Integration of RNA and Genome Sequences to Identify Genetic Risk in Hypoplastic Left Heart Syndrome

项目总结： 先天性心脏病是最常见的先天畸形，影响约1%的婴儿。 左心发育不全综合征(HLHS)，是一种严重的先天性心脏病，其左心室 不发达，10年死亡率为40%。只有6%的HLHS患者有遗传原因 外显子组测序，限制了患者接受诊断的能力，并可能受益于靶向 治疗。HLHS有两种理论机制：起源于心肌细胞，其中存在缺陷 心肌细胞导致心室发育不全，或内皮细胞起源，在那里 异常可使流经左心室的血流减弱。两个已知的HLHS基因，RBFOX2和NOTCH1，是 主要在心肌细胞和心肌内皮细胞中表达，并提供了一个机会 研究这些机制。发现更多致病的HLHS变异可能会增加 诊断患者，提高我们对心脏发育的分子理解。目前，大多数 外显子组测序中的致病变异体是减少基因表达的功能丧失变异体。测试我的 假设错义和非编码变异也通过改变基因表达或 活动，我建议使用机器学习对HLHS患者基因组测序，三维蛋白质 结构和增强子分析数据，以确定新的HLHS基因贡献者。通过实现这些目标，我 我将提高我在功能分析和机器学习方面的培训，以便为我的职业生涯做好准备 独立的内科科学家。 我的科学目标是识别导致HLHS的新变种和基因座。首先，在《目标1》中，我将使用机器 学习预测HLHS患者RBFOX2错义变异的致病性。这些数据的准确性 将通过对诱导多能干细胞进行基因组编辑来确定引入RBFOX2的预测 错义变体，随后评估RBFOX2在心肌细胞中的表达和功能 差异化。在目标2中，NOTCH1错义变体将被类似地评估在心脏疾病期间的致病性 内皮细胞分化。最后，在目标3中，我将使用大规模平行报告分析来确定活性 RBFOX2和NOTCH1途径基因附近的顺式调控区，然后确定是否存在罕见的变异 这些区域内的HLHS患者会导致基因失调。我将使用线性模型和机器学习来 确定哪些心脏基因组注释最能预测增强子活性，并使用这些注释 确定其他候选HLHS基因座。这项提案将在生物数据上使用机器学习 在某种程度上利用了我在发育生物学方面的背景，并开发了计算和 功能基因组学。这些结果将有助于实现理解 心脏发育和人类疾病的分子基础，以改进诊断，更好地定义风险，并激励 为患者提供新的治疗方法。

PROJECT SUMMARY: Congenital heart disease is the most common congenital anomaly and affects approximately 1% of infants. Hypoplastic left heart syndrome (HLHS), a severe form of congenital heart disease in which the left ventricle is underdeveloped, has a 10-year mortality of 40%. Only 6% of HLHS patients have a genetic cause identified on exome sequencing, limiting the ability of patients to receive a diagnosis and potentially benefit from targeted treatments. There are two theoretical mechanisms for HLHS: a cardiomyocyte origin, where there a defect in cardiac muscle cells causes underdevelopment of the ventricle, or an endothelial origin, where value abnormalities attenuate flow through the left ventricle. Two known HLHS genes, RBFOX2 and NOTCH1, are primarily expressed in cardiomyocytes and cardiac endothelial cells, respectively and provide an opportunity to study these mechanisms. Discovery of additional pathogenic HLHS variants could increase the proportion of diagnosed patients and improve our molecular understanding of cardiac development. Currently, most pathogenic variants in exome sequencing are loss-of-function variants that reduce gene expression. To test my hypothesis that missense and noncoding variants also contribute to HLHS by altering gene expression or activity, I propose to use machine learning on HLHS patient genome sequencing, three-dimensional protein structure, and enhancer assay data to identify new genetic contributors to HLHS. By completing these aims, I will advance my training in functional assays and machine learning to be best prepared for a career as an independent physician scientist. My scientific goal is to identify new variants and loci that contribute to HLHS. First, in Aim 1 I will use machine learning to predict the pathogenicity of missense variants in RBFOX2 from HLHS patients. Accuracy of these predictions will be determined by genome editing of induced pluripotent stem cells to introduce the RBFOX2 missense variants, followed by assessment of RBFOX2 expression and function during cardiomyocyte differentiation. In Aim 2, NOTCH1 missense variants will be similarly assessed for pathogenicity during cardiac endothelial cell differentiation. Finally, in Aim 3 I will use massively parallel reporter assays to identify active cis-regulatory regions near RBFOX2- and NOTCH1-pathway genes, and then determine if rare variants in HLHS patients within these regions cause gene dysregulation. I will use linear models and machine learning to determine which cardiac genomic annotations that best predict enhancer activity, and use those annotations to identify additional candidate HLHS loci. Together this proposal will employ machine learning on biological data in a way that uses my background in developmental biology and develops new skills in computational and functional genomics. These results will contribute towards the long-term objective of understanding the molecular basis of heart development and human disease to improve diagnosis, better define risks, and inspire novel treatments for patients.