bioRxivSystems BiologyPreprint
Differential Regulation of Rice Transcriptome to Rhizoctonia solani infection
Akash DasiD, Mazahar MoiniD, Ankur SahuiD, Mrinmoy Kshattry, P. B. Kirti, Pankaj BarahiD
Abstract
Sheath Blight (SB) disease in rice crop caused by the infection of the fungal pathogen Rhizoctonia solani (R. solani) is one of the severe rice diseases that can cause up to 50% yield losses. 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 its transcriptomic response signatures to R. solani infection at 1st, 2nd and 5th day post inoculation (dpi). In total, 428, 3225 and 1225 genes were differentially expressed in the treated rice plants post 1, 2 and 5 dpi, respectively. GO and KEGG enrichment analysis identified significant processes and pathways differentially altered in the rice plant after the fungal infection. Machine learning and network based integrative approach was used to construct Transcriptional Regulatory Networks (TRNs) of the rice plant at the three Time Points. Regulatory network analysis identified SUB1B, MYB30 and CCA1 as important regulatory hub Transcription Factors in rice during R. solani infection. Jasmonic acid signaling pathway was activated and in contrast, photosynthesis and carbon fixation processes were significantly compromised. Involvement of MAPK, CYPs, Peroxidases and PAL genes was observed in response to the fungal infection. Circadian clock was also strongly influenced by R. solani infection. Our integrative analysis identified 7 putative SB resistant genes altered in rice after R. solani infection and provided a better understanding of rice plant response to R. solani infection. One sentence summaryTime series expression analysis of rice variety BPT-5204 identifies key molecular signatures involved in rice plant response to R. solani infection.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: differential expression
- approach: machine learning (classical)
- approach: statistical model
- Citations
- 1
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