Introduction

Abiotic stress describes the negative impact of non-living environmental factors on plants, leading to a variety of responses that can alter biological processes like gene expression and cellular metabolism, as well as affect growth and development. This type of stress encompasses issues such as extreme temperatures, drought, flooding, salinity, metal toxicity, and nutrient deficiencies, each prompting distinct reactions. Key environmental challenges, particularly extreme temperatures, drought, and saline soils, significantly restrict plant survival and their distribution in natural ecosystems1,2. Soil salinization adversely affects both the yield and quality of crops. Salt stress poses a significant threat to plant growth, leading to decreased leaf expansion, stoma closure, a reduced photosynthetic rate, and a loss of biomass3,4. Salinization currently affects more than 800 million hectares of land on Earth, and projections suggested that by 2050, approximately half of cultivated land could be impacted by salinity5,6.

Plants have developed adaptive mechanisms to counter salt stress by making changes at the morphological, physiological, biochemical, and molecular levels. They also adjust metabolite and gene expression to combat stress and minimize damage7,8.

Rice (Oryza sativa L.), a glycophyte, is highly sensitive to salt stress9. The extent of salt tolerance varies among genotypes and stages of development. While rice is particularly vulnerable to salt stress during the seedling stage, it shows moderate tolerance during the tillering stage10,11. Certain genotypes have been recognized for their salt tolerance12. In various studies, the genotype IR28 has been utilized as salt-sensitive rice for molecular investigations of salinity tolerance13,14. Additionally, there are reports indicating the genetic analysis and high salinity tolerance of the genotype CSR2815,16,17.

Reactive oxygen species (ROS) are a group of highly reactive molecules that contain oxygen, such as superoxide, hydrogen peroxide, and hydroxyl radicals. They are produced as natural byproducts of cellular metabolism and play important roles in cellular signaling and defense against pathogens. However, under severe abiotic stress conditions, excess of ROS is produced, causing damage to various cellular components, such as DNA, proteins, carbohydrates, lipids, and enzymes, ultimately triggering programmed cell death18,19. To prevent injuries, plants regulate ROS production effectively by employing a range of enzymatic and nonenzymatic antioxidants. Enzymatic antioxidants belonging to the plant defense system include peroxidase (POD), superoxide dismutase (SOD), glutathione reductase (GR), catalase (CAT), dehydroascorbate reductase (DHAR), ascorbate peroxidase (APX), and monodehydroascorbate reductase (MDHAR), while nonenzymatic antioxidants include ascorbate (AsA), flavonoids, carotenoids, stilbenes, tocopherols, and various vitamins20,21.

Omics technologies, such as metabolomics, enable a system-wide analysis of metabolic processes, for example in response to salinity stress22. Metabolite profiling is conducted by instruments such as gas chromatography–mass spectrometry (GC‒MS) and permits the study of plant responses to environmental stresses at the molecular level. The comprehensive quantitative and qualitative measurements of the cellular metabolites acquired from stress-treated tissues provide a broad view of plant physiological and molecular reactions to stresses. Furthermore, metabolites are considered the final product of gene expression and are closely related to phenotype, which doubles the value of their study23,24,25. Recent reports have indicated the role of metabolites such as amino acids, sugar alcohols, and organic acids in osmotic adjustment as osmolytes, ionic homeostasis, photosynthesis and leaf senescence in salt-treated rice26,27,28,29,30.

Analyzing the metabolome and transcriptome together can provide precise insights into how genes and metabolites interact, allowing for a systematic exploration of metabolic pathway synthesis and regulation. This approach helps overcome the limitations of individual omics studies, providing a more detailed explanation of the expression patterns and involvement of key genes in metabolic adaptations31. Wang et al.32 reported several genes in OsDRAP1-mediated salt tolerance in rice by Pearson correlation analysis of transcript and metabolite levels. These genes were involved in key biosynthetic pathways of amino acids (proline, valine), organic acids (glyceric acid, phosphoenolpyruvic acid and ascorbic acid) and carbohydrate metabolism. In the present study, we used an association approach of metabolomics and gene expression data to elucidate metabolic adaptations of rice salt tolerance using various genotypes/organs/timepoints.

Results

Phenotypic evaluation confirmed the contrasting salinity tolerances of IR28 and CSR28

Phenotypic evaluation of IR28 and CSR28 rice seedlings 1 week after exposure to high salinity stress confirmed differences in their salinity tolerance (Fig. 1). The differences between the genotypes were more prominent in the shoots than in the roots. The difference in shoot length among the genotypes increased from 3.7% under control conditions to 47.1% under salinity stress. Furthermore, the difference in shoot dry weight increased from 1% in the control treatment to 57.9% in the salinity treatment. Compared with CSR28, IR28 exhibited greater reductions in both shoot length and dry weight under salinity stress. The leaf RWC of salt-stressed sensitive IR28 plants decreased significantly (23.4%) compared to that of the salt-tolerant CSR28 plants, while no significant difference was detected between the genotypes under control conditions. Brown and tubular leaves appeared in most IR28 seedlings after 1 week of salt stress, while CSR28 seedlings displayed more green leaves. The CSR28 genotype had a significantly lower mean salinity score than the IR28 genotype, which indicated that CSR28 was more salinity tolerant.

Fig. 1
figure 1

Phenotypic assessment of the salt sensitive (IR28) and tolerant (CSR28) rice genotypes after 1 week of 150 mM salt stress. (a) Length, (b) Fresh weight, (c) Dry weight, (d) Leaf RWC, (e) Salt score, including 1, 3, 5, 7 and 9 as very tolerant (normal growth), tolerant (relatively normal growth), relatively tolerant (delayed growth), sensitive (completely stopped growth) and very sensitive (death of all plants), respectively. (f) Phenotype of the seedlings under control and salt stress conditions. Different letters indicate significant difference between the genotypes based on Student’s t-test (P value ≤ 0.05).

Full size image

Effects of salt stress on H2O2 and MDA contents and antioxidant enzyme activity

Changes in the levels of H2O2 and MDA provide insights into the capacity to combat ROS and lipid peroxidation under stress. Both H2O2 (Fig. 2a) and MDA (Fig. 2b) levels increased in response to salinity stress in the organs of both genotypes. However, the increases were notably more pronounced under long-term stress in the sensitive genotype. The levels of H2O2 and MDA in the roots of IR28 at 54-h timepoint increased compared to CSR28 by 206.6% and 164.7%, respectively, while the increases in the shoots were 216.6% and 166.6%, respectively.

Fig. 2
figure 2

ROS and antioxidant enzyme levels under salinity stress. Levels of (a) H2O2, (b) MDA, (c) CAT, and (d) SOD in terms of salinity/control ratio. Different letters indicate significant differences among the combinations of organs, genotypes, and timepoints as determined by Duncan’s Multiple Range Test (P value ≤ 0.05).

Full size image

The results of the antioxidant enzyme activity revealed that the levels of both CAT and SOD increased in all conditions in response to salinity. Although no significant difference between the two genotypes in both organs at the 6-h timepoint, however CAT and SOD enzyme levels in the roots of CSR28 at 54-h timepoint, were elevated compared to IR28 by 226.6% and 162.1%, respectively, while those increases in the shoots were 214.6% and 180.1%, respectively (Fig. 2c,d).

The metabolic responses of salt-stressed rice seedlings revealed the specific functions of metabolites in salinity tolerance

GC‒MS analysis revealed 37 primary metabolites, including 18 amino acids (AAs), 5 sugars and sugar alcohols and 14 organic acids (OAs), in the roots and shoots of the CSR28 and IR28 genotypes at the 6-h and 54-h timepoints (the mean and standard variation values of relative metabolite levels are shown in Supplementary Table S1). ANOVA revealed significant differences between 35 metabolites in roots and shoots, with an average phenotypic variation of 35.6% based on the salinity/control ratio (Supplementary Table S2). Of the 37 identified metabolites, 26 presented a significant difference between two timepoints of 6 h and 54 h, explaining 8.2% of the phenotypic variation. The two genotypes displayed significant differences in 28 metabolites, which explained 9.7% of the phenotypic variation. Furthermore, the interaction of genotype × timepoint × organ was significant for 25 metabolites, which explained 4% of the phenotypic variation.

The relative changes of the metabolites are shown as ratio of salinity/control in all conditions (Table 1). In general, 89.3% of the metabolite changes were significant in response to salinity, of which 56.5% and 32.8% represented increased and decreased accumulation, respectively. Lactate had the smallest response to salinity under the different conditions.

Table 1 Metabolite alterations in the roots and shoots of salt-sensitive (IR28) and salt-tolerant (CSR28) rice seedlings were examined at two timepoints (6 h and 54 h) of high salt treatment.
Full size table

Amino acids (AAs)

Among the 18 identified AAs, 94.4% exhibited significant changes in response to salinity stress, of which 83.3% and 11.1% exhibited increased and decreased accumulation, respectively. The greatest increases were observed for the metabolites in the salt-stressed shoots of CSR28 at the 54-h timepoint. These metabolites included isoleucine (42.8-fold), leucine (31.06-fold) and proline (36.05-fold). Compared with those in the roots of the genotypes at the 6-h timepoint, only three AAs (α-alanine, GABA and methionine) were increased in CSR28 compared to those in IR28, while 14 AAs were remarkably increased in CSR28 compared to those in IR28 at the 54-h timepoint. Furthermore, the accumulation of six and 12 AAs was greater in the shoots of CSR28 than in those of IR28 at the 6-h and 54-h timepoints, respectively (Table 1).

Sugars and sugar alcohols

Out of the 5 sugars and sugar alcohols identified through GC‒MS analysis, 90% exhibited significant changes in response to salinity, with 55% and 35% increased and decreased accumulation, respectively. In the roots of CSR28, raffinose (45.2-fold) and fructose (-11.1-fold) had the greatest and greatest changes, respectively, at the 54-h timepoint. After 6 h of salinity treatment, the glucose and raffinose contents in the roots of IR28 were greater, and the fructose content was lower than those in the roots of CSR28. However, at the 54-h timepoint, the raffinose and myoinositol contents in CSR28 were significantly greater, and the glucose and glycerol contents were lower than those in IR28. In the shoots, the values in IR28 were greater than those in CSR28 at both timepoints (Table 1).

Organic acids (OAs)

Among the 14 OAs identified via metabolite profiling, 84.2% of the changes were significant in response to salinity under all conditions, including 22.4% and 61.8% increased and decreased accumulation, respectively. Furthermore, the maximum (8.4-fold) and minimum (-4.9-fold) changes were due to citrate in the roots of IR28 and quinate in the shoots of CSR28 at the 54-h timepoint, respectively. After 6 h of exposure to salinity, the concentrations of six OAs in CSR28 roots were greater than those in IR28 roots, while only the hydroxyglutarate concentration in CSR28 roots was greater than that in IR28 roots. Between CSR28 and IR28, nine OAs were differentially accumulated after 54 h of salinity treatment. After 6 h of salt exposure, the contents of six OAs in the shoots of IR28 were greater than those in the shoots of CSR28, while only the fumarate content in the shoots of CSR28 was greater than that in the shoots of IR28. Under long-term stress, a greater reduction in OAs was observed in CSR28 than in IR28 (Table 1).

Aspartate among AAs, myo-inositol among sugars and sugar alcohols and citrate, glycerate, isocitrate and shikimate among OAs showed organ-specific accumulation and increased only in roots in response to salinity stress. Among the OAs, only α-ketoglutarate and pyruvate were specifically accumulated between the genotypes in the salt-stressed shoots, so both decreased and increased in CSR28 and IR28, respectively.

Hierarchical cluster analysis (HCA) grouped the metabolites and samples

Heatmap was conducted to obtain an overview of metabolite profiling under different conditions. HCA grouped the metabolite data into two major clusters, roots and shoot, and each cluster into two distinct control and salinity stress subclusters (Fig. 3). Furthermore, each subcluster was classified with respect to the timepoints of 6 h and 54 h, and each subcluster included two tolerant and sensitive genotypes. Maximum similarity of the timepoints of 6 h and 54 h was observed under the control condition in both organs, and this similarity was greater in the shoots than in the roots, while the difference between the metabolites at both timepoints increased significantly under salinity stress. The genotypes in both organs exhibited a maximum correlation under the control condition, and the difference in their metabolome increased under salinity stress. In the roots, the difference between the two genotypes at 54 h was greater than that at 6 h, while the difference in the shoots was greater at 6 h than at 54 h. In general, the correlations between the samples were as follows: genotype > timepoint > treatment > organ.

Fig. 3
figure 3

Hierarchical cluster analysis (HCA) of the metabolite profiles grouped the samples based on organs, treatments, timepoints and genotypes. The data are z-score normalized means (n = 5). Heatmap represents high (red) to low (green) relative metabolite abundance.

Full size image

Correlations between metabolites

To further explain the relationships between metabolite contents in response to salinity stress, the correlations between amino acids, and between organic acids and carbohydrates were analyzed (Fig. 4). The results indicated that there was a significant positive correlation between the content of most amino acids, except for the correlations of glycine with putrescine (r= − 0.91, P value = 0.001), aspartate (r= − 0.85, P value = 0.02), asparagine (r= − 0.82, P value = 0.02), and β-alanine (r= − 0.81, P value = 0.04), and the correlations of putrescine with proline (r= − 0.85, P value = 0.03) and threonine (r= − 0.81, P value = 0.001), which were negatively correlated (Fig. 4a). In the correlation analysis between organic acids and carbohydrates, diverse patterns of both positive and negative correlations were observed. For example, except for glycerol, the other carbohydrates were positively correlated with each other. Furthermore, glycerol followed a pattern similar to that of organic acids such as lactate and pyruvate (Fig. 4b).

Fig. 4
figure 4

Correlation between the metabolites. Correlation between (a) Amino acids, (b) Sugars and organic acids. High and low significant correlations are shown as blue and red colors, respectively.

Full size image

Expression of genes involved in the metabolism of metabolites and antioxidant enzyme activity under salinity stress

Analysis of genes related to the accumulation of metabolites and antioxidant enzymes is highly important for understanding the synthesis of these compounds in response to salinity stress. Therefore, we focused on the key genes associated with the metabolites and antioxidant enzymes identified in this research (Fig. 5). The expression of key genes involved in proline biosynthesis demonstrated that salinity stress led to the up-regulation of the genes OsP5CS2, OsP5CR, and OsP5CS1 in most of the experimental samples. OsP5CS2 showed a significant increase in expression under all conditions except at the 6-h timepoint in the roots. The expression of this gene down-regulated in IR28, but did not change in CSR28. OsP5CR and OsP5CS1 showed elevated expression at both timepoints in the roots of CSR28, while a notable increase in the expression of OsP5CS1 occurred in all conditions in the shoots. The results of the expression of three genes involved in raffinose biosynthesis showed that OsRS2 had a significant increase in expression in response to salinity under all conditions, while OsNIN7 and OsEno5 were up-regulated in response to salinity in the roots, especially at the 54-h timepoint. Our findings also indicated an increase in the expression of the OsIMP-2 and OsMIOX genes involved in myoinositol biosynthesis in the roots. Among the four genes involved in glycolate metabolism, the expression of the OsGLO1, OsGLO6 and OsPLGG1 genes significantly increased in response to salinity in the roots of CSR28 at the 54-h timepoint. Finally, key genes involved in the synthesis of antioxidant enzymes were studied, and the results showed that except of OsCatB which encodes CAT and is specifically expressed in the shoots, other genes were up-regulated in the roots. Remarkably, OsSOD-Fe and OsNCA1a exhibited a significant increase in their expression in response to salinity only in the roots of the tolerant genotype CSR28 at the 54-h timepoint.

Fig. 5
figure 5

Gene expression analysis of the key encoding genes involved in metabolite biosynthesis and antioxidant enzyme activity. The data are salinity/control ratios. Yellow and blue colors indicate increased and decreased expression in response to salinity stress.

Full size image

Linear regression analysis reveals the relationships between metabolites and antioxidant enzymes with their relevant genes in response to salinity stress

Linear regression analysis was used to identify significant relationships between the contents of metabolites and antioxidant enzymes and their encoding genes. The results indicated that the proline and myoinositol contents were positively correlated with the expression of OsP5CS2 (R2 = 0.81, P value = 0.03) and OsIMP (R2 = 0.82, P value = 0.02), respectively. Among the three genes related to the CAT synthesis, only OsNCA1a (R2 = 0.84, P value = 0.01) was significantly correlated with the enzyme content, while OsSOD-Fe (R2 = 0.88, P value = 0.001) was positively related to the SOD content (Fig. 6).

Fig. 6
figure 6

Linear regression analysis between metabolites and antioxidant enzyme contents with their key related genes. Significant relationships between (a) Proline content and OsP5CS2 (R2 = 0.81, P value = 0.03), (b) Myoinositol content and OsIMP (R2 = 0.82, P value = 0.02), (c) SOD content and OsSOD-Fe (R2 = 0.88, P value = 0.001), (d) CAT content and OsNCA1a (R2 = 0.84, P value = 0.01).

Full size image

Discussion

The present study assessed the responses of the roots and shoots of rice seedlings of two contrasting genotypes to high salinity. After 1 week of high salinity treatment, the length, biomass and dry weight of the IR28 shoots were lower than those of the CSR28 shoots (Fig. 1). This is explained by the osmotic phase of salinity stress33 and consequently ionic toxicity, which accelerates the aging of older leaves and their necrosis due to salt accumulation5. It seems that IR28 experienced both osmotic and ionic toxicity phases earlier and more severely. The CSR28 genotype exhibited greater growth vigor than that of the IR28 genotype under salinity stress, which indicated greater salinity tolerance. A faster growth can transfer Na+ ions to shoots more slowly34,35. Furthermore, the rapid growth and development of cells prevent the accumulation of high salt concentrations15,36. The RWC, which is used to describe the water status of plant cells37 was significantly greater in salt-stressed CSR28 than in IR28. Numerous studies have reported that the RWC of tolerant genotypes is greater than that of sensitive ones38,39. An increased ability of plants to maintain water potential allows them to sustain photosynthetic activity, increase water use efficiency (WUE), and enhance their osmotic adjustment ability40,41.

Plants exposed to salt stress undergo diverse physiological alterations42. ROS such as H2O2 and O2− are extremely reactive molecules that can accumulate at elevated levels during environmental stresses such as salt, drought, and cold, causing oxidative damage to plant cells43. MDA is produced through lipid peroxidation and serves as a marker for oxidative damage in plant cell membranes induced by stress44. The H2O2 and MDA contents were greater in the roots and shoots of the sensitive genotype than in those of the tolerant plants in response to long-term salinity stress (Fig. 2a,b), which is an indication of greater oxidative stress damage in IR28.

ROS-scavenging enzymes and antioxidants such as CAT and SOD play important roles in reducing oxidative stress32,45. In the present study, the results revealed that the tolerant genotype had greater CAT and SOD contents than that of the sensitive genotype in response to salinity stress, particularly under long-term exposure (Fig. 2c and d), suggesting that these enzymes play vital roles in ROS scavenging and alleviating stress. Furthermore, our findings revealed the expression of the key encoding genes of the antioxidant enzymes (Fig. 5). Remarkably, linear regression analysis revealed that OsNCA1a and OsSOD-Fe had significant positive relationships with the contents of CAT and SOD enzymes, respectively (Fig. 6).

The GC-MS analysis revealed increased accumulation of AAs in both salt-stressed organs of the two genotypes (83.3%) in response to salinity. AAs act as osmolytes that maintain cellular turgor and protect molecules against damage caused by oxidative stresses through osmotic adjustment46. In the present study, the accumulation of AAs increased in both organs after 54 h of salinity treatment, indicating that long-term salinity stress results in increased Na+ accumulation and doubling of the role of the osmotic protection of AAs. The difference in AA accumulation between the two genotypes increased in both organs under long-term salinity stress. More AAs were detected in the roots of CSR28 than in those of IR28 at the 54-h timepoint (Table 1), suggesting the specific role of the metabolic pathways of roots in promoting salinity tolerance. Proline, as one of the key primary metabolites, possesses antioxidant activity and protects macromolecules against ROS, along with playing the role of osmolyte and osmotic adjustment47,48. Proline accumulation is directly related to abiotic stress tolerance49. Here, proline levels increased in response to salinity in both organs, genotypes and timepoints (Table 1). The tolerant genotype CSR28 possessed greater potential for coping with osmotic challenges via proline accumulation in the shoots of CSR28 than in those of IR28. On the other hand, a significant increase in GABA was observed in response to salinity stress in the roots of the tolerant genotype CSR28 at the 6-h timepoint. GABA, a non-protein amino acid, quickly builds up in plants under stress conditions50, helping to alleviate plant stress by regulating osmotic balance51. In general, the results of the present study were in agreement with previous findings on the role of AAs in inducing the salinity tolerance of rice at the seedling stage26,28.

Numerous studies have shown that genes related to proline biosynthesis are up-regulated under salt stress31,52. This study showed that the genes OsP5CS2, OsP5CR, and OsP5CS1 were up-regulated in response to salinity stress under most of the experimental conditions (Fig. 5). However, a significant gene-metabolite relationship was observed between the expression of the OsP5CS2 gene and the content of proline (Fig. 6); therefore, this gene is considered to play a key role in increasing proline under salinity stress. The overexpression of P5CS (pyrroline-5-carboxylate synthetase 5) could increase the proline content in potato53 and rice54 and enhanced salt tolerance of plants. Furthermore, p5cs1-4 mutants exhibited strongly impaired proline accumulation in response to NaCl, suggesting that P5CS1 contributes greater to stress-induced proline accumulation55.

Sugars and sugar alcohols act as osmolytes and antioxidants, in addition to being resources for metabolism and structural support56,57. Raffinose increased in response to salinity under all conditions, especially in roots, where its maximum accumulation was observed at the 54-h timepoint in CSR28 roots (Table 1). Nishizawa et al.58 reported that galactinol and raffinose protect plant cells against oxidative stress by scavenging hydroxyl radicals. Myo-inositol accumulated more in the roots of CSR28 under long-term salinity than in those of IR28. Using external myo-inositol in Malus hupehensis Rehd under salinity stress prevented the damage caused by salt accumulation through the support of the plant antioxidant defense system, Na+ and K+ ion homeostasis and osmotic balance59. IMP (L-myo-inositol monophosphatase) is a key enzyme in the last process of myoinositol biosynthesis. The present study revealed a significant correlation between the myoinositol content and the gene expression of OsIMP in response to salinity stress (Fig. 6). It has been reported that the overexpression of OsIMP in transgenic tobacco led to elevated inositol levels and improved cold tolerance by regulating antioxidant enzymes60.

Based on the assessment of primary metabolite data, although most OAs (61.8%) decreased in both salt-stressed organs, the value and pattern of their accumulation differed among organs, genotypes and timepoints. The lower reduction in OAs in the roots of both genotypes under long-term salinity stress could be due to the compensation of ionic imbalance61. Increasing the amount of citrate and isocitrate anions affects the maintenance of the ionic balance caused by the excessive entrance of the toxic cations of Na+. In addition, the accumulation of OAs in roots can play a role in osmotic adjustment. The results of the present study were consistent with those of Zhao et al.26.

Our study showed that three genes involved in glycolate metabolism were up-regulated in the roots of CSR28 in response to salinity stress (Fig. 5). Glycolate oxidase (GLO) is a key enzyme for photorespiratory metabolism in plants. The overexpression of four GLO-encoding genes has been shown in rice transgenic lines to enhance photosynthesis under conditions of high light and high temperature. Furthermore, H2O2, which can serve as a signaling molecule, was induced upon GLO overexpression62. Since, H2O2 and GLO were induced in the present study, we hypothesized that stress defense responses were triggered by the signaling function of H2O2 cooperated with GLO gene expression.

Conclusion

In this study, the impact of high salinity on rice genotypes was investigated at the seedling stage. The tolerant genotype (CSR28) exhibited better salt tolerance than the sensitive genotype (IR28). The osmoprotectants such as AAs and sugars increased, while OAs decreased in response to salinity stress. Strong correlations were observed between key genes and important compounds such as proline, myoinositol, CAT, and SOD under salt stress. This study highlighted the importance of gene expression and metabolomics data for understanding salt tolerance mechanisms and identified potential biomarkers for developing new salt-tolerant rice varieties.

Materials and methods

Plant materials and growth conditions

Seeds of two rice (Oryza sativa L. ssp. Indica) genotypes with varying salt tolerances were procured from the International Rice Research Institute (IRRI) in the Philippines. The sensitive genotype IR28 was developed at the IRRI, while the tolerant genotype CSR28 (IR51485-AC6534-4) was developed at the Central Soil Salinity Research Institute (CSSRI) in Karnal, India. The plants were cultivated hydroponically in the greenhouse at Heinrich-Heine-University (HHU) in Düsseldorf, Germany. Initially, the seeds were treated with 2.5% sodium hypochlorite for sterilization and then germinated at 28 °C in the absence of light. Subsequently, the seedlings were transplanted into 4-liter pots containing Yoshida culture medium63 and were grown under a light regime of 14 h light and 10 h dark at a temperature of 28 ± 2 °C. The culture medium at a pH of 5.5 was replaced every 3 days. After 2 weeks, the seedlings were subjected to 150 mM (15 dS/m) NaCl. The roots and shoots of both the untreated and salt-treated plants were collected at 6 h, 54 h, and 1 week after salt treatment.

Phenotypic evaluations of salinity tolerance

To evaluate the salinity tolerance of the IR28 and CSR28 genotypes, the length and the fresh and dry weights of roots and shoots, the leaf relative water content (RWC) and the salinity tolerance scores were assessed (in three replications of five seedlings each) 1 week after 150 mM salt treatment.

Root and shoot dry weight Dry weight was determined after placing the samples in a 72 °C oven for 48 h.

Leaf RWC Leaf RWC was calculated for the youngest fully developed leaves with the following equation.

$${\text{RWC}}\; (\%)=\left({\text{FW}} - {\text{DW}}\right)/\left({\text{TW}} - {\text{DW}}\right)\times 100$$

where FW, DW and TW represent the fresh, dry, and turgid weights, respectively.

Salt score 20 seedlings subjected to salinity treatment for 1 week were used to score the salinity tolerance of genotypes based on the method of Gregoria et al.64 in which 1, 3, 5, 7 and 9 refer to very tolerant (normal growth), tolerant (relatively normal growth), relatively tolerant (delayed growth), sensitive (completely stopped growth) and very sensitive (death of all plants), respectively.

Determination of H2O2 and MDA contents and antioxidant enzyme activity

The H2O2 and malondialdehyde (MDA) contents act as ROS and are indicators of stressful environments. The H2O2 content in the root and shoot samples was measured using the method described by Ghiazdowska et al.65. The method of Heath and Pacher66 was used to measure MDA as measure of lipid peroxidation. Catalase (CAT) and superoxide dismutase (SOD) are essential antioxidant enzymes that are required for ROS scavenging when plants experience salt stress. The CAT and SOD contents were measured according to the methods described by Scebba et al.67 and Giannopolitis68, respectively.

Metabolite profiling

The topmost parts of the plants were harvested (from five replications of 10 plants each) after 6 h and 54 h of salinity treatment, shock-frozen in liquid nitrogen and stored at 80 °C until further processing. The samples were ground in a mortar and freeze-dried. 10 mg of lyophilized material were extracted with 1.5 ml of a water, methanol and chloroform (1:2.5:1, v/v) mixture including 5 µM ribitol as an internal standard and stored at − 20 °C. GC–MS analysis was conducted using protocols adapted from Lisec et al.69 and Gu et al.70, as described previously by Shim et al.71. For relative quantification, the peak areas of all metabolites were normalized against sample weight and the peak area of the internal standard ribitol, which was added prior to extraction buffer.

Quantitative real-time PCR (qRT-PCR) analysis

qRT-PCR analysis was used to evaluate the expression of key genes encoding metabolites and antioxidant enzymes. Total RNA from the control and stressed samples was extracted with a P-Biozol kit (manufactured by Bio Flux-Bioer, Tokyo, Japan). Spectrophotometry and agarose gel electrophoresis were used to determine the quantity and quality of the extracted RNA after DNaseI treatment. cDNA was synthesized from 1 µg of total RNA by a cDNA reverse transcription kit (Applied Biosystems, California, USA), according to the manufacturer’s protocol. The primers (Table 2) were designed with Primer Express v3.0 software (Applied Biosystems, Foster City, CA). qRT-PCR analysis of three biological and two technical replicates was performed with an iCycler iQ5 thermocycler (Bio Rad Company) and SYBR Green I (SBP, Iran). All reactions were performed with the default parameters. The expression level of each gene was normalized to that of the internal control gene, elongation factor 1 alpha (OseEF-1a). The method72 of 2−ΔΔCT and log2 fold change (FC) were used to calculate the relative expression as a salinity/control ratio. The statistical significance of the ratios was considered to be │Log2 FC│≥ 1 and P value ≤ 0.05 (as calculated by Student’s t-test).

Table 2 Gene-specific primers used in gene expression analysis.
Full size table

Data analysis

The salinity tolerance of the two genotypes was assessed using phenotypic and physiological data through Student’s t-test (P value ≤ 0.05). The ROS and antioxidant contents and metabolite profiles were analyzed as factorial in a completely randomized design (CRD), and the significance level was tested using ANOVA in SAS v9.2 software. Relative metabolite abundances in roots and shoots of the two genotypes were compared at two timepoints based on the ratio of salinity/control, and their significance was determined using Student’s t-test. Furthermore, the means were compared through Duncan’s Multiple Range test (P value ≤ 0.05). MeV v4.9.0 (Multiple Experiment Viewer) software was used for heatmap and cluster analysis. We also used the “cor” function in R to calculate the Pearson correlation coefficient of metabolites with a threshold greater than 0.80 and a P value < 0.05. A linear regression analysis through R was performed to determine whether there was a significant relationship (P value ≤ 0.05) between metabolites and antioxidant enzymes with their corresponding coding genes.