Host-Pathogen Interaction Network Learning from In Vivo Gene Co-Expression

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DESCRIPTION (provided by applicant): The Phase I objective is to prototype a new systems biology software tool to identifying probable host-pathogen protein-protein interactive (PPI) relationships and networks from in vivo experiments where host-pathogen gene expressions are measured simultaneously. Computational methods for identifying such interactions are an important unsolved problem. New methods are emerging for simultaneous host-pathogen measurement of gene and protein expression. Tremendous amounts of data are generated from these methods and represent an increased analysis complexity that cannot be dealt with manually. New system biology computational techniques will be essential to aid in its analysis. Having new tools for identifying host-pathogen PPI relationships will significantly advance our understanding of immunologic mechanisms and has great implications in identifying potential targets/biomarkers for new immunotherapeutic drugs, adjuvants, and vaccines. This is extremely important knowledge for NIAID Category A, B and C priority pathogens and other common diseases of high concern. Proof-of-feasibility will be done using host and pathogen microarray and proteomic data from a bovine animal model challenged by Salmonella enterica Serovar Typhimurium (wild type S. Typhimurium) and an isogenic sipA, sopABDE2 mutant. Data will be provided to Seralogix by collaborators at Texas A&M National Center for Foreign Animal and Zoonotic Disease Defense. PUBLIC HEALTH RELEVANCE: The new system biology computational techniques, proposed herein, will be essential to aid in the analysis of complex host-pathogen interactions. Having new tools for identifying host-pathogen protein- protein relationships will significantly advance our understanding of immunologic mechanisms and has great implications in identifying potential targets/biomarkers for new immunotherapeutic drugs, adjuvants, and vaccines.