Unknown,Transcriptomics,Genomics,Proteomics

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Effect of acute ethanol on medidal prefronal cortex across BXD genetic mapping panel and progenitors.

ABSTRACT: In order to elucidate the molecular mechanisms underlying individual variation in sensitivity to ethanol we profiled the prefrontal cortex transcriptomes of two inbred strains that exhibit divergent responses to acute ethanol, the C57BL6/J (B6) and DBA/2J (D2) strains, as well as 27 members of the BXD recombinant inbred panel, which was derived from a B6 x D2 cross. With this dataset we were able to identify several gene co-expression networks that were robustly altered by acute ethanol across the BXD panel. These ethanol-responsive gene-enriched networks were heavily populated by genes regulating synaptic transmission and neuroplasticity, and showed strong genetic linkage to discreet chromosomal loci. Network-based measurements of node importance identified several hub genes as established regulators of ethanol response phenotypes, while other hubs represent novel candidate modulators of ethanol responses. Animals were injected intraperitoneally (IP) with saline or 1.8 g/kg of ethanol. As part of a parallel study of ethanol induced anxiolysis, all mice underwent behavioral testing that included 15 minutes of restraint in a 50 mL conical tube followed by 10 minutes in a light-dark chamber. Mice were killed by cervical dislocation four hours following IP injection. Immediately thereafter, brains were extracted and chilled for 1 minute in iced phosphate buffer before being microdissected into 8 constituent regions, including the medial prefrontal cortex. Samples were randomly assigned to batch groups prior to total RNA extraction, cRNA synthesis and hybridization. Each microarray represent a pooling of 4-5 animals.

ORGANISM(S): Mus musculus

SUBMITTER: Michael Miles

PROVIDER: E-GEOD-28515 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Individual genetic variation affects gene expression and cell phenotype by acting within complex molecular circuits, but this relationship is still largely unknown. Here, we combine genomic and meso-scale profiling with novel computational methods to detect genetic variants that affect the responsiveness of gene expression to stimulus (responsiveness QTLs) and position them in circuit diagrams. We apply this approach to study individual variation in transcriptional responsiveness to three different pathogen components in the model response of primary bone marrow dendritic cells (DCs) from recombinant inbred mice strains. We show that reQTLs are common both in cis (affecting a single target gene) and in trans (pleiotropically affecting co-regulated gene modules) and are specific to some stimuli but not others. Leveraging the stimulus-specific activity of reQTLs and the differential responsiveness of their associated targets, we show how to position reQTLs within the context of known pathways in this regulatory circuit. For example, we find that a pleiotropic trans-acting genetic factor in chr1:129-165Mb affects the responsiveness of 35 anti-viral genes only during an anti-viral like stimulus. Using RNAi we uncover RGS16 the likely causal gene in this interval, and an activator of the antiviral response. Our approach charts an experimental and analytic path to decipher the mechanisms underlying genetic variation in other complex circuits in primary mammalian cells. The transcriptional response of DCs to pathogen components in the context of the mouse BXD recombinant inbred lines. We isolated DCs from individual age-synchronized female mice of different BXD and parental strains. We measured 30 global transcription profiles in resting and stimulated DCs from two parental strains (B6 and D2) and six BXD strains. We used bone marrow derived primary DCs from a panel of recombinant inbred (RI) BXD mice derived from a cross of the parental C57BL/6J (B6) and DBA/2J (D2) strains. We measured transcriptional responses in DCs from each of two parental strains and 6 BXD strains stimulated by three ligands of Toll-like receptors: LPS (TLR4 ligand), Pam3CSK ('PAM', TLR2 ligand) and poly IC (TLR3/RIG-I ligand).

Project description:Purpose: The goal of the present study is to provide an independent assessment of the retinal transcriptome signatures of the C57BL/6J (B6) and DBA/2J (D2) mice and to enhance existing microarray datasets for accurately defining the allelic differences in the BXD recombinant inbred strains. Methods: Retinas from both B6 and D2 mice (3 of each) were used for the RNA-seq analysis. Transcriptome features were examined for both strains. Differentially expressed genes between the 2 strains were identified and bioinformatic analysis was performed to analyze the transcriptome differences between B6 and D2 strains, including Gene ontology (GO) analysis, Phenotype and Reactome enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The RNA-seq data were then directly compared with one of the microarray datasets (DoD Retina Normal Affy MoGene 2.0 ST RMA Gene Level Microarray Database) hosted on GeneNetwork (www.genenetwork.org). Results: RNA-seq provided an in-depth analysis of the transcriptome of the B6 and D2 retina with a total of more than 30,000,000 reads per sample. Over 70% of the reads were uniquely mapped, resulting in a total of 18,100 gene counts for all 6 samples. 1,665 genes were differentially expressed, with 858 of these more highly expressed in B6 and 807 more highly expressed in D2. Several molecular pathways were differentially active between the two strains, including the retinoic acid metabolic process, endoplasmic reticulum lumen, extracellular matrix organization, and PI3K-Akt signaling pathway. The most enriched KEGG pathways were the pentose and glucuronate interconversions pathway, the cytochrome P450 pathway, protein digestion and absorption pathway and the ECM-receptor interaction pathway. Each of these pathways had a more than 4-fold enrichment. The DoD normal retina microarray database provided expression profiling for 26,191 annotated transcripts for B6 mouse, D2 mouse and 53 BXD strains. A total of 13,793 genes in this microarray dataset were comparable to the RNA-seq dataset. For both B6 and D2, the RNA-seq data and microarray data were highly correlated with each other (Pearson's r = 0.780 for B6 and 0.784 for D2). Our results suggest that the microarray dataset can reliably detect differentially expressed genes between the B6 and D2 retinas, with a positive predictive value of 45.6%, and a negative predictive value of 93.6%. Examples of true positive and false positive genes are provided. Conclusions: Retinal transcriptome features of B6 and D2 mouse strains provide a useful reference for a better understanding of the mouse retina. Generally, the microarray database presented on GeneNetwork shows good agreement with the RNA-seq data, while we note that any allelic difference between B6 and D2 should be verified with the latter.

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Effect of acute ethanol on medidal prefronal cortex across BXD genetic mapping panel and progenitors.

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