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.

项目概要先天性心脏病（CHD）是最常见的出生缺陷，但这种疾病的遗传因素很差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。编码单核苷酸变异（SNV）的无其他先天性异常的CHD，或孤立的CHD，占75%。所有 CHD 的基因组测序 (GS) 研究主要集中在蛋白质编码区域，这种巨大的知识差距表明，仅约 10-20% 的受试者能够识别出致病变异。病因学，例如非编码基因组的变异，可能发挥了作用。 98% 的基因组，包含多种特征类型，包括非编码 RNA。长链非编码 RNA (lncRNA) 在疾病（包括心脏发育障碍）中作用的证据。因此，这项研究的长期目标是阐明 lncRNA 在导致心脏畸形中的作用。拟议调查的总体目标是开发计算方法来预测函数 lncRNA 参与心脏发育并预测影响这些分子的变异的致病影响我们将使用 Gabriella Miller Kids First (GMKF) 队列的 GS 数据进行关联。然后，我们将使用单细胞 RNA 测序 (scRNA-Seq) 数据来识别 lncRNA。我们的中心假设是变异在人类心脏发生的关键阶段的相关细胞类型中表达。 lncRNA 是未解决的 CHD 病例的一个可能原因，通过使用 scRNA-seq 数据，我们可以优先考虑我们提出了以下具体目标来应对这一挑战。 1，我们将开发一种机器学习（ML）工具来注释我们的CHD队列中缺乏的lncRNA变异。有效解释 CNV 和 SNV 影响 lncRNA 的生物学意义的工具。通过应用 ML 注释经临床验证的与孤立性 CHD 相关的 CNV。考虑影响 lncRNA 的 CNV 和 SNV 以及影响蛋白质编码基因的 CNV 和 SNV 将应用网络。分析 scRNA-Seq 数据以阐明 lncRNA 在心脏发育中的作用，我们将把 lncRNA 与蛋白质因果关系联系起来。通过使用基因调控网络（GRN）的推断来推断与一般心脏发育的关系。将根据单细胞转录组数据构建，有助于发现与心脏相关的 lncRNA 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 工具的人。变异注释并阐明 lncRNA 在 CHD 发展中可能发挥的作用。将实现 NHLBI 的使命：创建计算技术来理解机制正常心脏形成的调节以及 NICHD 的目标是了解心脏形成的遗传基础此外，这项研究意义重大，因为它可能导致新的遗传病因的发现。 CHD 和新治疗靶点的识别。