1. Introduction
Rice (Oryza sativa L.) is a staple food consumed by more than half of the world’s population, especially people in East and Southeast Asia [1]. Salinity is a major abiotic stress factor that severely affects the global rice production, especially in coastal and low-lying areas where soil salinity is widespread [2]. The areas affected by salinity will increase continuously, mainly due to climate change [3]. Rice is considered a salt-sensitive species (also known as glycophyte) and is the cereal species most sensitive to salt stress [4]. It is particularly sensitive in the early seedling and reproductive stages [2]. Salt stress inhibits photosynthesis and growth, leading to biomass losses and partial sterility, which ultimately results in rice yield losses [5]. Hence, salinity tolerance of rice is critical for ensuring food security and maintaining agricultural productivity in regions affected by salt stress.
Rice has shown considerable genetic variation in its ability to tolerate salt stress [6]. Understanding the genetic basis of salt tolerance in rice is essential for targeted breeding programs aimed at developing high-yielding, salt-tolerant varieties. Salt tolerance is known to be a complex trait controlled by multiple genes (quantitative trait loci—QTL) [7,8]. A limited number of rice varieties, such as Pokkali and Nona bokra, have been used as donors in breeding programs and in the mapping of salt-tolerant QTLs because of their ability to provide more salt tolerance [9]. Currently, hundreds of QTLs have been reported to be associated with salt tolerance at different growth stages [10]. However, the majority of QTLs identified were found to be marginally effective [11]. Saltol, located on chromosome 1, has been identified as a major QTL explaining 43–70% of the phenotypic variation associated with salt tolerance at the seedling stage [12,13]. SKC1 (OsHKT1:5), which encodes the HKT-type transporter, was cloned within the Saltol QTL region using a map-based cloning method [13]. This gene has been reported to play an important role in Na+/K+ homeostasis under salt stress [13]. The Saltol QTL has been transferred into several rice cultivars to improve salt tolerance at the seedling stage [14]. However, the application of this QTL in salt tolerance improvement had been reported to be limited in some genetic backgrounds [15]. Therefore, different QTLs from various donors are required to be applied in rice breeding programs for sustainable salt tolerance in rice.
With the advancement and feasibility of the next-generation sequencing (NGS), QTL-seq has emerged as a powerful genomic tool offering high-resolution and cost-effective solutions for dissecting the genetic basis of complex traits in plants [16]. This approach integrates NGS technologies with bulk-segregant analysis (BSA) in analyzing allelic variations between contrasting bulks [16]. QTL-seq has been widely applied in crop breeding programs, including rice, to identify QTLs for various agronomic traits, i.e., yield [17], quality [18], disease resistance [19,20], and abiotic stress tolerance [21,22]. Different types of segregating population, e.g., F2, F2:3, near-isogenic lines (NILs), and recombinant inbred lines (RILs) have been applied to identify QTLs for traits of interest using the QTL-seq approaches [23]. However, the use of chromosome segment substitution line (CSSL)-derived population in QTL-seq analysis has rarely been reported in rice.
Chromosome segment substitution lines (CSSLs) have been a crucial tool in rice genetics and breeding, offering insights into the genetic control of various genomic traits [24]. A population of 135 CSSLs with the KDML105 rice genetic background has been developed using the donors IR68586-F2-CA-31 (DH103) and IR68586-F2-CA-143 (DH212), which are double haploids of the cross of CT9993 × IR62266 [25]. These CSSLs were originally reported as carry substituted regions associated with drought tolerance; however, the chromosome substitutions in some of these CSSLs were also found to be associated with salt tolerance. Recently, a few lines of this CSSL population that are salt and drought tolerant have been characterized, and candidate genes have been proposed [26].
In this study, we sought to identify genomic regions associated with salt tolerance at the seedling stage of rice using the QTL-seq approach with an F2:3 population derived from a cross of KDML105 and one of its CSSL lines. The results of our study shed light on a QTL for salt tolerance that can be used in breeding programs for salt tolerance. In addition, several novel and known genes from this QTL have been proposed as candidate genes for salt tolerance at seedling stage of rice.
2. Materials and Methods
2.1. Plant Materials and Growth Conditions
A set of 454 F2:3 from a cross of KDML105 and KD-CSSL106 was used to evaluate salinity tolerance (150 mM NaCl) at the seedling stage (Figure S1). KDML105 was used as a salt-sensitive parent, and KD-CSSL106 was used as a salt-tolerant parent. KDML105 is a high-value aromatic rice variety from Thailand but is sensitive to various stress factors such as salt. KD-CSSL106 is a near-isogenic line (NIL, BC5F5) derived from a cross between KDML105 and IR68586-F2-CA-31 (DH103), with KDML105 serving as the recurrent parent (Figure S1). KD-CSSL106 exhibits moderate tolerance to salt stress at seedling stage. The experiment was conducted in a greenhouse at the Rice Science Center, Kasetsart University, Kamphaeng Saen, Nakhon Pathom, Thailand. For each F2:3 family, a total of 15 seeds were germinated at room temperature for 5 days and then transplanted into clay soil from a rice field and placed in a plastic tray with 200 wells (52 cm × 26 cm × 4 cm). For each F2:3 family, the plants were planted in three rows with five plants per row. The seedling trays were placed in a cement tank (900 cm × 900 cm × 50 cm) containing a nutrient solution (1:100) according to the instructions of Bangsai Agricultural Center Co. Ltd., Bangkok, Thailand. The details of the solution were described in the study by Phosuwan et al. [27]. The seedlings were allowed to grow for 21 days before being used for the salt stress experiment.
2.2. Salt Stress Experiment and Evaluation of Salt Tolerance
The experimental design for the salt stress experiment was a randomized complete block design (RCBD) with three replicates. The solution used for growing the seedlings was drained, and the tank was filled with a salt treatment solution (150 mM NaCl). This was the same nutrient solution to which 150 mM NaCl (EC 16–17 dS/m) was added. Evaluation of salt tolerance of the F2:3 progeny as well as the parental lines KDML105 and KD-CSSL106 and the reference variety, Pokkali (salt tolerant), was performed 12 days after treatment or when the susceptible parent showed a clear symptom of salt injury, using IRRI’s standard evaluation system (SES) for Salt Injury Score (SIS) of 1–9 [28] (Table 1). The mean SIS scores of fifteen plants in each F2:3 family were reported.
2.3. Bulk Construction, DNA Extraction, Whole-Genome Sequencing, and RNA-Seq
Twenty F2:3 families with the highest SIS values and another 20 F2:3 families with the lowest SIS values were selected to form the tolerant bulk (T-bulk) and the sensitive bulk (S-bulk), respectively. Leaf samples were pooled from 15 plants of each of the 40 selected F2:3 families (samples were taken before salt treatment) and the parental lines KDML105 and KD-CSSL106. The pooled leaf samples of each F2:3 family together with the parental lines were used for DNA extraction using the DNeasy® Plant Mini Kit (QIAGEN, Hilden, Germany). The quality and quantity of DNA were determined using Nanodrop 8000 (Thermo Fisher Scientific, Waltham, MA, USA). DNA was then collected in equal amounts (50 ng/µL, 80 µL) from individual F2:3 families in the salt-tolerant group and the salt-sensitive group, and the DNA from each group was pooled into a tolerant bulk (T-bulk) and a sensitive bulk (S-bulk), respectively. The DNA samples from the two bulks and the two parents were sent to Beijing Genome Institute, BGI (Shenzhen, China) for whole-genome sequencing using Illumina Hiseq 2500 sequencing platform.
For transcriptomic analyses of DH103 and KDML105, the growth of rice and salt stress experiments were performed as described in our previous study [27], and the RNA-seq analyses were performed as described by Sriden and Charoensawan [30], except that Nipponbare-IRGSP1.0 was used as the reference genome. Briefly, raw sequence reads were quality-checked using FastQC (
2.4. QTL-Seq Analysis and Candidate Gene Determination
Raw paired-end reads from each sample were filtered out of low-quality sequences using Trimmomatic software version 0.30 [31] to obtain clean reads. QTL-seq analysis was performed using the QTL-seq pipeline version 2.2.3 [35]. The parent KDML105 was used to generate the pseudo-reference genome according to [16,35]. Briefly, the clean reads of KDML105 were aligned to the publicly available reference genome (Nipponbare-IRGSP1.0), then the genome of Nipponbare was replaced by the variants of the KDML105 parents. The SNP index for each SNP position was calculated using the following formula: SNP index = (number of alternative bases)/(number of total aligned reads) [16]. The SNP positions with an SNP index of <0.3 and those with a read depth of <8 in both bulks were filtered out. Δ(SNP index) for each SNP position was calculated by subtracting the SNP index of the T-bulk from the SNP index of the S-bulk. A sliding window analysis was performed based on Δ(SNP index) with a window size of 750 kb and 10 kb increments. The cutoff for the minimum read depth to obtain SNPs was set to 12 reads. The genomic regions with the highest average ΔSNP index in the sliding windows were considered as the associated regions.
The genes located within the associated regions were determined based on the Rice Genome Annotation Project database (
2.5. KASP Marker Genotyping and Marker–Trait Association Analysis
The KASP markers developed for the SNPs within the discovered regions were used to validate the QTL using individual F2:3 families. These markers were courtesy of the Innovative Plant Biotechnology and Precision Agriculture Research Team, National Center for Genetic Engineering and Biotechnology, Thailand. The KASP reaction (LGC Biosearch, Hoddesdon, UK) contained 2× Master Mix 2.5 µL, KASP primer 0.075 µL, sample DNA 2 µL, and ddH2O to a final volume of 5 µL. The amplification process started at 94 °C for 5 min. It went through 10 cycles: 94 °C for 20 s, followed by 61 °C for 60 s, using a touchdown method with a gradual temperature decrease of 0.6 °C per cycle. This was followed by 27 cycles at 94 °C for 20 s and 55 °C for 30 s, with a subsequent rest period at 37 °C for 1 min. The fluorescence signals of the resulting PCR products were analyzed using the QuantStudio 6 Flex Real-Time PCR System (Thermo Fisher Scientific, Waltham, MA, USA). The analysis of marker–trait association was performed with a simple regression method using the lm() function in R (
2.6. Statistical Analysis
Data analyses were performed using the package “Agricolae” version 1.3–5 [37] available through R studio (R 4.3.3) for Windows. Graphs and plots were performed using the following R packages: “ggplot2” version 3.3.6 [38], “ggpubr” [39], “ggsignif” [40] and “RColorBrewer” [41], and “karyoploteR” [42].
3. Results
3.1. Evaluation of Salt Tolerance and Construction of the Two Extreme Bulks
Salt tolerance at the seedling stage (21 days old) was evaluated using the 454 F2:3 families together with the two parents, KDML105 and KD-CSSL106, 12 days after salt stress treatment (150 mM NaCl). Pokkali was used as a salt-tolerant reference cultivar. The salt injury phenotype of rice seedlings under salt stress was observed as leaf senescence from older to younger leaves. The average salt injury scores (SISs) of KDML105, KD-CSSL106, and Pokkali were 7.85, 6.13, and 4.99, respectively (Figure S2). Based on the SIS values, KD-CSSL106 was found to be moderately salt tolerant compared to Pokkali. The salt injury scores (SISs) determined in the F2:3 families ranged from 4.6 to 9.0, with an average of 6.77 (Figure 1). To construct the salt-tolerant and salt-sensitive bulks, we selected 20 F2:3 families with the highest salt tolerance (SIS values between 4.60 and 5.57) and another 20 F2:3 families with the highest salt sensitivity (SIS values between 7.80 and 9.00) to sequence the entire genome along with the two parents, KDML105 and KD-CSSL106.
3.2. Whole-Genome Sequencing and Variant Detection
Illumina sequencing generated 150 bp paired-end (PE) reads in a total of 53.40 million and 58.12 million of salt-tolerant bulk (T-bulk) and salt-sensitive bulk (S-bulk), respectively. In addition, a total of 58.12 million reads and 54.72 million reads were generated for the parents KD-CSSL106 and KDML105. After filtering out and trimming low-quality sequences, a total of 29.82 million reads, 33.48 million reads, 31.80 million reads, and 35.65 million reads were obtained for T-bulk, S-bulk, KD-CSSL106, and KDML105, respectively (Table 2). Based on the alignment of the clean reads of each sample to the Nipponbare reference genome (IRGSP 1.0), 93.07%, 93.04%, 93.26%, and 96.75% of the clean data were mapped for T-bulk, S-bulk, KD-CSSL106, and KDML105, respectively. These covered 97.90%, 98.00%, 97.85%, and 91.09% of the reference genome, respectively. The average depth of aligned reads for T-bulk, S-bulk, KD-CSSL106, and KDML105 was 17.26×, 18.88×, 18.72×, and 17.54× genome coverage, respectively (Table 2).
After mapping the cleaned reads from T-bulk and S-bulk to the KDML105 genome reference, 15,667 genome-wide SNPs and 11,935 InDels were detected with a read support of at least eight reads (Table 3). To obtain robust results, the common SNPs in the total of 8058 SNPs identified in T-bulk and S-bulk with a read support of at least 12 reads were used to perform the QTL-seq analysis (Table 3).
3.3. QTL-Seq Analysis for Salt Tolerance at Seedling Stage
To identify genomic regions associated with salt tolerance using the QTL-seq approach, we calculated the SNP index of T-bulk and S-bulk for each SNP identified in the genome. The ∆SNP index was also calculated using the formula: ∆SNP = (SNP index of T-bulk) − (SNP index of S-bulk). The SNP index of T-bulk and S-bulk and the ∆SNP index were then plotted over 12 rice chromosomes. The averaged SNP index and averaged ∆SNP index were also calculated using sliding windows within a 750 kb region with a step size of 10 kb and plotted on the graphs to identify candidate regions (Figure 2). Because the population used in the study descended from KDML105 and a CSSL line of KDML105, we found from the alignments at the KDML105 genome reference that most regions in each chromosome of the progeny were nearly identical to those of KML105, except for the introgression regions of the KD-CSSL. These included the regions on chromosomes 1, 2, 4, 5, 7, 8, and 10 (Figure 2). According to the plots of the SNP index of T-bulk and S-bulk and the ∆SNP index, a 920 kb region toward the end of chromosome 1 (40.20–41.12 Mb) was identified as the region most strongly associated with salt tolerance in this population, with the highest average ∆SNP index of 0.44 (Figure 3). We also verified the QTL region by single marker analysis using the KASP markers developed in the region and individual F2:3 families to confirm the QTL-seq results using bulked samples. A total of 28 KASP markers were developed covering the region between 40.20 and 41.12 Mb on chromosome 1. These markers were used to genotype the 40 individual F2:3 families. The result of the single marker analysis showed that the markers in this region could explain between 2.76 and 11.49% of the phenotypic variance (PVE) in the tested population. The markers with the highest PVE value were located at 40.50 Mb (Table S1).
3.4. Candidate Gene Annotation and Prioritization
A total of 124 genes were annotated within the QTL region based on the Rice Genome Annotation Project database (Table S2). Among these, there were genes previously proposed as candidate genes for salt tolerance, including potassium transporters: OsHAK5, OsHAK6, and OsHAK2. According to our preliminary analyses of the transcriptomic profiles of KDML105 and DH103 (the donor of the introgression region in chromosome 1 of KD-CSSL106) under salt stress at the seedling stage (the complete transcriptomic dataset and comprehensive analyses will be presented in a separate publication), at 1 day after salt stress, 16 genes appeared to be differentially expressed (Table S2; Figures S3 and S4). These include genes encoding a heparan-alpha-glucosaminide N-acetyltransferase, OsFBX32, BTBM1, a histidine kinase, a spermatogenesis-associated protein 20, OsMADS65, receptor-like protein kinase 2, a prefoldin subunit family protein, a histone-lysine N-methyltransferase, a WD domain G-beta repeat domain containing protein, a mitochondrial import receptor subunit TOM20, a DEK C terminal domain containing protein, a ribosomal protein L7Ae, and two expressed proteins. At 5 days after salt stress, only one gene was upregulated in DH103—OsMADS65, and another gene was downregulated in DH103—ribosomal protein L7Ae. Notably, both OsMADS65 and ribosomal protein L7Ae were found to be differentially expressed at both day 1 and day 5.
In addition to this, we prioritized the number of candidate genes based on the variants present in the genes and their SNP index in the comparison between the two bulks. After filtering out the genes that did not contain a functional SNP or indel with a contrasting SNP index in two bulks (∆SNP index > 3.0), we were able to prioritize 13 candidate genes (Table 4). These include genes encoding a MIKC-type MADS-domain protein (OsMADS65), a calmodulin-binding transcription activator, a ribosomal protein, a NB-ARC domain containing protein, an enoyl-CoA hydratase/isomerase, a zinc finger DHHC domain-containing protein, a no apical meristem (NAM), an exostosin family domain containing protein, a histone-lysine N-methyltransferase, an aspartokinase, an inducer of CBF expression, and an oxidoreductase, short chain dehydrogenase/reductase, and an expressed protein. The SNP index of the functional SNPs/Indels in these genes ranged between 0.63 and 0.91 in the S-bulk and between 0.17 and 0.50 in the T-bulk. ∆SNP index of those SNPs ranged between 0.33 and 0.63 (Table 4). Taken together, the genes that have a functional variant in the gene with contrasting SNPs and expressed differentially under salt stress in the tolerance donor, and the intolerant parents were OsMADS65 and histone-lysine N-methyltransferase.
4. Discussion
Soil salinity is increasingly recognized as a critical environmental problem that significantly affects agricultural productivity worldwide, especially in rice-growing regions of Asia [43]. Soil salinity hinders the development of rice, especially at the seedling and reproductive stages [44], highlighting the importance of understanding and improving the salt tolerance of rice. Salt tolerance is a complex quantitative trait that is controlled by multiple genes and is also affected by external environmental factors [45]. The introgression of quantitative trait loci (QTLs) into rice varieties to improve salt tolerance is an important aspect of modern rice breeding programs. QTL mapping and gene identification for salt tolerance in rice have been advancing through various methodologies. These methods include the traditional QTL mapping using a high-density set of SNP markers [46,47], genome-wide association studies (GWAS) [48,49,50], QTL-seq [15,21,51], and the integration of multiple approaches [52]. To date, hundreds of QTLs associated with salt stress response and salt tolerance at various growth stages of rice have been mapped across all rice chromosomes [53,54].
While numerous QTLs have been identified at different growth stages of rice that contribute to salt tolerance, the practical application of these QTLs in breeding programs has predominantly focused on the Saltol QTL [5,55,56]. The Saltol QTL, located on chromosome 1, is known to contribute significantly to salt tolerance at the seedling stage by enhancing the plant’s ability to take up and store sodium ions in the roots, thereby reducing the toxic effects of salt on plant growth and development [13]. It contributes to a large part of the phenotypic variance for salt tolerance at the seedling stage and is therefore a valuable element in breeding programs. While the Saltol QTL remains a cornerstone in the development of salt-tolerant rice varieties, expanding the use of other identified QTLs through advanced breeding techniques and a better understanding of their mechanisms promise to improve salt tolerance in a wider range of rice varieties. In addition to Saltol, qSt1b, located in the lower region of chromosome 1, significantly improved photosynthetic efficiency with less damage under salt stress [57].
A typical QTL mapping approach might identify a broad region of QTLs in early segregating populations such as F2 or F3, as there is little recombination between individuals in such mapping populations. Therefore, it is often difficult to identify candidate genes in these extended regions. In the present study, we used an F2:3 population derived from KDML105 and one of its CSSL, KD-CSSL106, for QTL identification by QTL-seq. The main advantage of this particular population is its ability to attenuate genomic background noise [58,59]. This substantial reduction in background noise greatly increases the accuracy of detecting phenotypic effects attributable to donor segments. Consequently, it enables the more accurate identification of specific QTLs and their associated effects, solving the general challenge of annotating candidate genes within large QTL regions. The QTL region detected on chromosome 1 at 40.20–41.12 Mb overlaps with qST1b identified by traditional QTL mapping [60], qST1.2 identified by QTL-seq [15], and qDTS1-1 identified by GWAS [48].
Several candidate genes identified in this study could be used in further studies on molecular or physiological mechanisms related to the salt response and tolerance mechanism in rice and could also be used in plant breeding programs for salt tolerance. The genes encoding MIKC-type MADS domain proteins are known to play roles in plant development from vegetative growth to reproduction and to function in various stress responses [61]. The importance of OsMADS genes for salt tolerance in rice has been investigated in recent studies, highlighting their significant role in stress response mechanisms, particularly OsMADS27 [62]. Calmodulin-binding transcription activator (CAMTA) is a transcription factor known to play an important role in various aspects of plant development, hormone signaling, and response to environmental stress, including salt stress [63,64]. Ribosomal proteins, including L27, are crucial for protein synthesis and are involved in various cellular processes, including responses to environmental stress [65]. Ribosomal proteins are known to play a role in stress responses, indirectly through their involvement in the translation of stress-responsive proteins. Changes in the expression of ribosomal protein genes have been observed under various stress conditions, suggesting that these proteins may be a part of the cellular machinery that helps plants cope with stress by ensuring the proper synthesis of proteins required for stress resistance [65,66]. Histone methylation is crucial in the epigenetic control of gene expression in plants, especially in response to abiotic stresses. Enzymes that either add or remove methyl groups from histones are actively involved in these stress responses. Furthermore, the relationship between changes in histone methylation and plant metabolism significantly influences how plants react to abiotic stress [67].
The NB-ARC domain is a common signaling motif of plant resistance proteins that play a critical role in the immune response to pathogen attack [68]. Although NB-ARC domain genes are primarily associated with plant immune responses, there is increasing evidence for their involvement in abiotic stress responses, including salt tolerance [69]. Transcriptome analysis of the halophyte Nitraria tangutorum revealed several genes, including those with the NB-ARC domain, which could contribute to salt tolerance via complex regulatory mechanisms [70]. In alfalfa, a gene encoding an NB-ARC domain-containing protein was reported to be involved in salt tolerance [71]. Chen et al. reported that many NB-ARC and NBS-LRR gene families were upregulated under seawater (SW) conditions [72], suggesting that they may play a role in adaptation to salt stress in rice. The INDUCER OF CBF EXPRESSION (ICE), an MYC-type transcription factor (basic helix-loop-helix (bHLH)), activates the expression of CBFs (C-repeat Binding Factors), which in turn regulate the expression of a number of genes involved in improving cold tolerance (chilling or low temperature) in plants [73]. The gene OsICE2 has been annotated within the QTL in this study. It has been suggested that OsICE1 (LOC_Os11g32100) and OsICE2 (LOC_Os01g70310) have similar functions in regulating chilling tolerance [74]. Although the function of ICE genes in rice for the mechanism of salt tolerance is not known, rice lines with the overexpression of Arabidopsis ICE1 (AtICE1) showed an improvement in tolerance to salt and other stress factors, i.e., cold and drought [75]. In addition, an ICE1-like MYC-type transcription factor from tomato was reported to confer salt as well as cold and osmotic tolerance in transgenic tobacco [76]. The regulation of transcription is a crucial component of the plant response to abiotic stress in plants. To date, many TFs that regulate the expression of downstream target genes under stress conditions have been identified and functionally characterized [77]. In rice, the major TF families that regulate salt tolerance are well studied. These include dehydration-responsive element (DRE) binding protein (DREB), ABA-responsive element (ABRE) binding protein/factor (AREB/ABF), and NAC (NAM, ATAF1/2, CUC2) [78]. Increasing research supports the role of NAC in plant response to environmental stress conditions, including salt stress [79,80]. Several studies indicate that genetically modified rice strains with enhanced expression of stress-responsive NAC transcription factor genes show improved salt tolerance without yield loss. This indicates the possible application of these NAC-TFs in the development of salt-resistant rice varieties [81].
It Is possible that the candidate genes proposed in this study may be involved in salt tolerance based on the annotated functions; however, further study, such as gene expression analysis and gene overexpression or knock-out analysis, is required to validate the functions of these candidate genes. It is important to note that the results of this study were derived from a greenhouse experiment. Therefore, the identified QTLs and gene expressions may differ from those obtained in field experiments or other environments. The SNP/indel variations identified in the genes can be used to develop DNA markers and used in marker-assisted selection programs for salt tolerance in rice.
5. Conclusions
In this study, a QTL-seq analysis was performed on F2:3 population derived from a chromosome segment substitution line (CSSL). A genomic region on chromosome 1 associated with salt tolerance in rice seedlings was identified. Through validation and annotation, important candidate genes were identified that are promising for breeding salt-tolerant rice varieties. However, it is important to note that the QTLs and gene expressions identified here are based on greenhouse experiments and may differ from those obtained in field experiments or other environments. The results of the present study provide insight into the genetic mechanisms of salt stress tolerance and offer a valuable resource for improving rice production in salt-affected regions.
Conceptualization, S.A., T.T., M.S., V.R. and J.L.S.; methodology, M.S. and S.W.; formal analysis, J.L., W.A., R.D., D.S., S.P., J.N. and S.K.; data curation, S.W. and V.C.; writing—original draft preparation, J.L. and M.S.; writing—review and editing, S.W., M.S., V.C. and S.A.; visualization, W.A.; supervision, S.A., M.S. and S.W.; funding acquisition, T.T., V.R., J.L.S. and S.A. All authors have read and agreed to the published version of the manuscript.
Data presented in this study are available in the
The authors thank the Innovative Plant Biotechnology and Precision Agriculture Research Team, National Center for Genetic Engineering and Biotechnology, Thailand, for providing the plant material used in this study and the KASP markers.
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 1. Phenotypic variation (salt injury score: SIS) in the parents and F2:3 population. (A) The difference of salt injury phenotypes between the two parents, KDML105 and KD-CSSL106 (CSSL106). (B) The distribution of SIS values in the F2:3 population. The SIS values of KDML105 and KD-CSSL106 were denoted by a black triangle. The selected plants for the QTL-seq analysis were those in the dotted rectangular.
Enlarge this image.Figure 2. Diagrams illustrating the SNP index of T-bulk and S-bulk, along with the Δ(SNP index) across 12 rice chromosomes. (A) The SNP index diagrams for the salt-sensitive bulk (S-bulk). (B) The SNP index diagrams for the salt-tolerant bulk (T-bulk). (C) The Δ(SNP index) diagram. The plots depict the moving averages of the SNP index with a window size of 750 kb and 10 kb steps, presented as blue lines in (A,B) and as a black line in (C). The blue and red paired dots in (C) correspond to the 95% and 99% confidence intervals, respectively. Additionally, a red vertical bar in the figure denotes the candidate region on chromosome 1.
Enlarge this image.Figure 3. The extent of the QTL region detected on chromosome 1. (A) Diagrams illustrating the SNP index of T-bulk and S-bulk together with the Δ(SNP index) on chromosome 1. (B) The magnified region containing the significant SNPs. In addition, a red vertical bar in the figure indicates the top of the region.
Modified standard evaluation score of visual salt injury at seedling stage [
| Score | Observation | Tolerance |
|---|---|---|
| 1 | Normal growth, no leaf symptoms | Highly tolerant |
| 3 | Nearly normal growth, but leaf tips or few leaves are whitish and rolled | Tolerant |
| 5 | Growth severely retarded; most leaves rolled; only a few are elongated | Moderately tolerant |
| 7 | Complete cessation of growth; most leaves are dry; some plants are dying | Susceptible |
| 9 | Almost all plants dead or dying | Highly susceptible |
Summary of whole-genome sequencing results for tolerant and susceptible bulks, as well as the parental lines.
| Sample | Raw Reads (Million) | Cleaned Reads | Clean Base | Read Alignment | Genome Coverage | Average Depth |
|---|---|---|---|---|---|---|
| Tolerance bulk | 53.40 | 29.82 | 4.25 | 93.07 | 97.90 | 17.26 |
| Susceptible bulk | 58.12 | 33.48 | 4.77 | 93.04 | 98.00 | 18.88 |
| KD-CSSL106 | 58.12 | 31.80 | 4.52 | 93.26 | 97.85 | 18.72 |
| KD | 54.72 | 35.65 | 5.30 | 96.75 | 91.09 | 17.54 |
Chromosome-wise distribution of common single nucleotide polymorphisms (SNPs) and insertion–deletion polymorphisms (InDels) identified in tolerant and susceptible bulks.
| Chromosome | Length (bp) | All Variants | Filtered Variants (Read Depths > 12) | ||
|---|---|---|---|---|---|
| SNPs | InDels | SNPs | InDels | ||
| 1 | 43,270,923 | 1579 | 1655 | 839 | 800 |
| 2 | 35,937,250 | 849 | 969 | 462 | 441 |
| 3 | 36,413,819 | 183 | 831 | 66 | 343 |
| 4 | 35,502,694 | 1668 | 1435 | 939 | 728 |
| 5 | 29,958,434 | 3985 | 1287 | 1847 | 586 |
| 6 | 31,248,787 | 460 | 719 | 235 | 307 |
| 7 | 29,697,621 | 1272 | 1006 | 661 | 469 |
| 8 | 28,443,022 | 3744 | 1346 | 2036 | 656 |
| 9 | 23,012,720 | 206 | 571 | 103 | 242 |
| 10 | 23,207,287 | 893 | 622 | 484 | 275 |
| 11 | 29,021,106 | 682 | 1121 | 330 | 529 |
| 12 | 27,531,856 | 146 | 373 | 56 | 133 |
| Total | 373,245,519 | 15,667 | 11,935 | 8058 | 5509 |
List of candidate genes containing SNPs/Indels with an effect contrasting the SNP index.
| Chr | Pos | Effect | Gene Name | Description | KD | KD-CSSL106 | SNP Index(S) | SNP Index(T) | ∆SNP Index |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 40344634 | missense | LOC_Os01g69850 | OsMADS65 | C | T | 0.71 | 0.17 | 0.55 |
| 1 | 40362776 | missense | LOC_Os01g69850 | OsMADS65 | A | C | 0.80 | 0.33 | 0.47 |
| 1 | 40401258 | missense | LOC_Os01g69910 | calmodulin-binding transcription activator | A | G | 0.82 | 0.38 | 0.44 |
| 1 | 40402065 | missense | LOC_Os01g69910 | calmodulin-binding transcription activator | G | A | 0.78 | 0.31 | 0.47 |
| 1 | 40402297 | missense | LOC_Os01g69910 | calmodulin-binding transcription activator | T | C | 0.86 | 0.50 | 0.36 |
| 1 | 40402429 | missense | LOC_Os01g69910 | calmodulin-binding transcription activator | G | A | 0.82 | 0.25 | 0.57 |
| 1 | 40402459 | missense | LOC_Os01g69910 | calmodulin-binding transcription activator | A | G | 0.75 | 0.36 | 0.39 |
| 1 | 40402504 | missense | LOC_Os01g69910 | calmodulin-binding transcription activator | G | A | 0.70 | 0.29 | 0.41 |
| 1 | 40453979 | missense | LOC_Os01g69950 | ribosomal protein L27 | A | C | 0.91 | 0.33 | 0.58 |
| 1 | 40556756 | frameshift | LOC_Os01g70080 | NB-ARC domain containing protein | C | CA | 0.71 | 0.36 | 0.36 |
| 1 | 40556758 | frameshift | LOC_Os01g70080 | NB-ARC domain containing protein | G | GC | 0.67 | 0.31 | 0.36 |
| 1 | 40556761 | frameshift | LOC_Os01g70080 | NB-ARC domain containing protein | T | TC | 0.78 | 0.42 | 0.36 |
| 1 | 40557831 | missense | LOC_Os01g70080 | NB-ARC domain containing protein | G | A | 0.80 | 0.33 | 0.47 |
| 1 | 40563787 | missense | LOC_Os01g70090 | enoyl-CoA hydratase/isomerase family protein | T | G | 0.80 | 0.17 | 0.63 |
| 1 | 40568751 | missense | LOC_Os01g70100 | zinc finger DHHC domain-containing protein | C | T | 0.80 | 0.17 | 0.63 |
| 1 | 40572974 | missense | LOC_Os01g70110 | No apical meristem protein | A | G | 0.73 | 0.33 | 0.39 |
| 1 | 40585106 | missense | LOC_Os01g70120 | expressed protein | CTCCTCCTCG | C | 0.86 | 0.40 | 0.46 |
| 1 | 40617122 | Inframe insertion | LOC_Os01g70180 | exostosin family domain containing protein | AATCCAC | A | 0.86 | 0.40 | 0.46 |
| 1 | 40663740 | missense | LOC_Os01g70220 | histone-lysine N-methyltransferase | C | T | 0.67 | 0.31 | 0.35 |
| 1 | 40711179 | missense | LOC_Os01g70300 | aspartokinase 3 | T | C | 0.78 | 0.38 | 0.40 |
| 1 | 40715330 | missense | LOC_Os01g70310 | inducer of CBF expression 2 | A | C | 0.75 | 0.42 | 0.33 |
| 1 | 40796815 | Inframe insertion | LOC_Os01g70430 | oxidoreductase | CCGGCGGCGG | CCGGCGG | 0.63 | 0.20 | 0.43 |
Supplementary Materials
The following supporting information can be downloaded at:
References
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; Charoensawan, Varodom 5
; Siangliw, Jonaliza L 3
; Ruanjaichon, Vinitchan 3
; Toojinda, Theerayut 3 ; Wanchana, Samart 3
; Siangliw, Meechai 3 ; Arikit, Siwaret 6
1 Plant Breeding Program, Faculty of Agriculture at Kamphaeng Saen, Kesetsart University, Nakhon Pathom 73140, Thailand;
2 Rice Science Center, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom 73140, Thailand;
3 National Center for Genetic Engineering and Biotechnology (BIOTEC), 113 Thailand Science Park, Pahonyothin Road, Khlong Luang, Pathum Thani 12120, Thailand;
4 Doctor of Philosophy Program in Biochemistry (International Program), Faculty of Science, Mahidol University, Bangkok 10400, Thailand;
5 Division of Medical Bioinformatics, Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand;
6 Rice Science Center, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom 73140, Thailand;