UNDERSTANDING THE COMPLEX RELATIONSHIP BETWEEN TF BINDING AND GENE EXPRESSION

了解 TF 结合和基因表达之间的复杂关系

• 批准号: 9789336
• 负责人: MICHAEL R BRENT
• 金额: $ 31.42万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-20 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9789336
• 关键词: Address; Affect; Binding; Binding Proteins; ChIP-seq; Chromatin; Complex; Computer Simulation; Data; Data Set; Ensure; Eukaryota; Future; Gene Expression; Genes; Genetic Transcription; Histones; Knock-out; Location; Logic; Machine Learning; Maps; Methods; Modeling; Modernization; Nucleic Acid Regulatory Sequences; Proteins; Regulation; Repression; Resources; Saccharomyces cerevisiae; Statistical Models; Testing; Textbooks; Thermodynamics; Training; Transcriptional Regulation; Ursidae Family; Writing; Yeasts; chromatin immunoprecipitation; experimental study; genome-wide; genomic data; human data; improved; knock-down; predicting response; predictive test; promoter; response; success; transcription factor

What determines which genes are activated by a transcription factor (TF), which are repressed, and which are not regulated? The simplest conceptual model is that a TF directly regulates the genes in whose regulatory regions it binds. In this “independent action model”, if the TF has an activation domain, it activates the genes whose promoters it binds; if it has a repression domain, it represses the genes whose promoters it binds. It is remarkable then, that in existing, comprehensive data sets, that the vast majority of genes whose promoters are bound by a TF show no change in expression when the TF is knocked down or knocked out. In yeast data where all TFs have been analyzed, only 3-5% of bound genes respond when the TF is knocked out; in more recent, but less comprehensive human data it is rarely more than 15%. Furthermore, nearly every TF appears to activate some of the genes it binds while repressing others. These data show that we do not have a credible rough draft map of the regulatory logic of any eukaryote, as most of the genes bound by a TF in existing data are not regulated by that TF and we do not know how to predict which ones are. Our preliminary results show that some, but clearly not all, of the problem can be explained by technical issues inherent to the existing datasets. We there propose to generate new, comprehensive data sets that are ideally suited to studying the relationship between binding and functional regulation (Aim 1). We will use these datasets to quantify the predictive power of the independent action model. In Aim 2, we will collect genome- wide data focused understanding the interactions between TF binding and local chromatin state to see we can use the information to extend the independent action model to increase its predictive power. Finally, we will collect genome-wide data to identify TF-TF interactions that affect the gene expression response to perturbation. We will assess the predictive power of this information using computational models. This project will produce the first consistent data set that is well suited to studying the relationship between TF binding and regulation as well as the first credible overview of regulatory logic for any eukaryote. At the conclusion, we will know why perturbation of a TF apparently activates some of the genes it binds, represses other genes it binds, and does nothing at all to most of the genes it binds. Is it just the limitations of existing high-throughput data sets, or do interactions dominate independent action?

是什么决定了哪些基因被转录因子 (TF) 激活、哪些被抑制、哪些被抑制 不受监管？最简单的概念模型是转录因子直接调控基因 它结合的区域。在这个“独立作用模型”中，如果 TF 有一个激活域，它就会激活基因 它结合其启动子；如果它有抑制域，它就会抑制其启动子所结合的基因。这是 值得注意的是，在现有的综合数据集中，绝大多数基因的启动子 当 TF 被敲低或敲除时，被 TF 结合的表达没有变化。在酵母数据中 当所有 TF 都已被分析时，当 TF 被敲除时，只有 3-5% 的结合基因有反应；在更多 最近但不太全面的人类数据很少超过 15%。此外，几乎每个TF都会出现 激活它结合的一些基因，同时抑制其他基因。这些数据表明我们没有可靠的数据 任何真核生物调控逻辑的粗略草图，因为现有数据中大多数基因都被 TF 结合 不受该 TF 监管，我们不知道如何预测哪些是受该 TF 监管的。 我们的初步结果表明，部分问题（但显然不是全部）可以用技术问题来解释 现有数据集所固有的。我们建议生成新的、全面的数据集，理想情况下 适合研究结合和功能调节之间的关系（目标 1）。我们将使用这些 数据集来量化独立行动模型的预测能力。在目标 2 中，我们将收集基因组- 广泛的数据集中于理解 TF 结合和局部染色质状态之间的相互作用，看看我们可以 使用这些信息来扩展独立行动模型以提高其预测能力。最后，我们将 收集全基因组数据以确定影响基因表达反应的 TF-TF 相互作用 扰动。我们将使用计算模型评估这些信息的预测能力。 该项目将产生第一个一致的数据集，非常适合研究 TF 之间的关系 结合和调节，以及对任何真核生物调节逻辑的第一个可信概述。在 结论，我们会知道为什么 TF 的扰动显然会激活它结合、抑制的一些基因 它结合的其他基因，并且对其结合的大多数基因没有任何作用。难道只是现有的限制 高通量数据集，还是相互作用主导独立行动？