Computational methods to elucidate the role of long non-coding RNA in Congenital Heart Disease

阐明长非编码RNA在先天性心脏病中作用的计算方法

基本信息

项目摘要

PROJECT SUMMARY Congenital Heart Disease (CHD) is the most common birth defect, yet the genetics of this disease are poorly understood. The genomic mechanisms of this disease include distinct rare copy number variants (CNVs) and protein- coding single nucleotide variants (SNVs). CHDs without other congenital anomalies, or isolated CHD, comprise 75% of all CHDs. Genome sequencing (GS) studies of isolated CHD have focused primarily on protein-coding regions, identifying disease-causal variants in only ~10-20% of subjects. This substantial knowledge gap suggests that other etiologies, such as variation in the non-coding genome, may play a role. The non-coding genome is vast, constituting 98% of the genome, and encompasses multiple feature types, including the non-coding RNAs. There is growing evidence for the role of long non-coding RNAs (lncRNAs) in disease, including developmental disorders of the heart. As such, the long-term goal of this study is to elucidate lncRNA’s role in contributing to cardiac malformations. The overarching objective of the proposed investigation is to develop computational methods to predict the function of lncRNAs involved in heart development and predict the pathogenic impact of variants impacting these molecules leading to heart maldevelopment. We will use GS data from the Gabriella Miller Kids First (GMKF) cohort to associate variation in lncRNAs to CHD. We will then use single-cell RNA-sequencing (scRNA-Seq) data to identify lncRNAs expressed in relevant cell types during crucial stages of human cardiogenesis. Our central hypothesis is that variants in lncRNAs are a probable cause in unsolved CHD cases and that by using scRNA-seq data, we can prioritize candidates for future functional validation. We propose the following specific aims to address this challenge. In Aim 1, we will develop a machine learning (ML) tool to annotate lncRNA variants in our CHD cohort. There is a lack of tools to interpret the biological implications of CNVs and SNVs impacting lncRNAs. Our preliminary data effectively annotated clinically validated CNVs associated with isolated CHD by applying ML. We will extend our methods to consider CNVs and SNVs impacting lncRNAs and those impacting protein-coding genes. Aim 2 will apply network analysis on scRNA-Seq data to elucidate lncRNA’s role in heart development. We will associate lncRNA-protein causal relationships with general heart development by using inference from the gene regulatory networks (GRN). GRN will be built from single-cell transcriptomics data to contribute to the discovery of lncRNAs involved in heart development. This work is innovative as we will be the first to construct an ML tool for cardiac-specific lncRNA variant annotation and clarify the role that lncRNAs may play in the development of CHD. Completing this project will achieve the NHLBI’s mission of creating computational techniques for understanding the mechanisms underlying the regulation of normal heart formation and NICHD’s objective of comprehending the genetic basis of heart defects. In addition, the research is significant since it may lead to the discovery of novel genetic etiologies in CHD and the identification of novel therapeutic targets.
项目摘要 先天性心脏病(CHD)是最常见的出生缺陷,但这种疾病的遗传学研究很少 明白这种疾病的基因组机制包括独特的罕见拷贝数变异(CNV)和蛋白质- 编码单核苷酸变体(SNV)。无其他先天性异常或孤立性CHD的CHD占75%, 所有CHD孤立CHD的基因组测序(GS)研究主要集中在蛋白质编码区, 仅在约10 - 20%的受试者中识别致病变异。这一巨大的知识差距表明, 病因学,如非编码基因组的变异,可能发挥作用。非编码基因组是巨大的, 98%的基因组,并涵盖多种特征类型,包括非编码RNA。人们越来越 长链非编码RNA(lncRNA)在疾病中的作用的证据,包括心脏发育障碍。 因此,本研究的长期目标是阐明lncRNA在心脏畸形中的作用。的 所提出的研究的首要目标是开发计算方法来预测的功能, 参与心脏发育的lncRNA,并预测影响这些分子的变体的致病影响 导致心脏发育不良我们将使用来自Gabriella米勒儿童优先(GMKF)队列的GS数据, lncRNA的变异与CHD的关系然后我们将使用单细胞RNA测序(scRNA-Seq)数据来鉴定lncRNA 在人类心脏发生的关键阶段在相关细胞类型中表达。我们的核心假设是, lncRNA是未解决的CHD病例的可能原因,通过使用scRNA-seq数据,我们可以优先考虑 未来功能验证的候选人。我们提出以下具体目标来应对这一挑战。在aim中 1,我们将开发一种机器学习(ML)工具来注释我们CHD队列中的lncRNA变体。都缺乏 解释影响lncRNA的CNV和SNV的生物学意义的工具。我们的初步数据 应用ML注释与孤立CHD相关的临床验证的CNV。我们将把我们的方法扩展到 考虑影响lncRNA的CNV和SNV以及影响蛋白质编码基因的那些。目标2将应用网络 分析scRNA-Seq数据以阐明lncRNA在心脏发育中的作用。我们将lncRNA-蛋白质因果 通过使用基因调控网络(GRN)的推断与一般心脏发育的关系。GRN 将从单细胞转录组学数据中构建,以有助于发现参与心脏的lncRNAs。 发展这项工作是创新的,因为我们将是第一个构建心脏特异性lncRNA的ML工具 变异注释,并阐明lncRNA可能在CHD的发展中发挥的作用。完成本项目 将实现NHLBI的使命,即创造计算技术来理解这些机制 正常心脏形成的调节和NICHD的目的是了解心脏病的遗传基础, 心脏缺陷此外,这项研究意义重大,因为它可能导致发现新的遗传病因, 冠心病和新的治疗靶点的鉴定。

项目成果

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