QTLs for seedling traits under salinity stress in hexaploid wheat/Caracterizacao de QTLs por tracos de sementes sob o estresse de salinidade em trigo Hexaploid.
Dryland salinity is a major limitation to crop production. About 20% of irrigated agricultural land in the world is affected by salinity (BOYER, 1982; FLOWERS et al., 1995). Soil salinity inhibits plant growth for not only osmotic stress but ion-excess stress. The presence of salt in the soil solution reduces the ability of plants to take up water, and then affects growth. This is the osmotic effect of salinity. Moreover, uptake excessive amounts of salt will eventually injure cells in the transpiring leaves and this may further reduce growth. This is the ion-excess effect of salinity (MUNNS, 1993; MUNNS, 2006). To survive, plants have evolved complex salt tolerance mechanisms (ZHU et al., 2002; HANIN et al., 2016). For example, the salt overly sensitive (SOS) pathway of salt tolerance is crucial for maintaining ion homeostasis under salt stress in model plant Arabidopsis. SOS pathway was also more active in salt tolerant cultivar PI365967 than in salt sensitive cultivar in tomato indicating that SOS pathway may be conserved across diverse plant species. Besides, salicylic acid (SA), abscisic acid (ABA), brassinosteroids (BRs) pathway and detoxification system may also be involved in salt perception or salt responses (KRISHNA et al., 2003; MARTINEZ-ATIENZA et al., 2007; SUN et al., 2010; SUN et al., 2015; CHEN et al., 2017).
Wheat is one of the most important food crops. Improving salt tolerance of wheat is needed to sustain food production in many regions in the world. To exploit variation in salt tolerance of wheat, large international collections have been screened in hydroponic or sand culture (KINGSBURY et al., 1984; SAYED, 1985; JAFARI-SHABESTARI et al., 1995). These researches laid a solid foundation for breeding wheat varieties with improved salt tolerance and provided us with research materials for probing into the mechanisms of wheat salt tolerance. In recent years, genome-wide transcriptomic and proteomic analysis for identification of salt-responsive genes in common wheat provided many useful clues (KAWAURA et al., 2008; GUO et al., 2012; CAPRIOTTI et al., 2014; GOYAL et al., 2016; JIANG et al., 2017). The differential expressed proteins/genes were involved primarily in carbon metabolism, detoxification and defense, chaperon and signal transduction. A number of salt tolerant related genes (FEKI et al., 2014; MAKHLOUFI et al., 2014; SUN et al., 2015; TOUNSI et al., 2016; GOYAL et al., 2016; CHEN et al., 2017) and QTLs (MEGAN et al., 2004; HUANG et al., 2006; WU et al., 2007; GENC et al., 2010; REN et al., 2012a; XU et al., 2013; MASOUDI et al., 2015; TOUNSI et al., 2016; OYIGA et al., 2017) have been identified in wheat in recent years. These provided useful information for genetic improvement of salt tolerance in wheat. However, for the intricate character of wheat salt tolerance, the underlying genetic basis was still unclear. Therefore, further exploiting QTLs that contribute to natural variation in salt tolerance would be helpful in understanding the mechanisms of wheat salt tolerance.
In wheat, salt tolerance is associated with low rates of transport of [Na.sup.+] to shoots and high selectivity for [K.sup.+] over [Na.sup.+] which were controlled by Knal located on chromosome 4D (GORHAM et al., 1987; GORHAM et al., 1990; DUBCOVSKY et al., 1996). Correlations between grain yield and [Na.sup.+] exclusion from leaves, along with the associated enhanced [K.sup.+]/[Na.sup.+] discrimination, have also been shown in wheat (CHHIPA et al., 1995; ASHRAF et al., 1997; XU et al., 2013; MASOUDI et al., 2015). Growth performance of seedlings of one specific wheat cultivar grown under salt stress can reflect its salt tolerance to a great extent. Root length, root fresh and dry weights, and shoot fresh and dry weights of wheat seedlings are associated with salt tolerance and could be used as selection criteria in wheat (SHAHZAD et al., 2012). In this paper, we mapped QTLs for seedling traits under normal and salt stress conditions using a recombinant inbred line (RIL) population derived from two Chinese wheat varieties Xiaoyan 54 and Jing 411. These results may provide useful information for molecular design of wheat varieties with improved salt tolerance.
MATERIALS AND METHODS
One recombinant inbred line (RIL) population was used in this study. The RIL population contained 142 RILs derived from two Chinese winter wheat varieties Xiaoyan 54 and Jing 411.
Hydroponic culture was used to investigate the seedling traits of wheat. Methods for seed sterilization, germination and the growth conditions of wheat plants were described previously (REN et al., 2012b). The growth chamber was set at 22[degrees]C in the day time and 18[degrees]C in the night time, 60% relative humidity and a 15-hour day length. The seedling traits of the "Xiaoyan 54 x Jing 411" RIL population were investigated after these lines were grown in nutrition solution in the growth chamber for 25 days under normal (CK) and salt stress (ST, 150mM NaCl) conditions. The maximum root length (MRL) of plant roots were measured using a ruler and then the shoot dry weight (SDW), root dry weight (RDW) and total dry weight (TDW) were measured after oven-drying at 80[degrees]C for 48h. The ratio of TDW (TDWR, ST/CK) of each line was also calculated.
The genetic map of the "Xiaoyan 54 x Jing 411" RIL population was described by REN et al., (2012b). Detection of QTLs for wheat seedling traits under normal and salt stress conditions in this RIL population was conducted by composite interval mapping (ZENG, 1994). Analyses of QTL location, additive effect and 95% confidence intervals of QTLs were performed using WinQTLCart 2.5 software (WANG et al., 2012). The stand model was employed and the parameters of forward regression analysis were set according to the method described by SU et al. (2009) and WANG et al. (2012). In brief, the walk speed and window size were set as 2cM and 10cm, respectively, with five control markers. Phenotypic variation explained by a single QTL was determined by the square of the partial correlation coefficient (R2). Threshold of LOD value for QTL detection was set as 3.0.
Evaluation of phenotypes
We measured root dry weight (RDW), maximum root length (MRL), shoot dry weight (SDW) and total dry weight (TDW) under normal (CK) and salt stress (ST) conditions. Under CK condition, the male parent Jing 411 had significant higher RDW, SDW and TDW than the female parent Xiaoyan 54, but it had shorter MRL than Xiaoyan 54 (Table 1). Salt stress significantly reduced the values of all these four traits in Xiaoyan 54 and Jing 411 compared to CK treatment. Jing 411 and Xiaoyan 54 had similar RDW and MRL, but Jing 411 had higher SDW and TDW than Xiaoyan 54 under ST condition (Table 1). These two parents also had similar relative TDW (TDWR, the ratio of TDW under ST and CK conditions), indicating that the plant growth of these two parents had similar sensitivity to salt stress.
The RIL lines showed large variations in all the investigated traits (Table 1). There existed RILs with values that were higher or lower than both parents among all these traits, indicating potential transgressive variations and the presence of positive and negative alleles in both parents.
We totally detected three QTLs for RDW, three for MRL, five for SDW, six for TDW and two for TDWR. These 19QTLs located on 12 chromosomes, and the percentage of phenotypic variation explained by individual QTL varied from 7.9% to 19.0% (Figure 1 and 2, Table 2).
We detected two and one QTLs for RDW under normal and 150mM NaCl salt stress condition respectively. qRDW.ST-4A, the QTL detected under ST condition located on chromosome 4A and explained 19.0% of phenotypic variation in RDW (Figure 1 and 2, Table 2).
One and two QTLs for MRL were detected under CK and ST conditions respectively (Figure 1 and 2, Table 2). qMRL.CK-2B for MRL were detected in CK which located on the short arm of chromosome 2B and the other two QTLs were detected under ST condition. qMRL.ST-4D located on chromosome 4D and explained 8.3% MRL variation. qMRL.ST-6B located on chromosome 6B and explained 16% MRL variation (Table 2).
Four and one QTLs for SDW were detected under CK and ST conditions respectively. The only QTL for SDW detected under ST condition, qSDW.ST7A, located on chromosome 7A and explained 12.4% SDW phenotypic variation (Figure 1 and 2, Table 2). We detected four and two QTLs for TDW under CK and ST conditions respectively. (Figure 1 and 2, Table 2). The six QTLs for TDW explained phenotypic variations varying from 9.3% to 15.3%. qTDW.ST-3A and qTDW.ST-7A were detected under ST condition and explained 12.3% and 12.0% TDW phenotypic variations respectively (Figure 1 and 2, Table 2).
We detected two QTLs for TDWR (Figure 1 and 2, Table 2). qTDWR-1A located on chromosome 1A and explained 13.7% TDWR variation. the other QTL, qTDWR-3A, located on chromosome 3A and explained 15% TDWR variation. (Table 2).
Although some single-gene effects for salinity tolerance have been identified in higher plants (for example AtNHX1 and RAS1 in Arabidopsis (APSE et al., 1999; REN et al., 2010), OsNHX1 and SKC1 in rice (FUKUDA et al., 1999; REN et al., 2005)), the tolerance of salinity is genetically and physiologically complex. Large international collections have been screened (KINGSBURY et al., 1984; SAYED, 1985; JAFARI-SHABESTARI et al., 1995), however, the application of salt-tolerant varieties to the improvement of cereal crops such as wheat remains hampered because of the quantitative nature of the genes involved. Identification of QTLs and some defined regions of chromosome are of crucial importance to enhance wheat salt tolerance. Markers closely associated with major QTLs for salt tolerance might be used for breeding programs in wheat using marker-assisted selection. QTL analyses for salt tolerance in wheat at seedling stage have been conducted in previous studies (MEGAN et al., 2004; HUANG et al., 2006; WU et al., 2007; GENC et al., 2010; REN et al., 2012a; XU et al., 2013; MASOUDI et al., 2015; OYIGA et al., 2017). However, these genes and/or QTLs are not sufficient for understanding the genetic basis and the genetic improvement of salt tolerance in wheat. To further exploit QTLs for wheat salt tolerance, we evaluated the seedling traits of a RIL population under normal and salinity stress conditions in this paper. We found that there exist no significant differences of RDW and MRL between Xiaoyan 54 and Jing411, but Jing 411 had higher SDW and TDW than Xiaoyan 54 under under 150mM salt stress condition. The RDW, MRL, SDW and TDW of the RIL population were significantly decreased under salt stress condition comparing to those of under normal condition (Table 1, 2), which is in consistence with previous study (WU et al., 2007; REN et al., 2012a; TOUNSI et al., 2016).
In total, we detected 11 and six QTLs under normal and salt stress conditions respectively and two QTLs for TDWR (Table 2). These 19 QTLs explained phenotypic variations varying from 7.9% to 19.0% (Table 2). The locus for MRL on chromosome 2B, which tightly linked with SSR marker Xbarc1138, explained 15.2% MRL phenotypic variation (Figure 1 and 2, Table 2). In fact, this locus has been reported controlling multi-root morphologic parameters and a number of QTLs for yield component (explained 19.1% and 17.3% of phenotypic variations in grain weight per ear and grain number per ear, respectively) (REN et al., 2012b; HAI et al., 2008).l However, we did not detect any QTLs for root traits in this chromosomal region under ST condition, indicated that the expression of this locus is inhibited and very sensitive to salt stress. Actually this QTL has been proved to be involved in brassinosteroids (BRs) signaling pathway (HE et al., 2014) and BRs is also known to confer salt stress tolerance (KRISHNA et al., 2003; ZHU et al., 2016). The qTDWR-3A located on chromosome 3A between SSR marker Xgwm156.2 and Xbarc324, and explained 15.0% TDWR phenotypic variation. We found that some chromosomal regions governed more than one trait under salt stress condition. For example, qTDW.ST3A and qTDWR-3A were mapped in the same marker interval (Xgwm156.2-Xbarc324) on chromosome 3A. The marker interval Xbarc1136.4- Xgdm14.3 on chromosome 7A also located two QTLs (qSDW.ST7A and qTDW.ST-7A). These salt-tolerance related loci may be pleiotropic.
We found that some QTLs detected in this study were tightly linked or coincided with previously reported salt tolerant QTLs in wheat. qTDWR-1A was located on chromosome 1A and tightly linked with SSR marker Xgwm558. Actually, this chromosomal region has been reported harboring two QTLs for salt tolerance, named QTdw-1A and QSkn-1A (controlling Shoot [K.sup.+]/[Na.sup.+] concentration ratio), which also linked with SSR marker Xgwm558 on chromosome 1A. qTDWR-3A and qTDW.ST-3A were located between SSR marker Xgwm156.2 and Xbarc324 on chromosome 3A and coincided with previously reported QTDW-3A and QSkn-3A (XU et al., 2012). Genc had proved that QTLs for [Na.sup.+] exclusion was associated with an increase (10%) in seedling biomass. Of the five QTLs identified for [Na.sup.+] exclusion in the literature, two were colocated with seedling biomass (GENC et al., 2010). SHAHZAD also verified that the biomass of wheat seedlings could be used as selection criteria in salt tolerance (SHAHZAD et al., 2012). Taken together, the clustering of qTDWR-3A and qTDW.ST-3A, and coinciding with previously reported QTL for [K.sup.+]/[Na.sup.+] concentration ratio indicated that this chromosomal region may harbor crucial salt-tolerance genes. The QTLs detected repeatedly in different trials described above may facilitate MAS of wheat salt tolerance.
We identified a total of 19QTLs for wheat seedling traits, of which 11 were detected under normal condition and six under salt stress condition. The other two QTLs controlled TDWR. Some salt-tolerance related loci may be pleiotropic. Chromosome 1A, 3A and 7A may harbor crucial salt-tolerance related loci and the linked marker could be utilized in wheat breeding for improving salt tolerance.
Received 07.01.17 Approved 01.23.18 Returned by the author 03.01.18
We are very grateful to Prof. Aimin Zhang and Dr. Dongcheng Liu for providing the genotype data of the genetic linkage map. This research was supported by the National Key Research and Development Program of China (2016YFD0300205), the National Natural Science Foundation of China (31401375) and Natural Science Foundation of Henan province (162300410133).
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Yongzhe Ren (1) * Yanhua Xu (1,2) Wan Teng (3) Bin Li (3) Tongbao Lin (1)
(1) College of Agronomy/State Key Laboratory of Wheat and Maize Crop Science/Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, 450002, Zhengzhou, China. E-mail: firstname.lastname@example.org. * Corresponding author.
(2) Shangqiu Normal University, Shangqiu, China.
(3) State Key Laboratory for Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Sciences, Chinese Academy of Sciences, Beijing, China.
Caption: Figure 1--QTL detected in the "Xiaoyan 54 x Jing 411" RIL population in this trial. MRL, RDW, SDW, TDW and TDWR indicated QTL for maximum root length, root dry weight, shoot dry weight, total dry weight and relative total root length, respectively. CK and ST indicated QTL detected under normal condition and salt stress condition respectively.
Caption: Figure 2--Continued. QTL detected in the "Xiaoyan 54 x Jing 411" RIL population in this trial. MRL, RDW, SDW, TDW and TDWR indicated QTL for maximum root length, root dry weight, shoot dry weight, total dry weight and relative total root length, respectively. CK and ST indicated QTL detected under normal condition and salt stress condition respectively.
Table--1 Mean values and ranges for the investigated traits in the RIL population and their parents at seedling stage in this trial. Trait Treatment Parent Xiaoyan54 Jing 411 RDW CK 9.5 [+ or -] 0.8(a) 11.7 [+ or -] 0.8(b) ST 8.0 [+ or -] 1.6 8.0 [+ or -] 0.5 MRL CK 26.4 [+ or -] 0.8(A) 19.8 [+ or -] 0.7(B) ST 16.5 [+ or -] 0.5 16.8 [+ or -] 1.5 SDW CK 61.2 [+ or -] 1.5(A) 76.1 [+ or -] 4.0(B) ST 41.2 [+ or -] 1.2(a) 52.4 [+ or -] 3.4(b) TDW CK 70.7 [+ or -] 2.2(a) 87.9 [+ or -] 3.1(b) ST 49.2 [+ or -] 1.6(a) 60.4 [+ or -] 2.9(b) Trait Treatment RIL Mean [+ or -] SD Min. Max. RDW CK 11.8 [+ or -] 3.3 6.0 23.0 ST 10.1 [+ or -] 2.8 3.6 34.0 MRL CK 23.2 [+ or -] 4.8 13.0 37.5 ST 15.6 [+ or -] 2.2 9.4 22.4 SDW CK 71.9 [+ or -] 17.3 39.0 127.7 ST 42.4 [+ or -] 8.2 21.7 66.0 TDW CK 83.7 [+ or -] 19.9 46.7 149.6 ST 52.5 [+ or -] 9.8 25.3 82.7 RDW, root dry weight (mg [plant.sup.-1]); MRL, maximum root length (cm); SDW, shoot dry weight (mg [plant.sup.-1]); TDW, total biomass dry weight (mg [plant.sup.-1]). Statistical difference between the two parents is indicated by different letters after the means. Lower case letters designate significance at P <0.05. Capital letters designate significance at P < 0.01. Table 2--QTLs detected in this trial using the "Xiaoyan 54 x Jing 411" RIL population. Trait Treat-ment QTL Chr (a) Marker interval (b) CK qRDW.CK-1B 1B XDuPw 7.2-Xbarc119.2 RDW CK qRDW.CK-4B 4B Xgwm192.1-Xgwm368 ST qRDW.ST-4A 4A Xbarc170-Xbarc1136.2 CK qMRL. CK-2B 2B Xbarc1138.2-Xcfd238 MRL ST qMRL.ST-4D 4D Xbarc98-Xgwm55.2 ST qMRL.ST-6B 6B X239630-Xcfd13 CK qSDW.CK-3D 3D Xbarc226-Xswm645 CK qSDW.CK-4B 4B Xbarc90-Xbarc20 SDW CK qSDW.CK-6D 6D Xgwm55.5-Xgwm133.4 CK qSDW.CK-7B 7B Xbarc1116-Xbarc258 ST qSDW.ST-7A 7A Xbarc1136.4-Xgdm14.3 CK qTDW.CK-3D 3D Xbarc226-Xgwm645 CK qTDW.CK-4B 4B XDuPw270-Xgwm192.1 TDW CK qTDW.CK-6D 6D Xgwm55.5-Xgwm133.4 CK qTDW.CK-7B 7B Xbarc1116-Xbarc258 ST qTDW.ST-3A 3A Xgwm156.2-Xbarc324 ST qTDW.ST-7A 7A Xbarc1136.4-Xgdm14.3 TDWR ST/CK qTDWR-1A 1A Xgwm558.2-XGluA1 ST/CK qTDWR-3A 3A Xgwm 156.2-Xbarc324 Trait Treat-ment LOD (c) [R.sup.2] x 100 Additive (d) CK 3.4 7.9 1.2 RDW CK 4.5 11.6 -1.4 ST 3.3 19.0 0.5 CK 5.1 15.2 2.1 MRL ST 3.0 8.3 0.4 ST 4.5 16.0 -0.6 CK 3.2 13.7 7.3 CK 4.2 9.8 -6.2 SDW CK 3.7 9.4 -6.1 CK 3.4 12.1 -6.9 ST 3.6 12.4 -1.7 CK 3. 7 15.3 8.8 CK 3.1 9.3 -7.0 TDW CK 4.1 10.2 -7.2 CK 3.3 9.5 -7.0 ST 3.51 12.3 -2.1 ST 3.8 12.0 -2.0 TDWR ST/CK 3.6 13.7 0.044 ST/CK 4.3 15.0 -0.038 (a) Chr means chromosome name. (b) Markers underlined were the nearest marker to the QTL. (c) LOD means Logarithm of odds. (d) Additive effects, a positive sign means that positive allele comes from the parent Xiaoyan 54, while a negative sign means positive allele comes from the parent Jing 411.