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; 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（由适用提供）：细菌基因组通常编码数百个转录因子（TFS）。大肠杆菌中的数十年来TFS的工作已导致对TF功能的深刻机械理解。但是，已经研究了相对较少的细菌TF。我们的数据和其他组的数据表明，即使对于经过良好研究的TF，也仅确定了一小部分TF结合位点。因此，众多利用公共数据库中广泛的大肠杆菌监管网络数据的调查人员依赖于高度不完整且潜在的误导性数据集。我们的长期目标是开发一个大肠杆菌中TF的转录调节的完全预测模型。该提案的果阿是通过映射所有大肠杆菌的法规，并将这些数据作为研究TF功能基本方面的基础来告知此类模型。细菌TFS的全局映射数据表明，完善的TF函数规则仅适用于结合位点的一部分。特别是，DNA序列通常不足以预测TF结合位置，这表明除DNA结合位点序列以外的其他因素有助于体内TF-DNA相互作用。鉴于基因调节的基本重要性，至关重要的是，我们必须更好地了解体内DNA序列与TF结合之间的关系。我们建议通过实验生成大肠杆菌的高分辨率调节网络，其中包括所有已知和预测的TF的监管信息。这将成为科学界的宝贵资源。我们将使用这些数据作为准确建模调节网络的框架，并为我们对体内DNA序列与TF结合之间关系的有针对性研究提供信息。我们期望为大肠杆菌生成高分辨率的监管网络。这将成为科学界的宝贵资源。十多年前产生的真核生物酿酒酵母模型的等效资源，为我们对真核转录调控的理解做出了巨大贡献。细菌最完整的资源是目前的结核分枝杆菌，该结核病缺乏实验性组织。为大肠杆菌生成等效资源将极大地支持细菌基因调节的研究。我们进一步期望将我们的调节网络模型作为基础，以了解DNA序列与体内TF结合之间的关系。我们期望揭示TFS对以及TFS和全球调节蛋白之间的复杂相互作用。这些相互作用的知识对于详细了解TF功能至关重要。共同描述的工作将使我们对细菌转录调节的理解进入后期时代。