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.
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