Prediction and validation of host-pathogen interactions by a versatile inference approach using Aspergillus fumigatus as a case study

Johannes Balkenhol; Elena Bencurova; Shishir K Gupta; Hella Schmidt; Thorsten Heinekamp; Axel Brakhage; Aparna Pottikkadavath; Thomas Dandekar

doi:10.1016/j.csbj.2022.07.050

Prediction and validation of host-pathogen interactions by a versatile inference approach using Aspergillus fumigatus as a case study

Comput Struct Biotechnol J. 2022 Aug 5:20:4225-4237. doi: 10.1016/j.csbj.2022.07.050. eCollection 2022.

Authors

Johannes Balkenhol¹, Elena Bencurova¹, Shishir K Gupta², Hella Schmidt³, Thorsten Heinekamp³, Axel Brakhage³, Aparna Pottikkadavath⁴, Thomas Dandekar¹

Affiliations

¹ Department of Bioinformatics, University of Würzburg, Würzburg, Germany.
² Evolutionary Genomics Group, Center for Computational and Theoretical Biology, University of Würzburg, 97078 Würzburg, Germany.
³ Department of Molecular and Applied Microbiology, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745 Jena, Germany.
⁴ Department of Structural Biology, Rudolf Virchow Center for Integrative and Translational Bioimaging, University of Würzburg, 97074 Würzburg, Germany.

Abstract

Biological networks are characterized by diverse interactions and dynamics in time and space. Many regulatory modules operate in parallel and are interconnected with each other. Some pathways are functionally known and annotated accordingly, e.g., endocytosis, migration, or cytoskeletal rearrangement. However, many interactions are not so well characterized. For reconstructing the biological complexity in cellular networks, we combine here existing experimentally confirmed and analyzed interactions with a protein-interaction inference framework using as basis experimentally confirmed interactions from other organisms. Prediction scoring includes sequence similarity, evolutionary conservation of interactions, the coexistence of interactions in the same pathway, orthology as well as structure similarity to rank and compare inferred interactions. We exemplify our inference method by studying host-pathogen interactions during infection of Mus musculus (phagolysosomes in alveolar macrophages) with Aspergillus fumigatus (conidia, airborne, asexual spores). Three of nine predicted critical host-pathogen interactions could even be confirmed by direct experiments. Moreover, we suggest drugs that manipulate the host-pathogen interaction.

Keywords: Aspergillus fumigatus; antifungal drugs; computational prediction; docking; host-pathogen interactions; ligand binding assay.