1. Introduction
Recently the European Commission announced the “European Green Deal”, a set of policy initiatives that aim to reform several European economic activities to be climate friendly by 2050 [1]. Among them, the “From Farm to Fork” strategy [2] highlights the need of food production chain sustainability, by decreasing the use of agrochemicals (fertilizers and pesticides) and, at the same time, ensuring the production of healthy, high quality, cost-effective food products for the consumers. Under this framework, studies on sustainable cropping systems aim to deliver innovative solutions and provide sufficient, safe, nutritious, healthy, and affordable food. Under that scope, there is a growing interest on molecules of biological or chemical origin, like elicitors, biostimulants and plant hormones with multiple modes of action, e.g., (i) acting against pests/pathogens, (ii) increasing nutrient acquisition and resource use efficiency (like nutrients and water), and (iii) ameliorate quality characteristics of the food product. Their application in crop management systems aims to increase agriculture sustainability and ensure environmental security.
Among various crops, viticulture has an important economic impact, especially on grape-growing countries, like Greece. Vines produce a variety of products, like dry grapes, table grapes, grape juice, and the most significant commercially product, wine. In order to ensure grape quality and productivity, common management plans include an intensive schedule of agro-chemicals application [3]. At the same time, there is a growing interest in management practices that overcome biotic and abiotic stresses [4], including new and innovative techniques, such as the application of elicitors and plant hormones [5,6].
Plant defense elicitors trigger one of the two main plant defense pathways, (i) the systemic acquired resistance (SAR) pathway, that is mediated by salicylic acid (SA), and (ii) the induced systemic resistance (ISR) pathway, which is mediated by jasmonic acid (JA) and ethylene [7,8]. Despite the fact that elicitors were first used as an alternative to conventional pesticides and fungicides [8], in the last few decades, they have been applied in vines with the aim to improve the grape quality, mainly by altering grape phenolic composition [9,10,11,12]. The most widely used plant defense molecules are the elicitors chitosan (CHT) and benzothiadiazole (BTH) and the plant hormone abscisic acid (ABA). Chitosan is a polysaccharide derived from chitin degradation with both antimicrobial activity and plant growth promoting activity [13]. Benzothiadiazole is a functional analog of SA that elicits the SAR defense pathway and the synthesis of a variety of bioactive secondary metabolites, like phenolic compounds [14]. Abscisic acid is a phytohormone with several roles, including growth regulation, stomatal control, leaf aging, and dormancy-induction [15].
Application of CHT in various grape varieties was reported to induce the production of chitinase and glucanase common pathogenesis-related proteins (PRPs) [16], plant defense enzymes precursors in phenolic compound biosynthesis, like phenylalanine ammonia-lyase (PAL), leading to accumulation of phenolic compounds [17], that add resistance to various abiotic stresses [18]. Application of BTH to the Monastrell variety increased the content of phenolic compounds in both berries and wine [19], increased the biosynthesis of anthocyanins, flavonolols and stilbenes in grapes in Monastrel clones [20], and the phenolic and chromatic characteristics in Merlot and Syrah varieties [5]. Application of both chitosan and benzothiadiazole changed the aromatic profile of wine produced by the Groppello Gentile variety [21] and increased anthocyanins and resveratrol in Merlot grape berries [22]. Finally, the use of ABA, improved the total anthocyanin concentration and the individual anthocyanins [20,23,24] and the C6 aroma content in Cabernet Sauvingnon [25]. Chemicals, including hormones, in plants are similar to those in mammals. In humans, for example, the neurotransmitter serotonin is synthesized from the essential amino acid tryptophan. In plants, auxin is made from tryptophan and related to serotonin. Auxin levels may have the potential to affect depression. Gibberellic acids are plant hormones produced by microbes. They have anti-inflammatory properties. However, the relationships between plant hormones, plant microbes, human gut microbes, and human hormones is complicated and poorly understood and their mode of action remains unclear [26].
Plants are the habitat of diverse microbial communities that interact with each other and with their host, forming the plant holobiont [27]. Grapevine-associated microorganisms have a documented effect during wine fermentation [28] and can act as signature of grape origin [29,30]. Moreover, it was recently demonstrated that microbial activity, combined with the abiotic and biotic factors, contributes to characterize the wine microbial “terroir” [31]. Although high-throughput sequencing technologies have been widely used to investigate the microbial ecology of various environments, their application in grapevine and wine fermentation microbial ecology is relatively recent [32]. Grape microbial diversity is found to be driven by biotic factors like cultivar and soil microbiome and abiotic factors like climatic conditions both macro- and micro-climate, the seasonal environmental conditions, and viticultural farming practices while the wine microbiome is affected by fermentation process applied during winemaking [28,29,33]. Soil acts as a microbial reservoir for plants, and usually, microbial diversity is higher in the rhizosphere than at aboveground parts, due to the more uniform micro-environmental conditions, the highly selective nutrient-poor conditions and high exposure to variable abiotic factors (i.e., temperature, humidity, and UV radiation intensity) of the phyllosphere and carposhere [30].
Plant-associated microbial communities are in constant communication, controlling for, and controlled by, the biosynthetic pathways of the plant [34,35]; thus, exogenous application of plant biosynthetic activators, such as elicitors, biostimulants and phytohormones, can bring modifications to these microbial communities. Thus far, little attention has been given to fungi and bacteria, which interact with the grapevine to trigger the secondary metabolism and promote the biosynthesis of phenolic compounds. Cappelletti et al. [36] observed that fungal and bacterial communities of the phyllosphere of the grapevine cultivar Pinot noir ENTAV115 were altered after the application of nutrient broth as a plant resistance inducer. Villegas et al. [37] recorded that pectin-derived oligosaccharide acquired from the Aspergillus niger increased the color and anthocyanin content of grape berries. Moreover, arbuscular mycorrhizal fungi and plant growth-promoting pseudomonads can increase anthocyanin concentrations in strawberry fruits [38]. In addition to this, the endophytic fungi Altenaria can alter anthocyanins in grape cells [39].
In the present study, we investigated the effects of foliar application of two elicitors, chitosan and benzothiadiazole, and a plant hormone, abscisic acid, on a local red grape variety Mouhtaro cultivated from the Muses Valley (Ascri, Viotia, Greece), by analyzing berry quality characteristics, as well as plant-associated microbial communities. To address this, we monitored berry quality characteristic at harvest in parallel with the bacterial and fungal communities of the rhizosphere, phyllosphere, and carposhpere.
2. Materials and Methods
2.1. Experimental Set up and Sampling
Experiments were carried out in a Vitis vinifera L. cv Mouhtaro non-irrigated vineyard of Muses Estate Winery at Muses Valley (PGI Thiva, Greece; 38°19′30.9′’ (N) and 23°05′37.7′’ (E)). Vines were planted in 2007 onto a R110 rootstocks (planting density 2.5 × 1.2 m) and trained on double cordon. The pruning system followed was 3 spurs in each cordon. Vineyard management was uniform within the whole experimental plot and in compliance with the recommended agricultural practices for the given viticulture location. Three different treatments were applied. Vines were sprayed with an aqueous solutions of (i) 400 mg/L abscisic acid (s-abscisic acid 10.4% w/v, Protone SL, Hellafarm, Greece) (Treatment ABA), (ii) 0.3 mM benzothiadiazole (benzo-(1,2,3)-thiadiazole-7-carbothioic acid S-methyl ester, BTH, trade name Bion, Syngenta, Basilea, CH) and (iii) 0.3% chitosan (chitosan hydrochloride 3% w/w, CHT, Project One, Phytorgan S.A., Greece). In BTH and CHT treatments, Tween 80 (Sigma–Aldrich), and in ABA treatment, Aquascope (Hellafarm, Greece), were used as wetting agents. No sprayed vines served as control (control treatment). All treatments were applied in triplicate, in sets of 10 vines in a row for each one, in three completely randomized blocks (Supplementary Figure S1a). For the ABA treatment, spraying was performed at the grape zone at veraison stage, and then 3 and 6 days after the first application. In the case of BTH and CHT applications were carried out in the whole vine canopy at veraison and then 7 and 14 days later (Table 1).
A total of 200 berries from each treatment were selected randomly. Grape maturity level was monitored weekly by measuring the sugar content, titratable acidity, pH and the weight of 100 berries. Additionally, at the optimum technological maturity stage berries were collected to determine their phenolic maturity level.
For microbial community analyses of the rhizosphere, the phyllosphere and the carposphere, sampling was performed at harvest as follows: from each experimental block of each treatment, four vines were selected, and four soil cores, four samples of leaves and four samples of berries were collected (Supplementary Figure S1b). For ABA-treated vines, since application was performed on the bunches, only berries were sampled for that treatment. The four soil cores were collected from a depth of 0–30 cm and by two were combined, homogenized, sieved (2-mm pore size) and stored at 4 °C until being transferred to the laboratory. For the epiphytic microbial communities of the phyllosphere and the carposphere, the four samples of leaves and berries that were collected were combined into two sterilized falcon tubes and stored at 4 °C until being transferred to the laboratory. A total of 60 samples (18 soil, 18 leaves, 24 fruits) were collected for the analysis of the prokaryotic and fungal microbiome, resulting in six biological replicates per treatment.
2.2. Must Conventional Analyses
The main must parameters, sugar content, titratable acidity and pH were determined in accordance with the official methods, as described in the compendium of international methods of wine and must analysis (OIV 2018). In addition, the weight of 100 berries was analyzed (g/berry). The color intensity, tonality, and percentage of blue tones (% blue) were evaluated as indicates in the Glories method (1984). Briefly, 100 grape berries were homogenized using a high-speed Ultra-Turrax at 11,500 rpm for 30 s (T25, IKA, Germany). Fifty grams of homogenized grapes were extracted with (a) 50 mL of 0.1 N HCl solution at pH 1.0 and (b) 50 mL of tartaric acid solution (5 g tartaric acid in 20 mL 1 N NaOH, made up to a 1 L volume with distilled water) at pH 3.2. The samples were shaken at room temperature for 4 h, and then they were centrifuged (13,440× g) at 4 °C for 10 min. The phenolic maturity parameters were determined through a spectrophotometric analysis of the juice. An ultraviolet–visible (UV–Vis) spectrophotometer was used (UV-1900, Shimadzu, Duisburg, Germany). The phenolic richness (A280) was obtained by measuring the absorbance at the wavelength of 280 nm, at pH 3.2, and was expressed as absorbance units (AU) × dilution factor. The anthocyanins extractable at pH 1.0 (ApH1.0) and pH 3.2 (ApH3.2) were measured at 520 nm and expressed as mg of malvidin-3-O-glucoside equivalents/L of extract. The seed maturity index (MP%) was calculated as MP% = [A280 nm − (ApH3.2/1000) × 40]/A280 nm and the skin maturity index was calculated as MS%= 100-MP%
2.3. DNA Extraction, Amplicon Sequencing and Bioinformatic Analysis
Soil DNA extraction was performed from 0.25 g of soil with the DNeasy Power soil DNA isolation kit following the manufacturers’ protocol (MoBio Laboratories, Carlsbad, CA, USA). Leaves (4–5 leaves) and berries (30–40 g) were washed with a sterilized isotonic solution of phosphate-buffered saline (PBS; 0.1 M) in 50 mL falcon tubes. The epiphytic microbial communities were detached from the surface with four-cycles of 2 min sonication/vortex intervals to achieve maximum recovery of the epiphytic biofilms. Following this, leaves and berries were removed from the tubes with a sterile tong and the solution centrifuged at 8000× g at 4 °C. DNA from the resulting microbial pellets was extracted with the DNeasy Power soil DNA isolation kit following the manufacturer’s protocol (MoBio Laboratories, Carlsbad, CA, USA). DNA was quantified using a Qubit 2.0 Fluorometer (Life Technologies, Paisley, UK).
Prokaryotic and fungal communities were analyzed by amplicon sequencing analysis. For prokaryotes, the V4 region of the 16S rRNA gene was amplified with 515f–806r primer pair [40], while for fungi, the ITS2 region was amplified with the primer pair fITS7 [41]–ITS4 [42]. Amplicon multiplexed libraries were constructed with a two-step amplification protocol where a unique 12-bp index was added, either to the forward or reverse primer, to serve as a barcode. Finally, a 2 × 250 bp pair-end sequencing was performed using a HiSeq2500 instrument in Rapid Mode (Illumina, San Diego, CA, USA) at the Genome Sequencing Center of the Brigham Young University (Provo, UT, USA).
The retrieved sequences were demultiplexed with Flexbar v3.0 [43]. In brief, DADA2 [44] was used to remove primers, filter, and denoise sequences, remove chimaeras, merge reads, and construct amplicon sequence variants (ASVs), identify representative sequences of ASVs, and to create an ASV table. The representative sequences of each ASV were classified against the Silva v138 for prokaryotes [45] and UNITE database for fungi [46]. Sequences classified in non-target taxa (e.g., unknown, chloroplasts, mitochondria for the 16S rRNA gene and unknown or protists for the ITS) were removed. Raw sequences were submitted to NCBI under the Bioproject ID PRJNA704932.
2.4. Statistical and Diversity Analysis
All analyses were performed with R software [47] and the same steps were followed for both prokaryotic and fungal communities. ASVs, with a relative abundance lower than 0.1% in the two samples, were excluded from downstream analyses. α-diversity indexes for richness (Observed) and diversity (Shannon) were determined with the phyloseq package [48]. The non-parametric Kruskal–Wallis test was used to investigate the main effects of the treatments on α-diversity. β-diversity patterns, thus variations in the structure of microbial communities, were visualized with nonmetric multidimensional scaling (nMDS) based on a Bray–Curtis dissimilarity matrix and permutational multivariate analysis of variance (PERMANOVA; adonis function, with 999 permutations) was applied to assess the effects of the treatments on β-diversity. Significance of differences in the microbial members of the prokaryotic and fungal community in each habitat (rhizosphere, phyllosphere and carposphere) between the treatments (control, CHT, BTH, ABA) were tested using linear discriminant analysis (LDA) and effect size (LEfSe) analysis [49]. Emerged taxa from LefSe analyses was used to generate taxonomic cladograms, illustrating differences between treatments. Finally, Spearman correlation was used to identify significant links between berry chemical characteristics and (i) α-diversity indices per treatment, and (ii) discriminant ASVs from LEfSe analysis. All plots were generated in R with the package ggplot2 [50]. All values are presented as the mean with standard deviation. The significance in the differences in grape berries characteristics was determined with an unpaired t-test or one-way ANOVA with Tukey’s test. A p-value ≤ 0.05 was considered to indicate statistical significance.
3. Results
3.1. Chemical Analysis of Grape Berries
Table 1 shows the influence of the treatments on the standard grape characteristics.
A decreasing trend for the mean berry weight after the application of all treatments was observed. The berries treated with CHT, showed a significantly lower value compared to the other treatments. Regarding the total soluble solids (TSS), grapes treated with ABA and BTH showed the highest values. Similar results were recorded for the total acidity (TA), where grapes treated with ABA and BTH showed increased TA. Finally, the values of pH tended to be slightly decreased compared to the control, with a lower value observed for BTH; however, no statistically significant differences were observed among the pH values (Table 2).
3.2. Phenolic Composition of the Grape Berries
The effects of the treatments on the grape berries phenolic composition using Glories assay are reported in Table 3; Table 4. All elicitors increased the anthocyanins concertation in comparison with the control. The CHT treatment presented the highest values followed by the BTH and ABA treatments. Control grapes had significantly lower anthocyanin concentrations. Extractable anthocyanins (pH 3.2) were also significantly lower in the control grapes. All elicitor/hormone treatments resulted in higher anthocyanin extractability levels, though the difference was not significant for BTH (Table 3). These data indicate that the vine treatments accelerated skin maturation in relationship with the anthocyanin composition.
Data related with the tannin’s maturity of the grapes are presented in Table 4.
The seed maturity index of the CHT-treated grapes (43.2%) was significantly lower compared to all other treatments, including the control (50.5–53.9%). On the contrary, the skin maturity index of the CHT grapes had higher values in comparison with the ABA, BTH, and control grapes. It seems, therefore, that for the CHT-treated berries, there is a higher extraction of tannins from skins than from seeds, in contrast with the other treatments. Hence, the maturity level of the CHT-treated berries is higher regarding the phenolics level, compared to the other treatments, including the control.
3.3. α-Diversity Patterns
Initially, quality filtering and ASVs construction, resulted in 12054 ASVs for prokaryotes and 4093 ASVs for fungi. After removal of the ASVs with extremely low relative abundance 1483 prokaryotic ASVs and 537 fungal ASVs, that represent more than 86% of the total communities, remained. α-diversity patterns were plant compartment dependent. For both microbial groups ASV-based richness and Shannon index was higher in the rhizosphere and gradually decreasing in the phyllosphere and the carposphere (Figure 1). The application of the elicitor/hormone treatments significantly affected prokaryote richness and diversity in the rhizosphere, where BTH treated vines showed the lowest index values (pKruskal-Wallis< 0.001) (Figure 1a). Phyllosphere’s richness and diversity were not affected (pKruskal-Wallis > 0.05), while carposphere’s richness and diversity tended to increase in BTH and ABA treated vines, but that increase was not significant (pKruskal-Wallis > 0.05) (Figure 1a). Rhizospheric fungal community richness and diversity did not change with respect to treatments (pKruskal-Wallis > 0.05), but differences were detected in phylosphere’s diversity and carposphere’s richness and diversity (Figure 1b). BTH treatment increased diversity in the phyllosphere and along with ABA decreased both richness and diversity in the carposphere (pKruskal-Wallis < 0.01) (Figure 1b).
Based on Spearman’s correlation, only non-treated control vines’ prokaryotic richness in the carposphere was positively correlated with the °Brix value (Spearman r: 0.77, p < 0.01). For AΒA, CHT, and BTH there was no link between α-diversity indexes and the physiochemical profile of the berries.
3.4. β-Diversity Patterns
In terms of β-diversity, the applied treatments were found to affect both prokaryotic and fungal communities differently for rhizosphere, phyllosphere, and carposphere. Bray–Curtis dissimilarity analysis and its visualization through nonmetric multidimensional scaling (NMDS) plots (Figure 2), accompanied by PERMANOVA analysis (Table 5), were adopted to investigate these effects.
In the rhizosphere, PERMANOVA analysis revealed that applied treatments significantly affected the β-diversity, explaining 26.5% (p < 0.001) and 17.7% (p < 0.001) of the prokaryotic and fungal community variations. NMDS plots demonstrated that, for the prokaryotic community, BTH application resulted in a clearer community separation from the control compared to CHT (Figure 2a), while for fungal communities, treatments were not distinct from the untreated vines (Figure 2b). In the phyllosphere, the applied treatments explained 13.4% of the prokaryotic community variation and 19.3% of the fungal community variation, while, from the NMDS plots, we see that for both microbial groups that both CHT and BTH did not separate from the control vines (Figure 2c,d). Finally, in the carposphere, we detected a larger effect of treatments, which explained 27.5% of the prokaryotic community variation and 29.5% of the fungal community variation. NMDS plots highlighted that BTH and ABA treatments had a larger effect on prokaryotes compared to control and CHT treated vines, which tended to group together (Figure 2e) and the fungal community was more distinct for ABA treatment (Figure 2f).
3.5. Community Structure and Discriminant ASVs
The composition of the prokaryotic microbial community at the phylum level across the different habitats and treatments is presented in Supplementary Figure S2a and, at the genus level, is presented in Figure 3a. At the phylum level, the rhizosphere was dominated by Actinobacteriota (30%), Proteobacteria (26.5%), Acidobacteriota (7.13%), Crenarchaeota (6.84%), Bacteroidota (5.75%), Chloroflexi (5.36%), Myxococcota (4%), Verrucomicrobiota (4%), Planctomycetota (3%), and Firmicutes (2.5%). The phyllosphere was dominated by Proteobacteria (46.8%), Actinobacteriota (23.6%), Firmicutes (15%), Bacteroidota (6%), Chloroflexi (2%), Myxococcota (1.5%) and Acidobacteriota (1%). Finally, the prokaryotic community of the carposphere was dominated by Proteobacteria (58%), Actinobacteriota (17.6%), Firmicutes (17%), Bacteroidota (3.5%), and Chloroflexi (1%). Regarding treatments, only BTH application altered the prokaryotic microbial community in all three habitats, with more profound effects on the rhizosphere, where a decrease in Actinobacteriota but an increase in Acidobacteriota, Crenarchaeota, Chloroflexi, Verrucomicrobiota and Planctomycetota were observed (Supplementary Table S1). Firmicutes decreased in both the phyllosphere and carposphere and Bacteroidota increased in the phyllosphere (Supplementary Table S1). Regarding the composition of the fungal microbial community at the phylum level, Ascomycota and Basidiomycota were the most dominant phyla across the different habitats and treatments (Supplementary Figure S2b; Supplementary Table S2), while no significant difference was recorded after elicitor/hormone application regarding their abundances (Supplementary Table S2).
At the genus level, sequences were assigned to 424 genera in total. In the rhizosphere, 22 had a relative abundance >1% on average and represented 44.6% of the total community (Figure 3a). The five more dominant genera of the rhizosphere were an unassigned genus of Solirubrobacterales (4.4%), an unassigned genus of Nitrososphaeraceae (3.8), an unassigned genus of Gaiellales (3.8%), Skermanella (3%) and Gaiella (2.3%). Twenty-two genera were found in the phyllosphere with relative abundance >1% on average and represented 56% of the total community (Figure 3a). The five most abundant were Skermanella (12%), Massilia (7.5%), Pseudarthrobacter (3.7%) Blastococcus (3.3%) and Microvirga (3%). In the carposphere, 27 genera showed abundance >1% on average and represented 62% of the total community (Figure 3a) and the five most abundant were Burkholderia-Caballeronia-Paraburkholderia (10.3%), Massilia (5%), Pantoea (4.2%), Cutibacterium (3.3%) and Skermanella (3.2%). Compared to rhizosphere, the genera Lactococcus, Bacillus, and Lactobacillus, were more abundant in both the phyllosphere and the carposphere. Among the treatments, the application of BTH affected the relative abundance of the bacterial genera more in the rhizosphere and the phyllosphere, while, in the carposphere, ABA treatment led to higher abundances of Massilia, an unassigned genus of Planococcaceae and Staphylococcus, and CHT treatment led to higher abundances of Cutibacterium, Lactococcus and Lactobacillus. It is worth mentioning that the genus Oenococcus, members of which are the most important lactic acid bacteria and are most frequently associated with malolactic fermentation in wine, was only present in the carposphere of the ABA-treated berries, but in low relative abundance, 0.08% of the total community.
Fungal sequences were assigned in 233 genera in total. In the rhizosphere, 19 had a relative abundance >1% on average and represented 76% of the total fungal community (Figure 3b). The five most dominant genera of the rhizosphere were Gibberella (12%), Fusarium (11%), Hormonema (8.3%), Solicoccozyma (6%) and an unassigned genus of Agaricomycetes (4.5%). In the phyllosphere, eleven genera had a relative abundance >1% on average and represented 87% of the total community (Figure 3b). The five most abundant were Alternaria (30%), Mycosphaerella (17%), an unassigned genus of Ustilaginaceae (14.5%), Aureobasidium (7.3%) and an unassigned genus of Didymellaceae (6%). In the carposphere, 14 genera had abundance >1% on average and represented 89% of the total community (Figure 3b) and the five most abundant were Alternaria (23%), Mycosphaerella (17.3%), Cladosporium (11.3%), Aureobasidium (10.5%) and an unassigned genus of Ustilaginaceae (7.5%). The application of the two elicitors and the plant hormone had a lesser effect on the fungal community compared to the prokaryotic community. More specifically, BTH in the rhizosphere increased the relative abundance of Gibberella and Hormonema compared to CHT and the control and in the phyllosphere both BTH and CHT increased the relative abundance of Pseudopithomyces and Epicoccum. In the carposphere among all applications, ABA treatment had a stronger effect by leading to an increased relative abundance of Aureobasidium and a decreased abundance of Cladosporium and Stemphylium. The Saccharomyces genus, members of which are yeasts involved in winemaking, was only present in the carposphere of the ABA-treated berries but in low relative abundance, 0.36% of the total community.
LEfSe results presented as cladograms (Supplementary Figure S3) highlight the significant differences in the microbial members of the prokaryotic and fungal communities in each habitat (rhizosphere, phyllosphere, and carposphere) and between the control and the treatments (CHT, BTH, and ABA). In the rhizosphere, from the 993 prokaryotic and 355 fungal ASVs used in the analyses, only 7% (69 ASVs, 37 for BTH and 16 for CHT) and 4.8% (17 ASVs; 7 for BTH and 10 for CHT) were found to be discriminant for the applied treatments. Discriminant prokaryotic ASVs with a relative abundance more than 1% in BTH treated rhizospheres belonged to genera Candidatus_Nitrososphaera, RB41, Blastococcus, Nitrososphaeraceae_Unknown, Candidatus_Nitrocosmicus, JG30_KF_CM45_Unknown, KD4_98_Unknown, Skermanella and in CHT-treated rhizospheres belonged to Bacillus (Supplementary Table 3). Regarding discriminant fungal members, in the BTH-treated rhizospheres, one ASV had a relative abundance more than 1% and was affiliated to the species Gibberella intricans and in the CHT-treated rhizospheres two ASVs had a relative abundance more than 1% and were affiliated to the species Botryotrichum spirotrichum and Alternaria alternata (Supplementary Table S4).
Treatments had an even milder effect on the microbial communities in the phyllosphere, where from 1015 prokaryotic and 282 fungal ASVs used in the analyses, only 2.4% (24 ASVs, 13 for BTH and 11 for CHT) and 2.5% (7 ASVs, 5 for BTH and 2 for CHT) were found to be discriminant for the applied treatments. Discriminant prokaryotic ASVs with relative abundance more than 1% in BTH-treated phyllospheres belonged to the genera Massilia and Pseudarthrobacter and in CHT-treated phyllospheres belonged to Skermanella (Supplementary Table S5). Regarding discriminant fungal members, in BTH-treated phyllospheres, one ASV, affiliated to Pseudopithomyces rosae, had a relative abundance more than 1% and one in CHT-treated phyllospheres affiliated to the species Epicoccum_Unknown (Supplementary Table S6).
For the carposphere, from 842 prokaryotic and 315 fungal ASVs used in the analyses, only 3.3% (28 ASVs, 8 for ABA, 13 for BTH, and 7 for CHT) and 4.7% (15 ASVs, 6 for ABA, 2 for BTH, and 7 for CHT) were found to be discriminant for the applied treatments. Discriminant prokaryotic ASVs with relative abundance more than 1% in ABA-treated carpospheres belonged to the genera Brevibacterium, Massilia, Planococcaceae_Unknown, Domibacillus in BTH-treated carpospheres belonged to the genera Burkholderia_Caballeronia_Paraburkholderia, Curvibacter, Magnetospirillaceae_Unknown, Novosphingobium, Paucibacter, Novosphingobium and in CHT-treated carpospheres belonged to Cutibacterium, Massilia, Lactococcus, Lawsonella, Staphylococcus, Lactobacillus, Paenibacillaceae_Unknown (Supplementary Table S7). Discriminant fungal members, in the ABA-treated carpospheres with relative abundance more than 1% were affiliated to Aureobasidium pullulans, Debaryomyces hansenii, Saccharomyces_Unknown, Vishniacozyma foliicola in the BTH-treated carpospheres they were affiliated to Sclerotiniaceae_Unknown and in the CHT-treated carpospheres were affiliated to Alternaria 12lternate, Stemphylium_Unknown and Didymellaceae_Unknown (Supplementary Table S8).
3.6. Linking Members of the Microbial Communitites in the Carposhere with Berries Chemical Characteristics
In a following step, correlation of the prokaryotic and fungal ASVs of the carposphere, which emerged from the LefSe analyses as a discriminant for the three treatments, with the chemical characteristics of berries was assessed (Figure 4). It should be mentioned that the observed associations indicate linkages rather than causality. Each treatment showed a unique pattern in the characteristics that the ASVs are correlated with, and this pattern is consistent across both prokaryotes and fungi. The majority of the ABA-discriminant prokaryotic and fungal ASVs had positive correlations with °Brix degrees. Several prokaryotic ASVs correlated positively with the seed and cellular maturity index and with extractable anthocyanins, while two showed negative associations with total acidity. Similarly, fungal ASVs, which are mainly yeasts, showed a positive association with extractable anthocyanins and a negative association with total acidity. Concerning BTH treatment, only the prokaryotic ASVs were associated with the berries’ chemical characteristics. These ASVs showed a positive correlation with the weight of the berry, °Brix degrees, and the seed maturity index, while they demonstrated negative correlations with total acidity, extractable and total anthocyanins, and the cellular maturity index. Finally, regarding CHT treatment, the emerged ASVs from both prokatyotic and fungal community showed the opposite patterns. Prokaryotic ASVs included some lactic acid bacterial genera, showed negative associations with the weight of the berry, °Brix degrees, and the seed maturity index but presented positive associations with total acidity, extractable and total anthocyanins, and the cellular maturity index. A similar pattern was recorded for fungal ASVs, as they showed negative associations with °Brix degrees, pH and berry weight and positive associations with total acidity.
4. Discussion
Exogenous application of molecules that elicit plant defense mechanisms have been thus far studied as an alternative to conventional pathogen management [51]. However, there is a growing body of literature that investigates the effects of elicitors and plant hormones on grape and wine quality characteristics and, more specifically, on potential improvements to the biosynthesis of phenolic compounds in grapes, and subsequently in produced wines [5,6,23,52]. From a nutritional point of view, grape products are functional foods that provide health benefits beyond basic nutrition, by virtue of their content of nutraceuticals and bioactive compounds, such as polyphenols, and their pharmaconutritional properties [53].
In the present study two elicitors and a major plant hormone were applied in a single vineyard of Mouhtaro, a wine grape variety commonly used in the production of a monovarietal wine, in Viotia, and more especially in the Muses Valley. Our main purpose was to determine the effect of these compounds on (i) the characteristics involved in the technological and phenolic maturity of grapes during the harvest stage, and (ii) the microbial ecology in the rhizosphere and on the surface of leaves and berries, which may also be directly or indirectly affected via metabolic and physiological plant responses. To our knowledge, this is the first report investigating the effect of two resistance elicitors, chitosan and benzothiadiazole, and a plant hormone, abscisic acid, on grape must standard characteristics and indices, which define the so-called technological maturity of the grapes, as well as on the phenolic composition of the grape berries and on the microbial communities in close association with different plant compartment.
One of the most crucial phenotypic stages in grapevine is veraison. During this stage, berries start gaining color due to anthocyanins and other phenolic compounds that accumulate in berry skins. The application of CHT, ABA, and BTH increased the synthesis of anthocyanins and other phenolics in berries compared to the untreated control berries, highlighting their potential for eliciting the biosynthesis of these secondary metabolites’ pathways during veraison stage. Results are in accordance with previous studies that showed increased values of anthocyanins and phenolic compounds in grape berries [12,54,55]. It is also important for wine production, as wines produced from ripper grapes had a more intense red color and were also less astringent due to the lesser contribution on the tannins of the seeds, which are more astringent due to the higher galloytation content [50]. There is evidence that chitosan treatment may activate key enzymes of the phenylpropanoid pathway, in particular phenylalanine ammonia lyase, which is the key enzyme that catalyzes the first step in phenolic biosynthesis [51].
The chemical analysis of the grape musts at the harvest stage did show significant differences (weight/berry, TSS and TA) related to the various treatments applied (Table 2). Romanazzi et al. [56] showed that grape musts from Montepulciano grapevines treated with laminarin and copper hydroxide were not significantly different from controls, while Rantsiou et al. [57] recorded similar results after the application of fungicides on the Nebbiolo variety. However, significant differences were highlighted in the total phenolics and anthocyanins in grape berries after the application of the treatments. The highest values of total anthocyanins (Table 3) and the skin maturity index (Table 4) were recorded for the CHT-treated grape berries among the grapes. This is an advantage from an oenological point of view, since the grape skins are the major source of color and aroma compounds [58]. However, the CHT-treated berries showed the lowest values in the seed maturity index (Table 4).
The goal was to better understand the induction and biochemical synthesis of grape polyphenolic compounds, and to improve viticultural techniques, both of which will prove very useful in the field of plant protection, such as conferring resistance in pathogenic micro-organisms, as well as obtaining the optimum organoleptic properties.
The role of microbial consortia residing inside and outside plant tissues, or even in the rhizosphere, should be considered as another important factor in the development of compounds important for grapevine protection and, potentially, for wine quality characteristics [59,60]. From an enological perspective, however, limited knowledge is available on how to practically manipulate microbial plant communities on/in the grapevine to improve vine/grape characteristics.
Even though these kinds of effects are of significant importance, they have still not been deeply investigated. Next generation sequencing and metagenomics tools are essential contributors in clarifying if there are differences in the microbial communities induced by the application of elicitors, and also if these communities are hidden producers of metabolites, including polyphenolic compounds. Due to the multiple microbial communities in the grapevine, however, it is difficult to determine which microorganisms can contribute to the biosynthesis of phenolic compounds.
Plants are a habitat for diverse microbial communities. The members of these communities interact with each other and with their host plant, and this communication is triggered by molecule and signals and, at the same time, triggers the biosynthesis of other molecules [61]. Similarly, biotic (e.g., plant pathogens or pests) and abiotic (e.g., exogenous application of elicitors or biostimulants) factors have direct or indirect effects (through changes in plant physiology) on the plant-associated microbial communities. In the present study, we examined both prokaryotic and fungal microbial communities in the rhizosphere, phyllosphere and carposphere of grapevines, which received a foliar spray of two elicitors, CHT and BTH, and a berry spray of a plant hormone, ABA. Among the different plant compartments, both nMDS plots (Figure 2) and PERMANOVA analysis (Table 5) highlighted that the prokaryotes of rhizospheric communities were mostly affected, followed by those of the carposphere, while for fungal communities, mainly those of the carposphere communities were affected. In general, fungal communities were found to be less sensitive to treatments compare to prokaryotic communities.
Concerning the effects of BTH and CHT on the microbial communities of the rhizosphere, this could reflect a systemic mode of action of these plant activators. Both molecules trigger either of the two main plant defense pathways, SAR and ISR. The activation of these pathways alters the chemical composition of plant root exudates [62], which in turn could lead to modifications to the rhizospheric microbial community [59,62]. Interestingly, even though CHT and BTH was sprayed on leaves, the microbial communities of the phyllosphere were less affected (Table 5). Likewise, phyllosphere microbial communities were slightly modified after foliar application of a protein-based resistance inducer of greenhouse-grown grapevines [36]. Leaves’ surfaces are subjected to continuous microbial “inoculation” from aerial transportation [62], this, combined with the long period between treatment application and harvest, gave time to the phyllosphere microbial communities to either return to their original or an alternative state, masking the treatment effects.
In the carposphere, treatments highly affected both prokaryotic and fungal communities. The treatment-discriminated microbes appeared to be zymogenous strategists (boosted by sugars and a higher pH) in the case of ABA and BTH, but were also autochtonous strategists (boosted by phenolics and acidity) in the case of CHT. More specifically, ABA treatment increased the relative abundance of native yeasts and decreased those of putative fungal pathogens, while CHT-treated grapes demonstrated an increase in lactic acid bacteria. Both microbial groups are of great importance for the winemaking process. The use of native yeasts could be considered a potential strategy for controlling the presence of mycotoxin-producing molds. According to the results of the present study, ABA treatment could increase the relative abundance of beneficial yeast on grapes and, at the same time, decrease the percentage of pathogenic fungi. Debaryomyces hansenii is the most famous species of the genus Debaryomyces, and is known for its antimicrobial properties towards undesirable species [63]. For instance, D. hansenii acts against the plant pathogen Aspergillus parasiticus through repression of regulatory genes implicated in aflatoxin biosynthesis, but other modes of action have been also mentioned, such as the competition for space and nutrients, as well as the production of enzymes, such as proteases and chitinases [64].
Lactic acid bacteria have also been used as biocontrol agents in winemaking [65]. The capacity of LAB to produce exopolysaccharides and form biofilms on different oenological surfaces help them to persist during the various steps of winemaking, from the grape berries to alcoholic and malolactic fermentation, up to the bottled wine [66]. Even if Oenococcus is the most widely used bacterial genus in winemaking [67], Lactobacillus strains have started to gain increasing interest in the wine industry due to their promising properties as malolactic starters [68]. Lucio et al. [69] tested different strains from various Lactobacillus species in order to drive malolactic fermentation in grape must. Based on their results, the tested strains of Lactiplantibacillus plantarum expressed the best overall performance in grape must and were, not only able to deplete malic acid, but also contributed to wine flavor. Interestingly, in our study we showed that CHT treatment could increase the abundance of members of the Lactobacillus genus on grapes compared to the control, as well as to the other tested treatments. It would be interesting in the future to examine the oenological properties of these bacteria to propose an intergraded biocontrol solution.
In addition, both prokaryotic and fungal ASVs that emerged from LefSe analyses as discriminants demonstrated the same correlation patterns at berries characteristics with respect to the ABA and CHT treatments, while BTH only had a significant effect on prokaryotic microbial communities (Figure 4). ABA discriminant prokaryotic and fungal ASVs correlated positively with berries’ physicochemical characteristics and the seed maturity index, while CHT-discriminant prokaryotic and fungal ASVs correlated negatively. The opposite pattern was observed for anthocyanins and the skin maturity index. These patterns could imply that discriminant ASVs probably act in a similar way or share a common functional traits.
5. Conclusions
The application of plant elicitors and hormones as a viticulture practice represents a sustainable way to reduce fertilizers and other chemicals, leading to better management of environmental contamination. Our results indicate that the use of plant elicitors and hormones deserves particular attention, not only because of their low environmental impact, but also for their implications in the improvement of quality traits. Their exogenous application during the veraison stage significantly changed grape chemical and phenolic parameters. Therefore, such plant treatments can be used in the field of viticulture in the new era as viticultural techniques to improve quality characteristics of berries and of produced wines. In addition, the application of these compounds may influence microbial communities, which reside on the surface of berries and leaves, as well as in the rhizosphere soil, which influences the health and metabolic responses of grapevines. As a consequence, the model of grapevine, microorganisms, quality characteristics of grape berries and plant activators could be seen in holistic way, where all these factors could all interact together in order to contribute to the sustainability of a vineyard. Due to the potential biotechnological application of microbes in agriculture, studies to link the microbial compositions of soil, grape and leaves and the wines features themselves have intensified. As this study indicates, plant activators should not be considered as a one-size-fits-all option and the significant challenge will be had in determining the best variety × environment × management practices (including plant activators) in order to select the best combinations.
Conceptualization, D.-E.M., M.D., N.K. and Y.K.; methodology, D.-E.M.; software, M.T., C.B.; validation, all authors; formal analysis, M.T. and D.-E.M.; resources, D.-E.M. and M.T.; data curation, M.T., D.-E.M.; writing—original draft preparation, D.-E.M., and M.T.; writing—review and editing, all authors; visualization, M.T.; supervision, D.-E.M. and M.T.; project administration, D.-E.M. and M.T.; funding acquisition, Y.K. All authors have read and agreed to the published version of the manuscript.
This research has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH–CREATE–INNOVATE (project code: T1EDK- 04200 (MU-SA)).
Raw sequences were submitted to NCBI under the Bioproject ID RJNA704932.
The authors would like to thank the winemaker Nikos Zacharias of the winery Muses Estate for providing the Mouhtaro single vineyard in the Muses Valley. Moreover, the authors would like to thank Constantinos Ehaliotis for helpful discussion and valuable comments on the manuscript.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Enlarge this image.Figure 1. The “Observed” (richness) and the “Shannon” (that takes both richness and evenness under account) α-diversity indicators for the ASVs-based proxies of (a) the prokaryotic and (b) the fungal communities of the rhizosphere (soil), phyllosphere (leaf), and carposphere (fruit) of vines under the application, or not (control), of chitosan (CHT), benzothiadiazole (BTH), and abscisic acid (ABA). The upper and lower box boundaries indicate the 75th and the 25th percentiles, respectively; the mid-line indicates the median, the diamond indicates the mean, and the whiskers above and below indicate the 90th and 10th percentiles, respectively; the black dots indicate outliers.
Enlarge this image.Figure 2. Nonmetric multidimensional scaling (NMDS) ordination plots based on the Bray-Curtis dissimilarity matrix of the (a,c,e) prokaryotic and (b,d,f) fungal communities among the sampling sites of the rhizosphere (a,b), phyllosphere (c,d) and carposphere (e,f). Each dot represents the corresponding community of a single sample (biological replicate). Lines connect the dots with the centroid of each grouping factor, the application or not (control) of chitosan (CTH), benzothiadiazole (BTH), and abscisic acid (ABA). Dashed ellipses represent the 95% confidence interval around the group’s centroid.
Enlarge this image.Figure 2. Nonmetric multidimensional scaling (NMDS) ordination plots based on the Bray-Curtis dissimilarity matrix of the (a,c,e) prokaryotic and (b,d,f) fungal communities among the sampling sites of the rhizosphere (a,b), phyllosphere (c,d) and carposphere (e,f). Each dot represents the corresponding community of a single sample (biological replicate). Lines connect the dots with the centroid of each grouping factor, the application or not (control) of chitosan (CTH), benzothiadiazole (BTH), and abscisic acid (ABA). Dashed ellipses represent the 95% confidence interval around the group’s centroid.
Enlarge this image.Figure 3. Taxonomy plots across plant compartments and treatments, at the genus level of (a) prokaryotic and (b) fungal microbial communities. In both plots, the top 24 genera are presented.
Enlarge this image.Figure 4. Heatmap plots of the correlation between (a) the prokaryotic and (b) the fungal communities of the carposphere at the ASV level and the berries’ chemical characteristics. Vertical dashed lines group the ASVs based on the treatments they were found to be discriminant, from left to right are the ABA group, the BTH group and the CHT group. Asterisks indicate the significance level of the correlation: * p < 0.05; ** p < 0.01; *** p < 0.001.
Enlarge this image.Figure 4. Heatmap plots of the correlation between (a) the prokaryotic and (b) the fungal communities of the carposphere at the ASV level and the berries’ chemical characteristics. Vertical dashed lines group the ASVs based on the treatments they were found to be discriminant, from left to right are the ABA group, the BTH group and the CHT group. Asterisks indicate the significance level of the correlation: * p < 0.05; ** p < 0.01; *** p < 0.001.
Treatment application schedule.
| Number of Application | Application Date | Treatment |
|---|---|---|
| 1st | 26/07/2019 | ABA, CTH, BTH |
| 2nd | 29/07/2019 | ABA |
| 2nd | 1/08/2019 | CHT and BTH |
| 3rd | 1/08/2019 | ABA |
| 3rd | 8/08/2019 | CHT and BTH |
Physicochemical characteristics of grapes at harvest.
| Treatments | Weight |
Total Soluble Solids (TSS) |
Total Acidity |
pH |
|---|---|---|---|---|
| Control | 2.14 ± 0.03a | 22.4 ± 0.62b | 6.68 ± 0.13b | 3.32 ± 0.02a |
| ABA | 2.11 ± 0.04a | 23.5 ± 0.04a | 7.90 ± 0.19a | 3.31 ± 0.02a |
| CHT | 1.97 ± 0.05b | 22.5 ± 0.47b | 6.69 ± 0.33b | 3.28 ± 0.20a |
| BTH | 2.09 ± 0.05a | 23.5 ± 0.12a | 8.35 ± 0.39a | 3.29 ± 0.02a |
Data represents mean ± std. deviation. Different letters in the same column indicate significant differences according to t-test at the 5% probability level.
Total and extractable anthocyanins of Mouhtaro grapes at harvest stage.
| Treatments | Total Anthocyanins |
Extractable Anthocyanins |
|---|---|---|
| Control | 0.67 ± 0.09c | 0.42 ± 0.03b |
| ABA | 0.76 ± 0.02b | 0.48 ± 0.03a |
| CHT | 1.06 ± 0.03a | 0.48 ± 0.01a |
| BTH | 0.83 ± 0.09ab | 0.46 ± 0.04ab |
Data represents mean ± std. deviation. Different letters in the same column indicate significant differences according to t-test at 5% probability level. Anthocyanin concentration is expressed as g/L of malvidin-3-O-glucoside.
Phenolic maturity of Mouhtaro grapes at harvest stage.
| Treatments | Seed Maturity Index (%) | Skin Maturity Index |
|---|---|---|
| Control | 53.8 ± 3.5a | 46.2 ± 3.5b |
| ABA | 53.9 ± 2.7a | 46.7 ± 2.7b |
| CHT | 43.2 ± 2.6b | 56.8 ± 2.7a |
| BTH | 50.5 ± 9.6a | 49.5 ± 9.6b |
Data represents mean ± std. deviation. Different letters in the same column indicate significant differences according to t-test at 5% probability level.
PERMANOVA results, under 999 permutations, using Bray–Curtis dissimilarity.
| Habitat | Prokaryotic Community | Fungal Community |
|---|---|---|
| Rhizosphere | 26.5% *** 1 | 17.7% *** |
| Phyllosphere | 13.4% *** | 19.3 * |
| Carposphere | 27.5% *** | 29.5% *** |
1 Significance: *: p < 0.05; ***: p < 0.001.
Supplementary Materials
The following are available online at
References
1. European Commission. The European Green Deal; COM/2019/640 Final European Commission: Brussels, Belgium, 2019.
2. European Commission. A Farm to Fork Strategy for A Fair, Healthy and Environmentally-Friendly Food System. 2020; Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52020DC0381 (accessed on 19 May 2021).
3. Wang, M.; Cernava, T. Overhauling the assessment of agrochemical-driven interferences with microbial communities for improved global ecosystem integrity. Environ. Sci. Ecotechnol.; 2020; 4, 100061. [DOI: https://dx.doi.org/10.1016/j.ese.2020.100061]
4. Lachman, J. Sulc Major factors influencing antioxidant contents and antioxidant activity in grapes and wines. Int. J. Wine Res.; 2009; 1, 101. [DOI: https://dx.doi.org/10.2147/IJWR.S4600]
5. Gil-Muñoz, R. Improving phenolic and chromatic characteristics of Monastrell, Merlot and Syrah wines by using two elicitors. OENO One; 2017; 51, 51.
6. Alenazi, M.M.; Shafiq, M.; AlObeed, R.S.; Alsdon, A.A.; Abbasi, N.A.; Ali, I.; Mubushar, M.; Javed, I. Application of abscisic acid at veraison improves red pigmentation and accumulation of dietary antioxidants in red table grapes cv. Red Globe at harvest. Sci. Hortic.; 2019; 257, [DOI: https://dx.doi.org/10.1016/j.scienta.2019.108672]
7. Pieterse, C.M.J.; Leon-Reyes, A.; Van Der Ent, S.; Van Wees, S.C.M. Networking by small-molecule hormones in plant immunity. Nat. Chem. Biol.; 2009; 5, pp. 308-316. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19377457][DOI: https://dx.doi.org/10.1038/nchembio.164]
8. Walters, D.R.; Havis, N.D.; Epaterson, L.; Etaylor, J.; Walsh, D.J.; Esablou, C. Control of foliar pathogens of spring barley using a combination of resistance elicitors. Front. Plant Sci.; 2014; 5, 241. [DOI: https://dx.doi.org/10.3389/fpls.2014.00241] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24904629]
9. Silva, V.; Singh, R.K.; Gomes, N.; Soares, B.G.; Silva, A.; Falco, V.; Capita, R.; Alonso-Calleja, C.; Pereira, J.E.; Amaral, J.S. et al. Comparative Insight upon Chitosan Solution and Chitosan Nanoparticles Application on the Phenolic Content, Antioxidant and Antimicrobial Activities of Individual Grape Components of Sousão Variety. Antioxidants; 2020; 9, 178. [DOI: https://dx.doi.org/10.3390/antiox9020178]
10. Wang, K.; Liao, Y.; Cao, S.; Di, H.; Zheng, Y. Effects of benzothiadiazole on disease resistance and soluble sugar accumulation in grape berries and its possible cellular mechanisms involved. Postharvest Biol. Technol.; 2015; 102, pp. 51-60. [DOI: https://dx.doi.org/10.1016/j.postharvbio.2015.02.011]
11. Gutiérrez-Gamboa, G.; Pérez-Álvarez, E.; Rubio-Bretón, P.; Garde-Cerdán, T. Changes on grape volatile composition through elicitation with methyl jasmonate, chitosan, and a yeast extract in Tempranillo (Vitis vinifera L.) grapevines. Sci. Hortic.; 2019; 244, pp. 257-262. [DOI: https://dx.doi.org/10.1016/j.scienta.2018.09.063]
12. Singh, R.K.; Martins, V.; Soares, B.; Castro, I.; Falco, V. Chitosan Application in Vineyards (Vitis vinifera L. cv. Tinto Cão) Induces Accumulation of Anthocyanins and Other Phenolics in Berries, Mediated by Modifications in the Transcription of Secondary Metabolism Genes. Int. J. Mol. Sci.; 2020; 21, 306. [DOI: https://dx.doi.org/10.3390/ijms21010306] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31906425]
13. Sharp, R.G. A Review of the Applications of Chitin and Its Derivatives in Agriculture to Modify Plant-Microbial Interactions and Improve Crop Yields. Agronomy; 2013; 3, pp. 757-793. [DOI: https://dx.doi.org/10.3390/agronomy3040757]
14. Gómez-Plaza, E.; Bautista-Ortín, A.B.; Ruiz-García, Y.; Fernández-Fernández, J.I.; Gil-Muñoz, R. Effect of elicitors on the evolution of grape phenolic compounds during the ripening period. J. Sci. Food Agric.; 2016; 97, pp. 977-983. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27235201][DOI: https://dx.doi.org/10.1002/jsfa.7823]
15. Zhang, Y.; Mechlin, T.; Dami, I. Foliar Application of Abscisic Acid Induces Dormancy Responses in Greenhouse-grown Grapevines. HortScience; 2011; 46, pp. 1271-1277. [DOI: https://dx.doi.org/10.21273/HORTSCI.46.9.1271]
16. Trotel-Aziz, P.; Couderchet, M.; Vernet, G.; Aziz, A. Chitosan Stimulates Defense Reactions in Grapevine Leaves and Inhibits Development of Botrytis Cinerea. Eur. J. Plant Pathol.; 2006; 114, pp. 405-413. [DOI: https://dx.doi.org/10.1007/s10658-006-0005-5]
17. Meng, X.; Li, B.; Liu, J.; Tian, S. Physiological responses and quality attributes of table grape fruit to chitosan preharvest spray and postharvest coating during storage. Food Chem.; 2008; 106, pp. 501-508. [DOI: https://dx.doi.org/10.1016/j.foodchem.2007.06.012]
18. Gornik, K.; Grzesik, M.; Romanowska-Duda, B. The Effect Of Chitosan On Rooting Of Grapevine Cuttings And On Subsequent Plant Growth Under Drought And Temperature Stress. J. Fruit Ornam. Plant Res.; 2008; 16, pp. 333-343.
19. Ruiz-García, Y.; Romero-Cascales, I.; Gil-Muñoz, R.; Fernández-Fernández, J.I.; López-Roca, J.M.; Gómez-Plaza, E. Improving Grape Phenolic Content and Wine Chromatic Characteristics through the Use of Two Different Elicitors: Methyl Jasmonate versus Benzothiadiazole. J. Agric. Food Chem.; 2012; 60, pp. 1283-1290. [DOI: https://dx.doi.org/10.1021/jf204028d]
20. Ruiz-García, Y.; Romero-Cascales, I.; Bautista-Ortín, A.B.; Gil-Muñoz, R.; Martínez-Cutillas, A.; Gómez-Plaza, E. Increasing Bioactive Phenolic Compounds in Grapes: Response of Six Monastrell Grape Clones to Benzothiadiazole and Methyl Jasmonate Treatments. Am. J. Enol. Vitic.; 2013; 64, pp. 459-465. [DOI: https://dx.doi.org/10.5344/ajev.2013.13038]
21. Vitalini, S.; Ruggiero, A.; Rapparini, F.; Neri, L.; Tonni, M.; Iriti, M. The application of chitosan and benzothiadiazole in vineyard (Vitis vinifera L. cv Groppello Gentile) changes the aromatic profile and sensory attributes of wine. Food Chem.; 2014; 162, pp. 192-205. [DOI: https://dx.doi.org/10.1016/j.foodchem.2014.04.040]
22. Iriti, M.; Rossoni, M.; Borgo, M.; Faoro, F. Benzothiadiazole Enhances Resveratrol and Anthocyanin Biosynthesis in Grapevine, Meanwhile Improving Resistance toBotrytis cinerea. J. Agric. Food Chem.; 2004; 52, pp. 4406-4413. [DOI: https://dx.doi.org/10.1021/jf049487b]
23. Pilati, S.; Bagagli, G.; Sonego, P.; Moretto, M.; Brazzale, D.; Castorina, G.; Simoni, L.; Tonelli, C.; Guella, G.; Engelen, K. et al. Abscisic Acid Is a Major Regulator of Grape Berry Ripening Onset: New Insights into ABA Signaling Network. Front. Plant Sci.; 2017; 8, 1093. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28680438][DOI: https://dx.doi.org/10.3389/fpls.2017.01093]
24. Peppi, M.C.; Fidelibus, M.W.; Dokoozlian, N. Abscisic Acid Application Timing and Concentration Affect Firmness, Pigmentation, and Color of ‘Flame Seedless’ Grapes. HortScience; 2006; 41, pp. 1440-1445. [DOI: https://dx.doi.org/10.21273/HORTSCI.41.6.1440]
25. Ju, Y.-L.; Liu, M.; Zhao, H.; Meng, J.-F.; Fang, Y.-L. Effect Of Exogenous Abscisic Acid And Methyl Jasmonate On Anthocyanin Composition, Fatty Acids, And Volatile Compounds Of Cabernet Sauvignon (Vitis Vinifera L.) Grape Berries. Mol. Basel Switz.; 2016; 21, 1354. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27754331][DOI: https://dx.doi.org/10.3390/molecules21101354]
26. Chanclud, E.; Lacombe, B. Plant Hormones: Key Players In Gut Microbiota And Human Diseases?. Trends Plant Sci.; 2017; 22, pp. 754-758. [DOI: https://dx.doi.org/10.1016/j.tplants.2017.07.003]
27. Uroz, S.; Courty, P.E.; Oger, P. Plant Symbionts Are Engineers Of The Plant-Associated Microbiome. Trends Plant Sci.; 2019; 24, pp. 905-916. [DOI: https://dx.doi.org/10.1016/j.tplants.2019.06.008]
28. Belda, I.; Zarraonaindia, I.; Perisin, M.; Palacios, A.; Acedo, A. From Vineyard Soil to Wine Fermentation: Microbiome Approximations to Explain the “terroir” Concept. Front. Microbiol.; 2017; 8, 821. [DOI: https://dx.doi.org/10.3389/fmicb.2017.00821]
29. Bokulich, N.A.; Thorngate, J.H.; Richardson, P.M.; Mills, D.A. PNAS Plus: From the Cover: Microbial biogeography of wine grapes is conditioned by cultivar, vintage, and climate. Proc. Natl. Acad. Sci. USA; 2014; 111, pp. E139-E148. [DOI: https://dx.doi.org/10.1073/pnas.1317377110]
30. Burns, K.N.; Bokulich, N.A.; Cantu, D.; Greenhut, R.F.; Kluepfel, D.A.; O’Geen, A.T.; Strauss, S.L.; Steenwerth, K.L. Vineyard soil bacterial diversity and composition revealed by 16S rRNA genes: Differentiation by vineyard management. Soil Biol. Biochem.; 2016; 103, pp. 337-348. [DOI: https://dx.doi.org/10.1016/j.soilbio.2016.09.007]
31. Bokulich, N.A.; Collins, T.S.; Masarweh, C.; Allen, G.; Heymann, H.; Ebeler, S.E.; Mills, D.A. Associations Among Wine Grape Microbiome, Metabolome, And Fermentation Behavior Suggest Microbial Contribution To Regional Wine Characteristics. Mbio; 2016; 7, e00631-16. [DOI: https://dx.doi.org/10.1128/mBio.00631-16]
32. Cao, Y.; Fanning, S.; Proos, S.; Jordan, K.; Srikumar, S. A Review on the Applications of Next Generation Sequencing Technologies as Applied to Food-Related Microbiome Studies. Front. Microbiol.; 2017; 8, 1829. [DOI: https://dx.doi.org/10.3389/fmicb.2017.01829]
33. Marzano, M.; Fosso, B.; Manzari, C.; Grieco, F.; Intranuovo, M.; Cozzi, G.; Mulè, G.; Scioscia, G.; Valiente, G.; Tullo, A. et al. Complexity and Dynamics of the Winemaking Bacterial Communities in Berries, Musts, and Wines from Apulian Grape Cultivars through Time and Space. PLoS ONE; 2016; 11, e0157383. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27299312][DOI: https://dx.doi.org/10.1371/journal.pone.0157383]
34. Jha, Y. Plant Microbiomes With Phytohormones: Attribute For Plant Growth And Adaptation Under The Stress Conditions. Advances in Plant Microbiome and Sustainable Agriculture: Functional Annotation and Future Challenges; Yadav, A.N.; Rastegari, A.A.; Yadav, N.; Kour, D. Microorganisms for Sustainability Springer: Singapore, 2020; pp. 85-103. ISBN 9789811532047
35. Tosi, M.; Mitter, E.K.; Gaiero, J.; Dunfield, K.E. It takes three to tango: The importance of microbes, host plant, and soil management to elucidate manipulation strategies for the plant microbiome. Can. J. Microbiol.; 2020; 66, pp. 413-433. [DOI: https://dx.doi.org/10.1139/cjm-2020-0085]
36. Cappelletti, M.; Perazzolli, M.; Antonielli, L.; Nesler, A.; Torboli, E.; Bianchedi, P.L.; Pindo, M.; Puopolo, G.; Pertot, I. Leaf Treatments with a Protein-Based Resistance Inducer Partially Modify Phyllosphere Microbial Communities of Grapevine. Front. Plant Sci.; 2016; 7, 1053. [DOI: https://dx.doi.org/10.3389/fpls.2016.01053] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27486468]
37. Villegas, D.; Handford, M.; Alcalde, J.A.; Perez-Donoso, A. Exogenous application of pectin-derived oligosaccharides to grape berries modifies anthocyanin accumulation, composition and gene expression. Plant Physiol. Biochem.; 2016; 104, pp. 125-133. [DOI: https://dx.doi.org/10.1016/j.plaphy.2016.03.020] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27031424]
38. Lingua, G.; Bona, E.; Manassero, P.; Marsano, F.; Todeschini, V.; Cantamessa, S.; Copetta, A.; D’Agostino, G.; Gamalero, E.; Berta, G. Arbuscular Mycorrhizal Fungi and Plant Growth-Promoting Pseudomonads Increases Anthocyanin Concentration in Strawberry Fruits (Fragaria x ananassa var. Selva) in Conditions of Reduced Fertilization. Int. J. Mol. Sci.; 2013; 14, pp. 16207-16225. [DOI: https://dx.doi.org/10.3390/ijms140816207] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23924942]
39. Yu, M.; Chen, J.-C.; Qu, J.-Z.; Liu, F.; Zhou, M.; Ma, Y.-M.; Xiang, S.-Y.; Pan, X.-X.; Zhang, H.-B.; Yang, M.-Z. Exposure To Endophytic Fungi Quantitatively And Compositionally Alters Anthocyanins In Grape Cells. Plant Physiol. Biochem.; 2020; 149, pp. 144-152. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32066082][DOI: https://dx.doi.org/10.1016/j.plaphy.2020.02.006]
40. Walters, W.; Hyde, E.R.; Berg-Lyons, D.; Ackermann, G.; Humphrey, G.; Parada, A.; Gilbert, J.A.; Jansson, J.K.; Caporaso, J.G.; Fuhrman, J.A. et al. Improved Bacterial 16S rRNA Gene (V4 and V4-5) and Fungal Internal Transcribed Spacer Marker Gene Primers for Microbial Community Surveys. mSystems; 2015; 1, e00009-15. [DOI: https://dx.doi.org/10.1128/mSystems.00009-15]
41. Ihrmark, K.; Bödeker, I.T.M.; Cruz-Martinez, K.; Friberg, H.; Kubartova, A.; Schenck, J.; Strid, Y.; Stenlid, J.; Brandström-Durling, M.; Clemmensen, K.E. et al. New Primers To Amplify The Fungal Its2 Region–Evaluation By 454-Sequencing Of Artificial And Natural Communities. FEMS Microbiol. Ecol.; 2012; 82, pp. 666-677. [DOI: https://dx.doi.org/10.1111/j.1574-6941.2012.01437.x]
42. White, T.J.; Bruns, T.; Lee, S.; Taylor, J. 38-Amplification And Direct Sequencing Of Fungal Ribosomal Rna Genes For Phylogenetics. PCR Protocols; Innis, M.A.; Gelfand, D.H.; Sninsky, J.J.; White, T.J. Academic Press: San Diego, CA, USA, 1990; pp. 315-322. ISBN 978-0-12-372180-8
43. Dodt, M.; Roehr, J.T.; Ahmed, R.; Dieterich, C. Flexbar-Flexible Barcode And Adapter Processing For Next-Generation Sequencing Platforms. Biology; 2012; 1, pp. 895-905. [DOI: https://dx.doi.org/10.3390/biology1030895]
44. Callahan, B.J.; Mcmurdie, P.J.; Rosen, M.J.; Han, A.W.; Johnson, A.J.A.; Holmes, S.P. Dada2: High-Resolution Sample Inference From Illumina Amplicon Data. Nat. Methods; 2016; 13, pp. 581-583. [DOI: https://dx.doi.org/10.1038/nmeth.3869]
45. Quast, C.; Pruesse, E.; Yilmaz, P.; Gerken, J.; Schweer, T.; Yarza, P.; Peplies, J.; Glöckner, F.O. The Silva Ribosomal Rna Gene Database Project: Improved Data Processing And Web-Based Tools. Nucleic Acids Res.; 2013; 41, pp. D590-D596. [DOI: https://dx.doi.org/10.1093/nar/gks1219] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23193283]
46. Nilsson, R.H.; Larsson, K.-H.; Taylor, A.F.S.; Bengtsson-Palme, J.; Jeppesen, T.S.; Schigel, D.; Kennedy, P.; Picard, K.; Glöckner, F.O.; Tedersoo, L. et al. The Unite Database For Molecular Identification Of Fungi: Handling Dark Taxa And Parallel Taxonomic Classifications. Nucleic Acids Res.; 2019; 47, pp. D259-D264. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30371820][DOI: https://dx.doi.org/10.1093/nar/gky1022]
47. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation For Statistical Computing: Vienna, Austria, 2013.
48. Mcmurdie, P.J.; Holmes, S. Phyloseq: An R Package For Reproducible Interactive Analysis And Graphics Of Microbiome Census Data. PLoS ONE; 2013; 8, e61217. [DOI: https://dx.doi.org/10.1371/journal.pone.0061217] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23630581]
49. Segata, N.; Izard, J.; Waldron, L.; Gevers, D.; Miropolsky, L.; Garrett, W.S.; Huttenhower, C. Metagenomic Biomarker Discovery And Explanation. Genome Biol.; 2011; 12, R60. [DOI: https://dx.doi.org/10.1186/gb-2011-12-6-r60]
50. Wickham, H. Ggplot2: Elegant Graphics for Data Analysis; 2nd ed. Springer: New York, NY, USA, 2009; ISBN 0-387-98140-3
51. Delaunois, B.; Farace, G.; Jeandet, P.; Clément, C.; Baillieul, F.; Dorey, S.; Cordelier, S. Elicitors As Alternative Strategy To Pesticides In Grapevine? Current Knowledge On Their Mode Of Action From Controlled Conditions To Vineyard. Environ. Sci. Pollut. Res.; 2014; 21, pp. 4837-4846. [DOI: https://dx.doi.org/10.1007/s11356-013-1841-4] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23719689]
52. Portu, J.; López, R.; Baroja, E.; Santamaría, P.; Garde-Cerdán, T. Improvement Of Grape And Wine Phenolic Content By Foliar Application To Grapevine Of Three Different Elicitors: Methyl Jasmonate, Chitosan, and Yeast Extract. Food Chem.; 2016; 201, pp. 213-221. [DOI: https://dx.doi.org/10.1016/j.foodchem.2016.01.086]
53. Iriti, M.; Faoro, F. Bioactivity of Grape Chemicals For Human Health. Nat. Prod. Commun.; 2009; 4, pp. 611-634. [DOI: https://dx.doi.org/10.1177/1934578X0900400502]
54. Paladines-Quezada, D.F.; Moreno-Olivares, J.D.; Fernández-Fernández, J.I.; Bautista-Ortín, A.B.; Gil-Muñoz, R. Influence Of Methyl Jasmonate And Benzothiadiazole On The Composition Of Grape Skin Cell Walls And Wines. Food Chem.; 2019; 277, pp. 691-697. [DOI: https://dx.doi.org/10.1016/j.foodchem.2018.11.029]
55. Villalobos-González, L.; Peña-Neira, A.; Ibáñez, F.; Pastenes, C. Long-term effects of abscisic acid (ABA) on the grape berry phenylpropanoid pathway: Gene expression and metabolite content. Plant Physiol. Biochem.; 2016; 105, pp. 213-223. [DOI: https://dx.doi.org/10.1016/j.plaphy.2016.04.012]
56. Romanazzi, G.; Mancini, V.; Feliziani, E.; Servili, A.; Endeshaw, S.; Neri, D. Impact Of Alternative Fungicides On Grape Downy Mildew Control And Vine Growth And Development. Plant Dis.; 2015; 100, pp. 739-748. [DOI: https://dx.doi.org/10.1094/PDIS-05-15-0564-RE]
57. Rantsiou, K.; Giacosa, S.; Pugliese, M.; Englezos, V.; Ferrocino, I.; Río Segade, S.; Monchiero, M.; Gribaudo, I.; Gambino, G.; Gullino, M.L. et al. Impact Of Chemical And Alternative Fungicides Applied To Grapevine Cv Nebbiolo On Microbial Ecology And Chemical-Physical Grape Characteristics At Harvest. Front. Plant Sci.; 2020; 11, 700. [DOI: https://dx.doi.org/10.3389/fpls.2020.00700] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32547588]
58. Ortega-Regules, A.; Ros-García, J.M.; Bautista-Ortín, A.B.; López-Roca, J.M.; Gómez-Plaza, E. Differences In Morphology And Composition Of Skin And Pulp Cell Walls From Grapes (Vitis Vinifera L.): Technological Implications. Eur. Food Res. Technol.; 2007; 227, 223. [DOI: https://dx.doi.org/10.1007/s00217-007-0714-9]
59. Liu, D.; Zhang, P.; Chen, D.; Howell, K. From the Vineyard to the Winery: How Microbial Ecology Drives Regional Distinctiveness of Wine. Front. Microbiol.; 2019; 10, 2679. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31824462][DOI: https://dx.doi.org/10.3389/fmicb.2019.02679]
60. Morgan, H.H.; Du Toit, M.; Setati, M.E. The Grapevine and Wine Microbiome: Insights from High-Throughput Amplicon Sequencing. Front. Microbiol.; 2017; 8, 820. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28553266][DOI: https://dx.doi.org/10.3389/fmicb.2017.00820]
61. Tsiknia, M.; Tsikou, D.; Papadopoulou, K.K.; Ehaliotis, C. Multi-species relationships in legume roots: From pairwise legume-symbiont interactions to the plant–microbiome–soil continuum. FEMS Microbiol. Ecol.; 2021; 97, [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33155054][DOI: https://dx.doi.org/10.1093/femsec/fiaa222]
62. Wei, Y.; Wu, Y.; Yan, Y.; Zou, W.; Xue, J.; Ma, W.; Wang, W.; Tian, G.; Wang, L. High-Throughput Sequencing Of Microbial Community Diversity In Soil, Grapes, Leaves, Grape Juice And Wine Of Grapevine From China. PLoS ONE; 2018; 13, e0193097. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29565999][DOI: https://dx.doi.org/10.1371/journal.pone.0193097]
63. Çorbacı, C.; Uçar, F.B. Purification, Characterization And In Vivo Biocontrol Efficiency Of Killer Toxins From Debaryomyces Hansenii Strains. Int. J. Biol. Macromol.; 2018; 119, pp. 1077-1082. [DOI: https://dx.doi.org/10.1016/j.ijbiomac.2018.07.121]
64. Peromingo, B.; Andrade, M.J.; Delgado, J.; Sánchez-Montero, L.; Núñez, F. Biocontrol of Aflatoxigenic Aspergillus Parasiticus By Native Debaryomyces Hansenii In Dry-Cured Meat Products. Food Microbiol.; 2019; 82, pp. 269-276. [DOI: https://dx.doi.org/10.1016/j.fm.2019.01.024] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31027783]
65. Mtshali, P.S.; Divol, B.; Van Rensburg, P.; Du Toit, M. Genetic Screening Of Wine-Related Enzymes In Lactobacillus Species Isolated From South African Wines. J. Appl. Microbiol.; 2010; 108, pp. 1389-1397. [DOI: https://dx.doi.org/10.1111/j.1365-2672.2009.04535.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19793136]
66. Dols-Lafargue, M.; Lonvaud-Funel, A. Polysaccharide Production By Grapes, Must, and Wine Microorganisms. Biology of Microorganisms on Grapes, in Must and in Wine; König, H.; Unden, G.; Fröhlich, J. Springer: Berlin/Heidelberg, Germany, 2009; pp. 241-258. ISBN 978-3-540-85463-0
67. Lonvaud-Funel, A. Lactic Acid Bacteria In The Quality Improvement and Depreciation of Wine. Antonie Leeuwenhoek; 1999; 76, pp. 317-331. [DOI: https://dx.doi.org/10.1023/A:1002088931106]
68. Du Toit, M.; Engelbrecht, L.; Lerm, E.; Krieger-Weber, S. Lactobacillus: The Next Generation of Malolactic Fermentation Starter Cultures—An Overview. Food Bioprocess Technol.; 2011; 4, pp. 876-906. [DOI: https://dx.doi.org/10.1007/s11947-010-0448-8]
69. Lucio, O.; Pardo, I.; Heras, J.; Krieger-Weber, S.; Ferrer, S. Use of starter cultures ofLactobacillusto induce malolactic fermentation in wine. Aust. J. Grape Wine Res.; 2017; 23, pp. 15-21. [DOI: https://dx.doi.org/10.1111/ajgw.12261]
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Dimopoulou, Maria 3 ; Bouyioukos, Costas 4 ; Kotseridis, Yorgos 1 1 Laboratory of Enology and Alcoholic Drinks, Department of Food Science and Human Nutrition, Agricultural University of Athens, Athens, 75 Iera Odos, 11855 Athens, Greece;
2 Soil Science and Agricultural Chemistry Lab, Department of Natural Resources and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece
3 Department of Wine, Vine, and Beverage Sciences, School of Food Science, University of West Attica, 12243 Athens, Greece;
4 Epigenetics and Cell Fate, CNRS UMR7216, Université de Paris, F-75013 Paris, France;