Whole Brain Gene Expression Correlates for ST_PCT_PPI_85 measured in BXD RI Females obtained using INIA Brain mRNA M430 (Jun06) RMA. The ST_PCT_PPI_85 measures Prepulse inhibition at 85db 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
Whole Brain Gene Expression Correlates for ST_PCT_STARTLE_85 measured in BXD RI Females obtained using INIA Brain mRNA M430 (Jun06) RMA. The ST_PCT_STARTLE_85 measures Acoustic Startle Response Percentage of maximum response at 85 db 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
Striatum Gene Expression Correlates for ADRE_RIGHT_WT measured in BXD RI Females & Males obtained using GeneNetwork Striatum M430V2 (Apr05) RMA. The ADRE_RIGHT_WT measures Right adrenal weight under the domain Adrenals. 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
Striatum Gene Expression Correlates for ADRE_RIGHT_WT measured in BXD RI Females obtained using GeneNetwork Striatum M430V2 (Apr05) RMA. The ADRE_RIGHT_WT measures Right adrenal weight under the domain Adrenals. 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
A list of genes whose transcript abundance in the PFC changed significantly 4 hours after an acute dose of ethanol (1.8 g/kg). This list was generated using Fisher's Combined Probability test to analyze saline vs ethanol S-scores across B6 and D2 inbred strains (n=3) and 27 BXD RI lines (n=1). Statistical significance was determined using 1,000 permutations of S-score data and selecting for probe-sets with q-values < 0.05. Aaron Wolen 5-26-10.
Authors:
Wolen AR, Phillips CA, Langston MA, Putman AH, Vorster PJ, Bruce NA, York TP, Williams RW, Miles MF
These genes are a 1 class SAM significant (1% FDR) in nucleus accumbens (core + shell) for saline treated ("basal") control vs. Fyn KO mice. The list was filtered for an average Sscore >2.0 or <-2.0. Data from Farris and Miles, PLoS One, 2013.
Genes identified as expressed higher (up) 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 higher (up) 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 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 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 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 higher (up) 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 higher (up) 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 higher (up) 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 higher (up) 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.
Drug Naïve DO mice were tested for open field, light dark, hole board, novelty place preference before collecting the striatum. RNA-Seq data was analyzed with WGCNA using a soft thresholding power of 3 selected using the WGCNA scale-free topology R2 threshold of 0.9, signed network with a minimum module size of 30, correlation type is bicor, used numeric labels.
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
GeneWeaver