Sheath Blight (SB) disease in rice is caused by the infection from the fungal pathogen Rhizoctonia solani (R. solani). SB is one of the most severe rice diseases that can cause up to 50% yield losses in rice. Naturally occurring rice varieties resistant to SB have not been reported yet. We have performed a Time-Series RNA-Seq analysis on a widely cultivated rice variety BPT-5204 for identifying transcriptome level response signatures during R. solani infection at 1st, 2nd and 5th day post infection (dpi). In total, 428, 3225 and 1225 genes were differentially expressed in the treated rice plants on 1, 2 and 5 dpi, respectively. GO and KEGG enrichment analysis identified significant processes and pathways differentially altered in the rice plants during the fungal infection. Machine learning and network based integrative approach was used to construct rice Transcriptional Regulatory Networks (TRNs) for the three time points. TRN analysis identified SUB1B, MYB30 and CCA1 as important regulatory hub transcription factors in rice during R. solani infection. Jasmonic acid, salicylic acid, ethylene biogenesis and signaling were induced on infection. SAR was up regulated, while photosynthesis and carbon fixation processes were significantly down regulated. Involvement of MAPK, CYPs, peroxidase, PAL, chitinase genes were also observed in response to the fungal infection. The integrative analysis identified seven putative SB resistance genes differentially regulated in rice during R. solani infection.
Keywords: Differentially expressed genes; Machine learning; Oryza sativa; Rhizoctonia solani; Transcriptional Regulatory Networks; Transcriptomics.
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