Viral infection involves an ongoing battle between the replicating virus and the host defense mechanism which seeks to destroy infected cells or eliminate viral replication. Successful viruses have evolved elaborate mechanisms to evade the host response such as blocking the host interferon response and selective shutdown of host cell translation. Over the past few years, several investigators have begun to generate expression data which details how the host responds to pathogens. By monitoring the expression levels of thousands of genes simultaneously, microarray data has provided useful insights into the host response to pathogen infection and mechanisms of pathogen evasion of host cell defense mechanisms. We propose to create a database of all publicly available microarray data relevant to host/pathogen interaction in mammalian cell and to analyze that data to identify common mechanisms used by viruses to evade the host response. Advanced analytical techniques will be used to re-analyze all available data to produce appropriate error models where possible. Subsequent analyses (such as clustering, intersections and unions between sets of data, and self-organizing maps) will take advantage of these error estimates to produce more robust identifications of common pathways in the host response. In addition, we will take advantage of work underway here and elsewhere to provide common annotations to homologous mammalian genes (in particular human, mouse and rat) so that these analyses may be more readily performed across species. These studies may ultimately lead to the development of new strategies to prevent or treat viral infection via drug targeting of host cell genes necessary for infection or replication.
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