cocaine related behavior 7 (Cocrb7) spans 28.968906 - 78.968906 Mbp (NCBI Build 37) on Chr 6. Obtained from MGI (http://www.informatics.jax.org) by searching for QTLs containing the keyword .
QTL for METH responses for home cage activity on Chr6 at Met (13.99 Mbp , Build 37)
Description:
METH responses for home cage activity spans 0.00 - 38.99 Mbp (NCBI Build 37) on Chr6. This interval was obtained by using an interval width of 25 Mbp around the peak marker (Build 37, MGI, http://informatics.jax.org).
QTL for cocaine related behavior on Chr6 at D6Mit183 (53.97 Mbp , Build 37)
Description:
cocaine related behavior spans 28.97 - 78.97 Mbp (NCBI Build 37) on Chr6. This interval was obtained by using an interval width of 25 Mbp around the peak marker (Build 37, MGI, http://informatics.jax.org).
QTL for METH responses for body temperature on Chr6 at D6Ncvs34 (54.50 Mbp , Build 37)
Description:
METH responses for body temperature spans 29.50 - 79.50 Mbp (NCBI Build 37) on Chr6. This interval was obtained by using an interval width of 25 Mbp around the peak marker (Build 37, MGI, http://informatics.jax.org).
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 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
Alcohol hypothalamus 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 hypothalamus 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
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
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
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
Analysis using RNA-seq of FACS-purified oligodendrocytes revealed a large cohort of morphine-regulated genes. In addition, to investigate cell-type-specific opioid responses, we performed single-cell RNA sequencing (scRNA-seq) of the nucleus accumbens of mice following acute morphine treatment. Differential expression analysis uncovered unique morphine-dependent transcriptional responses by oligodendrocytes and astrocytes.
Authors:
Denis Avey, Sumithra Sankararaman, Aldrin K Y Yim, Ruteja Barve, Jeffrey Milbrandt, Robi D Mitra
Sig. DEG mouse PFC endothelial cells P21 vs. P60_pvalue
Description:
We performed single cell RNA sequencing (scRNA-seq) to classify all neuron subtypes in prefrontal cortex (PFC) of adolescent (P21) (N = 4) and adult (P60) (N = 12) male C57BL/6 mice (strain mentioned but not explicit in publication) to characterize the transcriptional changes associated with this period (P21-P60). 12 independent biological replicates were used for each age. Each biological replicate was generated by pooling brain tissue from two mice (see methods for more info). To detect similar populations and identify corresponding cell clusters between the 10,646 P21 cells and the 11, 886 P60 PFC cells, we aligned the two scRNA-seq data sets in t-SNE by cross-correlation analysis (CCA)17 (Fig. (Fig.4a).4a). Using bootstrapped correlation, all clusters identified in the adult PFC are detected in the P21 PFC. Based on the expression of cell type-specific markers, the non-neuronal cells were clustered as: astrocytes (Gja1+), oligodendrocyte (Aspa+), newly formed (NF) oligodendrocytes (Bmp4+), oligodendrocyte precursors (OPC) (Pdgfra+), microglia (C1qa+) and endothelial cells (Flt1+) (Fig. 1c, d). The neurons express Snap25 and can be divided into excitatory (Slc17a7+) and inhibitory (Gad2+) neurons (Fig. 1c, d). We then analyzed transcriptional dynamics in each of the neuron subtypes between adolescence (P21) and adulthood (P60) in mouse. The differentially expressed genes between P21 and P60 cells for each cluster was performed using the “FindMarkers” function from the Seurat package using a likelihood ratio test and correcting for the number of detected unique molecular identifier (UMI) bias. Genelists contain significantly differentiated genes in each cell population cluster with fold change > 1.5 and p < 0.05.
Authors:
Aritra Bhattacherjee, Mohamed Nadhir Djekidel, Renchao Chen, Wenqiang Chen, Luis M Tuesta, Yi Zhang
Sig. DEG mouse PFC endothelial cells P21 vs. P60_logFC
Description:
We performed single cell RNA sequencing (scRNA-seq) to classify all neuron subtypes in prefrontal cortex (PFC) of adolescent (P21) (N = 4) and adult (P60) (N = 12) male C57BL/6 mice (strain mentioned but not explicit in publication) to characterize the transcriptional changes associated with this period (P21-P60). 12 independent biological replicates were used for each age. Each biological replicate was generated by pooling brain tissue from two mice (see methods for more info). To detect similar populations and identify corresponding cell clusters between the 10,646 P21 cells and the 11, 886 P60 PFC cells, we aligned the two scRNA-seq data sets in t-SNE by cross-correlation analysis (CCA)17 (Fig. (Fig.4a).4a). Using bootstrapped correlation, all clusters identified in the adult PFC are detected in the P21 PFC. Based on the expression of cell type-specific markers, the non-neuronal cells were clustered as: astrocytes (Gja1+), oligodendrocyte (Aspa+), newly formed (NF) oligodendrocytes (Bmp4+), oligodendrocyte precursors (OPC) (Pdgfra+), microglia (C1qa+) and endothelial cells (Flt1+) (Fig. 1c, d). The neurons express Snap25 and can be divided into excitatory (Slc17a7+) and inhibitory (Gad2+) neurons (Fig. 1c, d). We then analyzed transcriptional dynamics in each of the neuron subtypes between adolescence (P21) and adulthood (P60) in mouse. The differentially expressed genes between P21 and P60 cells for each cluster was performed using the “FindMarkers” function from the Seurat package using a likelihood ratio test and correcting for the number of detected unique molecular identifier (UMI) bias. Genelists contain significantly differentiated genes in each cell population cluster with fold change > 1.5 and p < 0.05.
Authors:
Aritra Bhattacherjee, Mohamed Nadhir Djekidel, Renchao Chen, Wenqiang Chen, Luis M Tuesta, Yi Zhang
Small intestine transcriptome changes in morphine treated mice. Eight-week-old, pathogen free, C57BL/6 male mice were used for this study (morphine n = 5, control n = 5). The animals were anesthetized using isoflurane (Pivetal®) and a 25mg slow-release morphine pellet or placebo pellet was implanted subcutaneously. Treatment lasted 16 hours. mRNA was purified from total RNA from using poly T-magnetic beads and strand specific library was constructed by using NEBNext Ultra RNA library prep kit. After quality control, the libraries were sequenced paired end by using Illumina sequencers (Illumina HiSeq 4000) for a read length of 150 base pairs. Clean reads were mapped to the mouse transcriptome using “STAR” software. The subsequent differential gene expression analysis was performed using DESeq2 R package (log2 (Fold change) > 1, P adj<0.05).
DEG male mouse forebrain 3-tri morphine vs saline_pvalue
Description:
To examine forebrain transcriptomic changes that might elucidate mechanisms of withdrawal, delayed development, and any long-term behavior changes, we generated transcriptomic signatures following our “3-trimester” exposure model (3-Tri). In addition, we also examined transcriptomes from animals that received opioids only during the gestational period (PND1) or only during the last trimester from PND 1–14 (PND 14). We sought to determine whether transcriptomic signatures vary based on the window of exposure, perhaps contributing to the discrepancies in the literature regarding acute and long-term outcomes. Brains were dissected from PND 1 pups 6 h after discovery. Brains were dissected from post-natal exposure only (PND 14) or 3-trimester exposure (3-tri) 6 h after the last morphine or saline injection. The number of animals per group was similar (N = 5–7 animals, male and female C57Bl/6NTac mice), and the quality controls, library construction and sequence parameters were also identical across all groups. Libraries were sequenced on a NovaSeq 6000 at a depth of 30 million total reads/sample using paired-end sequencing of 150 base pairs (PE150), to a depth of 30 million total reads/sample. Reads were then mapped to the mouse reference genome (Mus Musculus, GRCm38/mm10) using HISAT2 (version 2.2.1), and duplicated fragments were removed using Picard MarkDuplicates. Differential expression analysis between two conditions (e.g., Morphine and Saline) was performed in R (version 4.1.1) with DESeq2 (v1.32.0) package. Genes were assigned by the authors as differentially expressed if the (adjusted) (nominal) p-value < 0.05. All genes/scores are presented here.
Authors:
Amelia D Dunn, Shivon A Robinson, Chiso Nwokafor, Molly Estill, Julia Ferrante, Li Shen, Crystal O Lemchi, Jordi Creus-Muncunill, Angie Ramirez, Juliet Mengaziol, Julia K Brynildsen, Mark Leggas, Jamie Horn, Michelle E Ehrlich, Julie A Blendy
DEG mouse DSTR 24hr withdrawal cocaine vs saline SA_pvalue
Description:
To determine how a history of cocaine self-administration (SA) influences circuit-wide transcriptomes, RNA-seq was performed on PFC, dorsal striatum (DStr), NAc, basolateral amygdala (BLA), ventral hippocampus (vHIP), and VTA, obtained from the following six groups of male C57BL/6J mice (Figure 1A): saline SA + 24 hr withdrawal (WD) (S24, n=5–8); cocaine SA + 24 hr WD (C24, n=5–8); saline SA + 30 d WD + saline re-exposure (SS, n=5–8); saline SA + 30 d WD + cocaine exposure (SC, n=5–8); cocaine SA + 30 d WD + saline exposure (CS, n=3–7); and cocaine SA + 30 d WD + cocaine re-exposure (CC, n=5–7). Genes presensted here are from the cocaine or saline SA + 24hr withdrawal paradigm for each brain region.
Authors:
Deena M Walker, Hannah M Cates, Yong-Hwee E Loh, Immanuel Purushothaman, Aarthi Ramakrishnan, Kelly M Cahill, Casey K Lardner, Arthur Godino, Hope G Kronman, Jacqui Rabkin, Zachary S Lorsch, Philipp Mews, Marie A Doyle, Jian Feng, Benoit Labonté, Ja Wook Koo, Rosemary C Bagot, Ryan W Logan, Marianne L Seney, Erin S Calipari, Li Shen, Eric J Nestler
Gene expression mouse BLA pattern C CC vs S24_pvalue
Description:
RNA-seq was performed on PFC, dorsal striatum (DStr), NAc, basolateral amygdala (BLA), ventral hippocampus (vHIP), and VTA, obtained from the following six groups of male C57BL/6J mice (Figure 1A): saline SA + 24 hr withdrawal (WD) (S24, n=5–8); cocaine SA + 24 hr WD (C24, n=5–8); saline SA + 30 d WD + saline re-exposure (SS, n=5–8); saline SA + 30 d WD + cocaine exposure (SC, n=5–8); cocaine SA + 30 d WD + saline exposure (CS, n=3–7); and cocaine SA + 30 d WD + cocaine re-exposure (CC, n=5–7). To focus on genes that were uniquely altered following context/drug re-exposure after WD, we compared all groups to the same baseline (S24). We focused on three patterns associated with drug use: first-ever exposure to cocaine (SC; Pattern A; Figure 2B), re-exposure to cocaine-paired context (CS, Pattern B, Figure 2C), and re-exposure to cocaine-paired context + cocaine (CC, Pattern C, Figure 2D). Each Pattern includes genes that were both differentially expressed from S24 (p<0.05; fold change>15%) and distinct from all other groups. The pattern C genes were significantly differentially expressed (see above) between the cocaine SA with cocaine re-exposure after 30 days and baseline (i.e. CC vs S4). Differential expression of the pattern C genes are presented for each group (C24 vs S24, SS vs S4, SC vs S4, CS vs S4, and CC vs S4).
Authors:
Deena M Walker, Hannah M Cates, Yong-Hwee E Loh, Immanuel Purushothaman, Aarthi Ramakrishnan, Kelly M Cahill, Casey K Lardner, Arthur Godino, Hope G Kronman, Jacqui Rabkin, Zachary S Lorsch, Philipp Mews, Marie A Doyle, Jian Feng, Benoit Labonté, Ja Wook Koo, Rosemary C Bagot, Ryan W Logan, Marianne L Seney, Erin S Calipari, Li Shen, Eric J Nestler
Gene expression mouse VTA pattern C CC vs S24_pvalue
Description:
RNA-seq was performed on PFC, dorsal striatum (DStr), NAc, basolateral amygdala (BLA), ventral hippocampus (vHIP), and VTA, obtained from the following six groups of male C57BL/6J mice (Figure 1A): saline SA + 24 hr withdrawal (WD) (S24, n=5–8); cocaine SA + 24 hr WD (C24, n=5–8); saline SA + 30 d WD + saline re-exposure (SS, n=5–8); saline SA + 30 d WD + cocaine exposure (SC, n=5–8); cocaine SA + 30 d WD + saline exposure (CS, n=3–7); and cocaine SA + 30 d WD + cocaine re-exposure (CC, n=5–7). To focus on genes that were uniquely altered following context/drug re-exposure after WD, we compared all groups to the same baseline (S24). We focused on three patterns associated with drug use: first-ever exposure to cocaine (SC; Pattern A; Figure 2B), re-exposure to cocaine-paired context (CS, Pattern B, Figure 2C), and re-exposure to cocaine-paired context + cocaine (CC, Pattern C, Figure 2D). Each Pattern includes genes that were both differentially expressed from S24 (p<0.05; fold change>15%) and distinct from all other groups. The pattern C genes were significantly differentially expressed (see above) between the cocaine SA with cocaine re-exposure after 30 days and baseline (i.e. CC vs S4). Differential expression of the pattern C genes are presented for each group (C24 vs S24, SS vs S4, SC vs S4, CS vs S4, and CC vs S4).
Authors:
Deena M Walker, Hannah M Cates, Yong-Hwee E Loh, Immanuel Purushothaman, Aarthi Ramakrishnan, Kelly M Cahill, Casey K Lardner, Arthur Godino, Hope G Kronman, Jacqui Rabkin, Zachary S Lorsch, Philipp Mews, Marie A Doyle, Jian Feng, Benoit Labonté, Ja Wook Koo, Rosemary C Bagot, Ryan W Logan, Marianne L Seney, Erin S Calipari, Li Shen, Eric J Nestler
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