Mouse dorsal striatum RNA levels correlated to oxycodone SA levels
Description:
6 genes differentially expressed in the 11-week old C57BL/6J male mouse dorsal striatum correlating to levels of oxycodone self-administration based on RNA-seq data. Values presented are p-adjusted values. Data were taken from Table 4. GEO accession: GSE113948.
Authors:
Yong Zhang, Yupu Liang, Matthew Randesi, Vadim Yuferov, Connie Zhao, Mary Jeanne Kreek
32 genes differentially expressed in the 11-week old C57BL/6J male mouse ventral striatum following oxycodone self-administration based on RNA-seq data. The first five genes in this GeneSet are ones displaying experiment-wise significant changes in RNA levels; the remaining show point-wise significance. Data were taken from Table 1. Values presented are p-adjusted values.
GEO accession: GSE113948.
Authors:
Yong Zhang, Yupu Liang, Matthew Randesi, Vadim Yuferov, Connie Zhao, Mary Jeanne Kreek
7 genes differentially expressed in the 11-week old C57BL/6J male mouse dorsal striatum following oxycodone self-administration based on RNA-seq data. Values presented are p-adjusted values. Data were taken from Table 2. GEO accession: GSE113948.
Authors:
Yong Zhang, Yupu Liang, Matthew Randesi, Vadim Yuferov, Connie Zhao, Mary Jeanne Kreek
Mouse ventral striatum RNA levels correlated to oxycodone SA levels
Description:
3 genes differentially expressed in the 11-week old C57BL/6J male mouse ventral striatum correlated to levels of oxycodone self-administration based on RNA-seq data. Values presented are p-adjusted values. Data are from Table 3. GEO accession: GSE113948.
Authors:
Yong Zhang, Yupu Liang, Matthew Randesi, Vadim Yuferov, Connie Zhao, Mary Jeanne Kreek
Statistical analysis of the 224 reward-related genes differentially expressed in the 11-week old C57BL/6J male mouse ventral striatum following oxycodone self-administration based on RNA-seq data. Values presented are p-adjusted values. Note: genes with a p-adjusted value of "NA" were excluded. Data were take from Table 6. GEO accession: GSE113948.
Authors:
Yong Zhang, Yupu Liang, Matthew Randesi, Vadim Yuferov, Connie Zhao, Mary Jeanne Kreek
Genes that are suppressed by THC in total lymph node (LN) cells in C57BL/6J mice. Gene expression was evaluated via RNA-seq. Values presented are "1" for effect presence. Data taken from Supplemental Data 1.
Authors:
Xiaoming Yang, Marpe Bam, Prakash S Nagarkatti, Mitzi Nagarkatti
Genes that are induced by THC in total lymph node (LN) cells in C57BL/6J mice. Gene expression was evaluated via RNA-seq. Values presented are "1" for effect presence. Data taken from Supplemental Data 1.
Authors:
Xiaoming Yang, Marpe Bam, Prakash S Nagarkatti, Mitzi Nagarkatti
Genes that are suppressed by THC in CD4 cells in C57BL/6J mice. Gene expression was evaluated via RNA-seq. Values presented are "1" for effect presence. Data taken from Supplemental Data 1.
Authors:
Xiaoming Yang, Marpe Bam, Prakash S Nagarkatti, Mitzi Nagarkatti
Genes that are induced by THC in CD4 cells in C57BL/6J mice. Gene expression was evaluated via RNA-seq. Values presented are "1" for effect presence. Data taken from Supplemental Data 1.
Authors:
Xiaoming Yang, Marpe Bam, Prakash S Nagarkatti, Mitzi Nagarkatti
Genes that are suppressed by THC in both CD4+ cells and total lymph node (LN) cells in C57BL/6J mice. Gene expression was evaluated via RNA-seq. Values presented are "1" for effect presence. Data taken from Supplemental Data 1.
Authors:
Xiaoming Yang, Marpe Bam, Prakash S Nagarkatti, Mitzi Nagarkatti
Genes that are induced by THC in both CD4+ cells and total lymph node (LN) cells in C57BL/6J mice. Gene expression was evaluated via RNA-seq. Values presented are "1" for effect presence. Data taken from Supplemental Data 1.
Authors:
Xiaoming Yang, Marpe Bam, Prakash S Nagarkatti, Mitzi Nagarkatti
Heroin_mice_ongoing/early withdrawal from heroin intake
Description:
Authors develop a brain reward circuit-wide atlas of opioid-induced transcriptional regulation by combining RNA sequencing (RNA-seq) and heroin self-administration in male mice modeling multiple OUD-relevant conditions: acute heroin exposure, chronic heroin intake, context-induced drug-seeking following abstinence, and relapse. Bioinformatics analysis of this rich dataset identified numerous patterns of transcriptional regulation, with both region-specific and pan-circuit biological domains affected by heroin. Integration of RNA-seq data. This design enabled investigation of the transcriptomic landscape in multiple conditions that model distinct aspects of the OUD syndrome: first-ever heroin exposure (SH), ongoing/early withdrawal from heroin intake (H24), context-induced heroin-seeking following protracted abstinence (HS), and a combination of drug-induced and context-induced heroin-seeking representative of a relapse-like episode (HH). with OUD-relevant behavioral outcomes uncovered region-specific molecular changes and biological processes that predispose to OUD vulnerability.
Authors develop a brain reward circuit-wide atlas of opioid-induced transcriptional regulation by combining RNA sequencing (RNA-seq) and heroin self-administration in male mice modeling multiple OUD-relevant conditions: acute heroin exposure, chronic heroin intake, context-induced drug-seeking following abstinence, and relapse. Bioinformatics analysis of this rich dataset identified numerous patterns of transcriptional regulation, with both region-specific and pan-circuit biological domains affected by heroin. Integration of RNA-seq data. This design enabled investigation of the transcriptomic landscape in multiple conditions that model distinct aspects of the OUD syndrome: first-ever heroin exposure (SH), ongoing/early withdrawal from heroin intake (H24), context-induced heroin-seeking following protracted abstinence (HS), and a combination of drug-induced and context-induced heroin-seeking representative of a relapse-like episode (HH). with OUD-relevant behavioral outcomes uncovered region-specific molecular changes and biological processes that predispose to OUD vulnerability.
Heroin_mice_context-induced heroin-seeking following protracted abstinence
Description:
Authors develop a brain reward circuit-wide atlas of opioid-induced transcriptional regulation by combining RNA sequencing (RNA-seq) and heroin self-administration in male mice modeling multiple OUD-relevant conditions: acute heroin exposure, chronic heroin intake, context-induced drug-seeking following abstinence, and relapse. Bioinformatics analysis of this rich dataset identified numerous patterns of transcriptional regulation, with both region-specific and pan-circuit biological domains affected by heroin. Integration of RNA-seq data. This design enabled investigation of the transcriptomic landscape in multiple conditions that model distinct aspects of the OUD syndrome: first-ever heroin exposure (SH), ongoing/early withdrawal from heroin intake (H24), context-induced heroin-seeking following protracted abstinence (HS), and a combination of drug-induced and context-induced heroin-seeking representative of a relapse-like episode (HH). with OUD-relevant behavioral outcomes uncovered region-specific molecular changes and biological processes that predispose to OUD vulnerability.
Authors develop a brain reward circuit-wide atlas of opioid-induced transcriptional regulation by combining RNA sequencing (RNA-seq) and heroin self-administration in male mice modeling multiple OUD-relevant conditions: acute heroin exposure, chronic heroin intake, context-induced drug-seeking following abstinence, and relapse. Bioinformatics analysis of this rich dataset identified numerous patterns of transcriptional regulation, with both region-specific and pan-circuit biological domains affected by heroin. Integration of RNA-seq data. This design enabled investigation of the transcriptomic landscape in multiple conditions that model distinct aspects of the OUD syndrome: first-ever heroin exposure (SH), ongoing/early withdrawal from heroin intake (H24), context-induced heroin-seeking following protracted abstinence (HS), and a combination of drug-induced and context-induced heroin-seeking representative of a relapse-like episode (HH). with OUD-relevant behavioral outcomes uncovered region-specific molecular changes and biological processes that predispose to OUD vulnerability.
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 amygdala gene expression in females q-value
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
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
Alcohol prefrontal cortex gene expression in females q-value
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
Alcohol prefrontal cortex gene expression in males 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
Alcohol prefrontal cortex gene expression in males q-value
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
Alcohol hypothalamus 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
Alcohol hypothalamus gene expression in females q-value
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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