Evaluation of salinity tolerance of Egyptian barley genotypes and their dehydrin 6-based single nucleotide polymorphisms (SNPs) diversity

Abstract

In many countries, freshwater sources for agricultural irrigation are scarce, making it challenging to meet the food production needs of the growing human population. Utilizing seawater for agriculture could be a potential solution for limited water resources. A lysimeter experiments evaluated fifteen barley genotypes irrigated with different levels of diluted seawater (S1 = 4.0, S2 = 8.0, and S3 = 12.0 dSm⁻¹). Morpho-physiological traits and exploring single nucleotide polymorphism (SNP) diversity among the most tolerant and sensitive genotypes during the winter seasons (2019/2020 and 2020/2021) were examined. Irrigation with diluted seawater at ECw 12.0 dSm⁻¹ significantly decreased leaf area index (by 35.54 %), total chlorophyll content (by 18.97 %), chlorophyll fluorescence (by 46.36 %), plant height (by 29.17 %), number of tillers per square meter (by 43.73 %), number of grains per spike (by 30.56 %), thousand-kernel weight (by 36.07 %), and grain yield (by 34.27 %). In contrast, early flowering was increased by 21.67 %. The Dehydrin 6 gene (Dhn6) partial sequences, 770 bp long, were blasted and used to detect SNPs associated with salinity among genotypes. Several SNPs were identified, with 26 variable sites when aligning the partial Dhn6 sequences. Eleven SNPs were identified between the salt-tolerant Giza 137 and salt-sensitive Giza 132 genotypes, all located in exonic regions. These results indicate a potential role in salt tolerance for cultivar Giza 137, and the SNP markers effectively differentiated barley genotypes, which could be useful in salinity breeding programs aimed at developing salinity-tolerant barley and addressing climate change.

1. Introduction

Due to climate change, there will be insufficient freshwater sources to increase food production and feed growing human populations. Increasing water scarcity in arid and semiarid regions is dynamic, necessitating searching for non-conventional water resources in irrigated agriculture.¹

Egypt is one of the countries suffering from limited conventional water resources for agriculture in the Western Desert, Sinai, the North Coast, and the large border regions that depend on well water and rainwater for agricultural irrigation. Groundwater in the Delta and the Nile River water, as a dependent source for agriculture, is unavailable in most of Egyptian lands.² Using seawater as a water irrigation source in Egypt will be a good choice, as Egypt has about 2400 km of shorelines on both the Red Sea and the Mediterranean Sea.3, 4, 5

However, irrigation by seawater could be used as an alternative water resource for irrigation, but it will not be without problems. The problems are caused by salinity, which can affect plant growth and cause significant yield loss.6, 7, 8 Salinity is a common problem affecting about 20 % of the world's cultivated area and about 50 % of the world's irrigated lands. Where salinity problems are widespread, nearly 30 % of the irrigated farmlands are salt-affected.9, 10

Due to its widely available genetic information, barley (Hordeum vulgare L.) is a model plant species for salinity tolerance. This information helps barley grow in bordering environments and gives high yields with low damage. The ability of barley to survive under salt stress (up to 8 dsm⁻¹) depends on its wide genetic diversity.¹¹

Hence, plant breeders apply various tools for improving salt tolerance in barley, such as morpho-physiological character screening, which are more effective in breeding for salt resilience.12, 13, 4, 14, 15 However, morpho-physiological characters were affected by the environment. The plant breeder was seeking tools to assess genetic variability among closed genotypes while minimizing environmental influences. These tools are known as DNA markers. DNA markers are powerfull for evaluating genetic differences because the DNA content of a cell remains unaffected by environmental conditions, stages of plant development, or the source of the plant. This reliability significantly enhances the consistency of the results.¹⁶ A single nucleotide polymorphism (SNP) marker is defined as a single base pair position in genomic DNA in which a high substitution rate exists in normal individuals in some populations.¹⁷ SNP markers are reflected as one of the most recent molecular markers that could be useful in marker-assisted selection and high-resolution genetic maps.¹⁸ Large-scale approaches, including DNA sequencing, have been employed to identify SNPs in genes responding to salinity stress in plants.19, 20 In barley, there are many forms of each gene; accordingly, the potential of many SNPs exists.²¹

Dehydrins (DHNs) are a class of hydrophilic stress proteins that are expressed during late embryogenesis and in vegetative tissues exposed to salinity stress. They protect cells from damage caused by stress-induced dehydration.22, 23 DHNs play a critical role in stress tolerance and are considered important members of the late embryogenesis abundant (LEA) proteins. Their expression is regulated by dehydration-responsive element binding (DREB) transcription factors. Dehydration-responsive element (DRE) is the cis-acting element located in the promoter region of salt- and drought-inducible genes, including DHNs, and these elements are conserved motifs in plant genomes.24, 25

DHN proteins are stored differentially in cereal plants, mainly primarily contributing to drought and salt tolerance.²⁶ Many studies have investigated the association between the accumulation of mRNA from Dhn genes (Dhn6 and Dhn13) and various biotic-tolerant genotypes in barley. Notable differences among these genotypes were found, particularly in the Dhn6 gene, depending on the duration of dehydration stress.²⁷ In barley, thirteen Dhn genes have been identified that respond to salinity, drought, and low temperatures.28, 29. These genes are located on chromosomes 3H, 4H, and 6H.³⁰ Specifically, Pan et al.³¹ mapped the Dhn6 gene to the plus arm of chromosome 4, suggesting its potential role in stress tolerance, especially as it is predominantly induced by salt stress. Additionally, Dhn5 exhibits a different phosphorylation pattern in two wheat cultivars that show contrasting tolerance to salt stress.²⁶

This study investigated variations in salinity tolerance among newly selected promising barley lines. It also analyzed their response mechanisms to salinity stress and the effects on crop yield. The findings highlight the potential to enhance salt tolerance in barley. Specifically, our research evaluated fifteen barley genotypes irrigated with different levels of seawater, examining various morpho-physiological traits and exploring the diversity of single nucleotide polymorphisms (SNPs) in the Dhn6 gene. The main goal is to promote the use of these lines in salinity breeding programs, thereby improving barley production through the sustainable utilization of saline or poor-quality water resources in agriculture.

2. Materials and methods

2.1. Barley genotypes

In this study, fifteen barley genotypes were utilized, consisting of twelve promising barley lines and three existing cultivars. The Barley Research section of the Field Crops Research Institute, Agriculture Research Center, Egypt, generously provided these genotypes, as detailed in Table 1.

Table 1.

Names and pedigree of 15 barley genotypes (cultivars and promising lines).

No.	Name	Pedigree
1	Giza 123	Giza 117/FAO 86
2	Giza 132	Rihane,05//AS 46/Aths*2Athe/ Lignee 686
3	Giza 137	Giza 118 /4/Rhn,03/3/Mr25,//Att//Mari/Aths*3,02
4	Promising Line 1	C.C 89/3/Alanda/Hamra//Alanda,01
5	Promising Line 2	Giza 124/6/Alanda//Lignee527/Arar/5/Ager//Api/CM67/3/ Cel/WI2269//Ore/4/ Hamra,01
6	Promising Line 3	BLLU/PETUNIA1//CABUYA/3/Alanda// Lignee527 / Arar
7	Promising Line 4	Giza 118/3/Alanda/Hamra//Alanda,01
8	Promising Line 5	Rihane03/7/Bda/5/Cr.115/Pro/Bc/3/Api/CM67/4/Giza120/6/Dd/4/Rihane,03
9	Promising Line 6	Giza 2000/6/Alanda//Lignee527/Arar/5/Ager//Api/CM67/3/ Cel/WI2269//Ore/4/ Hamra,01
10	Promising Line 7	Giza 119/3/Alanda/Hamra//Alanda,01
11	Promising Line 8	Giza 117/6/Alanda//Lignee527/Arar/5/Ager//Api/CM67/3/ Cel/WI2269//Ore/4/ Hamra,01
12	Promising Line 9	Giza 123/5/Furat 1/4/M,Att,73,337,1/3/Mari/Aths*2//Attiki
13	Promising Line 10	ICB91,0343,0AP,0AP,0AP,281AP,0AP
14	Promising Line 11	ICB91,0343,0AP,0AP,0AP,289AP,0AP
15	Promising Line 12	Acsad1164/3/Mari/Aths*2//M,Att,73,337,1/5/Aths/ lignee686 /3/Deir Alla 106//Sv.Asa/ Attiki /4/Cen/Bglo.“S”)

2.2. Lysimeter experimental conditions

Four lysimeter experiments were conducted during two winter cropping seasons (2019/2020 and 2020/2021) at the Soil Improvement and Conservation Research Department of Sakha Agricultural Research Station in Kafr El Sheikh Governorate. The experiments took place at 134 North Cairo, specifically at latitude 31° 06′ 25.20″ N and longitude 30° 56′ 26.99″ E, with an elevation of 6 m above sea level. Each lysimeter was square, with an area of 0.64 m² and a height of 0.6 m. At the bottom of each lysimeter, there was a 0.1 m layer of sand and gravel. Additionally, each lysimeter was filled with 458.25 kg of clay soil (Fig. 1).

Fig. 1.

Design of the lysimeter shows how salinity stress experiments were conducted.

Three replications evaluated two factors in a completely randomized design (CRD). The first factor involved four levels of salinity in irrigation water. Lysimeters were divided into four groups; each group included 32 lysimeters, totaling 180 experiments (4 seawater irrigation levels × 15 barley genotypes × 3 replicates). The second factor involved fifteen barley genotypes (Table 2), which were sown on November 25^th and 27^th, 2019 and 2020, respectively, and harvested on April 25^th and 28^th, 2020 and 2021. All local recommendations for growing barley plants were followed to ensure they were stress-free, except for the treatments of seawater irrigation every 15 days until maturity.

Table 2.

Means of the chemical properties of the irrigation seawater used in the experiments during the two winter cropping seasons.

Seawater treatments	SAR*	ECw*	pH*	Cations Meq. L−1				Anions Meq. L−1
				K+	Na+	Mg+2	Ca+2	SO4	Cl-	HCO3–	CO3–
C	8.04	2.33	7.96	0.6	15.8	2.81	4.92	9.63	12.5	2	–
S1	11.04	4	8.12	0.8	29.5	4.85	9.43	19.59	22.5	3.5	–
S2	17.98	8	8.23	1.5	61.62	10.67	12.81	37.03	43.1	6.5	–
S3	19.43	12	8.27	2.5	88.65	15.43	26.2	57.14	65.14	10.5	–

*pH: was determined in soil water suspension (1:2.5). EC: was determined in saturated soil paste extract. SAR: sodium adsorption ratio.

The effect of irrigation levels was achieved by diluting seawater (ECw = 54.1 dSm⁻¹, equivalent to 43.28 g/L) with well water as a control (ECw = 2.33 dSm⁻¹, equivalent to 0.6 g/L) to produce seawater irrigation. The different saline irrigation water concentrations were prepared by mixing 20 L of well water with 0.512, 1.737, and 2.962 L of seawater to obtain irrigation seawater levels of ECw = 4, 8, and 12 dSm⁻¹, respectively. The irrigation was done every 15 days until maturity. The chemical properties of the different irrigation seawaters were analyzed according to Ayers et al.,³² as shown in Table 2.

2.3. Analysis of soil samples from lysimeter experiments

Soil samples were collected before the experiment and after planting during the two winter cropping seasons (2019/2020 and 2020/2021). Soil texture was determined using the pipette method described by Dewis and Freitas³³ at three consecutive depths: 0–20, 20–40, and 40–60 cm. Soil moisture characteristics were measured using a time domain reflectometry (TDR) probe. The particle size distribution percentage and chemical soil properties were analyzed according to Black et al.³⁴ The results are presented in Table 3.

Table 3.

Percentages of physical properties, soil moisture, and chemical properties were recorded before and after planting at the experimental site during the two winter cropping seasons.

Physical and soil moisture properties
Soil depth (cm)	Particle size distribution (%)			Texture		Soil moisture Characteristics (%)			Bulk density (kg/m3)
	Sand	Silt	Clay	Grade		F.C*	P.W.P.*	A.W.*
0–20	18.23	24.80	56.97	clay		43.90	22.63	21.27	1.16
20–40	18.78	25.50	55.72	clay		41.30	20.80	20.50	1.23
40–60	18.96	25.80	55.24	clay		38.60	19.70	18.90	1.32
Mean	18.66	25.37	57.97	clay		41.27	21.04	20.22	1.24
Chemical soil properties
Soil depth (cm)	Soluble cation (Meq L-1)				Soluble anion (Meq L-1)			pH*	EC*	Esp*	SAR*
	Na+	Mg++	Ca++	K+	Cl-	SO4- -	HCO3–
0–20	24.40	4.16	7.25	0.71	18.50	15.52	2.50	7.91	3.44	12.13	10.21
20–40	25.80	4.45	7.76	0.86	20.25	15.62	3.00	8.05	3.66	12.35	10.44
40–60	38.00	6.49	12.25	0.96	28.90	24.30	4.50	8.15	5.32	14.57	12.41
Mean	29.40	5.03	9.09	0.84	22.60	18.48	3.33	–	4.14	13.02	10.02

*F.C.: Field Capacity. P.W.P: Permanent wilting point. A.W.: Available soil water. pH: was determined in soil water suspension (1:2.5). EC: was determined in saturated soil paste extract. SAR: Sodium adsorption ratio. ESP: exchange sodium percentage.

2.4. Physiological characteristics

During the heading stage, three physiological traits were assessed: leaf area index (cm²) using the LI3100C Licor leaf area meter; relative chlorophyll content (SPAD values) with a chlorophyll meter (SPAD-502 from Minolta Camera Co. Ltd., Japan); and the maximum photochemical efficiency of PSII, which was estimated by measuring chlorophyll fluorescence (Fv/Fm) ratio using an OptiScan OS-30P fluorometer (Opti-Science, Hudson, NH, USA).

2.5. Morphological characteristics

Days to heading (HD) were recorded as the number of days from sowing until 50 % of the initial spikes reached the heading stage. At the harvest stage, after physiological maturity, plant height (PH) was measured from the soil surface to the tip of the spike on a random sample of five plants from each plot. The total yield was harvested from each plot, and the number of tillers was calculated to determine the tillers per square meter (NT/m²). Grains were counted from spikes collected in each plot and multiplied by six to calculate the number of grains per spike (NGS). Grain yield (GY) was reported on a per square meter basis (1.0 m²).

2.6. Dhn6 gene amplification and sequencing

Genomic DNA was extracted from fresh young leaves of barley genotypes following the protocol of the Bio-Spin Plant Genomic DNA extraction kit (Gentech Biosciences). The complete Dhn6 sequence (AF043091) from Choi et al.³⁰ was used to design primers using Primer3 web version 4.1.0 (https://primer3.ut.ee/). Forward primer 5′-GACGGTCAGGAGGACGTGAT-3′ and Reverse primer 5′-CAATCCCAGAGCCTCCATAGC-3′ were used to amplify Dhn6 from tolerant (Giza 137) and sensitive genotypes (Giza 132). The PCR reaction was conducted in a total volume of 20 µl using a multiplex PCR approach, following the manufacturer's instructions. The PCR program began with an initial denaturation step at 95 °C for 5 min. This was followed by 35 cycles of denaturation at 95 °C for 30 s, annealing at 57 °C for 30 s, and extension at 72 °C for 1 min. After the cycles, a final extension was performed at 72 °C for 7 min, followed by a hold at 4 °C. After performing agarose gel electrophoresis, the PCR products were found to be approximately 830 bp in length. The PCR products were then purified and sequenced using the Sanger sequencing method with an ABI automatic sequencer.

DNA sequence analysis was conducted using the BioEdit program.³⁶ After removing primer sequences and ambiguous peaks, the final sequence length was 770 bp. Subsequently, the Dhn6 sequences were used to search the GenBank database with the BLASTN algorithm. Multiple sequence alignment was carried out against identified consistent SNPs from GenBank using CLUSTAL X as Thomson et al.³⁷ described. The Dhn6 sequences of Giza 137 and Giza 132 have been submitted to NCBI GenBank. They were given the accession numbers OQ123824 for Giza 137 and OQ123825 for Giza 132. To determine the relationships between two genotypes and other sequences in the database, neighbor-joining (NJ) trees were constructed.³⁸

2.7. Data analysis

The morpho-physiological data were consistently collected and integrated over multiple years. A two-way ANOVA was conducted using a completely randomized design (RCD) model with SPSS software version 22.0 (SPSS Inc., Chicago, IL) to analyze homogeneity. No significant interaction was observed between the year and treatment, allowing the results to be pooled across years. Fisher’s protected least significant difference (LSD) test was applied at the 5 % level of significance to assess treatment means. Principal Component Analysis (PCA) was performed with Minitab 18.1 software (Minitab Inc., Coventry, UK). Additionally, ClustVis,³⁵ an online tool for visualizing the clustering of multivariate data, was used to create heatmaps.

3. Results

3.1. Effects of different salinity irrigation levels on the morpho-physiological characters

The combined analysis of variance (ANOVA) of the examined morpho-physiological traits, including leaf area index (LAI), total chlorophyll content (SPAD), chlorophyll fluorescence (Fv/Fm), days to heading (HD), plant height (PH), number of tillers per m² (TM), number of grains per spike (NGS), thousand kernel weight (TKW), and grain yield (GY) has been conducted. The results revealed a significant statistical effect (P < 0.01) from different diluted seawater irrigation levels (S) and fifteen barley genotypes (G) across two seasons, as shown in Table 4. A significant two-way interaction between genotype (G) and salinity level (S) was observed for all studied traits. We calculated the mean performances and comparative changes of all agro-physiological traits under well-water irrigation (C = 2.35 dSm⁻¹) and three different levels of diluted seawater (S1 = 4.0 dSm⁻¹, S2 = 8.0 dSm⁻¹, and S3 = 12.0 dSm⁻¹) during the 2019/2020 and 2020/2021 growing seasons.

Table 4.

Effects of different levels of salinity irrigation on the average performance of morpho-physiological characters (combined data from two years).

Treatments/parameters	LAI	SPAD		Fv/Fm	HD	PH	TM	NGS	TKW (kg)	GY (g)/plot
Salinity irrigation levels (dSm−1)
Well water (2.35) (C)	9.51	48.34		0.698	70.09	104.71	269.07	65.67	5.49	157.14
(S1) 4.0	9.04	47.98		0.632	75.52	104.4	264.9	64.95	5.43	155.7
(S2) 8.0	7.66	43.86		0.526	80.65	89.42	200.55	54.07	4.30	118.53
(S3) 12.0	6.13	39.17		0.373	85.34	74.17	151.41	45.60	3.51	89.15
LSD (0.05)	0.273	0.97		0.027	0.57	2.85	12.51	1.94	0.406	7.55
CV %	4.59	2.98		6.57	0.99	3.79	8.41	4.66	6.29	8.92
Barley Genotypes (G)
Giza 123	8.93	45.93		0.442	73.73	97.65	234.90	59.00	4.88	166.58
Giza 132	6.00	38.33		0.234	85.83	85.03	149.43	48.25	3.65	90.90
Giza 137	9.09	48.19		0.454	73.45	101.80	255.23	64.00	5.13	183.25
Promising Line 1	7.13	40.33		0.298	83.70	88.73	142.58	49.00	3.95	90.35
Promising Line 2	8.90	47.18		0.446	74.70	99.13	231.58	62.50	4.90	161.70
Promising Line 3	5.41	37.87		0.302	82.90	87.55	141.40	50.50	4.00	91.38
Promising Line 4	7.65	43.12		0.365	80.83	90.45	226.45	59.75	4.03	109.40
Promising Line 5	8.91	46.83		0.431	75.23	98.95	193.05	64.00	4.88	157.53
Promising Line 6	8.13	44.03		0.344	76.80	95.48	177.73	50.00	4.53	102.05
Promising Line 7	8.72	47.25		0.412	73.55	97.50	246.98	63.00	4.78	162.20
Promising Line 8	7.72	46.50		0.310	80.60	92.08	189.43	53.00	4.50	117.43
Promising Line 9	8.73	47.68		0.434	73.95	100.03	244.75	61.25	5.33	163.90
Promising Line 10	8.75	43.59		0.342	82.28	96.35	165.50	55.00	4.20	110.55
Promising Line 11	8.49	44.53		0.432	82.93	91.65	198.90	50.75	4.60	112.00
Promising Line 12	8.51	45.53		0.345	77.98	97.68	204.43	59.75	4.38	122.48
LSD (0.05)	0.529	1.87		0.053	1.12	5.14	24.23	3.71	0.41	14.62
Analysis of variance (F-test)
Salinity (S)	**	**	**	**	*	**	**	**	*	**
Genotypes (G)	**	*	**	**	**	*	**	**	**	*
Interaction
S × G	*	*	**	*	**	**	*	*	**	*

* and ** indicate significance at 0.05 and 0.01 levels. LAI: leaf area index. Fv/Fm: chlorophyll fluorescence. SPAD: total chlorophyll content. HD: days to heading. PH: plant height (cm). TM: numbers of tiller m². NGS: number of grains spike-1. TKW: thousand kernel weight (kg). GY: grain yield (g).

The phenotypic diversity of fifteen barley genotypes and their responses to salinity stress tolerance are presented in Table 4. Salinity stress significantly reduced the leaf area index (LAI), with average values of 9.04, 7.66, and 6.13 cm under S1, S2, and S3, respectively. When irrigated with S3 compared to well water at 2.35 dSm⁻¹ (C), the average decrease was 35.54 %. The cultivar Giza 137 recorded the highest LAI, averaging 9.09 cm², while Line 3 and Giza 132 had the lowest LAI values, at 5.41 and 6.00 cm², respectively. Irrigating with diluted seawater resulted in significant decreases in both total chlorophyll content (SPAD values) and chlorophyll fluorescence (indicated by the Fv/Fm ratio) across all 15 genotypes studied. Specifically, using diluted seawater at a salinity of 12.0 dSm⁻¹ (S3) led to average reductions of 18.97 % in total chlorophyll content and 46.56 % in chlorophyll fluorescence compared to well water at a salinity of 2.35 dSm⁻¹ The results presented in Table 5 indicate that the Egyptian barley cultivar Giza137 achieved the highest values for SPAD and Fv/Fm ratio, with averages of 48.19 and 0.454, respectively. In contrast, the Egyptian barley cultivar Giza 132 displayed the lowest values, averaging 36.33 and 0.234, respectively. Increasing salinity levels in irrigation significantly reduced all studied agronomic traits, including plant height (PH), the number of tillers per square meter (TM), the number of grains per spike (NGS), and thousand kernel weights (TKW). At an irrigation salinity level of 12.0 dSm⁻¹ (S3), the average reductions were as follows compared to an irrigation level of 2.33 dSm⁻¹ (C): 29.17 % for plant height, 43.73 % for the number of tillers per square meter, 30.56 % for the number of grains per spike, and 36.07 % for thousand kernel weight.

Table 5.

Summary of polymorphic sites in the Dhn6 gene region, which includes 26 SNPs. The SNP positions are specified relative to the beginning of the sequence.

SNP position (bp)	Genotype
	Giza 137	GU216661	GU216683	GU216664	GU216676	GU216681	Giza 132	GU216692
16	G	.	.	.	.	.	.	C
17	C	.	G	.	.	.	.	.
20	G	.	.	.	.	.	A	.
25	G	.	.	.	A	.	.	.
35	T	A	.	.	.	.	.	.
39	C	.	G	G	G	G	G	G
46	G							A
80	T	.	.	.	.	.	.	C
81	G	.	.	.	.	.	.	T
103	C	.	.	.	.	.	.	A
106	A	.	G	.	.	.	.	.
178	T	A	A	A	A	A	A	A
188	G	.	.	.	.	.	A	A
274	G	.	A	.	.	.	.	.
366	G	.	.	.	.	.	A	.
436	A	.	.	.	.	.	.	G
439	C	.	.	T	.	.	.	.
450	C	.	.	.	.	.	.	T
549	G	.	.	.	.	.	.	C
556	C	.	.	.	.	.	.	G
685	G	C	C	C	C	C	C	C
689	G	.	.	.	.	.	A	.
693	G	.	.	.	.	.	A	.
696	C	.	.	.	.	.	T	.
703	C	.	.	.	.	.	T	.
767	C	G	G	G	G	G	G	G

The results regarding grain yield (GY) showed that irrigation with different seawater levels caused a significant decrease in GY, with average values of 155.7, 118.53, and 89.15 g per plot. When irrigated with diluted seawater levels S1, S2, and S3, the average reductions were 0.92 %, 24.57 %, and 43.27 % respectively, compared to well water (C) (Table 4). On the other hand, increasing salinity levels during irrigation from 2.35 dSm⁻¹ to 4.0, 8.0, and 12.0 dSm⁻¹ encouraged all fifteen barley genotypes to flower earlier, with averages of 7.75 %, 15.07 %, and 21.76 % under S1, S2, and S3, respectively. The earliest flowering cultivars were Giza 137 and Giza 123, with average flowering times of 73.45 and 73.73 days, respectively (Table 5).

The results indicated that the cultivar Giza 137 had the highest average values across all studied traits, including 9.09 for LAI, 48.19 for SPAD, 73.45 days to flowering, 0.454 for Fv/Fm ratio, 101.80 cm for plant height, 255.23 t/m² for TMA, 64.00 grains per spike, 5.12 g per seed, and 183.25 g per plot for grain yield (Table 4).

3.2. Principal component analysis (PCA)

3.2.1. PCA loading plot

As shown in the PCA loading plot (Fig. 2), the distance matrix along the horizontal axis showed the direction of relationships among all the morpho-physiological characteristics. The results indicate that the first and second principal components, PCA1 = 84.96 % and PCA2 = 4.78 %, explain a significant 89.74 % of the total variance. PCA1, accounting for 84.96 % of the variation, positions characters such as LAI, SPAD, Fv/Fm, PH, NT, NGS, TKW, and GY on the positive side (right) of the horizontal axis, reflecting their significant positive correlations with other traits. Conversely, PCA2, explaining 4.78 % of the total variability, is primarily influenced by HD, positioning it on the negative side (left) of the horizontal axis due to its significant negative correlations with other traits.

Fig. 2.

Loading a plot graph showing the first two principal components (PCA) of the correlation matrix among the studied characters.

3.2.2. PCA scatter plot

The scatter plot of PCA analysis is based on all studied trait groups and all the genotypes and is divided into three categories, as shown in Fig. 3. PCA analysis revealed that the six barley genotypes (Giza 137; Giza 123, and promising lines 2, 7, 5, and 9) are separate from the other genotypes in the first group, which is located on the right side. This group accounts for 85.1 % of the PCA1 variance, a significant portion that shows a clear distinction and indicates they have a high average across all studied traits, qualifying them as saline-tolerant genotypes. The second group includes promising lines 4, 6, 8, 10, 11, and 12, which are scattered at various distances from each other in the PCA scatter plot, based on their moderate average values of studied traits. Meanwhile, Giza 132, promising lines 1 and 3, is positioned far from the other two groups on the left side, as shown by the PCA2 cluster analysis, which accounts for 4.8 % of the variance. This grouping is primarily based on their days to heading, the trait most influenced by saline water irrigation (Fig. 3), and their lowest values of all studied traits, indicating high reduction and signifying they are saline-sensitive genotypes.

Fig. 3.

A PCA scatter plot displaying all fifteen barley genotypes, analyzed based on nine morpho-physiological traits.

3.3. Heatmap cluster analysis

The heatmap cluster analysis was performed to investigate the effect of irrigation at different salinity levels on the morpho-physiological traits of fifteen barley genotypes using ClustVis of R software (Fig. 4). This analysis grouped genotypes and traits into two main dendrograms. The first is called column dendrograms, which display all nine morpho-physiological traits. The second is called row dendrograms, showing the fifteen barley genotypes clustered into two primary groups. The first group was split into subclusters: one containing saline-tolerant genotypes and another with moderately saline-tolerant genotypes. The second leading group included the saline-sensitive genotypes (Fig. 4).

Fig. 4.

A multivariate heatmap showing the phenotypic diversity of fifteen barley genotypes based on nine morpho-physiological traits was used using the heatmap module of ClustVis in R software.

3.4. SNP markers analysis

The genomic DNA sequences of the barley Dhn6 gene was amplified, resulting in PCR products measuring 830 bp in length. After removing the primer sequences, the final Dhn6 partial sequences for the barley genotypes were 770 bp long. These sequences were then compared to the NCBI database and displayed a high similarity of 99.48 % to the Dhn6 gene sequence. The partial DNA sequences of the Dhn6 gene were used to detect SNPs related to salinity tolerance between the most tolerant cultivar (Giza 137) and the most sensitive (Giza 132) genotypes. A total 26 SNPs variable (polymorphic) sites were detected when comparing their Dhn6 sequences with other sequences in GenBank Database. Only 11 of these were identified between the salt-tolerant (Giza 137) and sensitive (Giza 132) genotypes (Table 5). The eleven SNP substitutions occurred at the following positions: 20 bp (G/A), 39 bp (C/G), 178 bp (T/A), 188 bp (G/A), 366 bp (G/A), 685 bp (G/C), 689 bp (G/A), 693 bp (G/A), 696 bp (C/T), 703 bp (C/T), and 767 bp (C/G). The average nucleotide diversity (p) between the two sequences was 0.014. Most of the detected SNPs (7) are transitions (G/A), while only four are transversions (C/G, G/C, and T/A). Comparison of Dhn6 partial sequences from two cultivars with the complete sequence (AF043091) of the gene showed that all the SNPs are exonic and located in exon 1 of the gene.

The results indicate significant genetic diversity between the two barley genotypes studied, Giza 132 and Giza 137, as illustrated in Table 5. Fig. 5 displays constructed phylogenetic trees, which clearly separate the two genotypes, showing that they are highly divergent in their response to salinity. The most salt-tolerant cultivar, Giza 137, was aligned with all barley Dhn6 sequences available in the gene bank using BLAST analysis to enhance accuracy. This alignment confirms that Giza 137 belongs to the barley Dhn6 group and is associated with the Dhn6 gene, as demonstrated in Fig. 6.

Fig. 5.

A phylogenetic tree for two selected barley genotypes was generated using CLUSTAL_X and compared with other Dhn6 gene sequences.

Fig. 6.

Phylogenetic tree of nucleotide sequences from the Dhn6 gene of Giza 137 and closely related barley sequences from GenBank based on BLAST analysis.

4. Discussion

Water scarcity currently affects arid and drought-prone regions, hindering agricultural growth. In the Mediterranean basin, limited resources constrain farming production. Seawater is viewed as a viable alternative for irrigation and agriculture in Mediterranean countries.¹ Therefore, scientists are examining the various challenges associated with growing plants with saline water. Describing phenotypic diversity is vital for genotypes containing economically important traits to understand their response to saline water as a solution to water scarcity.3, 39

Salinity stress significantly impacts morpho-physiological traits. Many studies in barley have shown how salinity stress influences physiological characteristics. They demonstrated that it adversely impacts almost all stages of plant growth and development.40, 41, 15 Chlorophyll fluorescence is regarded as a reliable method for evaluating photosynthetic capacity under stress. Salinity-induced oxidative stress reduces photosystem II (PSII) activity through negative reactions and decreases the activity of the quinone acceptor.42, 43 In the current study, salinity stress led to a decrease in SPAD readings across all studied genotypes. High SPAD readings were observed in Giza 137, Line 7, Line 9, and Line 5 under different salinity levels, among other genotypes. This finding aligns with studies by Shah et al.⁴⁴ and Mariey et al.,45, 15 who reported a reduction in total chlorophyll content (SPAD) in barley leaves due to salinity. They suggested that SPAD readings could be a useful tool for screening large numbers of genotypes for salt tolerance.

The Fv/Fm ratio shows the photochemical efficiency of PSII and highlights the negative effects of stress on plants, which decreases the maximum PSII yield. This ratio indicates how effectively light drives primary photosynthetic reactions. In our study, irrigation with saline water lowered the Fv/Fm ratio in all genotypes compared to the control (well water). It has been reported that salt stress reduces photochemical efficiency, and electron transport in PSI and PSII declines due to structural changes in the thylakoids, which also limits chlorophyll biosynthesis.44, 45, 15 This observation was demonstrated by a reduction in the SPAD values of barley genotypes.

Agronomical descriptions are vital for evaluating the phenotypic variation in salinity tolerance among barley germplasm. In this study, irrigation with saline water at 12.0 dSm⁻¹ resulted in an average of 21.7 fewer days to heading, showing that barley genotypes Giza 137, Giza 123, Line 7, Line 9, Line 2, and Line 5 headed earlier than other genotypes. This reduction in days to heading results from genetic adaptation involving changes in gene expression in response to environmental challenges such as climate and soil salinity. Genetic variability is essential for effective adaptation to salt stress and helps the spread of different barley genotypes into extreme environments. Therefore, the results of the present study show that plant growth habit and heading date are key traits involved in barley's adaptation to salinity stress. Similar findings were reported by Mansour et al.¹³, Mariey et al.,⁴¹ and Zeeshan et al.,³⁹ who observed that earlier flowering in barley is connected with tolerance to high salinity.

In our study, irrigation with saline water (12.0 dSm⁻¹) caused an average decrease of 43.37 % in grain yield, along with reductions in related traits such as NGS (30.56 %), TM (43.73 %), and TKW (36.07 %). Interestingly, barley genotypes Giza 137, Giza 123, line 7, line 9, line 2, and line 5 exhibited the highest grain yields and all their component traits with minimal reductions, suggesting that these genotypes could be considered salt-tolerant. Conversely, Giza 132, line 1, and line 3 showed the lowest values in these traits, indicating they may be sensitive to salinity. Similar findings39, 43, 41, 45, 7, 15 confirm that deceased grain yield results from reductions in yield components, which are affected by various environmental, agronomic, and physiological factors. Notably, the reduction in grain yield for Giza 137, Giza 123, Line 7, Line 9, Line 2, and Line 5 genotypes was less than 50 %. Therefore, these genotypes could be effectively cultivated with seawater irrigation up to ECw12 dSm⁻¹ while maintain an economic yield.

Various multivariable statistical methods were used to identify relationships among studied traits, aiming to improve yields, select the best genotypes, and understand their responses to environmental stress. These methods can be applied in breeding programs to tackle salinity stress.46, 47 PCA analysis has often been utilized to clarify phenotypic diversity and categorize genotypes, serving as an effective tool that assists breeders in designing successful breeding programs under stress conditions.⁴⁸ In this study, the results showed that the loading and scatter PCA plot classified fifteen genotypes based on their average performance of morpho-physiological traits into three groups: tolerant, moderate, and sensitive to salinity. These findings are consistent with those of Al Lawati et al.,⁴⁸ Oubaidou et al.,⁴⁹ and Mariey et al.,15, 47 who used PCA analysis to evaluate barley genotypes under salinity stress.

Heatmap cluster analysis was used to interpret phenotypic and genotypic evaluation data, helping breeders develop effective strategies for their programs in specific environments.⁵⁰ In this study, the heatmap analysis successfully grouped all genotypes and traits into two main clusters: rows and columns. The row clusters clustered all fifteen barley genotypes into two primary groups: saline-tolerant and moderately tolerant genotypes in one cluster, which was further divided into two subgroups, while saline-sensitive genotypes formed the other cluster. These findings closely match those of Mohamed et al.,⁵⁰ Mariey et al.,⁷ and Mariey et al.,15, 47 who used hierarchical cluster analysis and principal PCA to classify barley genotypes into genetic groups under salinity stress.

Studying genetic diversity among genotypes is valuable for collecting factual information for conservation and developing effective breeding programs. DNA markers are essential for identifying genetic variations among different plant species. Dehydrin genes (Dhns) are highly expressed during embryonic stages and are involved in plant tolerance to salinity stress, as reported by Brini et al.²⁶ Phylogenetic analysis showed that the dehydrin gene (Dhn6) had the highest sequence identity and similarity with salinity transcription factors in barley.51, 52 In our study, 26 single-nucleotide polymorphisms (SNPs) were identified at particular sites in the dehydrin gene 6 (Dhn6), including nine SNPs between the salt-tolerant (Giza 137) and salt-sensitive (Giza 132) genotypes. These findings emphasize notable genetic variation within this gene among the barley genotypes analyzed.

A relationship has been observed between mRNA levels of Dhn genes (Dhn6 and Dhn13) and phenotypically diverse salt-tolerant barley genotypes, with notable differences. Particularly, the Dhn6 gene showed notably significant differences,²⁷ which is located in the short arm of chromosome 4.³¹ Dhn genes have been studied in various plants, including barley, and physiological studies show a positive correlation between dehydrin transcripts and their stress tolerance. A study by Drine et al.¹⁹ showed that the sequences of drought-sensitive barley varieties included a 62 bp deletion and six amino acid substitutions. Additionally, notable differences in the expression profiles of the Dhn6 gene were observed among drought-tolerant and susceptible barley cultivars studied. Similarly, our results revealed genetic variation between Giza 132 (salt-sensitive) and Giza 137 (salt-tolerant), but these differences were at the SNP level, and no InDel variations were observed.

Furthermore, research shows that the protein encoded by the Dhn6 gene has the highest content of Gly (32.5 %) and His (9.2 %) among barley dehydrins.¹⁹ Consequently, its possible function in antioxidative defense and stress resistance was suggested. Interestingly, our findings showed all the SNPs detected are exonic, which suggest a possible role in salt tolerance of cultivar Giza 137. However, this idea needs to be confirmed in future studies.

Other genes, including dehydration-responsive element-binding (DREB) gene and betaine aldehyde dehydrogenase (BADH) gene are known as functional gene families involved in salt tolerance during barley germination. The beta aldehyde dehydrogenase 1 (BADH-1) gene sequence was compared across two highly tolerant (Giza 2000 and Wadi Sedr) and one sensitive barley (Giza 129) cultivar to identify genetic differences among them.⁵³ Sequence analysis revealed that the BADH-1 gene was 728 bp long in both the salt-tolerant and sensitive cultivars. However, the study did not report any genetic variation at the nucleotide level.

Although barley is the most salt-tolerant cereal, salinity stress is considered a major abiotic factor affecting barley yield and productivity.⁵⁴ Additionally, its salinity tolerance varies among different genotypes.⁵⁵ Therefore, the Dhn6 gene could be a promising candidate for using omics and genome editing technologies to improve barley's salt tolerance, which could allow it to be irrigated with saline water.

5. Conclusions

This study demonstrated that the fifteen barley genotypes responded differently to irrigation under varying levels of seawater stress, resulting in changes to their agronomic and physiological traits. Cultivars Giza 123 and Giza 137, along with promising lines 2, 5, 7, and 9, exhibited notably high average values across all tested traits, indicating greater tolerance to salinity stress. Additionally, eleven SNPs were identified when comparing the Dhn6 gene sequence amplified from cultivars Giza 137 and Giza 123 and were exonic. This finding highlighted that SNP markers can effectively differentiate between salt-tolerant and salt-sensitive barley genotypes. These markers can be transformed into PCR-based markers and utilized in salinity breeding programs to develop more resilient barley cultivars. Further research is required to analyze Dhn6 gene expression to understand the relationship between nucleotide substitutions and salt tolerance in the two Egyptian barley cultivars studied.

CRediT authorship contribution statement

Reda M. Gaafar: Conceptualization, Investigation, Methodology, Supervision, Validation, Writing – review & editing. Ismael A. Khatab: Conceptualization, Data curation, Formal analysis, Methodology, Resources, Software, Visualization, Writing – original draft. Samah A. Mariey: Conceptualization, Data curation, Investigation, Methodology, Resources, Validation, Writing – original draft.

6. Ethics approval and consent to participate

6.1. Clinical trial

Not applicable.

6.2. Clinical Trial Number

Not applicable.

6.3. Consent for publication

Not applicable.

Data Availability

Data used during the preparation of this manuscript is available within the article.

Funding

This work has received no funding.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.