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

项目概要一个尚未解答的主要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如何受到差异性调节。人们认为，三—— 三维（3D）染色质组织由转录调节因子驱动，但基本机制这种与疾病相关的人类细胞相关的调节尚未得到很好的探索。我们建议阐明人类心脏分化背后的时间动态 3D 核组 (4DN)，其分子基础，以及人类 4DN 组织缺陷的突变的影响先天性心脏病（CHD）。 CHD 是最常见的出生缺陷，由心脏异常引起发展。 CHD 的遗传基础主要是编码染色质修饰基因（例如染色质修饰基因）的突变。 WDR5、KMT2D）和转录因子（TF，例如 TBX5、GATA4），其中许多也会导致成人发病心律失常。 CHD 突变对 4DN 的影响尚未被探索。我们假设 3D 基因组折叠在心脏分化过程中受到高度调控，并受到致病突变的影响转录调控因子和非编码元件。我们将使用 iPS 细胞模型和机器学习来阐明正常情况下人心肌细胞和内皮细胞的动态 3D 染色质组织和患病的心脏分化。我们提出了 3 个具体目标：目标 1：建立千碱级 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。我们将使用人类 iPS 细胞定向分化为发育中心脏的两种主要细胞类型：CM 和 EC，并在分化的精细时间过程中使用 microC，我们将在千碱基尺度上定义 3D 基因组的组织，捕获发育中间体和最终分化的状态细胞。这一目标将生成一个重要的集成 4DN 模板，用于发现心脏分化。在目标2：我们将确定心脏3D染色质的监管和疾病相关基础组织。我们将在具有 CHD 相关转录突变的 iPS 细胞系中进行 microC 监管机构，分为 CM 和 EC。这些发现将确定引起冠心病的程度由异常的基因组折叠和染色质状态引起，与其他人类心血管疾病具有重要相关性疾病。最后，目标 3 将解决数以百万计的 CHD 和合成非药物的高通量筛选问题。使用动态基因组折叠的深度学习模型编码突变。我们将建立一个深度学习以千碱基分辨率预测心脏分化过程中 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，我们将优先考虑变异可能与转录调节因子相互作用，通过破坏基因组折叠而引起疾病。几种候选药物将在分化为 CM 和 EC 的工程化 iPS 细胞中得到验证。这些结果将为计算发现不同人类疾病的疾病变异影响提供一个新的平台