Comparative Leaf and Root Transcriptomic Analysis of two Rice Japonica Cultivars Reveals Major Differences in the Root Early Response to Osmotic Stress

Rice (N Y). 2016 Dec;9(1):25. doi: 10.1186/s12284-016-0098-1. Epub 2016 May 23.

Abstract

Background: Rice (Oryza sativa L.) is one of the most important crops cultivated in both tropical and temperate regions and is characterized by a low water-use efficiency and a high sensitivity to a water deficit, with yield reductions occurring at lower stress levels compared to most other crops. To identify genes and pathways involved in the tolerant response to dehydration, a powerful approach consists in the genome-wide analysis of stress-induced expression changes by comparing drought-tolerant and drought-sensitive genotypes.

Results: The physiological response to osmotic stress of 17 japonica rice genotypes was evaluated. A clear differentiation of the most tolerant and the most sensitive phenotypes was evident, especially after 24 and 48 h of treatment. Two genotypes, which were characterized by a contrasting response (tolerance/sensitivity) to the imposed stress, were selected. A parallel transcriptomic analysis was performed on roots and leaves of these two genotypes at 3 and 24 h of stress treatment. RNA-Sequencing data showed that the tolerant genotype Eurosis and the sensitive genotype Loto mainly differed in the early response to osmotic stress in roots. In particular, the tolerant genotype was characterized by a prompt regulation of genes related to chromatin, cytoskeleton and transmembrane transporters. Moreover, a differential expression of transcription factor-encoding genes, genes involved in hormone-mediate signalling and genes involved in the biosynthesis of lignin was observed between the two genotypes.

Conclusions: Our results provide a transcriptomic characterization of the osmotic stress response in rice and identify several genes that may be important players in the tolerant response.

Keywords: Hormones; Lignin; Oryza sativa; Osmotic stress; RNA-Seq analysis; Transcription factors.