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），数十年对大肠杆菌中转录因子的研究已经使人们对转录因子功能有了深入的了解，然而，对基因组转录因子的研究相对较少。我们的数据和其他小组的数据表明，即使对于经过充分研究的 TF，也只识别出了一小部分 TF 结合位点，大量研究人员利用了广泛的研究。公共数据库中的大肠杆菌调控网络数据依赖于高度不完整且可能具有误导性的数据集。我们的长期目标是开发大肠杆菌中 TF 转录调控的完全预测模型。通过绘制所有大肠杆菌 TF 的调节子来为这样的模型提供信息，并使用这些数据作为研究 TF 功能的基本方面的基础。细菌 TF 的全局绘图数据表明，公认的 TF 功能规则仅适用于特定的细菌。子集特别是，DNA 序列通常不足以预测 TF 结合位置，这表明 DNA 结合位点序列以外的因素有助于体内 TF-DNA 相互作用，因此我们必须更好地进行研究。了解 DNA 序列和 TF 体内结合之间的关系，我们建议通过实验生成大肠杆菌的高分辨率调控网络，其中包括所有已知和预测的 TF 的调控子信息，这将成为科学界的宝贵资源。我们将使用这些数据作为框架为调控网络进行精确建模，并为我们对 DNA 序列与 TF 体内结合之间的关系进行有针对性的研究提供信息，我们希望为大肠杆菌生成高分辨率的调控网络。科学界的模型真核生物酿酒酵母的等效资源是在 10 多年前生成的，为我们对真核转录调控的理解做出了巨大贡献。结核分枝杆菌作为实验生物体缺乏易处理性，生成大肠杆菌的等效资源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来了解 DNA 序列和 TF 结合之间的关系。我们希望揭示 TF 对之间以及 TF 与全局调节蛋白之间的复杂相互作用对于详细了解 TF 功能至关重要。细菌转录调控进入后基因组时代。