A Systems Biology Approach to Study Cardiac Arrhythmias: iPS Cells and In Silico Modeling

 描述（由申请人提供）：长QT综合征是最常见的心脏病性疾病，易导致心源性猝死。存在导致长QT综合征的巨大遗传异质性，这导致疾病严重程度和临床病程的相当大的变化。药物治疗通常无效，在某些人群中产生不良影响，对心脏性猝死风险的预测仍然相当原始。最近出现的新技术突破，如诱导多能干细胞（iPSC），提供了一个前所未有的机会，研究遗传变异性，药物反应性和疾病易感性之间的关联。此外，长QT诱发的心律失常的生物学在很大程度上是可量化的，因此可采用系统生物学方法。我们的系统生物学协作R 01提案的总体目标是开发一种综合的实验和计算方法来预测患者的特定药物反应。为此，我们建议利用来自患者特异性iPSC衍生的心肌细胞（iPSC-CM）的实验数据结合临床和基因组数据来构建第一个据我们所知的患者特异性心脏电活动计算模型。我们假设，我们可以使用这个模型，以提高我们的能力，预测心律失常的易感性的基础上，患者的基因型以及药物反应表型与遗传变异的硅。我们已经组建了一支在心脏干细胞生物学、基因组学、药物基因组学、分子遗传学/表观遗传学、生物信息学和计算机建模方面成就卓著的研究人员团队。我们有能力在五年内实现项目目标。根据个体患者的遗传特征预测其QT反应的能力将是一种新的个性化方法，可以更好地了解心源性猝死的潜在机制，最终可能彻底改变治疗策略。

 DESCRIPTION (provided by applicant): Long QT syndrome is the most common cardiac arrhythmic disorder, predisposing to sudden cardiac death. There is tremendous genetic heterogeneity leading to long QT syndrome, which leads to considerable variations in disease severity and clinical course. Drug treatments are often ineffective, producing adverse effects in certain populations and prediction of the risk of sudden cardiac death remains fairly primitive. The recent advent of new technological breakthroughs, such as induced pluripotent stem cells (iPSCs), provides an unprecedented opportunity to study associations between genetic variability, drug responsiveness, and disease susceptibility. In addition, the biology of long QT-induced arrhythmia is largely quantifiable and thus amendable to a systems biology approach. The overarching goal of our Systems Biology Collaborative R01 Proposal is to develop an integrative experimental and computational approach to predict patient specific drug responses. To this end, we propose to utilize experimental data from patient-specific iPSC-derived cardiomyocytes (iPSC-CMs) in conjunction with clinical and genomic data, to construct the first, to our knowledge, patient-specific computational model of cardiac electrical activity. We hypothesize that we can use this model to improve our capability to predict arrhythmia susceptibility based on patient genotype as well as drug-response phenotypes associated with genetic variations in silico. We have assembled a team of highly accomplished researchers in cardiac stem cell biology, genomics, pharmacogenomics, molecular genetics/epigenetics, bioinformatics, and in silico modeling. We are well positioned to achieve the project goals within five years. The ability to predict QT response of an individual patient based on their genetic profile would be a novel personalized approach to better understand the mechanisms underlying sudden cardiac death that could ultimately revolutionize treatment strategies.