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Article

Integrative Analysis of Transcriptome and Metabolome Reveals the Regulatory Network Governing Aroma Formation in Grape

by
Liping Huang
,
Yue Zhu
,
Min Wang
,
Zhili Xun
,
Xiaohe Ma
* and
Qifeng Zhao
*
Pomology Institute, Shanxi Agricultural University, Jinzhong 030801, China
*
Authors to whom correspondence should be addressed.
Horticulturae 2024, 10(11), 1159; https://doi.org/10.3390/horticulturae10111159
Submission received: 10 September 2024 / Revised: 25 October 2024 / Accepted: 29 October 2024 / Published: 31 October 2024
(This article belongs to the Special Issue New Insights into the Genetic Regulation and Quality Improvement of Grapes)
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Abstract

:
The aroma metabolites in grape berries have received attention in recent years, but a global analysis of gene-regulated metabolites is still lacking. In this study, three grape cultivars, “Kyoho”, “Adenauer Rose”, and “Mei Xiangbao”, were used to determine the differential accumulation of metabolites and identify candidate genes related to grape berry aroma. A total of 27,228 genes were detected from the transcriptome, and 128 differentially accumulated metabolites (DAMs) were identified. Terpenoids and ester were the major substances in these three cultivars. KEGG enrichment showed that 12, 8, and 5 compounds were significantly enriched during the maturation process of these three grape cultivars, with most being terpenoids. A combined transcriptome and metabolome analysis found that the associated genes and metabolites were enriched in the following pathways: “Glycine, serine, and threonine metabolism”, “Cysteine and methionine metabolism”, “Tyrosine metabolism”, “Phenylalanine metabolism”, and “Phenylalanine, tyrosine, and tryptophan biosynthesis”. Seven structural genes (VvOMR1, VvGLYK, VvLPD2, VvAK2, VvSHM7, VvASP3, and VvASP1) and four transcription factors (VvERF053, VvERF4, VvMYB46, and VvMYB340) related to grape berry aroma accumulation were discovered. Our findings provide new insights into grape aroma formation and regulatory mechanism research, and the results will be beneficial for grape aroma breeding in the future.

1. Introduction

Grape berry aroma plays an essential role in consumers’ choice of fruits and promotes their desire for re-consumption [1,2]; it is also a crucial criterion in grape breeding. Generally, the interaction among multiple metabolites contributes to the formation of aroma. Many studies have found that the volatile compounds in lipids and amino acids contribute to the enhancement of aroma in fruits [3]. Esters, as key odorants, have been found to influence the aroma quality of fruits such as apples and bananas [4,5], and polyunsaturated fatty acids have a considerable influence on the formation of esters in fruits. Ester formation in yellow-fleshed peaches has been related to the activity of alcohol acyltransferase (AAT) and gene expression [6].
Grape crops are a commercially significant fruit crop with a long history of cultivation, and their produce can be categorized as table grapes, raisins, juice, or wine [7]. Grape quality is determined by size, color, and aroma, among which aroma has received more attention in recent years. As a non-respiratory and non-climacteric fruit, grape berries have three development stages: the early stage of berry development, a short growth stagnation stage, and a maturation stage with rapid growth. The ripening of grape berries is accompanied by many physiological changes; for example, phenols (such as tannins) and aroma substances (such as pyrazines) accumulate in the early stage, while many aroma substances and their precursors accumulate in the maturation stage. The volatile aroma in grape berries usually exists in the form of a “free” or “bound” glycoside [8]. A large number of volatile aroma compounds in grape berries are found in the “bound” form and have no aroma; however, upon hydrolysis of the glycoside, the compounds can be volatilized. The volatile aroma in grape berries consists of hexacarbons, esters, terpenoids, alcohols, ketones, and aldehydes [9]. These compounds interact with each other and determine the aroma of grape berries. As direct precursors of fruit aromatic substances, amino acids can produce large amounts of aromatic substances, such as alcohols, aldehydes, esters, ketones, volatile terpenes, and carbonyls; these are the key components of grape aroma and can be used to classify neutral, strawberry, and muscat grape resources [10,11,12,13,14,15].
With the development of sequencing technology, combined RNA-Seq and GC/MS have been widely utilized to explore metabolites and related genes to reveal the coloration and quality formation in fruit crops [16,17,18]. Although aroma is an important characteristic of grapes, the molecular regulation mechanism is still unclear. In this study, three grape cultivars were used: “Adenauer Rose”, which originated in France, belongs to V. vinifera L. with a strong muscat fragrance, and is a diploid cultivar. “Kyoho” belongs to V. vinifera × V. labrusca L. and has a strong strawberry aroma. “Mei Xiangbao” is a new tetraploid variety which possesses muscat and strawberry fragrances; and is crossbred from “Adenauer Rose” (female parent) and “Kyoho” (male parent). In this study, berries of these three grape cultivars in two developmental stages were explored using transcriptome and metabolome analyses, and then an integrative analysis of the transcriptome and metabolome was performed. A number of metabolites and genes associated with the formation of grape aroma were identified and quantified. The results will further our understanding of the molecular mechanism of the regulatory network governing aroma formation in grape berries and also provide us with a valuable reference for grape aroma breeding research.

2. Materials and Methods

2.1. Sample Collection

The grape cultivars (10-year-old seedlings) were planted in experimental arenaceous soil with a pH value of 7.8 at the Fruit Research Institute of Shanxi Agricultural University (39°58′ N and 116°13′ E) under a plastic cover and were trained into a two-wire vertical trellis system with a 2.5 m row space and a 1.2 m plant space. Organic fertilizer was applied in autumn, and phosphorus and potassium fertilizers were applied during the flowering and fruiting period. To avoid pests and fungal diseases, bagging was performed after pollination. Berry samples from three vines in 2017 were harvested at the development stages corresponding to the turning stage (when the berry begins to color and soften) and the maturation stage (when the berry reaches harvest ripeness). “Adenauer Rose” berries were collected on July 23 (58 days after full bloom) and August 10 (77 days after full bloom) at their turning and maturation stage; “Kyoho” berries were collected on July 25 (60 days after full bloom) and September 12 (110 days after full bloom) at their turning and maturation stage; and “Mei Xiangbao” berries were collected on July 23 (58 days after full bloom) and August 16 (83 days after full bloom) at their turning and maturation stage. Three biological replicates were used for each grape cultivar, and approximately 100 grape berries were randomly collected for each replicate. The whole berries were frozen in liquid nitrogen after removal of the skin and seeds and stored at −80 °C until needed.

2.2. mRNA Library Construction and Sequencing

The total RNA was isolated and purified using the TRIzol reagent (Invitrogen, Carlsbad, CA, USA), following the manufacturer’s instructions. A NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA) and a Bioanalyzer 2100 (Agilent, Palo Alto, CA, USA) were used to determine the RNA concentration and integrity of each sample. Poly (A) RNA was achieved by using Dynabeads Oligo (dT) 25-61005 (Thermo Fisher, Waltham, MA, USA) and then fragmented into small pieces using the Magnesium RNA Fragmentation Module (New Englnd Biolabs (Beijing, China) LTD. cat. e6150, USA). After reverse-transcribing to cDNA, dUTP Solution (Thermo Fisher, cat.R0133, USA), E. coli DNA polymerase I (New Englnd Biolabs (Beijing) LTD. cat. m0209, USA), and RNase H (New Englnd Biolabs (Beijing) LTD. cat. m0297, USA) were used to synthesize U-labeled second-stranded DNAs, which were assembled in an adapter. After the heat-labile UDG enzyme (New Englnd Biolabs (Beijing) LTD., cat. m0280, USA) treatment of the U-labeled second-stranded DNAs, the ligated products were amplified with PCR.
Sequencing and analysis: Low-quality bases were removed using Cutadapt software (https://cutadapt.readthedocs.io/en/stable/, accessed on 28 October 2024, version:cutadapt-1.9) and then the clean reads were mapped to the reference genome and assembled using HISAT2 (https://daehwankimlab.github.io/hisat2/, accessed on 28 October 2024, version:hisat2-2.2.1) and StringTie software (http://ccb.jhu.edu/software/stringtie/, accessed on 28 October 2024, version:stringtie-2.1.6). After generating the final transcriptome, the transcript expression levels were then calculated and represented with FPKM (FPKM = [total_exon_fragments/mapped_reads (millions) × exon_length (kB)]). Genes with fold change >2 or fold change <0.5 and p-value < 0.05 were recognized as differentially expressed genes (DEGs), and then KEGG enrichment was applied to these DEGs.

2.3. Metabolite Extraction

The whole berries of the three cultivars at different development stages were ground to a powder in liquid nitrogen. Then, 500 mg (1 mL) of the powder was transferred immediately to a 20 mL headspace vial (Agilent, Palo Alto, CA, USA) containing a NaCl-saturated solution to inhibit any enzyme reaction. The vials were sealed using crimp-top caps with TFE-silicone headspace septa (Agilent). At the time of the SPME analysis, each vial was kept at 60 °C for 5 min, and then a 120 µm DVB/CWR/PDMS fiber (Agilent) was exposed to the headspace of the sample for 15 min at 60 °C.

2.4. GC-MS Analysis

After sampling, desorption of the VOCs from the fiber coating was carried out in the injection port of the GC apparatus (Model 8890; Agilent, CA, USA) at 250 °C for 5 min in the splitless mode. The identification and quantification of VOCs was carried out using an Agilent Model 8890 GC and a 7000D mass spectrometer (Agilent) equipped with a 30 m × 0.25 mm × 0.25 μm DB-5MS (5% phenyl-polymethylsiloxane) capillary column. Helium was used as the carrier gas at a linear velocity of 1.2 mL/min. The injector temperature was kept at 250 °C, and the detector at 280 °C. The oven temperature was programmed from 40 °C (3.5 min), increasing at 10 °C/min to 100 °C, at 7 °C/min to 180 °C, at 25 °C/min to 280 °C, and held for 5 min. Mass spectra were recorded in electron-impact (EI) ionization mode at 70 eV. The quadrupole mass detector, ion source, and transfer line temperatures were set, respectively, at 150, 230, and 280 °C. The MS was selected, and the ion monitoring (SIM) mode was used for the identification and quantification of the analytes.

2.5. Metabolomics Data Processing

Unsupervised PCA (principal component analysis) was performed using the statistics function within R (www.r-project.org/, accessed on 28 October 2024, 3.6). Before the unsupervised PCA was conducted, log2 transform and mean centering were used as preprocessing methods. The HCA (hierarchical cluster analysis) results for the samples and metabolites were presented as heatmaps with dendrograms, while Pearson correlation coefficients (PCCs) between the samples were calculated using the cor function in R and presented as heatmaps only. Both HCA and PCC were carried out using the R package ComplexHeatmap (http://bioconductor.org/packages/ComplexHeatmap/, accessed on 28 October 2024, version 2.8.0). For the HCA, the normalized signal intensities of the metabolites (unit variance scaling) were visualized as a color spectrum. For the two-group analysis, differential metabolites were determined using VIP (VIP > 1) and absolute Log2FC (|Log2FC| ≥ 1.0). The VIP values were extracted from the OPLS-DA result, which also contained score plots and permutation plots, generated using the R package MetaboAnalystR (https://www.metaboanalyst.ca/docs/RTutorial.xhtml, accessed on 28 October 2024). The data were log-transformed (log2) and mean-centered before the OPLS-DA. In order to avoid overfitting, a permutation test (200 permutations) was performed.

2.6. qRT-PCR Validation

Total RNA was extracted from the berries of the grape cultivars “Kyoho”, “Adenauer Rose”, and “Mei Xiangbao” using the Plant Total RNA Isolation Kit (SK8631; Sangon Biotech, Shanghai, China) according to the manufacturer’s instructions. cDNA was obtained using PrimeScript™ RT-PCR Kit (RR047A; TaKaRa Bio, Kusatsu, Japan) according to the manufacturer’s instructions. Quantitative real-time PCR (qRT-PCR) was conducted in the ABI QuantStudio™ 6 Flex System (Applied Biosystems, Waltham, MA, USA). The relative expression level of the selected genes was normalized to grapevine β-actin and calculated using the 2−ΔΔCT method. All reactions were performed using three biological replicates. The primers used in this study are listed in Table S1.

3. Results

3.1. Identification of Metabolites in Three Grape Cultivars

A total of 346 metabolites were identified at two developmental stages in three cultivars. The metabolites were divided into nucleosides, alkaloids and derivatives, benzenoids, lipids and lipid-like molecules, nucleotides and analogs, organic acids and derivatives, phenylpropanoids and polyketides, and other compounds (Table S2). Among them, lipids and lipid-like molecules contributed the most, followed by organic acids and derivatives and phenylpropanoids, which were the main components in these metabolites. Furthermore, hierarchal clustering of the metabolite profile during grape berry ripeness was performed, and the results are shown in the heatmap (Figure S1).

3.2. Differentially Accumulated Metabolites at Different Stages of Ripeness

For the analysis of differentially accumulated metabolites (DAMs), the two developmental stages of each cultivar and different cultivars were screened using the following criteria: (1) Metabolite accumulation levels with a fold change ≥2; (2) based on the OPLS-DA model analysis results, metabolites with VIP (variable importance in project) ≥ 1 [18]; and (3) with a |[log2 (fold change)]| > 1 and adjusted p < 0.05. A total of 128 DAMs were discovered in these three cultivars, and 94, 52, and 53 DAMs were screened by comparing A2 vs. A1, J2 vs. J1, and M2 vs. M1, respectively (Table S3). A total of 12 substances were found in the “Adenauer Rose”, “Kyoho”, and “Mei Xiangbao” cultivars (Figure 1a).
Terpenoids and esters were the major substances in these three cultivars. Most of the aromatic compound content in “Adenauer Rose” was significantly higher than in the other two cultivars (Table 1). Twelve compounds were discovered in A2 vs. A1, J2 vs. J1, and M2 vs. M1. Interestingly, three compounds were down-regulated in the grape cultivar “Kyoho” and up-regulated in “Adenauer Rose” and “Mei Xiangbao”, including 6,6-dimethyl-Bicyclo [3.1.1]hept-2-ene-2-methanol, 1-methyl-4-(1-methyl ethylidene)-Cyclohexanol, and 2-methyl-5-(1-methylethenyl)-Cyclohexanol, which are terpenoids (Tables S4 and S5). However, 24 compounds were only discovered in A2 vs. A1 and M2 vs. M1, with most of these compounds being up-regulated in “Adenauer Rose” and “Mei Xiangbao”. Three compounds, (E)-3-Hexen-1-ol-acetate, 2-methyl-5-(1-methylethyl)-Pyrazine, and (1R,2S,5S)-2-methyl-5-(1-methylethyl)-Bicyclo [3.1.0] hexan-2-ol, were up-regulated in “Adenauer Rose” and down-regulated in “Mei Xiangbao” (Tables S4 and S5). Twelve compounds were only discovered in J2 vs. J1 and M2 vs. M1; these compounds showed the same variation tendency in “Kyoho” and “Mei Xiangbao” (Tables S4 and S5). To explore the function of the metabolites, the differentially accumulated metabolites were then analyzed via KEGG enrichment (Figure 1b–d). In total, 12, 8, and 5 compounds were significantly enriched during the maturation process of these three grape cultivars, and most of them were terpenoids (Figure 1b–d, Table S6).

3.3. Expression Analysis of Key Metabolites Affecting Aroma Formation

In order to identify key metabolites affecting aroma formation at the maturation stage of these three grape cultivars, we conducted a KEGG enrichment analysis for the comparison of the groups J2 vs. A2, J2 vs. M2, and A2 vs. M2. “Biosynthesis of secondary metabolites”, “Monoterpenoid biosynthesis”, “Limonene and pinene degradation”, and “Metabolic pathways” were all discovered in the comparison of the groups J2 vs. A2 and J2 vs. M2. Besides these four KEGG pathways, “Tropane, piperidine and pyridine alkaloid biosynthesis” and “Phenylalanine metabolism” were discovered in the comparison of the groups A2 vs. M2 (Figure 2a–c, Table S6). In total, 14 metabolites were significantly enriched in these pathways, of which 10 were significantly enriched in the comparison of J2 vs. A2; 9 were significantly enriched in the comparison of J2 vs. M2; and 12 were significantly enriched in the comparison of A2 vs. M2 (Figure 3a, Table S6).
The grape cultivar “Adenauer Rose” possesses a strong muscat fragrance and “Kyoho” possesses a strong strawberry fragrance. To further discover the key metabolites that could significantly affect the flavor formation of these two cultivars, six metabolites were selected according to the KEGG pathway enrichment analysis, i.e., 2,7,7-trimethyl-3-Oxatricyclo[4.1.1.0(2,4)]octane(D197), Myrcene(KMW0199), D-Limonene(KMW0217), 4-methyl-Benzenemethanol (NMW0038), L-alpha-Terpineol(NMW0071), and 6,6-dimethyl-Bicyclo[3.1.1]hept-2-ene-2-methanol (NMW0074) in J2 vs. A2, J2 vs. M2, and A2 vs. M2. Two metabolites, 3-Carene(KMW0220) and (1R,2S,5S)-2-methyl-5-(1-methylethyl)-Bicyclo [3.1.0] hexan-2-ol(KMW0257), were only selected according to J2 vs. A2 and J2 vs. M2. Two metabolites, Citral (KMW0446) and Geraniol (KMW0460) were only selected according to J2 vs. A2 and A2 vs. M2. One metabolite, α-Pinene (KMW0148), was only selected according to J2 vs. M2 and A2 vs. M2, and three metabolites, BenzeneacetAldehyde (KMW0212), (-)-trans-Isopiperitenol (NMW0082), and 2,3,4,5-tetrahydro-Pyridine(XMW0132), were only selected according to A2 vs. M2 (Figure 3b, Table S6).

3.4. RNA-Seq Analysis of Grape Berries at Different Stages of Ripeness

RNA-Seq was performed to explore the gene expression in the different samples. The Q30 scores of all samples were >98%, and 4.96–8.03 GB of valid data were produced for each sample (Table S7). A total of 27,228 genes were identified from all transcriptome samples. The annotated numbers of GO and KEGG were 22697 and 4280, respectively. The PCA analysis demonstrated the good reproducibility of the transcriptome data (Figure S1). Then, the DEGs were identified by following the standard |log2Fold Change| ≥ 1 and FDR < 0.05, 3784, 7416, and 6159 DEGs were identified in J2 vs. J1, A2 vs. A1, and M2 vs. M1, respectively. Among them, there were 969, 2832, and 1765 up-regulated genes and 2815, 3327, and 5651 down-regulated genes in J2 vs. J1, A2 vs. A1, and M2 vs. M1, respectively (Figure 4). The numbers of DEGs in the different cultivars are exhibited in Figure 4.

3.5. Figures, Tables, and Schemes

In terms of identifying the relevant genes enriched in the metabolic or signaling pathways involved in grape aroma formation, the results showed that 793, 743, and 719 DEGs were enriched in the comparison of J2 vs. A2, M2 vs. J2, and M2 vs. A2 (Figure 5, Table S8). The aromatic substances in the fruits were mainly synthesized through fatty acid metabolism, amino acid metabolism, terpene metabolism, and monosaccharide and glycoside metabolism. In this study, “Glycine, serine and threonine metabolism”, “Cysteine and methionine metabolism”, “Glycine, serine and threonine metabolism”, “Tyrosine metabolism”, “Phenylalanine metabolism”, and “Phenylalanine, tyrosine, and tryptophan biosynthesis” were selected according to the KEGG enrichment analysis of J2 vs. A2, M2 vs. J2, and M2 vs. A2. A total of 34, 18, and 12 DEGs were selected from the J2 vs. A2, M2 vs. J2, and M2 vs. A2 groups (Table S9). Five DEGs, i.e., VvOMR1 (VIT_08s0007g04310), VvGLYK (VIT_09s0002g05200), VvLPD2 (VIT_14s0060g01330), VvAK2 (VIT_14s0068g01190), and VvSHM7 (VIT_18s0001g -04340), were discovered in both J2 vs. A2 and M2 vs. J2 comparisons, while two DEGs, VvASP3(VIT_04s0008g03770) and VvASP1(VIT_08s0058g01000), were discovered in both J2 vs. A2 and M2 vs. A2 comparisons (Table S9). VvOMR1 was significantly down-regulated in the “Kyoho” and “AR” and up-regulated in “MXB” from the turning stage to the maturation stage; VvGLYK was significantly down-regulated in “AR” and “MXB”; VvLPD2 was significantly down-regulated in “Kyoho” and up-regulated in “AR” and “MXB”; VvAK2 was significantly down-regulated in all three cultivars; VvSHM7 and VvASP3 were both up-regulated in “Kyoho” and “AR”; and VvASP1 was significantly up-regulated in all three cultivars (Figure 6). Moreover, four common differentially expressed transcription factors, namely, VvERF053(VIT_12s0059g00280), VvERF4(VIT_19s0014g02240), VvMYB46(VIT_16s0039g01920), and VvMYB340(VIT_14s0066g01090), were discovered. qRT-PCR verification showed that VvERF053 was significantly down-regulated in “AR” and up-regulated in “MXB” from the turning stage to the maturation stage; VvERF4 was significantly down-regulated in “AR” and up-regulated in “Kyoho” and “MXB”; VvMYB46 was significantly up-regulated in all three cultivars; and VvMYB340 was significantly down-regulated in all three cultivars (Figure 7a).
To discover the transcription factors significantly related to structural genes and key metabolites, we calculated the Pearson correlation coefficients (p < 0.05) for 11 candidate genes and 14 key metabolites (Figure 7b). The result showed that VvGLYK was negative with most of these metabolites; VvAK2 was positive with XMW0132 and negative with KMW0212; VvASP3 was positive with KMW0148 and VvLPD2 and negative with NMW0071; VvASP1 was positive with VvSHM7 and negative with VvOMR1 and VvAK2; VvERF053 was significantly positive with VvOMR1 and VvLPD2; VvERF4 was significantly positive with VvOMR1 and negative with VvASP3; VvMYB46 was significantly positive with KMW0148, KMW0257, VvOMR1, and VvAK2; and VvMYB340 was significantly positive with VvSHM7 and VvASP1.

4. Discussion

In grape aroma research, more than 2000 volatile components have been identified [19], and many studies have focused on the classification of aroma in different grapes. Terpenes, including sesquiterpenes and monoterpenes, are usually the main ingredient in muscat-scented cultivars; for instance, during the development of muscat-scented cultivars, linalool, and geraniol are the most important contributors to the aroma [13,20,21,22]. Recent research on the muscat grape cultivars “Shine Muscat”, “Midnight Beauty”, and “Centennial Seedless” showed that the contents of linalool, geraniol, geranic acid, and terpenes were higher than for other grape cultivars [23]. In our study, terpenoids were also enriched the most in the muscat cultivar, “Adenauer Rose”, and linalool (KMW0291) was the highest aromatic substance, followed by 2,7,7-trimethyl- 3-Oxatricyclo [4.1.1.0(2,4)] octane (D197), fenchone (NMW0034), and geraniol (KMW0460) (Table S3). It was interesting that in the “Mei Xiangbao” grape cultivar, fenchone was the terpenoid with the highest content. Based on the KEGG enrichment analysis of “Adenauer Rose” and “Kyoho” at the maturation stage, 2,7,7-trimethyl- 3-Oxatricyclo [4.1.1.0(2,4)] octane (D197) was the most enriched aromatic substance in “Adenauer Rose”.
Esters including ethyl acetate, ethyl butyrate, ethyl 2-butenoate, and ethyl 2-hexenoate are the main ingredients in strawberry-scented cultivars [12,24]. In our study, the esters in “Kyoho” were less enriched than in the other two cultivars. At the maturation stage, α-Terpineol (NMW0071), butanoic acid-ethyl ester (KMW0074), and benzeneacetic acid-ethyl ester (KMW0441) were the three highest aromatic substances in “Kyoho”, while α-Terpineol was down-regulated from the turning stage to the maturation stage and butanoic acid-ethyl ester and benzeneacetic acid-ethyl ester were up-regulated. Esters and terpenoids were the aromatic substances with the highest content in the “Mei Xiangbao” cultivar. Differing from “Adenauer Rose” and “Kyoho”, the ketone (methyl 2,2,3-trimethylcyclopentyl) (XMW1442) content in “Mei Xiangbao” was the highest, and it was also up-regulated from the turning stage to the maturation stage.
Amino acids and their biosynthetic intermediates play key roles as precursors for the biosynthesis of plant volatiles [25]. For instance, phenylalanine biosynthesis and metabolism pathways have been demonstrated to relate to plants’ volatile diversity [26,27,28,29]. In our study, the two candidate genes VvASP3(VIT_04s0008g03770) and VvASP1(VIT_08 s0058g01000), which are involved in phenylalanine biosynthesis and the metabolism pathway, were first discovered based on the KEGG enrichment analysis. The Pearson correlation coefficients showed that these two structural genes were positively related to the content of α-Pinene (KMW0148), which is a terpenoid. Moreover, we also discovered a further four structural genes also belonging to amino acid metabolism pathways; until now, no studies have reported their role in grape berry aroma formation, and the function of these three genes still needs further investigation.
Ethylene response factors (ERFs), which belong to the AP2 family, play crucial roles in plant development and in the response to environmental stress. They can bind to the cis-acting element of the target gene promoter and promote the expression of ethylene-responsive genes [30,31,32,33,34]. Until now, little was known about ERFs’ role in fruit aroma biosynthesis. In peach aroma biosynthesis, PpERF5 and PpERF7 bind together to form a protein complex that enhances the transcription of LOX4, which plays a key role in volatile biosynthesis during peach fruit ripening [35]. In the petals of sweet osmanthus, OfERF61 can positively regulate the expression of OfCCD4 and increase the production of β-ionone [36]. In our study, two ERFs were discovered: VvERF053 and VvERF4. Based on the Pearson correlation coefficient analysis, they were positively related to the expression of the structural genes VvOMR1 and VvLPD2, while VvERF053 was found to be negatively related to most of the aromatic substances detected in this study.
MYB-related transcription factors also play crucial roles in plant development and the response to environmental stress through promoting their target gene expression [37,38,39]. Some reports have focused on the regulation effect of MYB transfactors in aromatic substance biosynthesis. In petunia, PhMYB4 can suppress the production of benzenoid/phenylpropanoid biosynthesis by regulating the expression of their downstream genes [40]. In apples, MdMYB85 directly interacts with the promoter region of MdAAT1 and enhances ester aroma synthesis [23], while in sweet osmanthus, many MYB transfactors have been discovered, among which OfMYB1R201 shows transcriptional activity in regulating the expression of genes related to floral volatile organic compounds [41]. HcMYB genes in H. coronarium can promote the key structural genes related to terpenoid and benzenoid biosynthesis [42]. In our study, two MYBs, VvMYB46 and VvMYB340, were discovered. Based on the Pearson correlation coefficient analysis, VvMYB46 was positively related to the expression of the structural genes VvOMR1 and VvAK2 and the production of α-Pinene (KMW0148) and 2-methyl-1-methylethyl-Bicyclo-hexan-2-ol(KMW0257). VvMYB340 was positively related to the expression of the structural genes VvSHM7 and VvAPS1 and negatively related to the expression of the structural gene VvAK2.

5. Conclusions

In this study, we conducted RNA-seq and GC-MS analyses for the grape cultivars “Kyoho”, “Adenauer Rose”, and “Mei Xiangbao” to provide novel insights into the regulation of grape berry aroma formation. A total of 128 DAMs were identified; 12, 8, and 5 compounds were significantly enriched during the maturation process of these three grape cultivars, with most being terpenoids. Seven structural genes (VvOMR1, VvGLYK, VvLPD2, VvAK2, VvSHM7, VvASP3, and VvASP1) and four transcription factors (VvERF053, VvERF4, VvMYB46, and VvMYB340) involved in the amino acid metabolism pathway were discovered, although their function in grape berry aroma formation still requires further investigation. Our results will provide a reference for grape aroma breeding in the future.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/horticulturae10111159/s1, Figure S1: Hierarchal clustering of the metabolite profile during grape berry ripeness; Table S1: The primers used in this study; Table S2: Metabolite identification at two developmental stages in three cultivars; Table S3: Significantly differentially accumulated metabolites at different stages of ripeness; Table S4: Differential metabolic substance changes at different development stages; Table S5: Differential metabolic substance statistics between different groups; Table S6: Differentially accumulated metabolites analyzed via KEGG enrichment; Table S7: RNA-seq data statistics; Table S8: KEGG analysis of all DEGs between different groups; Table S9: KEGG enrichment analysis for all DEGs in different groups.

Author Contributions

Conceptualization, L.H. and Q.Z.; methodology, L.H.; software, X.M.; validation, L.H., Q.Z. and M.W.; formal analysis, Z.X.; investigation, Y.Z.; resources, Z.X.; data collection, L.H.; writing—original draft preparation, L.H.; writing—review and editing, L.H.; visualization, Y.Z.; supervision, X.M.; project administration, L.H.; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Shanxi Province Science Foundation for Youths, grant number 202103021223140, and the Open Bidding for Selecting the Best Candidates for Key Technology Research Projects in Shanxi Province, grant number 202201140601027-3.

Data Availability Statement

The article contains all raw data. For further inquiries, please contact the corresponding author.

Acknowledgments

We thank Tao Xu and Hangzhou Lianchuan Biotechnology Co., Ltd.

Conflicts of Interest

The authors declare no conflict of interest.

References

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Figure 1. KEGG enrichment analysis of different accumulated metabolites for these three cultivars at different development stages. (a) Venn diagram for different groups. (b–d) Scatter plot of KEGG enrichment analysis for different development stages.
Figure 1. KEGG enrichment analysis of different accumulated metabolites for these three cultivars at different development stages. (a) Venn diagram for different groups. (b–d) Scatter plot of KEGG enrichment analysis for different development stages.
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Figure 2. KEGG enrichment analysis of different accumulated metabolites for three cultivars at their maturation stage. (a) KEGG enrichment analysis of the groups J2 vs. A2, (b) KEGG enrichment analysis of the groups J2 vs. M2. (c) KEGG enrichment analysis of the groups A2 vs. M2.
Figure 2. KEGG enrichment analysis of different accumulated metabolites for three cultivars at their maturation stage. (a) KEGG enrichment analysis of the groups J2 vs. A2, (b) KEGG enrichment analysis of the groups J2 vs. M2. (c) KEGG enrichment analysis of the groups A2 vs. M2.
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Figure 3. Statistical analysis of differentially accumulated metabolites for three cultivars at their maturation stage. (a) Venn diagram for different groups. (b) Heatmap of dynamic change for all significantly accumulated metabolites at different development stages.
Figure 3. Statistical analysis of differentially accumulated metabolites for three cultivars at their maturation stage. (a) Venn diagram for different groups. (b) Heatmap of dynamic change for all significantly accumulated metabolites at different development stages.
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Figure 4. The number of DEGs (differentially expressed genes) in different groups. J: “Kyoho”; M: “Mei Xiangbao”; A: “Adenauer Rose”; 1: grape berries in the turning stage; 2: grape berries in the maturation stage.
Figure 4. The number of DEGs (differentially expressed genes) in different groups. J: “Kyoho”; M: “Mei Xiangbao”; A: “Adenauer Rose”; 1: grape berries in the turning stage; 2: grape berries in the maturation stage.
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Figure 5. KEGG enrichment analysis of differentially expressed genes for three cultivars at their maturation stage.(a) DEGs of the groups J2 vs. A2, (b) DEGs of the groups J2 vs. M2. (c) DEGsof the groups A2 vs. M2.
Figure 5. KEGG enrichment analysis of differentially expressed genes for three cultivars at their maturation stage.(a) DEGs of the groups J2 vs. A2, (b) DEGs of the groups J2 vs. M2. (c) DEGsof the groups A2 vs. M2.
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Figure 6. Candidate genes and qRT-PCR verification. Green bars represent grape turning stage and red bars represent grape maturation stage. Error bars represent the SD of three biological replicates.
Figure 6. Candidate genes and qRT-PCR verification. Green bars represent grape turning stage and red bars represent grape maturation stage. Error bars represent the SD of three biological replicates.
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Figure 7. Correlation coefficients analysis for candidate genes and aromatic substances. (a) Transcription factor discovery and qRT-PCR verification. Green bars represent grape turning stage and red bars represent grape maturation stage. Error bars represent the SD of three biological replicates. (b) Pearson correlation coefficient analysis.
Figure 7. Correlation coefficients analysis for candidate genes and aromatic substances. (a) Transcription factor discovery and qRT-PCR verification. Green bars represent grape turning stage and red bars represent grape maturation stage. Error bars represent the SD of three biological replicates. (b) Pearson correlation coefficient analysis.
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Table 1. Dynamic change in aromatic compounds in three cultivars at different development stages.
Table 1. Dynamic change in aromatic compounds in three cultivars at different development stages.
ClassA2 vs. A1J2 vs. J1M2 vs. M1
No.UpDownNo.UpDownNo.UpDown
Acids101211110
Alcohols770651660
Aldehydes550220330
Amines110110110
Aromatics330624312
Esters9726421284
Heterocyclic compounds17161633852
Hydrocarbons871532321
Ketones660431331
Nitrogen compounds220000000
Terpenoids34331148612111
Other101101101
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Huang, L.; Zhu, Y.; Wang, M.; Xun, Z.; Ma, X.; Zhao, Q. Integrative Analysis of Transcriptome and Metabolome Reveals the Regulatory Network Governing Aroma Formation in Grape. Horticulturae 2024, 10, 1159. https://doi.org/10.3390/horticulturae10111159

AMA Style

Huang L, Zhu Y, Wang M, Xun Z, Ma X, Zhao Q. Integrative Analysis of Transcriptome and Metabolome Reveals the Regulatory Network Governing Aroma Formation in Grape. Horticulturae. 2024; 10(11):1159. https://doi.org/10.3390/horticulturae10111159

Chicago/Turabian Style

Huang, Liping, Yue Zhu, Min Wang, Zhili Xun, Xiaohe Ma, and Qifeng Zhao. 2024. "Integrative Analysis of Transcriptome and Metabolome Reveals the Regulatory Network Governing Aroma Formation in Grape" Horticulturae 10, no. 11: 1159. https://doi.org/10.3390/horticulturae10111159

APA Style

Huang, L., Zhu, Y., Wang, M., Xun, Z., Ma, X., & Zhao, Q. (2024). Integrative Analysis of Transcriptome and Metabolome Reveals the Regulatory Network Governing Aroma Formation in Grape. Horticulturae, 10(11), 1159. https://doi.org/10.3390/horticulturae10111159

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