Metabolomics of the Virus-host Cell Interaction

描述(申请人提供)：病毒是寄生虫。它们依赖于宿主细胞的代谢网络来提供复制所需的能量和大分子亚单位。在初步实验中，我们发现人成纤维细胞感染巨细胞病毒会产生戏剧性的代谢组变化，突出了病毒与宿主代谢相互作用的重要性。尽管最近取得了这些进展，但病毒对宿主细胞新陈代谢的影响仍然知之甚少。在这里，我们建议结合最先进的新陈代谢、基因组和贝叶斯建模技术，以彻底改变对病毒-宿主代谢相互作用的理解。将研究三种不同的病毒，每种病毒都是人类的重要病原体：甲型流感、单纯疱疹病毒和巨细胞病毒。在正常调节宿主细胞环境时发生的动态代谢变化(例如，在有营养的情况下)将与病毒感染时发生的代谢变化进行比较，使用液相色谱-串联质谱仪对100+代谢物和微阵列进行量化，以测量完整的转录反应。产生的数据将存储在一个可公开访问的数据库中，并通过集群和分解技术进行分析，以确定数据中的主要趋势，例如所有病毒普遍存在的新陈代谢影响。然后将使用贝叶斯分析来确定病毒感染、代谢物和基因之间的功能相互作用。最终模型的预测能力将通过模型指导的实验来测试，例如，涉及病毒基因敲除或抑制特定宿主细胞代谢途径的实验。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，特别是对病毒的研究，病毒是美国传染病最重要和最难治疗的原因。相关性：病毒会引起从普通感冒到流感再到艾滋病的各种疾病。在所有情况下，为了让病毒生存和生长，它们必须从它们感染的细胞中获得能量和生化积木。我们的目标是应用先进的测量技术和计算建模的混合来确定病毒用来欺骗受感染的宿主细胞制造它们所需的材料的途径。一旦发现这种途径，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.