System-wide Study of Transcriptional Control of Metabolism

描述(由申请人提供)：这项建议是为了响应NIH呼吁与国家生物医学计算中心进行探索性合作的呼吁，PAR-06-223，它将涉及哥伦比亚大学的磁铁NCBC和洛斯阿拉莫斯国家实验室的一个团队之间的合作。该提案的目的是在全系统范围内研究大肠杆菌K-12菌株的整合转录和代谢网络，旨在以后对病原体炭疽芽孢杆菌进行类似的分析。LANL主持了一项关于细菌代谢组学的实验研究计划。代谢物有几种功能。最常见的一种是各种细胞成分的前体。它们也是细胞功能的调节者，通过调节代谢反应或与转录因子结合，随后调节基因表达。相反，受转录因子调控的基因通常编码酶，调节新陈代谢反应的速度。因此，为了理解并最终预测细胞对感兴趣的环境变化(例如，病原体进入宿主环境)的反应，我们必须将转录组和代谢组的分析结合起来。为了满足这一需求，我们将与Pat Unkefer和John Dunbar的实验室合作，他们将产生约300个不同稳定生长条件下的大肠杆菌联合代谢/转录图谱的数据集。Magic NCBC的资源，特别是该中心生产的geWorkbench生物信息学平台内的算法，将被用于重建蜂窝网络。具体地说，我们预计ARACNE，一种最初由Magic开发的用于哺乳动物细胞转录网络高保真分析的算法，可以很好地从高通量系统范围的代谢活动数据中重建代谢网络，前提是对代谢数据的细节进行了适当的修改。我们还将调整算法以发现调制相互作用，即以调节器基因(酶)的活性为条件的代谢相互作用，或需要代谢物存在才能进行的转录相互作用。这种完整的基因组/代谢组分析还没有尝试过。这将是向完全了解一个重要有机体中的细胞过程迈出的一大步。由于细菌基因组和代谢组的规模相对较小，因此有可能对整个整合的基因组和代谢组进行系统范围的相互作用分析。虽然这项研究本身很重要，特别是考虑到炭疽杆菌的致病性质，但它也将为随后对包括人类在内的高等动物的代谢性疾病的研究提供一个重要的试验台。

DESCRIPTION (provided by applicant): This proposal is in response to the NIH call for Exploratory Collaborations with National Centers for Biomedical Computing, PAR-06-223, and it will involve a collaboration between Columbia University's MAGNet NCBC and a team at Los Alamos National Laboratory. The aim of the proposal is a system-wide tudy of integrated transcriptional and metabolic networks in Eschericia coli K-12 strain, aiming at a similar analysis of a pathogen, Bacillus anthracis, at a later date. LANL hosts an experimental research program on bacterial metabolomics. Metabolites serve several functions. The most common one is being the precursors to various cellular components. They are also regulators of cellular functions by means of modulating metabolic reactions or binding to transcription factors and subsequently regulating gene expression. Conversely, the genes regulated by a transcription factor often encode enzymes, modulating the speed of metabolic reactions. Thus, to understand and ultimately predict the cellular response to an environmental change of interest (e.g., pathogen entry into its host environment), we must integrate the analysis of the transcriptome and metabolome. To address this need, we will work with the laboratories of Pat Unkefer and John Dunbar, which will produce data sets of about 300 joint metabolic/transcriptional profiles of E.coli under different steady-state growth conditions. The resources of MAGnet NCBC, specifically the algorithms within the geWorkbench bioinformatics platform produced by the center, will be leveraged to reconstruct cellular networks. Specifically, we expect that ARACNE, an algorithm originally developed by MAGNet for high-fidelity analysis of transcriptional networks in mammalian cells, is well positioned for reconstruction of metabolic networks from high throughput system-wide metabolic activity data, provide that appropriate modifications to deal with the specifics of the metabolic data are made. We will also adapt the algorithm to discover modulated interactions, that is, metabolic interactions that are conditional on the activity of a modulator gene (enzyme), or transcriptional interactions that require the presence of a metabolite to proceed. Such integrated genome/metabolome analysis has not been attempted yet. It will be a giant leap towards a complete understanding of cellular processes in an important organism. Because of the comparatively small size of bacterial genomes and metabolomes, it will be possible to perform system-wide analyses of interactions for the entire integrated genome and metabolome. While important in its own right, especially in view of the pathogenic nature of B. anthracis, this research would also represent an important test bed for a subsequent study of metabolic diseases in higher animals, including humans.