Introduction

Salinity is one of the abiotic factors that harm agriculture by limiting plant growth and yield all over the world (Thabet et al. 2021a, b). The most fundamental physiological and biochemical mechanisms governing plant growth and yield were substantially hampered by soil salinity (Thabet et al. 2021a). High salt concentration in the form of sodium and chloride ions causes several undesirable effects on plant growth and performance, leading to disruption in the biochemical and molecular processes of the plant cell (Shahid et al. 2020). Na and Cl toxicity induces nutritional disorders by limiting the uptake of some nutrients (K+, Ca2+, P, N) from the soil (Rehman et al. 2019). Salt stress retards seed germination and seedling-related traits through osmotic and ionic imbalance (Tester and Davenport 2003). Salinity enhances reactive oxygen species (ROS) molecules in plant cells which causes toxic effects on lipid peroxidation, membrane deterioration, as well as DNA and protein damage, finally leading to causes oxidative damage. At the vegetative developmental stage, salt stress disturbs plant water relations, reduces cell turgor, and depresses plant biomass, which ultimately leads to osmotic stress (Munns and Tester 2008). This suggests that water uptake is the primary effect of environmental salinity. In general, salinity stress interferes with many physiological and biochemical activities, such as lowering water and nutrients uptake into plant tissues, changing metabolic processes, disturbing photosynthetic machinery, transpiration, and gaseous exchange by reducing the content of chlorophyll and carotenoids, and decreasing stomatal conductance, all of which impede plant growth and development.

Leaf protein content is one of the crucial indicators of the effects of salt stress (Isayenkov and Maathuis 2019). Therefore, plants respond to salinity stress conditions by accumulating small molecular mass proteins, which act as a source of storage nitrogen and also involve in osmotic adjustment (Ashraf and Harris 2004; Singh et al. 1987). On the other hand, it has been reported that a higher accumulation of osmolytes like proline could enhance salinity stress tolerance. Proline can modulate redox potential, thereby facilitating osmotic adjustment, safeguarding cellular membranes, and maintaining enzyme stability during abiotic stress (Raza et al. 2007). The most prevalent secondary metabolites, such as phenolic compounds, act as the most powerful antioxidants for scavenging the free radicals generated in response to different environmental stimuli. Another category of phenolic compounds known for having antioxidant effects is flavonoids (Tohidi et al. 2017). As a result, plants move toward cellular ion homeostasis, free radical scavenging, and stimulation of antioxidant machinery, polyamines biosynthesis, and mineral uptake leading to the maintenance of plant growth and productivity.

Barley (Hordeum vulgare L.), a cereal crop, is among the most well-known cereals in the world that can tolerate abiotic stresses (Thabet and Alqudah 2019). Since barley is one of the most saline-tolerant crops and has a higher tolerance to salt stress than other cereals like wheat, corn, and rice, it is commonly used as a model for studying plant resistance mechanisms (Thabet and Alqudah 2023; Thabet et al. 2021b). Identifying quantitative trait loci (QTLs) controlling salt tolerance is a promising strategy for enhancing salinity tolerance in barley. Thabet et al. (2021b) identified QTLs associated with salt tolerance at seed germination and seedling-related traits in barley. Thabet et al. (2021a) identified several QTLs for agronomic traits such as plant height, spikelet per spike, grains per spike, spike density, and thousand kernel weight under salt stress. Moreover, multiple QTLs were identified for several physiological traits of different plant species under salinity stress. For example, Rivandi et al. (2011) detected a QTL associated with the shoot Na+ exclusion trait that mapped to HvNax4. Shavrukov et al. (2010) identified a QTL mapped to HvNax3, a locus controlling shoot sodium content in the wild barley. Interestingly, Thabet et al. (2021a) identified several QTLs associated with enzymatic and non-enzymatic antioxidant components that cope with salt stress tolerance in barley using a genome-wide association study (GWAS).

GWAS is an effective approach to map QTLs more accurately and pinpoint the genes underlying several biochemical and agronomic parameters with relative ease in many crop species (Alqudah et al. 2020, 2021). In barley, GWASs have focused on complex traits in barley such as drought resistance, salt tolerance, and frost tolerance (Mwando et al. 2020; Thabet and Alqudah 2023; Thabet et al. 2018, 2020). In our recent work, we have explored several biochemical and agronomic traits as potential selection criteria for plant salt tolerance. Here, we measured bio-physiological and agronomic traits in a core set of 138 barley accessions to identify significant QTNs/candidate genes. We hypothesized that the studied components are controlled by multiple genes and would result in multiple QTNs for salt tolerance in barley. Our results shed light on the understanding of the genetic basis of plant architecture based on morpho-physiological attributes, provided QTNs and markers that can be used by breeders, and constructed a theoretical basis for fine mapping and for marker-assist-selected breeding.

Materials and Methods

Germplasm and Genotyping

A core set collection consisting of 138 worldwide spring barley accessions was selected out of 22,621 wild and domesticated barley accessions hosted in the German ex-situ IPK-Gatersleben Gene Bank as highly diverse accessions. The collection is originally from different geographical origins including 54 cultivars and 84 landraces. The core set/panel was split into 63 two-rowed and 75 six-rowed. All the accessions were genotyped using GBS that yielded 19,276 SNPs as reported by Milner et al. (2019). This collection was tested for the population structure that is considered in the GWAS analysis. Principal component analysis was used to check the structure within the population and plotted based on geographical regions, row-type, and biological status. Detailed information on barley accessions is provided in Table S1.

Experimental Setup

A core collection of 138 barley accessions was evaluated under field conditions at Fayoum University (29° 11′ 20.36″ N latitude, 30° 10′ 06.45″ E longitude) from November 2021 to April 2022. Five seeds from each accession were planted in plastic pots (40 cm × 25 cm × 25 cm) that were filled with silty loam soil and each pot was manually irrigated as required. Each accession was grown under two treatments, including control and salinity (200 mM NaCl), and was replicated three times for each treatment using a randomized complete block design (RCBD). At the early reproductive phase (25 days after sowing (DAS)), the plants were thinned into three plants per pot then exposed to both well-watered and salt treatments (200 mM NaCl). In this experiment, we used standard techniques for irrigation, fertilization, and pest and disease management. Salt treatment was conducted up to 50 DAS, after which distilled water irrigation was used until harvest. After 8 weeks of planting, leaf samples from each accession were collected to measure the physiological traits, including proline content (ProC), soluble protein content (SP), total phenolic (TPC), total flavonoid (TFC), and total antioxidant capacity (TAC). Five phenotypic traits such as spike length (SL), spikelet number per spike (SS), grain number per spike (GS), the weight of grains per spike (WGS) in gram (g), and thousand-grain weight (TGW) in gram (g) were measured as described in Thabet et al. (2021a). Additionally, salt tolerance indices (STI) for all traits were calculated according to the following equation: STI = trait value under salinity/trait value under control.

Estimation of Proline and Protein Content

Proline content (ProC) was determined and extracted as described in methods by Bates et al. (1973). Briefly, 400 ml of the mixture (1.25 g ninhydrin, 20 mL phosphoric acid, and 30 mL glacial acetic acid) was added to a 200 mL supernatant aliquot and heated for 1 h at 100 °C. The mixture was then allowed to cool, and 4 mL of toluene was added. At 520 nm, the absorbance was taken and the results were recorded as mmol g−1 DW. Soluble protein (SP) was determined by using a dye-binding method and bovine serum albumin as a standard. Then, the absorbance was measured spectrophotometrically at 595 nm as mg g DW−1.

Determination of Total Antioxidant Capacity (TAC)

The total antioxidant capacity (TAC) was estimated according to Prieto et al. (1999). Briefly, barley extract of 10 µL was mixed with 1 mL of 250 µM DPPH solution and 4 mL distilled water, then the extract was placed in the dark for 30 min. The absorbance was measured at 517 nm. The reference standard was Trolox, and also the results were recorded as Trolox equivalent mg g−1 DW. The inhibition (%) of free radical DPPH was calculated using the equation as follows:

Antioxidant activity (I %) = [(A0 − Ae)/A0] × 100, where A0 is the absorbance of the blank sample and Ae is the absorbance of the tested sample.

Determination of Metabolites

The content of total phenolic (TPC) was estimated and extracted according to Song et al. (2010). Briefly, 100 µL of leaf extract sample was added to 50 µL of 2N Folin–Ciocalteau reagent. After 5 min, 400 µL of 2N Na2CO3 and 1 mL of water were added and incubated for 90 min at room temperature and then removed to a microplate. The absorbance was measured at 740 nm, and the results were expressed as gallic acid (GAE) equivalent µg g−1 of DW.

Total flavonoid content (TFC) was measured and extracted using the method described in Zou et al. (2004). Briefly, 400 µL of barley extracts was mixed with 60 µL of 5% NaNO2, 500 µL of distilled water, and 140 µL of 10% AlCl3. After 10 min, 400 µL of 1 mM NaOH was added, and incubated for 10 min at room temperature, and then removed to a microplate. The absorbance was measured at 500 nm and the results were expressed as rutin (RE) equivalent µg g−1 of DW.

Data Analysis

Two-way ANOVA was determined using R software to compare the mean values of the different traits under both treatments at a P-value < 0.05. A mean value comparison of different traits was made with Student’s t-test. Measured trait relationships under each treatment were calculated using Pearson correlation analysis applying the corrplot function in the R package corrplot (Wei and Simko 2017). The best linear unbiased estimations (BLUEs) for the mean values of the measured traits were calculated by assuming the intercept and genotype effects using the lme4 package (Bates et al. 2015). The broad-sense heritability (H2) was performed according to Schmidt et al. (2019).

Genome-Wide Association Scan (GWAS)

GWAS was conducted using Genomic Association and Prediction Integrated Tool (GAPIT) in R software on a diverse set of 138 barley accessions (Lipka et al. 2012). The accessions were genotyped by sequencing (GBS) that yielded 19,276 SNPs. First, the GWAS analysis was computed using a mixed linear model (MLM) that took into account the variance–covariance kinship matrix and principal component analysis (PCA) and was conducted by incorporating the phenotypic and genotypic dataset. The kinship matrix was calculated using the VanRaden method (VanRaden 2008) to determine relative kinship among the sampled individuals. Both PCA and kinship matrix were used for population correction and stratification. We also applied a GAPIT model known as “fixed and random model circulating probability unification” (FarmCPU) to our data analysis. This was applied by considering the incorporation of multiple markers simultaneously as a covariate in a fixed-effects model and optimization on the associated covariate markers separately in a random effects model, which enabled us to avoid any false-negatives and to control the false-positive associations by preventing model over-fitting (Liu et al. 2016). Thus, FarmCPU is a powerful tool with less false-positives than MLM. The selection of an appropriate model and thresholds is important steps in identifying markers that are truly associated with specific traits and that might be located within or very close to genes that control the trait variation while also controlling both false-positive and false-negative associations. To determine which of the tested models best fitted the data, we plotted quantile–quantile (Q-Q) plots based on the observed and expected − log10(P) values. Based on the GWAS output of three models (GLM, MLM, and FarmCPU), the number of significant associations, and the resulting Q-Q plots, we then selected the FarmCPU model. A threshold P-value 0.001 equal to − log10(P) ≥ 4 was used to determine the significance of marker–trait associations. The region of interest was defined using the linkage disequilibrium (LD) decay around the most significantly associated SNP markers on each chromosome, which extended until a squared correlation (r2) decayed to < 0.5 within LD ± 1.2 Mbp. The physical locations of the SNPs were identified based on the barley database BARLEX used to mine the candidate genes (Mascher et al. 2021). Gene annotations, gene ontology (GO), and even gene annotations are all included in the web-based platform known as BARLEX. The annotation of the high-confidence (HC) candidate gene was detected using Morex v3 (Mascher et al. 2021).

Results

Molecular Markers, Population Structure, and Linkage Disequilibrium (LD)

The core collection of barley accessions belongs to ten geographical regions (Table S1 and Fig. 1). There are two main clusters among the accessions with biological status and row-type whereas most of the accessions were 2-rowed and landraces were 6-rowed (Table S1 and Fig. 2a and b). The accessions of the collection were originally from different geographical origins and could see a trend among the accessions that most of Northern Europe, Northern America, and South America were clustered around the center while the accessions from other origins were distributed (Table S1 and Fig. 2c). These results showed a wide diversity among the landraces coming from central Asia, the Near and the Far East, North Africa, and Ethiopia compared to accessions mostly coming from Europe and the Americas (Table S1 and Fig. 2).

Fig. 1
figure 1

Geographical distribution of barley population

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Fig. 2
figure 2

Population structure based on a biological status, b row-type, and c geographical region

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All of the barley accessions were genotyped using GBS, which yielded 19,276 SNPs. Information on the number of markers for each chromosome, with the map length and marker density for each chromosome, is presented in Figure S1. The largest number of markers was detected on chromosome 2H by 3,373 SNPs, while the highest marker density was detected on chromosome 7H. Only 2193 markers were noted on chromosome 1H with the lowest marker density among chromosomes. For the whole barley genome, the overall average values of r2 decreased as the distance between SNPs increased as well as the average LD decay distance was approximately 2.4 Mbp (Alqudah et al. 2020) (Figure S1).

Natural Variation of Biochemical Attributes

Natural variation was detected for metabolites and biochemical compounds that expressed normal distribution under salt stress (Fig. 3 and Figure S2). Proline content (ProC) accumulation remarkably increased by 47% under salt treatment (Fig. 3a), compared to the control condition (Table S2). Soluble protein (SP) significantly decreased under salt stress by 57% when compared to the control (Fig. 3b). The mean salt tolerance indices (STI) for SP and ProC were 43 and 190, respectively. A significant increase in TPC, TFC, and TAC was detected under salt treatment by 32%, 66%, and 72%, respectively, in comparison to the control across over genotypes (Table S2). Genotypic mean values for such traits STI ranged from 42.99 (SP) to 359.69 (TAC) (Fig. 4a and Table S2). H2 values ranged from 91.44 for TFC_C to 61.78 for TAC_C. Under salt treatment, moderate H2 was detected for TFs by 72.43. H2 values for STI were low for ProC_STI (34.70) and reached 89.31 in TFC_STI.

Fig. 3
figure 3

Natural phenotypic variation of physiological and agronomic traits: a proline content (ProC) mmol g−1 DW, b soluble protein (SP) mg g DW−1, c total antioxidant capacity (TAC) µg g−1 DW, d total phenolic content (TPC) µg g−1 DW, e total flavonoid content (TFC) µg g−1 DW, f spike length (SL) cm, g spikelet per spike (SS), h grains per spike (GS), i weight of grains per spike (WGS) gm, and j thousand kernel weight (TKW) gm in barley accessions under control and salinity

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Fig. 4
figure 4

Natural phenotypic variation of all of the studied traits for salt tolerant indices (STI): a biochemical attributes including, proline content (ProC) mmol g−1 DW, soluble protein (SP) mg g DW−1, total antioxidant capacity (TAC) µg g−1 DW, total phenolic content (TPC) µg g−1 DW, and total flavonoid content (TFC) µg g−1 DW. b Agronomic traits including spike length (SL) cm, spikelet per spike (SS), grains per spike (GS), weight of grains per spike (WGS) gm, and thousand kernel weight (TKW) gm in barley accessions

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Natural Variation of Morphological Attributes

Natural phenotypic variation was detected for all agronomic traits with normal distribution under both control and salt treatment (Fig. 3 and Figure S3–S4). Means for all of the studied morphological traits showed a significant decrease in response to salt treatment (Fig. 1f–j and Table S3). Yield traits, including WGS, showed a highly significant reduction under salt treatment by 48% compared to control treatment, indicating that salt stress has negative effects on barely yield grains (Fig. 3i and Table S3). The highest significant mean value was detected for GS_STI by 85.26% (Fig. 4b and Table S3). Moreover, high heritability was detected for GS under both treatments by 99.3% (Table S3).

Correlation

Pearson correlation was detected among all the studied traits under both treatments. Under control conditions, weight grains per spike showed a positive correlation with total phenolic content and soluble protein (Fig. 5a). In parallel, a positive correlation was detected under salinity treatment between proline content and the content of total antioxidant capacity, phenolics, and flavonoids; meanwhile, it showed negative correlation with all of the studied agronomic traits (Fig. 5b). This indicated that vigorous relationships between proline content and the secondary metabolites or antioxidants, including total phenolics and flavonoids, would imply a sophistication mechanism to scavenge the ROS molecules generated under salt stress.

Fig. 5
figure 5

Pearson correlation among all the studied traits under a control condition and b salinity condition. Soluble protein (SP) µg g−1, proline content (ProC) µg g−1, total phenolics (TPC) µg g−1, total flavonoid content (TFC) µg g−1, total antioxidant capacity (TAC), spike length (SL) cm, spikelet per spike (SS), grains per spike (GS), weight of grains per spike (WGS) gm, and thousand kernel weight (TKW) gm in barley accessions

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GWAS Analysis and Candidate Genes

Based on the GWAS output of three models (GLM, MLM, and FarmCPU), the number of significant associations, and the resulting Q-Q plots, we then selected the FarmCPU model. A threshold P-value 0.001 equal to –log10(P) ≥ 4 was used to determine the significance of marker–trait associations (Figures S5–S6). On all seven chromosomes, 122 significant QTNs passed the FDR level of P ≤ 0.05 were found for ten measured traits using GWAS analysis (Table S4). Forty-one QTNs were identified for plants grown under control conditions and 53 QTNs were detected for plants exposed to salt treatment, while 28 QTNs were identified for STI of all of the measured traits (Table S4). The highest number of QTNs was found on chromosome 2H (31 QTNs), followed by chromosomes 3H and 7H (21 QTNs for each), 5H (16 QTNs), 1H (14 QTNs), 6H (10 QTNs), and 4H (9 QTNs) (Table S4).

Thirty-six QTNs were found for the physiological traits, including SP (12 QTNs), TFC (11 QTNs), TPC (7 QTNs), TAC (5 QTNs), and ProC (1 QTN) (Table S4 and Fig. 6). For TFC, most QTNs were localized on chromosome 3H (570,752,043–570,752,136 bp). For TPC_STI, the most significant QTN was associated for chr2H:607,230,828:G:C which positively influenced the trait by + 17.8 g (–log10(P) = 6.62748789570469E-07) resulting in regulating the antioxidant defense system in response to salt stress (Table S4 and Fig. 6).

Fig. 6
figure 6

a Manhattan plot of the traits under control (C): ProC_C—proline content control, SP_C—soluble protein control, TAC_C—total antioxidant capacity control, TFC_C—total flavonoid content control, and TPC_C—total phenolic content control in barley. b Manhattan plot of the traits under salt treatment (S): ProC_S—proline content salinity, and SP_S—soluble protein, TAC_S—total antioxidant capacity salinity, TFC_S—total Flavonoid content salinity, and TPC_S—total phenolic content salinity in barley. The x-axis shows the chromosomes and the SNP order. The y-axis shows the − Log10 (P-value) for each SNP marker. c Manhattan plot of all biochemical attributes based on salt tolerant indices (STI) in spring barley. The x-axis shows the chromosomes and the SNP order. The y-axis shows the − Log10 (P-value) for each SNP marker

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Most QTNs for TFC_STI showed the highest positive effect (~ + 35.02 µg g−1 DW) and were influenced by G:A (at a position 570,7520,43 bp) on chromosome 3H, followed by TPC_STI which showed a positive effect of 17.77 µg g−1 DW by G:C (at position 607,230,828 bp) on chromosome 2H (Table S4 and Fig. 6).

The highest negative effect was detected for TAC_STI by − 27.02 mg g−1 DW and influenced by QTN T:A located at position 579,232,393 bp on chromosome 4H. In general, GWAS outputs revealed several QTNs that affected traits like TFC and TPC positively under salinity conditions. This information might be useful for the selection of salt-tolerant accessions and help to extend the understanding of genetic mechanisms of plants exposed to salt stress.

Eighty-six QTNs were found for the morphological-related traits (Table S4 and Fig. 7). Most QTNs were detected for SL (36) and TKW (19) for plants grown either under control or salinity. Highly significant QTNs (− log10(P) > 8.66E-08) were found to be associated with GS and TKW under both treatments (Table S4 and Fig. 7). The most significant QTN (− log10(P) = 2.81809401251597E-13) was detected for grain number per spike under control conditions and was associated with the marker chr1H:58,239,661:C:A located on chromosome 1H at 582,396,61 bp (Table S4 and Fig. 7). In response to salinity conditions, the most significant QTN (− log10(p) = 9.09430571441244E-13) was also found for grain number per spike on chromosome 2H at position 572,438,008 bp and was associated with the marker chr2H:572,438,008:C:A (Table S4 and Fig. 7).

Fig. 7
figure 7

a Manhattan plot of the morphological traits under control (C): SL_C—spike length control, SS_C—spikelets per spike control, grains per spike control, WGS_C—weight grains per spike control, and TKW_C—thousand kernel weight control in barley. b Manhattan plot of the traits under salt condition: SL_S—spike length salinity, SS_S—spikelet per spike salinity, GS_S—grains per spike salinity, WGS_S—weight grains per spike salinity, and TKW_S—thousand kernel weight salinity in barley. The x-axis shows the chromosomes and the SNP order. The y-axis shows the − Log10 (P-value) for each SNP marker. c Manhattan plot of all morphological traits based on salt tolerant indices (STI) in spring barley. The x-axis shows the chromosomes and the SNP order. The y-axis shows the − Log10 (P-value) for each SNP marker

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According to the physical position of QTNs, 203 putative candidate genes were detected within the defined interval (Table 1 and Table S5). Out of the high number of candidate genes, three important putative genes were found to be associated with involved salt-stress tolerance. For example, the QTN G:C was found inside the candidate gene HORVU.MOREX.r3.2HG0193490 at position 607,230,828 on chromosome 2H that annotated as a growth-regulating factor, controlling the variation of SP_STI and TPC_STI. This candidate showed overexpression in response to salt stress in several plant species (Fig. 8a–c).

Table 1 Marker trait association with the multi traits. Physical position of markers which are passing –log10(4)
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Fig. 8
figure 8

a Manhattan plot of SP_STI and TPC_STI, respectively, SP: soluble protein, TPC: total phenolics, and STI: salt tolerant index. The x-axis shows the chromosomes and the SNP order. The y-axis shows the –log10 (P-value) for each SNP marker. b Locus zoom in on regional plot colocalization of highly associated SNP (G:C). c Structure of the candidate gene (HORVU.MOREX.r3.2HG0193490) with sequence variation in the second exon inside LD heat map. d Allele effect on the accessions at the natural genetic variant (G:C) of single gene-based. e Quantile–quantile scale representing expected versus observed − log10 (P-value)

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The allelic variation explained by this marker G:C showed that accessions carrying allele C had slightly lower soluble proteins and total phenolics compared to the accessions carrying G allele (Fig. 8d). This indicates that accessions carrying allele G showed more resistance to salinity stress, thereby scavenging the deleterious effects of the free radicals generated under salt stress conditions.

The HORVU.MOREX.r3.2HG0217090 annotates the C2 calcium/lipid-binding and GRAM domain protein which were found to have higher functional activity in the transmembrane regions and are involved in abiotic stress, including drought and salinity (Table S5 and S6). Interestingly, the QTN G:A was located inside the candidate gene HORVU.MOREX.r3.3HG0291720 at position 501,703,401 on chromosome 3H and annotated as Ca-binding protein that harbors the variation of thousand kernel weight under both control and salinity conditions (Fig. 9a–c). Ca-binding protein is a type of protein that binds to specific intracellular free Ca2+ and contributes to the signalling process that triggers the expression of salt-stress responsive genes. The allelic variation explained by this marker G:A showed that accessions carrying allele G had a lower thousand kernel weight compared to the accessions carrying A allele (Fig. 9d). This indicates that accessions carrying allele G are more sensitive to salinity stress and thereby produced fewer grains under such conditions.

Fig. 9
figure 9

a Manhattan plot of TKW_C and TKW_S, respectively, TKW: thousand kernel weight, C: control, and S: salt. The x-axis shows the chromosomes and the SNP order. The y-axis shows the –log10 (P-value) for each SNP marker. b Locus zoom in on regional plot colocalization of highly associated SNP (G:A). c Structure of the candidate gene (HORVU.MOREX.r3.3HG0291720) with sequence variation in the second exon inside LD heat map. d Allele effect on the accessions at the natural genetic variant (G:A) of single gene-based. e Quantile–quantile scale representing expected versus observed − log10 (P-value)

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Discussion

Globally, salinity stress is one of the abiotic factors that greatly affect agriculture by limiting plant growth and production. Therefore, plants respond to the detrimental effects of salt stress through triggering antioxidant defense mechanisms (Thabet et al. 2021a, 2022). The leaf proteins have a major role in plant development, reproduction, and ultimately final grain yield. It is generally known that proteins in biological systems are the initial targets of ROS, and thus, decrease in protein content in plant leaves as a result of salt stress is expected. One of the main targets of ROS is the chloroplast, which modifies a wide range of proteins, such as stromal and thylakoid, significantly, including the degradation and inactivation of Rubiscom (Ishida et al. 1997). Therefore, leaf protein content is one of the crucial indicators of the effects of salt stress (Isayenkov and Maathuis 2019). Our results showed that soluble protein (SP) was significantly decreased under salinity conditions in comparison to control. Our results are in agreement with Khosravinejad et al. (2009), those who demonstrated a considerable decline in total protein content as a result of specific stress protein synthesis produced under salinity stress. Soluble protein content decreased as salinity increased, owing to a disruption in the protein synthesis mechanism or increased proteolytic activity, which eventually induced programmed cell death (Kovács 2017). Therefore, plants may accumulate small molecular mass proteins in response to salt stress, which may be a source of storage nitrogen and may also involve osmotic adjustment (Ashraf and Harris 2004; Singh et al. 1987).

On the other hand, it has been proposed that a higher accumulation of compatible solutes, such as proline, can improve salinity tolerance. Proline, along with other osmolytes, modulates redox potential in the end by facilitating osmotic adjustment, safeguarding cellular membranes, and maintaining enzyme stability during abiotic stress (Raza et al. 2007). Our study detected a significant increase in proline contents (ProC) for all barley accessions in response to salinity stress compared to the control condition. This finding is consistent with observations on wheat by Kumar et al. (2017). As a result, the osmoadaptive response involves the accumulation of proline, suggesting an imprecise involvement in the resistance to abiotic stress such as drought and salinity.

Moreover, plants respond to the detrimental effects of salt stress through the evolvement of a variety of metabolic mechanisms. Phenolic compounds are not only the most prevalent secondary metabolites in higher plants, but they also serve as the most powerful antioxidants for scavenging the excessive ROS generated in response to different environmental stimuli. Another category of phenolic compounds known for having antioxidant effects is the flavonoids (Tohidi et al. 2017). Our findings showed a significant increase in the accumulation of total phenolic and flavonoids compounds under salt treatment. This agrees with the observations made by Hichem et al. (2009), who reported the significant effect of salt stress on the total phenolic and flavonoids compounds in two maize (Zea mays L.) cultivars. The well-established link between polyphenols and abiotic stress tolerance, including salinity, is a strong indicator of tolerance and may therefore be utilized as a marker for the maintenance of the redox state in the cells (Sharma et al. 2019).

Under salt stress, our results detected a high positive correlation between proline content and the content of total antioxidant capacity, phenolics, and flavonoids; meanwhile, it showed a negative correlation with all of the studied agronomic traits, indicating that the vigorous relationships between proline content and the secondary metabolites, including total phenolics and flavonoids, would imply a sophistication adaptive mechanism to scavenge the free-radicals generated under salt stress (Hodaei et al. 2018).

Understanding the molecular basis of plant responses to salt stresses depends on identifying and characterizing salt stress-responsive genes and, ultimately, developing salt-tolerant crops. Here, we identify several candidate genes that might control the biological mechanisms of plant crop adaptation to salt stress, including barley. The most important candidate is located on chromosome 2H at position (607,227,256–607,231,009 bp), namely HORVU.MOREX.r3.2HG0193490, that encodes growth-regulating factors (GRFs). GRFs are transcription factors that are specific to plants and play significant roles in the plant’s response to abiotic stress (Omidbakhshfard et al. 2015). The GRFs have been discovered in a variety of plant species, including Arabidopsis, rice, rapeseed, Chinese cabbage, tobacco, and wheat (Li et al. 2021). Sakuma et al. (2006) reported that the overexpression of AtGRF7 in Arabidopsis results in improved tolerance to salt and drought stress. In cotton, Cao et al. (2020) reported that expression patterns of GhGRF1A, GhGRF1D, and GhGRF17D confer increased in response to salt stress. In strawberry, expression profile analysis detected that FvGRF3, FvGRF6, and FvGRF8 were up-regulated under salt stress (Li et al. 2021).

Moreover, the HORVU.MOREX.r3.2HG0217090 annotates the C2 calcium/lipid-binding and GRAM domain protein which were found to have higher functional activity in the transmembrane regions and are involved in abiotic stress, including drought and salinity. C2 domain-containing proteins (C2DPs), a type of Ca-dependent protein kinase, have higher functional activity in the transmembrane regions and are involved in abiotic stress, including drought and salinity (Zhang et al. 2022). In apple, overexpression of a gene encoding a C2-domain phospholipid-binding protein MdCAIP1 conferred tolerance to osmotic and salt stress (Liu et al. 2019). In transgenic Arabidopsis, a rice gene encoding a small C2 domain protein OsSMCP1 was up-regulated under biotic and abiotic stress responses (Yokotani et al. 2009). Based on RNA-seq data, the transcript profiles of GmC2-58, GmC2-88, and GmC2-148 in soybean showed overexpression in response to drought, salinity, and abscisic acid (ABA) treatments. Furthermore, the soybean plants with GmC2-148 transgenic hairy roots had delayed leaf rolling, increased the contents of proline, and reduced the contents of H2O2, O2−, and malondialdehyde under both salt and drought stress conditions (Sun et al. 2021). Altogether, these findings reported that C2 domain genes are implicated in the response to salt and drought conditions.

Interestingly, the QTN G:A was located inside the candidate gene HORVU.MOREX.r3.3HG0291720 at position 501,703,401 on chromosome 3H that harbors the variation of thousand kernel weight under both control and salinity conditions. Ca-binding protein is a type of protein that binds to specific intracellular free Ca2+ and contributes to the signaling process that triggers the expression of salt-stress responsive genes. Calcium ion (Ca2+) is an important second messenger for plant development as it serves as a signaling factor for plant transduction pathways regulating stress-induced gene expression (Kudla et al. 2010). Ca2+ maintains the steadiness of the cell wall, membrane, and membrane-binding protein and improves the plant responses to abiotic and biotic stresses, thereby triggering multiple signaling pathways (Cho et al. 2016). Ca-binding protein is a type of protein that binds to specific intracellular free Ca2+ and contributes to the signalling process that triggers the expression of stress response genes. Under salt stress, high levels of cytosolic Ca2+ can also be captured by three main Ca-binding proteins, namely calmodulins, Ca-dependent protein kinase, and calcineurin B-like proteins (Galon et al. 2010). Genetic evidence suggests that perception of salt stress leads to cytosolic Ca2+-mediated signal transduction that activates the Ca2+ sensor SOS3 (calcineurin-like protein [CBL4]). SOS3 senses the Na+-induced elevation of cytosolic Ca2+ via the interaction with SOS2 (calcineurin-interacting protein kinase [CIPK24]), which phosphorylates and activates of the plasma membrane Na+/H+ exchanger SOS1. The activation of SOS1 results in either the extrusion of Na+ out of cytosol or its compartmentation in the vacuole (El Mahi et al. 2019). Similar to SOS3, when calmodulins (CaMs) perceive a change in cytosolic Ca2+ associated with a particular stress response, they may modify the affinity of the Ca2+/CaM complex by controlling specific downstream receptors (such as phosphodiesterase) and targets (such as Cyclic Nucleotide-Gated Channel12) (DeFalco et al. 2016). Kaewneramit et al. (2019) reported that the overexpression of OsCaM1-1 enhanced salt stress tolerance in transgenic rice. Yoo et al. (2005) reported that overexpression of GmCaM4 in soybean conferred salt stress tolerance via modulating the expression levels of salt stress-responsive genes. Shen et al. (2020) reported that HvCaM1 (gene identifier HORVU0Hr1G001270) negatively regulates salt tolerance in barley via interaction with the transcriptional activator HvCAMTA4 to modulate the down-regulation of HvHKT1;5 and/or the up-regulation of HvHKT1;1, certain class I high-affinity potassium transporter (HvHKT1;5 and HvHKT1;1)-mediated Na+ translocation in roots, and ultimately reduce Na+ accumulation rates in shoots. Our findings detected the role of our candidate in the regulation of ion homeostasis was enhanced by Ca2+/CaM, thus providing a new strategy for increasing the tolerance of plants to salt stress.

Conclusion

To our knowledge, this is the first report that establishes the genetic factors determining the protective role of polyphenols (TPC and TFC) and antioxidants against salt stress-induced oxidative damage and the differential response of barley accessions, using GWAS. The vigorous activity of antioxidants would imply significant roles in the evolvement of defense-oriented strategies to scavenge the ROS molecules generated under salt stress. Highly significant QTN G:A was located inside the candidate HORVU.MOREX.r3.3HG0291720 gene on chromosome 3H. This gene annotates as Ca-binding protein that binds to specific intracellular free Ca2+ and contributes to the signalling process that in turn activates the expression of stress response genes. The identified QTNs/ candidate genes shed light on the genetic improvement of barley genotypes under salt stress.