Metabolomics of the Virus-host Cell Interaction

性状（由申请方提供）：病毒为寄生虫。它们依赖于宿主细胞的代谢网络来提供其复制所需的能量和大分子亚基。在初步实验中，我们发现巨细胞病毒感染人成纤维细胞会产生显著的代谢组学改变，突出了病毒-宿主代谢相互作用的重要性。尽管最近取得了这一进展，病毒对宿主细胞代谢的影响仍然知之甚少。在这里，我们建议结合联合收割机国家的最先进的代谢组学，基因组学和贝叶斯建模技术，以彻底改变病毒宿主代谢相互作用的理解。将研究三种不同的病毒，每种病毒都是重要的人类病原体：甲型流感病毒、单纯疱疹病毒和巨细胞病毒。在宿主细胞环境的正常调节时发生的动态代谢变化（例如，与营养物）将与病毒感染时发生的那些进行比较，使用液相色谱-串联质谱法来定量100+代谢物，并使用微阵列来测量完整的转录应答。由此产生的数据将储存在一个可公开访问的数据库中，并通过聚类和分解技术进行分析，以确定数据中的主要趋势，例如，所有病毒都有共同的代谢效应。贝叶斯分析将用于识别病毒感染、代谢物和基因之间的功能性相互作用。将通过模型指导的实验来测试所得到的模型的预测能力，所述实验涉及例如病毒基因敲除或特定宿主细胞代谢途径的抑制。这项研究的成功完成将大大推进病原体-宿主代谢相互作用的整体生物学知识，特别关注病毒，这是美国传染病最重要和最难治疗的原因。相关性：病毒引起的疾病从普通感冒到流感再到艾滋病。在所有情况下，为了使病毒存活和生长，它们必须从它们感染的细胞中获得能量和生化构建块。我们的目标是应用先进的测量技术和计算建模的混合物来确定病毒用来欺骗感染的宿主细胞制造它们所需的材料的途径。这些途径一旦被发现，将成为抗病毒治疗的新靶点。

DESCRIPTION (provided by applicant): Viruses are parasites. They depend on the metabolic network of the host cell to provide the energy and macromolecule subunits necessary for their replication. In preliminary experiments we have found that infection of human fibroblasts with cytomegalovirus produces dramatic metabolome alterations, highlighting the importance of virus-host metabolic interactions. Despite this recent progress, the effect of viruses on host cell metabolism remains little understood. Here we propose to combine state-of-the art metabolomic, genomic, and Bayesian modeling techniques to revolutionize understanding of virus-host metabolic interactions. Three different viruses, each important human pathogens, will be investigated: influenza A, herpes simplex, and cytomegalovirus. The dynamic metabolic changes that occur upon normal modulation of the host cell environment (e.g., with nutrients) will be compared to those that occur upon viral infection, using liquid chromatography-tandem mass spectrometry to quantitate 100+ metabolites and microarrays to measure the complete transcriptional response. The resulting data will be stored in a publicly accessible database and analyzed by clustering and decomposition techniques to identify major trends in the data, e.g., metabolic effects that are common across all of the viruses. Bayesian analysis will then be used to identify functional interactions between viral infection, metabolites, and genes. The predictive power of the resulting models will be tested through model-guided experiments, involving, for example, viral gene knockouts or inhibition of specific host cell metabolic pathways. Successful completion of this research will dramatically advance overall biological knowledge of pathogen-host metabolic interactions, with a specific focus on viruses, the most important and least treatable causes of infectious disease in the United States. Relevance: Viruses cause diseases ranging from the common cold to influenza to AIDS. In all cases, for the viruses to survive and grow, they must acquire energy and biochemical building blocks from the cells that they infect. We aim to apply a mixture of advanced measurement technologies and computational modeling to determine the pathways that viruses use to trick the infected host cells into making the materials they need. Such pathways, once identified, will be attractive new targets for antiviral therapy.