PDF(Pubmed)
Abstract：
BACKGROUND: Increasing evidence suggests that a substantial proportion of disease-associated mutations occur in enhancers, regions of non-coding DNA essential to gene regulation. Understanding the structures and mechanisms of the regulatory programs this variation affects can shed light on the apparatuses of human diseases.
RESULTS: We collect epigenetic and gene expression datasets from seven early time points during neural differentiation. Focusing on this model system, we construct networks of enhancer-promoter interactions, each at an individual stage of neural induction. These networks serve as the base for a rich series of analyses, through which we demonstrate their temporal dynamics and enrichment for various disease-associated variants. We apply the Girvan-Newman clustering algorithm to these networks to reveal biologically relevant substructures of regulation. Additionally, we demonstrate methods to validate predicted enhancer-promoter interactions using transcription factor overexpression and massively parallel reporter assays.
CONCLUSIONS: Our findings suggest a generalizable framework for exploring gene regulatory programs and their dynamics across developmental processes; this includes a comprehensive approach to studying the effects of disease-associated variation on transcriptional networks. The techniques applied to our networks have been published alongside our findings as a computational tool, E-P-INAnalyzer. Our procedure can be utilized across different cellular contexts and disorders.
RESULTS: We collect epigenetic and gene expression datasets from seven early time points during neural differentiation. Focusing on this model system, we construct networks of enhancer-promoter interactions, each at an individual stage of neural induction. These networks serve as the base for a rich series of analyses, through which we demonstrate their temporal dynamics and enrichment for various disease-associated variants. We apply the Girvan-Newman clustering algorithm to these networks to reveal biologically relevant substructures of regulation. Additionally, we demonstrate methods to validate predicted enhancer-promoter interactions using transcription factor overexpression and massively parallel reporter assays.
CONCLUSIONS: Our findings suggest a generalizable framework for exploring gene regulatory programs and their dynamics across developmental processes; this includes a comprehensive approach to studying the effects of disease-associated variation on transcriptional networks. The techniques applied to our networks have been published alongside our findings as a computational tool, E-P-INAnalyzer. Our procedure can be utilized across different cellular contexts and disorders.
摘要：
背景：越来越多的证据表明，相当比例的疾病相关突变发生在增强子中，基因调控所必需的非编码DNA区域。了解这种变化影响的监管计划的结构和机制可以阐明人类疾病的设备。
结果：我们从神经分化的七个早期时间点收集表观遗传和基因表达数据集。围绕这个模型系统，我们构建了增强子-启动子相互作用的网络，每个都处于神经诱导的个体阶段。这些网络是一系列丰富分析的基础，通过它，我们证明了它们对各种疾病相关变异的时间动态和富集。我们将Girvan-Newman聚类算法应用于这些网络，以揭示生物学相关的调控子结构。此外,我们展示了使用转录因子过表达和大规模平行报告子试验验证预测的增强子-启动子相互作用的方法。
结论：我们的研究结果为探索基因调控程序及其在发育过程中的动态提供了一个可推广的框架；这包括研究疾病相关变异对转录网络影响的综合方法。应用于我们网络的技术已经作为计算工具与我们的发现一起发布，E-P-INAnalyzer。我们的程序可以在不同的细胞环境和疾病中使用。
结果：我们从神经分化的七个早期时间点收集表观遗传和基因表达数据集。围绕这个模型系统，我们构建了增强子-启动子相互作用的网络，每个都处于神经诱导的个体阶段。这些网络是一系列丰富分析的基础，通过它，我们证明了它们对各种疾病相关变异的时间动态和富集。我们将Girvan-Newman聚类算法应用于这些网络，以揭示生物学相关的调控子结构。此外,我们展示了使用转录因子过表达和大规模平行报告子试验验证预测的增强子-启动子相互作用的方法。
结论：我们的研究结果为探索基因调控程序及其在发育过程中的动态提供了一个可推广的框架；这包括研究疾病相关变异对转录网络影响的综合方法。应用于我们网络的技术已经作为计算工具与我们的发现一起发布，E-P-INAnalyzer。我们的程序可以在不同的细胞环境和疾病中使用。
