Genetic Analysis of the Diabetes-Prone C57BLKS Strain

描述（由申请人提供）：携带瘦素受体基因突变（BKS-db）的C57 BLKS/J小鼠（BKS）是小鼠中肥胖诱导的糖尿病的经典模型。有趣的是，>70%的BKS基因组与C57 BL/6 J（B6）的基因组相同，剩余的大部分来自DBA/2样菌株。然而，相同的瘦素受体突变在B6小鼠中诱导的糖尿病严重程度远低于BKS小鼠，这表明渗入的DMA区域赋予糖尿病易感性。在初步研究中，我们发现4周龄的糖尿病前期BKS-db小鼠已经严重胰岛素抵抗，并且与B6-db相比，肝脏脂肪生成基因通常受到抑制。此外，我们已经使用了超精细的SNP定位，以精确地定位负责这些表型的基因渗入的DMA区域。最后，我们已经开发了一套全面的同源小鼠品系的DBA/2 DNA渗入的B6背景的片段。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，我们将采取全面的方法来分析这种引人注目的糖尿病易感性，包括（1）识别和表征脂质，葡萄糖和胰岛素代谢的相关变化（2）绘制负责的染色体位点，（3）识别潜在的遗传变异和（4）表征由这些变异引起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进展，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性作图数据，应用于遗传杂交中所有动物的大规模表达阵列分析，一组新出现的生物信息学工具，使用这些数据来优先考虑每个位点内的候选基因，并确定所涉及的代谢网络。这些结果将有助于我们更好地了解肥胖导致糖尿病的机制。

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.