Gene expression networks for metabolic syndrome traits in mice

University Of California Los Angeles, Los Angeles CA

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Abstract

[unreadable] DESCRIPTION (provided by applicant): The metabolic syndrome is a major public health problem because of the long-term health problems it causes. This work uses genetic studies with mice to find new genes that are critical for its development. Identifying these genes will help guide studies in humans that will eventually lead to better ways to detect and treat the metabolic syndrome, and so reduce diabetes and heart disease. The overall goal of this proposal is to utilize gene expression data in a novel manner to identify and validate key genes that influence the phenotypic expression of metabolic syndrome related traits in relevant mouse models. The approach is to integrate genomic and gene expression data from F2 intercross populations to identify causative genes and construct gene expression networks that model the important gene to gene and gene to trait relationships that control metabolic syndrome related trait expression. From such causative gene analyses and gene network model construction, the most important genes that are suggested to regulate the expression the metabolic syndrome will be screened for in vivo validation using transgenic or knockout technologies. Genes found to influence metabolic syndrome traits on screening will undergo further validation and characterization. The groundwork for this project has been completed as part of prior work. The model sets for study are completed intercrosses between DBA2/J and C57BL/6J inbred strains (BXD set; n=111) and C3H/HeJ and C57BL6/J inbred strains on an apoE null background (BXH set; n=334). Both intercross populations show significant variation in expression of traits associated with the metabolic syndrome. Genome wide expression microarrays (approximately 23,000 transcripts) from all F2 mice have been completed on liver for both sets, and additionally on adipose tissue, skeletal muscle, and brain in the BXH set. There are 4 basic components to this approach that make up our specific aims. (1) To identify candidate genes underlying QTL for metabolic syndrome related traits; (2) To identify trans-regulated genes whose transcript levels in turn regulate (are "causal" with regard to) clinical trait expression; (3) To identify regulated metabolic pathways and to develop causal gene expression network models for metabolic syndrome traits; and (4) To screen and validate candidate genes that are most strongly suggested to control metabolic syndrome related-trait expression. [unreadable] [unreadable] [unreadable]