Collaborative Research: Experimental and Computational Studies of DNA Binding by Human Paralogous Transcription Factors

合作研究：人类旁系同源转录因子 DNA 结合的实验和计算研究

• 批准号: 1412045
• 负责人: Raluca Gordan
• 金额: $ 48.08万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1412045&HistoricalAwards=false
• 关键词: Collaborative; Research; Experimental; Computational; Studies

All cells in an individual contain the same genetic information. However, different cells use this information differently, and each cell expresses only a small fraction of genes to produce the corresponding proteins. This process is tightly regulated by specialized proteins called transcription factors, which bind DNA in the neighborhood of specific genes and influence their expression. This project studies human transcription factors to understand how they identify their specific DNA targets across the genome. The project focuses on human transcription factors that have similar structures but interact with different genomic regions in the cell, and thus perform different functions. The goal of this research is to understand how closely related factors are able to recognize distinct genomic sites, a question that cannot be thoroughly addressed using current DNA binding specificity models. During the course of the project, high-quality transcription factor-DNA binding data and DNA binding specificity models will be generated. The data and models will be made available to the scientific community. The data generated in this study is expected to become a valuable resource for future development and testing of protein-DNA binding models, and for studies of differences among related transcription factors. Graduate, undergraduate, and high school students will participate in data generation and analysis, as well as dissemination of the results using visualization software, web platforms, and scientific posters and articles. Thus, students of various age groups will be introduced to molecular and structural biology through practical studies of protein-DNA interactions. In addition, students will become contributors to a bioinformatics research database and will have the opportunity to co-author scientific publications early in their career.Characterizing protein-DNA interactions and understanding the role of both proteins and DNA is vital to interpreting regulatory elements in the genome, and to understanding how changes in the protein or the DNA binding sites will affect cell function. Focusing on 16 transcription factors (TFs) from six different protein families, the goal of this project is to understand how paralogous TFs from each family are able to recognize distinct genomic sites, a question that cannot be addressed using current data and models. The project will use a combined experimental and computational approach to determine how DNA sequence and shape contribute to differential DNA binding by paralogous TFs. First, the project will use carefully designed high-throughput assays to measure in vitro binding of related TFs to thousands of putative genomic binding sites. These assays, called genomic-context protein-binding microarrays (gcPBM), minimize the noise and bias in the experimental measurements, making the data ideal for comparing the intrinsic sequence preferences of closely related factors. These high quality data will then be used to characterize the DNA binding preferences of paralogous TFs using regression models based on the DNA sequence content of putative binding sites. Next, the project will investigate the contribution of DNA shape, compared to high order DNA sequence features, to differential DNA binding specificities of paralogous TFs. Finally, the new computational models of DNA binding specificity will be validated using two approaches: 1) the models will be tested in vitro by introducing mutations in the DNA binding sites and testing them in new gcPBM assays; and 2) the models will be validated against in vivo TF binding data to verify that the new models are able to explain, at least in part, the differential in vivo binding patterns of paralogous TFs. Through the identification of characteristics that contribute to differential DNA binding of closely related TFs, this project represents a significant step forward in understanding how these factors are able to select different genomic binding sites, despite sharing a common DNA binding domain. This will ultimately lead to a better understanding of how TFs have evolved to regulate different target genes and perform different functions in the cell.This award is co-funded by the Directorate of Biological Sciences Division of Emerging Frontiers and Division of Molecular and Cellular Biosciences Program in Genetic Mechanisms and by the Directorate of Mathematical and Physical Sciences Division of Mathematical Sciences Program in Mathematical Biology.

个体中的所有细胞都包含相同的遗传信息。然而，不同的细胞以不同的方式使用这些信息，并且每个细胞仅表达一小部分基因来产生相应的蛋白质。这一过程受到称为转录因子的特殊蛋白质的严格调控，这些蛋白质将 DNA 结合在特定基因附近并影响其表达。该项目研究人类转录因子，以了解它们如何识别整个基因组中的特定 DNA 靶标。该项目重点关注具有相似结构但与细胞中不同基因组区域相互作用，从而执行不同功能的人类转录因子。这项研究的目的是了解相关因素如何能够识别不同的基因组位点，这是一个使用当前 DNA 结合特异性模型无法彻底解决的问题。在项目过程中，将生成高质量的转录因子-DNA结合数据和DNA结合特异性模型。数据和模型将提供给科学界。这项研究产生的数据预计将成为未来开发和测试蛋白质-DNA结合模型以及研究相关转录因子之间差异的宝贵资源。研究生、本科生和高中生将参与数据生成和分析，并使用可视化软件、网络平台以及科学海报和文章传播结果。因此，不同年龄段的学生将通过蛋白质-DNA 相互作用的实际研究了解分子和结构生物学。此外，学生将成为生物信息学研究数据库的贡献者，并有机会在职业生涯早期共同撰写科学出版物。表征蛋白质-DNA 相互作用并了解蛋白质和 DNA 的作用对于解释生物信息学中的调控元件至关重要。基因组，并了解蛋白质或 DNA 结合位点的变化将如何影响细胞功能。该项目重点关注来自 6 个不同蛋白质家族的 16 种转录因子 (TF)，其目标是了解每个家族的旁系同源 TF 如何能够识别不同的基因组位点，这是使用当前数据和模型无法解决的问题。该项目将使用实验和计算相结合的方法来确定 DNA 序列和形状如何影响旁系同源 TF 的差异 DNA 结合。首先，该项目将使用精心设计的高通量测定来测量相关转录因子与数千个假定基因组结合位点的体外结合。这些测定被称为基因组背景蛋白结合微阵列（gcPBM），可最大限度地减少实验测量中的噪音和偏差，使数据成为比较密切相关因素的内在序列偏好的理想选择。然后，这些高质量数据将用于使用基于推定结合位点 DNA 序列内容的回归模型来表征旁系同源 TF 的 DNA 结合偏好。接下来，该项目将研究与高阶 DNA 序列特征相比，DNA 形状对旁系同源 TF 的差异 DNA 结合特异性的贡献。最后，新的 DNA 结合特异性计算模型将使用两种方法进行验证：1）通过在 DNA 结合位点引入突变并在新的 gcPBM 测定中测试它们来对模型进行体外测试； 2) 将根据体内 TF 结合数据对模型进行验证，以验证新模型能够至少部分解释旁系同源 TF 的差异体内结合模式。通过识别有助于密切相关的 TF 差异 DNA 结合的特征，该项目在理解这些因素如何能够选择不同的基因组结合位点（尽管共享共同的 DNA 结合域）方面迈出了重要一步。这最终将导致人们更好地了解转录因子如何进化来调节不同的靶基因并在细胞中执行不同的功能。该奖项由新兴前沿生物科学部和分子与细胞生物科学计划部共同资助遗传机制和数学与物理科学局数学科学项目数学生物学。