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

 描述（由申请人提供）：左心室肥大（LVH）是心血管疾病（CVD）的最强风险因素之一，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，研究心肌细胞（CMs）已经对疾病机制产生了重要的见解。现在有越来越多的证据表明，心脏基质组成的变化，特别是在糖尿病中，有助于CM的疾病过程。我们建议使用人类诱导多能干细胞衍生的心肌细胞（hiPSC-CMs）作为“盘中病人”模型，研究“糖尿病”心脏基质的作用和影响，其与心肌细胞的相互作用以及遗传因素的作用。我们的建议建立在广泛的数据基础上，这些数据是NHLBI HyperGen-LVH研究的一部分。这包括GWAS和全外显子组序列数据。此外，我们已经从250名HyperGen参与者中开发了hiPSC-CM细胞系。在目标1中，我们建议在从db/db小鼠的脱细胞心脏获得的基质上培养来自这些个体的hiPSC-CM，以研究细胞和分子变化。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全局表达变化，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性（SNP）。利用WES数据，我们还将进行组合序列和表达分析，以识别潜在的罕见变异。我们的建议利用现有资源，包括遗传和表型数据，以及先前建立的hiPSC-CM。我们的实验将为心肌细胞-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。更好地理解这些相互作用和网络的作用可以为开发新的治疗方案以及识别风险增加的个体的标记物奠定基础。随着人口老龄化和糖尿病患病率的增加，这变得尤为重要。

 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.