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

批准号：
10680021
负责人：
Jacqueline Stephany Penaloza
金额：
$ 3.93万
依托单位：
RESEARCH INST NATIONWIDE CHILDREN'S HOSP
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-18 至 2025-05-17
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10680021
关键词：
Address Architecture Automobile Driving Biological Biological Process Candidate Disease Gene Cardiac Cardiovascular system Cells Child Clinical Code Computational Technique Computing Methodologies Congenital Abnormality Copy Number Polymorphism Data Data Analyses Data Set Development Disease Etiology Frequencies Future Genes Genetic Genetic Diseases Genetic Predisposition to Disease Genetic Research Genetic Variation Genetic study Genome Genomics Goals Heart Heart Abnormalities Heart Diseases Human Human Genetics Human Genome Infant Mortality Information Theory Investigation Knowledge Length Machine Learning Methods Micro Array Data Mission Molecular Mus National Heart, Lung, and Blood Institute National Institute of Child Health and Human Development Open Reading Frames Orthologous Gene Pathogenesis Pathogenicity Pathway Analysis Patients Pattern Play Population Probability Process Proteins Publishing RNA-Protein Interaction Regulation Research Role Single Nucleotide Polymorphism Structure Tissues Training Transcript Untranslated RNA Validation Variant Work aggregation database algorithmic methodologies base cardiogenesis causal variant cell type cohort congenital anomaly congenital heart disorder developmental disease disease phenotype gene regulatory network genetic variant genome sequencing global health heart function improved outcome innovation laboratory experiment machine learning model malformation mouse model new therapeutic target novel single-cell RNA sequencing stem time use tool transcriptomics

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）是最常见的先天缺陷，但这种疾病的遗传学很差理解齿。该疾病的基因组机制包括不同的稀有拷贝数变异（CNV）和蛋白质 - 编码单核苷酸变体（SNV）。没有其他先天性异常或孤立的CHD的冠心病占75％所有CHD。分离的CHD的基因组测序（GS）研究主要集中于蛋白质编码区域，仅在约10-20％的受试者中鉴定疾病毒害变异。这种大量知识差距表明其他病因，例如非编码基因组的变异，可能起作用。非编码基因组是巨大的，构成 98％的基因组，包括多种特征类型，包括非编码RNA。有增长长期非编码RNA（LNCRNA）在疾病中的作用的证据，包括心脏的发育障碍。因此，这项研究的长期目标是阐明LNCRNA在导致心脏畸形的作用中的作用。拟议的调查的总体目标是开发计算方法来预测参与心脏发育的LNCRNA并预测影响这些分子的变体的致病影响导致心脏发展。我们将使用Gabriella Miller Kids（GMKF）队列的GS数据进行合作 LNCRNA与CHD的变化。然后，我们将使用单细胞RNA-Sequencing（SCRNA-SEQ）数据来识别lncRNA 在人类心脏病的关键阶段中以相关细胞类型表达。我们的中心假设是变体在LNCRNA中是未解决的冠心病案例中的一个问题原因，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的，以应对这一挑战。目标 1，我们将开发一种机器学习（ML）工具，以注释我们的CHD队列中的lncrna变体。缺乏解释CNV和SNV影响LNCRNA的生物学意义的工具。我们的初步数据有效通过应用ML进行注释的临床验证与孤立的CHD相关的CNV。我们将将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CNV和SNV。 AIM 2将应用网络对SCRNA-SEQ数据的分析，以阐明LNCRNA在心脏发育中的作用。我们将关联lncrna-蛋白质因果关系通过使用基因调节网络（GRN）的推断，与一般心脏发展的关系。 grn 将由单细胞转录组学数据构建发展。这项工作具有创新性，因为我们将成为第一个为心脏特异性lncrna构建ML工具变体注释并阐明了LNCRNA在CHD发展中可能起的作用。完成这个项目将实现NHLBI创建计算技术以理解机制的使命基本的调节正常心脏形成和NICHD的目标是理解遗传基础心脏缺陷。此外，这项研究很重要，因为它可能导致发现新的遗传病因 CHD和新型治疗靶标的鉴定。