Global mapping and analysis of a bacterial transcriptional regulatory network

细菌转录调控网络的全局绘图和分析

• 批准号: 9307942
• 负责人: James E Galagan
• 金额: $ 55.28万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9307942
• 关键词: Anti-Bacterial Agents; Bacteria; Bacterial Genes; Bacterial Genome; Binding; Binding Sites; Cells; ChIP-on-chip; ChIP-seq; Communities; Complex; Computer Analysis; Computer Simulation; DNA; DNA Binding; DNA Sequence; Data; Data Set; Databases; Escherichia coli; Eukaryota; Gene Expression Regulation; Genetic Transcription; Genome; Genomics; Goals; In Vitro; Knowledge; Laboratory Organism; Location; Maps; Modeling; Mycobacterium tuberculosis; Proteins; Regulon; Research Personnel; Resolution; Resources; Saccharomyces cerevisiae; Sigma Factor; Signal Transduction; Specificity; Transcription Initiation; Transcription Initiation Site; Transcriptional Regulation; Work; genetic regulatory protein; in vivo; network models; predictive modeling; public health relevance; response; transcription factor; transcriptome sequencing

 DESCRIPTION (provided by applicant): Bacterial genomes typically encode hundreds of transcription factors (TFs). Decades of work on TFs in Escherichia coli has led to a deep mechanistic understanding of TF function. However, relatively few bacterial TFs have been studied on a genomic scale. Our data and those of other groups indicate that only a small fraction of TF binding sites have been identified, even for well-studied TFs. Consequently, the numerous investigators utilizing the extensive E. coli regulatory network data in public databases are relying on a highly incomplete, and potentially misleading, dataset. Our long-term goal is to develop a fully predictive model for transcription regulation by TFs in E. coli. The goa of this proposal is to inform such a model by mapping the regulons of all E. coli TFs and to use these data as the basis to investigate fundamental aspects of TF function. Global mapping data for bacterial TFs indicate that well-established rules of TF function apply to only a subset of binding sites. In particular, DNA sequence is often insufficient to predict TF binding location, suggesting that factors other than DNA binding site sequence contribute to TF-DNA interactions in vivo. Given the fundamental importance of gene regulation, it is vital that we better understand the relationship between DNA sequence and TF binding in vivo. We propose to experimentally generate a high-resolution regulatory network for E. coli that includes regulon information for all known and predicted TFs. This will serve as a valuable resource for the scientific community. We will use these data as a framework for accurate modeling of the regulatory network, and to inform our targeted studies of the relationship between DNA sequence and TF binding in vivo. We expect to generate a high-resolution regulatory network for E. coli. This will serve as a valuable resource for the scientific community. The equivalent resource for the model eukaryote, Saccharomyces cerevisiae, was generated over 10 years ago and has contributed greatly to our understanding of eukaryotic transcription regulation. The most complete resource for a bacterium is currently that for Mycobacterium tuberculosis, which lacks tractability as an experimental organism. Generating an equivalent resource for E. coli will greatly facilitate studies of bacterial gene regulation. We further expect to use our regulatory network model as a basis to understand the relationship between DNA sequence and TF binding in vivo. We expect to reveal complex interplay between pairs of TFs and between TFs and global regulatory proteins. Knowledge of these interactions is critical for a detailed understanding of TF function. Together, the work described in this proposal will bring our understanding of bacterial transcription regulation into the post-genomic era.

 描述（由申请人提供）：细菌基因组通常编码数百个转录因子（TF）。几十年来对大肠杆菌中TF的研究使人们对TF功能有了深入的了解。然而，在基因组规模上研究了相对较少的细菌TF。我们的数据和其他小组的数据表明，只有一小部分的TF结合位点已被确定，即使是充分研究的TF。因此，许多研究人员利用广泛的E.公共数据库中的大肠杆菌监管网络数据依赖于高度不完整且可能误导的数据集。我们的长期目标是建立一个完整的预测模型，在E。杆菌这个提议的果阿是通过映射所有E.大肠杆菌TF，并使用这些数据为基础，调查TF功能的基本方面。细菌TF的全局映射数据表明，TF功能的既定规则仅适用于结合位点的子集。特别是，DNA序列往往是不足以预测TF结合的位置，这表明DNA结合位点序列以外的因素有助于TF-DNA在体内的相互作用。鉴于基因调控的重要性，我们必须更好地了解DNA序列和TF结合之间的关系。我们建议实验性地产生一个高分辨率的调控网络E。包括所有已知和预测的TF的调节子信息。这将成为科学界的宝贵资源。我们将使用这些数据作为一个框架，准确建模的监管网络，并告知我们有针对性的研究之间的关系DNA序列和TF结合在体内。我们希望建立一个高分辨率的E.杆菌这将成为科学界的宝贵资源。模式真核生物酿酒酵母的等效资源是在10多年前产生的，并且极大地促进了我们对真核生物转录调控的理解。目前，细菌最完整的资源是结核分枝杆菌，它缺乏作为实验生物体的易处理性。为E生成等价资源。大肠杆菌的研究将极大地促进细菌基因调控的研究。我们进一步期望使用我们的调控网络模型作为基础，以了解DNA序列和TF结合在体内之间的关系。我们希望揭示复杂的相互作用对TF和TF和全球调节蛋白之间。了解这些相互作用对于详细了解TF功能至关重要。总之，本提案中描述的工作将使我们对细菌转录调控的理解进入后基因组时代。