Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Profiling of liver tissue from the F2 M16xICR mouse cross.

ABSTRACT: We profiled gene expression in liver 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 liver 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 302 liver samples.

ORGANISM(S): Mus musculus

SUBMITTER: Radu Dobrin

PROVIDER: E-GEOD-13752 | 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 map the genetics of gene expression in metabolically relevant tissues and investigate the diversity of expression SNPs (eSNPs) in multiple tissues from the same individual, we collected four tissues from approximately 1,000 patients undergoing Roux-en-y gastric bypass and clinical traits associated with their weight loss and co-morbidities. We then performed high-throughput genotyping and gene expression profiling and carried out a genome-wide association analyses for more than one hundred thousand gene expression traits representing four metabolically relevant tissues; liver, omental adipose, subcutaneous adipose and stomach. We successfully identified 24,531 eSNPs corresponding to ~10,000 distinct genes. This represents the greatest number of eSNPs identified to our knowledge by any study to date and the first study to identify eSNPs from stomach tissue. We then demonstrate how these eSNPs provide a high quality disease map for each tissue in morbidly obese patients to not only inform genetic associations indentified in this cohort, but in previously published genome wide association studies as well. eSNPs and gene co-expression modules identification in morbidly obese patients represent a great resource that will aid in elucidating the key networks associated with morbid obesity, response to RYGB and disease as a whole. Keywords: Tissue profiling in a human cohort. Liver tissue was collected from patients at the time of RYGB surgery at Massachusetts General Hospital between 2000 and 2007. Samples were collected in RNAlater (Ambion/Applied Biosystems), stored at -80° and shipped to Rosetta Inpharmatics Gene Expression Laboratory Seattle, WA for extraction, amplification, labeling, and microarray processing. Samples processed ranged in size from 100-200mg. Total RNA extracted from liver was converted to fluorescently labeled cRNA that was hybridized to custom 44K DNA oligonucleotide microarrays manufactured by Agilent Technologies as described previously (Hughes et al. 2001; Schadt et al. 2008). Successful gene expression profiling results were collected from 651 liver samples.

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 liver tissue from the F2 M16xICR mouse cross.

Publications

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

Similar Datasets

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