High-throughput computational modeling to assess the role of 3D genome folding in human congenital anomalies

高通量计算模型评估 3D 基因组折叠在人类先天异常中的作用

• 批准号: 10569063
• 负责人: Maureen Pittman
• 金额: $ 0.45万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10569063
• 关键词: 3-Dimensional; Acute; Affect; Biological; Brain; Calibration; Cardiac; Cardiac Myocytes; Caring; Cell Line; Cell model; ChIP-seq; Child; Chromatin; Chromatin Loop; Chromatin Structure; Code; Computer Models; Computing Methodologies; Congenital Abnormality; Congenital Heart Defects; Data; Defect; Development; Developmental Delay Disorders; Developmental Gene; Diagnosis; Diagnostic; Disease; Elements; Endothelial Cells; Enhancers; Epigenetic Process; Etiology; Family; Fetal Heart; Frequencies; Gene Expression; Genes; Genetic; Genetic Transcription; Genetic study; Genome; Genomics; Heart; Heart Abnormalities; Heritability; Hi-C; Histones; Human; Human Development; Individual; Infant; Infant Mortality; Lead; Learning; Limb structure; Live Birth; Machine Learning; Malignant Neoplasms; Measures; Methods; Modeling; Mutation; Nucleic Acid Regulatory Sequences; Nucleotides; Parents; Pathogenesis; Pathogenicity; Pathway interactions; Patients; Pediatric Cardiac Genomics Consortium; Persons; Phenotype; Proteins; Regulator Genes; Regulatory Element; Resolution; Role; Scientist; Shapes; Site; Source; Statistical Methods; Technology; Testing; Tissues; Training; Transcriptional Regulation; Trees; Untranslated RNA; Validation; Variant; autism spectrum disorder; cell type; comorbidity; congenital anomaly; congenital heart disorder; data resource; deep learning; deep learning model; design; developmental disease; disease phenotype; disorder risk; epigenomics; exome; falls; genetic variant; genome wide association study; genomic locus; gradient boosting; human disease; in silico; induced pluripotent stem cell; insight; machine learning method; machine learning prediction; malformation; mutant; novel; predictive modeling; prevent; promoter; risk variant; therapeutic target; transcription factor; transcriptome sequencing; transcriptomics

PROJECT SUMMARY Congenital heart defects (CHD) occur in nearly one percent of live births each year and are the leading cause of defect-associated infant mortality. The majority of genetic studies in CHD have focused on variation within the protein-coding exome; however, most disease-risk loci fall in noncoding regions, and it is presumed that some of these represent important regulators of gene expression such as cis-acting enhancers and insulators. In spite of these studies, less than half of the heritability of CHD has been explained via genome-wide associations or burden testing of protein-coding genes and putative regulatory elements. In this proposal, we hypothesize that genetic variants that alter 3D genome folding contribute to the etiology of CHD by disrupting the contacts of key cis-regulatory mechanisms in development. One type of chromatin structure that could be affected is the Topologically Associating Domain (TAD), which refers to a level of chromatin organization characterized by higher contact frequency within the domain relative to loci outside of that domain. It has been shown that while unaffected controls show a clear depletion of SVs at TAD boundary regions across the genome, individuals diagnosed with Developmental Delay (DD) and autism showed no bias in the genomic location of SVs. Based on these findings and the high rates of co-morbidity between DD and CHD, the first aim characterizes structural variation at TAD boundaries and other non-coding regulatory regions in CHD relative to controls, and will further determine whether TADs are enriched for SVs in a region-specific manner based on proximity to genes active in the developing heart. In the second aim, we use a complementary annotation- agnostic deep learning approach developed in our group to predict chromatin contact changes as a result of genetic variants in CHD patients. We will use Hi-C sequencing to confirm the model-predicted chromatin contact effects in iPSC-derived endothelial cells and cardiomyocytes, and additionally use RNA sequencing to determine whether the hypothesized transcriptional disruption occurred. Finally, in the third aim we will create a model that uses genetic, epigenetic, and transcriptional features of genetic variants found in each individual to predict their phenotypic status. By analyzing the relative importance of features used to make predictions, we will be able to determine what types of biological mechanisms and pathways are most predictive for congenital heart anomalies. Collectively, these findings will elucidate a currently underappreciated source of regulatory disruption in human development, identify new disease-relevant genes and potential therapeutic targets, and will refine and validate a method for the high-throughput prediction of chromatin contact frequency to advance the field of 3D genomics.

项目摘要 先天性心脏缺陷（CHD）发生在近百分之一的活产婴儿，每年是主要原因 与缺陷相关的婴儿死亡率。大多数冠心病的遗传学研究都集中在基因组内的变异上。 蛋白质编码外显子组;然而，大多数疾病风险基因座位于非编码区域，并且据推测 其中一些代表基因表达的重要调节因子，例如顺式作用增强子和绝缘子。 尽管有这些研究，但只有不到一半的冠心病遗传性是通过全基因组来解释的。 蛋白质编码基因和推定调节元件的关联或负担测试。在本提案中，我们 假设改变3D基因组折叠的遗传变异通过破坏 发展中关键顺式调节机制的联系。有一种染色质结构 受影响的是拓扑相关结构域（Topologically Associating Domain，简称RANDO），它是指染色质组织的一个水平 其特征在于相对于该域外部的基因座，该域内的接触频率更高。已经 显示，虽然未受影响的对照组显示在整个组织的边缘区域SV明显耗尽， 在基因组中，被诊断患有发育迟缓（DD）和自闭症的个体在基因组中没有表现出偏见， SV的位置。基于这些发现以及DD和CHD之间的高共病率， 表征CHD相关基因中的线粒体边界和其他非编码调控区的结构变异， 对照，并将进一步确定TADs是否以区域特异性方式富集SV， 接近发育中心脏的活跃基因在第二个目标中，我们使用一个补充注释- 我们小组开发了一种不可知的深度学习方法，用于预测染色质接触变化， CHD患者的遗传变异。我们将使用Hi-C测序来确认模型预测的染色质 iPSC衍生的内皮细胞和心肌细胞中的接触效应，并另外使用RNA测序， 确定假设的转录破坏是否发生。最后，在第三个目标中，我们将创建 一个模型，使用遗传，表观遗传和转录特征的遗传变异中发现的每个人 来预测它们的表型状态通过分析用于进行预测的特征的相对重要性， 我们将能够确定哪些类型的生物学机制和途径最具预测性， 先天性心脏畸形总的来说，这些发现将阐明一个目前被低估的来源， 人类发育中的调控中断，确定新的疾病相关基因和潜在的治疗 目标，并将完善和验证的方法，高通量预测染色质接触频率 来推进3D基因组学的发展