Genetic determinants of 4D genome folding in human cardiac development

项目摘要 一个主要的悬而未决的问题是染色质拓扑结构如何协调人类发育和细胞 分化，以及基因组折叠如何在人类疾病中差异调节。据认为，三- 三维（3D）染色质组织是由转录调控驱动的，但基本机制 这种调节与疾病相关的人类细胞的关系尚未得到很好的研究。我们建议 阐明了人类心脏分化的时间动态3D核组（4DN），其 分子基础，以及人类4DN组织缺陷突变的影响 先天性心脏病（CHD）。先天性心脏病是最常见的出生缺陷， 发展CHD的遗传基础很大程度上是编码染色质修饰物的基因突变（例如， WDR 5、KMT 2D）和转录因子（TF，例如TBX 5、GATA 4），其中许多也引起成人发作性 心律不齐CHD突变对4DN的影响尚未探讨。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 在转录调节子和非编码元件中。我们将使用iPS细胞模型和机器学习， 阐明正常心肌细胞和内皮细胞中动态3D染色质组织 和病变的心脏分化。我们提出了3个具体目标：目标1：建立一个大规模的4D地图 在人心肌细胞（CM）和内皮细胞（EC）分化中的基因组折叠。我们将使用 人iPS细胞向发育中心脏的两种主要细胞类型定向分化：CM和 EC，并使用microC在一个很好的时间过程中的分化，我们将定义在3D的规模， 组织的基因组，捕捉状态的发展中间和最终分化的 细胞这一目标将产生一个重要的整合4DN模板，用于心脏分化的发现。在 目的2：我们将确定心脏3D染色质的调控和疾病相关基础 organization.我们将在iPS细胞系中进行microC，该细胞系具有CHD相关的转录突变， 监管机构，分为CM和EC。这些发现将确定冠心病的发病程度 通过异常的基因组折叠和染色质状态，与其他人类心血管疾病有重要的相关性， 疾病最后，目标3将解决数百万CHD和合成非- 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 模型预测3D染色质接触频率在心脏分化在酶的分辨率。通过 通过计算机模拟引入数千例CHD患者缺失和其他非编码突变，我们将优先考虑 变异体可能与转录调节因子相互作用，通过破坏基因组折叠引起疾病。 将在分化成CM和EC的工程化iPS细胞中验证几种候选物。这些结果将 提供了一个新的平台，用于计算发现不同人类疾病的疾病变体影响

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