Introduction

Grape is an important cash crop, and China is one of the most important grape-growing countries with the production and area of table grapes ranked top around the world for a long time [1]. Microbes are an important part of the vineyard ecosystem, which participate in multiple physiological and biochemical processes during the grapevine cultivation [2,3]. In grapevine agriculture, disease occurs frequently, resulting in greatly-reduced production and huge economic losses. Besides, during postharvest storage of grapes, many endophytic bacteria also easily cause rot and deterioration, affecting normal sales. However, many grape growers and sellers are mostly unaware of the microbes that cause these diseases. So, it is of great significance to study the microbiome composition and microbial diversity in grapevines [4].

The microbial compositions are various for different tissues of the grapevines. In the rhizosphere (the area around the root), microbes are more numerous and complex due to their direct contact with the soil [5–7]. Some important microbes have been identified from the grapevine rhizosphere, such as Clostridium, Bacillus, Rhizobium, Acinetobacter, Streptococcus, Paenibacillus and other bacteria, as well as some fungi, such as Filobasidium capsuligenum, Aureobasidium pullulans and Hanseniaspora [4,8–10]. Rhizosphere microbes are affected by plant uptake, root exudates, and soil activities. At the same time, rhizosphere microbes also directly affect nutrient uptake, nutrient utilization, growth, development, and disease occurrence for the grapevines [11–13]. For example, Proteobacteria, with high abundance in grapevines, is involved in the cycling process of major nutrient elements, which can improve nitrogen utilization efficiency [14,15]. Microbial diversity in the phyllosphere (or leaf surface) is also one of the focuses of current researches. Leaves are the main dynamic habitats for microbes. Phyllospheric microbes mainly affect the fixation of carbon and nitrogen, thereby affecting plant growth and development [3,7]. In addition, some harmful microbes in the phyllosphere are also the main sources of some diseases [6,16,17]. Grape berries are also important habitats for microbes, which directly affect the economic value and nutritional value of grapes, especially for wine grapes, and the microbes inhabiting wine grapes have a direct impact on the aroma, color and quality of wine [18,19]. The microbes on grape berries can also cause some serious diseases, resulting in a decrease in yield and quality. For example, Alternaria sp., a bacterium on grape berries, can produce a variety of toxic metabolites, which cause the disease of black spot, and even giving rise to the poisoning and cancer after human ingestion [20,21]. Beyond rhizosphere, phyllosphere and berry, some studies have focused on the microbes in the other tissues of the grapevines, like Xanthobcter, Xanthomonas, Cellulomonas, and Xylella from the stems, and Pseudomonas sp. and Bacillus ssp. from the flowers [13,15].

The diversity of microbial community has been the focus for the researchers in microbiology, ecology and phytopathology in recent years [5,22]. By studying the dynamic changes of microbial community, we can understand the ecological functions of microbes and optimize community structure, contributing to the control and prevention of plant diseases. The current research approaches on microbial diversity has extended from traditional microorganism culture to high-throughput sequencing methods [23]. It has been very easy to understand all the microbial species and compositions of plants through high-throughput sequencing, which is commonly referred to as microbiome [24,25]. Through the comparative study of microbiome, we can systematically analyze the effects of different varieties, different ecological environments, and different treatment factors on the microbial community of fruit trees, so as to better guide the production and disease control for the orchard.

The microbiome diversity has been explored in grapevines not only in various plant parts (like berry, leaf, root, bark and bud), but also in various grape varieties. Awad et al. [26] investigated the microbiome in the tissues of bud and bark in 37 different grapevine varieties under the same viticulture environment, revealing that the genotypes of grapevines may also influence microbiome diversity regardless of growth conditions. At the same time, Awad et al. [27] further confirmed this conclusion by exploring microbial diversity in 36 grapevine varieties, indicating the impact of the genotypes and the phenological stages on the microbial communities. These researches unveiled and characterized the microbiome diversity in grapevines, suggesting that it is necessary for researchers to identify more microbial species in more tissues and terroirs. Except for these two studies, the researches on grapevine’s microbial communities are inadequate. On the other hand, current studies on microbial diversity mainly focus on different cultivars or species [14,15,28], while some natural mutant varieties (such as bud mutant in many horticultural plants) have not been studied yet.

Previously, we identified a bud mutant from the ‘Kyoho’ grape, named as ‘Fengzao’, which is typically characterized by early-ripening, with a maturity period of 30 days earlier than ‘Kyoho’ [29]. We have also compared the developmental process, the fruit physiology, and the transcriptome between the two cultivars [30–32], but the microbiome differences between them have not been investigated. Besides, the variation of microbial community for bud mutant variety in horticultural plants has not been studied yet. Therefore, in this study, we systematically compared the microbiome in different tissues of ‘Kyoho’ and ‘Fengzao’ in order to dissect the microbiome diversity and the microbial composition structure in both grapevine and its bud mutant variety, which will provide an important reference for relevant researches and lay a foundation for the future studies on the mechanisms underlying the microbial community’ variation.

Materials and methods

Plant materials

Grapevines of ‘Kyoho’ and ‘Fengzao’ were planted in the experimental fields of Henan University of Science and Technology (Luoyang, China) under the same viticulture management practices. Samples of fruit, stem and leaf were collected on April 15, 2020. Three trees were selected for ‘Fengzao’ and ‘Kyoho’, and a bunch of berries, 5 leaves, and 10 cm-length stem segments were respectively taken from each vine. Pooled samples of the stems, leaves and berries from each variety were immediately frozen in liquid nitrogen, and stored in a -80°C for further use. The samples of fruit (F), leaf (L) and stem (S) labeled as ‘KF’, ‘KL’, ‘KS’ for ‘Kyoho’ variety and as ‘FF’, ‘FL’, ‘FS’ for ‘Fengzao’ variety were formed, respectively (Table 1).

Download:

• PNG
  larger image
• TIFF
  original image

Table 1. Experimental samples in this study.

https://doi.org/10.1371/journal.pone.0290853.t001

Results

OTU (operational taxonomic unit) analysis

OTU is artificially-assigned taxon (strain, species, genus, group, etc.) for the convenience of analysis in phylogenetic studies or population genetics studies [20]. In general, if the similarity between sequences is higher than 97%, it can be defined as an OTU, and each OTU corresponds to a representative sequence [38]. Accordingly, at 97% similarity level, we conducted OTU analysis with the obtained sequences, and performed taxonomic annotation on OTU based on Silva (Release 119, bacteria) and UNITE (Release 7.0, fungi) taxonomic databases. A total of 34 OTUs were obtained from the six samples (Table 2, S2 Table in S1 File, Fig 1A), with 27 OTUs in FF, 30 OTUs in FL, 33 OTUs in FS, 34 OTUs in KF, 30 OTUs in KL, and 32 OTUs in KS (Fig 1A). The Venn diagrams showed that 28 OTUs were commonly present in different tissues of ‘Kyoho’ (KF, KL, KS), and 26 OTUs were commonly present in different tissues of ‘Fengzao’ (FF, FL, FS). Only a few OTUs are tissue specific (6 in ‘Kyoho’ and 8 in ‘Fengzao’) (Fig 1B and 1C).

Download:

• PNG
  larger image
• TIFF
  original image

Fig 1. Statistics of OTU (operational taxonomic unit) in different samples.

(A) OTU number in each sample and total number of the identified OTU. (B-C) Venn diagrams showing the OTU numbers among different samples of ‘Kyoho’ (B) and ‘Fengzao’ (C). ‘FF’, ‘FL’ and ‘FS’ indicate samples of fruit, leaf and stem in ‘Fengzao’; ‘KF’, ‘KL’ and ‘KS’ indicate samples of fruit, leaf and stem in ‘Kyoho’.

https://doi.org/10.1371/journal.pone.0290853.g001

Download:

• PNG
  larger image
• TIFF
  original image

Table 2. Information of the 34 OUTs from each sample.

https://doi.org/10.1371/journal.pone.0290853.t002

Species annotation and taxonomic analysis

In order to analyze the community composition of each sample, we compared the representative sequence of OTU with the microbial reference database to obtain the corresponding species classification information for each OTU, and then obtained the classification information of each OTU at various levels (phylum, class, order, family, genus) (Table 2). By statistics, at the level of phylum, the microbial community with a relatively higher abundance in the six samples were Cyanobacteria and Proteobacteria (S2A Fig in S1 File). At the level of class, Chloroplast and Alphaproteobacteria had higher abundance in the six samples (S2B Fig in S1 File). And at the level of order and family, Rickettsiales and mitochondria with known functions had relatively higher abundance in the six samples, while much more microbial communities with highest abundance were unknown (S2C and S2D Fig in S1 File). At the level of genus, eight OTUs had annotation, like OTU477 (Spirochaeta), OTU3488 (Wolbachia), OTU3748 (Chlorobaculum), OTU4313 (Tetragenococcus), OTU4701 (Sphingomonas), OTU6854 (Incertae_Sedis), OTU7563 (Pseudomonas), and OTU7879 (Enterobacter) (Table 2).

To specify and compare the bacterial compositions in the six samples, we used heatmaps to show the relative abundance in each sample at different levels (Fig 2). At the level of phylum, Proteobacteria had the highest abundance in the sample of FF; Chlorobi and Spirochaetae had the highest abundance in KF; Cyanobacteria had the highest abundance in KL; Firmicutes and Synergistetes had the highest abundance in KS (Fig 2A). At the level of class, Chloroplast and Alphaproteobacteria had the highest abundance in KL and FF, respectively; Bacilli and Synergistia had the highest abundance in KS; Chlorobia and Spirochaetes had the highest abundance in KF; Clostridia, Betaproteobacteria and Gammaproteobacteria had the highest abundance in FS (Fig 2B). At the level of order, Lactobacillales, Pseudomonadales and Synergistales had the highest abundance in KS; Rhizobiales, Sphingomonadales, Rhodobacterales, Gerbera_hybrid_cultivar and Rickettsiales had relatively higher abundance in FF and KF, indicating their abundance in fruit samples; Chlorobiales and Spirochaetales had the highest abundance in KF; Clostridiales, Burkholderiales and Enterobacteriales had the highest abundance in FS (Fig 2C). At the level of family, the microbial communities with the highest abundance were Enterococcaceae, Pseudomonadaceae, and Synergistaceae in the sample of KS, Ruminococcaceae, Enterobacteriaceae, and Oxalobacteraceae in FS, Spirochaetaceae, Anaplasmataceae, Chlorobiaceae, and Sphingomonadaceae in KF, Rhodobacteraceae and mitochondria in FF (Fig 2D). At the level of genus, Enterobacter and Incertae_Sedis had the highest abundance in FS; Tetragenococcus and Pseudomonas had the highest abundance in KS; Spirochaeta, Sphingomonas, Chlorobaculum and Wolbachia had the highest abundance in KF (Fig 2E).

Download:

• PNG
  larger image
• TIFF
  original image

Fig 2.

Clustering heat maps of bacterial abundance at all levels, including phylum (A), class (B), order (C), family (D), genus (E). Horizontal clustering refers to sample information and vertical clustering refers to bacterial information. Heatmap shows the bacterial abundance in each sample, with red and blue color representing high and low abundance, respectively.

https://doi.org/10.1371/journal.pone.0290853.g002

Additionally, we also returned our sequenced OTU information to the taxonomic system of NCBI database, so as to comprehensively understand the evolutionary relationships and abundance differences of all microbes from the samples. The evolutionary tree showed the relationship of all microbial communities and the differences of their relative abundance between the samples of ‘Kyoho’ and ‘Fengzao’ (Fig 3). The results indicated obvious abundance differences between ‘Kyoho’ and ‘Fengzao’. For example, Cyanobacteria, with highest abundance among all microbial communities, had higher bacterial abundance in ‘Fengzao’ than ‘Kyoho’; Ruminococcaceae, Rhizobiales, Rhodobacteraceae, Oxalobacteraceae, and Enterobacter all had relatively higher abundance in ‘Fengzao’ than ‘Kyoho’; while Chlorobaculum, Tetragenococcus, Wolbachia, Pseudomonas, and Synergistaceae had relatively higher abundance in ‘Kyoho’ than ‘Fengzao’ (Fig 3).

Download:

• PNG
  larger image
• TIFF
  original image

Fig 3. Tree diagram showing the bacterial abundance at each taxonomic level in ‘Fengzao’ and ‘Kyoho’.

The bacterial abundance is compared between ‘Fengzao’ (blue) and ‘Kyoho’ (gray) with pie charts.

https://doi.org/10.1371/journal.pone.0290853.g003

Alpha diversity analysis

Alpha diversity reflects the species diversity within a single sample, which can be measured by multiple indexes, like Ace, Chao1, Simpson and Shannon [39]. A larger value of Chao1, Ace and Shannon, and a smaller value of Simpson indicate a higher species diversity of the sample. So, based on Ace and Chao1, the species diversity ranking for the six samples is KF, FS, KS, FL, KL, and FF. And based on Simpson and Shannon, this ranking is FF, KF, FS, KS, FL, and KL (Table 3). We also calculated the Coverage of the sample library, which indicated whether the sequencing results represent the true situation of the microbes in the sample, with a higher value representing a higher probability that all sequences in the sample has been measured. The results showed that the Coverage values of the six samples were all very high (more than 0.9999) (Table 3). In addition, we also plotted the Rarefaction curve and the Shannon Index curve to verify whether the amount of sequencing data was sufficient to reflect the species diversity in the samples (S3Fig in S1 File). The two kinds of curves both tended to be flat for all the six samples, indicating that the sequencing data was reasonable and saturated, and no additional sequencing was needed.

Download:

• PNG
  larger image
• TIFF
  original image

Table 3. Alpha diversity index statistics.

https://doi.org/10.1371/journal.pone.0290853.t003

Beta diversity analysis

We employed two methods, PCoA (Principal coordinates analysis) and NMDS (Non-metric multi-dimensional scaling), to conduct beta diversity analysis, in order to further investigate the differences of microbial communities among the six samples. PCoA and NMDS exhibited similar results, both showing that samples of ‘Kyoho’ and ‘Fengzao’ were roughly divided into two classes (Fig 4), indicating the diversity of microbial communities between ‘Kyoho’ and ‘Fengzao’.

Download:

• PNG
  larger image
• TIFF
  original image

Fig 4.

PCoA (principal coordinates analysis) (A) and NMDS (non-metric multi-dimensional scaling) diagrams (B) showing sample diversity in ‘Fengzao’ and ‘Kyoho’. (A) The red and blue dots represent samples of ‘Fengzao’ and ‘Kyoho’, respectively. Horizontal and vertical coordinates are the two characteristic values that lead to the biggest difference between samples, and the influence degree is reflected by the percentage. (B) The red and blue dots represent samples of ‘Fengzao’ and ‘Kyoho’, respectively. When the Stress is less than 0.2, it indicates that the NMDS analysis has reliability.

https://doi.org/10.1371/journal.pone.0290853.g004

Prediction of the functional potential of the microbial community

In order to predict the functional potential of the microbial communities in different samples, we mapped the obtained OTUs to the corresponding COG (Clusters of orthologous groups of proteins) database to calculate the abundance of each COG, and performed pairwise tests for significant differences between different samples at the genus level. The different tissues (fruit, leaf and stem) of ‘Kyoho’ and ‘Fengzao’ were analyzed, respectively. The predicted COG pathway with most obviously differences between ‘Kyoho’ and ‘Fengzao’ was ‘Translation, ribosomal structure and biogenesis’ (Fig 5, S4-S5 Fig in S1 File). The results from fruit, leaf and stem were similar, with some predicted pathways identified in all the three tissues, like ‘Translation, ribosomal structure and biogenesis’, ‘Cell motility’, ‘Energy production and conversion’, ‘Function unknown’, ‘Carbohydrate transport and metabolism’, ‘Inorgenic ion transport and metabolism’, ‘Intracellular trafficking, secretion, and vesicular transport’, ‘General function prediction only’, and so on (Fig 5).

Download:

• PNG
  larger image
• TIFF
  original image

Fig 5. Analysis of COG metabolic pathways in fruits between ‘Fengzao’ (FF) and ‘Kyoho’ (KF).

The figure shows the abundance ratio of different functions in the two cultivars. The middle bar plots and dot plots shows the difference ratio of different functions under the 95% confidence intervals, and the values on the right show the p values.

https://doi.org/10.1371/journal.pone.0290853.g005

Discussion

Microbes are an important part of the grape ecosystem, which directly affect the yield, quality, stress resistance, growth and development of grapes. Especially for wine grapes, the microbial composition and content directly determine the quality of wine product [40]. ‘Kyoho’ is mainly used as table grapes, but recent studies have also shown that the microbes inside table grapes can also significantly affect grape quality and disease resistance [41]. However, studies on the microbial composition of table grapes are far behind that of wine grapes, and most of them focused on the rhizosphere, with relatively less attention paid to leaves, fruits, and stems [26,27,42,43]. Therefore, in this study, we investigated the microbiome in leaves, stems and fruits of ‘Kyoho’, a representative of table grapes. Most importantly, we previously identified a bud mutant derived from ‘Kyoho’, namely ‘Fengzao’ [29], and conducted deep studies on the comparisons of maturity period, fruit quality, physiological and molecular mechanisms of fruit development between ‘Fengzao’ and ‘Kyoho’ [29–31]. Considering the poor knowledge of microbial variation between plants and their bud mutants, especially for horticultural plants, this bud mutant germplasm can be a good resource for the further studies. Therefore, in this study, we investigated the microbial community’s variation with ‘Kyoho’ and ‘Fengzao’ using high-throughput sequencing, and the results assessed the microbial communities’ structure and diversity in various plant tissues in two grape varieties.

We identified 34 OTUs from the different grape tissues of ‘Kyoho’ and ‘Fengzao’ (Fig 1A). Although the identified microbial communities are not so rich, many representative microbes (such as Proteobacteria, Firmicutes, Synergistetes, Pseudomonadales, etc.) were identified, which are common in the horticultural plants as revealed by the previous studies [44–46]. The microbes we identified were mainly endogenous microbes (or named endophyte) which existed in the interior of plants. The communities of endophytes and epiphytes are usually different, as the epiphytes directly contact with the external environment and were influenced by the growth conditions, while the endophytes are relatively more stable with less microbial diversity in a same plant individual. So, the endophytes of plants may be less abundant than the epiphytes on the surface of the plant bodies (especially the rhizosphere) [22], while to some extent, the endophytes might affect plant normal growth even more, for example many diseases are caused by the endophytes. Previous studies mainly focused on the epiphyte [47], but in fact, the endophyte might play a more significant role for plants [25]. In recent years, more and more studies have started to investigate the endogenous microbes of plants, including Arabidopsis [25,28,48], apples [49] and grapes [19]. Lundberg et al. [25] rigorously defined the rhizosphere and the endophytic compartment (within the root) in Arabidopsis, and revealed the important functions of endophytic microbiome for plant-microbe interactions. So, while continuing to pay attention to rhizosphere microbes, we should also strengthen the researches on the endogenous microbes of other plant tissues (like leaf, fruit, stem, flower, etc.).

It is worth mentioning that our study revealed the significant variation in microbial composition between grape and its bud mutant variety (Fig 6), which is the first report in horticultural plants to explore microbial community’s variation in bud mutant variety. Current studies mainly focused on the microbial diversity among different plant species and different varieties of same plant species. For example, Portillo et al. [9] found differences in the microbial communities of fruit surface between two grape varieties, Grenache and Carignan. Yang et al. [50] compared the microbial communities in the rhizosphere soils from two varieties of Camellia sinensis, indicating that the composition of bacterial and fungal communities of two tea varieties was remarkably different. Similar results were obtained in tobacco and olive, in which the structure and composition of bacterial and fungal communities in different varieties were different [51,52]. So, from these studies, we can deduce that microbiome is genome- or genotype- specific in plants, which should be further investigated in more plant varieties [53,54], because current researches are still limited and insufficient. Additionally, there is no relevant research concerning the microbial variation in bud mutant, which is mostly occurred in horticultural plants. Accordingly, even though obvious variation was observed between ‘Kyoho’ and ‘Fengzao’ in this study, it is still uncertain whether bud mutant will influence microbial community diversity in all the horticultural plants. Therefore, it is of vital importance to deeply investigate the microbiome in bud mutant varieties in more horticultural plants to reveal the mechanisms underlying the bud mutant-related microbial diversity.

Download:

• PNG
  larger image
• TIFF
  original image

Fig 6. A model map showing the representative microbes in different samples of ‘Fengzao’ and ‘Kyoho’.

Only the microbial species with relatively high abundance are shown in the map, based on the results from Fig 2. The different taxonomic levels (p: phylum, c: class, o: order, f: family, g: genus) are indicated with different colored background.

https://doi.org/10.1371/journal.pone.0290853.g006

In addition, our study also found that different grape tissues harbored significantly different microbial communities. Fruits and stems were rich in microbes, while leaves contained very few microbes, with almost no microbes detected in ‘Fengzao’ leaves (Fig 6). Of course, the microbes inside the roots should be the most abundant. Previous studies have conducted detailed microbiome studies on grape roots, so our study did not replicate the study of the root. Martins et al. [19] examined the microbiome in the roots, barks, leaves and fruits of grapes, and found that the roots were the most abundant in microbes, followed by barks, fruits and leaves. The unique microbial community variation in root or rhizosphere mainly due to the specificity of the host plants themselves [55]. Root is directed related to the external environment and is most affected by it, and plant activities directly affect the microbial diversity of rhizosphere. Therefore, microbial community in rhizosphere exhibited significant species- or genotype-specific [25]. The tissue specificity of microbial compositions reminds us that a whole understanding of the microbiome for a plant requires analysis of different tissues, and although microbes can move within a plant, their final hosts may be relatively stationary.

After identifying the microbes, we also predicted the functional potential of the microbial community based on the COG pathway enrichment (Fig 5) using PICRUSt2 software. We noticed that the COG pathway with most obviously differences between ‘Kyoho’ and ‘Fengzao’ was ‘translation, ribosomal structure and biogenesis’, which is understandable as many of plant microbes are correlated with the processes of translation, ribosomal structure and biogenesis, and the microbes might influence the grape’s life activities by these processes, leading to the variation between these two varieties. The predicted pathways from fruit, leaf and stem were similar, possibly due to the mobility and transference of microorganisms throughout the whole grapevine. It’s worth noting that some pathways related to grape quality physiology, like ‘energy production and conversion’, ‘carbohydrate transport and metabolism’, and ‘inorgenic ion transport and metabolism’, were found in the COG pathway enrichment, indicating that these identified microbes may play a role in the grape fruit quality. Our previous studies demonstrated the physiological differences between ‘Kyoho’ and ‘Fengzao’ [29,31], which might due to the involvement of these microbes, and these inferences of course are needed to be further explored in the future. Certainly, functional prediction with PICRUSt2 can only be used for hypothesis generation, as criticisms for this software often exist. First, this prediction greatly depends on the existing reference genomes, so some specific functions may be less likely to be identified. Second, the strain-specific functions for the same species can’t be distinguished efficiently [37]. Nevertheless, this software is still the most reliable software available compared to the others, with expanded reference database and more compatible and novel approaches [37].

Many microbes identified in this study have been proved as key regulators in plant growth and development [18,44–46,56]. For example, Proteobacteria was found significantly enriched in the fruits of ‘Fengzao’. The phylum of Proteobacteria can be further categorized as Alphaproteobacteria, Betaproteobacteria, Gammarproteobacteria and Deltaproteobacteria, which were also identified in ‘Fengzao’ with a high abundance in fruits and stems (Fig 6). Rhodobacteraceae, one of the major subdivisions of Alphaproteobacteria [56], was also identified in the fruits of ‘Fengzao’ with a high abundance (Fig 6). Studies have found that Proteobacteria can promote growth in polluted farmland [46]. Interestingly, Proteobacteria and its subphylum were only identified and enriched in the fruits and stems of ‘Fengzao’, with a significantly lower abundance in ‘Kyoho’ (Figs 2 and 6), suggesting that bacterial abundance of Proteobacteria may have an impact on the differences of fruit development between ‘Fengzao’ and ‘Kyoho’. Firmicutes were also the main phyla at the early fruit enlargement stage and in the rhizosphere soil in vineyards [45], which were identified from the stems of ‘Kyoho’ in this study. Zhang et al. [45] found that Firmicutes were sensitive to abiotic stresses, especially drought. The Pseudomonas was found most significantly enriched in the stems of ‘Kyoho’(Fig 6). The content of Pseudomonas was high especially on the surface of wine grapes [44]. The Pseudomonas can produce extracellular polysaccharide, which is conducive to the formation of microbial membranes and affects the colonization of microbes on the fruit surface [44]. The Enterobacteriaceae family or the Enterobacter genus was identified with a significantly higher abundance in the stems of ‘Fengzao’ (Fig 6). The Enterobacteriaceae is thought to be beneficial for vineyards, as it can produce glucanases, chitinases and proteases to provide host resistance [18]. Additionally, some other identified microbes, like Ruminococcaceae family, Oxalobacteraceae family and Rhodobacteraceae genus from ‘Fengzao’, Tetragenococcus genus, Sphingomonas genus and Chlorobaculum genus from ‘Kyoho’, can be further investigated to understand their roles in grapevines.

Conclusion

In this study, we analyzed the microbiomes of the ‘Kyoho’ grape and its bud mutant variety (named ‘Fengzao’), identified a total of 34 OTUs from stems, leaves and fruits. There were obvious differences in the microbial communities between ‘Fengzao’ and ‘Kyoho’. The microbes in different grape tissues also showed remarkable differences, and fruits and stems are the tissues with relatively higher abundance of microbes, while the leaves contained less microbes. Proteobacteria phylum, Enterobacteriaceae family and Rhodobacteraceae were abundant in ‘Fengzao’, and Firmicutes phylum, Pseudomonas genus, family of Spirochaetaceae, Anaplasmataceae, Chlorobiaceae, and Sphingomonadaceae, genera of Spirochaeta, Sphingomonas, Chlorobaculum and Wolbachia were abundant in ‘Kyoho’. This study first demonstrated the microbial community’s variation between grapevine and its bud mutant variety, and these identified microbes will be significant resources for the future researches on the grapevine microbiology.

Supporting information