Genetic determinants of 4D genome folding in human cardiac development

项目总结 一个尚未回答的主要问题是染色质拓扑结构如何协调人类发育和细胞 分化，以及基因组折叠在人类疾病中是如何差异调节的。据认为，有三个- 三维(3D)染色质组织由转录调节因子驱动，但基本机制 这一规定与疾病相关的人类细胞有关，还没有得到很好的研究。我们建议 阐明人类心脏分化的时间动态三维核组(4dN)，其 分子基础，以及支持人类4dN组织缺陷的突变的影响 先天性心脏病(CHD)。先天性心脏病是最常见的出生缺陷，由心脏异常引起。 发展。CHD的遗传基础主要是染色质修饰物编码基因的突变(例如 WDR5，KMT2D)和转录因子(转录因子，如TBX5，GATA4)，其中许多也引起成人发病 心律不齐。CHD突变对4DN的影响还没有被探索。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 在转录调节因子和非编码元件中。我们将使用iPS细胞模型和机器学习来 人正常心肌细胞和内皮细胞动态三维染色质组织的研究 并导致心脏分化障碍。我们提出了三个具体目标：目标1：建立千基比例尺的四维图 人类心肌细胞(CM)和内皮细胞(EC)分化中基因组折叠的研究。我们将使用 人iPS细胞定向分化为发育中心脏的两种主要细胞类型：CMS和CMS ECS，并在精细的时间进程中使用MicroC，我们将在千基尺度上定义3D 基因组的组织，捕捉发育中间体和最终分化的状态 细胞。这一目标将产生一个发现心脏分化的基本整合的4dN模板。在……里面 目的2：我们将确定心脏3D染色质的调节基础和疾病相关基础 组织。我们将在具有CHD相关转录突变的iPS细胞系中进行Microc 监管者，分为CMS和ECs。这些发现将确定导致CHD的程度 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关联 疾病。最后，目标3将解决数百万冠心病和合成非冠心病的高通量筛查问题 用动态基因组折叠的深度学习模型编码突变。我们将构建深度学习 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。通过 引入数以千计的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控因子相互作用，通过破坏基因组折叠而导致疾病。 几个候选细胞将在分化为CMS和ECs的工程化iPS细胞中得到验证。这些结果将 为计算发现各种人类疾病的疾病变异影响提供了一个新的平台

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