Skip to main content
NCBI home page
Plant Biotechnology Journal logoLink to Plant Biotechnology Journal
. 2025 Sep 13;24(2):504–519. doi: 10.1111/pbi.70356

Metabolo‐Transcriptomics Analyses Reveal Alfalfa Adaptation to Combined Saline‐Alkali and Low‐Temperature Stress in the Field

Rui Guo 1, Lei Liu 1, Jiaqi Li 1, Hongyu Qu 1, Weileng Guo 1, Lishuang Zhang 1, Dihe Yang 1, Ruixin Wang 1, Changhong Guo 1,✉
PMCID: PMC12906809  PMID: 40944398

ABSTRACT

Alfalfa ( Medicago sativa L.) is known as the ‘King of Forage’ because of its high protein content and excellent palatability. Alfalfa productivity is adversely affected in cold regions with saline‐alkali soils. However, the mechanisms underlying the response of alfalfa to combined saline‐alkali and low‐temperature stress under field conditions remain unknown. The resistance of two alfalfa cultivars (resistant, ZD; sensitive, BM) in saline‐alkali soil during cold and freezing periods was investigated using transcriptomic and metabolomic analyses. Metabolomic analysis revealed specific accumulation of amino acids, organic acids, carbohydrates, fatty acids and flavonoids in ZD compared with that in BM. The core differential metabolites associated with ZD stress resistance included isoleucine, fumaric acid, raffinose, 2‐hydroxydocosanoic acid and isovitexin. Transcriptomic analysis revealed that ZD enriched more upregulated differentially expressed genes in galactose, starch and sucrose metabolism and flavonoid biosynthesis pathways than BM. Integrated metabolo‐transcriptomic analysis highlighted flavonoid, carbohydrate, glutathione and salicylic acid biosynthesis as key pathways in alfalfa stress response. WGCNA identified 10 hub genes responsive to combined stress, with MsBAM1 potentially regulating the carbohydrate synthesis pathway and its silencing impaired alfalfa resistance to combined saline‐alkali and low‐temperature stress.

Keywords: alfalfa, combined saline‐alkali and low‐temperature stress, metabolome, MsBAM1, transcriptome

1. Introduction

Saline‐alkali soils threaten the sustainable development of agriculture and animal husbandry. These soils exhibit reduced fertility and poor permeability, directly hindering plant growth (Wang, Gang, et al. 2024). Global saline‐alkali land reached approximately 10.7% of the total land area in 2024 (FAO 2024). China currently has a saline‐alkali soil area of approximately 9.91 × 107 ha (Ren et al. 2022), with carbonate‐dominated saline‐alkali soils primarily concentrated in the northeast (Zhang and Shijie 2001). These areas experience significant annual temperature variations, necessitating that plants cope with the combined stresses of salinity and low temperatures. The comprehensive utilisation of saline‐alkali land is of significant strategic importance for ensuring food security.

Plant responses to combined stressors are highly complex and involve additive, synergistic and antagonistic effects. Under simultaneous salt and heat stress, the damage caused by salt stress may be exacerbated as transpiration induced by heat stress increases salt uptake (Wen et al. 2005). Freeze–thaw cycles can intensify saline‐alkali stress damage by increasing malondialdehyde (MDA) content, altering biomembrane permeability and subsequently impairing photosynthesis (Bao et al. 2022). Genzel et al. (2021) found that cold treatment induced flavonoid accumulation (graveobioside A and cynaroside) in pepper leaves, whereas salt stress had a weaker effect on flavonoid accumulation. Combined salt and cold stress decreased flavonoid levels.

Saline‐alkali stress adversely affects plant growth through ionic toxicity and high pH effects, damaging the chloroplast structure and impairing photosynthetic efficiency (Ye et al. 2019). It also disrupts the reactive oxygen species (ROS) balance in plants, interfering with normal growth and metabolic pathways (Rao et al. 2023). Low‐temperature stress includes chilling stress (0°C–15°C) and freezing stress (< 0°C). Most plants acquire freezing tolerance through cold acclimation. Under freezing, intracellular and intercellular water form ice crystals, causing mechanical damage to cell membranes and reducing water availability (Chinnusamy et al. 2007; Knight and Knight 2012; Casano et al. 2018). Under saline‐alkali and low‐temperature stress, multiple metabolic pathways synergistically trigger cellular signalling and defensive compound synthesis (Lopez‐Delacalle et al. 2021; Lyu et al. 2022). In response to saline‐alkali stress, plants synthesise osmoregulatory substances, such as soluble sugar and proline, to adjust the cellular osmotic potential and stabilise cell membranes and protoplasm (Birhanie et al. 2022). Similarly, in response to low‐temperature stress, plants produce soluble sugars (trehalose) and quaternary ammonium compounds (betaine) that stabilise proteins and cellular structures (Ďúranová et al. 2023). Soluble sugars also help maintain water balance, increase cytoplasmic concentration and lower the freezing point, preventing mechanical membrane damage caused by ice formation (Bao et al. 2020). While individual stress responses have been well studied, the mechanisms by which plants respond to combined saline‐alkali and low‐temperature stresses remain poorly understood. In addition, laboratory studies have limitations in reflecting plant stress resistance mechanisms in natural environments (Suzuki et al. 2014). Therefore, field research is crucial. However, multi‐omics research on plants under field conditions remains insufficient (Ma et al. 2017; Gu et al. 2024).

β‐Amylase (BAM) is a key enzyme that catalyses starch hydrolysis (Vu and Marletta 2016). BAM genes are involved in energy metabolism and are closely associated with plant adaptation to abiotic stress. The expression of CsBAM3 is significantly induced by drought and salt stress, and its metabolites act as osmoprotectants to maintain cellular homeostasis (Yue et al. 2019). Under low temperatures, BAM genes in Arabidopsis enhance cold tolerance by regulating soluble sugar accumulation (Kaplan and Guy 2004), whereas the overexpression of PbrBAM3 reduces ROS levels and increases antioxidant enzyme activity through maltose accumulation (Zhao et al. 2019). Although BAM is crucial for plant resistance to abiotic stresses, its function under combined saline‐alkali and low‐temperature stress remains poorly understood.

Alfalfa is a widely cultivated perennial leguminous forage crop valued for its high protein content and palatability (Nichols et al. 2010). In northeastern China, the primary alfalfa‐producing region, crops face cold waves, often resulting in overwintering failure. To enhance the utilisation of saline‐alkali lands, alfalfa is often cultivated in marginal soils facing saline‐alkali and low temperature stress.

In this study, two alfalfa varieties previously screened by our laboratory, a stress‐resistant (ZD) and a stress‐sensitive (BM) genotype (Liu et al. 2024) were cultivated in saline‐alkali soil. Biomass and physiological parameters were measured in October (combined saline‐alkali and cold acclimation stress, above‐zero temperatures) and December (combined saline‐alkali and freezing stress, subzero temperatures). Integrated transcriptomic and metabolomic analyses were conducted to elucidate the molecular and metabolic responses of alfalfa to combined saline‐alkali and low‐temperature stresses. These findings provide a theoretical foundation for breeding stress‐resistant alfalfa and the sustainable utilisation of saline‐alkali lands.

2. Results

2.1. Phenotypic and Biomass Analysis of Alfalfa

In August, no significant differences were observed in field phenotypes or biomass indicators between the varieties. However, from September to October, the plant height, aboveground fresh weight and dry weight of ZD were significantly lower than those of BM. In contrast, ZD exhibited significantly greater root length, underground fresh weight and dry weight (Figure 1b–g). The aboveground parts of ZD exhibited wilting and yellowing in October (Figure 1a). In October, the proline content of the roots of 10ZD and 10BM increased 3.37‐fold and 2.40‐fold, respectively, compared with that of the control (9ZD and 9BM). The total flavonoid content increased 1.49‐fold and 1.13‐fold, and the soluble sugar content increased 1.79‐fold and 1.34‐fold, respectively. By December, the proline content of 12ZD and 12BM increased 4.33‐fold and 3.20‐fold, the total flavonoid content increased 1.19‐fold and 1.07‐fold, and the soluble sugar content increased 2.81‐fold and 1.81‐fold, respectively, compared to the control (9ZD and 9BM) (Figure 1h–j). Furthermore, the H2O2 content of 10ZD and 10BM increased 1.68‐fold and 2.16‐fold, respectively, whereas the MDA content of 12ZD and 12BM increased 1.64‐fold and 1.95‐fold, respectively, compared with the control (Figure 1k,l). Root activity measurements showed no significant differences between 10ZD and 10BM, but 12BM had significantly lower root activity than 12ZD. Compared with 10ZD, 12ZD root activity decreased by 18.45%, whereas that of 12BM decreased by 68.50% compared to 10BM (Figure 1m). Under combined stress conditions, ZD accumulated higher levels of total flavonoids, proline and soluble sugar, and maintained robust root activity. In contrast, BM exhibited significantly increased MDA and H2O2 levels and a notable decline in root activity, indicating that BM is more susceptible to the combined effects of saline‐alkali and low‐temperature stress.

FIGURE 1.

FIGURE 1

Open in a new tab

Changes in field phenotype, biomass and physiological indicators of alfalfa varieties Zhaodong (ZD) and Blue Moon (BM) grown in saline‐alkali soil under low‐temperature conditions. (a) Comparison of field phenotypes between ZD and BM grown in saline‐alkali soil in August and October 2022. (b) Plant height. (c) Root length. (d) Shoot fresh weight. (e) Root fresh weight. (f) Shoot dry weight. (g) Root dry weight. (h) Proline content. (i) Total flavonoid content (TFC). (j) Soluble sugar content. (k) Hydrogen peroxide (H2O2) content. (l) Malondialdehyde (MDA) content. (m) Root vitality. Asterisks indicate significant differences between ZD and BM under the same growth conditions (*p < 0.05, **p < 0.01 and ***p < 0.001).

2.2. Differences in Metabolomics of ZD and BM Under Combined Stress Conditions

Principal component analysis (PCA) based on all differential metabolites showed tight quality control samples clustering, indicating the stability of the LC–MS system. Biological replicates within groups demonstrated clear clustering and high reproducibility of intragroup data, whereas different group samples were distinctly separated, suggesting significant differences in metabolite accumulation patterns between the ZD and BM groups under combined stress (Figure S2). A Sankey diagram was used to visualise the number and classification of differential metabolites in each comparison group. In total, 4605 differential accumulation metabolites (DAMs) belonging to 14 main metabolite categories were detected under the combined stress conditions. In December, ZD exhibited significantly more increased metabolites than BM (ZD: 703, BM: 345) and fewer decreased metabolites (ZD: 503, BM: 757). The increased DAMs in the ZD group included flavonoids and organooxygen compounds, whereas the decreased DAMs included fatty acyls compounds (Figure 2, Table S3). This indicates the robust metabolic activity of ZD under the combined stress of saline‐alkali and freezing conditions. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the increased metabolites of ZD were enriched in phenylpropanoid, isoflavonoid, flavonoid, flavone and flavonol biosynthesis pathways in October. In December, ZD exhibited enrichment of increased metabolites in amino acid synthesis and metabolism and isoflavonoid biosynthesis pathways. In October and December, most metabolites in these pathways were significantly decreased in BM. Under combined stress, DAMs in both ZD and BM were enriched in the plant hormone signal transduction pathway, with indole‐3‐acetic acid and salicylic acid (SA) being significantly increased in ZD in December. These findings suggest a close association between the aforementioned metabolic pathways and stress tolerance in alfalfa (Figure S3).

FIGURE 2.

FIGURE 2

Open in a new tab

Statistics on the quantity and classification of differential accumulation metabolites (DAMs) between ZD and BM under saline‐alkali and low‐temperature stress. The connecting bands are coloured according to the type of metabolites.

2.3. Core Metabolites Confer Distinct Composite Stress Tolerance Capabilities to ZD and BM

To identify the core metabolites in ZD resistance to combined stress, we screened for DAMs that were increased in ZD under combined stress conditions in October and December that remained elevated compared to BM. ZD exhibited 136 and 467 specifically increased metabolites under the combined stress conditions in October and December, respectively (Figure 3). After screening and classification, 89 DAMs were obtained and visualised on a heatmap based on their relative abundance (Figure 3). These increased metabolites in ZD accumulated to higher levels than in BM, participating in the combined stress defence. The core responsive metabolites primarily include amino acids, organic acids, carbohydrates, fatty acids and flavonoids. Heatmap analysis revealed that some metabolites in ZD showed lower accumulation in October, whereas the levels of various metabolite types increased significantly by December, indicating that a robust metabolic regulatory response across the entire alfalfa plant is required to combat the combined saline‐alkali and freezing stress. In contrast, BM exhibited fewer increased metabolites than ZD in October and December, with some metabolites showing significant decreases, suggesting a lack of metabolic regulation or metabolite synthesis inhibition. We ranked the core metabolites of ZD under December stress conditions by log2FC and displayed the top five metabolites with the highest log2FC across the five substance categories (Table S4). Core metabolites, such as isoleucine, fumaric acid, raffinose, 2‐hydroxydocosanoic acid and isovitexin, were significantly elevated in ZD under combined stress.

FIGURE 3.

FIGURE 3

Open in a new tab

Core metabolites of ZD and BM under saline‐alkali and low‐temperature stress. Venn diagram analysis was employed to identify the upregulated differential metabolites in ZD under combined stress conditions (October and December). The heatmap displays the relative abundance of core metabolites in ZD and BM.

2.4. Transcriptomic Analysis of ZD and BM Under Combined Stress Conditions

Transcriptomic sequencing of ZD and BM yielded over 34 million clean reads, with a quality score of 30 exceeding 94.10%, indicating high‐quality RNA‐sequencing data (Table S5). PCA revealed clear separation between the varieties at different sampling times and at the transcriptomic level (Figure 4a). Sample correlation analysis (Figure S4a) demonstrated high intragroup consistency in gene expression, confirming the reproducibility of the transcriptomic samples at each sampling point. In December, the number of differentially expressed genes (DEGs) significantly increased in both varieties, with ZD exhibiting notably more DEGs than BM (ZD: 34910, BM: 28374) (Figure S4b). This indicates distinct gene expression patterns in both varieties under combined stress conditions in October and December. The expression levels of the 11 selected genes for qRT‐PCR validation (Figure S5a–k) were linearly fitted to the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) values (Table S6) with R 2 = 0.946 (Figure S5l), confirming the transcriptomic data. To identify the core responsive genes, we screened for DEGs that were upregulated in ZD during October and December that remained higher than in BM. Under combined stress, ZD exhibited 3717 and 5630 specifically upregulated genes, with expression levels significantly higher than those in BM (Figure 4b). KEGG enrichment analysis revealed significant enrichment of the upregulated DEGs in pathways such as galactose metabolism, starch and sucrose metabolism, flavonoid biosynthesis and the pentose phosphate pathway in ZD, suggesting that these pathways are highly active under combined stress (Figure 4c). These findings align with the observed increase in carbohydrate and flavonoid content at the metabolic level in response to combined stress.

FIGURE 4.

FIGURE 4

Open in a new tab

Overall analysis of transcriptomic data. (a) Principal component analysis (PCA), where the x‐axis and y‐axis represent the scores of PC1 and PC2, respectively. (b) Venn diagram analysis, identifying differentially expressed genes specifically upregulated in ZD under combined stress conditions in October and December compared to the September control. (c) KEGG enrichment analysis of core genes.

2.5. WGCNA Identifies Hub Genes Associated With Core Metabolites

Based on RNA‐Seq data, we used Weighted Gene Co‐expression Network Analysis (WGCNA) to identify hub genes associated with core metabolites. Using the relative abundances of 12 core metabolites (isovitexin, isoorientin, bavachin, leucanthoside, vitexin, proline, arginine, threonine, isoleucine, raffinose, trehalose and sucrose) that were specifically upregulated in the ZD group in December as trait files, WGCNA was conducted with a soft threshold of β = 12, and the scale‐free network met the fitting index criteria (Figure 5a). Hierarchical clustering identified 15 gene modules with strong co‐expression relationships among the genes within each module (Figure 5b). The MEturquoise module showed a significant positive correlation with the core metabolite content (Figure 5c). Focusing on the hub genes in this module, the gene interaction network was visualised using Cytoscape, considering node connectivity (degree value) (Figure 5d). The top 10 hub genes with the highest degree values included four transcription factors: MYB (MYB2, MS.gene004711), bZIP (ABF2, MS.gene48512), MYB (RVE7, MS.gene001169) and MYB (RVE5, MS.gene000650). The remaining genes included BAM1 (MS.gene40443), UGE1 (MS.gene000931), CIPK7 (MS.gene004886), TPS1 (MS.gene007836), AGPS1 (MS.gene003731) and GolS2 (MS.gene005335).

FIGURE 5.

FIGURE 5

Open in a new tab

Results of WGCNA analysis. (a) Determination of the soft threshold for the gene co‐expression network (x‐axis represents the β value, y‐axis represents the scale‐free network model index R 2). (b) Cluster dendrogram of gene co‐expression modules, where the main branches form 15 modules marked with different colours. (c) Relationship between modules and the content of core metabolites. Each row and column represents a module and a metabolite, respectively. (d) Co‐expression network among hub genes under combined stress conditions.

2.6. Conjoint Transcriptomic‐Metabolomic Profiling Analysis

We integrated the differential metabolites and gene expression patterns in the starch and sucrose (map00500) and galactose metabolism (map00052) pathways. Decreasing temperature caused alfalfa grown in saline‐alkali soil to accumulate carbohydrates (Figure 6a). Under combined stress in October and December, the raffinose, glucose, sucrose, trehalose‐6‐phosphate and trehalose contents increased significantly in ZD and BM, with higher accumulation in ZD, particularly in December (Table S7). Carbohydrate accumulation was aligned with biosynthesis‐related gene expression. In October, 14 and 15 genes were upregulated in the ZD and BM groups, respectively, encoding galactose mutarotase (GALM), galactinol synthase 2 (GolS2), raffinose synthetase (RFS) and trehalose‐6‐phosphate synthase (TPS). By December, the number of upregulated genes increased to 26 in ZD and 17 in BM, and 10 and 5 genes encoding α‐amylase (AMY) and β‐amylase were upregulated in ZD and BM, respectively (Table S8). This indicates the strong capacity of ZD to degrade starch and produce carbohydrates. These results show that starch degradation and sugar accumulation, such as raffinose, enhance ZD resistance to combined saline‐alkali and low‐temperature stress.

FIGURE 6.

FIGURE 6

Open in a new tab

Differences in gene expression and metabolite content between ZD and BM in metabolic pathways related to carbohydrate, glutathione, flavonoid and salicylic acid (SA). (a) Carbohydrate‐related pathway. (b) Glutathione metabolism‐related pathway. (c) Flavonoid metabolism‐related pathway. (d) SA biosynthesis and signalling pathway. In the heatmap, each row represents a gene or metabolite, while the six columns represent ZD and BM from different months. The heatmap in red and blue indicates gene expression abundance, whereas the heatmap in orange and green represents the relative abundance of metabolites. Enzyme annotations: GALM, Galactose mutarotase; GolS2, Galactinol synthase 2; RFS, Raffinose synthase; TPS, Trehalose‐6‐phosphate synthase; AMY, α‐amylase; BAM, β‐amylase; GS, Glutathione synthase; GR, Glutathione reductase; G6PD, Glucose‐6‐phosphate 1‐dehydrogenase; PGD, 6‐phosphogluconate dehydrogenase; GPX, Glutathione peroxidase; CHS, Chalcone synthase; CA4H, Cinnamate‐4‐hydroxylase; CHR, Chalcone reductase; CHI, Chalcone isomerase; PAL, Phenylalanine ammonia‐lyase.

Stress‐responsive pathways were explored by integrating the expression profiles of differential metabolites and genes involved in glutathione (GSH) metabolism (map00480). The expression levels of nine genes (Figure 6b), including 6‐phosphogluconate dehydrogenase (PGD), glucose‐6‐phosphate 1‐dehydrogenase (G6PD) and glutathione synthetase (GS), were higher in ZD than in BM in October. In December, the expression levels of eight genes, including GS, glutathione reductase (GR) and G6PD, were higher in ZD than in BM (Table S8). In December, the glutamate, glucose‐6‐P and ribulose‐5‐P contents in ZD were significantly higher than those in BM. Additionally, in October, BM accumulated more oxidised glutathione (GSSG) than ZD (Table S7), indicating that BM was under severe stress. The higher expression of genes related to GSH synthesis and the elevated levels of associated metabolites in ZD compared to BM suggest that the metabolic pathway for synthesising GSH is more active in ZD.

To explore stress‐responsive pathways, we integrated the expression profiles of DAMs and DEGs in the biosynthesis pathways of flavonoids (map00941), isoflavonoids (map00943), flavones and flavonols (map00944). As shown in Figure 6c, 20 flavonoids accumulated in ZD in October, with a significant upregulation of 28 genes associated with the synthesis of these metabolites, including chalcone isomerase (CHI), chalcone synthase (CHS), chalcone reductase (CHR) and cinnamate‐4‐hydroxylase (CA4H). In contrast, BM significantly increased only two metabolites, xanthohumol and medicarpin, with 10 related genes. As environmental temperatures decreased and combined stresses intensified by December, 18 and 16 genes in ZD and BM, respectively, from the CHS, CHR, CHI and CA4H families were significantly downregulated (Table S8). However, ZD significantly increased 16 metabolites, whereas only naringin and liquiritigenin were significantly increased in BM (Table S7). These findings indicate that the more severe combined stress in December suppressed flavonoid biosynthesis‐related genes, whereas ZD had already accumulated substantial amounts of flavonoids by October.

A dynamic analysis of the DEGs and DAMs in the biosynthesis (map01070) and signal transduction (map04075) pathways of plant hormones was conducted. In the SA biosynthesis pathway, phenylalanine ammonia‐lyase (PAL) expression levels in ZD and BM were significantly elevated in October, with ZD exhibiting a higher increase than that in BM (Figure 6d). Phenylalanine, trans‐cinnamic acid and SA accumulated significantly in ZD by December (Table S7), indicating a stronger capacity for SA synthesis. In the SA signal transduction pathway, Nonexpressor of pathogenesis‐related genes 1 (NPR1) (MS.gene003232 and MS.gene017050) and pathogenesis‐related (PR) (MS.gene00224 and MS.gene51245) were significantly upregulated in ZD in October. Several TGA family transcription factors, TGA1 (MS.gene88455), TGA3 (MS.gene43041, MS.gene73963), TGA9 (MS.gene35407, MS.gene28123) and TGA10 (MS.gene30742) showed significantly higher expression levels in ZD by December, with ZD consistently exhibiting higher expression levels than in BM (Table S8). The SA signal transduction pathway was more active in ZD under combined stress conditions than in BM.

2.7. MsBAM1 ‐RNAi Reduces Alfalfa's Resistance to Combined Stress

Based on the WGCNA findings, 10 hub genes with high degree values were identified. Metabolo‐transcriptomic integration revealed that under combined stress conditions, ZD significantly enhanced carbohydrate biosynthesis by activating saccharide‐related metabolism pathways, indicating the critical role of carbohydrate in plant stress resistance. To validate the function of carbohydrate synthesis‐related genes under combined stress conditions, a hub gene annotated as MS.gene40443 (MsBAM1) was selected for cloning. Domain analysis showed that MS.gene40443 contained a Glyco_hydro_14 domain (amino acids 110–533, Figure S6a) and belongs to the BAM gene family. The upregulation of MsBAM1 suggests its pivotal role in alfalfa tolerance to combined stress. To analyse its biological function, two independent MsBAM1‐RNAi interference lines were generated in alfalfa (Figure S6b). Compared to the wild‐type (WT), MsBAM1 expression was significantly reduced in MsBAM1‐RNAi alfalfa (Figure S6c). Under normal growth conditions, no phenotypic differences were observed between WT and MsBAM1‐RNAi alfalfa plants. However, under combined stress, WT leaves exhibited partial wilting and apical bending, whereas MsBAM1‐RNAi plants exhibited severe wilting with dry leaf edges and a brittle texture (Figure 7a). Before stress, β‐amylase activity in MsBAM1‐RNAi plants was lower than that in the WT plants. Although β‐amylase activity increased after combined stress, the extent of this increase was significantly lower in MsBAM1‐RNAi plants than in WT plants (Figure 7b). Consistent with β‐amylase activity, the soluble sugar content in MsBAM1‐RNAi alfalfa was significantly lower than that in WT after stress (Figure 7c). MsBAM1‐RNAi plants had significantly higher starch contents than WT under combined stress (Figure 7d). Under stress, MDA, superoxide anion (O2 −) and electrolyte leakage rate (EL) were significantly higher in MsBAM1‐RNAi plants than in WT (Figure 7e–g). Combined stress exacerbated chlorophyll fluorescence damage in MsBAM1‐RNAi alfalfa (Figure 7h), manifesting as lower F v /F m , ETR and φPSII than in WT (Figure 7i–k). Thus, MsBAM1 played a crucial role in the response of alfalfa to combined stresses by regulating soluble sugar biosynthesis.

FIGURE 7.

FIGURE 7

Open in a new tab

MsBAM1‐RNAi alfalfa reduces tolerance to combined stress. (a) Phenotypic changes of MsBAM1‐RNAi alfalfa under combined stress conditions for 7 days, scale bar = 10 cm. (b) β‐amylase activity. (c) Soluble sugar content. (d) Starch content. (e) MDA content. (f) O2 −. (g) Electrolyte leakage. (h) False‐colour fluorescence image, colour‐coded according to the scale (0 to 1) displayed on the right. (i) F v /Fm . (j) Electron transport rate (ETR). (k) φPSII. Data are mean ± SE (n = 3). Bars with different letters indicate a significant difference at p < 0.05.

3. Discussion

In October, the aboveground tissues of ZD withered and yellowed earlier than those of BM, and plant height, aboveground dry weight and fresh weight were significantly lower in ZD than in BM. However, ZD exhibited significantly greater root length and underground dry and fresh weights than BM. Overwintering rates are lower in alfalfa with upright growth and greater height and are significantly positively correlated with root length (You‐jun et al. 2022). Cunningham and Volenec (1998) found that the growth of stems and leaves in non‐dormant alfalfa varieties during autumn competes with non‐structural carbohydrate accumulation in roots (root starch and sugar), resulting in poor winter survival rates. This is consistent with the findings of the present study. Dynamic resistance adjustment occurs through altered gene expression in metabolic pathways, affecting metabolite composition. Combined transcriptomic and metabolomic analyses in this study revealed that carbohydrate, GSH, flavonoid and SA synthesis pathways are key to alfalfa's response to combined stress.

The synthesis and metabolism of carbohydrates play crucial roles in plant growth, development and abiotic stress resistance (Kim et al. 2024). Soluble sugars act as osmotic protectants, lowering the freezing point of the cytoplasm and mitigating stress‐induced damage under abiotic stress (Li, Tian, et al. 2023). In the present study, both varieties exhibited higher expression of sugar synthesis genes and sugar content in December than in October. Most TPS, GALM, GolS2 and RFS genes in ZD showed higher expression than those in BM, with significant accumulation of trehalose, glucose and raffinose. The soluble sugar content in roots aligned with the dynamic trends of sugar‐related metabolites in the metabolome (Figure 1j). Meanwhile, the expression of AMY and BAM in ZD under combined stress was higher than that in BM, indicating continuous sugar accumulation from September to December, with ZD exhibiting a stronger capacity to degrade starch and produce sugar under combined stress. Sugars, such as raffinose and trehalose, can stabilise cytoplasmic structures, enhance energy metabolism and activate antioxidant systems, improving plant resistance to abiotic stress (Nishizawa et al. 2008; ElSayed et al. 2014; Luo et al. 2022). Our previous study also revealed the accumulation of sugars (sucrose, raffinose and trehalose) and high expression of genes, such as MsBAM, MsTPS and MsGolS, under combined stress (Liu et al. 2024), suggesting consistent regulatory patterns of sugar metabolism under field and pot experiments. Based on these findings, we identified TPS1 (MS.gene007836), GolS2 (MS.gene005335), AGPS1 (MS.gene003731) and BAM1 (MS.gene40443) as hub genes responsive to combined stress using WGCNA. Transgenic MsBAM1‐RNAi plants exhibited significantly reduced β‐amylase activity, resulting in lower soluble sugar content and weakening alfalfa tolerance to saline‐alkali and low‐temperature combined stress. Thus, sugars play a crucial role in plant defence against combined stress. Higher expression of carbohydrate‐synthesising genes in ZD resulted in greater accumulation of raffinose, glucose, trehalose and sucrose than in BM, which may contribute to the stronger resistance of ZD to combined stress.

GSSG is reduced to GSH by GR in the presence of NADPH. GR maintains GSH levels and preserves the redox state of the cellular GSH pool to scavenge ROS (Noctor et al. 2012). Studies have shown that GSH mitigates salt‐induced oxidative stress in tomatoes, with GSH synthesis metabolites generally increased in salt‐tolerant species (Mittova et al. 2003). Syntrichia caninervis significantly increases its GSH content under cold stress (Salih et al. 2024). In this study, the expression of PGD and P6GD genes in ZD was significantly higher than that in BM in October, leading to substantial NADPH accumulation in ZD. By December, higher GR and GS expressions in ZD than in BM indicated ZD's stronger capacity for GSH synthesis and GSSG reduction than that of BM. Similarly, in maize, ZmG6PDH1 expression generates NADPH, and the AsA‐GSH cycle utilises NADPH to alleviate cold‐induced oxidative damage (Li, Cai, et al. 2023). In December, metabolomic analysis revealed that substrates related to GSH synthesis (glutamate, glucose‐6‐phosphate and ribulose‐5‐phosphate) were more abundant in ZD than in BM. In October, BM accumulated more GSSG than ZD, indicating a weaker GSSG reduction capacity and greater susceptibility to stress. Cold‐tolerant alfalfa varieties exhibit more active GSH metabolic pathway gene expression and stronger antioxidant activity than sensitive varieties under cold stress (Zhang, Lv, et al. 2024). Wang et al. (2012) enhanced the alkaline stress resistance of alfalfa by overexpressing the glutathione‐S‐transferase related gene GsGST14. Our study demonstrated that under combined saline‐alkali and low‐temperature stress, ZD exhibited more active expression of GSH‐related genes and effective ROS scavenging. These findings suggest that the GSH metabolic pathway plays a positive role in plant resistance to combined stress.

Low temperature and saline‐alkali stress can induce the production of large amounts of ROS in plants, leading to lipid membrane peroxidation, oxidative damage to proteins and genetic material, and cell death (Karonen 2022). Plants produce various flavonoids, including chalcones, flavanones, flavonols, anthocyanins and proanthocyanidins, which act as antioxidants that scavenge ROS (Brunetti et al. 2013). The synthesis of luteolin and chrysoeriol enhances plant antioxidant capacity, improving salt tolerance (Mishra et al. 2003; Zhang, Li, et al. 2024). In October, most genes in the flavonoid biosynthesis pathway were expressed at higher levels in ZD than in BM, resulting in greater flavonoid accumulation. Significant accumulation of naringenin and eriodictyol in ZD serves as a precursor for the synthesis of downstream flavonoid products (Lam et al. 2023). In December, the flavonoid content decreased, and the expression of related genes was downregulated in ZD and BM. However, pseudobaptigenin, glycitein, naringenin, biochanin A, isoliquiritigenin, cosmosiin and naringin accumulated in ZD (Table S7), suggesting that combined stress conditions may impair the synthesis of certain flavonoids, a trend also reflected in the root total flavonoid content (Figure 1i). In our previous study, we observed the accumulation of vitexin, chrysin and cosmosiin in ZD plants under combined stress conditions (Liu et al. 2024). Pot and field experiments confirmed the crucial role of these flavonoids in alfalfa resistance to combined stress and identified them as biomarkers of stress resistance. The flavonoids naringenin, luteolin, glycitein and eriodictyol have also been associated with abiotic stresses, such as saline‐alkali and low‐temperature conditions (Wang et al. 2023; Yang et al. 2024). However, isovitexin and isoliquiritigenin have not yet been related to plant abiotic stress responses, indicating that field trials induce broader flavonoid production in alfalfa to combat combined stress than pot experiments. In future research, these compounds and their associated biosynthesis genes may serve as novel biomarkers for saline‐alkali and low‐temperature tolerance. Under combined stress conditions, the flavonoid, isoflavone, flavone and flavonol biosynthesis pathways were more active in ZD than in BM. Therefore, the successful overwintering of ZD may be attributed to its greater accumulation of flavonoids in October compared to that of BM.

SA synthesis begins with the conversion of shikimic acid to phenylalanine, which is converted into transcinnamic acid via PAL catalysis, hydroxylated to form 2‐hydroxycinnamic acid, oxidised to produce benzoic acid and converted to SA by benzoic acid 2‐hydroxylase (Yalpani et al. 1993). Under salt stress, exogenous SA promotes root growth and development, enhancing plant stress tolerance (Kwon et al. 2023). Under low‐temperature conditions, SA regulates ROS levels by increasing the activity of antioxidant enzymes, improving resistance to cold (Mutlu et al. 2013). In October, PAL expression in the SA synthesis pathway was higher in ZD than in BM (Table S8). Phenylalanine and trans‐cinnamic acid accumulated significantly in ZD in December, exceeding BM levels (Table S7). Ten genes in the SA signal transduction pathway were differentially expressed between ZD and BM, including two NPR1, six TGA transcription factors, and two PR genes (Table S8). Consistent with our findings, Ning et al. (2024) found that under low‐temperature stress, NPR1 and TGA‐related genes were significantly upregulated in a resistant variety. NPR1 protein levels are regulated in an SA‐dependent manner in the nucleus. When pathogens attack, NPR1 interacts with TGA family transcription factors to induce PR gene expression and defence responses (Olate et al. 2018). Many PR proteins accumulate in winter rye under cold stress conditions (Hon et al. 1995; Pihakaski‐Maunsbach et al. 2001), playing a role in pathogen resistance and plant freezing tolerance (Griffith and Yaish 2004). Olate et al. (2018) found that NPR1 expression can be induced by low temperatures, with NPR1 protein accumulating in the nucleus in a monomeric form, interacting with HSFA1 transcription factors to activate HSFA1‐regulated gene expression and induce cold adaptation. Their triple mutant tga2/5/6 demonstrated that NPR1 in Arabidopsis can activate cold‐induced gene expression without binding to TGA transcription factors. In contrast, this study found that in December, the expression of the TGA1 (MS.gene88455), TGA3 (MS.gene43041, MS.gene73963), TGA9 (MS.gene35407, MS.gene28123) and TGA10 (MS.gene30742) was significantly upregulated in ZD compared to BM, suggesting that NPR1 and TGA‐related genes are specifically expressed under combined stress conditions. However, Olate et al. (2018) conducted their study on Arabidopsis under laboratory‐simulated conditions, which may account for the slight variations. Therefore, we hypothesised that NPR1 may be co‐regulated by SA and combined stress in ZD, interacting with TGA transcription factors to enhance their DNA‐binding activity, thereby inducing PR gene expression and strengthening alfalfa resistance to combined stress.

Our study utilised WGCNA to screen hub genes associated with the accumulation of core metabolites and constructed a gene‐visualisation network. The expression levels of TPS1 (MS.gene007836) and GolS2 (MS.gene005335), key genes in the trehalose and raffinose synthesis pathways, respectively (Li et al. 2011; Sun et al. 2013; Thalmann et al. 2016); AGPS1 (MS.gene003731), the coding gene for ADP‐glucose pyrophosphorylase, which controls starch content and regulates growth and root development (Sulmon et al. 2011); BAM1 (MS.gene40443), responsible for starch degradation and sugar compound synthesis (Peng et al. 2014); and UGE1 (MS.gene000931), the coding gene for UDP‐glucose 4‐epimerase (UDP‐Glucose 4‐Epimerase), which promotes galactan synthesis, thereby altering the mechanical properties of cell walls and enhancing cold resistance (Takahashi et al. 2024), were higher in ZD than in BM under combined stress conditions, indicating that the regulation of starch synthesis and degradation, sugar compound synthesis and cell wall structural modifications are key pathways for ZD's resistance to combined stress. RVE5 (MS.gene000650) and RVE7 (MS.gene001169) play important roles in regulating the circadian clock and stress responses by activating the expression of cold‐induced genes (de Barros Dantas et al. 2023; Kim et al. 2024). In castor beans, RcRVE7 is induced by cold stress, demonstrating its involvement in cold stress responses (Wei et al. 2024). Overexpression of SgRVE1 in Arabidopsis activates the transcription of AtCBF1, AtCBF2, and AtCBF3, promotes osmoprotectant accumulation and enhances cold tolerance (Wang, Liang, et al. 2024). We speculate that RVE5 and RVE7 help ZD rapidly respond to low‐temperature changes and accumulate osmotic regulators in the roots to resist combined stress. ABF2 (MS.gene48512), a bZIP transcription factor, binds to ABA‐responsive elements. Under combined drought and cold stress, ABA levels and transcripts of genes involved in raffinose, trehalose and proline biosynthesis increase, indicating that ABA‐dependent transcriptional regulation plays a crucial role in plant responses to combined stress (Kim et al. 2004; Cao et al. 2018; Guo et al. 2021). In the present study, glucose, raffinose, trehalose and proline levels were significantly increased in ZD, and ABF2 was identified as a hub gene involved in combined stress. Therefore, ABA may play an important role in alfalfa resistance to combined saline‐alkali and low temperature stress. Qi et al. (2015) demonstrated that tobacco overexpressing SbMYB2 under NaCl stress exhibited enhanced expression of genes in the flavonoid biosynthesis pathway (CHS, CHI and PAL) and accumulated higher levels of flavonoids, which enhanced the scavenging of excess ROS, confirming improved NaCl stress tolerance in these plants. In the present study, the accumulation of flavonoids and the expression of their biosynthetic genes under combined stress conditions may have been regulated by MYB2 (MS.gene004711). Calcineurin B‐like proteins transmit calcium signals by interacting with and regulating CBL‐interacting protein kinases. CIPK7, through CBL1 interaction, plays a role in cold responses (Huang et al. 2011). Among the 10 hub genes, four were related to starch degradation and carbohydrate biosynthesis, three were associated with the cold stress response and signal transduction, one was involved in the ABA signalling pathway, one was linked to the mechanical properties of the cell wall and one was associated with flavonoid biosynthesis. The discovery of these genes provides important insights into stress‐resistant breeding in this species.

Based on biomass, physiological indicators, transcriptomics and metabolomics data, we proposed a schematic diagram illustrating the differential response mechanisms of the ZD and BM alfalfa varieties under combined low‐temperature and saline‐alkali stress (Figure 8). In October, the aboveground parts of ZD wilted and turned yellow earlier than those of BM, whereas ZD roots exhibited significantly greater length, dry and fresh weights, and accumulated higher levels of total flavonoids, proline and soluble sugars than BM. Integrated transcriptomic and metabolomic analyses revealed increased activation of the biosynthesis pathways for flavonoids, carbohydrates, GSH and SA in ZD compared to BM, with key metabolites therein accumulated to higher levels; therefore, these pathways were identified as the key pathways for alfalfa resistance to combined stress. WGCNA identified 10 hub genes regulating ABA signal transduction, stress signal responses and the synthesis of osmoregulatory and antioxidant substances. Transgenic MsBAM1‐RNAi alfalfa exhibited significantly reduced β‐amylase activity, leading to decreased soluble sugar content and impaired ability to withstand combined stress. This study revealed the metabolic and molecular regulatory mechanisms of alfalfa under combined saline‐alkali and low‐temperature stress, providing a theoretical foundation for the genetic improvement of stress‐resistant alfalfa and the utilisation of saline‐alkali soils in cold regions.

FIGURE 8.

FIGURE 8

Open in a new tab

Model diagram of the physiological and molecular mechanisms of alfalfa in response to combined saline‐alkali and low‐temperature stress. Top‐left (Transcriptome): The 10 hub genes screened by WGCNA. Bottom‐left (Metabolome): Coloured circles represent metabolite classes, with larger sizes indicating higher content. Top‐right (Morphology): Arrows indicate significantly enhanced morphological parameters (FW, fresh weight, DW, dry weight). Bottom‐right (Physiology): Triangles represent physiological indices, with darker colours indicating higher contents (SS, soluble sugar; Pro, proline; TFC, total flavonoid content; MDA, malondialdehyde).

4. Materials and Methods

4.1. Plant Materials and Experimental Design

Two alfalfa varieties were studied: Zhaodong alfalfa ( Medicago sativa cv. Zhaodong [ZD]), a fall‐dormant type with saline‐alkali tolerance, and Blue Moon alfalfa ( Medicago sativa cv. Blue Moon [BM]), a saline‐alkali sensitive non‐fall‐dormant type. The experiments were conducted at the Grassland Research Base of Heilongjiang Academy of Agricultural Sciences in Shengli Village, Yuanda Township, Lanxi County, Heilongjiang Province (geographic coordinates: 46°12′ N, 126°08′ E; soil physicochemical properties are listed in Table S1). Seeds were sown in strips: after soil ploughing and levelling, 8 g of seeds were evenly distributed into 5 m‐long furrows (2–3 cm deep). When the seedlings reached a height of 5–10 cm, they were thinned to 10 cm plant spacing and 30 cm row spacing. Routine field management, including regular weeding, was implemented to ensure normal growth, with a single mowing in August. In the pot experiment for gene validation, we followed the stress conditions from previous trials (Liu et al. 2024) with minor modifications. The combined stress conditions included a combination of low temperature (−1°C) and irrigation with a saline‐alkali solution for 7 days (NaHCO3, Na2CO3, NaCl and Na2SO4 in a Na+ molar ratio of 2:1:2:1, with a Na+ concentration of 200 mM, and 100 mL applied every 2 days). Figure S1 shows the maximum, minimum and average temperatures at the experimental site from August to December 2022. The biomass and root physiological indices of both alfalfa varieties were measured on 18 August 2022 (Sampling1), 12 September 2022 (Sampling2) and 25 October 2022 (Sampling3), with only root physiological indices on 11 December 2022 (Sampling4) due to snow. Root samples for transcriptomic and metabolomic sequencing were collected on 12 September 2022 (soil temperature: 20°C, control group), 25 October 2022 (soil temperature: 5°C, combined saline‐alkali and cold acclimation stress group) and 11 December 2022 (soil temperature: −8°C, combined saline‐alkali and freezing stress group). The samples were labelled as 9ZD, 10ZD, 12ZD, 9BM, 10BM and 12BM, respectively. All samples were wrapped in aluminium foil, flash‐frozen in liquid nitrogen and stored at −80°C.

4.2. Determination of Biomass and Physiological Indicators of Alfalfa

Plant height, root length and fresh/dry weights of the aboveground and underground parts of alfalfa were measured as described by Volenec (1999). EL was measured as described by Wang et al. (2006).

The contents of soluble sugars, proline, MDA, H2O2, O2 −, starch and β‐amylase activity were determined using a reagent kit (Sangon Biotech, Shanghai, China) according to the manufacturer's instructions.

Total flavonoid content was estimated using a colorimetric method with rutin as the standard (Marinova et al. 2005). The sample (1 mL) (or rutin standard) was mixed with 1 mL of 5% (w/v) NaNO2 and incubated for 6 min. Next, 1 mL of 10% (w/v) AlCl3 was added and allowed to react for 6 min. The reaction was stopped by adding 4 mL of 4% (w/v) NaOH, and the final volume was adjusted to 10 mL with 60% (v/v) ethanol. After 15 min, the absorbance was measured at 510 nm. Total flavonoid content was expressed as rutin equivalents per gram of dry weight (mg RE/g DW).

For root activity (Towill and Mazur 1975), 0.1 g root tip samples were placed in a 10 mL beaker and fully submerged in an equal‐volume mixture of 0.4% 2,3,5‐triphenyltetrazolium chloride (TTC) solution and PBS buffer. After dark incubation at 37°C for 1–3 h, 2 mL of 1 mol·L−1 H2SO4 was added to stop the reaction. The extract was diluted with ethyl acetate to a final volume of 10 mL, and the absorbance was measured at 485 nm using a spectrophotometer to determine the amount of reduced TTC.

4.3. Chlorophyll Fluorescence Parameter Determination

Chlorophyll fluorescence imaging was performed using an IMAGING‐PAM‐MAXI system (Walz Heinz GmbH, Effeltrich, Bavaria, Germany). After 30 min of dark adaptation, key photosynthetic parameters, including the maximum quantum yield of photosystem (PS) II (F v /F m ), effective quantum yield of PSII (φPSII) and electron transport rate (ETR), were calculated using ImagingWin v2.56zg software (Calzadilla et al. 2022). A false‐colour representation of the fluorescence parameters was generated by normalising the values from the original image, with the colour scale ranging from black (0.00) to red, yellow, green, blue and pink (1.00) (Dong et al. 2019).

4.4. Metabolomic Profiling and Statistical Analysis

Root sample (0.2 g) was added to a pre‐chilled methanol/acetonitrile/water solution (2:2:1, v/v) and subjected to low‐temperature ultrasonication for 30 min, followed by centrifugation at 14 000 × g and 4°C for 20 min. The supernatant was collected and dried under vacuum. For mass spectrometry analysis, the sample was reconstituted in 100 μL of acetonitrile: water solution (1:1, v/v), centrifuged at 14 000 × g and 4°C for 15 min, and the supernatant was analysed using an Agilent 1290 Infinity LC ultra‐high‐performance liquid chromatography system with a HILIC column, followed by mass spectrometry analysis using an AB Sciex TripleTOF 6600. Database searches were performed using both proprietary in‐house databases from Personal Biotechnology Co. Ltd. and public databases (Fiehn HILIC, ReSpect, GNPS, MetaboBASE, OTCML, Fiehn/Vaniya, natural product, HMDB, Mass Bank). The raw MS data were converted to MzXML files using ProteoWizard MSConvert before importing into MS‐DIAL software. For peak picking, the following parameters were used: MS1 m/z Tolerance: ±5 ppm, MS2 similarity score: > 0.7. Collection of Algorithms of MEtabolite pRofile Annotation (CAMERA) was used for the annotation of isotopes and adducts. In the extracted ion features, only the variables with more than 50% of the nonzero measurement values in at least one group were retained. Compound identification of metabolites was performed by comparing minimum ±5 ppm and MS/MS spectra with an in‐house database established with available authentic standards. After data pre‐processing and quality evaluation, data analysis was performed using differential metabolite screening criteria of variable importance in projection > 1 and p < 0.05.

4.5. RNA‐Seq and Statistical Analysis

Sequencing was performed using the Illumina NovaSeq 6000 platform. The reference genome index was constructed using HISAT2 (v2.1.0), and the gene expression levels were normalised using FPKM. Differential expression analysis between the two comparison groups was conducted using DESeq (v1.38.3) with screening for DEGs with |log2FoldChange| > 1 and p < 0.05. Statistical enrichment of DEGs in the KEGG pathways was analysed using clusterProfiler (v4.6.0), with a significance threshold of p < 0.05 for identifying significantly enriched KEGG pathways. Bioinformatic analysis was performed using the GenesCloud platform (https://www.genescloud.cn). WGCNA was used to investigate the relationships between genes and core metabolites. Genes were input into the WGCNA for network construction (using the WGCNA v1.69 package in R) (Langfelder and Horvath 2008). Pearson correlation matrices (R ≥ 0.75, p ≤ 0.05) were used to establish relationships between feature genes and core metabolites, with the parameters set to default values. The co‐expression network was visualised using Cytoscape v3.10.1. Hub genes in the network were identified based on the degree centrality (Shannon et al. 2003).

4.6. Gene Cloning and Plasmid Construction

The coding sequence of MsBAM1 was amplified from alfalfa cDNA using PCR. The domain structure of MsBAM1 was analysed using the SMART website (http://smart.embl‐heidelberg.de/). A 200 bp non‐conserved fragment of MsBAM1 was amplified and inserted into the pANDA35HK vector for RNA interference. The pANDA35HK‐MsBAM1 plasmid was transformed into Agrobacterium tumefaciens strain GV3101. Strains carrying the recombinant plasmid were transformed to generate transgenic alfalfa plants (Fu et al. 2015).

4.7. qRT‐PCR Analysis

Eleven genes were selected for qRT‐PCR validation to ensure the accuracy of the RNA‐seq. Total RNA was extracted from the root samples using a TRIzol reagent kit (Invitrogen, Carlsbad, CA, USA). MonScript RTIII All‐in‐One Mix with dsDNase kit (Monad, China) was used for reverse transcription into cDNA. qRT‐PCR analysis was performed using the SYBR Green Realtime PCR Master Mix Kit (TOYOBO CO. LTD) and the Bio‐RAD CFX96 Touch system (Bio‐Rad Laboratories Inc., Hercules, CA, USA). Primers were designed using the National Center for Biotechnology Information (NCBI) online tool (http://www.ncbi.nlm.nih.gov/). The qRT‐PCR primer sequences are listed in Table S2.

4.8. Statistical Analysis

All experiments were performed in at least three biological replicates using independent samples. Data were analysed using SPSS version 26.0 (IBM, Armonk, NY, USA). Bars with different letters indicate significant differences at p < 0.05. Statistical analysis was performed using a one‐way analysis of variance with Tukey's multiple comparison test. Student's t‐test was used to analyse significance, *p < 0.05, **p < 0.01 and ***p < 0.001.

Author Contributions

C.G. designed and coordinated all the work for the study. R.G. conceived the study and wrote the whole manuscript. R.G., J.L. and H.Q. performed the experiment. R.G., L.L. and R.W. carried out data analysis. W.G., L.Z. and D.Y. helped to revise the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Figure S1: Temperature variations from September 2022 to December 2022.

Figure S2: Principal component analysis.

Figure S3: KEGG enrichment analysis of Zhaodong (ZD) and Blue moon (BM) under combined stress conditions in October and December.

Figure S4: Correlation analysis of gene expression across samples and analysis of the number of differentially expressed genes in each comparison group.

Figure S5: qRT‐PCR analysis of ZD and BM samples.

Figure S6: Conserved domain of MsBAM1, PCR identification of MsBAM1‐RNAi alfalfa, and the relative expression of the MsBAM1.

PBI-24-504-s001.docx (19.9MB, docx)

Table S1: Soil physicochemical properties.

Table S2: Primers used in this study.

Table S3: Differentially accumulated metabolites (DAMs) in the ZD and BM under combined stress compared to the control condition.

Table S4: Core DAMs between ZD and BM under saline‐alkali and low‐temperature stress.

Table S5: Quality of the sequencing data.

Table S6: Complete list of the calculated Fragments Per Kilobase of transcript per Million mapped reads (FPKM) values.

Table S7: List of metabolites exhibiting significant changes (relative content) in key metabolic pathways under combined saline‐alkali and low‐temperature stress conditions.

Table S8: List of genes exhibiting significant changes (FPKM) in key metabolic pathways under saline‐alkali and low‐temperature stress.

PBI-24-504-s002.xlsx (28.3MB, xlsx)

Funding: This work was supported by the National Natural Science Foundation of China (U21A20182, 31972507) and the Graduate Innovation Fund of Harbin Normal University (HSDBSCX2024‐07).

Data Availability Statement

The RNA‐seq data were submitted to the Sequence Read Archive (SRA), accession no. PRJNA1261659. All untargeted metabolomic data used in this publication have been deposited in the EMBL‐EBl MetaboLights database with the identifier MTBLS12611. The complete dataset can be accessed at https://www.ebi.ac.uk/metabolights/MTBLS12611.

References

  1. Bao, G. , Tang W., An Q., et al. 2020. “Physiological Effects of the Combined Stresses of Freezing‐Thawing, Acid Precipitation and Deicing Salt on Alfalfa Seedlings.” BMC Plant Biology 20: 204. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bao, L. , Bao G., Zhang X., Qu Y., Guo J., and Pan X.. 2022. “Short‐Term Effects of Combined Freeze–Thaw and Saline–Alkali Stresses on the Physiological Response in Highland Barley ( Hordeum vulgare ).” Functional Plant Biology 49: 970–979. [DOI] [PubMed] [Google Scholar]
  3. Birhanie, Z. M. , Yang D., Luan M., et al. 2022. “Salt Stress Induces Changes in Physiological Characteristics, Bioactive Constituents, and Antioxidants in Kenaf (Hibiscus cannabinus L.).” Antioxidants 11: 2005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Brunetti, C. , Di Ferdinando M., Fini A., Pollastri S., and Tattini M.. 2013. “Flavonoids as Antioxidants and Developmental Regulators: Relative Significance in Plants and Humans.” International Journal of Molecular Sciences 14: 3540–3555. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Calzadilla, P. I. , Carvalho F. E. L., Gomez R., Lima Neto M. C., and Signorelli S.. 2022. “Assessing Photosynthesis in Plant Systems: A Cornerstone to Aid in the Selection of Resistant and Productive Crops.” Environmental and Experimental Botany 201: 104950. [Google Scholar]
  6. Cao, F. Y. , DeFalco T. A., Moeder W., et al. 2018. “ Arabidopsis ETHYLENE RESPONSE FACTOR 8 (ERF8) Has Dual Functions in ABA Signaling and Immunity.” BMC Plant Biology 18: 211. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Casano, L. M. , Martin M., and Sabater B.. 2018. “Sensitivity of Superoxide Dismutase Transcript Levels and Activities to Oxidative Stress Is Lower in Mature‐Senescent Than in Young Barley Leaves.” Plant Physiology 106: 1033–1039. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Chinnusamy, V. , Zhu J., and Zhu J. K.. 2007. “Cold Stress Regulation of Gene Expression in Plants.” Trends in Plant Science 12: 444–451. [DOI] [PubMed] [Google Scholar]
  9. Cunningham, S. M. , and Volenec J. J.. 1998. “Seasonal Carbohydrate and Nitrogen Metabolism in Roots of Contrasting Alfalfa ( Medicago sativa L.) Cultivars.” Journal of Plant Physiology 153: 220–225. [Google Scholar]
  10. de Barros Dantas, L. L. , Eldridge B. M., Dorling J., Dekeya R., Lynch D. A., and Dodd A. N.. 2023. “Circadian Regulation of Metabolism Across Photosynthetic Organisms.” Plant Journal 116: 650–668. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Dong, Z. , Men Y., Li Z., Zou Q., and Ji J.. 2019. “Chlorophyll Fluorescence Imaging as a Tool for Analyzing the Effects of Chilling Injury on Tomato Seedlings.” Scientia Horticulturae 246: 490–497. [Google Scholar]
  12. Ďúranová, H. , Šimora V., Ďurišová Ľ., Olexiková L., Kovár M., and Požgajová M.. 2023. “Modifications in Ultrastructural Characteristics and Redox Status of Plants Under Environmental Stress: A Review.” Plants 12: 1666. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. ElSayed, A. I. , Rafudeen M. S., and Golldack D.. 2014. “Physiological Aspects of Raffinose Family Oligosaccharides in Plants: Protection Against Abiotic Stress.” Plant Biology 16: 1–8. [DOI] [PubMed] [Google Scholar]
  14. FAO . 2024. “Global Map of Salt‐Affected Soils: FAO Rome, Italy.”
  15. Fu, C. , Hernandez T., Zhou C., and Wang Z.‐Y.. 2015. “Alfalfa (Medicago sativa L.).” Methods in Molecular Biology 1: 213–221. [DOI] [PubMed] [Google Scholar]
  16. Genzel, F. , Dicke M. D., Junker‐Frohn L. V., et al. 2021. “Impact of Moderate Cold and Salt Stress on the Accumulation of Antioxidant Flavonoids in the Leaves of Two Capsicum cultivars .” Journal of Agricultural and Food Chemistry 69: 6431–6443. [DOI] [PubMed] [Google Scholar]
  17. Griffith, M. , and Yaish M. W. F.. 2004. “Antifreeze Proteins in Overwintering Plants: A Tale of Two Activities.” Trends in Plant Science 9: 399–405. [DOI] [PubMed] [Google Scholar]
  18. Gu, K. , Li X., Su J., et al. 2024. “Physiological and Ecological Responses of Flue‐Cured Tobacco to Field Chilling Stress: Insights From Metabolomics and Proteomics.” Frontiers in Plant Science 15: 1490633. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Guo, Q. , Li X., Niu L., Jameson P. E., and Zhou W.. 2021. “Transcription‐Associated Metabolomic Adjustments in Maize Occur During Combined Drought and Cold Stress.” Plant Physiology 186: 677–695. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hon, W. C. , Griffith M., Mlynarz A., Kwok Y. C., and Yang D.. 1995. “Antifreeze Proteins in Winter Rye Are Similar to Pathogenesis‐Related Proteins.” Plant Physiology 109: 879–889. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Huang, C. , Ding S., Zhang H., Du H., and An L.. 2011. “CIPK7 Is Involved in Cold Response by Interacting With CBL1 in Arabidopsis thaliana .” Plant Science 181: 57–64. [DOI] [PubMed] [Google Scholar]
  22. Kaplan, F. , and Guy C. L.. 2004. “β‐Amylase Induction and the Protective Role of Maltose During Temperature Shock.” Plant Physiology 135: 1674–1684. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Karonen, M. 2022. “Insights Into Polyphenol‐Lipid Interactions: Chemical Methods, Molecular Aspects and Their Effects on Membrane Structures.” Plants 11: 1809. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kim, J.‐S. , Kidokoro S., Yamaguchi‐Shinozaki K., and Shinozaki K.. 2024. “Regulatory Networks in Plant Responses to Drought and Cold Stress.” Plant Physiology 195: 170–189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Kim, S. , Kang J.‐y., Cho D.‐I., Park J. H., and Kim S. Y.. 2004. “ABF2, an ABRE‐Binding bZIP Factor, Is an Essential Component of Glucose Signaling and Its Overexpression Affects Multiple Stress Tolerance.” Plant Journal 40: 75–87. [DOI] [PubMed] [Google Scholar]
  26. Knight, M. R. , and Knight H.. 2012. “Low‐Temperature Perception Leading to Gene Expression and Cold Tolerance in Higher Plants.” New Phytologist 195: 737–751. [DOI] [PubMed] [Google Scholar]
  27. Kwon, E.‐H. , Adhikari A., Imran M., et al. 2023. “Exogenous SA Applications Alleviate Salinity Stress via Physiological and Biochemical Changes in St John's Wort Plants.” Plants 12: 310. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Lam, L. P. Y. , Wang L., Lui A. C. W., et al. 2023. “Flavonoids in Major Cereal Grasses: Distribution, Functions, Biosynthesis, and Applications.” Phytochemistry Reviews 22: 1399–1438. [Google Scholar]
  29. Langfelder, P. , and Horvath S.. 2008. “WGCNA: An R Package for Weighted Correlation Network Analysis.” BMC Bioinformatics 9: 559. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Li, H.‐W. , Zang B.‐S., Deng X.‐W., and Wang X.‐P.. 2011. “Overexpression of the Trehalose‐6‐Phosphate Synthase Gene OsTPS1 Enhances Abiotic Stress Tolerance in Rice.” Planta 234: 1007–1018. [DOI] [PubMed] [Google Scholar]
  31. Li, X. , Cai Q., Yu T., et al. 2023. “ ZmG6PDH1 in Glucose‐6‐Phosphate Dehydrogenase Family Enhances Cold Stress Tolerance in Maize.” Frontiers in Plant Science 14: 1116237. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Li, Y. , Tian Q., Wang Z., et al. 2023. “Integrated Analysis of Transcriptomics and Metabolomics of Peach Under Cold Stress.” Frontiers in Plant Science 14: 1153902. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Liu, L. , Si L., Zhang L., et al. 2024. “Metabolomics and Transcriptomics Analysis Revealed the Response Mechanism of Alfalfa to Combined Cold and Saline‐Alkali Stress.” Plant Journal 119: 1900–1919. [DOI] [PubMed] [Google Scholar]
  34. Lopez‐Delacalle, M. , Silva C. J., Mestre T. C., Martinez V., Blanco‐Ulate B., and Rivero R. M.. 2021. “Synchronization of Proline, Ascorbate and Oxidative Stress Pathways Under the Combination of Salinity and Heat in Tomato Plants.” Environmental and Experimental Botany 183: 104351. [Google Scholar]
  35. Luo, Y. , Wang Y., Xie Y., Gao Y., Li W., and Lang S.. 2022. “Transcriptomic and Metabolomic Analyses of the Effects of Exogenous Trehalose on Heat Tolerance in Wheat.” International Journal of Molecular Sciences 23: 5194. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Lyu, J. , Jin N., Meng X., et al. 2022. “Exogenous Silicon Alleviates the Adverse Effects of Cinnamic Acid‐Induced Autotoxicity Stress on Cucumber Seedling Growth.” Frontiers in Plant Science 13: 968514. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Ma, J. , Li R., Wang H., et al. 2017. “Transcriptomics Analyses Reveal Wheat Responses to Drought Stress During Reproductive Stages Under Field Conditions.” Frontiers in Plant Science 8: e00592. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Marinova, D. , Ribarova F., and Atanassova M.. 2005. “Total Phenolics and Flavonoids in Bulgarian Fruits and Vegetables.” Journal of Chemical Technology and Metallurgy 40: 255–260. [Google Scholar]
  39. Mishra, B. , Priyadarsini K. I., Kumar M. S., Unnikrishnan M. K., and Mohan H.. 2003. “Effect of O‐Glycosilation on the Antioxidant Activity and Free Radical Reactions of a Plant Flavonoid, Chrysoeriol.” Bioorganic & Medicinal Chemistry 11: 2677–2685. [DOI] [PubMed] [Google Scholar]
  40. Mittova, V. , Theodoulou F. L., Kiddle G., et al. 2003. “Coordinate Induction of Glutathione Biosynthesis and Glutathione‐Metabolizing Enzymes Is Correlated With Salt Tolerance in Tomato.” FEBS Letters 554: 417–421. [DOI] [PubMed] [Google Scholar]
  41. Mutlu, S. , Karadağoğlu Ö., Atici Ö., and Nalbantoğlu B.. 2013. “Protective Role of Salicylic Acid Applied Before Cold Stress on Antioxidative System and Protein Patterns in Barley Apoplast.” Biologia Plantarum 57: 507–513. [Google Scholar]
  42. Nichols, N. N. , Dien B. S., and Cotta M. A.. 2010. “Fermentation of Bioenergy Crops Into Ethanol Using Biological Abatement for Removal of Inhibitors.” Bioresource Technology 101: 7545–7550. [DOI] [PubMed] [Google Scholar]
  43. Ning, Y. , Liu Z., Liu J., et al. 2024. “Comparative Transcriptomics Analysis of Tolerant and Sensitive Genotypes Reveals Genes Involved in the Response to Cold Stress in Bitter Gourd ( Momordica charantia L.).” Scientific Reports 14: 16564. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Nishizawa, A. , Yabuta Y., and Shigeoka S.. 2008. “Galactinol and Raffinose Constitute a Novel Function to Protect Plants From Oxidative Damage.” Plant Physiology 147: 1251–1263. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Noctor, G. , Mhamdi A., Chaouch S., et al. 2012. “Glutathione in Plants: An Integrated Overview.” Plant, Cell & Environment 35: 454–484. [DOI] [PubMed] [Google Scholar]
  46. Olate, E. , Jiménez‐Gómez J. M., Holuigue L., and Salinas J.. 2018. “NPR1 Mediates a Novel Regulatory Pathway in Cold Acclimation by Interacting With HSFA1 Factors.” Nature Plants 4: 811–823. [DOI] [PubMed] [Google Scholar]
  47. Peng, T. , Zhu X., Duan N., and Liu J. H.. 2014. “ PtrBAM1, a β‐Amylase‐Coding Gene of Poncirus trifoliata , Is a CBF Regulon Member With Function in Cold Tolerance by Modulating Soluble Sugar Levels.” Plant, Cell & Environment 37: 2754–2767. [DOI] [PubMed] [Google Scholar]
  48. Pihakaski‐Maunsbach, K. , Moffatt B., Testillano P., et al. 2001. “Genes Encoding Chitinase‐Antifreeze Proteins Are Regulated by Cold and Expressed by All Cell Types in Winter Rye Shoots.” Physiologia Plantarum 112: 359–371. [DOI] [PubMed] [Google Scholar]
  49. Qi, L. , Yang J., Yuan Y., Huang L., and Chen P.. 2015. “Overexpression of Two R2R3‐MYB Genes From Scutellaria baicalensis Induces Phenylpropanoid Accumulation and Enhances Oxidative Stress Resistance in Transgenic Tobacco.” Plant Physiology and Biochemistry 94: 235–243. [DOI] [PubMed] [Google Scholar]
  50. Rao, Y. , Peng T., and Xue S.. 2023. “Mechanisms of Plant Saline‐Alkaline Tolerance.” Journal of Plant Physiology 281: 153916. [DOI] [PubMed] [Google Scholar]
  51. Ren, J. , Xie R., Zhu H., Zhao Y., and Zhang Z.. 2022. “Comparative Study on the Abilities of Different Crack Parameters to Estimate the Salinity of Soda Saline‐Alkali Soil in Songnen Plain, China.” Catena 213: 106221. [Google Scholar]
  52. Salih, H. , Bai W., Liang Y., et al. 2024. “ROS Scavenging Enzyme‐Encoding Genes Play Important Roles in the Desert Moss Syntrichia caninervis Response to Extreme Cold and Desiccation Stresses.” International Journal of Biological Macromolecules 254: 127778. [DOI] [PubMed] [Google Scholar]
  53. Shannon, P. , Markiel A., Ozier O., et al. 2003. “Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks.” Genome Research 13: 2498–2504. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Sulmon, C. , Gouesbet G., Ramel F., et al. 2011. “Carbon Dynamics, Development and Stress Responses in Arabidopsis: Involvement of the APL4 Subunit of ADP‐Glucose Pyrophosphorylase (Starch Synthesis).” PLoS One 6: e26855. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Sun, Z. , Qi X., Wang Z., et al. 2013. “Overexpression of TsGOLS2, a Galactinol Synthase, in Arabidopsis thaliana Enhances Tolerance to High Salinity and Osmotic Stresses.” Plant Physiology and Biochemistry 69: 82–89. [DOI] [PubMed] [Google Scholar]
  56. Suzuki, N. , Rivero R. M., Shulaev V., Blumwald E., and Mittler R.. 2014. “Abiotic and Biotic Stress Combinations.” New Phytologist 203: 32–43. [DOI] [PubMed] [Google Scholar]
  57. Takahashi, D. , Soga K., Kikuchi T., et al. 2024. “Structural Changes in Cell Wall Pectic Polymers Contribute to Freezing Tolerance Induced by Cold Acclimation in Plants.” Current Biology 34: 958–968. [DOI] [PubMed] [Google Scholar]
  58. Thalmann, M. , Pazmino D., Seung D., et al. 2016. “Regulation of Leaf Starch Degradation by Abscisic Acid Is Important for Osmotic Stress Tolerance in Plants.” Plant Cell 28: 1860–1878. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Towill, L. E. , and Mazur P.. 1975. “Studies on the Reduction of 2,3,5‐Triphenyltetrazolium Chloride as a Viability Assay for Plant Tissue Cultures.” Canadian Journal of Botany 53: 1097–1102. [Google Scholar]
  60. Volenec, J. J. 1999. “Physiological Control of Alfalfa Growth and Yield.” In Crop Yield: Physiology and Processes, edited by Smith D. L. and Hamel C., 425–442. Springer Berlin Heidelberg. [Google Scholar]
  61. Vu, V. V. , and Marletta M. A.. 2016. “Starch‐Degrading Polysaccharide Monooxygenases.” Cellular and Molecular Life Sciences 73: 2809–2819. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Wang, C. , Liang C., Li H., et al. 2024. “Over‐Expression of SgRVE1 From Fine‐Stem Stylo (Stylosanthes guianensis Var. Intermedia) Up‐Regulates CBF Gene Expression and Enhances Cold Tolerance in Arabidopsis .” Plant Cell, Tissue and Organ Culture 158: 42. [Google Scholar]
  63. Wang, G. , Gang N. I., Feng G., et al. 2024. “Saline‐Alkali Soil Reclamation and Utilization in China: Progress and Prospects.” Frontiers of Agricultural Science and Engineering 11: 216–228. [Google Scholar]
  64. Wang, X. , Yin J., Wang J., and Li J.. 2023. “Integrative Analysis of Transcriptome and Metabolome Revealed the Mechanisms by Which Flavonoids and Phytohormones Regulated the Adaptation of Alfalfa Roots to NaCl Stress.” Frontiers in Plant Science 14: 1117868. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Wang, Y. , Jiang J., Zhao X., Liu G., Yang C., and Zhan L.. 2006. “A Novel LEA Gene From Tamarix androssowii Confers Drought Tolerance in Transgenic Tobacco.” Plant Science 171: 655–662. [Google Scholar]
  66. Wang, Z. Y. , Song F. B., Cai H., et al. 2012. “Over‐Expressing GsGST14 From Glycine soja Enhances Alkaline Tolerance of Transgenic Medicago Sativa .” Biologia Plantarum 56: 516–520. [Google Scholar]
  67. Wei, X. , Zhang J., Wang N., et al. 2024. “Genome‐Wide Identification and Analysis of Expression Pattern of REVEILLE Transcription Factors in Castor ( Ricinus communis L.).” Horticulture, Environment and Biotechnology 65: 453–463. [Google Scholar]
  68. Wen, X. , Qiu N., Lu Q., and Lu C.. 2005. “Enhanced Thermotolerance of Photosystem II in Salt‐Adapted Plants of the Halophyte Artemisia anethifolia .” Planta 220: 486–497. [DOI] [PubMed] [Google Scholar]
  69. Yalpani, N. , Leon J., Lawton M. A., and Raskin I.. 1993. “Pathway of Salicylic Acid Biosynthesis in Healthy and Virus‐Inoculated Tobacco.” Plant Physiology 103: 315–321. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Yang, X. , Han Y., Huo G., et al. 2024. “Integrated Transcriptomic and Metabolomic Analysis Provides Insights Into Cold Tolerance in Lettuce ( Lactuca sativa L.).” BMC Plant Biology 24: 442. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Ye, L. , Zhao X., Bao E., Cao K., and Zou Z.. 2019. “Effects of Arbuscular Mycorrhizal Fungi on Watermelon Growth, Elemental Uptake, Antioxidant, and Photosystem II Activities and Stress‐Response Gene Expressions Under Salinity‐Alkalinity Stresses.” Frontiers in Plant Science 10: e00863. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. You‐jun, C. , Wen‐hui L., Hui‐ling M., et al. 2022. “Root Physiology and Characteristics of Different Alfalfa Varieties During Overwintering in Alpine Sub‐Humid Area.” Acta Agrestia Sinica 30: 919–930. [Google Scholar]
  73. Yue, C. , Cao H., Lin H., et al. 2019. “Expression Patterns of Alpha‐Amylase and Beta‐Amylase Genes Provide Insights Into the Molecular Mechanisms Underlying the Responses of Tea Plants ( Camellia sinensis ) to Stress and Postharvest Processing Treatments.” Planta 250: 281–298. [DOI] [PubMed] [Google Scholar]
  74. Zhang, D. , and Shijie W.. 2001. “Mechanism of Freeze–Thaw Action in the Process of Soil Salinization in Northeast China.” Environmental Geology 41: 96–100. [Google Scholar]
  75. Zhang, L. , Li W., Li Y., et al. 2024. “Overexpression of GiLEA5‐2.1, a Late Embryogenesis Abundant Gene LEA3 From Glycyrrhiza inflata Bat., Enhances the Drought and Salt Stress Tolerance of Transgenic Tobacco (Nicotiana benthamiana).” Industrial Crops and Products 211: 118308. [Google Scholar]
  76. Zhang, Z. , Lv Y., Sun Q., Yao X., and Yan H.. 2024. “Comparative Phenotypic and Transcriptomic Analyses Provide Novel Insights Into the Molecular Mechanism of Seed Germination in Response to Low Temperature Stress in Alfalfa.” International Journal of Molecular Sciences 25: 7244. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Zhao, L. , Yang T., Xing C., et al. 2019. “The β‐Amylase PbrBAM3 From Pear (Pyrus betulaefolia) Regulates Soluble Sugar Accumulation and ROS Homeostasis in Response to Cold Stress.” Plant Science 287: 110184. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1: Temperature variations from September 2022 to December 2022.

Figure S2: Principal component analysis.

Figure S3: KEGG enrichment analysis of Zhaodong (ZD) and Blue moon (BM) under combined stress conditions in October and December.

Figure S4: Correlation analysis of gene expression across samples and analysis of the number of differentially expressed genes in each comparison group.

Figure S5: qRT‐PCR analysis of ZD and BM samples.

Figure S6: Conserved domain of MsBAM1, PCR identification of MsBAM1‐RNAi alfalfa, and the relative expression of the MsBAM1.

PBI-24-504-s001.docx (19.9MB, docx)

Table S1: Soil physicochemical properties.

Table S2: Primers used in this study.

Table S3: Differentially accumulated metabolites (DAMs) in the ZD and BM under combined stress compared to the control condition.

Table S4: Core DAMs between ZD and BM under saline‐alkali and low‐temperature stress.

Table S5: Quality of the sequencing data.

Table S6: Complete list of the calculated Fragments Per Kilobase of transcript per Million mapped reads (FPKM) values.

Table S7: List of metabolites exhibiting significant changes (relative content) in key metabolic pathways under combined saline‐alkali and low‐temperature stress conditions.

Table S8: List of genes exhibiting significant changes (FPKM) in key metabolic pathways under saline‐alkali and low‐temperature stress.

PBI-24-504-s002.xlsx (28.3MB, xlsx)

Data Availability Statement

The RNA‐seq data were submitted to the Sequence Read Archive (SRA), accession no. PRJNA1261659. All untargeted metabolomic data used in this publication have been deposited in the EMBL‐EBl MetaboLights database with the identifier MTBLS12611. The complete dataset can be accessed at https://www.ebi.ac.uk/metabolights/MTBLS12611.

Articles from Plant Biotechnology Journal are provided here courtesy of Society for Experimental Biology (SEB) and the Association of Applied Biologists (AAB) and John Wiley and Sons, Ltd

RESOURCES