Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, 679 Hoes Lane West, Piscataway, NJ, 08854, USA.
Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.
Genome Biol. 2024 Aug 14;25(1):221. doi: 10.1186/s13059-024-03365-w.
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.
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.
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.
越来越多的证据表明，相当一部分与疾病相关的突变发生在增强子中，这些增强子是调控基因所必需的非编码 DNA 区域。了解这种变异所影响的调控程序的结构和机制，可以揭示人类疾病的机制。
我们从神经分化的七个早期时间点收集了表观遗传和基因表达数据集。我们专注于这个模型系统，构建了每个神经诱导阶段的增强子-启动子相互作用网络。这些网络为一系列丰富的分析提供了基础，通过这些分析，我们展示了它们的时间动态和对各种与疾病相关的变体的富集。我们将 Girvan-Newman 聚类算法应用于这些网络，以揭示调控的生物学相关亚结构。此外，我们还展示了使用转录因子过表达和大规模平行报告基因检测来验证预测的增强子-启动子相互作用的方法。
我们的研究结果表明了一种可用于探索发育过程中基因调控程序及其动态的通用框架；包括一种全面的方法来研究与疾病相关的变异对转录网络的影响。应用于我们网络的技术与我们的发现一起作为一个计算工具 E-P-INAnalyzer 发表。我们的方法可以在不同的细胞环境和疾病中使用。
