WeCoNET: a host–pathogen interactome database for deciphering crucial molecular networks of wheat-common bunt cross-talk mechanisms

Abstract

Background Triticum aestivum is the most important staple food grain of the world. In recent years, the outbreak of a major seed-borne disease, common bunt, in wheat resulted in reduced quality and quantity of the crop. The disease is caused by two fungal pathogens, Tilletia caries and Tilletia laevis, which show high similarity to each other in terms of life cycle, germination, and disease symptoms. The host–pathogen protein–protein interactions play a crucial role in initiating the disease infection mechanism as well as in plant defense responses. Due to the availability of limited information on Tilletia species, the elucidation of infection mechanisms is hampered. Results We constructed a database WeCoNET (http://bioinfo.usu.edu/weconet/), providing functional annotations of the pathogen proteins and various tools to exploit host–pathogen interactions and other relevant information. The database implements a host–pathogen interactomics tool to predict protein–protein interactions, followed by network visualization, BLAST search tool, advanced ‘keywords-based’ search module, etc. Other features in the database include various functional annotations of host and pathogen proteins such as gene ontology terms, functional domains, and subcellular localization. The pathogen proteins that serve as effector and secretory proteins have also been incorporated in the database, along with their respective descriptions. Additionally, the host proteins that serve as transcription factors were predicted, and are available along with the respective transcription factor family and KEGG pathway to which they belong. Conclusion WeCoNET is a comprehensive, efficient resource to the molecular biologists engaged in understanding the molecular mechanisms behind the common bunt infection in wheat. The data integrated into the database can also be beneficial to the breeders for the development of common bunt-resistant cultivars.

Fungal Effectoromics: A World in Constant Evolution

“…These targets in the host that attract many effectors have been denominated ‘hubs’ and are central in plant protein-protein interaction networks [ 189 , 190 ]. 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 ].…”

TritiKBdb: A Functional Annotation Resource for Deciphering the Complete Interaction Networks in Wheat-Karnal Bunt Pathosystem

“…The molecular techniques to study these interactions are efficient but time-consuming and expensive. Integrating computational approaches with comprehensive systems biology can enhance the study of protein-protein interactions [16][17][18]. The primary goal of developing TritiKBdb is to provide the research community with a comprehensive platform containing the respective functional annotations of the host and pathogen involved in the proteinprotein interactions during the Karnal bunt disease infection in wheat.…”

A Transformer-Based Ensemble Framework for the Prediction of Protein–Protein Interaction Sites

“…PPI sites refer to the interfacial residues of proteins that are involved in these interactions, and the identification of PPI sites is of utmost importance for unraveling the mysteries of cell processes and promoting the development of new drugs [ 6 – 8 ]. Experimental approaches for identifying PPI sites, including affinity purification coupled to mass spectrometry [ 9 , 10 ], coimmunoprecipitation [ 11 , 12 ] and 2-hybrid screening [ 13 , 14 ], face challenges due to their intricate and time-consuming procedures [ 15 – 18 ]. Therefore, the development of efficient computational methods to accelerate the identification of PPI sites is of vital importance [ 19 – 22 ].…”