Prediction of protein–protein interactions between fungus (Magnaporthe grisea) and rice (Oryza sativa L.)

Abstract

Rice blast disease caused by the fungus Magnaporthe grisea (M. grisea) is one of the most serious diseases for the cultivated rice Oryza sativa (O. sativa). A key factor causing rice blast disease and defense might be protein-protein interactions (PPIs) between rice and fungus. In this research, we have developed a computational pipeline to predict PPIs between blast fungus and rice. After cross-prediction by interolog-based and domain-based method, we achieved 532 potential PPIs between 27 fungus proteins and 236 rice proteins. Accuracy of jackknife test, 10-fold cross-validation test and independent test for these PPIs were 90.43, 93.85 and 84.67%, respectively, by using support vector machine classification method. Meanwhile, the pathogenic genes of blast fungus were enriched in the predicted PPIs network when compared with 1000 random interaction networks. The rice regulatory network was downloaded and divided into 228 subnetworks with over six nodes, and the top seven subnetworks affected by blast fungus through PPIs were investigated. The results indicated that 34 upregulated and 12 downregulated master regulators in rice interacting with the fungus proteins in response to the infection of blast fungus. The common master regulators in rice in response to the infection of M. grisea, Xanthomonas oryzae pv.oryzae and rice stripe virus were analyzed. The ubiquitin proteasome pathway was the common pathway in rice regulated by these three pathogens, while apoptosis signaling pathway was induced by fungus and bacteria. In summary, the results in this article provide insight into the process of blast fungus infection.

Predicting Protein–Protein Interactions Between Rice and Blast Fungus Using Structure-Based Approaches

“…Of the predicted PPIs, only 29.9% (604) of interactions were supported with the evidences of sequence similarity and interacting domain pairs. Moreover, we found that 17 predicted PPIs could be identified by the previous study with the sequence-based method (Ma et al, 2019). These results indicated that the structure-based method could efficiently discover new rice-blast fungus PPIs beyond those interactions inferred from sequence similarity.…”

“…Over 70% of PPIs between rice and blast fungus were inferred from structural similarity, which greatly expanded the landscape of the rice-blast fungus PPI network. Compared with the previous PPI network, 17 of PPIs were identified by the structure-based method (Ma et al, 2019). Although the number of common PPIs was relatively small, the results were significantly overlapped between the two independent studies (i.e., Fisher's exact test p < 5.6e-39).…”

Expanding Interactome Analyses beyond Model Eukaryotes

“…Remmele and co-workers used interolog mapping to identify host-pathogen interactions of A. fumigatus during human and mouse infection, highlighting the roles of the PLB1 virulence factor and APP anti-fungal host protein [213]. Several network studies have also investigated yeast infection in plants [61,89,91,[214][215][216][217].…”

Large Scale Proteomic Data and Network-Based Systems Biology Approaches to Explore the Plant World

“…On the other hand, the lack on PPIs knowledge about plant organisms is driving many biologists to reconstruct PPI networks experimentally ( Table 2 ). Of note, a number of studies and databases ( Table 3 ) focus on host-pathogen PPI, suggesting that this field of PPIs is of great interest to improve strategies oriented to manage destructive pathogens that cause huge crop losses every year worldwide [ 35 , 184 , 185 , 188 , 189 , 190 ].…”