System-wide Study of Transcriptional Control of Metabolism

描述（由申请人提供）：本提案是为了响应美国国立卫生研究院与国家生物医学计算中心探索性合作的呼吁，PAR-06-223，它将涉及哥伦比亚大学磁铁NCBC和洛斯阿拉莫斯国家实验室的一个团队之间的合作。该提案的目的是对大肠杆菌K-12菌株的综合转录和代谢网络进行系统范围的研究，目的是在以后对病原体炭疽芽孢杆菌进行类似的分析。LANL主持细菌代谢组学的实验研究项目。代谢物有几种功能。最常见的是成为各种细胞成分的前体。它们还通过调节代谢反应或与转录因子结合并随后调节基因表达来调节细胞功能。相反，受转录因子调控的基因通常编码酶，调节代谢反应的速度。因此，为了理解并最终预测细胞对环境变化的反应（例如，病原体进入其宿主环境），我们必须整合转录组和代谢组的分析。为了满足这一需求，我们将与Pat Unkefer和John Dunbar的实验室合作，他们将在不同的稳态生长条件下产生大约300个大肠杆菌联合代谢/转录谱的数据集。MAGnet NCBC的资源，特别是该中心生产的geWorkbench生物信息学平台中的算法，将用于重建蜂窝网络。具体来说，我们期望ARACNE（最初由MAGNet开发的用于哺乳动物细胞中转录网络的高保真分析的算法）能够很好地定位于从高通量全系统代谢活动数据中重建代谢网络，前提是对代谢数据的具体情况进行适当的修改。我们还将调整算法来发现调制的相互作用，即以调节基因（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。这种整合的基因组/代谢组分析尚未尝试过。这将是对一个重要生物体中细胞过程的全面理解的一个巨大飞跃。由于细菌基因组和代谢组相对较小，因此有可能对整个整合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，特别是考虑到炭疽芽孢杆菌的致病性，但它也将为随后在包括人类在内的高等动物中进行代谢性疾病的研究提供一个重要的试验平台。

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.