Integrated physiological, transcriptomic and metabolomic analysis reveals differential cold response in wheat seedlings across varieties

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This study employed integrated physiological, transcriptomic, and metabolomic analyses to dissect cold adaptation mechanisms in cold-tolerant "Luyan951" and cold-sensitive "Luyan955" wheat sister lines, which share a close genetic background but exhibit contrasting cold responses. Results Physiological assays demonstrated that Luyan951 exhibited a 52.67% survival rate under cold stress—significantly higher than Luyan955 (20.67%)—alongside enhanced antioxidant enzyme activities (SOD, CAT, POD) and superior osmotic adjustment (elevated proline and soluble sugars). Pathway enrichment analysis revealed that phenylpropanoid biosynthesis and jasmonic acid (JA) signaling pathways are critical for cold adaptation, with the cultivar "Luyan951" exhibiting stronger activation of these pathways. Key genes ( CAD , 4CL , JAZ , MYC2 ) and JA metabolism-related genes were significantly upregulated in "Luyan951", which was validated by qRT-PCR. Bioinformatic analysis indicated that these pathways may be regulated by AP2/ERF transcription factors. Subcellular localization and transcriptional activation experiments confirmed the nuclear localization and transactivation function of three AP2/ERF genes ( TraesCS5D02G318400 , TraesCS6A02G381000, TraesCS6D02G366100 ). Conclusions This study provides the first evidence in wheat that phenylpropanoid biosynthesis contributes to cold tolerance, and together with jasmonate signaling, constitutes a core regulatory network for freezing resistance by enabling ROS scavenging, osmotic homeostasis, and metabolic stability; we further speculate this pathway is potentially regulated by upstream ERF transcription factors, offering multi-faceted evidence to elucidate cold adaptation mechanisms and advance precision breeding in wheat. Transcriptome Metabolome Cold stress Phenylpropanoid Jasmonic acid signaling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background As a crucial global staple crop central to food security, wheat ( Triticum aestivum L.) plays a vital role in sustaining dietary energy supply. However, its yield and grain quality are profoundly constrained by abiotic stresses, particularly low-temperature injury [ 1 ]. Plant responses to cold stress involve coordinated physiological and molecular processes, including osmotic regulation, reactive oxygen species (ROS) scavenging, phytohormone-mediated signaling, and ion homeostasis [ 2 , 3 ]. Upon exposure to cold, plants rapidly activate complex defense mechanisms: osmoprotectants such as proline (PRO) and soluble sugars accumulate to maintain cellular integrity, while key antioxidant enzymes—superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD)—are upregulated to mitigate excessive ROS and prevent oxidative damage [ 4 , 5 ]. Plant cold-stress responses are governed by multilayered regulatory networks that occur at molecular, transcriptional, metabolic, and signaling levels. Transcription factors (TFs) encoded in the genome function as central regulatory switches that orchestrate plant adaptation to abiotic stress [ 6 – 9 ]. Plants possess a large number of transcription factor families, whose structures and functions exhibit great diversity. They play crucial roles in growth and development as well as in stress resistance [ 10 ], major TF families such as AP2/ERF, NAC, MYB, and WRKY modulate coordinated defense responses by regulating stress-responsive transcriptional networks [ 11 – 14 ]. Cold exposure activates a suite of metabolic and signaling pathways; for example, the phenylpropanoid biosynthesis pathway contributes to ROS detoxification by supporting the production of peroxidase, a key antioxidant enzyme mitigating cold-induced oxidative stress [ 15 , 16 ]. Hormone-mediated regulation also plays a critical role: salicylic acid (SA) alleviates cold damage by enhancing sugar accumulation and inducing the expression of cold-responsive genes [ 17 ], while TaSAMT1 serves as an important regulatory node linking brassinolide (BR) and SA signaling during cold adaptation [ 18 ]. In the jasmonic acid (JA) pathway, MYC2—a core JA-responsive TF—interacts with ICE1 to mediate MeJA-induced cold tolerance, as demonstrated in banana [ 19 ]. In wheat, cold tolerance is a complex quantitative trait, with the ICE–CBF–COR signaling cascade recognized as a pivotal regulatory module [ 20 ]. In addition, global crotonyltome profiling and genome-wide association studies (GWAS) have revealed the TaSRT1 – TaPGK regulatory model [ 21 ], which contributes to cold tolerance by modulating pyruvate metabolism. Despite these advances, comprehensive insights into the key genes and pathways underlying cold resistance in wheat remain limited, underscoring the need to identify critical cold-responsive regulators. However, merely relying on phenotypic identification, physiological and hormonal analyses cannot fully explain the complex mechanisms of cold responses. Therefore, more in-depth studies on metabolic products and gene expression changes are needed [ 22 ]. Transcriptomics reveals the dynamic network of gene expression regulation, while metabolomics depicts the global map of metabolic pathways. These two techniques are the key approaches for studying the cold resistance of plants [ 23 ]. Conventional single-omics techniques have significant limitations in dissecting complex biological mechanisms. Metabolic-transcriptional integration analysis has been widely used to elucidate the gene-metabolite regulatory network, providing a multi-omics perspective for understanding the mechanism of plant responses to cold stress [ 24 – 26 ]. Currently, the combined analysis of transcriptomics and metabolomics has been widely applied to various species (such as citrus, peach, alfalfa, potato and wheat), enabling cross-disciplinary exploration of the cold stress response mechanisms among multiple species and the identification of related genes and metabolites [ 27 – 32 ]. In the research on the mechanism of cold stress response, selecting varieties with different cold resistance for study can make the results more convincing, especially sister varieties with the same genetic basis and similar genetic background [ 33 ]. However, cases of studying wheat under cold stress from this perspective are still relatively rare. In this study, wheat sister lines (Luyan951 and Luyan955) were employed to systematically dissect cold-stress responses through integrated physiological, hormonal, transcriptomic, and metabolomic analyses, thereby pioneering the integration of multi-omics approaches with wheat sister-line analysis to decode cold tolerance regulation mechanisms. The aim was to elucidate the synergistic regulatory networks comprising key biosynthetic and signaling pathways, their candidate genes, and functional metabolites that contribute to enhanced cold resistance in wheat. By revealing these molecular and metabolic mechanisms, this study provides a theoretical foundation for the identification of cold-resistance genes and supports the development of high-yield, stress-resistant wheat varieties. Materials and Methods Plant growth conditions and stress treatments Both Luyan951(951) and Luyan955(955), high-yielding and widely adapted wheat sister lines, share common parentage were Luyuan502/Jimai22, were developed by the Crop Research Institute of Shandong Academy of Agricultural Sciences (SAAS). As sister lines, these accessions share a close genetic background, with Luyan951 exhibiting significantly greater cold tolerance than Luyan955. All plant materials were cultivated under uniform growth conditions at the experimental field station of SAAS. Seeds of the M15 generation were harvested and used for subsequent experiments. Seeds of wheat were surface-sterilized in 5% (v/v) sodium hypochlorite for 10 min, thoroughly rinsed with deionized water, and germinated on moist filter paper at 22 ± 1°C in the dark for 48 h. Uniform seedlings were transplanted into plastic pots (10 cm x 10cm, 50 seedlings per variety were planted) containing a 3:1 (w/w) mixture of soil and vermiculite and maintained in a controlled growth chamber under a 14 h light/10 h dark photoperiod (light from 6:00 a.m. to 8:00 p.m.), with day/night temperatures of 22°C/18°C, and 60% relative humidity. Seedlings were irrigated daily with modified Hoagland’s solution until reaching the two-leaf and one-tiller stage. Cold-stress treatment was applied in two stages: Seedlings were first acclimated at 4°C for 24 h, followed by exposure to −10°C for 12 h. Leaf tissue samples were collected at 0, 3, and 6 h during freezing, immediately flash-frozen in liquid nitrogen, and stored at −80°C for physiological assays and multi-omics analyses. Following treatment, plants were recovered under control conditions (22°C/60% RH) for 7 d to determine survival rates. All experiments were conducted with a minimum of three biological replicates arranged in a randomized block design to ensure statistical robustness. Quantification of physiological indicators and phytohormones Antioxidant enzyme activities and osmotic substance concentrations were determined using commercial assay kits (Solarbio, Beijing, China). POD activity was measured using the caffeic acid colorimetric method (BC0090), SOD activity using the nitroblue tetrazolium colorimetric method (BC5160), and CAT activity using the ammonium molybdate colorimetric method (BC0200). Further, MDA content was measured using the thiobarbituric acid method (BC0200), PRO content using the ninhydrin colorimetric method (BC0290), and soluble sugar content using the anthrone colorimetric method (BC0030). For phytohormone analysis, 50 mg of frozen leaf tissue powder (stored at − 80°C) was homogenized in liquid nitrogen (30 Hz, 1 min). Metabolites were extracted with 1 mL methanol/water/formic acid (15:4:1, v/v/v) spiked with 10 µL internal standard mix (100 ng·mL⁻¹). After vortexing (10 min) and centrifugation (12,000 ×g, 4°C, 5 min), the supernatants were dried under N₂, reconstituted in 80% methanol, and filtered (0.22 µm). Analyses were performed using an ultra-performance liquid chromatography coupled with electrospray ionization tandem mass spectrometry (UPLC–ESI–MS/MS) system (ExionLC™ AD/QTRAP® 6500+) equipped with a Waters ACQUITY UPLC® BEH C18 column (1.7 µm, 2.1 × 100 mm). The column temperature was maintained at 40°C, and the flow rate was 0.35 mL·min⁻¹ [ 34 ]. ESI conditions were set at 550°C, ± 5.5 kV (ion mode), and a curtain gas pressure of 35 psi. Quantitation was achieved using scheduled multiple reaction monitoring (MRM), with optimized analyte-specific declustering potential (DP)/collision energy (CE) values, calibrated against certified standards [ 35 ]. RNA extraction, sequencing, and transcriptome analysis Total RNA was isolated from wheat leaf tissues using the TIANGEN RNAprep Pure Plant Plus Kit (DP441). RNA concentration was quantified via Qubit™ 4.0 fluorometer, and integrity was assessed with an Agilent 2100 Bioanalyzer, accepting only samples with an RNA Interity Number (RIN) ≥ 8.0. Qualified RNA samples were sent to Metware Biotechnology Co., Ltd. (Wuhan, China) for cDNA library preparation and 150-bp paired-end sequencing on an Illumina NovaSeq 6000 platform [ 36 ]. Raw sequencing reads were processed with fastp (v0.22.0) using default parameters to remove adapters and low-quality bases, generating high-quality clean data [ 37 ]. The clean reads were then aligned to the Triticum aestivum IWGSC RefSeq genome ( http://plants.ensembl.org/Triticum_aestivum/Info/Index ; accessed) using HISAT2 (v2.1.0). Gene expression levels were estimated as Fragments Per Kilobase of transcript per Million mapped reads (FPKM). Differential expression analysis was performed using DESeq2 (v1.38.3). Differentially expressed genes (DEGs) were identified with the following thresholds: |log₂FC| ≥ 1.0 and a Benjamini–Hochberg FDR-adjusted 𝑃 < 0.05. Finally, KEGG (v2023.01) pathway enrichment analysis was performed on the identified DEGs. Based on phenylpropanoid biosynthesis, plant hormone signaling, and jasmonic acid response pathway enrichment, GSVA quantified AP2/ERF transcription factor activity using transcriptome data, while GSEA revealed significant correlations between these pathways and AP2/ERF, with differential expression shown in AP2/ERF genes across groups [ 38 , 39 ]. Metabolomic profiling Metabolite extraction and mass spectrometry (MS) analysis were conducted by MetWare Biotechnology Co., Ltd (Wuhan, China) following their established protocols. Briefly, 50 mg of cryodesiccated leaf tissue was extracted with 70% aqueous methanol (4°C, 12 h) under intermittent vortex agitation (30-s pulses at 30-min intervals for 6 cycles). After centrifugation (13,200 ×g, 4°C, 3 min), supernatants were filtered through 0.22-µm organic membranes. Chromatographic separation was achieved using an ExionLC™ AD UPLC system (SCIEX) fitted with an Agilent SB-C18 column (1.8 µm × 2.1 mm × 100 mm) under binary gradient conditions: solvent A (0.1% formic acid/water) and solvent B (0.1% formic acid/acetonitrile). Metabolite characterization utilized electrospray ionization-triple quadrupole MS/MS (SCIEX QTRAP® 6500+) in dynamic MRM mode, with spectral matching against MetWare's proprietary MWDB database v3.0. Differentially accumulated metabolites (DAMs)​ were screened based on a variable importance in projection (VIP) score > 1.0 from orthogonal projections to latent structures-discriminant analysis (OPLS-DA) and a |log₂(fold change)| ≥ 1.0. KEGG enrichment analysis was performed on the identified DAMs [ 40 ]. Correlation analysis of transcriptomic and metabolomic data Integration of transcriptomic and metabolomic data was performed by mapping both DEGs and DAMs to KEGG pathways. Pairwise Pearson correlation coefficients (PCC) between the expression levels of DEGs and the accumulation levels of DAMs were calculated using cor function in R software (v4.1.2). Associations with an absolute PCC (|r|) > 0.8 and an adjusted p-value < 0.05 were considered significant. Based on a predefined set of core cold-response KEGG pathways (ko00052, ko00196, ko00592, ko00940, ko00941, ko04016, and ko04075), co-enriched pathways were identified. A pathway was considered co-enriched if it showed significant enrichment in both the transcriptomic (DEGs) and metabolomic (DAMs) datasets. Quantitative real-time PCR assay Verification of RNA-seq data utilized identical RNA samples to those used for transcriptome sequencing. Quantitative PCR was performed using SYBR Green-based Super Real PreMix Plus (TIANGEN, Beijing, China) on a Roche Light Cycler® 480 II system. Gene-specific primers were designed using SnapGene v6.0 and validated through NCBI Primer-BLAST (Table S4). ACTIN served as the internal reference gene based on its uniform expression across treatments (|ΔCt| ≤ 0.5). Relative expression was calculated using the 2^ (−ΔΔCt) method with three technical replicates [ 41 ]. Subcellular localization The wheat protoplast system first isolates the mesophyll protoplasts of wheat leaves. The target gene - GFP fusion vector is transfected into the cells using PEG-mediated transformation method [ 42 ]. The tobacco transient expression system first transfers the fusion vector into the Agrobacterium EHA105 engineering strain, injects it into the leaves of Nicotiana tabacum , and 48 hours later, the fluorescence signal is observed using a laser confocal microscope (excitation wavelength 488 nm). The primer sequences are listed in Supplementary Table S4. Transcriptional activation experiments The transcriptional activation test vector used in this study is pGBKT7, as cited in Liu's research [ 43 ]. The coding regions of TraesCS5D02G318400 , TraesCS6A02G381000 , and TraesCS6D02G366100 were respectively integrated into the pGBKT7 vector using double enzyme digestion and homologous recombination methods, resulting in the construction of fusion expression vectors, pGBKT7 empty vector (negative control), and pGBKT7 -VP16 (positive control). These were respectively transformed into yeast strain Y2HGold. The transformed yeast cells were cultured at 30°C for 3 days, and their growth status was observed. The primer sequences are listed in Supplementary Table S4. Statistical analyses Unless otherwise specified, all physiological and biochemical assays were performed with six independent biological replicates. For both transcriptomics and metabolomics analyses, three independent biological replicates were used. Quantitative data are presented as mean ± standard deviation (SD). Statistical significance was assessed using Student's t-test in SPSS Statistics v26.0 (IBM®), with significance levels denoted as 𝑃 < 0.05 (*) and 𝑃 < 0.01 (**). Data organization and preliminary calculations were conducted in Microsoft® Excel 16.0. Graphs and visualizations were generated using GraphPad Prism v10.0. Results Phenotypic responses to cold stress Seedlings of Luyan951 and Luyan955 at the two-leaf, one-tiller stage were subjected to low-temperature treatment to assess cultivar-specific cold responses. Low-temperature stress significantly impaired normal growth and development in wheat and reduced post-recovery survival in both accessions. However, Luyan951 exhibited substantially higher cold tolerance, with a survival rate of 52.67%, compared with only 20.67% in Luyan955 (Fig. 1A, B). To further characterize physiological differences underlying these contrasting phenotypes, key antioxidant enzyme activities and osmotic adjustment indicators were quantified. Relative to their respective untreated controls, Luyan951 displayed significant increases in SOD, CAT, and POD activities at both 3 h and 6 h post-treatment. In contrast, Luyan955 showed delayed activation, with statistically significant induction detected only at 6 h (Fig. 1C). Cold stress also induced lipid peroxidation in both cultivars, as indicated by significantly elevated MDA contents at 3 h and 6 h. However, the magnitude of Malondialdehyde accumulation at 3 h was less pronounced in Luyan951 (𝑃 < 0.05) than in Luyan955 (𝑃 < 0.01). Similarly, PRO and soluble sugar concentrations increased significantly in both genotypes following cold exposure, reflecting enhanced osmotic adjustment. Notably, Luyan951 exhibited stronger early induction (𝑃 < 0.01), whereas Luyan955 showed weaker statistical significance at 3 h (𝑃 < 0.05), further supporting the superior early-stage stress response capacity of Luyan951. Transcriptome analysis To elucidate the molecular mechanisms underlying differential cold tolerance, RNA-seq was conducted on Luyan951 and Luyan955 under control and low-temperature conditions. Sequencing generated 304.19 Gb of nucleotide data yielding 2,027,908,974 clean reads, with an average GC content of 53.49% and high base-calling accuracy (Q20 > 96% and Q30 > 91%; Supplementary Table S1). Across all 18 samples, a total of 13,024 differentially expressed genes (DEGs) were identified using stringent criteria (|log 2 Fold Change (FC)| ≥ 1 and False Discovery Rate (FDR)-adjusted 𝑃 < 0.05; Supplementary Table S2). Quality assessments confirmed strong reliability of the transcriptomic datasets. Pearson correlation coefficients (𝑟) exceeded 0.95, indicating high reproducibility among biological replicates (Supplementary Fig. S1A). Principal component analysis (PCA) further revealed tight clustering of replicate samples within treatment groups and clear separation between cold-stressed and control samples, with PC1 accounting for 33.4% of the variance (Supplementary Fig. S1B). Cold treatment induced markedly different transcriptomic responses between the two genotypes. In Luyan951, 993 DEGs at 3 h and 7,673 DEGs at 6 h were detected, whereas Luyan955 exhibited 461 DEGs at 3 h and 5,823 DEGs at 6 h. Notably, the number of upregulated and downregulated DEGs was consistently significantly higher in Luyan951, particularly at 6 h (Fig. 2A). Heatmap clustering further illustrated distinct expression patterns between treatments and genotypes (Fig. 2B). Comparative analysis identified 228 coexpressed DEGs in Luyan951 and 304 coexpressed DEGs in Luyan955, with 167 genes shared between the two genotypes (Fig. 2C). To further investigate the functional roles of DEGs, Gene Ontology (GO, Fig. 2D) and Kyoto Encyclopedia of Genes and Genomes (KEGG, Fig. 2E) pathway enrichment analyses were performed. GO enrichment highlighted that DEGs were predominantly involved in stress-related pathways, including “cell wall remodeling,” “photosynthesis,” “membrane lipid metabolism,” “osmotic adjustment,” “antioxidant response,” and “hormone regulation.” Whether the gene ontology (GO) terms are positively enriched (indicating upregulation of metabolites) or negatively enriched (indicating downregulation of metabolites), their presence highlights their functional importance in the cold stress response. KEGG pathway analysis revealed genotype-specific transcriptional reprogramming. Most stress-associated pathways exhibited higher enrichment levels in Luyan951 relative to Luyan955, except for the “linoleic acid metabolism pathway.” Notably, Luyan951 demonstrated stronger activation of several key stress-responsive pathways, including “phenylpropanoid biosynthesis,” “arginine and proline metabolism,” “biosynthesis of unsaturated fatty acids,” and “amino sugar and nucleotide sugar metabolism.” Metabolome analysis To further characterize differential cold stress responses between Luyan951 and Luyan955, untargeted metabolomic profiling was performed. Pearson correlation and PCA of the metabolite datasets revealed distinct separation between control and cold-treated groups and exhibited strong concordance with transcriptomic profiles (Supplementary Fig. S2A, B). Differentially accumulated metabolites (DAMs) were defined as those with Variable Importance in Projection (VIP) > 1.5 and FC ≥ 2 or ≤ 0.5. Following cold exposure, Luyan951 accumulated 138 DAMs (70 upregulated and 68 downregulated), whereas Luyan955 exhibited 182 DAMs (127 upregulated and 55 downregulated; Fig. 3A). Hierarchical clustering analysis (HCA) based on Pearson correlation further revealed clear cultivar-specific segregation under both control and cold-stressed conditions. Among the DAMs, flavonoids and amino acid derivatives were the most abundant classes (Fig. 3B). Comparative analysis identified 48 DAMs unique to Luyan951, 90 unique to Luyan955, and 56 shared between the two cultivars, with 10 core DAMs conserved across all comparative groups (Fig. 3C). K-means clustering of DAMs grouped metabolites with concordant expression patterns into six distinct co-expression clusters (Fig. 3D). Centroid analysis revealed two superclusters: Clusters 1, 5, and 6 exhibited concordant temporal dynamics, whereas Clusters 2, 3, and 4 displayed cultivar-divergent accumulation patterns. Cluster sizes varied considerably, ranging from 34 DAMs in Cluster 1 to 147 DAMs in Cluster 2.A targeted examination of key metabolites identified 40 significantly altered metabolites between Luyan951 and Luyan955 (based on the highest FoldChange), classified into six categories. Flavonoids represented a major proportion, with Luyan951 exhibiting a greater number of upregulated flavonoid species than Luyan955 (Fig. 3E). KEGG annotation and classification of significantly altered metabolites revealed pathways consistent with transcriptomic analyses, including “cell wall remodeling,” “membrane lipid metabolism,” “osmotic adjustment,” and “hormone regulation.” Notably, Luyan951 showed higher enrichment in “phenylpropanoid biosynthesis,” “salicylic acid derivative biosynthesis,” “amino sugar and nucleotide sugar metabolism,” and “glycerophospholipid metabolism,” relative to Luyan955 (Fig. 3F). Integrated transcriptome and metabolome analysis To elucidate the molecular mechanisms underlying cold tolerance in Luyan951 and Luyan955, integrated transcriptomic and metabolomic analyses were performed. Gene–metabolite pairs were screened within each comparison group using Pearson correlation coefficients (|r| > 0.8, 𝑃 < 0.05). Qualified correlations were subsequently visualized using a nine-quadrant plot, which delineates fold-change patterns between corresponding genes and metabolites. Comparative analysis of quadrants 1, 3, 7, and 9, representing synergistic or antagonistic gene–metabolite interactions, revealed significantly higher area density in Luyan951 (Fig. 4A) than in Luyan955 (Fig. 4B), indicating more extensive co-regulation of metabolic and transcriptional responses in the cold-tolerant cultivar. Conversely, quadrants 2, 4, 5, 6, and 8, which mismatched or uninformative gene–metabolite changes. Focusing on core cold stress-responsive pathways, multi-omics integration identified pathways characterized by simultaneous differential expression of DEGs and DAMs. Hierarchical clustering of these pathways (Fig. 4C) highlighted “phenylpropanoid biosynthesis,” “plant hormone signal transduction,” and “flavonoid biosynthesis” as the most prominently enriched. Within the phenylpropanoid biosynthesis pathway, both gene expression and metabolite accumulation were substantially higher in Luyan951 than in Luyan955, indicating that the tolerant cultivar possesses a stronger capacity for metabolic regulation under low-temperature stress. Transcription factor analysis TFs are key regulators of cold tolerance in plants. In total, 735 TFs were identified in Luyan951 and Luyan955. Among these, families previously associated with stress responses—APETALA2/ethylene-responsive factor (AP2/ERF), basic helix-loop-helix (bHLH), myeloblastosis (MYB), no apical meristem (NAC), and WRKY—comprised 3.21% to 6.04% of the total TFs (Fig. 5A, Supplementary Table S3). Expression analysis revealed significant differential regulation within these five TF families, although the directionality of expression changes varied. Notably, AP2/ERF TFs were predominantly upregulated, whereas no distinct TF family exhibited a consistent trend of downregulation (Fig. 5B). Based on the transcriptome data, we used the GSVA algorithm to calculate the activity of the AP2/ERF transcription factor family, the activity of the AP2/ERF transcription factor family, the phenylpropanoid synthesis pathway, the plant hormone signal transduction pathway, and the activation level of jasmonic acid signal transduction in each sample. Specifically, AP2/ERF activity exhibited a strong positive correlation with phenylpropanoid biosynthesis (R = 0.73, P = 0.0006), plant hormone signal transduction (R = 0.88, P = 0), and the response to jasmonic acid (GO:0009753, R = 0.74, P = 0.0005) by using GSVA. Furthermore, GSEA demonstrated that samples with elevated AP2/ERF activity were significantly enriched in the phenylpropanoid biosynthesis pathway (Fig. 5C), plant hormone signal transduction pathway (Fig. 5D), and the response to jasmonic acid pathway (Fig. 5E). Meanwhile, the co-expression network illustrates the interactions among 55 significantly differentially expressed genes from the AP2/ERF gene family and there are certain correlations among these genes (Supplementary Fig. S3). Phenylpropanoid biosynthesis and jasmonic acid signal transduction To further delineate the molecular basis underlying differential cold adaptation between Luyan951 and Luyan955 at the seedling stage, we examined the pathways previously identified as co-enriched through transcriptomic–metabolomic analysis. Using R-based KEGG pathway mapping, a total of 44 genes, 2 significantly altered metabolites, and 8 key enzymes were localized to the phenylpropanoid biosynthesis pathway (ko00940), with redundant branches removed for clarity. Genes encoding phenylalanine ammonia-lyase (PAL), 4-coumarate-CoA ligase (4CL), hydroxycinnamoyl transferase (HCT), cinnamoyl-CoA reductase (CCR), and cinnamyl-alcohol dehydrogenase (CAD) constituted the dominant components of this pathway accounted for the majority of transcriptional variation (Fig. 6A). Quantification of phytohormone dynamics under cold stress revealed contrasting jasmonate-related responses between the two cultivars. Both cultivars exhibited similar accumulation trends for 1-aminocyclopropanecarboxylic acid (ACC), JA, jasmonoyl-L-isoleucine (JA-Ile), salicylic acid (SA), and Salicylic acid 2-O-β-glucoside (SAG). However, cis(+)-12-oxophytodienoic acid (OPDA) remained consistently elevated in Luyan951 throughout the time course, whereas Luyan955 exhibited a progressive decline. Furthermore, methyl jasmonate (MeJA) was significantly upregulated in Luyan951 but downregulated in Luyan955. Overall, all four jasmonate-related metabolites (JA, JA-Ile, OPDA, and MeJA) showed consistently higher concentrations in Luyan951 compared to Luyan955 across all time points (Fig. 6B). Given these differences, we further analyzed the JA signaling pathway (KEGG pathway ko04075) and its associated DEGs. A comparative schematic (Fig. 6C) revealed that 18 genes encoding four central functional components—jasmonic acid-amino synthetase (JAR1), coronatine-insensitive protein 1 (COI1), jasmonate ZIM domain-containing protein (JAZ), and TF MYC2—were differentially expressed between the two cultivars. These components form the core JA signaling module, and their transcriptional divergence accounts for the majority of genotype-specific variations in stress response pathways. Fig. 6. Expression Profiles of the Phenylpropanoid Biosynthesis Pathway and Jasmonic Acid Signaling Transduction Pathway in Wheat Cultivars under Cold Stress. (A) Integrated map of phenylpropanoid biosynthesis metabolites and their corresponding key enzymes. DEGs and DAMs were mapped onto KEGG pathways to elucidate systemic biological functions (www.kegg.jp/kegg/kegg1.html). (B) Temporal abundance profiles of 14 phytohormone compounds across six major phytohormone classes in wheat cultivars Luyan951 and Luyan955 under cold stress at 3 h and 6 h post-treatment. (C) Integrated map of key enzymes and their associated genes involved in jasmonic acid signal transduction. Red indicates high expression, while blue represents low expression. (D) qRT-PCR Validation of Expression Patterns of Nine Cold Stress-Related Genes in Wheat. Expression levels in control samples were normalized to 100%. Data are presented as the mean ± standard deviation (SD) from at least three independent biological replicates. TaACTIN was used as the internal reference gene for normalization. To validate the reliability of the RNA-seq expression profiles, we conducted quantitative real-time PCR (qRT-PCR) on cold resistance-associated key genes mapped to KEGG pathways phenylpropanoid biosynthesis (ko00940) and JA signaling (ko04075) pathways (Fig. 6D). The qRT-PCR results showed strong concordance with RNA-seq–derived expression patterns for nine DEGs, with the exception of COI1 ( TraesCS3B02G399200 ), which exhibited an initial transient upregulation followed by downregulation. In contrast, CAD ( TraesCS5D02G210600 ), 4CL ( TraesCS4D02G268400 ), HCT ( TraesCS4D02G300400 ), CCR ( TraesCS5B02G223700 , TraesCS5A02G225100 , TraesCS5D02G232400 ), JAZ ( TraesCS4A02G007800 ), and MYC2 ( TraesCS5A02G489500 ) exhibited a progressive upregulation trend in both cultivars. Notably, the magnitude of induction was significantly higher in Luyan951. Together, these results suggest that genes such as CAD , 4CL , HCT , CCR , JAZ , and MYC2 may contribute to the enhanced cold tolerance of Luyan951 and thus represent promising candidates for future functional studies. Subcellular localization and transcriptional activation analysis Based on the high specificity of expression observed in the AP2/ERF subfamily and its significant correlation with key pathways in this study, we conducted further investigation into this group of genes. Quantitative validation of six significantly differentially expressed AP2/ERF genes revealed consistent expression trends with transcriptome data, particularly TraesCS5D02G318400 , TraesCS6A02G381000 , and TraesCS6D02G366100 which exhibited more pronounced differential expression under cold stress (Fig. 7A). Confocal laser scanning microscopy confirmed nuclear localization of these three genes in wheat protoplasts and tobacco epidermal cells (Fig. 7B). Furthermore, we conducted transcriptional activation experiments. On the SD-TH+X and SD-THA+X plates, the experimental groups without the fusion positive control VP16 could grow and turn blue, while the experimental groups with the fusion VP16 could grow and their growth rate was stronger than that of pGBKT7-VP16 (Fig. 7C). These three genes exhibited transcriptional activation activity in the yeast system. In summary, this indicates that it is involved in the transcriptional regulation of various genes in wheat under cold stress conditions. Discussion Understanding the molecular and metabolic responses of wheat to low-temperature stress is crucial for elucidating the regulatory mechanisms underlying cold tolerance and for improving the resilience of wheat cultivars. Although several studies have addressed cold tolerance in wheat [ 44 ], investigations in sister lines remain limited. By integrating physiological traits, hormonal measurements, transcriptomics, and metabolomics in Luyan951 and Luyan955, this study identified key cold-responsive genes and metabolites—particularly those associated with phenylpropanoid biosynthesis and JA signaling—thus providing new insights into the regulatory framework of wheat cold resistance. Cold stress induces the rapid accumulation of ROS, triggering antioxidant defenses and altering protein, lipid, and osmolyte levels [ 45 , 46 ]. Phenotypically, Luyan951 exhibited a higher survival rate and significantly greater antioxidant enzyme activities of SOD, CAT, and POD, together with increased PRO and soluble sugar content. In contrast, Luyan955 showed elevated MDA levels, indicating more severe membrane lipid peroxidation. These findings suggest that Luyan951 possesses stronger antioxidant capacity and osmotic regulation ability, enabling superior protection under cold stress. Transcriptome analysis further revealed that cold-resistant Luyan951 exhibited a substantially higher number of DEGs than Luyan955 (Fig. 2 A). GO enrichment analysis revealed that these DEGs were significantly enriched in biological processes central to cold adaptation—including membrane lipid remodeling, osmotic regulation, and antioxidant activity [ 47 , 48 ]. KEGG mapping showed consistent enrichment patterns with previous studies [ 49 , 50 ], yet Luyan951 displayed markedly higher activation across most pathways (Fig. 2 D-E). Enhanced phenylpropanoid biosynthesis suggests reinforced cell wall structure and stronger antioxidant potential through increased secondary metabolite production. Likewise, elevated arginine and proline metabolism implies robust osmotic balance, while the significant upregulation of biosynthesis of unsaturated fatty acids and amino sugar and nucleotide sugar metabolism reflects broader metabolic adjustments. Together, these pathway-level differences indicate that Luyan951 exhibits a more coordinated molecular response to cold stress compared with the susceptible cultivar. Metabolomic profiling, which provides a direct reflection of physiological state, complements transcriptome analysis by capturing downstream biochemical changes [ 51 ]. The DAM analysis indicated that the cold-resistant cultivar Luyan951 exhibited fewer DAMs than the sensitive cultivar Luyan955 (Fig. 3 A), a pattern opposite to that observed for DEGs. Similar trends have been reported previously, where sensitive genotypes often display a greater number of altered genes and metabolites compared with tolerant genotypes [ 52 , 53 ]. We propose that although Luyan951 involves a larger number of transcriptionally regulated genes within metabolic pathways, these genes act in a coordinated manner, generating more concentrated and efficient metabolic outputs. Flavonoids—key regulators of cold tolerance—support coordinated response measures against cold stress by modulating cold-responsive genes (such as calcium-dependent protein kinase CPK27) and influencing key signaling pathways, including the CBF–COR pathway, MYB TFs, ABA and JA-mediated networks [ 54 , 55 ]. Consistent with this, our study revealed that Luyan951 accumulates significantly higher basal flavonoid levels than Luyan955, indicating greater stress-responsive potential (Fig. 3 E). Luyan955 displayed a substantial post-stress surge in flavonoid content (Fig. 3 B, 4 C), whereas Luyan951 exhibited larger fold-changes (Fig. 3 E), suggesting that Luyan951 relies on efficient flavonoid regulatory amplification, while Luyan955 engages in broad compensatory accumulation to reduce damage. Phenylpropanoid compounds, derived from phenylpropane via the shikimate metabolic pathway [ 56 ], have been extensively studied; However, their mechanistic involvement in cold stress responses remains inadequately elucidated. Previous studies have demonstrated their importance in enhancing cold tolerance in rice, Chinese cabbage, and oil-tea camellia [ 57 , 58 ], yet systematic evidence in wheat is still limited. In our study, both transcriptomic and metabolomic analyses revealed substantially higher enrichment of phenylpropanoid-related compounds in the cold-tolerant cultivar Luyan951 following cold exposure (Fig. 3 B, 4 C). Recent findings further indicate that coordinated regulation of lignin biosynthesis and ROS scavenging significantly enhances cold tolerance in citrus [ 59 , 60 ]. This provides a mechanistic basis for the strong upregulation of CAD, 4CL, HCT, and CCR observed in Luyan951 (Fig. 6 A, 6 D), thereby underscoring the central role of the phenylpropanoid pathway in wheat cold resistance and highlighting its conserved function across species plant. This is also the first direct evidence to be discovered in wheat that the synthesis of phenylpropanoids is associated with cold resistance. Jasmonates (JAs) are key plant hormones that coordinate stress responses and developmental processes [ 61 ]. Previous studies have revealed that the jasmonate signaling pathway regulates cold tolerance in species such as tomato, cucumber, melon, and Arabidopsis [ 62 – 64 ]. In wheat, several JA-related genes—including TaMED25 , TaSnRK1α-TaPAP6L— have been implicated in cold stress responses [ 65 , 66 ], although the complete regulatory network remains poorly understood. In our study, KEGG analysis of DAMs indicated comparable enrichment in hormone signal transduction between Luyan951 and Luyan955 (Fig. 3 F), yet Luyan955 exhibited a significantly higher absolute number of DAMs (Fig. 4 C). Targeted hormone quantification confirmed elevated JA levels in Luyan951 relative to Luyan955, consistent with activation of the JA signaling pathway (Fig. 6 B). These results suggest that Luyan955 exhibits a widespread stress response with metabolic dispersion, while Luyan951 relies on precise JA-mediated regulation to effectively activate defense mechanisms. Cold stress triggers JA-Ile accumulation, facilitating COI1–JAZ interactions that target JAZ for ubiquitin–proteasome-mediated degradation, thereby releasing MYC2 to activate downstream cold-resistance responses [ 67 ]. Consistently, TraesCS3B02G399200 in Luyan951 exhibited transient upregulation followed by decline, whereas excessive accumulation of TraesCS4A02G007800 in Luyan955 appears to impede MYC2 activation, potentially compromising cold tolerance (Fig. 6 C, 6 D). TF families known to mediate cold responses—including MYB, WRKY, NAC, and bZIP, the latter of which includes calmodulin-binding proteins involved in cold induction in plants—were prominently represented among the DEGs [ 68 ]. By contrast, studies on the roles of AP2/ERF TFs in plant cold resistance remain relatively limited [ 69 , 70 ], with only scattered reports in species such as rice and ginseng. Our findings demonstrate that members of the AP2/ERF family showed specific upregulation in wheat under cold stress, with representative genes such as TraesCS5D02G318400, TraesCS6A02G381000, and TraesCS6D02G366100 displaying cultivar-specific upregulation (Fig. 5 B, 7 A). Their subcellular localization and transcriptional activation experiment also confirmed that they could function normally as transcriptional activators. The correlation between AP2/ERF and the synthesis of phenylpropanoids as well as the hormone signal transduction is a strong support for our previous work (Fig. 5 C-E). Both GSVA and GSEA analyses demonstrated that AP2/ERF proteins potentially regulate downstream phenylpropanoid biosynthesis and hormone signal transduction pathways, with the possibility of crosstalk among these pathways not excluded, which provides novel insights and directions for further research on cold tolerance in wheat Therefore, our study underscores the pivotal roles of phenylpropanoid biosynthesis and jasmonate (JA) signaling in conferring cold tolerance in wheat ( Triticum aestivum L.), with genes associated with these pathways representing promising targets for developing cold-resistant cultivars. While key regulatory mechanisms have been identified, further validation is required, including temporal expression profiling, weighted gene co-expression network analysis (WGCNA), and in-depth exploration of AP2/ERF-related genes to elucidate co-expression modules and enriched pathways, as well as plant responses to cold stress under field conditions or across populations—the focal point of our subsequent validation. Functional characterization of candidate genes through genetic modification or genome editing will provide conclusive evidence for their roles in cold adaptation and offer robust theoretical guidance for breeding stress-resilient wheat varieties Conclusions This integrated physiological-transcriptomic-metabolomic study reveals that phenylpropanoid biosynthesis and JA signaling pathways form the core regulatory network for cold tolerance in wheat sister varieties Luyan951. Critically, we provide the first evidence in wheat that phenylpropanoid biosynthesis directly underpins cold adaptation—synergistically activated with JA signaling to establish systemic defense. This coordination enables efficient ROS scavenging, osmotic homeostasis, and metabolic stability under cold stress. We also speculate that these pathways may be regulated by upstream ERF transcription factors, though further experimental validation is required. These findings elucidate molecular mechanisms of cold adaptation and offer defined genetic targets for breeding cold-tolerant wheat. Abbreviations DAMs Differentially Accumulated Metabolites DEGs Differentially Expressed Genes GSEA Gene Set Enrichment Analysis GSVA Gene Set Variation Analysis GO Gene Ontology GWAS Genome-Wide Association Study JA Jasmonic Acid PCA Principal Component Analysis TFs Transcription Factors VIP Variable Importance in Projection WGCNA Weighted Gene Co-expression Network Analysis Declarations Author Contributions W.Z. designed and completed most experiments and wrote the majority of the manuscripts. P.L., X.S., and Y.L. assisted phenotypic identification and sample collection. M.S. assisted in seedling management. P.L., Z.C., and B.Y. revised and approved the final version of the manuscript. All authors contributed to the article and approved the submitted version. Funding This research was supported by the Taishan Scholars Program (tsqn202312291), the Shandong Provincial Natural Science Foundation (ZR2024QC377), the Research Start-up Foundation for Young Talent of Shandong Academy of Agricultural Sciences (CXGX2024F01). Data Availability The datasets generated and analysed during the current study are available in the supplementary information files. The transcriptome data were deposited in the NCBI Sequence Read Archive (SRA) under accession PRJNA1355034. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Hui S, Yujian Y, Yamiao Z, Yadong W, Ashley J, Jinpeng L, et al. Autonomous recovery of wheat spikelet development following cold stress arrest mediated by modulation of sucrose degradation and IAA/ABA homeostasis. Journal of Experimental Botany. 2025;77(2):492-510. Zhuang K, Kong F, Zhang S, Meng C, Yang M, Liu Z, et al. 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Additional Declarations No competing interests reported. Supplementary Files SupplementaryData.zip Supplementary Information The following supporting information can be downloaded at: Fig. S1: Correlation analysis and principal component analysis (PCA) of transcriptome data; Fig. S2: Correlation analysis and principal component analysis (PCA) of metabolomics data; Fig. S3: Shared expression network map; Table S1: Sequencing output statistics of 18 samples; Table S2: Detailed information of identified differentially expressed genes; Table S3: Information on related transcription factor genes; Table S4: Sequences of primers. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9047620","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625466633,"identity":"e30910f0-d98e-4a94-8e28-b6bbc3a5414c","order_by":0,"name":"Wenjie Zheng","email":"","orcid":"","institution":"Crop Research Institute, Shandong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Wenjie","middleName":"","lastName":"Zheng","suffix":""},{"id":625466634,"identity":"d871409e-b633-4285-b011-1765b633346c","order_by":1,"name":"Peng Li","email":"","orcid":"","institution":"Crop Research Institute, Shandong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Li","suffix":""},{"id":625466635,"identity":"4cd459ee-200c-48ac-81cb-682b85329bdd","order_by":2,"name":"Xin Sun","email":"","orcid":"","institution":"Shandong Luyan Seed Co., Ltd.,","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Sun","suffix":""},{"id":625466636,"identity":"b990e108-bfcb-4ce9-87b7-d49cabadedcc","order_by":3,"name":"Ying Liu","email":"","orcid":"","institution":"Shandong Luyan Seed Co., Ltd.,","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Liu","suffix":""},{"id":625466637,"identity":"721fa0fb-3190-413e-9a9b-2240b3073cf2","order_by":4,"name":"Mingzhu Sun","email":"","orcid":"","institution":"Crop Research Institute, Shandong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mingzhu","middleName":"","lastName":"Sun","suffix":""},{"id":625466639,"identity":"20682d59-81b6-4217-8357-cc9dae33bfbf","order_by":5,"name":"Baiqiang Yan","email":"","orcid":"","institution":"Crop Research Institute, Shandong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Baiqiang","middleName":"","lastName":"Yan","suffix":""},{"id":625466641,"identity":"0ae53653-dc11-4cb0-91fd-2eacbf9494fd","order_by":6,"name":"Zhengyong Cui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYJACZhDBz8x8+AFJWiQk29nSDEjTYnCeR0GCKOX8/WfMpAsq7OqMD/MwGDDU2EQT1CJxAKhlxplkCbPDvAceMBxLy20gpMWAscfsNm8bM1ALX4IBY8NhIrQw8wC1/KuXMG7mMZAgTgsbSEvDYQmgXiK1SJxhK//Nc+y45IzDwEBOIMYv/P2HNxvz1FTzAxmHH3yosSGshYGBAykCEwgrBwH2B8SpGwWjYBSMgpELAJZiN3swOOckAAAAAElFTkSuQmCC","orcid":"","institution":"Crop Research Institute, Shandong Academy of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Zhengyong","middleName":"","lastName":"Cui","suffix":""}],"badges":[],"createdAt":"2026-03-06 07:54:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9047620/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9047620/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107871849,"identity":"391c1e87-24c9-494a-a292-dab4c977bd4a","added_by":"auto","created_at":"2026-04-27 07:54:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2209396,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of Low-Temperature Stress on Growth and Physiological Responses of Wheat Cultivars Luyan951 and Luyan955. (A) Representative phenotypes of Luyan951 and Luyan955 seedlings under cold treatment. Scale bar = 10 cm. (B) Survival rate (%). (C) Physiological responses under cold stress, including superoxide dismutase activity (U/g FW), catalase activity (mmoL/g FW), peroxidase activity (U/g FW), malondialdehyde content (nmoL/g FW), proline content (ug/g FW), and soluble sugar contents (mg/g FW). Data are presented as means ± standard deviation (SD) (n ≥ 10). Statistical significance was determined using Student’s t test (* 𝑃\u0026lt; 0.05, ** 𝑃 \u0026lt; 0.01). 951, Luyan951; 955, Luyan955\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9047620/v1/4865ed33ce1a9c667da01a81.png"},{"id":107873987,"identity":"6fac2358-cfb4-4d35-883d-0dd063a39b00","added_by":"auto","created_at":"2026-04-27 08:04:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":18133600,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptomic Analysis of Wheat Cultivars Luyan951 and Luyan955 under Cold Stress. (A) Number of differentially expressed genes (DEGs) and their regulation patterns (up- or downregulated) in Luyan951 (951) and Luyan955 (955) following cold treatment. (B) Heatmap showing gene expression profiles of Luyan951 and Luyan955 under cold stress. (C) Venn Diagram depicting shared and unique DEGs between Luyan951 and Luyan955 under cold stress. (D) Gene Ontology (GO) enrichment of signaling pathways across experimental groups. (E) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment across groups. Orange bars indicate positively enriched signaling pathways, while blue bars indicate negatively enriched pathways. CK, untreated control; TC3 and TC6, cold stress treatment for 3 h and 6 h, respectively\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9047620/v1/9181c797bef44ffc7c95b047.png"},{"id":107871132,"identity":"1e14c35b-029b-4e3a-8531-2401e278db45","added_by":"auto","created_at":"2026-04-27 07:45:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9202161,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially Accumulated Metabolites (DAMs) in Luyan951 and Luyan955 under Cold Stress. (A) Number of DAMs identified between the two wheat cultivars. (B) Hierarchical clustering analysis of metabolite abundance across all samples; red indicates high abundance, while green indicates low abundance. (C) Venn diagram showing shared and unique DAMs (false discovery rate, FDR \u0026lt; 0.05; fold change (FC) ≥ 2). (D) K-means clustering of DAMs based on accumulation profiles, partitioned into six distinct co-expression clusters. (E) Heat map of key metabolites responding to cold treatment in Luyan951 and Luyan955. Green indicates low-fold changes, while pink indicates high-fold changes. (F) KEGG pathway enrichment of differentially accumulated metabolites. The pink bars on the x-axis represent the number of metabolites significantly regulated within each metabolic pathway. Ame, Aestivum Metabolome; CK, untreated controls; TC6, cold stress treatment for 6 h.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9047620/v1/ac5f2c8203f4f3537934a18b.png"},{"id":107871157,"identity":"08e43f4f-069e-40ac-b58b-798fe3c1415b","added_by":"auto","created_at":"2026-04-27 07:46:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2220949,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated Transcriptomic and Metabolomic\u003cstrong\u003e \u003c/strong\u003eAnalysis of Luyan951 and Luyan955 under Cold Stress. (A) Nine-quadrant correlation analysis of gene–metabolite pairs in Luyan951. (B) Nine-quadrant correlation analysis of gene–metabolite pairs in Luyan955. Quadrants 1–9 are arranged left to right, top to bottom. (C) Heatmap of expression profiles for genes and metabolites associated with cold stress. Purple indicates low expression, while red indicates high expression.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9047620/v1/af922c7ae34f7fa738b78ca9.png"},{"id":108491064,"identity":"e637fbc1-733e-45f5-a847-e969a1abd30a","added_by":"auto","created_at":"2026-05-05 09:51:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5676154,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of Transcription Factors (TFs) and Gene Expression Patterns under Cold Stress.\u003c/p\u003e\n\u003cp\u003e(A) Classification of TF families identified in Luyan951 and Luyan955. (B) Heatmap of expression profiles for cold-responsive TFs. Expression levels are normalized, with blue indicating low expression and red indicating high expression. The GSEA diagram illustrates the relationship between the activity of the AP2/ERF transcription factor family and the activities of phenylpropanoid synthesis (C), plant hormone signal transduction (D), and JA acid response (E).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9047620/v1/1c3d3adc03c2a3b9edabc73e.png"},{"id":107874152,"identity":"da3a0b78-331f-4f1a-a624-a3519221c3f9","added_by":"auto","created_at":"2026-04-27 08:05:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":7385300,"visible":true,"origin":"","legend":"\u003cp\u003eExpression Profiles of the Phenylpropanoid Biosynthesis Pathway and Jasmonic Acid Signaling Transduction Pathway in Wheat Cultivars under Cold Stress. (A) Integrated map of phenylpropanoid biosynthesis metabolites and their corresponding key enzymes. DEGs and DAMs were mapped onto KEGG pathways to elucidate systemic biological functions (www.kegg.jp/kegg/kegg1.html). (B) Temporal abundance profiles of 14 phytohormone compounds across six major phytohormone classes in wheat cultivars Luyan951 and Luyan955 under cold stress at 3 h and 6 h post-treatment. (C) Integrated map of key enzymes and their associated genes involved in jasmonic acid signal transduction. Red indicates high expression, while blue represents low expression. (D) qRT-PCR Validation of\u003cstrong\u003e \u003c/strong\u003eExpression Patterns of\u003cstrong\u003e \u003c/strong\u003eNine Cold Stress-Related Genes in Wheat. Expression levels in control samples were normalized to 100%. Data are presented as the mean ± standard deviation (SD) from at least three independent biological replicates. \u003cem\u003eTaACTIN\u003c/em\u003e was used as the internal reference gene for normalization.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9047620/v1/d94d0fe439a71f30f0b1c93f.png"},{"id":107871191,"identity":"44ea94f0-d033-4bb6-973e-77e71226cb8f","added_by":"auto","created_at":"2026-04-27 07:46:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":15942245,"visible":true,"origin":"","legend":"\u003cp\u003eSubcellular localization and activation activity analysis. (A) Quantitative Real-time PCR Analysis of AP2/ERF-related Genes. (B) Subcellular localization. (C) Transcriptional activation analysis.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-9047620/v1/2bded4e3074190956a1ce426.png"},{"id":108803926,"identity":"bec77f29-a42c-4ccd-bbdb-812cfeca6d4c","added_by":"auto","created_at":"2026-05-08 15:11:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":59285793,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9047620/v1/27fea6cc-fda2-4c3c-a7bc-aabfbd5ae185.pdf"},{"id":107871161,"identity":"f508a446-e4e9-47b9-ba79-89ddeb57b76d","added_by":"auto","created_at":"2026-04-27 07:46:13","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21034837,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following supporting information can be downloaded at:\u003c/p\u003e\n\u003cp\u003eFig. S1: Correlation analysis and principal component analysis (PCA) of transcriptome data;\u003c/p\u003e\n\u003cp\u003eFig. S2: Correlation analysis and principal component analysis (PCA) of metabolomics data;\u003c/p\u003e\n\u003cp\u003eFig. S3: Shared expression network map;\u003c/p\u003e\n\u003cp\u003eTable S1: Sequencing output statistics of 18 samples;\u003c/p\u003e\n\u003cp\u003eTable S2: Detailed information of identified differentially expressed genes;\u003c/p\u003e\n\u003cp\u003eTable S3: Information on related transcription factor genes;\u003c/p\u003e\n\u003cp\u003eTable S4: Sequences of primers.\u003c/p\u003e","description":"","filename":"SupplementaryData.zip","url":"https://assets-eu.researchsquare.com/files/rs-9047620/v1/fb55e64bed2c7279836062a0.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated physiological, transcriptomic and metabolomic analysis reveals differential cold response in wheat seedlings across varieties","fulltext":[{"header":"Background","content":"\u003cp\u003eAs a crucial global staple crop central to food security, wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) plays a vital role in sustaining dietary energy supply. However, its yield and grain quality are profoundly constrained by abiotic stresses, particularly low-temperature injury [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Plant responses to cold stress involve coordinated physiological and molecular processes, including osmotic regulation, reactive oxygen species (ROS) scavenging, phytohormone-mediated signaling, and ion homeostasis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Upon exposure to cold, plants rapidly activate complex defense mechanisms: osmoprotectants such as proline (PRO) and soluble sugars accumulate to maintain cellular integrity, while key antioxidant enzymes\u0026mdash;superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD)\u0026mdash;are upregulated to mitigate excessive ROS and prevent oxidative damage [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePlant cold-stress responses are governed by multilayered regulatory networks that occur at molecular, transcriptional, metabolic, and signaling levels. Transcription factors (TFs) encoded in the genome function as central regulatory switches that orchestrate plant adaptation to abiotic stress [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Plants possess a large number of transcription factor families, whose structures and functions exhibit great diversity. They play crucial roles in growth and development as well as in stress resistance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], major TF families such as AP2/ERF, NAC, MYB, and WRKY modulate coordinated defense responses by regulating stress-responsive transcriptional networks [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Cold exposure activates a suite of metabolic and signaling pathways; for example, the phenylpropanoid biosynthesis pathway contributes to ROS detoxification by supporting the production of peroxidase, a key antioxidant enzyme mitigating cold-induced oxidative stress [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Hormone-mediated regulation also plays a critical role: salicylic acid (SA) alleviates cold damage by enhancing sugar accumulation and inducing the expression of cold-responsive genes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], while \u003cem\u003eTaSAMT1\u003c/em\u003e serves as an important regulatory node linking brassinolide (BR) and SA signaling during cold adaptation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In the jasmonic acid (JA) pathway, MYC2\u0026mdash;a core JA-responsive TF\u0026mdash;interacts with \u003cem\u003eICE1\u003c/em\u003e to mediate MeJA-induced cold tolerance, as demonstrated in banana [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In wheat, cold tolerance is a complex quantitative trait, with the ICE\u0026ndash;CBF\u0026ndash;COR signaling cascade recognized as a pivotal regulatory module [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In addition, global crotonyltome profiling and genome-wide association studies (GWAS) have revealed the \u003cem\u003eTaSRT1\u003c/em\u003e\u0026ndash;\u003cem\u003eTaPGK\u003c/em\u003e regulatory model [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which contributes to cold tolerance by modulating pyruvate metabolism. Despite these advances, comprehensive insights into the key genes and pathways underlying cold resistance in wheat remain limited, underscoring the need to identify critical cold-responsive regulators.\u003c/p\u003e \u003cp\u003eHowever, merely relying on phenotypic identification, physiological and hormonal analyses cannot fully explain the complex mechanisms of cold responses. Therefore, more in-depth studies on metabolic products and gene expression changes are needed [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Transcriptomics reveals the dynamic network of gene expression regulation, while metabolomics depicts the global map of metabolic pathways. These two techniques are the key approaches for studying the cold resistance of plants [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Conventional single-omics techniques have significant limitations in dissecting complex biological mechanisms. Metabolic-transcriptional integration analysis has been widely used to elucidate the gene-metabolite regulatory network, providing a multi-omics perspective for understanding the mechanism of plant responses to cold stress [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Currently, the combined analysis of transcriptomics and metabolomics has been widely applied to various species (such as citrus, peach, alfalfa, potato and wheat), enabling cross-disciplinary exploration of the cold stress response mechanisms among multiple species and the identification of related genes and metabolites [\u003cspan additionalcitationids=\"CR28 CR29 CR30 CR31\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In the research on the mechanism of cold stress response, selecting varieties with different cold resistance for study can make the results more convincing, especially sister varieties with the same genetic basis and similar genetic background [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, cases of studying wheat under cold stress from this perspective are still relatively rare.\u003c/p\u003e \u003cp\u003eIn this study, wheat sister lines (Luyan951 and Luyan955) were employed to systematically dissect cold-stress responses through integrated physiological, hormonal, transcriptomic, and metabolomic analyses, thereby pioneering the integration of multi-omics approaches with wheat sister-line analysis to decode cold tolerance regulation mechanisms. The aim was to elucidate the synergistic regulatory networks comprising key biosynthetic and signaling pathways, their candidate genes, and functional metabolites that contribute to enhanced cold resistance in wheat. By revealing these molecular and metabolic mechanisms, this study provides a theoretical foundation for the identification of cold-resistance genes and supports the development of high-yield, stress-resistant wheat varieties.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant growth conditions and stress treatments\u003c/h2\u003e \u003cp\u003eBoth Luyan951(951) and Luyan955(955), high-yielding and widely adapted wheat sister lines, share common parentage were Luyuan502/Jimai22, were developed by the Crop Research Institute of Shandong Academy of Agricultural Sciences (SAAS). As sister lines, these accessions share a close genetic background, with Luyan951 exhibiting significantly greater cold tolerance than Luyan955. All plant materials were cultivated under uniform growth conditions at the experimental field station of SAAS. Seeds of the M15 generation were harvested and used for subsequent experiments.\u003c/p\u003e \u003cp\u003eSeeds of wheat were surface-sterilized in 5% (v/v) sodium hypochlorite for 10 min, thoroughly rinsed with deionized water, and germinated on moist filter paper at 22\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C in the dark for 48 h. Uniform seedlings were transplanted into plastic pots (10 cm x 10cm, 50 seedlings per variety were planted) containing a 3:1 (w/w) mixture of soil and vermiculite and maintained in a controlled growth chamber under a 14 h light/10 h dark photoperiod (light from 6:00 a.m. to 8:00 p.m.), with day/night temperatures of 22\u0026deg;C/18\u0026deg;C, and 60% relative humidity. Seedlings were irrigated daily with modified Hoagland\u0026rsquo;s solution until reaching the two-leaf and one-tiller stage. Cold-stress treatment was applied in two stages: Seedlings were first acclimated at 4\u0026deg;C for 24 h, followed by exposure to \u0026minus;10\u0026deg;C for 12 h. Leaf tissue samples were collected at 0, 3, and 6 h during freezing, immediately flash-frozen in liquid nitrogen, and stored at \u0026minus;80\u0026deg;C for physiological assays and multi-omics analyses. Following treatment, plants were recovered under control conditions (22\u0026deg;C/60% RH) for 7 d to determine survival rates. All experiments were conducted with a minimum of three biological replicates arranged in a randomized block design to ensure statistical robustness.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuantification of physiological indicators and phytohormones\u003c/h3\u003e\n\u003cp\u003eAntioxidant enzyme activities and osmotic substance concentrations were determined using commercial assay kits (Solarbio, Beijing, China). POD activity was measured using the caffeic acid colorimetric method (BC0090), SOD activity using the nitroblue tetrazolium colorimetric method (BC5160), and CAT activity using the ammonium molybdate colorimetric method (BC0200). Further, MDA content was measured using the thiobarbituric acid method (BC0200), PRO content using the ninhydrin colorimetric method (BC0290), and soluble sugar content using the anthrone colorimetric method (BC0030).\u003c/p\u003e \u003cp\u003eFor phytohormone analysis, 50 mg of frozen leaf tissue powder (stored at \u0026minus;\u0026thinsp;80\u0026deg;C) was homogenized in liquid nitrogen (30 Hz, 1 min). Metabolites were extracted with 1 mL methanol/water/formic acid (15:4:1, v/v/v) spiked with 10 \u0026micro;L internal standard mix (100 ng\u0026middot;mL⁻\u0026sup1;). After vortexing (10 min) and centrifugation (12,000 \u0026times;g, 4\u0026deg;C, 5 min), the supernatants were dried under N₂, reconstituted in 80% methanol, and filtered (0.22 \u0026micro;m). Analyses were performed using an ultra-performance liquid chromatography coupled with electrospray ionization tandem mass spectrometry (UPLC\u0026ndash;ESI\u0026ndash;MS/MS) system (ExionLC\u0026trade; AD/QTRAP\u0026reg; 6500+) equipped with a Waters ACQUITY UPLC\u0026reg; BEH C18 column (1.7 \u0026micro;m, 2.1 \u0026times; 100 mm). The column temperature was maintained at 40\u0026deg;C, and the flow rate was 0.35 mL\u0026middot;min⁻\u0026sup1; [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. ESI conditions were set at 550\u0026deg;C, \u0026plusmn;\u0026thinsp;5.5 kV (ion mode), and a curtain gas pressure of 35 psi. Quantitation was achieved using scheduled multiple reaction monitoring (MRM), with optimized analyte-specific declustering potential (DP)/collision energy (CE) values, calibrated against certified standards [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eRNA extraction, sequencing, and transcriptome analysis\u003c/h3\u003e\n\u003cp\u003eTotal RNA was isolated from wheat leaf tissues using the TIANGEN RNAprep Pure Plant Plus Kit (DP441). RNA concentration was quantified via Qubit\u0026trade; 4.0 fluorometer, and integrity was assessed with an Agilent 2100 Bioanalyzer, accepting only samples with an RNA Interity Number (RIN)\u0026thinsp;\u0026ge;\u0026thinsp;8.0. Qualified RNA samples were sent to Metware Biotechnology Co., Ltd. (Wuhan, China) for cDNA library preparation and 150-bp paired-end sequencing on an Illumina NovaSeq 6000 platform [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRaw sequencing reads were processed with fastp (v0.22.0) using default parameters to remove adapters and low-quality bases, generating high-quality clean data [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The clean reads were then aligned to the \u003cem\u003eTriticum aestivum\u003c/em\u003e IWGSC RefSeq genome (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://plants.ensembl.org/Triticum_aestivum/Info/Index\u003c/span\u003e\u003cspan address=\"http://plants.ensembl.org/Triticum_aestivum/Info/Index\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed) using HISAT2 (v2.1.0). Gene expression levels were estimated as Fragments Per Kilobase of transcript per Million mapped reads (FPKM). Differential expression analysis was performed using DESeq2 (v1.38.3). Differentially expressed genes (DEGs) were identified with the following thresholds: |log₂FC| \u0026ge; 1.0 and a Benjamini\u0026ndash;Hochberg FDR-adjusted \u0026#119875; \u0026lt; 0.05. Finally, KEGG (v2023.01) pathway enrichment analysis was performed on the identified DEGs.\u003c/p\u003e \u003cp\u003eBased on phenylpropanoid biosynthesis, plant hormone signaling, and jasmonic acid response pathway enrichment, GSVA quantified AP2/ERF transcription factor activity using transcriptome data, while GSEA revealed significant correlations between these pathways and AP2/ERF, with differential expression shown in AP2/ERF genes across groups [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eMetabolomic profiling\u003c/h3\u003e\n\u003cp\u003eMetabolite extraction and mass spectrometry (MS) analysis were conducted by MetWare Biotechnology Co., Ltd (Wuhan, China) following their established protocols. Briefly, 50 mg of cryodesiccated leaf tissue was extracted with 70% aqueous methanol (4\u0026deg;C, 12 h) under intermittent vortex agitation (30-s pulses at 30-min intervals for 6 cycles). After centrifugation (13,200 \u0026times;g, 4\u0026deg;C, 3 min), supernatants were filtered through 0.22-\u0026micro;m organic membranes. Chromatographic separation was achieved using an ExionLC\u0026trade; AD UPLC system (SCIEX) fitted with an Agilent SB-C18 column (1.8 \u0026micro;m \u0026times; 2.1 mm \u0026times; 100 mm) under binary gradient conditions: solvent A (0.1% formic acid/water) and solvent B (0.1% formic acid/acetonitrile). Metabolite characterization utilized electrospray ionization-triple quadrupole MS/MS (SCIEX QTRAP\u0026reg; 6500+) in dynamic MRM mode, with spectral matching against MetWare's proprietary MWDB database v3.0. Differentially accumulated metabolites (DAMs)​ were screened based on a variable importance in projection (VIP) score\u0026thinsp;\u0026gt;\u0026thinsp;1.0 from orthogonal projections to latent structures-discriminant analysis (OPLS-DA) and a |log₂(fold change)| \u0026ge; 1.0. KEGG enrichment analysis was performed on the identified DAMs [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eCorrelation analysis of transcriptomic and metabolomic data\u003c/h3\u003e\n\u003cp\u003eIntegration of transcriptomic and metabolomic data was performed by mapping both DEGs and DAMs to KEGG pathways. Pairwise Pearson correlation coefficients (PCC) between the expression levels of DEGs and the accumulation levels of DAMs were calculated using cor function in R software (v4.1.2). Associations with an absolute PCC (|r|)\u0026thinsp;\u0026gt;\u0026thinsp;0.8 and an adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant. Based on a predefined set of core cold-response KEGG pathways (ko00052, ko00196, ko00592, ko00940, ko00941, ko04016, and ko04075), co-enriched pathways were identified. A pathway was considered co-enriched if it showed significant enrichment in both the transcriptomic (DEGs) and metabolomic (DAMs) datasets.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative real-time PCR assay\u003c/h2\u003e \u003cp\u003eVerification of RNA-seq data utilized identical RNA samples to those used for transcriptome sequencing. Quantitative PCR was performed using SYBR Green-based Super Real PreMix Plus (TIANGEN, Beijing, China) on a Roche Light Cycler\u0026reg; 480 II system. Gene-specific primers were designed using SnapGene v6.0 and validated through NCBI Primer-BLAST (Table S4). ACTIN served as the internal reference gene based on its uniform expression across treatments (|ΔCt| \u0026le; 0.5). Relative expression was calculated using the 2^\u003csup\u003e(\u0026minus;ΔΔCt)\u003c/sup\u003e method with three technical replicates [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSubcellular localization\u003c/h3\u003e\n\u003cp\u003eThe wheat protoplast system first isolates the mesophyll protoplasts of wheat leaves. The target gene - GFP fusion vector is transfected into the cells using PEG-mediated transformation method [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The tobacco transient expression system first transfers the fusion vector into the Agrobacterium EHA105 engineering strain, injects it into the leaves of \u003cem\u003eNicotiana tabacum\u003c/em\u003e, and 48 hours later, the fluorescence signal is observed using a laser confocal microscope (excitation wavelength 488 nm). The primer sequences are listed in Supplementary Table S4.\u003c/p\u003e\n\u003ch3\u003eTranscriptional activation experiments\u003c/h3\u003e\n\u003cp\u003eThe transcriptional activation test vector used in this study is pGBKT7, as cited in Liu's research [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The coding regions of \u003cem\u003eTraesCS5D02G318400\u003c/em\u003e, \u003cem\u003eTraesCS6A02G381000\u003c/em\u003e, and \u003cem\u003eTraesCS6D02G366100\u003c/em\u003e were respectively integrated into the pGBKT7 vector using double enzyme digestion and homologous recombination methods, resulting in the construction of fusion expression vectors, pGBKT7 empty vector (negative control), and pGBKT7 -VP16 (positive control). These were respectively transformed into yeast strain Y2HGold. The transformed yeast cells were cultured at 30\u0026deg;C for 3 days, and their growth status was observed. The primer sequences are listed in Supplementary Table S4.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eUnless otherwise specified, all physiological and biochemical assays were performed with six independent biological replicates. For both transcriptomics and metabolomics analyses, three independent biological replicates were used. Quantitative data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Statistical significance was assessed using Student's t-test in SPSS Statistics v26.0 (IBM\u0026reg;), with significance levels denoted as \u0026#119875; \u0026lt; 0.05 (*) and \u0026#119875; \u0026lt; 0.01 (**). Data organization and preliminary calculations were conducted in Microsoft\u0026reg; Excel 16.0. Graphs and visualizations were generated using GraphPad Prism v10.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePhenotypic responses to cold stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeedlings of Luyan951 and Luyan955 at the two-leaf, one-tiller stage were subjected to low-temperature treatment to assess cultivar-specific cold responses.\u0026nbsp;Low-temperature stress significantly impaired normal growth and development in wheat and reduced post-recovery survival in both accessions. However, Luyan951 exhibited substantially higher cold tolerance, with a survival rate of 52.67%, compared with only 20.67% in Luyan955 (Fig. 1A, B). To further characterize physiological differences underlying these contrasting phenotypes, key antioxidant enzyme activities and osmotic adjustment indicators were quantified. Relative to their respective untreated controls, Luyan951 displayed significant increases in SOD, CAT, and POD activities at both 3 h and 6 h post-treatment. In contrast, Luyan955 showed delayed activation, with statistically significant induction detected only at 6 h (Fig. 1C). Cold stress also induced lipid peroxidation in both cultivars, as indicated by significantly elevated MDA contents at 3 h and 6 h. However, the magnitude of Malondialdehyde accumulation at 3 h was less pronounced in Luyan951 (𝑃\u0026nbsp;\u0026lt; 0.05) than in Luyan955 (𝑃\u0026nbsp;\u0026lt; 0.01).\u0026nbsp;Similarly, PRO and soluble sugar concentrations increased significantly in both genotypes following cold exposure, reflecting enhanced osmotic adjustment. Notably, Luyan951 exhibited stronger early induction (𝑃\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01), whereas Luyan955 showed weaker statistical significance at 3 h (𝑃 \u0026lt; 0.05), further supporting the superior early-stage stress response capacity of Luyan951.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptome analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the molecular mechanisms underlying differential cold tolerance, RNA-seq was conducted on Luyan951 and Luyan955 under control and low-temperature conditions. Sequencing generated 304.19 Gb of nucleotide data yielding 2,027,908,974 clean reads, with an average GC content of 53.49% and high base-calling accuracy (Q20 \u0026gt; 96% and Q30 \u0026gt; 91%; Supplementary Table S1).\u0026nbsp;Across all 18 samples, a total of 13,024 differentially expressed genes (DEGs) were identified using stringent criteria (|log\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eFold Change (FC)| \u0026ge; 1 and False Discovery Rate (FDR)-adjusted 𝑃 \u0026lt; 0.05; Supplementary Table S2). Quality assessments confirmed strong reliability of the transcriptomic datasets. Pearson correlation coefficients (𝑟) exceeded 0.95, indicating high reproducibility among biological replicates (Supplementary Fig. S1A). Principal component analysis (PCA) further revealed tight clustering of replicate samples within treatment groups and clear separation between cold-stressed and control samples, with PC1 accounting for 33.4% of the variance (Supplementary Fig. S1B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCold treatment induced markedly different transcriptomic responses between the two genotypes. In Luyan951, 993 DEGs at 3 h and 7,673 DEGs at 6 h were detected, whereas Luyan955 exhibited 461 DEGs at 3 h and 5,823 DEGs at 6 h. Notably, the number of upregulated and downregulated DEGs was consistently significantly higher in Luyan951, particularly at 6 h (Fig. 2A). Heatmap clustering further illustrated distinct expression patterns between treatments and genotypes (Fig. 2B). Comparative analysis identified 228 coexpressed DEGs in Luyan951 and 304 coexpressed DEGs in Luyan955, with 167 genes shared between the two genotypes (Fig. 2C).\u003c/p\u003e\n\u003cp\u003eTo further investigate the functional roles of DEGs, Gene Ontology (GO, Fig. 2D) and Kyoto Encyclopedia of Genes and Genomes (KEGG, Fig. 2E) pathway enrichment analyses were performed. GO enrichment highlighted that DEGs were predominantly involved in stress-related pathways, including \u0026ldquo;cell wall remodeling,\u0026rdquo; \u0026ldquo;photosynthesis,\u0026rdquo; \u0026ldquo;membrane lipid metabolism,\u0026rdquo; \u0026ldquo;osmotic adjustment,\u0026rdquo; \u0026ldquo;antioxidant response,\u0026rdquo; and \u0026ldquo;hormone regulation.\u0026rdquo; Whether the gene ontology (GO) terms are positively enriched (indicating upregulation of metabolites) or negatively enriched (indicating downregulation of metabolites), their presence highlights their functional importance in the cold stress response. KEGG pathway analysis revealed genotype-specific transcriptional reprogramming. Most stress-associated pathways exhibited higher enrichment levels in Luyan951 relative to Luyan955, except for the \u0026ldquo;linoleic acid metabolism pathway.\u0026rdquo; Notably, Luyan951 demonstrated stronger activation of several key stress-responsive pathways, including \u0026ldquo;phenylpropanoid biosynthesis,\u0026rdquo; \u0026ldquo;arginine and proline metabolism,\u0026rdquo; \u0026ldquo;biosynthesis of unsaturated fatty acids,\u0026rdquo; and \u0026ldquo;amino sugar and nucleotide sugar metabolism.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolome analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further characterize differential cold stress responses between Luyan951 and Luyan955, untargeted metabolomic profiling was performed. Pearson correlation and PCA of the metabolite datasets revealed distinct separation between control and cold-treated groups and exhibited strong concordance with transcriptomic profiles (Supplementary Fig. S2A, B). Differentially accumulated metabolites (DAMs) were defined as those with Variable Importance in Projection (VIP) \u0026gt; 1.5 and FC \u0026ge; 2 or \u0026le; 0.5. Following cold exposure, Luyan951 accumulated 138 DAMs (70 upregulated and 68 downregulated), whereas Luyan955 exhibited 182 DAMs (127 upregulated and 55 downregulated; Fig. 3A). Hierarchical clustering analysis (HCA) based on Pearson correlation further revealed clear cultivar-specific segregation under both control and cold-stressed conditions. Among the DAMs, flavonoids and amino acid derivatives were the most abundant classes (Fig. 3B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eComparative analysis identified 48 DAMs unique to Luyan951, 90 unique to Luyan955, and 56 shared between the two cultivars, with 10 core DAMs conserved across all comparative groups (Fig. 3C).\u0026nbsp;K-means clustering of DAMs grouped metabolites with concordant expression patterns into six distinct co-expression clusters (Fig. 3D). Centroid analysis revealed two superclusters: Clusters 1, 5, and 6 exhibited concordant temporal dynamics, whereas Clusters 2, 3, and 4 displayed cultivar-divergent accumulation patterns. Cluster sizes varied considerably, ranging from 34 DAMs in Cluster 1 to 147 DAMs in Cluster 2.A targeted examination of key metabolites identified 40 significantly altered metabolites between Luyan951 and Luyan955 (based on the highest FoldChange), classified into six categories. Flavonoids represented a major proportion, with Luyan951 exhibiting a greater number of upregulated flavonoid species than Luyan955 (Fig. 3E). KEGG annotation and classification of significantly altered metabolites revealed pathways consistent with transcriptomic analyses, including \u0026ldquo;cell wall remodeling,\u0026rdquo; \u0026ldquo;membrane lipid metabolism,\u0026rdquo; \u0026ldquo;osmotic adjustment,\u0026rdquo; and \u0026ldquo;hormone regulation.\u0026rdquo; Notably, Luyan951 showed higher enrichment in \u0026ldquo;phenylpropanoid biosynthesis,\u0026rdquo; \u0026ldquo;salicylic acid derivative biosynthesis,\u0026rdquo; \u0026ldquo;amino sugar and nucleotide sugar metabolism,\u0026rdquo; and \u0026ldquo;glycerophospholipid metabolism,\u0026rdquo; relative to Luyan955 (Fig. 3F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegrated transcriptome and metabolome analysis\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the molecular mechanisms underlying cold tolerance in Luyan951 and Luyan955, integrated transcriptomic and metabolomic analyses were performed. Gene\u0026ndash;metabolite pairs were screened within each comparison group using Pearson correlation coefficients (|r| \u0026gt; 0.8, 𝑃 \u0026lt; 0.05). Qualified correlations were subsequently visualized using a nine-quadrant plot, which delineates fold-change patterns between corresponding genes and metabolites. Comparative analysis of quadrants 1, 3, 7, and 9, representing synergistic or antagonistic gene\u0026ndash;metabolite interactions, revealed significantly higher area density in Luyan951 (Fig. 4A) than in Luyan955 (Fig. 4B), indicating more extensive co-regulation of metabolic and transcriptional responses in the cold-tolerant cultivar. Conversely, quadrants 2, 4, 5, 6, and 8, which mismatched or uninformative gene\u0026ndash;metabolite changes. Focusing on core cold stress-responsive pathways, multi-omics integration identified pathways characterized by simultaneous differential expression of DEGs and DAMs. Hierarchical clustering of these pathways (Fig. 4C) highlighted \u0026ldquo;phenylpropanoid biosynthesis,\u0026rdquo; \u0026ldquo;plant hormone signal transduction,\u0026rdquo; and \u0026ldquo;flavonoid biosynthesis\u0026rdquo; as the most prominently enriched. Within the phenylpropanoid biosynthesis pathway, both gene expression and metabolite accumulation were substantially higher in Luyan951 than in Luyan955, indicating that the tolerant cultivar possesses a stronger capacity for metabolic regulation under low-temperature stress.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscription factor analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTFs are key regulators of cold tolerance in plants. In total, 735 TFs were identified in Luyan951 and Luyan955. Among these, families previously associated with stress responses\u0026mdash;APETALA2/ethylene-responsive factor (AP2/ERF), basic helix-loop-helix (bHLH), myeloblastosis (MYB), no apical meristem (NAC), and WRKY\u0026mdash;comprised 3.21% to 6.04% of the total TFs (Fig. 5A, Supplementary Table S3). Expression analysis revealed significant differential regulation within these five TF families, although the directionality of expression changes varied. Notably, AP2/ERF TFs were predominantly upregulated, whereas no distinct TF family exhibited a consistent trend of downregulation (Fig. 5B). Based on the transcriptome data, we used the GSVA algorithm to calculate the activity of the AP2/ERF transcription factor family, the activity of the AP2/ERF transcription factor family, the phenylpropanoid synthesis pathway, the plant hormone signal transduction pathway, and the activation level of jasmonic acid signal transduction in each sample. Specifically, AP2/ERF activity exhibited a strong positive correlation with phenylpropanoid biosynthesis (R = 0.73, P = 0.0006), plant hormone signal transduction (R = 0.88, P = 0), and the response to jasmonic acid (GO:0009753, R = 0.74, P = 0.0005) by using GSVA. Furthermore, GSEA demonstrated that samples with elevated AP2/ERF activity were significantly enriched in the phenylpropanoid biosynthesis pathway (Fig. 5C), plant hormone signal transduction pathway (Fig. 5D), and the response to jasmonic acid pathway (Fig. 5E). Meanwhile, the co-expression network illustrates the interactions among 55 significantly differentially expressed genes from the AP2/ERF gene family and there are certain correlations among these genes (Supplementary Fig. S3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhenylpropanoid biosynthesis and jasmonic acid signal transduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further delineate the molecular basis underlying differential cold adaptation between Luyan951 and Luyan955 at the seedling stage, we examined the pathways previously identified as co-enriched through transcriptomic\u0026ndash;metabolomic analysis. Using R-based KEGG pathway mapping, a total of 44 genes, 2 significantly altered metabolites, and 8 key enzymes were localized to the phenylpropanoid biosynthesis pathway (ko00940), with redundant branches removed for clarity. Genes encoding phenylalanine ammonia-lyase (PAL), 4-coumarate-CoA ligase (4CL), hydroxycinnamoyl transferase (HCT), cinnamoyl-CoA reductase (CCR), and cinnamyl-alcohol dehydrogenase (CAD) constituted the dominant components of this pathway accounted for the majority of transcriptional variation (Fig. 6A).\u003c/p\u003e\n\u003cp\u003eQuantification of phytohormone dynamics under cold stress revealed contrasting jasmonate-related responses between the two cultivars. Both cultivars exhibited similar accumulation trends for 1-aminocyclopropanecarboxylic acid (ACC), JA, jasmonoyl-L-isoleucine (JA-Ile), salicylic acid (SA), and Salicylic acid 2-O-\u0026beta;-glucoside (SAG). However, cis(+)-12-oxophytodienoic acid (OPDA) remained consistently elevated in Luyan951 throughout the time course, whereas Luyan955 exhibited a progressive decline. Furthermore, methyl jasmonate (MeJA) was significantly upregulated in Luyan951 but downregulated in Luyan955. Overall, all four jasmonate-related metabolites (JA, JA-Ile, OPDA, and MeJA) showed consistently higher concentrations in Luyan951 compared to Luyan955 across all time points (Fig. 6B). Given these differences, we further analyzed the JA signaling pathway (KEGG pathway ko04075) and its associated DEGs. A comparative schematic (Fig. 6C) revealed that 18 genes encoding four central functional components\u0026mdash;jasmonic acid-amino synthetase (JAR1), coronatine-insensitive protein 1 (COI1), jasmonate ZIM domain-containing protein (JAZ), and TF MYC2\u0026mdash;were differentially expressed between the two cultivars. These components form the core JA signaling module, and their transcriptional divergence accounts for the majority of genotype-specific variations in stress response pathways.\u003c/p\u003e\n\u003cp\u003e\n \u003cv:shape id=\"图片_x0020_6\" o:spid=\"_x0000_i1025\" type=\"#_x0000_t75\"\u003e\u0026nbsp;\u003cv:imagedata src=\"file:///C%3A/Users/btr8097/AppData/Local/Packages/oice_16_974fa576_32c1d314_28ff/AC/Temp/msohtmlclip1/01/clip_image006.jpg\" o:title=\"\"\u003e\u0026nbsp;\u003c/v:imagedata\u003e\n \u003c/v:shape\u003e\n\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. 6.\u0026nbsp;\u003c/strong\u003eExpression Profiles of\u0026nbsp;the Phenylpropanoid Biosynthesis Pathway and Jasmonic Acid Signaling Transduction Pathway in Wheat Cultivars under Cold Stress.\u0026nbsp;(A) Integrated map of phenylpropanoid biosynthesis metabolites and their corresponding key enzymes. DEGs and DAMs were mapped onto KEGG pathways to elucidate systemic biological functions (www.kegg.jp/kegg/kegg1.html). (B) Temporal abundance profiles of 14 phytohormone compounds across six major phytohormone classes in wheat cultivars Luyan951 and Luyan955 under cold stress at 3 h and 6 h post-treatment. (C) Integrated map of key enzymes and their associated genes involved in jasmonic acid signal transduction. Red indicates high expression, while blue represents low expression. (D) qRT-PCR Validation of\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eExpression Patterns of\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNine Cold Stress-Related Genes in Wheat. Expression levels in control samples were normalized to 100%. Data are presented as the mean \u0026plusmn; standard deviation (SD) from at least three independent biological replicates. \u003cem\u003eTaACTIN\u003c/em\u003e was used as the internal reference gene for normalization.\u003c/p\u003e\n\u003cp\u003eTo validate the reliability of the RNA-seq expression profiles, we conducted quantitative real-time PCR (qRT-PCR) on cold resistance-associated key genes mapped to KEGG pathways phenylpropanoid biosynthesis (ko00940) and JA signaling (ko04075) pathways (Fig. 6D). The qRT-PCR results showed strong concordance with RNA-seq\u0026ndash;derived expression patterns for nine DEGs, with the exception of \u003cem\u003eCOI1\u003c/em\u003e (\u003cem\u003eTraesCS3B02G399200\u003c/em\u003e), which exhibited an initial transient upregulation followed by downregulation. In contrast, \u003cem\u003eCAD\u003c/em\u003e (\u003cem\u003eTraesCS5D02G210600\u003c/em\u003e), \u003cem\u003e4CL\u003c/em\u003e (\u003cem\u003eTraesCS4D02G268400\u003c/em\u003e), \u003cem\u003eHCT\u003c/em\u003e (\u003cem\u003eTraesCS4D02G300400\u003c/em\u003e), \u003cem\u003eCCR\u003c/em\u003e (\u003cem\u003eTraesCS5B02G223700\u003c/em\u003e, \u003cem\u003eTraesCS5A02G225100\u003c/em\u003e, \u003cem\u003eTraesCS5D02G232400\u003c/em\u003e), \u003cem\u003eJAZ\u003c/em\u003e (\u003cem\u003eTraesCS4A02G007800\u003c/em\u003e), and \u003cem\u003eMYC2\u003c/em\u003e (\u003cem\u003eTraesCS5A02G489500\u003c/em\u003e) exhibited a progressive upregulation trend in both cultivars. Notably, the magnitude of induction was significantly higher in Luyan951. Together, these results suggest that genes such as \u003cem\u003eCAD\u003c/em\u003e, \u003cem\u003e4CL\u003c/em\u003e, \u003cem\u003eHCT\u003c/em\u003e, \u003cem\u003eCCR\u003c/em\u003e, \u003cem\u003eJAZ\u003c/em\u003e, and \u003cem\u003eMYC2\u003c/em\u003e may contribute to the enhanced cold tolerance of Luyan951 and thus represent promising candidates for future functional studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubcellular localization and transcriptional activation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the high specificity of expression observed in the AP2/ERF subfamily and its significant correlation with key pathways in this study, we conducted further investigation into this group of genes. Quantitative validation of six significantly differentially expressed AP2/ERF genes revealed consistent expression trends with transcriptome data, particularly \u003cem\u003eTraesCS5D02G318400\u003c/em\u003e, \u003cem\u003eTraesCS6A02G381000\u003c/em\u003e, and \u003cem\u003eTraesCS6D02G366100\u003c/em\u003e which exhibited more pronounced differential expression under cold stress (Fig. 7A). Confocal laser scanning microscopy confirmed nuclear localization of these three genes in wheat protoplasts and tobacco epidermal cells (Fig. 7B). Furthermore, we conducted transcriptional activation experiments. On the SD-TH+X and SD-THA+X plates, the experimental groups without the fusion positive control VP16 could grow and turn blue, while the experimental groups with the fusion VP16 could grow and their growth rate was stronger than that of pGBKT7-VP16 (Fig. 7C). These three genes exhibited transcriptional activation activity in the yeast system. In summary, this indicates that it is involved in the transcriptional regulation of various genes in wheat under cold stress conditions.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUnderstanding the molecular and metabolic responses of wheat to low-temperature stress is crucial for elucidating the regulatory mechanisms underlying cold tolerance and for improving the resilience of wheat cultivars. Although several studies have addressed cold tolerance in wheat [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], investigations in sister lines remain limited. By integrating physiological traits, hormonal measurements, transcriptomics, and metabolomics in Luyan951 and Luyan955, this study identified key cold-responsive genes and metabolites\u0026mdash;particularly those associated with phenylpropanoid biosynthesis and JA signaling\u0026mdash;thus providing new insights into the regulatory framework of wheat cold resistance.\u003c/p\u003e \u003cp\u003eCold stress induces the rapid accumulation of ROS, triggering antioxidant defenses and altering protein, lipid, and osmolyte levels [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Phenotypically, Luyan951 exhibited a higher survival rate and significantly greater antioxidant enzyme activities of SOD, CAT, and POD, together with increased PRO and soluble sugar content. In contrast, Luyan955 showed elevated MDA levels, indicating more severe membrane lipid peroxidation. These findings suggest that Luyan951 possesses stronger antioxidant capacity and osmotic regulation ability, enabling superior protection under cold stress.\u003c/p\u003e \u003cp\u003eTranscriptome analysis further revealed that cold-resistant Luyan951 exhibited a substantially higher number of DEGs than Luyan955 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). GO enrichment analysis revealed that these DEGs were significantly enriched in biological processes central to cold adaptation\u0026mdash;including membrane lipid remodeling, osmotic regulation, and antioxidant activity [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. KEGG mapping showed consistent enrichment patterns with previous studies [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], yet Luyan951 displayed markedly higher activation across most pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-E). Enhanced phenylpropanoid biosynthesis suggests reinforced cell wall structure and stronger antioxidant potential through increased secondary metabolite production. Likewise, elevated arginine and proline metabolism implies robust osmotic balance, while the significant upregulation of biosynthesis of unsaturated fatty acids and amino sugar and nucleotide sugar metabolism reflects broader metabolic adjustments. Together, these pathway-level differences indicate that Luyan951 exhibits a more coordinated molecular response to cold stress compared with the susceptible cultivar.\u003c/p\u003e \u003cp\u003eMetabolomic profiling, which provides a direct reflection of physiological state, complements transcriptome analysis by capturing downstream biochemical changes [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The DAM analysis indicated that the cold-resistant cultivar Luyan951 exhibited fewer DAMs than the sensitive cultivar Luyan955 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), a pattern opposite to that observed for DEGs. Similar trends have been reported previously, where sensitive genotypes often display a greater number of altered genes and metabolites compared with tolerant genotypes [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. We propose that although Luyan951 involves a larger number of transcriptionally regulated genes within metabolic pathways, these genes act in a coordinated manner, generating more concentrated and efficient metabolic outputs. Flavonoids\u0026mdash;key regulators of cold tolerance\u0026mdash;support coordinated response measures against cold stress by modulating cold-responsive genes (such as calcium-dependent protein kinase CPK27) and influencing key signaling pathways, including the CBF\u0026ndash;COR pathway, MYB TFs, ABA and JA-mediated networks [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Consistent with this, our study revealed that Luyan951 accumulates significantly higher basal flavonoid levels than Luyan955, indicating greater stress-responsive potential (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Luyan955 displayed a substantial post-stress surge in flavonoid content (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), whereas Luyan951 exhibited larger fold-changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), suggesting that Luyan951 relies on efficient flavonoid regulatory amplification, while Luyan955 engages in broad compensatory accumulation to reduce damage. Phenylpropanoid compounds, derived from phenylpropane via the shikimate metabolic pathway [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], have been extensively studied; However, their mechanistic involvement in cold stress responses remains inadequately elucidated. Previous studies have demonstrated their importance in enhancing cold tolerance in rice, Chinese cabbage, and oil-tea camellia [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], yet systematic evidence in wheat is still limited. In our study, both transcriptomic and metabolomic analyses revealed substantially higher enrichment of phenylpropanoid-related compounds in the cold-tolerant cultivar Luyan951 following cold exposure (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Recent findings further indicate that coordinated regulation of lignin biosynthesis and ROS scavenging significantly enhances cold tolerance in citrus [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. This provides a mechanistic basis for the strong upregulation of CAD, 4CL, HCT, and CCR observed in Luyan951 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), thereby underscoring the central role of the phenylpropanoid pathway in wheat cold resistance and highlighting its conserved function across species plant. This is also the first direct evidence to be discovered in wheat that the synthesis of phenylpropanoids is associated with cold resistance.\u003c/p\u003e \u003cp\u003eJasmonates (JAs) are key plant hormones that coordinate stress responses and developmental processes [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Previous studies have revealed that the jasmonate signaling pathway regulates cold tolerance in species such as tomato, cucumber, melon, and Arabidopsis [\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. In wheat, several JA-related genes\u0026mdash;including \u003cem\u003eTaMED25\u003c/em\u003e, \u003cem\u003eTaSnRK1α-TaPAP6L\u0026mdash;\u003c/em\u003ehave been implicated in cold stress responses [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], although the complete regulatory network remains poorly understood. In our study, KEGG analysis of DAMs indicated comparable enrichment in hormone signal transduction between Luyan951 and Luyan955 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eF), yet Luyan955 exhibited a significantly higher absolute number of DAMs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Targeted hormone quantification confirmed elevated JA levels in Luyan951 relative to Luyan955, consistent with activation of the JA signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). These results suggest that Luyan955 exhibits a widespread stress response with metabolic dispersion, while Luyan951 relies on precise JA-mediated regulation to effectively activate defense mechanisms. Cold stress triggers JA-Ile accumulation, facilitating COI1\u0026ndash;JAZ interactions that target JAZ for ubiquitin\u0026ndash;proteasome-mediated degradation, thereby releasing MYC2 to activate downstream cold-resistance responses [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Consistently, \u003cem\u003eTraesCS3B02G399200\u003c/em\u003e in Luyan951 exhibited transient upregulation followed by decline, whereas excessive accumulation of \u003cem\u003eTraesCS4A02G007800\u003c/em\u003e in Luyan955 appears to impede MYC2 activation, potentially compromising cold tolerance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eTF families known to mediate cold responses\u0026mdash;including MYB, WRKY, NAC, and bZIP, the latter of which includes calmodulin-binding proteins involved in cold induction in plants\u0026mdash;were prominently represented among the DEGs [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. By contrast, studies on the roles of AP2/ERF TFs in plant cold resistance remain relatively limited [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], with only scattered reports in species such as rice and ginseng. Our findings demonstrate that members of the AP2/ERF family showed specific upregulation in wheat under cold stress, with representative genes such as \u003cem\u003eTraesCS5D02G318400, TraesCS6A02G381000, and TraesCS6D02G366100\u003c/em\u003e displaying cultivar-specific upregulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Their subcellular localization and transcriptional activation experiment also confirmed that they could function normally as transcriptional activators. The correlation between AP2/ERF and the synthesis of phenylpropanoids as well as the hormone signal transduction is a strong support for our previous work (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-E). Both GSVA and GSEA analyses demonstrated that AP2/ERF proteins potentially regulate downstream phenylpropanoid biosynthesis and hormone signal transduction pathways, with the possibility of crosstalk among these pathways not excluded, which provides novel insights and directions for further research on cold tolerance in wheat\u003c/p\u003e \u003cp\u003eTherefore, our study underscores the pivotal roles of phenylpropanoid biosynthesis and jasmonate (JA) signaling in conferring cold tolerance in wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.), with genes associated with these pathways representing promising targets for developing cold-resistant cultivars. While key regulatory mechanisms have been identified, further validation is required, including temporal expression profiling, weighted gene co-expression network analysis (WGCNA), and in-depth exploration of AP2/ERF-related genes to elucidate co-expression modules and enriched pathways, as well as plant responses to cold stress under field conditions or across populations\u0026mdash;the focal point of our subsequent validation. Functional characterization of candidate genes through genetic modification or genome editing will provide conclusive evidence for their roles in cold adaptation and offer robust theoretical guidance for breeding stress-resilient wheat varieties\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis integrated physiological-transcriptomic-metabolomic study reveals that phenylpropanoid biosynthesis and JA signaling pathways form the core regulatory network for cold tolerance in wheat sister varieties Luyan951. Critically, we provide the first evidence in wheat that phenylpropanoid biosynthesis directly underpins cold adaptation\u0026mdash;synergistically activated with JA signaling to establish systemic defense. This coordination enables efficient ROS scavenging, osmotic homeostasis, and metabolic stability under cold stress. We also speculate that these pathways may be regulated by upstream ERF transcription factors, though further experimental validation is required. These findings elucidate molecular mechanisms of cold adaptation and offer defined genetic targets for breeding cold-tolerant wheat.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDAMs \u0026nbsp; Differentially Accumulated Metabolites\u003c/p\u003e\n\u003cp\u003eDEGs \u0026nbsp; Differentially Expressed Genes\u003c/p\u003e\n\u003cp\u003eGSEA \u0026nbsp; Gene Set Enrichment Analysis\u003c/p\u003e\n\u003cp\u003eGSVA \u0026nbsp; Gene Set Variation Analysis\u003c/p\u003e\n\u003cp\u003eGO \u0026nbsp; \u0026nbsp; Gene Ontology\u003c/p\u003e\n\u003cp\u003eGWAS \u0026nbsp; Genome-Wide Association Study\u003c/p\u003e\n\u003cp\u003eJA \u0026nbsp; \u0026nbsp; \u0026nbsp;Jasmonic Acid\u003c/p\u003e\n\u003cp\u003ePCA \u0026nbsp; \u0026nbsp; Principal Component Analysis\u003c/p\u003e\n\u003cp\u003eTFs \u0026nbsp; \u0026nbsp; Transcription Factors\u003c/p\u003e\n\u003cp\u003eVIP \u0026nbsp; \u0026nbsp; Variable Importance in Projection\u003c/p\u003e\n\u003cp\u003eWGCNA Weighted Gene Co-expression Network Analysis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW.Z. designed and completed most experiments and wrote the majority of the manuscripts. P.L., X.S., and Y.L. assisted phenotypic identification and sample collection. M.S. assisted in seedling management. P.L., Z.C., and B.Y. revised and approved the final version of the manuscript. All authors contributed to the article and approved the submitted version.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Taishan Scholars Program (tsqn202312291), the Shandong Provincial Natural Science Foundation (ZR2024QC377), the Research Start-up Foundation for Young Talent of Shandong Academy of Agricultural Sciences (CXGX2024F01).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available in the supplementary information files. The transcriptome data were deposited in the NCBI Sequence Read Archive (SRA) under accession PRJNA1355034.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHui S, Yujian Y, Yamiao Z, Yadong W, Ashley J, Jinpeng L, et al. Autonomous recovery of wheat spikelet development following cold stress arrest mediated by modulation of sucrose degradation and IAA/ABA homeostasis. 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Plants.\u003cem\u003e \u003c/em\u003e2025;14(18):2922.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Transcriptome, Metabolome, Cold stress, Phenylpropanoid, Jasmonic acid signaling","lastPublishedDoi":"10.21203/rs.3.rs-9047620/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9047620/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCold stress severely constrains global wheat productivity, yet the molecular basis for differential cold tolerance in genetically similar cultivars remains poorly understood. This study employed integrated physiological, transcriptomic, and metabolomic analyses to dissect cold adaptation mechanisms in cold-tolerant \"Luyan951\" and cold-sensitive \"Luyan955\" wheat sister lines, which share a close genetic background but exhibit contrasting cold responses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePhysiological assays demonstrated that Luyan951 exhibited a 52.67% survival rate under cold stress\u0026mdash;significantly higher than Luyan955 (20.67%)\u0026mdash;alongside enhanced antioxidant enzyme activities (SOD, CAT, POD) and superior osmotic adjustment (elevated proline and soluble sugars). Pathway enrichment analysis revealed that phenylpropanoid biosynthesis and jasmonic acid (JA) signaling pathways are critical for cold adaptation, with the cultivar \"Luyan951\" exhibiting stronger activation of these pathways. Key genes (\u003cem\u003eCAD\u003c/em\u003e, \u003cem\u003e4CL\u003c/em\u003e, \u003cem\u003eJAZ\u003c/em\u003e, \u003cem\u003eMYC2\u003c/em\u003e) and JA metabolism-related genes were significantly upregulated in \"Luyan951\", which was validated by qRT-PCR. Bioinformatic analysis indicated that these pathways may be regulated by AP2/ERF transcription factors. Subcellular localization and transcriptional activation experiments confirmed the nuclear localization and transactivation function of three AP2/ERF genes (\u003cem\u003eTraesCS5D02G318400\u003c/em\u003e, \u003cem\u003eTraesCS6A02G381000, TraesCS6D02G366100\u003c/em\u003e).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study provides the first evidence in wheat that phenylpropanoid biosynthesis contributes to cold tolerance, and together with jasmonate signaling, constitutes a core regulatory network for freezing resistance by enabling ROS scavenging, osmotic homeostasis, and metabolic stability; we further speculate this pathway is potentially regulated by upstream ERF transcription factors, offering multi-faceted evidence to elucidate cold adaptation mechanisms and advance precision breeding in wheat.\u003c/p\u003e","manuscriptTitle":"Integrated physiological, transcriptomic and metabolomic analysis reveals differential cold response in wheat seedlings across varieties","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-24 17:54:58","doi":"10.21203/rs.3.rs-9047620/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-06T07:29:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T17:37:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163500740023650985584218206546906975604","date":"2026-04-18T14:42:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36525364312951308785895550333966218033","date":"2026-04-17T12:12:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184085226413587717019983750118182430135","date":"2026-04-17T07:18:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-16T12:54:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-07T10:06:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-07T10:06:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2026-03-06T07:45:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"81a1aac0-28c9-4d32-beec-4324c5f6cc71","owner":[],"postedDate":"April 24th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-06T07:29:33+00:00","index":31,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T17:37:01+00:00","index":30,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-24T17:54:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-24 17:54:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9047620","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9047620","identity":"rs-9047620","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}