Inferring gene regulatory circuitry from functional genomics data

描述(由申请人提供)：基因调控网络是由数千个独特分子之间高度特异的相互作用定义的。转录因子在这些网络中发挥着核心作用，但其序列特异性的结构基础以及信号通路和转录因子之间的连通性仍不清楚。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结合蛋白对转录后稳定性的调节。我们的大部分研究工作将集中在酵母上，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中推断出基于结构的蛋白质-DNA识别码。通过对广泛范围的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势。这将使我们能够仅根据TF的氨基酸序列来预测序列特异性，并设计具有指定序列特异性的TF。我们的第二个目标是利用网络水平的遗传连锁分析来确定转铁蛋白活性的调节因子。我们将开发一种将遗传连锁分析的力量与转录网络连通性的先验信息相结合的方法，并确定其等位基因状态影响转铁蛋白活性的数量性状基因座。使用这种方法，我们将对酵母中信号和转录网络之间的连接性进行全面的分析。我们的第三个目标是从功能上剖析转录后对mRNA稳定性的调控。我们先前证明，稳态mRNA表达数据包含关于RNA结合蛋白(RBPs)对mRNA半衰期的条件特异性控制的详细信息。通过将40个限制性商业惯例的高通量免疫沉淀数据集与大量生理条件下的全基因组mRNA表达数据整合在一起，我们将预测特定限制性商业惯例活跃的条件。我们将分析与辅助因子的组合顺式调控相互作用，并使用连锁分析来绘制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的预后。我们的工作将带来理论和生物学的见解，以及实用的软件工具和数据库，这些工具和数据库将帮助基础和应用研究人员了解和预测基因调控网络的行为。 与公共卫生相关：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，以便以特定于基因和细胞类型的方式控制基因表达。这些工具将使研究人员了解基因调控网络的行为是如何由基因组序列塑造的，以及如何受到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。

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.