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Article

Mining Minor Cold Resistance Genes in V. vinifera Based on Transcriptomics

by
Junli Liu
1,
Yihan Li
1,
Zhilei Wang
2,
Hua Li
1,3,4,* and
Hua Wang
1,3,4,*
1
College of Enology, Northwest A&F University, Xianyang 712100, China
2
College of Enology and Horticulture, Ningxia University, Yinchuan 750021, China
3
China Wine Industry Technology Institute, Yinchuan 750021, China
4
Engineering Research Center for Viti-Viniculture, National Forestry and Grassland Administration, Xianyang 712100, China
*
Authors to whom correspondence should be addressed.
Horticulturae 2025, 11(12), 1538; https://doi.org/10.3390/horticulturae11121538
Submission received: 10 November 2025 / Revised: 8 December 2025 / Accepted: 17 December 2025 / Published: 18 December 2025
(This article belongs to the Special Issue Research on Grape Stress Resistance Cultivation and Genetic Breeding)
Browse Figures
Versions Notes

Abstract

Cold resistance is an important characteristic of sustainable development in the grape industry. The intraspecific recurrent selection in the Vitis vinifera (V. vinifera) method uses high-quality varieties as breeding materials and the substitution and accumulation of minor resistance genes, breeding high-quality grapes with cold resistance. This study was conducted to identify and genetically analyse the cold resistance of a V. vinifera hybrid population (Ecolly × Dunkelfelder), screen for highly resistant and sensitive plant samples, and use high-throughput sequencing to perform transcriptome sequencing and related differential gene expression analysis on each sample. The results revealed that the cold resistance of the hybrid offspring population was characterised by continuous quantitative trait inheritance, with 38 differentially expressed genes (7 upregulated genes and 31 downregulated genes) between the high resistance and high-sensitivity types. Analysis of genes related to various pathways, related to cold resistance, revealed that CYP76F10, Dxs, GERD, NMT, GDE1, glgC, and DHQ-SDH, as well as transcription factor MYB, HB, and MADS family genes, are key candidate genes for V. vinifera cold resistance research. Real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) was used to investigate the expression characteristics of the six genes that were differentially expressed genes, the results of which were essentially consistent with the results of RNA-seq. Specifically, NMT may enhance cold resistance by enhancing membrane lipid stability. The synergistic expression pattern of CYP76F14 and Dxs suggests its key role in terpene synthesis. By exploring potential genes related to micro effects, a theoretical foundation for further exploration of new high-quality cold-resistant grape varieties has been provided.

1. Introduction

The climate zone with the most grape growth is the temperate climate zone, among which the Mediterranean climate, due to its high compatibility with the grape growth cycle, has become the most typical advantageous climate zone, supplemented by temperate continental climate and high-altitude cool regions, together forming the core area of global grape cultivation. As a temperate crop, vitis has high nutritional value and economic benefits, and its growth conditions require high light and suitable temperatures [1]. Low temperatures and cold weather in the winter affect the healthy development of the grape industry. Traditional soil burial and cold prevention techniques not only cause surface damage and soil wind erosion, but also increase labour costs, leading to a decline in market competitiveness [2,3]. Thus, hybrid breeding is a conventional method of grape breeding and is currently the main way to breed cold-resistant grapes. The cold resistance of grape plants mainly occurs in wild mountain grape varieties [4], Vitis riparia, Vitis labrusca, and the interspecific hybrid offspring of these grapes [5,6]. However, the fruit quality of interspecific hybridization is more inclined towards that of cold-resistant parents and is not suitable for large-scale promotion [7,8]. The V. vinifera intraspecific recurrent selection method uses high-quality grape as hybrid materials, fundamentally ensuring the fruit quality of the hybrid population. Although they exhibit low resistance or sensitivity to cold, there are multiple genes with slight cold resistance in V. vinifera, which can be stably inherited [9,10]. The V. vinifera intraspecific recurrent selection method can ensure the continuous improvement of cold resistance in breeding materials and the breeding of high-quality new, cold-resistant varieties while ensuring high-quality fruit quality.
The cold resistance of grapes significantly affects the content of bioactive compounds through mechanisms such as regulating osmoregulatory substances, antioxidant enzyme activity, and secondary metabolite synthesis [11]. For example, regulation can be achieved through indicators such as soluble sugars, proline, total protein, and peroxidase activity. The cold resistance of grapes is a quantitative trait controlled by the effects of multiple genes [12]. This type of gene is rapidly expressed in grape plants when they sense cold signals, improving their cold tolerance and regulation by multiple pathways [13]. Cold resistance functional genes regulate cold resistance by synthesising corresponding functional proteins, such as cold-regulated (COR) genes, plant antifreeze protein (AFP) genes, fatty acid desaturase (FAD) genes, and osmoregulation-related genes. The expression of the phospholipid-binding protein-encoding gene VvBAP1, VvERF117 in V. vinifera can increase the activity of antioxidant enzymes (SOD and POD) by regulating and controlling the soluble sugar content, thus enhancing the cold resistance of grapes [14,15]. The accumulation of ABA (abscisic acid) in woody tissue during grape cold acclimation upregulates the expression of the grape raffinose family gene VivRafS5, leading to the synthesis of raffinose [16]; the late embryo enriched protein (LEA) family gene VamDHN3 participates in the adaptive response of grape plants to cold and osmotic stress by increasing the stability of grape cell membranes [17]. Several types of transcription factors, including WRKY family transcription factors, MYB family transcription factors [18], and bHLH-type transcription factors, have been isolated and shown to participate in plant responses to cold stress [19,20,21]. The overexpression of the transcription factor genes mentioned above can activate the expression of downstream genes and improve plant cold resistance. Low temperature can promote the release of ethylene (ET), and endogenous ET can increase the cold resistance of grapes. The ERF transcription factor VaERF057 is located downstream of the ET signalling pathway and is considered a positive regulator of cold tolerance [22]. This study focuses on the micro effect cold resistance genes of V. vinifera hybrid offspring under natural conditions, while young leaf tissue cells undergo vigorous cell division and are in a rapid growth stage, with active gene expression and high integrity. Therefore, selecting young tissues can help to fully screen out all micro effect cold-resistant genes.
This study used the high-quality hybrid varieties “Ecolly” and “Dunkelfelder” of V. vinifera as breeding intermediate materials, and cultivated new varieties with high cold resistance through intraspecific recurrent selection. Based on transcriptome analysis of young leaves of high/low resistance plants under natural conditions, we systematically analyse the differential expression patterns of trace cold resistance genes in V. vinifera. This study provides molecular theoretical support for the intraspecific genetic improvement of V. vinifera through transcriptomics technology and genetic analysis, overcoming the quality trade-off limitations of traditional hybrid breeding and having significant industrial application value.

2. Materials and Methods

2.1. Experimental Materials

The hybrid progeny plants of the high-quality variety Ecolly (ECL) and Dunkelfelder (DKF) (35 ECL × DKF, 35 individual plants in total) were planted in the vineyard of Century Chateau in Yangling (34° N, 108° E), using a single hedge frame, single stem, double arm shaping mode, with a row spacing of 1.0 m × 2.5 m, and cultivated in the open field. The soil in the Yangling area is fertile, consisting of loess soil with a sticky texture and deep layers, suitable for the growth of various crops. Plants grow in the same soil, irrigation, pruning, and disease control management conditions. All plants receive unified management: drip irrigation (twice a week), winter short shoot pruning.

2.2. Evaluation and Classification of Cold Resistance

2.2.1. Determination of the Semilethal Temperature of Dormant Winter Buds of Grape Plants

Kaya & Köse’s method was used, with a modified cooling rate and extreme temperature [23]. Differential thermal analysis was carried out on the winter buds of dormant branches in an alternating high and low-temperature hygrothermal test chamber (model: YSGJS-408; Shanghai Lanhao Instrument and Equipment Co., Ltd., Shanghai, China); at a constant cooling rate of 10 °C·h−1, the cooling temperature started at 4 °C and ended at −26 °C. A 48-channel paperless recorder (model: R8000, Anhui Jujie Technology Co., Ltd., Wuhu, China) was used to record the cooling curve. As the temperature decreases, two peaks appear in the cooling curve, namely, high temperature exothermic and low temperature exothermic peaks, corresponding to the high temperature exotherm (HTE). The HTE of winter buds is related to the first freezing or non-lethal extracellular freezing. mHTE or HTE50 is used to represent the high-temperature semi-lethal temperature, which is the median of the high-temperature supercooling point generated during the high-temperature heat release process.

2.2.2. Classification of the Chilling Resistance of Dormant Winter Buds

The affiliation function method was used to calculate the affiliation values of winter bud injury for the test materials:
S V i j = 1 − x i j − x j   m i n x j   m a x − x j   m i n
In the formula, SVij is the subordinate function value of cold resistance, i represents the plant number of a variety or hybrid progeny population, j represents the corresponding temperature in a population number of branches subjected to low temperature semilethal conditions, xj max is the maximum, and xj min is the minimum of the numbered low temperature semilethal conditions in the population. The arithmetic mean of the two years was taken as the average membership degree of the winter buds of the parents or offspring. The cold hardiness of the grape germplasm was graded according to the subordinate degree of cold hardiness [24].
Sensitivity: 0.00 ≤ SV ≤ 0.29 (S)
Low Resistance: 0.30 ≤ SV ≤ 0.39 (LR)
Middle Resistance: 0.40 ≤ SV ≤ 0.59 (MR)
Resistance: 0.60 ≤ SV ≤ 0.69 (R)
High Resistance: 0.70 ≤ SV ≤ 1.00 (HR)

2.3. Transcriptome Material

Based on the results of the identification of cold resistance traits, two highly resistant and two sensitive plants were selected as biological replicates, and young leaf tissues were collected in the natural state for transcriptome sequencing to screen for cold resistance-related genes.

2.4. Transcriptome Sequencing and Data Analysis

High-cold-resistant plants S1022 and S1024 were selected from the hybrid population, denoted as Group C, and sensitive plants S1023 and S1035 were simultaneously selected as control groups, denoted as Group D. Candidate genes related to cold resistance in grape plants were explored.
In May 2023, the apical meristem of S1022, S1023, S1024, and S1035 individual plants was wrapped in tin foil and frozen in liquid nitrogen for 1 h, after which they were placed in a foam box prefilled with dry ice and mailed to Wuhan Feisha Genetic Information Co., Ltd Wuhan, China. In June 2023, the company completed RNA extraction and sequencing of the samples.
FASTQ was used to evaluate the quality of the RNA-Seq libraries. The raw reads were filtered by SOAPnuke software v2.1.0 (https://scicrunch.org/resolver/RRID:SCR_015025, (accessed on 8 December 2025)) to obtain clean reads. Using HISAT2 v2.2.1 (https://daehwankimlab.github.io/hisat2/, (accessed on 8 December 2025)), the clean reads were compared with the high-quality grape reference genome. Gene expression levels were quantified using RSEM with default parameters. DEGs were identified using the DESeq2 package (v1.46.0) in R. DEGs were determined with adjusted p values < 0.05 and |log2fc| ≥ 1.
GO enrichment analysis of the genes in the gene set was carried out by using Goatools software v1.0.0 (https://geneontology.org/, (accessed on 8 December 2025)), and the GO functions of the genes in the gene set were obtained. Using Fisher’s exact test, the GO function was considered to be significantly enriched when the adjusted p value (p adjust) was <0.05. The Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for biochemical pathway annotation and enrichment analysis. When the adjusted p value (p adjust) was <0.05, the KEGG pathway function was considered to be significantly enriched.

2.5. Data Processing

Microsoft Excel 2021 was used to record, arrange, and analyse the original data, SPSS 22.0 was used to analyse the significance, and Origin Pro 2021 was used to construct a histogram of the frequency distribution and fit the normal curve.

3. Results

3.1. Evaluation and Classification of Winter Bud Cold Resistance

Genetic analyses of the hybrid parents ECL and DKF, as well as the cross progeny populations, using differential thermal analysis (DTA) in two consecutive overwintering seasons, revealed that there was no significant difference in cold tolerance between the hybrid parents ECL and DKF across different years (Figure 1A). The mHTE values of the winter buds of the ECL were lower than those of the DKF in both overwintering seasons. Genetic analysis of the winter bud mHTE values of the hybrid populations during the two overwintering seasons revealed that the winter bud mHTE values of the progeny populations of the hybrid combinations showed a skewed normal distribution, which was characterised by the inheritance of quantitative traits under the control of multiple genes (Figure 1B,C). The mHTE values of this population ranged from −14 to −7 °C, and the cold resistance class was categorised into five classes, namely, high resistance, resistance, moderate resistance, low resistance, and sensitivity based on the average affiliation value. Therefore, in the subsequent transcriptomics experiments, representative high-resistance (Group C) and sensitivity (Group D) plant samples were selected for analysis (Table 1).

3.2. Transcriptome Sequencing Data Analysis

The results of electrophoresis of 4 treated grape samples (Group C and Group D) revealed that the RNA of all the samples was intact and could be used in follow-up experiments. As shown in Table 2, clean data for all the samples were obtained from the cDNA library, with more than 7G of clean data per sample. The proportions of Q20 and Q30 bases were more than 96.8 and 90%, respectively, and the GC content was between 45 and 46%. A large number of FM indices were used to cover the entire genome, and the reads from RNA-seq were quickly and accurately compared with those from the genome by HISAT2. Moreover, the proportion of the sequenced reads that were successfully aligned to the genome was greater than 87%. To determine the reliability of the samples, correlation analysis of the biological replicates of each group was carried out. The results revealed that the correlations between the replicates were all R2 ≥ 98%, which further indicated that the transcriptome sequencing quality was high and that the samples were reliable and available for follow-up analysis.

3.3. Overall Analysis of Differentially Expressed Genes

To assess the overall quality of the RNA-seq data to further understand the differences in the transcriptome between the high cold resistance group (Group C) and the control group (Group D) of V. vinifera, we analysed the distribution and characteristics of the expression of FPKM across samples (Figure 2A) and found that more than 50% of the genes in all the samples had log10(FPKM) values between 0 and 2, with the mean log10(FPKM) at positions of approximately 1. The genes in the cold resistance classes were analysed for both upregulated and downregulated DEGs overall, with a log2fold change > 1, a log2fold change < −1, and an FDR < 0.05. In this study, the transcription samples were compared, and the differences in gene expression among grape plants with different resistance levels were determined. In total, 38 genes were differentially expressed between Group C and Group D, with 7 upregulated and 31 downregulated genes (Figure 2B). The volcano plot shows the distribution of upregulated and downregulated genes (Figure 2C).

3.4. Differential Gene Analysis of Different Plant Samples

3.4.1. GO Enrichment Analysis

To determine the function of the differentially expressed genes, we performed GO enrichment analysis on the DEGs of the transcriptome samples from plants with differential cold tolerance (Figure 3A), which were distributed into three major categories: biological processes, cellular components, and molecular functions. In general, GO terms were most abundant in biological processes, followed by molecular functions and cellular components. These results suggest that the genes involved in the regulation of cold hardiness in V. vinifera are involved mainly in biological processes and molecular functions, and are not strongly correlated with cell classification. In the classification of biological processes, the differentially expressed genes were enriched mainly in 5 GO terms: cellular process, metabolic process, biological regulation and stimulus response, followed by developmental processes, multicellular biological processes, and reproductive processes. In the category of cellular components, differentially expressed genes are associated only with anatomical entities of the cell. In the molecular function categories, the differentially expressed genes were enriched mainly in the binding and catalytic activities of GO terms but were less enriched in transcriptional regulatory activity and transporter activity.
Further analysis of the top 20 FDR values enriched in the GO term for both groups (Figure 3B) revealed that the 5 pathways with the greatest numbers of differentially expressed genes were aromaticity biosynthesis (GO: 0019438), organic cyclic compound biosynthesis (GO: 1901362), transcriptional cis-regulatory region binding (GO: 000976), transcriptional regulatory region nucleic acid binding (GO: 0001067), sequence-specific double-stranded DNA binding (GO: 1990837), double-stranded DNA binding (GO: 0003690) and sequence-specific DNA binding (GO: 0043565). The 5 pathways with the greatest differences were sequence-specific DNA binding in the cis regulatory region of RNA polymerase II (GO: 0000978), sequence-specific DNA binding in the cis regulatory region of RNA polymerase II (GO: 0000987), sequence-specific DNA binding in the transcription regulatory region of RNA polymerase II (GO: 0000977), sequence-specific DNA binding in the transcription regulatory region of RNA polymerase II (GO: 000976) and DNA binding transcription factor activity, and RNA polymerase II-specific (GO: 000981). These results indicated that the cold hardiness gene of V. vinifera might be related to a DNA-binding pathway specific to the RNA polymerase II transcription regulatory region. In addition, RNA polymerase II (GO: 0006357), RNA polymerase II (GO: 0006366), flower development (GO: 0009908), meristems (GO: 0010022), and flower meristems (GO: 0010582) were also enriched.

3.4.2. KEGG Enrichment Analysis

KEGG enrichment analysis of the DEGs from both groups of plants (Figure 4A) revealed codistribution in two categories, including cellular processes and metabolic pathways. The genes associated with cellular processes were enriched only in the transport and catabolic pathways. Metabolic pathways included genes related to lipid metabolism, terpenoids and polyketides metabolism, secondary metabolite biosynthesis, carbohydrate metabolism, amino acid metabolism, cofactors and vitamins metabolism, and metabolic pathways involving other amino acids. KEGG functional enrichment analysis was performed for the two comparison groups, the FDR values were calculated by a test of assumptions, and the top 20 FDR values were analysed for significant levels of pathway enrichment (Figure 4B). Pathways were enriched mainly in the stilbenoid, diarylheptanoid, and gingerol biosynthesis, flavonoid biosynthesis, glycerophospholipid metabolism, and phenylpropanoid biosynthesis pathways.
There are 38 annotated genes in Groups C and D, which encode spermidine hydroxycinnamoyl transferase, 1-deoxy-D-ketose-5-phosphate synthase 2, and gimerene D synthase. KEGG enrichment analysis of the differentially expressed genes (Table 3) revealed that they were enriched mainly in 8 pathways related to the effects of secondary metabolites, lipid metabolism, terpene and polyketone metabolism, and carbohydrate and amino acid transport and catabolism.

3.5. Expression Analysis of Genes Related to Cold Resistance in Grape

Through natural selection and its own genetic variation, plants can adapt long-term to low-temperature environments to obtain cold resistance. The cold resistance of V. vinifera is distributed continuously and controlled by multiple genes. The difference in cold resistance between different plants may be controlled by minor genes; to compare the two groups of plants with different levels of cold resistance, we carried out GO enrichment and KEGG enrichment analyses and screened several different genes. Based on these differences, we analysed the expression of genes related to secondary metabolism, lipid metabolism, amino acid metabolism, carbohydrate metabolism, and transcription factors in a V. vinifera intraspecific hybrid population.

3.5.1. Anabolism of Secondary Metabolites

Among the genes related to secondary metabolite anabolism, the expression level of Group C was significantly greater than that of Group D (Figure 5A), and the expression level of the CYP76F14 gene in S1022 was approximately twice that of S1024. Dxs and GERD genes were also expressed differently between the two groups, and Dxs was hardly expressed in Group D. The expression level of the GERD gene in S1022 was 6.3-fold higher than that in S1024, while Group D displayed inverted expression patterns. The VIT_00023653001 and VIT_00023651001 genes, whose genes simultaneously regulate flavonoid biosynthesis, diphenylethene, diarylheptane, and gingerol biosynthesis and phenylpropanoid biosynthesis, were expressed only in Group D, and the expression level of S1023 was significantly greater than that of S1035.

3.5.2. Lipid Anabolism

Among the genes related to lipid metabolism, three were differentially expressed (Figure 5B). The expression of genes encoding NMT exhibited 3.2× higher expression in Group C, and the expression of genes encoding GDE1 showed 2.8× higher expression in Group D; genes encoding ACOX1/ACOX3 were expressed only in Group D. Among the genes related to cofactor and vitamin metabolism, only the Dxs gene was differentially expressed, and only trace expression was detected in Group D.

3.5.3. Carbohydrate and Amino Acid Metabolism

In this study, carbohydrate metabolism genes displayed distinct patterns. We identified two differentially expressed genes related to carbohydrate anabolism, and VIT_00018579001 coregulated carbohydrate anabolism and amino acid metabolism. Overall, compared with that in Group C, VIT_00018579001 gene expression in Group D was significantly greater, and almost no expression was detected in Group C (Figure 5C). The GLGC gene expression level in Group C was greater than that in Group D, and the S1022 gene expression level was 2-fold greater than that of S1024. The expression level of DHQ-SDH in S1024 was 3.2× higher than in S1022, with Group C showing 4.8× higher levels than Group D.

3.5.4. Transcription Factors

The transcription factors identified in this study included members of the MYB, HB, and MADS families, and the transcription factors of these families were analysed. A total of 1 MYB transcription factor was detected (Figure 5D). Compared with those in Group D, the expression levels of MYB transcription factor genes in the two samples in Group C were significantly greater. Three transcription factors (VIT_00012250001, VIT_00018450001, and VIT_00036549001, named VvMADS-1, VvMADS-2, and VvMADS-3) were detected in the Mads family. Three transcription factors (VIT_00009273001, VIT_00031241001, and VIT_00004811001, named VvHB-1, VvHB-2, and VvHB-3) were also detected in the two comparison groups, and no expression of the VvHB-1 and VvHB-3 genes was detected in Group D; moreover, HB-2 was only slightly expressed.

3.6. Candidate Gene Expression Analysis: Quantitative Real-Time Polymerase Chain Reaction

To test the reliability of the data, six differentially expressed genes located within the localization interval were selected for quantitative real-time polymerase chain reaction (RT-qPCR), and the results are shown in Figure 6. The trends of the RNA-seq data and the RT-qPCR results were essentially the same, indicating that the sequencing data were reliable.

4. Discussion

4.1. Evaluation of Cold Resistance in Hybrid Population

Winter freezing injury is an important factor that restricts the growth of grapes in northern China, which causes decreases in grape yield and quality and even economic losses for fruit farmers [25]. Therefore, selecting high-quality and cold-resistant grape varieties is very important for the development of the grape industry in northern China. Wang evaluated the cold hardiness of V. vinifera hybrid germplasms and reported that Chinese grape-growing areas can be divided into 4 cold hardiness regions according to the lethal temperature (LT50) and that the LT50 values of different germplasm resources in the same production area differ [26]. The cold resistance of hybrid offspring of different varieties is isolated, and there is a phenomenon of super-parental inheritance, which is a quantitative trait controlled by multiple genes [27,28]. The cold resistance traits of V. vinifera have quantitative genetic characteristics. In this study, the mHTE of the progenies of V. vinifera intraspecific hybrids was determined; the cold hardiness of the hybrid population had a continuous partial normal distribution, further verifying the genetic characteristics of cold resistance in V. vinifera.

4.2. Cold Resistance Gene and Metabolites

Cold-resistant genes affect the physicochemical parameters of grapes through multidimensional regulatory mechanisms [29]. Firstly, cold-resistant genes promote the breakdown of starch into soluble sugars such as sucrose, glucose, and fructose, lowering the freezing point of cells, maintaining osmotic pressure, and enhancing the cell’s ability to resist dehydration [30]. Secondly, cold-resistant genes increase soluble protein, proline content, and antioxidant enzyme (SOD, POD, CAT) activity [31], reduce membrane lipid peroxidation (such as malondialdehyde accumulation), and maintain membrane structural integrity. Cold-resistant genes regulate water metabolism, increase bound water content, reduce free water, and prevent cell freezing. In addition, cold-resistant genes can activate the antioxidant enzyme system in low-temperature environments [32], eliminate ROS such as superoxide radicals and hydroxyl radicals, and reduce membrane lipid peroxidation. In summary, cold-resistant genes comprehensively affect the physicochemical parameters of grapes (such as sugar content, membrane permeability, antioxidant enzyme activity, water status, etc.) through multidimensional mechanisms such as sugar accumulation, cell membrane stability, antioxidant system activation, secondary metabolite accumulation, and hormone signal regulation, ultimately enhancing their cold resistance.

4.2.1. Cold Resistance and Secondary Metabolites

Plants can defend against cold through a multilayered defence strategy in which secondary metabolites strongly affect the cold resistance response of plants [33,34]. Stilbene is a class of plant phenolic secondary metabolites, among which trans resveratrol (3,5,4′-trihydroxytrans stilbene) is considered the most prominent and studied member. Resveratrol, as a stilbeneid bioactive compound, is a key enzyme complex highly dependent on the phenylpropanoid metabolic pathway, including phenylalanine ammonia lyase (PAL), cinnamic acid-4-hydroxylase (C4H), 4-coumaric acid CoA ligase (4CL), and stilbene synthase (STS). In addition, Vitis Myb14 plays a key regulatory role in grape stress response, helping to improve defence against necrotic pathogens, enhance the production of plant antitoxin resveratrol, and improve drought or cold resistance [35]. The release of secondary metabolite terpenoids can be induced by conditioned stresses such as light and low temperature, leading to plant defence responses and plant–plant interactions [36]. These compounds have a wide range of functions in plant responses to cold and are important components of the aroma of wine [37]. Among them, (E)-8-carboxylinalool, which is catalysed by the multifunctional single-titritol oxidase CYP76F14, is a key precursor of wine aroma [38,39]. With respect to the carotenoid synthesis pathway of terpenoid Dxs in different colours of mature pepper fruit, Berry revealed a correlation between carotenoid content and Dxs transcriptional levels [40]. Simpson and Kevin studied the major flow control steps in the methyl erythritol 4-phosphate (MEP) pathway and reported that increased Dxs expression levels result in increased abundance of gene transcripts for major rate-determining enzymes in the carotenoid pathway [41,42]. Although these studies did not explain the direct relationship between CYP76F14 and Dxs genes and cold resistance, their interactions with grape aroma and carotenoids may indirectly affect grape cold resistance. In this study, the expression of the CYP76F14 and Dxs genes differed between the C and D groups, and the expression level in Group C was significantly greater than that in Group D; therefore, these two genes can be regarded as key candidate genes for improving the cold resistance of V. vinifera.

4.2.2. Cold Resistance and Lipid Metabolism

Low temperature is a key environmental stress factor that restricts plant growth and significantly affects quality and yield [43,44]. To relieve cold stress, a series of physiological and biochemical, and complex molecular regulatory mechanisms occur in plants [45]. The importance of the plant membrane and stored lipid levels for improving the cold resistance and cold germination of seeds has been extensively studied in a range of plant species, and stored lipid components are energy sources for plant growth [46]. In a study of the regulatory mechanism of cold resistance in gramineous plants, lipids and lipid molecules were found to be the most important metabolites, accounting for 14.26% of the total metabolites [47]. NMT and GDE1 genes showed differential expression in Group C and Group D, and no relevant reports have been found before. This gene may enhance cold resistance by enhancing membrane lipid stability. The peroxidase (ACOX) gene encoding acyl-COA oxidase was characterised by Joo [48]. The results revealed that this gene is a key rate-limiting enzyme for the fatty acid metabolism of domperidone and that fatty acid oxidation is significantly promoted by the upregulation of ACOX1 expression [49,50]. In this study, ACOX1/ACOX3 were expressed only in Group D but not in Group C, which was consistent with the findings of previous studies. The expression of ACOX1/ACOX3 decreased the synthesis of lipids, which affected the cold resistance of the plants.

4.2.3. Cold Resistance and Carbohydrates

By stimulating photosynthesis and carbohydrate metabolism, plants improve winter viability [51], and soluble carbohydrates play an important role in improving plant cold hardiness [52]. Mutants with significant cold resistance exhibit increased carbohydrate biosynthesis. Cryoprotective effect of soluble carbohydrates at lower temperatures, with increased carbohydrate concentrations at cold-resistant sites in plants [53]. 3-Dehydroquinate dehydratase/shikimate dehydrogenase (DHQ-SDH) is a gene whose expression is upregulated in higher plants under low-temperature conditions [54]. In this study, the expression level of the DHQ-SDH gene in the cold-resistant group was significantly greater than that in the sensitive group, indicating that this gene could help plants form self-protection under cold stress. ADP glucose pyrophosphatase (GLGC) is highly expressed in the starch synthesis pathway in rice seeds [55], and its overexpression in cassava roots increases starch accumulation and its subsequent hydrolysis to sugars [56,57]. In this study, GLGC expression differed significantly between the two comparison groups: that in Group C was significantly greater than that in Group D, indicating that this gene may be a candidate gene for cold resistance in V. vinifera.

4.2.4. Cold Resistance and Transcription Factor Regulation

Transcription factors are major regulators that control plant responses to external stimuli and the ability of plants to evolve resistance after exposure to harsh environments [58]. MYB transcription factors play important roles in abiotic tolerance in plants [59], and reducing the accumulation of reactive oxygen species in turn increases cold resistance [60,61,62]. Gene regulation is determined by the combination of transcriptional regulators present on specific cis-regulatory elements at specific times [63], and MADS family transcription factors play important roles in regulating plant growth and signal transduction [64]. The overexpression of MADS in Arabidopsis thaliana enhances adaptation to abiotic stresses (drought and low temperature) [65], and the changes in the expression of MADS transcription factors in winter wheat from high-altitude areas are similar to those in cold-resistant plants [66], but have not been reported in grapes. In this study, 3 transcription factor family genes were identified and can be used as candidate genes for exploring cold resistance in V. vinifera.

5. Conclusions

Based on the above results, gene enrichment was carried out mainly with the following GO terms: cellular process, metabolic process, biological regulation, stimulus response, etc. The results of the KEGG enrichment analysis revealed that lipid metabolism, metabolism of terpenes and polyketones, biosynthesis of other secondary metabolites, carbohydrate metabolism, metabolism of amino acids, metabolism of cofactors and vitamins, and metabolic pathways of other amino acids were the most common differential pathways of KEGG enrichment. We identified CYP76F14, Dxs, and GERD genes in the secondary metabolite synthesis pathway; NMT and GDE1 family-related genes in lipid metabolism; GLGC in carbohydrate synthesis; and DHQ-SDH related to amino acid metabolism. MYB, HB, and MADS genes were the key candidate genes for exploring cold resistance in V. vinifera.
Based on current transcriptome data, this study preliminarily screened 38 candidate DEGs. The differentially expressed genes can be used to develop molecular markers and validate the accumulation of substitution genes. However, there are still some limitations to the research results. For example, this study investigated the expression of cold-resistant genes in V. vinifera under natural conditions. In the future, gradient cold treatment experiments will be designed to dynamically monitor gene expression changes to verify the conclusions of this study. The original number of biological replicates was 2, and the number of DEGs was relatively small (only 38). In the future, at least 3 replicates will be used, and the DESeq2 likelihood ratio test model will be used to control dispersion. In this study, a genetic map was not constructed directly, but a combination of a cold-resistant phenotype and transcriptome analysis was performed to identify cold resistance genes. In future research, we will identify cold resistance and true or false hybrid progeny, construct a high-density genetic map, and combine the results with the results of the cold resistance phenotype and transcriptome analysis to continue mining cold resistance genes. We will systematically analyse the accumulation of minor genes during intraspecific hybridization in V. vinifera.

Author Contributions

Conceptualization, J.L., Z.W. and Y.L.; Data curation, J.L. and Z.W.; Funding acquisition, H.L., H.W.; Investigation, Z.W. and Y.L.; Methodology, J.L. and Z.W.; Project administration, H.L. and H.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Key Research and Development Project (grant number, 2019YFD1002500), Key Research and Development Project of Shaanxi Province (grant number, 2020ZDLNY07-08), Ningxia Hui Nationality Autonomous Region Major Research and Development Project, (grant number, 2020BCF01003).

Data Availability Statement

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

Conflicts of Interest

The authors declare no conflict of interest.

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Horticulturae 11 01538 g001
Figure 1. Cold resistance and genetic analysis of parents and hybrid offspring in 2021 and 2022. (A) Two consecutive years of mHTE in parents; (B) Normal distribution of cold resistance in 2021; (C) Normal distribution of cold resistance in 2022.
Figure 1. Cold resistance and genetic analysis of parents and hybrid offspring in 2021 and 2022. (A) Two consecutive years of mHTE in parents; (B) Normal distribution of cold resistance in 2021; (C) Normal distribution of cold resistance in 2022.
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Figure 2. FPKM distribution, gene expression characteristics, and up-regulated and down-regulated genes of each sample. (A) FPKM box plots of different transcripts; (B) Distribution map of upregulated and downregulated genes on the sample; (C) The number of upregulated and downregulated genes on both combinations.
Figure 2. FPKM distribution, gene expression characteristics, and up-regulated and down-regulated genes of each sample. (A) FPKM box plots of different transcripts; (B) Distribution map of upregulated and downregulated genes on the sample; (C) The number of upregulated and downregulated genes on both combinations.
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Figure 3. GO enrichment analysis of differentially expressed genes in different cold resistance combinations. (A) The histogram displays GO analysis significantly enriched in DEGs, divided into biological processes (red), cellular components (blue), and molecular functions (green); (B) Bubble chart, enrichment factors for various differentially expressed genes.
Figure 3. GO enrichment analysis of differentially expressed genes in different cold resistance combinations. (A) The histogram displays GO analysis significantly enriched in DEGs, divided into biological processes (red), cellular components (blue), and molecular functions (green); (B) Bubble chart, enrichment factors for various differentially expressed genes.
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Figure 4. KEGG enrichment of differentially expressed genes in two combinations. (A) A histogram displaying significant enrichment of KEGG, with red indicating cellular processes, blue indicating genetic information processing, and green indicating metabolic pathways; (B) Bubble plots of enrichment factors for various differentially expressed genes.
Figure 4. KEGG enrichment of differentially expressed genes in two combinations. (A) A histogram displaying significant enrichment of KEGG, with red indicating cellular processes, blue indicating genetic information processing, and green indicating metabolic pathways; (B) Bubble plots of enrichment factors for various differentially expressed genes.
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Figure 5. Expression levels of differentially expressed genes in different combinations. S1022 and S1024 represent Group C, S1023 and S1035 represent Group D. (A) Expression levels of secondary metabolite-related genes; (B) Expression levels of lipid anabolism-related genes; (C) Carbohydrate and amino acid-related gene expression amount; (D) Expression level of transcription factor-related genes.
Figure 5. Expression levels of differentially expressed genes in different combinations. S1022 and S1024 represent Group C, S1023 and S1035 represent Group D. (A) Expression levels of secondary metabolite-related genes; (B) Expression levels of lipid anabolism-related genes; (C) Carbohydrate and amino acid-related gene expression amount; (D) Expression level of transcription factor-related genes.
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Figure 6. RT-qPCR validation of differentially expressed genes. (A) RT-qPCR validation of gene CYP76F14; (B) RT-qPCR validation of gene Dxs; (C) RT-qPCR validation of gene GERD; (D) RT-qPCR validation of gene NMT; (E) RT-qPCR validation of gene GDE1; (F) RT-qPCR validation of gene MYB.
Figure 6. RT-qPCR validation of differentially expressed genes. (A) RT-qPCR validation of gene CYP76F14; (B) RT-qPCR validation of gene Dxs; (C) RT-qPCR validation of gene GERD; (D) RT-qPCR validation of gene NMT; (E) RT-qPCR validation of gene GDE1; (F) RT-qPCR validation of gene MYB.
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Table 1. Cold resistance indicators and grades of each sample for two consecutive years.
Table 1. Cold resistance indicators and grades of each sample for two consecutive years.
NO.20212022
mHTESVijLevelmHTESVijLevel
Group CS1022−13.360.81HR−12.780.75HR
S1024−14.430.89HR−14.320.88HR
Group DS1023−11.440.28S−11.160.26S
S1035−8.130.11S−8.310.13S
Table 2. Quality statistics of RNA sequences in each sample.
Table 2. Quality statistics of RNA sequences in each sample.
NameClean Reads PairsClean Base (Gp)LengthQ20 (%)Q30 (%)GC (%)Total Mapped Ratio%
S102226.147.84150; 15097.0; 97.391.1; 91.746.0; 46.087.38
S102329.748.92150; 15096.9; 97.090.9; 90.746.0; 45.989.44
S102435.5910.68150; 15096.8; 97.190.7; 91.145.8; 45.790.09
S103539.7111.91150; 15097.2; 97.491.5; 91.946.0; 45.992.20
Table 3. KEGG enrichment analysis of differentially expressed genes in various combinations.
Table 3. KEGG enrichment analysis of differentially expressed genes in various combinations.
ClassificationPathway IDPathwayGeneFunctional Annotations
Biosynthesis of other secondary metabolitesko00941Flavonoid biosynthesisVIT_00023653001HCT; Spermidine hydroxycinnamoyl transferase
VIT_00023651001HCT
ko00945Biosynthesis of dibenzene, diarylheptane, and gingerolVIT_00023653001HCT; Spermidine hydroxycinnamoyl transferase
VIT_00023651001HCT
ko00940Biosynthesis of phenylpropanoidVIT_00023653001HCT; Spermidine hydroxycinnamoyl transferase
VIT_00023651001HCT
Lipid metabolismko00564Glycerophospholipid metabolismVIT_00011715001NMT; Phosphate ethanolamine N-methyltransferase 3 subtype X2; Phosphate methylethanolamine N-methyltransferase isomer X1
VIT_00033033001GDE1
ko01040Biosynthesis of unsaturated fatty acidsVIT_00018579001ACOX1/ACOX3
Cofactors and vitamins metabolismko00730Thiamine metabolismVIT_00029109001Dxs; 1-deoxy-D-ketose-5-phosphate synthetase 2, chloroplast isomer X2
Terpenoids and polyketones metabolismko00902Monoterpenoid biosynthesisVIT_00019905001CYP76F14; Geraniol 8-hydroxylase
ko00900Terpenoid skeleton biosynthesisVIT_00029109001Dxs; 1-deoxy-D-ketose-5-phosphate synthetase 2, chloroplast isomer X2
ko00909Biosynthesis of sesquiterpenes and triterpenesVIT_00014175001GERD; (-)—Gemasene D Synthase
Amino acid metabolismko00400Biosynthesis of phenylalanine, tyrosine, and tryptophanVIT_00021979001Aro DE, DHQ-SDH
Carbohydrate metabolismko00640Propionic acid metabolismVIT_00018579001ACOX1/ACOX3
ko00592Alpha linolenic acid metabolism
ko00071Fatty acid degradation
ko00520Amino sugar and nucleotide sugar metabolismVIT_00023805001glgC
1-Phosphoglucosadenyltransferase
ko00500Starch and sucrose metabolism
Metabolism of other amino acidsko00410Beita—alanine metabolismVIT_00018579001ACOX1/ACOX3
Transportation and catabolismko04146Peroxisome
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Liu, J.; Li, Y.; Wang, Z.; Li, H.; Wang, H. Mining Minor Cold Resistance Genes in V. vinifera Based on Transcriptomics. Horticulturae 2025, 11, 1538. https://doi.org/10.3390/horticulturae11121538

AMA Style

Liu J, Li Y, Wang Z, Li H, Wang H. Mining Minor Cold Resistance Genes in V. vinifera Based on Transcriptomics. Horticulturae. 2025; 11(12):1538. https://doi.org/10.3390/horticulturae11121538

Chicago/Turabian Style

Liu, Junli, Yihan Li, Zhilei Wang, Hua Li, and Hua Wang. 2025. "Mining Minor Cold Resistance Genes in V. vinifera Based on Transcriptomics" Horticulturae 11, no. 12: 1538. https://doi.org/10.3390/horticulturae11121538

APA Style

Liu, J., Li, Y., Wang, Z., Li, H., & Wang, H. (2025). Mining Minor Cold Resistance Genes in V. vinifera Based on Transcriptomics. Horticulturae, 11(12), 1538. https://doi.org/10.3390/horticulturae11121538

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