Inferring gene regulatory circuitry from functional genomics data

从功能基因组数据推断基因调控电路

• 批准号: 7943348
• 负责人: Harmen J Bussemaker
• 金额: $ 30.09万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7943348
• 关键词: Address; Affect; Amino Acid Sequence; Amino Acids; Attention; Base Pairing; Behavior; Binding; Biological; Cells; Code; Computational algorithm; Computer software; Computing Methodologies; DNA; DNA Binding; Data; Data Set; Databases; Disease; Eukaryota; Family; Free Energy; Funding; Gene Expression; Gene Expression Regulation; Genes; Genetic; Grant; Half-Life; Immunoprecipitation; In Vitro; Individual; Laboratories; Lead; Maps; Measures; Mediating; Messenger RNA; Methods; Modeling; Nucleosomes; Peptide Sequence Determination; Pharmaceutical Preparations; Physiological; Play; Post-Transcriptional Regulation; Proteins; Quantitative Trait Loci; RNA Binding; RNA-Binding Proteins; Reading; Regulator Genes; Research; Research Personnel; Role; Shapes; Signal Pathway; Signal Transduction; Software Tools; Specificity; Statistical Mechanics; Structure; Transcript; Validation; Work; Yeasts; base; cell type; combinatorial; design; follow-up; functional genomics; gene function; genetic linkage analysis; genetic regulatory protein; genome sequencing; genome-wide; insight; mRNA Expression; mRNA Stability; novel; outcome forecast; protein protein interaction; public health relevance; research study; response; tool; transcription factor

DESCRIPTION (provided by applicant): Gene regulatory networks are defined by highly specific interactions between thousands of unique molecules. Transcription factors (TFs) play a central role in these networks, but much remains unknown regarding the structural basis of their sequence specificity and the connectivity between signaling pathways and TFs. We will develop novel computational methods to address these fundamental questions. We will also analyze post-transcriptional regulation of transcript stability by RNA-binding proteins. Most of our research effort will focus on yeast, but our methods will be applicable in all eukaryotes. For data access and experimental validation of our results, we will work with excellent high-throughput experimental collaborators. We will also perform more traditional follow- up experiments within our own laboratory. Our first specific aim is to infer a structure- based protein-DNA recognition code from high-throughput binding data. By performing a simultaneous fit to in vitro binding data for a wide range of TFs, we will estimate free energy potentials for base-pair/amino-acid recognition. These will allow us to predict sequence specificity from the amino-acid sequence of the TF alone and design TFs with prescribed sequence specificity. Our second aim is to identify modulators of TF activity using network-level genetic linkage analysis. We will develop a method that combines the power of genetic linkage analysis with prior information about transcriptional network connectivity, and identify quantitative trait loci whose allelic status affects TF activity. Using this approach, we will perform a comprehensive analysis of the connectivity between the signaling and the transcriptional networks in yeast. Our third aim is to functionally dissect post-transcriptional regulation of mRNA stability. We previously demonstrated that steady-state mRNA expression data contains detailed information about the condition-specific control of mRNA half-life by RNA-binding proteins (RBPs). By integrating a novel high-throughput immunoprecipitation dataset for >40 RBPs with genome wide mRNA expression data for a large number of physiological conditions, we will predict the conditions in which specific RBPs are active. We will analyze combinatorial cis-regulatory interactions with co-factors and use linkage analysis to map connectivity between signaling pathways and post-transcriptional networks. Aberrant regulation of gene expression is often associated with disease. Furthermore, genetic differences between individuals affect responsiveness to drugs as well as disease prognosis. Our work will lead to theoretical and biological insights, as well as practical software tools and databases that will help basic and applied researchers to understand and predict the behavior of gene regulatory networks. PUBLIC HEALTH RELEVANCE: This project aims to further develop computational algorithms and software that can be used to predict how DNA- and RNA-binding "read" the genome sequence in order to control gene expression in a gene- and cell type-specific manner. These tools will allow researchers to understand how the behavior of gene regulatory networks is shaped by the genome sequence, and affected by genetics differences between individuals. Aberrant regulation of gene expression is often associated with disease.

描述（由申请人提供）：基因调控网络由数千种独特分子之间的高度特异性相互作用定义。转录因子（TF）在这些网络中起着核心作用，但关于其序列特异性的结构基础以及信号通路和TF之间的连接仍有很多未知之处。我们将开发新的计算方法来解决这些基本问题。我们还将分析RNA结合蛋白对转录物稳定性的转录后调节。我们的大部分研究工作将集中在酵母，但我们的方法将适用于所有真核生物。对于我们的结果的数据访问和实验验证，我们将与优秀的高通量实验合作者合作。我们还将在自己的实验室内进行更传统的后续实验。我们的第一个具体目标是从高通量结合数据推断基于结构的蛋白质-DNA识别代码。通过对各种TF的体外结合数据进行同时拟合，我们将估计碱基对/氨基酸识别的自由能潜力。这些将使我们能够预测单独的TF的氨基酸序列的序列特异性和设计具有规定的序列特异性的TF。我们的第二个目标是使用网络水平的遗传连锁分析来确定TF活性的调节剂。我们将开发一种方法，将遗传连锁分析的力量与转录网络连接的先验信息相结合，并确定其等位基因状态影响TF活性的数量性状位点。使用这种方法，我们将对酵母中信号传导和转录网络之间的连接进行全面分析。我们的第三个目标是从功能上剖析mRNA稳定性的转录后调控。我们先前证明，稳态mRNA表达数据包含有关RNA结合蛋白（RBP）对mRNA半衰期的条件特异性控制的详细信息。通过将>40种RBP的新型高通量免疫沉淀数据集与大量生理条件的全基因组mRNA表达数据整合，我们将预测特定RBP活跃的条件。我们将分析组合顺式调控与辅因子的相互作用，并使用连锁分析来映射信号通路和转录后网络之间的连接。基因表达的异常调节通常与疾病有关。此外，个体之间的遗传差异影响对药物的反应以及疾病预后。我们的工作将导致理论和生物学的见解，以及实用的软件工具和数据库，这将有助于基础和应用研究人员了解和预测基因调控网络的行为。 公共卫生相关性：该项目旨在进一步开发可用于预测DNA和RNA结合如何“读取”基因组序列的计算算法和软件，以便以基因和细胞类型特异性的方式控制基因表达。这些工具将使研究人员能够了解基因调控网络的行为如何受到基因组序列的影响，以及个体之间遗传差异的影响。基因表达的异常调节通常与疾病有关。