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: 10544796
• 负责人: Shaun Aengus Mahony
• 金额: $ 45.53万
• 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/10544796
• 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; Specific qualifier value; Specificity; System; algorithm development; cell type; cohort; computerized tools; genome-wide; machine learning method; neural network architecture; novel; preference; programs; synergism; 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.

项目摘要 /摘要 我的研究计划的目的是了解转录因素（TFS）如何指导监管 基于细胞命运决定的计划。我的实验室目前侧重于TF监管的基本步骤 活动：新诱导的TF如何建立其DNA结合模式？ TFS应该具有约束力的亲和力 沿着典型的脊椎动物基因组的数百万个位点，但只有一小部分似乎是在给定的 细胞类型。此外，在细胞类型和发育时间点之间的结合变化的队列。我们 已经开发了开创性的机器学习方法，以表征调节性基因组事件和 了解TF结合特异性。我们已协作将计算方法应用于 了解细胞分化系统中的细胞命运决策，找到诱导结合的新方法 TF可以受到染色质环境的影响。该建议旨在集成算法 对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节状态预先确定。 尽管许多人已经分类了与静态中TF结合位点共存的并发染色质特征 上下文，该提案着重于细胞命运决策的典型动态设置。如何 在给定的细胞类型形状的染色质景观中，新诱导的TF将结合？主题1将继续我们 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 神经网络体系结构可以分开序列和染色质特征以解释诱导的TF结合 模式。利用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-DNA结合亚型模式（例如，直接与间接DNA结合）可以解释为什么 某些站点被诱导的TF束缚。我们将进一步询问DNA结合预定剂是否是 可转让：我们可以预测给定的TF如果引入新的单元格类型将在哪里结合？ 主题2将分析TFS在细胞命运决策过程中如何与已建立的染色质环境相互作用。 我们将询问寄生虫叉子盒TF如何识别独特的绑定目标，即使它们具有相似的绑定目标 DNA结合偏好，并在相同的染色质环境中表达。了解TF 绑定位点和调节活动可能会随着细胞的下降轨迹而变化，我们将 继续长期合作，以检查依赖染色质的TF调节行为 神经元亚型规范和造血。与这些努力相辅相成，我们将建立综合性 单细胞水平上时间染色质可及性动力学的调节模型。 这两个主题将协同作用，以提供计算工具和应用分析，以实现A 在细胞命运决策过程中，对TF调节特异性有更全面的了解。