Molecular Mechanisms of Drought Stress Response in Medicago ruthenica: Insights from Transcriptome Analysis and Functional Validation of Key Genes

Abstract

Drought stress severely limits plant growth and productivity, yet the molecular basis of drought tolerance and post-drought recovery remains incompletely understood in many forage legumes. Medicago ruthenica is a perennial legume native to arid and cold regions and exhibits strong drought resilience. Results: We integrated key physiological traits related to stomatal regulation, photosynthesis, osmotic adjustment and antioxidant defense with RNA-seq across four stages (well-watered control, CK; drought for 9 days, D9; drought for 12 days, D12; and rewatering for 4 days, RW). Drought triggered stage-dependent physiological shifts, and transcriptome profiling identified >3000 drought- and rewatering-responsive genes enriched in primary metabolism, redox homeostasis and hormone signaling. WGCNA highlighted two drought-associated modules (MEcyan and MEcoral1) and prioritized three hub transcription factors for functional validation: 861 (AP2/ERF), 22 (WRKY) and 89 (bZIP). Overexpression of each gene in tobacco improved drought tolerance, as indicated by enhanced growth/root traits, increased osmolyte accumulation and antioxidant enzyme activities, and reduced membrane damage. Conclusions: Together, these results provide an integrated view of drought stress response and recovery in M. ruthenica and identify 861, 22 and 89 as candidate regulatory genes for engineering drought resilience in legumes.

1. Introduction

Drought is one of the major environmental stresses globally that restricts agricultural productivity and the stability of ecosystems. Water deficit not only leads to reduced crop yields but also impacts the sustainable utilization of grasslands and the development of livestock industries [1,2]. Over the course of long-term evolution, plants have developed complex drought-adaptive mechanisms, including morphological structures, physiological metabolism, and multi-level molecular regulation [3]. Understanding these regulatory mechanisms is crucial for breeding drought-tolerant crops and ensuring agricultural production security. Medicago ruthenica is a perennial leguminous forage species native to the arid and cold regions of northern China. Known for its excellent traits such as cold tolerance, drought resistance, and strong regeneration capacity, it is regarded as an important genetic resource for improving the drought resistance of cultivated Medicago sativa [4,5,6].

Existing studies have shown that plants adopt various strategies to maintain cellular homeostasis and physiological balance under drought conditions. Stomatal closure reduces water loss; the accumulation of osmotic regulators, such as proline and soluble sugars, helps maintain cellular osmotic pressure [7,8]; concurrently, the increased activity of antioxidant enzymes, including superoxide dismutase (SOD) and peroxidase (POD), aids in scavenging excessive reactive oxygen species (ROS), thus alleviating oxidative damage [9,10]. On the other hand, drought inhibits photosynthesis, leading to a decline in chlorophyll content and weakened activity of photosynthetic enzymes, and it induces lipid peroxidation in membranes, resulting in the accumulation of malondialdehyde (MDA) in tissues [11]. At the molecular level, hormone signaling pathways such as abscisic acid (ABA) are rapidly activated, and a large number of response genes are induced, including multiple transcription factor families such as bZIP, AP2/ERF, and WRKY, which play central roles in regulating drought responses [12].

However, compared to Medicago sativa, research on the drought adaptation mechanisms of its wild relative Medicago ruthenica is still limited. There is a lack of systematic studies to reveal the physiological, biochemical dynamics, and transcriptomic regulatory patterns of M. ruthenica under different drought intensities and the subsequent rehydration recovery process. Furthermore, there is a scarcity of reports based on transcriptomics to identify key drought-related genes and validate their functions. This research gap restricts the potential application of M. ruthenica in molecular improvement of M. sativa and drought-resistant breeding.

In this study, M. ruthenica was subjected to progressive drought and rewatering treatments, and we integrated morphological observations, physiological/biochemical assays and transcriptome profiling to characterize the drought–rewatering response. WGCNA was further used to identify drought-associated co-expression modules and hub genes. Three transcription factors were selected for functional validation: evm.TU.original_scaffold_861_pilon.70 (hereafter 861; AP2/ERF), evm.TU.fragScaff_scaffold_22_pilon.68 (hereafter 22; WRKY) and evm.TU.fragScaff_scaffold_89_pilon.7 (hereafter 89; bZIP). Their coding sequences were cloned and overexpressed in tobacco to evaluate their potential roles in drought tolerance. This work provides candidate regulatory genes and a framework for improving drought resilience in forage crops such as alfalfa (Medicago sativa).

2. Materials and Methods

2.1. Experimental Materials and Treatment

In this study, Medicago ruthenica (L.) Ledeb. ‘Zhilixing’, a perennial leguminous forage species, was used. This variety was cultivated through multiple selective breeding by the Inner Mongolia Agricultural University and registered in December 1992. The seeds used in the experiment were collected from the grassland experimental station of Inner Mongolia Agricultural University in 2008 and have been stored in the Grassland Resource Laboratory.

The seeds were first soaked in 70% ethanol for 1 min, followed by treatment with a 2.5% sodium hypochlorite solution for 10 min. After that, the seeds were rinsed several times with sterile distilled water and placed on moist filter paper to germinate. After germination, the seedlings were transplanted into nutrient pots containing a mixture of field soil and vermiculite (3:1, v/v). The plants were grown in an artificial greenhouse under controlled conditions: 16 h of light/8 h of dark photoperiod, temperature maintained at 25 ± 2 °C, relative humidity at 60–70%, and light intensity of approximately 6000 lx.

When the plants reached the 6-leaf stage, uniform and healthy plants were selected for the experiment. Four treatment groups were set: normal watering control (CK), drought for 9 days (D9), drought for 12 days (D12), and rewatering for 4 days after 12 days of drought (RW). The drought treatment was achieved by withholding water, and rewatering was carried out by restoring normal watering. Each treatment group had three biological replicates, and each replicate contained 10 individual plants.

2.2. Morphological and Physiological Measurements

Functional leaves (the third fully expanded leaf) from each treatment group were collected for scanning electron microscopy (SEM) analysis. Leaf segments of approximately 5 mm × 5 mm were fixed in 2.5% glutaraldehyde (4 °C, 12 h), dehydrated using gradient ethanol, critically point-dried, and sputter-coated with gold. The leaf epidermis and stomatal morphology were observed using a Hitachi SU8010 scanning electron microscope (Hitachi, Tokyo, Japan), and stomatal density, length, width, and opening/closing state were measured using ImageJ software (version 1.5, National Institutes of Health, Bethesda, MD, USA).

Simultaneously, physiological parameters were measured on the functional leaves of each treatment group. Chlorophyll content was determined using a UV-2550 spectrophotometer (Kyoto, Japan) based on the method of Arnon at wavelengths of 663, 645, and 470 nm. Free proline content was measured by the acid ninhydrin method at 520 nm. Soluble sugar content was measured by the anthrone colorimetric method at 620 nm. Soluble protein content was quantified using the Bradford method with Coomassie Brilliant Blue G-250 at 595 nm, using BSA as a standard curve. Malondialdehyde (MDA) content was measured by the thiobarbituric acid (TBA) method at the absorbance difference between 532 and 600 nm. Superoxide dismutase (SOD) activity was measured using the nitroblue tetrazolium (NBT) photoreduction method at 560 nm, and peroxidase (POD) activity was determined by the guaiacol-H₂O₂ colorimetric method at 470 nm.

2.3. Transcriptome Sequencing and Differential Gene Analysis

Functional leaves from each treatment group (CK, drought for 9 days, drought for 12 days, and rewatering for 4 days) were collected for RNA extraction. Total RNA was extracted using the RNAprep Pure Plant Kit (Tiangen, Beijing, China). The RNA concentration and purity were measured using a Nanodrop 2000 spectrophotometer (Thermo Scientific, Waltham, MA, USA), and RNA integrity was assessed on an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA), ensuring a RIN value ≥ 7.0 prior to cDNA library construction. Three biological replicates were prepared for each treatment, resulting in a total of 12 samples.

Following rRNA depletion, cDNA libraries were constructed using the NEBNext Ultra RNA Library Prep Kit (New England Biolabs, Ipswich, MA, USA), and paired-end sequencing (PE150) was conducted on the Illumina NovaSeq 6000 platform (Illumina, San Diego, CA, USA).

For data analysis, raw reads were assessed using FastQC (v0.11.9) for quality control. Low-quality sequences and adapter contamination were removed using Trimmomatic (v0.39). Clean reads were aligned to the reference genome (if available) using HISAT2 (v2.2.1) to generate aligned BAM files. The gene expression levels were quantified based on the mapped reads using featureCounts (v2.0.0) with gene annotations derived from the reference genome. Differential expression analysis was performed using DESeq2 (v1.30.0), with a threshold of |log2FoldChange| ≥ 1 and FDR < 0.05.

Gene Ontology (GO) enrichment analysis and KEGG pathway analysis were conducted using KOBAS 3.0 to identify the biological functions and pathways enriched among the differentially expressed genes (DEGs). For further validation, seven representative DEGs were selected for qRT-PCR analysis, and their expression profiles were compared to transcriptome data to validate the sequencing results.

2.4. Weighted Gene Co-Expression Network Analysis (WGCNA)

Based on the transcriptome sequencing data, a weighted gene co-expression network analysis (WGCNA) was performed using the WGCNA R package (v1.70-3). FPKM values of all samples were used as input for the matrix, and genes with high coefficient of variation (CV) were selected (top 50%) to ensure the stability of the network construction. The soft threshold β was determined by the pickSoftThreshold function, with an optimal R² value of ≥0.85. An adjacency matrix was constructed, and a topological overlap matrix (TOM) was calculated. Genes were clustered into modules based on TOM, with a minimum module size of 30, and dynamic tree-cutting was used to identify module boundaries. Each module’s feature vector (module eigengene, ME) was calculated, and Pearson correlation analysis was performed between the ME and sample treatments (CK, drought for 9 d, drought for 12 d, rewatering for 4 d) as well as physiological indicators (chlorophyll, proline, soluble sugar, MDA, SOD, POD, etc.) to identify key modules related to drought stress and resistance traits. In key modules, hub genes with high connectivity (kME) were extracted, and functional annotation based on GO and KEGG was performed. Drought-related transcription factor families such as AP2/ERF, bZIP, and WRKY were prioritized for further analysis.

2.5. Validation of Transcriptome Sequencing Accuracy by qRT-PCR

To validate the RNA-seq results, seven representative differentially expressed genes (DEGs) were selected for real-time quantitative PCR (qRT-PCR) verification. Primers were designed using Premier 5.0. and synthesized by Sangon Biotech (Shanghai, China). The selected genes included glucan endo-1,3-β-glucosidase (carbohydrate metabolism), ERF034 (ethylene-responsive transcription factor), zeta-carotene isomerase (photosynthesis-related carotenoid biosynthesis), GDSL esterase/lipase (lipid metabolism), DRT100 (DNA-damage-repair/toleration protein 100), fatty acid desaturase, and an uncharacterized gene. qRT-PCR expression patterns were compared with RNA-seq fold changes to evaluate the reliability of transcriptome profiling.

2.6. Cloning and Functional Validation of Drought-Related Genes

Based on WGCNA hub-gene screening and expression patterns, three transcription factor genes were selected for cloning and functional validation: evm.TU.original_scaffold_861_pilon.70 (861; AP2/ERF), evm.TU.fragScaff_scaffold_22_pilon.68 (22; WRKY) and evm.TU.fragScaff_scaffold_89_pilon.7 (89; bZIP). cDNA was synthesized using the PrimeScript RT Reagent Kit (Takara, Shiga, Japan), and the open reading frames (ORFs) of the target genes were amplified and cloned into the plant expression vector pCAMBIA2300-GFP under the CaMV 35S promoter. Recombinant plasmids were transformed into Agrobacterium tumefaciens GV3101 and introduced into tobacco (Nicotiana tabacum cv. NC89) by the leaf-disk method. Regenerated plants were selected on medium containing kanamycin (100 mg/L). Positive lines were screened by genomic PCR. PCR was performed with an initial denaturation at 95 °C for 3 min, followed by 35 cycles of 95 °C for 30 s, 55–60 °C for 30 s and 72 °C for 45–60 s, (Supplementary Table S1) with a final extension at 72 °C for 5 min and transgene expression was evaluated by qRT-PCR in independent T1 lines (Supplementary Figure S1). Representative lines with high expression were used for downstream drought assays.

2.7. Drought Resistance Assessment of Transgenic Tobacco

Wild-type (WT) and transgenic tobacco seedlings were grown in a greenhouse until they were 5 weeks old, and then subjected to drought treatment (withholding water for 9 days). During the treatment, wilting symptoms and survival rates were observed and recorded daily, and plant height, above-ground and below-ground biomass were measured before and after the drought treatment. After drought treatment, functional leaves were sampled to measure physiological parameters: free proline was determined using the acidic ninhydrin method, soluble sugars using the anthrone colorimetric method, MDA using the TBA method, SOD activity using the NBT photoreduction method, and POD activity using the guaiacol-H₂O₂ colorimetric method. Tobacco root systems were scanned using the WinRHIZO Pro 2019b root scanning system (Regent Instruments, Québec, QC, Canada), and root parameters such as main root length, number of lateral roots, and root-shoot ratio were calculated. All experiments were performed with at least three biological replicates (n = 3). For transgenic tobacco assays, n indicates three independent T1 lines (one plant per line) unless otherwise stated, and data were analyzed by one-way analysis of variance (ANOVA) using SPSS 26.0 software. Significant differences between treatments were determined at p < 0.05 using Duncan’s multiple range test.

2.8. Sequence Analysis and Identification of Arabidopsis Homologs

Protein family/domain annotation of 861, 22 and 89 was performed based on conserved motifs/domains, and putative Arabidopsis thaliana homologs were inferred from the assigned transcription factor families and well-characterized Arabidopsis drought/ABA-responsive members. Because definitive orthology requires BLASTp and phylogenetic analysis, the homolog information is provided as a working inference to support discussion and guide future validation (Supplementary Table S3).

3. Results

3.1. Drought Stress on the Morphology and Physiology of M. ruthenica

To assess how progressive drought and rewatering affect leaf anatomy in M. ruthenica, we quantified epidermal cell traits and stomatal characteristics using SEM (Figure 1).

Drought altered epidermal cell density and cell dimensions in a stage-dependent manner, and most traits showed partial recovery after rewatering (Figure 1a–c).

Stomatal aperture and stomatal density were significantly reduced under drought, consistent with a water-conservation strategy; rewatering promoted reopening and partial restoration of stomatal traits (Figure 1d,e).

Stomatal size (length and width) also decreased under prolonged drought and tended to recover following rewatering (Figure 1f,g).

Overall, drought induced coordinated anatomical adjustments (reduced stomatal opening and altered epidermal cell traits), and rewatering triggered recovery, supporting the strong drought resilience of M. ruthenica.

3.2. Effects of Drought Stress on the Physiology of M. ruthenica

We further evaluated key physiological and biochemical indicators related to photosynthesis, osmotic adjustment and oxidative stress in M. ruthenica across drought and rewatering stages (Figure 2).

Chlorophyll contents increased during early drought and then declined under prolonged drought, while rewatering promoted recovery towards control levels, indicating dynamic regulation of photosynthesis-related traits (Figure 2a–c).

MDA accumulation increased with drought duration, indicating enhanced membrane lipid peroxidation under severe drought; rewatering reduced MDA compared with prolonged drought (Figure 2d).

Osmotic regulators (proline, soluble sugars and soluble proteins) accumulated during drought and generally decreased after rewatering, suggesting active osmotic adjustment during dehydration and relief upon rehydration (Figure 2e–g).

Antioxidant enzyme activities (SOD and POD) increased during drought and returned towards baseline after rewatering, supporting an ROS-scavenging response to drought-induced oxidative stress (Figure 2h,i).

These results indicate that M. ruthenica coordinates photosynthesis-related adjustments, osmolyte accumulation and antioxidant defense to cope with drought stress and rapidly transitions these processes during recovery.

3.3. Transcriptomic Analysis

3.3.1. Transcriptome Sequencing Quality Assessment and Data Overview

To explore the molecular response mechanisms of M. ruthenica during drought stress and rewatering processes, this study conducted Illumina high-throughput transcriptome sequencing on leaf samples from four treatments: normal water supply (CK), drought for 9 days (D9), drought for 12 days (D12), and rewatering for 4 days (RW). A total of approximately 573 million raw reads were obtained, corresponding to a data volume of approximately 87.34 Gb (

Table 1. Transcriptome Sequencing Data Volume and Quality Control Statistics for M. ruthenica Under Different Treatments.

Sample	Raw Reads	Raw Bases	Clean Reads	Clean Bases	Error Rate	Q20	Q30	GC Pct
A1	53,622,240	8.04 G	53,524,174	8.03 G	0.01	98.07	94.78	41.94
A2	66,713,486	10.01 G	66,603,458	9.99 G	0.01	97.74	93.89	41.7
A3	51,701,418	7.76 G	51,614,756	7.74 G	0.01	98.05	94.73	41.82
B1	52,827,488	7.92 G	52,718,956	7.91 G	0.01	97.87	94.45	41.68
B2	48,787,544	7.32 G	48,676,006	7.30 G	0.01	98.09	94.83	41.57
B3	44,637,736	6.70 G	44,369,144	6.66 G	0.01	97.85	94.43	41.77
C1	54,689,354	8.20 G	54,591,888	8.19 G	0.01	97.9	94.49	41.79
C2	45,379,566	6.81 G	45,084,400	6.76 G	0.01	98.06	94.86	41.88
C3	45,217,556	6.78 G	44,890,000	6.73 G	0.01	97.81	94.37	41.71
D1	56,358,828	8.45 G	56,253,308	8.44 G	0.01	97.9	94.54	41.6
D2	54,745,144	8.21 G	54,650,950	8.20 G	0.01	98.03	94.68	41.87
D3	54,319,404	8.15 G	54,219,798	8.13 G	0.01	98.1	94.84	41.86

). After quality control, the number of clean reads was consistent with raw reads, and the total clean bases were 86.98 Gb, accounting for more than 99% of the data, indicating high data integrity. The sequencing error rate for all samples was 0.01%, with Q20 and Q30 values ranging from 97.7% to 98.1% and 93.9% to 94.9%, respectively, which are well above the standard sequencing thresholds (Q30 > 85%), demonstrating high sequencing quality. The GC content was between 41.6% and 41.9%, which is typical for leguminous plant leaf transcriptomes, suggesting that the sample data were unbiased.

In terms of functional annotation, the majority of unigenes were successfully annotated in multiple public databases (Figure 3). Specifically, 79.15% of unigenes were annotated in the NT database, 64.33% in the NR database, 47.16% in SwissProt, 45.82% in GO, 43.3% in Pfam, 24.82% in KEGG, and 19.55% in KOG. These results indicate that the transcriptome data have a high functional annotation rate and reliability, laying a solid foundation for subsequent differential gene analysis and functional research.

3.3.2. Differential Gene Expression Analysis

To further reveal the transcriptomic response characteristics of M. ruthenica under drought stress and rewatering conditions, a systematic analysis of differentially expressed genes (DEGs) was conducted between treatments (Figure 4). The results show that for drought stress at 9 days (B vs. A), a total of 3984 DEGs were identified, with 1720 upregulated genes and 2264 downregulated genes. For drought stress at 12 days (C vs. A), 3425 DEGs were identified, with 1264 upregulated genes and 2161 downregulated genes. After 4 days of rewatering (D vs. A), 2259 DEGs were identified, with 845 upregulated genes and 1414 downregulated genes. The overall trend indicates that as the drought severity increases, the number of DEGs increases, with the number of downregulated genes consistently higher than the number of upregulated genes. This suggests that prolonged drought has a stronger inhibitory effect on gene expression, and after rewatering, many genes remain downregulated (Figure 4a).

Venn diagram analysis further revealed the overlap of DEGs between different treatments (Figure 4b). In the comparisons of B vs. A, C vs. A, and D vs. A, a total of 527 genes were significantly differentially expressed across all treatments (p < 0.05), which may represent core regulatory genes involved in the drought-rewatering response. In addition, there were a considerable number of unique DEGs for each treatment, such as 1578 unique genes in B vs. A, 1274 unique genes in C vs. A, and 893 unique genes in D vs. A. This indicates that M. ruthenica exhibits stage-specific and distinct molecular regulatory patterns at different stages.

The heatmap of DEG clustering clearly displays the expression differences among the samples (Figure 4c). The three biological replicates within each treatment group clustered together, demonstrating good reproducibility of the sequencing data. The control group samples (A1–A3) and the drought 12-day samples (C1–C3) showed the most significant differences in expression patterns, indicating that prolonged drought has the strongest impact on the transcriptome. The drought 9-day group (B1–B3) and the rewatering group (D1–D3) displayed relatively similar expression patterns, suggesting that rewatering could restore the expression of some genes to a state closer to that of the mid-drought stage.

In summary, drought stress causes large-scale reprogramming of gene expression in M. ruthenica, primarily downregulating genes, with distinct molecular responses at different stages. After rewatering, some gene expressions were restored, but a significant proportion of genes remained differentially expressed, indicating that rewatering does not completely reverse the drought effects.

3.3.3. GO Functional Enrichment Analysis

To explore the biological functions of the differentially expressed genes (DEGs) under drought and rewatering treatments, Gene Ontology (GO) enrichment analysis was performed for the three comparison groups: B vs. A (Drought for 9 days vs. control), C vs. A (Drought for 12 days vs. control), and D vs. A (Rewatering for 4 days vs. control) (Figure 5).

In the B vs. A comparison, DEGs were primarily enriched in molecular functions such as “transcription factor activity,” “glycosyltransferase activity,” “DNA-binding transcription factor activity,” and “iron ion binding,” while also being significantly enriched in cellular components like “photosystem II,” “thylakoid membrane,” and “photosynthetic membrane.” Additionally, biological processes such as “protein folding,” “photosynthesis,” and “alcohol metabolic process” were significantly enriched (p < 0.05). These results suggest that after 9 days of drought, M. ruthenica primarily responds through transcriptional regulation and protein folding protection, maintaining the stability of the photosystem while also engaging in osmotic regulation through metabolic processes to combat mid-term drought stress. This aligns with the observed significant increase in chlorophyll content, SOD, and POD activity during this stage.

In the C vs. A comparison, DEGs continued to be significantly enriched in molecular functions like “oxidoreductase activity,” “electron transport activity,” and “glycoside transferase activity,” and were also concentrated in cellular components such as “cell wall,” “external encapsulating structure,” and “extracellular region.” Furthermore, biological processes related to “glucan metabolic process,” “cell wall organization,” and “defense response” were significantly enriched (p < 0.05). This indicates that during prolonged drought, the focus of M. ruthenica’s response shifts from photosynthesis to cell wall remodeling and activation of defense mechanisms, which help maintain cell morphology and mechanical strength while alleviating ongoing oxidative damage. This corresponds to the observed physiological changes during 12 days of drought, including a decrease in chlorophyll content, reduced proline and soluble sugar levels, and a significant increase in MDA content.

In the D vs. A comparison, DEGs were mainly enriched in molecular functions such as “transcription factor activity,” “DNA-binding transcription factor activity,” “iron ion binding,” and “hydrolase activity,” while also being significantly involved in cellular components related to “chromosomes,” “nucleosomes,” and “chromatin.” Additionally, biological processes related to “oligosaccharide synthesis and metabolism,” “trehalose metabolism,” “stress response,” and “biosynthetic processes” were significantly enriched (p < 0.05). This suggests that rewatering not only induced gene responses related to DNA repair, chromatin remodeling, and transcriptional resetting but also promoted the reactivation of sugar metabolism and stress response, enabling the plant to restore some physiological functions. In particular, the enrichment of “trehalose metabolism” indicates that rewatering supports the repair process through the accumulation of energy compounds and osmotic regulators.

In summary, the GO enrichment results reveal the stage-specific molecular responses of M. ruthenica during the drought-rewatering process: mid-term drought primarily involves transcriptional regulation and photosynthetic adaptation, while prolonged drought relies on cell wall remodeling and defense responses. After rewatering, metabolic balance is restored through nuclear-level epigenetic remodeling and reactivation of sugar metabolism. Notably, the functional transitions at different stages are highly consistent with the physiological data, demonstrating the continuous adaptive mechanism of M. ruthenica through “transcriptional regulation—structural defense—transcriptional resetting” during the drought-rewatering cycle.

3.3.4. KEGG Pathway Enrichment Analysis

To further explore the molecular response mechanisms of M. ruthenica under drought and rewatering conditions, a KEGG pathway enrichment analysis was performed on the differentially expressed genes (DEGs) (Figure 6). The results indicated that the DEGs from the three comparisons (B vs. A, C vs. A, D vs. A) were significantly enriched in key pathways related to photosynthesis, energy metabolism, osmotic regulation, stress signaling transduction, and secondary metabolism.

In the B vs. A comparison (drought for 9 days vs. control), DEGs were significantly enriched in pathways such as “photosynthesis,” “photosystem antenna proteins,” “carbon fixation,” “plant hormone signal transduction,” and “MAPK signaling pathway—plant” (p < 0.05). The upregulation of genes related to photosynthesis and the photosystem is likely associated with the increase in chlorophyll content during the mid-drought phase (Figure 2a–d), indicating that M. ruthenica mitigates stress by enhancing photosynthetic efficiency and energy acquisition early during drought. At the same time, the activation of MAPK cascades and hormone signaling pathways (especially ABA-related factors) suggests that the signaling network plays a key role in the rapid response to drought stress.

In the C vs. A comparison (drought for 12 days vs. control), the focus of the enriched DEGs shifted significantly towards metabolic pathways, including “glycolysis/gluconeogenesis,” “pentose and glucuronic acid interconversion,” “galactose metabolism,” “fatty acid degradation,” and “isoflavonoid biosynthesis” (p < 0.05). These pathways are closely linked to osmotic regulation and antioxidant defenses. For instance, the changes in carbohydrate and fatty acid metabolism may correlate with the decreased levels of osmotic substances like proline and soluble sugars at 12 days (Figure 2f,g), suggesting that energy metabolism is suppressed under prolonged drought, and cellular osmotic protection is diminished. Additionally, the activation of “cell wall metabolism” and “defense response” pathways indicates that cell wall remodeling and secondary metabolite accumulation are important mechanisms for maintaining cell integrity and resisting stress under prolonged drought conditions.

In the D vs. A comparison (rewatering vs. control), the enriched pathways showed a new dynamic feature, primarily focusing on “plant hormone signal transduction,” “circadian rhythm—plants,” “starch and sucrose metabolism,” and “phenylpropanoid, flavonoid, and isoflavonoid biosynthesis” (p < 0.05). After rewatering, hormone signaling pathways such as ABA, IAA, and ethylene became reactivated, driving the expression recovery of downstream defense-related genes. The enrichment of the “circadian rhythm—plants” pathway suggests that rewatering triggered the regulatory networks related to photoperiod and metabolic rhythms, which helped to restore metabolic balance and physiological rhythms. Meanwhile, the reactivation of carbohydrate metabolism (e.g., sucrose metabolism) provided energy for recovery, and the enrichment of flavonoid and phenylpropanoid pathways indicated that antioxidant and secondary defense mechanisms continued to play a critical role during the rewatering phase.

In conclusion, the KEGG analysis revealed the molecular dynamics of M. ruthenica during the drought-rewatering process: in the mid-drought stage, the plant primarily relies on photosynthetic system regulation and rapid hormone signaling response; during prolonged drought, it shifts towards weakening basic metabolism and activating defense pathways; while during rewatering, by reactivating hormone signaling, circadian rhythms, and energy metabolism, combined with the accumulation of secondary metabolites, the plant partially restores its physiological function. These results are highly consistent with the physiological data from earlier measurements (Figure 2), collectively providing the molecular basis for M. ruthenica’s drought tolerance and recovery mechanisms.

3.3.5. WGCNA Analysis

A systematic clustering of the transcriptome data was performed using Weighted Gene Co-expression Network Analysis (WGCNA), and the resulting gene dendrogram (Figure 7a) clearly displayed the distribution patterns of gene expression. Based on the branching structure of the dendrogram and the dynamic tree cutting algorithm, all genes were classified into multiple co-expression modules, which were marked with different colors. The modules exhibited good independence on the dendrogram, indicating that the expression features of genes could be accurately grouped, providing a solid foundation for subsequent analysis of module–trait relationships.

In the module–trait correlation heatmap (Figure 7b), significant differences in the correlation between modules and physiological/anatomical traits were observed, with correlation coefficients (r) ranging from −1.00 to 1.00. Overall, some modules had weak correlations with traits (|r| < 0.5), showing only nonspecific background-level associations. However, a few modules showed high correlations with several key traits, indicating their core role in regulating physiological and structural responses.

Among all the modules, MEcyan and MEcoral1 showed the most significant correlation features, and were identified as the two key modules in this study. Specifically, MEcyan displayed strong positive correlations with photosynthetic traits: chlorophyll a (r = 0.99), total chlorophyll (r = 0.98), soluble protein (r = 0.96), and proline (r = 0.93), as well as a high coupling relationship with SOD (r = 0.94) and POD (r = 0.91) activities. In terms of anatomical traits, MEcyan was closely correlated with upper epidermal stomatal length (r = 0.91) and lower epidermal stomatal opening rate (r = 0.92). This suggests that MEcyan may play an important role in enhancing photosynthetic metabolism and antioxidant responses, and is highly consistent with the dynamic regulation of stomata in leaves.

Similarly, MEcoral1 also exhibited significant integrated correlations. It showed strong positive correlations with chlorophyll a (r = 0.97), total chlorophyll (r = 0.96), soluble protein (r = 0.95), proline (r = 0.91), and was highly correlated with SOD (r = 0.92) and POD (r = 0.89) activities. Regarding anatomical traits, MEcoral1 showed strong relationships with upper epidermal cell density (r = 0.94), upper epidermal stomatal length (r = 0.90), and lower epidermal stomatal opening rate (r = 0.91). This indicates that MEcoral1 is involved in both metabolic regulation and leaf structure formation, and may maintain a balance between photosynthesis and stress resistance by regulating leaf anatomical features under drought stress conditions.

In contrast, other modules (such as MEviolet, MEred, MEblue, etc.) showed moderate correlations with certain traits (e.g., MEviolet with soluble sugars, r = 0.68, MEred with MDA, r = −0.59), but their overall correlation levels were lower than those of MEcyan and MEcoral1, and the traits associated with these modules were more limited. This further highlights the central roles of MEcyan and MEcoral1 in network regulation.

In conclusion, the WGCNA results indicate that MEcyan and MEcoral1 are the two key modules most closely related to major physiological and anatomical traits under cold stress. Future functional enrichment and hub gene screening will focus on these two modules to further investigate their molecular mechanisms in drought stress response.

3.3.6. Key Module Gene Co-Expression Network Analysis

Based on the key modules identified by WGCNA (MEcyan and MEcoral1), we visualized the co-expression networks to prioritize potential hub regulators associated with drought-related traits (Figure 8).

In the MEcyan module, 22 showed high intramodular connectivity and occupied a central position in the network, suggesting that it may act as a hub regulator in this module.

In the MEcoral1 module, 861 formed a highly centralized network with extensive connections to other genes, indicating strong regulatory potential within this module.

In addition, 89 was also highlighted as a highly connected transcription factor in the drought-associated network. Together, the network features support the selection of three candidate transcription factors—861 (AP2/ERF), 22 (WRKY) and 89 (bZIP)—for downstream functional validation.

3.3.7. qRT-PCR Validation

In this experiment, the expression levels of 7 differentially expressed genes (DEGs) were measured to validate the accuracy of the transcriptome sequencing. These genes included two involved in carbohydrate metabolism: glucan endo-1,3-beta-glucosidase (Figure 9a); one ethylene response transcription factor, ERF034 (Figure 9b); one photosynthesis-related gene, zeta-carotene isomerase (Figure 9c); one lipid metabolism-related gene, GDSL esterase/lipase (Figure 9d); one DNA damage repair/tolerance protein, DRT100 (Figure 9e); fatty acid desaturase (Figure 9f); and one unknown gene (Figure 9g).

The results showed that with increasing drought stress, the fold changes of ERF034, zeta-carotene isomerase, and DRT100 genes significantly decreased, while the fold changes of glucan endo-1,3-beta-glucosidase, GDSL esterase/lipase, fatty acid desaturase, and the unknown gene significantly increased. After rewatering, the fold changes of glucan endo-1,3-beta-glucosidase, ERF034, GDSL esterase/lipase, and the unknown gene significantly decreased, while the fold changes of zeta-carotene isomerase and DRT100 significantly increased. The fold change of fatty acid desaturase showed no significant changes, which was consistent with the transcriptome sequencing results.

3.4. Identification of Key Drought Resistance Genes in M. ruthenica and Functional Validation

3.4.1. Generation of Transgenic Lines and Molecular Identification

We constructed CaMV 35S-driven overexpression vectors for the three candidate transcription factors identified above (referred to as MrERF (861), MrWRKY (22) and MrbZIP (89) in the Supplementary Materials): 861 (AP2/ERF; MEcoral1 module), 22 (WRKY; MEcyan module) and 89 (bZIP; drought-associated module). Transgenic tobacco plants were generated via Agrobacterium-mediated transformation and selected on kanamycin-containing medium. Putative transformants were screened by genomic PCR using gene-specific primers (Figure 10). To verify that each transgene was overexpressed, transcript levels in independent T1 lines were quantified by qRT-PCR (Supplementary Figure S1), and representative high-expression lines were used for subsequent drought tolerance assays.

3.4.2. Transgenic Tobacco Drought Tolerance Phenotype Observation and Analysis

WT, empty-vector (EV) and overexpression lines (861, 22 and 89) were subjected to progressive drought stress followed by rewatering. Compared with WT/EV plants, the overexpression lines showed delayed wilting and improved recovery, with the 861 line exhibiting the most pronounced tolerance (Figure 11a).

Before drought treatment (day 0), EV and WT plants displayed comparable growth, whereas the 861, 22 and 89 overexpression lines tended to be larger. This suggests that the baseline size differences are likely associated with transgene effects rather than tissue-culture/regeneration artifacts, because EV plants were regenerated and handled in parallel with the overexpression lines (Figure 11b).

During drought stress, growth inhibition was less severe in the overexpression lines, and the 861 line maintained a higher plant height relative to WT/EV across the treatment period (Figure 11b).

Root traits were also enhanced in the overexpression lines. In particular, the 861 line consistently showed longer roots and a higher root number than WT/EV plants during drought and after rewatering (Figure 11c,d).

The 22 and 89 lines also showed improved root development relative to WT/EV plants, although their advantages were smaller than those observed for the 861 line (Figure 11c,d).

Overall, these phenotypic results indicate that overexpression of 861 confers the strongest drought-tolerance phenotype among the three transcription factors, potentially by promoting root-system development and sustaining growth under water deficit.

3.4.3. Transgenic Tobacco Drought Physiological Response Analysis

Under drought stress, the transgenic tobacco lines 861, 22, and 89 exhibited significant drought physiological responses. By measuring physiological indicators such as soluble sugar, proline, SOD activity, POD activity, and MDA content, we conducted an in-depth analysis of the drought tolerance of these transgenic lines.

Firstly, regarding soluble sugars, which are important osmotic regulators in plants, the 861 transgenic line demonstrated strong drought tolerance. At day 9 of drought, the soluble sugar content of the 861 line was 107.58 mg·g⁻¹, significantly higher than that of the other treatment groups (e.g., WT at 62.72 mg·g⁻¹, 2300-GFP at 63.09 mg·g⁻¹, 22 at 85.65 mg·g⁻¹) (Figure 12a). The higher soluble sugar content helped the 861 line maintain cell osmotic pressure, mitigating water loss under drought stress.

Secondly, the accumulation of proline, a critical component in plant drought tolerance, also played an important role. The proline content in the 861 transgenic line reached 31.46 μg·g⁻¹ at day 9, significantly higher than that in the other lines (e.g., WT at 19.24 μg·g⁻¹, 2300-GFP at 18.32 μg·g⁻¹, 22 at 24.18 μg·g⁻¹) (Figure 12b). Proline not only helps with osmotic regulation but also protects cells from oxidative damage, particularly during the early and mid-stages of drought stress.

In terms of antioxidant enzyme activity, the 861 group also exhibited a significant advantage. SOD (superoxide dismutase) and POD (peroxidase) are key components of the plant antioxidant system. The SOD activity of the 861 line reached 64.51 U·g⁻¹·min⁻¹ at day 9, and the POD activity was 7015 U·g⁻¹·min⁻¹, both significantly higher than those in the WT group (SOD at 41.64 U·g⁻¹·min⁻¹, POD at 4356 U·g⁻¹·min⁻¹) and the 2300-GFP group (SOD at 42.35 U·g⁻¹·min⁻¹, POD at 4442 U·g⁻¹·min⁻¹) (Figure 12c,d). These results indicate that the 861 line can effectively scavenge reactive oxygen species (ROS) induced by drought and reduce oxidative damage.

Moreover, MDA (malondialdehyde) is an important indicator of cell membrane damage. At day 9 of drought, the MDA content in the 861 line was 40.27 μmol·g⁻¹, significantly lower than that in the WT group (65.49 μmol·g⁻¹) and the 2300-GFP group (66.14 μmol·g⁻¹), suggesting that the 861 line can effectively mitigate lipid peroxidation of cell membranes under drought stress (Figure 12e).

In conclusion, the 861 transgenic line showed significant drought tolerance by accumulating soluble sugars and proline, enhancing antioxidant enzyme activity, and reducing MDA levels. In comparison, the 22 and 89 lines also exhibited some drought tolerance, but their performance in physiological indicators was not as strong as the 861 line. These results indicate that the 861 gene plays a crucial role in improving drought resistance in transgenic tobacco, providing potential candidate genes for the improvement of drought-resistant crops in the future.

4. Discussion

4.1. Morphological and Physiological Strategies for Drought Adaptation

This study reveals the adaptive morphological and physiological strategies of M. ruthenica under drought stress. Drought stress has become a major challenge to global agricultural productivity, and understanding how plants respond through morphological and physiological mechanisms is crucial for drought-resistant crop breeding. M. ruthenica exhibits significant stomatal regulation, accumulation of osmotic regulators, enhanced antioxidant defenses, and efficient recovery capabilities under drought stress.

First, under drought stress, M. ruthenica reduces water loss by closing stomata. The significant reduction in stomatal density and aperture helps minimize transpiration, protecting the plant from excessive water loss. Previous studies have shown that many drought-resistant plants employ similar stomatal regulation mechanisms to adapt to drought [13]. However, while stomatal closure effectively reduces water loss, it inevitably leads to a decline in photosynthetic efficiency. This phenomenon was also confirmed in our study, where the photosynthetic rate of M. ruthenica significantly decreased at the early stage of drought. Compared to existing studies [14], our findings show that M. ruthenica possesses a more robust stomatal regulation mechanism, capable of quickly recovering photosynthesis to counteract the adverse effects of drought.

Second, proline and soluble sugars are key osmotic regulators in plants’ response to drought. In our study, M. ruthenica showed significant proline accumulation under drought stress, which is consistent with findings in Oryza sativa [15] and Solanum lycopersicum [16]. Proline plays a crucial role in maintaining cellular osmotic balance and protecting cell membranes and proteins. More importantly, our research further demonstrates that proline accumulation is not only a rapid response to water stress in drought-tolerant plants but is also closely related to plant recovery. Specifically, the recovery of proline levels during rewatering indicates that M. ruthenica can quickly restore its physiological functions after drought stress. Similarly, soluble sugars also play an essential role in maintaining cellular water balance and participating in antioxidant responses, a mechanism widely verified in Triticum aestivum [17] and Zea mays [18].

Regarding antioxidant defense, M. ruthenica demonstrated strong antioxidant capacity under drought stress, especially with enhanced superoxide dismutase (SOD) and peroxidase (POD) activities. These increases in antioxidant enzymes help eliminate reactive oxygen species (ROS) generated under drought stress, thereby alleviating oxidative damage. Our results are consistent with studies on Populus davidiana [19] and Panicum miliaceum [20], demonstrating the crucial role of antioxidant defense in drought-resistant plants. However, we also observed that, under extreme drought conditions, despite an increase in antioxidant system activity, ROS accumulation still caused some cellular damage. This suggests that antioxidant defense in plants depends not only on enzyme systems but also on the regulation of endogenous hormones and metabolic networks. Our study indicates that M. ruthenica maintains high physiological stability during prolonged drought by integrating various physiological mechanisms, including osmotic regulation, antioxidant defense, and water regulation, and quickly recovers growth.

Compared to many drought-resistant plants, M. ruthenica exhibited strong recovery capability. After rewatering, it was able to quickly restore chlorophyll content, photosynthetic rate, and antioxidant enzyme activities. This recovery process is consistent with findings in other drought-resistant plants, suggesting that the plant can rapidly restore physiological functions by regulating metabolic pathways after experiencing drought stress [21,22]. Our research provides an in-depth understanding of how M. ruthenica rapidly recovers through enhanced antioxidant defense, osmotic regulation, and water balance after rewatering, offering new insights into its drought tolerance mechanism.

4.2. Molecular Mechanisms of Drought Adaptation in M. ruthenica

In this study, we extensively explored the dynamic changes in gene expression of M. ruthenica during different drought phases and rewatering processes. In the early drought phase, M. ruthenica significantly upregulated carbohydrate and amino acid metabolism pathways to provide energy and protective substances for the plant. Specifically, M. ruthenica quickly activated proline synthesis and other metabolic pathways, effectively enhancing its cellular osmotic regulation capacity and antioxidant defense system. This mechanism enabled M. ruthenica to rapidly respond to water loss in the early drought phase and maintain cellular physiological stability. Furthermore, upregulation of carbohydrate metabolism genes, such as ADP-glucose pyrophosphorylase (AGP) and fructose-1,6-bisphosphatase (FBPase), reflected the plant’s strategy of storing sugars to maintain osmotic balance and cellular hydration, consistent with drought response mechanisms observed in Solanum lycopersicum [23] and Abies koreana [24], although M. ruthenica displayed a stronger emergency response capacity.

In the prolonged drought phase, the ABA (abscisic acid) signaling pathway in M. ruthenica was significantly activated. Our data indicate that key genes involved in ABA synthesis and signaling (e.g., ABA1, NCED, and PYR/PYL) were upregulated under drought stress, enhancing the plant’s water retention and drought resistance. This finding aligns with studies on Sophora alopecuroides [25] and Phellodendron Chinense [26], which also regulated drought resistance through the ABA signaling pathway. However, M. ruthenica exhibited a more complex ethylene signaling regulation in drought responses, particularly the significant upregulation of ETHYLENE INSENSITIVE 3 (EIN3) and ETHYLENE RESPONSE FACTOR (ERF), indicating the more critical role of ethylene in M. ruthenica’s drought response. This ethylene-ABA signaling interaction might provide an additional layer of regulation for M. ruthenica’s rapid response in drought environments. Compared to other leguminous plants, such as M. ruthenica [27] and Lens culinaris [28], M. ruthenica significantly downregulated photosynthesis-related genes, such as RBCS and PSII, to conserve water. This phenomenon suggests that M. ruthenica effectively mitigates drought stress by reducing water consumption through the downregulation of photosynthesis. This response, similar to other drought-resistant plants, highlights M. ruthenica’s water-saving mechanism, with more pronounced responsiveness in this aspect.

During the rewatering phase, M. ruthenica exhibited excellent repair and regeneration capabilities. Transcriptome analysis revealed that antioxidant-related genes, such as SOD and POD, rapidly restored to normal levels after rewatering, promoting plant recovery by repairing the damaged cellular structures caused by drought. Compared to Medicago sativa, M. ruthenica’s recovery after rewatering was not only reflected in the upregulation of antioxidant genes but also included the activation of cell wall repair and growth-regeneration genes, such as Expansin and XET. This suggests that M. ruthenica possesses a more efficient repair and regeneration capacity [29]. Compared to existing studies [30,31], M. ruthenica’s drought tolerance transcriptome response demonstrates its unique gene regulatory network, particularly in the interaction between ABA and ethylene signaling pathways and the regulation of carbohydrate and amino acid metabolism. This unique gene regulatory mechanism may be a key factor behind M. ruthenica’s outstanding drought tolerance in extreme drought environments. Through these studies, we have not only revealed the molecular mechanisms behind M. ruthenica’s response to drought stress but also provided strong support for future drought-resistant crop breeding.

4.3. Transcription Factor Roles and Co-Expression Network Analysis in Drought Response of M. ruthenica

Using weighted gene co-expression network analysis (WGCNA), we identified two drought-associated modules (MEcyan and MEcoral1) that were strongly correlated with physiological traits during drought and rewatering. From these modules, three hub transcription factors were prioritized for validation: 861 (AP2/ERF family), 22 (WRKY family) and 89 (bZIP family).

AP2/ERF transcription factors are widely involved in abiotic stress responses and can modulate drought tolerance through regulation of stress-responsive genes and protective metabolites. In legumes, the AP2-domain transcription factor WXP1 from Medicago truncatula activates cuticular wax production and confers drought tolerance when overexpressed in alfalfa [32], highlighting a plausible link between AP2/ERF regulators and water-loss control. Thus, the AP2/ERF hub gene 861 may participate in drought adaptation by coordinating transcriptional programs related to water conservation and stress protection.

WRKY transcription factors have been repeatedly implicated in drought-stress regulation, often through interactions with ABA and ROS signaling. In Arabidopsis, activated expression of WRKY57 improves drought tolerance and is associated with elevated ABA levels [33]. In Medicago truncatula, MtWRKY76 overexpression enhances salt and drought tolerance and triggers abiotic stress-inducible genes [34]. These reports support the hypothesis that the WRKY hub gene 22 may modulate drought adaptation through ABA-associated transcriptional reprogramming and stress-inducible downstream networks.

bZIP transcription factors, particularly the AREB/ABF subgroup, are central regulators of ABRE-dependent ABA signaling in plant drought responses. AREB1 functions as an ABRE-binding transcription activator that enhances drought stress tolerance in Arabidopsis [35,36], and the four AREB/ABFs act as predominant downstream transcription factors of SnRK2 kinases in ABA signaling during osmotic-stress responses [37,38]. Therefore, the bZIP hub gene 89 identified here may contribute to drought tolerance by integrating ABA signaling with osmotic adjustment and antioxidant defense pathways.

Notably, co-expression connections among 861, 22 and 89 (Figure 8) suggest that AP2/ERF, WRKY and bZIP regulators may act in a coordinated manner to balance growth maintenance, stress protection and recovery following rewatering.

In addition to these three transcription factors, several other drought-responsive genes were present in the same networks and may contribute to the overall drought resilience of M. ruthenica. Further functional studies will help disentangle the relative contributions and regulatory hierarchies within these modules.

Overall, our network and enrichment analyses support a multi-layer regulatory framework in which transcription factors (including 861/AP2–ERF, 22/WRKY and 89/bZIP) interact with metabolic adjustment and ROS-scavenging pathways to shape drought tolerance and recovery.

4.4. Functional Validation of Key Drought-Resistant Genes in M. ruthenica Through Transgenic Tobacco

To functionally validate the three candidate transcription factors from M. ruthenica, we generated transgenic tobacco plants overexpressing 861, 22 or 89 and evaluated their drought tolerance. Compared with wild-type (WT) and empty-vector control (EV) plants, all three overexpression lines showed improved performance under drought stress, with the 861 line showing the most pronounced tolerance in our assays.

Overexpression of 861 was associated with enhanced growth and root development and with improved physiological protection under drought, including higher levels of osmolytes (e.g., soluble sugars and proline), increased antioxidant enzyme activities (SOD and POD) and reduced membrane lipid peroxidation (lower MDA).

The 22 and 89 overexpression lines also exhibited improved drought-related traits relative to WT/EV plants, consistent with their predicted roles as WRKY and bZIP transcription factors in stress signaling, ROS homeostasis and osmotic regulation.

Root-system enhancement was a common feature among the transgenic lines, which could increase water uptake capacity and thus contribute to drought tolerance at the whole-plant level.

We also observed genotype-dependent developmental changes (e.g., altered growth rate/phenology) in some overexpression lines. Such changes may reflect a drought-escape or growth–stress trade-off strategy, and they warrant further investigation in controlled genetic backgrounds.

Taken together, the heterologous overexpression results in tobacco support 861, 22 and 89 as positive regulators associated with drought tolerance, while the phenotypic differences among lines suggest distinct regulatory contributions for each gene.

Importantly, because these functional assays were performed in tobacco, the results demonstrate that these M. ruthenica genes can enhance drought tolerance in a heterologous system, but they do not by themselves prove improved drought tolerance in M. ruthenica. In addition, PCR-based screening confirms the presence of transgene fragments but does not resolve copy number or insertion sites, and it can be confounded by residual Agrobacterium; therefore, additional validation (e.g., Southern blot, qPCR-based copy-number estimation, or T-DNA border junction sequencing) is recommended. Species-specific validation in M. ruthenica (e.g., hairy-root transformation, VIGS or gene editing) will also be required. A working model summarizing the proposed drought-response framework is shown in Figure 13.

5. Conclusions

This study combines physiological assays and transcriptome profiling to characterize the responses of Medicago ruthenica to progressive drought stress and subsequent rewatering, providing a systems-level view of drought tolerance and recovery.

WGCNA identified two key trait-associated co-expression modules (MEcyan and MEcoral1) and highlighted three candidate hub transcription factors—861 (AP2/ERF), 22 (WRKY) and 89 (bZIP)—that are strongly linked to drought-related traits.

Heterologous overexpression of these three genes in tobacco enhanced drought tolerance, supporting their potential as candidate regulators for improving drought resilience. Further functional validation in M. ruthenica and additional molecular evidence for transgene integration/expression are needed to translate these findings into breeding applications (Figure 13).