PHI-Nets: A Network Resource for Ascomycete Fungal Pathogens to Annotate and Identify Putative Virulence Interacting Proteins and siRNA Targets

Abstract

Interactions between proteins underlie all aspects of complex biological mechanisms. Therefore, methodologies based on complex network analyses can facilitate identification of promising candidate genes involved in phenotypes of interest and put this information into appropriate contexts. To facilitate discovery and gain additional insights into globally important pathogenic fungi, we have reconstructed computationally inferred interactomes using an interolog and domain-based approach for 15 diverse Ascomycete fungal species, across nine orders, specifically Aspergillus fumigatus, Bipolaris sorokiniana, Blumeria graminis f. sp. hordei, Botrytis cinerea, Colletotrichum gloeosporioides, Colletotrichum graminicola, Fusarium graminearum, Fusarium oxysporum f. sp. lycopersici, Fusarium verticillioides, Leptosphaeria maculans, Magnaporthe oryzae, Saccharomyces cerevisiae, Sclerotinia sclerotiorum, Verticillium dahliae, and Zymoseptoria tritici. Network cartography analysis was associated with functional patterns of annotated genes linked to the disease-causing ability of each pathogen. In addition, for the best annotated organism, namely F. graminearum, the distribution of annotated genes with respect to network structure was profiled using a random walk with restart algorithm, which suggested possible co-location of virulence-related genes in the protein–protein interaction network. In a second ‘use case’ study involving two networks, namely B. cinerea and F. graminearum, previously identified small silencing plant RNAs were mapped to their targets. The F. graminearum phenotypic network analysis implicates eight B. cinerea targets and 35 F. graminearum predicted interacting proteins as prime candidate virulence genes for further testing. All 15 networks have been made accessible for download at www.phi-base.org providing a rich resource for major crop plant pathogens.

PHI-base in 2022: a multi-species phenotype database for Pathogen–Host Interactions

“…Other authors have used various computational methods to overcome a similar lack of data: these methods include an interolog approach that relies on sequence similarity between proteins from different species; identification of conserved Pfam molecule binding domains in PHI-base proteins to identify interactors; and generation of network-extracted ontologies to annotate transcriptomics data ( 52 , 53 ). These methods were used by three recent studies that specifically took the high-level phenotype annotations assigned to PHI-base proteins to construct networks of rice-pathogen interactions ( 54 ), to identify and build annotated networks for putative virulence factors for 14 Ascomycete fungal pathogens ( 55 ), and to generate ontologies, extracted from an interaction network, that led to the identification of the PEP8 protein in the human infecting fungal pathogen Candida albicans . PEP8 is likely involved in retrograde vesicle transport, with a function in hyphal development and immune evasion ( 56 ).…”

Understanding Host–Pathogen Interactions in Brassica napus in the Omics Era

“…A web-based database called the Pathogen-Host Interactions database (PHI-base) has been set up that stores curated experimental data obtained from host–pathogen studies, encompassing phenotypic data and biological data on pathogenicity, virulence, and effector gene functions from fungal, oomycete and bacterial pathogens from animal, plant, fungal and insect host species, with embedded search links including BLAST, PubMed, UniProt Knowledgebase and others [ 212 ]. Complementing PHI-base, PHI-Nets provides information related to networks of molecular and biological protein–protein interactions for the understanding of pathogenicity and virulence mechanisms in host–pathogen relationships [ 213 ].…”