Transcriptomics

Dataset Information

0

Comparative physiological and transcriptomic profiling reveals the characteristics of tissue tolerance mechanisms in Japanese rice landrace under salt stress


ABSTRACT: The aim of this study was to characterize the tissue tolerance mechanisms of rice under salt stress. Our preliminary experiment identified a japonica rice landrace Shuzenji-kokumai (SZK), which is considered to be tissue-tolerant because it can maintain better growth than salt-sensitive rice while having a high Na+ concentration in the shoots under salt stress. These mechanisms differ from those of most salt-tolerant rice varieties, which have low Na+ concentrations in the shoots. We compared the physiological and molecular characteristics of SZK with those of FL478, a salt-tolerant variety, and Kunishi, a salt-sensitive variety. Under salt stress conditions, SZK accumulated high levels of Na+ in roots, leaf sheaths, and leaf blades, which were almost as high as those in the salt-sensitive Kunishi. Simultaneously, SZK maintained better growth and physiological status, as determined by its higher dry weight, lower electrolyte leakage ratio, and lower malondialdehyde concentration. OsNHX1 and OsNHX2 were up-regulated in the leaf sheaths of SZK, suggesting that Na+ is compartmentalized in the vacuole to avoid Na+ toxicity. In contrast, FL478 showed up-regulation of OsHKT1;5 and OsSOS1 in the roots, which exclude Na+ from the shoots. RNA-seq analysis showed that 4623 and 1998 differentially expressed genes (DEGs) were detected in the leaf sheaths and leaf blades of SZK, respectively. Among them, the HSP (heat shock protein) gene expression was highly up-regulated only in SZK, indicating that SZK protects against the protein damage caused by Na+ toxicity. Our findings suggest that SZK has atypical survival mechanisms under salt-stress conditions. These mechanisms offer potential traits for improving salt tolerance in rice.

ORGANISM(S): Oryza sativa

PROVIDER: GSE266657 | GEO | 2024/05/08

REPOSITORIES: GEO

Similar Datasets

2019-04-16 | GSE129844 | GEO
2022-04-18 | GSE200863 | GEO
2016-06-30 | E-GEOD-76613 | biostudies-arrayexpress
2014-09-27 | GSE61788 | GEO
2014-09-27 | E-GEOD-61788 | biostudies-arrayexpress
2016-06-30 | GSE76613 | GEO
2010-07-07 | GSE13735 | GEO
2017-08-10 | GSE102422 | GEO
2007-12-21 | GSE6600 | GEO
2019-11-01 | GSE122850 | GEO