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.

项目概要 转录因子 (TF) 是调节多种基因表达的蛋白质子集 在细胞中。指导体内许多组织和细胞类型特异性基因表达程序，转录 调节是 TF 诱导细胞分化和极化的主要机制之一。理解 转录调控的动态至关重要，因为它是细胞、组织、器官和细胞的重要组成部分。 系统发育及其失调可导致许多复杂的疾病。转录调控是最好的 通过有向网络建模，其中边缘源自转录因子到其下游 目标代表监管关系。然而，构建、优化和分析转录调控 网络（TRN）由于其固有的复杂性而极具挑战性。此外，动态接线 这些网络在细胞和组织分化过程中随着时间的推移而演变，并呈现出连续的轨迹， 而不是离散状态。目前理解这一动态系统的方法主要基于基因 由于基因调控的改变，表达能力不足，无法准确模拟此类网络 通常是通过染色质结构的变化而发生的。此外，现有方法要么忽视或过于简单化 细胞群中网络状态的异质性，从而导致分辨率的损失。在这个 提议，我们假设连续的细胞分化轨迹是由进化驱动的 转录网络布线，由染色质结构的改变引起。我们的首要任务 该研究计划的目标是阐明与相关的监管线路的不断演变 使用由单细胞构建的细胞特异性 TRN 来确定发育阶段或疾病状况 表观基因组数据。为了实现这一目标，我们将使用基序分析结合多个单细胞构建 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 单细胞分辨率的修饰 (ChIP-Seq) 和三维染色质相互作用 (Hi-C)。我们将使用 这些网络揭示与细胞分化相关的调控变化并发现关键 驱动细胞沿着发育轨迹或跨越疾病状态的转录调节因子。我们 还将检测这些网络内的转录调控模块，以发现与 细胞分化。最后，我们将把我们的方法应用于多个领域，并使用生物学来检验我们的假设 模型。总之，这些研究将建立一个动态网络模型系统，用于阐明表观遗传 高分辨率调节。这套集成的模型不仅有助于准确理解 发育中的表观遗传调控也将成为发现治疗靶点的强大资产 对与转录失调相关的多种复杂疾病进行干预。