Genetic determinants of 4D genome folding in human cardiac development

人类心脏发育中 4D 基因组折叠的遗传决定因素

基本信息

批准号：
10118056
负责人：
Benoit Gaetan Bruneau
金额：
$ 74.22万
依托单位：
J. DAVID GLADSTONE INSTITUTES
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-21 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10118056
关键词：
3-Dimensional ATAC-seq Address Adult Architecture Arrhythmia Artificial Intelligence Birth CCCTC-binding factor Cardiac Cardiac Myocytes Cardiac development Cardiovascular Diseases Cell Differentiation process Cell Line Cell model Cells ChIP-seq Child Chromatin Chromatin Remodeling Factor Chromosome Structures Code Complement Complex Congenital Abnormality Congenital Heart Defects Data Data Set Development Diagnosis Disease Elements Endothelial Cells Engineering Enhancers Family Frequencies GATA4 gene Gene Expression Gene Expression Regulation Genes Genetic Genetic Determinism Genetic Transcription Genome Genomics Heart Heart Abnormalities Heart Diseases Histones Human Human Biology Human Development Lead Machine Learning Maps Measures Modeling Molecular Mutate Mutation Patients Phenotype Process Proteins Regulation Regulator Genes Regulatory Element Resolution SMARCA2 gene Structure Time Untranslated RNA Variant Work cardiogenesis cell type cohesin computational platform congenital heart disorder deep learning disease-causing mutation high throughput screening histone modification human disease human model human pluripotent stem cell improved in silico machine learning method novel predictive modeling promoter stem cell differentiation stem cell model transcription factor

项目摘要

PROJECT SUMMARY A major unanswered question is how chromatin topology coordinates human development and cellular differentiation, and how genome folding is differentially regulated in human disease. It is thought that three- dimensional (3D) chromatin organization is driven by transcriptional regulators, but fundamental mechanisms of this regulation as it relates to disease-relevant human cells have not been well explored. We propose to elucidate the temporally dynamic 3D nucleome (4DN) that underlies human cardiac differentiation, its molecular underpinnings, and the impact of mutations that underly defective 4DN organization in human congenital heart disease (CHD). CHDs are the most common birth defect and arise from abnormal heart development. The genetic basis of CHD is largely mutations in genes encoding chromatin modifiers (e.g. WDR5, KMT2D) and transcription factors (TFs, e.g. TBX5, GATA4), many of which also cause adult-onset arrhythmias. The impact of CHD mutations on the 4DN has not been explored. We hypothesize that 3D genome folding is highly regulated during cardiac differentiation and is impacted by disease-causing mutations in transcriptional regulators and non-coding elements. We will use iPS cell models and machine learning to elucidate dynamic 3D chromatin organization in human cardiomyocytes and endothelial cells during normal and diseased cardiac differentiation. We propose 3 specific aims: Aim 1: Establish a kilobase-scale 4D map of genome folding in human cardiomyocytes (CM) and endothelial cell (EC) differentiation. We will use directed differentiation of human iPS cells towards the two major cell types of the developing heart: CMs and ECs, and using microC across a fine time course of differentiation we will define at kilobase scale the 3D organization of the genome, capturing the states of developmental intermediates and the final differentiated cells. This aim will generate an essential integrated 4DN template for discovery in cardiac differentiation. In Aim 2: we will Determine the regulatory and disease-related basis for cardiac 3D chromatin organization. We will perform microC in iPS cell lines with CHD-associated mutations in transcriptional regulators, differentiated into CMs and ECs. These findings will establish the degree to which CHD is caused by abnormal genome folding and chromatin states, with important relevance to other human cardiovascular diseases. Finally, Aim 3 will address High-throughput screening of millions of CHD and synthetic non- coding mutations with a deep-learning model of dynamic genome folding. We will build a deep-learning model predicting 3D chromatin contact frequencies across cardiac differentiation at kilobase-resolution. By introducing thousands of CHD patient deletions and other non-coding mutations in silico, we will prioritize variants likely to interact with transcriptional regulators to cause disease through disrupted genome folding. Several candidates will be validated in engineered iPS cells differentiated into CMs and ECs. These results will provide a novel platform for computational discovery of disease variant impact across diverse human diseases

项目摘要一个主要的未解决的问题是染色质拓扑如何协调人类发育和细胞分化以及基因组折叠在人类疾病中的差异调节。据认为三尺寸（3D）染色质组织是由转录调节器驱动的，但基本机制与疾病相关的人类细胞有关的调节尚未得到很好的探索。我们建议阐明了人类心脏分化的基础的时间动态3D核心（4DN）分子基础以及人类中基本缺陷组织的突变的影响先天性心脏病（CHD）。 CHD是最常见的先天缺陷，是由异常心脏引起的发展。 CHD的遗传基础在很大程度上是编码染色质修饰剂的基因中的突变（例如 WDR5，KMT2D）和转录因子（例如TFS，例如TBX5，GATA4），其中许多也会引起成人发作心律不齐。尚未探索冠心病突变对4DN的影响。我们假设3D 基因组折叠在心脏分化过程中受到高度调节，并受到致病突变的影响在转录调节器和非编码元件中。我们将使用IPS细胞模型和机器学习在正常情况下阐明人类心肌细胞和内皮细胞中的动态3D染色质组织和患病的心脏分化。我们提出了3个特定目的：目标1：建立千目标尺度4D地图人类心肌细胞（CM）和内皮细胞（EC）分化中的基因组折叠。我们将使用将人IPS细胞的定向分化为发育中心的两种主要细胞类型：CMS和 ECS，以及在差分差异过程中使用Microc，我们将以千目标尺度定义3D 基因组的组织，捕获发展中间体的状态和最终差异化细胞。该目标将生成一个必不可少的集成4DN模板，以在心脏分化中发现。在目标2：我们将确定心脏3D染色质的调节和疾病相关的基础组织。我们将在转录中具有与CHD相关的突变的IPS细胞系中执行Microc 监管机构，分为CMS和EC。这些发现将确定引起冠心病的程度通过异常基因组折叠和染色质状态，与其他人类心血管具有重要意义疾病。最后，AIM 3将解决数百万冠心和合成非 - 的高通量筛查具有动态基因组折叠的深度学习模型的编码突变。我们将建立一个深度学习在千目标分辨率下预测跨心脏分化的3D染色质接触频率的模型。经过在计算机中引入数千个CHD患者缺失和其他非编码突变，我们将优先考虑变体可能与转录调节剂相互作用，通过破坏基因组折叠引起疾病。在工程的IPS细胞中，将对几个候选物进行验证，分为CMS和EC。这些结果将会提供了一个新颖的平台，用于计算各种人类疾病的疾病变异影响