Abstract

   Soil mulching is a useful agronomic practice that promotes early fruit
   maturation and affects fruit quality. However, the regulatory mechanism
   of fruit metabolites under soil-mulching treatments remains unknown. In
   this study, variations in the gene sets and metabolites of grape
   berries after mulching (rice straw + felt + plastic film) using
   transcriptome and metagenomic sequencing were investigated. The results
   of the cluster analysis and orthogonal projection to latent structures
   discriminant analysis of the metabolites showed a difference between
   the mulching and control groups, as did the principal component
   analysis results for the transcriptome. In total, 36 differentially
   expressed metabolites were identified, of which 10 (resveratrol,
   ampelopsin F, piceid, 3,4′-dihydroxy-5-methoxystilbene, ε-viniferin,
   trans resveratrol, epsilon-viniferin, 3′-hydroxypterostilbene,
   1-methyl-resveratrol, and pterostil-bene) were stilbenes. Their content
   increased after mulching, indicating that stilbene synthase activity
   increased after mulching. The weighted gene co-expression network
   analysis revealed that the turquoise and blue modules were positively
   and negatively related to stilbene compounds. The network analysis
   identified two seed genes (VIT_09s0054g00610, VIT_13s0156g00260) and
   two transcription factors (VIT_13s0156g00260, VIT_02s0025g04590).
   Overall, soil mulching promoted the accumulation of stilbene compounds
   in grapes, and the results provided key genetic information for further
   studies.

   Keywords: mulching, grape, stilbene, transcriptome, metabolomics, berry
   quality, weighted gene co-expression network analysis (WGCNA)

1. Introduction

   Grapevines are among the most economically important fruit trees
   worldwide [[32]1]. In most southern areas of China, the maturity period
   of table grapes is from July to August, whereas in most northern areas
   of China, the maturity period is in September. The concentration of
   maturity often decreases the price of table grapes. Early maturation is
   a key agronomic trait of plants; however, the common breeding procedure
   for early maturing varieties is challenging in fruit tree breeding
   [[33]2,[34]3]. Promoting early maturity through agronomic practices
   such as dormancy breaking or soil mulching—which can help raise the
   selling price of grapes, reduce the use of pesticides, and avoid the
   hazard of typhoons—is common and effective for boosting production
   [[35]4,[36]5].

   Yield and quality are important factors for producers when promoting
   early mature cultivation systems. Previous studies have shown that crop
   or fruit yield, nutritional content, and flavor change in an
   early-maturity cultivation model [[37]6,[38]7]. For example, a
   combination of chemical application treatments significantly advanced
   budbreak, and apple fruit quality and yield increased after treatment
   [[39]8]. Another study showed that different plastic-film mulch
   treatments accelerated tomato ripening and changed yield and quality
   [[40]9,[41]10]. Although agronomic practices for promoting early
   maturity can affect plant growth and quality performance, the
   underlying regulatory mechanisms remain unclear.

   Stilbenes are natural phenolic compounds synthesized in plants that
   mainly act as phytoalexins in response to abiotic- or biotic-stress
   conditions [[42]11,[43]12]. In addition, previous studies have
   demonstrated that stilbenes possess various health-promoting
   properties, including antioxidant, anti-aging, and anticancer effects
   [[44]13]. Stilbene occurred in limited plant species because stilbene
   synthase (STS), the key enzyme in stilbene biosynthesis, is not
   commonly distributed in all plant species [[45]14]. Table grapes or
   wine are primary sources of stilbenes for humans [[46]15]. The content
   of stilbenes in grapes can be affected by factors such as grape
   variety, growth stage, agronomy practices, and environmental conditions
   [[47]16,[48]17]. It remains unclear how the content of stilbenes in
   grapes varies under early maturing cultivation systems.

   A weighted gene co-expression network analysis (WGCNA) was used to
   determine modules of highly correlated genes and identify gene modules
   related to external phenotypic traits [[49]18]. This is useful for
   identifying genes associated with numerous gene sets and target traits.
   Thus far, it has been used to screen for key genes associated with
   plant development and quality as well as abiotic and biotic stresses
   [[50]19,[51]20,[52]21,[53]22]. Sheng et al. [[54]23] used transcriptome
   sequencing to identify gene co-expression patterns of gene networks
   highly related to sesame plant height; a module showed a significantly
   positive relationship with plant height, and a hub gene in the
   regulation of plant height was found using WGCNA. Wang et al. [[55]19]
   used metabolome- and genome-wide transcriptome analyses to reveal
   flavor formation in kiwifruit, and WGCNA was conducted to investigate
   the co-expression patterns of differentially expressed genes (DEGs),
   finding that gene sets in three modules were associated with
   flavor-associated metabolites. The same analysis method has also been
   used to investigate key gene modules associated with heat stress,
   drought stress, and powdery-mildew resistance [[56]19,[57]24,[58]25].

   Our previous study found that grape ripening occurred 5 to 7 days
   earlier in the soil mulch group than in the control group, but changes
   in fruit quality were not evaluated. The present study determined fruit
   quality during the maturation period using conventional physical
   testing and transcriptomic and metabolomic methods. This study aimed to
   reveal and explain the changes in grape quality during soil mulching.

2. Materials and Methods

2.1. Plant Materials and Field Experiment

   The field experiment was conducted in a vineyard within a plastic
   greenhouse. The study site was located at the Jiangsu Academy of
   Agricultural Sciences, Nanjing, Jiangsu Province, China (32°02′ N,
   118°52′ E, approximately above sea level, with an average annual
   precipitation of 1106 mm, frost-free 225 d). Zijinhongxia (Vitis
   vinifera L.) was used as the plant material in this study, with a
   planting density of 1.8 m between lines. There were two treatments with
   three replicates in the present study: (1) conventional tillage with no
   mulching (control) and (2) mulching with rice straw + felt + plastic
   film (treatment). Each replicate consisted of 3 grapevine trees. The
   field experiments were conducted between December 2021 and July 2022.
   In December 2021, the grapevines were spur-pruned. In February 2022,
   the plastic film was closed, and the grapevines were irrigated. Two
   days after irrigation, the soil was covered with rice straw (5 cm
   thickness), and then, an arched shed (0.57 m height, 0.8 m width) was
   constructed with iron wire (3.5 mm thickness), glass fiber bar (5 mm
   thickness, 1.5 m length), felt (200 g∙m^2), and plastic film (0.08 mm).

2.2. Plant Sampling

   The grape berries were sampled 90 d after flowering. Three biological
   replicates were obtained, each consisting of 54 berries from 6
   clusters. The berries were immediately frozen in liquid nitrogen and
   stored at −80 °C for ribonucleic acid (RNA) extraction and metagenomic
   analyses. In addition, fruit quality, including single-berry weight,
   longitudinal and transverse diameters, total soluble solids, total
   soluble sugar, and total titratable acid, was determined according to a
   handbook of plant physiology experiments and specifications for grape
   germplasm description [[59]26,[60]27]. Stilbene synthase activity was
   determined using an astragalus synthase (STS) ELISA kit (KIRbio,
   Beijing, China).

2.3. RNA Extraction, High-Throughput Sequencing, and Data Processing and
Analysis

   Using the RNAprep Pure Plant Kit (TIANGEN, Beijing, China), total RNA
   was extracted. Its quantity was measured by Nanodrop ND-2000 (Nanodrop
   technologies, Wilmington, DE, USA). The NEBNext Ultra II FS DNA Library
   Kit for Illumina^® (NEB, Ipswich, MA, USA) was used for complementary
   deoxyribonucleic acid library construction and then sequencing on
   Illumina sequencing platform (Metware Biotechnology Co., Ltd., Wuhan,
   China) [[61]28]. Gene expression was estimated using the fragments per
   kilobase of transcript per million mapped reads, which was computed
   using featureCounts v1.6.2/StringTie v1.3.4d [[62]29,[63]30]. The
   differential expression between the two groups was analyzed using
   DESeq2 v1.22.1/edgeR v3.24.3 [[64]31,[65]32], and the
   Benjamini–Hochberg technique was employed to adjust the p-values. For
   varying expression levels, the corrected p-value and |log2foldchange|
   were employed as thresholds. A hypergeometric test was used to perform
   an enrichment analysis. Pathway units were used in a hypergeometric
   distribution test for a Kyoto Encyclopedia of Genes and Genomes (KEGG)
   analysis. Finally, an analysis was conducted using gene ontology (GO).

2.4. Metabolomic Analysis

   Using a vacuum freeze-dryer (Scientz-100F, Ningbo Scientz Biotechnology
   Co., Ltd., Ningbo, China), the fruit samples were freeze-dried and then
   dissolved in 70% methanol solution. Using a UPLC-ESI-MS/MS system
   (ExionLCTM AD; MS, Applied Biosystems 6500 Q TRAP (ABsciex, Framingham,
   MA, USA)), the supernatant was analyzed under the analytical conditions
   specified by [[66]33]. The procedure outlined by [[67]34] was used to
   identify the differentially expressed metabolites (DEMs). The
   differences between the control and treatment groups were investigated
   using an orthogonal projection to latent structures discriminant
   analysis (OPLS-DA) model.

2.5. WGCNA and Correlation Analysis

   The software WGCNAv1.69 R was used for the gene co-expression network
   analysis [[68]35]. The networks were visualized by Cytoscape (3.8.2).
   Correlations between different modules derived from WGCNA and stilbene
   compounds were evaluated using Pearson correlation coefficients. The
   functions of the DEMs were determined using the KEGG database. The
   functions of the DEGs were annotated using GO and KEGG databases.

3. Results

3.1. Fruit Appearance and Quality

   Mulching treatment did not result in obvious differences in fruit
   appearance or quality ([69]Table S1). Various parameters including
   single-berry weight, transverse diameter, soluble solids, soluble
   sugar, and titratable acid did not show any significant variation after
   mulching. Only the longitudinal diameter increased under the mulching
   treatment ([70]Table S1). In both groups, the shape index fell in the
   range of 1.1–1.3, showing that berry shape did not change under
   mulching treatment.

3.2. Metabolomic Level Analysis of Grape Berries under Mulching Treatment

   Based on the metagenome results, two distinct distinctions were
   observed in both cluster analysis and OPLS-DA ([71]Figure 1). The
   results indicated clear differences between the mulch and control
   groups. All the metabolites determined were divided into nine classes
   under Class I, and alkaloid, nucleotide, derivative, and phenolic acid
   contents were higher in the mulching treatment than in the control
   ([72]Figure 1C, [73]Table S2). The detected compounds from the other
   Class I classes were also divided into nine classes, and aldehyde
   compounds and lactones were more abundant in the mulch treatment than
   in the control ([74]Figure 1D, [75]Table S3).

Figure 1.

   [76]Figure 1
   [77]Open in a new tab

   Metabolites of berry fruit in the control and mulch treatment groups:
   (A) Cluster analysis based on Bray–Curtis distances of metabolites; (B)
   OPLS-DA based on Bray–Curtis distances of metabolites; (C) Relative
   abundance of metabolites in Class I and (D) others in Class I.

   The metabolites that differed significantly between the control and
   mulch treatment groups were largely related to metabolism, particularly
   the biosynthesis of secondary metabolites ([78]Figure 2A). According to
   the KEGG enrichment results ([79]Figure 2B, [80]Table S4), the
   differential metabolites were assigned to 23 KEGG pathways, and the top
   4 significant pathways were stilbenoid, diarylheptanoid, and gingerol
   biosynthesis (ko00945); linoleic acid metabolism (ko00591); ether lipid
   metabolism (ko00565); and the biosynthesis of various plant secondary
   metabolites (ko00999).

Figure 2.

   [81]Figure 2

   [82]Figure 2
   [83]Open in a new tab

   Functions of differentially expressed metabolites (DEMs) in grape
   berries: (A) KEGG annotation pathways of DEMs; (B) KEGG pathway
   analysis of the DEMs. * p < 0.05, ** 0.001 < p < 0.01.

   Thirty-six differential metabolites were identified, as shown in the
   heatmap ([84]Figure 3A). Phenolic acids, flavonoids, and other
   compounds accounted for 66.67% of the differential metabolites, whereas
   stilbene metabolites accounted for more than 99% of the other
   metabolites ([85]Figure 3A). All stilbene metabolites observed
   (resveratrol, ampelopsin F, piceid, 3,4′-dihydroxy-5-methoxystilbene,
   ε-viniferin, trans resveratrol, epsilon-viniferin,
   3′-hydroxypterostilbene, 1-methyl-resveratrol, and pterostilbene) in
   this study were higher in the mulching treatment than in the control.
   Similarly, all phenolic acids (monogalloyl-diglucose,
   1,6-Di-O-galloyl-β -D-glucose, 6′-O-feruloyl-D-sucrose, sibiricose A5,
   gallic acid ethyl ester, ethyl gallate, gallic acid, 1-O-feruloylquinic
   acid, and 3,5-dihydroxytoluene) were higher in the treatment group than
   in the control group ([86]Figure 3A). The differential radar chart
   shows that the five most significant metabolites were
   2,3-dihydroxy-5(6),12(13)-diene-ursolic acid, 3,5-dihydroxytoluene,
   pterostilbene, gallic acid ethyl ester, ethyl gallate, and
   3,5-dihydroxytoluene ([87]Figure 3B).

Figure 3.

   [88]Figure 3

   [89]Figure 3
   [90]Open in a new tab

   Differentially expressed metabolites (DEMs) in the control and mulch
   treatment groups: (A) Heatmap of the DEMs between the control and mulch
   treatments. Green indicates downregulated metabolites and red indicates
   upregulated metabolites; (B) Radar charts of the DEMs in the control
   and mulch treatment groups.

3.3. Transcriptomic Level Analysis of Grape Berries between the Control and
Mulching Groups

   This study identified 289,253,990 reads from the six treatments; the
   Q20 rates were all greater than 97%, and the Q30 rates were all greater
   than 92% ([91]Table S5). As [92]Figure 4A shows, 641 DEGs (upregulated:
   357, downregulated: 284) were identified when the control and mulch
   treatment groups were compared. Based on the PCA results, the control
   and mulch treatments were grouped into two significantly different
   clusters ([93]Figure 4B). GO enrichment of the DEGs showed that
   cellular processes, metabolic processes, responses to stimuli in
   biological processes (BPs), cellular anatomical entities in cellular
   components (CCs), and binding and catalytic activities in molecular
   functions (MFs) were the most likely targets of mulch treatment
   ([94]Figure 4C). In addition, the KEGG pathway enrichment analysis
   results ([95]Figure 4D, [96]Table S6) showed that the top five enriched
   pathways were (ko04141), photosynthesis–antenna proteins (ko00196),
   galactose metabolism (ko00052), thiamine metabolism (ko00730), and
   steroid biosynthesis (ko00100). However, only one significantly
   enriched pathway (protein processing in the endoplasmic reticulum,
   corrected p < 0.001) was identified.

Figure 4.

   [97]Figure 4
   [98]Open in a new tab

   Expressions of berry fruit genes in the control and mulch treatment
   groups: (A) Counts of differently expressed genes in the control and
   mulch-treated groups; (B) PCA of berry fruits based on Bray–Curtis
   distances; (C) Number of upregulated and downregulated genes in the
   gene ontology (GO) classification; (D) KEGG pathways enriched with
   DEGs.

3.4. Integrated Transcriptomic and Metabolomic Analysis

3.4.1. KEGG Enrichment Analysis

   Furthermore, a combination of transcriptome and metabolomic analyses
   were used to determine the co-enrichment of DEGs and DEMs. The results
   showed that 14 KEGG pathways were mapped for the enrichment of DEGs and
   DEMs ([99]Figure 5). Among the DEM-enriched KEGG pathways, four
   pathways (stilbenoid, diarylheptanoid, gingerol biosynthesis, linoleic
   acid metabolism, ether lipid metabolism, and biosynthesis of various
   plant secondary metabolites), stilbenoid, diarylheptanoid, and gingerol
   biosynthesis were significantly enriched. In addition, the stilbene
   synthase activity in the mulch treatment was higher than that in the
   control ([100]Figure 5). These results indicate a greater effect of
   mulching on metabolites related to stilbene compounds.

Figure 5.

   [101]Figure 5
   [102]Open in a new tab

   Statistical diagrams of KEGG pathways co-enriched with DEMs and DEGs.

3.4.2. Weighted Gene Co-Expression Network Analysis of DEGs and Correlation
with Stilbene Compounds

   To further determine the molecular mechanism of the change in stilbene
   caused by mulching, 24,760 genes were subjected to WGCNA, which
   identified 10 co-expression modules ([103]Figure 6A). A module–trait
   heatmap showed that genes belonging to the turquoise and blue modules
   were more strongly correlated with stilbene compounds than genes from
   the other modules ([104]Figure 6A).

Figure 6.

   [105]Figure 6
   [106]Open in a new tab

   Weighted correlation network analysis modules of all genes established
   after soil mulch treatment: (A) Stilbene activity in the control and
   mulch treatment groups; (B) Tree graphs of 3000 genes by hierarchical
   clustering of overlapping topological dissimilarities. Red indicates a
   positive correlation, and blue indicates a negative correlation; (C)
   Heatmap showing module–stilbene compound correlations. Each row
   corresponds to a module indicated by a different color. Red indicates a
   positive correlation, and blue indicates a negative correlation; (D)
   Co-expression gene network of MEblue and MEturquoise. Blue indicates
   gene expression in the control, and pink indicates gene expression in
   the mulch. * p < 0.05, ** 0.001 < p < 0.01, *** p < 0.001.

   Next, a correlation analysis was conducted between the contents of the
   10 stilbene compounds and the 10 modules. The correlation analysis
   results showed that the accumulation of transcripts in turquoise and
   blue was significantly positively and negatively correlated with
   stilbene-associated metabolites, including
   3,4′-dihydroxy-5-methoxystilbene, piceid, and 1-methyl-resveratrol,
   respectively ([107]Figure 6B). These results indicate that the genes in
   these two modules were mainly associated with stilbene compounds.
   Furthermore, hub gene networks of the turquoise and blue modules were
   obtained ([108]Figure 6C). The number of hub genes in the turquoise
   module in the mulching treatment was higher than that in the control,
   whereas the counts of hub genes in the blue module were higher in the
   control than in the mulching treatment; VIT_09s0054g00610 was the seed
   gene in the turquoise module, and VIT_13s0156g00260 was the seed gene
   in the blue module. In these two modules, transcription factors
   HB-HD-ZIP TCP (VIT_13s0156g00260) and TCP (VIT_02s0025g04590) were
   observed.

4. Discussion

   As a conventional soil management practice, mulching affects not only
   soil conditions but also plant yield and quality. Previous studies have
   shown that mulch strategies can enhance fruit production and quality
   [[109]36]. For example, ref. [[110]37] found that organic and plastic
   mulches had significant positive effects on pomegranate fruit size,
   which contributed to an increase in fruit yield, while [[111]38] showed
   that tomato yield decreased with plastic-film mulching treatment. In
   the present study, no significant differences were observed in the
   berry fruit weight. The berry shape index showed that the shapes of the
   berries were the same in the control and mulch treatment groups. This
   result was consistent with that reported by [[112]39]. In this study,
   soluble solids, sugars, and titratable acids were evaluated. Some
   studies have found that plastic films or a combination of plastic films
   and straw mulches improve the total soluble-solid content
   [[113]40,[114]41]. However, another study found that mulched soil in
   double-layer high tunnels led to lower total soluble solids and total
   soluble-acid values in tomatoes than those in bare soil [[115]42]. No
   obvious differences were observed between the three indicators.
   Therefore, variations in fruit weight, yield, and quality among the
   different mulching treatments were not always the same, and the
   treatment period, plant species, and mulching materials might have
   affected these indicators.

   Metagenomic sequencing results showed that the content of several
   stilbene compounds increased with the mulching treatment ([116]Figure
   3). KEGG pathways of the DEMs related to stilbenoid, diarylheptanoid,
   and gingerol biosynthesis were identified ([117]Figure 2). The results
   showed that the grapes subjected to the mulching treatment in our study
   had higher antioxidant properties. Previous studies have shown that the
   KEGG pathway of DEGs in roots between plastic-film mulching and clean
   tillage is linked to stilbenoid, diarylheptanoid, and gingerol
   biosynthesis; however, no stilbene compounds were found in this
   treatment. In addition, our previous study showed that, under mulching
   treatment (rice straw + felt + plastic film), grapevine root tissue
   DEGs were significantly enriched in stilbenoid, diarylheptanoid, and
   gingerol biosynthesis [[118]5]. Previous studies have shown that
   stilbenes are involved in grapevine defense responses against fungal
   pathogens and abiotic stress [[119]43,[120]44,[121]45]. Our previous
   study found that the plant–pathogen pathway was enriched under mulching
   [[122]5], which may explain why stilbene-related pathways were enriched
   after treatment.

   Two modules significantly related to stilbene compounds
   (3,4′-dihydroxy-5-methoxystilbene, piceid, and 1-methyl-resveratrol)
   were determined by integrated transcriptomic and metabolomic analyses
   using WGCNA. Two key transcription factors were identified in both the
   modules. Several transcription factors that are involved in regulating
   stilbene biosynthesis have been reported [[123]46]. MYBs are among the
   largest families of plant transcription factors that regulate stilbenes
   [[124]47]. The Chinese wild grape VdMYB1 transcription factor might
   activate the gene expression of stilbenoid biosynthesis enzymes
   [[125]48]. WRKY and ERF have also been found to regulate stilbene
   synthesis by interacting with MYB [[126]49,[127]50]. In the present
   study, HB-HD-ZIP TCP (VIT_13s0156g00260) and TCP (VIT_02s0025g04590)
   had positive and negative relationships, respectively, with the number
   of stilbene compounds (3,4′-dihydroxy-5-methoxystilbene, piceid, and
   1-methyl-resveratrol). The transcription factors related to stilbene
   compounds that we identified were different from those identified in
   previous studies, and the target metabolism differed between the
   present and previous studies. While the previous study focused on
   resveratrol synthesis, our study focused on
   3,4′-dihydroxy-5-methoxystilbene, piceid, and 1-methyl-resveratrol. Two
   key seed genes (VIT_09s0054g00610 and VIT_13s0156g00260) were
   identified in the turquoise and blue modules; however, their function
   and structure require further investigation. In addition, the results
   were obtained during the grape maturity period, and all berry
   development periods should be considered in further research, which
   would provide more sufficient and comprehensive data for the study of
   related mechanisms.

   Stilbenes, a major family of polyphenols, exhibit a wide range of
   pharmacological properties and various health benefits
   [[128]51,[129]52]. In our study, 10 stilbene compounds were detected,
   which showed a significantly increasing trend after mulch treatment
   ([130]Figure 3). Resveratrol, ampelopsin F, piceid,
   ε-viniferintrans-resveratroll, epsilon-viniferin, and pterostilbene
   possess anticancer, antioxidant, anti-inflammatory,
   cardiovascular-protective, and anti-aging properties
   [[131]53,[132]54,[133]55,[134]56,[135]57,[136]58]. Overall, the
   mulching treatment used in this study may have beneficial effects on
   grapes.

Supplementary Materials

   The following supporting information can be downloaded at
   [137]https://www.mdpi.com/article/10.3390/foods13193208/s1, Table S1.
   Fruit quality under the control and mulch treatment; Table S2. Detected
   Class I compounds; Table S3. Compounds detected from classes other of
   Class I; Different lowercase letters indicate significant differences
   among groups (p < 0.05) in Tables S1 to S3. Table S4. KEGG enrichment
   of differential metabolites between the control and mulching; Table S5.
   Quality control results of transcriptional sequencing; Table S6. KEGG
   pathway enrichment of DEGs.
   [138]foods-13-03208-s001.zip^ (18.1KB, zip)

Author Contributions

   Conceptualization, B.W. and W.W.; Software, B.W.; Formal analysis,
   B.W.; Investigation, B.W. and Z.C.; Resources, W.W. and Z.C.; Data
   curation, B.W., Z.W., Z.C. and X.W.; Writing—original draft, B.W.;
   Writing—review and editing, Z.W. and X.W.; Visualization, B.W.; Project
   administration, W.W.; Funding acquisition, X.W. All authors have read
   and agreed to the published version of the manuscript.

Institutional Review Board Statement

   Not applicable.

Informed Consent Statement

   Not applicable.

Data Availability Statement

   The original contributions presented in this study are included in this
   article/[139]Supplementary Materials; further inquiries can be directed
   to the corresponding author.

Conflicts of Interest

   The authors declare no conflicts of interest.

Funding Statement

   This research was funded by the Jiangsu Agricultural Science and
   Technology Innovation Fund [CX(21)2027].

Footnotes

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References