Understanding the predeterminants of transcription factor regulatory activity

项目摘要/摘要 我的研究项目的目标是了解转录因子(TF)是如何指导调控的 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 新诱导的转录因子是如何建立其DNA结合模式的？TFS应具有以下内容的绑定亲和力 典型脊椎动物基因组上有数百万个位点，但只有一小部分似乎与给定的 单元类型。此外，被绑定的队列会随着细胞类型和发育时间点的变化而变化。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和 了解转铁蛋白结合的特异性。我们合作地将我们的计算方法应用于 了解细胞分化系统中细胞命运的决定，寻找结合诱导 TFS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 管理系统的开发和应用分析，以全面了解 全基因组的转录因子结合模式是由染色质调节状态预先确定的。 虽然许多人已经将与TF结合位点共存的并发染色质特征编目为静态的 在此背景下，本提案侧重于细胞命运决定的典型动态设置。这是怎么回事 在给定的细胞类型形状中的染色质景观，新诱导的转铁蛋白将结合在哪里？主题1将继续我们的 研究动态转铁蛋白结合活性的机器学习方法的发展。我们将专注于小说 神经网络结构可以分离序列和染色质特征来解释诱导的转铁蛋白结合 模式。凭借我们独特的专业知识和方法，我们会问，将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)可以解释为什么 某些位点被诱导的转录因子结合。我们将进一步询问DNA结合的前置决定因素是否 可转移性：如果引入一种新的细胞类型，我们能预测一个给定的转铁蛋白结合在哪里吗？ 主题2将分析在细胞命运决定过程中，转录因子如何与已建立的染色质环境相互作用。 我们将询问类似的叉头盒TF如何识别不同的结合靶标，即使它们具有类似的 DNA结合偏好和在相同的染色质环境中表达。要了解TF如何 结合部位和调节活动可以随着细胞向下分化的轨迹而改变，我们将 继续长期合作，检查染色质依赖的转铁蛋白调节行为 神经元亚型指定与造血。与这些努力相辅相成，我们将建立一体化的 单细胞水平时间染色质可及性动力学的调控模型。 这两个主题将协同作用，提供计算工具和应用分析，使 更全面地了解细胞命运决定过程中转铁蛋白的调节特异性。

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.