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

基本信息

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

项目摘要

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.

细胞经常使用相似的蛋白质来执行非常不同的细胞功能。该项目重点关注决定每个细胞（在 20,000 个人类基因的庞大库中）表达哪些基因的调节蛋白，将破译允许相似蛋白与基因组不同区域相互作用以执行不同功能的机制。这很重要，因为大多数人类蛋白质，包括本项目中研究的转录因子，都是大家族中密切相关的成员。随着时间的推移，这些蛋白质的功能多样化，蛋白质序列通常几乎没有变化。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。为了解决这一知识空白，该项目将生成高质量的转录因子-DNA 结合数据和模型，并分析这些数据以研究转录因子针对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之间差异的宝贵资源。这项研究将使博士后、研究生、本科生、高中生和中学生了解人类细胞调节基因表达的遗传机制。学员将参与数据生成和分析以及结果传播。因此，他们将通过蛋白质-DNA 相互作用的实际研究被引入分子和计算生物学。学员还将体验跨学科研究的力量，因为他们将结合计算模型和生物实验来解决有关人类细胞的基本问题。此外，通过利用生物学问题作为教授计算技术的动力，该项目旨在吸引各个年龄段的女学生进入 STEM 领域。蛋白质和 DNA 之间的相互作用对于许多细胞过程至关重要，包括基因表达的调节。该项目重点研究密切相关的转录因子蛋白如何识别人类基因组中独特的 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较短的区域，通常是 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（即来自同一蛋白质家族的因子的结合位点的存在）其他家族），以及重复DNA序列元件的存在（最近被证明会影响蛋白质-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）与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。将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量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 检测非常适合该项目。生成的数据将用于开发蛋白质-DNA 结合特异性的计算模型，并识别相关因素之间特异性的差异。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在多大程度上解释细胞中差异基因组结合。通过确定导致密切相关的转录因子的差异 DNA 结合的遗传机制，该项目在理解这些调节蛋白如何选择不同的基因组靶标并在人类细胞中执行不同的功能方面迈出了重要的一步。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

QBiC-Pred: quantitative predictions of transcription factor binding changes due to sequence variants

QBiC-Pred：定量预测由于序列变异导致的转录因子结合变化

DOI：
10.1093/nar/gkz363
发表时间：
2019-05
期刊：
Nucleic Acids Research
影响因子：
14.9
作者：
Martin, Vincentius;Zhao, Jingkang;Afek, Ariel;Mielko, Zachery;Gordân, Raluca
通讯作者：
Gordân, Raluca

Mutational processes in cancer preferentially affect binding of particular transcription factors

癌症中的突变过程优先影响特定转录因子的结合