Deciphering the Host–Pathogen Interactome of the Wheat–Common Bunt System: A Step towards Enhanced Resilience in Next Generation Wheat

Abstract

Common bunt, caused by two fungal species, Tilletia caries and Tilletia laevis, is one of the most potentially destructive diseases of wheat. Despite the availability of synthetic chemicals against the disease, organic agriculture relies greatly on resistant cultivars. Using two computational approaches—interolog and domain-based methods—a total of approximately 58 M and 56 M probable PPIs were predicted in T. aestivum–T. caries and T. aestivum–T. laevis interactomes, respectively. We also identified 648 and 575 effectors in the interactions from T. caries and T. laevis, respectively. The major host hubs belonged to the serine/threonine protein kinase, hsp70, and mitogen-activated protein kinase families, which are actively involved in plant immune signaling during stress conditions. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the host proteins revealed significant GO terms (O-methyltransferase activity, regulation of response to stimulus, and plastid envelope) and pathways (NF-kappa B signaling and the MAPK signaling pathway) related to plant defense against pathogens. Subcellular localization suggested that most of the pathogen proteins target the host in the plastid. Furthermore, a comparison between unique T. caries and T. laevis proteins was carried out. We also identified novel host candidates that are resistant to disease. Additionally, the host proteins that serve as transcription factors were also predicted.

Fungal Effectoromics: A World in Constant Evolution

“…Common hubs include the serine/threonine protein kinase and mitogen-activated protein kinase families which are actively involved in plant immune signaling, transcription factors, enzymes involved in the biosynthesis or regulation of jasmonic acid and salicylic acid pathways, and other phytoregulators involved in host responses [ 191 ]. Similarly, common proteins or hubs in pathogens have been identified in interaction networks, for example, ubiquitin-like activating enzymes, small GTPases such as Rho, Ran, and Ras, SUMO-conjugating enzymes, thioredoxin reductase, among others [ 192 ]. Table 3 highlights examples of hubs in plant hosts and pathogens.…”

A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts

“…Existing MHI computational prediction methods have mainly depended on the evolutionary information of protein sequence [ 11–13 ]. Generally, the approaches for MHI prediction can be classified into three categories: structure-based approaches [ 14 ], domain-based approaches [ 15 ] and sequence-based approaches [ 16 ]. The structure-based methods are not applicable when the 3-dimensional (3D) structures of the target proteins are unknown.…”

Deciphering the complete human-monkeypox virus interactome: Identifying immune responses and potential drug targets

“…These models include homology-based interolog and domain-based prediction, which have been widely used in several host-pathogen interaction studies in the past ( 15 , 16 ). The molecular techniques available for the detection and/or validation of PPIs are laborious and costly, while the computational approaches, on the other hand, provide a comprehensive understanding of the biological function and cellular behaviour of the proteins involved in the interactions in rapid and economical manner ( 17 ). To the best of our knowledge, our study is the foremost report that provides novel insights into the sequence-based PPIs prediction between human and various monkeypox virus strains using computational approaches, and unravelling the candidate drug targets in human against MPXV.…”