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

RNA 和基因组序列整合识别左心发育不全综合征的遗传风险

基本信息

  • 批准号:
    10544300
  • 负责人:
  • 金额:
    $ 16.79万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-07-01 至 2027-06-30
  • 项目状态:
    未结题

项目摘要

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.
项目概要: 先天性心脏病是最常见的先天性异常,影响约1%的婴儿。 左心发育不良综合征(HLHS),一种严重的先天性心脏病,其中左心室 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,并可能从靶向治疗中获益。 治疗。HLHS有两种理论机制:心肌细胞起源,其中存在缺陷, 心肌细胞引起心室发育不全,或内皮起源,其中价值 异常使通过左心室的血流减弱。两个已知的HLHS基因,RBFOX 2和NOTCH 1, 主要分别在心肌细胞和心脏内皮细胞中表达,并提供了一个机会, 研究这些机制。发现额外的致病性HLHS变异可能会增加 诊断的患者,并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性变体是降低基因表达的功能丧失变体。测试我 假设错义和非编码变体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 结构和增强子测定数据,以鉴定HLHS的新遗传贡献者。通过实现这些目标,我 我将推进我在功能分析和机器学习方面的培训,为我的职业生涯做好最好的准备, 独立的医学科学家。 我的科学目标是确定导致HLHS的新变异和基因座。首先,在目标1中,我将使用机器 学习预测HLHS患者RBFOX 2错义变体的致病性。信赖从此类 预测将通过诱导多能干细胞的基因组编辑来确定,以引入RBFOX 2。 错义变体,随后评估心肌细胞分化过程中RBFOX 2的表达和功能。 分化在目标2中,将类似地评估NOTCH 1错义变体在心脏病期间的致病性。 内皮细胞分化最后,在目标3中,我将使用大规模平行的报告基因测定来鉴定活性 RBFOX 2-和NOTCH 1-通路基因附近的顺式调节区域,然后确定是否存在罕见的变异。 这些区域内的HLHS患者引起基因失调。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,并使用这些注释来 鉴定另外候选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,利用我在发育生物学方面的背景, 功能基因组学这些结果将有助于实现了解 心脏发育和人类疾病的分子基础,以改善诊断,更好地定义风险,并激发 为患者提供新的治疗方法。

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Sarah Morton其他文献

Sarah Morton的其他文献

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{{ truncateString('Sarah Morton', 18)}}的其他基金

Integration of RNA and Genome Sequences to Identify Genetic Risk in Hypoplastic Left Heart Syndrome
RNA 和基因组序列整合识别左心发育不全综合征的遗传风险
  • 批准号:
    10369414
  • 财政年份:
    2022
  • 资助金额:
    $ 16.79万
  • 项目类别:
Computational Prioritization of Coding and Non-Coding Variants in Congenital Heart Disease
先天性心脏病编码和非编码变体的计算优先级
  • 批准号:
    10469306
  • 财政年份:
    2021
  • 资助金额:
    $ 16.79万
  • 项目类别:

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