Metabolomics of the Virus-host Cell Interaction

描述（由申请人提供）：病毒是寄生虫。它们依靠宿主细胞的代谢网络来提供它们复制所必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改变，这突出了病毒与宿主代谢相互作用的重要性。尽管最近取得了这一进展，但病毒对宿主细胞代谢的影响仍然知之甚少。在这里，我们建议结合最先进的代谢组学，基因组学和贝叶斯建模技术来彻底改变对病毒-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。将宿主细胞环境正常调节（如营养物）时发生的动态代谢变化与病毒感染时发生的动态代谢变化进行比较，使用液相色谱-串联质谱法定量测定100多种代谢物，并使用微阵列测量完整的转录反应。所产生的数据将存储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，例如所有病毒的共同代谢效应。然后，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相互作用。由此产生的模型的预测能力将通过模型指导的实验进行测试，例如，涉及病毒基因敲除或抑制特定宿主细胞代谢途径。这项研究的成功完成将极大地提高对病原体-宿主代谢相互作用的整体生物学知识，并特别关注病毒，这是美国最重要和最难以治疗的传染病原因。相关性：病毒引起各种疾病，从普通感冒到流感再到艾滋病。在所有情况下，病毒要生存和生长，它们必须从被感染的细胞中获取能量和生化基础。我们的目标是将先进的测量技术和计算模型相结合，以确定病毒用来欺骗受感染的宿主细胞制造所需材料的途径。这些途径一旦被确定，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.