Striatum Gene Expression Correlates for LD_PCT_DISTLIGHT measured in BXD RI Females obtained using GeneNetwork Striatum M430V2 (Apr05) RMA. The LD_PCT_DISTLIGHT measures Light-Dark Box Percentage of distance traveled in light compartment 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
Genes that are differentially expressed in response to over expressing MECP2 (causes anxiety phenotype) in the amygdala. Statistics reported as fold change.
Authors:
Samaco RC, Mandel-Brehm C, McGraw CM, Shaw CA, McGill BE, Zoghbi HY
Here, female High Drinking in the Dark (HDID) mice were stereotaxically injected with 0.5uL rAAV2/5-CMV-Cre-GFP and 0.5uL rAAV2-hSyn-DIO-hM3Dq-mCherry bilaterally into the NAc. A Drinking in the Dark (DID) experiment lasting 6 weeks was carried out with 2 fluid groups (water or ethanol) and 2 treatment groups (VEH/VEH/VEH or VEH/CNO/VEH). Mice were serially treated with vehicle prior to DID during week 1 to establish baseline drinking, CNO (1mg/kg) during weeks 2-5 to measure the effects of chronic treatment, and then mice were treated with vehicle again during week 6 to determine if there were any lasting effects of chronic CNO treatment. This gene set comprises 1,473 genes that were differentially expressed in the nucleus accumbens of ethanol drinking HDID mice treated with vehicle as compared to the water drinking and vehicle treated control group.
Authors:
Darya Y. Pozhidayeva, Sean P. Farris, Calla M. Goeke, Evan J. Firsick, Kayla G. Townsley, Marina Guizzetti, and Angela R. Ozburn
Here, female High Drinking in the Dark (HDID) mice were stereotaxically injected with 0.5uL rAAV2/5-CMV-Cre-GFP and 0.5uL rAAV2-hSyn-DIO-hM3Dq-mCherry bilaterally into the NAc. A Drinking in the Dark (DID) experiment lasting 6 weeks was carried out with 2 fluid groups (water or ethanol) and 2 treatment groups (VEH/VEH/VEH or VEH/CNO/VEH). Mice were serially treated with vehicle prior to DID during week 1 to establish baseline drinking, CNO (1mg/kg) during weeks 2-5 to measure the effects of chronic treatment, and then mice were treated with vehicle again during week 6 to determine if there were any lasting effects of chronic CNO treatment. This gene set comprises 688 genes that were uniquely differentially expressed in the nucleus accumbens of only ethanol drinking HDID mice treated with vehicle as compared to the water drinking and vehicle treated control group.
Authors:
Darya Y. Pozhidayeva, Sean P. Farris, Calla M. Goeke, Evan J. Firsick, Kayla G. Townsley, Marina Guizzetti, and Angela R. Ozburn
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 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 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 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 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 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 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 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 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
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
Alcohol prefrontal cortex gene expression in females logFC
Description:
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
Gene expression in female mice PFC associated with chronic alcohol exposure
Description:
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
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