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
Ethanol Induced Ataxia Chr#17 rs3672987(33247165) with right flanking marker rs4136382(3388912) and left marker rs3715723(58810428). This was mapped in 300 + (b6x129)F2 mice.
Differential gene expression in nucleus accumbens somatostatin interneurons_cocaine_mice_pvalue
Description:
To characterize transcriptional alterations that cocaine induces in these cells, we perform cell type-specific RNA-sequencing on FACS-isolated nuclei of somatostatin interneurons and identified 1100 DETs enriched for processes related to neural plasticity. To profile the entire (non poly-A selected) transcriptome of NAc somatostatin interneurons, we generated a transgenic reporter line (SST-TLG498 mice) to label the nuclei of these cells with a modified form of EGFP that is retained in the nuclear membrane (EGFP-F)22, enabling their isolation from NAc dissections using FACS. We succeeded in FACS-isolating nuclei suitable for RNA-sequencing from individual SST-TLG498 mice. We proceeded with differential expression analysis of the RNA-sequencing data to identify differentially expressed transcripts (DETs) in NAc somatostatin interneurons in response to repeated cocaine exposure: 778 transcripts were upregulated by cocaine and 322 were downregulated.
Authors:
Efrain A Ribeiro, Marine Salery, Joseph R Scarpa, Erin S Calipari, Peter J Hamilton, Stacy M Ku, Hope Kronman, Immanuel Purushothaman, Barbara Juarez, Mitra Heshmati, Marie Doyle, Casey Lardner, Dominicka Burek, Ana Strat, Stephen Pirpinias, Ezekiell Mouzon, Ming-Hu Han, Rachael L Neve, Rosemary C Bagot, Andrew Kasarskis, Ja Wook Koo, Eric J Nestler
Ethanol induced LORR Chr# 2 rs13476399(28144658) with right flanking marker rs3713997(3151175) and left marker rs3679483 (179861211). This was mapped in 300 + (b6x129)F2 mice.
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
Differentially expressed geens in the central amygdala (CeA) of male Sprague-Dawley rats (300-350 g prior to surgery, 325-375 g at start of self-administration) on day 2 following methamphetamine withdrawal. Gene Expression was evaluated via RNA-seq. Data taken from Supplementary Table S2. Values presented are adjusted p-values. Data available from GEO with accession number GSE111243."
Authors:
Hannah M Cates, Xuan Li, Immanuel Purushothaman, Pamela J Kennedy, Li Shen, Yavin Shaham, Eric J Nestler
Differentially expressed geens in the central amygdala (CeA) of male Sprague-Dawley rats (300-350 g prior to surgery, 325-375 g at start of self-administration) on day 35 following methamphetamine withdrawal. Gene Expression was evaluated via RNA-seq. Data taken from Supplementary Table S2. Values presented are adjusted p-values. Data available from GEO with accession number GSE111243."
Authors:
Hannah M Cates, Xuan Li, Immanuel Purushothaman, Pamela J Kennedy, Li Shen, Yavin Shaham, Eric J Nestler
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 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.
Gene expression correlation with morphine response in BXD mice
Description:
Dataset represents the correlation between gene expression in prefrontal cortex from GeneNetwork dataset DOD BXD PFC GWI CTL RNA-seq ComB (Dec19) TPM Log2 with GeneNetwork dataset for BXD mice labeled as: Central nervous system, pharmacology, behavior: Morphine response (50 mg/kg ip), locomotion (open field) from 45-60 min after injection in an activity chamber for males [cm] with peak at Chr10: 5.636905. Data shown here are p-adjusted values < 0.05.
Genes associated with Mus musculus that interact with the MeSH term 'Dibutyl Phthalate' (D003993). Incorporates data from 52 publications curated by the Comparative Toxicogenomics Database (CTD). ODE Gene scores represent number of supporting publications per gene.
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 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.
"A metabolic process - chemical reactions and pathways, including anabolism and catabolism, by which living organisms transform chemical substances - which involves a single organism." [GOC:jl]
Genes with particular expression in the Orbital area, ventrolateral part, layer 1. Data represent fold expression difference in structure versus grey matter average expression.
The effects of aging on gene expression in hippocampal tissues dissected from C57BL/6 mice was profiled. Mice were of ages 1, 6, 16, and 24 mo, with ten mice per age cohort (five mice of each sex). mRNA from each tissue sample was sampled and radiolabeled cDNA was generated, which was then hybridized to two filter membranes containing a total of 16,896 cDNA clones corresponding to 8,932 genes. p-value was uploaded.
Authors:
Zahn JM, Poosala S, Owen AB, Ingram DK, Lustig A, Carter A, Weeraratna AT, Taub DD, Gorospe M, Mazan-Mamczarz K, Lakatta EG, Boheler KR, Xu X, Mattson MP, Falco G, Ko MS, Schlessinger D, Firman J, Kummerfeld SK, Wood WH 3rd, Zonderman AB, Kim SK, Becker KG
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