Genome-scale host-pathogen prediction for non-medical microbes Article Swipe

Background Network studies of host-pathogen interactions (HPI) are critical in understanding the mechanisms of pathogenesis. However, accessible HPI data for agriculturally important pathogens are limited. This lack of HPI data impedes network analysis to study agricultural pathogens, for preventing and reducing the severity of diseases of relevance to agriculture. Results To rapidly provide HPIs for a broad range of pathogens, we use an interolog-based approach. This approach uses sequence similarity to transfer known HPIs from better studied host-pathogen pairs and predicts 389,878 HPIs for 23 host-pathogen systems of relevance to US agriculture. Each predicted HPI is qualitatively assessed using co-localization, infection related processes, and interacting domains and this information is provided as a confidence indicator for the prediction. Evaluation of predicted HPIs demonstrates that the host proteins predicted to be involved in pathogen interactions include hubs and bottlenecks in the network, as reported in curated host proteins. Moreover, we demonstrate that the use of the predicted HPIs adds value to network analysis and recapitulates known aspects of host-pathogen biology. Access to the predicted HPIs for these agricultural host-pathogen systems is available via the Host Pathogen Interaction Database (HPIDB, .igbb.msstate.edu ), and can be downloaded in standard MITAB file format for subsequent network analysis. Conclusions This core set of interolog-based HPIs will enable animal health researchers to incorporate network analysis into their research and help identify host-pathogen interactions that may be tested and experimentally validated. Moreover, the development of a larger set of experimentally validated HPI will inform future predictions. Our approach of transferring biologically relevant HPIs based on interologs is broadly applicable to many host-microbe systems and can be extended to support network modeling of other pathogens, as well as interactions between non-pathogenic microbes.