Integrated frameworks for single-cell epigenomics based transcriptional regulatory networks

基于单细胞表观基因组学的转录调控网络的集成框架

• 批准号: 10713209
• 负责人: Yasin Uzun
• 金额: $ 40.42万
• 依托单位: PENNSYLVANIA STATE UNIV HERSHEY MED CTR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10713209
• 关键词: 3-Dimensional; ATAC-seq; Architecture; Biological Models; Cell Differentiation process; Cells; Cellular Structures; ChIP-seq; Chromatin; Complex; Coupled; DNA Methylation; DNA Sequence; Data; Development; Developmental Process; Disease; Epigenetic Process; Evolution; Gene Expression; Gene Expression Regulation; Genes; Genetic Transcription; Goals; Mammals; Methods; Modeling; Nature; Pathway interactions; Population; Proteins; Regulation; Research; Resolution; Systems Development; Testing; Therapeutic Intervention; Time; Tissue Differentiation; Tissues; Transcriptional Regulation; bisulfite sequencing; cell type; dynamic system; epigenetic regulation; epigenomics; histone modification; network models; organ growth; programs; targeted treatment; therapeutic target; transcription factor; transcription regulatory network

PROJECT SUMMARY Transcription factors (TFs) compose a subset of proteins that regulate the expression of a wide range of genes in cells. Instructing many tissue- and cell-type specific gene expression programs in the body, transcriptional regulation is one of the major mechanisms of cell differentiation and polarization induced by TFs. Understanding the dynamics of transcriptional regulation is crucial since it is a critical component of cell, tissue, organ and system development and its dysregulation can lead to many complex diseases. Transcriptional regulation is best modeled via directed networks in which the edges originating from transcription factors to their downstream targets represent regulatory relationships. However, building, optimizing and analyzing transcriptional regulatory networks (TRNs) is highly challenging due to inherent complexity of such networks. Moreover, the dynamic wiring in these networks evolves over time during cell and tissue differentiation and presents a continuous trajectory, instead of discrete states. Current approaches for understanding this dynamic system are mainly based on gene expression and are underpowered to accurately model such networks because alterations in gene regulation often take place via changes in chromatin architecture. Further, existing methods either disregard or oversimplify the heterogeneous nature of network states in cell populations, thereby leading to a loss of resolution. In this proposal, we hypothesize that continuous cell differentiation trajectories are driven by evolutions in the transcriptional network wiring, which are induced by alterations in the chromatin architecture. Our overarching goal in this research program is to elucidate the continuous evolution of regulatory wirings associated with developmental stages or disease conditions using cell-specific TRNs that are constructed from single-cell epigenomic data. To reach this goal, we will build TRNs using motif analysis coupled with multiple single-cell epigenomic sequencing data, including chromatin accessibility (ATAC-Seq), DNA methylation (BS-Seq), histone modification (ChIP-Seq) and three-dimensional chromatin interaction (Hi-C) at single-cell resolution. We will use these networks to uncover the regulatory changes associated with cell differentiation and discover the key transcriptional regulators that drive the cells along developmental trajectories or across the disease states. We will also detect transcriptional regulatory modules within these networks to discover pathways associated with cell differentiation. Finally, we will apply our approach to multiple domains and test our hypothesis using biological models. Altogether these studies will establish a system of dynamic network models for unraveling epigenetic regulation at a high resolution. This integrated set of models will not only facilitate an accurate understanding of epigenetic regulation in development but will also be a powerful asset for discovering targets for therapeutic interventions for a wide range of complex diseases associated with transcriptional dysregulation.

项目摘要 转录因子（TFS）组成了调节广泛基因表达的蛋白质的子集 在细胞中。指导人体中许多组织和细胞类型的特定基因表达程序，转录 调节是TFS引起的细胞分化和极化的主要机制之一。理解 转录调控的动力学至关重要，因为它是细胞，组织，器官和 系统开发及其失调会导致许多复杂的疾病。转录法规是最好的 通过定向网络建模，其中边缘从转录因子到下游建模 目标代表监管关系。但是，构建，优化和分析转录调节 由于此类网络的固有复杂性，网络（TRN）极具挑战性。而且，动态布线 在这些网络中，在细胞和组织分化过程中会随着时间的流逝而演变，并提出连续的轨迹， 而不是离散状态。当前理解该动态系统的方法主要基于基因 表达，并且无法准确建模此类网络，因为基因调节的改变 通常是通过染色质体系结构的变化进行的。此外，现有方法要么无视或过度简化 细胞种群中网络状态的异质性质，从而导致分辨率损失。在这个 提案，我们假设连续的细胞分化轨迹是由演变驱动的 转录网络接线是由染色质体系结构的改变引起的。我们的总体 该研究计划中的目标是阐明与 使用由单细胞构建的细胞特异性TRN的发育阶段或疾病条件 表观基因组数据。为了实现此目标，我们将使用主题分析构建TRN，并与多个单细胞一起构建TRN 表观基因组测序数据，包括染色质可及性（ATAC-SEQ），DNA甲基化（BS-SEQ），组蛋白 单细胞分辨率的修改（CHIP-SEQ）和三维染色质相互作用（HI-C）。我们将使用 这些网络以发现与细胞分化相关的调节变化并发现密钥 转录调节因子驱动细胞沿发育轨迹或跨疾病状态的调节剂。我们 还将检测这些网络中的转录调节模块，以发现与 细胞分化。最后，我们将使用我们的方法应用我们的方法，并使用生物学检验我们的假设 型号。这些研究总之将建立一个动态网络模型系统，用于揭示表观遗传 高分辨率的调节。这组集成的模型不仅将有助于准确理解 发育中的表观遗传调节，但也将是发现治疗目标的有力资产 与转录失调相关的多种复杂疾病的干预措施。