The study of molecular host-parasite interactions is essential to understand parasitic infection and adaptation within the host system. As well, prevention and treatment of infectious diseases require a clear understanding of the molecular crosstalk between parasites and their hosts. Yet, large-scale experimental identification of host-parasite molecular interactions remains challenging, and the use of computational predictions becomes then necessary. Here, we propose a computational integrative approach to predict host-parasite protein-protein interaction (PPI) networks resulting from the human infection by 15 different eukaryotic parasites. We used an orthology-based approach to transfer high-confidence intraspecies interactions obtained from the STRING database to the corresponding interspecies homolog protein pairs in the host-parasite system. Our approach uses either the parasites predicted secretome and membrane proteins, or only the secretome, depending on whether they are uni- or multi-cellular, respectively, to reduce the number of false predictions. Moreover, the host proteome is filtered for proteins expressed in selected cellular localizations and tissues supporting the parasite growth. We evaluated the inferred interactions by analyzing the enriched biological processes and pathways in the predicted networks and their association with known parasitic invasion and evasion mechanisms. The resulting PPI networks were compared across parasites to identify common mechanisms that may define a global pathogenic hallmark. We also provided a study case focusing on a closer examination of the human-S. mansoni predicted interactome, detecting central proteins that have relevant roles in the human-S. mansoni network, and identifying tissue-specific interactions with key roles in the life cycle of the parasite. The predicted PPI networks can be visualized and downloaded at http://orthohpi.jensenlab.org.
Keywords: biological networks; computational biology; host–parasite interactions; parasitology; schistosomiasis; systems biology.