Genetic Analysis of the Diabetes-Prone C57BLKS Strain

描述(申请人提供)：C57BLKS/J小鼠(BKS)，当携带瘦素受体基因突变(BKS-db)时，是肥胖诱导的糖尿病小鼠的经典模型。有趣的是，&gt；70%的BKS基因组与C57BL/6J(B6)的基因组相同，其余大部分来自类似DBA/2的菌株。然而，同样的瘦素受体突变在B6小鼠中引起的糖尿病比在BKS小鼠中要小得多，这表明导入的DMA区域赋予糖尿病易感性。在前期工作中，我们发现4周大的糖尿病前期BKS-db小鼠已经存在严重的胰岛素抵抗，与B6-db相比，肝脏脂肪生成基因普遍受到抑制。此外，我们使用了超精细的SNP作图来精确定位负责这些表型的导入的DMA区域。最后，我们开发了一套全面的同源小鼠品系，将DBA/2 DNA片段导入到B6背景中。这些菌株将使我们能够测试单个DBA区域对BKS-db小鼠糖尿病易感性的影响。在这个项目中，我们将采用一种全面的方法来分析这种引人注目的糖尿病易感性，包括(1)识别和表征与脂、糖和胰岛素代谢相关的变化(2)绘制负责的染色体位置图，(3)识别潜在的遗传变异，以及(4)表征由这些变异导致的代谢途径和网络的特定变化。为了实现这一目标，我们将利用小鼠遗传学的一些最新发展，包括几个关键小鼠品系的完整DNA序列信息，BKS和相关品系的高密度单核苷酸多态图谱数据，应用于遗传杂交中所有动物的大规模表达阵列分析，以及一套新出现的生物信息学工具，这些工具使用这些数据来确定每个基因座内候选基因的优先顺序，并确定涉及的代谢网络。这一结果将加深人们对肥胖导致糖尿病的机制的理解。

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.