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 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 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 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 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 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 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 complex psychiatric disorder with strong genetic and environmental risk factors. We studied the molecular perturbations underlying risky drinking behavior by measuring transcriptome changes across the neurocircuitry of addiction in a genetic mouse model of binge drinking. Sixteen generations of selective breeding for high blood alcohol levels after a binge drinking session produced global changes in brain gene expression in alcohol-naïve High Drinking in the Dark (HDID-1) mice. Using gene expression profiles to generate circuit-level hypotheses, we developed a systems approach that integrated regulation of gene coexpression networks across multiple brain regions, neuron-specific transcriptional signatures, and knowledgebase analytics. Whole-cell, voltage-clamp recordings from nucleus accumbens shell neurons projecting to the ventral tegmental area showed differential ethanol-induced plasticity in HDID-1 and control mice and provided support for one of the hypotheses. There were similarities in gene networks between HDID-1 mouse brains and postmortem brains of human alcoholics, suggesting that some gene expression patterns associated with high alcohol consumption are conserved across species. This study demonstrated the value of gene networks for data integration across biological modalities and species to study mechanisms of disease.
Authors:
Laura B Ferguson, Lingling Zhang, Daniel Kircher, Shi Wang, R Dayne Mayfield, John C Crabbe, Richard A Morrisett, R Adron Harris, Igor Ponomarev
Alcohol use disorder (AUD) is a complex psychiatric disorder with strong genetic and environmental risk factors. We studied the molecular perturbations underlying risky drinking behavior by measuring transcriptome changes across the neurocircuitry of addiction in a genetic mouse model of binge drinking. Sixteen generations of selective breeding for high blood alcohol levels after a binge drinking session produced global changes in brain gene expression in alcohol-naïve High Drinking in the Dark (HDID-1) mice. Using gene expression profiles to generate circuit-level hypotheses, we developed a systems approach that integrated regulation of gene coexpression networks across multiple brain regions, neuron-specific transcriptional signatures, and knowledgebase analytics. Whole-cell, voltage-clamp recordings from nucleus accumbens shell neurons projecting to the ventral tegmental area showed differential ethanol-induced plasticity in HDID-1 and control mice and provided support for one of the hypotheses. There were similarities in gene networks between HDID-1 mouse brains and postmortem brains of human alcoholics, suggesting that some gene expression patterns associated with high alcohol consumption are conserved across species. This study demonstrated the value of gene networks for data integration across biological modalities and species to study mechanisms of disease.
Authors:
Laura B Ferguson, Lingling Zhang, Daniel Kircher, Shi Wang, R Dayne Mayfield, John C Crabbe, Richard A Morrisett, R Adron Harris, Igor Ponomarev
Alcohol use disorder (AUD) is a complex psychiatric disorder with strong genetic and environmental risk factors. We studied the molecular perturbations underlying risky drinking behavior by measuring transcriptome changes across the neurocircuitry of addiction in a genetic mouse model of binge drinking. Sixteen generations of selective breeding for high blood alcohol levels after a binge drinking session produced global changes in brain gene expression in alcohol-naïve High Drinking in the Dark (HDID-1) mice. Using gene expression profiles to generate circuit-level hypotheses, we developed a systems approach that integrated regulation of gene coexpression networks across multiple brain regions, neuron-specific transcriptional signatures, and knowledgebase analytics. Whole-cell, voltage-clamp recordings from nucleus accumbens shell neurons projecting to the ventral tegmental area showed differential ethanol-induced plasticity in HDID-1 and control mice and provided support for one of the hypotheses. There were similarities in gene networks between HDID-1 mouse brains and postmortem brains of human alcoholics, suggesting that some gene expression patterns associated with high alcohol consumption are conserved across species. This study demonstrated the value of gene networks for data integration across biological modalities and species to study mechanisms of disease.
Authors:
Laura B Ferguson, Lingling Zhang, Daniel Kircher, Shi Wang, R Dayne Mayfield, John C Crabbe, Richard A Morrisett, R Adron Harris, Igor Ponomarev
Peroxisome proliferator-activated receptors (PPARs) are nuclear hormone receptors that act as ligand-activated transcription factors. Although prescribed for dyslipidemia and type-II diabetes, PPAR agonists also possess anti-addictive characteristics. PPAR agonists decrease ethanol consumption and reduce withdrawal severity and susceptibility to stress-induced relapse in rodents. However, the cellular and molecular mechanisms facilitating these properties have yet to be investigated. We tested three PPAR agonists in a continuous access two-bottle choice (2BC) drinking paradigm and found that tesaglitazar (PPARα/γ; 1.5 mg/kg) and fenofibrate (PPARα; 150 mg/kg) decreased ethanol consumption in male C57BL/6J mice while bezafibrate (PPARα/γ/β; 75 mg/kg) did not. We hypothesized that changes in brain gene expression following fenofibrate and tesaglitazar treatment lead to reduced ethanol drinking. We studied unbiased genomic profiles in areas of the brain known to be important for ethanol dependence, the prefrontal cortex (PFC) and amygdala, and also profiled gene expression in liver. Genomic profiles from the non-effective bezafibrate treatment were used to filter out genes not associated with ethanol consumption. Because PPAR agonists are anti-inflammatory, they would be expected to target microglia and astrocytes. Surprisingly, PPAR agonists produced a strong neuronal signature in mouse brain, and fenofibrate and tesaglitazar (but not bezafibrate) targeted a subset of GABAergic interneurons in the amygdala. Weighted gene co-expression network analysis (WGCNA) revealed co-expression of treatment-significant genes. Functional annotation of these gene networks suggested that PPAR agonists might act via neuropeptide and dopaminergic signaling pathways in the amygdala. Our results reveal gene targets through which PPAR agonists can affect alcohol consumption behavior.
Authors:
Laura B Ferguson, Dana Most, Yuri A Blednov, R Adron Harris
Peroxisome proliferator-activated receptors (PPARs) are nuclear hormone receptors that act as ligand-activated transcription factors. Although prescribed for dyslipidemia and type-II diabetes, PPAR agonists also possess anti-addictive characteristics. PPAR agonists decrease ethanol consumption and reduce withdrawal severity and susceptibility to stress-induced relapse in rodents. However, the cellular and molecular mechanisms facilitating these properties have yet to be investigated. We tested three PPAR agonists in a continuous access two-bottle choice (2BC) drinking paradigm and found that tesaglitazar (PPARα/γ; 1.5 mg/kg) and fenofibrate (PPARα; 150 mg/kg) decreased ethanol consumption in male C57BL/6J mice while bezafibrate (PPARα/γ/β; 75 mg/kg) did not. We hypothesized that changes in brain gene expression following fenofibrate and tesaglitazar treatment lead to reduced ethanol drinking. We studied unbiased genomic profiles in areas of the brain known to be important for ethanol dependence, the prefrontal cortex (PFC) and amygdala, and also profiled gene expression in liver. Genomic profiles from the non-effective bezafibrate treatment were used to filter out genes not associated with ethanol consumption. Because PPAR agonists are anti-inflammatory, they would be expected to target microglia and astrocytes. Surprisingly, PPAR agonists produced a strong neuronal signature in mouse brain, and fenofibrate and tesaglitazar (but not bezafibrate) targeted a subset of GABAergic interneurons in the amygdala. Weighted gene co-expression network analysis (WGCNA) revealed co-expression of treatment-significant genes. Functional annotation of these gene networks suggested that PPAR agonists might act via neuropeptide and dopaminergic signaling pathways in the amygdala. Our results reveal gene targets through which PPAR agonists can affect alcohol consumption behavior.
Authors:
Laura B Ferguson, Dana Most, Yuri A Blednov, R Adron Harris
The current study used two inbred mouse strains, C57BL/6 J and A/J, to investigate the genetics of behavioral responses to fentanyl. Mice were tested for conditioned place preference and fentanyl-induced locomotor activity. C57BL/6J mice formed a conditioned place preference to fentanyl injections and fentanyl increased their activity. Neither effect was noted in A/J mice. We conducted RNA-sequencing on the nucleus accumbens of mice used for fentanyl-induced locomotor activity. Surprisingly, we noted few differentially expressed genes using treatment as the main factor. However many genes differed between strains.
Authors:
Samuel J Harp, Mariangela Martini, Will Rosenow, Larry D Mesner, Hugh Johnson, Charles R Farber, Emilie F Rissman
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