Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Profiling of hypothalamus tissue from the F2 M16xICR mouse cross.

ABSTRACT: We profiled gene expression in hypothalamus tissue from F2 progeny from a cross between the outbred M16 (selectively bred for rapid weight gain) and ICR (control) mouse strains. We developed a framework for reconstructing tissue-to-tissue coexpression networks between genes in hypothalamus, liver or hypothalamus tissues that are independent of networks constructed from single tissue analyses. The subnetworks we identify as specific to tissue-to-tissue interactions associate with multiple obesity-relevant biological functions like circadian rhythm, energy balance, stress response, or immune response. Keywords: Tissue profiling in a mouse F2 cross. We analyzed 308 hypothalamus samples.

ORGANISM(S): Mus musculus

SUBMITTER: Eric Schadt

PROVIDER: E-GEOD-13745 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease.

Dobrin Radu R Zhu Jun J Molony Cliona C Argman Carmen C Parrish Mark L ML Carlson Sonia S Allan Mark F MF Pomp Daniel D Schadt Eric E EE

Genome biology 20090522 5

<h4>Background</h4>Obesity is a particularly complex disease that at least partially involves genetic and environmental perturbations to gene-networks connecting the hypothalamus and several metabolic tissues, resulting in an energy imbalance at the systems level.<h4>Results</h4>To provide an inter-tissue view of obesity with respect to molecular states that are associated with physiological states, we developed a framework for constructing tissue-to-tissue coexpression networks between genes in ...[more]

PMID: 19463160

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Project description:To identify the genes and pathways that underlie cardiovascular and metabolic phenotypes we performed an integrated analysis of a mouse C57BL/6J x A/J F2 (B6AF2) cross by relating genome-wide gene expression data from adipose, kidney, and liver tissues to physiological endpoints measured in the population. We have identified a large number of trait QTLs including loci driving variation in cardiac function on chromosomes 2 and 6 and a hotspot for adiposity, energy metabolism, and glucose traits on chromosome 8. Integration of adipose gene expression data identified a core set of genes that drive the chromosome 8 adiposity QTL. This chromosome 8 trans eQTL signature contains genes associated with mitochondrial function and oxidative phosphorylation and maps to a subnetwork with conserved function in humans that was previously implicated in human obesity. In addition, human eSNPs corresponding to orthologous genes from the signature show enrichment for association to type II diabetes in the DIAGRAM cohort, supporting the idea that the chromosome 8 locus perturbs a molecular network that in humans senses variations in DNA and in turn affects metabolic disease risk. We functionally validate predictions from this approach by demonstrating metabolic phenotypes in knockout mice for three genes from the trans eQTL signature, Akr1b8, Emr1, and Rgs2. In addition we show that the transcriptional signatures for knockout of two of these genes, Akr1b8 and Rgs2, map to the F2 network modules associated with the chromosome 8 trans eQTL signature and that these modules are in turn very significantly correlated with adiposity in the F2 population. Overall this study demonstrates how integrating gene expression data with QTL analysis in a network-based framework can aid in the elucidation of the molecular drivers of disease that can be translated from mice to humans. An F2 population was derived from a C57BL/6J x A/J mouse cross (B6AF2) and tissues were collected in 360 male and female progeny for microarray analysis. RNA extraction, probe preparation, and array hybridizations were all carried out at the Rosetta Inpharmatics Gene Expression Laboratory (Seattle, WA). Mouse tissues (gonadal adipose, kidney medulla, kidney cortex, hypothalamus) were pulverized prior to homogenization in a solution of GITC/BME (1:50 ratio) using a Covaris S2 cryo-prep (Covaris, Inc, Woburn, MA), followed by addition of a TRIzol water solution (4:1 ratio). 100% Chloroform was added to the TRIzol/GITC lysate (1:5 ratio) to facilitate separation of the organic and aqueous components using the phaselock (Eppendorf) system. The aqueous supernatant was further purified using a Promega SV-96 total RNA kit (Promega, Madison, WI), incorporating a DNase treatment. Total RNA samples were assayed for quality using an Agilent Bioanalyzer (Agilent Technologies, Santa Clara, CA) and for yield using Ribogreen (Ambion, Austin, TX) metrics prior to amplification. All samples, with the exception of kidney medulla, were amplified and labeled using a custom automated version of a 5 Âµg RT/IVT protocol and hybridizations to custom Agilent microarrays were performed as described [58]. The custom ink-jet microarrays used in this study were manufactured by Agilent Technologies and consisted of 4,732 control probes and 39,558 non-control oligonucleotides derived from mouse Unigene clusters, combined with RefSeq sequences and RIKEN full-length cDNA clones. For each individual animal tissue sample, labeled complementary RNA (cRNA) was hybridized against a pool of labeled cRNAs constructed from equal aliquots of RNA for that specific tissue from at least 200 individuals.

Dataset Information

Profiling of hypothalamus tissue from the F2 M16xICR mouse cross.

Publications

Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease.

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