Genetic Analysis of the Diabetes-Prone C57BLKS Strain

点击翻译按钮获取中文摘要

DESCRIPTION (provided by applicant): The C57BLKS/J mouse (BKS), when carrying a mutation to the leptin receptor gene (BKS-db) is a classic model of obesity-induced diabetes in the mouse. Interestingly, >70% of the BKS genome is identical to that of C57BL/6J (B6) with the bulk of the remainder deriving from a DBA/2-like strain. And yet, the same leptin receptor mutation induces much less severe diabetes in the B6 than in the BKS mouse suggesting that the regions of introgressed DMA confer diabetes susceptibility. In preliminary work, we show that the 4-week old prediabetic BKS-db mouse is already severely insulin resistant and that hepatic lipogenic genes are generally suppressed compared to B6-db. In addition, we have used ultra-fine SNP mapping to precisely localize the introgressed DMA regions responsible for these phenotypes. Finally, we have developed a comprehensive set of congenic mouse strains with segments of DBA/2 DNA introgressed on a B6 background. These strains will allow us to test the impact of individual DBA regions on diabetes susceptibility in the BKS-db mouse. In this project, we will take a comprehensive approach to analysis of this striking diabetes susceptibility including (1) identifying and characterizing the associated shifts in lipid, glucose and insulin metabolism (2) mapping the responsible chromosomal loci, (3) identifying the underlying genetic variations and (4) characterizing specific shifts in metabolic pathways and networks that result from these variations. To accomplish this, we will take advantage of a number of recent developments in mouse genetics including complete DNA sequence information for several key mouse strains, high-density single nucleotide polymorphism mapping data for BKS and related strains, large scale expression array analysis applied to all animals in a genetic cross and, a set of newly emerging bioinformatics tools that use these data to prioritize candidate genes within each locus and to determine the metabolic networks involved. The results will provide an enhanced understanding of the mechanisms of obesity-induced diabetes.