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: 8274820
• 负责人: Harmen J Bussemaker
• 金额: $ 38.1万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-13 至 2013-07-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8274820
• 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; research study; response; tool; transcription factor

Project Summary 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 genomewide 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.

项目摘要 基因调控网络是由数千个基因之间的高度特异性相互作用定义的。 独特的分子。转录因子（TF）在这些网络中起着核心作用， 但是关于它们的序列特异性的结构基础仍有许多未知之处 以及信号通路和TF之间的连接。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。我们的大多数 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。 对于数据访问和实验验证我们的结果，我们将与优秀的 高通量实验合作者。我们也将进行更多的传统跟踪- 在我们自己的实验室里进行实验。我们的第一个目标是推断出一种结构- 基于高通量结合数据的蛋白质-DNA识别代码。通过执行 同时拟合各种TF的体外结合数据，我们将估计游离 碱基对/氨基酸识别的能量潜力。这些将使我们能够预测 序列特异性，并设计TF， 规定的序列特异性。我们的第二个目标是确定TF活性的调节剂 使用网络级遗传连锁分析。我们将开发一种方法， 利用转录网络的先验信息进行遗传连锁分析的能力 连接性，并鉴定其等位基因状态影响TF活性的数量性状基因座。 使用这种方法，我们将对连接性进行全面分析 在酵母中的信号和转录网络之间。我们的第三个目标是 从功能上剖析了mRNA稳定性的转录后调控。我们之前 表明稳态mRNA表达数据包含详细的信息， 关于RNA结合蛋白（RBP）对mRNA半衰期的条件特异性控制。 通过整合一个新的高通量免疫沉淀数据集，用于>40个RBP， 在大量生理条件下的全基因组mRNA表达数据，我们 将预测特定RBP活跃的条件。我们将分析 与辅因子的组合顺式调节相互作用，并使用连锁分析来定位 信号通路和转录后网络之间的连接。异常 基因表达的调节通常与疾病有关。此外，基因 个体之间的差异影响对药物和疾病的反应 预后我们的工作将导致理论和生物学的见解，以及实际的 软件工具和数据库，这将有助于基础和应用研究人员了解 并预测基因调控网络的行为。