Abstract Fungal pathogens pose a major threat to coffee production, yet the molecular mechanisms underlying these infections remain poorly understood. Omics data from fungal pathogens affecting coffee plants offer valuable but largely unexplored insights into host-pathogen interactions. In this study, we applied a computational approach to analyze publicly available genomic and proteomic data from three coffee cultivars and three fungal pathogens, including Hemileia vastatrix (coffee leaf rust fungus) and Colletotrichum higginsianum (anthracnose fungus). We identified candidate genes involved in the plant’s defense response and potential fungal effector proteins associated with pathogenesis, providing novel targets for disease monitoring and management. Fungal analysis revealed in H. vastatrix a total of 2,058 potential effectors and in C. higginsianum 4,475, these effectors were categorized as cytoplasmic or apoplastic. Orthofinder analysis highlighted four informative groups, clustered based on function, role in infection processes, and total count of positive parameters allowing the identification of kinases, deacetylases, chitin-binding, recognition, GTP-binding, Ras, and Rho family proteins. Our results provide the first set of computationally called proteomes for the analyzed species, contextualized to coffee-fungi pathogenesis and provide a set of genes and proteins to further validate experimentally.
Rojas-Rojas et al. (Thu,) studied this question.