Striatum Gene Expression Correlates for COCA_TIME_COND_CHG measured in BXD RI Females & Males obtained using GeneNetwork Striatum M430V2 (Apr05) RMA. The COCA_TIME_COND_CHG measures Cocaine CPP - difference in time spent relative to baseline drug exposure under the domain Cocaine. The correlates were thresholded at a p-value of less than 0.001.
Authors:
Philip VM, Duvvuru S, Gomero B, Ansah TA, Blaha CD, Cook MN, Hamre KM, Lariviere WR, Matthews DB, Mittleman G, Goldowitz D, Chesler EJ
Cerebellum Gene Expression Correlates for ZM_PCT_OPEN measured in BXD RI Females obtained using SJUT Cerebellum mRNA M430 (Mar05) RMA. The ZM_PCT_OPEN measures Zero Maze - Percentage open time under the domain Basal Behavior. The correlates were thresholded at a p-value of less than 0.001.
Authors:
Philip VM, Duvvuru S, Gomero B, Ansah TA, Blaha CD, Cook MN, Hamre KM, Lariviere WR, Matthews DB, Mittleman G, Goldowitz D, Chesler EJ
Cerebellum Gene Expression Correlates for ZM_TCLOSED measured in BXD RI Females obtained using SJUT Cerebellum mRNA M430 (Mar05) RMA. The ZM_TCLOSED measures Zero Maze - Time in Closed Arms under the domain Basal Behavior. The correlates were thresholded at a p-value of less than 0.001.
Authors:
Philip VM, Duvvuru S, Gomero B, Ansah TA, Blaha CD, Cook MN, Hamre KM, Lariviere WR, Matthews DB, Mittleman G, Goldowitz D, Chesler EJ
Cerebellum Gene Expression Correlates for ZM_TOPEN measured in BXD RI Females obtained using SJUT Cerebellum mRNA M430 (Mar05) RMA. The ZM_TOPEN measures Zero Maze - total time in open quadrants under the domain Basal Behavior. The correlates were thresholded at a p-value of less than 0.001.
Authors:
Philip VM, Duvvuru S, Gomero B, Ansah TA, Blaha CD, Cook MN, Hamre KM, Lariviere WR, Matthews DB, Mittleman G, Goldowitz D, Chesler EJ
The GEO2R tool was used to analyze microarray data from mice either mock-infected or infected with SARS-CoV1. The Gene sets used in the analysis were from GSE59185. GEO2R was used with default parameters. Genes with an adjusted p-value of <0.05 and a log fold change <-1.0 are included in this set. EntrezGene identifiers or sequence identifiers were converted to MGI identifiers. Genes that could not be converted were omitted. If a gene was represented more than once, the largest fold-change was chosen.
Authors:
Jose A Regla-Nava, Jose L Nieto-Torres, Jose M Jimenez-Guardeño, Raul Fernandez-Delgado, Craig Fett, Carlos Castaño-RodrĂguez, Stanley Perlman, Luis Enjuanes, Marta L DeDiego
Genes identified as expressed higher (up) in the AJ strain than in the PWK strain. Differentially expressed genes had a Q-value < 0.05 following the Benjamini-Hochberg methodology for false discovery rates in the limma+voom pipeline within edgeR. Q-value is reported from the topTable function.
Genes identified as expressed higher (up) in the AJ strain than in the PWK strain. Differentially expressed genes had a Q-value < 0.05 following the Benjamini-Hochberg methodology for false discovery rates in the limma+voom pipeline within edgeR. Q-value is reported from the topTable function.
Genes identified as expressed lower (down) in the AJ strain than in the AJ strain. Differentially expressed genes had a Q-value < 0.05 following the Benjamini-Hochberg methodology for false discovery rates in the limma+voom pipeline within edgeR. Q-value is reported from the topTable function.
Genes identified as expressed lower (down) in the AJ strain than in the CAST strain. Differentially expressed genes had a Q-value < 0.05 following the Benjamini-Hochberg methodology for false discovery rates in the limma+voom pipeline within edgeR. Q-value is reported from the topTable function.
Genes identified as expressed lower (down) in the AJ strain than in the NOD strain. Differentially expressed genes had a Q-value < 0.05 following the Benjamini-Hochberg methodology for false discovery rates in the limma+voom pipeline within edgeR. Q-value is reported from the topTable function.
Genes identified as expressed lower (down) in the AJ strain than in the NZO strain. Differentially expressed genes had a Q-value < 0.05 following the Benjamini-Hochberg methodology for false discovery rates in the limma+voom pipeline within edgeR. Q-value is reported from the topTable function.
Genes identified as expressed lower (down) in the AJ strain than in the S129 strain. Differentially expressed genes had a Q-value < 0.05 following the Benjamini-Hochberg methodology for false discovery rates in the limma+voom pipeline within edgeR. Q-value is reported from the topTable function.
Genes identified as expressed lower (down) in the AJ strain than in the WSB strain. Differentially expressed genes had a Q-value < 0.05 following the Benjamini-Hochberg methodology for false discovery rates in the limma+voom pipeline within edgeR. Q-value is reported from the topTable function.
Genes identified as expressed lower (down) in the AJ strain than in the AJ strain. Differentially expressed genes had a Q-value < 0.05 following the Benjamini-Hochberg methodology for false discovery rates in the limma+voom pipeline within edgeR. Q-value is reported from the topTable function.
Genes identified as expressed lower (down) in the AJ strain than in the CAST strain. Differentially expressed genes had a Q-value < 0.05 following the Benjamini-Hochberg methodology for false discovery rates in the limma+voom pipeline within edgeR. Q-value is reported from the topTable function.
Genes identified as expressed lower (down) in the AJ strain than in the NOD strain. Differentially expressed genes had a Q-value < 0.05 following the Benjamini-Hochberg methodology for false discovery rates in the limma+voom pipeline within edgeR. Q-value is reported from the topTable function.
Genes identified as expressed lower (down) in the AJ strain than in the NZO strain. Differentially expressed genes had a Q-value < 0.05 following the Benjamini-Hochberg methodology for false discovery rates in the limma+voom pipeline within edgeR. Q-value is reported from the topTable function.
Genes identified as expressed lower (down) in the AJ strain than in the S129 strain. Differentially expressed genes had a Q-value < 0.05 following the Benjamini-Hochberg methodology for false discovery rates in the limma+voom pipeline within edgeR. Q-value is reported from the topTable function.
Genes identified as expressed lower (down) in the AJ strain than in the WSB strain. Differentially expressed genes had a Q-value < 0.05 following the Benjamini-Hochberg methodology for false discovery rates in the limma+voom pipeline within edgeR. Q-value is reported from the topTable function.
Alcohol Use Disorder (AUD) is a chronic, relapsing syndrome diagnosed by a heteroge- neous set of behavioral signs and symptoms. There are no laboratory tests that provide direct objective evidence for diagnosis. Microarray and RNA-Seq technologies enable genome-wide transcriptome profiling at low costs and provide an opportunity to identify bio- markers to facilitate diagnosis, prognosis, and treatment of patients. However, access to brain tissue in living patients is not possible. Blood contains cellular and extracellular RNAs that provide disease-relevant information for some brain diseases. We hypothesized that blood gene expression profiles can be used to diagnose AUD. We profiled brain (prefrontal cortex, amygdala, and hypothalamus) and blood gene expression levels in C57BL/6J mice using RNA-seq one week after chronic intermittent ethanol (CIE) exposure, a mouse model of alcohol dependence. We found a high degree of preservation (rho range: [0.50, 0.67]) between blood and brain transcript levels. There was small overlap between blood and brain DEGs, and considerable overlap of gene networks perturbed after CIE related to cell- cell signaling (e.g., GABA and glutamate receptor signaling), immune responses (e.g., anti- gen presentation), and protein processing / mitochondrial functioning (e.g., ubiquitination, oxidative phosphorylation). Blood gene expression data were used to train classifiers (logis- tic regression, random forest, and partial least squares discriminant analysis), which were highly accurate at predicting alcohol dependence status (maximum AUC: 90.1%). These results suggest that gene expression profiles from peripheral blood samples contain a bio- logical signature of alcohol dependence that can discriminate between CIE and Air subjects.
Authors:
Laura B Ferguson, Amanda J Roberts, R Dayne Mayfield, Robert O Messing
Microglia depletion and alcohol gene expression logFC
Description:
Alcohol abuse induces changes in microglia morphology and immune function, but whether microglia initiate or simply amplify the harmful effects of alcohol exposure is still a matter of debate. Here, we determine microglia function in acute and voluntary drinking behaviors using a colony-stimulating factor 1 receptor inhibitor (PLX5622). We show that microglia depletion does not alter the sedative or hypnotic effects of acute intoxication. Microglia depletion also does not change the escalation or maintenance of chronic voluntary alcohol consumption. Transcriptomic analysis revealed that although many immune genes have been implicated in alcohol abuse, down regulation of microglia genes does not necessitate changes in alcohol intake. Instead, microglia depletion and chronic alcohol result in compensatory upregulation of alcohol-responsive, reactive astrocyte genes, indicating astrocytes may play a role in regulation of these alcohol behaviors. Taken together, our behavioral and transcriptional data indicate that microglia are not theprimary effector cell responsible for regulation of acute and voluntary alcohol behaviors. Because microglia depletion did not regulate acute or voluntary alcohol behaviors, we hypothesized that these doses were insufficient to activate microglia and recruit them to an effector phenotype. Therefore, we used a model of repeated immune activation using polyinosinic:polycytidylic acid
We investigated the role of microglia in a mouse model of alcohol dependence using a colony stimulating factor 1 receptor inhibitor (PLX5622) to deplete microglia and a chronic intermittent ethanol vapor two-bottle choice drinking procedure. Additionally, we examined anxiety-like behavior during withdrawal. We then analyzed synaptic neuroadaptations in the central nucleus of the amygdala (CeA) and gene expression changes in the medial prefrontal cortex (mPFC) and CeA from the same animals used for behavioral studies.
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