Genetics of cardiomyocyte and cardiac matrix interaction: The HyperGen iPSC Study

 简介(申请人提供)：左心室肥厚(LVH)是导致心血管疾病(CVD)的最重要的危险因素之一，包括缺血性心脏病、慢性心力衰竭和CVD死亡。常见的风险因素包括高血压和糖尿病，而很大一部分风险也是由基因决定的。在细胞水平上，对心肌细胞(CMS)的研究对疾病发生机制有了重要的认识。现在有越来越多的证据表明，心脏基质成分的变化，特别是在糖尿病患者，有助于CMS的疾病过程。我们建议以人诱导多能干细胞来源的心肌细胞(hiPSC-CMS)作为“盘中病人”模型，研究糖尿病心肌基质的作用和影响、它与心肌细胞的相互作用以及遗传因素的作用。我们的建议基于大量数据，这些数据是NHLBI HyperGen-LVH研究的一部分。这包括GWAS和整个外显子组序列数据。此外，我们已经从250名HyperGen参与者中开发出HiPSC-CMS系。在目标1中，我们建议将这些个体的HiPSC-CMS培养在从db/db小鼠的去细胞心脏获得的基质上，以研究细胞和分子的变化。在目标2中，我们进行表达分析以确定与糖尿病基质相关的整体表达变化，然后进行通路分析以确定功能网络。最后，在目标3中，我们进行eQTL分析以确定与反应相关的单核苷酸多态(SNPs)。利用WES数据，我们还将进行联合序列和表达分析，以确定潜在的稀有变体。除了先前建立的hiPSC-CMS外，我们的建议还利用了现有的资源，包括遗传和表型数据。我们的实验将对心肌细胞-心脏基质相互作用的分子机制、其途径和调节反应的遗传因素提供新的见解。更好地了解这些相互作用和网络的作用可以为开发新的治疗选择以及识别风险增加的个人的标记奠定基础。随着人口老龄化和糖尿病患病率的增加，这一点变得尤为重要。

 DESCRIPTION (provided by applicant): Left ventricular hypertrophy (LVH) is one of the most potent risk factors for cardiovascular disease (CVD), including ischemic heart disease, chronic heart failure and CVD death. Common risk factors include hypertension and diabetes while a significant portion of the risk is also determined by genes. On a cellular level, studying cardiomyocytes (CMs) has yielded important insights into disease mechanism. There is now growing evidence suggesting that changes in the composition of the cardiac matrix particularly in diabetes contributes to the disease process in CMs. We propose to examine the role and impact of a 'diabetic' cardiac matrix, its interaction with cardiomyocytes and the role of genetic factors by using human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) as a `patient in a dish' model. Our proposal builds on extensive data which is available as part of the NHLBI HyperGen-LVH study. This includes GWAS and Whole Exome Sequence data. In addition, we have already developed hiPSC-CMs lines from 250 HyperGen participants. In Aim 1, we propose to culture hiPSC-CMs from these individuals on a matrix obtained from decellularized hearts of the db/db mouse to investigate cellular and molecular changes. In Aim 2 we perform expression analysis to determine global expression changes associated with the diabetic matrix followed by a pathway analysis to determine functional networks. Finally, in Aim 3, we perform eQTL analysis to determine single nucleotide polymorphisms (SNPs) associated with the response. Utilizing WES data, we will also perform a combined sequence and expression analysis to identify potential rare variants. Our proposal utilizes existing resources, including genetic and phenotypic data in addition to previously established hiPSC-CMs. Our experiments will provide novel insights into the molecular mechanisms underlying the cardiomyocyte-cardiac matrix interaction, its pathways and genetic factors modulating the response. A better understanding of the role of these interactions and networks can build the basis to develop novel treatment options as well as markers for the identification of individuals at increased risk. This becomes particularly important with an aging population and the increase in prevalence of diabetes.