Dissection of salt tolerance in cotton germplasm by analyzing agro-physiological traits and ERF genes expression

doi:10.21203/rs.3.rs-3232079/v1

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Dissection of salt tolerance in cotton germplasm by analyzing agro-physiological traits and ERF genes expression

https://doi.org/10.21203/rs.3.rs-3232079/v1

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The development of genotypes that can tolerate high levels of salt is crucial for the efficient use of salt-affected land and for enhancing crop productivity worldwide. Therefore, incorporating salinity tolerance is a critical trait that crops must possess. Salt resistance is a complex character, controlled by multiple genes both physiologically and genetically. To examine the genetic foundation of salt tolerance, we assessed 16 F1 hybrids and their eight parental lines under normal and salt stress (15 dS m^− 1) conditions. Under salt stress conditions significant reduction was observed for plant height (PH), bolls/plant (NBP), boll weight (BW), seed cotton yield (SCY), lint% (LP), fiber length (FL), fiber strength (FS), potassium to sodium ratio (K+/Na+), potassium contents (K+), total soluble proteins (TSP), carotenoids (Car) and chlorophyll contents. Furthermore, the mean values for hydrogen peroxide (H2O2), sodium contents (Na+), catalase (CAT), superoxidase (SOD), peroxidase (POD), and fiber fineness (FF) were increased under salt stress. Moderate to high heritability and genetic advance was observed for NBP, BW, LP, SCY, K+/Na+, SOD, CAT, POD, Car, TSP, FL, and FS. Mean performance, and multivariate analysis of 24 cotton genotypes based on various agro-physiological and biochemical parameters suggested that the genotypes FBS-Falcon, Barani-333, JSQ-White Hold, Ghauri, along with crosses FBS- FALCON × JSQ- White Hold, FBG-222 × FBG-333, FBG-222 × Barani-222, and Barani-333 × FBG-333 achieved the maximum values for K+/Na+, K+, TSP, POD, Chlb, CAT, Car, LP, FS, FL, PH, NBP, BW, and SCY under salt stress and declared as salt resistant genotypes. The above-mentioned genotypes also showed relatively higher expression levels of Ghi-ERF-2D.6 and Ghi-ERF-7A.6 at 15 dSm^− 1 and proved the role of these ERF genes in salt tolerance in cotton. The findings suggest that these genotypes have the potential for the development of salt-tolerant cotton varieties with desirable fiber quality traits.

Biological sciences/Genetics

Biological sciences/Plant sciences

Earth and environmental sciences/Climate sciences

salt tolerance

natural variation

antioxidants

upland cotton

sustainable agriculture

Soil salinization is a significant obstacle to the sustainable growth of global agriculture and a major threat to the ecological health of the soil. Approximately 37% of all cultivated lands globally are categorized as sodic. It is estimated that almost 50% of the world's (2.5 billion hectares) of irrigated lands are significantly impacted by salinization (Suhani, Vaish, Singh, & Singh, 2020). Additionally, every year nearly four million acres of cropland are abandoned due to severe salt damage (Mau & Porporato, 2015). Besides the existing salt-affected soil, ongoing secondary salinization in farmable land is caused by several factors such as climate change, excessive irrigation, irrigation with saline water, and excessive use of mineral fertilizers (Zafar, Razzaq, et al., 2022). When crop plants experienced salt stress, several physiological and biochemical changes occur, including water imbalance, ionic toxicity, nutrient deficiency, oxidative stress and hormonal imbalance which ultimately reduced the crop yield (Guo, Huang, Li, & Hou, 2020).

Cotton (Gossypium spp.), a main agro-industrial crop, is grown worldwide for its significant production of natural fibers and seed oil. India, China, USA, Brazil, Pakistan, and Australia are amongst the major cotton-growing countries. Despite being in the top 5 globally, Pakistan has low yield compared to other leading countries due to various biotic and abiotic factors (drought, heat, and salinity) (Chaudhry UF, 2022). Among all these abiotic stresses salinity is a major threat to cotton production. In Pakistan due to competition between cereals and other cash crops, the cultivation of cotton is being moved to saline and alkaline soils. Besides these factors, the increasing demand of natural fiber for textiles, due to population growth and societal reasons, it's essential to produce high-quality cotton fibers in abundance (Zafar, Shakeel, et al., 2021).

Cotton is tolerant to moderate salinity (7.7 ds/m), but increased soil salinity negatively affects its growth and yield. At plant level, salinity disrupt various physiological and biochemical processes like ionic toxicity, photosynthesis process and oxidative injury. As a consequence of this phenomenon, there is inhibition of cell division and expansion in leaf surface area, leading to developmental changes, metabolic adaptations, and decreased stem growth and root cell proliferation. These factors directly contribute to a reduction in the NBP, BW, SCY and fiber quality parameters (Abdelraheem, Esmaeili, O’Connell, Zhang, & Products, 2019; Farooq et al., 2020). Salt stress triggers the production of higher levels of reactive oxygen species (ROS) such as superoxide radicals (O₂⁻), H₂O₂, singlet oxygen (¹O₂) and hydroxyl radicals (OH⁻), which induce oxidative stress in the plant (Farooq, Zhang, Zafar, Ma, & Zhao, 2021). The production of ROS at higher level leads to oxidation and damage of plant cell organelles like mitochondria, peroxisomes and chloroplasts. In response, the cell produces antioxidant enzymes such as superoxidase dismutase (SOD), peroxidases (POD), catalase (CAT), and non-enzymatic antioxidants (Munawar, Hameed, & Khan, 2021) to counter the detrimental effects of oxidation, safeguard the organelles, and uphold the normal functioning of the cell (Hu & Schmidhalter, 2023).

Genetic switches also referred as transcription factors (TFs), attach themselves to specific cis-acting elements, regulating downstream expression of the gene and aiding in the management of stress-induced impairment. In various plant species, many TFs are either induced or suppressed after exposure to salinity stress, facilitating adaptation to saline environments (Zafar, Rehman, et al., 2022). The AP2/ERF gene family is a plant-specific group of TFs characterized by an AP2/ERF-type DNA-binding domain that consists of roughly 60–70 amino acids (Long et al., 2019). This family was initially discovered in the Arabidopsis homeotic gene APETALA2 (AP2). The AP2/ERF gene family is subdivided into four subfamilies, namely AP2, ERF, RAV, and DREB. The AP2 subfamily differs from the other three subfamilies in that it possesses double AP2/ERF domains, while the other three subfamilies have a single AP2/ERF domain (Qiao, Huang, & Liu, 2008). In the past two decades, the ERF family of genes has garnered significant importance, as overexpression of ERF genes in various plants has been shown to enhance tolerance against abiotic stresses and pathogen resistance in genetically modified plants. In previous study, based on transcriptome and qRT-PCR analysis, Zafar et al. (2022) reported several potential candidate genes (Ghi-ERF-6D.1, Ghi-ERF-2D.6, Ghi-ERF-12D.13, Ghi-ERF-11D.5, and Ghi-ERF-7A.6) that promote salinity tolerance in upland cotton (Zafar, Rehman, et al., 2022).

Salinity poses a significant economic and environmental challenge to the global cotton industry, which highlights the need to enhance the salt tolerance of cotton and develop germplasm capable of producing higher yields under changing climatic situations (Zafar, Razzaq, et al., 2022). To mitigate the impact of salinity stress, several strategies have been employed to enhance resilience in cotton plants. Among these, the development of salt-tolerant germplasm is regarded by cotton breeders as the most effective and sustainable solution (Zafar, Zhang, et al., 2022). Utilizing conventional breeding techniques to screen available germplasm for morphological, physiological, and biochemical traits can leverage natural variations and significantly enhance cotton plant tolerance to salt stress. Measuring the levels of antioxidant enzymes produced in response to salt stress can serve as an effective screening strategy for identifying germplasm with the potential to yield higher crops under saline conditions (Farooq et al., 2020). In this experiment, cotton genotypes were evaluated under salt stress with the aim to develop salt resilient cultivars. The present research aims to achieve three objectives: (i) identification of cotton genotypes that exhibit salt tolerance and have potential for use in cotton breeding programs, (ii) development of a selection criterion that incorporates agro-physiological and biochemical characters for the production of salt-resistant cotton cultivars, and (iii) evaluation of the expression levels of Ghi-ERF-2D.6 and Ghi-ERF-7A.6 genes in salt-tolerant cotton genotypes.

Under normal field conditions, the F₀ seeds of eight genotypes were sown and then, in a Line × Tester manner, were crossed. Four genotypes namely (FBS- FALCON, FBS- SMART, FBG-222 and Barani-333) were used as lines and four genotypes namely (JSQ- White Hold, FBG-333, Barani-222 and Ghauri-3) were used as testers. Following the crosses, selfed and crossed bolls were gathered separately upon boll opening. Subsequently, the cotton fibers from each group were processed separately using a single roller ginning machine (Testex, Model: TB510C) after being picked. In the next growing season, 24 genotypes (including 8 parents and their 16F1 hybrids, Table 1) were grown in containers with normal soil (1.9 dSm^− 1) under RCBD with three replications. After the emergence of seedlings, healthy specimens were selected for each genotype in every replication, all of which were subsequently subjected to salt stress through irrigation water with a known EC. The salt stress was induced following a well-established procedure (Farooq, Shakeel, Chattha, & Tahir, 2020; Hariadi, Marandon, Tian, Jacobsen, & Shabala, 2011; Shabala et al., 2012). This method involves gradually increasing the salinity to a target level of ~ 15 dS m − 1 in two distinct phases to avert seedling injury and ensure survival. Sodium chloride (NaCl) was calculated and added to the groundwater using the U.S Salinity Lab formula (Richards, 1954).

Amount of sodium chloride (g/kg) =\(\frac{Equiv. Wt of NaCl \times Saturation Percentage }{100 \times 1000} X TSS\)

In this equation, TSS (Total Soluble Salts) represents the difference in EC (desired EC - initial EC) multiplied by 12.66, and the saturation percentage of the soil was established at 42%.

During the initial phase, the 2-week-old seedlings were subjected to a salinity level of 7.5 dS m − 1, which necessitated the addition of approximately 17.74 g of NaCl to each 10 kg pot. Subsequently, in the second phase, the 4-week-old seedlings were subjected to an escalated salinity level of 15 dS m − 1, which required a further addition of about 23.72 g of NaCl per 10 kg pot. Thus, a total of approximately 41.46 g (17.74 g + 23.72 g) of NaCl was added per 10 kg pot in the two phases to attain the desired salinity level. After reaching this level, no further salt was added. Following this precise modulation of salinity, standard agricultural procedures were then followed for irrigation. This careful approach enabled us to achieve gradual increments in salinity while preventing seedling injury and ensuring survival throughout the experiment.

All agronomic procedures were appropriately carried out from the time of sowing until harvesting. Figure S1 displays the environmental conditions, including temperature, humidity, and rainfall, during crop growth. Upon reaching maturity (i.e., after 140 days of growth and > 80% completion of boll formation), the following parameters were measured from five chosen plants from each replication of both normal and saline treatment: plant height, bolls plant^− 1, boll weight, seed cotton yield plant^− 1, lint%, H₂O₂, SOD, POD, CAT, TSP, chlorophyll contents (a & b), carotenoids, fiber strength, fiber length, fiber fineness, Na⁺, K⁺ and K⁺/Na⁺ ratio.

Table 1

Material used in breeding study
Code	Genotype name
1	FBS- FALCON
2	FBS- SMART
3	FBG-222
4	Barani-333
5	JSQ- White Hold
6	FBG-333
7	Barani-222
8	Ghauri-3
9	FBS- FALCON × JSQ- White Hold
10	FBS- FALCON × FBG-333
11	FBS- FALCON × Barani-222
12	FBS- FALCON × Ghauri-3
13	FBS- SMART × JSQ- White Hold
14	FBS- SMART × FBG-333
15	FBS- SMART × Barani-222
16	FBS- SMART × Ghauri-3
17	FBG-222 × JSQ- White Hold
18	FBG-222 × FBG-333
19	FBG-222 × Barani-222
20	FBG-222 × Ghauri-3
21	Barani-333 × JSQ- White Hold
22	Barani-333 × FBG-333
23	Barani-333 × Barani-222
24	Barani-333 × Ghauri-3

Table 2

Analysis of soil properties used in this study
Parameters	Unit	Normal Field
Textural Class (USDA)		Sandy Loam
ECe	dS/m	1.9
pHs		8.4
Organic matter	%	0.5
Na⁺	meq L^− 1	3.05

Ion analysis:

The analysis of sodium and potassium ions involved grinding hot air-dried leaves using a pestle and mortar. Concentrated nitric acid and sulfuric acid in a 2:1 ratio was used for digestion of ground leaves on hot plate. Then digested samples were cooled down by adding distilled water at room temperature. Flame photometer (410 Flame Photometer) was used to take out readings and then K+ /Na + ratio was calculated.

Bernt and Bergmeyer (Allen, Farmer, & Sohal, 1983) method was used to measure the hydrogen peroxide of treated and control samples. 0.5 g of leaf sample was homogenized using liquid nitrogen for each control and treated group. 1.5 mL of 100 mM potassium phosphate buffer (pH 6.8) was used for suspension of ground leaves then suspension was centrifuged at 18000 ×g for 20 min at 40°C. Supernatant (0.25 ml) was taken out and mixed with 1.25 ml of peroxidase reagent containing; 40 µg peroxidase/ml, 0.005% (w/v) O-dianizidine and of 83 mM potassium phosphate buffer (pH 7.0) at 30°C to initiate the reaction. After 10 min, the reaction was halted by adding 0.25 ml of 1 N perchloric acid and centrifuging the mixture at 5,000× g for 5 min. Spectrophotometer (NanoDrop™ 8000 Spectrophotometer Thermo Fisher Scientific, Sweden) was used to measure the absorbance at 436 nm and the amount of H₂O₂ was determined based on an extinction coefficient of 39.4 mM^− 1cm^− 1 (L. Zhang et al., 2014). The SOD activity was measured in terms of enzyme units that inhibited the photochemical reduction of nitro blue tetrazolium (NBT). To quantify SOD, a reaction mixture containing 100 µl of enzyme extract, potassium phosphate buffer (pH 5), 200 µl of methionine, 200 µl of Triton X, 100 µl of NBT, and 800 µl of distilled water was prepared. The mixture was exposed to ultraviolet light for 15 min, followed by the addition of 100 µl of Riboflavin. The absorbance at 560 nm was measured using a spectrophotometer. The same enzyme extracted for measuring SOD was used to measure POD values. It calculated as the number of enzyme units that oxidized guaiacol. The reaction mixture was prepared by mixing 100 µl H₂O₂ (40 mM), 100 µl guaiacol (20 mM) mixed with 100 µl of enzyme extract and 800 µl potassium phosphate buffer (pH 5) in Eppendorf tube and spectrophotometer was used to measure absorbance at 470 nm wavelength (Liu, Zou, Meng, Zou, & Jiang, 2009). To determine the Total Soluble Proteins (TSP), leaf tissues extraction was carried out using vortex and centrifugation in a phosphate buffer (pH 4). Furthermore, a 40 µl portion of the same enzyme extract was mixed with 160 µl of Bradford reagent, added to an ELISA plate, and subjected to spectrophotometer readings at a wavelength of 595 nm absorbance (Bradford, 1976). Catalase activity was determined by measuring the amount of H₂O₂ consumed and transformed into H₂O and O₂. Spectrophotometer values were recorded at 240 nm absorbance to calculate CAT activity (Liu et al., 2009). The chlorophyll and carotenoid contents were measured by following (Arnon, 1949).

qRT‑PCR of Ghi-ERF-2D.6 and Ghi-ERF-7A.6 in tolerant cotton genotypes

The newly emerged leaves of control and stressed (15 ds⁻m) genotypes at two leaves stage were sampled. The RNA was extracted using a kit protocol (RNAprep Pure Plant Kit by Tiangen, Beijing, China) and the quality and integrity of the RNA was checked using gel electrophoresis and NanoDrop 2000 spectrophotometer (Thermo Scientific, USA) respectively. The cDNA was prepared using (PrimeScript® RT Reagent Kit, Takara Biotechnology Co., China). Three repeats of both genes were used technically. The real time differential expression of mRNA of both genes were measured using qRT-PCR (Maxima SYBR Green)/ROX qPCR Master mix (2X), cat#K0221, Thermo scientific, USA). Gene normalization was obtained using GAPDH as an internal control. The gene specific primers were used for the expression of both genes (Table 3).

Table 2

The list of the primers
qReal-time PCR Primers
ERF2-RT-F	AGGTACGGGTTCCTTATGGC	120
ERF2-RT-R	CCGTCTCTGCAGGAACAATC
ERF7-RT_F	GGGAGTGCATGGGGATTTAC
ERF7-RT-R	CAATCTTCCGAACTCTTCTGTGT

Fiber quality parameters

A seed cotton sample was weighed and subjected to a single roller ginning machine, in order to separate lint from the seeds. The percentage (%) of the lint was then calculated. This was achieved by dividing the weight of the lint by the weight of the seed cotton in the sample, the obtained value was then finally expressed as a percentage. Furthermore, employing a high-volume instrument (HVI-900, USTER, USA), the resulting lint was given an in-depth analysis in order to determine characteristics such as fiber strength, fiber fineness and length parameters.

Statistical analysis

The split-plot analysis of variance (ANOVA) was performed with two factors (Steel & Torrie, 1980). The statistical tools prcomp, ggplot2, and Hmisc from R 4.1.1 were used to conduct principal components, and correlation analyses respectively on the mean data. Falconer and Mackay's method was followed to estimate heritability and genetic advance (Falconer, 1996; Racine, 2012).

The results of the ANOVA suggested significant variations among parents and their F1 hybrids under salt stress conditions. These differences indicated the existence of genetic diversity in the studied germplasm for salinity tolerance, as shown in (Table 4). Moreover, the treatment and Genotypes × Treatment interaction for all characteristics was highly significant, indicating that all genotypes responded differently to salt stress (Table 4). All-cotton genotypes experienced a negative impact on their agronomic and yield-related traits under salt stress. The reduction was observed in agronomic traits such as PH, NBP, BW, SCY, lint%, FL, and FS as shown in (Table 5). Interestingly, fiber fineness (FF) was increased under saline environments. Furthermore, the mean values for H₂O₂, Na⁺, CAT, POD, and SOD were increased under salt stress, while the mean values for K⁺/Na⁺, K⁺, TSP, Chla and b were decreased under saline conditions.

The GAM was classified into three categories: low (0–10%), moderate (10–20%), and high (20% or higher). In terms of heritability, values exceeding 80% were considered very high, values ranging from 60–79% were moderately high, values between 40–59% were medium, and values lower than 40% were regarded as low (Johnson, Robinson, & Comstock, 1955). Under saline conditions, high genetic advance as a percentage of the mean (GAM) was observed for BW, CAT, Chla, Chlb, FF, NBP, POD, SCY, SOD and TSP whereas moderate GAM was observed for PH, LP, Na⁺, K⁺, FS, FL, and Car. The heritability estimates for BW, CAT, Car, Chlb, FL, FS, K⁺, K⁺/Na⁺, LP, NBP, PH, POD, SOD and TSP, were very high whilst moderate heritability estimates were observed for SCY, Na⁺, H₂O₂, FF, and Chla under saline conditions (Table 5).

Table 4

ANOVA split plot design for different agronomic and physiological traits
SOV	Replication	Treatments	Error A	Genotypes	Treatments × Genotypes	Error B
DF	2	1	2	23	23	92
BW	0.14	66.59**	0.05	2.04**	9.94**	0.14
CAT	23.68	2218.17*	34.99	577.3**	9.37**	10.30
Car	0.04	4.62**	0.00	0.16**	5.74**	0.02
Chla	0.13	17.32**	0.11	0.41**	2.86**	0.07
Chlb	0.003	1.20**	0.00	0.04**	4.17**	0.00
FF	0.06	81.48**	0.23	5.24**	7.18**	0.14
FL	0.09	5335.82**	0.28	151.9**	14.64**	4.89
FS	2.43	3235.33**	1.63	127.1**	8.93**	5.98
H₂O₂	0.00	2.84**	0.02	0.2**	1.32	0.02
K⁺	104.10	12511.4*	23.50	1166.1**	6.29**	42.70
K⁺/Na⁺	1.00	76.07**	0.01	1.17 **	10.94**	0.14
LP	9.70	11507.4**	19.10	356.4**	14.31**	14.40
Na⁺	120.04	7863.99**	5.16	89.38**	7.23**	20.32
NBP	7.34	4841.84**	3.55	108.73**	8.11**	9.96
PH	62.80	11612.8**	38.20	429.8**	5.32**	30.20
POD	11.82	762.86**	1.48	165.7**	6.32**	4.11
SCY	23.92	5451.73**	40.26	130.77**	14.36**	6.27
SOD	0.83	304.39**	0.87	22.7**	17.96**	0.77
TSP	772.40	71679.4**	53.40	73718.5**	5.36**	308.20
* Significant at the 0.05 level
** Significant at the 0.01 level

Table 5

Genetic components of variability, genetic advance percentage means and heritability (broad sense) estimates for studied traits across control and salt stress conditions
SOV	Treatment	Min	Max	Mean	H2b	GAM
BW	Control	2.0973	4.838	3.4328	30.61	31.39
	Stress	0.121	4.129	2.0733	89.86	22.19
CAT	Control	14.05	45.6091	29.5031	84.88	20.77
	Stress	16.4609	59.7862	37.3531	93.98	44.39
Car	Control	0.43	1.49	0.7058	47	39.57
	Stress	0.0863	1.2997	0.3475	83.98	16.43
Chla	Control	0.36	1.985	1.2565	14.37	40.35
	Stress	0.0607	1.6396	0.5637	75.45	33.07
Chlb	Control	0.2059	0.6768	0.3822	12.90	44.47
	Stress	0.0145	0.533	0.1993	90.11	38.05
FF	Control	0.3462	4.4903	2.1767	93.14	18.13
	Stress	2.05	5.15	3.6812	73.79	20.48
FL	Control	16.29	31.785	26.3596	78.50	14.58
	Stress	2.93	28.42	14.1857	90.92	11.19
FS	Control	17.672	29.814	23.7402	25.41	21.09
	Stress	4.148	27.932	14.2603	93.25	12.27
H₂O₂	Control	0.303	1.166	0.6221	68.85	36.76
	Stress	0.4579	1.3713	0.9033	62.50	38.25
K⁺	Control	135.34	179.78	156.89	73	13.87
	Stress	100.29	178.67	138.25	88	26.30
K⁺/Na⁺	Control	2.77	7.22	4.12	60	21.41
	Stress	1.68	5.09	2.67	86	30.74
LP	Control	32.1954	57.5275	44.502	16.01	13.55
	Stress	7.9245	49	26.6242	94.80	11.48
Na⁺	Control	20.4	56.06	38.9845	65.03	21.92
	Stress	33.18	69.98	53.7644	59.49	17.39
NBP	Control	13	32	24.6389	18.82	36.23
	Stress	3	27	13.0417	88.20	22.56
PH	Control	50.29	93.22	66.941	19.64	12.05
	Stress	28.36	70.49	48.9806	90.56	14.89
POD	Control	5.25	19.7774	11.9577	81.13	54.77
	Stress	7.1406	31.7854	16.5612	90.14	82.40
SCY	Control	22.32	32.658	27.8429	81.07	29.61
	Stress	5.4634	30.3084	15.5381	73.27	61.97
SOD	Control	3.04	7.904	5.5599	55.94	19.11
	Stress	4.1312	16.845	8.4673	90.63	77.37
TSP	Control	336.8	739.6	553.3943	97.07	37.43
	Stress	288	736.8	508.7726	97.96	48.25

Mean performance of agronomic, and fiber quality traits

Under the influence of salt stress conditions (15dsm^− 1), the growth and agronomic characteristics of the examined genotypes were negatively impacted. Notably, the effects of salt stress were particularly evident in terms of plant height, the number of bolls per plant, and boll weight, lint percentage, fiber fineness, and fiber strength for all the genotypes studied along with crosses, as depicted in (Figs. 1 & 2). Under salt stress conditions (15dsm^− 1), certain genotypes and crosses demonstrated remarkable growth and yield performance compared to other studied genotypes. Genotypes FBS-Falcon, Barani-333, JSQ-White Hold, Ghauri, along with crosses FBS- FALCON × JSQ- White Hold, FBG-222 × FBG-333, FBG-222 × Barani-222, and Barani-333 × FBG-333 displayed notable resilience and exhibited superior performance for following traits such as PH, NBP, BW, SCY, lint%, FL, FF, and FS. Conversely, the genotypes (FBS-Smart, FBG-222, FBG-333, and Barani-222), and crosses (FBS- FALCON × Ghauri-3, FBS- SMART × JSQ- White Hold, FBS- SMART × Barani-222, FBS- SMART × Ghauri-3, FBG-222 × JSQ- White Hold, FBG-222 × Ghauri-3, Barani-333 × JSQ- White Hold, and Barani-333 × Ghauri-3) demonstrated lower performance for agronomic and fiber quality traits under salt stress conditions. Notably, among the studied crosses (FBS- FALCON × FBG-333, FBS- FALCON × Barani-222, FBS- SMART × FBG-333, and Barani-333 × Barani-222), revealed moderate performance for PH, NBP, BW, SCY, lint%, FL, FF, and FS under salt stress conditions. These specific crosses demonstrated moderate resilience to the adverse effects of salt stress, indicating their potential for further exploration and breeding programs (Figs. 1 & 2).

Biochemical traits

The biochemical traits such chlorophyll a (Chla), chlorophyll b (Chlb), total soluble proteins (TSP), and carotenoid (CAR) exhibited a statistically significant decrease in all genotypes under salt stress (15dsm^− 1) (Fig. 3). The application of salt stress treatment led to a more significant reduction in Chla, Chlb, TSP, and CAR levels in specific genotypes and crosses, such as FBS-Smart, FBG-222, FBG-333, Barani-222, FBS- FALCON × Ghauri-3, FBS- SMART × JSQ- White Hold, FBS- SMART × Barani-222, FBS- SMART × Ghauri-3, FBG-222 × JSQ- White Hold, FBG-222 × Ghauri-3, Barani-333 × JSQ- White Hold, and Barani-333 × Ghauri-3. However, the genotypes FBS-Falcon, Barani-333, JSQ-White Hold, Ghauri, FBS- FALCON × JSQ- White Hold, FBG-222 × FBG-333, FBG-222 × Barani-222, and Barani-333 × FBG-333 exhibited the ability to maintain moderate level of Chla, Chlb, TSP, and CAR (Fig. 3). Furthermore, certain genotypes and crosses, such as FBS- FALCON × FBG-333, FBS- FALCON × Barani-222, FBS- SMART × FBG-333, and Barani-333 × Barani-222 exhibited less reduction in Chla, Chlb, TSP, and CAR level compared to susceptible genotypes (Fig. 3).

The application of NaCl treatment had a significant impact on the ionic homeostasis of plants, particularly in relation to Na⁺, K⁺, and the K⁺/Na⁺ ratio. Notably, the Na⁺ content in NaCl-treated plants was found to be significantly higher compared to the control group, indicating an increase in sodium accumulation as a result of the treatment. The mean graph of (Fig. 4) depicted that certain genotype and crosses, including FBS-Falcon, Barani-333, JSQ-White Hold, Ghauri, FBS- FALCON × JSQ- White Hold, FBG-222 × FBG-333, FBG-222 × Barani-222, and Barani-333 × FBG-333 lower increase in sodium contents compared other genotypes. Compared to other genotypes, these genotypes also revealed less reduction in potassium contents. Consequently, these genotypes and crosses maintained a higher ratio of potassium to sodium content, similar to that of the control group and performed well for biochemical, agronomic and fiber quality characters under saline environments (Fig. 4). However, specific crosses such as FBS- FALCON × FBG-333, FBS- FALCON × Barani-222, FBS- SMART × FBG-333, and Barani-333 × Barani-222 demonstrated a moderate accumulation of Na⁺, and exhibited a moderate potassium to sodium ratio. The mean graph reveals that the genotypes and crosses, including FBS-Smart, FBG-222, FBG-333, Barani-222, FBS- FALCON × Ghauri-3, FBS- SMART × JSQ- White Hold, FBS- SMART × Barani-222, FBS- SMART × Ghauri-3, FBG-222 × JSQ- White Hold, FBG-222 × Ghauri-3, Barani-333 × JSQ- White Hold, and Barani-333 × Ghauri-3 exhibited higher levels of Na⁺, along with a lower potassium to sodium ratio under salt stress conditions and declared as salt susceptible genotypes (Fig. 4).

Under salt treatment (15 dsm^− 1), the cotton genotypes displayed a significant rise in the levels of reactive oxygen species (ROS). In contrast to the control group, all genotypes exhibited noticeable increases in the levels of H₂O₂, indicating the presence of oxidative stress induced by salt stress. However, it is noteworthy that susceptible genotypes and specific crosses, including FBS-Smart, FBG-222, FBG-333, Barani-222, FBS- FALCON × Ghauri-3, FBS- SMART × JSQ- White Hold, FBS- SMART × Barani-222, FBS- SMART × Ghauri-3, FBG-222 × JSQ- White Hold, FBG-222 × Ghauri-3, Barani-333 × JSQ- White Hold, and Barani-333 × Ghauri-3, exhibited greater levels of oxidative damage and showed higher value for H₂O₂ under saline conditions (Fig. 5A). This observation suggests that these particular cultivars possess lower tolerance to salt stress when compared to tolerant genotypes such as FBS-Falcon, Barani-333, JSQ-White Hold, Ghauri, FBS- FALCON × JSQ- White Hold, FBG-222 × FBG-333, FBG-222 × Barani-222, and Barani-333 × FBG-333. In contrast, moderate levels of H₂O₂ were observed in the following crosses such as FBS- FALCON × FBG-333, FBS- FALCON × Barani-222, FBS- SMART × FBG-333, and Barani-333 × Barani-222 (Fig. 5A). Theses genotypes also showed a moderate level of SOD, POD, and CAT activities under saline environmental conditions. Our findings indicate that under the salt stress condition of (15 dsm^− 1), there was a higher level of SOD, CAT and POD activity observed compared to the control group. The tolerant genotypes exhibited optimal level of SOD, CAT and POD activity under saline conditions. The tolerant genotypes FBS-Falcon, Barani-333, JSQ-White Hold, Ghauri, FBS- FALCON × JSQ- White Hold, FBG-222 × FBG-333, FBG-222 × Barani-222, and Barani-333 × FBG-333 revealed high performance for SOD, POD and CAT as compared to moderately tolerant and susceptible genotypes. The susceptible genotypes FBS-Smart, FBG-222, FBG-333, Barani-222, FBS- FALCON × Ghauri-3, FBS- SMART × JSQ- White Hold, FBS- SMART × Barani-222, FBS- SMART × Ghauri-3, FBG-222 × JSQ- White Hold, FBG-222 × Ghauri-3, Barani-333 × JSQ- White Hold, and Barani-333 × Ghauri-3 showed lesser increase in SOD, POD and CAT activities under saline conditions (Fig. 5B, 5C, 5D).

Correlation analysis

The relationship between various morpho-physiological and biochemical traits is crucial in determining the most suitable genotype under both normal and saline environment. The Pearson correlation coefficients were computed separately for normal and saline conditions. Under normal conditions, FL, Na⁺, TSP, CAT, FS, PH, FF, POD, and SOD showed positive association with each other. Lint % revealed significant positive relationship with FL, TSP, CAT, FS, FF, POD, Chla, Chlb, BW, and CAR contents under control conditions (Fig. 6). Under salt stress conditions, H₂O₂ and Na⁺ revealed significant negative relationship with all morphological, agronomical (SCY, BW, NBP, and LP), fiber quality traits (FL, FS, & FF) and physiological characters (CAT, TSP, SOD, POD, Car, chlorophyll contents, K⁺/Na⁺, and K⁺ respectively (Fig. 2). Under salt stress environments all agronomic characters (SCY, BW, NBP, and LP) revealed positive relationship with physiological traits CAT, TSP, POD, Car, chlorophyll contents, K⁺/Na⁺, and K⁺ respectively. The fiber quality parameters showed positive association with all antioxidant and biochemical traits under salt stress conditions (Fig. 7).

Principal component analysis (PCA) for agro-physiological, biochemical and fiber quality traits

PCA is a statistical approach for analyzing and simplifying complex datasets. We used PCA to evaluate the correlation among variables and the genetic diversity in studied accessions and their association with studied characters under normal and saline conditions. The PC-1 and PC-2 explained 37.6% and 13% of the whole variation under control conditions, respectively (Fig. 8). The relative distance of variables from the origin in PC1 and PC2 showed how much each variable contributes to the total variation among the studied accessions. Under normal conditions, the biplot demonstrated that genotypes of quadrant-IV had high potential for SCY, LP, BW, chlorophyll contents, POD, FS, SOD, K⁺, and carotenoids contents. The genotypes of quadrant-III were performed better for NBP, TSP, CAT, PH, Na⁺, and FL (Fig. 8). The genotypes of quadrant-I revealed good performance for potassium to sodium ratio whereas the genotypes of quadrant-II showed optimal performance for H₂O₂ under control conditions (Fig. 8).

Under saline conditions, the biplot of PC-1 and PC-2, which together account for 90.20% of the total variation. In biplot, traits lied close to each other were positively associated with each other (Fig. 9). Under salt stress conditions, the biplot of PC1 and PC2, revealed positive association between Na⁺ and H₂O₂ and the genotypes FBG-222, FBS- SMART, FBG-333, Barani-333 × JSQ-White Hold, FBS- SMART × Ghauri-3, Barani-333 × Ghauri-3, FBG-222 × JSQ- White Hold, FBS- SMART × JSQ-White Hold, FBS- SMART × Ghauri-3, FBS- FALCON × Ghauri-3, Barani-222, and FBS- SMART × Barani-222 showed positive relationship with these traits and declared as salt susceptible. Whereas the genotypes FBS- FALCON × JSQ- White Hold, FBS- FALCON, JSQ- White Hold, FBG-222 × FBG-333, FBG-222 × Barani-222, Barani-333, Ghauri-3, and Barani-333 × FBG-333 revealed positive correlation with K⁺/Na⁺, K⁺, POD, SOD, CAT, TSP, Chla, Chlb, CAR, LP, FS, FL, FS, NBP, BW, and SCY and declared as salt tolerant (Fig. 9). The genotypes FBS- FALCON × FBG-333, FBS- FALCON × Barani-222, and FBS- SMART × FBG-333 showed significant association with FF under saline conditions. The insights obtained from PCA can be utilized to obtain valuable outcomes (Fig. 9).

Examination of the expression of ERF genes (Ghi-ERF-2D.6 and Ghi-ERF-7A.6) by qPCR

The quantitative measurement of mRNA expression in the leaf of genotypes at salt stress levels of control and 15 dsm^− 1 was performed. As an internal control of gene normalization, the GAPDH was employed. In Fig. 10, the genes Ghi-ERF-2D.6 and Ghi-ERF-7A.6 showed relatively higher expression at 15 dsm^− 1. In genotype JSQ-white Hold, the expression of Ghi-ERF-2D.6 is 1.5 folds higher at 15 dsm^− 1 than control whereas the expression of Ghi-ERF-7A.6 is 2.2 folds higher at 15 dsm^− 1 than control. The genotype Barani-333 × FBG-333 showed 2 folds higher expression of Ghi-ERF-2D.6 at 15 dsm^− 1 than control whereas the expression of Ghi-ERF-7A.6 is 2.7 folds higher at 15 dsm^− 1 than control. The expression of both Ghi-ERF-2D.6 and Ghi-ERF-7A.6 in genotype FBG-222 × Barani-222 was measured 2 folds higher at 15 dsm^− 1 than control. In genotype Ghauri, the expression of Ghi-ERF-2D.6 and Ghi-ERF-7A.6 was 2.5 folds and 2.2 folds higher at 15 dsm^− 1 than control respectively. The genotype FBG-222 × FBG-333 showed 1.5 folds and 3 folds higher expression of Ghi-ERF-2D.6 and Ghi-ERF-7A.6 at 15 dsm^− 1 than control respectively (Fig. 10). In genotype FBS- FALCON × JSQ-white Gold, the expression of Ghi-ERF-2D.6 is 2 folds higher whereas the expression of Ghi-ERF-7A.6 is 2.7 folds higher at 15 dsm^− 1 than control respectively. The genotype Barani-333 showed 2.5 folds and 3 folds higher expression of Ghi-ERF-2D.6 and Ghi-ERF-7A.6 at 15 dsm^− 1 than control respectively. The expression of Ghi-ERF-2D.6 and Ghi-ERF-7A.6 in genotype FBS- FALCON was measured 2.1 folds and 2.3 folds higher at 15 dsm^− 1 than control respectively. The expression level of both the genes was increased at 15 dsm^− 1 and showed significant tolerance against salt stress (Fig. 10).

Cotton is an economically important crop that is grown worldwide for its fiber and seed oil. However, cotton growth and productivity are often limited by various abiotic stresses, including salt stress (Guo et al., 2020). Salt stress can have a significant impact on cotton physiology, leading to reduced growth, disrupted water and ionic balance, oxidative stress, and decrease in yield. Understanding these physiological effects is critical for developing strategies to mitigate the negative effects of salt stress and improve cotton productivity in salt-affected soils (Noman, 2023; Wang et al., 2017).

A significant amount of work has been done so far to develop cotton cultivars that can tolerate stress. In this pursuit, plant breeders often depend on the genetic variability present in the available germplasm to select desirable traits (Zafar, Jia, et al., 2021). To improve breeding programs aimed at producing salt-tolerant cotton genotypes, it is crucial to have current knowledge on genetic variability and heritability (Zafar, Jia, et al., 2021). In this study, Line × Tester approach was used, where four lines and four testers were crossed, resulting in the production of 16 F1 hybrids. The success of improving crop plants genetically is dependent on the level of heritability of traits that are economically valuable (Kumar, Raju, Rajan, Muraleedharan, & Suji, 2019). Traits that exhibit high heritability and genetic advance are more likely to be transmitted to the next generation in greater proportions. When there is both a high H²b and high GAM, this can lead to genetic gains through the selection process (Manan et al., 2022). In this study, traits such as NBP, BW, LP, SCY, K⁺/Na⁺, CAT, SOD, POD, TSP, Car, FL and FS demonstrated moderate to high heritability and GAM, suggesting the presence of additive gene action. These traits can be useful for early-stage selection of genotypes, which can then be utilized in breeding programs focused on improvement (SAHAR et al., 2021). Under salt stress environment, significant reduction was observed in agronomic traits such as PH, NBP, BW, SCY, and lint%. Interestingly, fiber fineness (FF) was increased under saline environments. Furthermore, the mean values for H₂O₂, CAT, SOD, and POD, were increased under salt stress, while the mean values for TSP, Car, Chl a and b were decreased under saline conditions. (Farooq et al., 2020) reported similar findings. In a stress breeding program, measuring chlorophyll content is crucial in identifying salt-tolerant genotypes, as higher chlorophyll contents correlate with greater salt tolerance. In our study chlorophyll contents were reduced under salt stress conditions (Saleh & analysis, 2012). The reduction in chlorophyll contents, leads to increased production of H₂O₂. This creates oxidative stress due to the strong negative association between H₂O₂ and chlorophyll content under salt treatment (Dong et al., 2020). One of the ways in which oxidative stress can reduce cotton yield under salt stress is by inhibiting photosynthesis. Salt stress-induced oxidative stress can damage the photosynthetic machinery, resulting in a decrease in the efficiency of photosynthesis. This, in turn, reduces the plant's ability to produce energy and fix carbon dioxide, which are essential for growth and yield (Ibrahim et al., 2019). Sodium and potassium are essential ions that play critical roles in different biochemical processes in plants, including osmoregulation, ion homeostasis, and enzyme activation (Hamani et al., 2020). Salt stress can cause an influx of toxic ions such as sodium and chloride into the plant and decrease the potassium contents in plants, which disrupt ion homeostasis, leading to ion toxicity and osmotic stress (Zafar et al., 2020). This can impair the plant's ability to maintain normal cellular function, resulting in reduced cotton yield and boll weight. In our study, the genotypes FBS- FALCON × Ghauri-3, FBS- SMART × JSQ- White Hold, FBS- SMART × Barani-222, FBS- SMART × Ghauri-3, FBG-222 × JSQ- White Hold, FBG-222 × Ghauri-3, Barani-333 × JSQ- White Hold, and Barani-333 × Ghauri-3 revealed higher Na⁺ contents and lower values for the K⁺ and K⁺/Na⁺ and performed poor for agronomic physiological and fiber quality traits. Farooq et al. (2020) reported that excess Na + accumulation can also reduce water uptake by the plant, as well as it also competes with other ions, such as potassium and calcium, for binding sites on the root surface and within the plant. This can reduce the uptake of other vital nutrients and lead to nutrient deficiencies, which can further reduce yield of the seed cotton and boll weight (Farooq et al., 2020). In our study, negative association of Na⁺ and H₂O₂ with fiber quality traits and agronomic as well as antioxidant traits was observed under salt stress conditions. Ju et al. (2023) reported that tolerant plants accumulated a lower amount of Na⁺ ions, while sensitive plants accumulated a higher amount of Na⁺ ions within their cells. Salt-tolerant plants maintain their Na⁺/K⁺ ratio by reducing the uptake of Na + from the roots, sequestering the excess Na⁺ present in the cytosol into the vacuole, and promoting the efflux of Na⁺ from root cells (Ju et al., 2023).

Antioxidants play a critical role in protecting cotton plants from oxidative stress under salt stress. Antioxidants such as catalase, peroxidase, superoxidase and non-enzymatic antioxidants (carotenoids, ascorbate and flavonoids) can neutralize ROS by donating electrons to them, preventing the formation of more ROS and the damage they can cause (Guo et al., 2020). In response to salt stress, plants activate protective enzymes such as SOD, POD, and CAT, to enhance their ability to eliminate ROS (Wang et al., 2017; Zafar et al., 2020). The current study observed an increase in the activities of SOD, POD, and CAT in leaves under salt stress. The increased SOD activity in leaves may aid in scavenging oxygen-free radicals, while the elevated CAT activity may facilitate the breakdown of H₂O₂ into water and oxygen, thus reducing its levels. This finding is consistent with previous reports by (Farooq et al., 2020; Munawar et al., 2021; Zafar et al., 2020) who also noted an increase in SOD, POD, and CAT activities in cotton leaves under salinity stress. Ibrahim et al. (2019) reported that POD activity was enhanced up to 53% in resistant cultivars. The enhancement of POD activity contributes to the improvement of photosynthetic activity, thereby highlighting the important role of antioxidant defense mechanisms in mitigating salt stress (Ibrahim et al., 2019). According to Sharif et al. (2019), the enhanced activity of antioxidants such as SOD, POD, and CAT were associated with salt tolerance during fiber development (Sharif et al., 2019). Under stressful conditions, carotenoids tend to increase as their primary function is to protect against singlet oxygen (Azeem et al., 2023). High-yielding cultivars often exhibit elevated levels of CAT, TSP, and POD as they play an active role in regulating H₂O₂ levels by scavenging it to maintain optimal levels. The genotypes FBS-Falcon, Barani-333, JSQ-White Hold, Ghauri, along with crosses FBS- FALCON × JSQ- White Hold, FBG-222 × FBG-333, FBG-222 × Barani-222, and Barani-333 × FBG-333 revealed higher level of K⁺/Na⁺, K⁺, POD, SOD, CAT, TSP, Chla, Chlb, and CAR and declared as salt tolerant cultivars. The above-mentioned genotypes also showed relatively higher expression level of Ghi-ERF-2D.6 and Ghi-ERF-7A.6 at 15 dS/m and proved the role of ERF genes in salt tolerance in cotton. Similar findings also reported in cotton (J. Zhang et al., 2021), maize (Ashraf et al., 2018), quinoa (Shabbir et al., 2021) and wheat (Abbas et al., 2022).

The genetic variability study of cotton germplasm for morphological, biochemical, and fiber quality traits under salt stress revealed that the genotypes the FBS-Falcon, Barani-333, JSQ-White Hold, Ghauri, along with crosses FBS- FALCON × JSQ- White Hold, FBG-222 × FBG-333, FBG-222 × Barani-222, and Barani-333 × FBG-333 performed optimally and exhibited salt tolerance based on their K+/Na + ratio, K + levels, and various enzyme activities such as POD, SOD, and CAT. Furthermore, these genotypes also exhibited higher levels of TSP, Chla, Chlb, and Car under salt stress, indicating their ability to survive under the effects of salt stress. These genotypes also showed relatively higher expression level of Ghi-ERF-2D.6 and Ghi-ERF-7A.6 at 15 dS/m. Importantly, these genotypes also demonstrated favorable fiber quality traits, making them potential candidates for the development of salt-tolerant cotton varieties with desirable fiber properties. Future studies focused on the molecular foundation of salt tolerance in these genotypes could facilitate the development of improved cotton varieties that can thrive in salt-affected regions and contribute to the sustainable production of cotton. Further research on the mechanisms underlying salt tolerance in cotton plants is warranted to develop a better grasp of the molecular foundation of salt tolerance and to facilitate the development of improved salt-tolerant cotton varieties.

PH = Plant Height (cm), NBP= number of bolls/plant, BW= Boll weight (g), SCY= Seed cotton yield (g), LP= lint (%), Na⁺ =sodium concentration, K⁺= potassium concentration, K⁺/Na⁺ =potassium to sodium ratio, H₂O₂ = Hydrogen peroxide (μmol/g), CAT = Catalase (U mg^-1 protein), SOD=Super-oxidase dismutase (U mg^-1 protein), POD = Peroxidase (U mg^-1 protein), TSP = Total soluble Protein (mg g^-1 FW), Caro. = Carotenoid (mg g^-1 FW), Chl a&b= Chlorophyll contents a & b (mg g^-1 FW), FF= Fiber fineness (µg/in), FL=Fiber length (mm), FS= Fiber strength (g/tex).

ACKNOWLEDGEMENTS

The authors would like to thank the FB Genetics, Four Brothers Group, Lahore, Pakistan for providing space and facilities to conduct this experiment.

AUTHORS’ CONTRIBUTIONS

MMZ, AR and WSC wrote the initial draft of the manuscript. A.A. provided the space and help to conduct the experiment. JA, AP, AS, SZ, ABS, FQ and HS. helped in statistical analysis. FQ, MMZ and AS review and edit the manuscript. The final approval was given by AR, MMZ and FQ.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

Study protocol complies with relevant institutional, national, and international guidelines and legislation.

CONSENT FOR PUBLICATION

Not Applicable

AVAILABILITY OF DATA AND MATERIALS

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.

COMPETING INTERESTS

The authors declare no conflict of interest.

FUNDING

SUPPLEMENTARY FIGURES

Supplementary figure is attached

SUPPLEMENTARY TABLES

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No competing interests reported.

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Dissection of salt tolerance in cotton germplasm by analyzing agro-physiological traits and ERF genes expression

Status:

Version 1

Abstract

Figures

INTRODUCTION

MATERIALS AND METHODS

Ion analysis:

qRT‑PCR of Ghi-ERF-2D.6 and Ghi-ERF-7A.6 in tolerant cotton genotypes

Fiber quality parameters

Statistical analysis

RESULTS

Mean performance of agronomic, and fiber quality traits

Biochemical traits

Correlation analysis

Principal component analysis (PCA) for agro-physiological, biochemical and fiber quality traits

Examination of the expression of ERF genes (Ghi-ERF-2D.6 and Ghi-ERF-7A.6) by qPCR

DISCUSSION

CONCLUSIONS

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1