Physiological mechanism dissection and QTL mapping of salinity tolerance in rice (<i>Oryza sativa </i>L.)
Creators: name: Chen, Tianxiao
Publisher: name: University of Tasmania
Year: 2024

description: Rice is one of the most important crops. Its planting area, however, is being occupied by other crops in China. The extension of rice cultivation in saline soils can significantly increase rice production. Hence, improving salinity tolerance in rice has become one of the major goals in breeding programs. Mining salinity tolerant resources and understanding physiological and molecular mechanisms of salinity tolerance could lay a foundation for the improvement of salinity tolerance in rice. In this study, a series of salinity tolerance related morphological and physiological traits were investigated in 46 rice germplasms including Sea Rice 86 to reveal main strategies of rice in responding to salinity stress. At the same time, two bi-parental populations were used to identify QTL for salinity tolerance and investigate physiological mechanisms underlying tolerant QTL. The main results are as follows:<br> (1) Abundant genetic variations among 46 rice genotypes were observed for salinity tolerance and related parameters. Sea Rice 86 seedlings showed moderate tolerance to salinity stress under hydroponic conditions, surviving for 16.9 days after the treatment of 140 mM NaCl. Correlation and principal component analyses revealed that salinity tolerance of rice seedlings is not only controlled by growth vigour, but also regulated by ion transport pathways such as long-distance Na<sup>+</sup> transport and root Na<sup>+</sup> sequestration. <br>(2) Stomatal adjustment and root K<sup>+</sup> retention are the two important strategies employed by rice to respond to salinity stress. Salinity tolerant genotypes had better abilities of stomatal adjustment and root K<sup>+ </sup>retention than sensitive ones. <br>(3) A high-density genetic map covering 1,680.9 cM was constructed using a RIL population derived from a cross between IR29 and Pokkali. A total of 23 QTL for morphological and physiological traits which are related to salinity tolerance were identified on chromosomes 1, 2, 3, 4, 6, 7, 8, and 12. Three QTL (<i>qSIS1</i>, <i>qSIS4</i>, and <i>qSIS12</i>) for salt injury scores were mapped on chromosomes 1, 4 and 12, which are at the same positions to the three QTL (<i>qSNaC1.2</i>, <i>qSNaC4</i>, and <i>qSNaC12</i>) for shoot Na<sup>+</sup> concentration. Further analysis suggested that these QTL controlled salinity tolerance via mediating long-distance Na<sup>+</sup> transport from roots to shoots. Moreover, these three QTL showed addictive effects, thus can be effectively used in breeding programs to improve rice salinity tolerance through QTL pyramiding. <br>(4) Sea Rice 86 was salinity-sensitive at the seedling stage but showed strong salinity tolerance during the adult stage. Fast stomatal closure and early heading could be the main adaptive mechanisms of Sea Rice 86 responding to salinity stress. <br>(5) A DH population was developed from a cross between Sea Rice 86 and Nipponbare. A high?density genetic map with a total length of 1563.8 cM and an average spacing of 0.9 cM between markers was subsequently constructed. High collinearity was observed between the genetic map and the physical map of rice reference genome. The map has been validated by accurate mapping genes controlling quantitative traits with high heritability such as plant height, heading date, and awn length. <br>(6) A total of 17 QTL for different salinity tolerance indices measured at the seedling stage and adult stage were identified. These QTL are located on chromosomes 1, 2, 4, 5, 6, and 12. A major QTL controlling seedling stage tolerance was mapped in the same position as the semidwarfing gene <i>sd1</i> which participates in GA biosynthesis. It was speculated that <i>sd1</i> could improve salinity tolerance through retarding shoot growth. Two QTL conferring adult stage tolerance were identified from Sea Rice 86, determining 15.0% of total phenotypic variation. <br>(7) SR86, Nona Bokra, and Pokkali had the same sequence of <i>SKC1</i> in the coding region, indicating that SR86 carried the favorable allele at <i>SKC1.</i>