Mining minor cold-resistant genes in V. vinifera based on transcriptomics

doi:10.21203/rs.3.rs-5012434/v1

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Research Article

Mining minor cold-resistant genes in V. vinifera based on transcriptomics

https://doi.org/10.21203/rs.3.rs-5012434/v1

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Background

Cold resistance is an important characteristic of sustainable development in the grape industry. Analyzing cold resistance genes provides molecular theoretical support for high-quality cold resistance breeding through cross breeding between grape varieties. The intraspecific recurrent selection in Vitis vinifera (V. vinifera) method uses high-quality varieties as breeding materials, and utilizes the substitution and accumulation of minor resistance genes, which is an effective method for high-quality grape disease resistance breeding.

Results

This study aimed to identify and genetically analyze the cold resistance of the V. viniferahybrid population (Ecolly x 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 showed that the cold resistance of the hybrid offspring population was a continuous quantitative trait inheritance, with 38 differentially expressed genes (7 upregulated genes and 31 downregulated genes) between the high resistance and sensitive types. GO enrichment analysis showed that differential genes were mainly enriched in the biosynthesis process of aromatic compounds, organic cyclic compounds, transcription cis regulatory region binding, transcription regulatory region nucleic acid binding, sequence specific double stranded DNA binding, double stranded DNA binding, and sequence specific DNA binding. KEGG analysis revealed differentially expressed genes, with pathways mainly enriched in the biosynthesis pathways of hexene, diarylheptanoid and gingerol, flavonoid biosynthesis, glycerophospholipid metabolism, and phenylpropanoid biosynthesis.

Conclusion

Through the analysis of cold resistance related genes in various pathways, it was found that the cold resistance genes of V. vinifera were mainly related to secondary metabolites, lipid, carbohydrate, amino acid synthesis metabolism, and transcription factor regulation.

Cold resistance

Minor gene

Transcriptome

Vitis vinifera

Intraspecific hybridization

As a temperate crop, vitis have high nutritional value and economic benefits, and their growth conditions require high light and suitable temperature [1]. The low temperature and cold weather in 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 labor costs, leading to a decline in market competitiveness [2, 3]. Different grape varieties have different cold tolerance, so hybrid breeding is a conventional method of grape breeding, and it is also the main way of breeding cold resistant grapes at present. The cold resistance of grape plants mainly exists in wild mountain grape varieties [4], Vitis riparia, Vitis labrusca, and the interspecific hybrid offspring of these grapes [5]. However, the traditional breeding method for cold resistance mainly involves simple interspecific hybridization, which neglects the quality improvement of hybrid offspring. The fruit quality of interspecific hybridization is more inclined towards cold resistant parents and is not suitable for large-scale promotion [6, 7]. The fruit quality of V. vinifera is excellent. Although they exhibit low resistance or sensitivity to cold, there are differences in cold resistance among varieties. The reason for this difference is the existence of multiple genes with slight cold resistance in Eurasian grape varieties, which can be stably inherited [8, 9]. The use of Vitis. vinifera (V. vinifera) intraspecific recurrent selection method can ensure continuous improvement of cold resistance in breeding materials and the breeding of high-quality cold resistant new varieties while ensuring high-quality fruit quality.

The cold resistance of grapes is comprehensively constrained by their own structure and external conditions, and is a quantitative trait controlled by micro effect multi genes [10]. This type of gene is rapidly expressed in grape plants when they sense cold signals, improving their cold tolerance and being regulated by multiple pathways [11]. Cold resistance functional genes regulate cold resistance by synthesizing corresponding functional proteins, such as cold regulated genes (COR), plant antifreeze protein (AFP) genes, fatty acid desaturase (FAD) genes, and osmoregulation related genes. The phospholipid binding protein gene VvBAP1 in Eurasian grapes can enhance the activity of antioxidant enzymes (SOD and POD) by regulating and controlling soluble sugar content, thereby enhancing the cold resistance of grapes [12]. The accumulation of ABA in woody tissue during grape cold acclimation upregulates the grape raffinose family gene VivRafS5, leading to the synthesis of raffinose [13]; Overexpressed late embryo enriched protein (LEA) family gene VamDHN3 participates in the adaptive response of grapes to cold and osmotic stress by enhancing the stability of grape cell membranes [14]. Several types of transcription factors have been isolated and identified to participate in plant responses to cold stress, including WRKY family transcription factors, MYB family transcription factors, bHLH type transcription factors [15–17]. 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 enhance the cold resistance of grapes. The ERF transcription factor VaERF057 is located downstream of the ET signaling pathway and is considered a positive regulator of cold tolerance [18].

In order to fully utilize the micro effective cold resistance multi genes present in V. vinifera and select high-quality cold resistance new varieties, we used high-quality V. vinifera hybrid varieties "Ecolly" and "Dunkelfelder" as breeding intermediate materials to continue intra specific hybridization, and identified and genetically analyzed the cold resistance of the hybrid offspring. Selecting plants with high cold resistance and sensitivity under natural conditions for transcriptomic analysis, starting from the synthesis and metabolism of secondary metabolites, lipids, carbohydrates, and amino acids, as well as transcription factors, to explore the expression differences of micro effective cold resistance genes in V. vinifera, providing molecular theoretical references for the selection of high-quality cold resistance varieties through V. vinifera intraspecific recurrent selection.

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 the results of differential thermal analysis (DTA) in two consecutive overwintering seasons, showed that there was no significant difference in cold tolerance between the hybrid parents ECL and DKF between different years (Fig. 1A). The mHTE values of the winter buds of ECL were lower than those of DKF in both overwintering seasons. Genetic analysis of the winter bud mHTE values of the hybrid populations in the two overwintering seasons showed that the winter bud mHTE values of the progeny populations of the hybrid combinations showed a skewed normal distribution, which was characterized by the inheritance of quantitative traits under the control of multiple genes (Fig. 1B, 1C). The mHTE values of this population were distributed from − 14℃ to -7℃, and the cold resistance class was determined to be in five classes of high resistance, resistance, middle resistance, low resistance and sensitivity based on the average affiliation value. Therefore, in the subsequent transcriptomics experiments, representative plant samples were selected for analysis in the two classes of high resistance (Group C) and sensitivity (Group D), respectively (Table 1).

Table 1

Cold resistance indicators and grades of each sample for two consecutive years
	NO.	2021			2022
	NO.	mHTE	SVij	Level	mHTE	SVij	Level
Group C	S1022	-13.36	0.81	HR	-12.78	0.75	HR
Group C	S1024	-14.43	0.89	HR	-14.32	0.88	HR
Group D	S1023	-11.44	0.28	S	-11.16	0.26	S
Group D	S1035	-8.13	0.11	S	-8.31	0.13	S

Transcriptome sequencing data analysis

The result of electrophoresis of 4 grape treated samples (Group C, Group D) showed that the RNA of all samples was intact, which could be used in follow-up experiments. As shown in the Table 2, clean data for all samples was obtained from the cDNA library, with clean data greater than 7G per sample. The proportion 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 indexes were used to cover the whole genome, and the reads from RNA-seq were quickly and accurately compared with the genome by HISAT2, it was found that the proportion of the sequenced reads successfully aligned to the genome was higher than 87%. In order to determine the reliability of the samples, the correlation analysis of the biological replicates of each group was carried out. The results showed that the correlations between the replicates were all R²≥98%, which further indicated that the transcriptome sequencing quality was high and the samples were reliable, available for follow-up analysis.

Table 2

Quality statistics of RNA sequences in each sample
Name	Clean Reads Pairs	Clean base (Gp)	Length	Q20 (%)	Q30 (%)	GC (%)	Total mapped ratio%
S1022	26.14	7.84	150;150	97.0;97.3	91.1;91.7	46.0;46.0	87.38
S1023	29.74	8.92	150;150	96.9;97.0	90.9;90.7	46.0;45.9	89.44
S1024	35.59	10.68	150;150	96.8;97.1	90.7;91.1	45.8;45.7	90.09
S1035	39.71	11.91	150;150	97.2;97.4	91.5;91.9	46.0;45.9	92.20

Overall analysis of differential genes

For the overall quality assessment of RNA-seq to further understand the transcriptome differences between the high cold resistance group (Group C) and the control group (Group D) of V. vinifera, we performed the distribution and characteristics of the expression quantities FPKM across samples were also analyzed (Fig. 2A), and it was found that more than 50% of the genes in all samples had log10(FPKM) concentrated between 0 and 2, with the mean log10(FPKM) at positions around 1. Gene cold resistance classes were analyzed for both upregulated and downregulated DEGs overall, with log2fold-change > 1 and log2fold-change < -1, and FDR < 0.05. In this study, the transcription samples were compared and the differences of gene expression among grape plants with different resistance were presented. Between Group C and Group D, the total number of differential genes was 38, with 7 up-regulated and 31 down-regulated (Fig. 2B). The volcano plot shows the distribution of up-and down-regulated genes (Fig. 2C).

Differential gene analysis of different plant samples

GO enrichment analysis

To determine the function of the differentially expressed genes, we performed GO enrichment analysis on DEGs of transcriptome samples from plants with differential cold tolerance (Fig. 3A), and 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 least cellular components. These results suggest that the genes involved in the regulation of cold hardiness in V. vinifera are mainly involved in biological processes and molecular functions, and have little correlation with cell classification. In the classification of biological processes, the differential genes are mainly enriched in 5 GO terms: cellular process, metabolic process, biological regulation, biological regulation and stimulus response, the next are developmental processes, multicellular biological processes, reproductive and reproductive processes. In the category of cellular components, differential genes are associated only with anatomical entities of the cell. In the molecular functional categories, the differential genes were mainly enriched in the binding and catalytic activities of GO terms, but less enriched in transcriptional regulatory activity and transporter activity.

Further analysis of the top 20 FDR values enriched to GO term for both groups (Fig. 3B) showed that, the top 5 pathways with the highest number 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 first 5 pathways with the highest coefficient of difference were sequence-specific DNA binding in cis regulatory region of RNA polymerase II (GO: 0000978), sequence-specific DNA binding in cis regulatory region of RNA polymerase II (GO: 0000987), sequence-specific DNA binding in transcription regulatory region of RNA polymerase II (GO: 0000977), sequence-specific DNA binding in transcription regulatory region of RNA polymerase II (GO: 000976) and DNA binding transcription factor activity, RNA polymerase II-specific (GO: 000981). The results indicated that the cold hardiness gene of V. vinifera may be related to DNA binding pathway specific to RNA polymerase II transcription regulatory region. In addition, RNA polymerase II (GO: 0006357), RNA polymerase II (GO: 0006366), Flower Development (GO: 0009908), the identification of meristems (GO: 0010022), and the identification of flower meristems (GO: 0010582) were also enriched.

KEGG enrichment analysis

KEGG enrichment analysis of DEGs from both groups of plants (Fig. 4A) revealed co-distribution in two categories, including cellular processes and metabolic pathways. Cellular processes had differential genes only in the transport and catabolic pathways. Metabolic pathways had differential genes in lipid metabolism, metabolism of terpenoids and polyketides, biosynthesis of other secondary metabolites, carbohydrate metabolism, amino acid metabolism, metabolism of cofactors and vitamins, and metabolic pathways of other amino acids. KEGG functional enrichment analysis was performed for the two comparison groups, calculated to FDR values by a test of assumptions, and the top 20 FDR values were analyzed for significant levels of pathway enrichment (Fig. 4B). Pathways were mainly enriched in stilbenoid, diarylheptanoid and gingerol biosynthesis, flavonoid biosynthesis, glycerophospholipid metabolism and phenylpropanoid biosynthesis pathways.

There are 38 annotated genes in Group C and Group D, which encode spermidine hydroxycinnamoyl transferase, 1-deoxy-D-ketose-5-phosphate synthase 2, and gimerene D synthase and so on. KEGG enrichment analysis of the differential genes (Table 3) showed that they were mainly enriched in 8 pathways, effects of secondary metabolites, lipid metabolism, terpenes and polyketones metabolism, carbohydrate and amino acid transport and catabolism.

Table 3

KEGG enrichment analysis of differentially expressed genes in various combinations
Classification	Pathway ID	Pathway	Gene	Functional annotations
Biosynthesis of other secondary metabolites	ko00941	Flavonoid biosynthesis	VIT_00023653001	HCT;Spermidine hydroxycinnamoyl transferase
	ko00941	Flavonoid biosynthesis	VIT_00023651001	HCT
	ko00945	Biosynthesis of dibenzene, diarylheptane, and gingerol	VIT_00023653001	HCT;Spermidine hydroxycinnamoyl transferase
	ko00945	Biosynthesis of dibenzene, diarylheptane, and gingerol	VIT_00023651001	HCT
	ko00940	Biosynthesis of phenylpropanoid	VIT_00023653001	HCT;Spermidine hydroxycinnamoyl transferase
	ko00940	Biosynthesis of phenylpropanoid	VIT_00023651001	HCT
Lipid metabolism	ko00564	Glycerophospholipid metabolism	VIT_00011715001	NMT;Phosphate ethanolamine N-methyltransferase 3 subtype X2; Phosphate methylethanolamine N-methyltransferase isomer X1
	ko00564	Glycerophospholipid metabolism	VIT_00033033001	GDE1
	ko01040	Biosynthesis of unsaturated fatty acids	VIT_00018579001	ACOX1;ACOX3
Cofactors and vitamins metabolism	ko00730	Thiamine metabolism	VIT_00029109001	Dxs;1-deoxy-D-ketose-5-phosphate synthetase 2, chloroplast isomer X2
Terpenoids and polyketones metabolism	ko00902	Monoterpenoid biosynthesis	VIT_00019905001	CYP76F14;Geraniol 8-hydroxylase
	ko00900	Terpenoid skeleton biosynthesis	VIT_00029109001	Dxs;1-deoxy-D-ketose-5-phosphate synthetase 2, chloroplast isomer X2
	ko00909	Biosynthesis of sesquiterpenes and triterpenes	VIT_00014175001	GERD;(-) - Gemasene D Synthase
Amino acid metabolism	ko00400	Biosynthesis of phenylalanine, tyrosine, and tryptophan	VIT_00021979001	Aro DE, DHQ-SDH
Carbohydrate metabolism	ko00640	Propionic acid metabolism	VIT_00018579001	ACOX1;ACOX3
	ko00592	Alpha linolenic acid metabolism
	ko00071	Fatty acid degradation
	ko00520	Amino sugar and nucleotide sugar metabolism	VIT_00023805001	glgC 1-Phosphoglucosadenyltransferase
	ko00500	Starch and sucrose metabolism	VIT_00023805001	glgC 1-Phosphoglucosadenyltransferase
Metabolism of other amino acids	ko00410	Beita - alanine metabolism	VIT_00018579001	ACOX1;ACOX3
Transportation and catabolism	ko04146	Peroxisome	VIT_00018579001	ACOX1;ACOX3

Expression analysis of cold resistance-related genes in grape

Plant cold resistance is a long-term adaptation to low temperature environment process, through natural selection and its own genetic variation to obtain cold resistance. The cold resistance of V. vinifera is distributed continuously and controlled by multi-genes. The difference of cold resistance between different plants may be controlled by minor gene in the comparison between the two groups of plants with different cold resistance, we carried out GO enrichment and KEGG enrichment analysis, and screened a number of different genes. According to this difference, we will analyze the expression of cold resistance genes in V. vinifera intraspecific hybrid population from the aspects of secondary metabolites, lipid metabolism, amino acid metabolism, carbohydrate metabolism and transcription factors.

Anabolism of secondary metabolite

Among the genes related to secondary metabolite anabolism, the expression level of Group C was significantly higher than that of Group D (Fig. 5A), the expression level of CYP76F14 gene in Group C was about 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 GERD gene in Group C was 6.3-fold higher than that in S1024, the amount of GERD gene expression in Group D was 6.3-fold higher for S1035 than for S1023. The VIT_00023653001 and VIT_00023651001 genes, which simultaneously regulate flavonoid biosynthesis, diphenylethene, diarylheptane and gingerol biosynthesis and phenylpropanoid biosynthesis, were only expressed in Group D, the expression level of S1023 was significantly higher than that of S1035.

Lipid anabolism

Among the genes related to lipid metabolism, there were three differentially expressed genes (Fig. 5B). The gene encoding NMT was significantly higher in Group C than in Group D, and the gene encoding GDE1 was significantly higher in Group D than in Group C, genes encoding ACOX1; ACOX3 were only expressed in Group D. Among the genes related to cofactor and vitamin metabolism, only the Dxs gene showed differential expression, and only trace expression in Group D.

Carbohydrate and amino acid metabolism

In this study, we identified two differentially expressed genes related to carbohydrate anabolism, and VIT_00018579001 coworkers regulate carbohydrate anabolism and amino acid metabolism. Overall, VIT_00018579001 gene expression in Group D was significantly higher than that in Group C, and almost no expression in Group C (Fig. 5C). The GLGC gene expression level in Group C was higher than that in Group D, and the S1022 gene expression level was 2-fold higher than that of S1024. The expression level of DHQ-SDH in Group C was significantly higher than that in Group D, and the expression level of S1024 was 3.2-fold higher than that of S1022.

Transcription factors

The transcription factors identified in this study included MYB, HB and MADS families, and the transcription factors of these families were analyzed. A total of 1 MYB transcription factors were detected (Fig. 5D). The expression level of MYB transcription factor genes in two samples of Group C was significantly higher than that in Group D. Three transcription factors (VIT_00012250001, VIT_00018450001, VIT_00036549001 named MADS-1, MADS-2, MADS-3, respectively) were detected in the Mads family. Three transcription factors VIT_00009273001, VIT_00031241001 and VIT_00004811001 (named HB-1, HB-2 and HB-3, respectively) were also found in the two comparison groups, and no expression of HB-1 and HB-3 genes was detected in Group D, HB-2 was also only slightly expressed.

Evaluation of cold resistance in hybrid population

Winter freezing injury is an important factor that restricts the growth of grape in the north of our country, which causes the decrease of grape yield and quality and even the economic loss of fruit farmers [19]. Therefore, it is very important for the development of grape industry in the north of our country to select high-quality and cold-resistant grape varieties. Wang evaluated the cold hardiness of V. vinifera hybrid germplasm and found that Chinese grape growing areas could be divided into 4 cold hardiness regions according to the lethal temperature (LT50), the LT50 of different germplasm resources in the same producing area was different [20]. The cold hardiness of hybrid progenies of 'Beihong' and '7-11-49' was comprehensively evaluated by Wang [21]. The results showed that the cold hardiness of hybrid progenies was segregated and there was a phenomenon of superparent inheritance. The cold-resistance phenotype of 'Shuangyou' and ' Italian Riesling ' hybrids showed normal distribution, and the cold-resistance traits were controlled by multiple genes [22]. In this study, the LT50 of the progenies of V. vinifera intraspecific hybrids was determined, and the cold hardiness grades were classified by the subordinate function method. It was found that the cold hardiness of the hybrid population was a continuous partial normal distribution, and it belonged to the super-parent quantitative trait inheritance, this is consistent with previous studies. Selection of varieties with high cold resistance as intermediate materials in cross breeding and introduction of molecular breeding can speed up the breeding process. Therefore, this study screened breeding materials with high cold resistance, combined with molecular breeding, this study provides a basis for mining the low-effective cold-resistance genes in V. vinifera.

Cold resistance and secondary metabolite

Plants can defend against cold through a multi-layered defense strategy, in which secondary metabolite has an important impact on the cold-resistant response of plants [23, 24]. The release of secondary metabolite terpenoids can be induced by conditioned stresses such as light and low temperature, leading to plant defense responses and plant-plant interactions [25]. These compounds have a wide range of functions in plant responses to cold and are also important components of wine aroma [26]. Among them, (E)-8-carboxylinalool, which is catalyzed by the multifunctional single-titritol oxidase CYP76F14, is a key precursor of wine aroma [27, 28]. Carotenoid synthesis pathway of terpenoid Dxs in different colors of mature pepper fruit, Berry revealed a correlation between carotenoid content and Dxs transcriptional levels [29]. Simpson and Kevin studied the major flow control steps in the methyl erythritol 4-phosphate (MEP) pathway, increased Dxs expression levels result in increased abundance of gene transcripts for carotenoid pathway major rate-determining enzymes [30]. Genetically modified crops accumulate different levels of isoprene, which is one of the rate-limiting steps in the catalytic MEP biosynthesis pathway [31]. In this study, CYP76F14 and Dxs genes were differentially expressed in both C and D groups, and the expression level in Group C was significantly higher than that in Group D, therefore, these two genes can be regarded as the key candidate genes for improving cold resistance of V. vinifera.

Cold resistance and lipid metabolism

Low temperature is a key environmental stress factor that restricts plant growth and has a significant impact on its quality and yield [32, 33]. In order to relieve cold stress, a series of physiological and biochemical and complex molecular regulation mechanisms occur in plants [34]. The importance of plant membrane and stored lipid levels in improving cold resistance and cold germination of seeds has been extensively studied in a range of plant species, storage lipid components are an energy source for plant growth [35]. In the 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 [36]. Peroxidase (ACOX) gene encoding acyl-COA oxidase was characterized by Joo [37]. The results showed that this gene is a key rate-limiting enzyme for the fatty acid metabolism of domperidone, fatty acid oxidation is significantly promoted by upregulating the expression of ACOX1 [38, 39]. In this study, ACOX1 and ACOX3 were only expressed in Group D, but not in Group C, which was consistent with previous studies. The expression of ACOX1 and ACOX3 decreased the synthesis of lipids, which affected the cold resistance of plants.

Cold hardiness and carbohydrates

Plants improve winter viability by stimulating photosynthesis and carbohydrate metabolism [40], and soluble carbohydrates play an important role in improving plant cold hardiness [41]. Studies on the molecular mechanisms of cold resistance in banana mutants have shown that mutants with significant cold resistance enhance carbohydrate biosynthesis [24]. Yan revealed the resistance mechanism of tea plants to freezing stress, elucidating the cryoprotective effect of soluble carbohydrates at lower temperatures [23], with increased carbohydrate concentrations at cold-resistant sites in plants, whereas the concentration at cold-resistant sites decreased [42]. 3-dehydroquinate dehydratase/shikimate dehydrogenase (DHQ-SDH) in higher plants is a cold-regulated gene with up-regulated expression under low temperature conditions [43]. In this study, the expression level of DHQ-SDH gene in the cold-resistant group was significantly higher than that in the sensitive group, indicating that this gene could help plants form self-protection under cold stress. ADP glucose pyrophosphatase (GLGC) shows high expression levels in the starch synthesis pathway in rice seeds [44], and overexpression in cassava roots increases starch accumulation and its subsequent hydrolysis to sugars [45, 46]. In this study, the gene expression in the two comparison groups showed significant difference, Group C was significantly higher than Group D, may be used as a candidate gene for cold resistance in V. vinifera.

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 [47]. MYB transcription factors play an important role in abiotic tolerance in plants [48], reducing the accumulation of reactive oxygen species in turn increases cold resistance [49–51]. Gene regulation is determined by the combination of transcriptional regulators present on specific cis-regulatory elements at specific times [52], and MADS family transcription factors play important roles in regulating plant growth and signal transduction [53]. Overexpression of MADS in Arabidopsis thaliana enhances adaptation to abiotic stresses (drought, low temperature) [54], the expression changes of MADS transcription factors in winter wheat from cold high altitude areas also showed a similar trend to that of cold resistance [55], but have not been reported in grapes. In this study, 3 transcription factor family genes were identified, which can be used as candidate genes for exploring cold resistance in V. vinifera.

Experimental materials

The hybrid progeny of high-quality variety Ecolly (ECL) and Dunkelfelder (DKF) (ECL× DKF) was planted in the vineyard of Shengtang Winery in Yangling, under similar management conditions of soil, irrigation, pruning and disease control, the plants were controlled.

Evaluation and classification of cold resistance

Determination of semi-lethal temperature of dormant winter buds of grape

Refer to Kaya & Köse's method with minor modifications [56]. A 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 4 ℃·h-1, the cooling temperature starts at 4°C and ends 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, corresponding to high temperature exotherm (HTE) and low temperature exotherm (LTE).

Classification of chilling resistance of dormant winter buds

The affiliation function method was utilized to calculate the affiliation values of winter bud injury for the test materials:

$$\:SVij=1-\frac{{x}_{ij}-{x}_{j\:min}}{{x}_{j\:max}-{x}_{j\:min}}$$

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 semi-lethal, x_{j max} is the maximum and x_{j min} is the minimum of the numbered low temperature semi-lethal temperature in a population. Arithmetic mean of two years was taken as the average membership degree of winter bud of the parent or offspring. The cold hardiness of grape germplasm was graded according to the subordinate degree of cold hardiness.

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)

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 the young leaf tissues were collected in the natural state for transcriptome sequencing to screen for cold resistance-related genes.

Transcriptome sequencing and data analysis

Select high cold resistant plants S1022 and S1024 from the hybrid population, denoted as Group C; Simultaneously select sensitive plants S1023 and S1035 as control groups, denoted as Group D. Explore candidate genes related to grape cold resistance.

In May 2023, the apical meristem of S1022, S1023, S1024 and S1035 individual plants were wrapped in tin foil and frozen in liquid nitrogen for 1 hour, they were placed in a foam box pre-filled with dry ice and mailed to Wuhan Feisha Genetic Information Co, Ltd. In June 2023, the company completed RNA extraction and sequencing of the samples.

FASTQ was used to evaluate the quality of RNA-Seq libraries. The Raw reads were filtered by SOAPnuke software (v2.1.0) to obtain clean reads. Using HISAT2 v2.2.1, the clean reads were compared with the high-quality grape reference genome. Gene expression levels were quantified using RSEM with default parameters. Differentially expressed genes (DEGs) were identified using the DEseq2 package in R. DEGs were determined with adjusted P-value < 0.05 and | log2fc | ≥1.

The GO enrichment analysis of the genes in the gene set was carried out by using software Goattools, and the GO functions of the genes in the gene set were obtained. Using Fisher's exact test, the GO function is considered to be significantly enriched when the adjusted p-value (P adjust) < 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) < 0.05, the KEGG PATHWAY function was considered to be significantly enriched.

Data processing

Microsoft Excel 2021 was used to record, arrange and analyze the original data, SPSS22.0 was used to analyze the significance, and Origin Pro 2021 was used to draw the histogram of frequency distribution and fit the normal curve.

According to the above results, the different genes were mainly enriched in GO items: cellular process, metabolic process, biological regulation, biological regulation, stimulus response, etc. The results of KEGG enrichment analysis showed 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 are the common differential pathways of KEGG enrichment We obtained CYP76F14, Dxs, and GERD genes in the secondary metabolite synthesis pathway, NMT, GDE1 family-related genes in lipid metabolism, glgC in carbohydrate synthesis, and DHQ-SDH related to amino acid metabolism, MYB, HB and MADS genes are the key candidate genes for exploring cold resistance in V. vinifera.

This study provides a theoretical basis for analyzing the cold resistance mechanism of V. vinifera at the molecular level. The differential genes can be used to develop molecular markers and validate the accumulation of substitution genes. Because of the number of hybrid population, this study did not directly construct genetic map, but combined with cold-resistant phenotype and transcriptome analysis to mine cold-resistant genes. In the future research, we will identify the cold resistance and the true or false of the hybrid progeny, then construct the high density genetic map, combine with the cold resistance phenotype and transcriptome analysis to continue to mine the cold resistance genes, we systematically analyzed the accumulation of minor genes during intraspecific hybridization in V. vinifera.

Acknowledgements

Not applicable.

Authors’ contributions

JL, ZW, HL and HW planed and designed the experiments. JL, ZW, LW and SJ collected the samples and performed experiments. JL analyzed and interpreted the sequencing data. JL and ZW wrote the manuscript. HL and HW provided funding for sequencing. All authors read and approved the final manuscript.

Funding

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

Availability of data and materials

Data and materials will be made available on request.

Ethics approval and consent to participate

Experimental research and feld studies on plants including the collection of plant material are comply with relevant guidelines and regulation.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interest.

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No competing interests reported.

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Mining minor cold-resistant genes in V. vinifera based on transcriptomics

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Abstract

Figures

Introduction

Results

Evaluation and classification of winter bud cold resistance

Transcriptome sequencing data analysis

Overall analysis of differential genes

Differential gene analysis of different plant samples

GO enrichment analysis

KEGG enrichment analysis

Expression analysis of cold resistance-related genes in grape

Anabolism of secondary metabolite

Lipid anabolism

Carbohydrate and amino acid metabolism

Transcription factors

Discussion

Evaluation of cold resistance in hybrid population

Cold resistance and secondary metabolite

Cold resistance and lipid metabolism

Cold hardiness and carbohydrates

Cold resistance and transcription factor regulation

Materials and methods

Experimental materials

Evaluation and classification of cold resistance

Determination of semi-lethal temperature of dormant winter buds of grape

Classification of chilling resistance of dormant winter buds

Transcriptome material

Transcriptome sequencing and data analysis

Data processing

Conclusion

Declarations

References

Additional Declarations

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