Deciphering early responsive signature genes in rice blast disease: an integrated temporal transcriptomic study.

Abstract

Rice blast disease, caused by Magnaporthe oryzae, reigns as the top-most cereal killer, jeopardizing global food security. This necessitates the timely scouting of pathogen stress-responsive genes during the early infection stages. Thus, we integrated time-series microarray (GSE95394) and RNA-Seq (GSE131641) datasets to decipher rice transcriptome responses at 12- and 24-h post-infection (Hpi). Our analysis revealed 1580 differentially expressed genes (DEGs) overlapped between datasets. We constructed a protein-protein interaction (PPI) network for these DEGs and identified significant subnetworks using the MCODE plugin. Further analysis with CytoHubba highlighted eight plausible hub genes for pathogenesis: RPL8 (upregulated) and RPL27, OsPRPL3, RPL21, RPL9, RPS5, OsRPS9, and RPL17 (downregulated). We validated the expression levels of these hub genes in response to infection, finding that RPL8 exhibited significantly higher expression compared with other downregulated genes. Remarkably, RPL8 formed a distinct cluster in the co-expression network, whereas other hub genes were interconnected, with RPL9 playing a central role, indicating its pivotal role in coordinating gene expression during infection. Gene Ontology highlighted the enrichment of hub genes in the ribosome and protein translation processes. Prior studies suggested that plant immune defence activation diminishes the energy pool by suppressing ribosomes. Intriguingly, our study aligns with this phenomenon, as the identified ribosomal proteins (RPs) were suppressed, while RPL8 expression was activated. We anticipate that these RPs could be targeted to develop new stress-resistant rice varieties, beyond their housekeeping role. Overall, integrating transcriptomic data revealed more common DEGs, enhancing the reliability of our analysis and providing deeper insights into rice blast disease mechanisms.