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Article

Identification of QTLs for Salt Tolerance at the Germination and Seedling Stages in Rice

by
Walid Raafat Nakhla
1,2,
Wenqiang Sun
1,2,
Kai Fan
1,2,
Kang Yang
1,2,
Chaopu Zhang
1,2 and
Sibin Yu
1,2,*
1
National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
2
College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Plants 2021, 10(3), 428; https://doi.org/10.3390/plants10030428
Submission received: 24 January 2021 / Revised: 8 February 2021 / Accepted: 19 February 2021 / Published: 24 February 2021
(This article belongs to the Special Issue Genetic Resources and Crop Improvement)
Browse Figures
Versions Notes

Abstract

:
Rice is highly sensitive to salinity stress during the seedling establishment phase. Salt stress is widely occurring in cultivated areas and severely affects seed germination ability and seedling establishment, which may result in a complete crop failure. The objective of the present study is to identify quantitative trait loci (QTLs) related to salt tolerance of the germination and seedling stages in a rice backcross inbred line (BIL) population that was derived from a backcross of an Africa rice ACC9 as donor and indica cultivar Zhenshan97 (ZS97) as the recurrent parent. Under salt stress, ACC9 exhibited a higher germination percentage, but more repressed seedling growth than ZS97. Using the BIL population, 23 loci for germination parameters were detected at the germination stage and 46 loci were identified for several morphological and physiological parameters at the seedling stage. Among them, nine and 33 loci with the ACC9 alleles increased salt tolerance at the germination and seedling stages, respectively. Moreover, several major QTLs were found to be co-localized in the same or overlapping regions of previously reported genes for salt stress. These major loci will facilitate improving salt-tolerance rice in genome-breeding programs.

1. Introduction

The world population is estimated to increase by more than two billion in the next 30 years while agricultural areas are facing a real decrease [1]. Agriculture production is principally influenced by prevailing unfavorable environmental conditions, such as drought and salinity, which are challenging global food security. Drought and salinity are the most serious factors that affect rice production worldwide [2]. It is estimated that more than 7% of arable land areas for crop cultivation are affected by salinity due to improper irrigation and fertilizer abuse [3]. It is an emergent need to develop tolerance varieties to combat the stresses and to sustain food production. Rice is one of the staple foods for two-thirds of the world population [4]. Hence, the increase in food production, particularly rice production, is urgent to meet the demand of the growing population. To increase rice production of saline-affected areas, identification of quantitative trait loci (QTLs) associated with salt tolerance is an essential step for the improvement in varieties of salt-tolerant rice.
Salinity is defined as the accumulation of a high concentration of salt, especially NaCl as well as other soluble salt ions in either soil or water, which has a deleterious effect on plant development and growth [5]. Rice is extremely susceptible to salt stress at the germination and seedling stages [6]. Salt tolerance of rice is a quantitative character controlled by many genes and influenced by environmental factors [7]. Significant efforts and considerable advancements have been made in the detection of QTLs to understand the genetic basis of salinity tolerance of rice [7,8,9,10,11,12]. Several QTLs associated with salinity tolerance have been identified at the germination and seedling stages in various genetic populations [10,11,12]. More than 20 QTLs related to salt sensitivity, sodium (Na+) concentration, and sodium-to-potassium (K+) ratio within roots and shoots are recorded on the GRAMENE website (https://archive.gramene.org). The locus Saltol was first detected on chromosome 1 and explained 43% of the total phenotypic variation in the Na+/K+ ratio of the seedling in the IR29/Pokkali recombinant inbred line population [13]. Sixteen QTLs for salt tolerance were detected by using an F2 population developed from Cheriviruppu × Pusa Basmati 1 at both the seedling and reproductive stages [11]. Recently, genome-wide association studies (GWAS) in natural populations of Asian cultivated rice (Oryza sativa L.) have been conducted to map QTLs for salt tolerance [14,15,16,17,18,19]. Moreover, some genes associated with salt tolerance were cloned in rice. For example, the SKC1 (also known as OsHKT1;5) gene that encodes a high affinity K+ type transporter regulating K+/Na+ homeostasis has been cloned using a map-based approach [20]. The cation-chloride cotransporter gene (OsCCC1) associated with salt tolerance was identified with the role of maintaining K+ and Cl− homeostasis in rice [21].
Africa rice (Oryza glaberrima Steud) carries many genes of desirable characters that can be transferred to Asian cultivated varieties, especially those responsible for resistance to biotic and abiotic stresses [22]. However, there are a limited number of studies using Africa rice to identify salt-associated QTLs. For example, Meyer et al. (2016) performed GWAS in a panel of germplasms that comprised 93 African rice varieties and found 11 regions significantly associated with six salt-tolerance traits at the seedlings stage, highlighting the value of this unique genetic resource [23]. To explore genetic variation of African rice in salt tolerance, the main objective of this study is to use a genome-sequenced Africa rice IRGC96717 (ACC9) and a genome-sequenced indica variety Zhenshan 97 (ZS97) to discover QTLs for salinity tolerance of the germination and seedling stages for improving rice varieties in breeding programs.

2. Results

2.1. Responses of ACC9 and ZS97 to Salt Stress

At the germination stage, the seed germination parameters (GR-3d, GR-7d) of ACC9 and ZS97 were similar under control conditions (CK) (Figure 1A); however, under salt stress (18 dS m−1 NaCl), ACC9 displayed better performance in terms of germination percentage than ZS97 (Figure 1B,C). ACC9 showed a significantly lower germination index (GI) than ZS97 under CK, but an opposite pattern was observed under salt stress (Figure 1D). ACC9 had a significantly lower mean of germination time (GT) than ZS97 under salt stress (Figure 1E). The relative values of the above four traits (GR-3d, GR-7d, GI, and GT) at the germination stage between salt stress and control were calculated and referred to as sensitivity index. The sensitivity indices revealed that ACC9 was more tolerant to salt stress than ZS97 at the germination stage (Figure 1F).
At the seedling stage, salt stress repressed plant growth of both parents (Figure 2). ACC9 displayed better performance in terms of seedling growth than ZS97 (Figure 2A), but the plant growth of ACC9 was more affected by the stress of 10 dS m−1 NaCl than ZS97 (Figure 2B). The differences between the two parents concerning seedling height (SH), shoot fresh weight (SFW), shoot dry weight (SDW), fresh leaf weight (FLW) and dead leaf weight (DLW) as well as their sensitivity indices are investigated. As shown in Figure 2 and Table S1, ACC9 had significantly higher SH, SFW, and SDW than ZS97 under control. There was no significant difference in SH between the two parents under salt stress (Figure 2C). Conversely, ZS97 had significantly higher SFW and SDW than ACC9 under salt stress (Figure 2D). ACC9 revealed significantly higher FLW than ZS97 under salt-stress conditions, while no significant DLW was observed under control conditions (Figure 2E). On the other hand, ZS97 had significantly higher FLW and lower DLW than ACC9 under stress conditions (Figure 2E). The sensitivity indices of the above five seedling traits in ACC9 were significantly higher than those in ZS97 (Figure 2F), suggesting that ACC9 may be less tolerant to salt stress than ZS97 in terms of seedling growth.
Concerning the biochemical traits, ACC9 had higher Na+ content and Na+/K+ ratio (NaKR) under control conditions but lower K+ content than ZS97, while ACC9 still had higher Na+ content and NaKR, but lower contents of K+ under salt stress than ZS97 (Table S1).

2.2. Phenotype Variation in BIL Population under Salt Stress

The backcross inbred line (BIL) population exhibited wide variations in germination parameters (GR-3d, GR-7d, GI, GT) and its sensitivity indices, which showed nearly normal distributions (Figure S1). At the seedling stage, SH, SFW, SDW, DLW, Na+ content, and K+ content also showed normal distributions (Figure S2). This indicates the assayed traits as quantitative traits are governed by multiple genes. In addition, the frequency distribution of the SH sensitivity index (SHI) was continuous but with 2 peaks.
Significant correlations were observed among many assayed traits at the germination and seedling stages: positive correlations among GR-3d, GR-7d, and GI under 18 dS m−1 NaCl; negative correlation between GT and GR-3d, and between GT and GI (Figure S3). In addition, there were positive and significant correlations between SH, SFW, and SDW under salt stress (Figure S3). SFW was negatively correlated with DLW and DLSFR under salt stress. Concerning the correlation between physiological and morphological traits, Na+ content and NaKR had significantly positive correlations with DLW and DLSFR, but revealed significantly negative correlations with SFW (Figure S3). Notably, the seedling traits were not significantly correlated with the germination traits under salt stress.

2.3. QTLs Identified for Germination Traits

A total of 23 QTLs were identified for four germination traits, which distributed on 9 chromosomes (Table 1). For GR-3d, three QTLs—qGR-3d2, qGR-3d7, and qGR-3d12—were identified under control conditions, and qGR-3d12 explained the highest phenotypic variation (PVE) (9.5%) of germination percentage at 3d after seed imbibition (Table 1). For GR-7d, seven QTLs were identified, of which six loci had ACC9 alleles increasing germination percentage. Under salt stress, two QTLs were detected for the germination parameters; of them, qGR-3d12 explained the largest phenotypic variation (24.5%).
Regarding germination index (GI), no QTL was identified under control conditions. One QTL, qGI11, was identified under salt stress (Table 1), explaining 11.5% of PVE. The additive effect of ZS97 allele increased GI. For GT, two QTLs, qGT1.1 and qGT1.2, were detected under control, and two QTLs, qGT4 and qGT9, were identified under salt stress (Table 1). The QTL qGT1.1 explained 18.8% of PVE under control conditions, followed by qGT4 (15.5%) under salt stress. The additive effect of the ACC9 alleles decreased GT at both loci. For the sensitivity index, three QTLs, qGRI-3d12, qGRI-7d10, and qGTI4, were identified, with the decreased effects from the ACC9 alleles at qGRI-3d12 and qMGTI4 (Table 1).

2.4. QTLs Identified for Morphological Traits at the Seedling Stages

QTLs detected for SH, SFW, SDW, DLW, DLSFR, and their sensitivity indices are shown in Table 2. One QTL, qSH1, was identified as a major locus for seedling height under both control and salt-stress conditions, explaining 61.9% of PVE under control and 57.1% of PVE under salt-stress conditions. Concerning the sensitivity index, two QTLs, qSHI1 and qSHI7, were mapped on chromosome 1 and 7, respectively. The additive effect of the ACC9 alleles increased seedling height under control and stress conditions, and decreased its sensitivity index (Table 2).
One QTL, qSDW1, was detected for SDW and explained 28.6% of PVE under salt stress (Table 2). Three QTLs, qDLW1, qDLW8.1 and qDLW8.2, were detected for DLW under control conditions. Moreover, three QTLs, qDLW2, qDLW6 and qDLW9, were identified for DLW under salt stress. Of these, the alleles from ACC9 at all loci increased dead leaf weight. Seven QTLs, qDLWI2, qDLWI4.1, qDLWI4.2, qDLWI6.1, qDLWI6.2, qDLWI9, and qDLWI10, were detected for DLW sensitivity index (Table 2). The ACC9 alleles reduced dead leaf weight index at all detected QTLs.
Concerning QTLs for Na+ content, one QTL, qNa1, was mapped on chromosome 1 under control (Table 2). Under salt stress, two QTLs, qNa2.1 and qNa2.2, were detected on chromosome 2, respectively with 5.3% and 17.8% of PVE. The additive effect of the ACC9 alleles decreased Na+ contents under both control and stress conditions. For K+ content, qK1 and qK10 were identified under control conditions, contributing 10.3% and 13.1% of PVE, respectively. Under salt stress, three QTLs, qK2, qK6 and qK11, were detected with a range of 9.3% to 26.1% of PVE. The ACC9 alleles increased the K+ concentration of shoots. For Na+/K+ ratio, two QTLs (qNaKR1 and qNaKR11) under control conditions and two QTLs (qNaKR2.1 and qNaKR2.2) were detected under salt stress. The largest phenotypic variance (PVE = 19.1%) was explained by qNaKR11 under control, followed by qNaKR2.2 (17.8%) under salt stress conditions. The ACC9 alleles at these four detected loci decreased the Na+/K+ ratio.

2.5. Common QTLs Identified under Salt Stress

Several QTLs were identified with PVE ≥10% for the assayed traits under salt stress and colocated in the same or overlapping regions (Figure 3). For example, six QTLs, such as qSH1, qSDW1, and qSHI1, were located in a small bin (B01C254, 190 kb) on chromosome 1. Five QTLs, qDLW2, qDLSFR2, qNa+2.2, qDLWI2, and qDLSFRI2, were overlapped on the bin B02C61 in chromosome 2. Two common QTLs, qGR-3d12 and qGRI-3d12, were located on B12C6 on chromosome 12. In addition, three QTLs—each with minor effect—qDLSFR4.1, qDLWI4.1, and qDLSFRI4.1, were colocalized in B04C175, whose size is 300 kb; and four QTLs, qGR-7d4.5, qDLWI4.2, qDLSFR4.2, and qDLSFRI4.2, were mapped on the same bin B04C302 (400 kb) (Table 1 and Table 2). There are two common QTL clusters located on chromosome 6. One cluster includes three QTLs, qGR-7d6, qDLSFR6.1, and qDLWI6.1; another cluster includes four QTLs, qDLW6, qDLSFR6.2, qDLWI6.2, and qDLSFRI6. Four QTLs, qGR-7d10, qGRI-7d10, qDLSFR10, and qDLWI10 were overlapped on B10C43 on chromosome 10 (Table 1 and Table 2). These QTLs may be the genetic bases of significant correlations between GR, GI and GT at the germination stage, and among SH, SFW, SDW, DLW, DLSFR, Na+ content, K+ content and Na+/K+ ratio at the seedling stage.

3. Discussion

Previous studies have reported that the sensitivity of rice to salinity varies depending on different growth stages. Salt stress hinders germination rate, leading to inferior plant growth and reduced yield production [24]. Therefore, salinity tolerance of rice at the germination and seedling stages is an extremely important parameter in the improvement of rice productivity under salt stress, particularly in the regions where the cultivation mainly depends on direct-seeding systems [25]. Developing rice varieties of salinity tolerance at early growth stages is the utmost important step in rice breeding programs.
Our results showed that germination parameters were extremely affected by salt stress (18 dS m−1 NaCl) (Figure 1). They are consistent with previous reports [26]. Importantly, ACC9 was more tolerant to salt stress than ZS97 at the germination stage. The present results are in agreement with a previous study that salinity reduced the germination rate of ZS97. The reduction of germination rate was found to be due to a decrease in α-Amylase gene activity, which affects the bioactive GA in the germinating seeds [27]. Five major QTLs were identified, each explaining 10% and more of PVE in the germination characters in the BIL population. On the other hand, ZS97 showed salinity tolerance compared with ACC9 at the seedling stage (Figure 2). Eight major QTLs were identified for seedling growth and biomass. It is unexpected that there are no significant correlations between the germination parameters and the seedling biomass under salt-stress conditions (Figure S3). In line with this, the common loci for the germination and seedling traits were hardly identified with the BIL population under the stress conditions (Figure 3). This insignificant correlation between the germination parameters and seedling biomass under salt stress is consistent with the previous study [18]. Therefore, ZS97 would be used as an elite tolerance germplasm for the improvement in rice salt tolerance at the seedling stage and ACC9 as a tolerance source for germination ability.
Mapping of QTLs associated with salt tolerance is considered the basic step in marker-assisted breeding of salt-tolerance varieties. In the current study, the BIL population derived from the Africa rice ACC9 and indica cultivar ZS97 was used to identify the QTLs associated with salt tolerance at early growth stages. Among 23 QTLs identified at the germination stage, nine loci with ACC9 alleles increased salt tolerance; among 46 QTLs detected at the seedling stage, 33 loci with ACC9 alleles enhanced salt tolerance. Africa rice ACC9 was reported to be a suitable donor for many desirable traits such as drought tolerance and early vigor characteristics [28,29]. Moreover, several major QTLs (with PVE ≥ 10%) for different traits were found to be colocalized in the same or overlapping regions (Figure 3), indicating there is the same gene or linked genes having a pleiotropic effect on these multiple traits. For example, three loci (qSH1, qSDW1 and qSHI1) colocalized in B01C254; five loci (qDLW2, qNa+2.2, qDLSFR2, qDLWI2, and qDLSFRI2) overlapped in B02C61. These colocalized QTLs for various traits could be very beneficial for a rice breeding program via marker-assisted selection to improve salt tolerance.
Some loci in the current study were identified in small bins (or regions) in the BIL population and localized within or near previously reported genes or loci associated with salt tolerance in rice (Figure 3; Table 1 and Table 2), which allowed naming of candidate genes at the given bin using the available gene annotation database (http://rice.plantbiology.msu.edu/). For example, six major loci colocalized in B01C254 within a 190-kb size encompassing two known genes: the green revolution gene sd1 [30] and SNAC6 [31]. As qSH1 explained the highest phenotypic variation in seedling height under salt stresses, the gene sd1 may be the most likely candidate for qSH1. qNaKR2.1 was found near OsPgk2 (Figure 3), which was extremely induced by salt stress in a salt-tolerance variety Pokkali [32]; qGT4 was found in proximity to OsBADH1, which could enhance rice adaptability to salt stress [33]. In addition, qDLW6 for dead leaf weight was located within the same region of previously identified qDSW6.2 [34]. The qDLW6 region harbors a candidate gene OsSIDP366, which was reported to be a positive regulator responding to drought and salt stress in rice [35]. qGR-3d12 and GRI-3d12 are located in B12C6 (Figure 3), where CYP94C2b (LOC_Os12g05440) was reported to enhance salt tolerance of rice [36]. Thus it may be a possible candidate gene. Among these loci of interest, three loci (qSHI1, qGT4 and qGRI-3d12) with salt tolerance alleles from ACC9 could be interpreted as promising ones for the improvement in salt tolerance of Asian cultivated rice. These data indicate that the identified QTLs with possible candidate genes could be useful for developing varieties of salinity tolerance at early seedling stages in rice breeding programs.

4. Materials and Methods

4.1. Plant Materials

The population used at the present study is a backcross inbred line (BIL) produced by single-seed descend method from a backcross between an Africa landrace IRGC96717 (ACC9) as donor and an indica cultivar Zhenshan 97 (ZS97) as recurrent parent. The first backcross (BC1F1) had been self-crossed for 11 generations (BC1F11) (Figure S4). The BIL population was grown at the experimental field of Huazhong Agricultural University in 2018 from May to September at Wuhan (30.48° N, 114.2° E), China. The seeds of each line were harvested at 35 days after heading and equilibrated in a storage chamber with low relative humidity (18% RH) for further analyses.

4.2. Germination Evaluation of the Parents and BIL Population

Germination tests under salt conditions were conducted using 50 healthy seeds per sample in a petri plate containing three layers of filter paper moistened with 15 mL of 18 dS m−1 NaCl solutions [26,37]. As a control, a germination test was carried out using 15 mL distilled water instead of the saline solution. Germination tests under both the saline and control conditions were kept in a growth chamber with 12 h light/12 h dark circle, 25 ± 2 °C, and 60% RH. The germinated seeds were recorded every 24 h for seven days. The germination experiment was repeated three times for each sample of the parents and BILs. Germination rate (GR), germination index (GI) and mean germination time (GT) were calculated as described previously [34].

4.3. Evaluation of Seedling Performance of the Parents and BIL Population

Thirty uniformly germinated seeds were transferred to planting plates filled with moist soil. The seedlings at 14d after seed sowing were then transplanted into hydroponics solution [38] and grown in a greenhouse, where room temperature ranged from 28 to 30 °C during the day and 18 to 23 °C during the night. Relative humidity was set at 85%, and light set at 671 µmol·m−2·S−1 with a minimum of 100 and a maximum 1400 µmol·m−2·s−1 during the day [25].
For evaluation of salt tolerance, 14 day-old seedlings were treated two weeks with salinized nutrient solution with the level of NaCl (10 dS m−1) for BIL lines. The nutrient solutions were replenished every 3d. The pH range was fixed from 5.00 to 5.50. Seedling performances were recorded after 14d of NaCl application. Five traits, seedling height (SH), shoot fresh weight (SFW), shoot dry weight (SDW), dead leaf weight (DLW) and dead leaf to shoot fresh weight ratio (DLSFR), were determined as described previously [34]. All collected samples were dried immediately at 70 °C for 72 h using a forced-air oven [25]. The relative values of the aforementioned traits (the sensitivity index) assayed at the seedling stage between the salt stress and control were calculated according to the formula [39] (Equation (1)):
SI = ( control   value − treatment ) /   control   value   × 100
Na+ and K+ contents in dry shoot samples were measured according to the method reported previously [40]. Approximately 1 g of each sample was put into a 50 mL centrifuge tube with 20 mL of 1N HCL for digestion and kept the sample in a water bath at 85 °C for 30 min with shaking manually each 10 m interval. The homogenized samples were filtered using Whatman papers and kept at room temperature overnight. Na+ and K+ contents were measured using a FP6431 flame photometer (Shanghai Instrument, China) as reported previously [40].

4.4. Linkage Map Construction and QTL Analysis

The CTAB method was applied to extract DNA from young seedling leaves [41]. A genotyping-by-sequencing strategy was used to analyze the genotypes of BIL population with a minor modification [42]. Briefly, two restriction enzymes, EcoRI and MspI, were used to digest the genome DNA into fragments. Single-nucleotide polymorphism (SNP) calling and real site frequency spectrum (realSFS) were performed based on estimation of site frequency. A Practical Extraction and Report Language (PERL) was used for filtration SNPs as well as indels taking into consideration of the following criteria: the missing data greater than 50% and allele frequency less than 5% in the population. According to SNP genotyping, 714 bin markers covered the rice genome were used for constructing molecular linkage map. Bin name and order on different chromosomes of ACC9/ZS97 population are presented in the Supplemental Table S2. IciMapping software with inclusive composite interval mapping method was used for QTL analysis [43]. The existence of a QTL with an additive effect was declared by the logarithm of odds (LOD) threshold of 2.5. Correlations of traits, mean and standard error were implemented using the software SPSS version 23 (SPSS Inc., Chicago, IL, USA).

5. Conclusions

Salinity is one of the most important abiotic stresses that influence the global production of rice. Salt tolerance is a quantitative character that depends upon the cumulative action of many genes. In this study, germination parameters and seedling growth were used to evaluate salt tolerance of the BILs derived from the Africa rice ACC9 and indica cultivar ZS97. A total of 23 and 46 QTLs were identified for the assayed traits associated with salt tolerance at the germination and seedling stages, respectively. At the germination stage, three major QTLs, qGT4, qGR-3d12, and qGRI-3d12, explained more than 15% of the phenotypic variance on corresponding traits with ACC9 alleles enhancing germination parameters under salt stress. At the seedling stage, major common QTL regions (e.g., qSH1, qSDW1, and qSHI1) were identified for multiple traits, with the ACC9 alleles showing increased seedling growth under salt stress. In addition, at least three loci were identified in small bins (or regions) and localized in or nearby previously reported genes associated with salt stress. These data of the promising loci with a possible candidate gene will be useful for improving rice with salt tolerance through the genomic breeding approaches.

Supplementary Materials

The following are available online at https://www.mdpi.com/2223-7747/10/3/428/s1, Figure S1: Frequency distribution of germination parameters in the BIL population derived from ACC9 x ZS97 under salt stress with 18 dS m−1 NaCl. Figure S2: Frequency distribution of seedling traits in the BIL population derived from ACC9 x ZS97 under 10 dS m−1 NaCl salt stress. Figure S3: Correlation of morpho–physiological traits examined in the BIL population derived from ACC9 x ZS97 under salt stress at the germination and seedling stages. Figure S4: Construction procedure of the ACC9/ZS97 BIL population. Table S1: Morphological and physiological traits of ACC9 and ZS97 and BILs under control and salt stress conditions. Table S2: Bin information of ACC9/ZS97 BIL population.

Author Contributions

Conceptualization, S.Y.; methodology, W.S.; formal analysis, K.Y. and W.R.N.; investigation, K.Y., W.R.N., and K.F.; resources, K.Y. and C.Z.; data curation, W.R.N. and W.S.; writing—original draft preparation, W.R.N.; writing—review and editing, W.R.N. and S.Y.; funding acquisition, S.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by grants from the National Natural Science Foundation of China (31971864, 31671656), Special Major Projects for Technological Innovation (2019ABA104) and the Earmarked Fund for the China Agriculture Research System of China (CARS-01-01).

Data Availability Statement

Data is contained within the article and supplementary material.

Acknowledgments

We would like to thank Hani Shalabi, He Hanzi, and Hesham Agrama for reviewing and editing a previous version of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Mohanty, S.; Wassmann, R.; Nelson, A.; Moya, P.; Jagadish, S. Rice and climate change: Significance for food security and vulnerability. Int. Rice Res. Inst. 2013, 14, 1–14. [Google Scholar]
  2. Dahanayaka, B.; Gimhani, D.; Kottearachchi, N.; Samarasighe, W. QTL mapping for salinity tolerance using an elite rice (Oryza sativa) breeding population. SABRAO J. Breed. Genet. 2017, 49, 123–134. [Google Scholar]
  3. Szabolcs, I. Soils and salinization. In Handbook of Plant and Crop Stress; Pessarakali, M., Ed.; Marcel Dekker: New York, NY, USA, 1994; pp. 3–11. [Google Scholar]
  4. Zeigler, R.S.; Barclay, A. The relevance of rice. Rice 2008, 1, 3–10. [Google Scholar] [CrossRef] [Green Version]
  5. Munns, R. Genes and salt tolerance: Bringing them together. New Phytol. 2005, 167, 645–663. [Google Scholar] [CrossRef] [PubMed]
  6. Heenan, D.; Lewin, L.; McCaffery, D. Salinity tolerance in rice varieties at different growth stages. Aust. J. Exp. Agric. 1988, 28, 343–349. [Google Scholar] [CrossRef]
  7. Roy, S.J.; Negrão, S.; Tester, M. Salt resistant crop plants. Curr. Opin. Biotechnol. 2014, 26, 115–124. [Google Scholar] [CrossRef]
  8. Rahman, M.A.; Bimpong, I.K.; Bizimana, J.B.; Pascual, E.D.; Arceta, M.; Swamy, B.P.M.; Diaw, F.; Rahman, M.S.; Singh, R.K. Mapping QTLs using a novel source of salinity tolerance from Hasawi and their interaction with environments in rice. Rice 2017, 10, 47. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Rahman, M.A.; Thomson, M.J.; De Ocampo, M.; Egdane, J.A.; Salam, M.A.; Shah-E-Alam, M.; Ismail, A.M. Assessing trait contribution and mapping novel QTL for salinity tolerance using the Bangladeshi rice landrace Capsule. Rice 2019, 12, 63. [Google Scholar] [CrossRef] [Green Version]
  10. Singh, R.; Flowers, T. The Physiology and molecular biology of the effect of salinity on rice. In Handbook of Plant and Crop Stress; Taylor and Francis: Boca Raton, FL, USA, 2010; pp. 910–942. [Google Scholar]
  11. Hossain, H.; Rahman, M.A.; Alam, M.S.; Singh, R.K. Mapping of quantitative trait loci associated with reproductive-stage salt tolerance in rice. J. Agron. Crop Sci. 2015, 201, 17–31. [Google Scholar] [CrossRef]
  12. Ghomi, K.; Rabiei, B.; Sabouri, H.; Sabouri, A. Mapping QTLs for traits related to salinity tolerance at seedling stage of rice (Oryza sativa L.): An agrigenomics study of an Iranian rice population. OMICS J. Integr. Biol. 2013, 17, 242–251. [Google Scholar] [CrossRef]
  13. Bonilla, P.; Dvorak, J.; Mackell, D.; Deal, K.; Gregorio, G. RFLP and SSLP mapping of salinity tolerance genes in chromosome 1 of rice (Oryza sativa L.) using recombinant inbred lines. Philipp. Agric. Sci. 2002, 85, 68–72. [Google Scholar]
  14. Batayeva, D.; Labaco, B.; Ye, C.; Li, X.; Usenbekov, B.; Rysbekova, A.; Dyuskalieva, G.; Vergara, G.; Reinke, R.; Leung, H. Genome-wide association study of seedling stage salinity tolerance in temperate japonica rice germplasm. BMC Genet. 2018, 19, 2. [Google Scholar] [CrossRef] [Green Version]
  15. Lekklar, C.; Pongpanich, M.; Suriya-arunroj, D.; Chinpongpanich, A.; Tsai, H.; Comai, L.; Chadchawan, S.; Buaboocha, T. Genome-wide association study for salinity tolerance at the flowering stage in a panel of rice accessions from Thailand. BMC Genom. 2019, 20, 76. [Google Scholar] [CrossRef] [Green Version]
  16. Liu, C.; Chen, K.; Zhao, X.; Wang, X.; Shen, C.; Zhu, Y.; Dai, M.; Qiu, X.; Yang, R.; Xing, D.; et al. Identification of genes for salt tolerance and yield-related traits in rice plants grown hydroponically and under saline field conditions by genome-wide association study. Rice 2019, 12, 88. [Google Scholar] [CrossRef]
  17. Rohila, J.S.; Edwards, J.D.; Tran, G.D.; Jackson, A.K.; McClung, A.M. Identification of superior alleles for seedling stage salt tolerance in the USDA rice mini-core collection. Plants 2019, 8, 472. [Google Scholar] [CrossRef] [Green Version]
  18. Naveed, S.A.; Zhang, F.; Zhang, J.; Zheng, T.-Q.; Meng, L.-J.; Pang, Y.-L.; Xu, J.-L.; Li, Z.-K. Identification of QTN and candidate genes for salinity tolerance at the germination and seedling stages in rice by genome-wide association analyses. Sci. Rep. 2018, 8, 6505. [Google Scholar] [CrossRef] [Green Version]
  19. Yu, J.; Zhao, W.; Tong, W.; He, Q.; Yoon, M.Y.; Li, F.P.; Choi, B.; Heo, E.B.; Kim, K.W.; Park, Y.J. A genome-wide association study reveals candidate genes related to salt rolerance in rice (Oryza sativa) at the germination stage. Int. J. Mol. Sci. 2018, 19, 3145. [Google Scholar] [CrossRef] [Green Version]
  20. Ren, Z.H.; Gao, J.P.; Li, L.G.; Cai, X.L.; Huang, W.; Chao, D.Y.; Zhu, M.Z.; Wang, Z.Y.; Luan, S.; Lin, H.X. A rice quantitative trait locus for salt tolerance encodes a sodium transporter. Nat. Genet. 2005, 37, 1141–1146. [Google Scholar] [CrossRef]
  21. Kong, X.Q.; Gao, X.H.; Sun, W.; An, J.; Zhao, Y.X.; Zhang, H. Cloning and functional characterization of a cation–chloride cotransporter gene OsCCC1. Plant Mol. Biol. 2011, 75, 567–578. [Google Scholar] [CrossRef]
  22. Brar, D.S.; Khush, G.S. Alien introgression in rice. In Oryza: From Molecule to Plant; Sasaki, T., Moore, G., Eds.; Springer: Dordrecht, The Netherlands, 1997; pp. 35–47. [Google Scholar]
  23. Meyer, R.S.; Choi, J.Y.; Sanches, M.; Plessis, A.; Flowers, J.M.; Amas, J.; Dorph, K.; Barretto, A.; Gross, B.; Fuller, D.Q.; et al. Domestication history and geographical adaptation inferred from a SNP map of African rice. Nat. Genet. 2016, 48, 1083–1088. [Google Scholar] [CrossRef]
  24. Ashraf, M.; Foolad, M.R. Pre-sowing seed treatment—A shotgun approach to improve germination, plant growth, and crop yield under saline and non-saline conditions. Adv. Agron. 2005, 88, 223–271. [Google Scholar]
  25. Zeng, L.; Shannon, M.C.; Grieve, C.M. Evaluation of salt tolerance in rice genotypes by multiple agronomic parameters. Euphytica 2002, 127, 235–245. [Google Scholar] [CrossRef]
  26. Shi, Y.; Gao, L.; Wu, Z.; Zhang, X.; Wang, M.; Zhang, C.; Zhang, F.; Zhou, Y.; Li, Z. Genome-wide association study of salt tolerance at the seed germination stage in rice. BMC Plant Biol. 2017, 17, 92. [Google Scholar] [CrossRef] [Green Version]
  27. Liu, L.; Xia, W.; Li, H.; Zeng, H.; Wei, B.; Han, S.; Yin, C. Salinity inhibits rice seed germination by reducing α-amylase activity via decreased bioactive gibberellin content. Front. Plant Sci. 2018, 9. [Google Scholar] [CrossRef] [PubMed]
  28. Ali, M.L.; Sanchez, P.L.; Yu, S.-B.; Lorieux, M.; Eizenga, G.C. Chromosome segment substitution lines: A powerful tool for the introgression of valuable genes from Oryza wild species into cultivated rice (O. sativa). Rice 2010, 3, 218–234. [Google Scholar] [CrossRef] [Green Version]
  29. Ndjiondjop, M.N.; Manneh, B.; Cissoko, M.; Drame, N.K.; Kakai, R.G.; Bocco, R.; Baimey, H.; Wopereis, M. Drought resistance in an interspecific backcross population of rice (Oryza spp.) derived from the cross WAB56-104 (O. sativa) × CG14 (O. glaberrima). Plant Sci. 2010, 179, 364–373. [Google Scholar] [CrossRef]
  30. Spielmeyer, W.; Ellis, M.H.; Chandler, P.M. Semidwarf (sd-1), “green revolution” rice, contains a defective gibberellin 20-oxidase gene. Proc. Natl. Acad. Sci. USA 2002, 99, 9043–9048. [Google Scholar] [CrossRef] [Green Version]
  31. Nakashima, K.; Tran, L.P.; Nguyen, D.V.; Fujita, M.; Maruyama, K.; Todaka, D.; Ito, Y.; Hayashi, N.; Shinozaki, K.; Yamaguchi-Shinozaki, K. Functional analysis of a NAC-type transcription factor OsNAC6 involved in abiotic and biotic stress-responsive gene expression in rice. Plant J. 2007, 51, 617–630. [Google Scholar] [CrossRef]
  32. Joshi, R.; Karan, R.; Singla-Pareek, S.L.; Pareek, A. Ectopic expression of Pokkali phosphoglycerate kinase-2 (OsPGK2-P) improves yield in tobacco plants under salinity stress. Plant Cell Rep. 2016, 35, 27–41. [Google Scholar] [CrossRef]
  33. Hasthanasombut, S.; Supaibulwatana, K.; Mii, M.; Nakamura, I. Genetic manipulation of Japonica rice using the OsBADH1 gene from Indica rice to improve salinity tolerance. Plant Cell Tissue Organ Cult. 2011, 104, 79–89. [Google Scholar] [CrossRef]
  34. Wang, Z.; Cheng, J.; Chen, Z.; Huang, J.; Bao, Y.; Wang, J.; Zhang, H. Identification of QTLs with main, epistatic and QTL × environment interaction effects for salt tolerance in rice seedlings under different salinity conditions. Theor. Appl. Genet. 2012, 125, 807–815. [Google Scholar] [CrossRef]
  35. Guo, C.; Luo, C.; Guo, L.; Li, M.; Guo, X.; Zhang, Y.; Wang, L.; Chen, L. OsSIDP366, a DUF1644 gene, positively regulates responses to drought and salt stresses in rice. J. Integr. Plant Biol. 2016, 58, 492–502. [Google Scholar] [CrossRef]
  36. Kurotani, K.I.; Hayashi, K.; Hatanaka, S.; Toda, Y.; Ogawa, D.; Ichikawa, H.; Ishimaru, Y.; Tashita, R.; Suzuki, T.; Ueda, M.; et al. Elevated levels of CYP94 family gene expression alleviate the jasmonate response and enhance salt tolerance in rice. Plant Cell Physiol. 2015, 56, 779–789. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Wang, Z.F.; Wang, J.F.; Bao, Y.M.; Wu, Y.Y.; Su, X.; Zhang, H.S. Inheritance of rice seed germination ability under salt stress. Rice Sci. 2010, 17, 105–110. [Google Scholar] [CrossRef]
  38. Yoshida, S.; Forno, D.A.; Cock, J.H. Laboratory Manual for Physiological Studies of Rice, 3rd ed.; IRRI: Los Baños, Laguna, Philippines, 1971; p. 61. [Google Scholar]
  39. Fischer, R.; Maurer, R. Drought resistance in spring wheat cultivars. I. grain yield responses. Aust. J. Agric. Res. 1978, 29, 897–912. [Google Scholar] [CrossRef]
  40. Ferdose, J.; Kawasaki, M.; Taniguchi, M.; Miyake, H. Differential sensitivity of rice cultivars to salinity and its relation to ion accumulation and root tip structure. Plant Prod. Sci. 2009, 12, 453–461. [Google Scholar] [CrossRef]
  41. Murray, M.; Thompson, W.F. Rapid isolation of high molecular weight plant DNA. Nucleic Acids Res. 1980, 8, 4321–4326. [Google Scholar] [CrossRef] [Green Version]
  42. Elshire, R.J.; Glaubitz, J.C.; Sun, Q.; Poland, J.A.; Kawamoto, K.; Buckler, E.S.; Mitchell, S.E. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE. 2011, 6, e19379. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Li, H.; Ribaut, J.M.; Li, Z.; Wang, J. Inclusive composite interval mapping (ICIM) for digenic epistasis of quantitative traits in biparental populations. Theor. Appl. Genet. 2008, 116, 243–260. [Google Scholar] [CrossRef]
Plants 10 00428 g001 550
Figure 1. Seed germination parameters of the parental lines, ACC9 and ZS97, under control (CK) and salt treatment (S). Morphological differences of seed germination between ACC9 and ZS97 under control CK (A) and salt stress (B); (C) seed germination percentage at 3d and 7d after imbibition, (D) germination index (GI), (E) mean germination time (MGT), (F) sensitivity index. The error bars represent the means and standard errors of three replications. Asterisks (*, **) indicate significant differences according to Student’s t-test at p < 0.05 and 0.01, respectively. Scale bar = 2 cm in (A,B).
Figure 1. Seed germination parameters of the parental lines, ACC9 and ZS97, under control (CK) and salt treatment (S). Morphological differences of seed germination between ACC9 and ZS97 under control CK (A) and salt stress (B); (C) seed germination percentage at 3d and 7d after imbibition, (D) germination index (GI), (E) mean germination time (MGT), (F) sensitivity index. The error bars represent the means and standard errors of three replications. Asterisks (*, **) indicate significant differences according to Student’s t-test at p < 0.05 and 0.01, respectively. Scale bar = 2 cm in (A,B).
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Figure 2. Seedling growth and biomass at 14 d after salt treatment with 10 dS m−1. Morphological differences of seedlings between ACC9 and ZS97 under control CK (A) and salt stress (B); (C) seedling height (SH); (D) shoot fresh (SFW) and dry weight (SDW); (E) fresh leaf weight (FLW) and dead leaf weight (DLW); (F), sensitivity index for SH, SFW, SDW, FLW, and DLW under stress. Values are the means and standard errors of two replications. Asterisks (**) represent significant differences according to Student’s t-test at p < 0.01, respectively. Scale bar = 10 cm in (A,B).
Figure 2. Seedling growth and biomass at 14 d after salt treatment with 10 dS m−1. Morphological differences of seedlings between ACC9 and ZS97 under control CK (A) and salt stress (B); (C) seedling height (SH); (D) shoot fresh (SFW) and dry weight (SDW); (E) fresh leaf weight (FLW) and dead leaf weight (DLW); (F), sensitivity index for SH, SFW, SDW, FLW, and DLW under stress. Values are the means and standard errors of two replications. Asterisks (**) represent significant differences according to Student’s t-test at p < 0.01, respectively. Scale bar = 10 cm in (A,B).
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Plants 10 00428 g003 550
Figure 3. Locations of the common QTLs identified for germination and seedling traits in the backcross inbred line (BIL) population derived from ACC9 and ZS97. Some reported genes for salt tolerance are indicated. The trait abbreviations are explained in Table 2.
Figure 3. Locations of the common QTLs identified for germination and seedling traits in the backcross inbred line (BIL) population derived from ACC9 and ZS97. Some reported genes for salt tolerance are indicated. The trait abbreviations are explained in Table 2.
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Table
Table 1. Putative QTLs identified for salinity tolerance in the BIL population at the germination stage.
Table 1. Putative QTLs identified for salinity tolerance in the BIL population at the germination stage.
TreatTraits aQTLChrBin bLeft Marker
(Mb)		Right Marker
(Mb)		Bin Size
(Mb)		Add cLOD dPVE (%) e
ControlGR-3dqGR-3d22B02C464.265.431.17−0.135.66.4
qGR-3d77B07C2116.2722.846.56−0.052.71.9
qGR-3d1212B12C72.986.623.65−0.1412.99.5
GR-7dqGR-7d4.14B04C6710.7211.070.34−0.093.65.8
qGR-7d4.24B04C13416.3316.410.08−0.104.15.6
qGR-7d4.34B04C25417.1417.170.03−0.094.35.7
qGR-7d4.44B04C34818.7318.830.10−0.079.15.1
qGR-7d1010B10C430.6113.8913.28−0.107.35.8
qGR-7d1111B11C92.297.395.10−0.092.85.2
qGR-7d1212B12C4519.5420.380.830.055.22.6
GTqGT1.11B01C10511.9311.960.030.215.418.8
qGT1.21B01C23633.4733.590.120.213.013.1
StressGR-3dqGR-3d44B04C37224.6827.202.510.092.76.0
qGR-3d1212B12C62.986.623.650.207.324.5
GR-7dqGR-7d4.54B04C30218.2718.670.40−0.223.35.1
qGR-7d66B06C464.625.200.58−0.257.55.1
qGR-7d1010B10C430.6113.8913.28−0.223.35.1
GIqGI1111B11C5519.0219.100.08−1.952.611.5
GTqGT44B04C35319.4619.970.51−0.346.115.5
qGT99B09C1808.118.130.02−0.244.110.8
IndexGRI-3dqGRI-3d1212B12C62.986.623.65−12.283.311.5
GRI-7dqGRI-7d1010B10C430.6113.8913.287.093.84.9
GTIqGTI44B04C18316.4116.720.31−19.003.39.0
Putative QTLs at germination stage: a, GR-3d and GR-7d, germinated seed percentage at 3rd and 7th day after imbibition, respectively; GI germination index; GT mean germination time; GRI-3d, GRI-7d, GII and GTI represent the sensitivity index of GR-3d, GR-7d, GI, and GT, respectively; b, location of quantitative trait loci (QTLs) on chromosome; c, additive effect of the ACC9 allele; the negative value indicating the ACC9 allele decreased the trait; d, logarithm of odds; and e, the phenotypic variance explained by each QTL.
Table
Table 2. Putative QTLs identified for salinity tolerance in the BIL population at the seedling stage.
Table 2. Putative QTLs identified for salinity tolerance in the BIL population at the seedling stage.
TreatsTraits aQTLChrBin bLeft
Marker		Right
Marker		Bin SizeAdd cLOD dPVE (%) e
ControlSHqSH11B01C25436.0936.280.197.7821.461.9
SFWqSFW1.11B01C25436.0936.280.190.1711.734.8
qSFW44B04C32218.738.830.100.093.07.2
DLWqDLW11B01C25436.0936.280.190.016.511.5
qDLW8.18B08C301.942.200.26−0.018.816.4
qDLW8.28B08C406.176.540.370.014.27.0
Na+qNa 11B01C27338.5038.710.210.403.13.6
K+qK11B01C737.738.260.531.343.110.3
qK1010B10C430.6113.8913.281.613.813.1
Na+/K+qNaKR11B01C26937.8137.950.13−0.023.19.8
qNaKR1111B11C9225.7525.860.11−0.025.819.1
StressSHqSH11B0C25436.0936.280.195.0818.457.1
SFWqSFW1.21B01C23634.4935.501.010.063.215.2
SDWqSDW11B01C25436.0936.280.190.027.328.6
DLWqDLW22B02C614.265.431.170.023.510.0
qDLW66B06C16125.3227.642.320.024.410.2
qDLW99B09C280.660.680.010.012.62.0
DLSFRqDLSFR22B02C614.265.431.170.086.34.3
qDLSFR4.14B04C17516.4116.720.310.093.34.1
qDLSFR4.24B04C30218.2718.670.400.094.04.0
qDLSFR6.16B06C494.625.200.580.093.53.3
qDLSFR6.26B06C13825.3227.642.320.095.74.2
qDLSFR99B09C902.402.530.130.032.70.8
qDLSFR1010B10C430.6113.8913.280.095.04.2
Na+qNa2.12B02C224.154.260.11−4.373.25.3
qNa2.22B02C614.265.431.17−6.803.217.8
K+qK22B02C18019.8020.150.351.958.026.1
qK66B06C00.110.150.041.153.49.3
qK1111B11C9225.752.590.111.233.49.6
Na+/K+qNaKR2.12B02C202.823.270.44−0.234.210.4
qNaKR2.22B02C1105.435.560.13−0.236.417.4
IndexSHIqSHI11B01C25436.0936.280.19−3.426.116.4
qSHI77B07C32.488.215.73−3.103.08.0
DLWIqDLWI22B02C614.265.431.17−0.945.42.8
qDLWI4.14B04C17516.4116.720.31−0.944.92.7
qDLWI4.24B04C30218.2718.670.40−0.966.74.0
qDLWI6.16B06C484.625.200.58−0.992.82.6
qDLWI6.26B06C14325.3227.642.32−0.955.12.7
qDLWI99B09C1053.253.300.05−0.373.20.6
qDLWI1010B10C430.6113.8913.28−0.955.42.8
DLSFRIqDLSFRI22B02C614.265.431.17−2.446.05.3
qDLSFRI4.14B04C17516.4116.720.31−2.443.95.2
qDLSFRI4.24B04C30218.2718.670.40−2.487.35.1
qDLSFRI66B06C14025.3227.642.32−2.485.15.3
qDLSFRI99B09C1002.712.970.26−1.033.21.2
qDLSFRI1010B10C430.6113.8913.28−2.474.35.2
Putative QTLs at seedling stage: a, SH, seedling height; SFW, shoot fresh weight; SDW, shoot dry weight; DLW, dead leaf weight; DLSFR, dead leaf to shoot fresh weight ratio; Na+, Na+ content in shoot; K+, K+ content in shoot; SHI, DLWI, and DLSFRI represent the sensitivity index of SH, DLW, and DLSFR, respectively; b, location of QTLs on chromosome; c, additive effect of the ACC9 allele; d, logarithm of odds; and e, the phenotypic variance explained by each QTL.
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Nakhla, W.R.; Sun, W.; Fan, K.; Yang, K.; Zhang, C.; Yu, S. Identification of QTLs for Salt Tolerance at the Germination and Seedling Stages in Rice. Plants 2021, 10, 428. https://doi.org/10.3390/plants10030428

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Nakhla WR, Sun W, Fan K, Yang K, Zhang C, Yu S. Identification of QTLs for Salt Tolerance at the Germination and Seedling Stages in Rice. Plants. 2021; 10(3):428. https://doi.org/10.3390/plants10030428

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Nakhla, Walid Raafat, Wenqiang Sun, Kai Fan, Kang Yang, Chaopu Zhang, and Sibin Yu. 2021. "Identification of QTLs for Salt Tolerance at the Germination and Seedling Stages in Rice" Plants 10, no. 3: 428. https://doi.org/10.3390/plants10030428

APA Style

Nakhla, W. R., Sun, W., Fan, K., Yang, K., Zhang, C., & Yu, S. (2021). Identification of QTLs for Salt Tolerance at the Germination and Seedling Stages in Rice. Plants, 10(3), 428. https://doi.org/10.3390/plants10030428

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