Hydrotime analysis and gene expression reveal that cytokinin mitigates the detrimental effects of salinity on seed vigor

Quinoa (Chenopodium quinoa Willd.) has gained prominence as a nutritionally rich and climate-resilient crop, well-suited for cultivation in semi-arid and arid environments¹. Its inherent traits—including drought and salinity tolerance, short growing season, and low water requirements—render quinoa a strategic crop for regions facing water scarcity and poor soil fertility^2,3. Nevertheless, quinoa cultivation still faces considerable challenges, particularly during critical stages such as seed germination and seedling establishment. Water stress, high salinity, temperature fluctuations, and nutrient-poor soils can all negatively affect crop performance. Understanding how these stressors influence seed quality and vigor is essential for optimizing quinoa production under adverse conditions^4,5.

Among abiotic stresses, soil salinity poses a widespread threat to agricultural productivity, affecting nearly one-third of the world’s arable land. Salinity impacts plant physiology in two distinct phases: an initial osmotic phase that limits water uptake, followed by an ionic phase characterized by toxic ion accumulation⁶. The osmotic phase reduces cell expansion, impairs shoot and root development, and disrupts reproductive processes, including seed development. The ionic phase, marked by excessive sodium (Na⁺) and chloride (Cl⁻) accumulation, damages cellular membranes, interferes with enzyme activity, and accelerates leaf senescence, thereby restricting the supply of nutrients to developing seeds⁷.

Salinity has been shown to influence progeny seed traits. In Suaeda salsa, a halophytic species, exposure to high salinity during seed development enhanced seed germination, size, and biochemical quality through increased accumulation of compatible solutes like glycine betaine and proline^8,9. While similar findings are emerging in quinoa, they remain limited. Bouras et al.¹⁰ reported increased quinoa seed yield under saline irrigation (EC 12–17 dS m⁻¹), while Razzaghi et al.¹¹ and Roman et al.¹² demonstrated quinoa’s ability to maintain yield at salinity levels up to 25 dS m⁻¹ and tolerate conditions as high as 51.5 dS m⁻¹. These studies highlight quinoa’s resilience and underscore the need to understand how maternal salinity exposure influences seed development, vigor, and stress tolerance.

Plant growth regulators, particularly cytokinins, have emerged as promising tools for modulating plant responses to abiotic stress. Cytokinins play essential roles in regulating plant growth, seed formation, and nutrient partitioning^13,14. Exogenous cytokinin application has been shown to mitigate stress-induced damage and enhance seed traits such as size, weight, and nutrient content¹⁵. In loquat (Eriobotrya japonica), for example, trans-zeatin application significantly improved seed development by enhancing metabolic activity¹⁶. Cytokinins also reduce salinity-induced oxidative stress by enhancing antioxidant enzyme activity, thereby limiting reactive oxygen species (ROS) accumulation and protecting cellular structures¹⁷. Their ability to modulate stress-responsive pathways makes cytokinins a potential intervention for improving seed quality under salt stress^18,19.

To better quantify seed responses to water stress, the hydrotime model has become a widely accepted analytical tool in seed science^20,21,22. The model estimates key physiological parameters: the hydrotime constant (θ_H), median base water potential (Ψ_b(50)), and the standard deviation of base water potential (σ_Ψb). Lower θ_H values indicate faster germination, while more negative Ψ_b(50) values reflect greater vigor and tolerance to low water potentials. A smaller σ_Ψb denotes a more uniform seed lot. This model has been applied successfully across crops such as sugar beet²³, cotton²⁴, and rapeseed²⁵. In quinoa, Mamedi and Sharifzadeh²⁶ demonstrated that osmopriming with CaCl₂ improved germination under saline and osmotic conditions, validating the hydrotime model’s utility in salt-affected environments. In castor bean (Ricinus communis L.), a crop known for its rusticity, the model successfully identified significant genetic variability in salinity tolerance among genotypes, with the most tolerant genotype exhibiting a Ψ_b(50) of −1.39 MPa²⁷. Similarly, application of the model to Amaranthus species under salinity stress revealed that the tolerant A. giganticus was characterized by a lower θ_H (40.3 MPa h) and a more negative Ψ_b(50) (−1.55 MPa), effectively quantifying its superior germination speed and stress tolerance²⁸. By applying this well-established model to quinoa, our study aims to provide similarly quantifiable and comparable metrics of seed vigor and salinity tolerance, thereby contributing to the broader effort of selecting resilient crops for marginal environments.

At the molecular level, quinoa’s remarkable salinity tolerance is believed to be linked to its broad genetic diversity²⁹. Salt stress activates complex physiological and genetic responses, including the transcription of genes involved in osmotic regulation and ion homeostasis^30,31. For example, Na⁺/H⁺ antiporters (NHXs) are essential for vacuolar Na⁺ sequestration and pH regulation, contributing to cellular protection under stress. These proteins also play roles in osmotic adjustment, protein trafficking, and organ development³². The salt overly sensitive (SOS) signaling pathway is another key mechanism in salinity tolerance. In this pathway, calcium signaling activates SOS3, which forms a complex with SOS2 to phosphorylate and activate SOS1, a plasma membrane Na⁺/H⁺ antiporter^33,34. SOS1 genes have been identified in several crops including rice, sugarcane, and bread wheat³⁴–³⁷. Another key gene, BADH, encodes betaine aldehyde dehydrogenase, an enzyme crucial for the synthesis of glycine betaine, a compatible solute that protects cells under osmotic stress³⁸.

Given these complex physiological and molecular interactions, this study seeks to address the following questions: (1) To what extent does salinity stress during maternal plant growth impact quinoa seed quality, and how do these effects differ between salt-tolerant (‘Titicaca’, ‘Q12’) and salt-sensitive (‘Redcarina’, ‘Giza1’) cultivars? (2) Can the application of exogenous cytokinin (0.75 µM) mitigate the negative impacts of moderate (25 dS m⁻¹) and high (40 dS m⁻¹) salinity on the quality of seeds produced by quinoa plants? (3) How do quinoa seeds, influenced by both maternal salinity and cytokinin treatment, respond to subsequent water stress conditions during germination, as quantified by the hydrotime model across a range of water potentials (0 to −1.5 MPa)? (4) Can the hydrotime model, along with its parameters, quantify and predict germination responses of seeds produced under these complex maternal environments? (5) Is the physiological tolerance conferred by maternal treatments associated with the regulation of key stress-responsive genes (BADH, SOS1, and NHX1), providing a molecular basis for the observed improvements? By addressing these questions, this work aims to provide a comprehensive understanding of how maternal environment and hormonal priming interact to shape seed vigor and stress tolerance in the next generation.

Seed production under salinity stress and cytokinin application

An experiment was conducted within the greenhouse facilities of Zanjan University’s Department of Plant Production and Genetics Engineering. This experiment followed a three-factorial design with three replications, examining three key factors: cultivar, salinity (EC: 0, 25, 40 dS m⁻¹), and cytokinin (0 and 75 µM). The cultivars selected for this study represented varying degrees of salinity tolerance, encompassing two salt-tolerant cultivars, ‘Titicaca’ and ‘Q12’, and two environmentally stress-sensitive cultivars, ‘Redcarina’ and ‘Giza1’, resulting in a total of four cultivars studied. The seeds were obtained from the National Salinity Research Center, Yazd, Iran.

To facilitate cultivation, the required amount of soil was transported from the farm to the greenhouse. Each experimental unit comprised two pots, with a total of 144 plastic pots measuring 20 cm in diameter and 25 cm in height, each filled with 8 kg of sieved soil. The soil mixture consisted of farm soil, sand, and animal manure in a 1:1:2 ratio. This ratio was selected to ensure a well-balanced growth medium with appropriate drainage, aeration, and nutrient availability. The sand component improved soil texture and drainage, preventing waterlogging, while the higher proportion of animal manure enriched the soil with organic matter and essential nutrients, supporting vigorous plant growth and simulating typical fertilized field conditions.

Determination of irrigation quantities involved measuring the soil’s field capacity (FC), which was calculated based on three soil samples, with the average FC value applied across the experimental setup. To induce salinity in the soil, “Saltcalc” tool was utilized to calculate the precise quantity of salt required to achieve the desired EC within a specific soil volume^39,40. The resulting salt solution was then applied to the designated pots. To prevent salt runoff, a sub-pot system was employed to recapture water drainage.

For planting, seeds of the selected cultivars were sown and immediately watered, with 15 seeds placed at a depth of 3 centimeters in each pot. Tap water, with an electrical conductivity (EC) measurement of 0.6, was used for irrigation. Growing conditions within the greenhouse were meticulously controlled, maintaining a 14 h light cycle, 70% humidity, and day and night temperatures of 25 °C and 15 °C, respectively. Thinning of seedlings occurred at the 4-leaf stage, with five healthy seedlings retained in each pot until the harvest period when seeds were collected.

The cytokinin hormone used in this experiment, 6-Benzylaminopurine (6-BA), was obtained from SIGMA and prepared in aqueous solution form. To enhance foliar absorption, the hormone solution was mixed with 0.5 mL of Tween-20 as a surfactant to reduce water surface tension. The cytokinin spray was applied twice: first during the flowering stage and again three days later, both at 10 AM to ensure consistent environmental conditions for foliar uptake. Sampling was conducted seven days after the second application, allowing sufficient time for the hormone to be absorbed and exert its physiological and biochemical effects on the plants.

A concentration of 0.75 µM 6-BA was selected for this study based on its effectiveness reported in previous studies, which showed it to significantly enhance stress tolerance and promote reproductive development without causing phytotoxicity^16,17. This concentration falls within the commonly applied range (0.1–10 µM) for exogenous cytokinin treatments in foliar sprays, making it a suitable and practical choice for optimizing plant response under saline conditions.

Seed germination test

After harvest, quinoa seeds were transferred to the laboratory of the Department of Agronomy and Plant Breeding, Tehran University, for germination tests. The experiment followed a factorial design with three replications, considering produced seeds of four quinoa cultivars (‘Titicaca’, ‘Q12’, ‘Redcarina’, and ‘Giza1’) under three salinity levels (0, 25, and 40 dS m⁻¹) and two cytokinin hormone levels (0 and 0.75 µM), and six water potentials (0, −0.3, −0.6, −0.9, −1.2, and − 1.5 MPa) during this test at the laboratory. Osmotic potentials were maintained using polyethylene glycol 6000 (PEG) solutions at 20°C⁴¹.

Each treatment and replication involved placing 50 seeds in 90 mm-diameter Petri dishes on filter paper at 20 °C under each of the six water potentials. Filter papers were pre-soaked in solutions matching the desired water potentials for 24 h before seed placement. Germination was monitored regularly until no further germination was observed for three days, with seeds considered germinated when radicle protrusion reached approximately 2 mm.

The hydrotime model was applied to quantify seed germination in response to water potentials as following Eqs^20,21. :

where ψ is the actual water potential (MPa), θ_H is the hydrotime constant (MPa h), ψ_b(g) is the base water potential (MPa) defined for a specific germination fraction (g), and t_g is the time (h) to radicle protrusion of fraction g (%) of the seed population. The normal distribution of ψ_b(g) values among seeds in a population is characterized by its median ψ_b(50) and standard deviation (σ_ψb) and they were estimated using repeated probit analyses, varying θ_H until the best fit is reached (Mesgaran et al., 2013) as follows⁴²:

These equations form the basis of the hydrotime model analysis, allowing for the characterization of germination responses under different water potential conditions and the estimation of key parameters used to assess quinoa seed germination.

Candidate genes expression

Gene expression analysis was performed on two quinoa cultivars with contrasting salinity tolerance: the salt-sensitive ‘Redcarina’ and the salt-tolerant ‘Titicaca.’ Total RNA was extracted from frozen leaf and root tissues using TRIzol^® reagent (Invitrogen, Life Technologies, USA), following the manufacturer’s protocol. To eliminate potential genomic DNA contamination, RNA samples were treated with RNase-free DNase I (Promega, USA). RNA integrity and concentration were verified spectrophotometrically and by agarose gel electrophoresis.

First-strand complementary DNA (cDNA) was synthesized from 1 µg of total RNA using the iScript™ cDNA Synthesis Kit (Bio-Rad, USA), according to the manufacturer’s instructions. Gene-specific primers for SOS1, BADH, and NHX1 were designed using Oligo v.5.1 software and synthesized by MWG (Germany). Real-time quantitative PCR (RT-qPCR) was carried out using the iQ™ SYBR^® Green Supermix (Bio-Rad, USA) on an iCycler iQ™ Real-Time PCR Detection System (Bio-Rad, USA).

Each reaction was conducted in triplicate with three biological and three technical replicates per treatment. Relative gene expression levels were calculated using the 2^–ΔΔCt method, with normalization to an internal reference gene. Amplification specificity was confirmed by melt-curve analysis and gel electrophoresis of PCR products. All protocols followed standard procedures as described in Zare et al.⁴³.

Data analysis

Normal distribution, having been formulated into the hydrotime model, were fitted to data using the PROC NLMIXED procedure of SAS, with the default optimization technique of dual quasi-Newton algorithm as mentioned by Mesgaran et al.⁴². This procedure implements maximum likelihood (ML) to estimate the model parameters and is preferred to the least squares method (PROC NLIN). In addition, using the PROC NLMIXED with its ML method, more appropriate criteria than R² can be obtained, such as the Akaike information criterion (AIC) or the Bayesian information criterion (BIC)⁴⁴ and root mean square error (RMSE) for the purpose of model selection. Distribution properties (Table 1) were estimated using the Estimate statement, and their corresponding standard errors were approximated with the default delta method⁴⁵.

The model was run in two ways: (1) First, the model was fitted to the cumulative germination fraction data of each treatment with all replications and (2) The model was fitted to the cumulative germination fraction data of each treatment for each replication separately. In this way, the hydrotime model parameters were obtained for each replication separately and these data were subjected to analysis of variance and mean comparison for further analysis. LSD method was used for mean comparison of these data and the effect of cytokinin hormone was compared separately in each cultivar and each salinity level.

Hydrotime analysis to study effects of salinity stress and cytokinin application

Differences in germination were observed in quinoa seeds produced under various salinity conditions, both with and without the application of the cytokinin hormone (Fig. 1). These differences were particularly evident in the cumulative germination patterns, which exhibited distinct responses to drought stress. Increasing drought intensity delayed the initiation and completion of seed germination. Moreover, heightened drought stress levels led to a substantial reduction in germination percentages. This decline in germination was a consistent trend across all treatments, though the degree of severity varied by water potentials. The fittings of hydrotime models were represented by the lines (Fig. 1).

The precision and goodness of fit of the hydrotime model (Table 1) were rigorously evaluated using RMSE and AIC. RMSE values, ranging from approximately 0.1297 to 0.2088, quantified the extent of error in the model’s predictions. Lower RMSE values indicated a superior fit, signifying that the hydrotime model closely aligned with the observed data for treatments featuring lower RMSE values. Conversely, higher RMSE values indicated a less precise fit. AIC values, as shown in Table 1, accompanied each treatment, spanning from − 306.1 to −66.3. Lower AIC values indicated a better balance between model fit and complexity. Treatments with lower AIC values represented models that effectively captured the data without excessive complexity.

The hydrotime parameters (θ_H, σ_ψb, and ψ_b(50)) displayed notable variability among different cultivars (Table 1). On average, ‘Q12’ exhibited the lowest θ_H value at 23.5 MPa-hours, while ‘Redcarina’ showcased the highest values at 40.9 MPa-hours, with variations attributed to salinity and cytokinin treatments applied during seed production. Base water potential (ψ_b(50)) ranged between − 1.8 MPa in ‘Q12’ seeds produced under 40 dS m^− 1 and − 1.3 MPa in ‘Titicaca’ seeds produced under 40 dS m^− 1 with 0.75 µM cytokinin (Table 1). Additionally, σ_ψb values in Table 1 exhibited variations among treatments, influenced by different cultivars, salinity levels, and cytokinin treatments. These variations underscore the sensitivity of σ_ψb to the specific combinations of factors applied in the study. Notably, certain treatments saw an increase in σ_ψb with rising salinity or the presence of cytokinin, indicating greater variability in the base water potential within the seeds. Conversely, in other instances, σ_ψb remained relatively stable despite fluctuations in these experimental factors.

The ANOVA results in Table 2 revealed significant differences and interactions among cultivars, salinity levels, and hormone treatments concerning the parameters θ_H and σ_Ψb. Significance levels, represented by “Pr > F” values, indicate the statistical importance of these factors in explaining variations in θ_H and σ_Ψb. Notably, for ψ_b(50), cultivar were found to be significant, while the hormone and salinity treatments did not exhibit significance. Additionally, the interaction terms underscored the complexity of the relationships among these factors.

It is evident that different cultivars responded differently to salinity and cytokinin hormone treatments (Fig. 2). In the ‘Giza1’, the application of cytokinin led to a significant increase in the hydrotime constant at 0 and 25 dS m^− 1 salinity levels. However, at 40 dS m^− 1, no significant difference was observed between cytokinin application and non-application. In the absence of cytokinin, the hydrotime constant increased with rising salinity, but with cytokinin application, it decreased under salinity stress. In contrast, the ‘Q12’ showed no significant difference in the hydrotime constant between cytokinin application and non-application at 0 and 25 dS m^− 1, but a sharp increase was observed at 40 dS m^− 1, with cytokinin application significantly reducing the hydrotime constant. In the ‘Redcarina’, cytokinin application increased the hydrotime constant at all salinity levels, with significance noted at 0 and 25 dS m^− 1. Conversely, in the ‘Titicaca’ cultivar, cytokinin application reduced the hydrotime constant at all salinity levels, with significance observed at 25 dS m^− 1 (Fig. 2).

In both the ‘Giza1’ and ‘Redcarina’ cultivars, cytokinin hormone reduced (resulting in more negative values) the base potential for germination (ψ_b(50)) at most salinity levels, with significance at 25 dS m^− 1 (Fig. 3). Conversely, in the other two cultivars, cytokinin hormone increased the base potential for germination at most salinity levels. Significance was observed in the ‘Q12’ cultivar without salinity and in the ‘Titicaca’ cultivar at 40 dS m^− 1 (Fig. 3).

The alterations in the scale parameter (σ_Ψb) varied among different cultivars (Fig. 4). In ‘Giza1’, cytokinin hormone application increased the scale at 0 and 25 dS m^− 1 salinity levels. In ‘Redcarina’, cytokinin hormone significantly increased the scale at 25 dS m^− 1 but decreased it significantly at 40 dS m^− 1. In ‘Titicaca,’ cytokinin hormone also significantly decreased the scale at 25 dS m^− 1. Notably, in the ‘Q12’ cultivar, no significant difference was observed in cytokinin hormone application at any salinity level (Fig. 4).

Candidate genes expression

Results from the real-time RT-PCR analysis revealed that the salt-tolerant ‘Titicaca’ cultivar exhibited the most pronounced molecular response, showing the strongest upregulation of SOS1, BADH, and NHX1 genes under the combined stress of high salinity and cytokinin application (Fig. 5). Specifically, in ‘Titicaca’, the transcription level of SOS1 increased by approximately 23-fold under these conditions, a significantly higher expression than observed in the salt-sensitive ‘Redcarina’. A similar genotype-specific pattern was evident for the BADH gene, where expression was upregulated by approximately 18.7-fold in ‘Titicaca’ compared to an 11-fold increase in ‘Redcarina’ under the highest stress treatment, indicating a stronger osmoprotective response in the tolerant cultivar. Likewise, NHX1 transcription increased by 12-fold in ‘Titicaca’ at 40 dS m⁻¹ salinity with cytokinin application, markedly higher than in ‘Redcarina’. Overall, both salinity (40 dS m⁻¹) and cytokinin treatment (0.75 µM) independently and synergistically led to a substantial upregulation of these key stress-responsive genes, with the most robust activation occurring in the salt-tolerant genotype.

Impact of salinity stress on seed quality

Our study revealed that in certain cultivars, salinity had discernible impact on seed quality and, in fact, may have contributed to increased seed germination. For instance, in the ‘Giza1’ cultivar, known for its sensitivity to environmental stresses, salinity led to a decrease in base water potential (Fig. 3). This decrease translated to an improvement in seed vigor, highlighting the nuanced ways in which quinoa responds to salinity stress. Furthermore, our hydrotime analysis provided valuable insights into the germination speed of quinoa seeds under different conditions. In the salt-tolerant ‘Titicaca’ cultivar, salinity stress was associated with a decrease in hydrotime, indicating a faster germination rate. This suggests that salinity, at certain levels, may act as a stimulant in tolerant genotypes, accelerating germination and potentially promoting more rapid seedling establishment.

Previous studies have shown that salinity during seed development can enhance seed quality by increasing seed size and nutrient reserves, particularly under moderate salinity levels that stimulate plant growth^8,9,10,11,12. Interestingly, our findings suggest that seed quality was not negatively affected by salinity stress during maternal growth, and in some cases, it may have improved, as reflected in increased germination rates. This improvement could be attributed to adaptive physiological responses such as enhanced osmotic adjustment or hormonal regulation during seed maturation, which may contribute to better seed vigor and germination potential.

Cytokinin-mediated improvement of seed quality

Exogenous cytokinin treatment aimed to counteract the known detrimental effects of salinity on seed quality, which often manifest through disruptions to nutrient partitioning and hormonal imbalances during seed development⁶. Our findings demonstrated that cytokinin application to salt-stressed maternal plants significantly enhanced the seed vigor of their progeny, as quantified by a reduction in the Ψ_b(50) in salt-sensitive cultivars like ‘Giza1’ and ‘Redcarina’ (Fig. 3). These results reinforce previous work showing the beneficial roles of cytokinins in seed development and stress mitigation^13,14. The mechanisms underlying these improvements are likely multifaceted. Cytokinins are known to modulate hormonal crosstalk by antagonizing abscisic acid (ABA) and promoting gibberellin (GA)-related pathways, both of which influence seed dormancy and germination¹⁷. They also activate the antioxidant defense system, reducing the oxidative damage typically induced by salinity-related ROS, which can impair cellular structures during seed maturation^6,17. Furthermore, cytokinins promote nutrient mobilization and remobilization, improving seed reserve accumulation, and have been shown to regulate aquaporin gene expression, potentially enhancing water uptake during germination under osmotic stress¹⁷. Together, these processes contribute to enhanced seed vigor and viability.

Our study indicated that cytokinin application was particularly effective in the salt-sensitive cultivars ‘Giza1’ and ‘Redcarina’ under high salinity conditions (25 dS m⁻¹), where a significant reduction in Ψ_b(50) was observed (Fig. 3). This suggests that cytokinin may enhance seed vigor by improving water relations and membrane function, likely linked to aquaporin-mediated water transport. In contrast, salt-tolerant cultivars such as ‘Titicaca’ and ‘Q12’ exhibited limited improvements from cytokinin treatment, possibly due to their already robust stress mitigation mechanisms. This differential response suggests that cytokinin’s efficacy in ameliorating salinity-induced stress may be more prominent in sensitive cultivars, emphasizing the importance of genotype-specific approaches in enhancing seed quality under challenging environmental conditions. The success of the hydrotime model in quantifying these cultivar-specific responses in quinoa aligns with its application in other stress-tolerant species, such as castor bean, where it effectively discriminated salinity tolerance among genotypes based on Ψ_b(50)²⁷, and in Amaranthus spp., where it identified A. giganticus as the most tolerant species through a lower θ_H and more negative Ψ_b(50)²⁸. This consistency across diverse crops underscores the robustness and broad applicability of the hydrotime model as a tool for screening seed vigor and stress tolerance.

In salt-sensitive genotypes, cytokinin increased the θ_H, indicating a longer time to reach germination completion—potentially due to more synchronized germination across seeds with varying initial vigor (Fig. 2). Conversely, salt-tolerant cultivars showed reduced hydrotime constants following cytokinin treatment, signifying faster germination, which may facilitate rapid seedling establishment in saline conditions. These improvements in seed germination parameters are likely mediated by cytokinin-induced regulation of hormonal signaling, oxidative stress alleviation, enhanced nutrient use, and improved water uptake mechanisms. This work contributes to the broader understanding of hormonal priming as a strategy to support seed resilience under abiotic stress and highlights the potential for using cytokinin-based treatments in saline agriculture.

Hydrotime model application

The hydrotime model provided a valuable tool for quantifying these germination dynamics. Statistical evaluation using RMSE and AIC confirmed acceptable model fit, particularly in treatments with less extreme stress levels (Table 1). These indices affirmed the model’s utility in comparing physiological responses among cultivars and treatments²¹. Hydrotime parameters— θ_H, σ_Ψb, and Ψ_b(50)—revealed genotype-dependent variation. For instance, lower Ψ_b(50) values in cytokinin-treated seeds suggested improved vigor, particularly in salt-sensitive cultivars. Variations in σ_Ψb also indicated differing synchrony of germination, with cytokinin potentially narrowing the distribution of Ψ_b(50) and enhancing uniform emergence. These patterns align with studies demonstrating that θ_H and Ψ_b(50) are reliable physiological indicators of seed vigor and response to stress^23,24,26. However, while the hydrotime model served as a powerful interpretive framework, it does carry inherent limitations—especially under complex or interacting stresses. One key assumption of the model is that the germination rate is linearly related to water potential above a base threshold (Ψ_b), with constant θ_H across seeds within a population²¹. This relationship may become non-linear under high salinity or combined stress conditions involving hormones, ions, and osmotic agents. For example, cytokinin-induced physiological shifts—such as changes in membrane permeability, aquaporin activity, or ABA-GA balance—may alter the effective water potential sensed by seeds, confounding model assumptions^17,18. Moreover, under high salt stress, both osmotic and ionic components can interact in ways that affect seed hydration status and metabolic readiness, further challenging the model’s predictive capability^27,28,42. While θ_H values were still useful in distinguishing cultivar responses, extreme stress conditions may reduce the reliability of parameter interpretations, particularly when hormonal treatments modify seed physiology in non-additive ways.

Candidate genes expression

The expression analysis of SOS1, BADH, and NHX1 genes revealed significant differences between the quinoa cultivars in response to salinity and cytokinin treatments. In particular, the tolerant cultivar ‘Titicaca’ exhibited a superior molecular response, as evidenced by its significantly higher transcription levels of SOS1 (23-fold), BADH (18.7-fold), and NHX1 (12-fold) under high salinity and cytokinin, compared to the sensitive ‘Redcarina’ genotype (e.g., 11-fold for BADH) (Fig. 5). The SOS1 gene, which encodes a plasma membrane Na⁺/H⁺ antiporter, plays a crucial role in excluding Na⁺ from the cytosol and maintaining ion homeostasis under saline conditions^34,35,36,37. In our study, SOS1 expression was up-regulated by 23-fold under 40 dS m^{− 1} salinity stress with cytokinin in the ‘Titicaca’ cultivar. This aligns with previous studies showing that overexpression of SOS1 enhances salt tolerance in transgenic plants such as Arabidopsis and Tobacco by improving Na⁺ exclusion and maintaining higher K⁺/Na⁺ ratios^34,46. The interaction between salinity-induced calcium signals and the SOS signaling cascade—including SOS2 and SOS3—is essential for activating SOS1 function^35,36. Our findings support the hypothesis that SOS1 is a key component of the salinity response in quinoa and that its enhanced expression contributes to better Na⁺ regulation in tolerant cultivars.

The BADH gene, which encodes betaine aldehyde dehydrogenase, catalyzes the synthesis of glycine betaine—a vital osmoprotectant that stabilizes membranes and proteins under osmotic stress. In our study, BADH was significantly up-regulated (up to 18.7-fold) in the ‘Titicaca’ cultivar under combined salinity and cytokinin treatment. This gene also showed increased expression (up to 11-fold) in the sensitive ‘Redcarina’ cultivar, though to a lesser extent. These findings are consistent with earlier research in spinach, barley, and sugar beet, where BADH transcript levels increased under salinity and drought stress^47,48. The regulation of BADH expression is closely tied to stress-responsive cis-elements that facilitate rapid transcriptional responses to osmotic challenges³⁸. Thus, the up-regulation of BADH in quinoa likely enhances glycine betaine accumulation, contributing to cellular osmoprotection and improved stress resilience.

Similarly, NHX1—a tonoplast-localized Na⁺/H⁺ antiporter—is critical for vacuolar Na⁺ sequestration, which mitigates cytotoxic effects of Na⁺ and aids in osmotic adjustment. Our results demonstrated a 12-fold increase in NHX1 expression under high salinity and cytokinin in Titicaca, supporting its role in ion compartmentalization. These findings mirror those from other studies in wheat, Arabidopsis, and Miscanthus, where NHX1 overexpression enhanced salt tolerance by promoting Na⁺ storage in vacuoles^33,34,49. Beyond Na⁺ homeostasis, NHX genes are involved in pH regulation, protein processing, and cell development, further emphasizing their multifaceted role in plant stress adaptation³².

Cytokinin treatment amplified the expression of all three genes in both cultivars, suggesting a hormonal regulatory effect on salt-responsive gene pathways. Cytokinins have been shown to influence gene expression by modulating signaling cascades and transcription factor activity under abiotic stress⁵⁰. In our study, cytokinin application may have acted as a priming agent, enhancing the transcriptional readiness of stress-related genes, particularly in the tolerant genotype. This highlights the potential of cytokinin in activating key protective mechanisms at the molecular level, complementing its known physiological roles in water balance, antioxidative defense, and ion transport^51,52.

These findings deepen our understanding of quinoa’s adaptive mechanisms and highlight the importance of cultivar-specific strategies for optimizing production in saline environments. However, it is important to note that this study was conducted under controlled greenhouse conditions; therefore, further validation under field conditions is necessary to confirm the practical efficacy of cytokinin application for improving quinoa seed quality and stress resilience. Additionally, further research is needed to investigate the molecular and physiological bases of these responses, particularly in relation to stress-induced hormonal changes and seed maturation pathways.

This study provides comprehensive evidence that salinity stress during maternal plant growth, in conjunction with cytokinin application, has a significant influence on quinoa seed quality and germination dynamics. The effects were highly genotype-dependent, with tolerant cultivars like ‘Titicaca’ exhibiting enhanced performance under salinity, both physiologically and molecularly. Notably, cytokinin application improved seed vigor and reduced base water potential in sensitive cultivars, indicating its potential to counteract salinity-induced stress during seed development.

Hydrotime modeling effectively quantified differences in germination response, highlighting the influence of genotype, salinity, and hormonal treatments on seed behavior. The model’s precision, validated by AIC and RMSE metrics, emphasized its utility for predicting germination under stress, although limitations remain under extreme or combined stress conditions. Gene expression analysis revealed that SOS1, BADH, and NHX1 were significantly up-regulated in cytokinin-treated seeds from saline conditions, particularly in the tolerant ‘Titicaca’ genotype. These genes likely contribute to enhanced ion regulation, osmotic adjustment, and cellular protection, reinforcing their value as markers in future quinoa breeding programs.

Together, these findings underscore the importance of integrated physiological, biochemical, and molecular approaches for improving quinoa’s adaptability to salinity. The synergistic effect of genotype selection and exogenous cytokinin treatment offers a promising and economically viable strategy to enhance seed performance in saline-prone regions. Specifically, the targeted use of cytokinin on salt-sensitive cultivars could provide a cost-effective intervention for breeding programs and farmers to improve stand establishment and yield stability in marginal lands. Future work should focus on field validation, optimization of hormone application protocols, and deeper functional characterization of key stress-related genes to fully harness quinoa’s potential in sustainable agriculture.

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Authors and Affiliations

1. Department of Agronomy and Plant Breeding Sciences, Faculty of Agricultural Science & Engineering, University of Tehran, Tehran, Iran

   Sama Osali & Ali Ahmadi

2. Department of Plant Production and Genetics, University of Zanjan, Zanjan, Iran

   Afshin Tavakoli

3. University of Zanjan, Zanjan, Iran

   Negin Idelouei

4. Department of Agronomy and Plant Breeding Sciences, College of Agricultural Technology (Aburaihan), University of Tehran, Tehran, Iran

   Elias Soltani

Contributions

Conceptualization, S.O., A.A., E.S.; Methodology, S.O. and N.I; Validation, A.A. and E.S.; Formal Analysis, S.O. and E.S.; Investigation, S.O. and N.I.; Resources, A.T., A.A.; Data Curation, S.O.; Writing – Original Draft Preparation, S.O. and E.S.; Writing – Review & Editing, All Authors; Visualization, S.O.; Supervision, A.A.; Project Administration, A.A. and A.T.

Corresponding author

Correspondence to Elias Soltani.

Competing interests

The authors declare no competing interests.

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