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: 10798541
• 负责人: Shaun Aengus Mahony
• 金额: $ 11.37万
• 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/10798541
• 关键词: 3-Dimensional; Affinity; Behavior; Binding; Binding Proteins; Binding Sites; Biological Assay; Cell Differentiation process; Cells; Chromatin; Collaborations; DNA; DNA Binding; DNA Sequence; DNA-Binding Proteins; Data; Development; Disease; Embryo; Environment; Equipment; Event; Generations; Genes; Genome; Genomics; Goals; Health; Hematopoiesis; Human; Human Genome; Human body; Machine Learning; Memory; Methodology; Modeling; Mus; Neurons; Pathway Analysis; Pattern; Research; Research Support; Resolution; Shapes; Site; Specific qualifier value; Specificity; System; algorithm development; cell type; cohort; computerized tools; computing resources; convolutional neural network; embryonic stem cell; equipment acquisition; genome-wide; machine learning method; neural network; neural network architecture; neurodevelopment; novel; preference; programs; stem; 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.

项目总结/摘要 我的研究项目的目标是了解转录因子（TF）如何指导调节 决定细胞命运的程序。我的实验室目前专注于TF调节的基本步骤 活性：新诱导的TF如何建立它们的DNA结合模式？TF应具有结合亲和力， 在典型的脊椎动物基因组中有沿着数百万个位点，但只有一小部分似乎与给定的 细胞类型。此外，被结合的群体在细胞类型和发育时间点上发生变化。我们 已经开发出用于表征调控基因组事件的开创性机器学习方法， 了解TF结合特异性。我们已经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，寻找新的方法， 转录因子可以受到预先存在的染色质环境的影响。该提案旨在整合算法 制定和应用分析监管制度，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。 虽然许多人已经将与TF结合位点共存的静态染色质特征编目， 在这种背景下，该建议侧重于动态设置，这是典型的细胞命运的决定。如何 在给定的细胞类型形状中，新诱导的TF将结合的染色质景观？主题1将继续我们的 开发用于研究动态TF结合活性的机器学习方法。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，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，我们将询问整合3D基因组是否 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TF结合。我们将进一步询问DNA结合决定因素是否 transferable：我们能预测一个给定的TF如果被引入一个新的细胞类型中会在哪里结合吗？ 主题2将分析在决定细胞命运的过程中，转录因子如何与已建立的染色质环境相互作用。 我们将询问旁系同源叉头盒转录因子如何识别不同的结合靶点，即使它们具有相似的 DNA结合偏好和在相同的染色质环境中表达。了解TF如何 结合位点和调节活性可以随着细胞分化轨迹的进展而改变，我们将 继续长期的合作，检查染色质依赖的TF调节行为， 神经元亚型特化和造血。作为对这些努力的补充，我们将建立一个一体化的 在单细胞水平的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， 更全面地了解细胞命运决定期间TF调节特异性。

项目成果

Foxa2 and Pet1 Direct and Indirect Synergy Drive Serotonergic Neuronal Differentiation.

• DOI: 10.3389/fnins.2022.903881
• 发表时间: 2022
• 期刊: Frontiers in neuroscience
• 影响因子: 4.3
• 作者: Aydin B;Sierk M;Moreno-Estelles M;Tejavibulya L;Kumar N;Flames N;Mahony S;Mazzoni EO
• 通讯作者: Mazzoni EO