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Article

Adaptive Mechanisms of White-Flowered Alfalfa (Medicago sativa L.) in High-Altitude Cold and Saline–Alkali Environments

by
Xiaoli Wei
,
Wei Wang
,
Yuanyuan Zhao
,
Xiaojian Pu
,
Guangxin Lu
* and
Chengti Xu
*
College of Animal Husbandry and Veterinary Science, Qinghai University, Xining 810016, China
*
Authors to whom correspondence should be addressed.
Biology 2026, 15(5), 414; https://doi.org/10.3390/biology15050414
Submission received: 14 February 2026 / Revised: 1 March 2026 / Accepted: 2 March 2026 / Published: 3 March 2026
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Simple Summary

This study explored why some alfalfa plants on the Qinghai–Tibet Plateau have white flowers instead of the usual purple. We found that the white-flowered plants have made a trade-off: they invest less energy in flower size and growth, and more in defending themselves against the harsh, salty environment. Specifically, these plants have boosted their chemical defenses, including antioxidant enzymes and protective compounds. By analyzing the plants’ genes and metabolites, we discovered that the cellular pathway responsible for purple flower color was dialed down, which redirected the plant’s resources towards producing these defensive chemicals instead. This shows that the white flower is not just a color change, but a marker of a major shift in the plant’s survival strategy. This knowledge could be very useful for developing new alfalfa varieties that are better equipped to handle stressful growing conditions.

Abstract

White-flowered alfalfa (Medicago sativa L.) persisting in Qinghai–Tibet Plateau’s saline–alkali habitats provides a unique model to explore floral color polymorphism-linked ecological adaptation. We systematically compared phenotypic, physiological, transcriptomic, and metabolomic profiles of white-flowered (WF) and purple-flowered (PF) alfalfa under high-altitude cold/saline–alkali field conditions (three biological replicates; Student’s t-test). WF showed a significant growth-defense trade-off: flower size and chlorophyll a content decreased by 18.9% and 46.0%, with reduced gibberellin levels, while jasmonic acid (36.3%), proline (51.5%), antioxidant enzyme activities, total flavonoids (17.7%), and condensed tannins (18.2%) were significantly increased (p < 0.001). Multi-omics analysis revealed phenylpropanoid pathway reprogramming (suppressed anthocyanin biosynthesis, precursor accumulation) and coordinated hormone signaling (jasmonic acid activation, salicylic acid inhibition). Our findings confirm the white-flower trait is not an isolated mutation. It is a key component of a coordinated adaptive syndrome, mediated by metabolic reprogramming and hormonal crosstalk. These results provide theoretical and technical support for breeding stress-resistant alfalfa varieties suitable for marginal land cultivation.

1. Introduction

Salinity–alkalinity stress significantly limits crop productivity worldwide [1]. Alfalfa (Medicago sativa L.), the most important high-quality leguminous forage, faces substantial cultivation constraints due to salt-alkali stress [2]. Investigating stress-resistant germplasm and its adaptive mechanisms is therefore of strategic importance. During domestication, alfalfa has predominantly developed purple corollas [3]. However, stable white-flowered individuals have been found in the saline–alkali habitats of the Qinghai–Tibet Plateau. Traditionally, the white-flower trait was considered a simple defect in anthocyanin synthesis, a neutral or slightly disadvantageous morphological marker [4]. However, increasing evidence suggests that white-flowered individuals may have adaptive advantages under certain stressful environments [5]. The occurrence of white-flowered alfalfa in harsh regions such as the Qinghai–Tibet Plateau appears non-coincidental [6], raising the question of whether the white-flower trait is linked to systemic stress adaptation.
Plant adaptation to stress involves multi-level metabolic and regulatory responses. The phenylpropanoid pathway produces anthocyanins for flower coloration [7] as well as flavonoids and tannins that function as antioxidants and defense compounds [8]. Anthocyanins and many stress-resistant flavonoids share upstream biosynthetic pathways [9]. Thus, metabolic flux trade-offs between pigment synthesis and defense metabolites may represent a key link between flower color and stress tolerance [10]. We hypothesized that the white-flower trait is not an isolated mutation, but a coordinated adaptive syndrome involving redirected phenylpropanoid metabolism and hormonal signaling.
To date, extensive studies have focused on salt-alkali tolerance in purple-flowered alfalfa [11], mainly addressing ion homeostasis and reactive oxygen species scavenging [12]. However, no study has systematically analyzed adaptive mechanisms using flower color variation as a model by integrating phenotypic, physiological, transcriptomic, and metabolomic data.
To address this gap, this study investigates white-flowered alfalfa that exhibits stable growth in the mildly saline–alkali soils of the Qinghai Plateau, using typical purple-flowered alfalfa as a control. Our objectives were to: (1) systematically compare differences in floral morphology and key physiological and biochemical indicators between white- and purple-flowered alfalfa; (2) elucidate global gene expression profiles and metabolite accumulation patterns associated with the white-flower trait through leaf transcriptome and metabolome analyses; and (3) identify core pathways and key candidate genes regulating the relationship between flower color variation and stress resistance through transcript–metabolite association analysis. This study aims not only to clarify the adaptation mechanisms of white-flowered alfalfa in specific habitats, but also to provide new insights into the intricate connections among plant phenotypic plasticity, metabolic trade-offs, and environmental adaptation, thereby offering theoretical foundations and genetic resources for breeding novel alfalfa germplasms that integrate distinctive ornamental value with high stress resistance.

2. Materials and Methods

2.1. Overview of the Experimental Site and Material Sources

The experimental site is situated in Bazangou Township, Pingan District, which is characterized by a temperate continental climate with distinct features: prolonged sunshine duration, intense solar radiation, dry and windy springs, cool summers, cold winters, and subtle seasonal transitions. The average annual sunshine duration is 2864.4 h, equating to approximately 7.7 h per day. The mean annual temperature is 7.6 °C, with total annual precipitation recorded at 310.1 mm and annual evaporation at 1836.3 mm.
Soil sampling and analysis: Soil samples (0–30 cm depth) were collected from three independent plots (n = 3). Each plot followed an S-shaped pattern with five cores pooled per plot. Soil pH and electrical conductivity (EC) were determined in 1:5 soil:water suspensions [13]. Exchangeable Na+ and Ca2+ were extracted with 1 M ammonium acetate and quantified by ICP-OES [14]. Soil organic matter was determined by potassium dichromate oxidation [15]. All analyses were performed in triplicate, with results expressed as mean ± SD (Table 1).
Based on EC (0.48 ± 0.001 ms/cm in 1:5 extract, equivalent to ~2.4 ms/cm saturated paste) and pH (8.19 ± 0.010), the soil is classified as mildly saline–alkali according to established criteria (saturated paste EC 2–4 ms/cm indicates mildly saline) [16].
Plant materials: Purple-flowered alfalfa (PF, cv. ‘Qinghai local’) was established in 2023. In July 2023, white-flowered (WF) phenotypes were first observed within the population, accounting for approximately 10% of individuals at peak flowering. These WF individuals were bagged for self-pollination, and seeds were collected. In 2024 and 2025, the progeny of these WF individuals were sown in separate plots alongside PF plants under the same field conditions. The WF progeny consistently exhibited stable white-flowered phenotypes across both growing seasons, with no reversion to purple flowering, confirming the genetic stability of the trait.
For sampling in 2025, we selected: (1) WF plants from the confirmed stable lineage; (2) PF plants from the original population; (3) all plants at initial flowering stage with comparable vigor. On 25 July 2025, at 15:00, the third fully expanded leaves were collected from 30 plants per phenotype (10 plants pooled per replicate, three replicates). The 15:00 time point was selected based on a preliminary diurnal pilot study showing peak phenylpropanoid gene expression and flavonoid accumulation at this time, and to minimize diurnal variation as a confounding factor [17]. Samples were immediately frozen in liquid nitrogen and stored at −80 °C for subsequent analyses.

2.2. Measurement of Leaf Surface Area and Physiological-Biochemical Parameters

Flower length: Measured from calyx base to standard petal tip using a digital caliper. Six flowers per biological replicate were measured.
Photosynthetic pigments: Chlorophyll a (Chl a), chlorophyll b (Chl b), and total carotenoids (Car) were extracted from fresh leaf tissue (0.2 g) with 10 mL of 80% acetone at 4 °C for 24 h in darkness. After centrifugation (5000× g, 10 min, 4 °C), absorbance was measured at 470, 646, and 663 nm (UV-2600, Shimadzu, Tokyo, Japan). Pigment content was calculated using Lichtenthaler’s equations [18] and expressed as mg/g fresh weight (FW).
Plant hormones: Gibberellins (GAs), abscisic acid (ABA), and jasmonic acid (JA) were quantified by HPLC-MS/MS [19]. Fresh leaf tissue (0.5 g) was extracted with 5 mL of 80% methanol containing 0.1% formic acid and isotope-labeled internal standards (d2-GA, d6-ABA, d5-JA). After centrifugation (12,000× g, 15 min, 4 °C), the supernatant was purified on C18 solid-phase extraction (SPE) cartridges (Waters, Milford, MA, USA), dried under nitrogen, and reconstituted in 200 μL of 80% methanol. Separation used a Waters ACQUITY UPLC BEH C18 column (2.1 × 100 mm, 1.7 μm) (Waters, Milford, MA, USA) at 40 °C. Mobile phases: (A) 0.1% formic acid in water, (B) 0.1% formic acid in acetonitrile. Gradient: 0–2 min, 95% A; 2–10 min, 95–60% A; 10–12 min, 60–5% A; 12–14 min, 5% A; 14–16 min, 95% A. Flow rate: 0.3 mL/min. Detection used negative electrospray ionization-multiple reaction monitoring (ESI-MRM) on a Xevo TQ-S mass spectrometer (Waters, Milford, MA, USA). Quantification used calibration curves with authentic standards (0.1–500 ng/mL, R2 > 0.99). Hormone content was expressed as ng/g FW.
Catalase (CAT) activity was assayed by monitoring H2O2 decomposition at 240 nm [20]. Peroxidase (POD) activity was measured using guaiacol oxidation at 470 nm [21]. Superoxide dismutase (SOD) activity was determined by nitroblue tetrazolium (NBT) photoreduction inhibition at 560 nm [22]. All activities were expressed as units per mg protein (U/mg protein).
Proline (Pro): Fresh leaf tissue (0.2 g) was extracted with 3 mL of 3% sulfosalicylic acid. After centrifugation, 1 mL supernatant was mixed with 1 mL acetic acid and 1 mL ninhydrin reagent, boiled for 30 min, and extracted with 3 mL toluene. Absorbance was measured at 520 nm. Proline content was calculated using a standard curve (0–50 μg/mL, R2 = 0.998) and expressed as μg/g FW [23].
Malondialdehyde (MDA): Fresh leaf tissue (0.2 g) was homogenized in 3 mL of 10% trichloroacetic acid (TCA) and centrifuged. Then, 1 mL supernatant was mixed with 1 mL of 0.67% thiobarbituric acid (TBA), boiled for 30 min, and centrifuged. Absorbance was measured at 450, 532, and 600 nm. MDA content was calculated as described [24] and expressed as nmol/g FW.
Soluble sugars (SS): Fresh leaf tissue (0.1 g) was extracted with 5 mL of 80% ethanol at 80 °C for 30 min. After centrifugation, 1 mL supernatant was mixed with 5 mL anthrone reagent and boiled for 10 min. Absorbance was measured at 625 nm. Glucose was used as standard (0–200 μg/mL, R2 = 0.997), and results expressed as mg/g FW [22].
Condensed tannins (CT): Fresh leaf tissue (0.2 g) was extracted with 5 mL of 70% acetone at 4 °C for 24 h. After centrifugation, 1 mL supernatant was mixed with 3 mL vanillin reagent (4% vanillin in methanol) and 1.5 mL concentrated HCl. After 20 min at 30 °C, absorbance was measured at 500 nm. Catechin was used as standard (0–200 μg/mL, R2 = 0.995), and results expressed as mg/g FW [25].
Total flavonoids (TF): Fresh leaf tissue (0.2 g) was extracted with 5 mL of 70% ethanol at 60 °C for 2 h. After centrifugation, 1 mL supernatant was mixed sequentially with 0.3 mL of 5% NaNO2 (6 min), 0.3 mL of 10% Al(NO3)3 (6 min), and 4 mL of 4% NaOH. Volume was adjusted to 10 mL with 70% ethanol. After 15 min, absorbance was measured at 510 nm. Rutin was used as standard (0–500 μg/mL, R2 = 0.996), and results expressed as mg/g FW [26].
All data are presented as mean ± standard deviation (SD) of three biological replicates (n = 3). For each biological replicate, three technical replicates were performed.

2.3. RNA Extraction, Library Construction, and Transcriptome Sequencing

Total RNA for transcriptome sequencing analysis was extracted from fresh leaves of Medicago sativa using the TIANGEN Polysaccharides & Polyphenolics-rich Plant Total RNA Extraction Kit (DP441, Tiangen Biotech (Beijing) Co., Ltd., Beijing, China). The integrity (RIN ≥ 7.0) and purity (A260/A280 ratio of 1.8–2.0, A260/A230 > 2.0) of the RNA were verified through agarose gel electrophoresis, Nanodrop 2000 (Thermo Fisher Scientific, Wilmington, DE, USA), and the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) [27]. Subsequently, strand-specific libraries were constructed using the Hieff NGS® Ultima Dual-mode mRNA Library Prep Kit (12309ES, Yeasen Biotechnology Co., Ltd., Shanghai, China). The workflow involved mRNA magnetic bead enrichment, fragmentation, double-stranded cDNA synthesis, end repair and adapter ligation, library PCR amplification, and bead purification [28]. The final libraries passed quality control on the Agilent 2100 system and underwent paired-end 150 bp sequencing on the Illumina NovaSeq 6000 platform, with each sample yielding no less than 6 Gb of raw data.
High-quality transcriptome sequencing data were initially subjected to quality control and filtering using fastp (version 0.23.2) to obtain valid reads. Subsequently, the valid reads were aligned to the Medicago reference genome with HISAT2 (version 2.2.1), and the raw read count data for each gene were quantified from the alignment results using featureCounts (version 2.0.3). Differential analysis of gene expression levels was conducted based on the raw read counts, with TMM normalization performed using the edgeR package (version 0.13.5). Gene expression differences were tested through a generalized linear model. The screening threshold for differentially expressed genes was established as a false discovery rate (FDR) ≤ 0.05 and an absolute log2 fold change (|log2FC|) > 1.

2.4. RMetabolite Extraction and Liquid Chromatography-Mass Spectrometry/Mass(LC-MS/MS) Spectrometry Analysis

Sample preparation: Three independent biological replicates per phenotype (WF and PF) were analyzed, each derived from 10 pooled plants (Section 2.1). Fresh leaf tissue (100 mg) was ground in liquid nitrogen and extracted with 500 μL of pre-cooled 80% methanol containing 0.1% formic acid. After vortexing and ice incubation (5 min), samples were centrifuged (15,000× g, 20 min, 4 °C). The supernatant was diluted with water to 53% methanol, re-centrifuged, and transferred to LC vials [29].
Quality control: A pooled QC sample was prepared by mixing equal volumes of all six individual samples and injected every 10 samples throughout the run. Features with relative standard deviation (RSD) > 30% in QC samples were excluded [30].
LC-MS/MS conditions: Separation used a SCIEX Exion LC AC system coupled to a QTRAP® 6500+ mass spectrometer (SCIEX, Framingham, MA, USA) with a Waters Xselect HSS T3 column (2.1 × 150 mm, 2.5 μm) at 50 °C. Mobile phases: (A) 0.1% formic acid in water, (B) 0.1% formic acid in acetonitrile. Gradient: 0–1 min (5% B), 1–8 min (5–50% B), 8–10 min (50–95% B), 10–12 min (95% B), 12–15 min (5% B). Flow rate: 0.4 mL/min, injection volume: 2 μL. Detection used positive/negative ESI-MRM mode.
Metabolite identification: Metabolites were identified by matching retention time and MS/MS spectra against an in-house database of >2500 authentic standards (validated with reference compounds) and cross-verified with public databases (KEGG, HMDB). Identification confidence follows Metabolomics Standards Initiative (MSI) guidelines [31]: Level 1 (matched to authentic standard) and Level 2 (putatively annotated). All reported differential metabolites achieved Level 1 or 2.
Data processing: Raw data were processed using SCIEX OS 1.4 for peak integration and alignment. Peak areas were normalized by total ion current (TIC) [32]. For untargeted profiling, relative quantification was based on normalized peak areas.
Statistical analysis: After log transformation and Pareto scaling, principal component analysis (PCA) was performed to examine overall profiles and QC clustering. Orthogonal partial least squares–discriminant analysis (OPLS-DA) was applied for supervised discrimination between WF and PF, validated by 7-fold cross-validation and 200 permutation tests. Differential metabolites were identified using criteria of variable importance in projection (VIP) ≥ 1 and t-test p < 0.05. KEGG pathway enrichment used hypergeometric tests with FDR correction (q < 0.05).

2.5. Statistical Analysis

The data were statistically analyzed using SPSS version 26.0. Differences in various indicators between three-leaf and five-leaf alfalfa were assessed using independent sample t-tests, with significance levels defined as * p < 0.05, ** p < 0.01, *** p < 0.001, and **** p < 0.0001. All data are presented as mean ± standard error of the mean (Mean ± SD). Relevant figures were generated using GraphPad Prism version 10.1.2.
Correlation network analysis was conducted to explore the associations between metabolites and gene expression. Pearson correlation coefficients were calculated between the abundance levels of each metabolite and the expression levels of each differentially expressed gene. A significant interaction was defined if the absolute correlation coefficient was |r| > 0.8 with a p-value < 0.05. The resulting metabolite-gene correlation network was visualized and analyzed using Cytoscape software (version 3.10.1).

3. Results

3.1. Phenotypic Variation

Significant phenotypic differences in flower color were observed between the two alfalfa materials: the PF material exhibited deep purple pigmentation, while the WF material completely lacked anthocyanins, resulting in white flowers (Figure 1A). Quantitative analysis of floral organ morphology revealed that the flower size of WF was significantly reduced compared to PF. Specifically, the average flower length of WF was 3.04 cm, representing an 18.9% decrease compared to PF (3.75 cm) (p < 0.001, Figure 1B). This association between anthocyanin deficiency and reduced floral organ size suggests that the genetic mutation present in WF may exert pleiotropic effects on plant development beyond merely influencing pigment synthesis.

3.2. Differences in Physiological and Biochemical Parameters

In comparison to PF materials, WF materials exhibited a significant decrease of 46.0% in Chl a content (p < 0.001), a 14.9% reduction in Chl b (p < 0.01), and a 13.6% decline in Car (p < 0.05) (Figure 2A–C). The altered Chl a/b ratio indicates potential adaptive modifications in the thylakoid membrane structure or the composition of the light-harvesting complex in WF materials. Furthermore, the SS content in WF materials increased significantly by 74.0% (p < 0.0001) (Figure 2D), which may reflect the feedback accumulation of carbon assimilation products resulting from reduced photosynthetic capacity. Hormonal analysis demonstrated that GA content in WF materials was significantly lower than that in PF materials (p < 0.0001) (Figure 2E), whereas ABA levels did not show a significant difference (Figure 2F). Additionally, the JA content in WF materials increased by 36.3% (p < 0.001) (Figure 2G). The antagonistic changes observed between GA and JA suggest that WF materials may undergo metabolic reprogramming related to growth-defense trade-offs: downregulated GA suppresses vegetative growth, while upregulated JA activates defense responses.
The accumulation of Pro in the WF material increased by 51.5% (p < 0.001) (Figure 2H), while the activities of CAT, POD, and SOD rose by 188.2% (p < 0.0001), 12.3% (p < 0.001), and 26.6% (p < 0.001), respectively (Figure 2I–K). The level of MDA decreased by 33.4% (p < 0.001) (Figure 2L), indicating enhanced oxidative stress tolerance in the WF material. The observed association between GA reduction and the upregulation of antioxidant enzymes aligns with the framework of the growth-defense trade-off theory, suggesting that resources were reallocated from growth metabolism to stress defense. Additionally, the TF content in the WF material increased by 17.7% (p < 0.001), and CT rose by 18.2% (p < 0.0001) (Figure 2M,N). Notably, the absence of anthocyanins did not result in a decrease in total flavonoids. It is speculated that the metabolic block in the WF material occurs at the terminal end of the anthocyanin branch pathway, with upstream carbon flow being redirected towards the synthesis of other flavonoids and tannins.

3.3. Transcriptome Analysis and Differentially Expressed Genes (DEGs) Analysis

RNA-seq analysis revealed extensive transcriptome reprogramming between PF and WF alfalfa. Principal component analysis (PCA) results demonstrated complete separation of PF and WF samples along the PC1 axis, which accounts for 99.4% of the variation. This finding indicates a systemic restructuring of gene expression associated with flower color variation (Figure 3A). Differential expression analysis identified 21,456 significantly differentially expressed genes (DEGs), with 10,515 genes upregulated and 10,941 downregulated in WF, illustrating a nearly balanced distribution of up- and down-regulated genes (Figure 3B). The volcano plot revealed a broad distribution of DEG fold changes, highlighting numerous highly significant differentially expressed genes (Figure 3C). This suggests that the transcriptional regulatory network in WF underwent profound alterations rather than localized adjustments.
KEGG pathway enrichment analysis revealed that differentially expressed genes (DEGs) were significantly concentrated in core metabolic and biosynthetic processes (Figure 3D). The “Biosynthesis of amino acids” pathway exhibited the highest enrichment level, accounting for 10.94% of the genes, followed by the “Metabolic pathways” (8.71%), with “Photosynthesis” (6.69%) and “Carbon metabolism” (5.76%) ranking subsequently. This enrichment pattern closely aligns with physiological data: the decline in WF photosynthetic pigment content corresponds to significant alterations in photosynthesis pathway genes, while soluble sugar accumulation reflects reprogramming in carbon metabolism. Furthermore, changes in proline and secondary metabolites are consistent with the observed enrichment in amino acid synthesis and secondary metabolite biosynthetic pathways. Notably, genes in the “Biosynthesis of secondary metabolites” pathway account for 4.44%, providing candidate genetic resources for deciphering the metabolic compensation mechanism underlying anthocyanin deficiency.
The results indicate that variations in flower color trigger a coordinated regulation of primary and secondary metabolism at the transcriptional level. These systematic changes in gene expression form the molecular basis for the physiological phenotypic alterations observed in WF.

3.4. qRT-PCR Validation of RNA-Seq Data

We screened three significantly enriched pathways from the integrated analysis results and further identified nine highly expressed genes (HEGs). The accuracy of the transcriptomic data for these nine HEGs was verified by quantitative real-time polymerase chain reaction (qRT-PCR). The relative expression profile analysis shown in Figure S1 revealed that these nine HEGs exhibited similar expression trends, and a high correlation was observed between qRT-PCR and RNA-sequencing data, indicating that the transcriptomic data obtained in this study are highly reliable.

3.5. Metabolomic Analysis and Differential Metabolites (DMs) Analysis

Metabolite composition analysis revealed a total of 3130 metabolites, classified into 11 major compound categories. The primary metabolite classes included lipids (479 species, 15.30%), keto-aldehyde esters (456 species, 14.57%), and terpenoids (368 species, 11.76%). Additionally, amino acids (252 species, 8.05%), organic acids (222 species, 7.09%), carbohydrates (212 species, 6.77%), and flavonoids (183 species, 5.85%) represented significant proportions (Figure 4A). As the main site of photosynthesis and primary metabolism, leaves demonstrated high abundances of lipids and carbohydrates, which align with their roles in energy storage and membrane structure characteristic of source organs. The substantial proportions of flavonoids and terpenoids indicate the active defensive secondary metabolism occurring in leaves.
Quality control and multivariate statistical analysis confirmed the reliability of the data. QC samples were closely clustered in the PCA plot (Figure 4B), indicating excellent instrument stability. The OPLS-DA model exhibited complete separation of PF and WF along the T score [1] axis, which accounts for 87.6% of the variation, and demonstrated high consistency among intra-group biological replicates (Figure 4C). This suggests that variation in flower color drives systematic differentiation of the metabolome, showcasing outstanding model predictive capability (R2Y = 0.99, Q2 = 0.98).
KEGG pathway enrichment analysis revealed that the differential metabolites were significantly concentrated in amino acid metabolic networks (Figure 4D). The aminoacyl-tRNA biosynthesis pathway exhibited the highest level of enrichment, followed by metabolic pathways, biosynthesis of plant secondary metabolites, biosynthesis of amino acids, and the phenylalanine, tyrosine, and tryptophan biosynthesis pathways. This enrichment pattern aligns closely with the transcriptomic data; both omics analyses collectively indicate a remodeling of amino acid metabolism, with significant enrichment in the phenylpropanoid pathway (phenylalanine metabolism) providing direct evidence for elucidating the metabolic compensation underlying anthocyanin deficiency. This suggests that carbon flux may be redirected from the anthocyanin branch to the synthesis of lignin, flavonols, or alkaloids.

3.6. Integrated Analysis of Transcriptome and Metabolome

Through an integrated analysis of the transcriptome and metabolome, the phenylpropanoid biosynthesis pathway was identified as being closely associated with variations in flower color. The annotated differential metabolites included L-phenylalanine and L-tyrosine. A correlation analysis between these two metabolites and 40 related genes is illustrated in Figure 5. In this figure, circles represent metabolites, triangles denote genes, solid red lines indicate positive correlations, and gray dashed lines indicate negative correlations. The thickness of the lines corresponds to the strength of the correlations.
Phenylalanine exhibited a significant negative correlation with At5g37930, the homologous gene MsSINAL10, which encodes a MYB-type transcription factor. The downregulation of this gene’s expression may alleviate the inhibition of the phenylalanine ammonia-lyase (PAL) pathway, thereby facilitating the conversion of phenylalanine to cinnamic acid and supplying precursors for flavonoid and lignin biosynthesis. This regulatory pattern is consistent with the observed phenotype of increased total flavonoids and tannins in WF leaves.
The association network of L-Tyrosine is extensive, exhibiting distinct characteristics of functional differentiation. It demonstrates significant positive correlations with genes such as ECT2, WCRKC1, Thumpd1, CD4B, HCF152, and TIM, which are involved in various biological processes: RNA epigenetic modification (ECT2), calcium signaling transduction (WCRKC1), ribosome biogenesis (Thumpd1), cell cycle regulation (CD4B), chloroplast development (HCF152), and mitochondrial import (TIM). This suggests that L-Tyrosine metabolism may be intricately linked to the regulatory mechanisms governing leaf cell proliferation, energy metabolism, and the integrity of the photosynthetic apparatus. Notably, the positive correlation with HCF152, which encodes a PPR protein involved in chloroplast RNA splicing, indicates that its high expression may partially compensate for the functional loss associated with the decline in WF photosynthetic pigments.
The negatively correlated gene clusters were enriched in functions related to oxidative stress and secondary metabolic regulation. CYP71AU50 encodes a cytochrome P450 monooxygenase involved in the synthesis of terpenoid indole alkaloids; NRG2 is a member of the NAC transcription factor family that regulates the deposition of secondary cell walls; XYL1 encodes a xylose isomerase that influences the composition of cell wall polysaccharides; UGD4 participates in the synthesis of UDP-glucuronic acid; and CCD1 is a carotenoid cleavage dioxygenase that modulates stress responses. The negative correlation between L-Tyrosine and these genes suggests that in WF leaves, the carbon flux of L-Tyrosine may shift from cell wall synthesis and hormone metabolism toward the production of defensive alkaloids and antioxidant compounds, thereby forming a metabolic compensation mechanism for anthocyanin deficiency.
Multi-omics integrative analysis identified two core pathways, Phenylpropanoid biosynthesis and Plant hormone signal transduction, systematically elucidating the molecular mechanisms underlying flower color variation (Figure 6).
Phenylpropanoid biosynthesis exhibited significant gene-metabolite co-variation. Within the PAL gene family, MS.gene064490 demonstrated marked downregulation, while members such as MS.gene005954 and MS.gene055905 were found to be upregulated. This indicates intra-family differentiation, suggesting adjustments in substrate allocation strategies. The downregulated expression of the 4CL encoding gene MS.gene017240 may restrict the conversion of cinnamic acid to cinnamoyl-CoA, thereby diverting carbon flow toward the p-coumaric acid branch. Furthermore, the significant downregulation of COMT genes, including MS.gene31229 and MS.gene09096, aligns with the observed metabolite accumulation pattern during the conversion step from caffeic acid to ferulic acid. It is particularly crucial that CYP73A (MS.gene28756, etc.) exhibits an antagonistic expression pattern with E2.1.1.104. The overall downregulation of the CYP73A family inhibits the hydroxylation of p-coumaroyl-CoA to caffeoyl-CoA, whereas the upregulation of certain members of E2.1.1.104 promotes the methylation of caffeoyl-CoA. This interplay between hydroxylation inhibition and methylation promotion may redirect the carbon flux towards lignin monomer synthesis rather than the anthocyanin branch.
The plant hormone signal transduction exhibits an abnormal regulatory pattern characterized by concurrent receptor activation and downstream suppression. The NPR1 receptor protein-encoding gene MS.gene017050 shows significant upregulation, indicating an enhanced capability for salicylic acid (SA) perception. However, the TGA family transcription factors demonstrate overall significant downregulation, with only MS.gene045075 being upregulated, resulting in severely impaired effector function. The PR-1 encoding genes MS.gene01270 and MS.gene02249 are significantly downregulated, while other members show significant upregulation, revealing functional differentiation within the gene family. This suggests a bottleneck in the WF hormone signal transduction pathway, where receptor activation fails to effectively transmit signals to defense response genes. In conjunction with the physiological phenotype of significantly elevated jasmonic acid (JA) content in WF leaves, the systemic activation of the JA signaling pathway may competitively inhibit downstream SA responses, resulting in a hormonal balance dominated by JA with suppressed SA. This leads to a defensive strategy that favors JA-mediated induced resistance.

4. Discussion

4.1. Metabolic Reprogramming of Flower Color Variation

The stable occurrence of white-flowered alfalfa in plateau saline–alkali environments reveals a coordinated metabolic phenotype. While silencing of anthocyanin biosynthesis is often viewed as a simple loss-of-function [33], our multi-omics data demonstrate that this trait is associated with systemic remodeling of the phenylpropanoid network. Specifically, we observed accumulation of L-phenylalanine and L-tyrosine in WF leaves (Figure 5), suggesting redirection of metabolic flux rather than passive end-product accumulation. This pattern is consistent with metabolic rebalancing observed in other plants when primary pathways are perturbed [34]. For instance, under low-phosphorus stress, soybean redirects phenylpropanoid metabolism from anthocyanins to flavonols via transcription factor GmPHR1 [35]. While our transcriptomic data reveal differential expression of multiple transcription factors (Figure 3), their specific regulatory roles in WF remain to be functionally validated.
It should be noted that the observed metabolite and gene expression changes represent correlations, not proven causal relationships. The proposed metabolic flux redirection is inferred from precursor accumulation and downstream product increases, but direct evidence (e.g., isotope tracing) would be required to confirm flux dynamics. Additionally, given that Medicago species possess complex isoflavonoid pathways involved in both defense and rhizobial signaling [36], the metabolic changes observed in WF may have implications beyond above-ground stress adaptation, potentially affecting below-ground interactions—a hypothesis warranting future investigation.

4.2. Response of the Antioxidant Defense System

White-flowered alfalfa exhibited enhanced antioxidant capacity, with SOD and POD activities increasing by 26.6% and 12.3%, respectively, and CAT activity rising by 188.2% (Figure 2I–K). Concurrently, MDA content decreased by 33.4% (Figure 2L), indicating reduced oxidative damage. These quantitative changes suggest improved oxidative stress tolerance in WF compared to PF. Similar coordinated antioxidant responses have been reported in salt-adapted faba bean [37] and drought-stressed chickpea [38]. However, the mechanisms linking flower color variation to antioxidant enhancement remain correlative. The simultaneous increase in proline (51.5%, Figure 2H) may contribute to redox homeostasis beyond its established role in osmoregulation [39], but this interpretation requires direct testing.
Notably, the reduced chlorophyll content in WF (Chl a −46.0%, Chl b −14.9%, Car −13.6%, Figure 2A–C) could be interpreted either as photosynthetic impairment or as an adaptive strategy to limit ROS production under high-light stress [40]. Similar adjustments in photosynthetic apparatus composition have been observed in clover under fluctuating light [41]. At present, our data cannot distinguish between these possibilities; both may contribute to the observed phenotype.

4.3. Reconstruction of Hormone Signaling Networks

WF plants displayed reduced GA content (−36.3%, Figure 2E) and increased JA content (+36.3%, Figure 2G), consistent with a growth-defense trade-off framework [42]. While this pattern aligns with resource allocation theory, the proposed molecular mechanisms remain speculative. Based on studies in other species, one might hypothesize involvement of DELLA-JAZ interactions as described in soybean drought responses [43], or SA-JA antagonism observed in Medicago-pathogen interactions [44]. Our transcriptomic data show differential expression of multiple hormone signaling genes (Figure 6), but these observations are correlational. Direct evidence for specific regulatory mechanisms—such as protein–protein interactions or transcription factor binding—would require targeted molecular analyses beyond the scope of this study.
The SA signaling pathway showed complex expression patterns (Figure 6), with receptor upregulation but downstream effector downregulation. This may indicate pathway bottleneck or redirection of resources toward JA-mediated abiotic stress responses. Similar hormone signaling prioritization has been reported in common bean under combined stress [45]. However, these interpretations remain hypothetical pending functional validation.

4.4. Alternative Interpretations and Study Limitations

An alternative interpretation warrants consideration: the white-flower trait could arise from neutral mutation without direct adaptive significance. Under this view, the associated physiological changes (enhanced antioxidants, metabolic shifts) might represent pleiotropic consequences of the same mutation, or result from linkage disequilibrium, rather than coordinated adaptive responses. While the stable inheritance of WF across three generations and the consistent multi-omics patterns favor the adaptive syndrome interpretation, we cannot definitively exclude neutral or pleiotropic explanations. Future studies involving independent white-flowered lines, segregating populations, or functional validation of candidate genes would help distinguish between these possibilities.
Additional limitations include: (1) this study relies on correlational data requiring functional validation (e.g., transgenics, gene editing); (2) analysis focused on leaves rather than flowers; (3) single-location design limits generalizability; (4) single time-point sampling cannot capture stress dynamics. (5) agronomic traits such as biomass production and yield were not assessed—future studies should evaluate these parameters to determine the practical implications of the white-flower trait for forage production. Addressing these in future work will strengthen mechanistic understanding.

4.5. Implications for Alfalfa Breeding

Despite these limitations, this study provides valuable resources for alfalfa breeding. The 18.9% reduction in flower size, 46.0% decrease in Chl a, and 188.2% increase in CAT activity collectively define a phenotypic syndrome associated with the white-flower trait. The identified candidate genes—particularly those involved in phenylpropanoid pathway regulation and hormone signaling—may serve as molecular markers for selecting breeding materials that combine white-flower ornamental traits with enhanced stress resistance. The observed metabolic reprogramming model suggests that precise manipulation of phenylpropanoid branch points could potentially overcome the traditional trade-off between ornamental quality and stress tolerance, facilitating development of alfalfa varieties suitable for cultivation in marginal lands.

5. Conclusions

The stable existence of white clover in saline–alkaline plateau habitats highlights its adaptive strategies developed through systemic regulation. Research indicates that this phenotype reallocates resources toward defense systems via floral morphological convergence, evidenced by an 18.9% reduction in flower length, and attenuation of the photosynthetic system, marked by a 46.0% decrease in chlorophyll a. The activation of the jasmonic acid signaling pathway, coupled with the inhibition of the gibberellin pathway, collaboratively directs metabolic flux toward proline accumulation, which shows a 51.5% increase, as well as synergistically enhancing the antioxidant enzyme system, with catalase activity rising by 188.2%. Ultimately, these adaptations contribute to a 33.4% reduction in oxidative damage. The core mechanism underlying these changes is the restructuring of the phenylpropane metabolic network. The interruption of anthocyanin synthesis did not diminish the secondary metabolic flux; rather, it redirected precursors such as L-Phenylalanine and L-Tyrosine toward the biosynthesis of flavonoids and tannins, which increased by 17.7% and 18.2%, respectively. This observation establishes a coupling effect between metabolic defense mechanisms and the loss of floral color. The integration of physiological metabolism and molecular regulation suggests that the white-flower trait represents a comprehensive characteristic that plants develop through multi-level synergistic adaptations to stressful environments. Consequently, this provides a novel theoretical framework and breeding approach for the utilization of wild germplasm resources.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biology15050414/s1, Figure S1: Expression analysis of 9 HEGs in different samples determined by RT-qPCR.

Author Contributions

X.W.: Writing—original draft, Resources, Investigation. W.W.: Visualization, Formal analysis. Y.Z.: Project administration, Conceptualization. X.P.: Conceptualization, Funding acquisition. G.L.: Writing—review and editing, Project administration, Supervision. C.X.: Writing—review and editing, Project administration, Conceptualization. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Qinghai Provincial Major Science and Technology Special Project (2023-NK-A3) and Processes, Mechanisms, and Research Methods of Rhizosphere Synthetic Microbial Communities Promoting Vegetation Restoration in Saline-Alkali Lands of the Qaidam Basin (U23A2043).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.

Acknowledgments

Thanks to the support of project funding project: Qinghai Province Major Science and Technology Project (2023-NK-A3) and Processes, Mechanisms, and Research Methods of Rhizosphere Synthetic Microbial Communities Promoting Vegetation Restoration in Saline-Alkali Lands of the Qaidam Basin (U23A2043).

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Ahmad, R.; Hussain, S.; Anjum, M.A.; Khalid, M.F.; Saqib, M.; Zakir, I.; Hassan, A.; Fahad, S.; Ahmad, S. Oxidative Stress and Antioxidant Defense Mechanisms in Plants Under Salt Stress. In Plant Abiotic Stress Tolerance: Agronomic, Molecular and Biotechnological Approaches; Hasanuzzaman, M., Hakeem, K.R., Nahar, K., Alharby, H.F., Eds.; Springer International Publishing: Cham, Switzerland, 2019; pp. 191–205. [Google Scholar]
  2. Peel, M.D.; Anower, M.R.; Wu, Y. Breeding Efficiency for Salt Tolerance in Alfalfa. Life 2023, 13, 2188. [Google Scholar] [CrossRef]
  3. Chen, H.; Zeng, Y.; Yang, Y.; Huang, L.; Tang, B.; Zhang, H.; Hao, F.; Liu, W.; Li, Y.; Liu, Y.; et al. Allele-aware chromosome-level genome assembly and efficient transgene-free genome editing for the autotetraploid cultivated alfalfa. Nat. Commun. 2020, 11, 2494. [Google Scholar] [CrossRef]
  4. Ho, W.W.; Smith, S.D. Molecular evolution of anthocyanin pigmentation genes following losses of flower color. BMC Evol. Biol. 2016, 16, 98. [Google Scholar] [CrossRef]
  5. Zhou, B.; Zheng, B.; Wu, W. The ncRNAs Involved in the Regulation of Abiotic Stress-Induced Anthocyanin Biosynthesis in Plants. Antioxidants 2023, 13, 55. [Google Scholar] [CrossRef]
  6. Wang, X.-M.; Zhao, G.-Q.; Chen, J.; Jing, F.-Z.; Li, L.; Wang, Y.-Y.; Ma, P.; Wu, Y.-H.; Xu, S.-J.; He, W.-L. Adaptation of Plants to UV-B Radiation with Altitude in Tuha Basin: Synergistic Regulation of Epidermal Structure, Secondary Metabolites, and Organic Element Allocation. Life 2025, 15, 1375. [Google Scholar] [CrossRef]
  7. Grünig, N.; Horz, J.M.; Pucker, B. Diversity and ecological functions of anthocyanins. BMC Plant Biol. 2025, 26, 146. [Google Scholar] [CrossRef] [PubMed]
  8. Ninkuu, V.; Aluko, O.O.; Yan, J.; Zeng, H.; Liu, G.; Zhao, J.; Li, H.; Chen, S.; Dakora, F.D. Phenylpropanoids metabolism: Recent insight into stress tolerance and plant development cues. Front. Plant Sci. 2025, 16, 1571825. [Google Scholar] [CrossRef] [PubMed]
  9. Wu, X.; Yang, L.; Han, J.; Liu, H.; Chen, G.; Wang, H.; Feng, X.; Zhang, W.; Liu, K.; Zhang, Z. Integrated transcriptomic and metabolomic analysis unveils heat-tolerance-associated flavonoid metabolites and genes in the rice rel1-D mutant. BMC Genom. 2025, 26, 792. [Google Scholar] [CrossRef] [PubMed]
  10. Qin, W.; Deng, J.; Guo, J.; Yang, W.; Liu, J. Metabolic reallocation in soybeans under shade stress alters phenylpropanoid profiles with implications for stress adaptation and seed composition. BMC Plant Biol. 2025, 25, 870. [Google Scholar] [CrossRef]
  11. Kaundal, R.; Duhan, N.; Acharya, B.R.; Pudussery, M.V.; Ferreira, J.F.S.; Suarez, D.L.; Sandhu, D. Transcriptional profiling of two contrasting genotypes uncovers molecular mechanisms underlying salt tolerance in alfalfa. Sci. Rep. 2021, 11, 5210. [Google Scholar] [CrossRef]
  12. Daud, M.; Qiao, H.; Xu, S.; Hui, X.; Adil, M.; Lu, Y. Understanding abiotic stress in alfalfa: Physiological and molecular perspectives on salinity, drought, and heavy metal toxicity. Front. Plant Sci. 2025, 16, 1627599. [Google Scholar] [CrossRef] [PubMed]
  13. Wang, Y.; Yang, X.; Lv, X.; He, W.; Shao, M.; Liu, H.; Jia, C. Depth-Specific Prediction of Coastal Soil Salinization Using Multi-Source Environmental Data and an Optimized GWO–RF–XGBoost Ensemble Model. Remote Sens. 2025, 17, 4043. [Google Scholar] [CrossRef]
  14. Hu, P.; Zhang, W.; Wanek, W.; Chen, J.; Abalos, D.; Zhao, J.; Xiao, D.; Hou, X.; Li, J.; Chen, H.; et al. Calcium-rich parent materials enhance multiple soil functions and bacterial network complexity. Commun. Earth Environ. 2025, 6, 797. [Google Scholar] [CrossRef]
  15. Yohannes, D.; Kolhe, K.P.; Dananto, M.; Mathewos, M. Investigation of the impact of short-term tillage practices on soil properties at various depths in light soils of the highlands of Central Ethiopia. Sci. Rep. 2025, 15, 39920. [Google Scholar] [CrossRef]
  16. Du, D.; He, B.; Luo, X.; Ma, S.; Song, Y.; Yang, W. Spatio-Temporal Variation Analysis of Soil Salinization in the Ougan-Kuqa River Oasis of China. Sustainability 2024, 16, 2706. [Google Scholar] [CrossRef]
  17. Chen, C.; Huang, T.; Wang, L.-X.; Xiong, J.-S.; Li, M.-Y.; Tan, G.-F.; Liu, P.-Z.; Li, Y.-P.; Hu, Z.-H.; Liu, H.; et al. High-throughput transcriptomic analysis of circadian rhythm of flavonoid metabolism under different photoperiods in celery. Veg. Res. 2025, 5, e028. [Google Scholar] [CrossRef]
  18. Castillo, J.M.; Grewell, B.J.; Pickart, A.; Bortolus, A.; Peña, C.; Figueroa, E.; Sytsma, M. Phenotypic plasticity of invasive Spartina densiflora (Poaceae) along a broad latitudinal gradient on the Pacific Coast of North America. Am. J. Bot. 2014, 101, 448–458. [Google Scholar] [CrossRef]
  19. Israeli, A.; Burko, Y.; Shleizer-Burko, S.; Zelnik, I.D.; Sela, N.; Hajirezaei, M.R.; Fernie, A.R.; Tohge, T.; Ori, N.; Bar, M. Coordinating the morphogenesis-differentiation balance by tweaking the cytokinin-gibberellin equilibrium. PLoS Genet. 2021, 17, e1009537. [Google Scholar] [CrossRef]
  20. Zhang, Z.; Peng, H.; Yang, D.; Zhang, G.; Zhang, J.; Ju, F. Polyvinyl chloride degradation by a bacterium isolated from the gut of insect larvae. Nat. Commun. 2022, 13, 5360. [Google Scholar] [CrossRef]
  21. Huang, J.; Zheng, W.; Liang, Z.; Zhang, Z.; Li, J.; Zhang, H.; Wang, H.; Ao, X.; Yao, X.; Xie, F. Integrated Transcriptomic and Metabolomic Analyses of Seed-Filling Disorders in Soybeans Under Different Ecological Conditions. Agronomy 2025, 15, 2266. [Google Scholar] [CrossRef]
  22. Wang, C.; Wei, X.; Wang, Y.; Wu, C.; Jiao, P.; Jiang, Z.; Liu, S.; Ma, Y.; Guan, S. Metabolomics and Transcriptomic Analysis Revealed the Response Mechanism of Maize to Saline-Alkali Stress. Plant Biotechnol. J. 2025, 23, 5397–5416. [Google Scholar] [CrossRef]
  23. Ma, X.; Zhou, S.; Guo, X.; Xin, L.; Guan, Y. Assessment of various salt water stresses with mineralization on plant growth and antioxidants activity to regulate oxidative stress and ROS scavenging of halophyte ice plant. Front. Plant Sci. 2025, 16, 1626676. [Google Scholar] [CrossRef]
  24. Popržen, T.; Antonić Reljin, D.; Uzelac, B.; Milovančević, M.; Paunović, D.; Trifunović-Momčilov, M.; Marković, M.; Raspor, M.; Nikolić, I.; Stanković, S.; et al. Pseudomonas oryzihabitans D1-104/3 and P. gessardii C31-106/3 differentially modulate the antioxidative response of duckweed (Lemna minor L.) to salt stress. Front. Microbiol. 2024, 15, 1481437. [Google Scholar] [CrossRef] [PubMed]
  25. Yadav, N.K.; Patel, A.B.; Rajesh, S.D.; Parida, P.K.; Baidya, S. Organic fertilization with cow dung modulates growth, bioactive compounds, and antioxidant capacity in duckweed (Wolffia globosa). Front. Plant Sci. 2026, 17, 1751657. [Google Scholar] [CrossRef] [PubMed]
  26. Wei, Y.; Hu, H.; Yuan, M.; Xu, H.; Mao, X.; Zhao, Y.; Huang, L. Determination of Bioactive Components in Chrysanthemum Tea (Gongju) Using Hyperspectral Imaging Technique and Chemometrics. Foods 2024, 13, 4145. [Google Scholar] [CrossRef] [PubMed]
  27. Kuang, Q.; He, C.; Huang, H.; Jiang, H. Multi-omic analysis on the molecular mechanisms of rapid growth in ‘Deqin’ alfalfa after space mutagenesis. BMC Plant Biol. 2025, 25, 34. [Google Scholar] [CrossRef]
  28. Gao, Y.; Liu, X.; Wu, B.; Wang, H.; Xi, F.; Kohnen, M.V.; Reddy, A.S.N.; Gu, L. Quantitative profiling of N(6)-methyladenosine at single-base resolution in stem-differentiating xylem of Populus trichocarpa using Nanopore direct RNA sequencing. Genome Biol. 2021, 22, 22. [Google Scholar] [CrossRef]
  29. Li, Z.; Cheng, B.; Zhao, Y.; Luo, L.; Zhang, Y.; Feng, G.; Han, L.; Peng, Y.; Zhang, X. Metabolic Regulation and Lipidomic Remodeling in Relation to Spermidine-induced Stress Tolerance to High Temperature in Plants. Int. J. Mol. Sci. 2022, 23, 12247. [Google Scholar] [CrossRef]
  30. Lin, J.; Dai, H.; Yuan, J.; Tang, C.; Ma, B.; Xu, J. Arsenic-induced enhancement of diazotrophic recruitment and nitrogen fixation in Pteris vittata rhizosphere. Nat. Commun. 2024, 15, 10003. [Google Scholar] [CrossRef]
  31. Fang, T.; Chen, L.; Yao, H.; Li, Y.; Liu, G.; Wu, S.; Lan, J.; Chen, X. Combined Transcriptomic and Metabolomic Analyses of the Response of Ganoderma lucidum to Elevated CO2. J. Fungi 2026, 12, 5. [Google Scholar] [CrossRef]
  32. Van Puyvelde, B.; Hunter, C.L.; Zhgamadze, M.; Savant, S.; Wang, Y.O.; Hoedt, E.; Raedschelders, K.; Pope, M.; Huynh, C.A.; Ramanujan, V.K.; et al. Acoustic ejection mass spectrometry empowers ultra-fast protein biomarker quantification. Nat. Commun. 2024, 15, 5114. [Google Scholar] [CrossRef]
  33. Li, J.; Wei, Y.; Jin, J.; Gao, J.; Xie, Q.; Yang, F.; Zhu, G. A Fragment Insertion of AgDFR Results in a White Flower Phenotype in Arundina graminifolia (Orchidaceae). Plants 2025, 14, 1680. [Google Scholar] [CrossRef] [PubMed]
  34. Wu, X.; Zhu, S.; He, L.; Cheng, G.; Li, T.; Meng, W.; Wen, F. Phenylalanine Ammonia-Lyase: A Core Regulator of Plant Carbon Metabolic Flux Redistribution-From Molecular Mechanisms and Growth Modulation to Stress Adaptability. Plants 2025, 14, 3811. [Google Scholar] [CrossRef]
  35. Yao, Y.; Yuan, H.; Liu, D.; Cheng, L. Response of soybean root exudates and related metabolic pathways to low phosphorus stress. PLoS ONE 2024, 19, e0314256. [Google Scholar] [CrossRef]
  36. Yang, Q.; Wang, G. Isoflavonoid metabolism in leguminous plants: An update and perspectives. Front. Plant Sci. 2024, 15, 1368870. [Google Scholar] [CrossRef]
  37. Liu, C.; Mao, B.; Yuan, D.; Chu, C.; Duan, M. Salt tolerance in rice: Physiological responses and molecular mechanisms. Crop J. 2022, 10, 13–25. [Google Scholar] [CrossRef]
  38. El-Beltagi, H.S.; Mohamed, H.I.; Sofy, M.R. Role of Ascorbic acid, Glutathione and Proline Applied as Singly or in Sequence Combination in Improving Chickpea Plant through Physiological Change and Antioxidant Defense under Different Levels of Irrigation Intervals. Molecules 2020, 25, 1702. [Google Scholar] [CrossRef]
  39. Trovato, M.; Forlani, G.; Signorelli, S.; Funck, D. Proline Metabolism and Its Functions in Development and Stress Tolerance. In Osmoprotectant-Mediated Abiotic Stress Tolerance in Plants: Recent Advances and Future Perspectives; Hossain, M.A., Kumar, V., Burritt, D.J., Fujita, M., Mäkelä, P.S.A., Eds.; Springer International Publishing: Cham, Switzerland, 2019; pp. 41–72. [Google Scholar]
  40. Chauhan, J.; Prathibha, M.D.; Singh, P.; Choyal, P.; Mishra, U.N.; Saha, D.; Kumar, R.; Anuragi, H.; Pandey, S.; Bose, B.; et al. Plant photosynthesis under abiotic stresses: Damages, adaptive, and signaling mechanisms. Plant Stress 2023, 10, 100296. [Google Scholar] [CrossRef]
  41. Tikkanen, M.; Grieco, M.; Nurmi, M.; Rantala, M.; Suorsa, M.; Aro, E.M. Regulation of the photosynthetic apparatus under fluctuating growth light. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 2012, 367, 3486–3493. [Google Scholar] [CrossRef] [PubMed]
  42. Li, C.; Xu, M.; Cai, X.; Han, Z.; Si, J.; Chen, D. Jasmonate Signaling Pathway Modulates Plant Defense, Growth, and Their Trade-Offs. Int. J. Mol. Sci. 2022, 23, 3945. [Google Scholar] [CrossRef] [PubMed]
  43. Hou, X.; Lee, L.Y.C.; Xia, K.; Yan, Y.; Yu, H. DELLAs Modulate Jasmonate Signaling via Competitive Binding to JAZs. Dev. Cell 2010, 19, 884–894. [Google Scholar] [CrossRef] [PubMed]
  44. Gupta, A.; Awasthi, P.; Sharma, N.; Parveen, S.; Vats, R.P.; Singh, N.; Kumar, Y.; Goel, A.; Chandran, D. Medicarpin confers powdery mildew resistance in Medicago truncatula and activates the salicylic acid signalling pathway. Mol. Plant Pathol. 2022, 23, 966–983. [Google Scholar] [CrossRef] [PubMed]
  45. Zamani, F.; Hosseini, N.M.; Oveisi, M.; Arvin, K.; Rabieyan, E.; Torkaman, Z.; Rodriguez, D. Rhizobacteria and Phytohormonal interactions increase Drought Tolerance in Phaseolus vulgaris through enhanced physiological and biochemical efficiency. Sci. Rep. 2024, 14, 30761. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Phenotypic and morphological comparison of floral organs between purple-flowered alfalfa (PF) and white-flowered alfalfa (WF). (A) Representative inflorescence phenotypes of PF (left) and WF (right). (B) Quantitative analysis of flower length. Data are presented as mean ± standard error, *** p < 0.001, Student’s t-test.
Figure 1. Phenotypic and morphological comparison of floral organs between purple-flowered alfalfa (PF) and white-flowered alfalfa (WF). (A) Representative inflorescence phenotypes of PF (left) and WF (right). (B) Quantitative analysis of flower length. Data are presented as mean ± standard error, *** p < 0.001, Student’s t-test.
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Figure 2. Analysis of physiological and biochemical characteristics in leaves of purple-flowered alfalfa (PF) and white-flowered alfalfa (WF). Data are presented as mean ± standard deviation. ns, not significant; * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, Student’s t-test. (A–N) represent chlorophyll a, chlorophyll b, carotenoid, soluble sugar, gibberellin, abscisic acid, jasmonic acid, proline, catalase, peroxidase, superoxide dismutase, malondialdehyde, total flavonoids, and condensed, respectively.
Figure 2. Analysis of physiological and biochemical characteristics in leaves of purple-flowered alfalfa (PF) and white-flowered alfalfa (WF). Data are presented as mean ± standard deviation. ns, not significant; * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, Student’s t-test. (A–N) represent chlorophyll a, chlorophyll b, carotenoid, soluble sugar, gibberellin, abscisic acid, jasmonic acid, proline, catalase, peroxidase, superoxide dismutase, malondialdehyde, total flavonoids, and condensed, respectively.
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Figure 3. Transcriptome analysis of leaves from purple-flowered alfalfa (PF) and white-flowered alfalfa (WF). (A) Principal component analysis (PCA) plot. (B) Statistics of differentially expressed genes (DEGs). (C) Volcano plot showing the distribution of DEGs. (D) KEGG pathway enrichment analysis. The bar chart displays the top 15 significantly enriched pathways, with the x-axis representing the gene ratio (%).
Figure 3. Transcriptome analysis of leaves from purple-flowered alfalfa (PF) and white-flowered alfalfa (WF). (A) Principal component analysis (PCA) plot. (B) Statistics of differentially expressed genes (DEGs). (C) Volcano plot showing the distribution of DEGs. (D) KEGG pathway enrichment analysis. The bar chart displays the top 15 significantly enriched pathways, with the x-axis representing the gene ratio (%).
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Figure 4. Broad-target metabolomics analysis of leaves in purple-flowered alfalfa (PF) and white-flowered alfalfa (WF). (A) Pie chart of metabolite classification. (B) Principal component analysis (PCA) plot. (C) Orthogonal partial least-squares discriminant analysis (OPLS-DA) score plot. (D) KEGG pathway enrichment analysis. Bubble chart shows the top 15 significantly enriched pathways, with the x-axis representing the proportion of metabolites, bubble size indicating the number of enriched metabolites, and color depth representing the Q value.
Figure 4. Broad-target metabolomics analysis of leaves in purple-flowered alfalfa (PF) and white-flowered alfalfa (WF). (A) Pie chart of metabolite classification. (B) Principal component analysis (PCA) plot. (C) Orthogonal partial least-squares discriminant analysis (OPLS-DA) score plot. (D) KEGG pathway enrichment analysis. Bubble chart shows the top 15 significantly enriched pathways, with the x-axis representing the proportion of metabolites, bubble size indicating the number of enriched metabolites, and color depth representing the Q value.
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Figure 5. Key metabolite-gene association network. Circles represent metabolites, triangles represent genes; node sizes indicate metabolite connectivity or gene abundance respectively; solid red lines indicate positive correlations, dashed gray lines indicate negative correlations; line thickness represents the absolute value of correlation coefficients.
Figure 5. Key metabolite-gene association network. Circles represent metabolites, triangles represent genes; node sizes indicate metabolite connectivity or gene abundance respectively; solid red lines indicate positive correlations, dashed gray lines indicate negative correlations; line thickness represents the absolute value of correlation coefficients.
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Figure 6. Key pathway flowchart of multi-omics integrated analysis. The left side shows Phenylpropanoid biosynthesis, while the right side displays Plant hormone signal transduction. Red dots represent upregulated metabolites, green dots indicate downregulated metabolites; orange boxes mark key enzymes; the heatmap shows annotated differentially expressed genes, with red denoting upregulation, blue indicating downregulation, and color intensity representing the magnitude of log2FC.
Figure 6. Key pathway flowchart of multi-omics integrated analysis. The left side shows Phenylpropanoid biosynthesis, while the right side displays Plant hormone signal transduction. Red dots represent upregulated metabolites, green dots indicate downregulated metabolites; orange boxes mark key enzymes; the heatmap shows annotated differentially expressed genes, with red denoting upregulation, blue indicating downregulation, and color intensity representing the magnitude of log2FC.
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Table 1. Soil Nutrient Content of the Experimental Site.
Table 1. Soil Nutrient Content of the Experimental Site.
Soil pHElectrical Conductivity (ms/cm)Total Soluble Salts (g/kg)Na+ (g/kg)Ca2+ (g/kg)Soil Organic Matter (g/kg)
8.19 ± 0.0100.48 ± 0.0011.79 ± 0.0100.22 ± 0.0010.18 ± 0.00115.8 ± 0.100
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Wei, X.; Wang, W.; Zhao, Y.; Pu, X.; Lu, G.; Xu, C. Adaptive Mechanisms of White-Flowered Alfalfa (Medicago sativa L.) in High-Altitude Cold and Saline–Alkali Environments. Biology 2026, 15, 414. https://doi.org/10.3390/biology15050414

AMA Style

Wei X, Wang W, Zhao Y, Pu X, Lu G, Xu C. Adaptive Mechanisms of White-Flowered Alfalfa (Medicago sativa L.) in High-Altitude Cold and Saline–Alkali Environments. Biology. 2026; 15(5):414. https://doi.org/10.3390/biology15050414

Chicago/Turabian Style

Wei, Xiaoli, Wei Wang, Yuanyuan Zhao, Xiaojian Pu, Guangxin Lu, and Chengti Xu. 2026. "Adaptive Mechanisms of White-Flowered Alfalfa (Medicago sativa L.) in High-Altitude Cold and Saline–Alkali Environments" Biology 15, no. 5: 414. https://doi.org/10.3390/biology15050414

APA Style

Wei, X., Wang, W., Zhao, Y., Pu, X., Lu, G., & Xu, C. (2026). Adaptive Mechanisms of White-Flowered Alfalfa (Medicago sativa L.) in High-Altitude Cold and Saline–Alkali Environments. Biology, 15(5), 414. https://doi.org/10.3390/biology15050414

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