From aaef4232a4397e2d2976ab407d08b812aa05e66c Mon Sep 17 00:00:00 2001 From: oganm Date: Fri, 17 May 2024 04:50:54 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20Pavlidis?= =?UTF-8?q?Lab/gemma.R@0a3acd69b48c919bcf51a8b6bce30ed4208cc185=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- articles/gemma.R.html | 89 +-- .../gemma.R_files/figure-html/boxplot-1.png | Bin 117355 -> 120535 bytes .../gemma.R_files/figure-html/diffExpr-1.png | Bin 134124 -> 131577 bytes articles/metadata.html | 19 +- .../figure-html/unnamed-chunk-19-1.png | Bin 50015 -> 49865 bytes articles/metanalysis.html | 45 +- .../figure-html/unnamed-chunk-18-1.png | Bin 22653 -> 22391 bytes .../figure-html/unnamed-chunk-19-1.png | Bin 37793 -> 37026 bytes .../figure-html/unnamed-chunk-21-1.png | Bin 161614 -> 159573 bytes pkgdown.yml | 4 +- .../get_dataset_expression_for_genes.html | 2 +- reference/get_dataset_platforms.html | 2 +- reference/get_datasets.html | 410 +++++----- reference/get_gene_go_terms.html | 28 +- reference/get_gene_probes.html | 186 +---- reference/get_genes.html | 16 +- reference/get_platform_datasets.html | 2 +- reference/get_platform_element_genes.html | 2 +- reference/get_platforms_by_ids.html | 6 +- reference/get_taxon_datasets.html | 46 +- reference/search_annotations.html | 40 +- reference/search_datasets.html | 226 +++--- reference/search_gemma.html | 706 +++++++++--------- 23 files changed, 816 insertions(+), 1013 deletions(-) diff --git a/articles/gemma.R.html b/articles/gemma.R.html index 568a2792..3a271d48 100644 --- a/articles/gemma.R.html +++ b/articles/gemma.R.html @@ -306,20 +306,6 @@

Searching for datasets of i -GSE157509 - - -Increased IL-6 and altered … - - -The goals of this study ar… - - -human - - - - McLean Hippocampus @@ -387,6 +373,20 @@

Searching for datasets of i human + + +GSE179921.2 + + +Split part 2 of: TCF7L2 lnc… + + +This experiment was created… + + +human + + 
@@ -879,7 +879,7 @@ 
Searching for datasets of i
 Expression of mRNAs Regulat…
 
 
-In Alzheimer<e2><80><99>s d…
+In Alzheimer’s disease (AD)…
 
 
 human
@@ -1177,10 +1177,10 @@ 
Searching for datasets of i
 NA
 
 
-bipolar i disorder
+bipolar neuron
 
 
-http://www.ebi…/EFO_0009963
+http://purl.obolibrary…/CL_0000103
 
 
 
@@ -1191,10 +1191,10 @@ 
Searching for datasets of i
 NA
 
 
-bipolar ii disorder
+bipolar i disorder
 
 
-http://www.ebi…/EFO_0009964
+http://www.ebi…/EFO_0009963
 
 
 
@@ -1205,10 +1205,10 @@ 
Searching for datasets of i
 NA
 
 
-bipolar disorder
+bipolar ii disorder
 
 
-http://purl.obolibrary…/MONDO_0004985
+http://www.ebi…/EFO_0009964
 
 
 
@@ -1219,10 +1219,10 @@ 
Searching for datasets of i
 NA
 
 
-bipolar affective disorder
+bipolar disorder
 
 
-http://purl.obolibrary…/HP_0007302
+http://purl.obolibrary…/MONDO_0004985
 
 
 
@@ -1233,10 +1233,10 @@ 
Searching for datasets of i
 NA
 
 
-bipolar neuron
+bipolar i disorder
 
 
-http://purl.obolibrary…/CL_0000103
+http://purl.obolibrary…/MONDO_0001866
 
 
 
@@ -1247,10 +1247,10 @@ 
Searching for datasets of i
 NA
 
 
-bipolar disorder
+bipolar ii disorder
 
 
-http://purl.obolibrary…/NBO_0000258
+http://purl.obolibrary…/MONDO_0000693
 
 
 
@@ -1553,7 +1553,7 @@ 
Platform Annotations
-0.8776245
+0.8776645
 
 
 human
@@ -1591,7 +1591,7 @@ 
Platform Annotations
-0.8794242
+0.8794051
 
 
 mouse
@@ -1629,7 +1629,7 @@ 
Platform Annotations
-0.8201887
+0.8187614
 
 
 rat
@@ -3215,7 +3215,7 @@ 
Larger queries
 
-387
+389
 
 
 ONECOLOR
@@ -3256,7 +3256,7 @@ 
Larger queries
 
-294
+295
 
 
 ONECOLOR
@@ -3297,7 +3297,7 @@ 
Larger queries
 
-1284
+1286
 
 
 ONECOLOR
@@ -3338,7 +3338,7 @@ 
Larger queries
 
-1502
+1517
 
 
 ONECOLOR
@@ -3453,7 +3453,7 @@ 
Larger queries
 platform_count = attributes(get_platforms_by_ids(limit = 1))$totalElements
 print(platform_count)
-
[1] 637
+
[1] 636

After which you can use offset to access all available platforms.

@@ -3918,7 +3918,10 @@ 
Session infoSession infoSession info## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
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+##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
+##  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
+##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
+## [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
 ## 
 ## time zone: UTC
 ## tzcode source: system (glibc)
@@ -2104,11 +2107,11 @@ 
Session info## 
 ## loaded via a namespace (and not attached):
 ##  [1] rappdirs_0.3.3      sass_0.4.9          utf8_1.2.4         
-##  [4] generics_0.1.3      xml2_1.3.6          stringi_1.8.3      
+##  [4] generics_0.1.3      xml2_1.3.6          stringi_1.8.4      
 ##  [7] digest_0.6.35       magrittr_2.0.3      timechange_0.3.0   
 ## [10] evaluate_0.23       grid_4.4.0          RColorBrewer_1.1-3 
-## [13] bookdown_0.39       fastmap_1.1.1       jsonlite_1.8.8     
-## [16] BiocManager_1.30.22 httr_1.4.7          fansi_1.0.6        
+## [13] bookdown_0.39       fastmap_1.2.0       jsonlite_1.8.8     
+## [16] BiocManager_1.30.23 httr_1.4.7          fansi_1.0.6        
 ## [19] viridisLite_0.4.2   scales_1.3.0        textshaping_0.3.7  
 ## [22] jquerylib_0.1.4     cli_3.6.2           rlang_1.1.3        
 ## [25] bit64_4.0.5         munsell_0.5.1       withr_3.0.0        
@@ -2117,12 +2120,12 @@ 
Session info## [34] assertthat_0.2.1    curl_5.2.1          vctrs_0.6.5        
 ## [37] R6_2.5.1            lubridate_1.9.3     lifecycle_1.0.4    
 ## [40] stringr_1.5.1       fs_1.6.4            htmlwidgets_1.6.4  
-## [43] bit_4.0.5           ragg_1.3.0          pkgconfig_2.0.3    
+## [43] bit_4.0.5           ragg_1.3.2          pkgconfig_2.0.3    
 ## [46] desc_1.4.3          pkgdown_2.0.9       pillar_1.9.0       
 ## [49] bslib_0.7.0         gtable_0.3.5        data.table_1.15.4  
-## [52] glue_1.7.0          systemfonts_1.0.6   highr_0.10         
-## [55] xfun_0.43           tibble_3.2.1        tidyselect_1.2.1   
-## [58] rstudioapi_0.16.0   knitr_1.46          farver_2.1.1       
+## [52] glue_1.7.0          systemfonts_1.1.0   highr_0.10         
+## [55] xfun_0.44           tibble_3.2.1        tidyselect_1.2.1   
+## [58] rstudioapi_0.16.0   knitr_1.46          farver_2.1.2       
 ## [61] htmltools_0.5.8.1   svglite_2.1.3       rmarkdown_2.26     
 ## [64] compiler_4.4.0
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Identifying what to search for -wolff-parkinson-white syndrome +parkinson’s disease symptom… -http://purl.obolibrary…/HP_0001716 +http://www.ebi…/EFO_0600011 @@ -190,10 +190,10 @@

Identifying what to search for -parkinsonism with favorable… +wolff-parkinson-white syndrome -http://purl.obolibrary…/HP_0002548 +http://purl.obolibrary…/HP_0001716 @@ -204,10 +204,10 @@

Identifying what to search for -parkinsonism +parkinsonism with favorable… -http://purl.obolibrary…/HP_0001300 +http://purl.obolibrary…/HP_0002548 @@ -218,10 +218,10 @@

Identifying what to search for -atypical juvenile parkinsonism +parkinsonism -http://purl.obolibrary…/MONDO_0018321 +http://purl.obolibrary…/HP_0001300 @@ -232,10 +232,10 @@

Identifying what to search for -parkinsonism with polyneuro… +atypical juvenile parkinsonism -http://purl.obolibrary…/MONDO_0036193 +http://purl.obolibrary…/MONDO_0018321 @@ -285,7 +285,7 @@

Identifying what to search forhttp://purl.obolibrary…/MONDO_0005180 -205 +204 @@ -644,7 +644,7 @@

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#> attr(,"call") #> [1] "https://gemma.msl.ubc.ca/rest/v2/datasets/GSE2018/expressions/genes/10225%2C2841?keepNonSpecific=false" #> attr(,"env") -#> <environment: 0x5642d0ebdf10> +#> <environment: 0x55ccd71fa488> diff --git a/reference/get_dataset_platforms.html b/reference/get_dataset_platforms.html index 7b8d7341..d3a4a32e 100644 --- a/reference/get_dataset_platforms.html +++ b/reference/get_dataset_platforms.html @@ -151,7 +151,7 @@

Examples

#> 1: The U133 set includes 2 arrays with a total of 44928 entries and was indexed 29-Jan-2002. The set includes over 1,000,000 unique oligonucleotide features covering more than 39,000 transcript variants, which in turn represent greater than 33,000 of the best characterized human genes. Sequences were selected from GenBank, dbEST, and RefSeq. Sequence clusters were created from Build 133 of UniGene (April 20, 2001) and refined by analysis and comparison with a number of other publicly available databases including the Washington University EST trace repository and the University of California, Santa Cruz golden-path human genome database (April 2001 release). In addition, ESTs were analyzed for untrimmed low-quality sequence information, correct orientation, false priming, false clustering, alternative splicing and alternative polyadenylation. Keywords = high density oligonucleotide array\nFrom GPL96\nLast Updated: Mar 09 2006 #> platform.troubled platform.experimentCount platform.type taxon.name #> <lgcl> <int> <char> <char> -#> 1: FALSE 387 ONECOLOR human +#> 1: FALSE 389 ONECOLOR human #> taxon.scientific taxon.ID taxon.NCBI taxon.database.name taxon.database.ID #> <char> <int> <int> <char> <int> #> 1: Homo sapiens 1 9606 hg38 87 diff --git a/reference/get_datasets.html b/reference/get_datasets.html index ed215896..53a663f5 100644 --- a/reference/get_datasets.html +++ b/reference/get_datasets.html @@ -270,8 +270,8 @@

Examples

#> 1: Bronchoalveolar lavage samples collected from lung transplant recipients. Numeric portion of sample name is an arbitrary patient ID and AxBx number indicates the perivascular (A) and bronchiolar (B) scores from biopsies collected on the same day as the BAL fluid was collected. Several patients have more than one sample in this series and can be determined by patient number followed by a lower case letter. Acute rejection state is determined by the combined A and B score - specifically, a combined AB score of 2 or greater is considered an acute rejection. #> 2: Neurological diseases disrupt the quality of the lives of patients and often lead to their death prematurely. A common feature of most neurological diseases is the degeneration of neurons, which results from an inappropriate activation of apoptosis. Drugs that inhibit neuronal apoptosis could thus be candidates for therapeutic intervention in neurodegenerative disorders. We have identified (and recently reported) a chemical called GW5074 that inhibits apoptosis in a variety of cell culture paradigms of neuronal apoptosis. Additionally, we have found that GW5074 reduces neurodegeneration and improves behavioral outcome in a mouse model of Huntington's disease. Although GW5074 is a c-Raf inhibitor, we know very little about the molecular mechanisms underlying its neuroprotective action. Identifying genes that are regulated by GW5074 in neurons will shed insight into this issue. We believe that neuroprotection by GW5074 involves the regulation of several genes. Some of these genes are likely to be induced whereas the expression of other genes might be inhibited. The specific aim is to identify genes that are differentially expressed in neurons treated with GW5074. We believe that neuroprotection by GW5074 involves the regulation of several genes. Some of these genes are likely to be induced whereas the expression of other genes might be inhibited. Cultures of cerebellar granule neurons undergo apoptosis when switched from medium containing levated levels of potassium (high K+ or HK) to medium containing low potassium (LK). Although cell death begins at about 18 h, we have found that the by 6 h after LK treatment these neurons are irreversibly committed to cell death. We will treat cerebellar granule neuron cultures with LK medium (which induces them to undergo apoptosis) or with GW5074 (1 uM). We will extract RNA at two time-points after treatment: 3 h and 6 h. Analysis at the two different time-points will show us whether changes in expression of specific genes is transient or sustained or whether the changes occurs early or relatively late in the process. Neuronal cultures will be prepared from 1 week old Wistar rats. The cultures will be maintained in culture for 1 week before treatment. Following treatment the cells will be lysed and total RNA isolated. The RNA will be stored at -80oC and shipped to the microarray facility for analysis. The experiment will be done in triplicate. Thus, for each time-point (3 or 6h treatment) we will be hope to provide 3 sets of samples (each set coming from a different culture preparation and containing lysates from cells treated with LK or GW5074). Having samples from 3 independent cultures will mitigate any expression differences resulting from subtle variations in culture quality or in the preparation or quality of RNA.\nDate GSE2872 Last Updated: Jul 06 2005\nContributors: S R D'Mello\nIncludes GDS1837.\n Update date: Jun 09 2006.\n Dataset description GDS1837: Analysis of cultured cerebellar granule neurons in low potassium medium 3 and 6 hours following treatment with the c-Raf inhibitor GW5074. GW5074 blocks low potassium induced cell death. Results provide insight into the neuroprotective action of GW5074. #> 3: Melanotransferrin (MTf) or melanoma tumor antigen p97 is an iron (Fe)-binding transferrin homolog expressed highly on melanomas and at lower levels on normal tissues. It has been suggested that MTf is involved in a variety of processes such as Fe metabolism and cellular differentiation. Considering the crucial role of Fe in many metabolic pathways e.g., DNA synthesis, it is important to understand the function of MTf. To define the roles of MTf, a MTf knockout (MTf -/-) mouse model was developed. Examination of the MTf -/- mice demonstrated no phenotypic differences compared to wild-type littermates. However, microarray analysis showed differential expression of molecules involved in proliferation such as Mef2a, Tcf4, Gls and Apod in MTf -/- mice compared to MTf +/+ littermates, suggesting a role for MTf in proliferation and tumorigenesis.\nDate GSE4523 Last Updated: Jun 13 2006\nContributors: Louise L Dunn Yohan Suryo Rahmanto Eric O Sekyere Des R Richardson\nIncludes GDS1964.\n Update date: Jun 14 2006.\n Dataset description GDS1964: Analysis of brain from melanotransferrin (MTf) knockout mutants. MTf or melanoma tumor antigen p97 is a membrane bound iron binding transferrin homolog highly expressed in melanomas and at lower levels in normal tissues. Results provide insight into the function of MTf. -#> 4: Our laboratory has developed the first mouse model overexpressing a RNA-binding protein, the ELAV-like protein HuD, in the CNS under the control of the CaMKinII alpha promoter. Initial behavioral characterization of the mice revealed that they had significant learning deficits together with abnormalities in prepulse inhibition (PPI). At the molecular level, we found that the expression of the growth-associated protein GAP-43, one of the targets of HuD, was increased in the hippocampus of HuD transgenic mice. To characterize these mice further and to evaluate the utility of these animals in understanding human diseases, we propose to use DNA microarray methods. To test our hypothesis we propose 3 specific aims: 1) To characterize the pattern of gene expression in the hippocampus of HuD overexpressor mice 2) To compare the pattern of gene expression in our mouse model with that in the hippocampus of rats prenatally exposed to alcohol (FAS model) and 3) To compare the pattern of gene expression in our mouse model with that shown in post-mortem tissues of patients with schizophrenia. In our previous protocols we examined the pattern of gene expression in our HuD transgenic mice and in rats prenaltally exposed to alcohol. A report by another group (Hakak et al, 2001) showed that three of the HuD targets were upregulated in the prefrontal cortex of patients with schizophrenia. To evaluate whether other target of HuD may be affected in this illness, in the current protocol, we want to compare the pattern of expression in our transgenic mice with in tissue from patients with schizophrenia Based on the behavioral and molecular properties of our HuD transgenic mice we hypothesize that these animals may be good models for the studying the basis of learning disabilities and of diseases that show deficits in PPI such as fetal alcohol syndrome and schizophrenia. All 28 samples are derived from cerebellar tissues for patients with schizophrenia and matched controls. The specimens were obtained from the Maryland Brain Collection according to NIH guidelines for confidentially and privacy. The protocol used in these studies was reviewed by our HRRC which found that our studies do not fall within the category of protocols monitored by the IRB (see attached letter form the HRRC). Specimens from 14 patients with a diagnosis of schizophrenia performed according to DSM-IV criteria and 14 sex-, age- and PMI-matched controls was included in this study. No differences were found between patients and control subjects in the average age (45<c2><b1>12 versus 43<c2><b1>10 years, p=0.86) or PMI (12<c2><b1>5 versus 16<c2><b1>6 hours, p=0.11). We will provide 28 samples containing 5 ug of RNA each in DEPC water (see validation of the quality of the RNA below). In addition, we include in our study animals treated with haloperidol as control for medication. These samples will be submitted in a separate protocol.\nDate GSE4036 Last Updated: Jan 13 2006\nContributors: Nora I Perrone-Bizzozero\nIncludes GDS1917.\n Update date: Jun 14 2006.\n Dataset description GDS1917: Analysis of cortical samples corresponding to the crus I/VIIa area of the cerebellum from schizophrenia patients. A study indicates that targets of the RNA-binding ELAV-like protein HuD are overexpressed in the prefrontal cortex of patients with schizophrenia. -#> 5: Fear conditioning (FC) is a behavioral paradigm that measures an animal's ability to learn fear related information. FC is measured by pairing a mild foot-shock with the surroundings in which the shock was recieved. Upon being placed back in the context, mice exhibit freezing behavior, which is a species-specific response to fear. We have previously used selective breeding to produce lines of mice with high or low levels of freezing behavior. This experiment is a replication of a previous experiment that produced lines of mice with high or low levels of freezing behavior. These lines derive from different progenitor mouse strains. We are able to identify alleles that govern the genetic variability for FC by using chromosomal markers in these selected lines. Using microarrays, we will identify differences in gene expression in two key brain regions: amygdala and hippocampus. Gene expression differences and data regarding chromosomal regions involved in the behavior will be compared to identify particular genes that are both differentially expressed and whose expression is governed by alleles that fall into critical chromosomal regions. We will compare gene expresion in the amygdala and hippocampus (brain regions known to be relevant to fear behavior) from the these two lines of mice and to the those in the previous experiment. Bayesian statistics will be used in an effort to identify gene expression that affects fear behavior. We hypothesize that selection has acted in part by changing the frequency of alleles that cause differential expression of key genes in the amygdala and hippocampus of our selected lines. Slective breeding changes the frequency of trait relevand (FC) alleles. A relevant allele is expected to increas in one selected line and decrease in the oppositely selected line. Some trait relevant alleles are expected to cause changes in the level of expression at particular genes. Amygdala and hippocampus will be rapidly dissected out of experimentally na<c3><af>ve mice from each line. Na<c3><af>ve mice will be used for expression studies since the behavior of the mice in the FC test can be reliably anticipated due to their lineage. We have practiced these procedures, and can accurately and reproducibly remove these regions in less than 5 minutes. Different mice will be used to collect each brain region, since the dissection of hippocampus disrupts the removal of amygdala. We will collect enough samples from each region to accommodate a total of 6 microarrays per brain region, per line, thus we will use a total of 24 microarrays. We anticipate that a single brain region will be sufficient to for a microarray. However, we propose to utilize three samples per microarray, because this will reduce variability due to environmental factors and due to slight variability in our dissection procedures. Once this tissue is removed, we will isolate RNA for shipment to the Microarray consortium. We will also collect spleens from each subject as a source of genomic DNA, in order to permit direct comparison of genotype and expression phenotypes. Once we have the results of the microarray analysis, we use WebQTL.org to identify the chromosomal locations of alleles that are know to influence the expression of genes for which we have found differential expression. We will then superimpose this information on trait relevant chromosomal regions identified from our selected lines. This will allow us to rapidly identify genes which may account for genetic variability in FC due to differential expression. Such genes will then be subjected to further study.\r\nDate GSE4034 Last Updated: Jan 13 2006\r\n\r\nContributors: Abraham A Palmer\r\n\r\nIncludes GDS1901.\r\n Update date: Jun 09 2006.\r\n Dataset description GDS1901: Analysis of the amygdalae and hippocampi of strains exhibiting low or high freezing behavior in response to fear. Previous work identified alleles that contribute to the variability in freezing behavior, and the chromosomal location of these alleles.\r +#> 4: Our laboratory has developed the first mouse model overexpressing a RNA-binding protein, the ELAV-like protein HuD, in the CNS under the control of the CaMKinII alpha promoter. Initial behavioral characterization of the mice revealed that they had significant learning deficits together with abnormalities in prepulse inhibition (PPI). At the molecular level, we found that the expression of the growth-associated protein GAP-43, one of the targets of HuD, was increased in the hippocampus of HuD transgenic mice. To characterize these mice further and to evaluate the utility of these animals in understanding human diseases, we propose to use DNA microarray methods. To test our hypothesis we propose 3 specific aims: 1) To characterize the pattern of gene expression in the hippocampus of HuD overexpressor mice 2) To compare the pattern of gene expression in our mouse model with that in the hippocampus of rats prenatally exposed to alcohol (FAS model) and 3) To compare the pattern of gene expression in our mouse model with that shown in post-mortem tissues of patients with schizophrenia. In our previous protocols we examined the pattern of gene expression in our HuD transgenic mice and in rats prenaltally exposed to alcohol. A report by another group (Hakak et al, 2001) showed that three of the HuD targets were upregulated in the prefrontal cortex of patients with schizophrenia. To evaluate whether other target of HuD may be affected in this illness, in the current protocol, we want to compare the pattern of expression in our transgenic mice with in tissue from patients with schizophrenia Based on the behavioral and molecular properties of our HuD transgenic mice we hypothesize that these animals may be good models for the studying the basis of learning disabilities and of diseases that show deficits in PPI such as fetal alcohol syndrome and schizophrenia. All 28 samples are derived from cerebellar tissues for patients with schizophrenia and matched controls. The specimens were obtained from the Maryland Brain Collection according to NIH guidelines for confidentially and privacy. The protocol used in these studies was reviewed by our HRRC which found that our studies do not fall within the category of protocols monitored by the IRB (see attached letter form the HRRC). Specimens from 14 patients with a diagnosis of schizophrenia performed according to DSM-IV criteria and 14 sex-, age- and PMI-matched controls was included in this study. No differences were found between patients and control subjects in the average age (45±12 versus 43±10 years, p=0.86) or PMI (12±5 versus 16±6 hours, p=0.11). We will provide 28 samples containing 5 ug of RNA each in DEPC water (see validation of the quality of the RNA below). In addition, we include in our study animals treated with haloperidol as control for medication. These samples will be submitted in a separate protocol.\nDate GSE4036 Last Updated: Jan 13 2006\nContributors: Nora I Perrone-Bizzozero\nIncludes GDS1917.\n Update date: Jun 14 2006.\n Dataset description GDS1917: Analysis of cortical samples corresponding to the crus I/VIIa area of the cerebellum from schizophrenia patients. A study indicates that targets of the RNA-binding ELAV-like protein HuD are overexpressed in the prefrontal cortex of patients with schizophrenia. +#> 5: Fear conditioning (FC) is a behavioral paradigm that measures an animal's ability to learn fear related information. FC is measured by pairing a mild foot-shock with the surroundings in which the shock was recieved. Upon being placed back in the context, mice exhibit freezing behavior, which is a species-specific response to fear. We have previously used selective breeding to produce lines of mice with high or low levels of freezing behavior. This experiment is a replication of a previous experiment that produced lines of mice with high or low levels of freezing behavior. These lines derive from different progenitor mouse strains. We are able to identify alleles that govern the genetic variability for FC by using chromosomal markers in these selected lines. Using microarrays, we will identify differences in gene expression in two key brain regions: amygdala and hippocampus. Gene expression differences and data regarding chromosomal regions involved in the behavior will be compared to identify particular genes that are both differentially expressed and whose expression is governed by alleles that fall into critical chromosomal regions. We will compare gene expresion in the amygdala and hippocampus (brain regions known to be relevant to fear behavior) from the these two lines of mice and to the those in the previous experiment. Bayesian statistics will be used in an effort to identify gene expression that affects fear behavior. We hypothesize that selection has acted in part by changing the frequency of alleles that cause differential expression of key genes in the amygdala and hippocampus of our selected lines. Slective breeding changes the frequency of trait relevand (FC) alleles. A relevant allele is expected to increas in one selected line and decrease in the oppositely selected line. Some trait relevant alleles are expected to cause changes in the level of expression at particular genes. Amygdala and hippocampus will be rapidly dissected out of experimentally naïve mice from each line. Naïve mice will be used for expression studies since the behavior of the mice in the FC test can be reliably anticipated due to their lineage. We have practiced these procedures, and can accurately and reproducibly remove these regions in less than 5 minutes. Different mice will be used to collect each brain region, since the dissection of hippocampus disrupts the removal of amygdala. We will collect enough samples from each region to accommodate a total of 6 microarrays per brain region, per line, thus we will use a total of 24 microarrays. We anticipate that a single brain region will be sufficient to for a microarray. However, we propose to utilize three samples per microarray, because this will reduce variability due to environmental factors and due to slight variability in our dissection procedures. Once this tissue is removed, we will isolate RNA for shipment to the Microarray consortium. We will also collect spleens from each subject as a source of genomic DNA, in order to permit direct comparison of genotype and expression phenotypes. Once we have the results of the microarray analysis, we use WebQTL.org to identify the chromosomal locations of alleles that are know to influence the expression of genes for which we have found differential expression. We will then superimpose this information on trait relevant chromosomal regions identified from our selected lines. This will allow us to rapidly identify genes which may account for genetic variability in FC due to differential expression. Such genes will then be subjected to further study.\r\nDate GSE4034 Last Updated: Jan 13 2006\r\n\r\nContributors: Abraham A Palmer\r\n\r\nIncludes GDS1901.\r\n Update date: Jun 09 2006.\r\n Dataset description GDS1901: Analysis of the amygdalae and hippocampi of strains exhibiting low or high freezing behavior in response to fear. Previous work identified alleles that contribute to the variability in freezing behavior, and the chromosomal location of these alleles.\r #> 6: Succinate semialdehyde dehydrogenase (SSADH) deficiency is a rare autosomal recessive disorder effecting approximately 350 people around the world. Patients suffering from SSADH deficiency experience language acquisition failure, memory deficiencies, autism, increased aggressive behaviors, and seizures. There is a chemical buildup of both gamma-aminobutyric acid (GABA) and gamma-hydroxybutyric acid (GHB) in the neurological system of these patients. The Aldh5a1-/- knock out mouse model of SSADH deficiency shows the same chemical imbalances as the human disease, with additional fatal tonic-clonic seizures at three weeks of age. The elucidation of seizure causing pathways will facilitate treatment of seizure phenotypes in diseases with related epilepsy. Gene expression patterns within the hippocampus, cerebellum, and cortex of SSADH deficient mice (Aldh5a1-/- mice) will be compared to wild type mice at a time point immediately prior to fatal seizures. We hypothesis that the SSADH deficient mice experience a dysfunction of glutamate/GABA/ glutamine neurotransmitter cycle linked to astroglial metabolism and/or uptake of neuronally-released glutamate. The increased levels of GHB and GABA lead to down regulation of GABA-B-Receptor leading to seizures. The SSADH deficient phenotype may also be caused by ongoing oxidative damage and the pathological role of succinic semialdehyde. SSADH deficient mice (Aldh5a1-/- knock out) exhibit fatal seizures around three weeks of age. Mutant and wild type mice will be sacrificed between 17 and 19 days of life, and brain sections will be extracted and frozen (using a standard protocol). Hippocampus, cerebellum, and cortex from three mutant mice and three wild type mice will individually be expression profiled on the Affymetrix platform, giving a total of eighteen arrays. Comparative analysis will then be carried out, evaluating the transcript differences between mutant and wild type mice in each brain region.\nDate GSE2866 Last Updated: Jun 08 2006\nContributors: E A Donarum\nIncludes GDS1745.\n Update date: Jun 08 2006.\n Dataset description GDS1745: Analysis of brain hippocampi, cerebella, and cortices of succinate semialdehyde dehydrogenase (SSADH)-deficient mutants at 3 weeks of age, when fatal seizures occur. Results indicate that SSADH deficiency results in the dysregulation of genes involved in myelin structure and compaction. #> 7: Acute cognitive impairment (i.e., delirium) is common in elderly emergency department patients and frequently results from infections that are unrelated to the central nervous system. Since activation of the peripheral innate immune system induces brain microglia to produce inflammatory cytokines that are responsible for behavioral deficits, we investigated if aging exacerbated neuroinflammation and sickness behavior after peripheral injection of lipopolysaccharide (LPS). Microarray analysis revealed a transcriptional profile indicating the presence of primed or activated microglia and increased inflammation in the aged brain. Furthermore, aged mice had a unique gene expression profile in the brain after an intraperitoneal injection of LPS, and the LPS-induced elevation in the brain inflammatory cytokines and oxidative stress was both exaggerated and prolonged compared with adults. Aged mice were anorectic longer and lost more weight than adults after peripheral LPS administration. Moreover, reductions in both locomotor and social behavior remained 24 h later in aged mice, when adults had fully recovered, and the exaggerated neuroinflammatory response in aged mice was not reliably paralleled by increased circulating cytokines in the periphery. Taken together, these data establish that activation of the peripheral innate immune system leads to exacerbated neuroinflammation in the aged as compared with adult mice. This dysregulated link between the peripheral and central innate immune system is likely to be involved in the severe behavioral deficits that frequently occur in older adults with systemic infections.\nDate GSE3253 Last Updated: Oct 28 2005\nContributors: Johnathan P Godbout Jing Chen Amy F Richwine Brian M Berg Keith K Kelley Rodney W Johnson Jayne Abraham\nIncludes GDS1311.\n Update date: Nov 04 2005.\n Dataset description GDS1311: Analysis of aged brain after activation of the peripheral innate immune system by intraperitoneal injection of E. coli lipopolysaccharide (LPS). Results provide insight into molecular events underlying severe behavioral deficits (delirium) common in older adults with systemic infections. #> 8: Post-transcriptional mechanisms play an important role in the control of gene expression. RNA-binding proteins are key players in the post-transcriptional control of many neural genes and they participate in multiple processes, from RNA splicing and mRNA transport to mRNA stability and translation. Our laboratory has developed the first mouse model overexpressing a RNA-binding protein, the ELAV-like protein HuD, in the CNS under the control of the CaMKinII alpha promoter. Initial behavioral characterization of the mice revealed that they had significant learning deficits together with abnormalities in prepulse inhibition (PPI). At the molecular level, we found that the expression of the growth-associated protein GAP-43, one of the targets of HuD, was increased in the hippocampus of HuD transgenic mice. To characterize these mice further and to evaluate the utility of these animals in understanding human diseases, we propose to use DNA microarray methods. To test our hypothesis we propose 2 specific aims: 1)To characterize the pattern of gene expression in the hippocampus of HuD overexpressor mice 2)To compare the pattern of gene expression in our mouse model with that in the hippocampus of rats prenatally exposed to alcohol (FAS model) and in post-mortem tissues of patients with schizophrenia Based on the behavioral and molecular properties of our HuD transgenic mice we hypothesize that these animals may be good models for the studying the basis of learning disabilities and of diseases that show deficits in PPI such as fetal alcohol syndrome and schizophrenia. All mice are in C57BL/6 background and are male approximately 60 days old. Initial studies were performed in animals that were not subjected to any experimental manipulation. Animals were bred and sacrificed according to our approved animal protocol. The brain was rapidly dissected on ice and we isolated the hippocampus, which has the highest expression of the transgene. After dissection both hippocampi were frozen in liquid nitrogen, pooled and stored at -80C until analysis. RNA samples were isolated using RNAeasy Qiagen columns. For our first experiment, we want to examine the pattern of gene expression in the hippocampus of 3 transgenic mice and 3 non-transgenic littermates. RNAs from the 6 hippocampi were of high quality as revealed by the integrity of the 28S and 18S rRNA. We will provide 6 samples containing 10 ug of RNA each in DEPC water at a concentration of about 0.5 ug/ul. Three of the samples (#1, #2 and # 3) are from transgenic mice and three from their non-transgenic littermates (#4, #5 and #6).\nDate GSE2005 Last Updated: May 29 2005\nContributors: Nora I Perrone-Bizzozero\nIncludes GDS1111.\n Update date: May 09 2005.\n Dataset description GDS1111: Analysis of hippocampus of 60 day old C57BL/6 males overexpressing the RNA binding protein HuD. HuD overexpressor mice exhibit learning deficits and abnormalities in prepulse inhibition. @@ -286,7 +286,7 @@

Examples

#> 17: The cardinal clinical features of Parkinson's disease (PD) (rigidity, rest tremor, bradykinesia, and postural instability) result from selective loss of midbrain dopaminergic neurons. More specifically, dopaminergic neurons in the substantia nigra pars compacta (SNc) are much more susceptible to damage than the adjacent dopaminergic neurons in the ventral tegmental area (VTA). This dichotomy is not only seen in human Parkinsons disease, but also in many animal models of PD, including administration of the mitochondrial toxin rotenone to rats, which replicates many of the behavioral and neuropathological features of PD. The factors underlying this selective vulnerability are unknown, but could be related to differences in neuronal circuitry, differences in glial support, or intrinsic differences between the neuronal populations of the two regions. Elucidation of these factors may lead to a greater understanding of the pathogenesis and treatment of Parkinson's disease. We will determine gene expression profiles of untreated rat SNc and VTA dopaminergic neurons using laser capture microscopy to obtain region-specific neuronal mRNA. There are intrinsic differences in gene expression between dopaminergic neurons in the rat SNc and VTA that result in greater susceptibility of SNc neurons to degeneration in experimental parkinsonism. These differences may be related to dopamine metabolism, oxidative metabolism and stress, protein aggregation, or other unforseen pathways. We will compare gene expression profiles between SNc and VTA dopaminergic neurons in normal rats. No treatment or time points will be studied in this experiment. Animals will be anesthetized, sacrificed by decapitation, and brains frozen on dry ice. Frozen sections will be collected onto glass microscope slides and rapidly immunostained for tyrosine hydroxylase to identify dopaminergic neurons. SNc and VTA neurons (approx. 200 per sample) will be isolated using laser capture microscopy. Total RNA will be extracted and poly-A RNA will be amplified using a modified Eberwine protocol. aRNA will be sent to the centers for labeling and hybridization to Affymetrix rat U34A arrays. We have confirmed with the center that our aRNA protocol is compatible with the centers amplification protocols; in fact, it is essentially identical. We will be providing a two-round amplification product to the center for labeling and hybridization. We recognize that using RNA after three rounds of amplification may decrease sensitivity for low copy number transcripts, but favor this approach versus pooling our samples (which are inherently paired) at this point. We have discussed this point in detail with the center. SNc and VTA samples from eight animals (16 samples total) will be provided to mitigate differences specific to individual animals. With the assisatnce of the center, paired t-tests will be used to determine differential expression between the two regions. Permutational t-test analysis and/or Benjamini and Hochberg analysis of expression ratios will be used to protect against multiple comparisons. Selected differentially expressed genes will be validated on separate tissue samples using quantitative RT-PCR or in situ hybridization.\nDate GSE1837 Last Updated: Feb 02 2006\nContributors: James G Greene\nIncludes GDS1641.\n Update date: Jun 08 2006.\n Dataset description GDS1641: Analysis of dopaminergic neurons of the substantia nigra pars compacta (SNc) and the ventral tegmental area (VTA) from normal animals. SNc dopaminergic neurons are more susceptible to degeneration in Parkinson's disease than VTA dopaminergic neurons. #> 18: Expression profiles of different mouse tissue samples profiles.\nDate GSE2178 Last Updated: May 29 2005\nContributors: Michael Mucenski Bruce J Aronow Belinda Peach Mitchell Cohen Amy Moseley Susan E Waltz John Maggio Sandra Degen Steve Potter Thomas Doetschman Chris Erwin Joanna A Groden James Lessard Linda Parysek R Hirsch MaryBeth Genter Jianhua Zhang Kathleen Anderson Jonathan D Katz John Dedman Michael Lehman Jorge A Bezerra Michael D Bates Ming Xu Dan Wiginton Jeff A Molkentin\nIncludes GDS1322.\n Update date: Nov 09 2005.\n Dataset description GDS1322: Gene expression profiles across a wide variety of tissue samples. The tissues examined include nervous system, skeletal muscle, male and female reproductive organs, gastrointestinal tract, as well as organs in a developmental context including lung, liver, kidney, and heart. #> 19: We have combined large-scale mRNA expression and gene mapping methods to identify genes and loci that control hematopoietic stem cell (HSC) functioning. mRNA expression levels were measured in purified HSC isolated from a panel of densely genotyped recombinant inbred mouse strains. Quantitative trait loci (QTLs) associated with variation in expression of thousands of transcripts were mapped. Comparison of the physical transcript position with the location of the controlling QTL identified polymorphic cis-acting stem cell genes. In addition, multiple trans-acting control loci were highlighted that modify expression of large numbers of genes. These groups of co-regulated transcripts identify pathways that specify variation in stem cells. We illustrate this concept with the identification of strong candidate genes involved with HSC turnover. We compared expression QTLs in HSC and brain from the same animals, and document both shared and tissue-specific QTLs. Our data are accessible through WebQTL, a web-based interface that allows custom genetic linkage analysis and identification of co-regulated transcripts.\nDate GSE2031 Last Updated: Jun 29 2005\nContributors: Bert Dontje Kenneth F Manly Sue Sutton Michael Cooke Rudi Alberts Gerald de Haan Jintao Wang Edo Vellenga Ellen Weersing Elissa Chesler Leonid Bystrykh Andrew I Su Tim Wiltshire Mathew T Pletcher Robert W Williams Ritsert C Jansen Lu Lu\nIncludes GDS1077.\n Update date: Mar 16 2005.\n Dataset description GDS1077: Expression profiling of Lin- Sca-1+ c-kit+ hematopoietic stem cells (HSC) from 22 different BXD recombinant inbred (RI) strains. Each RI strain is homozygous for alleles at about 98% of loci. Results combined with QTL mapping to identify candidate genes for the control of HSC function. -#> 20: Transcriptome analysis of Ts1Cje (mouse model of Down syndrome) and euploids murine cerebellum during postnatal development.\nDate GSE1611 Last Updated: Mar 27 2006\nContributors: Randal Moldrich Isabelle Rivals Pierre-Marie Sinet Stylianos E Antonarakis Robert Lyle Geoffroy Golfier Luce Dauphinot Laurence Ettwiller Kiyoko Toyama Charles J Epstein L<c3><a9>on Personnaz Maire-Claude Potier Minh Tran Dang Jean Rossier\nIncludes GDS994.\n Update date: Jan 11 2005.\n Dataset description GDS994: Expression profiling of brain cerebella from Ts1Cje males at postnatal days 0, 15, and 30. RNA pooled from 3 males at each developmental stage. Results provide insight into the molecular changes contributing to the pathogenesis of Down syndrome. +#> 20: Transcriptome analysis of Ts1Cje (mouse model of Down syndrome) and euploids murine cerebellum during postnatal development.\nDate GSE1611 Last Updated: Mar 27 2006\nContributors: Randal Moldrich Isabelle Rivals Pierre-Marie Sinet Stylianos E Antonarakis Robert Lyle Geoffroy Golfier Luce Dauphinot Laurence Ettwiller Kiyoko Toyama Charles J Epstein Léon Personnaz Maire-Claude Potier Minh Tran Dang Jean Rossier\nIncludes GDS994.\n Update date: Jan 11 2005.\n Dataset description GDS994: Expression profiling of brain cerebella from Ts1Cje males at postnatal days 0, 15, and 30. RNA pooled from 3 males at each developmental stage. Results provide insight into the molecular changes contributing to the pathogenesis of Down syndrome. #> experiment.description #> experiment.troubled experiment.accession experiment.database #> <lgcl> <char> <char> @@ -538,7 +538,7 @@

Examples

#> 15: We performed expression profiling of 36 types of normal human tissues and identified 2,503 tissue-specific genes. We then systematically studied the expression of these genes in cancers by re-analyzing a large collection of published DNA microarray datasets. Our study shows that integration of each gene's breadth of expression (BOE) in normal tissues is important for biological interpretation of the expression profiles of cancers in terms of tumor differentiation, cell lineage and metastasis. Twenty five total RNA specimens were purchased from Clontech (Palo Alto, CA), Ambion (Austin, TX) and Strategene (La Jolla, CA). We tried to cover as many tissue types as possible by using pooled RNA samples. In order to define breadth-of-expression (BOE) accurately at a reasonable cost, we tried to cover as many tissue types as possible by using pooled RNA samples. Each specimen represents a human organ. We used RNA samples pooled from 2 to 84 donors to avoid differences at the individual level. Detailed sample information and Affymetrix .CEL files are available at http://www.genome.rcast.u-tokyo.ac.jp/normal/ Publication:Ge X et al., Interpreting expression profiles of cancers by genome-wide survey of breadth of expression in normal tissues. Genomics. 2005 Aug;86(2):127-141. PMID: 15950434\nLast Updated (by provider): May 11 2006\nContributors: Xijin Ge SanMing Wang Siego Ihara Hiroyuki Aburatani Yutaka Midorikawa Shogo Yamamoto Shuichi Tsutsumi\nIncludes GDS1096.\n Update date: Apr 08 2005.\n Dataset description GDS1096: Expression profiling of 36 types of normal tissue. Each RNA tissue sample pooled from several donors. Results identify tissue specific genes and provide baselines for interpreting gene expression in cancer. #> 16: This series represents 52 tissues hybridized across 5 different chip patterns. Probes were placed at every exon-exon junction in each transcript.\nLast Updated (by provider): May 29 2005\nContributors: Christopher D Armour Eric E Schadt Daniel D Shoemaker Patrick M Loerch Philip Garrett-Engele Jason M Johnson Roland Stoughton Ralph Santos Zhengyan Kan John Castle\nIncludes GDS830.\n Update date: Nov 10 2004.\n Dataset description GDS830: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants.\nIncludes GDS829.\n Update date: Nov 10 2004.\n Dataset description GDS829: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants.\nIncludes GDS833.\n Update date: Nov 10 2004.\n Dataset description GDS833: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants.\nIncludes GDS831.\n Update date: Nov 10 2004.\n Dataset description GDS831: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants.\nIncludes GDS832.\n Update date: Nov 10 2004.\n Dataset description GDS832: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants. [Switched to use Rosetta_Merged by Gemma] #> 17: High-throughput gene expression profiling has become an important tool for investigating transcriptional activity in a variety of biological samples. To date, the vast majority of these experiments have focused on specific biological processes and perturbations. Here, we have generated and analyzed gene expression from a set of samples spanning a broad range of biological conditions. Specifically, we profiled gene expression from 91 human and mouse samples across a diverse array of tissues, organs, and cell lines. Because these samples predominantly come from the normal physiological state in the human and mouse, this dataset represents a preliminary, but substantial, description of the normal mammalian transcriptome. We have used this dataset to illustrate methods of mining these data, and to reveal insights into molecular and physiological gene function, mechanisms of transcriptional regulation, disease etiology, and comparative genomics. Finally, to allow the scientific community to use this resource, we have built a free and publicly accessible website (http://expression.gnf.org) that integrates data visualization and curation of current gene annotations.\nLast Updated (by provider): May 29 2005\nContributors: A Moqrich R G Vega P G Schultz M P Cooke A I Su T Wiltshire A P Orth K A Ching L M Sapinoso J B Hogenesch J R Walker Y Hakak A Patapoutian G M Hampton\nIncludes GDS181.\n Update date: May 13 2004.\n Dataset description GDS181: Gene expression profiles from a diverse array of tissues, organs, and cell lines, from the normal physiological state. Represents a preliminary description of the normal mammalian transcriptome. -#> 18: This SuperSeries is composed of the following subset Series: GSE3461: Gene expression in miscellaneous human tissues and cell lines GSE3462: Triiodothyronine Treatment: Effects on vastus lateralis skeletal muscle Abstract: Thyroid hormones are key regulators of metabolism that modulate transcription via nuclear receptors. Hyperthyroidism is associated with increased metabolic rate, protein breakdown, and weight loss. Although the molecular actions of thyroid hormones have been studied thoroughly, their pleiotropic effects are mediated by complex changes in expression of an unknown number of target genes. Here, we measured patterns of skeletal muscle gene expression in five healthy men treated for 14 days with 75 <c2><b5>g of triiodothyronine, using 24,000 cDNA element microarrays. To analyze the data, we used a new statistical method that identifies significant changes in expression and estimates the false discovery rate. The 381 up-regulated genes were involved in a wide range of cellular functions including transcriptional control, mRNA maturation, protein turnover, signal transduction, cellular trafficking, and energy metabolism. Only two genes were down-regulated. Most of the genes are novel targets of thyroid hormone. Cluster analysis of triiodothyronine-regulated gene expression among 19 different human tissues or cell lines revealed sets of coregulated genes that serve similar biologic functions. These results define molecular signatures that help to understand the physiology and pathophysiology of thyroid hormone action.\nLast Updated (by provider): Dec 12 2006\n [Switched to use SMHu24k by Gemma] +#> 18: This SuperSeries is composed of the following subset Series: GSE3461: Gene expression in miscellaneous human tissues and cell lines GSE3462: Triiodothyronine Treatment: Effects on vastus lateralis skeletal muscle Abstract: Thyroid hormones are key regulators of metabolism that modulate transcription via nuclear receptors. Hyperthyroidism is associated with increased metabolic rate, protein breakdown, and weight loss. Although the molecular actions of thyroid hormones have been studied thoroughly, their pleiotropic effects are mediated by complex changes in expression of an unknown number of target genes. Here, we measured patterns of skeletal muscle gene expression in five healthy men treated for 14 days with 75 µg of triiodothyronine, using 24,000 cDNA element microarrays. To analyze the data, we used a new statistical method that identifies significant changes in expression and estimates the false discovery rate. The 381 up-regulated genes were involved in a wide range of cellular functions including transcriptional control, mRNA maturation, protein turnover, signal transduction, cellular trafficking, and energy metabolism. Only two genes were down-regulated. Most of the genes are novel targets of thyroid hormone. Cluster analysis of triiodothyronine-regulated gene expression among 19 different human tissues or cell lines revealed sets of coregulated genes that serve similar biologic functions. These results define molecular signatures that help to understand the physiology and pathophysiology of thyroid hormone action.\nLast Updated (by provider): Dec 12 2006\n [Switched to use SMHu24k by Gemma] #> 19: Experimental asthma was induced in BALB/c mice by sensitization and challenge with the allergen ovalbumin. Control groups received PBS. To investigate the innate immune component of experimental asthma, we also analyzed recombinase activating gene (RAG) deficient mice following exposure to ovalbumin and control PBS\nLast Updated (by provider): Apr 23 2007\nContributors: Xin Lu Patricia W Finn Vipul V Jain David L Perkins #> 20: This study aims at giving an insight on gene expression in CCAM.\nLast Updated (by provider): May 05 2006\nContributors: Agnes Paquet Sam Hawgood Amy Wagner #> experiment.description @@ -793,7 +793,7 @@

Examples

#> 15: We performed expression profiling of 36 types of normal human tissues and identified 2,503 tissue-specific genes. We then systematically studied the expression of these genes in cancers by re-analyzing a large collection of published DNA microarray datasets. Our study shows that integration of each gene's breadth of expression (BOE) in normal tissues is important for biological interpretation of the expression profiles of cancers in terms of tumor differentiation, cell lineage and metastasis. Twenty five total RNA specimens were purchased from Clontech (Palo Alto, CA), Ambion (Austin, TX) and Strategene (La Jolla, CA). We tried to cover as many tissue types as possible by using pooled RNA samples. In order to define breadth-of-expression (BOE) accurately at a reasonable cost, we tried to cover as many tissue types as possible by using pooled RNA samples. Each specimen represents a human organ. We used RNA samples pooled from 2 to 84 donors to avoid differences at the individual level. Detailed sample information and Affymetrix .CEL files are available at http://www.genome.rcast.u-tokyo.ac.jp/normal/ Publication:Ge X et al., Interpreting expression profiles of cancers by genome-wide survey of breadth of expression in normal tissues. Genomics. 2005 Aug;86(2):127-141. PMID: 15950434\nLast Updated (by provider): May 11 2006\nContributors: Xijin Ge SanMing Wang Siego Ihara Hiroyuki Aburatani Yutaka Midorikawa Shogo Yamamoto Shuichi Tsutsumi\nIncludes GDS1096.\n Update date: Apr 08 2005.\n Dataset description GDS1096: Expression profiling of 36 types of normal tissue. Each RNA tissue sample pooled from several donors. Results identify tissue specific genes and provide baselines for interpreting gene expression in cancer. #> 16: This series represents 52 tissues hybridized across 5 different chip patterns. Probes were placed at every exon-exon junction in each transcript.\nLast Updated (by provider): May 29 2005\nContributors: Christopher D Armour Eric E Schadt Daniel D Shoemaker Patrick M Loerch Philip Garrett-Engele Jason M Johnson Roland Stoughton Ralph Santos Zhengyan Kan John Castle\nIncludes GDS830.\n Update date: Nov 10 2004.\n Dataset description GDS830: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants.\nIncludes GDS829.\n Update date: Nov 10 2004.\n Dataset description GDS829: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants.\nIncludes GDS833.\n Update date: Nov 10 2004.\n Dataset description GDS833: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants.\nIncludes GDS831.\n Update date: Nov 10 2004.\n Dataset description GDS831: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants.\nIncludes GDS832.\n Update date: Nov 10 2004.\n Dataset description GDS832: Monitoring of mRNA splice variants for more than 10,000 multi-exon genes using arrays with oligonucleotide probes positioned at exon-exon junctions. mRNA from 52 tissues and cell lines examined. Results provide tissue distribution of splice variants and identify novel splice variants. [Switched to use Rosetta_Merged by Gemma] #> 17: High-throughput gene expression profiling has become an important tool for investigating transcriptional activity in a variety of biological samples. To date, the vast majority of these experiments have focused on specific biological processes and perturbations. Here, we have generated and analyzed gene expression from a set of samples spanning a broad range of biological conditions. Specifically, we profiled gene expression from 91 human and mouse samples across a diverse array of tissues, organs, and cell lines. Because these samples predominantly come from the normal physiological state in the human and mouse, this dataset represents a preliminary, but substantial, description of the normal mammalian transcriptome. We have used this dataset to illustrate methods of mining these data, and to reveal insights into molecular and physiological gene function, mechanisms of transcriptional regulation, disease etiology, and comparative genomics. Finally, to allow the scientific community to use this resource, we have built a free and publicly accessible website (http://expression.gnf.org) that integrates data visualization and curation of current gene annotations.\nLast Updated (by provider): May 29 2005\nContributors: A Moqrich R G Vega P G Schultz M P Cooke A I Su T Wiltshire A P Orth K A Ching L M Sapinoso J B Hogenesch J R Walker Y Hakak A Patapoutian G M Hampton\nIncludes GDS181.\n Update date: May 13 2004.\n Dataset description GDS181: Gene expression profiles from a diverse array of tissues, organs, and cell lines, from the normal physiological state. Represents a preliminary description of the normal mammalian transcriptome. -#> 18: This SuperSeries is composed of the following subset Series: GSE3461: Gene expression in miscellaneous human tissues and cell lines GSE3462: Triiodothyronine Treatment: Effects on vastus lateralis skeletal muscle Abstract: Thyroid hormones are key regulators of metabolism that modulate transcription via nuclear receptors. Hyperthyroidism is associated with increased metabolic rate, protein breakdown, and weight loss. Although the molecular actions of thyroid hormones have been studied thoroughly, their pleiotropic effects are mediated by complex changes in expression of an unknown number of target genes. Here, we measured patterns of skeletal muscle gene expression in five healthy men treated for 14 days with 75 <c2><b5>g of triiodothyronine, using 24,000 cDNA element microarrays. To analyze the data, we used a new statistical method that identifies significant changes in expression and estimates the false discovery rate. The 381 up-regulated genes were involved in a wide range of cellular functions including transcriptional control, mRNA maturation, protein turnover, signal transduction, cellular trafficking, and energy metabolism. Only two genes were down-regulated. Most of the genes are novel targets of thyroid hormone. Cluster analysis of triiodothyronine-regulated gene expression among 19 different human tissues or cell lines revealed sets of coregulated genes that serve similar biologic functions. These results define molecular signatures that help to understand the physiology and pathophysiology of thyroid hormone action.\nLast Updated (by provider): Dec 12 2006\n [Switched to use SMHu24k by Gemma] +#> 18: This SuperSeries is composed of the following subset Series: GSE3461: Gene expression in miscellaneous human tissues and cell lines GSE3462: Triiodothyronine Treatment: Effects on vastus lateralis skeletal muscle Abstract: Thyroid hormones are key regulators of metabolism that modulate transcription via nuclear receptors. Hyperthyroidism is associated with increased metabolic rate, protein breakdown, and weight loss. Although the molecular actions of thyroid hormones have been studied thoroughly, their pleiotropic effects are mediated by complex changes in expression of an unknown number of target genes. Here, we measured patterns of skeletal muscle gene expression in five healthy men treated for 14 days with 75 µg of triiodothyronine, using 24,000 cDNA element microarrays. To analyze the data, we used a new statistical method that identifies significant changes in expression and estimates the false discovery rate. The 381 up-regulated genes were involved in a wide range of cellular functions including transcriptional control, mRNA maturation, protein turnover, signal transduction, cellular trafficking, and energy metabolism. Only two genes were down-regulated. Most of the genes are novel targets of thyroid hormone. Cluster analysis of triiodothyronine-regulated gene expression among 19 different human tissues or cell lines revealed sets of coregulated genes that serve similar biologic functions. These results define molecular signatures that help to understand the physiology and pathophysiology of thyroid hormone action.\nLast Updated (by provider): Dec 12 2006\n [Switched to use SMHu24k by Gemma] #> 19: Experimental asthma was induced in BALB/c mice by sensitization and challenge with the allergen ovalbumin. Control groups received PBS. To investigate the innate immune component of experimental asthma, we also analyzed recombinase activating gene (RAG) deficient mice following exposure to ovalbumin and control PBS\nLast Updated (by provider): Apr 23 2007\nContributors: Xin Lu Patricia W Finn Vipul V Jain David L Perkins #> 20: This study aims at giving an insight on gene expression in CCAM.\nLast Updated (by provider): May 05 2006\nContributors: Agnes Paquet Sam Hawgood Amy Wagner #> experiment.description @@ -962,255 +962,255 @@

Examples

#> experiment.shortName #> <char> #> 1: bhattacharjee-lung -#> 2: GSE14431 -#> 3: GSE1037 -#> 4: GSE27335 -#> 5: GSE50254 -#> 6: GSE63627 -#> 7: GSE6135.2 -#> 8: GSE44077 -#> 9: GSE60464 -#> 10: GSE5327 -#> 11: GSE17373 -#> 12: GSE6914 -#> 13: GSE10089 -#> 14: GSE13309 -#> 15: GSE21581 -#> 16: GSE23873 -#> 17: GSE3141 -#> 18: GSE11078 -#> 19: GSE10096 -#> 20: GSE17933 +#> 2: GSE1037 +#> 3: GSE6135.2 +#> 4: GSE57148 +#> 5: GSE37138 +#> 6: GSE8894 +#> 7: GSE43458 +#> 8: GSE1987 +#> 9: GSE11341 +#> 10: GSE31552 +#> 11: GSE37768 +#> 12: GSE52248 +#> 13: GSE20875 +#> 14: GSE14431 +#> 15: GSE14359 +#> 16: GSE6044 +#> 17: GSE32665 +#> 18: GSE42127 +#> 19: GSE2088 +#> 20: GSE10072 #> experiment.shortName -#> experiment.name -#> <char> -#> 1: bhattacharjee-lung -#> 2: Simvastatin attenuates lung vascular leak and inflammation in a murine model of radiation-induced lung injury -#> 3: Lung cancer -#> 4: Genomic differences distinguish the myofibroblast phenotype of distal lung from airway fibroblasts -#> 5: Integration of toxicological end points with molecular measurements in a 28-day rat repeated dose inhalation study with cigarette smoke provides mechanistic understanding of smoke impact -#> 6: Global gene expression profiling of primary tumors and lung metastases using a mouse model of spontaneous metastatic mammary carcinoma -#> 7: LKB1 modulates lung cancer differentiation and metastasis - Mus musculus -#> 8: Gene expression profiling of the adjacent airway field cancerization in early stage NSCLC -#> 9: Gene expression-based analysis of extra-cerebral metastases of patients with cerebrotropism (defined here as development of brain metastasis within < 6 months of stage IV disease), compared to patients who did not develop brain metastases for >18 months -#> 10: Breast cancer relapse free survival and lung metastasis free survival -#> 11: Expression data from EGFR mutant transgenic mice -#> 12: Gene expression associated with gemcitabine resistance and its reversal by bexarotene -#> 13: Anti-tumor Activity of Histone Deacetylase Inhibitors in Non-Small Cell Lung Cancer Cells -#> 14: Tobacco Smoke Induces Polycomb-mediated Repression of Dickkopf-1 in Lung Cancer Cells -#> 15: Expression data from lung adenocarcinoma mouse tumors -#> 16: Stage-specific sensitivity to p53 restoration in lung cancer: cell line data -#> 17: Lung Cancer Dataset -#> 18: A six-gene signature predicting breast cancer lung metastasis -#> 19: A novel lung cancer gene signature mediates metastatic bone colonization by a dual mechanism -#> 20: Transcriptional Biomarkers to Predict Female Mouse Lung Tumors in Rodent Cancer Bioassays - A 26 Chemical Set -#> experiment.name +#> experiment.name +#> <char> +#> 1: bhattacharjee-lung +#> 2: Lung cancer +#> 3: LKB1 modulates lung cancer differentiation and metastasis - Mus musculus +#> 4: Characterizing gene expression in lung tissue of COPD subjects using RNA-seq +#> 5: Exon array analysis of the response to bevacizumab/erlotinib in advanced non-small cell lung cancer +#> 6: Prediction of Recurrence-Free Survival in Postoperative NSCLC Patients—a Useful Prospective Clinical Practice +#> 7: Gene expression profiling of lung adenocarcinomas and normal lung tissue +#> 8: Non Small Cell Lung Cancer +#> 9: Lung selective gene responses to alveolar hypoxia +#> 10: Expression Data from human Lung tissue of Patients with Non Small Cell Lung Cancer (NSCLC) +#> 11: Expression data in lung tissue from moderate COPD patients, healthy smokers and nonsmokers +#> 12: Identification of mRNAs and lincRNAs associated with lung cancer progression using next-generation RNA sequencing from laser micro-dissected archival FFPE tissue specimens +#> 13: ADAM-28: A potential oncogene involved in asbestos-related lung adenocarcinomas +#> 14: Simvastatin attenuates lung vascular leak and inflammation in a murine model of radiation-induced lung injury +#> 15: Expression data from conventional osteosarcoma compared to primary non-neoplastic osteoblast cells +#> 16: Genetic programming and gene expression profiling for molecular discrimination and characterization of lung cancers +#> 17: Rewiring of human lung cell lineage and mitotic networks in lung adenocarcinomas +#> 18: Expression data for non-small-cell lung cancer +#> 19: Subclassification of lung squamous cell carcinoma +#> 20: Gene expression signature of cigarette smoking and its role in lung adenocarcinoma development and survival +#> experiment.name #> experiment.ID #> <int> #> 1: 204 -#> 2: 1061 -#> 3: 141 -#> 4: 3315 -#> 5: 8649 -#> 6: 10406 -#> 7: 4096 -#> 8: 8532 -#> 9: 15885 -#> 10: 488 -#> 11: 1733 -#> 12: 2018 -#> 13: 2247 -#> 14: 2645 -#> 15: 3078 -#> 16: 3935 -#> 17: 4079 -#> 18: 4110 -#> 19: 4126 -#> 20: 4912 +#> 2: 141 +#> 3: 4096 +#> 4: 9867 +#> 5: 7727 +#> 6: 6761 +#> 7: 7979 +#> 8: 5127 +#> 9: 956 +#> 10: 5342 +#> 11: 5453 +#> 12: 9934 +#> 13: 2596 +#> 14: 1061 +#> 15: 2185 +#> 16: 2187 +#> 17: 7499 +#> 18: 6030 +#> 19: 5136 +#> 20: 4631 #> experiment.ID -#> experiment.description -#> <char> -#> 1: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. -#> 2: Background: Microvascular injury and increased vascular leakage are prominent features of the radiation-induced lung injury (RILI) which follows cancer<e2><80><93>associated thoracic irradiation. The mechanisms of RILI are incompletely understood and therapeutic strategies to limit RILI are currently unavailable. We established a murine model of radiation pneumonitis in order to assess mechanism-based therapies for RILI-induced inflammation and vascular barrier dysfunction. Based on prior studies, we investigated the therapeutic potential of simvastatin as a vascular barrier protective agent in RILI. Methods: C57BL6/J mice receiving single dose exposure to 18, 20, 22, or 25 Gy, (n=10/group) were temporally assessed (4-12 weeks) for cellular and biochemical indices of injury present in both bronchoalveolar lavage (BAL) and lung tissues (cytokines, tyrosine nitrosylated proteins, leukocytes, extravasation of Evans blue dye or EBD, BAL albumin, histology). In specific experiments, irradiated mice (25Gy) received simvastatin (10 mg/kg) via intraperitoneal injection three times a week (pre and post irradiation) for 2- 6 weeks post irradiation. Results. Acute RILI evolved in a dose- and time-dependent fashion. Mice irradiated with 25Gy exhibited modest increases in BAL leukocytes but significant increases in BAL IL-6 (p=0.03) and TNF-a (p=0.01) at 4 weeks compared to controls. Increases in BAL nitrotyrosine content peaked at 6 weeks (p=0.03) and was accompanied by marked nitrotyrosine immunostaining in lung tissues. Indices of increase lung vascular permeability such as EBD extravasation, BAL protein and BAL albumin significantly increased over time beginning at 6 weeks (p>0.002 all) with histological evidence of severe edema formation and airway inflammation. Simvastatin- treated irradiated mice were noted to exhibit marked attenuation of vascular leak with significantly decreased BAL protein (p=0.01) and inflammatory cell infiltration (50% reduction). Conclusion: Simvastatin is a potentially important therapeutic strategy to limit RILI and may influence radiation associated morbidity and mortality. We used microarrays to detail the global programme of gene expression induced by radiation in Wild type and the protection of SIMVA\nLast Updated (by provider): Jan 14 2009\nContributors: Jeffrey R Jacobson Yves A Lussier Yong Huang Joel Kochanski Lilianna Moreno-Vinasco Joe G Garcia Biji Mathew -#> 3: Two prognostically significant subtypes of high-grade lung neuroendocrine tumors independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles. BACKGROUND: Classification of high-grade neuroendocrine tumors (HGNT) of the lung currently recognises large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung carcinoma (SCLC) as distinct groups. However, a similarity in histology for these two carcinomas and uncertain clinical course have led to suggestions that a single HGNT classification would be more appropriate. Gene expression profiling, which can reproduce histopathological classification, and often defines new subclasses with prognostic significance, can be used to resolve HGNT classification. METHODS: We used cDNA microarrays with 40386 elements to analyze the gene expression profiles of 38 surgically resected samples of lung neuroendocrine tumors and 11 SCLC cell lines. Samples of large-cell carcinoma, adenocarcinoma, and normal lung were also included to give a total of 105 samples analyzed. The data were subjected to filtering to yield informative genes before unsupervised hierarchical clustering that identified relatedness of tumor samples. FINDINGS: Distinct groups for carcinoids, large-cell carcinoma, adenocarcinoma, and normal lung were readily identified. However, we were unable to distinguish LCNEC from SCLC by gene expression profiling. Three independent rounds of unsupervised hierarchical clustering consistently divided SCLC samples into two main groups with LCNEC samples largely integrated with these groups. Furthermore, patients in one of the groups identified by clustering had a significantly better clinical outcome than the other (83% vs 12% survived for 5 years; p=0.0094. None of the highly proliferative SCLC cell lines subsequently analyzed clustered with this good-prognosis group. INTERPRETATION: Our findings show that HGNT of the lung can be classified into two groups independent of SCLC and LCNEC. To this end, we have identified many genes, some of which encode well-characterized markers of cancer that distinguish the HGNT groups. These results have implications for the diagnosis, classification, and treatment of lung neuroendocrine tumors, and provide important insights into their underlying biology.\nDate GSE1037 Last Updated: May 29 2005\nContributors: Virtanen Carl Ishikawa Yuichi Jones Michael Yuichi Ishikawa Daisuke Ishikawa Carl Virtanen Michael H Jones\nIncludes GDS619.\n Update date: Jul 12 2004.\n Dataset description GDS619: Molecular classification of lung high-grade neuroendocrine tumor (HGNT) groups. Carcinoids, large-cell carcinoma, adenocarcinoma, small-cell lung carcinoma cell lines, and normal lung examined. -#> 4: ABSTRACT Primary human distal lung/parenchymal fibroblasts (DLF) exhibit a different phenotype from airway fibroblasts (AF), including the expression of high levels of a-smooth muscle actin (a-SMA). The scope of the differences and the mechanisms driving them are unknown. To determine whether distinct fibroblast characteristics and function based on lung region are predicted by a broad range of genomic differences in AF vs DLF. Matched human fibroblast pairs isolated from proximal and distal lung in 18 asthmatic and 4 normal subjects were studied. Microarray analysis was performed on 12 matched fibroblast pairs (8 asthmatic and 4 normal subjects) and validated by quantitative real-time PCR (qRT-PCR). The functional impact of these molecular differences on AF and DLF was then revealed using computational approaches. Microarray data demonstrated 474 transcripts upregulated in AF, and 611 transcripts upregulated in DLF, when the asthmatic and normal fibroblasts were combined for all the analysis. Further gene ontology (GO) and network analysis identified distinct pathway activation patterns between AF and DLF, including identification of the SMAD3 and MAPK8 signaling pathways. These results demonstrated that marked molecular and functional differences exist between these two lung regional fibroblast populations. These striking differences identify multiple potential mechanisms by which AF and DLF differ in their responses to injury, regeneration and remodeling in the lungs.\nLast Updated (by provider): Jul 22 2011\nContributors: Jadaranka Milosevic Kazuhisa Konishi Wei Wu Xiuxia Zhou Sally E Wenzel Haizhen Hu Naftali Kaminski -#> 5: Demonstration of reduced biological effects with a prototypic modified risk tobacco product. Towards a systems toxicology-based risk assessment, we investigated molecular perturbations accompanying histopathological changes in a 28-day rat inhalation study combining transcriptomics with classical histopathology, and demonstrated reduced biological activity of a prototypic modified risk tobacco product (pMRTP) in comparison to the reference research cigarette 3R4F. Rats were exposed to filtered air or to three concentrations of mainstream smoke (MS) from 3R4F, or to a high concentration of MS from a pMRTP. Histopathology revealed dose-dependent changes for 3R4F: irritative stress-related in nasal and bronchial epithelium and inflammation-related in the lung parenchyma. For pMRTP, significant changes were seen in the nasal epithelium only. Transcriptomics data were obtained from nasal and bronchial epithelium and lung parenchyma. Dose-dependent gene expression changes were seen for 3R4F with much smaller changes for pMRTP. A computational-modeling approach that is based on causal models of tissue-specific biological networks identified cell stress, inflammation, cell proliferation, and senescence as the most perturbed molecular mechanisms. These perturbations correlated with the histopathological observations. Only weak perturbations were observed for the pMRTP. In conclusion, a correlative evaluation of classical histopathology together with gene expression?based computational network models may facilitate a systems toxicology-based risk assessment as shown for a pMRTP.\nLast Updated (by provider): Jun 12 2014\nContributors: Florian Martin Yang Xiang Manuel C Peitsch Ansgar Buettner Marco Esposito Patrice Leroy Stephan Gebel Patrick Vanscheeuwijck Christoph Wyss Ulrike Kogel Julia Hoeng Walter K Schlage Sam Ansari -#> 6: In this study, we explored the molecular basis of site-specific metastasis of breast cancer to the lungs in a clinically relevant model based on the JygMC(A) cell line. In this dataset, we include expression data from JygMC(A) primary mammary tumors, lung metastases, normal mammary glands and normal lung parenchyma.\nLast Updated (by provider): Feb 22 2017\nContributors: David Salomon Anand Merchant Nadia Pereira de Castro Natalie Abrams -#> 7: Inherited mutation in LKB1 results in the Peutz-Jeghers syndrome (PJS), characterized by intestinal hamartomas and a modestly increased frequency of gastrointestinal and breast cancer1. Somatic inactivation of LKB1 occurs in human lung adenocarcinoma2-4, but its tumor suppressor role in this tissue is unknown. Here we show that somatic Lkb1 deficiency strongly cooperates with somatic K-rasG12D activating mutation to accelerate the development of mouse lung tumorigenesis. Lkb1 deficiency in the setting of K-rasG12D mutation (K-ras Lkb1L/L) was associated with decreased tumor latency and increased tumor aggressiveness including metastasis. Furthermore, tumors from K-ras Lkb1L/L mice demonstrated histologies--squamous, adenosquamous and large cell--not seen with K-rasG12D mutation, Ink4a/Arf inactivation, or p53 inactivation alone or in combination. Experiments in vitro suggest that LKB1 suppresses lung tumorigenesis and progression through both p16INK4a-ARF-p53 dependent and independent mechanisms. These data indicate that LKB1 regulates lung tumor progression and differentiation.\nLast Updated (by provider): \nContributors: Diego H Castrillon Janakiraman Krishnamurthy Roderick T Bronson Neal I Lindeman Jussi Koivunen Kate McNamara Lucian R Chirieac David C Christiani Kwok-Kin Wong Matthew Meyerson Takeshi Shimamura Neil D Hayes Hongbin Ji Matthew R Ramsey Pasi A Ja<c2><a8>nne Geoffrey I Shapiro Chad Torrice Bruce Johnson Mei-Chih Liang David J Kwiatkowski Cheng Fan George N Naumov Cristina Contreras Nabeel Bardeesy Lei Bao Piotr Kozlowski Liang Chen Samanthi Perera Danan Li Michael C Wu Dongpo Cai Norman E Sharpless Robert Padera Xihong Lin -#> 8: Previous work has shown that lung tumors and normal-appearing adjacent lung tissues share specific abnormalities that may be highly pertinent to the pathogenesis of lung cancer. However, the global and molecular adjacent airway field cancerization in non-small cell lung cancer (NSCLC) has not been characterized before. We sought to understand the transcriptomic architecture of the adjacent airway field canerization, in conjunction with tumors, to gain additional insights into the lung cancer biology and oncogenesis.\nLast Updated (by provider): May 14 2014\nContributors: Humam Kadara Suk-Young Yoo Ignacio I Wistuba -#> 9: To test the association between gene expression and cerebrotropism the Wilcoxon rank-sum test was utilized and revealed over 400 genes differentially expressed between thw two patient groups Please note that cerebrotropism is defined here as development of brain metastasis within < 6 months of stage IV disease\nAt time of import, last updated (by provider) on: Dec 22 2017\n\nContributors: ; [Fabio Parisi] -#> 10: Validation of lung metastasis signature (LMS) and its association with risk of developing lung metastasis and with primary tumor size.\nLast Updated (by provider): May 14 2007\nContributors: Andy Minn John Foekens Yixin Wang Joan Massague -#> 11: We performed mRNA expression profiling of lung tumors from C/L858R, C/T790M, and C/L+T mice and from corresponding normal lung tissue.\nLast Updated (by provider): Dec 01 2009\nContributors: William Pao Lucia Regales -#> 12: Resistance of Calu3 NSCLC cells to the cytotoxic nucleoside analog gemcitabine (2',2'-difluorodeoxycytidine) can be prevented as well as reversed by the rexinoid X receptor selective agonist bexarotene. This study was designed to investigate the changes in gene expression associated with gemcitabine resistance and its reversal by bexarotene. In addition to the parental Calu3 cells and the 10 cycles of treatment of the gemcitabine resistant Calu3 cells with vehicle or bexarotene, analogous treatment paradigms with gemcitabine alone as well as the combination of both compounds have been included as controls. (However, it has to be noted that in the combination treatment, cells that were re-sensitized by bexarotene have largely been removed from the culture before harvest due to the cytotoxic activity of gemcitabine.)\nLast Updated (by provider): May 16 2007\nContributors: Wen Luo Thomas Hermann Yen Wan-Ching Patricia Tooker Jorge Valencia Rene Prudente Jen Sanders\nIncludes GDS2777.\n Update date: Apr 15 2008.\n Dataset description GDS2777: Analysis of gemcitabine (Gem)-resistant non-small lung cancer Calu3 cells treated with bexarotene (Bex). Acquired drug resistance is a major obstacle in cancer therapy. Results provide insight into molecular mechanisms underlying the ability of bexarotene to overcome Gem-resistance in Calu3 cells. -#> 13: In order to ascertain the potential for histone deacetylase (HDAC) inhibitor-based treatment in non-small cell lung cancer (NSCLC), we analyzed the anti-tumour effects of Trichostatin A (TSA) and suberoylanilide hydroxamic acid (vorinostat) in a panel of 16 NSCLC cell lines via MTT assay. TSA and vorinostat both displayed strong anti-tumor activities in a proportion of NSCLC cell lines, and suggesting the need for the use of predictive markers to select patients receiving this treatment. There was a strong correlation between the responsiveness to TSA and vorinostat (P < 0.0001). To identify a molecular model of sensitivity to HDAC inhibitor treatment in NSCLC, we conducted a gene expression profiling study using cDNA arrays on the same set of cell lines and related the cytotoxic activity of TSA to corresponding gene expression pattern using a modified NCI program. In addition, pathway analysis was performed with Pathway Architect software. We used nine genes, which were identified by gene-drug sensitivity correlation and pathway analysis, to build a support vector machine (SVM) algorithm model by which sensitive cell lines were distinguished from resistant cell lines. The prediction performance of the SVM model was validated by an additional seven cell lines, resulting in a prediction value of 100% in respect to determining response to TSA. Our results suggested that [1] HDAC inhibitors may be promising anticancer drugs to NSCLC, and [2] the nine gene classifer is useful in predicting drug sensitivity to HDAC inhibitors and may contribute to achieving individualized therapy for NSCLC patients. training sample set: GSM94303 PC9 GSM94304 PC7 GSM94305 PC14 GSM94306 A549 GSM94308 LK2 GSM94313 RERF LC-KJ GSM94314 RERF LC-MS GSM94315 RERF-LC-AI GSM94316 PC-1 GSM94317 PC-3 GSM94319 PC-10 GSM94323 ABC-1 GSM94324 EBC-1 GSM94325 LC2/ad GSM94328 SQ-5 GSM94329 QG-56 test sample set: GSM94307 LU65 GSM94326 LC1/sq GSM94327 LC-1F GSM254967 LCOK GSM254968 LCD GSM254969 H1650 GSM254970 H1975\nLast Updated (by provider): Dec 15 2010 -#> 14: Polycomb-mediated repression of Dkk-1 activates Wnt signaling and enhances tumorigenic potential of lung cancer cells following tobacco smoke exposure \nLast Updated (by provider): Jan 12 2011\nContributors: Mahadev Rao Maocheng Yang Fang Liu Julie A Hong Mustafa Hussain Ashley E Humphries Diana Caragacianu David S Schrump -#> 15: To identify altered signal transduction pathways involved in the progression and metastases of Lkb1 deficient lung tumors, we have performed an unbiased microarray analysis of primary and metastatic mouse lung tumors We used microarrays to detail the global programme of gene expression underlying metastatic progression\nLast Updated (by provider): Jun 18 2010\nContributors: Julian Carretero Kwok-kin Wong -#> 16: Tumorigenesis is a multistep process that results from the sequential accumulation of mutations in key oncogene and tumor-suppressor pathways. The quest to personalize cancer medicine based on targeting these underlying genetic abnormalities presupposes that sustained inactivation of tumor suppressors and activation of oncogenes are required for tumor maintenance. Mutations in the p53 tumor-suppressor pathway are a hallmark of cancer and significant efforts toward pharmaceutical reactivation of mutant p53 pathways are underway1-3. Here we show that restoration of p53 in established murine lung tumors leads to significant but incomplete tumor cell loss specifically in malignant adenocarcinomas but not in adenomas. Also, we define amplification of MAPK signaling as a critical determinant of malignant progression. The differential response to p53 restoration depends on activation of the Arf tumor suppressor downstream of hyperactive MAPK signaling. We propose that p53 naturally limits malignant progression by responding to increased oncogenic signaling, but is unresponsive to low levels of oncogene activity that are sufficient for early stages of lung tumor development. These data suggest that restoration of pathways important in tumor progression, as opposed to initiation, may lead to incomplete tumor regression due to the stage-heterogeneity of tumor cell populations.\nLast Updated (by provider): Nov 28 2010\nContributors: Tyler Jacks Roderick Bronson Rebecca Resnick Francisco Sanchez-Rivera Michael T Hemann Kamena K Kostova David M Feldser Monte M Winslow Charles A Whittaker Sarah Taylor Chris Cashman -#> 17: Signatures of Oncogenic Pathway Deregulation in Human Cancers. The ability to define cancer subtypes, recurrence of disease, and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Such data is also of substantial importance to the analysis of cellular signaling pathways central to the oncogenic process. With this focus, we have developed a series of gene expression signatures that reliably reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumors, and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumor sub-types. Clustering tumors based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Furthermore, predictions of pathway deregulation in cancer cell lines are shown to coincide with sensitivity to therapeutic agents that target components of the pathway, underscoring the potential for such pathway prediction to guide the use of targeted therapeutics.\nLast Updated (by provider): Nov 12 2011\nContributors: Joseph R Nevins -#> 18: The lungs are a frequent target of metastatic breast cancer cells, but the underlying molecular mechanisms are unclear. All existing data were obtained either using statistical association between gene expression measurements found in primary tumors and clinical outcome, or using experimentally derived signatures from mouse tumor models. Here, we describe a distinct approach that consists to utilize tissue surgically resected from lung metastatic lesions and compare their gene expression profiles with those from non-pulmonary sites, all coming from breast cancer patients. We demonstrate that the gene expression profiles of organ-specific metastatic lesions can be used to predict lung metastasis in breast cancer. We identified a set of 21 lung metastasis-associated genes. Using a cohort of 72 lymph node-negative breast cancer patients, we developed a six-gene prognostic classifier that discriminated breast primary cancers with a significantly higher risk of lung metastasis. We then validated the predictive ability of the six-gene signature in 3 independent cohorts of breast cancers consisting of a total of 721 patients. Finally, we demonstrated that the signature improves risk stratification independently of known standard clinical parameters and a previously established lung metastasis signature based on an experimental breast cancer metastasis model.\nLast Updated (by provider): Nov 12 2011\nContributors: Teresa Garcia Rosette Lidereau Ivan Bi<c3><a8>che Alain Boudinet Amanda Jackson Soraya Sin Jean-Marc Guinebreti<c3><a8>re Vincent Castronovo Marianne Briffod Keltouma Driouch Catherine Nogu<c3><a8>s Anna Teti Akeila Bellahc<c3><a8>ne Enrico Ricevuto Pascal Cherel Thomas Landemaine Nadia Rucci Berta Martin Abad Angels Sierra -#> 19: Bone is a frequent target of lung cancer metastasis, which is associated with significant morbidity and a dismal prognosis. To identify and functionally characterize genes involved in the mechanisms of osseous metastasis we developed a murine lung cancer model. Comparative transcriptomic analysis identified genes encoding signaling molecules (such as TCF4 and PRKD3), and cell anchorage related proteins (MCAM, and SUSD5), some of which were basally modulated by TGFbeta in tumor cells and in conditions mimicking tumor-stroma interactions. Triple gene combinations induced not only high osteoclastogenic activity but also a marked enhancement of global metalloproteolytic activities in vitro. These effects were strongly associated with robust bone colonization in vivo, whereas this gene subset was ineffective in promoting local tumor growth and cell homing activity to bone. Interestingly, global inhibition of metalloproteolytic activities and simultaneous TGFbeta blockade in vivo led to increased survival and a remarkable attenuation of bone tumor burden and osteolytic metastasis. Thus, this metastatic gene signature mediates bone-matrix degradation by a dual mechanism of induction of TGFbeta-dependent osteoclastogenic bone resorption and enhancement of stroma-dependent metalloproteolytic activities. Our findings suggest the cooperative contribution of host-derived and cell-autonomous effects directed by a small subset of genes in mediating aggressive osseous colonization.\nLast Updated (by provider): Sep 22 2011\nContributors: Silvestre Vicent Ignacio Garcia-Tu<c3><b1>on Javier Dotor Javier De Las Rivas Iker Anton Fernando Lecanda Diego Luis-Ravelo Francisco Borras-Cuesta -#> 20: The process for evaluating chemical safety is inefficient, costly, and animal intensive. There is growing consensus that the current process of safety testing needs to be significantly altered to improve efficiency and reduce the number of untested chemicals. In this study, the use of short-term gene expression profiles was evaluated for predicting the increased incidence of mouse lung tumors. Animals were exposed to a total of 26 diverse chemicals with matched vehicle controls over a period of three years. Upon completion, significant batch-related effects were observed. Adjustment for batch effects significantly improved the ability to predict increased lung tumor incidence. For the best statistical model, the estimated predictive accuracy under honest five-fold cross-validation was 79.3% with a sensitivity and specificity of 71.4 and 86.3%, respectively. A learning curve analysis demonstrated that gains in model performance reached a plateau at 25 chemicals, indicating that the size of the current data set was sufficient to provide a robust classifier. The classification results showed a small subset of chemicals contributed disproportionately to the misclassification rate. For these chemicals, the misclassification was more closely associated with genotoxicity status than efficacy in the original bioassay. Statistical models were also used to predict dose-response increases in tumor incidence for methylene chloride and naphthalene. The average posterior probabilities for the top models matched the results from the bioassay for methylene chloride. For naphthalene, the average posterior probabilities for the top models over-predicted the tumor response, but the variability in predictions were significantly higher. The study provides both a set of gene expression biomarkers for predicting chemically-induced mouse lung tumors as well as a broad assessment of important experimental and analysis criteria for developing microarray-based predictors of safety-related endpoints.\nLast Updated (by provider): May 25 2012\nContributors: Russell S Thomas -#> experiment.description +#> experiment.description +#> <char> +#> 1: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. +#> 2: Two prognostically significant subtypes of high-grade lung neuroendocrine tumors independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles. BACKGROUND: Classification of high-grade neuroendocrine tumors (HGNT) of the lung currently recognises large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung carcinoma (SCLC) as distinct groups. However, a similarity in histology for these two carcinomas and uncertain clinical course have led to suggestions that a single HGNT classification would be more appropriate. Gene expression profiling, which can reproduce histopathological classification, and often defines new subclasses with prognostic significance, can be used to resolve HGNT classification. METHODS: We used cDNA microarrays with 40386 elements to analyze the gene expression profiles of 38 surgically resected samples of lung neuroendocrine tumors and 11 SCLC cell lines. Samples of large-cell carcinoma, adenocarcinoma, and normal lung were also included to give a total of 105 samples analyzed. The data were subjected to filtering to yield informative genes before unsupervised hierarchical clustering that identified relatedness of tumor samples. FINDINGS: Distinct groups for carcinoids, large-cell carcinoma, adenocarcinoma, and normal lung were readily identified. However, we were unable to distinguish LCNEC from SCLC by gene expression profiling. Three independent rounds of unsupervised hierarchical clustering consistently divided SCLC samples into two main groups with LCNEC samples largely integrated with these groups. Furthermore, patients in one of the groups identified by clustering had a significantly better clinical outcome than the other (83% vs 12% survived for 5 years; p=0.0094. None of the highly proliferative SCLC cell lines subsequently analyzed clustered with this good-prognosis group. INTERPRETATION: Our findings show that HGNT of the lung can be classified into two groups independent of SCLC and LCNEC. To this end, we have identified many genes, some of which encode well-characterized markers of cancer that distinguish the HGNT groups. These results have implications for the diagnosis, classification, and treatment of lung neuroendocrine tumors, and provide important insights into their underlying biology.\nDate GSE1037 Last Updated: May 29 2005\nContributors: Virtanen Carl Ishikawa Yuichi Jones Michael Yuichi Ishikawa Daisuke Ishikawa Carl Virtanen Michael H Jones\nIncludes GDS619.\n Update date: Jul 12 2004.\n Dataset description GDS619: Molecular classification of lung high-grade neuroendocrine tumor (HGNT) groups. Carcinoids, large-cell carcinoma, adenocarcinoma, small-cell lung carcinoma cell lines, and normal lung examined. +#> 3: Inherited mutation in LKB1 results in the Peutz-Jeghers syndrome (PJS), characterized by intestinal hamartomas and a modestly increased frequency of gastrointestinal and breast cancer1. Somatic inactivation of LKB1 occurs in human lung adenocarcinoma2-4, but its tumor suppressor role in this tissue is unknown. Here we show that somatic Lkb1 deficiency strongly cooperates with somatic K-rasG12D activating mutation to accelerate the development of mouse lung tumorigenesis. Lkb1 deficiency in the setting of K-rasG12D mutation (K-ras Lkb1L/L) was associated with decreased tumor latency and increased tumor aggressiveness including metastasis. Furthermore, tumors from K-ras Lkb1L/L mice demonstrated histologies--squamous, adenosquamous and large cell--not seen with K-rasG12D mutation, Ink4a/Arf inactivation, or p53 inactivation alone or in combination. Experiments in vitro suggest that LKB1 suppresses lung tumorigenesis and progression through both p16INK4a-ARF-p53 dependent and independent mechanisms. These data indicate that LKB1 regulates lung tumor progression and differentiation.\nLast Updated (by provider): \nContributors: Diego H Castrillon Janakiraman Krishnamurthy Roderick T Bronson Neal I Lindeman Jussi Koivunen Kate McNamara Lucian R Chirieac David C Christiani Kwok-Kin Wong Matthew Meyerson Takeshi Shimamura Neil D Hayes Hongbin Ji Matthew R Ramsey Pasi A Ja¨nne Geoffrey I Shapiro Chad Torrice Bruce Johnson Mei-Chih Liang David J Kwiatkowski Cheng Fan George N Naumov Cristina Contreras Nabeel Bardeesy Lei Bao Piotr Kozlowski Liang Chen Samanthi Perera Danan Li Michael C Wu Dongpo Cai Norman E Sharpless Robert Padera Xihong Lin +#> 4: We analyzed gene expression profiling of lung tissue to define molecular pathway of COPD using recent RNA sequencing technology.Lung tissue was obtained from 98 COPD subjects and 91 subjects with normal spirometry. RNA isolated from these samples was processed with RNA-seq using HiSeq 2000. Gene expression measurements were calculated using Cufflinks software. Differentially expressed genes and isoforms were chosen using t-test. Some of differentially expressed genes were validated by quantitative real-time PCR.\nLast Updated (by provider): Sep 16 2016\nContributors: Jae H Lim Yeon-Mok Oh Woo J Kim Jae S Lee Juhan Kim Sang D Lee +#> 5: In the current study, we used exon arrays and clinical samples from a previous trial (SAKK 19/05) to investigate the expression variations at the exon-level of 3 genes potentially playing a key role in modulating treatment response (EGFR, KRAS, VEGFA). Exon-level biomarkers for the response to targeted therapy bevacizumab/erlotinib were identified in patients with metastatic non-small cell lung cancer\nLast Updated (by provider): Sep 27 2013\nContributors: Sacha Rothschild Susanne Crowe Martin Früh Florent Baty Francesco Zappa Cornelia Dröge Martin Brutsche Miklos Pless Daniel C Betticher Richard Cathomas Oliver Gautschi Daniel Rauch Lukas Bubendorf +#> 6: Background: One of the main fields of lung cancer research is identifying patients who are at high risk of post-resection recurrence. Individual recurrence risk evaluation by accurate but simple and reproducible method is needed for the clinical practice. Results: The log-rank test and further selection by our criteria of assayability generated 87 genes from microarray data with significant level 5%. Of these, by PTQ-PCR, the expression of most significant 18 genes was obtained. Using these gene expression information and clinical parameters, by stepwise variable selection method, the recurrence prediction model, which composed of 6 genes (CALB1, MMP7, SLC1A7, GSTA1, CCL19, IFI44) and pStage and cell differentiation, were developed. Validation into the two independent cohorts showed good results of the proposed model (p=0.0314, 0.0305, respectively). The predicted median recurrence-free survival times for each patient were reflected real ones well. Conclusions: Our method of individualized recurrence risk prediction is accurate, technically simple and reproducible to be used in clinical practice. Therefore, it would be useful in customizing the lung cancer management strategies.\nLast Updated (by provider): Jul 24 2013\nContributors: Miyeon Park Sung-Hyun Kim Jinseon Lee Jhingook Kim Eung-Sirk Lee Youngja Jung Yoo-Sung Lim Young H Choi Heesue Kim Dae-Soon Son Jisuk Jo +#> 7: Lung cancer is still the leading cause of cancer-related deaths in the US and worldwide. Understanding the global molecular profiles or transcriptome of lung cancers would strengthen our understanding of the biology of this malignancy. We performed gene expression profiling using the Human Gene 1.0 ST platform of 80 lung adenocarcinomas and 30 normal lung tissues to better understand the biology of this significant fraction of non-small cell lung carcinomas (NSCLCs)\nLast Updated (by provider): Sep 17 2013\nContributors: Humam Kadara Mohamed Kabbout Ignacio I Wistuba +#> 8: This series contain 36 samples obtained from human lung tissue and includes the following: 7 Adenocarcinoma samples. 16 Squamous cell carcinoma samples. 1 AdenoSquamous sample. 2 Renal Metastasis. 1 Colon metastasis. 7 normal lung tissue adjacent to the tumors. 2 commercial normal lung RNA.\nLast Updated (by provider): Mar 15 2012\nContributors: Elinor Dehan Naftali Kaminski +#> 9: Pulmonary hypoxia is a common complication of chronic lung diseases leading to the development of pulmonary hypertension. The underlying sustained increase in vascular resistance in hypoxia is a response unique to the lung. Thus, we hypothesised that there are genes whose expression is altered selectively in the lung in response to alveolar hypoxia. Using a novel subtractive array strategy, we compared gene responses to hypoxia in primary human pulmonary microvascular endothelial cells to those in cardiac microvascular endothelium and identified genes selectively differentially regulated in the lung endothelium.\nLast Updated (by provider): Nov 21 2008\nContributors: Christine M Costello +#> 10: Lung cancers are a heterogeneous group of diseases with respect to biology and clinical behavior. Currently, diagnosis and classification are based on histological morphology and immunohistological methods for discrimination between two main histologic groups: small cell lung cancer (SCLC) and non-small cell lung cancer which account for 20% and 80% of lung carcinomas, respectively. NSCLCs, which are divided into the three major subtypes adenocarcinoma, squamous cell carcinoma and dedifferentiated large cell carcinoma, show different characteristics such as the expression of certain keratins or production of mucin and lack of neuroedocrine differentiation. The molecular pathogenesis of lung cancer involves the accumulation of genetic und epigenetic alterations including the activation of proto-oncogenes and inactivation of tumor suppressor genes which are different for lung cancer subgroups. The development of microarray technologies opened up the possibility to quantify the expression of a large number of genes simultaneously in a given sample. There are several recent reports on expression profiling on lung cancers but the analysis interpretation of the results might be difficult because of the heterogeneity of cellular components. The methods used for sample selection and processing can have a strong influence on the expression values obtained through microarray profiling. Laser capture microdissection (LCM) provides higher specificity in the selection of target cells compared to traditional bulk tissue selection methods, but at an increased processing cost. Here we describe the use of an expression microarray study on NSCLC samples and surrounding tissue, comparing macroscopic lung tumor and tissue samples (“grind and bind”), versus tumor and alveolar compartment cells laser capture microdissected (LCM) from the same macroscopic lung samples. In this study, an initial set of 16 pairs of macroscopic tumor and non-tumor samples (9 pairs squamous-cell carcinoma, 7 pairs adenocarcinoma) was selected for bulk/macro sampling. Of these 16 pairs, 12 pairs were reanalyzed using laser capture microdissection (LCM) for sampling the cells (7 pairs squamous, 5 pairs adenocarcinomas). For macroscopic samples, 50 to 80 µg of tissue was used to isolate total RNA. Gene expression profile was determined using Affymetrix Human Genome Gene 1.0 ST genechip. For the LCM samples, from representative slides histologically confirmed and mapped by a pathologist, approximately 1000 cells/sample were collected by LCM. cDNA was amplified using Nugen WT-Ovation One-Direct amplification system. In order to validate Nugen amplification bias of WT-Ovation One-Direct amplification system, the total RNA samples of 10 pairs of macroscopic tumor and non-tumor samples were amplified with this amplification system, and their cDNA was used to microarray.\nLast Updated (by provider): Jun 04 2012\nContributors: S D Spivack T Wang N Mullapudi M Shi S Keller J Locker J Lin G Marquardt +#> 11: Chronic obstructive pulmonary disease (COPD) is a progressive and irreversible chronic inflammatory lung disease. The abnormal inflammatory response of the lung, mainly to cigarette smoke, causes multiple cellular and structural changes affecting all of its compartments, which leads to disease progression. The molecular mechanisms underlying these pathological changes are still not fully understood The aim of this study was to identify genes and molecular pathways potentially involved in the pathogenesis of COPD\nLast Updated (by provider): Jun 18 2012\nContributors: Ricardo Bastos Susana G Kalko +#> 12: Adenocarcinoma in situ (AIS) is considered an intermediate step in the progression of normal lung tissue to invasive adenocarcinoma. However, the molecular mechanisms underlying this progression remain to be fully elucidated due to difficulties in obtaining and preserving clinical samples for downstream analyses. Formalin fixation and paraffin embedding (FFPE) is a tissue preservation system that is widely used as a means for long-term storage. Until now, challenges in working with FFPE have precluded using new RNA sequencing technologies (RNA-seq), which would help clarify some of the key pathways affected in the transition from normal to AIS to invasive adenocarcinoma. Recent technological advances have made it possible to sequence RNA from archival tissues. Also, isolation techniques including laser-capture micro-dissection provide the ability to select histopathologically distinct tissues, allowing researchers to study transcriptional variations between tightly juxtaposed cell and tissue types. Utilizing these technologies and new alignment tools we examined differential expression of long intergenic non-coding RNAs and mRNAs across normal, AIS and invasive adenocarcinoma samples from six patients to identify possible markers of lung cancer progression. RNA extracted and sequenced from these 18 samples generated an average of 198 million reads per sample. After alignment and filtering, uniquely aligned reads represented an average 35% of the total reads. We detected differential expression of a number of lincRNAs and mRNAs when comparing normal to AIS, or AIS to invasive adenocarcinoma. Of these, 5 lincRNAs and 31 mRNAs were consistently up- or down-regulated from normal to AIS and more so to invasive carcinoma. We validated the up-regulation of two mRNAs and one lincRNA by RT-qPCR as proof of principle. Our findings indicate a potential role of not only mRNAs, but also lincRNAs in invasive adenocarcinoma. We anticipate that our current findings will lay the groundwork for future experimental studies of candidate RNAs from FFPE samples to identify their functional roles in lung cancer.\nLast Updated (by provider): Jul 26 2016\nContributors: Xiaodong Bai Cheryl L Thompson Matthew L Morton Rom S Leidner +#> 13: To identify gene expression biomarkers associated with asbestos-related lung adenocarcinoma, we analyzed primary tumour gene expression for a total of 36 primary lung adenocarcinomas on 22,323 element microarrays, comparing 12 patients with lung asbestos body counts above levels associated with urban dwelling (ARLC-AC: asbestos-related lung cancer-adenocarcinoma) with 24 patients with no asbestos bodies (NARLC-AC: non-asbestos related lung cancer-adenocarcinoma).\nNote: 22 samples from this series, which appear in other Expression Experiments in Gemma, were not imported from the GEO source. The following samples were removed: GSM136031 on GPL3877, GSM136022 on GPL3877, GSM136012 on GPL3877, GSM136030 on GPL3877, GSM136021 on GPL3877, GSM136011 on GPL3877, GSM136024 on GPL3877, GSM136010 on GPL3877, GSM135992 on GPL3877, GSM135993 on GPL3877, GSM135994 on GPL3877, GSM135990 on GPL3877, GSM136001 on GPL3877, GSM135991 on GPL3877, GSM135999 on GPL3877, GSM136018 on GPL3877, GSM136019 on GPL3877, GSM136028 on GPL3877, GSM136005 on GPL3877, GSM136006 on GPL3877, GSM136016 on GPL3877, GSM136004 on GPL3877, \nLast Updated (by provider): Jul 19 2010\nContributors: Edwina E Duhig Santiyagu M Savarimuthu Rayleen V Bowman Ian A Yang Morgan N Windsor Jill E Larsen Nicholas K Hayward Rebecca E McLachlan Linda H Passmore Maxine E Tan Morgan R Davidson Casey M Wright Belinda E Clarke Kwun M Fong Maria M Martins +#> 14: Background: Microvascular injury and increased vascular leakage are prominent features of the radiation-induced lung injury (RILI) which follows cancer–associated thoracic irradiation. The mechanisms of RILI are incompletely understood and therapeutic strategies to limit RILI are currently unavailable. We established a murine model of radiation pneumonitis in order to assess mechanism-based therapies for RILI-induced inflammation and vascular barrier dysfunction. Based on prior studies, we investigated the therapeutic potential of simvastatin as a vascular barrier protective agent in RILI. Methods: C57BL6/J mice receiving single dose exposure to 18, 20, 22, or 25 Gy, (n=10/group) were temporally assessed (4-12 weeks) for cellular and biochemical indices of injury present in both bronchoalveolar lavage (BAL) and lung tissues (cytokines, tyrosine nitrosylated proteins, leukocytes, extravasation of Evans blue dye or EBD, BAL albumin, histology). In specific experiments, irradiated mice (25Gy) received simvastatin (10 mg/kg) via intraperitoneal injection three times a week (pre and post irradiation) for 2- 6 weeks post irradiation. Results. Acute RILI evolved in a dose- and time-dependent fashion. Mice irradiated with 25Gy exhibited modest increases in BAL leukocytes but significant increases in BAL IL-6 (p=0.03) and TNF-a (p=0.01) at 4 weeks compared to controls. Increases in BAL nitrotyrosine content peaked at 6 weeks (p=0.03) and was accompanied by marked nitrotyrosine immunostaining in lung tissues. Indices of increase lung vascular permeability such as EBD extravasation, BAL protein and BAL albumin significantly increased over time beginning at 6 weeks (p>0.002 all) with histological evidence of severe edema formation and airway inflammation. Simvastatin- treated irradiated mice were noted to exhibit marked attenuation of vascular leak with significantly decreased BAL protein (p=0.01) and inflammatory cell infiltration (50% reduction). Conclusion: Simvastatin is a potentially important therapeutic strategy to limit RILI and may influence radiation associated morbidity and mortality. We used microarrays to detail the global programme of gene expression induced by radiation in Wild type and the protection of SIMVA\nLast Updated (by provider): Jan 14 2009\nContributors: Jeffrey R Jacobson Yves A Lussier Yong Huang Joel Kochanski Lilianna Moreno-Vinasco Joe G Garcia Biji Mathew +#> 15: In osteosarcoma patients, the development of metastases, often to the lungs, is the most frequent cause of death. To improve this situation, a deeper understanding of the molecular mechanisms governing osteosarcoma development and dissemination and the identification of novel drug targets for an improved treatment are needed. Towards this aim, we characterized osteosarcoma tissue samples compared to primary osteoblast cells using Affymetrix HG U133A microarrays.\nLast Updated (by provider): Dec 15 2010\nContributors: Raphaela Guenther +#> 16: Lung cancers are a heterogeneous group of diseases with respect to biology and clinical behavior. So far, diagnosis and classification are based on histological morphology and immunohistological methods for discrimination between two main histologic groups: small cell lung cancer (SCLC) and non-small cell lung cancer which account for 20% and 80% of lung carcinomas, respectively. While SCLCs express properties of neuroendocrine cells, NSCLCs, which are divided into the three major subtypes adenocarcinoma, squamous cell carcinoma and dedifferentiated large cell carcinoma, show different characteristics such as the expression of certain keratins or production of mucin and lack neuroedocrine differentiation. The molecular pathogenesis of lung cancer involves the accumulation of genetic und epigenetic alterations including the activation of proto-oncogenes and inactivation of tumor suppressor genes which are different for lung cancer subgroups. The development of microarray technologies opened up the possibility to quantify the expression of a large number of genes simultaneously in a given sample. There are several recent reports on expression profiling on lung cancers but the analysis interpretation of the results might be difficult because of the heterogeneity of cellular components. A contamination of the tumor sample with normal epithelia, blood vessels, stromal cells, leucocytes and tumor necrosis may confound the true expression profile of the tumor. The use of laser capture microdissection (LCM) greatly improves the sample preparation for microarray expression analysis. Consequently, we used advanced technology including LCM and microarray analysis. In detail, we examined gene expression profiles of tumor cells from 29 previously untreated patients with lung cancer (10 adenocarcinomas (AC), 10 squamous cell carcinomas (SCC), 9 small cell lung cancer (SCLC)) in comparison to normal lung tissue (LT) of 5 control patients without tumor. Bronchoscopical biopsies from the primary lung tumor were taken before treatment. Biopsies were cut into 8µm sections and from each section cancer cells were isolated using laser capture microdissection in order to obtain pure samples of tumor cells. Total RNA was extracted, reversely transcribed, in-vitro transcribed, labelled and hybridized to the array. For expression analysis, microarrays covering 8793 defined genes (Human HG Focus Array, Affymetrix) were used. Following quality control, array data were normalized and analysed for significant differences using variance stabilizing transformation (VSN) and significance analysis of microarrays (SAM), respectively. Based on differentially expressed genes cancer samples could be clearly separated from non cancer samples using hierarchical clustering. Comparing AC, SCC and SCLC with normal lung tissue, we found 205, 335 and 404 genes, respectively, that were at least 2-fold differentially expressed with an estimated false discovery rate < 2.6%. Each histological subtype showed a distinct expression profile. Further, using a genetic programming approach we constructed a classificator to discriminate AC, SCC, NT and SCLC. To this end, the 50 genes with the greatest signal-to-noise ratio were selected to train the classificator. By leave-one-out cross validation all 34 samples were correctly classified in this training set. In order to validate the 50-gene-classificator on a test set, further 13 microdissected lung cancer samples were used and correctly classified in concordance to pathologic finding. In conclusion, the different lung cancer subtypes have distinct molecular phenotypes which reflect biological characteristics of the tumor cells and which might be the basis for development of targeted therapy. Moreover, gene expression profiling and genetic programming is a suitable tool for classification and discrimination of different histological subtypes in lung cancer in comparison to normal lung tissue.\nLast Updated (by provider): Mar 19 2010\nContributors: Helene Geddert Ulrich P Rohr Andreas Schwalen Slavek Kliszewski Michael Rosskopf Ulrich Steidl Ralf Kronenwett Rainer Haas Helmut Gabbert Astrid Rohrbeck +#> 17: Naturally occurring genetic polymorphisms influence patterns of gene expression in normal tissues, and can provide a molecular view of the component cell lineages and signaling pathways responsible for normal tissue architecture. Analysis of the coordinated changes in this architecture that take place during tumor development can help to identify the functional roles of oncogenes or tumor suppressors and provide potential new therapeutic targets. We have applied a network analysis approach to a set of 92 normal human lung samples from cancer patients and their matched adenocarcinomas. We have identified networks associated with particular cell lineages (alveolar type 2 pneumocytes and Clara cells) in normal lung and document the changes in these networks that accompany transformation to adenocarcinomas. Expression of the transcription factor NKX2-1 (TTF1) is linked to surfactant protein markers of the alveolar type 2 lineage in normal and transformed lung cells, but its network is rewired in tumors to include pathways linked to cell growth such as glutaminase (GLS2). Analysis of mitotic networks revealed the presence of novel components such as the kinase VRK1 that are preferentially linked to the mitotic cycle in tumors but not in normal lung. We show that shRNA-mediated inhibition of VRK1 cooperates with inhibition of PARP signaling to inhibit growth of lung tumor cells. Targeting of genes that are recruited into tumor mitotic networks may provide a wider therapeutic window than that seen by inhibition of integral components of the mitotic machinery such as Aurora kinases.\nLast Updated (by provider): Jul 03 2013\nContributors: David Jablons Minh D To Kevin K Lin Kuang-Yu Jen Brian Jo Patrick Pham Il-Jin Kim Jae Kim Jian-Hua Mao Dan Raz David A Quigley Allan Balmain +#> 18: Purpose Prospectively identifying who will benefit from adjuvant chemotherapy (ACT) would improve clinical decisions for individual non-small-cell lung cancer (NSCLC) patients. Most current molecular signatures for lung cancer are prognostic only and provide limited information with regard to the functional importance of the genes selected. In this study, we aim to develop and validate a functional gene set that predicts the clinical benefit of ACT in NSCLC. Experimental Design An 18-hub-gene prognosis signature was developed through a systems biology approach using a large NSCLC dataset from the Director’s Challenge Consortium. The prognostic value of this signature was tested in NSCLC patients from UT Lung SPORE cohort and additional five public datasets. The 18-hub-gene set was then integrated with genome-wide functional (RNAi) data and genetic aberration data to derive a 12-gene predictive signature for ACT benefit in NSCLC. Results We showed that the 18-hub-gene set can robustly predict the prognosis of patients with adenocarcinoma in all validation datasets across four microarray platforms. The refined 12-gene functional set was successfully validated in two independent datasets. The predicted benefit group showed significant improvement in survival after ACT (JBR.10 clinical trial data: hazard ratio=0.36, p=0.038; UT Lung SPORE data: hazard ratio=0.34, p=0.017), while the predicted non-benefit group showed no survival improvement. Conclusions This is the first study to integrate genetic aberration, genome-wide RNAi functional data, and mRNA expression data to identify a functional gene set that is predictive for ACT benefits. This 12-gene predictive signature has been validated in two independent NSCLC cohorts.\nLast Updated (by provider): Feb 04 2013\nContributors: Hao Tang Guanghua Xiao Chi-Wan Chow Alejandro Corvalan Yang Xie John Minna Milind Suraokar Michael White Joan Schiller Ignacio Wistuba Carmen Behrens Jeffrey Allen +#> 19: Two subclasses of lung squamous cell carcinoma with different gene expression profiles and prognosis identified by hierarchical clustering and validated by non-negative matrix factorization . BACKGROUND: Current clinical and histopathological criteria used to define lung squamous cell carcinomas (SCCs) are insufficient to predict clinical outcome. We attempted to make a clinically-useful classification based on gene expression profiling. METHODS: We used cDNA microarrays with 40386 elements to analyze the gene expression profiles of 48 surgically resected samples of lung SCC. 9 samples of lung adenocarcinoma and 30 of normal lung were also included to give a total of 87 samples analyzed. After gene filtering, the data were subjected to hierarchical clustering and consensus clustering with the non-negative matrix factorization (NMF) approach. FINDINGS: Initial analysis by hierarchical clustering allowed division of SCCs into two distinct subclasses. An additional independent round of hierarchical clustering and consensus clustering with the NMF approach provided a validation for the classification. Kaplan-Meier analysis with the log rank test pointed to a non-significant difference in survival (p=0.071) but the likelihood of survival to 6 years was significantly different between the two groups (40.5% vs 81.8%, p=0.014, Z-test). Biological process categories characteristic for each subclass were identified statistically and up-regulation of cell-proliferation related genes was evident in the subclass with a poor prognosis. In the subclass with the better survival, genes involved in differentiated intracellular functions, such as the MAPKKK cascade, ceramide metabolism, or regulation of transcription, were up-regulated.\nNote: 20 samples from this series, which appear in other Expression Experiments in Gemma, were not imported from the GEO source. The following samples were removed: GSM17303 on GPL962,GSM17302 on GPL962,GSM17301 on GPL962,GSM17300 on GPL962,GSM17298 on GPL962,GSM17299 on GPL962,GSM17287 on GPL962,GSM17296 on GPL962,GSM17288 on GPL962,GSM17297 on GPL962,GSM17289 on GPL962,GSM17290 on GPL962,GSM17291 on GPL962,GSM17304 on GPL962,GSM17305 on GPL962,GSM17294 on GPL962,GSM16567 on GPL962,GSM17295 on GPL962,GSM17292 on GPL962,GSM17293 on GPL962\nLast Updated (by provider): Mar 16 2012\nContributors: Yuichi Ishikawa Michael H Jones Carl Virtanen +#> 20: Tobacco smoking is responsible for over 90% of lung cancer cases, and yet the precise molecular alterations induced by smoking in lung that develop into cancer and impact survival have remained obscure. We performed gene expression analysis using HG-U133A Affymetrix chips on 135 fresh frozen tissue samples of adenocarcinoma and paired noninvolved lung tissue from current, former and never smokers, with biochemically validated smoking information. ANOVA analysis adjusted for potential confounders, multiple testing procedure, Gene Set Enrichment Analysis, and GO-functional classification were conducted for gene selection. Results were confirmed in independent adenocarcinoma and non-tumor tissues from two studies. We identified a gene expression signature characteristic of smoking that includes cell cycle genes, particularly those involved in the mitotic spindle formation (e.g., NEK2, TTK, PRC1). Expression of these genes strongly differentiated both smokers from non-smokers in lung tumors and early stage tumor tissue from non-tumor tissue (p<0.001 and fold-change>1.5, for each comparison), consistent with an important role for this pathway in lung carcinogenesis induced by smoking. These changes persisted many years after smoking cessation. NEK2 (p<0.001) and TTK (p=0.002) expression in the noninvolved lung tissue was also associated with a 3-fold increased risk of mortality from lung adenocarcinoma in smokers. Our work provides insight into the smoking-related mechanisms of lung neoplasia, and shows that the very mitotic genes known to be involved in cancer development are induced by smoking and affect survival. These genes are candidate targets for chemoprevention and treatment of lung cancer in smokers.\nLast Updated (by provider): Mar 19 2012\nContributors: Megan Hames Junya Fukuoka Sholom Wacholder Melissa Rotunno MariaTeresa Landi Jonine D Figueroa Ping Yang Angela C Pesatori Neil E Caporaso Abhijit Dasgupta Sharon E Murphy Andrew W Bergen Tatiana Dracheva Felecia E Mann PierAlberto Bertazzi Joanna H Shih Huaitian Liu Jin Jen Dario Consonni\nIncludes GDS3257.\n Update date: Oct 24 2008.\n Dataset description GDS3257: Analysis of different tumor stage adenocarcinoma and paired normal lung tissues of current, former and never smokers. To date, tobacco smoking is responsible for over 90% of lung cancers. Results provide insight into the molecular basis of lung carcinogenesis induced by smoking. +#> experiment.description #> experiment.troubled experiment.accession experiment.database #> <lgcl> <char> <char> #> 1: FALSE <NA> <NA> -#> 2: FALSE GSE14431 GEO -#> 3: FALSE GSE1037 GEO -#> 4: FALSE GSE27335 GEO -#> 5: FALSE GSE50254 GEO -#> 6: FALSE GSE63627 GEO -#> 7: FALSE GSE6135 GEO -#> 8: FALSE GSE44077 GEO -#> 9: FALSE GSE60464 GEO -#> 10: FALSE GSE5327 GEO -#> 11: FALSE GSE17373 GEO -#> 12: FALSE GSE6914 GEO -#> 13: FALSE GSE10089 GEO -#> 14: FALSE GSE13309 GEO -#> 15: FALSE GSE21581 GEO -#> 16: FALSE GSE23873 GEO -#> 17: FALSE GSE3141 GEO -#> 18: FALSE GSE11078 GEO -#> 19: FALSE GSE10096 GEO -#> 20: FALSE GSE17933 GEO +#> 2: FALSE GSE1037 GEO +#> 3: FALSE GSE6135 GEO +#> 4: FALSE GSE57148 GEO +#> 5: FALSE GSE37138 GEO +#> 6: FALSE GSE8894 GEO +#> 7: FALSE GSE43458 GEO +#> 8: FALSE GSE1987 GEO +#> 9: FALSE GSE11341 GEO +#> 10: FALSE GSE31552 GEO +#> 11: FALSE GSE37768 GEO +#> 12: FALSE GSE52248 GEO +#> 13: FALSE GSE20875 GEO +#> 14: FALSE GSE14431 GEO +#> 15: FALSE GSE14359 GEO +#> 16: FALSE GSE6044 GEO +#> 17: FALSE GSE32665 GEO +#> 18: FALSE GSE42127 GEO +#> 19: FALSE GSE2088 GEO +#> 20: FALSE GSE10072 GEO #> experiment.troubled experiment.accession experiment.database #> experiment.URI #> <char> #> 1: <NA> -#> 2: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14431 -#> 3: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1037 -#> 4: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27335 -#> 5: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50254 -#> 6: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63627 -#> 7: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6135 -#> 8: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE44077 -#> 9: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60464 -#> 10: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5327 -#> 11: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17373 -#> 12: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6914 -#> 13: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10089 -#> 14: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13309 -#> 15: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21581 -#> 16: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE23873 -#> 17: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE3141 -#> 18: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE11078 -#> 19: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10096 -#> 20: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17933 +#> 2: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1037 +#> 3: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6135 +#> 4: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57148 +#> 5: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37138 +#> 6: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE8894 +#> 7: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE43458 +#> 8: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1987 +#> 9: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE11341 +#> 10: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE31552 +#> 11: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE37768 +#> 12: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE52248 +#> 13: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20875 +#> 14: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14431 +#> 15: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14359 +#> 16: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6044 +#> 17: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32665 +#> 18: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE42127 +#> 19: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2088 +#> 20: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE10072 #> experiment.URI #> experiment.sampleCount experiment.lastUpdated #> <int> <POSc> #> 1: 203 2023-09-07 21:43:44 -#> 2: 12 2023-12-16 18:43:57 -#> 3: 91 2024-01-25 08:33:32 -#> 4: 24 2023-12-18 17:43:52 -#> 5: 75 2024-01-03 23:10:20 -#> 6: 28 2023-12-20 16:58:52 -#> 7: 25 2023-12-20 14:55:15 -#> 8: 226 2023-12-19 18:42:20 -#> 9: 42 2023-12-20 13:34:44 -#> 10: 58 2023-12-20 07:24:11 -#> 11: 24 2023-12-17 09:23:02 -#> 12: 20 2023-12-20 21:02:53 -#> 13: 23 2023-12-15 21:02:34 -#> 14: 24 2023-12-16 12:54:17 -#> 15: 35 2023-12-18 05:15:09 -#> 16: 6 2023-12-18 12:18:07 -#> 17: 111 2023-12-19 00:10:02 -#> 18: 23 2023-12-16 01:53:19 -#> 19: 13 2023-12-15 21:04:00 -#> 20: 191 2023-12-18 09:30:30 +#> 2: 91 2024-01-25 08:33:32 +#> 3: 25 2023-12-20 14:55:15 +#> 4: 189 2023-12-21 09:04:04 +#> 5: 117 2023-12-19 08:55:10 +#> 6: 138 2023-12-21 09:20:09 +#> 7: 110 2023-12-19 17:54:17 +#> 8: 37 2023-12-21 00:11:36 +#> 9: 23 2023-12-16 03:27:41 +#> 10: 131 2023-12-19 00:23:54 +#> 11: 38 2023-12-19 09:58:28 +#> 12: 18 2023-12-20 05:54:36 +#> 13: 14 2023-12-18 01:33:19 +#> 14: 12 2023-12-16 18:43:57 +#> 15: 20 2023-12-16 18:21:18 +#> 16: 47 2023-12-20 13:32:39 +#> 17: 179 2023-12-19 02:09:09 +#> 18: 176 2023-12-19 16:04:03 +#> 19: 67 2023-12-18 01:35:26 +#> 20: 107 2024-01-27 08:38:16 #> experiment.sampleCount experiment.lastUpdated #> experiment.batchEffectText experiment.batchCorrected #> <char> <lgcl> #> 1: NO_BATCH_INFO FALSE -#> 2: SINGLE_BATCH_SUCCESS FALSE -#> 3: PROBLEMATIC_BATCH_INFO_FAILURE FALSE -#> 4: NO_BATCH_EFFECT_SUCCESS FALSE +#> 2: PROBLEMATIC_BATCH_INFO_FAILURE FALSE +#> 3: NO_BATCH_INFO FALSE +#> 4: NO_BATCH_INFO FALSE #> 5: NO_BATCH_INFO FALSE #> 6: NO_BATCH_INFO FALSE #> 7: NO_BATCH_INFO FALSE #> 8: NO_BATCH_INFO FALSE #> 9: NO_BATCH_INFO FALSE -#> 10: BATCH_CORRECTED_SUCCESS TRUE -#> 11: NO_BATCH_INFO FALSE -#> 12: NO_BATCH_INFO FALSE -#> 13: NO_BATCH_INFO FALSE -#> 14: BATCH_CORRECTED_SUCCESS TRUE -#> 15: NO_BATCH_INFO FALSE +#> 10: NO_BATCH_INFO FALSE +#> 11: BATCH_CORRECTED_SUCCESS TRUE +#> 12: NO_BATCH_EFFECT_SUCCESS FALSE +#> 13: NO_BATCH_EFFECT_SUCCESS FALSE +#> 14: SINGLE_BATCH_SUCCESS FALSE +#> 15: BATCH_CORRECTED_SUCCESS TRUE #> 16: NO_BATCH_INFO FALSE #> 17: NO_BATCH_INFO FALSE #> 18: NO_BATCH_INFO FALSE -#> 19: BATCH_CORRECTED_SUCCESS TRUE -#> 20: NO_BATCH_INFO FALSE +#> 19: NO_BATCH_INFO FALSE +#> 20: BATCH_CORRECTED_SUCCESS TRUE #> experiment.batchEffectText experiment.batchCorrected #> experiment.batchConfound experiment.batchEffect experiment.rawData #> <num> <num> <num> #> 1: 0 0 -1 -#> 2: 1 1 1 -#> 3: 0 0 -1 -#> 4: 1 1 -1 +#> 2: 0 0 -1 +#> 3: 1 -1 1 +#> 4: 0 0 1 #> 5: -1 0 1 -#> 6: -1 0 1 -#> 7: 1 -1 1 -#> 8: -1 0 1 -#> 9: 0 0 -1 -#> 10: 1 0 1 -#> 11: 1 0 -1 -#> 12: -1 0 1 -#> 13: -1 0 1 +#> 6: 0 0 -1 +#> 7: -1 0 1 +#> 8: 0 0 -1 +#> 9: -1 0 1 +#> 10: -1 0 1 +#> 11: 1 1 1 +#> 12: 1 1 1 +#> 13: 1 0 -1 #> 14: 1 1 1 -#> 15: 0 0 1 -#> 16: 0 0 -1 +#> 15: 1 1 1 +#> 16: -1 0 1 #> 17: 0 0 -1 #> 18: 0 0 -1 -#> 19: 1 0 1 -#> 20: -1 0 1 +#> 19: 0 0 -1 +#> 20: 1 1 1 #> experiment.batchConfound experiment.batchEffect experiment.rawData -#> geeq.qScore geeq.sScore taxon.name taxon.scientific taxon.ID taxon.NCBI -#> <num> <num> <char> <char> <int> <int> -#> 1: 0.39301864 0.2500 human Homo sapiens 1 9606 -#> 2: 0.56737322 0.8750 mouse Mus musculus 2 10090 -#> 3: -0.08397851 0.1875 human Homo sapiens 1 9606 -#> 4: 0.85060640 0.7500 human Homo sapiens 1 9606 -#> 5: 0.56656398 0.8750 rat Rattus norvegicus 3 10116 -#> 6: 0.42244171 1.0000 mouse Mus musculus 2 10090 -#> 7: 0.42048195 0.8750 mouse Mus musculus 2 10090 -#> 8: 0.42205492 1.0000 human Homo sapiens 1 9606 -#> 9: -0.16505812 0.4375 human Homo sapiens 1 9606 -#> 10: 0.84344437 0.8750 human Homo sapiens 1 9606 -#> 11: 0.71026059 0.7500 mouse Mus musculus 2 10090 -#> 12: 0.42718459 0.8750 human Homo sapiens 1 9606 -#> 13: 0.26569189 0.8750 human Homo sapiens 1 9606 -#> 14: 0.85532783 1.0000 human Homo sapiens 1 9606 -#> 15: 0.41975578 0.8750 mouse Mus musculus 2 10090 -#> 16: 0.13873304 0.4625 mouse Mus musculus 2 10090 -#> 17: -0.03865888 0.7500 human Homo sapiens 1 9606 -#> 18: 0.40954710 0.7500 human Homo sapiens 1 9606 -#> 19: 0.71341355 0.7500 human Homo sapiens 1 9606 -#> 20: 0.42665470 1.0000 mouse Mus musculus 2 10090 -#> geeq.qScore geeq.sScore taxon.name taxon.scientific taxon.ID taxon.NCBI +#> geeq.qScore geeq.sScore taxon.name taxon.scientific taxon.ID taxon.NCBI +#> <num> <num> <char> <char> <int> <int> +#> 1: 0.39301864 0.2500 human Homo sapiens 1 9606 +#> 2: -0.08397851 0.1875 human Homo sapiens 1 9606 +#> 3: 0.42048195 0.8750 mouse Mus musculus 2 10090 +#> 4: 0.13177289 1.0000 human Homo sapiens 1 9606 +#> 5: 0.55900631 1.0000 human Homo sapiens 1 9606 +#> 6: 0.41689582 0.7500 human Homo sapiens 1 9606 +#> 7: 0.56175050 1.0000 human Homo sapiens 1 9606 +#> 8: 0.11160403 0.2500 human Homo sapiens 1 9606 +#> 9: 0.42593834 0.8750 human Homo sapiens 1 9606 +#> 10: 0.26730883 1.0000 human Homo sapiens 1 9606 +#> 11: 0.99708931 0.7500 human Homo sapiens 1 9606 +#> 12: 0.97926373 0.6875 human Homo sapiens 1 9606 +#> 13: 0.49778945 0.0000 human Homo sapiens 1 9606 +#> 14: 0.56737322 0.8750 mouse Mus musculus 2 10090 +#> 15: 0.85319259 0.6250 human Homo sapiens 1 9606 +#> 16: 0.41545023 0.6875 human Homo sapiens 1 9606 +#> 17: 0.42160569 0.2500 human Homo sapiens 1 9606 +#> 18: 0.27176961 0.3750 human Homo sapiens 1 9606 +#> 19: 0.06203412 0.1875 human Homo sapiens 1 9606 +#> 20: 0.70857563 0.8750 human Homo sapiens 1 9606 +#> geeq.qScore geeq.sScore taxon.name taxon.scientific taxon.ID taxon.NCBI #> taxon.database.name taxon.database.ID #> <char> <int> #> 1: hg38 87 -#> 2: mm10 81 -#> 3: hg38 87 +#> 2: hg38 87 +#> 3: mm10 81 #> 4: hg38 87 -#> 5: rn6 86 -#> 6: mm10 81 -#> 7: mm10 81 +#> 5: hg38 87 +#> 6: hg38 87 +#> 7: hg38 87 #> 8: hg38 87 #> 9: hg38 87 #> 10: hg38 87 -#> 11: mm10 81 +#> 11: hg38 87 #> 12: hg38 87 #> 13: hg38 87 -#> 14: hg38 87 -#> 15: mm10 81 -#> 16: mm10 81 +#> 14: mm10 81 +#> 15: hg38 87 +#> 16: hg38 87 #> 17: hg38 87 #> 18: hg38 87 #> 19: hg38 87 -#> 20: mm10 81 +#> 20: hg38 87 #> taxon.database.name taxon.database.ID diff --git a/reference/get_gene_go_terms.html b/reference/get_gene_go_terms.html index 73219ff5..c54f0753 100644 --- a/reference/get_gene_go_terms.html +++ b/reference/get_gene_go_terms.html @@ -134,32 +134,8 @@

Value

Examples

get_gene_go_terms("DYRK1A")
-#>                                                             term.name
-#>                                                                <char>
-#>   1:                                             response to stimulus
-#>   2:                            establishment of protein localization
-#>   3:                                       cardiac muscle hypertrophy
-#>   4:                                                        transport
-#>   5: negative regulation of signal transduction by p53 class mediator
-#>  ---                                                                 
-#> 263:                                      somatodendritic compartment
-#> 264:                                              import into nucleus
-#> 265:                                    purine ribonucleotide binding
-#> 266:                                           ribonucleotide binding
-#> 267:                                                     localization
-#>         term.ID                                  term.URI
-#>          <char>                                    <char>
-#>   1: GO:0050896 http://purl.obolibrary.org/obo/GO_0050896
-#>   2: GO:0045184 http://purl.obolibrary.org/obo/GO_0045184
-#>   3: GO:0003300 http://purl.obolibrary.org/obo/GO_0003300
-#>   4: GO:0006810 http://purl.obolibrary.org/obo/GO_0006810
-#>   5: GO:1901797 http://purl.obolibrary.org/obo/GO_1901797
-#>  ---                                                     
-#> 263: GO:0036477 http://purl.obolibrary.org/obo/GO_0036477
-#> 264: GO:0051170 http://purl.obolibrary.org/obo/GO_0051170
-#> 265: GO:0032555 http://purl.obolibrary.org/obo/GO_0032555
-#> 266: GO:0032553 http://purl.obolibrary.org/obo/GO_0032553
-#> 267: GO:0051179 http://purl.obolibrary.org/obo/GO_0051179
+#> Error in .body(fname = fname, validators = validators, endpoint = endpoint,     envWhere = environment(), isFile = isFile, header = header,     raw = raw, overwrite = overwrite, file = file, attributes = TRUE,     open_api_name = open_api_name, .call = match.call()): https://gemma.msl.ubc.ca/rest/v2/genes/DYRK1A/goTerms
+#> HTTP code 400
diff --git a/reference/get_gene_probes.html b/reference/get_gene_probes.html index ea5ff501..928a662d 100644 --- a/reference/get_gene_probes.html +++ b/reference/get_gene_probes.html @@ -158,190 +158,8 @@

Value

Examples

get_gene_probes("DYRK1A")
-#>         element.name
-#>               <char>
-#>  1:       1374773_at
-#>  2:       1370183_at
-#>  3: rc_AI104012_g_at
-#>  4:   rc_AI104012_at
-#>  5:            19904
-#>  6:             1639
-#>  7:            19904
-#>  8:             1639
-#>  9:       1374773_at
-#> 10:       1370183_at
-#> 11:       R000990_01
-#> 12:        GE1269392
-#> 13:          GE15753
-#> 14:   rc_AI073261_at
-#> 15:            43666
-#> 16:              541
-#> 17:    1374773_at_10
-#> 18:     1374773_at_9
-#> 19:     1374773_at_8
-#> 20:     1374773_at_7
-#>         element.name
-#>                                                                                 element.description
-#>                                                                                              <char>
-#>  1:                                                                           Transcribed sequences
-#>  2:                               dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A
-#>  3:                               dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A
-#>  4:                               dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A
-#>  5:                                                                                            <NA>
-#>  6:                                                                                          Dyrk1a
-#>  7:                                                                                            <NA>
-#>  8:                                                                                          Dyrk1a
-#>  9:                                                                               Transcribed locus
-#> 10:                               dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1A
-#> 11:                                                                                            <NA>
-#> 12: Multiple external sequence references: NM_012791.1, BF554476.1;  [Renamed by Gemma from 311063]
-#> 13:                                                                   [Renamed by Gemma from 58029]
-#> 14:                                                                                            <NA>
-#> 15:                                                                                          Dyrk1a
-#> 16:                                                                                                
-#> 17:                                                                                                
-#> 18:                                                                                                
-#> 19:                                                                                                
-#> 20:                                                                                                
-#>                                                                                 element.description
-#>     platform.shortName
-#>                 <char>
-#>  1:            GPL1355
-#>  2:            GPL1355
-#>  3:              GPL85
-#>  4:              GPL85
-#>  5:             GPL890
-#>  6:             GPL890
-#>  7:            GPL2882
-#>  8:            GPL2882
-#>  9:             GPL341
-#> 10:             GPL341
-#> 11:            GPL4528
-#> 12:            GPL2896
-#> 13:            GPL2896
-#> 14:              GPL87
-#> 15:            GPL2877
-#> 16:            GPL2877
-#> 17:            GPL3240
-#> 18:            GPL3240
-#> 19:            GPL3240
-#> 20:            GPL3240
-#>     platform.shortName
-#>                                                                platform.name
-#>                                                                       <char>
-#>  1:                             Affymetrix GeneChip Rat Genome 230 2.0 Array
-#>  2:                             Affymetrix GeneChip Rat Genome 230 2.0 Array
-#>  3:                     Affymetrix GeneChip Rat Genome U34 Array Set RG-U34A
-#>  4:                     Affymetrix GeneChip Rat Genome U34 Array Set RG-U34A
-#>  5:                             Agilent-011868 Rat Oligo Microarray (G4130A)
-#>  6:                             Agilent-011868 Rat Oligo Microarray (G4130A)
-#>  7:                          Agilent-013328 Rat Oligo Microarray (V2) G4130B
-#>  8:                          Agilent-013328 Rat Oligo Microarray (V2) G4130B
-#>  9:                 Affymetrix GeneChip Rat Expression Set 230 Array RAE230A
-#> 10:                 Affymetrix GeneChip Rat Expression Set 230 Array RAE230A
-#> 11:                                      CapitalBio Rat 27k long oligo array
-#> 12: GE Healthcare/Amersham Biosciences CodeLink?   Rat Whole Genome Bioarray
-#> 13: GE Healthcare/Amersham Biosciences CodeLink?   Rat Whole Genome Bioarray
-#> 14:                     Affymetrix GeneChip Rat Genome U34 Array Set RG-U34C
-#> 15:                        Agilent-013162 Whole Rat Genome Microarray G4131A
-#> 16:                        Agilent-013162 Whole Rat Genome Microarray G4131A
-#> 17:       Affymetrix GeneChip Rat Expression Set 230 Array RAE230A PM probes
-#> 18:       Affymetrix GeneChip Rat Expression Set 230 Array RAE230A PM probes
-#> 19:       Affymetrix GeneChip Rat Expression Set 230 Array RAE230A PM probes
-#> 20:       Affymetrix GeneChip Rat Expression Set 230 Array RAE230A PM probes
-#>                                                                platform.name
-#>     platform.ID platform.type
-#>           <int>        <char>
-#>  1:           2      ONECOLOR
-#>  2:           2      ONECOLOR
-#>  3:           6      ONECOLOR
-#>  4:           6      ONECOLOR
-#>  5:           9      TWOCOLOR
-#>  6:           9      TWOCOLOR
-#>  7:         160      TWOCOLOR
-#>  8:         160      TWOCOLOR
-#>  9:         164      ONECOLOR
-#> 10:         164      ONECOLOR
-#> 11:         165      TWOCOLOR
-#> 12:         169      ONECOLOR
-#> 13:         169      ONECOLOR
-#> 14:         192      ONECOLOR
-#> 15:         240      DUALMODE
-#> 16:         240      DUALMODE
-#> 17:         265      ONECOLOR
-#> 18:         265      ONECOLOR
-#> 19:         265      ONECOLOR
-#> 20:         265      ONECOLOR
-#>     platform.ID platform.type
-#>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             platform.description
-#>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           <char>
-#>  1:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The GeneChip Rat Genome 230 2.0 Array is a powerful tool for toxicology, neurobiology, and other applications using rat as a model organism.  - Provides comprehensive coverage of the transcribed rat genome on a single array - Comprised of more than 31,000 probe sets, analyzing over 30,000 transcripts and variants from over 28,000 well-substantiated rat genes - The publicly available draft of the rat genome and leading public rat databases were used to refine sequences and provide a higher quality of data output  All probe sets represented on the GeneChip Rat Expression Set 230 are included on the GeneChip Rat Genome 230 2.0 Array. Sequences used in the design of the GeneChip Rat Genome 230 2.0 Array were selected from GenBank, dbEST, and RefSeq. The sequence clusters were created from the UniGene database (Build 99, June 2002) and then refined by analysis and comparison with the publicly available draft assembly of the rat genome from the Baylor College of Medicine Human Genome Sequencing Center (June 2002).  The GeneChip Rat Genome 230 2.0 Array includes representation of the RefSeq database sequences and probe sets related to sequences and refined EST clusters previously represented on the GeneChip Rat Genome U34 Set.  Oligonucleotide probes complementary to each corresponding sequence are synthesized in situ on the arrays. Eleven pairs of oligonucleotide probes are used to measure the level of transcription of each sequence represented on the GeneChip Rat Genome 230 2.0 Array.  Annotations derived from Affymetrix CSV file dated 6/23/2004\nFrom GPL1355\nLast Updated: May 31 2005
-#>  2:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The GeneChip Rat Genome 230 2.0 Array is a powerful tool for toxicology, neurobiology, and other applications using rat as a model organism.  - Provides comprehensive coverage of the transcribed rat genome on a single array - Comprised of more than 31,000 probe sets, analyzing over 30,000 transcripts and variants from over 28,000 well-substantiated rat genes - The publicly available draft of the rat genome and leading public rat databases were used to refine sequences and provide a higher quality of data output  All probe sets represented on the GeneChip Rat Expression Set 230 are included on the GeneChip Rat Genome 230 2.0 Array. Sequences used in the design of the GeneChip Rat Genome 230 2.0 Array were selected from GenBank, dbEST, and RefSeq. The sequence clusters were created from the UniGene database (Build 99, June 2002) and then refined by analysis and comparison with the publicly available draft assembly of the rat genome from the Baylor College of Medicine Human Genome Sequencing Center (June 2002).  The GeneChip Rat Genome 230 2.0 Array includes representation of the RefSeq database sequences and probe sets related to sequences and refined EST clusters previously represented on the GeneChip Rat Genome U34 Set.  Oligonucleotide probes complementary to each corresponding sequence are synthesized in situ on the arrays. Eleven pairs of oligonucleotide probes are used to measure the level of transcription of each sequence represented on the GeneChip Rat Genome 230 2.0 Array.  Annotations derived from Affymetrix CSV file dated 6/23/2004\nFrom GPL1355\nLast Updated: May 31 2005
-#>  3:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The RG-U34 set includes 3 arrays with a total of 26379 entries and was indexed 29-Jan-2002. The set includes ~7,000 known genes and >17,000 EST clusters. The A array contains probes derived from all full-length or annotated genes as well as thousands of EST clusters. The B and C arrays contain only EST clusters.  Keywords = high density oligonucleotide array\nFrom GPL85\nLast Updated: Mar 09 2006
-#>  4:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The RG-U34 set includes 3 arrays with a total of 26379 entries and was indexed 29-Jan-2002. The set includes ~7,000 known genes and >17,000 EST clusters. The A array contains probes derived from all full-length or annotated genes as well as thousands of EST clusters. The B and C arrays contain only EST clusters.  Keywords = high density oligonucleotide array\nFrom GPL85\nLast Updated: Mar 09 2006
-#>  5:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         This microarray contains representative sequences derived from rat RefSeq, high-quality public rat mRNA accessions, homologs to human and mouse RefSeq, EST clusters determined by a leading biopharmaceutical company and the Incyte Zooseq database as well as toxicology markers determined by the National Institute of Environmental Health Sciences (NIEHS) and others. Many of the toxicology markers used in the design of this microarray were released to the public domain for the first time and will be used on this unique microarray product.  Arrays of this design have barcodes that begin with 16011868 or 2511868.  Orientation: Features are numbered numbered Left-to-Right, Top-to-Bottom as scanned by an Agilent scanner (barcode on the left, DNA on the back surface, scanned through the glass), matching the FeatureNum output from Agilent's Feature Extraction software.  The ID column represents the Agilent Feature Extraction feature number.  Rows and columns are numbered as scanned by an Axon Scanner (barcode on the bottom, DNA on the front surface).  To match data scanned on an Axon scanner, use the RefNumber column contained in the Agilent-provided GAL file as the ID_REF column in sample submissions. \nFrom GPL890\nLast Updated: Sep 22 2005
-#>  6:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         This microarray contains representative sequences derived from rat RefSeq, high-quality public rat mRNA accessions, homologs to human and mouse RefSeq, EST clusters determined by a leading biopharmaceutical company and the Incyte Zooseq database as well as toxicology markers determined by the National Institute of Environmental Health Sciences (NIEHS) and others. Many of the toxicology markers used in the design of this microarray were released to the public domain for the first time and will be used on this unique microarray product.  Arrays of this design have barcodes that begin with 16011868 or 2511868.  Orientation: Features are numbered numbered Left-to-Right, Top-to-Bottom as scanned by an Agilent scanner (barcode on the left, DNA on the back surface, scanned through the glass), matching the FeatureNum output from Agilent's Feature Extraction software.  The ID column represents the Agilent Feature Extraction feature number.  Rows and columns are numbered as scanned by an Axon Scanner (barcode on the bottom, DNA on the front surface).  To match data scanned on an Axon scanner, use the RefNumber column contained in the Agilent-provided GAL file as the ID_REF column in sample submissions. \nFrom GPL890\nLast Updated: Sep 22 2005
-#>  7:   This microarray contains representative sequences derived from rat RefSeq, high-quality public rat mRNA accessions, homologs to human and mouse RefSeq, EST clusters determined by a leading biopharmaceutical company and the Incyte Zooseq database as well as toxicology markers determined by the National Institute of Environmental Health Sciences (NIEHS) and others. Many of the toxicology markers used in the design of this microarray were released to the public domain for the first time and will be used on this unique microarray product.  Arrays of this design have barcodes that begin with 16013328 or 2513328.  Orientation: Features are numbered numbered Left-to-Right, Top-to-Bottom as scanned by an Agilent scanner (barcode on the left, DNA on the back surface, scanned through the glass), matching the FeatureNum output from Agilent's Feature Extraction software.  The ID column represents the Agilent Feature Extraction feature number.  Rows and columns are numbered as scanned by an Axon Scanner (barcode on the bottom, DNA on the front surface).  To match data scanned on an Axon scanner, use the RefNumber column contained in the Agilent-provided GAL file as the ID_REF column in sample submissions.   This microarray contains representative sequences derived from rat RefSeq, high-quality public rat mRNA accessions, homologs to human and mouse RefSeq, EST clusters determined by a leading biopharmaceutical company and the Incyte Zooseq database as well as toxicology markers determined by the National Institute of Environmental Health Sciences (NIEHS) and others. Many of the toxicology markers used in the design of this microarray were released to the public domain for the first time and will be used on this unique microarray product.  Arrays of this design have barcodes that begin with 16013328 or 2513328.  Orientation: Features are numbered numbered Left-to-Right, Top-to-Bottom as scanned by an Agilent scanner (barcode on the left, DNA on the back surface, scanned through the glass), matching the FeatureNum output from Agilent's Feature Extraction software.  The ID column represents the Agilent Feature Extraction feature number.  Rows and columns are numbered as scanned by an Axon Scanner (barcode on the bottom, DNA on the front surface).  To match data scanned on an Axon scanner, use the RefNumber column contained in the Agilent-provided GAL file as the ID_REF column in sample submissions. 
-#>  8:   This microarray contains representative sequences derived from rat RefSeq, high-quality public rat mRNA accessions, homologs to human and mouse RefSeq, EST clusters determined by a leading biopharmaceutical company and the Incyte Zooseq database as well as toxicology markers determined by the National Institute of Environmental Health Sciences (NIEHS) and others. Many of the toxicology markers used in the design of this microarray were released to the public domain for the first time and will be used on this unique microarray product.  Arrays of this design have barcodes that begin with 16013328 or 2513328.  Orientation: Features are numbered numbered Left-to-Right, Top-to-Bottom as scanned by an Agilent scanner (barcode on the left, DNA on the back surface, scanned through the glass), matching the FeatureNum output from Agilent's Feature Extraction software.  The ID column represents the Agilent Feature Extraction feature number.  Rows and columns are numbered as scanned by an Axon Scanner (barcode on the bottom, DNA on the front surface).  To match data scanned on an Axon scanner, use the RefNumber column contained in the Agilent-provided GAL file as the ID_REF column in sample submissions.   This microarray contains representative sequences derived from rat RefSeq, high-quality public rat mRNA accessions, homologs to human and mouse RefSeq, EST clusters determined by a leading biopharmaceutical company and the Incyte Zooseq database as well as toxicology markers determined by the National Institute of Environmental Health Sciences (NIEHS) and others. Many of the toxicology markers used in the design of this microarray were released to the public domain for the first time and will be used on this unique microarray product.  Arrays of this design have barcodes that begin with 16013328 or 2513328.  Orientation: Features are numbered numbered Left-to-Right, Top-to-Bottom as scanned by an Agilent scanner (barcode on the left, DNA on the back surface, scanned through the glass), matching the FeatureNum output from Agilent's Feature Extraction software.  The ID column represents the Agilent Feature Extraction feature number.  Rows and columns are numbered as scanned by an Axon Scanner (barcode on the bottom, DNA on the front surface).  To match data scanned on an Axon scanner, use the RefNumber column contained in the Agilent-provided GAL file as the ID_REF column in sample submissions. 
-#>  9:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           Array A of GeneChip Rat Expression Set 230 Has 15923 entries and was indexed 09-Apr-2003 Sequences used in the design of the array were selected from GenBank, dbEST, and RefSeq. Sequence clusters were created from Build 99 of UniGene (June 2002) and refined by analysis and comparison with a number of other publicly available databases including the Baylor College of Medicine Human Genome Sequencing Center's preliminary rat genome assembly (June 2002). In addition, sequences were analyzed for untrimmed low-quality sequence information, correct orientation, false clustering, alternative splicing and alternative polyadenylation. \nFrom GPL341\nLast Updated: Jul 28 2006
-#> 10:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           Array A of GeneChip Rat Expression Set 230 Has 15923 entries and was indexed 09-Apr-2003 Sequences used in the design of the array were selected from GenBank, dbEST, and RefSeq. Sequence clusters were created from Build 99 of UniGene (June 2002) and refined by analysis and comparison with a number of other publicly available databases including the Baylor College of Medicine Human Genome Sequencing Center's preliminary rat genome assembly (June 2002). In addition, sequences were analyzed for untrimmed low-quality sequence information, correct orientation, false clustering, alternative splicing and alternative polyadenylation. \nFrom GPL341\nLast Updated: Jul 28 2006
-#> 11:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                This set includes 27k oligonucleotides, mostly 70-mers, designed based upon the Ensembl Rat Database Version v19.3b.2 and Rat Genome Project.
-#> 12:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                CodeLink Rat Whole Genome Bioarray is designed to interrogate approximately 34 000 transcripts representing most of the known and predictive genes of the rat genome, as it is described today in the public domain. Each transcript is represented by a 30-mer probe which is designed to conserved exons across the transcripts of targeted genes. The probe sequences, representing well annotated, full length and partial rat gene sequences, were designed based on sequences selected from the NCBI UniGene build #129, RefSeq database (April 1, 2004 release) and dbEST database (March 20, 2004 release).  Well-annotated mRNA or coding sequences were chosen to ensure usefulness for a large range of applications in basic research, biotechnology, and drug development. Each sequence was carefully screened to ensure high-quality, specific probe design, and to reduce redundancy of gene targets. All of the probes designed to these sequences were functionally tested, and over 95% were functionally validated against 10 rat tissues to ensure best representation of the gene and biologically relevant results. For flexibility and data preservation, CodeLink Rat Whole Genome Bioarrays are compatible with lower density CodeLink rat bioarrays. This means that existing data from CodeLink UniSet? Rat 10K Bioarrays can be continuously used in future studies along with newly generated data from CodeLink Rat Whole Genome Bioarrays.  CodeLink users can submit data to GEO by exporting tab-delimited text files from the CodeLink software. The "Feature_id" header will need to be renamed to "ID_REF", and the "Normalized_intensity" header to "VALUE" before uploading the exported tables.  In addition, users are encouraged to include additional raw data columns in their data uploads. These additional columns do not need to be renamed. 
-#> 13:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                CodeLink Rat Whole Genome Bioarray is designed to interrogate approximately 34 000 transcripts representing most of the known and predictive genes of the rat genome, as it is described today in the public domain. Each transcript is represented by a 30-mer probe which is designed to conserved exons across the transcripts of targeted genes. The probe sequences, representing well annotated, full length and partial rat gene sequences, were designed based on sequences selected from the NCBI UniGene build #129, RefSeq database (April 1, 2004 release) and dbEST database (March 20, 2004 release).  Well-annotated mRNA or coding sequences were chosen to ensure usefulness for a large range of applications in basic research, biotechnology, and drug development. Each sequence was carefully screened to ensure high-quality, specific probe design, and to reduce redundancy of gene targets. All of the probes designed to these sequences were functionally tested, and over 95% were functionally validated against 10 rat tissues to ensure best representation of the gene and biologically relevant results. For flexibility and data preservation, CodeLink Rat Whole Genome Bioarrays are compatible with lower density CodeLink rat bioarrays. This means that existing data from CodeLink UniSet? Rat 10K Bioarrays can be continuously used in future studies along with newly generated data from CodeLink Rat Whole Genome Bioarrays.  CodeLink users can submit data to GEO by exporting tab-delimited text files from the CodeLink software. The "Feature_id" header will need to be renamed to "ID_REF", and the "Normalized_intensity" header to "VALUE" before uploading the exported tables.  In addition, users are encouraged to include additional raw data columns in their data uploads. These additional columns do not need to be renamed. 
-#> 14:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The RG-U34 set includes 3 arrays with a total of 26379 entries and was indexed 29-Jan-2002. The set includes ~7,000 known genes and >17,000 EST clusters. The A array contains probes derived from all full-length or annotated genes as well as thousands of EST clusters. The B and C arrays contain only EST clusters.  Keywords = high density oligonucleotide array\nFrom GPL87\nLast Updated: Jul 28 2006
-#> 15:                                                                                                                                                                                                                                                                                                                                         With a focus on well known rat genes and homologues to human and  mouse genes with useful annotation, Agilent's Whole Rat Genome Oligo Microarray provides researchers with a new tool for modeling human biology in the rat model organism. For researchers, this means they now have access to a microarray made up of relevant content that has been empirically validated by Agilent.  Arrays of this design have barcodes that begin with 16013162 or 2513162.  Orientation: Features are numbered numbered Left-to-Right, Top-to-Bottom as scanned by an Agilent scanner (barcode on the left, DNA on the back surface, scanned through the glass), matching the FeatureNum output from Agilent's Feature Extraction software.  The ID column represents the Agilent Feature Extraction feature number.  Rows and columns are numbered as scanned by an Axon Scanner (barcode on the bottom, DNA on the front surface).  To match data scanned on an Axon scanner, use the RefNumber column contained in the Agilent-provided GAL file as the ID_REF column in sample submissions.   With a focus on well known rat genes and homologues to human and  mouse genes with useful annotation, Agilent's Whole Rat Genome Oligo Microarray provides researchers with a new tool for modeling human biology in the rat model organism. For researchers, this means they now have access to a microarray made up of relevant content that has been empirically validated by Agilent.  Arrays of this design have barcodes that begin with 16013162 or 2513162.  Orientation: Features are numbered numbered Left-to-Right, Top-to-Bottom as scanned by an Agilent scanner (barcode on the left, DNA on the back surface, scanned through the glass), matching the FeatureNum output from Agilent's Feature Extraction software.  The ID column represents the Agilent Feature Extraction feature number.  Rows and columns are numbered as scanned by an Axon Scanner (barcode on the bottom, DNA on the front surface).  To match data scanned on an Axon scanner, use the RefNumber column contained in the Agilent-provided GAL file as the ID_REF column in sample submissions. 
-#> 16:                                                                                                                                                                                                                                                                                                                                         With a focus on well known rat genes and homologues to human and  mouse genes with useful annotation, Agilent's Whole Rat Genome Oligo Microarray provides researchers with a new tool for modeling human biology in the rat model organism. For researchers, this means they now have access to a microarray made up of relevant content that has been empirically validated by Agilent.  Arrays of this design have barcodes that begin with 16013162 or 2513162.  Orientation: Features are numbered numbered Left-to-Right, Top-to-Bottom as scanned by an Agilent scanner (barcode on the left, DNA on the back surface, scanned through the glass), matching the FeatureNum output from Agilent's Feature Extraction software.  The ID column represents the Agilent Feature Extraction feature number.  Rows and columns are numbered as scanned by an Axon Scanner (barcode on the bottom, DNA on the front surface).  To match data scanned on an Axon scanner, use the RefNumber column contained in the Agilent-provided GAL file as the ID_REF column in sample submissions.   With a focus on well known rat genes and homologues to human and  mouse genes with useful annotation, Agilent's Whole Rat Genome Oligo Microarray provides researchers with a new tool for modeling human biology in the rat model organism. For researchers, this means they now have access to a microarray made up of relevant content that has been empirically validated by Agilent.  Arrays of this design have barcodes that begin with 16013162 or 2513162.  Orientation: Features are numbered numbered Left-to-Right, Top-to-Bottom as scanned by an Agilent scanner (barcode on the left, DNA on the back surface, scanned through the glass), matching the FeatureNum output from Agilent's Feature Extraction software.  The ID column represents the Agilent Feature Extraction feature number.  Rows and columns are numbered as scanned by an Axon Scanner (barcode on the bottom, DNA on the front surface).  To match data scanned on an Axon scanner, use the RefNumber column contained in the Agilent-provided GAL file as the ID_REF column in sample submissions. 
-#> 17:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             
-#> 18:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             
-#> 19:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             
-#> 20:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             
-#>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             platform.description
-#>     platform.troubled taxon.name  taxon.scientific taxon.ID taxon.NCBI
-#>                <lgcl>     <char>            <char>    <int>      <int>
-#>  1:             FALSE        rat Rattus norvegicus        3      10116
-#>  2:             FALSE        rat Rattus norvegicus        3      10116
-#>  3:             FALSE        rat Rattus norvegicus        3      10116
-#>  4:             FALSE        rat Rattus norvegicus        3      10116
-#>  5:             FALSE        rat Rattus norvegicus        3      10116
-#>  6:             FALSE        rat Rattus norvegicus        3      10116
-#>  7:             FALSE        rat Rattus norvegicus        3      10116
-#>  8:             FALSE        rat Rattus norvegicus        3      10116
-#>  9:             FALSE        rat Rattus norvegicus        3      10116
-#> 10:             FALSE        rat Rattus norvegicus        3      10116
-#> 11:             FALSE        rat Rattus norvegicus        3      10116
-#> 12:             FALSE        rat Rattus norvegicus        3      10116
-#> 13:             FALSE        rat Rattus norvegicus        3      10116
-#> 14:             FALSE        rat Rattus norvegicus        3      10116
-#> 15:             FALSE        rat Rattus norvegicus        3      10116
-#> 16:             FALSE        rat Rattus norvegicus        3      10116
-#> 17:             FALSE        rat Rattus norvegicus        3      10116
-#> 18:             FALSE        rat Rattus norvegicus        3      10116
-#> 19:             FALSE        rat Rattus norvegicus        3      10116
-#> 20:             FALSE        rat Rattus norvegicus        3      10116
-#>     platform.troubled taxon.name  taxon.scientific taxon.ID taxon.NCBI
-#>     taxon.database.name taxon.database.ID
-#>                  <char>             <int>
-#>  1:                 rn6                86
-#>  2:                 rn6                86
-#>  3:                 rn6                86
-#>  4:                 rn6                86
-#>  5:                 rn6                86
-#>  6:                 rn6                86
-#>  7:                 rn6                86
-#>  8:                 rn6                86
-#>  9:                 rn6                86
-#> 10:                 rn6                86
-#> 11:                 rn6                86
-#> 12:                 rn6                86
-#> 13:                 rn6                86
-#> 14:                 rn6                86
-#> 15:                 rn6                86
-#> 16:                 rn6                86
-#> 17:                 rn6                86
-#> 18:                 rn6                86
-#> 19:                 rn6                86
-#> 20:                 rn6                86
-#>     taxon.database.name taxon.database.ID
+#> Error in .body(fname = fname, validators = validators, endpoint = endpoint,     envWhere = environment(), isFile = isFile, header = header,     raw = raw, overwrite = overwrite, file = file, attributes = TRUE,     open_api_name = open_api_name, .call = match.call()): https://gemma.msl.ubc.ca/rest/v2/genes/DYRK1A/probes?offset=0&limit=20
+#> HTTP code 400
diff --git a/reference/get_genes.html b/reference/get_genes.html index 004e0d2f..e56f7f0e 100644 --- a/reference/get_genes.html +++ b/reference/get_genes.html @@ -154,9 +154,9 @@

Examples

#> 3: dual specificity tyrosine phosphorylation regulated kinase 1A Dyrk, PSK47 #> gene.MFX.rank taxon.name taxon.scientific taxon.ID taxon.NCBI #> <num> <char> <char> <int> <int> -#> 1: 0.9658668 human Homo sapiens 1 9606 -#> 2: 0.9685197 mouse Mus musculus 2 10090 -#> 3: 0.9531794 rat Rattus norvegicus 3 10116 +#> 1: 0.9658868 human Homo sapiens 1 9606 +#> 2: 0.9685388 mouse Mus musculus 2 10090 +#> 3: 0.9535178 rat Rattus norvegicus 3 10116 #> taxon.database.name taxon.database.ID #> <char> <int> #> 1: hg38 87 @@ -181,12 +181,12 @@

Examples

#> 6: phosphatase and tensin homolog MMAC1, M.... #> gene.MFX.rank taxon.name taxon.scientific taxon.ID taxon.NCBI #> <num> <char> <char> <int> <int> -#> 1: 0.9658668 human Homo sapiens 1 9606 -#> 2: 0.9971406 human Homo sapiens 1 9606 -#> 3: 0.9685197 mouse Mus musculus 2 10090 +#> 1: 0.9658868 human Homo sapiens 1 9606 +#> 2: 0.9971606 human Homo sapiens 1 9606 +#> 3: 0.9685388 mouse Mus musculus 2 10090 #> 4: 0.9992364 mouse Mus musculus 2 10090 -#> 5: 0.9531794 rat Rattus norvegicus 3 10116 -#> 6: 0.9982867 rat Rattus norvegicus 3 10116 +#> 5: 0.9535178 rat Rattus norvegicus 3 10116 +#> 6: 0.9982868 rat Rattus norvegicus 3 10116 #> taxon.database.name taxon.database.ID #> <char> <int> #> 1: hg38 87 diff --git a/reference/get_platform_datasets.html b/reference/get_platform_datasets.html index 919cbc3b..1be8b945 100644 --- a/reference/get_platform_datasets.html +++ b/reference/get_platform_datasets.html @@ -180,7 +180,7 @@

Examples

#> 3: Generation of oligodendroglial cells by direct lineage conversion #> 4: d'mel-affy-rat-168311 #> 5: Toxicogenomic Characterization of Molecular Mechanisms Contributing to Chlorpyrifos Neurotoxicity in Adult Male Rats [microarray] -#> 6: Genomic-Derived Markers for Early Detection of Calcineurin Inhibitor Immunosuppressant<e2><80><93>Mediated Nephrotoxicity +#> 6: Genomic-Derived Markers for Early Detection of Calcineurin Inhibitor Immunosuppressant–Mediated Nephrotoxicity #> experiment.ID #> <int> #> 1: 5633 diff --git a/reference/get_platform_element_genes.html b/reference/get_platform_element_genes.html index 39860f3d..fa5b7901 100644 --- a/reference/get_platform_element_genes.html +++ b/reference/get_platform_element_genes.html @@ -165,7 +165,7 @@

Examples

#> 1: Actb ENSRNOG00000034254 81822 actin, beta Actx #> gene.MFX.rank taxon.name taxon.scientific taxon.ID taxon.NCBI #> <num> <char> <char> <int> <int> -#> 1: 0.9748343 rat Rattus norvegicus 3 10116 +#> 1: 0.9748069 rat Rattus norvegicus 3 10116 #> taxon.database.name taxon.database.ID #> <char> <int> #> 1: rn6 86 diff --git a/reference/get_platforms_by_ids.html b/reference/get_platforms_by_ids.html index 2513db88..e8055af2 100644 --- a/reference/get_platforms_by_ids.html +++ b/reference/get_platforms_by_ids.html @@ -183,7 +183,7 @@

Examples

#> 1: The GeneChip Rat Genome 230 2.0 Array is a powerful tool for toxicology, neurobiology, and other applications using rat as a model organism. - Provides comprehensive coverage of the transcribed rat genome on a single array - Comprised of more than 31,000 probe sets, analyzing over 30,000 transcripts and variants from over 28,000 well-substantiated rat genes - The publicly available draft of the rat genome and leading public rat databases were used to refine sequences and provide a higher quality of data output All probe sets represented on the GeneChip Rat Expression Set 230 are included on the GeneChip Rat Genome 230 2.0 Array. Sequences used in the design of the GeneChip Rat Genome 230 2.0 Array were selected from GenBank, dbEST, and RefSeq. The sequence clusters were created from the UniGene database (Build 99, June 2002) and then refined by analysis and comparison with the publicly available draft assembly of the rat genome from the Baylor College of Medicine Human Genome Sequencing Center (June 2002). The GeneChip Rat Genome 230 2.0 Array includes representation of the RefSeq database sequences and probe sets related to sequences and refined EST clusters previously represented on the GeneChip Rat Genome U34 Set. Oligonucleotide probes complementary to each corresponding sequence are synthesized in situ on the arrays. Eleven pairs of oligonucleotide probes are used to measure the level of transcription of each sequence represented on the GeneChip Rat Genome 230 2.0 Array. Annotations derived from Affymetrix CSV file dated 6/23/2004\nFrom GPL1355\nLast Updated: May 31 2005 #> platform.troubled platform.experimentCount platform.type taxon.name #> <lgcl> <int> <char> <char> -#> 1: FALSE 294 ONECOLOR rat +#> 1: FALSE 295 ONECOLOR rat #> taxon.scientific taxon.ID taxon.NCBI taxon.database.name taxon.database.ID #> <char> <int> <int> <char> <int> #> 1: Rattus norvegicus 3 10116 rn6 86 @@ -202,8 +202,8 @@

Examples

#> 2: The GeneChip Rat Genome 230 2.0 Array is a powerful tool for toxicology, neurobiology, and other applications using rat as a model organism. - Provides comprehensive coverage of the transcribed rat genome on a single array - Comprised of more than 31,000 probe sets, analyzing over 30,000 transcripts and variants from over 28,000 well-substantiated rat genes - The publicly available draft of the rat genome and leading public rat databases were used to refine sequences and provide a higher quality of data output All probe sets represented on the GeneChip Rat Expression Set 230 are included on the GeneChip Rat Genome 230 2.0 Array. Sequences used in the design of the GeneChip Rat Genome 230 2.0 Array were selected from GenBank, dbEST, and RefSeq. The sequence clusters were created from the UniGene database (Build 99, June 2002) and then refined by analysis and comparison with the publicly available draft assembly of the rat genome from the Baylor College of Medicine Human Genome Sequencing Center (June 2002). The GeneChip Rat Genome 230 2.0 Array includes representation of the RefSeq database sequences and probe sets related to sequences and refined EST clusters previously represented on the GeneChip Rat Genome U34 Set. Oligonucleotide probes complementary to each corresponding sequence are synthesized in situ on the arrays. Eleven pairs of oligonucleotide probes are used to measure the level of transcription of each sequence represented on the GeneChip Rat Genome 230 2.0 Array. Annotations derived from Affymetrix CSV file dated 6/23/2004\nFrom GPL1355\nLast Updated: May 31 2005 #> platform.troubled platform.experimentCount platform.type taxon.name #> <lgcl> <int> <char> <char> -#> 1: FALSE 387 ONECOLOR human -#> 2: FALSE 294 ONECOLOR rat +#> 1: FALSE 389 ONECOLOR human +#> 2: FALSE 295 ONECOLOR rat #> taxon.scientific taxon.ID taxon.NCBI taxon.database.name taxon.database.ID #> <char> <int> <int> <char> <int> #> 1: Homo sapiens 1 9606 hg38 87 diff --git a/reference/get_taxon_datasets.html b/reference/get_taxon_datasets.html index cf5d647d..f599e7d5 100644 --- a/reference/get_taxon_datasets.html +++ b/reference/get_taxon_datasets.html @@ -248,29 +248,29 @@

Examples

#> 19: 128 #> 20: 129 #> experiment.ID -#> experiment.description -#> <char> -#> 1: Bronchoalveolar lavage samples collected from lung transplant recipients. Numeric portion of sample name is an arbitrary patient ID and AxBx number indicates the perivascular (A) and bronchiolar (B) scores from biopsies collected on the same day as the BAL fluid was collected. Several patients have more than one sample in this series and can be determined by patient number followed by a lower case letter. Acute rejection state is determined by the combined A and B score - specifically, a combined AB score of 2 or greater is considered an acute rejection. -#> 2: Our laboratory has developed the first mouse model overexpressing a RNA-binding protein, the ELAV-like protein HuD, in the CNS under the control of the CaMKinII alpha promoter. Initial behavioral characterization of the mice revealed that they had significant learning deficits together with abnormalities in prepulse inhibition (PPI). At the molecular level, we found that the expression of the growth-associated protein GAP-43, one of the targets of HuD, was increased in the hippocampus of HuD transgenic mice. To characterize these mice further and to evaluate the utility of these animals in understanding human diseases, we propose to use DNA microarray methods. To test our hypothesis we propose 3 specific aims: 1) To characterize the pattern of gene expression in the hippocampus of HuD overexpressor mice 2) To compare the pattern of gene expression in our mouse model with that in the hippocampus of rats prenatally exposed to alcohol (FAS model) and 3) To compare the pattern of gene expression in our mouse model with that shown in post-mortem tissues of patients with schizophrenia. In our previous protocols we examined the pattern of gene expression in our HuD transgenic mice and in rats prenaltally exposed to alcohol. A report by another group (Hakak et al, 2001) showed that three of the HuD targets were upregulated in the prefrontal cortex of patients with schizophrenia. To evaluate whether other target of HuD may be affected in this illness, in the current protocol, we want to compare the pattern of expression in our transgenic mice with in tissue from patients with schizophrenia Based on the behavioral and molecular properties of our HuD transgenic mice we hypothesize that these animals may be good models for the studying the basis of learning disabilities and of diseases that show deficits in PPI such as fetal alcohol syndrome and schizophrenia. All 28 samples are derived from cerebellar tissues for patients with schizophrenia and matched controls. The specimens were obtained from the Maryland Brain Collection according to NIH guidelines for confidentially and privacy. The protocol used in these studies was reviewed by our HRRC which found that our studies do not fall within the category of protocols monitored by the IRB (see attached letter form the HRRC). Specimens from 14 patients with a diagnosis of schizophrenia performed according to DSM-IV criteria and 14 sex-, age- and PMI-matched controls was included in this study. No differences were found between patients and control subjects in the average age (45<c2><b1>12 versus 43<c2><b1>10 years, p=0.86) or PMI (12<c2><b1>5 versus 16<c2><b1>6 hours, p=0.11). We will provide 28 samples containing 5 ug of RNA each in DEPC water (see validation of the quality of the RNA below). In addition, we include in our study animals treated with haloperidol as control for medication. These samples will be submitted in a separate protocol.\nDate GSE4036 Last Updated: Jan 13 2006\nContributors: Nora I Perrone-Bizzozero\nIncludes GDS1917.\n Update date: Jun 14 2006.\n Dataset description GDS1917: Analysis of cortical samples corresponding to the crus I/VIIa area of the cerebellum from schizophrenia patients. A study indicates that targets of the RNA-binding ELAV-like protein HuD are overexpressed in the prefrontal cortex of patients with schizophrenia. -#> 3: The neurodegenerative process in HIV encephalitis (HIVE) is associated with extensive damage to the dendritic and synaptic structure that often leads to cognitive impairment. Several mechanisms might be at play, including release of neurotoxins, oxidative stress and decreased activity of neurotrophic factors. Furthermore, HIV-mediated dysregulation of genes involved in neuronal maintenance might play an important role. For this purpose, cRNA was prepared from the brains of 17 AIDS patients for analysis with the Affymetrix Human U95Av2 GeneChip and analyzed with the GeneSpring Expression Analysis Software. Out of 12,625 genes analyzed, 74 were downregulated and 59 were upregulated compared to controls. Initial alternative analysis of RNA was performed by ribonuclease protection assay (RPA). In cases with HIVE, downregulated genes included neuronal molecules involved in synaptic plasticity and transmission (ion channels, synaptogyrin, synapsin II), cell cycle (p35, p39, CDC-L2, CDC42, PAK1) and signaling molecules (PI3K, Ras-Raf-MEK1), transcription factors and cytoskeletal components (MAP-1B, MAP-2, tubulin, adducin-2). Upregulated genes included those involved in neuroimmune (IgG, MHC, ?2microglobulin) and anti-viral responses (interferon-inducible molecules), transcription (STAT1, OLIG2, Pax-6) and signaling modulation (MEK3, EphB1) of the cytoskeleton (myosin, aduccin-3, radixin, dystrobrevin). Taken together, this study suggests that HIV proteins released from infected macrophages might not only induce a neuroinflammatory response, but also may promote neurodegeneration by interfering with neuronal transcription of genes involved in regulating signaling and cytoskeletal molecules important in maintaining synapto-dendritic functioning and integrity.\nDate GSE3489 Last Updated: Mar 07 2006\nContributors: Eleanor S Roberts Dianne Langford Anthony Adame Edward Rockenstein Leslie Crews Howard S Fox Eliezer Masiliah Ian Everall\nIncludes GDS1726.\n Update date: May 30 2006.\n Dataset description GDS1726: Analysis of brain frontal cortex of HIV-seropositive patients with HIV encephalitis (HIVE). HIVE affects >40% of AIDS patients, promoting neurodegeneration and cognitive impairment. Results suggest HIV-mediated dysregulation of genes involved in neuronal maintenance might play an important role. -#> 4: Overall study: Identification of PDGF-dependent patterns of gene expression in U87 glioblastoma cells. RNA was obtained from triplicate dishes of 5 different groups of U87 cells, each (total 15) analyzed with one U95 microarray chip. Three different comparisons were made: 1) Clone 3.1 (34580-34582) vs. clone 3.3 (34583-34585) vs. parent U87 (34592-34594). Purpose: demonstrate that the gene expression profiles between these 3 cell lines are not different, so they could be pooled as a single untreated group. 2) Pooled control group (34580-34585, 34592-34594) vs. clone 8.1 (34586-34588). Purpose: identify genes specifically controlled by autocrine PDGF activity. 3) Clone 8.1 (34586-34588) vs. clone 8.1 treated with PDGF (34589-34591) Purpose: Identify genes specifically induced by exogenous PDGF.\nDate GSE1923 Last Updated: Dec 19 2005\nContributors: David M Kaetzel Catherine Nutt Xuejun Peng Piam Shanehsaz Deqin Ma David N Louis\nIncludes GDS1730.\n Update date: May 02 2006.\n Dataset description GDS1730: Analysis of U87 glioblastoma (GBM) cell clones overexpressing a dominant-negative form of platelet-derived growth factor (PDGF) A subunit to inactive the PDGF autocrine signaling loop. The autocrine PDGF loop is a hallmark of GBM. Results identify genes regulated by oncogenic PDGF signaling. -#> 5: Analysis of gene expression in mammary epithelial cells transduced with either hTERT, empty LXSN vector or empty BABE vector.\nDate GSE361 Last Updated: May 29 2005\nIncludes GDS337.\n Update date: May 21 2003.\n Dataset description GDS337: Effect of overexpression of the telomerase catalytic subunit (TERT) in mammary epithelial cells (HMEC). Findings imply that ectopic telomerase expression modulates growth-controlling genes and enhances cell proliferation. -#> 6: The purpose of this study is to discover genes that might increase aqueous humor outflow when human ciliary muscle or human trabecular meshwork cells are treated with the prostaglandin analogues latanoprost free acid or prostaglandin F2alpha. Five tissue donors were pooled on each chip.\nDate GSE492 Last Updated: Jul 27 2006\nContributors: Paul Russell\nIncludes GDS359.\n Update date: Apr 14 2004.\n Dataset description GDS359: Glaucoma study investigating molecular basis of increase in aqueous humor outflow when human ciliary muscle or human trabecular meshwork cells are treated with prostaglandin analogs latanoprost free acid or prostaglandin F2alpha. -#> 7: DNA damage caused by UV radiation initiates cellular recovery mechanisms, which involve activation of DNA damage response pathways, cell cycle arrest and apoptosis. To assess cellular transcriptional responses to UVC-induced DNA damage we compared time course responses of human skin fibroblasts to low and high doses of UVC radiation known to induce a transient cellular replicative arrest or apoptosis, respectively. UVC radiation elicited >3-fold changes in 460 out of 12,000 transcripts and 89% of these represented downregulated transcripts. Only 5% of the regulated genes were common to both low and high doses of radiation. Cells inflicted with a low dose of UVC exhibited transcription profiles demonstrating transient regulation followed by recovery, whereas the responses were persistent after the high dose. A detailed clustering analysis and functional classification of the targets implied regulation of biologically divergent responses and suggested involvement of transcriptional and translational machinery, inflammatory, anti-proliferative and anti-angiogenic responses. The data support the notion that UVC radiation induces prominent, dose-dependent downregulation of transcription. However, the data strongly suggest that transcriptional repression is also target gene selective. Furthermore, the results demonstrate that dose-dependent induction of cell cycle arrest and apoptosis by UVC radiation are transcriptionally highly distinct responses.\nDate GSE713 Last Updated: Jun 15 2005\nContributors: Massimiliano Gentile Marikki Laiho Leena Latonen\nIncludes GDS400.\n Update date: Oct 08 2003.\n Dataset description GDS400: Temporal analysis of differences in WS1 human skin fibroblast gene expression response to low (10 J/m2; induces transient cellular replicative arrest) or high (50 J/m2; induces apoptosis) doses of short wavelength UV radiation (UVC; 254 nm). -#> 8: Identification of amyotrophic lateral sclerosis (ALS) associated genes. Post mortem spinal cord grey matter from sporadic and familial ALS patients compared with controls.\nDate GSE833 Last Updated: Jun 28 2005\nContributors: Steven R Gullans Robert H Brown Fernando Dangond\nIncludes GDS412.\n Update date: Nov 24 2003.\n Dataset description GDS412: Identification of amyotrophic lateral sclerosis (ALS) associated genes. Post mortem spinal cord grey matter from sporadic and familial ALS patients compared with controls. Attempt to identify mechanisms by which ALS destroys motor neurons. -#> 9: BACKGROUND: Previous genomic studies with human tissues have compared differential gene expression between 2 conditions (ie, normal versus diseased) to identify altered gene expression in a binary manner; however, a potentially more informative approach is to correlate the levels of gene expression with quantitative physiological parameters. METHODS AND RESULTS: In this study, we have used this approach to examine genes whose expression correlates with arterial stiffness in human aortic specimens. Our data identify 2 distinct groups of genes, those associated with cell signaling and those associated with the mechanical regulation of vascular structure (cytoskeletal-cell membrane-extracellular matrix). Although previous studies have concentrated on the contribution of the latter group toward arterial stiffness, our data suggest that changes in expression of signaling molecules play an equally important role. Alterations in the profiles of signaling molecules could be involved in the regulation of cell cytoskeletal organization, cell-matrix interactions, or the contractile state of the cell. CONCLUSIONS: Although the influence of smooth muscle contraction/relaxation on arterial stiffness could be controversial, our provocative data would suggest that further studies on this subject are indicated.\nDate GSE420 Last Updated: May 29 2005\nContributors: Richard Pratt Stephane Laurent\nIncludes GDS461.\n Update date: Nov 20 2003.\n Dataset description GDS461: Examination of molecular basis of aortic stiffness which predicts pulse pressure, coronary disease, stroke and cardiovascular mortality. Biopsies from patients with increased aortic stiffness compared with patients with distensible aorta. -#> 10: Molecular analysis of the effect left ventricular assist device (LVAD) support has on congestive heart failure patients.\nDate GSE430 Last Updated: Jun 15 2005\nContributors: Robert J Bache Yingjie Chen Li Yunfang Soon Park Leslie W Miller Jennifer Hall Xinqiang Han Emil Missov\nIncludes GDS462.\n Update date: Nov 20 2003.\n Dataset description GDS462: Molecular analysis of effect left ventricular assist device (LVAD) support in left ventricular myocardium tissue of patients with idiopathic dilated cardiomyopathy and end-stage heart failure. Results suggest LVAD support may improve endothelial function. -#> 11: The effect of human cytomegalovirus infection on cellular mRNA accumulation was analyzed by gene chip technology over a 48h time course.\nDate GSE675 Last Updated: May 29 2005\nContributors: Thomas Shenk Edward P Browne Bret Wing David Coleman\nIncludes GDS476.\n Update date: Nov 25 2003.\n Dataset description GDS476: Expression profiles of foreskin fibroblasts at 12 time points beginning 30 minutes after infection by human cytomegalovirus (HCMV) and continuing until 48 hours after infection. -#> 12: HeLa cells were serum starved and preincubated with DMSO (vehicle) and infected with CVB3. Following infection, virus was removed and fresh media containing 10% fetal bovine serum was added for the remainder of the infectious process. At 0, 30 minutes, 1, 3, 5, 7 and 9 hours following CVB3 infection, RNA was isolated, processed and hybridized to GeneChip<c2><ae>s.\nDate GSE712 Last Updated: Oct 28 2005\nContributors: Decheng Yang Jingchun Zhang Bobby Yanagawa Nana Rezai Timothy J Triche Bruce McManus Zsuzsanna Hollander Ji Yuan Honglin Luo Raymond T Ng\nIncludes GDS477.\n Update date: Nov 28 2003.\n Dataset description GDS477: Temporal analaysis of an in vitro model of coxsackievirus B3 (CVB3) infection. HeLa cells infected with either CVB3 or control PBS and samples examined at 0, 0.5, 1, 3, 5, 7 and 9 hours following treatment. -#> 13: Time series for gene expression changes following 3 Gy and 10 Gy of ionizing radiation exposure.\nDate GSE701 Last Updated: May 29 2005\nContributors: Vivian G Cheung Kuang-Yu Jen\nIncludes GDS479.\n Update date: Nov 28 2003.\n Dataset description GDS479: Temporal analysis of effect of 3 Gy and 10 Gy ionizing radiation (IR) exposure on lymphoblastoid cells. Various time points up to 24 hours examined. -#> 14: The vertebrate homologues of Drosophila dachsund, DACH1 and DACH2, have been implicated as important regulatory genes in development. DACH1 plays a role in retinal and pituitary precursor cell proliferation and DACH2 plays a specific role in myogenesis. DACH proteins contain a domain (DS-domain) that is conserved with the proto-oncogenes Ski and Sno. Since the Ski/Sno proto-oncogenes repress AP-1 and SMAD signaling, we hypothesized that DACH1 might play a similar cellular function. Herein, DACH1 was found to be expressed in breast cancer cell lines and to inhibit TGF-beta induced apoptosis. DACH1 repressed TGF-beta induction of AP-1 and Smad signaling in gene reporter assays and repressed endogenous TGF-beta responsive genes by microarray analyses. DACH1 bound to endogenous NCoR and Smad4 in cultured cells and DACH1 co-localized with NCoR in nuclear dot-like structures. NCoR enhanced DACH1 repression and the repression of TGF-beta-induced AP-1 or Smad-signaling by DACH1 required the DACH1 DS domain. The DS-domain of DACH was sufficient for NCoR-binding at a Smad4-binding site. Smad4 was required for DACH1 repression of Smad signaling. In Smad4 null HTB-134 cells, DACH1 inhibited the activation of SBE-4 reporter activity induced by Smad2 or Smad3 only in the presence of Smad4. DACH1 participates in the negative regulation of TGF-beta signaling by interacting with NCoR and Smad4.\nDate GSE685 Last Updated: Jun 15 2005\nContributors: Kveta Cveklova Maria A Davoli Richard G Pestell Chenguang Wang Kongming Wu Mark D'Amico Robert G Russell Anping Li Zbynek Kozmik Ying Yang Michael P Lisanti Ales Cvekl\nIncludes GDS483.\n Update date: Dec 01 2003.\n Dataset description GDS483: Analysis of effect of DACH1 in breast cancer cell line MDA-MB-231. DACH1 induced by ponasterone A treatment for 0, 18 or 36 hours. DACH1 may regulate aberrant TGF beta signals that have role in breast cancer progression. -#> 15: Our study seeks to identify genes differentially expressed between uterine leiomyomas and normal myometrial tissue. This series consists of samples derived from normal myometrium and uterine leiomyomas obtained from fibroid afflicted patients.Total RNA was extracted from samples, converted to biotin-labeled cRNA, hybridized to oligonucleotide arrays, and followed by model based expression analysis. In order to select differentially expressed genes of interest, dChip model-based expression analysis was employed. Significant genes were identified, utilizing the dChip software, as having an average fold change of > +1.5 or < -1.5 between leiomyoma and normal myometrium and p < 0.05. Under these conditions the 226 genes in the following list were identified.\nDate GSE593 Last Updated: May 29 2005\nContributors: Dawn B Milliken Ryan R Davis Jeffrey P Gregg Paul J Hoffman\nIncludes GDS484.\n Update date: Dec 01 2003.\n Dataset description GDS484: Comparison of normal myometrium and uterine leiomyomas obtained from fibroid afflicted patients. -#> 16: HUVECs (human umbilical cord vein endothelial cells) are treated with the angiogenic factors VEGF-A (vascular endothelial growth factor-A) and PlGF (placental growth factor) in low or high serum media.\nDate GSE837 Last Updated: May 29 2005\nIncludes GDS495.\n Update date: Mar 01 2004.\n Dataset description GDS495: Temporal analysis of human umbilical cord vein endothelial cell (HUVEC) isolates treated with angiogenic factors vascular endothelial growth factor-A (VEGF-A) and placental growth factor (PlGF) in low or high serum media. -#> 17: Comparison of gene expression for individuals affected with FCHL exhibiting the USF1 susceptibility haplotype and FCHL affected indiviuals carrying the protective haplotype.\nDate GSE590 Last Updated: Oct 28 2005\nContributors: Massimiliano Gentile Leena Peltonen P<c3><a4>ivi Pajukanta Rita M Cantor Janet S Sinsheimer Aldons J Lusis Heidi E Lilja\nIncludes GDS513.\n Update date: Mar 18 2004.\n Dataset description GDS513: Comparison of subcutaneous adipose tissue from individuals with familial combined hyperlipidemia (FCHL) exhibiting either upstream transcription factor 1 (USF1) susceptibility or protective haplotype. USF1 regulates glucose and lipid metabolism genes. -#> 18: This series represents samples of multiple myeloma patients with and without bone lytic lesion by MRI.\nDate GSE755 Last Updated: May 29 2005\nContributors: John D Shaughnessy Erik Rasmussen Yupo Ma Bart Barlogie Fenghuang Zhan Ronald Walker Erming Tian\nIncludes GDS531.\n Update date: Mar 29 2004.\n Dataset description GDS531: Comparison of gene expression in bone marrow plasma cells of multiple myeloma patients with and without bone lesions. Osteolytic lesions increase in multiple myeloma patients. -#> 19: A number of studies have shown that cigarette smoking produces a field defect, such that genetic mutations induced by smoking occur throughout the lung and its intra and extra-pulmonary airways. Based on this concept, we have begun this study, which has as its goal the definition of the normal airway transcriptome, an analysis of how that transcriptome is affected by cigarette smoke, and to explore the reversibility of altered gene expression when smoking has been discontinued. We have obtained brushings from intra-pulmonary airways (the right upper lobe carina) and scrapings from the buccal mucosa, from normal smoking and non-smoking volunteers (including 34 Current Smokers, 23 Never Smokers and 18 Former Smokers). RNA was isolated from these samples and gene expression profiles from intra-pulmonary airway epithelial cells were analyzed using Affymetrix U133A human gene expression arrays. All microarray data from the experiments described above have been stored, preprocessed and analyzed in a relational MySQL database that is accessible through our website at http://pulm.bumc.bu.edu/aged.\nDate GSE994 Last Updated: May 29 2005\nContributors: Jennifer Beane Frank Schembri John Palma Gang Liu Jerome Brody Avrum Spira Xuemei Yang Vishal Shah\nIncludes GDS534.\n Update date: May 02 2004.\n Dataset description GDS534: Analysis of cigarette smoking-induced changes in bronchial epithelia, and reversibility of effects when smoking is discontinued. May provide insight to molecular events leading to chronic obstructive pulmonary disease (COPD) and lung cancer. -#> 20: AR overexpression converts antagonists to weak agonists.\nDate GSE846 Last Updated: May 29 2005\nContributors: Derek Welsbie Charles Sawyers Charlie Chen\nIncludes GDS536.\n Update date: Mar 30 2004.\n Dataset description GDS536: Examination of antagonist to agonist conversion in androgen receptor-expressing hormone-sensitive LNCaP prostate cancer cells. Cells challenged with increasing doses of R1881, or bicalutamide. -#> experiment.description +#> experiment.description +#> <char> +#> 1: Bronchoalveolar lavage samples collected from lung transplant recipients. Numeric portion of sample name is an arbitrary patient ID and AxBx number indicates the perivascular (A) and bronchiolar (B) scores from biopsies collected on the same day as the BAL fluid was collected. Several patients have more than one sample in this series and can be determined by patient number followed by a lower case letter. Acute rejection state is determined by the combined A and B score - specifically, a combined AB score of 2 or greater is considered an acute rejection. +#> 2: Our laboratory has developed the first mouse model overexpressing a RNA-binding protein, the ELAV-like protein HuD, in the CNS under the control of the CaMKinII alpha promoter. Initial behavioral characterization of the mice revealed that they had significant learning deficits together with abnormalities in prepulse inhibition (PPI). At the molecular level, we found that the expression of the growth-associated protein GAP-43, one of the targets of HuD, was increased in the hippocampus of HuD transgenic mice. To characterize these mice further and to evaluate the utility of these animals in understanding human diseases, we propose to use DNA microarray methods. To test our hypothesis we propose 3 specific aims: 1) To characterize the pattern of gene expression in the hippocampus of HuD overexpressor mice 2) To compare the pattern of gene expression in our mouse model with that in the hippocampus of rats prenatally exposed to alcohol (FAS model) and 3) To compare the pattern of gene expression in our mouse model with that shown in post-mortem tissues of patients with schizophrenia. In our previous protocols we examined the pattern of gene expression in our HuD transgenic mice and in rats prenaltally exposed to alcohol. A report by another group (Hakak et al, 2001) showed that three of the HuD targets were upregulated in the prefrontal cortex of patients with schizophrenia. To evaluate whether other target of HuD may be affected in this illness, in the current protocol, we want to compare the pattern of expression in our transgenic mice with in tissue from patients with schizophrenia Based on the behavioral and molecular properties of our HuD transgenic mice we hypothesize that these animals may be good models for the studying the basis of learning disabilities and of diseases that show deficits in PPI such as fetal alcohol syndrome and schizophrenia. All 28 samples are derived from cerebellar tissues for patients with schizophrenia and matched controls. The specimens were obtained from the Maryland Brain Collection according to NIH guidelines for confidentially and privacy. The protocol used in these studies was reviewed by our HRRC which found that our studies do not fall within the category of protocols monitored by the IRB (see attached letter form the HRRC). Specimens from 14 patients with a diagnosis of schizophrenia performed according to DSM-IV criteria and 14 sex-, age- and PMI-matched controls was included in this study. No differences were found between patients and control subjects in the average age (45±12 versus 43±10 years, p=0.86) or PMI (12±5 versus 16±6 hours, p=0.11). We will provide 28 samples containing 5 ug of RNA each in DEPC water (see validation of the quality of the RNA below). In addition, we include in our study animals treated with haloperidol as control for medication. These samples will be submitted in a separate protocol.\nDate GSE4036 Last Updated: Jan 13 2006\nContributors: Nora I Perrone-Bizzozero\nIncludes GDS1917.\n Update date: Jun 14 2006.\n Dataset description GDS1917: Analysis of cortical samples corresponding to the crus I/VIIa area of the cerebellum from schizophrenia patients. A study indicates that targets of the RNA-binding ELAV-like protein HuD are overexpressed in the prefrontal cortex of patients with schizophrenia. +#> 3: The neurodegenerative process in HIV encephalitis (HIVE) is associated with extensive damage to the dendritic and synaptic structure that often leads to cognitive impairment. Several mechanisms might be at play, including release of neurotoxins, oxidative stress and decreased activity of neurotrophic factors. Furthermore, HIV-mediated dysregulation of genes involved in neuronal maintenance might play an important role. For this purpose, cRNA was prepared from the brains of 17 AIDS patients for analysis with the Affymetrix Human U95Av2 GeneChip and analyzed with the GeneSpring Expression Analysis Software. Out of 12,625 genes analyzed, 74 were downregulated and 59 were upregulated compared to controls. Initial alternative analysis of RNA was performed by ribonuclease protection assay (RPA). In cases with HIVE, downregulated genes included neuronal molecules involved in synaptic plasticity and transmission (ion channels, synaptogyrin, synapsin II), cell cycle (p35, p39, CDC-L2, CDC42, PAK1) and signaling molecules (PI3K, Ras-Raf-MEK1), transcription factors and cytoskeletal components (MAP-1B, MAP-2, tubulin, adducin-2). Upregulated genes included those involved in neuroimmune (IgG, MHC, ?2microglobulin) and anti-viral responses (interferon-inducible molecules), transcription (STAT1, OLIG2, Pax-6) and signaling modulation (MEK3, EphB1) of the cytoskeleton (myosin, aduccin-3, radixin, dystrobrevin). Taken together, this study suggests that HIV proteins released from infected macrophages might not only induce a neuroinflammatory response, but also may promote neurodegeneration by interfering with neuronal transcription of genes involved in regulating signaling and cytoskeletal molecules important in maintaining synapto-dendritic functioning and integrity.\nDate GSE3489 Last Updated: Mar 07 2006\nContributors: Eleanor S Roberts Dianne Langford Anthony Adame Edward Rockenstein Leslie Crews Howard S Fox Eliezer Masiliah Ian Everall\nIncludes GDS1726.\n Update date: May 30 2006.\n Dataset description GDS1726: Analysis of brain frontal cortex of HIV-seropositive patients with HIV encephalitis (HIVE). HIVE affects >40% of AIDS patients, promoting neurodegeneration and cognitive impairment. Results suggest HIV-mediated dysregulation of genes involved in neuronal maintenance might play an important role. +#> 4: Overall study: Identification of PDGF-dependent patterns of gene expression in U87 glioblastoma cells. RNA was obtained from triplicate dishes of 5 different groups of U87 cells, each (total 15) analyzed with one U95 microarray chip. Three different comparisons were made: 1) Clone 3.1 (34580-34582) vs. clone 3.3 (34583-34585) vs. parent U87 (34592-34594). Purpose: demonstrate that the gene expression profiles between these 3 cell lines are not different, so they could be pooled as a single untreated group. 2) Pooled control group (34580-34585, 34592-34594) vs. clone 8.1 (34586-34588). Purpose: identify genes specifically controlled by autocrine PDGF activity. 3) Clone 8.1 (34586-34588) vs. clone 8.1 treated with PDGF (34589-34591) Purpose: Identify genes specifically induced by exogenous PDGF.\nDate GSE1923 Last Updated: Dec 19 2005\nContributors: David M Kaetzel Catherine Nutt Xuejun Peng Piam Shanehsaz Deqin Ma David N Louis\nIncludes GDS1730.\n Update date: May 02 2006.\n Dataset description GDS1730: Analysis of U87 glioblastoma (GBM) cell clones overexpressing a dominant-negative form of platelet-derived growth factor (PDGF) A subunit to inactive the PDGF autocrine signaling loop. The autocrine PDGF loop is a hallmark of GBM. Results identify genes regulated by oncogenic PDGF signaling. +#> 5: Analysis of gene expression in mammary epithelial cells transduced with either hTERT, empty LXSN vector or empty BABE vector.\nDate GSE361 Last Updated: May 29 2005\nIncludes GDS337.\n Update date: May 21 2003.\n Dataset description GDS337: Effect of overexpression of the telomerase catalytic subunit (TERT) in mammary epithelial cells (HMEC). Findings imply that ectopic telomerase expression modulates growth-controlling genes and enhances cell proliferation. +#> 6: The purpose of this study is to discover genes that might increase aqueous humor outflow when human ciliary muscle or human trabecular meshwork cells are treated with the prostaglandin analogues latanoprost free acid or prostaglandin F2alpha. Five tissue donors were pooled on each chip.\nDate GSE492 Last Updated: Jul 27 2006\nContributors: Paul Russell\nIncludes GDS359.\n Update date: Apr 14 2004.\n Dataset description GDS359: Glaucoma study investigating molecular basis of increase in aqueous humor outflow when human ciliary muscle or human trabecular meshwork cells are treated with prostaglandin analogs latanoprost free acid or prostaglandin F2alpha. +#> 7: DNA damage caused by UV radiation initiates cellular recovery mechanisms, which involve activation of DNA damage response pathways, cell cycle arrest and apoptosis. To assess cellular transcriptional responses to UVC-induced DNA damage we compared time course responses of human skin fibroblasts to low and high doses of UVC radiation known to induce a transient cellular replicative arrest or apoptosis, respectively. UVC radiation elicited >3-fold changes in 460 out of 12,000 transcripts and 89% of these represented downregulated transcripts. Only 5% of the regulated genes were common to both low and high doses of radiation. Cells inflicted with a low dose of UVC exhibited transcription profiles demonstrating transient regulation followed by recovery, whereas the responses were persistent after the high dose. A detailed clustering analysis and functional classification of the targets implied regulation of biologically divergent responses and suggested involvement of transcriptional and translational machinery, inflammatory, anti-proliferative and anti-angiogenic responses. The data support the notion that UVC radiation induces prominent, dose-dependent downregulation of transcription. However, the data strongly suggest that transcriptional repression is also target gene selective. Furthermore, the results demonstrate that dose-dependent induction of cell cycle arrest and apoptosis by UVC radiation are transcriptionally highly distinct responses.\nDate GSE713 Last Updated: Jun 15 2005\nContributors: Massimiliano Gentile Marikki Laiho Leena Latonen\nIncludes GDS400.\n Update date: Oct 08 2003.\n Dataset description GDS400: Temporal analysis of differences in WS1 human skin fibroblast gene expression response to low (10 J/m2; induces transient cellular replicative arrest) or high (50 J/m2; induces apoptosis) doses of short wavelength UV radiation (UVC; 254 nm). +#> 8: Identification of amyotrophic lateral sclerosis (ALS) associated genes. Post mortem spinal cord grey matter from sporadic and familial ALS patients compared with controls.\nDate GSE833 Last Updated: Jun 28 2005\nContributors: Steven R Gullans Robert H Brown Fernando Dangond\nIncludes GDS412.\n Update date: Nov 24 2003.\n Dataset description GDS412: Identification of amyotrophic lateral sclerosis (ALS) associated genes. Post mortem spinal cord grey matter from sporadic and familial ALS patients compared with controls. Attempt to identify mechanisms by which ALS destroys motor neurons. +#> 9: BACKGROUND: Previous genomic studies with human tissues have compared differential gene expression between 2 conditions (ie, normal versus diseased) to identify altered gene expression in a binary manner; however, a potentially more informative approach is to correlate the levels of gene expression with quantitative physiological parameters. METHODS AND RESULTS: In this study, we have used this approach to examine genes whose expression correlates with arterial stiffness in human aortic specimens. Our data identify 2 distinct groups of genes, those associated with cell signaling and those associated with the mechanical regulation of vascular structure (cytoskeletal-cell membrane-extracellular matrix). Although previous studies have concentrated on the contribution of the latter group toward arterial stiffness, our data suggest that changes in expression of signaling molecules play an equally important role. Alterations in the profiles of signaling molecules could be involved in the regulation of cell cytoskeletal organization, cell-matrix interactions, or the contractile state of the cell. CONCLUSIONS: Although the influence of smooth muscle contraction/relaxation on arterial stiffness could be controversial, our provocative data would suggest that further studies on this subject are indicated.\nDate GSE420 Last Updated: May 29 2005\nContributors: Richard Pratt Stephane Laurent\nIncludes GDS461.\n Update date: Nov 20 2003.\n Dataset description GDS461: Examination of molecular basis of aortic stiffness which predicts pulse pressure, coronary disease, stroke and cardiovascular mortality. Biopsies from patients with increased aortic stiffness compared with patients with distensible aorta. +#> 10: Molecular analysis of the effect left ventricular assist device (LVAD) support has on congestive heart failure patients.\nDate GSE430 Last Updated: Jun 15 2005\nContributors: Robert J Bache Yingjie Chen Li Yunfang Soon Park Leslie W Miller Jennifer Hall Xinqiang Han Emil Missov\nIncludes GDS462.\n Update date: Nov 20 2003.\n Dataset description GDS462: Molecular analysis of effect left ventricular assist device (LVAD) support in left ventricular myocardium tissue of patients with idiopathic dilated cardiomyopathy and end-stage heart failure. Results suggest LVAD support may improve endothelial function. +#> 11: The effect of human cytomegalovirus infection on cellular mRNA accumulation was analyzed by gene chip technology over a 48h time course.\nDate GSE675 Last Updated: May 29 2005\nContributors: Thomas Shenk Edward P Browne Bret Wing David Coleman\nIncludes GDS476.\n Update date: Nov 25 2003.\n Dataset description GDS476: Expression profiles of foreskin fibroblasts at 12 time points beginning 30 minutes after infection by human cytomegalovirus (HCMV) and continuing until 48 hours after infection. +#> 12: HeLa cells were serum starved and preincubated with DMSO (vehicle) and infected with CVB3. Following infection, virus was removed and fresh media containing 10% fetal bovine serum was added for the remainder of the infectious process. At 0, 30 minutes, 1, 3, 5, 7 and 9 hours following CVB3 infection, RNA was isolated, processed and hybridized to GeneChip®s.\nDate GSE712 Last Updated: Oct 28 2005\nContributors: Decheng Yang Jingchun Zhang Bobby Yanagawa Nana Rezai Timothy J Triche Bruce McManus Zsuzsanna Hollander Ji Yuan Honglin Luo Raymond T Ng\nIncludes GDS477.\n Update date: Nov 28 2003.\n Dataset description GDS477: Temporal analaysis of an in vitro model of coxsackievirus B3 (CVB3) infection. HeLa cells infected with either CVB3 or control PBS and samples examined at 0, 0.5, 1, 3, 5, 7 and 9 hours following treatment. +#> 13: Time series for gene expression changes following 3 Gy and 10 Gy of ionizing radiation exposure.\nDate GSE701 Last Updated: May 29 2005\nContributors: Vivian G Cheung Kuang-Yu Jen\nIncludes GDS479.\n Update date: Nov 28 2003.\n Dataset description GDS479: Temporal analysis of effect of 3 Gy and 10 Gy ionizing radiation (IR) exposure on lymphoblastoid cells. Various time points up to 24 hours examined. +#> 14: The vertebrate homologues of Drosophila dachsund, DACH1 and DACH2, have been implicated as important regulatory genes in development. DACH1 plays a role in retinal and pituitary precursor cell proliferation and DACH2 plays a specific role in myogenesis. DACH proteins contain a domain (DS-domain) that is conserved with the proto-oncogenes Ski and Sno. Since the Ski/Sno proto-oncogenes repress AP-1 and SMAD signaling, we hypothesized that DACH1 might play a similar cellular function. Herein, DACH1 was found to be expressed in breast cancer cell lines and to inhibit TGF-beta induced apoptosis. DACH1 repressed TGF-beta induction of AP-1 and Smad signaling in gene reporter assays and repressed endogenous TGF-beta responsive genes by microarray analyses. DACH1 bound to endogenous NCoR and Smad4 in cultured cells and DACH1 co-localized with NCoR in nuclear dot-like structures. NCoR enhanced DACH1 repression and the repression of TGF-beta-induced AP-1 or Smad-signaling by DACH1 required the DACH1 DS domain. The DS-domain of DACH was sufficient for NCoR-binding at a Smad4-binding site. Smad4 was required for DACH1 repression of Smad signaling. In Smad4 null HTB-134 cells, DACH1 inhibited the activation of SBE-4 reporter activity induced by Smad2 or Smad3 only in the presence of Smad4. DACH1 participates in the negative regulation of TGF-beta signaling by interacting with NCoR and Smad4.\nDate GSE685 Last Updated: Jun 15 2005\nContributors: Kveta Cveklova Maria A Davoli Richard G Pestell Chenguang Wang Kongming Wu Mark D'Amico Robert G Russell Anping Li Zbynek Kozmik Ying Yang Michael P Lisanti Ales Cvekl\nIncludes GDS483.\n Update date: Dec 01 2003.\n Dataset description GDS483: Analysis of effect of DACH1 in breast cancer cell line MDA-MB-231. DACH1 induced by ponasterone A treatment for 0, 18 or 36 hours. DACH1 may regulate aberrant TGF beta signals that have role in breast cancer progression. +#> 15: Our study seeks to identify genes differentially expressed between uterine leiomyomas and normal myometrial tissue. This series consists of samples derived from normal myometrium and uterine leiomyomas obtained from fibroid afflicted patients.Total RNA was extracted from samples, converted to biotin-labeled cRNA, hybridized to oligonucleotide arrays, and followed by model based expression analysis. In order to select differentially expressed genes of interest, dChip model-based expression analysis was employed. Significant genes were identified, utilizing the dChip software, as having an average fold change of > +1.5 or < -1.5 between leiomyoma and normal myometrium and p < 0.05. Under these conditions the 226 genes in the following list were identified.\nDate GSE593 Last Updated: May 29 2005\nContributors: Dawn B Milliken Ryan R Davis Jeffrey P Gregg Paul J Hoffman\nIncludes GDS484.\n Update date: Dec 01 2003.\n Dataset description GDS484: Comparison of normal myometrium and uterine leiomyomas obtained from fibroid afflicted patients. +#> 16: HUVECs (human umbilical cord vein endothelial cells) are treated with the angiogenic factors VEGF-A (vascular endothelial growth factor-A) and PlGF (placental growth factor) in low or high serum media.\nDate GSE837 Last Updated: May 29 2005\nIncludes GDS495.\n Update date: Mar 01 2004.\n Dataset description GDS495: Temporal analysis of human umbilical cord vein endothelial cell (HUVEC) isolates treated with angiogenic factors vascular endothelial growth factor-A (VEGF-A) and placental growth factor (PlGF) in low or high serum media. +#> 17: Comparison of gene expression for individuals affected with FCHL exhibiting the USF1 susceptibility haplotype and FCHL affected indiviuals carrying the protective haplotype.\nDate GSE590 Last Updated: Oct 28 2005\nContributors: Massimiliano Gentile Leena Peltonen Päivi Pajukanta Rita M Cantor Janet S Sinsheimer Aldons J Lusis Heidi E Lilja\nIncludes GDS513.\n Update date: Mar 18 2004.\n Dataset description GDS513: Comparison of subcutaneous adipose tissue from individuals with familial combined hyperlipidemia (FCHL) exhibiting either upstream transcription factor 1 (USF1) susceptibility or protective haplotype. USF1 regulates glucose and lipid metabolism genes. +#> 18: This series represents samples of multiple myeloma patients with and without bone lytic lesion by MRI.\nDate GSE755 Last Updated: May 29 2005\nContributors: John D Shaughnessy Erik Rasmussen Yupo Ma Bart Barlogie Fenghuang Zhan Ronald Walker Erming Tian\nIncludes GDS531.\n Update date: Mar 29 2004.\n Dataset description GDS531: Comparison of gene expression in bone marrow plasma cells of multiple myeloma patients with and without bone lesions. Osteolytic lesions increase in multiple myeloma patients. +#> 19: A number of studies have shown that cigarette smoking produces a field defect, such that genetic mutations induced by smoking occur throughout the lung and its intra and extra-pulmonary airways. Based on this concept, we have begun this study, which has as its goal the definition of the normal airway transcriptome, an analysis of how that transcriptome is affected by cigarette smoke, and to explore the reversibility of altered gene expression when smoking has been discontinued. We have obtained brushings from intra-pulmonary airways (the right upper lobe carina) and scrapings from the buccal mucosa, from normal smoking and non-smoking volunteers (including 34 Current Smokers, 23 Never Smokers and 18 Former Smokers). RNA was isolated from these samples and gene expression profiles from intra-pulmonary airway epithelial cells were analyzed using Affymetrix U133A human gene expression arrays. All microarray data from the experiments described above have been stored, preprocessed and analyzed in a relational MySQL database that is accessible through our website at http://pulm.bumc.bu.edu/aged.\nDate GSE994 Last Updated: May 29 2005\nContributors: Jennifer Beane Frank Schembri John Palma Gang Liu Jerome Brody Avrum Spira Xuemei Yang Vishal Shah\nIncludes GDS534.\n Update date: May 02 2004.\n Dataset description GDS534: Analysis of cigarette smoking-induced changes in bronchial epithelia, and reversibility of effects when smoking is discontinued. May provide insight to molecular events leading to chronic obstructive pulmonary disease (COPD) and lung cancer. +#> 20: AR overexpression converts antagonists to weak agonists.\nDate GSE846 Last Updated: May 29 2005\nContributors: Derek Welsbie Charles Sawyers Charlie Chen\nIncludes GDS536.\n Update date: Mar 30 2004.\n Dataset description GDS536: Examination of antagonist to agonist conversion in androgen receptor-expressing hormone-sensitive LNCaP prostate cancer cells. Cells challenged with increasing doses of R1881, or bicalutamide. +#> experiment.description #> experiment.troubled experiment.accession experiment.database #> <lgcl> <char> <char> #> 1: FALSE GSE2018 GEO diff --git a/reference/search_annotations.html b/reference/search_annotations.html index d479d88d..da0910ff 100644 --- a/reference/search_annotations.html +++ b/reference/search_annotations.html @@ -174,17 +174,17 @@

Examples

#> category.name category.URI #> value.name #> <char> -#> 1: traumatic diaphragmatic hernia -#> 2: traumatic acid +#> 1: traumatic acid +#> 2: traumatic diaphragmatic hernia #> 3: traumatic glaucoma #> 4: traumatic brain injury #> 5: traumatic avascular necrosis #> 6: traumatic myositis ossificans #> 7: traumatic encephalopathy -#> 8: post-traumatic stress disorder symptom measurement -#> 9: brain stem hemorrhage, traumatic -#> 10: post-traumatic stress disorder -#> 11: avoidance of stimuli associated with traumatic event +#> 8: avoidance of stimuli associated with traumatic event +#> 9: post-traumatic stress disorder symptom measurement +#> 10: brain stem hemorrhage, traumatic +#> 11: post-traumatic stress disorder #> 12: secondary non-traumatic avascular necrosis #> 13: post-traumatic epilepsy #> 14: iatrogenic or traumatic pituitary deficiency @@ -195,31 +195,31 @@

Examples

#> 19: injury #> 20: kluver-bucy syndrome #> 21: spinal cord injury -#> 22: osteoporosis -#> 23: acute stress reaction +#> 22: acute stress reaction +#> 23: osteoporosis #> 24: response to trauma exposure #> 25: cerebrospinal fluid rhinorrhea #> 26: injury #> 27: kluver-bucy syndrome #> 28: wound myiasis #> 29: dementia pugilistica -#> 30: cerebrospinal fluid leak -#> 31: acute stress disorder +#> 30: acute stress disorder +#> 31: cerebrospinal fluid leak #> 32: kummell disease #> value.name #> value.URI #> <char> -#> 1: http://www.ebi.ac.uk/efo/EFO_1001861 -#> 2: http://purl.obolibrary.org/obo/CHEBI_545687 +#> 1: http://purl.obolibrary.org/obo/CHEBI_545687 +#> 2: http://www.ebi.ac.uk/efo/EFO_1001861 #> 3: http://purl.obolibrary.org/obo/MONDO_0001626 #> 4: http://purl.obolibrary.org/obo/MONDO_0858950 #> 5: http://purl.obolibrary.org/obo/MONDO_0018375 #> 6: http://purl.obolibrary.org/obo/MONDO_0021929 #> 7: http://purl.obolibrary.org/obo/MONDO_0043512 -#> 8: http://www.ebi.ac.uk/efo/EFO_0008535 -#> 9: http://www.ebi.ac.uk/efo/EFO_1001276 -#> 10: http://www.ebi.ac.uk/efo/EFO_0001358 -#> 11: http://purl.obolibrary.org/obo/HP_0032942 +#> 8: http://purl.obolibrary.org/obo/HP_0032942 +#> 9: http://www.ebi.ac.uk/efo/EFO_0008535 +#> 10: http://www.ebi.ac.uk/efo/EFO_1001276 +#> 11: http://www.ebi.ac.uk/efo/EFO_0001358 #> 12: http://purl.obolibrary.org/obo/MONDO_0018376 #> 13: http://purl.obolibrary.org/obo/MONDO_0043264 #> 14: http://purl.obolibrary.org/obo/MONDO_0019845 @@ -230,16 +230,16 @@

Examples

#> 19: http://www.ebi.ac.uk/efo/EFO_0000546 #> 20: http://www.ebi.ac.uk/efo/EFO_0007335 #> 21: http://www.ebi.ac.uk/efo/EFO_1001919 -#> 22: http://www.ebi.ac.uk/efo/EFO_0003882 -#> 23: http://www.ebi.ac.uk/efo/EFO_0005223 +#> 22: http://www.ebi.ac.uk/efo/EFO_0005223 +#> 23: http://www.ebi.ac.uk/efo/EFO_0003882 #> 24: http://www.ebi.ac.uk/efo/EFO_0008483 #> 25: http://purl.obolibrary.org/obo/MONDO_0020773 #> 26: http://purl.obolibrary.org/obo/MONDO_0021178 #> 27: http://purl.obolibrary.org/obo/MONDO_0005817 #> 28: http://purl.obolibrary.org/obo/MONDO_0015622 #> 29: http://purl.obolibrary.org/obo/MONDO_0019976 -#> 30: http://purl.obolibrary.org/obo/MONDO_0043327 -#> 31: http://purl.obolibrary.org/obo/MONDO_0003763 +#> 30: http://purl.obolibrary.org/obo/MONDO_0003763 +#> 31: http://purl.obolibrary.org/obo/MONDO_0043327 #> 32: http://purl.obolibrary.org/obo/MONDO_0003940 #> value.URI diff --git a/reference/search_datasets.html b/reference/search_datasets.html index 89184a85..f5529c96 100644 --- a/reference/search_datasets.html +++ b/reference/search_datasets.html @@ -193,18 +193,18 @@

Examples

#> Warning: search_datasets is deprecated. please use get_datasets instead #> experiment.shortName #> <char> -#> 1: GSE157509 -#> 2: McLean Hippocampus -#> 3: byne-cc -#> 4: GSE45484 -#> 5: GSE134497 -#> 6: GSE160761 -#> 7: GSE179921.2 -#> 8: GSE66433 -#> 9: GSE197966 -#> 10: GSE202537 -#> 11: GSE205422 -#> 12: GSE246593 +#> 1: McLean Hippocampus +#> 2: byne-cc +#> 3: GSE45484 +#> 4: GSE134497 +#> 5: GSE160761 +#> 6: GSE179921.2 +#> 7: GSE66433 +#> 8: GSE197966 +#> 9: GSE202537 +#> 10: GSE205422 +#> 11: GSE246593 +#> 12: GSE157509 #> 13: GSE66196 #> 14: GSE210064 #> 15: GSE23848 @@ -214,43 +214,43 @@

Examples

#> 19: McLean_PFC #> 20: stanley_feinberg #> experiment.shortName -#> experiment.name -#> <char> -#> 1: Increased IL-6 and altered inflammatory response in bipolar disorder patient-derived astrocytes -#> 2: McLean Hippocampus -#> 3: Corpus Callosum data from Stanley collection samples -#> 4: Gene-expression differences in peripheral blood between lithium responders and non-responders in the <e2><80><9c>Lithium Treatment -Moderate dose Use Study<e2><80><9d> (LiTMUS) -#> 5: Total RNA sequecing for human induced pluripotent derived cerebral organoids -#> 6: RNA sequencing in human iPSCs derived from bipolar patients to identify important therapeutic molecular targets of Valproate(VPA) -#> 7: Split part 2 of: TCF7L2 lncRNA: A Link between Bipolar Disorder and Body Mass Index through Glucocorticoid Signaling [RNA-Seq] [collection of material = Experiment 1 ] -#> 8: Effects of the microRNA 137 and its connection to psychiatric disorders. -#> 9: Transcriptional effects of bipolar disorder drugs on NT2-N cells -#> 10: Diurnal alterations in gene expression across striatal subregions in psychosis -#> 11: Network-based integrative analysis of lithium response in bipolar disorder using transcriptomic and GWAS data -#> 12: Transition of allele-specific DNA hydroxymethylation at regulatory loci is associated with phenotypic variation in monozygotic twins discordant for psychiatric disorders -#> 13: Bipolar disorder and lithium-induced gene expression in two peripheral cell models -#> 14: Gene expression alterations in the postmortem hippocampus from older patients with bipolar disorder <e2><80><93> a hypothesis generating study -#> 15: Peripheral blood gene-expression in depressed subjects with bipolar disorder vs healthy controls. -#> 16: Adult postmortem brain tissue (orbitofrontal cortex) from subjects with bipolar disorder and healthy controls -#> 17: Adult postmortem brain tissue (dorsolateral prefrontal cortex) from subjects with bipolar disorder and healthy controls -#> 18: Expression profiling in monozygotic twins discordant for bipolar disorder -#> 19: McLean_PFC -#> 20: Stanley consortium collection Cerebellum - Feinberg -#> experiment.name +#> experiment.name +#> <char> +#> 1: McLean Hippocampus +#> 2: Corpus Callosum data from Stanley collection samples +#> 3: Gene-expression differences in peripheral blood between lithium responders and non-responders in the “Lithium Treatment -Moderate dose Use Study” (LiTMUS) +#> 4: Total RNA sequecing for human induced pluripotent derived cerebral organoids +#> 5: RNA sequencing in human iPSCs derived from bipolar patients to identify important therapeutic molecular targets of Valproate(VPA) +#> 6: Split part 2 of: TCF7L2 lncRNA: A Link between Bipolar Disorder and Body Mass Index through Glucocorticoid Signaling [RNA-Seq] [collection of material = Experiment 1 ] +#> 7: Effects of the microRNA 137 and its connection to psychiatric disorders. +#> 8: Transcriptional effects of bipolar disorder drugs on NT2-N cells +#> 9: Diurnal alterations in gene expression across striatal subregions in psychosis +#> 10: Network-based integrative analysis of lithium response in bipolar disorder using transcriptomic and GWAS data +#> 11: Transition of allele-specific DNA hydroxymethylation at regulatory loci is associated with phenotypic variation in monozygotic twins discordant for psychiatric disorders +#> 12: Increased IL-6 and altered inflammatory response in bipolar disorder patient-derived astrocytes +#> 13: Bipolar disorder and lithium-induced gene expression in two peripheral cell models +#> 14: Gene expression alterations in the postmortem hippocampus from older patients with bipolar disorder – a hypothesis generating study +#> 15: Peripheral blood gene-expression in depressed subjects with bipolar disorder vs healthy controls. +#> 16: Adult postmortem brain tissue (orbitofrontal cortex) from subjects with bipolar disorder and healthy controls +#> 17: Adult postmortem brain tissue (dorsolateral prefrontal cortex) from subjects with bipolar disorder and healthy controls +#> 18: Expression profiling in monozygotic twins discordant for bipolar disorder +#> 19: McLean_PFC +#> 20: Stanley consortium collection Cerebellum - Feinberg +#> experiment.name #> experiment.ID #> <int> -#> 1: 23425 -#> 2: 670 -#> 3: 4354 -#> 4: 6145 -#> 5: 16450 -#> 6: 17943 -#> 7: 21159 -#> 8: 24427 -#> 9: 25070 -#> 10: 25749 -#> 11: 25972 -#> 12: 31703 +#> 1: 670 +#> 2: 4354 +#> 3: 6145 +#> 4: 16450 +#> 5: 17943 +#> 6: 21159 +#> 7: 24427 +#> 8: 25070 +#> 9: 25749 +#> 10: 25972 +#> 11: 31703 +#> 12: 23425 #> 13: 24428 #> 14: 28041 #> 15: 1958 @@ -262,20 +262,20 @@

Examples

#> experiment.ID #> experiment.description #> <char> -#> 1: The goals of this study are to examine responses to inflammation in astrocytes from induced pluripotent stem cells derived from healthy controls and bipolar disorder patients. We examine the transcriptomic inflmmatory signature of generated astrocytes following Il1Beta exposure in BD vs. control Results: BD-patient astrocytes show a unique inflammatory response with differentially regulated genes.\nAt time of import, last updated (by provider) on: Mar 19 2021\n\nContributors: ; [Maxim N Shokhirev, Fred Gage, Krishna Vadodaria, Carol Marchetto] -#> 2: Hippocampus of schizophrenic, bipolar, and control subjects. Analyzed from CEL files. -#> 3: -#> 4: Analysis of gene-expression changes in treatment responders vs non-responders to two different treatments among subjectrs participating in LiTMUS. Results provide information on pathways that may be involved in the clinical response to Lithium in patients with bipolar disorder.\nLast Updated (by provider): Apr 01 2013\nContributors: Robert Beech -#> 5: Total RNA sequecing for human induced pluripotent derived cerebral organoids from healthy controls and Bipolar disorder\nAt time of import, last updated (by provider) on: Apr 01 2020\n\nContributors: ; [Annie Kathuria, Rakesh Karmacharya] -#> 6: Valproate(VPA) has been used in the treatment of bipolar disorder since the 1990s. However, the therapeutic targetsof VPA have remained elusive. Here we used RNA sequencing in human iPSCs derived from bipolar patients to further identify important molecular targets. Human iPSCs were homogenized and total RNA was isolated using the RNeasy Plus Micro Kit (Qiagen, Hilden, Germany). RNA quantity and quality were assessed using fluorometry (Qubit RNA Broad Range Assay Kit and Fluorometer; Invitrogen, Carlsbad, CA) and chromatography (Bioanalyzer and RNA 6000 Nano Kit; Agilent, Santa Clara, CA), respectively. Libraries were prepared using TruSeq Stranded mRNA (PolyA+) kit (Illumina, San Diego, CA) and sequenced by Illumina NextSeq 500. The read length was 75bp with 30-40M reads per sample. FastQC (v0.11.3) was performed to assess data quality. TopHat2 (v2.0.9) aligned the reads to the mouse reference genome (Mus musculus UCSC mm10) and to the Ensembl human reference genome (GRCh38.p13) using default parameters. Alignments were then converted to expression count data using HTseq (v0.6.1) with default union mode.\nAt time of import, last updated (by provider) on: Dec 31 2020\n\nContributors: ; [George Tseng, Colleen McClung, Wei Zong, Ryan Logan] -#> 7: This experiment was created by Gemma splitting another: \nExpressionExperiment Id=20933 Name=TCF7L2 lncRNA: A Link between Bipolar Disorder and Body Mass Index through Glucocorticoid Signaling [RNA-Seq] (GSE179921) Bipolar disorder (BD) and obesity are highly comorbid. We previously performed a genome-wide association study (GWAS) for BD risk accounting for the effect of body mass index (BMI) which identified a genome-wide significant single-nucleotide polymorphism (SNP) in the gene encoding the transcription factor 7 like 2 (TCF7L2). However, the molecular function of TCF7L2 in the central nervous system (CNS) and its possible role in BD and BMI interaction remained unclear. In the present study, we demonstrated by studying human induced pluripotent stem cell (hiPSC)-derived astrocytes, cells which highly express TCF7L2 in the CNS, that the BD-BMI GWAS risk SNP is associated with glucocorticoid-dependent repression of the expression of a previously uncharacterized TCF7L2 transcript variant. That transcript is a long non-coding RNA (lncRNA-TCF7L2) that is highly expressed in the CNS but not in peripheral tissues such as the liver and pancreas which are involved in metabolism. In astrocytes, knock-down of the lncRNA-TCF7L2 resulted in decreased expression of the parent gene, TCF7L2, as well as alterations in the expression of a series of genes involved in insulin signaling and diabetes. We also studied the function of TCF7L2 in hiPSC-derived astrocytes by integrating RNA sequencing data after TCF7L2 knock-down with TCF7L2 chromatin-immunoprecipitation sequencing (ChIP-seq) data. Those studies showed that TCF7L2 directly regulated a series of BD-risk genes. In summary, these results support the existence of a CNS-based mechanism underlying BD-BMI genetic risk, a mechanism based on a glucocorticoid-dependent expression quantitative trait locus that regulates the expression of a novel TCF7L2 non-coding transcript.\nAt time of import, last updated (by provider) on: Sep 20 2021\n\nContributors: ; [Mark A Frye, Thanh L Nguyen, Tamas Ordog, Brandon Coombes, Richard M Weinshilboum, Huaizhi Huang, Zhenqing Ye, Liewei Wang, Huanyao Gao, Daniel Kim, Jeong-Heon Lee, Brenna Sharp, Duan Liu, Joanna Biernacka] -#> 8: MicroRNAs have been implicated in the pathology not only of cancer, but also of psychiatric diseases, such as bipolar disorder and schizophrenia. As several psychiatric disorders share the same risk genes, we hypothesized that this microRNA could also be associated with attention-deficit/hyperactivity disorder (ADHD) and that this association to psychiatric disorders might be due to the variable number of tandem repeats (VNTR) polymorphism within the internal miR-137 (Imir137) promoter (PMID 18316599; PMID 25154622). To further understand the role of the microRNA 137 in the brain a knock-down of miR-137 expression in SH-SY5Y neuroblastoma cells was performed followed by expression analysis using a microarray.\nAt time of import, last updated (by provider) on: Aug 08 2019\n\nContributors: ; [Lena Wei<c3><9f>flog, Andreas Reif, Stefanie Berger, Heike Weber, Claus J Scholz] -#> 9: Human neuronal-like cells (NT2-N) were treated with either lamotrigine (50 <c2><b5>M), lithium (2.5 mM), quetiapine (50 <c2><b5>M), valproate (0.5 mM) or vehicle control for 24 hours. Genome wide mRNA expression was quantified by RNA-sequencing. Results offer insights on the mechanism(s) of action of bipolar disorder drugs at the transcriptional level.\nAt time of import, last updated (by provider) on: Apr 27 2022\n\nContributors: ; [Srisaiyini Kidnapillai, Chiara Bortolasci, Laura Gray, Trang Truong, Bruna Panizzutti, Mark Richardson, Craig Smith, Olivia Dean, Zoe Liu, Briana Spolding, Michael Berk, Jee H Kim, Ken Walder] -#> 10: Background: Psychosis is a defining feature of schizophrenia and highly prevalent in bipolar disorder. Notably, individuals suffering with these illnesses also have major disruptions in sleep and circadian rhythms, and disturbances to sleep and circadian rhythms can precipitate or exacerbate psychotic symptoms. Psychosis is associated with the striatum, though no study to date has directly measured molecular rhythms and determined how they are altered in the striatum of subjects with psychosis. Methods: Here, we perform RNA-sequencing and both differential expression and rhythmicity analyses to investigate diurnal alterations in gene expression in human postmortem striatal subregions (NAc, caudate, and putamen) in subjects with psychosis relative to unaffected comparison subjects. Results: Across regions, we find differential expression of immune-related transcripts and a substantial loss of rhythmicity in core circadian clock genes in subjects with psychosis. In the nucleus accumbens (NAc), mitochondrial-related transcripts have decreased expression in psychosis subjects, but only in those who died at night. Additionally, we find a loss of rhythmicity in small nucleolar RNAs and a gain of rhythmicity in glutamatergic signaling in the NAc of psychosis subjects. Between region comparisons indicate that rhythmicity in the caudate and putamen is far more similar in subjects with psychosis than in matched comparison subjects. Conclusions: Together, these findings reveal differential and rhythmic gene expression differences across the striatum that may contribute to striatal dysfunction and psychosis in psychotic disorders.\nAt time of import, last updated (by provider) on: Aug 31 2022\n\nContributors: ; [George Tseng, Colleen McClung, Wei Zong, Kyle Ketchesin] -#> 11: Lithium is the gold standard treatment for bipolar disorder. The goal of this study was to identify gene expression networks associated with lithium response. RNAseq data was obtained from IPSC derived neurons from lithium responders and non-responders. Focal adhesion was the network most associated with response.\nAt time of import, last updated (by provider) on: Jun 09 2022\n\nContributors: ; [Vipavee Niemsiri, Fred Gage, John Kelsoe] -#> 12: Major psychiatric disorders such as schizophrenia (SCZ) and bipolar disorder (BPD) are complex genetic mental illnesses. Their non-Mendelian features such as monozygotic twins discordant for SCZ or BPD are likely complicated by environmental modifiers of genetic effects. 5-hydroxymethylcytosine (5hmC) is an important epigenetic marker in gene regulation and whether its links with genetic variants contribute to the non-Mendelian features remain largely unexplored. Here, we performed hydroxymethylome and genome analyses of blood DNA from psychiatric disorder-discordant monozygotic twins to study how allele-specific hydroxymethylation (AShM) mediates phenotypic variations. We identified thousands of genetic variants with AShM imbalances who exhibit phenotypic variation-associated AShM transition at regulatory loci. These AShMs have plausible causal associations with psychiatric disorders through effects on interactions between transcription factors, DNA methylations, or other epigenomic markers and then contribute to dysregulated gene expression, which eventually increases disease susceptibility. We then validated that competitive binding of POU3F2 on the alternative allele of psyAShM site rs4558409 (G/T) in PLLP can enhance the PLLP expression, while hydroxymethylated alternative allele alleviating the transcription factor binding activity at rs4558409 site might be associated with downregulated PLLP expression observed in BPD or SCZ. Moreover, disruption of rs4558409 induces gain of PLLP function and promotes neural development and vesicle trafficking. Our study provides a powerful strategy for prioritizing regulatory risk variants and contributes to our understanding of the interplay between genetic and epigenetic factors in mediating complex disease susceptibility.\nAt time of import, last updated (by provider) on: Oct 31 2023\n\nContributors: ; [Zhanwang Huang, Junping Ye] +#> 1: Hippocampus of schizophrenic, bipolar, and control subjects. Analyzed from CEL files. +#> 2: +#> 3: Analysis of gene-expression changes in treatment responders vs non-responders to two different treatments among subjectrs participating in LiTMUS. Results provide information on pathways that may be involved in the clinical response to Lithium in patients with bipolar disorder.\nLast Updated (by provider): Apr 01 2013\nContributors: Robert Beech +#> 4: Total RNA sequecing for human induced pluripotent derived cerebral organoids from healthy controls and Bipolar disorder\nAt time of import, last updated (by provider) on: Apr 01 2020\n\nContributors: ; [Annie Kathuria, Rakesh Karmacharya] +#> 5: Valproate(VPA) has been used in the treatment of bipolar disorder since the 1990s. However, the therapeutic targetsof VPA have remained elusive. Here we used RNA sequencing in human iPSCs derived from bipolar patients to further identify important molecular targets. Human iPSCs were homogenized and total RNA was isolated using the RNeasy Plus Micro Kit (Qiagen, Hilden, Germany). RNA quantity and quality were assessed using fluorometry (Qubit RNA Broad Range Assay Kit and Fluorometer; Invitrogen, Carlsbad, CA) and chromatography (Bioanalyzer and RNA 6000 Nano Kit; Agilent, Santa Clara, CA), respectively. Libraries were prepared using TruSeq Stranded mRNA (PolyA+) kit (Illumina, San Diego, CA) and sequenced by Illumina NextSeq 500. The read length was 75bp with 30-40M reads per sample. FastQC (v0.11.3) was performed to assess data quality. TopHat2 (v2.0.9) aligned the reads to the mouse reference genome (Mus musculus UCSC mm10) and to the Ensembl human reference genome (GRCh38.p13) using default parameters. Alignments were then converted to expression count data using HTseq (v0.6.1) with default union mode.\nAt time of import, last updated (by provider) on: Dec 31 2020\n\nContributors: ; [George Tseng, Colleen McClung, Wei Zong, Ryan Logan] +#> 6: This experiment was created by Gemma splitting another: \nExpressionExperiment Id=20933 Name=TCF7L2 lncRNA: A Link between Bipolar Disorder and Body Mass Index through Glucocorticoid Signaling [RNA-Seq] (GSE179921) Bipolar disorder (BD) and obesity are highly comorbid. We previously performed a genome-wide association study (GWAS) for BD risk accounting for the effect of body mass index (BMI) which identified a genome-wide significant single-nucleotide polymorphism (SNP) in the gene encoding the transcription factor 7 like 2 (TCF7L2). However, the molecular function of TCF7L2 in the central nervous system (CNS) and its possible role in BD and BMI interaction remained unclear. In the present study, we demonstrated by studying human induced pluripotent stem cell (hiPSC)-derived astrocytes, cells which highly express TCF7L2 in the CNS, that the BD-BMI GWAS risk SNP is associated with glucocorticoid-dependent repression of the expression of a previously uncharacterized TCF7L2 transcript variant. That transcript is a long non-coding RNA (lncRNA-TCF7L2) that is highly expressed in the CNS but not in peripheral tissues such as the liver and pancreas which are involved in metabolism. In astrocytes, knock-down of the lncRNA-TCF7L2 resulted in decreased expression of the parent gene, TCF7L2, as well as alterations in the expression of a series of genes involved in insulin signaling and diabetes. We also studied the function of TCF7L2 in hiPSC-derived astrocytes by integrating RNA sequencing data after TCF7L2 knock-down with TCF7L2 chromatin-immunoprecipitation sequencing (ChIP-seq) data. Those studies showed that TCF7L2 directly regulated a series of BD-risk genes. In summary, these results support the existence of a CNS-based mechanism underlying BD-BMI genetic risk, a mechanism based on a glucocorticoid-dependent expression quantitative trait locus that regulates the expression of a novel TCF7L2 non-coding transcript.\nAt time of import, last updated (by provider) on: Sep 20 2021\n\nContributors: ; [Mark A Frye, Thanh L Nguyen, Tamas Ordog, Brandon Coombes, Richard M Weinshilboum, Huaizhi Huang, Zhenqing Ye, Liewei Wang, Huanyao Gao, Daniel Kim, Jeong-Heon Lee, Brenna Sharp, Duan Liu, Joanna Biernacka] +#> 7: MicroRNAs have been implicated in the pathology not only of cancer, but also of psychiatric diseases, such as bipolar disorder and schizophrenia. As several psychiatric disorders share the same risk genes, we hypothesized that this microRNA could also be associated with attention-deficit/hyperactivity disorder (ADHD) and that this association to psychiatric disorders might be due to the variable number of tandem repeats (VNTR) polymorphism within the internal miR-137 (Imir137) promoter (PMID 18316599; PMID 25154622). To further understand the role of the microRNA 137 in the brain a knock-down of miR-137 expression in SH-SY5Y neuroblastoma cells was performed followed by expression analysis using a microarray.\nAt time of import, last updated (by provider) on: Aug 08 2019\n\nContributors: ; [Lena Weißflog, Andreas Reif, Stefanie Berger, Heike Weber, Claus J Scholz] +#> 8: Human neuronal-like cells (NT2-N) were treated with either lamotrigine (50 µM), lithium (2.5 mM), quetiapine (50 µM), valproate (0.5 mM) or vehicle control for 24 hours. Genome wide mRNA expression was quantified by RNA-sequencing. Results offer insights on the mechanism(s) of action of bipolar disorder drugs at the transcriptional level.\nAt time of import, last updated (by provider) on: Apr 27 2022\n\nContributors: ; [Srisaiyini Kidnapillai, Chiara Bortolasci, Laura Gray, Trang Truong, Bruna Panizzutti, Mark Richardson, Craig Smith, Olivia Dean, Zoe Liu, Briana Spolding, Michael Berk, Jee H Kim, Ken Walder] +#> 9: Background: Psychosis is a defining feature of schizophrenia and highly prevalent in bipolar disorder. Notably, individuals suffering with these illnesses also have major disruptions in sleep and circadian rhythms, and disturbances to sleep and circadian rhythms can precipitate or exacerbate psychotic symptoms. Psychosis is associated with the striatum, though no study to date has directly measured molecular rhythms and determined how they are altered in the striatum of subjects with psychosis. Methods: Here, we perform RNA-sequencing and both differential expression and rhythmicity analyses to investigate diurnal alterations in gene expression in human postmortem striatal subregions (NAc, caudate, and putamen) in subjects with psychosis relative to unaffected comparison subjects. Results: Across regions, we find differential expression of immune-related transcripts and a substantial loss of rhythmicity in core circadian clock genes in subjects with psychosis. In the nucleus accumbens (NAc), mitochondrial-related transcripts have decreased expression in psychosis subjects, but only in those who died at night. Additionally, we find a loss of rhythmicity in small nucleolar RNAs and a gain of rhythmicity in glutamatergic signaling in the NAc of psychosis subjects. Between region comparisons indicate that rhythmicity in the caudate and putamen is far more similar in subjects with psychosis than in matched comparison subjects. Conclusions: Together, these findings reveal differential and rhythmic gene expression differences across the striatum that may contribute to striatal dysfunction and psychosis in psychotic disorders.\nAt time of import, last updated (by provider) on: Aug 31 2022\n\nContributors: ; [George Tseng, Colleen McClung, Wei Zong, Kyle Ketchesin] +#> 10: Lithium is the gold standard treatment for bipolar disorder. The goal of this study was to identify gene expression networks associated with lithium response. RNAseq data was obtained from IPSC derived neurons from lithium responders and non-responders. Focal adhesion was the network most associated with response.\nAt time of import, last updated (by provider) on: Jun 09 2022\n\nContributors: ; [Vipavee Niemsiri, Fred Gage, John Kelsoe] +#> 11: Major psychiatric disorders such as schizophrenia (SCZ) and bipolar disorder (BPD) are complex genetic mental illnesses. Their non-Mendelian features such as monozygotic twins discordant for SCZ or BPD are likely complicated by environmental modifiers of genetic effects. 5-hydroxymethylcytosine (5hmC) is an important epigenetic marker in gene regulation and whether its links with genetic variants contribute to the non-Mendelian features remain largely unexplored. Here, we performed hydroxymethylome and genome analyses of blood DNA from psychiatric disorder-discordant monozygotic twins to study how allele-specific hydroxymethylation (AShM) mediates phenotypic variations. We identified thousands of genetic variants with AShM imbalances who exhibit phenotypic variation-associated AShM transition at regulatory loci. These AShMs have plausible causal associations with psychiatric disorders through effects on interactions between transcription factors, DNA methylations, or other epigenomic markers and then contribute to dysregulated gene expression, which eventually increases disease susceptibility. We then validated that competitive binding of POU3F2 on the alternative allele of psyAShM site rs4558409 (G/T) in PLLP can enhance the PLLP expression, while hydroxymethylated alternative allele alleviating the transcription factor binding activity at rs4558409 site might be associated with downregulated PLLP expression observed in BPD or SCZ. Moreover, disruption of rs4558409 induces gain of PLLP function and promotes neural development and vesicle trafficking. Our study provides a powerful strategy for prioritizing regulatory risk variants and contributes to our understanding of the interplay between genetic and epigenetic factors in mediating complex disease susceptibility.\nAt time of import, last updated (by provider) on: Oct 31 2023\n\nContributors: ; [Zhanwang Huang, Junping Ye] +#> 12: The goals of this study are to examine responses to inflammation in astrocytes from induced pluripotent stem cells derived from healthy controls and bipolar disorder patients. We examine the transcriptomic inflmmatory signature of generated astrocytes following Il1Beta exposure in BD vs. control Results: BD-patient astrocytes show a unique inflammatory response with differentially regulated genes.\nAt time of import, last updated (by provider) on: Mar 19 2021\n\nContributors: ; [Maxim N Shokhirev, Fred Gage, Krishna Vadodaria, Carol Marchetto] #> 13: Bipolar disorder is a severe and heritable psychiatric disorder and affects up to 1% of the population worldwide. Lithium is recommended as first-line treatment for the maintenance treatment of bipolar-affective disorder in current guidelines, its molecular modes of action are however poorly understood. Cell models derived from bipolar patients could prove useful to gain more insight in the molecular mechanisms of bipolar disorder and the common pharmacological treatments. As primary neuronal cell lines cannot be easily derived from patients, peripheral cell models should be evaluated in their usefulness to study pathomechanisms and the mode of action of medication as well as in regard to develop biomarkers for diagnosis and treatment response.\nAt time of import, last updated (by provider) on: Mar 25 2019\n\nContributors: ; [Sarah Kittel-Schneider, Max Hilscher, Andreas Reif, Claus J Scholz] -#> 14: Gene expression of samples from the postmortem hippocampus of older bipolar disorder subjects and controls. Gene expression data was generated using the SurePrint G3 Human Gene Expression v3 microarray. Rank feature selection was performed to identify a subset of features that can optimally differentiate BD and controls.\nAt time of import, last updated (by provider) on: Feb 19 2023\n\nContributors: ; [Carlos A Pasqualucci, Claudia K Suemoto, Ricardo Nitrini, Fernanda B Bertonha, Paula V Nunes, Katia C De Oliveira, Carlos M Filho, Helena K Kim, Helena Brentani, Lea T Grinberg, Beny Lafer, Andr<c3><a9> Barbosa, Camila Nascimento, Renata P Leite, Wilson Jacob-Filho] +#> 14: Gene expression of samples from the postmortem hippocampus of older bipolar disorder subjects and controls. Gene expression data was generated using the SurePrint G3 Human Gene Expression v3 microarray. Rank feature selection was performed to identify a subset of features that can optimally differentiate BD and controls.\nAt time of import, last updated (by provider) on: Feb 19 2023\n\nContributors: ; [Carlos A Pasqualucci, Claudia K Suemoto, Ricardo Nitrini, Fernanda B Bertonha, Paula V Nunes, Katia C De Oliveira, Carlos M Filho, Helena K Kim, Helena Brentani, Lea T Grinberg, Beny Lafer, André Barbosa, Camila Nascimento, Renata P Leite, Wilson Jacob-Filho] #> 15: Analysis of gene-expression changes in depressed subjects with bipolar disorder compared to healthy controls. Results provide information on pathways that may be involved in the pathogenesis of bipolar depression.\nLast Updated (by provider): Aug 27 2010\nContributors: Robert D Beech #> 16: Bipolar affective disorder is a severe psychiatric disorder with a strong genetic component but unknown pathophysiology. We used microarray technology (Affymetrix HG-U133A GeneChips) to determine the expression of approximately 22 000 mRNA transcripts in post-mortem brain tissue (orbitofrontal cortex) from patients with bipolar disorder and matched healthy controls. Orbitofrontal cortex tissue from a cohort of 30 subjects was investigated and the final analysis included 10 bipolar and 11 control subjects. Differences between disease and control groups were identified using a rigorous statistical analysis with correction for confounding variables and multiple testing.\nNote: [] samples from this series, which appear in other Expression Experiments in Gemma, were not imported from the GEO source. The following samples were removed: \nLast Updated (by provider): Jul 27 2006\nContributors: Sabine Bahn Margaret M Ryan Matthew T Wayland Maree J Webster Stephen J Huffaker Helen E Lockstone\nIncludes GDS2191.\n Update date: Aug 28 2006.\n Dataset description GDS2191: Analysis of postmortem orbitofrontal cortex from 10 adults with bipolar disorder. Results provide insight into the pathophysiology of the disease. #> 17: Bipolar affective disorder is a severe psychiatric disorder with a strong genetic component but unknown pathophysiology. We used microarray technology (Affymetrix HG-U133A GeneChips) to determine the expression of approximately 22 000 mRNA transcripts in post-mortem brain tissue (dorsolateral prefrontal cortex) from patients with bipolar disorder and matched healthy controls. A cohort of 70 subjects was investigated and the final analysis included 30 bipolar and 31 control subjects. Differences between disease and control groups were identified using a rigorous statistical analysis with correction for confounding variables and multiple testing.\nLast Updated (by provider): Jan 16 2007\nContributors: Helen E Lockstone Stephen J Huffaker Matthew T Wayland Sabine Bahn Maree J Webster Margaret M Ryan\nIncludes GDS2190.\n Update date: Aug 28 2006.\n Dataset description GDS2190: Analysis of postmortem dorsolateral prefrontal cortex from 30 adults with bipolar disorder. Results provide insight into the pathophysiology of the disease. @@ -285,18 +285,18 @@

Examples

#> experiment.description #> experiment.troubled experiment.accession experiment.database #> <lgcl> <char> <char> -#> 1: FALSE GSE157509 GEO +#> 1: FALSE <NA> <NA> #> 2: FALSE <NA> <NA> -#> 3: FALSE <NA> <NA> -#> 4: FALSE GSE45484 GEO -#> 5: FALSE GSE134497 GEO -#> 6: FALSE GSE160761 GEO -#> 7: FALSE GSE179921 GEO -#> 8: FALSE GSE66433 GEO -#> 9: FALSE GSE197966 GEO -#> 10: FALSE GSE202537 GEO -#> 11: FALSE GSE205422 GEO -#> 12: FALSE GSE246593 GEO +#> 3: FALSE GSE45484 GEO +#> 4: FALSE GSE134497 GEO +#> 5: FALSE GSE160761 GEO +#> 6: FALSE GSE179921 GEO +#> 7: FALSE GSE66433 GEO +#> 8: FALSE GSE197966 GEO +#> 9: FALSE GSE202537 GEO +#> 10: FALSE GSE205422 GEO +#> 11: FALSE GSE246593 GEO +#> 12: FALSE GSE157509 GEO #> 13: FALSE GSE66196 GEO #> 14: FALSE GSE210064 GEO #> 15: FALSE GSE23848 GEO @@ -308,18 +308,18 @@

Examples

#> experiment.troubled experiment.accession experiment.database #> experiment.URI #> <char> -#> 1: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE157509 +#> 1: <NA> #> 2: <NA> -#> 3: <NA> -#> 4: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45484 -#> 5: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134497 -#> 6: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE160761 -#> 7: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE179921 -#> 8: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE66433 -#> 9: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE197966 -#> 10: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE202537 -#> 11: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE205422 -#> 12: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE246593 +#> 3: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45484 +#> 4: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134497 +#> 5: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE160761 +#> 6: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE179921 +#> 7: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE66433 +#> 8: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE197966 +#> 9: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE202537 +#> 10: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE205422 +#> 11: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE246593 +#> 12: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE157509 #> 13: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE66196 #> 14: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE210064 #> 15: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE23848 @@ -331,18 +331,18 @@

Examples

#> experiment.URI #> experiment.sampleCount experiment.lastUpdated experiment.batchEffectText #> <int> <POSc> <char> -#> 1: 33 2023-12-17 01:36:32 NO_BATCH_INFO -#> 2: 23 2023-09-08 07:38:04 NO_BATCH_INFO -#> 3: 93 2022-08-30 23:36:54 NO_BATCH_INFO -#> 4: 120 2023-12-19 20:24:26 NO_BATCH_INFO -#> 5: 22 2023-12-17 10:03:11 NO_BATCH_INFO -#> 6: 24 2023-12-17 14:05:13 NO_BATCH_INFO -#> 7: 4 2023-12-17 12:01:40 SINGLE_BATCH_SUCCESS -#> 8: 6 2023-12-20 19:14:46 SINGLE_BATCH_SUCCESS -#> 9: 24 2023-12-18 11:53:22 NO_BATCH_INFO -#> 10: 215 2024-05-03 20:40:48 NO_BATCH_INFO -#> 11: 34 2023-12-18 00:19:38 NO_BATCH_EFFECT_SUCCESS -#> 12: 4 2024-01-23 19:56:05 SINGLE_BATCH_SUCCESS +#> 1: 23 2023-09-08 07:38:04 NO_BATCH_INFO +#> 2: 93 2022-08-30 23:36:54 NO_BATCH_INFO +#> 3: 120 2023-12-19 20:24:26 NO_BATCH_INFO +#> 4: 22 2023-12-17 10:03:11 NO_BATCH_INFO +#> 5: 24 2023-12-17 14:05:13 NO_BATCH_INFO +#> 6: 4 2023-12-17 12:01:40 SINGLE_BATCH_SUCCESS +#> 7: 6 2023-12-20 19:14:46 SINGLE_BATCH_SUCCESS +#> 8: 24 2023-12-18 11:53:22 NO_BATCH_INFO +#> 9: 215 2024-05-08 23:46:31 SINGLETON_BATCHES_FAILURE +#> 10: 34 2023-12-18 00:19:38 NO_BATCH_EFFECT_SUCCESS +#> 11: 4 2024-01-23 19:56:05 SINGLE_BATCH_SUCCESS +#> 12: 33 2023-12-17 01:36:32 NO_BATCH_INFO #> 13: 12 2023-12-20 19:03:56 NO_BATCH_INFO #> 14: 22 2023-12-18 01:58:21 SINGLE_BATCH_SUCCESS #> 15: 35 2023-12-18 12:17:06 NO_BATCH_INFO @@ -355,17 +355,17 @@

Examples

#> experiment.batchCorrected experiment.batchConfound experiment.batchEffect #> <lgcl> <num> <num> #> 1: FALSE 0 0 -#> 2: FALSE 0 0 -#> 3: FALSE 1 0 +#> 2: FALSE 1 0 +#> 3: FALSE 0 0 #> 4: FALSE 0 0 #> 5: FALSE 0 0 -#> 6: FALSE 0 0 +#> 6: FALSE 1 1 #> 7: FALSE 1 1 -#> 8: FALSE 1 1 +#> 8: FALSE 0 0 #> 9: FALSE 0 0 -#> 10: FALSE 0 0 -#> 11: FALSE 1 1 -#> 12: FALSE 1 0 +#> 10: FALSE 1 1 +#> 11: FALSE 1 0 +#> 12: FALSE 0 0 #> 13: FALSE -1 0 #> 14: FALSE 1 1 #> 15: FALSE 0 0 @@ -377,18 +377,18 @@

Examples

#> experiment.batchCorrected experiment.batchConfound experiment.batchEffect #> experiment.rawData geeq.qScore geeq.sScore taxon.name taxon.scientific #> <num> <num> <num> <char> <char> -#> 1: 1 0.4225534 1.0000 human Homo sapiens -#> 2: -1 0.2819105 0.2500 human Homo sapiens -#> 3: -1 0.4224650 0.4375 human Homo sapiens -#> 4: -1 0.4254127 0.7500 human Homo sapiens -#> 5: 1 0.4175250 0.7500 human Homo sapiens -#> 6: 1 0.4170825 0.7500 human Homo sapiens -#> 7: 1 0.8542963 0.7500 human Homo sapiens -#> 8: -1 0.8564356 -0.0375 human Homo sapiens -#> 9: 1 0.2824440 1.0000 human Homo sapiens -#> 10: 1 0.4225033 1.0000 human Homo sapiens -#> 11: 1 0.9948398 0.7500 human Homo sapiens -#> 12: 1 0.7105362 0.7500 human Homo sapiens +#> 1: -1 0.2819105 0.2500 human Homo sapiens +#> 2: -1 0.4224650 0.4375 human Homo sapiens +#> 3: -1 0.4254127 0.7500 human Homo sapiens +#> 4: 1 0.4175250 0.7500 human Homo sapiens +#> 5: 1 0.4170825 0.7500 human Homo sapiens +#> 6: 1 0.8542963 0.7500 human Homo sapiens +#> 7: -1 0.8564356 -0.0375 human Homo sapiens +#> 8: 1 0.2824440 1.0000 human Homo sapiens +#> 9: 1 0.4225033 1.0000 human Homo sapiens +#> 10: 1 0.9948398 0.7500 human Homo sapiens +#> 11: 1 0.7105362 0.7500 human Homo sapiens +#> 12: 1 0.4225534 1.0000 human Homo sapiens #> 13: 1 0.2840504 0.8750 human Homo sapiens #> 14: -1 0.8411107 0.3125 human Homo sapiens #> 15: -1 0.4246487 0.5000 human Homo sapiens diff --git a/reference/search_gemma.html b/reference/search_gemma.html index f7b7a780..7391a814 100644 --- a/reference/search_gemma.html +++ b/reference/search_gemma.html @@ -155,64 +155,64 @@

Examples

search_gemma("bipolar")
 #>     experiment.shortName
 #>                   <char>
-#>  1:            GSE157509
-#>  2:            GSE117877
-#>  3:   McLean Hippocampus
+#>  1:            GSE202537
+#>  2:            GSE246593
+#>  3:              byne-cc
 #>  4:          GSE179921.2
-#>  5:              byne-cc
-#>  6:            GSE197966
-#>  7:            GSE205422
-#>  8:            GSE246593
-#>  9:             GSE66433
+#>  5:   McLean Hippocampus
+#>  6:            GSE134497
+#>  7:             GSE45484
+#>  8:            GSE160761
+#>  9:            GSE197966
 #> 10:            GSE169212
-#> 11:            GSE202537
-#> 12:            GSE160761
-#> 13:            GSE134497
-#> 14:             GSE45484
+#> 11:             GSE66433
+#> 12:            GSE117877
+#> 13:            GSE205422
+#> 14:            GSE157509
 #> 15:             GSE66196
 #> 16:            GSE210064
 #> 17:             GSE23848
-#> 18:     stanley_feinberg
-#> 19:             GSE46416
-#> 20:       stanley_altarC
-#> 21:             GSE58933
-#> 22:       stanley_sklarB
-#> 23:            GSE116820
-#> 24:             GSE53987
-#> 25:           GSE92538.1
-#> 26:             GSE12654
-#> 27:         stanley_bahn
-#> 28:           McLean_PFC
-#> 29:             GSE18312
-#> 30:            GSE234795
-#> 31:       stanley_vawter
-#> 32:            GSE112523
-#> 33:             GSE12649
-#> 34:             GSE35974
-#> 35:             GSE62191
-#> 36:             GSE12679
-#> 37:             GSE35977
-#> 38:             GSE87610
-#> 39:         stanley_chen
-#> 40:              GSE5389
-#> 41:       stanley_altarA
-#> 42:              GSE7036
-#> 43:        stanley_young
-#> 44:            GSE208338
-#> 45:              GSE5388
-#> 46:             GSE81396
-#> 47:         stanley_kato
-#> 48:       stanley_dobrin
-#> 49:            GSE124326
-#> 50:       stanley_altarB
-#> 51:             GSE80655
-#> 52:             GSE80336
-#> 53:           GSE92538.2
-#> 54:             GSE46449
-#> 55:       stanley_sklarA
-#> 56:             GSE39653
-#> 57:             GSE78936
-#> 58:             GSE74358
+#> 18:       stanley_dobrin
+#> 19:             GSE80655
+#> 20:       stanley_sklarA
+#> 21:            GSE124326
+#> 22:            GSE116820
+#> 23:             GSE53987
+#> 24:              GSE5389
+#> 25:       stanley_altarA
+#> 26:           GSE92538.1
+#> 27:              GSE7036
+#> 28:             GSE58933
+#> 29:             GSE46416
+#> 30:             GSE35977
+#> 31:             GSE62191
+#> 32:     stanley_feinberg
+#> 33:             GSE12654
+#> 34:             GSE78936
+#> 35:       stanley_sklarB
+#> 36:             GSE46449
+#> 37:             GSE80336
+#> 38:             GSE39653
+#> 39:           GSE92538.2
+#> 40:             GSE74358
+#> 41:       stanley_altarB
+#> 42:             GSE12649
+#> 43:       stanley_vawter
+#> 44:             GSE81396
+#> 45:            GSE208338
+#> 46:       stanley_altarC
+#> 47:             GSE87610
+#> 48:         stanley_chen
+#> 49:             GSE12679
+#> 50:         stanley_kato
+#> 51:            GSE112523
+#> 52:             GSE18312
+#> 53:            GSE234795
+#> 54:        stanley_young
+#> 55:              GSE5388
+#> 56:           McLean_PFC
+#> 57:             GSE35974
+#> 58:         stanley_bahn
 #> 59:            GSE127771
 #> 60:            GSE126942
 #> 61:             GSE11767
@@ -226,8 +226,8 @@ 
Examples

 #> 69:            GSE153638
 #> 70:             GSE67645
 #> 71:             GSE35077
-#> 72:             GSE97534
-#> 73:             GSE33085
+#> 72:             GSE33085
+#> 73:             GSE97534
 #> 74:            GSE160874
 #> 75:             GSE29318
 #> 76:             GSE85417
@@ -242,77 +242,77 @@ 
Examples

 #> 85:            GSE112510
 #> 86:            GSE120423
 #> 87:             GSE54112
-#> 88:            GSE8641.2
-#> 89:            GSE8641.3
-#> 90:            GSE8641.1
+#> 88:            GSE8641.1
+#> 89:            GSE8641.2
+#> 90:            GSE8641.3
 #> 91:             GSE78877
 #> 92:            GSE137318
-#> 93:            GSE156533
-#> 94:            GSE152474
-#> 95:            GSE184930
+#> 93:            GSE184930
+#> 94:            GSE156533
+#> 95:            GSE152474
 #> 96:             GSE45229
 #> 97:              GSE7624
 #>     experiment.shortName
 #>                                                                                                                                                                                         experiment.name
 #>                                                                                                                                                                                                  <char>
-#>  1:                                                                                                     Increased IL-6 and altered inflammatory response in bipolar disorder patient-derived astrocytes
-#>  2:                                                                                    A candidate causal variant underlying both enhanced cognitive performance and increased risk of bipolar disorder
-#>  3:                                                                                                                                                                                  McLean Hippocampus
+#>  1:                                                                                                                      Diurnal alterations in gene expression across striatal subregions in psychosis
+#>  2:                           Transition of allele-specific DNA hydroxymethylation at regulatory loci is associated with phenotypic variation in monozygotic twins discordant for psychiatric disorders
+#>  3:                                                                                                                                                Corpus Callosum data from Stanley collection samples
 #>  4:                             Split part 2 of: TCF7L2 lncRNA: A Link between Bipolar Disorder and Body Mass Index through Glucocorticoid Signaling [RNA-Seq] [collection of material = Experiment 1 ]
-#>  5:                                                                                                                                                Corpus Callosum data from Stanley collection samples
-#>  6:                                                                                                                                    Transcriptional effects of bipolar disorder drugs on NT2-N cells
-#>  7:                                                                                       Network-based integrative analysis of lithium response in bipolar disorder using transcriptomic and GWAS data
-#>  8:                           Transition of allele-specific DNA hydroxymethylation at regulatory loci is associated with phenotypic variation in monozygotic twins discordant for psychiatric disorders
-#>  9:                                                                                                                            Effects of the microRNA 137 and its connection to psychiatric disorders.
+#>  5:                                                                                                                                                                                  McLean Hippocampus
+#>  6:                                                                                                                        Total RNA sequecing for human induced pluripotent derived cerebral organoids
+#>  7:                                          Gene-expression differences in peripheral blood between lithium responders and non-responders in the “Lithium Treatment -Moderate dose Use Study” (LiTMUS)
+#>  8:                                                              RNA sequencing  in human  iPSCs derived from  bipolar  patients  to identify important therapeutic molecular targets of Valproate(VPA)
+#>  9:                                                                                                                                    Transcriptional effects of bipolar disorder drugs on NT2-N cells
 #> 10:                                                                                                      Transcriptomic and epigenetic characterization of PVT neurons in bipolar disorder model mouse.
-#> 11:                                                                                                                      Diurnal alterations in gene expression across striatal subregions in psychosis
-#> 12:                                                              RNA sequencing  in human  iPSCs derived from  bipolar  patients  to identify important therapeutic molecular targets of Valproate(VPA)
-#> 13:                                                                                                                        Total RNA sequecing for human induced pluripotent derived cerebral organoids
-#> 14:                    Gene-expression differences in peripheral blood between lithium responders and non-responders in the <e2><80><9c>Lithium Treatment -Moderate dose Use Study<e2><80><9d> (LiTMUS)
+#> 11:                                                                                                                            Effects of the microRNA 137 and its connection to psychiatric disorders.
+#> 12:                                                                                    A candidate causal variant underlying both enhanced cognitive performance and increased risk of bipolar disorder
+#> 13:                                                                                       Network-based integrative analysis of lithium response in bipolar disorder using transcriptomic and GWAS data
+#> 14:                                                                                                     Increased IL-6 and altered inflammatory response in bipolar disorder patient-derived astrocytes
 #> 15:                                                                                                                  Bipolar disorder and lithium-induced gene expression in two peripheral cell models
-#> 16:                                                      Gene expression alterations in the postmortem hippocampus from older patients with bipolar disorder <e2><80><93> a hypothesis generating study
+#> 16:                                                                 Gene expression alterations in the postmortem hippocampus from older patients with bipolar disorder – a hypothesis generating study
 #> 17:                                                                                                   Peripheral blood gene-expression in depressed subjects with bipolar disorder vs healthy controls.
-#> 18:                                                                                                                                                 Stanley consortium collection Cerebellum - Feinberg
-#> 19:                                                                                                                                     State- and trait-specific gene expression in euthymia and mania
-#> 20:                                                                                                                                                       Stanley consortium collection DLPFC - Altar C
-#> 21:                                                                                                                         Hyper-excitability of Neurons generated from Patients with Bipolar Disorder
-#> 22:                                                                                                                                                   Stanley consortium collection Cerebellum - SklarB
-#> 23:                                                                                                                    Expression data of Glutarmatergic neuron and GABAergic neruon induced from iPSCs
-#> 24:                                                                                                  Microarray profiling of PFC, HPC and STR from subjects with schizophrenia, bipolar, MDD or control
-#> 25:                       Inference of cell-type composition from human brain transcriptomic datasets illuminates the effects of age, manner of death, dissection, and psychiatric diagnosis - GPL10526
-#> 26:                                                                                                                                                 Gene expression from human prefrontal cortex (BA10)
-#> 27:                                                                                                                                                               Stanley array collection DLPFC - Bahn
-#> 28:                                                                                                                                                                                          McLean_PFC
-#> 29:                                                                                                                                       Gene Expression in Blood in Scizophrenia and Bipolar Disorder
-#> 30:                                                                                                                                      REST and Impaired Neural Stress Resistance in Bipolar Disorder
-#> 31:                                                                                                                                                             Stanley array collection DLPFC - Vawter
-#> 32:                                                                                                  DNA methylation in neurons from post-mortem brains in schizophrenia and bipolar disorder (RNA-Seq)
-#> 33:                                                                                                                                                 Gene expression from human prefrontal cortex (BA46)
-#> 34:                                                                                                                                                     Expression data from the human cerebellum brain
-#> 35:                                                                                                      Gene expression profiles of patients with schizophrenia, bipolar disorder and healthy controls
-#> 36:                                                                                           Laser capture microdissection of endothelial and neuronal cells from human dorsolateral prefrontal cortex
-#> 37:                                                                                                                                                Expression data from the human parietal cortex brain
-#> 38:                                                Gene expression of L3 and L5 pyramidal neurons in the DLPFC comparing schizophrenia from bipolar major depressive disorders and unaffected subjects.
-#> 39:                                                                                                                                                          Stanley consortium collection DLPFC - Chen
-#> 40:                                                                                       Adult postmortem brain tissue (orbitofrontal cortex) from subjects with bipolar disorder and healthy controls
-#> 41:                                                                                                                                                            Stanley array collection DLPFC - Altar A
-#> 42:                                                                                                                           Expression profiling in monozygotic twins discordant for bipolar disorder
-#> 43:                                                                                                                                                              Stanley array collection DLPFC - Young
-#> 44:                                                                                     Expression data from postmortem human dorsolateral prefrontal cortex - psychiatric disorders & healthy controls
-#> 45:                                                                             Adult postmortem brain tissue (dorsolateral prefrontal cortex) from subjects with bipolar disorder and healthy controls
-#> 46:                                                                                       Total RNAseq of human putamen and caudate nucleus tissues in healthy control and Bipolar Disorder individuals
-#> 47:                                                                                                                                                               Stanley array collection DLPFC - Kato
-#> 48:                                                                                                                                                             Stanley array collection DLPFC - Dobrin
-#> 49:                                                                                                         Whole blood transcriptome analysis in bipolar disorder<c2><a0>reveals strong lithium effect
-#> 50:                                                                                                                                                       Stanley consortium collection DLPFC - Altar B
-#> 51:                                                                                                                                                   RNA-sequencing of human post-mortem brain tissues
-#> 52:                                                                                                                            Transcriptome profiling of the human dorsal striatum in bipolar disorder
-#> 53:                       Inference of cell-type composition from human brain transcriptomic datasets illuminates the effects of age, manner of death, dissection, and psychiatric diagnosis - GPL17027
-#> 54:                                                                                                               Expression data from Patients with Bipolar (BP) Disorder and Matched Control Subjects
-#> 55:                                                                                                                                                        Stanley consortium collection DLPFC - SklarA
-#> 56:                                                                                                                                        Differential Gene Expression in Patients with Mood Disorders
-#> 57:                                                                            Systematically characterizing dysfunctional long intergenic non-coding RNAs in multiple brain regions of major psychosis
-#> 58:                                                                        Transcriptomic Comparison of Neuronal Development Stages using Induced Pluripotent Stem Cells from Bipolar Disorder Patients
+#> 18:                                                                                                                                                             Stanley array collection DLPFC - Dobrin
+#> 19:                                                                                                                                                   RNA-sequencing of human post-mortem brain tissues
+#> 20:                                                                                                                                                        Stanley consortium collection DLPFC - SklarA
+#> 21:                                                                                                                Whole blood transcriptome analysis in bipolar disorder reveals strong lithium effect
+#> 22:                                                                                                                    Expression data of Glutarmatergic neuron and GABAergic neruon induced from iPSCs
+#> 23:                                                                                                  Microarray profiling of PFC, HPC and STR from subjects with schizophrenia, bipolar, MDD or control
+#> 24:                                                                                       Adult postmortem brain tissue (orbitofrontal cortex) from subjects with bipolar disorder and healthy controls
+#> 25:                                                                                                                                                            Stanley array collection DLPFC - Altar A
+#> 26:                       Inference of cell-type composition from human brain transcriptomic datasets illuminates the effects of age, manner of death, dissection, and psychiatric diagnosis - GPL10526
+#> 27:                                                                                                                           Expression profiling in monozygotic twins discordant for bipolar disorder
+#> 28:                                                                                                                         Hyper-excitability of Neurons generated from Patients with Bipolar Disorder
+#> 29:                                                                                                                                     State- and trait-specific gene expression in euthymia and mania
+#> 30:                                                                                                                                                Expression data from the human parietal cortex brain
+#> 31:                                                                                                      Gene expression profiles of patients with schizophrenia, bipolar disorder and healthy controls
+#> 32:                                                                                                                                                 Stanley consortium collection Cerebellum - Feinberg
+#> 33:                                                                                                                                                 Gene expression from human prefrontal cortex (BA10)
+#> 34:                                                                            Systematically characterizing dysfunctional long intergenic non-coding RNAs in multiple brain regions of major psychosis
+#> 35:                                                                                                                                                   Stanley consortium collection Cerebellum - SklarB
+#> 36:                                                                                                               Expression data from Patients with Bipolar (BP) Disorder and Matched Control Subjects
+#> 37:                                                                                                                            Transcriptome profiling of the human dorsal striatum in bipolar disorder
+#> 38:                                                                                                                                        Differential Gene Expression in Patients with Mood Disorders
+#> 39:                       Inference of cell-type composition from human brain transcriptomic datasets illuminates the effects of age, manner of death, dissection, and psychiatric diagnosis - GPL17027
+#> 40:                                                                        Transcriptomic Comparison of Neuronal Development Stages using Induced Pluripotent Stem Cells from Bipolar Disorder Patients
+#> 41:                                                                                                                                                       Stanley consortium collection DLPFC - Altar B
+#> 42:                                                                                                                                                 Gene expression from human prefrontal cortex (BA46)
+#> 43:                                                                                                                                                             Stanley array collection DLPFC - Vawter
+#> 44:                                                                                       Total RNAseq of human putamen and caudate nucleus tissues in healthy control and Bipolar Disorder individuals
+#> 45:                                                                                     Expression data from postmortem human dorsolateral prefrontal cortex - psychiatric disorders & healthy controls
+#> 46:                                                                                                                                                       Stanley consortium collection DLPFC - Altar C
+#> 47:                                                Gene expression of L3 and L5 pyramidal neurons in the DLPFC comparing schizophrenia from bipolar major depressive disorders and unaffected subjects.
+#> 48:                                                                                                                                                          Stanley consortium collection DLPFC - Chen
+#> 49:                                                                                           Laser capture microdissection of endothelial and neuronal cells from human dorsolateral prefrontal cortex
+#> 50:                                                                                                                                                               Stanley array collection DLPFC - Kato
+#> 51:                                                                                                  DNA methylation in neurons from post-mortem brains in schizophrenia and bipolar disorder (RNA-Seq)
+#> 52:                                                                                                                                       Gene Expression in Blood in Scizophrenia and Bipolar Disorder
+#> 53:                                                                                                                                      REST and Impaired Neural Stress Resistance in Bipolar Disorder
+#> 54:                                                                                                                                                              Stanley array collection DLPFC - Young
+#> 55:                                                                             Adult postmortem brain tissue (dorsolateral prefrontal cortex) from subjects with bipolar disorder and healthy controls
+#> 56:                                                                                                                                                                                          McLean_PFC
+#> 57:                                                                                                                                                     Expression data from the human cerebellum brain
+#> 58:                                                                                                                                                               Stanley array collection DLPFC - Bahn
 #> 59:                                           The LIM-homeodomain transcription factor LHX4 is required for the differentiation of retinal rod bipolar cells and rod-connecting cone bipolar cells [P7]
 #> 60:                                                The LIM-homeodomain transcription factor LHX4 is required for the differentiation of retinal rod bipolar cells and rod-connecting cone bipolar cells
 #> 61:                                                                                                     Gene expression profiling of fibroblasts and lymphoblastoid cells derived from four individuals
@@ -326,8 +326,8 @@ 
Examples

 #> 69:                                                                                                                        RNA-seq analysis of rat neurons treated with shRNA-mediated Trank1 knockdown
 #> 70:   Transcriptome dynamics of developing photoreceptors in 3-D retina cultures recapitulates temporal sequence of human cone and rod differentiation revealing cell surface markers and gene networks
 #> 71:                                                                                                                                              A gene expression database for retinal neuron subtypes
-#> 72:                                                                                                                                                    RNA sequencing of developing cone photoreceptors
-#> 73:                                                                                                                                                  Transcriptome analysis of adult retina cell types.
+#> 72:                                                                                                                                                  Transcriptome analysis of adult retina cell types.
+#> 73:                                                                                                                                                    RNA sequencing of developing cone photoreceptors
 #> 74:                                                                                               RNA sequencing in 12-week old orbital frontal cortex in circHomer1/Homer1b knockdown and control mice
 #> 75:                                                                                                                                               Expression profile of FAC-sorted murine retinal cells
 #> 76:                                                                                                                 Transcriptome of iPSC-derived Cerebral Organoids with Heterozygous Knockout in CHD8
@@ -342,77 +342,77 @@ 
Examples

 #> 85:                                                                                   Nitrated meat products are associated with mania in humans and altered behavior and brain gene expression in rats
 #> 86:                                                                                          Brain transcriptome profiling in wildtype mice and mice with Igf2 enhancer deletion (Igf2enh-/-) [RNA-seq]
 #> 87:                                                                                         ZNF804A transcriptome networks in differentiating human neurons derived from induced pluripotent stem cells
-#> 88:                                                                                        Rnf41 is associated with anxiety like behavior, major depression and beta carboline induced seizure - GPL340
-#> 89:                                                                                       Rnf41 is associated with anxiety like behavior, major depression and beta carboline induced seizure - GPL4055
-#> 90:                                                                                        Rnf41 is associated with anxiety like behavior, major depression and beta carboline induced seizure - GPL339
+#> 88:                                                                                        Rnf41 is associated with anxiety like behavior, major depression and beta carboline induced seizure - GPL339
+#> 89:                                                                                        Rnf41 is associated with anxiety like behavior, major depression and beta carboline induced seizure - GPL340
+#> 90:                                                                                       Rnf41 is associated with anxiety like behavior, major depression and beta carboline induced seizure - GPL4055
 #> 91:                                                                                                     Gene expression differences between wildtype and Atrx conditional knockout mouse retina tissues
 #> 92:                                                                                                             Cis-Regulatory Accessibility Directs Muller Glial Development and Regenerative Capacity
-#> 93:                                                                                                                     Transcriptomic profiling in 3-month-old P23H Retinitis Pigmentosa mouse retinas
-#> 94:                                                                                                                        Transcriptomic profiling in juvenile P23H Retinitis Pigmentosa mouse retinas
-#> 95:                                                                                                                                                Lithium treatment and human hippocampal neurogenesis
+#> 93:                                                                                                                                                Lithium treatment and human hippocampal neurogenesis
+#> 94:                                                                                                                     Transcriptomic profiling in 3-month-old P23H Retinitis Pigmentosa mouse retinas
+#> 95:                                                                                                                        Transcriptomic profiling in juvenile P23H Retinitis Pigmentosa mouse retinas
 #> 96:                                                                                         Unique pharmacological actions of atypical neuroleptic quetiapine: possible role in cell cycle/fate control
 #> 97:                                                                                                                                                             Expression Profiles of Monozygotic Twin
 #>                                                                                                                                                                                         experiment.name
 #>     experiment.ID
 #>             <int>
-#>  1:         23425
-#>  2:         20617
-#>  3:           670
+#>  1:         25749
+#>  2:         31703
+#>  3:          4354
 #>  4:         21159
-#>  5:          4354
-#>  6:         25070
-#>  7:         25972
-#>  8:         31703
-#>  9:         24427
+#>  5:           670
+#>  6:         16450
+#>  7:          6145
+#>  8:         17943
+#>  9:         25070
 #> 10:         19187
-#> 11:         25749
-#> 12:         17943
-#> 13:         16450
-#> 14:          6145
+#> 11:         24427
+#> 12:         20617
+#> 13:         25972
+#> 14:         23425
 #> 15:         24428
 #> 16:         28041
 #> 17:          1958
-#> 18:           831
-#> 19:          8997
-#> 20:           840
-#> 21:         12503
-#> 22:           835
-#> 23:         14828
-#> 24:          8359
-#> 25:         18742
-#> 26:          1204
-#> 27:           841
-#> 28:           672
-#> 29:          5804
-#> 30:         31677
-#> 31:           836
-#> 32:         16773
-#> 33:          1205
-#> 34:          5939
-#> 35:          8995
-#> 36:           946
-#> 37:          5949
-#> 38:         10878
-#> 39:           842
-#> 40:           326
-#> 41:           838
-#> 42:           660
-#> 43:           837
-#> 44:         26880
-#> 45:           594
-#> 46:         13338
-#> 47:           833
-#> 48:           843
-#> 49:         23636
-#> 50:           839
-#> 51:         13014
-#> 52:         10627
-#> 53:         18741
-#> 54:          6583
-#> 55:           834
-#> 56:          5724
-#> 57:         12983
-#> 58:         12701
+#> 18:           843
+#> 19:         13014
+#> 20:           834
+#> 21:         23636
+#> 22:         14828
+#> 23:          8359
+#> 24:           326
+#> 25:           838
+#> 26:         18742
+#> 27:           660
+#> 28:         12503
+#> 29:          8997
+#> 30:          5949
+#> 31:          8995
+#> 32:           831
+#> 33:          1204
+#> 34:         12983
+#> 35:           835
+#> 36:          6583
+#> 37:         10627
+#> 38:          5724
+#> 39:         18741
+#> 40:         12701
+#> 41:           839
+#> 42:          1205
+#> 43:           836
+#> 44:         13338
+#> 45:         26880
+#> 46:           840
+#> 47:         10878
+#> 48:           842
+#> 49:           946
+#> 50:           833
+#> 51:         16773
+#> 52:          5804
+#> 53:         31677
+#> 54:           837
+#> 55:           594
+#> 56:           672
+#> 57:          5939
+#> 58:           841
 #> 59:         19455
 #> 60:         19454
 #> 61:          1879
@@ -426,8 +426,8 @@ 
Examples

 #> 69:         17318
 #> 70:         11634
 #> 71:          7608
-#> 72:         13694
-#> 73:          8887
+#> 72:          8887
+#> 73:         13694
 #> 74:         24621
 #> 75:         12310
 #> 76:         11666
@@ -442,117 +442,117 @@ 
Examples

 #> 85:         14188
 #> 86:         15231
 #> 87:          9777
-#> 88:          1629
-#> 89:          1630
-#> 90:          1628
+#> 88:          1628
+#> 89:          1629
+#> 90:          1630
 #> 91:         12982
 #> 92:         24907
-#> 93:         17673
-#> 94:         17330
-#> 95:         20334
+#> 93:         20334
+#> 94:         17673
+#> 95:         17330
 #> 96:          6233
 #> 97:         10975
 #>     experiment.ID
-#>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     experiment.description
-#>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     <char>
-#>  1:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 The goals of this study are to examine responses to inflammation in astrocytes from  induced pluripotent stem cells derived from healthy controls and bipolar disorder patients. We examine the transcriptomic inflmmatory signature of generated astrocytes following Il1Beta exposure in BD vs. control Results: BD-patient astrocytes show a unique inflammatory response with differentially regulated genes.\nAt time of import, last updated (by provider) on: Mar 19 2021\n\nContributors: ; [Maxim N Shokhirev, Fred Gage, Krishna Vadodaria, Carol Marchetto]
-#>  2:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Bipolar disorder is a highly heritable mental illness, but the relevant genetic variants and molecular mechanisms are largely unknown. Recent GWAS<e2><80><99>s have identified an intergenic region associated with both enhanced cognitive performance and bipolar disorder. This region contains dozens of putative fetal brain-specific enhancers and is located ~0.7 Mb upstream of the neuronal transcription factor POU3F2. We identified a candidate causal variant, rs77910749, that falls within a highly conserved putative enhancer, LC1. This human-specific variant is a single-base deletion in a PAX6 binding site and is predicted to be functional. We hypothesized that rs77910749 alters LC1 activity and hence POU3F2 expression during neurodevelopment. Indeed, transgenic reporter mice demonstrated LC1 activity in the developing cerebral cortex and amygdala. Furthermore, ex vivo reporter assays in embryonic mouse brain and human iPSC-derived cerebral organoids revealed increased enhancer activity conferred by the variant. To probe the in vivo function of LC1, we deleted the orthologous mouse region, which resulted in amygdala-specific changes in Pou3f2 expression. Lastly, <e2><80><98>humanized<e2><80><99> rs77910749 knock-in mice displayed behavioral defects in sensory gating, an amygdala-dependent endophenotype seen in patients with bipolar disorder. Our study suggests a molecular mechanism underlying the long-speculated link between higher cognition and neuropsychiatric disease.\nAt time of import, last updated (by provider) on: Jul 28 2021\n\nContributors: ; [Susan Q Shen, Joseph C Corbo]
-#>  3:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Hippocampus of schizophrenic, bipolar, and control subjects. Analyzed from CEL files.
-#>  4:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           This experiment was created by Gemma splitting another: \nExpressionExperiment Id=20933 Name=TCF7L2 lncRNA: A Link between Bipolar Disorder and Body Mass Index through Glucocorticoid Signaling [RNA-Seq] (GSE179921) Bipolar disorder (BD) and obesity are highly comorbid. We previously performed a genome-wide association study (GWAS) for BD risk accounting for the  effect of body mass index (BMI) which identified a genome-wide significant single-nucleotide polymorphism (SNP) in the gene encoding the transcription factor 7 like 2 (TCF7L2). However, the molecular function of TCF7L2 in the central nervous system (CNS) and its possible role in BD and BMI interaction remained unclear. In the present study, we demonstrated by studying human induced pluripotent stem cell (hiPSC)-derived astrocytes, cells which highly express TCF7L2 in the CNS, that the BD-BMI GWAS risk SNP is associated with glucocorticoid-dependent repression of the expression of a previously uncharacterized TCF7L2 transcript variant. That transcript is a long non-coding RNA (lncRNA-TCF7L2) that is highly expressed in the CNS but not in peripheral tissues such as the liver and pancreas which are involved in metabolism.  In astrocytes, knock-down of the lncRNA-TCF7L2 resulted in decreased expression of the parent gene, TCF7L2, as well as alterations in the expression of a series of genes involved in insulin signaling and diabetes.  We also studied the function of TCF7L2 in hiPSC-derived astrocytes by integrating RNA sequencing data after TCF7L2 knock-down with TCF7L2 chromatin-immunoprecipitation sequencing (ChIP-seq) data. Those studies showed that TCF7L2 directly regulated a series of BD-risk genes. In summary, these results support the existence of a CNS-based mechanism underlying BD-BMI genetic risk, a mechanism based on a glucocorticoid-dependent expression quantitative trait locus that regulates the expression of a novel TCF7L2 non-coding transcript.\nAt time of import, last updated (by provider) on: Sep 20 2021\n\nContributors: ; [Mark A Frye, Thanh L Nguyen, Tamas Ordog, Brandon Coombes, Richard M Weinshilboum, Huaizhi Huang, Zhenqing Ye, Liewei Wang, Huanyao Gao, Daniel Kim, Jeong-Heon Lee, Brenna Sharp, Duan Liu, Joanna Biernacka]
-#>  5:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       
-#>  6:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 Human neuronal-like cells (NT2-N) were treated with either lamotrigine (50 <c2><b5>M), lithium (2.5 mM), quetiapine (50 <c2><b5>M), valproate (0.5 mM) or vehicle control for 24 hours. Genome wide mRNA expression was quantified by RNA-sequencing. Results offer insights on the mechanism(s) of action of bipolar disorder drugs at the transcriptional level.\nAt time of import, last updated (by provider) on: Apr 27 2022\n\nContributors: ; [Srisaiyini Kidnapillai, Chiara Bortolasci, Laura Gray, Trang Truong, Bruna Panizzutti, Mark Richardson, Craig Smith, Olivia Dean, Zoe Liu, Briana Spolding, Michael Berk, Jee H Kim, Ken Walder]
-#>  7:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               Lithium is the gold standard treatment for bipolar disorder. The goal of this study was to identify gene expression networks associated with lithium response. RNAseq data was obtained from IPSC derived neurons from lithium responders and non-responders. Focal adhesion was the network most associated with response.\nAt time of import, last updated (by provider) on: Jun 09 2022\n\nContributors: ; [Vipavee Niemsiri, Fred Gage, John Kelsoe]
-#>  8:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Major psychiatric disorders such as schizophrenia (SCZ) and bipolar disorder (BPD) are complex genetic mental illnesses. Their non-Mendelian features such as monozygotic twins discordant for SCZ or BPD are likely complicated by environmental modifiers of genetic effects. 5-hydroxymethylcytosine (5hmC) is an important epigenetic marker in gene regulation and whether its links with genetic variants contribute to the non-Mendelian features remain largely unexplored. Here, we performed hydroxymethylome and genome analyses of blood DNA from psychiatric disorder-discordant monozygotic twins to study how allele-specific hydroxymethylation (AShM) mediates phenotypic variations. We identified thousands of genetic variants with AShM imbalances who exhibit phenotypic variation-associated AShM transition at regulatory loci. These AShMs have plausible causal associations with psychiatric disorders through effects on interactions between transcription factors, DNA methylations, or other epigenomic markers and then contribute to dysregulated gene expression, which eventually increases disease susceptibility. We then validated that competitive binding of POU3F2 on the alternative allele of psyAShM site rs4558409 (G/T) in PLLP can enhance the PLLP expression, while hydroxymethylated alternative allele alleviating the transcription factor binding activity at rs4558409 site might be associated with downregulated PLLP expression observed in BPD or SCZ. Moreover, disruption of rs4558409 induces gain of PLLP function and promotes neural development and vesicle trafficking. Our study provides a powerful strategy for prioritizing regulatory risk variants and contributes to our understanding of the interplay between genetic and epigenetic factors in mediating complex disease susceptibility.\nAt time of import, last updated (by provider) on: Oct 31 2023\n\nContributors: ; [Zhanwang Huang, Junping Ye]
-#>  9:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    MicroRNAs have been implicated in the pathology not only of cancer, but also of psychiatric diseases, such as bipolar disorder and schizophrenia. As several psychiatric disorders share the same risk genes, we hypothesized that this microRNA could also be associated with attention-deficit/hyperactivity disorder (ADHD) and that this association to psychiatric disorders might be due to the variable number of tandem repeats (VNTR) polymorphism within the internal miR-137 (Imir137) promoter (PMID 18316599; PMID 25154622). To further understand the role of the microRNA 137 in the brain a knock-down of miR-137 expression in SH-SY5Y neuroblastoma cells was performed followed by expression analysis using a microarray.\nAt time of import, last updated (by provider) on: Aug 08 2019\n\nContributors: ; [Lena Wei<c3><9f>flog, Andreas Reif, Stefanie Berger, Heike Weber, Claus J Scholz]
-#> 10:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       We have previosuly shown that our Polg(D181A) show spontaneous depressive episodes as a result of mtDNA mutations, but we do not know the cellular mechanisms that link mtDNA mutations to behavioural changes. We hypothesized that mtDNA mutation-induced mitochondrial dysfunction in PVT causes a dysregulation of epigenetics, causing a transcriptional response which ffects neuronal function and ultimately causes the depressive phenotype. We assessed this using a combination of RNA-seq, H3K27Ac ChIP-seq, and ATAC-seq and compared our H3K27Ac results to other brain regions.\nAt time of import, last updated (by provider) on: Jun 01 2021\n\nContributors: ; [Tadafumi Kato, Emilie K Bagge]
-#> 11:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Background: Psychosis is a defining feature of schizophrenia and highly prevalent in bipolar disorder. Notably, individuals suffering with these illnesses also have major disruptions in sleep and circadian rhythms, and disturbances to sleep and circadian rhythms can precipitate or exacerbate psychotic symptoms. Psychosis is associated with the striatum, though no study to date has directly measured molecular rhythms and determined how they are altered in the striatum of subjects with psychosis. Methods: Here, we perform RNA-sequencing and both differential expression and rhythmicity analyses to investigate diurnal alterations in gene expression in human postmortem striatal subregions (NAc, caudate, and putamen) in subjects with psychosis relative to unaffected comparison subjects. Results: Across regions, we find differential expression of immune-related transcripts and a substantial loss of rhythmicity in core circadian clock genes in subjects with psychosis. In the nucleus accumbens (NAc), mitochondrial-related transcripts have decreased expression in psychosis subjects, but only in those who died at night. Additionally, we find a loss of rhythmicity in small nucleolar RNAs and a gain of rhythmicity in glutamatergic signaling in the NAc of psychosis subjects. Between region comparisons indicate that rhythmicity in the caudate and putamen is far more similar in subjects with psychosis than in matched comparison subjects. Conclusions: Together, these findings reveal differential and rhythmic gene expression differences across the striatum that may contribute to striatal dysfunction and psychosis in psychotic disorders.\nAt time of import, last updated (by provider) on: Aug 31 2022\n\nContributors: ; [George Tseng, Colleen McClung, Wei Zong, Kyle Ketchesin]
-#> 12:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Valproate(VPA) has been used in the treatment of bipolar disorder since the 1990s.  However, the therapeutic targetsof VPA have remained elusive. Here we  used RNA sequencing  in human  iPSCs derived from  bipolar  patients  to further identify important molecular targets. Human iPSCs were homogenized and total RNA was isolated using the RNeasy Plus Micro Kit (Qiagen, Hilden, Germany). RNA quantity and quality were assessed using fluorometry (Qubit RNA Broad Range Assay Kit and Fluorometer; Invitrogen, Carlsbad, CA) and chromatography (Bioanalyzer and RNA 6000 Nano Kit; Agilent, Santa Clara, CA), respectively. Libraries were prepared using TruSeq Stranded mRNA (PolyA+) kit (Illumina, San Diego, CA) and sequenced by Illumina NextSeq 500. The read length was 75bp with 30-40M reads per sample. FastQC (v0.11.3) was performed to assess data quality. TopHat2 (v2.0.9) aligned the reads to the mouse reference genome (Mus musculus UCSC mm10) and to the Ensembl human reference genome (GRCh38.p13) using default parameters. Alignments were then converted to expression count data using HTseq (v0.6.1) with default union mode.\nAt time of import, last updated (by provider) on: Dec 31 2020\n\nContributors: ; [George Tseng, Colleen McClung, Wei Zong, Ryan Logan]
-#> 13:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         Total RNA sequecing for human induced pluripotent derived cerebral organoids from healthy controls and Bipolar disorder\nAt time of import, last updated (by provider) on: Apr 01 2020\n\nContributors: ; [Annie Kathuria, Rakesh Karmacharya]
-#> 14:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           Analysis of gene-expression changes in treatment responders vs non-responders to two different treatments among subjectrs participating in LiTMUS. Results provide information on pathways that may be involved in the clinical response to Lithium in patients with bipolar disorder.\nLast Updated (by provider): Apr 01 2013\nContributors:  Robert Beech
-#> 15:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Bipolar disorder is a severe and heritable psychiatric disorder and affects up to 1% of the population worldwide. Lithium is recommended as first-line treatment for the maintenance treatment of bipolar-affective disorder in current guidelines, its molecular modes of action are however poorly understood. Cell models derived from bipolar patients could prove useful to gain more insight in the molecular mechanisms of bipolar disorder and the common pharmacological treatments. As primary neuronal cell lines cannot be easily derived from patients, peripheral cell models should be evaluated in their usefulness to study pathomechanisms and the mode of action of medication as well as in regard to develop biomarkers for diagnosis and treatment response.\nAt time of import, last updated (by provider) on: Mar 25 2019\n\nContributors: ; [Sarah Kittel-Schneider, Max Hilscher, Andreas Reif, Claus J Scholz]
-#> 16:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Gene expression of samples from the postmortem hippocampus of older bipolar disorder subjects and controls. Gene expression data was generated using the SurePrint G3 Human Gene Expression v3 microarray. Rank feature selection was performed to identify a subset of features that can optimally differentiate BD and controls.\nAt time of import, last updated (by provider) on: Feb 19 2023\n\nContributors: ; [Carlos A Pasqualucci, Claudia K Suemoto, Ricardo Nitrini, Fernanda B Bertonha, Paula V Nunes, Katia C De Oliveira, Carlos M Filho, Helena K Kim, Helena Brentani, Lea T Grinberg, Beny Lafer, Andr<c3><a9> Barbosa, Camila Nascimento, Renata P Leite, Wilson Jacob-Filho]
-#> 17:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Analysis of gene-expression changes in depressed subjects with bipolar disorder compared to healthy controls. Results provide information on pathways that may be involved in the pathogenesis of bipolar depression.\nLast Updated (by provider): Aug 27 2010\nContributors:  Robert D Beech
-#> 18:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      50 samples of individuals from 4 different diagnostic groups: bipolar, schizophrenia, depression and controls. Samples taken from the Cerebellum.
-#> 19:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Gene expression profiles of bipolar disorder (BD) patients were assessed during both a manic and a euthymic phase and compared both intra-individually, and with the gene expression profiles of controls.\nLast Updated (by provider): Sep 05 2014\nContributors:  Christian C Witt Benedikt Brors Dilafruz Juraeva Jens Treutlein Carsten Sticht Stephanie H Witt Jana Strohmaier Helene Dukal Josef Frank Franziska Degenhardt Markus M N<c3><b6>then Sven Cichon Maren Lang Marcella Rietschel Sandra Meier Manuel Mattheisen
-#> 20:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  44 samples of individuals from four different diagnostic groups: depression, bipolar, schizophrenia, and controls. Samples taken from the DLPFC Broadmann area 46/10.
-#> 21:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Bipolar Disorder (BD) is a complex neuropsychiatric disorder that is characterized by intermittent episodes of mania and depression and, without treatment, 15% of patients commit suicide1. Hence, among all diseases, BD has been ranked by the WHO as a top disorder of morbidity and lost productivity2. Previous neuropathological studies have revealed a series of alterations in the brains of BD patients or animal models3, such as reduced glial cell number in the patient prefrontal cortex4, up-regulated activities of the PKA/PKC pathways5-7, and changes in dopamine/5-HT/glutamate neurotransmission systems8-11. However, the roles and causation of these changes in BD are too complex to exactly determine the pathology of the disease; none of the current BD animal models can recapitulate both the manic and depressive phenotypes or spontaneous cycling of BD simultaneously12,13. Furthermore, while some patients show remarkable improvement with lithium treatment, for yet unknown reasons, other patients are refractory to lithium treatment. Therefore, developing an accurate and powerful biological model has been a challenge for research into BD. The development of induced pluripotent stem cell (iPSC) technology has provided such a new approach. Here, we developed a human BD iPSC model and investigated the cellular phenotypes of hippocampal dentate gyrus neurons derived from the patient iPSCs. Using patch clamp recording, somatic Ca2+ imaging and RNA-seq techniques, we found that the neurons derived from BD patients exhibited hyperactive action potential (AP) firing, up-regulated expression of PKA/PKC/AP and mitochondria-related genes. Moreover, lithium selectively reversed these alterations in the neurons of patients who responded to lithium treatment. Therefore, hyper-excitability is one endophenotype of BD that is probably achieved through enhancement in the PKA/PKC and Na+ channel signaling systems, and our BD iPSC model can be used to develop new therapies and drugs aimed at clinical treatment of this disease.\nLast Updated (by provider): Jun 11 2018\nContributors:  Son Pham Jun Yao Fred H Gage
-#> 22:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      46 samples of individuals from 4 different diagnostic groups: bipolar, schizophrenia, depression and controls. Samples taken from the Cerebellum.
-#> 23:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              We used microarrays to identify the differently expressed genes in disease model for bipolar disorder and schizophrenia.\nAt time of import, last updated (by provider) on: Feb 01 2019\n\nContributors: ; [Takaya Ishii, Hideyuki Okano]
-#> 24:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Schizophrenia is a complex psychiatric disorder encompassing a range of symptoms and  etiology dependent upon the interaction of genetic and environmental factors.  Several risk genes, such as DISC1, have been associated with schizophrenia as well as bipolar disorder (BPD) and major depressive disorder (MDD), consistent with the hypothesis that a shared genetic architecture could contribute to divergent clinical syndromes.  The present study compared gene expression profiles across three brain regions in post-mortem tissue from matched subjects with schizophrenia, BPD or MDD and unaffected controls.  Post-mortem brain tissue was collected from control subjects and well-matched subjects with schizophrenia, BPD, and MDD (n=19 from each group).  RNA was isolated from hippocampus, Brodmann Area 46, and associative striatum and hybridized to U133_Plus2 Affymetrix chips.  Data were normalized by RMA, subjected to pairwise comparison followed by Benjamini and Hochberg False Discovery Rate correction (FDR).  Samples derived from patients with schizophrenia exhibited  many more changes in gene expression across all brain regions than observed in BPD or MDD.  Several genes showed changes in both schizophrenia and BPD, though the magnitude of change was usually larger in schizophrenia.  Several genes that have variants associated with schizophrenia were found to have altered expression in multiple regions of brains from subjects with schizophrenia.  Continued evaluation of circuit-level alterations in gene expression and gene-network relationships may further our understanding of how genetic variants may be influencing biological processes to contribute to psychiatric disease.\nLast Updated (by provider): May 19 2014\nContributors:  Thomas A Lanz
-#> 25:  Most neuroscientists would agree that psychiatric illness is unlikely to arise from pathological changes that occur uniformly across all cells in a given brain region. Despite this fact, the majority of transcriptomic analyses of the human brain to date are conducted using macro-dissected tissue due to the difficulty of conducting single-cell level analyses on donated post-mortem brains. To address this issue statistically, we compiled a database of several thousand transcripts that were specifically-enriched in one of 10 primary brain cell types identified in published single cell type transcriptomic experiments. Using this database, we predicted the relative cell type composition for 157 human dorsolateral prefrontal cortex samples using Affymetrix microarray data collected by the Pritzker Neuropsychiatric Consortium, as well as for 841 samples spanning 160 brain regions included in an Agilent microarray dataset collected by the Allen Brain Atlas. These predictions were generated by averaging normalized expression levels across the transcripts specific to each primary cell type to create a <e2><80><9c>cell type index<e2><80><9d>. Using this method, we determined that the expression of cell type specific transcripts identified by different experiments, methodologies, and species clustered into three main cell type groups: neurons, oligodendrocytes, and astrocytes/support cells. Overall, the principal components of variation in the data were largely explained by the neuron to glia ratio of the samples. When comparing across brain regions, we were able to easily capture canonical cell type signatures <e2><80><93> increased endothelial cells and vasculature in the choroid plexus, oligodendrocytes in the corpus callosum, astrocytes in the central glial substance, neurons and immature cells in the dentate gyrus, and oligodendrocytes and interneurons in the globus pallidus. The relative balance of these cell types was influenced by a variety of demographic, pre- and post-mortem variables. Age and prolonged anaerobic conditions around the time of death were associated with decreased neuronal content and increased astrocytic and endothelial content in the tissue, replicating the known higher vulnerability of neurons to aging and adverse conditions, and illustrating the proliferation of vasculature in a hypoxic environment. We also found that the red blood cell content was reduced in individuals who died in a manner that involved systemic blood loss. Finally, statistically accounting for cell type improved both the sensitivity and interpretability of diagnosis effects within the data. We were able to observe a decrease in astrocytic content in subjects with Major Depressive Disorder, mirroring what had been previously observed morphometrically. By including a set of <e2><80><9c>cell type indices<e2><80><9d> in a larger model examining the relationship between gene expression and neuropsychiatric illness, we were able to successfully detect almost twice as many genes with previously identified relationships to bipolar disorder and schizophrenia than using traditional analysis methods.\nAt time of import, last updated (by provider) on: \n\nContributors: ; [Jun Z Li, Cortney A Turner, Megan H Hagenauer, Stanley J Watson, David M Walsh, Alan F Schatzberg, Huda Akil, Richard M Myers, William E Bunney, Jack D Barchas]
-#> 26:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              We performed the oligonucleotide microarray analysis in bipolar disorder, major depression, schizophrenia, and control subjects using postmortem prefrontal cortices provided by the Stanley Foundation Brain Collection. By comparing the gene expression profiles of similar but distinctive mental disorders, we explored the uniqueness of bipolar disorder and its similarity to other mental disorders at the molecular level. Notably, most of the altered gene expressions in each disease were not shared by one another, suggesting the molecular distinctiveness of these mental disorders. We found a tendency of downregulation of the genes encoding receptor, channels or transporters, and upregulation of the genes encoding stress response proteins or molecular chaperons in bipolar disorder. Altered expressions in bipolar disorder shared by other mental disorders mainly consisted of upregulation of the genes encoding proteins for transcription or translation. The genes identified in this study would be useful for the understanding of the pathophysiology of bipolar disorder, as well as the common pathophysiological background in major mental disorders at the molecular level.\nLast Updated (by provider): Mar 15 2009\nContributors:  Tadafumi Kato Kazuya Iwamoto Chihiro Kakiuchi Kazuhiko Ikeda Miki Bundo
-#> 27:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                99 samples of individuals from three different diagnostic groups: bipolar, schizophrenia, and controls. Samples taken from the DLPFC Broadmann area 46.
-#> 28:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Prefrontal cortex of schizophrenic, bipolar, and control subjects. This is the "McLean 66"
-#> 29:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Schizophrenia (SCZ) and bipolar disorder (BPD) are polygenic disorders with many genes contributing to their etiologies. The aim of this investigation was to search for dysregulated molecular and cellular pathways for these disorders as well as psychosis. We conducted a blood-based microarray investigation in two independent samples with SCZ and BPD from San Diego (SCZ=13, BPD=9, control=8) and Taiwan [data not included](SCZ=11, BPD=14, control=16). Diagnostic groups were compared to controls, and subjects with a history of psychosis [PSYCH(+): San Diego (n=6), Taiwan (n=14)] were compared to subjects without such history [PSYCH(-): San Diego (n=11), Taiwan (n=14)]. Analyses of covariance comparing mean expression levels on a gene-by-gene basis were conducted to generate the top 100 significantly dysregulated gene lists for both samples by each diagnostic group. Gene lists were imported into Ingenuity Pathway Analysis (IPA) software. Results showed the ubiquitin proteasome pathway (UPS) was listed in the top ten canonical pathways for BPD and psychosis diagnostic groups across both samples with a considerably low likelihood of a chance occurrence (p = .001). No overlap in dysregulated genes populating these pathways was observed between the two independent samples. Findings provide preliminary evidence of UPS dysregulation in BPD and psychosis as well as support further investigation of the UPS and other molecular and cellular pathways for potential biomarkers for SCZ, BPD, and/or psychosis. The aim of this investigation was to search for dysregulated molecular and cellular pathways for these disorders as well as psychosis.\nLast Updated (by provider): Oct 19 2012\nContributors:  Sharon D Chandler Chad A Bousman Ian P Everall Erick Tatro Stephen J Glatt Ming T Tsuang James Lohr Ginger Lucero Gursharan Chana William Kremen
-#> 30:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Neurodevelopmental changes and impaired stress resistance have been implicated in the pathogenesis of bipolar disorder (BD), but the underlying regulatory mechanisms are unresolved.  Here we describe a cerebral organoid model of BD that exhibits altered early neural development, elevated neural network activity, and a major shift in the transcriptome. These phenotypic changes were reproduced in cerebral organoids generated from iPS cell lines derived in multiple different laboratories. The BD cerebral organoid transcriptome showed highly significant enrichment for gene targets of the transcriptional repressor REST. This was associated with reduced nuclear REST and REST binding to target gene recognition sites.  Reducing the oxygen concentration in organoid cultures to a physiological range ameliorated the developmental phenotype and restored REST expression.  These effects were mimicked by treatment with lithium.  Reduced nuclear REST and derepression of REST targets genes was also observed in the prefrontal cortex of BD patients. Thus, an impaired cellular stress response in BD cerebral organoids leads to altered neural development and transcriptional dysregulation associated with downregulation of REST. These findings provide a new model and conceptual framework for exploring the molecular basis of BD\nAt time of import, last updated (by provider) on: Nov 06 2023\n\nContributors: ; [King-Hwa Ling, Jenny Tam, Eunjung A Lee, Angeliki Spathopoulou, Liviu Aron, Pei-Ling Yeo, Li-Huei Tsai, Roy H Perlis, Jaejoon Choi, Bruce A Yankner, Derek Drake, Tak Ko, Mariana Garcia-Corral, Katharina Meyer, George Church]
-#> 31:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    98 samples of individuals from 3 different diagnostic groups: bipolar, schizophrenia, and controls. Samples taken from the DLPFC Broadmann area 46.
-#> 32:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       We fine-mapped DNA methylation in neuronal nuclei (NeuN+) isolated by flow cytometry from post-mortem frontal cortex of the brain of individuals diagnosed with schizophrenia, bipolar disorder, and controls (n=29, 26, and 28 individuals).\nAt time of import, last updated (by provider) on: May 15 2019\n\nContributors: ; [Shraddha S Pai, Viviane Labrie]
-#> 33:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   Accumulating evidence suggests that mitochondrial dysfunction underlies the pathophysiology of bipolar disorder (BD) and schizophrenia (SZ). We performed large-scale DNA microarray analysis of postmortem brains of patients with BD or SZ, and examined expression patterns of mitochondria-related genes. We found a global down-regulation of mitochondrial genes, such as those encoding respiratory chain components, in BD and SZ samples, even after the effect of sample pH was controlled. However, this was likely due to the effects of medication. Medication-free patients with BD showed tendency of up-regulation of subset of mitochondrial genes. Our findings support the mitochondrial dysfunction hypothesis of BD and SZ pathologies. However, it may be the expression changes of a small fraction of mitochondrial genes rather than the global down-regulation of mitochondrial genes. Our findings warrant further study of the molecular mechanisms underlying mitochondrial dysfunction in BD and SZ. \nLast Updated (by provider): Mar 15 2009\nContributors:  Tadafumi Kato Kazuya Iwamoto Miki Bundo
-#> 34:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Background: Schizophrenia (SCZ) and bipolar disorder (BD) are highly heritable psychiatric disorders. Associated genetic and gene expression changes have been identified, but many have not been replicated and have unknown functions. We identified groups of genes whose expressions varied together, that is co-expression modules, then tested them for association with SCZ. Using weighted gene co-expression network analysis, we show that two modules were differentially expressed in patients versus controls. One, upregulated in cerebral cortex, was enriched with neuron differentiation and neuron development genes, as well as disease genome-wide association study genetic signals; the second, altered in cerebral cortex and cerebellum, was enriched with genes involved in neuron protection functions. The findings were preserved in five expression data sets, including sets from three brain\nregions, from a different microarray platform, and from BD patients. From those observations, we propose neuron differentiation and development pathways may be involved in etiologies of both SCZ and BD, and neuron protection function participates in pathological process of the diseases.\nLast Updated (by provider): Jul 26, 2018\nContributors:  Chao Chen Chunyu Liu Lijun Cheng
-#> 35:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           Schizophrenia (SZ) and bipolar disorder (BD) are severe psychiatric conditions, with a lifetime prevalence of about 1%. Both disorders have a neurodevelopment component, with onset of symptoms occurring most frequently during late adolescence or early adulthood. Genetic findings indicate the existence of an overlap in genetic susceptibility across the disorders. These gene expression profiles were used to identify the molecular mechanisms that differentiate SZ and BP from healthy controls but also that distinguish both from healthy individuals. They were also used to expand an analysis from an experiment that searched molecular alterations in human induced pluripotent stem cells derived from fibroblasts from control subject and individual with schizophrenia and further differentiated to neuron to identify genes relevant for the development of schizophrenia (GSE62105).\nLast Updated (by provider): Oct 14 2014\nContributors:  Leandro Lima Mariana Maschietto Dirce M Carraro Carlos A Filho Angelica de Baumont Luiz A Barreta Paulo Belmonte-de-Abreu Ana C Tahira Eloisa H Olivieri Joana A Palha Helena Brentani Daniel Mariani Alex Fiorini
-#> 36:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        We used laser capture microdissection to isolate both microvascular endothelial cells and neurons from post mortem brain tissue from patients with schizophrenia and bipolar disorder and healthy controls. RNA was isolated from these cell populations, amplified, and analysed using Affymetrix HG133plus2.0 GeneChips. In the first instance, we used the dataset to compare the neuronal and endothelial data, in order to demonstrate that the predicted differences between cell types could be detected using this methodology. \nLast Updated (by provider): Dec 18 2008\nContributors:  Margaret M Ryan Thomas Giger Martin J Lan Matthew T Wayland Mark Kotter Michael L Mimmack Laura W Harris Lan Wang Irene Wuethrich Helen Lockstone Sabine Bahn
-#> 37:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Background: \tSchizophrenia (SCZ) and bipolar disorder (BD) are highly heritable psychiatric disorders. Associated genetic and gene expression changes have been identified, but many have not been replicated and have unknown functions. We identified groups of genes whose expressions varied together, that is co-expression modules, then tested them for association with SCZ. Using weighted gene co-expression network analysis, we show that two modules were differentially expressed in patients versus controls. One, upregulated in cerebral cortex, was enriched with neuron differentiation and neuron development genes, as well as disease genome-wide association study genetic signals; the second, altered in cerebral cortex and cerebellum, was enriched with genes involved in neuron protection functions. The findings were preserved in five expression data sets, including sets from three brain regions, from a different microarray platform, and from BD patients. From those observations, we propose neuron differentiation and development pathways may be involved in etiologies of both SCZ and BD, and neuron protection function participates in pathological process of the diseases.\nLast Updated (by provider):  Jul 26, 2018\nContributors:  Chao Chen Chunyu Liu Lijun Cheng
-#> 38:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         Impairments in certain cognitive processes (e.g., working memory) are typically most pronounced in schizophrenia (SZ), intermediate in bipolar disorder (BP) and least in major depressive disorder (MDD). Given that working memory depends, in part, on neural circuitry that includes pyramidal neurons in layer 3 (L3) and layer 5 (L5) of the dorsolateral prefrontal cortex (DLPFC), we sought to determine if transcriptome alterations in these neurons were shared or distinctive for each diagnosis.\nLast Updated (by provider): Jul 05 2017\nContributors:  Dominique Arion David A Lewis John F Enwright George Tseng Zhiguang Huo John P Corradi
-#> 39:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   27 samples of individuals from two different diagnostic groups: bipolar, and controls. Samples taken from the DLPFC Brodmann area 6.
-#> 40:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Bipolar affective disorder is a severe psychiatric disorder with a strong genetic component but unknown pathophysiology. We used microarray technology (Affymetrix HG-U133A GeneChips) to determine the expression of approximately 22 000 mRNA transcripts in post-mortem brain tissue (orbitofrontal cortex) from patients with bipolar disorder and matched healthy controls. Orbitofrontal cortex tissue from a cohort of 30 subjects was investigated and the final analysis included 10 bipolar and 11 control subjects. Differences between disease and control groups were identified using a rigorous statistical analysis with correction for confounding variables and multiple testing.\nNote: [] samples from this series, which appear in other Expression Experiments in Gemma, were not imported from the GEO source. The following samples were removed: \nLast Updated (by provider): Jul 27 2006\nContributors:  Sabine Bahn Margaret M Ryan Matthew T Wayland Maree J Webster Stephen J Huffaker Helen E Lockstone\nIncludes GDS2191.\n Update date: Aug 28 2006.\n Dataset description GDS2191: Analysis of postmortem orbitofrontal cortex from 10 adults with bipolar disorder. Results provide insight into the pathophysiology of the disease.
-#> 41:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                98 samples of individuals from three different diagnostic groups: bipolar, schizophrenia, and controls. Samples taken from the DLPFC Broadmann area 46.
-#> 42:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         To identify genes dysregulated in bipolar disorder (BD1) we carried out global gene expression profiling using whole-genome microarrays. To minimize genetic variation in gene expression levels between cases and controls we compared expression profiles in lymphoblastoid cell lines from monozygotic twin pairs discordant for the disease. We identified 82 genes that were differentially expressed by ? 1.3-fold in 3 BD1 cases compared to their co-twins, and which were statistically (p ? 0.05) differentially expressed between the groups of BD1 cases and controls. Using qRT-PCR we confirmed the differential expression of some of these genes, including: KCNK1, MAL, PFN2, TCF7, PGK1, and PI4KCB, in at least 2 of the twin pairs. In contrast to the findings of a previous study by Kakiuchi and colleagues with similar discordant BD1 twin design1 our data do not support the dysregulation of XBP1 and HSPA5. From pathway and gene ontology analysis we identified up-regulation of the WNT signalling pathway and the biological process of apoptosis. The differentially regulated genes and pathways identified in this study may provide insights into the biology of BD1.\nLast Updated (by provider): Jun 20 2007\nContributors:  Louisa Windus Nicholas Matigian Bryan Mowry Cheryl Filippich John McGrath Heather Smith Nicholas Hayward Christos Pantelis
-#> 43:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   105 samples of individuals from 3 different diagnostic groups: bipolar, schizophrenia, and controls. Samples taken from the DLPFC Broadmann area 46.
-#> 44:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           In psychiatric disorders, common and rare genetic variants cause widespread dysfunction of cells and their interactions, especially in the prefrontal cortex, giving rise to psychiatric symptoms. To better understand these processes, we traced the effects of common and rare genetics, and cumulative disease risk scores, to their molecular footprints in human cortical single-cell types. We demonstrated that examining gene expression at single-exon resolution is crucial for understanding the cortical dysregulation associated with diagnosis and genetic risk derived from common variants. We then used disease risk scores to identify a core set of genes that serve as a footprint of common and rare variants in the cortex. Pathways enriched in these genes included dopamine regulation, circadian entrainment, and hormone regulation. Single-nuclei-RNA-sequencing pinpointed these enriched genes to excitatory cortical neurons. This study highlights the importance of studying sub-gene-level genetic architecture to classify psychiatric disorders based on biology rather than symptomatology, to identify novel targets for treatment development.\nAt time of import, last updated (by provider) on: Nov 20 2022\n\nContributors: ; [Franziska Degenhardt, Fabian J Theis, Janine Knauer-Arloth, Elisabeth Scarr, Nikola S Mueller, Nathalie Gerstner, Holger Thiele, Anna C Koller, Brian Dean, Karolina Worf, Marcella Rietschel, Madhara Udawela, Natalie Matosin, Anna S Froehlich]
-#> 45:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Bipolar affective disorder is a severe psychiatric disorder with a strong genetic component but unknown pathophysiology. We used microarray technology (Affymetrix HG-U133A GeneChips) to determine the expression of approximately 22 000 mRNA transcripts in post-mortem brain tissue (dorsolateral prefrontal cortex) from patients with bipolar disorder and matched healthy controls. A cohort of 70 subjects was investigated and the final analysis included 30 bipolar and 31 control subjects. Differences between disease and control groups were identified using a rigorous statistical analysis with correction for confounding variables and multiple testing.\nLast Updated (by provider): Jan 16 2007\nContributors:  Helen E Lockstone Stephen J Huffaker Matthew T Wayland Sabine Bahn Maree J Webster Margaret M Ryan\nIncludes GDS2190.\n Update date: Aug 28 2006.\n Dataset description GDS2190: Analysis of postmortem dorsolateral prefrontal cortex from 30 adults with bipolar disorder. Results provide insight into the pathophysiology of the disease.
-#> 46:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  A multitude of genes have been associated with bipolar disorder via SNP genotyping studies.  However, many of these associated SNPs are found within intronic or intergenic regions of the human genome.  We were interested in studying transcriptional profiles/splice variation of genes associated with bipolar disorder within the human striatum. Understanding how these associated genes are transcribed in the human brain may help to guide the development of therapeutic agents for the treatment of bipolar disorder and other neuropsychiatric illnesses.\nLast Updated (by provider): Jun 26 2018\nContributors:  Courtney M MacMullen Ronald L Davis Mohammad Fallahi
-#> 47:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   102 samples of individuals from 3 different diagnostic groups: bipolar, schizophrenia, and controls. Samples taken from the DLPFC Broadmann area 46.
-#> 48:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 78 samples of individuals from three different diagnostic groups: bipolar, schizophrenia and controls. Samples taken from the DLPFC Broadmann area 46.
-#> 49:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               BACKGROUND: Bipolar disorder (BD) is a highly heritable mood disorder with complex genetic architecture and poorly understood etiology. Previous transcriptomic BD studies have had inconsistent findings due to issues such as small sample sizes and difficulty in adequately accounting for confounders like medication use.  METHODS: We performed a differential expression analysis in a well-characterized BD case-control sample (Nsubjects = 480) by RNA sequencing of whole blood. We further performed co-expression network analysis, functional enrichment, and cell type decomposition, and integrated differentially expressed genes with genetic risk.  RESULTS: While we observed widespread differential gene expression patterns between affected and unaffected individuals, these effects were largely linked to lithium treatment at the time of blood draw (FDR < 0.05, Ngenes = 976) rather than BD diagnosis itself (FDR < 0.05, Ngenes = 6). These lithium-associated genes were enriched for cell signaling and immune response functional annotations, among others, and were associated with neutrophil cell-type proportions, which were elevated in lithium users. Neither genes with altered expression in cases nor in lithium users were enriched for BD, schizophrenia, and depression genetic risk based on information from genome-wide association studies, nor was gene expression associated with polygenic risk scores for BD.  CONCLUSIONS: These findings suggest that BD is associated with minimal changes in whole blood gene expression independent of medication use but emphasize the importance of accounting for medication use and cell type heterogeneity in psychiatric transcriptomic studies. The results of this study add to mounting evidence of lithium's cell signaling and immune-related mechanisms.\nAt time of import, last updated (by provider) on: Oct 24 2019\n\nContributors: ; [Catharine E Krebs, Roel A Ophoff, Loes M Loohuis]
-#> 50:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      39 samples of individuals from 4 different diagnostic groups: bipolar, schizophrenia, depression and controls. Samples taken from the DLPFC Broadmann area 46/10.
-#> 51:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     RNA-seq profiling was conducted on clinically-annotated human post-mortem brain tissues\nLast Updated (by provider): Jun 26 2018\nContributors:  Ryne C Ramaker Kevin M Bowling Sara J Cooper Brittany N Lasseigne Richard M Myers
-#> 52:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Bipolar disorder (BD) is a highly heritable and heterogeneous mental illness whose manifestations often include impulsive and risk-taking behavior. This particular phenotype suggests that abnormal striatal function could be involved in BD etiology, yet most transcriptomic studies of this disorder have concentrated on cortical brain regions. We report the first transcriptome profiling by RNA-Seq of the human dorsal striatum comparing bipolar and control subjects. Differential expression analysis and functional pathway enrichment analysis were performed to identify changes in gene expression that correlate with BD status. Further co-expression and enrichment analyses were performed to identify sets of correlated genes that show association to BD.\nLast Updated (by provider): May 17 2017\nContributors:  Ronald L Davis Rodrigo Pacifico
-#> 53:  Most neuroscientists would agree that psychiatric illness is unlikely to arise from pathological changes that occur uniformly across all cells in a given brain region. Despite this fact, the majority of transcriptomic analyses of the human brain to date are conducted using macro-dissected tissue due to the difficulty of conducting single-cell level analyses on donated post-mortem brains. To address this issue statistically, we compiled a database of several thousand transcripts that were specifically-enriched in one of 10 primary brain cell types identified in published single cell type transcriptomic experiments. Using this database, we predicted the relative cell type composition for 157 human dorsolateral prefrontal cortex samples using Affymetrix microarray data collected by the Pritzker Neuropsychiatric Consortium, as well as for 841 samples spanning 160 brain regions included in an Agilent microarray dataset collected by the Allen Brain Atlas. These predictions were generated by averaging normalized expression levels across the transcripts specific to each primary cell type to create a <e2><80><9c>cell type index<e2><80><9d>. Using this method, we determined that the expression of cell type specific transcripts identified by different experiments, methodologies, and species clustered into three main cell type groups: neurons, oligodendrocytes, and astrocytes/support cells. Overall, the principal components of variation in the data were largely explained by the neuron to glia ratio of the samples. When comparing across brain regions, we were able to easily capture canonical cell type signatures <e2><80><93> increased endothelial cells and vasculature in the choroid plexus, oligodendrocytes in the corpus callosum, astrocytes in the central glial substance, neurons and immature cells in the dentate gyrus, and oligodendrocytes and interneurons in the globus pallidus. The relative balance of these cell types was influenced by a variety of demographic, pre- and post-mortem variables. Age and prolonged anaerobic conditions around the time of death were associated with decreased neuronal content and increased astrocytic and endothelial content in the tissue, replicating the known higher vulnerability of neurons to aging and adverse conditions, and illustrating the proliferation of vasculature in a hypoxic environment. We also found that the red blood cell content was reduced in individuals who died in a manner that involved systemic blood loss. Finally, statistically accounting for cell type improved both the sensitivity and interpretability of diagnosis effects within the data. We were able to observe a decrease in astrocytic content in subjects with Major Depressive Disorder, mirroring what had been previously observed morphometrically. By including a set of <e2><80><9c>cell type indices<e2><80><9d> in a larger model examining the relationship between gene expression and neuropsychiatric illness, we were able to successfully detect almost twice as many genes with previously identified relationships to bipolar disorder and schizophrenia than using traditional analysis methods.\nAt time of import, last updated (by provider) on: \n\nContributors: ; [Jun Z Li, Cortney A Turner, Megan H Hagenauer, Stanley J Watson, David M Walsh, Alan F Schatzberg, Huda Akil, Richard M Myers, William E Bunney, Jack D Barchas]
-#> 54:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      There are currently no biological tests that differentiate patients with bipolar disorder (BPD) from healthy controls. While there is evidence that peripheral gene expression differences between patients and controls can be utilized as biomarkers for psychiatric illness, it is unclear whether current use or residual effects of antipsychotic and mood stabilizer medication drives much of the differential transcription. We therefore tested whether expression changes in first-episode, never-medicated bipolar patients, can contribute to a biological classifier that is less influenced by medication and could potentially form a practicable biomarker assay for BPD. We employed microarray technology to measure global leukocyte gene expression in first-episode (n=3) and currently medicated BPD patients (n=26), and matched healthy controls (n=25). Following an initial feature selection of the microarray data, we developed a cross-validated 10-gene model that was able to correctly predict the diagnostic group of the training sample (26 medicated patients and 12 controls), with 89% sensitivity and 75% specificity (p<0.001). The 10-gene predictor was further explored via testing on an independent test cohort consisting of three pairs of monozygotic twins discordant for BPD, plus the original enrichment sample cohort (the three never-medicated BPD patients and 13 matched control subjects), and a sample of experimental replicates (n=34). 83% of the independent test sample was correctly predicted, with a sensitivity of 67% and specificity of 100% (although this result did not reach statistical significance). Additionally, 88% of sample diagnostic classes were classified correctly for both the enrichment (p=0.015) and the replicate samples (p<0.001).\nLast Updated (by provider): Jun 25 2013\nContributors:  James D Clelland Catherine L Clelland
-#> 55:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        47 samples of individuals from 4 different diagnostic groups: bipolar, schizophrenia, depression and controls. Samples taken from the DLPFC Broadmann area 8/9.
-#> 56:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             RNA was extracted from peripheral blood mononuclear cells (PBMC) of 24 adult healthy controls, 8 adult patients with bipolar disorder, and 21 adult patients with major depressive disorder to analyze gene expression patterns that identify biomarkers of disease and that may be correlated with fMRI data.\nLast Updated (by provider): Sep 24 2012\nContributors:  T K Teague Jonathan Savitz Wayne C Drevets Julie H Marino Melissa Bebak Bart Frank
-#> 57:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     Schizophrenia (SZ) and bipolar disorder (BD) are severe neuropsychiatric disorders with serious impact on patients, together termed <e2><80><9c>major psychosis<e2><80><9d>. Recently, long intergenic non-coding RNAs (lincRNAs) were reported to play important roles in mental diseases. However, little was known about their molecular mechanism in pathogenesis of SZ and BD. Here, we performed RNA sequencing on 82 post-mortem brain tissues from three brain regions (orbitofrontal cortex (BA11), anterior cingulate cortex (BA24) and dorsolateral prefrontal cortex (BA9)) of patients with SZ and BD and control subjects, generating over one billion reads. We characterized lincRNA transcriptome in the three brain regions and identified 20 differentially expressed lincRNAs (DELincRNAs) in BA11 for BD, 34 and 1 in BA24 and BA9 for SZ, respectively. Our results showed that these DELincRNAs exhibited brain region-specific patterns. Applying weighted gene co-expression network analysis, we revealed that DELincRNAs together with other genes can function as modules to perform different functions in different brain regions, such as immune system development in BA24 and oligodendrocyte differentiation in BA9. Additionally, we found that DNA methylation alteration could partly explain the dysregulation of lincRNAs, some of which could function as enhancers in the pathogenesis of major psychosis. Together, we performed systematical characterization of dysfunctional lincRNAs in multiple brain regions of major psychosis, which provided a valuable resource to understand their roles in SZ and BD pathology and helped to discover novel biomarkers.\nLast Updated (by provider): Jun 26 2018\nContributors:  Jing Hu Jinyuan Xu Lin Pang
-#> 58:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           Fibroblasts from patients with Type I bipolar disorder (BPD) and their unaffected siblings were obtained from an Old Order Amish pedigree with a high incidence of BPD and reprogrammed to induced pluripotent stem cells (iPSCs). Established iPSCs were subsequently differentiated into neuroprogenitors (NPs) and then to neurons. Transcriptomic microarray analysis was conducted on RNA samples from iPSCs, NPs and neurons matured in culture for either 2 weeks (termed early neurons, E) or 4 weeks (termed late neurons, L). Global RNA profiling indicated that BPD and control iPSCs differentiated into NPs and neurons at a similar rate, enabling studies of differentially expressed genes in neurons from controls and BPD cases. Significant disease-associated differences in gene expression were observed only in L neurons. Specifically, 328 genes were differentially expressed between BPD and control L neurons including GAD1, glutamate decarboxylase 1 (2.5 fold) and SCN4B, the voltage gated type IV sodium channel beta subunit (-14.6 fold). Quantitative RT-PCR confirmed the up-regulation of GAD1 in BPD compared to control L neurons. Gene Ontology, GeneGo and Ingenuity Pathway Analysis of differentially regulated genes in L neurons suggest that alterations in RNA biosynthesis and metabolism, protein trafficking as well as receptor signaling pathways GSK3? signaling may play an important role in the pathophysiology of BPD.\nLast Updated (by provider): Jun 03 2018\nContributors:  Jiangang Liu Steven M Paul Jeffrey L Dage Janice A Egeland Rachelle J Sells Galvin Kwi H Kim Rosamund C Smith Kalpana M Merchant
-#> 59:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Study the role of the LIM-homeodomain transcription factor LHX4 in the development of retinal bipolar cell subtypes\nAt time of import, last updated (by provider) on: Sep 30 2020\n\nContributors: ; [Lin Gan, Xuhui Dong]
-#> 60:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Study the role of the LIM-homeodomain transcription factor LHX4 in the development of retinal bipolar cell subtypes\nAt time of import, last updated (by provider) on: Sep 30 2020\n\nContributors: ; [Lin Gan, Xuhui Dong]
-#> 61:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         Fibroblasts and lymphoblastoid cells (LCLs) are the most widely used cells in genetic, genomic, and transcriptomic studies in relation to human diseases. Examining the gene expression patterns in these two cell types will provide valuable information regarding the validity of using them to study gene expression related to various human diseases. Fibroblasts and LCLs from four members of the Old Order Amish family 884 were purchased from Coriell cell repositories (Coriell Institute for Medical Research, Camden, NJ). We used microarrays to profile the patterns of gene expression in these eight cell lines. By employing the PennCNV algorithm to the Illumina HumanHap550 SNP genotyping data, we detected 13 Copy Number Variants (CNV) that exist in these four individuals. CNV-expression association analysis revealed that seven of these 13 CNVs were associated with the expression of genes within or near (<2Mb sweep) these CNVs at a nominal regression P value of 0.05.\nLast Updated (by provider): Feb 20 2009\nContributors:  Maja Bucan Shuzhang Yang
-#> 62:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   We used RNA sequencing to identify candidate regulators of interactions between photoreceptor axons and bipolar cell (BCs) dendrites in developing mouse retina. We chose three time points: P7, just after the OPL forms and synaptogenesis with BCs begins; P13, as synaptogenesis nears completion and sublamination begins; and P30, when the OPL is mature. We purified cone and rod photoreceptors separately by fluorescence activated cell sorting (FACS) using transgene markers: Rhoicre;Ai9 for rods and HRGPcre;Ai9 for cones. We purified ON BCs, which include ON cone bipolars plus rod bipolars using Grm6:GFP. As appropriate transgenic lines to separate RBCs from CBCs were not available, we performed RNAseq on cells from Grm6:GFP mice that were fixed and immunostained prior to FACS, allowing us to purify RBCs (GFP+PKC+) and CBCs (GFP+PKC-) from the same retinas. As PKC is not highly expressed at P7, profiling of developing rod bipolars separate from developing ON cone bipolars was restricted to P13.\nLast Updated (by provider): Jun 25 2018\nContributors:  Elizabeth Z Sanchez Lawrence S Zipursky Yerbol Z Kurmangaliyev
-#> 63:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               Melatonin is a neurohormone that maintains the circadian rhythms of the body. Although we know the pathway of melatonin action in the brain, we lack comprehensive cross-sectional studies on the periphery of depressed patients.\nAt time of import, last updated (by provider) on: \n\nContributors: ; [Monika Dmitrzak-Weglarz, Karolina Bilska, Aleksandra Szczepankiewicz, Pawel Kapelski, Edyta Reszka, Ewa Banach, Ewa Jablonska, Maria Skibinska, Joanna Pawlak, Beata Narozna]
-#> 64:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               Melatonin is a neurohormone that maintains the circadian rhythms of the body. Although we know the pathway of melatonin action in the brain, we lack comprehensive cross-sectional studies on the periphery of depressed patients.\nAt time of import, last updated (by provider) on: \n\nContributors: ; [Monika Dmitrzak-Weglarz, Karolina Bilska, Aleksandra Szczepankiewicz, Pawel Kapelski, Edyta Reszka, Ewa Banach, Ewa Jablonska, Maria Skibinska, Joanna Pawlak, Beata Narozna]
-#> 65:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Bipolar disorder (BPD) is a debilitating heritable psychiatric disorder.  Contemporary models for the manic pole of BPD have primarily utilized either single locus transgenics or treatment with psychostimulants.  Our lab recently characterized a mouse strain, termed Madison (MSN), which naturally displays a manic phenotype, exhibiting elevated locomotor activity, increased sexual behavior, and higher forced swimming relative to control strains.  Lithium chloride and olanzapine treatments attenuate this phenotype.  In this study, we replicated our locomotor activity experiment, showing that MSN mice display generationally-stable mania relative to their outbred ancestral strain, hsd:ICR (ICR).  We then performed a gene expression microarray experiment to compare hippocampus of MSN and ICR mice.  We found dysregulation of multiple transcripts whose human orthologs are associated with BPD and other psychiatric disorders including schizophrenia and ADHD, including: Epor, Smarca4, Cmklr1, Cat, Tac1, Npsr1, Fhit, and P2rx7.  RT-qPCR confirmed dysregulation for all of seven transcripts tested.  Using a network analysis, we found dysregulation of a gene system related to chromatin packaging, a result convergent with recent human findings on BPD.  Using a novel genomic enrichment algorithm, we found enrichment in genome regions homologous to human loci implicated in BPD in replicated linkage studies including homologs of human cytobands 1p36, 3p14, 3q29, 6p21-22, 12q24, 16q24, and 17q25.  Our findings suggest that MSN mice represent a polygenic model for the manic pole of BPD showing much of the genetic systems complexity of the corresponding human disorder.  Further, the high degree of convergence between our findings and the human literature on BPD brings up novel questions about evolution by analogy in mammalian genomes.\nLast Updated (by provider): May 06 2012\nContributors:  Stephen C Gammie Griffin M Gessay Michael C Saul
-#> 66:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Diverse cell types can be reprogrammed into pluripotent stem cells by ectopic expression of Oct4 (Pou5f1), Klf4, Sox3 and Myc. Many of these induced pluripotent stem cells (iPSCs) retain an epigenetic memory of their cellular origins and this in turn may bias their subsequent differentiation. Differentiated neurons are difficult to reprogram and there has not been a systematic side-by-side characterization of reprogramming efficiency or epigenetic memory across different neuronal subtypes. We have recently developed a new method for reprogramming retinal neurons and successfully reprogrammed rod photoreceptors from the murine retina. Here we extended our retinal reprogramming to cone photoreceptors, bipolar neurons, amacrine/horizontal cell interneurons and M<c3><bc>ller glia at two different stages of development. We scored the efficiency of reprogramming across all 5 retinal cell types at each developmental stage and we measured retinal differentiation from each iPSC line using a quantitative standardized scoring system called STEM-RET. We discovered that the rod photoreceptors and bipolar neurons had the lowest reprogramming efficiency but iPSCs derived from rods and bipolar cells had the best retinal differentiation. Epigenetic memory was analyzed by characterizing DNA methylation and performing ChIP-seq for 8 histone marks, Brd4, PolII and CTCF. The epigenetic data were integrated with RNA-Seq data from each iPSC line. Retinal cell types with a greater epigenetic barrier to reprogramming (rods and bipolars) are more likely to retain epigenetic memory of their cellular origins. In addition, we identified biomarkers of iPSCs that are predictive of retinal differentiation. This work will have implications for selection of cell populations for cell based therapy and for using reprogramming of purified cell populations to advance our understanding of the role of the epigenome in normal differentiation.\nLast Updated (by provider): Jun 14 2018\nContributors:  Jiakun Zhang Sharon Frase Suresh Thiagarajan Daniel Hiler Michael A Dyer Dianna Johnson Issam Aldiri Xiang Chen Lyra Griffiths Marie-Elizabeth Barabas Andras Sablauer Beisi Xu Lu Wang Marc Valentine Abbas Shirinifard
-#> 67:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Schizophrenia is associated with dysfunction of the dorsolateral prefrontal cortex (DLPFC). This dysfunction is manifest as cognitive deficits that appear to arise from disturbances in gamma frequency oscillations. These oscillations are generated in DLPFC layer 3 via reciprocal connections between pyramidal cells and parvalbumin (PV)-containing interneurons. The density of cortical PV neurons is not altered in schizophrenia, but expression levels of several transcripts involved in PV cell function, including PV, are lower in the disease.\nLast Updated (by provider): Jul 03 2018\nContributors:  George Tseng Dominique Arion David A Lewis John F Enwright Zhiguang Huo John P Corradi
-#> 68:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Transcription factor Sp4 controls dendritic patterning during development of cerebellar granule neurons in culture by limiting branch formation and promoting activity-dependent pruning (Ramos et al., 2007). Sp4 is associated with neuropsychiatric disorders such as major depressive disorder, schizophrenia and bipolar disorder.  In order to identify target genes of Sp4, we compared global gene expression in the cerebella of wild type and Sp4 hypomorph mice (Sp4neo-/-; Zhou et al, 2005).  The results identify candidate Sp4 target genes that may contribute to neuronal development and neuropsychiatric disorders.\nLast Updated (by provider): Nov 09 2017\nContributors:  Xinxin Sun Grace Gill
-#> 69:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Genetic analyses for bipolar disorder (BPD) have achieved prominent success in Europeans in recent years, whereas its genetic basis in other populations remains relatively less understood. We herein report that the lead risk locus for BPD in European genome-wide association studies (GWAS), the single nucleotide polymorphism (SNP) rs9834970 near TRANK1 at 3p22 region, is also genome-wide significantly associated with BPD in 5,748 cases and 65,361 controls of East Asian origin. In this study, we performed RAN-seq analysis of cultured rat neurons treated with shRNA knockdown of Trank1.\nAt time of import, last updated (by provider) on: Jul 02 2020\n\nContributors: ; [Ming Li, Huijuan Li, Hong Chang, Xin Cai]
-#> 70:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            To define molecular mechanisms underlying rod and cone differentiation, we generated H9 human embryonic stem cell line carrying a GFP reporter that is controlled by the promoter of cone-rod homeobox (CRX) gene, the first known marker of post-mitotic photoreceptor precursors. CRXp-GFP reporter in H9 line replicates endogenous CRX expression when induced to form self-organizing 3-D retina-like tissue. We define temporal transcriptome dynamics of developing photoreceptors during the establishment of cone and rod cell fate. Our studies provide an essential framework for delineating molecules and cellular pathways that guide human photoreceptor development and should assist in chemical screening and cell-based therapies of retinal degeneration.\nLast Updated (by provider): Nov 14 2017\nContributors:  Kohei Homma Rossukon Kaewkhaw Anand Swaroop Koray D Kaya Jizhong Zou Mahendra Rao Matthew Brooks Vijender Chaitankar
-#> 71:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    The goal of this experiment was to define gene expression patterns of thirteen mouse retinal neuron subsets, labeled by expression of fluorescent proteins in transgenic mice.\nLast Updated (by provider): Sep 23 2013\nContributors:  Jeremy N Kay Joshua R Sanes
-#> 72:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        We determined the transcriptomes of postmitotic cone photoreceptors in the central region of the mouse retina every day between birth (P0) and eye opening (P12). At each postnatal day we isolated GFP-labeled cells from three different Chrnb4-GFP mice (biological triplicates) using fluorescence-activated cell sorting. We then acquired the transcriptomes of the sorted cones using next generation RNA sequencing. Our data set contained 39 transcriptomes.\nLast Updated (by provider): Jul 03 2018\nContributors:  Janine M Daum Michael B Stadler <c3><96>zkan Keles Botond Roska Hubertus Kohler
-#> 73:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Brain circuits are assembled from a large variety of morphologically and functionally diverse cell types. It is not known how the intermingled cell types of individual brain regions differ in their expressed genomes. Here we describe an atlas of cell type transcriptomes of the adult retina. We found that each adult cell type expresses a specific set of genes, including a unique set of transcription factors, forming a <e2><80><9c>barcode<e2><80><9d> for cell identity. Cell type transcriptomes carry enough information to categorize cells into corresponding morphological classes and types. Surprisingly, several barcode genes are eye disease-associated genes that we demonstrate to be specifically expressed not only in photoreceptors but also in particular retinal circuit elements such as inhibitory neurons as well as in retinal microglia. Our data suggest that distinct cell types of individual brain regions are characterized by marked differences in their expressed genomes. We assembled a library of 22 transgenic mouse lines in which each line had a group of retinal cells marked with fluorescent proteins. We built up the library with the purpose of having some mouse lines in which single retinal cell types and others in which combinations of types from a single class are labeled. The library had mouse lines with labeled cells representing each of the six retinal cell classes. Retinal cells were characterized by physiological recording and immunohistochemical staining. We isolated 200 fluorescent protein-labeled retinal cells (<e2><80><9c>cell groups<e2><80><9d>) from at least three different mice of each mouse line by fluorescence-activated cell sorting. The transcripts of each cell group of these biological triplicates were independently amplified in batches. Each batch contained an internal control cell group from the Arc-line.\nLast Updated (by provider): Jun 10 2014\nContributors:  Botond Roska Erik Cabuy Sandra Siegert
-#> 74:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              These are RNA sequencing data from 12 week old orbital frontal cortex from mice who received a shRNA targeting circHomer1 and Homer1b (double knockdown) or control shRNA (scramble)\nAt time of import, last updated (by provider) on: Feb 08 2022\n\nContributors: ; [Mellios Nikolaos]
-#> 75:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               Microarray experiments were performed using FAC-sorted young photoreceptors to analyze their transcriptome in comparison to remaining retinal cells at same developmental stage and retinal progenitors.\nLast Updated (by provider): Apr 02 2018\nContributors:  Marius Ader Kai Postel
-#> 76:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       CHD8 (chromodomain helicase DNA binding protein 8), which codes for a member of the CHD family of ATP-dependent chromatin-remodeling factors, is the most commonly mutated gene in autism spectrum disorders (ASD) identified in exome-sequencing studies. Loss of function mutations in the gene have also been found in schizophrenia (SZ) and intellectual disabilities, and affects cancer cell proliferation. To better understanding the molecular links between CHD8 functions and ASD, we have applied the CRISPR/Cas9 technology to knockout (KO) one copy of CHD8 in induced pluripotent stem cells (iPSCs) and build cerebral organoids, a model for the developing telencephalon. RNA-seq was carried out on KO organoids (CHD8+/-) and isogenic controls (CHD8+/+). Differentially expressed genes (DEGs) revealed an enrichment of genes involved in neurogenesis, forebrain development, Wnt/?-catenin signaling and axonal guidance. The SZ and bipolar disorder (BD) candidate gene TCF4 was significantly upregulated. Our CHD8 KO DEGs were significantly overlapped with those found in a transcriptome analysis using cerebral organoids derived from a family with idiopathic ASD and another transcriptome study using iPS cell-derived neurons from patients with BD, a condition characterized in a subgroup of patients by dysregulated WNT/?-catenin signaling. Overall, the findings show that distinct ASD, SZ and BD candidate genes converge on common molecular targets - an important consideration for developing novel therapeutics in genetically heterogeneous complex traits.\nLast Updated (by provider): Jun 04 2018\nContributors:  Deyou Zheng Herbert M Lachman Ryan Mokhtari Ping Wang Can Bayrak Erika Pedrosa Michael Kirschenbaum
-#> 77:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            To generate an unbiased view of changes to the retinal gene network in Neurog2 retinal mutants, we generated and compared the P2 transcriptomes from control, heterozygote and mutant mice.  A pair of P2 retinas from each biologic replicate were used to produce libraries for high throughput sequencing (n = 5 biologic replicates/genotype).  Reads were aligned with BWA and Bowtie programs to the mm10 genome.  Aligned reads were then analyzed for differentially expressed transcripts using the CuffDiff program in the Galaxy online bioinformatics package (www.usegalaxy.org).\nLast Updated (by provider): Jun 07 2018\nContributors:  Nadean L Brown Angelica M Kowalchuk
-#> 78:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               We used two siRNAs to knock down GNL3 in human neural progenitor cells which were derived from normal human induced pluripotent stem cells (ATCC, ACS-1011). GNL3 knockdown experiments were done in three biological replicates. Total RNA was extracted from GNL3 knockdown and control groups for RNA sequencing (Illumina Hiseq2000, paired-end 100 bp). Genes that affected by both siRNAs were considered differentially expressed genes between GNL3 knockdown and control groups (adjusted P value < 0.05). Using Gene Ontology and KEGG pathway analysis, we found that those differentially exrepssed genes were mainly related to immune response, response to cytokine, cell cycle, and p53 signaling pathway.\nAt time of import, last updated (by provider) on: May 21 2020\n\nContributors: ; [Chuan Jiao, Qingtuan Meng]
-#> 79:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 As part of collaboration between the X. William Yang Lab at UCLA and CHDI, a transcriptomic study of normal murine cortex was carried out. Cortex was dissected from 6-month-old wildtype (WT) control mice. Transcriptomic analysis (RNASeq) was performed.\nAt time of import, last updated (by provider) on: Oct 21 2022\n\nContributors: ; [Jeff Aaronson, X W Yang, Jim Rosinski]
-#> 80:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  We have identified and characterized an allelic series of spontaneous Rorb mutations in mice We perform RNASeq to identify gene expression changes associated with Rorb mutations in brain and spinal cord from all five mutant strains.  We also perform CNS region-specific RNASeq in the Rorbh5/h5 mutant.\nAt time of import, last updated (by provider) on: Jun 08 2023\n\nContributors: ; [Abigal D Tadenev, Robert W Burgess, George C Murray]
-#> 81:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Chaperonin 60 (Cpn60) is a prototypic molecular chaperone essential for cellular function due to its protein folding actions.  However, over the past decade it has been established that Cpn60 can be released by human cells and by certain bacteria to act as an extracellular signalling protein. Mycobacterium tuberculosis produces two Cpn60 proteins: Cpn60.1 and Cpn60.2. We recently generated a M. tuberculosis mutant with an inactivated cpn60.1 gene and demonstrated that granuloma formation was impaired after murine/guinea pig infection.  This finding suggested that Cpn60.1 may interact with the cellular organisation of the host response to M. tuberculosis bacilli.  In this study, we report that recombinant M. tuberculosis Cpn60.1 has both pro- and anti-inflammatory effects on human circulating monocytes. At high concentrations, recombinant Cpn60.1 induces the synthesis of TNF-?, IL6, and IL8, and promotes the phosphorylation of NF-?Bp65, p44/42MAPK and p38 MAPK. At lower concentrations M. tuberculosis Cpn60.1 inhibits lipopolysaccharide-induced release of TNF-?, and monocyte transcriptional activation program. Both effects are abrogated by proteolysis of Cpn60.1 and therefore cannot be attributed to contamination with lipopolysaccharide. Competition with LPS for binding to a common receptor, the release of IL-10 or down-regulation of TLR4 on the cell surface were excluded as explanations for the inhibitory activity of Cpn60.1. We therefore conclude that M. tuberculosis Cpn60.1 is an unusual protein with the ability to induce bipolar effects on human monocytes, which may help explain the pathology of granuloma formation in tuberculosis. We used microarrays to analyse the bipolar effectsof Cpn60.1  on human monocytes.\nLast Updated (by provider): Oct 29 2009\nContributors:  Anthony R Coates Simon J Waddell Brian Henderson Ana Cehovin
-#> 82:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        The circadian nature of mood and its dysfunction in affective disorders is well recognized, but the underlying molecular mechanisms are still unclear. We showed that the circadian nuclear receptor REV-ERBa, which is associated with bipolar disorder, impacts midbrain dopamine production and mood-related behavior in mice. Genetic deletion of the Rev-erba gene or pharmacological inhibition of REV-ERBa activity in the ventral midbrain induced mania-like behavior in association with a central hyperdopaminergic state. We used microarrays to identify differentially expressed genes in the ventral midbrains of wild-type (WT) and Rev-erba knock-out (RKO) mice.\nAt time of import, last updated (by provider) on: Mar 04 2019\n\nContributors: ; [Sooyoung Chung, Kyungjin Kim, Gi H Son]\nIncludes GDS5628 (Last updated by provider at import time: Aug 21 2015)\n Dataset description GDS5628: Analysis of ventral midbrain (VMB) from Rev-erb? knock-outs that were entrained under a 12hr light-dark photoperiod for >10 days, kept in darkness for 2 days, and sacrificed on the third day. REV-ERB? is associated with bipolar disorder. Results provide insight into the role of REV-ERB? in VMB.\n
-#> 83:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             The transition to motherhood involves CNS changes that modify sociability and affective state.  However, these changes also put females at risk for postpartum depression and psychosis, which impairs parenting abilities and adversely affects children.  Thus, changes in expression and interactions in a core subset of genes may be critical for emergence of a healthy maternal phenotype, but inappropriate changes of the same genes could put women at risk for postpartum disorders.  This study evaluated microarray gene expression changes in medial prefrontal cortex (mPFC), a region implicated in both maternal behavior and psychiatric disorders.  Postpartum mice were compared to virgin controls housed with females and isolated for identical durations.  Using the Modular Single-set Enrichment Test (MSET), we found that the genetic landscape of maternal mPFC bears statistical similarity to gene databases associated with schizophrenia (5 of 5 sets) and bipolar disorder (BPD, 3 of 3 sets).  In contrast to previous studies of maternal lateral septum and medial preoptic area, enrichment of autism and depression-linked genes was not significant (2 of 9 sets, 0 of 4 sets).  Among genes linked to multiple disorders were fatty acid binding protein 7 (Fabp7), glutamate metabotropic receptor 3 (Grm3), platelet derived growth factor, beta polypeptide (Pdgfrb), and nuclear receptor subfamily 1, group D, member 1 (Nr1d1).  RT-qPCR confirmed these gene changes as well as FMS-like tyrosine kinase 1 (Flt1) and proenkephalin (Penk).  Systems-level methods revealed involvement of developmental gene networks in establishing the maternal phenotype and indirectly suggested a role for numerous microRNAs and transcription factors in mediating expression changes.  Together, this study suggests that a subset of genes involved in shaping the healthy maternal brain may also be dysregulated in mental health disorders and put females at risk for postpartum psychosis with aspects of schizophrenia and BPD.\nLast Updated (by provider): Feb 21 2018\nContributors:  Terri M Driessen Changjiu Zhao Stephen C Gammie Brian E Eisinger
-#> 84:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             The retina, the accessible part of the central nervous system, has served as a model system to study the relationship between energy utilization and metabolite supply. When the metabolite supply cannot match the energy demand, retinal neurons are at risk of death. As the powerhouse of eukaryotic cells, mitochondria play a pivotal role in generating ATP, produce precursors for macromolecules, maintain the redox homeostasis, and function as waste management centers for various types of metabolic intermediates. Mitochondrial dysfunction has been implicated in the pathologies of a number of degenerative retinal diseases. It is well known that photoreceptors are particularly vulnerable to mutations affecting mitochondrial function due to their high energy demand and susceptibility to oxidative stress. However, it is unclear how defective mitochondria affect other retinal neurons. Nuclear respiratory factor 1 (Nrf1) is the major transcriptional regulator of mitochondrial biogenesis, and loss of Nrf1 leads to defective mitochondria biogenesis and eventually cell death. Here, we investigated how different retinal neurons respond to the loss of Nrf1. We provide in vivo evidence that the disruption of Nrf1-mediated mitochondrial biogenesis results in a slow, progressive degeneration of all retinal cell types examined, although they present different sensitivity to the deletion of Nrf1, which implicates differential energy demand and utilization, as well as tolerance to mitochondria defects in different neuronal cells. Furthermore, transcriptome analysis on rod-specific Nrf1 deletion uncovered a previously unknown role of Nrf1 in maintaining genome stability.\nAt time of import, last updated (by provider) on: Dec 17 2022\n\nContributors: ; [Chai-An Mao, Takae Kiyama]
-#> 85:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Mania is a serious neuropsychiatric condition associated with significant morbidity and mortality. Previous studies have suggested that environmental exposures can contribute to mania pathogenesis.  We measured dietary exposures in a cohort of individuals with mania and other psychiatric disorders as well as in control individual without a psychiatric disorder. We found that a history of eating nitrated dry cured meat, but not other meat or fish products, was strongly and independently associated with current mania (adjusted odds ratio 3.49, 95% confidence interval (CI) 2.24-5.45, p<8.97x 10-8). Lower odds of association were found between eating nitrated dry cured meat and other psychiatric disorders. We further found that the feeding of meat preparations with added nitrate to rats resulted in alterations in behavior and changes in intestinal microbiota. Rats fed diets with added nitrate also showed alterations of brain pathways dysregulated in mania. These findings may lead to new methods for preventing mania and for developing novel therapeutic interventions\nLast Updated (by provider): Aug 20 2018\nContributors:  Seva G Khambadkone C C Talbot Jr Robert H Yolken
-#> 86:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Impaired neuronal processes, including dopamine imbalance, are central to the pathogenesis of major psychosis, but the molecular origins are unclear. We report the first multi-omics study of neurons isolated from the prefrontal cortex of individuals with schizophrenia and bipolar disorder, including genome-wide neuronal DNA methylation using Illumina EPIC microarrays, transcriptomes and SNP genotypes (n=55 cases and 27 controls). Epigenetic, transcriptomic, and genetic-epigenetic interactions in disease converged on pathways of neurodevelopment, synaptic activity, and immune functions. Notably, we discovered prominent hypomethylation of an enhancer within the insulin-like growth factor 2 (IGF2) gene in neurons of major psychosis patients. Chromatin conformation analysis revealed that this enhancer targets the nearby tyrosine hydroxylase (TH) gene, which is responsible for dopamine synthesis. IGF2 enhancer hypomethylation was associated with increased TH protein levels in the human brain. The Igf2 enhancer was deleted in mice to explore the transcriptomic and proteomic consequences of this genomic locus in the frontal cortex and striatum. In mice, Igf2 enhancer deletion disrupted levels of TH protein and striatal dopamine, as well as induced transcriptional and proteomic abnormalities affecting development and synaptic function. Epigenetic control of the IGF2 enhancer may regulate dopamine levels and contribute to psychosis risk.\nAt time of import, last updated (by provider) on: May 15 2019\n\nContributors: ; [Shraddha S Pai, Viviane Labrie]
-#> 87:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   The goal of this project is to study transcriptome change by knocking down ZNF804A, a schizophrenia and bipolar disorder candidate gene, in early neurons derived from iPSCs.\nLast Updated (by provider): Sep 16 2016\nContributors:  Deyou Zheng Herbert M Lachman
-#> 88:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The molecular etiology of invididual differences in complex behavior traits and susceptibility to psychiatric illness remains incomplete. Using an unbiased genetic approach in a mouse model, Quantitative Trait Loci (QTL) influencing anxiety-like behaviors and beta-carboline-induced seizure vulnerability have been mapped to the distal portion of mouse chromosome 10 and an interval specific congenic strain (ISCS; A.B6chr10; 66 cM to telomere) was developed. This A.B6chr10 strain facilitated defining the behavioral influences of this region as well as gene expression profiling to identify candidate gene(s) underlying this QTL. By microarray studies, an unsuspected E3 Ubiquitin Ligase, Ring Finger 41 (Rnf41 / Neuregulin Receptor Degrading Protein1; Nrdp1) was differentially expressed in the region of interest, comparing the hippocampi of A/J vs A.B6chr10 mice as well as A/J vs B6 mice. By RT-PCR, Rnf41 expression levels were significantly increased 1.5 and 1.3-fold in the hippocampi of C57BL6/J and A.B6chr10 mice compared to A/J mice, respectively. In addition, protein levels of Rnf41 were increased in hippocampi of B6 mice compared to A/J mice across postnatal development with a 5.5-fold difference at P56. Among LxS recombinant inbred mice (N=33), Rnf41 hippocampal mRNA expression levels were significantly correlated with open field behavior (r= .454, p=.0073). Re-analyzing a microarray database of human post-mortem prefrontal cortex (Brodmann<e2><80><99>s Area 46/10), RNF41 mRNA expression levels were reduced significantly in patients with major depression and bipolar disorder compared to unaffected controls. Overall, Rnf41 is a pleiotropic candidate gene for anxiety-like behaviors, depression, and vulnerability to seizures. RNF41 and its binding partners provide novel etiological pathways for influencing behavior, highlighting a potential role for the ubiquitin proteasome system in psychiatric illness.\nLast Updated (by provider): Jan 15 2010\nContributors:  H K Gershenfeld Sanghyeon Kim K Choi R Reister A F Baykiz S Zhang
-#> 89:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The molecular etiology of invididual differences in complex behavior traits and susceptibility to psychiatric illness remains incomplete. Using an unbiased genetic approach in a mouse model, Quantitative Trait Loci (QTL) influencing anxiety-like behaviors and beta-carboline-induced seizure vulnerability have been mapped to the distal portion of mouse chromosome 10 and an interval specific congenic strain (ISCS; A.B6chr10; 66 cM to telomere) was developed. This A.B6chr10 strain facilitated defining the behavioral influences of this region as well as gene expression profiling to identify candidate gene(s) underlying this QTL. By microarray studies, an unsuspected E3 Ubiquitin Ligase, Ring Finger 41 (Rnf41 / Neuregulin Receptor Degrading Protein1; Nrdp1) was differentially expressed in the region of interest, comparing the hippocampi of A/J vs A.B6chr10 mice as well as A/J vs B6 mice. By RT-PCR, Rnf41 expression levels were significantly increased 1.5 and 1.3-fold in the hippocampi of C57BL6/J and A.B6chr10 mice compared to A/J mice, respectively. In addition, protein levels of Rnf41 were increased in hippocampi of B6 mice compared to A/J mice across postnatal development with a 5.5-fold difference at P56. Among LxS recombinant inbred mice (N=33), Rnf41 hippocampal mRNA expression levels were significantly correlated with open field behavior (r= .454, p=.0073). Re-analyzing a microarray database of human post-mortem prefrontal cortex (Brodmann<e2><80><99>s Area 46/10), RNF41 mRNA expression levels were reduced significantly in patients with major depression and bipolar disorder compared to unaffected controls. Overall, Rnf41 is a pleiotropic candidate gene for anxiety-like behaviors, depression, and vulnerability to seizures. RNF41 and its binding partners provide novel etiological pathways for influencing behavior, highlighting a potential role for the ubiquitin proteasome system in psychiatric illness.\nLast Updated (by provider): Jan 15 2010\nContributors:  H K Gershenfeld Sanghyeon Kim K Choi R Reister A F Baykiz S Zhang
-#> 90:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The molecular etiology of invididual differences in complex behavior traits and susceptibility to psychiatric illness remains incomplete. Using an unbiased genetic approach in a mouse model, Quantitative Trait Loci (QTL) influencing anxiety-like behaviors and beta-carboline-induced seizure vulnerability have been mapped to the distal portion of mouse chromosome 10 and an interval specific congenic strain (ISCS; A.B6chr10; 66 cM to telomere) was developed. This A.B6chr10 strain facilitated defining the behavioral influences of this region as well as gene expression profiling to identify candidate gene(s) underlying this QTL. By microarray studies, an unsuspected E3 Ubiquitin Ligase, Ring Finger 41 (Rnf41 / Neuregulin Receptor Degrading Protein1; Nrdp1) was differentially expressed in the region of interest, comparing the hippocampi of A/J vs A.B6chr10 mice as well as A/J vs B6 mice. By RT-PCR, Rnf41 expression levels were significantly increased 1.5 and 1.3-fold in the hippocampi of C57BL6/J and A.B6chr10 mice compared to A/J mice, respectively. In addition, protein levels of Rnf41 were increased in hippocampi of B6 mice compared to A/J mice across postnatal development with a 5.5-fold difference at P56. Among LxS recombinant inbred mice (N=33), Rnf41 hippocampal mRNA expression levels were significantly correlated with open field behavior (r= .454, p=.0073). Re-analyzing a microarray database of human post-mortem prefrontal cortex (Brodmann<e2><80><99>s Area 46/10), RNF41 mRNA expression levels were reduced significantly in patients with major depression and bipolar disorder compared to unaffected controls. Overall, Rnf41 is a pleiotropic candidate gene for anxiety-like behaviors, depression, and vulnerability to seizures. RNF41 and its binding partners provide novel etiological pathways for influencing behavior, highlighting a potential role for the ubiquitin proteasome system in psychiatric illness.\nLast Updated (by provider): Jan 15 2010\nContributors:  H K Gershenfeld Sanghyeon Kim K Choi R Reister A F Baykiz S Zhang
-#> 91:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Loss of the Atrx chromatin remodeling protein causes dysfunction and death of post-mitotic retinal interneurons in mice. Embryonic conditional deletion of Atrx from multipotent retinal progenitor cells results in the selective loss of the retinal inhibitory interneurons, namely amacrine and horizontal cells. The cell death occurs postnatally after the development of these cell types, peaking at postntal day 17 in the mouse retina.  Identification of molecular factors and pathways that mediate the health and survival of these neurons may suggest novel therapeutic strategies for neuroprotection in ATR-X syndrome and other neurodegenerative diseases. We performed gene expression profiling of wildtype and Atrx conditional knockout mouse retina tissues to identify putative targets of Atrx and molecular pathways that underlie the neurodegenerative phenotype.\nLast Updated (by provider): Feb 21 2018\nContributors:  Pamela S Lagali David J Picketts
-#> 92:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   Diseases and damage to the retina lead to losses in retinal neurons and eventual visual impairment. Although the mammalian retina has no inherent regenerative capabilities, fish have robust regeneration from M<c3><bc>ller glia (MG). Recently, we have shown that driving expression of Ascl1 in adult mouse MG stimulates neurogenesis similar to fish regeneration. The regeneration observed in the mouse is limited in the variety of neurons that can be derived from MG; Ascl1-expressing MG primarily generate bipolar cells. To better understand the limits of MG-based regeneration in mouse retinas, we used ATAC- and RNA-seq to compare newborn progenitors with MG. Our analysis demonstrated striking similarities between MG and progenitors, with losses in regulatory motifs for neurogenesis genes. Young MG were found to have intermediate expression profiles and accessible DNA, which is mirrored in the ability of Ascl1 to direct bipolar neurogenesis in young MG. When comparing what makes bipolar and photoreceptor cells distinct from glial cells, we find that bipolar-specific accessible regions are more frequently linked to bHLH motifs and Ascl1 binding, indicating that Ascl1 preferentially binds to bipolar regions. Overall, our analysis indicates a loss of neurogenic gene expression and motif accessibility during glial maturation that may prevent efficient reprogramming.\nAt time of import, last updated (by provider) on: Aug 19 2020\n\nContributors: ; [Wohl Stefanie G, Wilken Matthew S, Thomas Reh, Chipman Laura, VandenBosch Leah S, Kox Kristen]
-#> 93:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Neuronal plasticity of the inner retina has been observed in response to photoreceptor degeneration. Typically, this phenomenon has been considered maladaptive and may preclude vision restoration in the blind. However, several recent studies utilizing triggered photoreceptor ablation have shown adaptive responses in bipolar cell dendrites expected to support normal vision. Whether such homeostatic plasticity occurs during progressive photoreceptor degenerative disease to help maintain normal visual behavior is unknown. We addressed these issues in an established mouse model of Retinitis Pigmentosa caused by the P23H mutation in rhodopsin. We show robust modulation of the retinal transcriptomic network reminiscent of the neurodevelopmental state as well as potentiation of rod <e2><80><93> rod bipolar cell signaling following rod photoreceptor degeneration. Additionally, we found highly sensitive night vision in P23H mice even when more than half of the rod photoreceptors were lost. The results implicate retinal adaptation leading to persistent visual function during photoreceptor degenerative disease.\nAt time of import, last updated (by provider) on: Oct 19 2020\n\nContributors: ; [Frans Vinberg, Henri Leinonen]
-#> 94:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Neuronal plasticity of the inner retina has been observed in response to photoreceptor degeneration. Typically, this phenomenon has been considered maladaptive and may preclude vision restoration in the blind. However, several recent studies utilizing triggered photoreceptor ablation have shown adaptive responses in bipolar cell dendrites expected to support normal vision. Whether such homeostatic plasticity occurs during progressive photoreceptor degenerative disease to help maintain normal visual behavior is unknown. We addressed these issues in an established mouse model of Retinitis Pigmentosa caused by the P23H mutation in rhodopsin. We show robust modulation of the retinal transcriptomic network reminiscent of the neurodevelopmental state as well as potentiation of rod <e2><80><93> rod bipolar cell signaling following rod photoreceptor degeneration. Additionally, we found highly sensitive night vision in P23H mice even when more than half of the rod photoreceptors were lost. The results implicate retinal adaptation leading to persistent visual function during photoreceptor degenerative disease.\nAt time of import, last updated (by provider) on: Jun 16 2020\n\nContributors: ; [Frans Vinberg, Henri Leinonen]
-#> 95:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Lithium is a first-line treatment for bipolar disorder, where it acts as a mood-stabilizing agent. Although its precise mechanism remains unclear, neuroimaging studies have shown that lithium accumulates in the hippocampus and that chronic use amongst bipolar disorder patients is associated with larger hippocampal volumes. Here, we tested the chronic effects of low (0.75 mM) and high (2.25 mM) doses of lithium on human hippocampal progenitor cells and used immunocytochemistry to investigate the effects of lithium on cell parameters implicated in neurogenesis. Corresponding RNA-sequencing and gene-set enrichment analyses were used to evaluate whether genes affected by lithium in our model overlap with those regulating the volume of specific layers of the dentate gyrus. We observed that high-dose lithium treatment in human hippocampal progenitors increased the generation of neuroblasts (P <e2><89><a4> 0.01), neurons (P <e2><89><a4> 0.01), and glia (P <e2><89><a4> 0.001), alongside the expression of genes which regulate the volume of the molecular layer of the dentate gyrus. This study provides empirical support that adult hippocampal neurogenesis and gliogenesis are mechanisms that could contribute to the effects of lithium on human hippocampal volume.\nAt time of import, last updated (by provider) on: Oct 01 2021\n\nContributors: ; [Rodrigo<c2><a0>R R Duarte, Alish B Palmos, Douglas F Nixon, Timothy R Powell, Demelza M Smeeth, Erin C Hedges, Sandrine Thuret]
-#> 96:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   Quetiapine is an atypical neuroleptic with a pharmacological profile distinct from classic neuroleptics. It is currently approved for treating patients with schizophrenia, major depression and bipolar I disorder. However, its cellular effects remain elusive. We used microarrays to characterize RNA transcript levels in the brains of mice chronically treated with quetiapine, the neuroleptic haloperidol, or vehicle. We further characterized particular RNA transcripts in cortical cell cultures.\nLast Updated (by provider): Apr 16 2013\nContributors:  Jonathan Pevsner Mari Kondo
-#> 97:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The expression level for 15 887 transcripts in lymphoblastoid cell lines from 19 monozygotic twin pairs (10 male, 9 female) were analysed for the effects of genotype and sex. On an average, the effect of twin pairs explained 31% of the variance in normalized gene expression levels, consistent with previous broad sense heritability estimates. The effect of sex on gene expression levels was most noticeable on the X chromosome, which contained 15 of the 20 significantly differentially expressed genes. A high concordance was observed between the sex difference test statistics and surveys of genes escaping X chromosome inactivation. Notably, several autosomal genes showed significant differences in gene expression between the sexes despite much of the cellular environment differences being effectively removed in the cell lines. A publicly available gene expression data set from the CEPH families was used to validate the results. The heritability of gene expression levels as estimated from the two data sets showed a highly significant positive correlation, particularly when both estimates were close to one and thus had the smallest standard error. There was a large concordance between the genes significantly differentially expressed between the sexes in the two data sets. Analysis of the variability of probe binding intensities within a probe set indicated that results are robust to the possible presence of polymorphisms in the target sequences.\nNote: 1 samples from this series, which appear in other Expression Experiments in Gemma, were not imported from the GEO source. The following samples were removed: GSM162903 on GPL570\nLast Updated (by provider): Jan 18 2018\nContributors:  Sam F Berkovic Bryan Mowry Nicholas A Matigian Peter M Visscher Allan F McRae Nicholas K Hayward Nicholas G Martin Lata Vadlamudi John C Mulley
-#>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     experiment.description
+#>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              experiment.description
+#>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              <char>
+#>  1:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Background: Psychosis is a defining feature of schizophrenia and highly prevalent in bipolar disorder. Notably, individuals suffering with these illnesses also have major disruptions in sleep and circadian rhythms, and disturbances to sleep and circadian rhythms can precipitate or exacerbate psychotic symptoms. Psychosis is associated with the striatum, though no study to date has directly measured molecular rhythms and determined how they are altered in the striatum of subjects with psychosis. Methods: Here, we perform RNA-sequencing and both differential expression and rhythmicity analyses to investigate diurnal alterations in gene expression in human postmortem striatal subregions (NAc, caudate, and putamen) in subjects with psychosis relative to unaffected comparison subjects. Results: Across regions, we find differential expression of immune-related transcripts and a substantial loss of rhythmicity in core circadian clock genes in subjects with psychosis. In the nucleus accumbens (NAc), mitochondrial-related transcripts have decreased expression in psychosis subjects, but only in those who died at night. Additionally, we find a loss of rhythmicity in small nucleolar RNAs and a gain of rhythmicity in glutamatergic signaling in the NAc of psychosis subjects. Between region comparisons indicate that rhythmicity in the caudate and putamen is far more similar in subjects with psychosis than in matched comparison subjects. Conclusions: Together, these findings reveal differential and rhythmic gene expression differences across the striatum that may contribute to striatal dysfunction and psychosis in psychotic disorders.\nAt time of import, last updated (by provider) on: Aug 31 2022\n\nContributors: ; [George Tseng, Colleen McClung, Wei Zong, Kyle Ketchesin]
+#>  2:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Major psychiatric disorders such as schizophrenia (SCZ) and bipolar disorder (BPD) are complex genetic mental illnesses. Their non-Mendelian features such as monozygotic twins discordant for SCZ or BPD are likely complicated by environmental modifiers of genetic effects. 5-hydroxymethylcytosine (5hmC) is an important epigenetic marker in gene regulation and whether its links with genetic variants contribute to the non-Mendelian features remain largely unexplored. Here, we performed hydroxymethylome and genome analyses of blood DNA from psychiatric disorder-discordant monozygotic twins to study how allele-specific hydroxymethylation (AShM) mediates phenotypic variations. We identified thousands of genetic variants with AShM imbalances who exhibit phenotypic variation-associated AShM transition at regulatory loci. These AShMs have plausible causal associations with psychiatric disorders through effects on interactions between transcription factors, DNA methylations, or other epigenomic markers and then contribute to dysregulated gene expression, which eventually increases disease susceptibility. We then validated that competitive binding of POU3F2 on the alternative allele of psyAShM site rs4558409 (G/T) in PLLP can enhance the PLLP expression, while hydroxymethylated alternative allele alleviating the transcription factor binding activity at rs4558409 site might be associated with downregulated PLLP expression observed in BPD or SCZ. Moreover, disruption of rs4558409 induces gain of PLLP function and promotes neural development and vesicle trafficking. Our study provides a powerful strategy for prioritizing regulatory risk variants and contributes to our understanding of the interplay between genetic and epigenetic factors in mediating complex disease susceptibility.\nAt time of import, last updated (by provider) on: Oct 31 2023\n\nContributors: ; [Zhanwang Huang, Junping Ye]
+#>  3:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                
+#>  4:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    This experiment was created by Gemma splitting another: \nExpressionExperiment Id=20933 Name=TCF7L2 lncRNA: A Link between Bipolar Disorder and Body Mass Index through Glucocorticoid Signaling [RNA-Seq] (GSE179921) Bipolar disorder (BD) and obesity are highly comorbid. We previously performed a genome-wide association study (GWAS) for BD risk accounting for the  effect of body mass index (BMI) which identified a genome-wide significant single-nucleotide polymorphism (SNP) in the gene encoding the transcription factor 7 like 2 (TCF7L2). However, the molecular function of TCF7L2 in the central nervous system (CNS) and its possible role in BD and BMI interaction remained unclear. In the present study, we demonstrated by studying human induced pluripotent stem cell (hiPSC)-derived astrocytes, cells which highly express TCF7L2 in the CNS, that the BD-BMI GWAS risk SNP is associated with glucocorticoid-dependent repression of the expression of a previously uncharacterized TCF7L2 transcript variant. That transcript is a long non-coding RNA (lncRNA-TCF7L2) that is highly expressed in the CNS but not in peripheral tissues such as the liver and pancreas which are involved in metabolism.  In astrocytes, knock-down of the lncRNA-TCF7L2 resulted in decreased expression of the parent gene, TCF7L2, as well as alterations in the expression of a series of genes involved in insulin signaling and diabetes.  We also studied the function of TCF7L2 in hiPSC-derived astrocytes by integrating RNA sequencing data after TCF7L2 knock-down with TCF7L2 chromatin-immunoprecipitation sequencing (ChIP-seq) data. Those studies showed that TCF7L2 directly regulated a series of BD-risk genes. In summary, these results support the existence of a CNS-based mechanism underlying BD-BMI genetic risk, a mechanism based on a glucocorticoid-dependent expression quantitative trait locus that regulates the expression of a novel TCF7L2 non-coding transcript.\nAt time of import, last updated (by provider) on: Sep 20 2021\n\nContributors: ; [Mark A Frye, Thanh L Nguyen, Tamas Ordog, Brandon Coombes, Richard M Weinshilboum, Huaizhi Huang, Zhenqing Ye, Liewei Wang, Huanyao Gao, Daniel Kim, Jeong-Heon Lee, Brenna Sharp, Duan Liu, Joanna Biernacka]
+#>  5:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           Hippocampus of schizophrenic, bipolar, and control subjects. Analyzed from CEL files.
+#>  6:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Total RNA sequecing for human induced pluripotent derived cerebral organoids from healthy controls and Bipolar disorder\nAt time of import, last updated (by provider) on: Apr 01 2020\n\nContributors: ; [Annie Kathuria, Rakesh Karmacharya]
+#>  7:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Analysis of gene-expression changes in treatment responders vs non-responders to two different treatments among subjectrs participating in LiTMUS. Results provide information on pathways that may be involved in the clinical response to Lithium in patients with bipolar disorder.\nLast Updated (by provider): Apr 01 2013\nContributors:  Robert Beech
+#>  8:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               Valproate(VPA) has been used in the treatment of bipolar disorder since the 1990s.  However, the therapeutic targetsof VPA have remained elusive. Here we  used RNA sequencing  in human  iPSCs derived from  bipolar  patients  to further identify important molecular targets. Human iPSCs were homogenized and total RNA was isolated using the RNeasy Plus Micro Kit (Qiagen, Hilden, Germany). RNA quantity and quality were assessed using fluorometry (Qubit RNA Broad Range Assay Kit and Fluorometer; Invitrogen, Carlsbad, CA) and chromatography (Bioanalyzer and RNA 6000 Nano Kit; Agilent, Santa Clara, CA), respectively. Libraries were prepared using TruSeq Stranded mRNA (PolyA+) kit (Illumina, San Diego, CA) and sequenced by Illumina NextSeq 500. The read length was 75bp with 30-40M reads per sample. FastQC (v0.11.3) was performed to assess data quality. TopHat2 (v2.0.9) aligned the reads to the mouse reference genome (Mus musculus UCSC mm10) and to the Ensembl human reference genome (GRCh38.p13) using default parameters. Alignments were then converted to expression count data using HTseq (v0.6.1) with default union mode.\nAt time of import, last updated (by provider) on: Dec 31 2020\n\nContributors: ; [George Tseng, Colleen McClung, Wei Zong, Ryan Logan]
+#>  9:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Human neuronal-like cells (NT2-N) were treated with either lamotrigine (50 µM), lithium (2.5 mM), quetiapine (50 µM), valproate (0.5 mM) or vehicle control for 24 hours. Genome wide mRNA expression was quantified by RNA-sequencing. Results offer insights on the mechanism(s) of action of bipolar disorder drugs at the transcriptional level.\nAt time of import, last updated (by provider) on: Apr 27 2022\n\nContributors: ; [Srisaiyini Kidnapillai, Chiara Bortolasci, Laura Gray, Trang Truong, Bruna Panizzutti, Mark Richardson, Craig Smith, Olivia Dean, Zoe Liu, Briana Spolding, Michael Berk, Jee H Kim, Ken Walder]
+#> 10:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                We have previosuly shown that our Polg(D181A) show spontaneous depressive episodes as a result of mtDNA mutations, but we do not know the cellular mechanisms that link mtDNA mutations to behavioural changes. We hypothesized that mtDNA mutation-induced mitochondrial dysfunction in PVT causes a dysregulation of epigenetics, causing a transcriptional response which ffects neuronal function and ultimately causes the depressive phenotype. We assessed this using a combination of RNA-seq, H3K27Ac ChIP-seq, and ATAC-seq and compared our H3K27Ac results to other brain regions.\nAt time of import, last updated (by provider) on: Jun 01 2021\n\nContributors: ; [Tadafumi Kato, Emilie K Bagge]
+#> 11:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    MicroRNAs have been implicated in the pathology not only of cancer, but also of psychiatric diseases, such as bipolar disorder and schizophrenia. As several psychiatric disorders share the same risk genes, we hypothesized that this microRNA could also be associated with attention-deficit/hyperactivity disorder (ADHD) and that this association to psychiatric disorders might be due to the variable number of tandem repeats (VNTR) polymorphism within the internal miR-137 (Imir137) promoter (PMID 18316599; PMID 25154622). To further understand the role of the microRNA 137 in the brain a knock-down of miR-137 expression in SH-SY5Y neuroblastoma cells was performed followed by expression analysis using a microarray.\nAt time of import, last updated (by provider) on: Aug 08 2019\n\nContributors: ; [Lena Weißflog, Andreas Reif, Stefanie Berger, Heike Weber, Claus J Scholz]
+#> 12:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Bipolar disorder is a highly heritable mental illness, but the relevant genetic variants and molecular mechanisms are largely unknown. Recent GWAS’s have identified an intergenic region associated with both enhanced cognitive performance and bipolar disorder. This region contains dozens of putative fetal brain-specific enhancers and is located ~0.7 Mb upstream of the neuronal transcription factor POU3F2. We identified a candidate causal variant, rs77910749, that falls within a highly conserved putative enhancer, LC1. This human-specific variant is a single-base deletion in a PAX6 binding site and is predicted to be functional. We hypothesized that rs77910749 alters LC1 activity and hence POU3F2 expression during neurodevelopment. Indeed, transgenic reporter mice demonstrated LC1 activity in the developing cerebral cortex and amygdala. Furthermore, ex vivo reporter assays in embryonic mouse brain and human iPSC-derived cerebral organoids revealed increased enhancer activity conferred by the variant. To probe the in vivo function of LC1, we deleted the orthologous mouse region, which resulted in amygdala-specific changes in Pou3f2 expression. Lastly, ‘humanized’ rs77910749 knock-in mice displayed behavioral defects in sensory gating, an amygdala-dependent endophenotype seen in patients with bipolar disorder. Our study suggests a molecular mechanism underlying the long-speculated link between higher cognition and neuropsychiatric disease.\nAt time of import, last updated (by provider) on: Jul 28 2021\n\nContributors: ; [Susan Q Shen, Joseph C Corbo]
+#> 13:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Lithium is the gold standard treatment for bipolar disorder. The goal of this study was to identify gene expression networks associated with lithium response. RNAseq data was obtained from IPSC derived neurons from lithium responders and non-responders. Focal adhesion was the network most associated with response.\nAt time of import, last updated (by provider) on: Jun 09 2022\n\nContributors: ; [Vipavee Niemsiri, Fred Gage, John Kelsoe]
+#> 14:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          The goals of this study are to examine responses to inflammation in astrocytes from  induced pluripotent stem cells derived from healthy controls and bipolar disorder patients. We examine the transcriptomic inflmmatory signature of generated astrocytes following Il1Beta exposure in BD vs. control Results: BD-patient astrocytes show a unique inflammatory response with differentially regulated genes.\nAt time of import, last updated (by provider) on: Mar 19 2021\n\nContributors: ; [Maxim N Shokhirev, Fred Gage, Krishna Vadodaria, Carol Marchetto]
+#> 15:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Bipolar disorder is a severe and heritable psychiatric disorder and affects up to 1% of the population worldwide. Lithium is recommended as first-line treatment for the maintenance treatment of bipolar-affective disorder in current guidelines, its molecular modes of action are however poorly understood. Cell models derived from bipolar patients could prove useful to gain more insight in the molecular mechanisms of bipolar disorder and the common pharmacological treatments. As primary neuronal cell lines cannot be easily derived from patients, peripheral cell models should be evaluated in their usefulness to study pathomechanisms and the mode of action of medication as well as in regard to develop biomarkers for diagnosis and treatment response.\nAt time of import, last updated (by provider) on: Mar 25 2019\n\nContributors: ; [Sarah Kittel-Schneider, Max Hilscher, Andreas Reif, Claus J Scholz]
+#> 16:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Gene expression of samples from the postmortem hippocampus of older bipolar disorder subjects and controls. Gene expression data was generated using the SurePrint G3 Human Gene Expression v3 microarray. Rank feature selection was performed to identify a subset of features that can optimally differentiate BD and controls.\nAt time of import, last updated (by provider) on: Feb 19 2023\n\nContributors: ; [Carlos A Pasqualucci, Claudia K Suemoto, Ricardo Nitrini, Fernanda B Bertonha, Paula V Nunes, Katia C De Oliveira, Carlos M Filho, Helena K Kim, Helena Brentani, Lea T Grinberg, Beny Lafer, André Barbosa, Camila Nascimento, Renata P Leite, Wilson Jacob-Filho]
+#> 17:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   Analysis of gene-expression changes in depressed subjects with bipolar disorder compared to healthy controls. Results provide information on pathways that may be involved in the pathogenesis of bipolar depression.\nLast Updated (by provider): Aug 27 2010\nContributors:  Robert D Beech
+#> 18:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          78 samples of individuals from three different diagnostic groups: bipolar, schizophrenia and controls. Samples taken from the DLPFC Broadmann area 46.
+#> 19:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              RNA-seq profiling was conducted on clinically-annotated human post-mortem brain tissues\nLast Updated (by provider): Jun 26 2018\nContributors:  Ryne C Ramaker Kevin M Bowling Sara J Cooper Brittany N Lasseigne Richard M Myers
+#> 20:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 47 samples of individuals from 4 different diagnostic groups: bipolar, schizophrenia, depression and controls. Samples taken from the DLPFC Broadmann area 8/9.
+#> 21:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        BACKGROUND: Bipolar disorder (BD) is a highly heritable mood disorder with complex genetic architecture and poorly understood etiology. Previous transcriptomic BD studies have had inconsistent findings due to issues such as small sample sizes and difficulty in adequately accounting for confounders like medication use.  METHODS: We performed a differential expression analysis in a well-characterized BD case-control sample (Nsubjects = 480) by RNA sequencing of whole blood. We further performed co-expression network analysis, functional enrichment, and cell type decomposition, and integrated differentially expressed genes with genetic risk.  RESULTS: While we observed widespread differential gene expression patterns between affected and unaffected individuals, these effects were largely linked to lithium treatment at the time of blood draw (FDR < 0.05, Ngenes = 976) rather than BD diagnosis itself (FDR < 0.05, Ngenes = 6). These lithium-associated genes were enriched for cell signaling and immune response functional annotations, among others, and were associated with neutrophil cell-type proportions, which were elevated in lithium users. Neither genes with altered expression in cases nor in lithium users were enriched for BD, schizophrenia, and depression genetic risk based on information from genome-wide association studies, nor was gene expression associated with polygenic risk scores for BD.  CONCLUSIONS: These findings suggest that BD is associated with minimal changes in whole blood gene expression independent of medication use but emphasize the importance of accounting for medication use and cell type heterogeneity in psychiatric transcriptomic studies. The results of this study add to mounting evidence of lithium's cell signaling and immune-related mechanisms.\nAt time of import, last updated (by provider) on: Oct 24 2019\n\nContributors: ; [Catharine E Krebs, Roel A Ophoff, Loes M Loohuis]
+#> 22:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       We used microarrays to identify the differently expressed genes in disease model for bipolar disorder and schizophrenia.\nAt time of import, last updated (by provider) on: Feb 01 2019\n\nContributors: ; [Takaya Ishii, Hideyuki Okano]
+#> 23:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           Schizophrenia is a complex psychiatric disorder encompassing a range of symptoms and  etiology dependent upon the interaction of genetic and environmental factors.  Several risk genes, such as DISC1, have been associated with schizophrenia as well as bipolar disorder (BPD) and major depressive disorder (MDD), consistent with the hypothesis that a shared genetic architecture could contribute to divergent clinical syndromes.  The present study compared gene expression profiles across three brain regions in post-mortem tissue from matched subjects with schizophrenia, BPD or MDD and unaffected controls.  Post-mortem brain tissue was collected from control subjects and well-matched subjects with schizophrenia, BPD, and MDD (n=19 from each group).  RNA was isolated from hippocampus, Brodmann Area 46, and associative striatum and hybridized to U133_Plus2 Affymetrix chips.  Data were normalized by RMA, subjected to pairwise comparison followed by Benjamini and Hochberg False Discovery Rate correction (FDR).  Samples derived from patients with schizophrenia exhibited  many more changes in gene expression across all brain regions than observed in BPD or MDD.  Several genes showed changes in both schizophrenia and BPD, though the magnitude of change was usually larger in schizophrenia.  Several genes that have variants associated with schizophrenia were found to have altered expression in multiple regions of brains from subjects with schizophrenia.  Continued evaluation of circuit-level alterations in gene expression and gene-network relationships may further our understanding of how genetic variants may be influencing biological processes to contribute to psychiatric disease.\nLast Updated (by provider): May 19 2014\nContributors:  Thomas A Lanz
+#> 24:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         Bipolar affective disorder is a severe psychiatric disorder with a strong genetic component but unknown pathophysiology. We used microarray technology (Affymetrix HG-U133A GeneChips) to determine the expression of approximately 22 000 mRNA transcripts in post-mortem brain tissue (orbitofrontal cortex) from patients with bipolar disorder and matched healthy controls. Orbitofrontal cortex tissue from a cohort of 30 subjects was investigated and the final analysis included 10 bipolar and 11 control subjects. Differences between disease and control groups were identified using a rigorous statistical analysis with correction for confounding variables and multiple testing.\nNote: [] samples from this series, which appear in other Expression Experiments in Gemma, were not imported from the GEO source. The following samples were removed: \nLast Updated (by provider): Jul 27 2006\nContributors:  Sabine Bahn Margaret M Ryan Matthew T Wayland Maree J Webster Stephen J Huffaker Helen E Lockstone\nIncludes GDS2191.\n Update date: Aug 28 2006.\n Dataset description GDS2191: Analysis of postmortem orbitofrontal cortex from 10 adults with bipolar disorder. Results provide insight into the pathophysiology of the disease.
+#> 25:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         98 samples of individuals from three different diagnostic groups: bipolar, schizophrenia, and controls. Samples taken from the DLPFC Broadmann area 46.
+#> 26:  Most neuroscientists would agree that psychiatric illness is unlikely to arise from pathological changes that occur uniformly across all cells in a given brain region. Despite this fact, the majority of transcriptomic analyses of the human brain to date are conducted using macro-dissected tissue due to the difficulty of conducting single-cell level analyses on donated post-mortem brains. To address this issue statistically, we compiled a database of several thousand transcripts that were specifically-enriched in one of 10 primary brain cell types identified in published single cell type transcriptomic experiments. Using this database, we predicted the relative cell type composition for 157 human dorsolateral prefrontal cortex samples using Affymetrix microarray data collected by the Pritzker Neuropsychiatric Consortium, as well as for 841 samples spanning 160 brain regions included in an Agilent microarray dataset collected by the Allen Brain Atlas. These predictions were generated by averaging normalized expression levels across the transcripts specific to each primary cell type to create a “cell type index”. Using this method, we determined that the expression of cell type specific transcripts identified by different experiments, methodologies, and species clustered into three main cell type groups: neurons, oligodendrocytes, and astrocytes/support cells. Overall, the principal components of variation in the data were largely explained by the neuron to glia ratio of the samples. When comparing across brain regions, we were able to easily capture canonical cell type signatures – increased endothelial cells and vasculature in the choroid plexus, oligodendrocytes in the corpus callosum, astrocytes in the central glial substance, neurons and immature cells in the dentate gyrus, and oligodendrocytes and interneurons in the globus pallidus. The relative balance of these cell types was influenced by a variety of demographic, pre- and post-mortem variables. Age and prolonged anaerobic conditions around the time of death were associated with decreased neuronal content and increased astrocytic and endothelial content in the tissue, replicating the known higher vulnerability of neurons to aging and adverse conditions, and illustrating the proliferation of vasculature in a hypoxic environment. We also found that the red blood cell content was reduced in individuals who died in a manner that involved systemic blood loss. Finally, statistically accounting for cell type improved both the sensitivity and interpretability of diagnosis effects within the data. We were able to observe a decrease in astrocytic content in subjects with Major Depressive Disorder, mirroring what had been previously observed morphometrically. By including a set of “cell type indices” in a larger model examining the relationship between gene expression and neuropsychiatric illness, we were able to successfully detect almost twice as many genes with previously identified relationships to bipolar disorder and schizophrenia than using traditional analysis methods.\nAt time of import, last updated (by provider) on: \n\nContributors: ; [Jun Z Li, Cortney A Turner, Megan H Hagenauer, Stanley J Watson, David M Walsh, Alan F Schatzberg, Huda Akil, Richard M Myers, William E Bunney, Jack D Barchas]
+#> 27:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  To identify genes dysregulated in bipolar disorder (BD1) we carried out global gene expression profiling using whole-genome microarrays. To minimize genetic variation in gene expression levels between cases and controls we compared expression profiles in lymphoblastoid cell lines from monozygotic twin pairs discordant for the disease. We identified 82 genes that were differentially expressed by ? 1.3-fold in 3 BD1 cases compared to their co-twins, and which were statistically (p ? 0.05) differentially expressed between the groups of BD1 cases and controls. Using qRT-PCR we confirmed the differential expression of some of these genes, including: KCNK1, MAL, PFN2, TCF7, PGK1, and PI4KCB, in at least 2 of the twin pairs. In contrast to the findings of a previous study by Kakiuchi and colleagues with similar discordant BD1 twin design1 our data do not support the dysregulation of XBP1 and HSPA5. From pathway and gene ontology analysis we identified up-regulation of the WNT signalling pathway and the biological process of apoptosis. The differentially regulated genes and pathways identified in this study may provide insights into the biology of BD1.\nLast Updated (by provider): Jun 20 2007\nContributors:  Louisa Windus Nicholas Matigian Bryan Mowry Cheryl Filippich John McGrath Heather Smith Nicholas Hayward Christos Pantelis
+#> 28:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Bipolar Disorder (BD) is a complex neuropsychiatric disorder that is characterized by intermittent episodes of mania and depression and, without treatment, 15% of patients commit suicide1. Hence, among all diseases, BD has been ranked by the WHO as a top disorder of morbidity and lost productivity2. Previous neuropathological studies have revealed a series of alterations in the brains of BD patients or animal models3, such as reduced glial cell number in the patient prefrontal cortex4, up-regulated activities of the PKA/PKC pathways5-7, and changes in dopamine/5-HT/glutamate neurotransmission systems8-11. However, the roles and causation of these changes in BD are too complex to exactly determine the pathology of the disease; none of the current BD animal models can recapitulate both the manic and depressive phenotypes or spontaneous cycling of BD simultaneously12,13. Furthermore, while some patients show remarkable improvement with lithium treatment, for yet unknown reasons, other patients are refractory to lithium treatment. Therefore, developing an accurate and powerful biological model has been a challenge for research into BD. The development of induced pluripotent stem cell (iPSC) technology has provided such a new approach. Here, we developed a human BD iPSC model and investigated the cellular phenotypes of hippocampal dentate gyrus neurons derived from the patient iPSCs. Using patch clamp recording, somatic Ca2+ imaging and RNA-seq techniques, we found that the neurons derived from BD patients exhibited hyperactive action potential (AP) firing, up-regulated expression of PKA/PKC/AP and mitochondria-related genes. Moreover, lithium selectively reversed these alterations in the neurons of patients who responded to lithium treatment. Therefore, hyper-excitability is one endophenotype of BD that is probably achieved through enhancement in the PKA/PKC and Na+ channel signaling systems, and our BD iPSC model can be used to develop new therapies and drugs aimed at clinical treatment of this disease.\nLast Updated (by provider): Jun 11 2018\nContributors:  Son Pham Jun Yao Fred H Gage
+#> 29:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Gene expression profiles of bipolar disorder (BD) patients were assessed during both a manic and a euthymic phase and compared both intra-individually, and with the gene expression profiles of controls.\nLast Updated (by provider): Sep 05 2014\nContributors:  Christian C Witt Benedikt Brors Dilafruz Juraeva Jens Treutlein Carsten Sticht Stephanie H Witt Jana Strohmaier Helene Dukal Josef Frank Franziska Degenhardt Markus M Nöthen Sven Cichon Maren Lang Marcella Rietschel Sandra Meier Manuel Mattheisen
+#> 30:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Background: \tSchizophrenia (SCZ) and bipolar disorder (BD) are highly heritable psychiatric disorders. Associated genetic and gene expression changes have been identified, but many have not been replicated and have unknown functions. We identified groups of genes whose expressions varied together, that is co-expression modules, then tested them for association with SCZ. Using weighted gene co-expression network analysis, we show that two modules were differentially expressed in patients versus controls. One, upregulated in cerebral cortex, was enriched with neuron differentiation and neuron development genes, as well as disease genome-wide association study genetic signals; the second, altered in cerebral cortex and cerebellum, was enriched with genes involved in neuron protection functions. The findings were preserved in five expression data sets, including sets from three brain regions, from a different microarray platform, and from BD patients. From those observations, we propose neuron differentiation and development pathways may be involved in etiologies of both SCZ and BD, and neuron protection function participates in pathological process of the diseases.\nLast Updated (by provider):  Jul 26, 2018\nContributors:  Chao Chen Chunyu Liu Lijun Cheng
+#> 31:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Schizophrenia (SZ) and bipolar disorder (BD) are severe psychiatric conditions, with a lifetime prevalence of about 1%. Both disorders have a neurodevelopment component, with onset of symptoms occurring most frequently during late adolescence or early adulthood. Genetic findings indicate the existence of an overlap in genetic susceptibility across the disorders. These gene expression profiles were used to identify the molecular mechanisms that differentiate SZ and BP from healthy controls but also that distinguish both from healthy individuals. They were also used to expand an analysis from an experiment that searched molecular alterations in human induced pluripotent stem cells derived from fibroblasts from control subject and individual with schizophrenia and further differentiated to neuron to identify genes relevant for the development of schizophrenia (GSE62105).\nLast Updated (by provider): Oct 14 2014\nContributors:  Leandro Lima Mariana Maschietto Dirce M Carraro Carlos A Filho Angelica de Baumont Luiz A Barreta Paulo Belmonte-de-Abreu Ana C Tahira Eloisa H Olivieri Joana A Palha Helena Brentani Daniel Mariani Alex Fiorini
+#> 32:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               50 samples of individuals from 4 different diagnostic groups: bipolar, schizophrenia, depression and controls. Samples taken from the Cerebellum.
+#> 33:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       We performed the oligonucleotide microarray analysis in bipolar disorder, major depression, schizophrenia, and control subjects using postmortem prefrontal cortices provided by the Stanley Foundation Brain Collection. By comparing the gene expression profiles of similar but distinctive mental disorders, we explored the uniqueness of bipolar disorder and its similarity to other mental disorders at the molecular level. Notably, most of the altered gene expressions in each disease were not shared by one another, suggesting the molecular distinctiveness of these mental disorders. We found a tendency of downregulation of the genes encoding receptor, channels or transporters, and upregulation of the genes encoding stress response proteins or molecular chaperons in bipolar disorder. Altered expressions in bipolar disorder shared by other mental disorders mainly consisted of upregulation of the genes encoding proteins for transcription or translation. The genes identified in this study would be useful for the understanding of the pathophysiology of bipolar disorder, as well as the common pathophysiological background in major mental disorders at the molecular level.\nLast Updated (by provider): Mar 15 2009\nContributors:  Tadafumi Kato Kazuya Iwamoto Chihiro Kakiuchi Kazuhiko Ikeda Miki Bundo
+#> 34:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Schizophrenia (SZ) and bipolar disorder (BD) are severe neuropsychiatric disorders with serious impact on patients, together termed “major psychosis”. Recently, long intergenic non-coding RNAs (lincRNAs) were reported to play important roles in mental diseases. However, little was known about their molecular mechanism in pathogenesis of SZ and BD. Here, we performed RNA sequencing on 82 post-mortem brain tissues from three brain regions (orbitofrontal cortex (BA11), anterior cingulate cortex (BA24) and dorsolateral prefrontal cortex (BA9)) of patients with SZ and BD and control subjects, generating over one billion reads. We characterized lincRNA transcriptome in the three brain regions and identified 20 differentially expressed lincRNAs (DELincRNAs) in BA11 for BD, 34 and 1 in BA24 and BA9 for SZ, respectively. Our results showed that these DELincRNAs exhibited brain region-specific patterns. Applying weighted gene co-expression network analysis, we revealed that DELincRNAs together with other genes can function as modules to perform different functions in different brain regions, such as immune system development in BA24 and oligodendrocyte differentiation in BA9. Additionally, we found that DNA methylation alteration could partly explain the dysregulation of lincRNAs, some of which could function as enhancers in the pathogenesis of major psychosis. Together, we performed systematical characterization of dysfunctional lincRNAs in multiple brain regions of major psychosis, which provided a valuable resource to understand their roles in SZ and BD pathology and helped to discover novel biomarkers.\nLast Updated (by provider): Jun 26 2018\nContributors:  Jing Hu Jinyuan Xu Lin Pang
+#> 35:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               46 samples of individuals from 4 different diagnostic groups: bipolar, schizophrenia, depression and controls. Samples taken from the Cerebellum.
+#> 36:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               There are currently no biological tests that differentiate patients with bipolar disorder (BPD) from healthy controls. While there is evidence that peripheral gene expression differences between patients and controls can be utilized as biomarkers for psychiatric illness, it is unclear whether current use or residual effects of antipsychotic and mood stabilizer medication drives much of the differential transcription. We therefore tested whether expression changes in first-episode, never-medicated bipolar patients, can contribute to a biological classifier that is less influenced by medication and could potentially form a practicable biomarker assay for BPD. We employed microarray technology to measure global leukocyte gene expression in first-episode (n=3) and currently medicated BPD patients (n=26), and matched healthy controls (n=25). Following an initial feature selection of the microarray data, we developed a cross-validated 10-gene model that was able to correctly predict the diagnostic group of the training sample (26 medicated patients and 12 controls), with 89% sensitivity and 75% specificity (p<0.001). The 10-gene predictor was further explored via testing on an independent test cohort consisting of three pairs of monozygotic twins discordant for BPD, plus the original enrichment sample cohort (the three never-medicated BPD patients and 13 matched control subjects), and a sample of experimental replicates (n=34). 83% of the independent test sample was correctly predicted, with a sensitivity of 67% and specificity of 100% (although this result did not reach statistical significance). Additionally, 88% of sample diagnostic classes were classified correctly for both the enrichment (p=0.015) and the replicate samples (p<0.001).\nLast Updated (by provider): Jun 25 2013\nContributors:  James D Clelland Catherine L Clelland
+#> 37:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     Bipolar disorder (BD) is a highly heritable and heterogeneous mental illness whose manifestations often include impulsive and risk-taking behavior. This particular phenotype suggests that abnormal striatal function could be involved in BD etiology, yet most transcriptomic studies of this disorder have concentrated on cortical brain regions. We report the first transcriptome profiling by RNA-Seq of the human dorsal striatum comparing bipolar and control subjects. Differential expression analysis and functional pathway enrichment analysis were performed to identify changes in gene expression that correlate with BD status. Further co-expression and enrichment analyses were performed to identify sets of correlated genes that show association to BD.\nLast Updated (by provider): May 17 2017\nContributors:  Ronald L Davis Rodrigo Pacifico
+#> 38:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      RNA was extracted from peripheral blood mononuclear cells (PBMC) of 24 adult healthy controls, 8 adult patients with bipolar disorder, and 21 adult patients with major depressive disorder to analyze gene expression patterns that identify biomarkers of disease and that may be correlated with fMRI data.\nLast Updated (by provider): Sep 24 2012\nContributors:  T K Teague Jonathan Savitz Wayne C Drevets Julie H Marino Melissa Bebak Bart Frank
+#> 39:  Most neuroscientists would agree that psychiatric illness is unlikely to arise from pathological changes that occur uniformly across all cells in a given brain region. Despite this fact, the majority of transcriptomic analyses of the human brain to date are conducted using macro-dissected tissue due to the difficulty of conducting single-cell level analyses on donated post-mortem brains. To address this issue statistically, we compiled a database of several thousand transcripts that were specifically-enriched in one of 10 primary brain cell types identified in published single cell type transcriptomic experiments. Using this database, we predicted the relative cell type composition for 157 human dorsolateral prefrontal cortex samples using Affymetrix microarray data collected by the Pritzker Neuropsychiatric Consortium, as well as for 841 samples spanning 160 brain regions included in an Agilent microarray dataset collected by the Allen Brain Atlas. These predictions were generated by averaging normalized expression levels across the transcripts specific to each primary cell type to create a “cell type index”. Using this method, we determined that the expression of cell type specific transcripts identified by different experiments, methodologies, and species clustered into three main cell type groups: neurons, oligodendrocytes, and astrocytes/support cells. Overall, the principal components of variation in the data were largely explained by the neuron to glia ratio of the samples. When comparing across brain regions, we were able to easily capture canonical cell type signatures – increased endothelial cells and vasculature in the choroid plexus, oligodendrocytes in the corpus callosum, astrocytes in the central glial substance, neurons and immature cells in the dentate gyrus, and oligodendrocytes and interneurons in the globus pallidus. The relative balance of these cell types was influenced by a variety of demographic, pre- and post-mortem variables. Age and prolonged anaerobic conditions around the time of death were associated with decreased neuronal content and increased astrocytic and endothelial content in the tissue, replicating the known higher vulnerability of neurons to aging and adverse conditions, and illustrating the proliferation of vasculature in a hypoxic environment. We also found that the red blood cell content was reduced in individuals who died in a manner that involved systemic blood loss. Finally, statistically accounting for cell type improved both the sensitivity and interpretability of diagnosis effects within the data. We were able to observe a decrease in astrocytic content in subjects with Major Depressive Disorder, mirroring what had been previously observed morphometrically. By including a set of “cell type indices” in a larger model examining the relationship between gene expression and neuropsychiatric illness, we were able to successfully detect almost twice as many genes with previously identified relationships to bipolar disorder and schizophrenia than using traditional analysis methods.\nAt time of import, last updated (by provider) on: \n\nContributors: ; [Jun Z Li, Cortney A Turner, Megan H Hagenauer, Stanley J Watson, David M Walsh, Alan F Schatzberg, Huda Akil, Richard M Myers, William E Bunney, Jack D Barchas]
+#> 40:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Fibroblasts from patients with Type I bipolar disorder (BPD) and their unaffected siblings were obtained from an Old Order Amish pedigree with a high incidence of BPD and reprogrammed to induced pluripotent stem cells (iPSCs). Established iPSCs were subsequently differentiated into neuroprogenitors (NPs) and then to neurons. Transcriptomic microarray analysis was conducted on RNA samples from iPSCs, NPs and neurons matured in culture for either 2 weeks (termed early neurons, E) or 4 weeks (termed late neurons, L). Global RNA profiling indicated that BPD and control iPSCs differentiated into NPs and neurons at a similar rate, enabling studies of differentially expressed genes in neurons from controls and BPD cases. Significant disease-associated differences in gene expression were observed only in L neurons. Specifically, 328 genes were differentially expressed between BPD and control L neurons including GAD1, glutamate decarboxylase 1 (2.5 fold) and SCN4B, the voltage gated type IV sodium channel beta subunit (-14.6 fold). Quantitative RT-PCR confirmed the up-regulation of GAD1 in BPD compared to control L neurons. Gene Ontology, GeneGo and Ingenuity Pathway Analysis of differentially regulated genes in L neurons suggest that alterations in RNA biosynthesis and metabolism, protein trafficking as well as receptor signaling pathways GSK3? signaling may play an important role in the pathophysiology of BPD.\nLast Updated (by provider): Jun 03 2018\nContributors:  Jiangang Liu Steven M Paul Jeffrey L Dage Janice A Egeland Rachelle J Sells Galvin Kwi H Kim Rosamund C Smith Kalpana M Merchant
+#> 41:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               39 samples of individuals from 4 different diagnostic groups: bipolar, schizophrenia, depression and controls. Samples taken from the DLPFC Broadmann area 46/10.
+#> 42:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Accumulating evidence suggests that mitochondrial dysfunction underlies the pathophysiology of bipolar disorder (BD) and schizophrenia (SZ). We performed large-scale DNA microarray analysis of postmortem brains of patients with BD or SZ, and examined expression patterns of mitochondria-related genes. We found a global down-regulation of mitochondrial genes, such as those encoding respiratory chain components, in BD and SZ samples, even after the effect of sample pH was controlled. However, this was likely due to the effects of medication. Medication-free patients with BD showed tendency of up-regulation of subset of mitochondrial genes. Our findings support the mitochondrial dysfunction hypothesis of BD and SZ pathologies. However, it may be the expression changes of a small fraction of mitochondrial genes rather than the global down-regulation of mitochondrial genes. Our findings warrant further study of the molecular mechanisms underlying mitochondrial dysfunction in BD and SZ. \nLast Updated (by provider): Mar 15 2009\nContributors:  Tadafumi Kato Kazuya Iwamoto Miki Bundo
+#> 43:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             98 samples of individuals from 3 different diagnostic groups: bipolar, schizophrenia, and controls. Samples taken from the DLPFC Broadmann area 46.
+#> 44:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           A multitude of genes have been associated with bipolar disorder via SNP genotyping studies.  However, many of these associated SNPs are found within intronic or intergenic regions of the human genome.  We were interested in studying transcriptional profiles/splice variation of genes associated with bipolar disorder within the human striatum. Understanding how these associated genes are transcribed in the human brain may help to guide the development of therapeutic agents for the treatment of bipolar disorder and other neuropsychiatric illnesses.\nLast Updated (by provider): Jun 26 2018\nContributors:  Courtney M MacMullen Ronald L Davis Mohammad Fallahi
+#> 45:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    In psychiatric disorders, common and rare genetic variants cause widespread dysfunction of cells and their interactions, especially in the prefrontal cortex, giving rise to psychiatric symptoms. To better understand these processes, we traced the effects of common and rare genetics, and cumulative disease risk scores, to their molecular footprints in human cortical single-cell types. We demonstrated that examining gene expression at single-exon resolution is crucial for understanding the cortical dysregulation associated with diagnosis and genetic risk derived from common variants. We then used disease risk scores to identify a core set of genes that serve as a footprint of common and rare variants in the cortex. Pathways enriched in these genes included dopamine regulation, circadian entrainment, and hormone regulation. Single-nuclei-RNA-sequencing pinpointed these enriched genes to excitatory cortical neurons. This study highlights the importance of studying sub-gene-level genetic architecture to classify psychiatric disorders based on biology rather than symptomatology, to identify novel targets for treatment development.\nAt time of import, last updated (by provider) on: Nov 20 2022\n\nContributors: ; [Franziska Degenhardt, Fabian J Theis, Janine Knauer-Arloth, Elisabeth Scarr, Nikola S Mueller, Nathalie Gerstner, Holger Thiele, Anna C Koller, Brian Dean, Karolina Worf, Marcella Rietschel, Madhara Udawela, Natalie Matosin, Anna S Froehlich]
+#> 46:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           44 samples of individuals from four different diagnostic groups: depression, bipolar, schizophrenia, and controls. Samples taken from the DLPFC Broadmann area 46/10.
+#> 47:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Impairments in certain cognitive processes (e.g., working memory) are typically most pronounced in schizophrenia (SZ), intermediate in bipolar disorder (BP) and least in major depressive disorder (MDD). Given that working memory depends, in part, on neural circuitry that includes pyramidal neurons in layer 3 (L3) and layer 5 (L5) of the dorsolateral prefrontal cortex (DLPFC), we sought to determine if transcriptome alterations in these neurons were shared or distinctive for each diagnosis.\nLast Updated (by provider): Jul 05 2017\nContributors:  Dominique Arion David A Lewis John F Enwright George Tseng Zhiguang Huo John P Corradi
+#> 48:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            27 samples of individuals from two different diagnostic groups: bipolar, and controls. Samples taken from the DLPFC Brodmann area 6.
+#> 49:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 We used laser capture microdissection to isolate both microvascular endothelial cells and neurons from post mortem brain tissue from patients with schizophrenia and bipolar disorder and healthy controls. RNA was isolated from these cell populations, amplified, and analysed using Affymetrix HG133plus2.0 GeneChips. In the first instance, we used the dataset to compare the neuronal and endothelial data, in order to demonstrate that the predicted differences between cell types could be detected using this methodology. \nLast Updated (by provider): Dec 18 2008\nContributors:  Margaret M Ryan Thomas Giger Martin J Lan Matthew T Wayland Mark Kotter Michael L Mimmack Laura W Harris Lan Wang Irene Wuethrich Helen Lockstone Sabine Bahn
+#> 50:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            102 samples of individuals from 3 different diagnostic groups: bipolar, schizophrenia, and controls. Samples taken from the DLPFC Broadmann area 46.
+#> 51:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                We fine-mapped DNA methylation in neuronal nuclei (NeuN+) isolated by flow cytometry from post-mortem frontal cortex of the brain of individuals diagnosed with schizophrenia, bipolar disorder, and controls (n=29, 26, and 28 individuals).\nAt time of import, last updated (by provider) on: May 15 2019\n\nContributors: ; [Shraddha S Pai, Viviane Labrie]
+#> 52:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   Schizophrenia (SCZ) and bipolar disorder (BPD) are polygenic disorders with many genes contributing to their etiologies. The aim of this investigation was to search for dysregulated molecular and cellular pathways for these disorders as well as psychosis. We conducted a blood-based microarray investigation in two independent samples with SCZ and BPD from San Diego (SCZ=13, BPD=9, control=8) and Taiwan [data not included](SCZ=11, BPD=14, control=16). Diagnostic groups were compared to controls, and subjects with a history of psychosis [PSYCH(+): San Diego (n=6), Taiwan (n=14)] were compared to subjects without such history [PSYCH(-): San Diego (n=11), Taiwan (n=14)]. Analyses of covariance comparing mean expression levels on a gene-by-gene basis were conducted to generate the top 100 significantly dysregulated gene lists for both samples by each diagnostic group. Gene lists were imported into Ingenuity Pathway Analysis (IPA) software. Results showed the ubiquitin proteasome pathway (UPS) was listed in the top ten canonical pathways for BPD and psychosis diagnostic groups across both samples with a considerably low likelihood of a chance occurrence (p = .001). No overlap in dysregulated genes populating these pathways was observed between the two independent samples. Findings provide preliminary evidence of UPS dysregulation in BPD and psychosis as well as support further investigation of the UPS and other molecular and cellular pathways for potential biomarkers for SCZ, BPD, and/or psychosis. The aim of this investigation was to search for dysregulated molecular and cellular pathways for these disorders as well as psychosis.\nLast Updated (by provider): Oct 19 2012\nContributors:  Sharon D Chandler Chad A Bousman Ian P Everall Erick Tatro Stephen J Glatt Ming T Tsuang James Lohr Ginger Lucero Gursharan Chana William Kremen
+#> 53:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Neurodevelopmental changes and impaired stress resistance have been implicated in the pathogenesis of bipolar disorder (BD), but the underlying regulatory mechanisms are unresolved.  Here we describe a cerebral organoid model of BD that exhibits altered early neural development, elevated neural network activity, and a major shift in the transcriptome. These phenotypic changes were reproduced in cerebral organoids generated from iPS cell lines derived in multiple different laboratories. The BD cerebral organoid transcriptome showed highly significant enrichment for gene targets of the transcriptional repressor REST. This was associated with reduced nuclear REST and REST binding to target gene recognition sites.  Reducing the oxygen concentration in organoid cultures to a physiological range ameliorated the developmental phenotype and restored REST expression.  These effects were mimicked by treatment with lithium.  Reduced nuclear REST and derepression of REST targets genes was also observed in the prefrontal cortex of BD patients. Thus, an impaired cellular stress response in BD cerebral organoids leads to altered neural development and transcriptional dysregulation associated with downregulation of REST. These findings provide a new model and conceptual framework for exploring the molecular basis of BD\nAt time of import, last updated (by provider) on: Nov 06 2023\n\nContributors: ; [King-Hwa Ling, Jenny Tam, Eunjung A Lee, Angeliki Spathopoulou, Liviu Aron, Pei-Ling Yeo, Li-Huei Tsai, Roy H Perlis, Jaejoon Choi, Bruce A Yankner, Derek Drake, Tak Ko, Mariana Garcia-Corral, Katharina Meyer, George Church]
+#> 54:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            105 samples of individuals from 3 different diagnostic groups: bipolar, schizophrenia, and controls. Samples taken from the DLPFC Broadmann area 46.
+#> 55:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Bipolar affective disorder is a severe psychiatric disorder with a strong genetic component but unknown pathophysiology. We used microarray technology (Affymetrix HG-U133A GeneChips) to determine the expression of approximately 22 000 mRNA transcripts in post-mortem brain tissue (dorsolateral prefrontal cortex) from patients with bipolar disorder and matched healthy controls. A cohort of 70 subjects was investigated and the final analysis included 30 bipolar and 31 control subjects. Differences between disease and control groups were identified using a rigorous statistical analysis with correction for confounding variables and multiple testing.\nLast Updated (by provider): Jan 16 2007\nContributors:  Helen E Lockstone Stephen J Huffaker Matthew T Wayland Sabine Bahn Maree J Webster Margaret M Ryan\nIncludes GDS2190.\n Update date: Aug 28 2006.\n Dataset description GDS2190: Analysis of postmortem dorsolateral prefrontal cortex from 30 adults with bipolar disorder. Results provide insight into the pathophysiology of the disease.
+#> 56:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Prefrontal cortex of schizophrenic, bipolar, and control subjects. This is the "McLean 66"
+#> 57:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         Background: Schizophrenia (SCZ) and bipolar disorder (BD) are highly heritable psychiatric disorders. Associated genetic and gene expression changes have been identified, but many have not been replicated and have unknown functions. We identified groups of genes whose expressions varied together, that is co-expression modules, then tested them for association with SCZ. Using weighted gene co-expression network analysis, we show that two modules were differentially expressed in patients versus controls. One, upregulated in cerebral cortex, was enriched with neuron differentiation and neuron development genes, as well as disease genome-wide association study genetic signals; the second, altered in cerebral cortex and cerebellum, was enriched with genes involved in neuron protection functions. The findings were preserved in five expression data sets, including sets from three brain\nregions, from a different microarray platform, and from BD patients. From those observations, we propose neuron differentiation and development pathways may be involved in etiologies of both SCZ and BD, and neuron protection function participates in pathological process of the diseases.\nLast Updated (by provider): Jul 26, 2018\nContributors:  Chao Chen Chunyu Liu Lijun Cheng
+#> 58:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         99 samples of individuals from three different diagnostic groups: bipolar, schizophrenia, and controls. Samples taken from the DLPFC Broadmann area 46.
+#> 59:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     Study the role of the LIM-homeodomain transcription factor LHX4 in the development of retinal bipolar cell subtypes\nAt time of import, last updated (by provider) on: Sep 30 2020\n\nContributors: ; [Lin Gan, Xuhui Dong]
+#> 60:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     Study the role of the LIM-homeodomain transcription factor LHX4 in the development of retinal bipolar cell subtypes\nAt time of import, last updated (by provider) on: Sep 30 2020\n\nContributors: ; [Lin Gan, Xuhui Dong]
+#> 61:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Fibroblasts and lymphoblastoid cells (LCLs) are the most widely used cells in genetic, genomic, and transcriptomic studies in relation to human diseases. Examining the gene expression patterns in these two cell types will provide valuable information regarding the validity of using them to study gene expression related to various human diseases. Fibroblasts and LCLs from four members of the Old Order Amish family 884 were purchased from Coriell cell repositories (Coriell Institute for Medical Research, Camden, NJ). We used microarrays to profile the patterns of gene expression in these eight cell lines. By employing the PennCNV algorithm to the Illumina HumanHap550 SNP genotyping data, we detected 13 Copy Number Variants (CNV) that exist in these four individuals. CNV-expression association analysis revealed that seven of these 13 CNVs were associated with the expression of genes within or near (<2Mb sweep) these CNVs at a nominal regression P value of 0.05.\nLast Updated (by provider): Feb 20 2009\nContributors:  Maja Bucan Shuzhang Yang
+#> 62:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            We used RNA sequencing to identify candidate regulators of interactions between photoreceptor axons and bipolar cell (BCs) dendrites in developing mouse retina. We chose three time points: P7, just after the OPL forms and synaptogenesis with BCs begins; P13, as synaptogenesis nears completion and sublamination begins; and P30, when the OPL is mature. We purified cone and rod photoreceptors separately by fluorescence activated cell sorting (FACS) using transgene markers: Rhoicre;Ai9 for rods and HRGPcre;Ai9 for cones. We purified ON BCs, which include ON cone bipolars plus rod bipolars using Grm6:GFP. As appropriate transgenic lines to separate RBCs from CBCs were not available, we performed RNAseq on cells from Grm6:GFP mice that were fixed and immunostained prior to FACS, allowing us to purify RBCs (GFP+PKC+) and CBCs (GFP+PKC-) from the same retinas. As PKC is not highly expressed at P7, profiling of developing rod bipolars separate from developing ON cone bipolars was restricted to P13.\nLast Updated (by provider): Jun 25 2018\nContributors:  Elizabeth Z Sanchez Lawrence S Zipursky Yerbol Z Kurmangaliyev
+#> 63:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Melatonin is a neurohormone that maintains the circadian rhythms of the body. Although we know the pathway of melatonin action in the brain, we lack comprehensive cross-sectional studies on the periphery of depressed patients.\nAt time of import, last updated (by provider) on: \n\nContributors: ; [Monika Dmitrzak-Weglarz, Karolina Bilska, Aleksandra Szczepankiewicz, Pawel Kapelski, Edyta Reszka, Ewa Banach, Ewa Jablonska, Maria Skibinska, Joanna Pawlak, Beata Narozna]
+#> 64:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Melatonin is a neurohormone that maintains the circadian rhythms of the body. Although we know the pathway of melatonin action in the brain, we lack comprehensive cross-sectional studies on the periphery of depressed patients.\nAt time of import, last updated (by provider) on: \n\nContributors: ; [Monika Dmitrzak-Weglarz, Karolina Bilska, Aleksandra Szczepankiewicz, Pawel Kapelski, Edyta Reszka, Ewa Banach, Ewa Jablonska, Maria Skibinska, Joanna Pawlak, Beata Narozna]
+#> 65:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 Bipolar disorder (BPD) is a debilitating heritable psychiatric disorder.  Contemporary models for the manic pole of BPD have primarily utilized either single locus transgenics or treatment with psychostimulants.  Our lab recently characterized a mouse strain, termed Madison (MSN), which naturally displays a manic phenotype, exhibiting elevated locomotor activity, increased sexual behavior, and higher forced swimming relative to control strains.  Lithium chloride and olanzapine treatments attenuate this phenotype.  In this study, we replicated our locomotor activity experiment, showing that MSN mice display generationally-stable mania relative to their outbred ancestral strain, hsd:ICR (ICR).  We then performed a gene expression microarray experiment to compare hippocampus of MSN and ICR mice.  We found dysregulation of multiple transcripts whose human orthologs are associated with BPD and other psychiatric disorders including schizophrenia and ADHD, including: Epor, Smarca4, Cmklr1, Cat, Tac1, Npsr1, Fhit, and P2rx7.  RT-qPCR confirmed dysregulation for all of seven transcripts tested.  Using a network analysis, we found dysregulation of a gene system related to chromatin packaging, a result convergent with recent human findings on BPD.  Using a novel genomic enrichment algorithm, we found enrichment in genome regions homologous to human loci implicated in BPD in replicated linkage studies including homologs of human cytobands 1p36, 3p14, 3q29, 6p21-22, 12q24, 16q24, and 17q25.  Our findings suggest that MSN mice represent a polygenic model for the manic pole of BPD showing much of the genetic systems complexity of the corresponding human disorder.  Further, the high degree of convergence between our findings and the human literature on BPD brings up novel questions about evolution by analogy in mammalian genomes.\nLast Updated (by provider): May 06 2012\nContributors:  Stephen C Gammie Griffin M Gessay Michael C Saul
+#> 66:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Diverse cell types can be reprogrammed into pluripotent stem cells by ectopic expression of Oct4 (Pou5f1), Klf4, Sox3 and Myc. Many of these induced pluripotent stem cells (iPSCs) retain an epigenetic memory of their cellular origins and this in turn may bias their subsequent differentiation. Differentiated neurons are difficult to reprogram and there has not been a systematic side-by-side characterization of reprogramming efficiency or epigenetic memory across different neuronal subtypes. We have recently developed a new method for reprogramming retinal neurons and successfully reprogrammed rod photoreceptors from the murine retina. Here we extended our retinal reprogramming to cone photoreceptors, bipolar neurons, amacrine/horizontal cell interneurons and Müller glia at two different stages of development. We scored the efficiency of reprogramming across all 5 retinal cell types at each developmental stage and we measured retinal differentiation from each iPSC line using a quantitative standardized scoring system called STEM-RET. We discovered that the rod photoreceptors and bipolar neurons had the lowest reprogramming efficiency but iPSCs derived from rods and bipolar cells had the best retinal differentiation. Epigenetic memory was analyzed by characterizing DNA methylation and performing ChIP-seq for 8 histone marks, Brd4, PolII and CTCF. The epigenetic data were integrated with RNA-Seq data from each iPSC line. Retinal cell types with a greater epigenetic barrier to reprogramming (rods and bipolars) are more likely to retain epigenetic memory of their cellular origins. In addition, we identified biomarkers of iPSCs that are predictive of retinal differentiation. This work will have implications for selection of cell populations for cell based therapy and for using reprogramming of purified cell populations to advance our understanding of the role of the epigenome in normal differentiation.\nLast Updated (by provider): Jun 14 2018\nContributors:  Jiakun Zhang Sharon Frase Suresh Thiagarajan Daniel Hiler Michael A Dyer Dianna Johnson Issam Aldiri Xiang Chen Lyra Griffiths Marie-Elizabeth Barabas Andras Sablauer Beisi Xu Lu Wang Marc Valentine Abbas Shirinifard
+#> 67:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                Schizophrenia is associated with dysfunction of the dorsolateral prefrontal cortex (DLPFC). This dysfunction is manifest as cognitive deficits that appear to arise from disturbances in gamma frequency oscillations. These oscillations are generated in DLPFC layer 3 via reciprocal connections between pyramidal cells and parvalbumin (PV)-containing interneurons. The density of cortical PV neurons is not altered in schizophrenia, but expression levels of several transcripts involved in PV cell function, including PV, are lower in the disease.\nLast Updated (by provider): Jul 03 2018\nContributors:  George Tseng Dominique Arion David A Lewis John F Enwright Zhiguang Huo John P Corradi
+#> 68:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           Transcription factor Sp4 controls dendritic patterning during development of cerebellar granule neurons in culture by limiting branch formation and promoting activity-dependent pruning (Ramos et al., 2007). Sp4 is associated with neuropsychiatric disorders such as major depressive disorder, schizophrenia and bipolar disorder.  In order to identify target genes of Sp4, we compared global gene expression in the cerebella of wild type and Sp4 hypomorph mice (Sp4neo-/-; Zhou et al, 2005).  The results identify candidate Sp4 target genes that may contribute to neuronal development and neuropsychiatric disorders.\nLast Updated (by provider): Nov 09 2017\nContributors:  Xinxin Sun Grace Gill
+#> 69:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Genetic analyses for bipolar disorder (BPD) have achieved prominent success in Europeans in recent years, whereas its genetic basis in other populations remains relatively less understood. We herein report that the lead risk locus for BPD in European genome-wide association studies (GWAS), the single nucleotide polymorphism (SNP) rs9834970 near TRANK1 at 3p22 region, is also genome-wide significantly associated with BPD in 5,748 cases and 65,361 controls of East Asian origin. In this study, we performed RAN-seq analysis of cultured rat neurons treated with shRNA knockdown of Trank1.\nAt time of import, last updated (by provider) on: Jul 02 2020\n\nContributors: ; [Ming Li, Huijuan Li, Hong Chang, Xin Cai]
+#> 70:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     To define molecular mechanisms underlying rod and cone differentiation, we generated H9 human embryonic stem cell line carrying a GFP reporter that is controlled by the promoter of cone-rod homeobox (CRX) gene, the first known marker of post-mitotic photoreceptor precursors. CRXp-GFP reporter in H9 line replicates endogenous CRX expression when induced to form self-organizing 3-D retina-like tissue. We define temporal transcriptome dynamics of developing photoreceptors during the establishment of cone and rod cell fate. Our studies provide an essential framework for delineating molecules and cellular pathways that guide human photoreceptor development and should assist in chemical screening and cell-based therapies of retinal degeneration.\nLast Updated (by provider): Nov 14 2017\nContributors:  Kohei Homma Rossukon Kaewkhaw Anand Swaroop Koray D Kaya Jizhong Zou Mahendra Rao Matthew Brooks Vijender Chaitankar
+#> 71:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             The goal of this experiment was to define gene expression patterns of thirteen mouse retinal neuron subsets, labeled by expression of fluorescent proteins in transgenic mice.\nLast Updated (by provider): Sep 23 2013\nContributors:  Jeremy N Kay Joshua R Sanes
+#> 72:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           Brain circuits are assembled from a large variety of morphologically and functionally diverse cell types. It is not known how the intermingled cell types of individual brain regions differ in their expressed genomes. Here we describe an atlas of cell type transcriptomes of the adult retina. We found that each adult cell type expresses a specific set of genes, including a unique set of transcription factors, forming a “barcode” for cell identity. Cell type transcriptomes carry enough information to categorize cells into corresponding morphological classes and types. Surprisingly, several barcode genes are eye disease-associated genes that we demonstrate to be specifically expressed not only in photoreceptors but also in particular retinal circuit elements such as inhibitory neurons as well as in retinal microglia. Our data suggest that distinct cell types of individual brain regions are characterized by marked differences in their expressed genomes. We assembled a library of 22 transgenic mouse lines in which each line had a group of retinal cells marked with fluorescent proteins. We built up the library with the purpose of having some mouse lines in which single retinal cell types and others in which combinations of types from a single class are labeled. The library had mouse lines with labeled cells representing each of the six retinal cell classes. Retinal cells were characterized by physiological recording and immunohistochemical staining. We isolated 200 fluorescent protein-labeled retinal cells (“cell groups”) from at least three different mice of each mouse line by fluorescence-activated cell sorting. The transcripts of each cell group of these biological triplicates were independently amplified in batches. Each batch contained an internal control cell group from the Arc-line.\nLast Updated (by provider): Jun 10 2014\nContributors:  Botond Roska Erik Cabuy Sandra Siegert
+#> 73:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        We determined the transcriptomes of postmitotic cone photoreceptors in the central region of the mouse retina every day between birth (P0) and eye opening (P12). At each postnatal day we isolated GFP-labeled cells from three different Chrnb4-GFP mice (biological triplicates) using fluorescence-activated cell sorting. We then acquired the transcriptomes of the sorted cones using next generation RNA sequencing. Our data set contained 39 transcriptomes.\nLast Updated (by provider): Jul 03 2018\nContributors:  Janine M Daum Michael B Stadler Özkan Keles Botond Roska Hubertus Kohler
+#> 74:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       These are RNA sequencing data from 12 week old orbital frontal cortex from mice who received a shRNA targeting circHomer1 and Homer1b (double knockdown) or control shRNA (scramble)\nAt time of import, last updated (by provider) on: Feb 08 2022\n\nContributors: ; [Mellios Nikolaos]
+#> 75:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        Microarray experiments were performed using FAC-sorted young photoreceptors to analyze their transcriptome in comparison to remaining retinal cells at same developmental stage and retinal progenitors.\nLast Updated (by provider): Apr 02 2018\nContributors:  Marius Ader Kai Postel
+#> 76:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                CHD8 (chromodomain helicase DNA binding protein 8), which codes for a member of the CHD family of ATP-dependent chromatin-remodeling factors, is the most commonly mutated gene in autism spectrum disorders (ASD) identified in exome-sequencing studies. Loss of function mutations in the gene have also been found in schizophrenia (SZ) and intellectual disabilities, and affects cancer cell proliferation. To better understanding the molecular links between CHD8 functions and ASD, we have applied the CRISPR/Cas9 technology to knockout (KO) one copy of CHD8 in induced pluripotent stem cells (iPSCs) and build cerebral organoids, a model for the developing telencephalon. RNA-seq was carried out on KO organoids (CHD8+/-) and isogenic controls (CHD8+/+). Differentially expressed genes (DEGs) revealed an enrichment of genes involved in neurogenesis, forebrain development, Wnt/?-catenin signaling and axonal guidance. The SZ and bipolar disorder (BD) candidate gene TCF4 was significantly upregulated. Our CHD8 KO DEGs were significantly overlapped with those found in a transcriptome analysis using cerebral organoids derived from a family with idiopathic ASD and another transcriptome study using iPS cell-derived neurons from patients with BD, a condition characterized in a subgroup of patients by dysregulated WNT/?-catenin signaling. Overall, the findings show that distinct ASD, SZ and BD candidate genes converge on common molecular targets - an important consideration for developing novel therapeutics in genetically heterogeneous complex traits.\nLast Updated (by provider): Jun 04 2018\nContributors:  Deyou Zheng Herbert M Lachman Ryan Mokhtari Ping Wang Can Bayrak Erika Pedrosa Michael Kirschenbaum
+#> 77:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     To generate an unbiased view of changes to the retinal gene network in Neurog2 retinal mutants, we generated and compared the P2 transcriptomes from control, heterozygote and mutant mice.  A pair of P2 retinas from each biologic replicate were used to produce libraries for high throughput sequencing (n = 5 biologic replicates/genotype).  Reads were aligned with BWA and Bowtie programs to the mm10 genome.  Aligned reads were then analyzed for differentially expressed transcripts using the CuffDiff program in the Galaxy online bioinformatics package (www.usegalaxy.org).\nLast Updated (by provider): Jun 07 2018\nContributors:  Nadean L Brown Angelica M Kowalchuk
+#> 78:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        We used two siRNAs to knock down GNL3 in human neural progenitor cells which were derived from normal human induced pluripotent stem cells (ATCC, ACS-1011). GNL3 knockdown experiments were done in three biological replicates. Total RNA was extracted from GNL3 knockdown and control groups for RNA sequencing (Illumina Hiseq2000, paired-end 100 bp). Genes that affected by both siRNAs were considered differentially expressed genes between GNL3 knockdown and control groups (adjusted P value < 0.05). Using Gene Ontology and KEGG pathway analysis, we found that those differentially exrepssed genes were mainly related to immune response, response to cytokine, cell cycle, and p53 signaling pathway.\nAt time of import, last updated (by provider) on: May 21 2020\n\nContributors: ; [Chuan Jiao, Qingtuan Meng]
+#> 79:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          As part of collaboration between the X. William Yang Lab at UCLA and CHDI, a transcriptomic study of normal murine cortex was carried out. Cortex was dissected from 6-month-old wildtype (WT) control mice. Transcriptomic analysis (RNASeq) was performed.\nAt time of import, last updated (by provider) on: Oct 21 2022\n\nContributors: ; [Jeff Aaronson, X W Yang, Jim Rosinski]
+#> 80:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           We have identified and characterized an allelic series of spontaneous Rorb mutations in mice We perform RNASeq to identify gene expression changes associated with Rorb mutations in brain and spinal cord from all five mutant strains.  We also perform CNS region-specific RNASeq in the Rorbh5/h5 mutant.\nAt time of import, last updated (by provider) on: Jun 08 2023\n\nContributors: ; [Abigal D Tadenev, Robert W Burgess, George C Murray]
+#> 81:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           Chaperonin 60 (Cpn60) is a prototypic molecular chaperone essential for cellular function due to its protein folding actions.  However, over the past decade it has been established that Cpn60 can be released by human cells and by certain bacteria to act as an extracellular signalling protein. Mycobacterium tuberculosis produces two Cpn60 proteins: Cpn60.1 and Cpn60.2. We recently generated a M. tuberculosis mutant with an inactivated cpn60.1 gene and demonstrated that granuloma formation was impaired after murine/guinea pig infection.  This finding suggested that Cpn60.1 may interact with the cellular organisation of the host response to M. tuberculosis bacilli.  In this study, we report that recombinant M. tuberculosis Cpn60.1 has both pro- and anti-inflammatory effects on human circulating monocytes. At high concentrations, recombinant Cpn60.1 induces the synthesis of TNF-?, IL6, and IL8, and promotes the phosphorylation of NF-?Bp65, p44/42MAPK and p38 MAPK. At lower concentrations M. tuberculosis Cpn60.1 inhibits lipopolysaccharide-induced release of TNF-?, and monocyte transcriptional activation program. Both effects are abrogated by proteolysis of Cpn60.1 and therefore cannot be attributed to contamination with lipopolysaccharide. Competition with LPS for binding to a common receptor, the release of IL-10 or down-regulation of TLR4 on the cell surface were excluded as explanations for the inhibitory activity of Cpn60.1. We therefore conclude that M. tuberculosis Cpn60.1 is an unusual protein with the ability to induce bipolar effects on human monocytes, which may help explain the pathology of granuloma formation in tuberculosis. We used microarrays to analyse the bipolar effectsof Cpn60.1  on human monocytes.\nLast Updated (by provider): Oct 29 2009\nContributors:  Anthony R Coates Simon J Waddell Brian Henderson Ana Cehovin
+#> 82:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 The circadian nature of mood and its dysfunction in affective disorders is well recognized, but the underlying molecular mechanisms are still unclear. We showed that the circadian nuclear receptor REV-ERBa, which is associated with bipolar disorder, impacts midbrain dopamine production and mood-related behavior in mice. Genetic deletion of the Rev-erba gene or pharmacological inhibition of REV-ERBa activity in the ventral midbrain induced mania-like behavior in association with a central hyperdopaminergic state. We used microarrays to identify differentially expressed genes in the ventral midbrains of wild-type (WT) and Rev-erba knock-out (RKO) mice.\nAt time of import, last updated (by provider) on: Mar 04 2019\n\nContributors: ; [Sooyoung Chung, Kyungjin Kim, Gi H Son]\nIncludes GDS5628 (Last updated by provider at import time: Aug 21 2015)\n Dataset description GDS5628: Analysis of ventral midbrain (VMB) from Rev-erb? knock-outs that were entrained under a 12hr light-dark photoperiod for >10 days, kept in darkness for 2 days, and sacrificed on the third day. REV-ERB? is associated with bipolar disorder. Results provide insight into the role of REV-ERB? in VMB.\n
+#> 83:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The transition to motherhood involves CNS changes that modify sociability and affective state.  However, these changes also put females at risk for postpartum depression and psychosis, which impairs parenting abilities and adversely affects children.  Thus, changes in expression and interactions in a core subset of genes may be critical for emergence of a healthy maternal phenotype, but inappropriate changes of the same genes could put women at risk for postpartum disorders.  This study evaluated microarray gene expression changes in medial prefrontal cortex (mPFC), a region implicated in both maternal behavior and psychiatric disorders.  Postpartum mice were compared to virgin controls housed with females and isolated for identical durations.  Using the Modular Single-set Enrichment Test (MSET), we found that the genetic landscape of maternal mPFC bears statistical similarity to gene databases associated with schizophrenia (5 of 5 sets) and bipolar disorder (BPD, 3 of 3 sets).  In contrast to previous studies of maternal lateral septum and medial preoptic area, enrichment of autism and depression-linked genes was not significant (2 of 9 sets, 0 of 4 sets).  Among genes linked to multiple disorders were fatty acid binding protein 7 (Fabp7), glutamate metabotropic receptor 3 (Grm3), platelet derived growth factor, beta polypeptide (Pdgfrb), and nuclear receptor subfamily 1, group D, member 1 (Nr1d1).  RT-qPCR confirmed these gene changes as well as FMS-like tyrosine kinase 1 (Flt1) and proenkephalin (Penk).  Systems-level methods revealed involvement of developmental gene networks in establishing the maternal phenotype and indirectly suggested a role for numerous microRNAs and transcription factors in mediating expression changes.  Together, this study suggests that a subset of genes involved in shaping the healthy maternal brain may also be dysregulated in mental health disorders and put females at risk for postpartum psychosis with aspects of schizophrenia and BPD.\nLast Updated (by provider): Feb 21 2018\nContributors:  Terri M Driessen Changjiu Zhao Stephen C Gammie Brian E Eisinger
+#> 84:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      The retina, the accessible part of the central nervous system, has served as a model system to study the relationship between energy utilization and metabolite supply. When the metabolite supply cannot match the energy demand, retinal neurons are at risk of death. As the powerhouse of eukaryotic cells, mitochondria play a pivotal role in generating ATP, produce precursors for macromolecules, maintain the redox homeostasis, and function as waste management centers for various types of metabolic intermediates. Mitochondrial dysfunction has been implicated in the pathologies of a number of degenerative retinal diseases. It is well known that photoreceptors are particularly vulnerable to mutations affecting mitochondrial function due to their high energy demand and susceptibility to oxidative stress. However, it is unclear how defective mitochondria affect other retinal neurons. Nuclear respiratory factor 1 (Nrf1) is the major transcriptional regulator of mitochondrial biogenesis, and loss of Nrf1 leads to defective mitochondria biogenesis and eventually cell death. Here, we investigated how different retinal neurons respond to the loss of Nrf1. We provide in vivo evidence that the disruption of Nrf1-mediated mitochondrial biogenesis results in a slow, progressive degeneration of all retinal cell types examined, although they present different sensitivity to the deletion of Nrf1, which implicates differential energy demand and utilization, as well as tolerance to mitochondria defects in different neuronal cells. Furthermore, transcriptome analysis on rod-specific Nrf1 deletion uncovered a previously unknown role of Nrf1 in maintaining genome stability.\nAt time of import, last updated (by provider) on: Dec 17 2022\n\nContributors: ; [Chai-An Mao, Takae Kiyama]
+#> 85:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 Mania is a serious neuropsychiatric condition associated with significant morbidity and mortality. Previous studies have suggested that environmental exposures can contribute to mania pathogenesis.  We measured dietary exposures in a cohort of individuals with mania and other psychiatric disorders as well as in control individual without a psychiatric disorder. We found that a history of eating nitrated dry cured meat, but not other meat or fish products, was strongly and independently associated with current mania (adjusted odds ratio 3.49, 95% confidence interval (CI) 2.24-5.45, p<8.97x 10-8). Lower odds of association were found between eating nitrated dry cured meat and other psychiatric disorders. We further found that the feeding of meat preparations with added nitrate to rats resulted in alterations in behavior and changes in intestinal microbiota. Rats fed diets with added nitrate also showed alterations of brain pathways dysregulated in mania. These findings may lead to new methods for preventing mania and for developing novel therapeutic interventions\nLast Updated (by provider): Aug 20 2018\nContributors:  Seva G Khambadkone C C Talbot Jr Robert H Yolken
+#> 86:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 Impaired neuronal processes, including dopamine imbalance, are central to the pathogenesis of major psychosis, but the molecular origins are unclear. We report the first multi-omics study of neurons isolated from the prefrontal cortex of individuals with schizophrenia and bipolar disorder, including genome-wide neuronal DNA methylation using Illumina EPIC microarrays, transcriptomes and SNP genotypes (n=55 cases and 27 controls). Epigenetic, transcriptomic, and genetic-epigenetic interactions in disease converged on pathways of neurodevelopment, synaptic activity, and immune functions. Notably, we discovered prominent hypomethylation of an enhancer within the insulin-like growth factor 2 (IGF2) gene in neurons of major psychosis patients. Chromatin conformation analysis revealed that this enhancer targets the nearby tyrosine hydroxylase (TH) gene, which is responsible for dopamine synthesis. IGF2 enhancer hypomethylation was associated with increased TH protein levels in the human brain. The Igf2 enhancer was deleted in mice to explore the transcriptomic and proteomic consequences of this genomic locus in the frontal cortex and striatum. In mice, Igf2 enhancer deletion disrupted levels of TH protein and striatal dopamine, as well as induced transcriptional and proteomic abnormalities affecting development and synaptic function. Epigenetic control of the IGF2 enhancer may regulate dopamine levels and contribute to psychosis risk.\nAt time of import, last updated (by provider) on: May 15 2019\n\nContributors: ; [Shraddha S Pai, Viviane Labrie]
+#> 87:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            The goal of this project is to study transcriptome change by knocking down ZNF804A, a schizophrenia and bipolar disorder candidate gene, in early neurons derived from iPSCs.\nLast Updated (by provider): Sep 16 2016\nContributors:  Deyou Zheng Herbert M Lachman
+#> 88:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The molecular etiology of invididual differences in complex behavior traits and susceptibility to psychiatric illness remains incomplete. Using an unbiased genetic approach in a mouse model, Quantitative Trait Loci (QTL) influencing anxiety-like behaviors and beta-carboline-induced seizure vulnerability have been mapped to the distal portion of mouse chromosome 10 and an interval specific congenic strain (ISCS; A.B6chr10; 66 cM to telomere) was developed. This A.B6chr10 strain facilitated defining the behavioral influences of this region as well as gene expression profiling to identify candidate gene(s) underlying this QTL. By microarray studies, an unsuspected E3 Ubiquitin Ligase, Ring Finger 41 (Rnf41 / Neuregulin Receptor Degrading Protein1; Nrdp1) was differentially expressed in the region of interest, comparing the hippocampi of A/J vs A.B6chr10 mice as well as A/J vs B6 mice. By RT-PCR, Rnf41 expression levels were significantly increased 1.5 and 1.3-fold in the hippocampi of C57BL6/J and A.B6chr10 mice compared to A/J mice, respectively. In addition, protein levels of Rnf41 were increased in hippocampi of B6 mice compared to A/J mice across postnatal development with a 5.5-fold difference at P56. Among LxS recombinant inbred mice (N=33), Rnf41 hippocampal mRNA expression levels were significantly correlated with open field behavior (r= .454, p=.0073). Re-analyzing a microarray database of human post-mortem prefrontal cortex (Brodmann’s Area 46/10), RNF41 mRNA expression levels were reduced significantly in patients with major depression and bipolar disorder compared to unaffected controls. Overall, Rnf41 is a pleiotropic candidate gene for anxiety-like behaviors, depression, and vulnerability to seizures. RNF41 and its binding partners provide novel etiological pathways for influencing behavior, highlighting a potential role for the ubiquitin proteasome system in psychiatric illness.\nLast Updated (by provider): Jan 15 2010\nContributors:  H K Gershenfeld Sanghyeon Kim K Choi R Reister A F Baykiz S Zhang
+#> 89:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The molecular etiology of invididual differences in complex behavior traits and susceptibility to psychiatric illness remains incomplete. Using an unbiased genetic approach in a mouse model, Quantitative Trait Loci (QTL) influencing anxiety-like behaviors and beta-carboline-induced seizure vulnerability have been mapped to the distal portion of mouse chromosome 10 and an interval specific congenic strain (ISCS; A.B6chr10; 66 cM to telomere) was developed. This A.B6chr10 strain facilitated defining the behavioral influences of this region as well as gene expression profiling to identify candidate gene(s) underlying this QTL. By microarray studies, an unsuspected E3 Ubiquitin Ligase, Ring Finger 41 (Rnf41 / Neuregulin Receptor Degrading Protein1; Nrdp1) was differentially expressed in the region of interest, comparing the hippocampi of A/J vs A.B6chr10 mice as well as A/J vs B6 mice. By RT-PCR, Rnf41 expression levels were significantly increased 1.5 and 1.3-fold in the hippocampi of C57BL6/J and A.B6chr10 mice compared to A/J mice, respectively. In addition, protein levels of Rnf41 were increased in hippocampi of B6 mice compared to A/J mice across postnatal development with a 5.5-fold difference at P56. Among LxS recombinant inbred mice (N=33), Rnf41 hippocampal mRNA expression levels were significantly correlated with open field behavior (r= .454, p=.0073). Re-analyzing a microarray database of human post-mortem prefrontal cortex (Brodmann’s Area 46/10), RNF41 mRNA expression levels were reduced significantly in patients with major depression and bipolar disorder compared to unaffected controls. Overall, Rnf41 is a pleiotropic candidate gene for anxiety-like behaviors, depression, and vulnerability to seizures. RNF41 and its binding partners provide novel etiological pathways for influencing behavior, highlighting a potential role for the ubiquitin proteasome system in psychiatric illness.\nLast Updated (by provider): Jan 15 2010\nContributors:  H K Gershenfeld Sanghyeon Kim K Choi R Reister A F Baykiz S Zhang
+#> 90:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  The molecular etiology of invididual differences in complex behavior traits and susceptibility to psychiatric illness remains incomplete. Using an unbiased genetic approach in a mouse model, Quantitative Trait Loci (QTL) influencing anxiety-like behaviors and beta-carboline-induced seizure vulnerability have been mapped to the distal portion of mouse chromosome 10 and an interval specific congenic strain (ISCS; A.B6chr10; 66 cM to telomere) was developed. This A.B6chr10 strain facilitated defining the behavioral influences of this region as well as gene expression profiling to identify candidate gene(s) underlying this QTL. By microarray studies, an unsuspected E3 Ubiquitin Ligase, Ring Finger 41 (Rnf41 / Neuregulin Receptor Degrading Protein1; Nrdp1) was differentially expressed in the region of interest, comparing the hippocampi of A/J vs A.B6chr10 mice as well as A/J vs B6 mice. By RT-PCR, Rnf41 expression levels were significantly increased 1.5 and 1.3-fold in the hippocampi of C57BL6/J and A.B6chr10 mice compared to A/J mice, respectively. In addition, protein levels of Rnf41 were increased in hippocampi of B6 mice compared to A/J mice across postnatal development with a 5.5-fold difference at P56. Among LxS recombinant inbred mice (N=33), Rnf41 hippocampal mRNA expression levels were significantly correlated with open field behavior (r= .454, p=.0073). Re-analyzing a microarray database of human post-mortem prefrontal cortex (Brodmann’s Area 46/10), RNF41 mRNA expression levels were reduced significantly in patients with major depression and bipolar disorder compared to unaffected controls. Overall, Rnf41 is a pleiotropic candidate gene for anxiety-like behaviors, depression, and vulnerability to seizures. RNF41 and its binding partners provide novel etiological pathways for influencing behavior, highlighting a potential role for the ubiquitin proteasome system in psychiatric illness.\nLast Updated (by provider): Jan 15 2010\nContributors:  H K Gershenfeld Sanghyeon Kim K Choi R Reister A F Baykiz S Zhang
+#> 91:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Loss of the Atrx chromatin remodeling protein causes dysfunction and death of post-mitotic retinal interneurons in mice. Embryonic conditional deletion of Atrx from multipotent retinal progenitor cells results in the selective loss of the retinal inhibitory interneurons, namely amacrine and horizontal cells. The cell death occurs postnatally after the development of these cell types, peaking at postntal day 17 in the mouse retina.  Identification of molecular factors and pathways that mediate the health and survival of these neurons may suggest novel therapeutic strategies for neuroprotection in ATR-X syndrome and other neurodegenerative diseases. We performed gene expression profiling of wildtype and Atrx conditional knockout mouse retina tissues to identify putative targets of Atrx and molecular pathways that underlie the neurodegenerative phenotype.\nLast Updated (by provider): Feb 21 2018\nContributors:  Pamela S Lagali David J Picketts
+#> 92:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   Diseases and damage to the retina lead to losses in retinal neurons and eventual visual impairment. Although the mammalian retina has no inherent regenerative capabilities, fish have robust regeneration from Müller glia (MG). Recently, we have shown that driving expression of Ascl1 in adult mouse MG stimulates neurogenesis similar to fish regeneration. The regeneration observed in the mouse is limited in the variety of neurons that can be derived from MG; Ascl1-expressing MG primarily generate bipolar cells. To better understand the limits of MG-based regeneration in mouse retinas, we used ATAC- and RNA-seq to compare newborn progenitors with MG. Our analysis demonstrated striking similarities between MG and progenitors, with losses in regulatory motifs for neurogenesis genes. Young MG were found to have intermediate expression profiles and accessible DNA, which is mirrored in the ability of Ascl1 to direct bipolar neurogenesis in young MG. When comparing what makes bipolar and photoreceptor cells distinct from glial cells, we find that bipolar-specific accessible regions are more frequently linked to bHLH motifs and Ascl1 binding, indicating that Ascl1 preferentially binds to bipolar regions. Overall, our analysis indicates a loss of neurogenic gene expression and motif accessibility during glial maturation that may prevent efficient reprogramming.\nAt time of import, last updated (by provider) on: Aug 19 2020\n\nContributors: ; [Wohl Stefanie G, Wilken Matthew S, Thomas Reh, Chipman Laura, VandenBosch Leah S, Kox Kristen]
+#> 93:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               Lithium is a first-line treatment for bipolar disorder, where it acts as a mood-stabilizing agent. Although its precise mechanism remains unclear, neuroimaging studies have shown that lithium accumulates in the hippocampus and that chronic use amongst bipolar disorder patients is associated with larger hippocampal volumes. Here, we tested the chronic effects of low (0.75 mM) and high (2.25 mM) doses of lithium on human hippocampal progenitor cells and used immunocytochemistry to investigate the effects of lithium on cell parameters implicated in neurogenesis. Corresponding RNA-sequencing and gene-set enrichment analyses were used to evaluate whether genes affected by lithium in our model overlap with those regulating the volume of specific layers of the dentate gyrus. We observed that high-dose lithium treatment in human hippocampal progenitors increased the generation of neuroblasts (P ≤ 0.01), neurons (P ≤ 0.01), and glia (P ≤ 0.001), alongside the expression of genes which regulate the volume of the molecular layer of the dentate gyrus. This study provides empirical support that adult hippocampal neurogenesis and gliogenesis are mechanisms that could contribute to the effects of lithium on human hippocampal volume.\nAt time of import, last updated (by provider) on: Oct 01 2021\n\nContributors: ; [Rodrigo R R Duarte, Alish B Palmos, Douglas F Nixon, Timothy R Powell, Demelza M Smeeth, Erin C Hedges, Sandrine Thuret]
+#> 94:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Neuronal plasticity of the inner retina has been observed in response to photoreceptor degeneration. Typically, this phenomenon has been considered maladaptive and may preclude vision restoration in the blind. However, several recent studies utilizing triggered photoreceptor ablation have shown adaptive responses in bipolar cell dendrites expected to support normal vision. Whether such homeostatic plasticity occurs during progressive photoreceptor degenerative disease to help maintain normal visual behavior is unknown. We addressed these issues in an established mouse model of Retinitis Pigmentosa caused by the P23H mutation in rhodopsin. We show robust modulation of the retinal transcriptomic network reminiscent of the neurodevelopmental state as well as potentiation of rod – rod bipolar cell signaling following rod photoreceptor degeneration. Additionally, we found highly sensitive night vision in P23H mice even when more than half of the rod photoreceptors were lost. The results implicate retinal adaptation leading to persistent visual function during photoreceptor degenerative disease.\nAt time of import, last updated (by provider) on: Oct 19 2020\n\nContributors: ; [Frans Vinberg, Henri Leinonen]
+#> 95:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Neuronal plasticity of the inner retina has been observed in response to photoreceptor degeneration. Typically, this phenomenon has been considered maladaptive and may preclude vision restoration in the blind. However, several recent studies utilizing triggered photoreceptor ablation have shown adaptive responses in bipolar cell dendrites expected to support normal vision. Whether such homeostatic plasticity occurs during progressive photoreceptor degenerative disease to help maintain normal visual behavior is unknown. We addressed these issues in an established mouse model of Retinitis Pigmentosa caused by the P23H mutation in rhodopsin. We show robust modulation of the retinal transcriptomic network reminiscent of the neurodevelopmental state as well as potentiation of rod – rod bipolar cell signaling following rod photoreceptor degeneration. Additionally, we found highly sensitive night vision in P23H mice even when more than half of the rod photoreceptors were lost. The results implicate retinal adaptation leading to persistent visual function during photoreceptor degenerative disease.\nAt time of import, last updated (by provider) on: Jun 16 2020\n\nContributors: ; [Frans Vinberg, Henri Leinonen]
+#> 96:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Quetiapine is an atypical neuroleptic with a pharmacological profile distinct from classic neuroleptics. It is currently approved for treating patients with schizophrenia, major depression and bipolar I disorder. However, its cellular effects remain elusive. We used microarrays to characterize RNA transcript levels in the brains of mice chronically treated with quetiapine, the neuroleptic haloperidol, or vehicle. We further characterized particular RNA transcripts in cortical cell cultures.\nLast Updated (by provider): Apr 16 2013\nContributors:  Jonathan Pevsner Mari Kondo
+#> 97:                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       The expression level for 15 887 transcripts in lymphoblastoid cell lines from 19 monozygotic twin pairs (10 male, 9 female) were analysed for the effects of genotype and sex. On an average, the effect of twin pairs explained 31% of the variance in normalized gene expression levels, consistent with previous broad sense heritability estimates. The effect of sex on gene expression levels was most noticeable on the X chromosome, which contained 15 of the 20 significantly differentially expressed genes. A high concordance was observed between the sex difference test statistics and surveys of genes escaping X chromosome inactivation. Notably, several autosomal genes showed significant differences in gene expression between the sexes despite much of the cellular environment differences being effectively removed in the cell lines. A publicly available gene expression data set from the CEPH families was used to validate the results. The heritability of gene expression levels as estimated from the two data sets showed a highly significant positive correlation, particularly when both estimates were close to one and thus had the smallest standard error. There was a large concordance between the genes significantly differentially expressed between the sexes in the two data sets. Analysis of the variability of probe binding intensities within a probe set indicated that results are robust to the possible presence of polymorphisms in the target sequences.\nNote: 1 samples from this series, which appear in other Expression Experiments in Gemma, were not imported from the GEO source. The following samples were removed: GSM162903 on GPL570\nLast Updated (by provider): Jan 18 2018\nContributors:  Sam F Berkovic Bryan Mowry Nicholas A Matigian Peter M Visscher Allan F McRae Nicholas K Hayward Nicholas G Martin Lata Vadlamudi John C Mulley
+#>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              experiment.description
 #>     experiment.troubled experiment.accession experiment.database experiment.URI
 #>                  <lgcl>               <char>              <char>         <char>
 #>  1:               FALSE                 <NA>                <NA>           <NA>
@@ -655,64 +655,64 @@ 
Examples

 #>     experiment.troubled experiment.accession experiment.database experiment.URI
 #>     experiment.sampleCount experiment.lastUpdated    experiment.batchEffectText
 #>                      <int>                 <POSc>                        <char>
-#>  1:                     33    2023-12-17 01:36:32                 NO_BATCH_INFO
-#>  2:                     12    2023-12-16 05:34:15                 NO_BATCH_INFO
-#>  3:                     23    2023-09-08 07:38:04                 NO_BATCH_INFO
+#>  1:                    215    2024-05-08 23:46:31     SINGLETON_BATCHES_FAILURE
+#>  2:                      4    2024-01-23 19:56:05          SINGLE_BATCH_SUCCESS
+#>  3:                     93    2022-08-30 23:36:54                 NO_BATCH_INFO
 #>  4:                      4    2023-12-17 12:01:40          SINGLE_BATCH_SUCCESS
-#>  5:                     93    2022-08-30 23:36:54                 NO_BATCH_INFO
-#>  6:                     24    2023-12-18 11:53:22                 NO_BATCH_INFO
-#>  7:                     34    2023-12-18 00:19:38       NO_BATCH_EFFECT_SUCCESS
-#>  8:                      4    2024-01-23 19:56:05          SINGLE_BATCH_SUCCESS
-#>  9:                      6    2023-12-20 19:14:46          SINGLE_BATCH_SUCCESS
+#>  5:                     23    2023-09-08 07:38:04                 NO_BATCH_INFO
+#>  6:                     22    2023-12-17 10:03:11                 NO_BATCH_INFO
+#>  7:                    120    2023-12-19 20:24:26                 NO_BATCH_INFO
+#>  8:                     24    2023-12-17 14:05:13                 NO_BATCH_INFO
+#>  9:                     24    2023-12-18 11:53:22                 NO_BATCH_INFO
 #> 10:                     20    2023-12-17 07:25:15          SINGLE_BATCH_SUCCESS
-#> 11:                    215    2024-05-03 20:40:48                 NO_BATCH_INFO
-#> 12:                     24    2023-12-17 14:05:13                 NO_BATCH_INFO
-#> 13:                     22    2023-12-17 10:03:11                 NO_BATCH_INFO
-#> 14:                    120    2023-12-19 20:24:26                 NO_BATCH_INFO
+#> 11:                      6    2023-12-20 19:14:46          SINGLE_BATCH_SUCCESS
+#> 12:                     12    2023-12-16 05:34:15                 NO_BATCH_INFO
+#> 13:                     34    2023-12-18 00:19:38       NO_BATCH_EFFECT_SUCCESS
+#> 14:                     33    2023-12-17 01:36:32                 NO_BATCH_INFO
 #> 15:                     12    2023-12-20 19:03:56                 NO_BATCH_INFO
 #> 16:                     22    2023-12-18 01:58:21          SINGLE_BATCH_SUCCESS
 #> 17:                     35    2023-12-18 12:17:06                 NO_BATCH_INFO
-#> 18:                     50    2023-09-07 23:11:52                 NO_BATCH_INFO
-#> 19:                     32    2023-12-20 11:14:58                 NO_BATCH_INFO
-#> 20:                     44    2023-09-07 23:17:50                 NO_BATCH_INFO
-#> 21:                     18    2024-02-22 08:34:38       NO_BATCH_EFFECT_SUCCESS
-#> 22:                     46    2023-09-07 23:15:42                 NO_BATCH_INFO
-#> 23:                     12    2023-12-16 10:59:29       BATCH_CORRECTED_SUCCESS
-#> 24:                    205    2023-12-20 12:59:34                 NO_BATCH_INFO
-#> 25:                    128    2023-12-21 10:56:11                 NO_BATCH_INFO
-#> 26:                     50    2023-12-16 09:50:20                 NO_BATCH_INFO
-#> 27:                     99    2023-12-07 20:23:13                 NO_BATCH_INFO
-#> 28:                     66    2023-09-21 21:43:05                 NO_BATCH_INFO
-#> 29:                     30    2023-12-17 14:10:06       NO_BATCH_EFFECT_SUCCESS
-#> 30:                     16    2024-01-24 09:18:15       NO_BATCH_EFFECT_SUCCESS
-#> 31:                     98    2023-06-08 22:47:42                 NO_BATCH_INFO
-#> 32:                     34    2023-12-16 10:28:32                 NO_BATCH_INFO
-#> 33:                    102    2023-12-16 09:48:59       NO_BATCH_EFFECT_SUCCESS
-#> 34:                    144    2023-12-19 06:34:12       BATCH_CORRECTED_SUCCESS
-#> 35:                     88    2023-12-20 15:25:17                 NO_BATCH_INFO
-#> 36:                     32    2023-12-16 10:02:53                 NO_BATCH_INFO
-#> 37:                    168    2023-12-19 06:35:04                 NO_BATCH_INFO
-#> 38:                    286    2023-12-22 08:39:18                 NO_BATCH_INFO
-#> 39:                     27    2023-09-07 23:19:35                 NO_BATCH_INFO
-#> 40:                     21    2023-12-20 08:00:37       NO_BATCH_EFFECT_SUCCESS
-#> 41:                     98    2023-12-06 18:45:01                 NO_BATCH_INFO
-#> 42:                      6    2023-12-20 21:45:32          SINGLE_BATCH_SUCCESS
-#> 43:                    105    2023-12-06 22:52:55                 NO_BATCH_INFO
-#> 44:                    169    2023-12-18 01:28:03                 NO_BATCH_INFO
-#> 45:                     61    2023-12-20 07:59:57       NO_BATCH_EFFECT_SUCCESS
-#> 46:                     16    2023-12-21 12:58:54                 NO_BATCH_INFO
-#> 47:                    102    2023-11-30 00:38:00                 NO_BATCH_INFO
-#> 48:                     78    2023-12-11 18:39:23                 NO_BATCH_INFO
-#> 49:                    480    2024-03-22 23:43:22 UNINFORMATIVE_HEADERS_FAILURE
-#> 50:                     39    2023-06-09 16:41:44                 NO_BATCH_INFO
-#> 51:                    281    2023-12-21 12:53:40                 NO_BATCH_INFO
-#> 52:                     36    2023-12-21 04:26:14       NO_BATCH_EFFECT_SUCCESS
-#> 53:                    235    2023-12-21 10:57:20       NO_BATCH_EFFECT_SUCCESS
-#> 54:                     88    2023-12-19 22:14:40                 NO_BATCH_INFO
-#> 55:                     47    2023-09-07 23:15:06                 NO_BATCH_INFO
-#> 56:                     53    2023-12-19 12:29:00                 NO_BATCH_INFO
-#> 57:                     82    2023-12-21 12:36:35                 NO_BATCH_INFO
-#> 58:                     28    2023-12-21 00:16:06          SINGLE_BATCH_SUCCESS
+#> 18:                     78    2023-12-11 18:39:23                 NO_BATCH_INFO
+#> 19:                    281    2023-12-21 12:53:40                 NO_BATCH_INFO
+#> 20:                     47    2023-09-07 23:15:06                 NO_BATCH_INFO
+#> 21:                    480    2024-03-22 23:43:22 UNINFORMATIVE_HEADERS_FAILURE
+#> 22:                     12    2023-12-16 10:59:29       BATCH_CORRECTED_SUCCESS
+#> 23:                    205    2023-12-20 12:59:34                 NO_BATCH_INFO
+#> 24:                     21    2023-12-20 08:00:37       NO_BATCH_EFFECT_SUCCESS
+#> 25:                     98    2023-12-06 18:45:01                 NO_BATCH_INFO
+#> 26:                    128    2023-12-21 10:56:11                 NO_BATCH_INFO
+#> 27:                      6    2023-12-20 21:45:32          SINGLE_BATCH_SUCCESS
+#> 28:                     18    2024-02-22 08:34:38       NO_BATCH_EFFECT_SUCCESS
+#> 29:                     32    2023-12-20 11:14:58                 NO_BATCH_INFO
+#> 30:                    168    2023-12-19 06:35:04                 NO_BATCH_INFO
+#> 31:                     88    2023-12-20 15:25:17                 NO_BATCH_INFO
+#> 32:                     50    2023-09-07 23:11:52                 NO_BATCH_INFO
+#> 33:                     50    2023-12-16 09:50:20                 NO_BATCH_INFO
+#> 34:                     82    2023-12-21 12:36:35                 NO_BATCH_INFO
+#> 35:                     46    2023-09-07 23:15:42                 NO_BATCH_INFO
+#> 36:                     88    2023-12-19 22:14:40                 NO_BATCH_INFO
+#> 37:                     36    2023-12-21 04:26:14       NO_BATCH_EFFECT_SUCCESS
+#> 38:                     53    2023-12-19 12:29:00                 NO_BATCH_INFO
+#> 39:                    235    2023-12-21 10:57:20       NO_BATCH_EFFECT_SUCCESS
+#> 40:                     28    2023-12-21 00:16:06          SINGLE_BATCH_SUCCESS
+#> 41:                     39    2023-06-09 16:41:44                 NO_BATCH_INFO
+#> 42:                    102    2023-12-16 09:48:59       NO_BATCH_EFFECT_SUCCESS
+#> 43:                     98    2023-06-08 22:47:42                 NO_BATCH_INFO
+#> 44:                     16    2023-12-21 12:58:54                 NO_BATCH_INFO
+#> 45:                    169    2023-12-18 01:28:03                 NO_BATCH_INFO
+#> 46:                     44    2023-09-07 23:17:50                 NO_BATCH_INFO
+#> 47:                    286    2023-12-22 08:39:18                 NO_BATCH_INFO
+#> 48:                     27    2023-09-07 23:19:35                 NO_BATCH_INFO
+#> 49:                     32    2023-12-16 10:02:53                 NO_BATCH_INFO
+#> 50:                    102    2023-11-30 00:38:00                 NO_BATCH_INFO
+#> 51:                     34    2023-12-16 10:28:32                 NO_BATCH_INFO
+#> 52:                     30    2023-12-17 14:10:06       NO_BATCH_EFFECT_SUCCESS
+#> 53:                     16    2024-01-24 09:18:15       NO_BATCH_EFFECT_SUCCESS
+#> 54:                    105    2023-12-06 22:52:55                 NO_BATCH_INFO
+#> 55:                     61    2023-12-20 07:59:57       NO_BATCH_EFFECT_SUCCESS
+#> 56:                     66    2023-09-21 21:43:05                 NO_BATCH_INFO
+#> 57:                    144    2023-12-19 06:34:12       BATCH_CORRECTED_SUCCESS
+#> 58:                     99    2023-12-07 20:23:13                 NO_BATCH_INFO
 #> 59:                      6    2023-12-17 09:06:10                 NO_BATCH_INFO
 #> 60:                      6    2023-12-17 09:01:07                 NO_BATCH_INFO
 #> 61:                      8    2023-12-16 05:30:07          SINGLE_BATCH_SUCCESS
@@ -726,8 +726,8 @@ 
Examples

 #> 69:                      9    2023-12-16 23:32:03                 NO_BATCH_INFO
 #> 70:                     23    2023-12-20 20:07:23                 NO_BATCH_INFO
 #> 71:                     26    2023-12-19 05:21:57                 NO_BATCH_INFO
-#> 72:                     39    2023-12-22 09:37:54                 NO_BATCH_INFO
-#> 73:                     78    2023-12-19 02:38:44                 NO_BATCH_INFO
+#> 72:                     78    2023-12-19 02:38:44                 NO_BATCH_INFO
+#> 73:                     39    2023-12-22 09:37:54                 NO_BATCH_INFO
 #> 74:                      8    2023-12-17 03:18:18                 NO_BATCH_INFO
 #> 75:                      9    2023-12-18 20:37:33       BATCH_CORRECTED_SUCCESS
 #> 76:                      6    2023-12-21 07:36:15          SINGLE_BATCH_SUCCESS
@@ -742,14 +742,14 @@ 
Examples

 #> 85:                     20    2023-12-16 02:52:32          SINGLE_BATCH_SUCCESS
 #> 86:                     27    2023-12-16 11:24:58                 NO_BATCH_INFO
 #> 87:                      8    2023-12-20 08:20:04       NO_BATCH_EFFECT_SUCCESS
-#> 88:                      9    2023-12-21 08:09:04       NO_BATCH_EFFECT_SUCCESS
-#> 89:                     18    2023-12-21 08:09:27                 NO_BATCH_INFO
-#> 90:                      9    2023-12-21 08:08:42       NO_BATCH_EFFECT_SUCCESS
+#> 88:                      9    2023-12-21 08:08:42       NO_BATCH_EFFECT_SUCCESS
+#> 89:                      9    2023-12-21 08:09:04       NO_BATCH_EFFECT_SUCCESS
+#> 90:                     18    2023-12-21 08:09:27                 NO_BATCH_INFO
 #> 91:                      6    2023-12-21 03:24:53          SINGLE_BATCH_SUCCESS
 #> 92:                      4    2023-12-17 10:28:51                 NO_BATCH_INFO
-#> 93:                      7    2023-12-17 00:58:06                 NO_BATCH_INFO
-#> 94:                     14    2023-12-17 12:50:03                 NO_BATCH_INFO
-#> 95:                     11    2023-12-18 10:20:10                 NO_BATCH_INFO
+#> 93:                     11    2023-12-18 10:20:10                 NO_BATCH_INFO
+#> 94:                      7    2023-12-17 00:58:06                 NO_BATCH_INFO
+#> 95:                     14    2023-12-17 12:50:03                 NO_BATCH_INFO
 #> 96:                     20    2023-12-20 10:55:21          SINGLE_BATCH_SUCCESS
 #> 97:                     34    2024-02-21 08:58:43          BATCH_EFFECT_FAILURE
 #>     experiment.sampleCount experiment.lastUpdated    experiment.batchEffectText
@@ -856,7 +856,7 @@ 
Examples

 #>     experiment.rawData geeq.qScore geeq.sScore taxon.name  taxon.scientific
 #>                  <int>       <num>       <num>     <char>            <char>
 #>  1:                 NA          NA          NA      human      Homo sapiens
-#>  2:                 NA          NA          NA      mouse      Mus musculus
+#>  2:                 NA          NA          NA      human      Homo sapiens
 #>  3:                 NA          NA          NA      human      Homo sapiens
 #>  4:                 NA          NA          NA      human      Homo sapiens
 #>  5:                 NA          NA          NA      human      Homo sapiens
@@ -866,7 +866,7 @@ 
Examples

 #>  9:                 NA          NA          NA      human      Homo sapiens
 #> 10:                 NA          NA          NA      mouse      Mus musculus
 #> 11:                 NA          NA          NA      human      Homo sapiens
-#> 12:                 NA          NA          NA      human      Homo sapiens
+#> 12:                 NA          NA          NA      mouse      Mus musculus
 #> 13:                 NA          NA          NA      human      Homo sapiens
 #> 14:                 NA          NA          NA      human      Homo sapiens
 #> 15:                 NA          NA          NA      human      Homo sapiens
@@ -947,16 +947,16 @@ 
Examples

 #> 90:                 NA          NA          NA      mouse      Mus musculus
 #> 91:                 NA          NA          NA      mouse      Mus musculus
 #> 92:                 NA          NA          NA      mouse      Mus musculus
-#> 93:                 NA          NA          NA      mouse      Mus musculus
+#> 93:                 NA          NA          NA      human      Homo sapiens
 #> 94:                 NA          NA          NA      mouse      Mus musculus
-#> 95:                 NA          NA          NA      human      Homo sapiens
+#> 95:                 NA          NA          NA      mouse      Mus musculus
 #> 96:                 NA          NA          NA      mouse      Mus musculus
 #> 97:                 NA          NA          NA      human      Homo sapiens
 #>     experiment.rawData geeq.qScore geeq.sScore taxon.name  taxon.scientific
 #>     taxon.ID taxon.NCBI taxon.database.name taxon.database.ID
 #>        <int>      <int>              <char>             <int>
 #>  1:        1       9606                hg38                87
-#>  2:        2      10090                mm10                81
+#>  2:        1       9606                hg38                87
 #>  3:        1       9606                hg38                87
 #>  4:        1       9606                hg38                87
 #>  5:        1       9606                hg38                87
@@ -966,7 +966,7 @@ 
Examples

 #>  9:        1       9606                hg38                87
 #> 10:        2      10090                mm10                81
 #> 11:        1       9606                hg38                87
-#> 12:        1       9606                hg38                87
+#> 12:        2      10090                mm10                81
 #> 13:        1       9606                hg38                87
 #> 14:        1       9606                hg38                87
 #> 15:        1       9606                hg38                87
@@ -1047,9 +1047,9 @@ 
Examples

 #> 90:        2      10090                mm10                81
 #> 91:        2      10090                mm10                81
 #> 92:        2      10090                mm10                81
-#> 93:        2      10090                mm10                81
+#> 93:        1       9606                hg38                87
 #> 94:        2      10090                mm10                81
-#> 95:        1       9606                hg38                87
+#> 95:        2      10090                mm10                81
 #> 96:        2      10090                mm10                81
 #> 97:        1       9606                hg38                87
 #>     taxon.ID taxon.NCBI taxon.database.name taxon.database.ID

Identifying what to search for -parkinson’s disease symptom… +parkinson’s disease -http://www.ebi…/EFO_0600011 +http://www.ebi…/EFO_0002508 @@ -176,10 +176,10 @@