Chemogenomic Analysis of E. coli Response to NO species

描述（由申请人提供）：本申请的目标是开发一种化学基因组分析方法，用于重建细菌中的信号和转录网络。特别是，大肠杆菌对活性氮氧化物（RNOS）的反应，如一氧化氮和s -亚硝基硫醇，将被用作模型系统。拟议的研究将开发一种范式，将化学反应性信息纳入大肠杆菌的转录组学分析，而不是仅仅依赖于关联关系，这并不能揭示信号机制的化学细节。由此产生的网络在机械上是可行的，并且与基因表达数据一致。该方法由三个部分组成：转录组测量、网络成分分析（NCA）和化学信息学分析。研究从野生型大肠杆菌对RNOS反应的微阵列实验和文献数据库中的初始转录因子(TF)-启动子连通性开始，该数据库对于感兴趣的条件是不完整的。NCA首先基于野生型微阵列数据推断出TF在RNOS挑战下的活性。TFs的活性，而不是表达水平，为推断化学机制和信号通路提供了关键信息。在受干扰的tf中，化学信息学分析允许将受影响的tf分类为直接和间接目标，并确定潜在的信号传导化学机制。然后使用染色体敲除技术删除受干扰的tf。进一步测试敲除菌株对RNOS的反应，并在NCA中分析转录组数据，以推断修改后的靶tf和转录网络。如果预测到新的TF目标，则继续迭代。另外，用遗传和生化实验验证了预测的网络。

DESCRIPTION (provided by applicant): The goal of this application is to develop a chemo genomic analysis methodology for reconstructing signaling and transcription networks in bacteria. In particular, Escherichia coli responses to reactive nitrogen oxide species (RNOS), such as nitric oxide and S-nitrosothiols, will be used as a model system. Instead of relying solely on association relationships, which does not reveal chemical details of signaling mechanisms, the proposed research will develop a paradigm to incorporate chemical reactivity information in the transcriptomic analysis of E. coli. The resulting network will be mechanistically feasible and consistent with gene expression data. The proposed approach consists of iteration through three components: transcriptome measurements, Network Component Analysis (NCA), and chemoinfomatic analysis. The investigation starts from micro array experiments of wild-type E. coli response to RNOS and the initial transcription factor (TF)-promoter connectivity from the literature database, which is incomplete for the conditions of interest. NCA first deduces the activities of TF upon RNOS challenge based on wild-type micro array data. The activities of TFs, rather than the expression levels, provide critical information for deducing chemical mechanisms and signaling pathways. Among the TFs perturbed, chemoinfomatic analysis allows the classification of affected TFs into direct and indirect targets and the identification of potential chemical mechanisms for signaling. The perturbed TFs are then deleted using chromosomal knockout techniques. The resulting knockout strains are further tested for responses to RNOS and the transcriptome data are analyzed in NCA to deduce revised target TFs and transcription networks. If hew TF targets are predicted, then the iteration continues. Otherwise, the predicted networks are verified using genetic and biochemical experiments.