Beyond GWAS of insulin resistance: An integrated approach to translate genetic association to function

超越胰岛素抵抗的 GWAS：将遗传关联转化为功能的综合方法

• 批准号: 9174452
• 负责人: Erik Ingelsson
• 金额: $ 42.56万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9174452
• 关键词: Adipocytes; Atherosclerosis; Biochemical; Biological Models; Blood Glucose; CRISPR/Cas technology; Candidate Disease Gene; Cardiovascular Diseases; Cells; Code; Cohort Studies; Collaborations; Collection; Communities; Computer Simulation; Data; Development; Diagnosis; Drug Targeting; Etiology; Evaluation; Functional disorder; Gene Expression; Gene Expression Regulation; General Population; Genes; Genetic Determinism; Genetic Transcription; Glucose; Gold; Hepatocyte; Human; Human Biology; Imagery; In Vitro; Individual; Insulin; Insulin Resistance; Intravenous; Knock-out; Knowledge; Lead; Link; Measurement; Measures; Medicine; Metabolic Pathway; Metabolism; Methodology; Methods; Mining; Modeling; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Orthologous Gene; Phenotype; Physiological; Prevention approach; Proteins; Proteomics; Public Health; Research Proposals; Resistance development; Resources; Risk; Role; Sampling; Series; Susceptibility Gene; Techniques; Translating; Work; Zebrafish; base; cohort; design; epigenomics; follow-up; genetic association; genome wide association study; glucose uptake; high throughput screening; in vitro Model; in vivo; in vivo Model; insight; knock-down; lipid metabolism; metabolic phenotype; metabolomics; novel strategies; population based; prevent; research study; resistance gene; trait; transcriptomics

PROJECT SUMMARY/ABSTRACT Insulin resistance is a physiological state in which normal levels of insulin fail to regulate blood glucose levels, and even in the absence of type 2 diabetes, there is strong evidence that insulin resistance dramatically increases risk for atherosclerosis and overt cardiovascular disease. In the past few years, we have identified 13 susceptibility loci for insulin resistance, but the causal gene and mechanisms are unknown for all but three of these loci, and the role of the ten remaining loci for development of insulin resistance has not been studied systematically. This represents a gold mine for in-depth physiological and mechanistic studies as increased understanding of the links between obesity, insulin resistance and cardiovascular disease may lead to new approaches to prevention and treatment that could have a huge public health impact. To establish and characterize genes associated with insulin resistance, we plan experiments in large human cohorts with functional follow-up using zebrafish and cell-based models. We will characterize suggested insulin resistance loci using detailed phenotypic information from large population-based samples (total N=13,811) assessed with dynamic measures of glucose and insulin metabolism, metabolomic, transcriptomic, epigenomic and proteomic profiling together with in silico data on gene regulation and transcription from public resources. Next, we will take 55 candidate genes forward to our pipeline for efficient characterization in zebrafish using high-throughput visualization techniques and biochemical measurements. We use CRISPR-Cas9 techniques to knockout the orthologous 55 genes from the 10 loci that are uncharacterized to date, and study the effect of perturbing these genes on insulin resistance. Finally, we will prioritize five candidate genes for mechanistic studies using gene knockdown in adipocytes and hepatocytes to study glucose, insulin and lipid metabolism, gene expression and metabolic pathways. By performing detailed follow-up analyses of loci hypothesized to be involved in insulin resistance, we expect to establish causal genes and mechanisms of action for several of these loci. The in-depth characterization using in vivo and in vitro models will provide further evidence towards causality and the mechanisms of action, as well as a first evaluation of which could be viable drug targets. Our approach of integrating comprehensive characterization in humans with experiments in functional model systems provides a translational framework, which by design is more likely to yield findings relevant for human biology and medicine. Importantly, we have access to unique study materials, state-of-the art methodology, and have a strong track record of successful collaborations in this field. Our work is anticipated to benefit the scientific community, to lead to new important insights into insulin resistance, cardiovascular disease and type 2 diabetes.

项目总结/摘要 胰岛素抵抗是一种生理状态，其中正常水平的胰岛素不能调节血糖水平， 即使没有2型糖尿病，也有强有力的证据表明， 增加动脉粥样硬化和明显心血管疾病的风险。在过去的几年里，我们发现 胰岛素抵抗的13个易感基因座，但除3个外，所有致病基因和机制均未知 这些基因座中，和其余10个基因座的胰岛素抵抗的发展的作用还没有研究 系统地这代表了一个金矿，为深入的生理和机制研究，因为增加 了解肥胖、胰岛素抵抗和心血管疾病之间的联系可能会导致新的 预防和治疗方法可能会对公共卫生产生巨大影响。建立和 表征与胰岛素抵抗相关的基因，我们计划在大型人类队列中进行实验， 使用斑马鱼和基于细胞的模型进行功能随访。 我们将使用来自大样本的详细表型信息来表征建议的胰岛素抵抗基因座。 基于人群的样本（总N= 13，811），采用葡萄糖和胰岛素的动态测量进行评估 代谢、代谢组学、转录组学、表观基因组学和蛋白质组学分析以及以下方面的计算机数据： 基因调控和转录的公共资源。 接下来，我们将把55个候选基因带到我们的管道中，以便使用 高通量可视化技术和生化测量。我们使用CRISPR-Cas9技术 从10个至今尚未鉴定的基因座中敲除55个基因，并研究 干扰这些基因对胰岛素抵抗的影响。 最后，我们将优先考虑五个候选基因，用于在脂肪细胞中使用基因敲低的机制研究 和肝细胞来研究葡萄糖、胰岛素和脂质代谢、基因表达和代谢途径。 通过对假设与胰岛素抵抗有关的位点进行详细的随访分析，我们预期 以建立这些基因座中的几个的致病基因和作用机制。深入刻画 使用体内和体外模型将为因果关系和作用机制提供进一步的证据， 以及对可能成为可行药物靶点的第一次评估。我们的综合方法 通过功能模型系统中的实验在人类中进行表征提供了翻译框架， 通过设计，它更有可能产生与人类生物学和医学相关的发现。重要的是我们有 获得独特的学习材料，最先进的方法，并有成功的良好记录 在这个领域的合作。我们的工作预计将有利于科学界，导致新的重要 深入了解胰岛素抵抗、心血管疾病和2型糖尿病。