Genes and Gene Networks Associated with Obesity and Diabetes

描述（由申请人提供）：本项目的目的是鉴定在肥胖诱导的2型糖尿病发展中起作用的基因和基因网络。在上一个资助期，我们进行了有史以来第一次研究，调查胰岛基因表达的交叉。该杂交来自对leptinob突变诱导的糖尿病具有抗性（C57 BL/6或B6）或易感性（BTBR）的小鼠品系。除了胰岛，我们还调查了其他五种组织（肝脏、脂肪、肌肉、下丘脑和肾脏）中的基因表达。对所有小鼠进行B6和BTBR之间5，000个单核苷酸多态性的基因分型。我们调查了F2样本中的>100个临床特征，以及肝脏中的数百个微小RNA（miRNAs）。我们的研究产生了前所未有的数据集，使我们能够开发因果调控网络，预测基因之间的相互作用，中间表型如miRNA和mRNA，以及下游临床特征。具体而言，这些研究使我们能够：1）确定表达数量性状基因座（eQTL）; 2)鉴定“热点”，或似乎由单个基因座共调控的大量转录物; 3)利用遗传学作为锚来开发定向eQTL网络模型;以及4）将eQTL网络模型与肥胖和糖尿病相关的关键生理表型整合。总之，遗传结构（eQTL和热点）和重建的网络已经确定了基因组区域和基因，这些区域和基因可能包含肥胖依赖性生理学基础的调控元件。 2型糖尿病我们已经使用我们的网络构建方法来识别与临床表型密切相关的基因。该提案的具体目标是：1）鉴定和测试控制胰岛细胞复制所必需的胰岛细胞周期基因的调控基因组位点。完善和实验测试模型，预测调控基因座和负责胰岛细胞增殖能力的胰岛细胞周期基因之间的因果关系; 2）识别和测试控制肝脂肪变性的调控基因组基因座。构建和完善网络模型，将肝脏共调节基因与肝脏甘油三酯的积累联系起来; 3）利用miRNA丰度的遗传性来预测mRNA靶点，并将miRNA、mRNA和临床表型与可测试模型联系起来。具体目标1 - 3的成功完成主要取决于新的统计和计算方法的发展。这些将包括以下方法：a）解析多个生理和表达性状映射到的遗传区间; B）扩展我们的网络重构方法以推断性状之间的依赖性并结合先验信息;以及c）识别差异相关的基因集。 公共卫生相关性：该项目旨在了解肥胖引起的2型糖尿病的遗传因素。我们使用遗传学和基因组学方法来鉴定与这种疾病的关键代谢和病理过程有因果关系的基因。我们的研究将有助于了解一些但不是所有的肥胖者是如何成为糖尿病患者的，并可能开发生物标志物来识别患糖尿病风险最高的个体。

DESCRIPTION (provided by applicant): The objectives of this project are to identify genes and gene networks that play a role in the development of obesity-induced type 2 diabetes. In the previous grant period, we carried out the first-ever study that surveyed pancreatic islet gene expression in an intercross. The intercross was derived from mouse strains resistant (C57BL/6 or B6) or susceptible (BTBR) to diabetes induced by the leptinob mutation. In addition to islets, we surveyed gene expression in five other tissues (liver, adipose, muscle, hypothalamus, and kidney). All mice were genotyped for 5,000 single nucleotide polymorphisms between B6 and BTBR. We surveyed >100 clinical traits in the F2 sample, as well as hundreds of micro-RNAs (miRNAs) in liver. Our studies have yielded an unprecedented dataset, allowing us to develop causal regulatory networks that predict the interaction among genes, intermediate phenotypes such as miRNAs and mRNAs, and downstream clinical traits. Specifically these studies have allowed us to: 1) identify expression quantitative trait loci (eQTLs); 2) identify "hotspots", or large sets of transcripts that appear to be co-regulated by a single locus; 3) use genetics as an anchor to develop directional eQTL network models; and 4) integrate eQTL network models with key physiological phenotypes related to obesity and diabetes. Taken together, the genetic architecture (eQTL and hotspots) and reconstructed networks have identified genomic regions and genes that may contain regulatory elements that underlie the physiology of obesity-dependent type 2 diabetes. We have used our methods for network construction to identify genes that are closely associated with clinical phenotypes. The specific aims of this proposal are to: 1) Identify and test regulatory genomic loci that govern pancreatic islet cell cycle genes essential for ¿-cell replication. Refine and experimentally test models that predict the causal relationship between the regulatory loci and islet cell cycle genes responsible for the proliferative capacity of ¿-cells; 2) Identify and test the regulatory genomic loci governing hepatic steatosis. Construct and refine network models that relate co-regulated hepatic genes with accumulation of liver triglycerides; 3) Utilize the heritability of miRNA abundance to predict mRNA targets and relate miRNAs, mRNAs and clinical phenotypes with testable models. Successful completion of Specific Aims 1 - 3 is critically dependent on the development of novel statistical and computational methods. These will include methods to: a) resolve genetic intervals to which multiple physiological and expression traits map; b) extend our network reconstruction approach to infer dependence among traits and incorporate prior information; and c) identify differentially correlated gene sets. PUBLIC HEALTH RELEVANCE: This project is aimed at understanding the genetic factors responsible for obesity- induced type 2 diabetes. We use genetic and genomic approaches to identify genes that are causally related to the key metabolic and pathological processes underlying this disease. Our research will help to understand how some, but not all obese people become diabetic and perhaps develop biomarkers to identify individuals at greatest risk for developing diabetes.