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Comprehensive molecular insights into the stress response dynamics of rice (Oryza sativa L.) during rice tungro disease by RNA-seq-based comparative whole transcriptome analysis

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Abstract

Rice tungro is a serious viral disease of rice resulting from infection by two viruses, Rice tungro bacilliform virus and Rice tungro spherical virus. To gain molecular insights into the global gene expression changes in rice during tungro, a comparative whole genome transcriptome study was performed on healthy and tungro-affected rice plants using Illumina Hiseq 2500. About 10 GB of sequenced data comprising about 50 million paired end reads per sample were then aligned on to the rice genome. Gene expression analysis revealed around 959 transcripts, related to various cellular pathways concerning stress response and hormonal homeostasis to be differentially expressed. The data was validated through qRT-PCR. Gene ontology and pathway analyses revealed enrichment of transcripts and processes similar to the differentially expressed genes categories. In short, the present study is a comprehensive coverage of the differential gene expression landscape and provides molecular insights into the infection dynamics of the rice-tungro virus system.

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Acknowledgements

The work was supported by the financial support from University of Delhi (DU) in the form of grants of R&D, DU-DST PURSE and infrastructural support from DST-FIST. GK is indebted to University Grants Commission, New Delhi, for research fellowship during this work. The gift of PB1 seeds from IARI, New Delhi, and RTD-affected plants from ICAR-IIRR, Hyderabad, are gratefully acknowledged.

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  1. Department of Plant Molecular Biology, University of Delhi South Campus, New Delhi, 110 021, India

    Gaurav Kumar & Indranil Dasgupta

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  1. Gaurav Kumar
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Correspondence to Indranil Dasgupta.

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Communicated by BJ Rao.

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Kumar, G., Dasgupta, I. Comprehensive molecular insights into the stress response dynamics of rice (Oryza sativa L.) during rice tungro disease by RNA-seq-based comparative whole transcriptome analysis. J Biosci 45, 27 (2020). https://doi.org/10.1007/s12038-020-9996-x

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