Global mapping and analysis of a bacterial transcriptional regulatory network

 描述(申请人提供)：细菌基因组通常编码数百种转录因子(TF)。几十年来在大肠杆菌中对转铁蛋白功能的研究使人们对转铁蛋白的功能有了深入的了解。然而，在基因组水平上对细菌转录因子进行研究的相对较少。我们的数据和其他组的数据表明，即使是研究得很好的TF，也只鉴定了一小部分TF结合位点。因此，利用公共数据库中广泛的大肠杆菌调控网络数据的众多研究人员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一种完全可预测的模型，用于转录因子在大肠杆菌中的转录调控。这项建议的目的是通过映射所有E.ColiTf的调控子来告知这样一个模型，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的全球图谱数据表明，TF功能的成熟规则仅适用于结合位点的子集。特别是，DNA序列往往不足以预测TF结合位置，这表明除了DNA结合位点序列外，还有其他因素参与了体内TF-DNA的相互作用。鉴于基因调控的根本重要性，我们必须更好地了解DNA序列与体内转铁蛋白结合之间的关系。我们建议在实验中为大肠杆菌生成一个高分辨率的调控网络，其中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准确建模调控网络的框架，并为我们在体内DNA序列和TF结合之间的关系的有针对性的研究提供信息。我们希望为大肠杆菌产生一个高分辨率的调控网络。这将成为科学界的宝贵资源。与模式真核生物类似的资源酿酒酵母产生于10多年前，为我们理解真核生物的转录调控做出了巨大贡献。细菌最完整的资源目前是结核分枝杆菌的资源，作为实验有机体，它缺乏可管理性。为大肠杆菌创造一个等价的资源将极大地促进细菌基因调控的研究。我们进一步期望利用我们的调控网络模型作为基础来理解DNA序列和体内TF结合之间的关系。我们希望揭示成对的转录因子之间以及转化因子与全球调控蛋白之间的复杂相互作用。这些相互作用的知识对于详细理解Tf函数是至关重要的。总之，这项提案中描述的工作将把我们对细菌转录调控的理解带入后基因组时代。

 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.