Understanding the predeterminants of transcription factor regulatory activity

了解转录因子调节活性的决定因素

• 批准号: 10330514
• 负责人: Shaun Aengus Mahony
• 金额: $ 45.56万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10330514
• 关键词: 3-Dimensional; Affinity; Behavior; Binding; Binding Proteins; Binding Sites; Cell Differentiation process; Cells; Chromatin; Collaborations; DNA Binding; DNA-Binding Proteins; Development; Disease; Environment; Event; Genes; Genome; Genomics; Goals; Health; Hematopoiesis; Human; Human Genome; Human body; Machine Learning; Methodology; Modeling; Neurons; Pattern; Research; Shapes; Site; Specificity; System; algorithm development; cell type; cohort; computerized tools; genome-wide; machine learning method; neural network architecture; novel; preference; programs; tool; transcription factor; vertebrate genome

PROJECT SUMMARY / ABSTRACT The goal of my research program is to understand how transcription factors (TFs) direct the regulatory programs that underlie cell fate decisions. My lab currently focuses on a fundamental step in TF regulatory activity: how do newly induced TFs establish their DNA binding patterns? TFs should have binding affinity for millions of sites along the typical vertebrate genome, yet only a small fraction appears to be bound in a given cell type. Moreover, the cohort that are bound changes across cell types and developmental timepoints. We have developed pioneering machine learning approaches for characterizing regulatory genomic events and understanding TF binding specificity. We have collaboratively applied our computational approaches to understand cell fate decisions in cell differentiation systems, finding new ways in which the binding of induced TFs can be influenced by preexisting chromatin environments. This proposal aims to integrate algorithmic development and applied analysis of regulatory systems to gain a comprehensive understanding of how genome-wide TF binding patterns are predetermined by chromatin regulatory states. While many have cataloged the concurrent chromatin features that coexist with TF binding sites in a static context, this proposal focuses on the dynamic settings that are typical of cell fate decisions. How does the chromatin landscape in a given cell type shape where a newly induced TF will bind? Theme 1 will continue our development of machine learning methods for studying dynamic TF binding activities. We will focus on novel neural network architectures that can separate sequence and chromatin features to explain induced TF binding patterns. Drawing on our unique expertise and methodologies, we will ask whether integrating 3D genome organization or protein-DNA binding subtype modes (e.g., direct vs. indirect DNA binding) can explain why certain sites become bound by induced TFs. We will further ask if DNA binding predeterminants are transferrable: can we predict where a given TF will bind if introduced into a new cell type? Theme 2 will analyze how TFs interact with established chromatin environments during cell fate decisions. We will ask how paralogous Forkhead box TFs recognize distinct binding targets, even when they have similar DNA binding preferences and are expressed in the same chromatin environment. To understand how TF binding sites and regulatory activities can change as cells proceed down differentiation trajectories, we will continue long-standing collaborations that examine chromatin-dependent TF regulatory behaviors during neuronal subtype specification and hematopoiesis. Complementary to these efforts, we will build integrative regulatory models of temporal chromatin accessibility dynamics at the single cell level. The two themes will synergize to provide the computational tools and applied analyses that will enable a more complete understanding of TF regulatory specificity during cell fate decisions.

项目概要/摘要 我的研究项目的目标是了解转录因子 (TF) 如何指导调控 决定细胞命运的程序。我的实验室目前专注于 TF 监管的一个基本步骤 活性：新诱导的 TF 如何建立其 DNA 结合模式？ TF 应具有结合亲和力 典型的脊椎动物基因组上有数百万个位点，但似乎只有一小部分被结合在给定的位置上 细胞类型。此外，绑定的群体会随着细胞类型和发育时间点的不同而变化。我们 开发了开创性的机器学习方法来表征调控基因组事件， 了解 TF 结合特异性。我们合作应用我们的计算方法 了解细胞分化系统中的细胞命运决定，寻找诱导结合的新方法 TF 可能会受到先前存在的染色质环境的影响。该提案旨在整合算法 监管系统的开发和应用分析，以全面了解如何 全基因组 TF 结合模式由染色质调控状态预先确定。 虽然许多人已经对静态中与 TF 结合位点共存的并发染色质特征进行了分类。 在上下文中，该提案重点关注细胞命运决定的典型动态设置。如何 新诱导的 TF 将在给定细胞类型形状中结合的染色质景观？主题1将继续我们的 开发用于研究动态 TF 结合活动的机器学习方法。我们将专注于小说 神经网络架构可以分离序列和染色质特征来解释诱导的 TF 结合 模式。凭借我们独特的专业知识和方法，我们将询问是否整合 3D 基因组 组织或蛋白质-DNA 结合亚型模式（例如，直接与间接 DNA 结合）可以解释原因 某些位点会被诱导的转录因子结合。我们将进一步询问 DNA 结合的先决因素是否是 可转移：如果将给定的 TF 引入新的细胞类型，我们能否预测其将在何处结合？ 主题 2 将分析在细胞命运决定过程中 TF 如何与已建立的染色质环境相互作用。 我们将询问旁系同源 Forkhead box TF 如何识别不同的结合目标，即使它们具有相似的结合目标 DNA 结合偏好并在相同的染色质环境中表达。了解 TF 如何 随着细胞沿着分化轨迹前进，结合位点和调节活动可能会发生变化，我们将 继续长期合作，检查染色质依赖性 TF 调控行为 神经元亚型规范和造血。作为这些努力的补充，我们将建立一体化的 单细胞水平上时间染色质可及性动态的调控模型。 这两个主题将协同提供计算工具和应用分析，从而使 更全面地了解细胞命运决定过程中 TF 调控特异性。