Differential effects of genomic context on the binding specificity of paralogous transcription factors

基因组背景对旁系同源转录因子结合特异性的不同影响

• 批准号: 1715589
• 负责人: Raluca Gordan
• 金额: $ 72.18万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1715589&HistoricalAwards=false
• 关键词: Differential; effects; genomic; context; binding

Cells often use similar proteins to perform very different cellular functions. Focusing on regulatory proteins that dictate which genes are expressed in each cell (among the large repertoire of 20,000 human genes), this project will decipher mechanisms that allow similar proteins to interact with distinct regions of the genome in order to perform distinct functions. This is significant because most human proteins, including the transcription factors studied in this project, are closely-related members of large families. Over time, these proteins diversified in function, often with little change in the protein sequence. Our understanding of how closely-related transcription factors can bind to different regions of the genome is very limited. To address this gap in knowledge, this project will generate high-quality transcription factor-DNA binding data and models, and will analyze the data to study genetic mechanisms by which transcription factors target specific genomic regions. The data and models will be made available to the scientific community, and are expected to become a valuable resource for future studies of differences among related transcription factors. This research will expose postdoctoral fellows, graduate, undergraduate, high school, and middle school students to the genetic mechanisms used by human cells to regulate gene expression. Trainees will participate in data generation and analysis, as well as dissemination of results. Thus, they will be introduced to molecular and computational biology through practical studies of protein-DNA interactions. Trainees will also experience the power of interdisciplinary research, as they will combine computational modeling and biological experiments to address fundamental questions about human cells. In addition, by using biological questions as motivation to teach computational techniques, this project aims to attract female students of various age groups to STEM fields.Interactions between proteins and DNA are critical for many cellular processes, including the regulation of gene expression. This project focuses on how closely-related transcription factor proteins identify their unique DNA binding sites across the human genome, in order to regulate the expression of target genes. Interactions between transcription factors and DNA occur over short regions, oftentimes 10 nucleotides. However, the DNA binding sites always occur within a specific genomic context, which is very likely to influence the interactions with transcription factors. This project will investigate three mechanisms by which genomic context can influence binding of closely-related factors: homotypic clustering (i.e. the presence of additional binding sites for factors from the same protein family), heterotypic clustering (i.e. the presence of binding sites for factors from other families), and the presence of repetitive DNA sequence elements (which were recently shown to affect protein-DNA binding). The project will use high-throughput cell-free assays to quantitatively measure the binding levels of 21 human transcription factors (from 9 distinct proteins families) for tens of thousands of DNA sites in their native genomic sequence contexts. For each protein family, DNA sequences with different arrangements of binding sites and different repeat elements will be selected from the human genome and synthesized de novo on glass slides. Binding of related transcription factors to the selected genomic sequences will be measured quantitatively, on the slides, using genomic-context protein-binding microarray (gcPBM) assays. gcPBM data are quantitative, highly reproducible, and can detect differences in binding between closely related factors, which makes the gcPBM assay ideal for this project. The generated data will be used to develop computational models of protein-DNA binding specificity, and to identify differences in specificity between related factors. All models will take into account characteristics of the genomic context (homotypic clustering, heterotypic clustering, and repetitive DNA elements). The models will be validated computationally and experimentally. Finally, the project will assess the extent to which different mechanisms can explain differential genomic binding in the cell. By identifying genetic mechanism that contribute to the differential DNA binding of closely-related transcription factors, this project represents a significant step forward in understanding how these regulatory proteins select distinct genomic targets and perform distinct functions in human cells.

细胞通常使用相似的蛋白质来执行非常不同的细胞功能。该项目专注于决定哪些基因在每个细胞中表达的调节蛋白（在20，000个人类基因的大型库中），该项目将破译允许类似蛋白质与基因组的不同区域相互作用以执行不同功能的机制。这是重要的，因为大多数人类蛋白质，包括本项目中研究的转录因子，都是大家族的密切相关成员。随着时间的推移，这些蛋白质的功能多样化，通常蛋白质序列几乎没有变化。我们对密切相关的转录因子如何与基因组的不同区域结合的理解非常有限。为了解决这一知识差距，该项目将生成高质量的转录因子-DNA结合数据和模型，并将分析数据以研究转录因子靶向特定基因组区域的遗传机制。这些数据和模型将提供给科学界，并有望成为未来研究相关转录因子差异的宝贵资源。这项研究将使博士后研究员、研究生、本科生、高中生和中学生接触到人类细胞用来调节基因表达的遗传机制。受训人员将参与数据生成和分析以及结果的传播。因此，他们将通过蛋白质-DNA相互作用的实际研究引入分子和计算生物学。学员还将体验跨学科研究的力量，因为他们将结合联合收割机计算建模和生物实验，以解决有关人类细胞的基本问题。此外，本项目还以生物学问题为动力，教授计算技术，旨在吸引不同年龄段的女学生学习STEM领域。蛋白质和DNA之间的相互作用对许多细胞过程至关重要，包括基因表达的调节。该项目的重点是密切相关的转录因子蛋白如何在人类基因组中识别其独特的DNA结合位点，以调节靶基因的表达。转录因子和DNA之间的相互作用发生在短区域，通常为10个核苷酸。然而，DNA结合位点总是发生在特定的基因组环境中，这很可能影响与转录因子的相互作用。该项目将研究基因组环境影响密切相关因子结合的三种机制：同型聚类（即来自同一蛋白质家族的因子存在额外的结合位点），异型聚类（即来自其他家族的因子存在结合位点），以及重复DNA序列元件的存在（最近显示影响蛋白质-DNA结合）。该项目将使用高通量无细胞测定来定量测量21种人类转录因子（来自9个不同的蛋白质家族）在其天然基因组序列背景中数万个DNA位点的结合水平。对于每个蛋白质家族，将从人类基因组中选择具有不同结合位点排列和不同重复元件的DNA序列，并在载玻片上从头合成。使用基因组背景蛋白结合微阵列（gcPBM）测定，在载玻片上定量测定相关转录因子与选定基因组序列的结合。gcPBM数据是定量的、高度可重复的，并且可以检测密切相关的因子之间的结合差异，这使得gcPBM测定法成为该项目的理想选择。生成的数据将用于开发蛋白质-DNA结合特异性的计算模型，并确定相关因素之间的特异性差异。所有模型都将考虑基因组背景的特征（同型聚类、异型聚类和重复DNA元件）。这些模型将通过计算和实验进行验证。最后，该项目将评估不同机制可以解释细胞中差异基因组结合的程度。通过确定有助于密切相关的转录因子的差异DNA结合的遗传机制，该项目代表了理解这些调控蛋白如何选择不同的基因组靶点并在人类细胞中执行不同功能的重要一步。