QTL mapping of photosynthetic-related traits in rice under salt and alkali stresses
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Salt-alkali stress causes serious abiotic damage during the growth stage of rice. Photosynthetic characteristics and their related physiological traits affect the growth and development of rice under stressed conditions. In this study, using a recombinant inbred line (RIL) population derived from a cross between Dongnong 425 and Changbai 10 (CB10), quantitative trait loci (QTLs) for seven photosynthetic-related traits, including net photosynthetic rate (Pn), transpiration rate, stomatal conductance, intercellular CO2 concentration, chlorophyll content (Cc), canopy temperature and leaf area (La), were identified in control, salt and alkali stress conditions at the grain filling stage of rice over two years. In total, 23 QTLs were detected by using the CIM module of Windows QTL Cartographer 2.5 software, and 19 out of the 23 QTLs were salt-alkali stress-related. qPn3-2, qTr8, qCc3, qCc12 and qLa12 were specifically expressed in salt or alkali stress conditions. qTr6 expression was detected under both salt and alkali stresses for two consecutive years, suggesting it may have a key role in salt-alkali tolerance breeding. OsGLYII-2, which is a salt tolerant gene located in the mapping interval of major QTLs qPn3-2 and qCc3, was sequenced to verify the additive effect orientation of the two QTLs. The sequenced result showed that the OsGLYII-2 allele of extremely salt-tolerant RIL lines was consistent with that of the tolerant parent CB10 and showed that the positive alleles of qPn3-2 and qCc3 originated from CB10. The information obtained in this study may be useful for understanding the genetic basis of salt-alkali tolerance; it also provides an important base for the fine mapping and map-based cloning of the QTLs for photosynthetic-related traits under salt-alkali stress.
KeywordsOryza sativa L. Salt Alkali Photosynthesis QTLs
This work was supported by the National Natural Science Foundation (31701507), Youth Science Foundation of Heilongjiang Province (QC2017015), National Science and Technology Major Project (2018ZX0800912B-002), Postdoctoral Scientific Research Developmental Fund of Heilongjiang Province (LBH-Q17016), National Key Research and Development Plan (2017YFD0300501).
DZ and SJ designed research; SJ, DX, EZ, HZ, JW, HL, LY, SZ, LW performed research including phenotyping and genotyping; SJ wrote manuscript; DZ corrected manuscript. All authors have read and approved the manuscript.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Aminian R, Mohammadi S, Hoshmand S, Khodombashi M (2011) Chromosomal analysis of photosynthesis rate and stomatal conductance and their relationships with grain yield in wheat (Triticum aestivum L.) under water stressed and well-watered conditions. Acta Physiol Plant 33(3):755–764. https://doi.org/10.1007/s11738-010-0600-0 CrossRefGoogle Scholar
- Herve D, Fabre F, Berrios EF, Leroux N, Chaarani G, Planchon C, Sarrafi A, Gentzbittel L (2001) QTL analysis of photosynthesis and water status traits in sunflower (Helianthus annuus L.) under greenhouse conditions. J Exp Bot 52(362):1857–1864. https://doi.org/10.1093/jexbot/52.362.1857362.1857 CrossRefPubMedGoogle Scholar
- Juenger TE, McKay JK, Hausmann N, Keurentjes JJB, Sen S, Stowe KA, Dawson TE, Simms EL, Richards JH (2005) Identification and characterization of QTL underlying whole-plant physiology in Arabidopsis thaliana: delta C-13, stomatal conductance and transpiration efficiency. Plant Cell Environ 28(6):697–708. https://doi.org/10.1111/j.1365-3040.2004.01313.x CrossRefGoogle Scholar
- Lander ES, Green P, Abrahamson J, Barlow A, Daly MJ, Lincoln SE, Newburg L (1987) MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1:174–181. https://doi.org/10.1016/0888-7543(87)90010-3 CrossRefPubMedGoogle Scholar
- Li HY, Yang YM, Zhang HY, Chu SS, Zhang XG, Yin DM, Yu DY, Zhang D (2016) A genetic relationship between phosphorus efficiency and photosynthetic traits in soybean as revealed by QTL analysis using a high-density genetic map. Front Plant Sci. https://doi.org/10.3389/fpls.2016.00924 CrossRefPubMedPubMedCentralGoogle Scholar
- McCouch SR, Cho YG, Yano M, Paul E, Blinstrub M, Morishima H, Kinoshita T (1997) Report on QTL nomenclature. Rice Genet Newsl 14:11–13Google Scholar
- Sehgal A, Sita K, Kumar J, Kumar S, Singh S, Siddique KHM, Nayyar H (2017) Effects of drought, heat and their interaction on the growth, yield and photosynthetic function of lentil (Lens culinaris Medikus) genotypes varying in heat and drought sensitivity. Front Plant Sci 8:1776. https://doi.org/10.3389/fpls.2017.01776 CrossRefPubMedPubMedCentralGoogle Scholar
- Sun J, Zou DT, Luan FS, Zhao HW, Wang JG, Liu HL, Xie DW, Su DQ, Ma J, Liu ZL (2014) Dynamic QTL analysis of the Na+ content, K+ content, and Na+/K+ ratio in rice roots during the field growth under salt stress. Biol Plant 58(4):689–696. https://doi.org/10.1007/s10535-014-0445-2 CrossRefGoogle Scholar
- Teng S, Qian Q, Zeng DL, Kunihiro Y, Fujimoto K, Huang DN, Zhu LH (2002) QTL analysis of leaf photosynthetic rate and related physiological traits in rice (Oryza sativa L.). Chin Rice Res Newsl 135(4):1–7. https://doi.org/10.1023/B:EUPH.0000009487.89270.e9 CrossRefGoogle Scholar
- Wang ZF, Cheng JP, Chen ZW, Huang J, Bao YM, Wang JF, Zhang HS (2012a) Identification of QTLs with main, epistatic and QTL × environment interaction effects for salt tolerance in rice seedlings under different salinity conditions. Theor Appl Genet 125:807–815. https://doi.org/10.1007/s00122-012-1873-z CrossRefPubMedGoogle Scholar
- Wang ZF, Chen ZW, Cheng JP, Lai YY, Wang JF, Bao YM, Huang J, Zhang HS (2012b) QTL analysis of Na+ and K+ concentrations in roots and shoots under different levels of NaCl stress in Rice (Oryza sativa L.). PLoS ONE 7:e51202. https://doi.org/10.1371/journal.pone.0051202 CrossRefPubMedPubMedCentralGoogle Scholar
- Wang SC, Basten CJ, Zeng ZB (2012c) Windows QTL cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC. https://brcwebportal.cos.ncsu.edu/qtlcart/WQTLCart.htm
- Wei K, Wang J, Sang MM, Zhang SL, Zhou HC, Jiang LB, Clavijo Michelangeli JA, Eduardo Vallejos C, Wu RL (2018) An ecophysiologically based mapping model identifies a major pleiotropic QTL for leaf growth trajectories of Phaseolus vulgaris. Plant J 95(5):775–784. https://doi.org/10.1111/tpj.13986 CrossRefGoogle Scholar
- Xiong L, Zhu JK (2001) Abiotic stress signal transduction in plants: molecular and genetic perspectives. Physiol Plant 12:152–166. https://doi.org/10.1034/j.1399-3054.2001.1120202.x CrossRefGoogle Scholar