Agriculture is not only a primary source of food and employment but also a vital component of national economies, making its sustainability a global priority1. However, the productivity and quality of cultivated crops are heavily influenced by local meteorological conditions, with water availability being one of the most limiting factors2. The ongoing challenges posed by global climate change complicate the ability of plant breeders to anticipate and address the obstacles to future food security3,4. Among these challenges, drought is considered one of the most critical abiotic stressors, as it substantially reduces crop yields. In plants, drought disrupts key physiological processes, including respiration, photosynthesis, and stomatal function, thereby impairing overall growth and metabolic activity.

In response to drought stress, plants initiate a range of adaptive mechanisms, including morphological and structural changes, the activation of drought-responsive genes, hormone biosynthesis, and the accumulation of osmoprotectants5. Among crops recognized for their resilience to such stresses, quinoa (Chenopodium quinoa Willd.) has garnered increasing attention due to its exceptional nutritional profile and remarkable adaptability to adverse environmental conditions6. As a gluten-free pseudo-cereal, quinoa is notably rich in high-quality protein, unsaturated fatty acids, fiber, vitamins, magnesium, and iron, rendering it a valuable crop for promoting food and nutritional security in the 21st century7,8. Its seeds are widely consumed for their high crude protein content and dietary benefits. In addition to its nutritional value, quinoa exhibits notable tolerance to multiple abiotic stressors, including drought, salinity, heat, and cold9. It has demonstrated the ability to germinate, grow, and reproduce even in hyper-arid environments such as those of Chile, northwest Argentina, and the Altiplano region of Bolivia and Peru regions where water scarcity significantly limits agricultural productivity10. Despite this recognized resilience, the molecular basis of quinoa’s drought tolerance, particularly in terms of epigenetic and genome-wide responses, remains insufficiently understood, underscoring the importance of further research in this area.

Quinoa responds to limited water availability through a range of stress avoidance mechanisms that regulate water loss and uptake11. These include both morphological and physiological adaptations that enable the plant to maintain growth under drought stress. Notably, under dry conditions, quinoa modifies its root and leaf development, often exhibiting minimal ontogenic variation12. In arid or rainfed environments where other crops struggle due to shallow or ineffective root systems, quinoa’s ability to restrict leaf expansion contributes to improved water-use efficiency and enhanced drought tolerance. This adaptive response minimizes transpirational water loss and helps sustain plant survival during periods of water scarcity. Under water-deficit stress, quinoa increases solute accumulation, thereby lowering cellular water potential and stimulating root growth13. Concurrently, stomatal closure and reduced shoot elongation limit evaporation and further conserve water14. Thus, modulation of shoot development, which directly affects the transpiration surface area, constitutes a key component of the drought response strategy in quinoa and other resilient crop species.

Numerous studies have demonstrated that quinoa has evolved adaptive mechanisms to alleviate the effects of drought stress, including high water-use efficiency and an elevated shoot-to-root ratio15. Although the association between drought stress and DNA methylation has received less attention than other abiotic stress responses16, accumulating evidence suggests that water deficit can lead to notable epigenetic changes. For instance, drought stress has been shown to upregulate DNA methyltransferase genes in wheat17, and to induce genome-wide hypermethylation in upland cotton, which returned to baseline levels upon rehydration18. Such stress-induced genomic and epigenetic alterations have been analyzed using a variety of molecular marker systems, including random amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP), coupled restriction enzyme digestion-random amplification (CRED-RA), and methylation-sensitive amplified polymorphism (MSAP)19.

Among these, the inter-primer binding site (iPBS) technique is a PCR-based DNA fingerprinting method that does not require prior sequence knowledge. It utilizes primer-binding sites within the long terminal repeat (LTR) regions of retrotransposons and has been successfully applied to assess genome variability and stability in species such as guava, barley, wheat, pear, maize, and apricot20. The CRED approach, when combined with iPBS markers (CRED-iPBS), offers a robust method for detecting changes in cytosine methylation under environmental stress. This system uses methylation-sensitive restriction enzymes, particularly HpaII and MspI, to reveal differential methylation patterns at CCGG sequences21. These two isoschizomers differ in their sensitivity to cytosine methylation: HpaII cleaves DNA only when the external cytosine is hemi-methylated, while MspI is blocked by both hemi- and fully methylated external cytosines22,23. This differential cleavage pattern allows for detailed profiling of drought-induced methylation changes, especially in stress-adaptive crops like quinoa. The modulation of DNA methylation in response to adverse conditions, such as drought, low temperature, and high salinity, is a well-recognized phenomenon in plants 34. However, there is a scarcity of research aimed at elucidating the impact of restricted water treatment on DNA methylation. The DNA methylation patterns of quinoa plants exhibit significant variations under limited water conditions, including methylation, hypermethylation, and demethylation.

Although quinoa has been widely recognized for its resilience to abiotic stresses such as salinity and drought, the molecular mechanisms underlying this tolerance remain poorly understood. In particular, limited information is available regarding the role of genomic stability and epigenetic modifications, such as cytosine methylation, in response to prolonged drought conditions. Therefore, this study aims to investigate the genomic and epigenetic responses of six quinoa genotypes under varying irrigation levels using inter-primer binding site (iPBS) and coupled restriction enzyme digestion-iPBS (CRED-iPBS) markers. This approach enables the detection of both genomic rearrangements and DNA methylation alterations, offering insight into the molecular basis of drought stress adaptation in quinoa.

Results

Physiological responses A prior study by Akçay and Tan24 demonstrated that the physiological responses of quinoa to water deficit vary significantly depending on both irrigation level and genotype. Their findings indicated a gradual decline in key growth parameters such as shoot and root lengths, dry biomass accumulation, and drought tolerance indices with decreasing irrigation. Interestingly, an increased root-to-shoot ratio was observed at 50% field capacity, suggesting a compensatory mechanism that enhances water uptake under moderate drought stress. Among the genotypes assessed, Titicaca, Sandoval Mix, Moqu Arrochilla, and Mint Vanilla exhibited notable drought tolerance based on physiological indicators (Table 1). These results underscore the importance of evaluating morphological traits particularly root and shoot development as reliable indicators of drought-induced stress. Such parameters are not only essential for identifying tolerant genotypes but also for establishing meaningful correlations between physiological responses and subsequent changes in genomic DNA polymorphism and cytosine methylation patterns, particularly under varying irrigation conditions (Table 1).

iPBS analysis The iPBS analysis was performed to assess the genomic polymorphism induced by drought stress across six quinoa genotypes subjected to five different irrigation levels. Notable differences in banding patterns were observed between control (100% field capacity) and stressed plants, including the disappearance of control-specific bands and the appearance of novel bands. Each of the 10 iPBS primers (Table 2) used produced varying numbers of bands depending on genotype: in the control group, Moqu Arrochilla yielded 26 bands, White 40, Cherry Vanilla and Rainbow 50, Titicaca 54, and China 69. Individual primers generated 1–5 bands in Moqu Arrochilla, 2–7 in White, 2–10 in Cherry Vanilla, 1–8 in Rainbow, 1–9 in Titicaca, and 3–12 in China. Under drought conditions, new bands emerged in the genotypes White (80), Titicaca (42), Rainbow (38), Cherry Vanilla (37), Moqu Arrochilla (17), and China (13). Concurrently, previously existing bands were lost in China (54), Rainbow (35), Cherry Vanilla (27), Titicaca (20), Moqu Arrochilla (12), and White (8). These polymorphic changes, comprising both band gains and losses, reflect differential genomic responses to water stress. The extent of polymorphism varied across both genotypes and irrigation levels, indicating genotype-dependent genomic plasticity (Table 3).

The highest polymorphism rate (65.0%) was detected in the White genotype under 50% field capacity, whereas the lowest polymorphism (15.4%) was recorded in Moqu Arrochilla at 5% field capacity. In addition to polymorphism rates, changes in iPBS profiles were quantified using Genomic Template Stability (GTS) percentages, which serve as indicators of genomic stability in response to stress. GTS values were calculated for all 10 primers used in the study and are presented in Table 2. The response varied among genotypes: the highest GTS value (84.6%) was found in Moqu Arrochilla under the 5% FC treatment, while the lowest GTS (35.0%) was observed in White at 50% FC.

CRED-iPBS analysis The CRED-iPBS analysis was performed using ten primers (Table 3) to detect cytosine methylation alterations in genomic DNA. Genomic DNA was digested with MspI and HpaII, and polymorphism percentages were calculated based on changes relative to the control (undigested) DNA band profiles. The level of irrigation appeared to influence whether methylation changes represented hypermethylation or hypomethylation. In the MspI control group, the number of bands varied across genotypes: Moqu Arrochilla (63), China (70), Titicaca (72), White (74), Rainbow (75), and Cherry Vanilla (80). Compared to the controls, the number of newly formed bands in the MspI-digested experimental groups were as follows: China (58), Titicaca (51), Rainbow (46), White (46), Moqu Arrochilla (40), and Cherry Vanilla (30). Simultaneously, the number of lost bands was: White (57), Rainbow (53), Cherry Vanilla (51), Titicaca (46), Moqu Arrochilla (42), and China (35). The White genotype exhibited the greatest total number of altered bands (103), while Cherry Vanilla showed the lowest (81). The highest MspI polymorphism rate (42.9%) was observed in the China genotype under 50% field capacity, whereas the lowest (20.8%) was recorded in the Titicaca genotype under 5% field capacity.

The CRED-iPBS analysis using HpaII revealed varying band numbers across quinoa genotypes in the control groups: Moqu Arrochilla (59), Cherry Vanilla (69), Rainbow (71), White (72), Titicaca (72), and China (76). When experimental groups were compared to their respective controls, the number of newly formed bands was as follows: White (38), Moqu Arrochilla (37), Titicaca (32), Cherry Vanilla (31), China (30), and Rainbow (24). The corresponding number of lost bands was: Titicaca (65), China (61), White (59), Rainbow (53), Cherry Vanilla (46), and Moqu Arrochilla (42). The genotypes White and Titicaca exhibited the highest total number of altered bands (97 each), whereas Cherry Vanilla and Rainbow had the lowest (77 each). The highest polymorphism level for HpaII (39.0%) was recorded in Moqu Arrochilla at 5% field capacity, while the lowest (21.1%) was found in China at 25% field capacity (Table 4).

Discussion

Quinoa stands out as a model crop in global agriculture due to its exceptional tolerance to abiotic stresses. Its high nutritional value rich in protein, essential amino acids, and micronutrients makes it a valuable food source in efforts to combat global hunger. Numerous studies have highlighted quinoa’s efficient water use, high salinity tolerance, and adaptability to harsh environments, contributing to a growing global interest in its cultivation25. As a climate-resilient crop with notable resistance to salt, cold, and drought stress26, quinoa offers promising solutions for sustainable food production, especially under low-input agricultural systems. The worldwide decline in crop productivity is largely driven by limited irrigation resources and increasing soil salinity27. Quinoa’s ability to thrive under such conditions not only addresses food security concerns but also supports farmers in water-scarce regions and contributes to soil rehabilitation in saline lands28.

Plants respond to drought stress through a series of morphological, physiological, biochemical, and molecular changes29. While previous studies have largely emphasized plant responses to acute water stress at specific developmental stages30, little is known about quinoa’s molecular response to prolonged drought across its entire growth cycle. The present study addresses this gap by evaluating DNA methylation patterns and genomic stability in six quinoa genotypes exposed to varying irrigation regimes using iPBS and CRED-iPBS markers. These methods provide a comprehensive assessment of both genetic alterations and cytosine methylation under drought stress.

The analysis of iPBS profiles revealed genotype-specific polymorphic patterns. The White genotype showed the greatest number of newly formed bands (80) and the fewest lost bands (8), indicating a dynamic genomic response to water limitation. In contrast, the China genotype displayed the fewest new bands (13) and the highest number of lost bands (54), suggesting limited adaptability at the genomic level. The GTS values further supported this observation, with the highest value (84.6%) recorded in Moqu Arrochilla at 5% field capacity and the lowest (35.0%) in White at 50% field capacity. These results highlight that drought stress induces substantial genomic rearrangements in quinoa, possibly due to DNA damage, activation of retrotransposons, or point mutations affecting oligonucleotide priming sites. Such modifications can alter the accessibility of priming regions, leading to the observed polymorphisms31,32.

The findings of this study’s CRED-iPBS analysis indicate that significant differences in the DNA band profiles of various quinoa genotypes, with variations observed across different irrigation levels of field capacity. The China genotype, irrigated at 50% field capacity, exhibited the highest polymorphism value for MspI (42.9%), while the experimental group of the Titicaca genotype, irrigated at 5% field capacity, showed the lowest value (20.8%). Similarly, Moqu Arrochilla genotype, irrigated at 5% field capacity, demonstrated the highest polymorphism value for HpaII (39.0%), whereas the China genotype, irrigated at 25% field capacity, displayed the lowest value (21.1%). While quinoa demonstrates remarkable resistance to stress, the precise mechanisms underlying this effect remain poorly understood. Research has shown that plants respond to biotic and abiotic stress through various mechanisms, including molecular, physiological, and biochemical pathways, which regulate gene expression33. Several studies have also demonstrated that water deficit stress can induce changes in DNA methylation in plants34. However, the exact relationship between DNA methylation and the water deficit tolerance mechanism in quinoa remains uncertain. Although the CRED-iPBS technique effectively identifies relative variations in cytosine methylation across different treatment conditions, it does not provide site-specific resolution. Therefore, future studies employing high-resolution approaches, such as bisulfite sequencing or methylation-specific PCR (MSP), are recommended to validate and localize these epigenetic changes more precisely. Plants and animals both undergo DNA methylation at specific positions, such as the cytosine of the CG dinucleotide, CNG, and CNN sequences35. By altering the stability and positioning of nucleosomes, the basic units of chromatin, this hereditary epigenetic mark regulates gene expression and chromatin structure36. Consequently, it affects the accessibility of DNA to regulatory proteins or protein complexes involved in DNA replication, repair, and transcription. Thus, DNA methylation influences responses to stress, flowering, and gene development in plants37.

Approximately 20–30% of cytosines in the nuclear genome of plants are methylated, and methylation levels can vary significantly across tissues, organs, and developmental stages38. Alterations in DNA methylation may result in abnormal plant phenotypes, such as stunted growth, leaf clustering, or delayed flowering, which can be heritable across generations, as observed in Arabidopsis thaliana with reduced methylation39. DNA methylation patterns are known to be dynamic and responsive to environmental stimuli, including salinity40, drought41, low temperature42, heavy metal exposure21, and pathogen attacks43. The modulation of cytosine methylation under such adverse conditions plays a critical regulatory role in stress response and adaptation34. However, few studies have specifically addressed how water deficit influences DNA methylation in quinoa.

Conclusions

This study represents the first comprehensive evaluation of drought-induced genomic instability and cytosine methylation changes in quinoa genotypes using iPBS and CRED-iPBS marker systems. The findings demonstrate that limited irrigation triggers genotype-specific alterations in DNA band profiles, retrotransposon activity, and methylation patterns. iPBS analysis revealed substantial polymorphism and variable GTS values, indicating genomic rearrangements linked to water stress, while CRED-iPBS results confirmed both hypermethylation and hypomethylation events under different irrigation levels. These observations suggest that epigenetic reprogramming contributes significantly to quinoa’s adaptive response to drought. The combined use of iPBS and CRED-iPBS markers proved effective in detecting stress-responsive genomic and epigenetic variations, providing insights into the regulatory mechanisms underlying resilience. Future studies incorporating larger genotype panels, transcriptomic profiling, and high-resolution methylation techniques such as bisulfite sequencing are recommended to validate and expand these findings. Overall, this research underscores the importance of understanding methylation-mediated responses to drought and highlights quinoa’s potential as a model species for studying epigenetic adaptation in climate-resilient crops.

Materials and methods

Plant material The present study was conducted as a pot experiment in the greenhouse facilities of the Faculty of Agriculture at Atatürk University. Six quinoa (Chenopodium quinoa Willd.) genotypes, sourced from diverse geographical origins, were selected for evaluation. Detailed information on the genetic materials and their distinguishing characteristics is provided in Table 1.

Table 1. Quinoa genotypes used in the research and some of their properties.
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Plant growth conditions and application The experiment was conducted under controlled greenhouse conditions using a factorial arrangement based on a completely randomized design (CRD) with three replications. Ten seeds were initially sown per pot, and seedlings were thinned to three plants after emergence. Uniform irrigation was applied during the first two weeks to maintain field capacity across all treatments. From the third week onward, five irrigation levels 100% field capacity (FC, control), 50% FC (mild drought), 25% FC (moderate drought), 10% FC (severe drought), and 5% FC (extreme drought) were applied to simulate drought stress. The greenhouse was maintained at a day/night temperature regime of 25/15 ± 5 °C. Each pot was filled with 2 kg of loamy garden soil mixed with 10% decomposed farmyard manure. Soil analysis indicated high organic matter (4.7%), neutral pH (7.04), non-saline conditions (total salt: 0.032%), low lime content (0.72%), and sufficient available phosphorus (4.20 ppm). Pots were weighed daily at a fixed time, and the amount of water needed to restore field capacity was added accordingly. The experiment was concluded eight weeks after the application of drought treatments, after which shoot and root length, shoot and root dry weight, root-to-shoot ratio, and drought tolerance percentage were measured24.

DNA isolation, iPBS-PCR and CRED-iPBS amplification Leaf tissues were harvested from young seedlings of quinoa genotypes grown under both control and drought stress conditions. Genomic DNA was extracted using the method described by Zeinalzadehtabrizi et al.44 and stored at − 20 °C for downstream analysis. DNA concentration and quality were assessed using a spectrophotometer and by electrophoresis on a 0.8% agarose gel, respectively. A total of 20 iPBS primers were initially screened using the amplification protocol described by Kalendar et al. [45]. PCR reactions were prepared in a 20 µL volume containing 10× buffer, 2 mM MgCl₂, 0.25 mM of each dNTP, 2 µM (20 pmol) primer, 0.5 U Taq polymerase, and 1 µL of template DNA (50 ng µL−1). The amplification conditions included an initial denaturation at 95 °C for 3 min; 38 cycles of 95 °C for 15 s, 51–56 °C for 60 s, and 72 °C for 60 s; followed by a final extension at 72 °C for 5 min. PCR products were separated on 1.5% agarose gels prepared with 1× sodium borate (SB) buffer and run at 100 V/cm for 120 min. Gels were stained with ethidium bromide (1.3 mM) and visualized under UV illumination. A 100 bp DNA ladder (Vivantis, NM2421) was used to estimate fragment sizes. Ten of the twenty iPBS primers yielded reproducible and scorable banding patterns across the six quinoa genotypes (Table 2). For CRED-iPBS analysis, 1000 ng of genomic DNA was digested with 1 U of either HpaII or MspI (Thermo Scientific) according to the manufacturer’s instructions. The PCR conditions for CRED-iPBS were the same as those used for iPBS-PCR, except for the use of enzyme-digested DNA as the template. The CRED-iPBS amplification protocol consisted of an initial denaturation at 95 °C for 5 min; 40 cycles of 94 °C for 60 s, 51–56 °C for 60 s, and 72 °C for 120 s; and a final extension at 72 °C for 15 min. The resulting products were separated on 1.5% agarose gels in 1× SB buffer, stained with ethidium bromide (0.2 µg mL−1), and visualized under UV light21.

Table 2. iPBS-retrotransposons primer names, sequence, the melting temperature (Tm), CG content (%) and annealing temperature used in this study.
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Table 3. The number of bands in control and disappearance (−), and/or appearance ( +) of DNA bands with molecular sizes (base pair, bp), total band, polymorphism, and the average GTS value for all the primers of five Irrigation of field capacity (FC) treated quinoa genotypes.
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Table 4. The changes in methylation status (CRED–iPBS) of quinoa genotypes exposed to different Irrigation of field capacity (FC).
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iPBS and CRED-iPBS analyses The banding patterns generated from iPBS and CRED-iPBS analyses were evaluated using TotalLab TL120 software (Nonlinear Dynamics Ltd) (Figs. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 and 1112). Genomic Template Stability (GTS%) was calculated using the formula: GTS = 1 – (a/n), where a is the average number of polymorphic bands in each treated sample and n is the total number of bands in the control group46. Polymorphisms in iPBS profiles were identified based on the disappearance of existing bands and the appearance of novel bands relative to the control. The mean polymorphism rate for each treatment group was expressed as a percentage of the control (set at 100%). For the CRED-iPBS analysis, the average polymorphism percentage for each treatment concentration was calculated using the formula: 100 × a/n47.

Fig. 1
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iPBS banding profiles of iPBS-2078 marker across various drought stresstreatments in Titicaca genotype; 1: M 100–1000 bp DNA ladder; 2: control; 3: 5% FCtreatment; 4: 10% FC treatment; 5: 25% FC treatment; 6: 50% FC treatment.

Fig. 2
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CRED-iPBS profiles banding profiles of iPBS-2078 marker across various drought stress treatments in Titicaca genotype; 1: M, 100–1000 bp DNA ladder; 2: control Hpa II; 3: control Msp I; 4: 5% FC treatment Hpa II; 5: 5% FC treatment Msp I; 6: 10% FC treatment Hpa II; 7: 10% FC treatment Msp I; 8: 25% FC treatment Hpa II; 9: 25% FC treatment Msp I; 10: 50% FC treatment Hpa II; 11: 50% FC treatment Msp I.

Fig. 3
Fig. 3The alternative text for this image may have been generated using AI.
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iPBS banding profiles of iPBS-2077 marker across various drought stress treatments in Rainbow genotype; 1: M 100–1000 bp DNA ladder; 2: control; 3: 5% FC treatment; 4: 10% FC treatment; 5: 25% FC treatment; 6: 50% FC treatment.

Fig. 4
Fig. 4The alternative text for this image may have been generated using AI.
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CRED-iPBS profiles banding profiles of iPBS-2077 marker across various drought stress treatments in Rainbow genotype; 1: M, 100–1000 bp DNA ladder; 2: control Hpa II; 3: control Msp I; 4: 5% FC treatment Hpa II; 5: 5% FC treatment Msp I; 6: 10% FC treatment Hpa II; 7: 10% FC treatment Msp I; 8: 25% FC treatment Hpa II; 9: 25% FC treatment Msp I; 10: 50% FC treatment Hpa II; 11: 50% FC treatment Msp I.

Fig. 5
Fig. 5The alternative text for this image may have been generated using AI.
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iPBS banding profiles of iPBS-2381 marker across various drought stress treatments in Moqu Arrochilla genotype; 1: M 100–1000 bp DNA ladder; 2: control; 3: 5% FC treatment; 4: 10% FC treatment; 5: 25% FC treatment; 6: 50% FC treatment.

Fig. 6
Fig. 6The alternative text for this image may have been generated using AI.
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CRED-iPBS profiles banding profiles of iPBS-2381 marker across various drought stress treatments in Moqu Arrochilla genotype; 1: M, 100–1000 bp DNA ladder; 2: control Hpa II; 3: control Msp I; 4: 5% FC treatment Hpa II; 5: 5% FC treatment Msp I; 6: 10% FC treatment Hpa II; 7: 10% FC treatment Msp I; 8: 25% FC treatment Hpa II; 9: 25% FC treatment Msp I; 10: 50% FC treatment Hpa II; 11: 50% FC treatment Msp I.

Fig. 7
Fig. 7The alternative text for this image may have been generated using AI.
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iPBS banding profiles of iPBS-2389 marker across various drought stress treatments in Cherry Vanilla genotype; 1: M 100–1000 bp DNA ladder; 2: control; 3: 5% FC treatment; 4: 10% FC treatment; 5: 25% FC treatment; 6: 50% FC treatment.

Fig. 8
Fig. 8The alternative text for this image may have been generated using AI.
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CRED-iPBS profiles banding profiles of iPBS-2389 marker across various drought stress treatments in Cherry Vanilla genotype; 1: M, 100–1000 bp DNA ladder; 2: control Hpa II; 3: control Msp I; 4: 5% FC treatment Hpa II; 5: 5% FC treatment Msp I; 6: 10% FC treatment Hpa II; 7: 10% FC treatment Msp I; 8: 25% FC treatment Hpa II; 9: 25% FC treatment Msp I; 10: 50% FC treatment Hpa II; 11: 50% FC treatment Msp I.

Fig. 9
Fig. 9The alternative text for this image may have been generated using AI.
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iPBS banding profiles of iPBS-2390 marker across various drought stress treatments in China genotype; 1: M 100–1000 bp DNA ladder; 2: control; 3: 5% FC treatment; 4: 10% FC treatment; 5: 25% FC treatment; 6: 50% FC treatment.

Fig. 10
Fig. 10The alternative text for this image may have been generated using AI.
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CRED-iPBS profiles banding profiles of iPBS-2390 marker across various drought stress treatments in China genotype; 1: M, 100–1000 bp DNA ladder; 2: control Hpa II; 3: control Msp I; 4: 5% FC treatment Hpa II; 5: 5% FC treatment Msp I; 6: 10% FC treatment Hpa II; 7: 10% FC treatment Msp I; 8: 25% FC treatment Hpa II; 9: 25% FC treatment Msp I; 10: 50% FC treatment Hpa II; 11: 50% FC treatment Msp I.

Fig. 11
Fig. 11The alternative text for this image may have been generated using AI.
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iPBS banding profiles of iPBS-2391 marker across various drought stress treatments in White genotype; 1: M 100–1000 bp DNA ladder; 2: control; 3: 5% FC treatment; 4: 10% FC treatment; 5: 25% FC treatment; 6: 50% FC treatment.

Fig. 12
Fig. 12The alternative text for this image may have been generated using AI.
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CRED-iPBS profiles banding profiles of iPBS-2391 marker across various drought stress treatments in White genotype; 1: M, 100–1000 bp DNA ladder; 2: control Hpa II; 3: control Msp I; 4: 5% FC treatment Hpa II; 5: 5% FC treatment Msp I; 6: 10% FC treatment Hpa II; 7: 10% FC treatment Msp I; 8: 25% FC treatment Hpa II; 9: 25% FC treatment Msp I; 10: 50% FC treatment Hpa II; 11: 50% FC treatment Msp I.