Dataset Information

Genome-level analysis of genetic regulation of liver gene expression networks.

ABSTRACT: The liver is the primary site for the metabolism of nutrients, drugs, and chemical agents. Although metabolic pathways are complex and tightly regulated, genetic variation among individuals, reflected in variations in gene expression levels, introduces complexity into research on liver disease. This study dissected genetic networks that control liver gene expression through the combination of large-scale quantitative mRNA expression analysis with genetic mapping in a reference population of BXD recombinant inbred mouse strains for which extensive single-nucleotide polymorphism, haplotype, and phenotypic data are publicly available. We profiled gene expression in livers of naive mice of both sexes from C57BL/6J, DBA/2J, B6D2F1, and 37 BXD strains using Agilent oligonucleotide microarrays. These data were used to map quantitative trait loci (QTLs) responsible for variations in the expression of about 19,000 transcripts. We identified polymorphic local and distant QTLs, including several loci that control the expression of large numbers of genes in liver, by comparing the physical transcript position with the location of the controlling QTL.The data are available through a public web-based resource (www.genenetwork.org) that allows custom data mining, identification of coregulated transcripts and correlated phenotypes, cross-tissue, and cross-species comparisons, as well as testing of a broad array of hypotheses.

SUBMITTER: Gatti D

PROVIDER: S-EPMC3518845 | biostudies-literature | 2007 Aug

REPOSITORIES: biostudies-literature

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Genome-level analysis of genetic regulation of liver gene expression networks.

Gatti Daniel D Maki Akira A Chesler Elissa J EJ Kirova Roumyana R Kosyk Oksana O Lu Lu L Manly Kenneth F KF Williams Robert W RW Perkins Andy A Langston Michael A MA Threadgill David W DW Rusyn Ivan I

Hepatology (Baltimore, Md.) 20070801 2

<h4>Unlabelled</h4>The liver is the primary site for the metabolism of nutrients, drugs, and chemical agents. Although metabolic pathways are complex and tightly regulated, genetic variation among individuals, reflected in variations in gene expression levels, introduces complexity into research on liver disease. This study dissected genetic networks that control liver gene expression through the combination of large-scale quantitative mRNA expression analysis with genetic mapping in a reference ...[more]

PMID: 17542012

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Project description:BACKGROUND: In dynamical models with feedback and sigmoidal response functions, some or all variables have thresholds around which they regulate themselves or other variables. A mathematical analysis has shown that when the dose-response functions approach binary or on/off responses, any variable with an equilibrium value close to one of its thresholds is very robust to parameter perturbations of a homeostatic state. We denote this threshold robustness. To check the empirical relevance of this phenomenon with response function steepnesses ranging from a near on/off response down to Michaelis-Menten conditions, we have performed a simulation study to investigate the degree of threshold robustness in models for a three-gene system with one downstream gene, using several logical input gates, but excluding models with positive feedback to avoid multistationarity. Varying parameter values representing functional genetic variation, we have analysed the coefficient of variation (CV) of the gene product concentrations in the stable state for the regulating genes in absolute terms and compared to the CV for the unregulating downstream gene. The sigmoidal or binary dose-response functions in these models can be considered as phenomenological models of the aggregated effects on protein or mRNA expression rates of all cellular reactions involved in gene expression. RESULTS: For all the models, threshold robustness increases with increasing response steepness. The CVs of the regulating genes are significantly smaller than for the unregulating gene, in particular for steep responses. The effect becomes less prominent as steepnesses approach Michaelis-Menten conditions. If the parameter perturbation shifts the equilibrium value too far away from threshold, the gene product is no longer an effective regulator and robustness is lost. Threshold robustness arises when a variable is an active regulator around its threshold, and this function is maintained by the feedback loop that the regulator necessarily takes part in and also is regulated by. In the present study all feedback loops are negative, and our results suggest that threshold robustness is maintained by negative feedback which necessarily exists in the homeostatic state. CONCLUSION: Threshold robustness of a variable can be seen as its ability to maintain an active regulation around its threshold in a homeostatic state despite external perturbations. The feedback loop that the system necessarily possesses in this state, ensures that the robust variable is itself regulated and kept close to its threshold. Our results suggest that threshold regulation is a generic phenomenon in feedback-regulated networks with sigmoidal response functions, at least when there is no positive feedback. Threshold robustness in gene regulatory networks illustrates that hidden genetic variation can be explained by systemic properties of the genotype-phenotype map.

Project description:Numerous genome-wide screens for polymorphisms that influence gene expression have provided key insights into the genetic control of transcription. Despite this work, the relevance of specific polymorphisms to in vivo expression and splicing remains unclear. We carried out the first genome-wide screen, to our knowledge, for SNPs that associate with alternative splicing and gene expression in human primary cells, evaluating 93 autopsy-collected cortical brain tissue samples with no defined neuropsychiatric condition and 80 peripheral blood mononucleated cell samples collected from living healthy donors. We identified 23 high confidence associations with total expression and 80 with alternative splicing as reflected by expression levels of specific exons. Fewer than 50% of the implicated SNPs however show effects in both tissue types, reflecting strong evidence for distinct genetic control of splicing and expression in the two tissue types. The data generated here also suggest the possibility that splicing effects may be responsible for up to 13 out of 84 reported genome-wide significant associations with human traits. These results emphasize the importance of establishing a database of polymorphisms affecting splicing and expression in primary tissue types and suggest that splicing effects may be of more phenotypic significance than overall gene expression changes. We evaluated 93 autopsy-collected cortical brain tissue samples with no defined neuropsychiatric condition and 80 peripheral blood mononucleated cell samples collected from living healthy donors. Affymetrix Human ST 1.0 exon arrays were used to assess exon and transcript expression levels for all samples used in the study. Replicate analyses were not performed. Exon array sample preparation from total RNA was conducted based on standard Affymetrix protocols. Data were normalized separately within PBMC and brain sets Affymetrix PLIER protocol with a sketch-quantile normalization procedure (Affymetrix Expression Console). Normalized files include brain core transcripts (NABRAINSETCOMBplier-gene-core.summary), brain all transcripts (NABRAINSETCOMBplier-gene-full.summary), brain core exons (NABRAINSETCOMBplier-exon-core.summary), brain all exons (NABRAINSETCOMBplier-exon-all.summary), PBMC core transcripts (NAPBMCfullsetplier-gene-core.summary), PBMC all transcripts (NAPBMCfullsetplier-gene-full.summary), PBMC core exons (NAPBMCfullsetplier-exon-core.summary), PBMC all exons (NAPBMCfullsetplier-exon-all.summary). all, full and core definitions are provided by Affymetrix.

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Dataset Information

Genome-level analysis of genetic regulation of liver gene expression networks.

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Genome-level analysis of genetic regulation of liver gene expression networks.

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