bioRxivPlant BiologyPreprint
An in-silico comparative analysis of lncRNA expression and their role in the pathogenesis of representative fungal, bacterial and viral infections in rice
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Abstract
Long non-coding RNAs (lncRNAs) perform prominent role in the regulation of gene expression during plant development and stress response by directly interacting with DNA, RNA, proteins, and/or triggering production of small regulatory RNA molecules. The objective of our study is to understand the systems-level response of the same plant species to highly diverse pathogens across different kingdoms and evaluate the patterns of similarity vs differences, specifically in the context of lncRNAs. Towards this objective, we performed a comparative in silico analysis of lncRNAs of Rice that are differentially expressed in response to infection by bacteria (Xanthomonas oryzae), fungus (Magnaporthe oryzae) and virus (Rice black dwarf virus). Using a tailored lncRNA analysis pipeline, we successfully identified 1125, 719 and 240 lncRNAs in Xanthomonas oryzae infection susceptible cultivar CT9737-6-1-3P-M, Magnaporthe oryzae susceptible LTH accession, and Rice black streaked dwarf virus susceptible Wuyujing No. 7 rice cultivars respectively. The in-silico predicted Cis- and Trans-target genes of lncRNAs were subsequently used to identify the pathways modulated by these lncRNA and how they cluster into unique categories of plant responses to pathogen infections. To further substantiate the role of predicted lncRNAs in plant defence and immune response our analysis finds that many of the lncRNAs co-localize with the QTLs associated with Blast and Bacterial blight resistance in rice. Our in silico analysis provides a list of common and unique pathogen specific lncRNAs that can provide vital insights into the generic vs tailored mechanisms adopted by rice in different infection scenarios.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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