# Meta-analysis of grapevine microbiota: Insights into the influence of cultivars, plant parts, geography, and vineyard practices on bacterial diversity

Daniel Legesse, Yassine Bouhouch, Cédric Jacquard, Lisa Sanchez, Essaid Ait-Barka, Qassim Esmaeel

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## To cite this version:

Daniel Legesse, Yassine Bouhouch, Cédric Jacquard, Lisa Sanchez, Essaid Ait-Barka, et al.. Meta-analysis of grapevine microbiota: Insights into the influence of cultivars, plant parts, geography, and vineyard practices on bacterial diversity. Current Plant Biology, 2025, 42, pp.100478. 10.1016/j.cpb.2025.100478. hal-05210027

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# Meta-analysis of grapevine microbiota: Insights into the influence of cultivars, plant parts, geography, and vineyard practices on bacterial diversity

Daniel Legesse, Yassine Bouhouch, Cédric Jacquard, Lisa Sanchez, Essaid Ait-Barka, Qassim Esmaeel*

Université de Reims Champagne Ardenne, INRAE, RIBP USC 1488, 51100 Reims, France

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## ARTICLE INFO

**Keywords:**

Grapevine microbiome

Diversity analysis

Meta-analysis

Amplicon metagenomics

Microbial communities

Sustainable viticulture

## ABSTRACT

Investigating the grapevine microbiome is crucial for developing sustainable management practices in viticulture. This meta-analysis used amplicon metagenomics to analyze bacterial sequences from the Sequence Read Archive, covering 756 grapevine accessions. The dataset includes 378 paired end sequencing runs across twelve cultivars, four sample types, six origins, and two vineyard conditions. The analysis revealed significant variations in microbial communities across different grapevine cultivars and sample types, highlighting the influence of both the cultivar and ecological niche on microbial diversity. Differential abundance analyses further identified key microbial taxa driving variations across different grapevine plant parts. Furthermore, the study identified microbial taxa that consistently thrive across diverse conditions, suggesting their potential role in grapevine health and development. These findings support the possibility of utilizing specific microbial agents for biological control and sustainable vineyard management. This meta-analysis underscores the complexity of the grapevine microbiome and its pivotal role in vineyard ecosystems, providing valuable insights for future research and the advancement of sustainable viticulture practices.

## 1. Introduction

Grapevine (*Vitis vinifera* L.), is a perennial plant that ranks among the most cultivated fruit crops worldwide [1–3]. The economic importance of grapevine extends beyond its agricultural value, playing a pivotal role in the economic stability of many nations. Several factors influence grapevine production, including weather conditions and stressors such as nutrient deficiencies, all of which significantly impact grape yield and quality. Diseases such as downy mildew, powdery mildew, and gray mold severely affect grapevine production, highlighting the importance of effective disease management in grapevine cultivation [4]. Managing these diseases is crucial for ensuring successful wine grape production, requiring the prediction of disease outbreaks and disrupting the infection cycle at its weakest point [5].

Viticulture management practices are crucial for controlling the spread of a wide range of pathogens in vineyards, including fungi, bacteria and viruses. These pathogens can severely impact both grape yield quality [5–8]. Effective management relies on integrated

approaches combining preventive measures tailored to specific pathogens [9]. Breeding programs have focused on developing grapevine varieties with partial or complete resistance to downy and powdery mildews, offering a viable alternative to fungicides [10–13]. The use of chemical fungicides for disease management remains a common practice, with treatments typically applied every 7–10 days from bud break to bloom, depending on weather conditions [14]. Cultural control practices, such as reducing leaf and cluster wetness and improving air circulation, are also implemented to mitigate disease spread [15]. Additionally, beneficial microorganisms, used as biological control agents (BCAs), show promise in controlling downy mildew, although their effectiveness varies depending on environmental factors [16]. The study of grapevine cultivars, plant environments and their microbiota offers significant potential applications in viticulture [17]. Recent studies have shown that the grapevine microbiome influences a wide range of factors, ranging from field origin signatures to wine quality [1]. For example, research conducted in Argentina has shown that cultivars, soil properties, and vintage significantly affect the rhizospheric

<footnote>* Corresponding author.
E-mail address: qassim.esmaeel@univ-reims.fr (Q. Esmaeel).</footnote>
microbiome of Malbec and Cabernet Sauvignon varieties [18]. Similarly, research in California has demonstrated that rootstock genotype and environment shape the microbial communities of grafted grapevines, with leaves and berries exhibiting temporal shifts in their microbiomes [19]. Additionally, plant compartment and vineyard practices significantly influence bacterial communities. For example, research in France demonstrated that organic vineyards exhibit greater microbiome diversity across the rhizosphere, bulk soil, phyllosphere, and carposphere compared to conventional practices [20].

In viticulture, investigating the grapevine microbiome is essential not only for advancing ecological knowledge but also crucial for developing sustainable and environmentally friendly practices [1,21]. However, challenges remain in characterizing the grapevine microbiome and ensuring the persistence of BCAs in the soil and rhizosphere. Nevertheless, the core taxa of the grapevine microbiome play a crucial role in maintaining the balance of nutrition, health, and growth for the grapevine [22].

While there has been progress in understanding the grapevine microbiome, significant gaps remain in our comprehension of its responses to environmental triggers [23].

Metadata analysis of the grapevine microbiome across different cultivars and environmental niches represents a critical gap in current research. Despite growing interest, limited attention has focused to analyzing microbiome metadata across diverse countries, grapevine varieties, and environmental conditions [22]. This gap limits the development of a comprehensive understanding of the microbiome's role in viticulture and hinders insights crucial for advancing sustainable practices. Additionally, insufficient comprehensive examination of the microbiome across various grapevine cultivars restricts our broader understanding of microbial diversity in relation to vineyard management and grapevine health. Addressing this gap requires the use of multiple databases, such as the National Center for Biotechnology Information (NCBI), to access the metadata needed for in-depth investigations. Effective metadata searches and conducting meta-analyses of grapevine cultivars across diverse countries, vineyards, and plant parts are vital for advancing viticultural practices and ensuring reproducibility in research [17,24–27].

The main objective of the present study was to develop a comprehensive meta-analysis approach focused on grapevine microbiomes, particularly in relation to different cultivars and their associated ecosystems. Furthermore, it highlights the crucial need for depositing sequence datasets into public repositories, fostering future research and enabling more robust meta-analyses.

Understanding grapevine microbiome diversity and dynamics requires analysis of various cultivars, their origin, and their effects on grapevine health and productivity. However, retrieving metadata necessitates careful consideration of data quality, statistical methods, and result interpretation. This study aggregates metadata on cultivar, plant ecology, geographical origin, and vineyard conditions, highlighting the complex interactions between microbial taxa and their ecological roles within the grapevine ecosystem. We specifically examined whether distinct grapevine cultivars shared patterns of abundance within their microbial communities.

# 2. Material and methods

## 2.1. Database search and selection criteria

To identify relevant studies related to our research topic, a comprehensive literature review was conducted using the keywords "grapevine microbiome" "grapevine microbial interaction", "grapevine metagenomics", and "grapevine phyllosphere microbiome". The search was performed in April 2023, utilizing the SCOPUS® (http://www.scopus.com) and Web of Science® (https://webofknowledge.com/) databases. To ensure robustness and relevance, stringent eligibility criteria were applied to mitigate potential selection bias:

- **Metadata coverage:** studies providing comprehensive metadata, including detailed experimental protocols, technical specifications, and other relevant information, were selected.

- **Technical aspects:** priority was given to entries offering thorough technical descriptions, such as sequencing library preparation and data files formats.

- **Structured metadata:** preference was given to entries in the Bio-Sample Sequence Read Archive (SRA) format, which utilizes structured 'attribute-value' pairs for better organization and analysis.

- **Compliance with standards:** Selected entries ensured data consistency and interoperability

[28]. The SRA dataset was retrieved from the NCBI and the European Nucleotide Archive (ENA), focusing on high-throughput sequencing data. A total of 756 run accessions (378 paired-end runs) of 16SrRNA amplicon metagenomic sequence data were retrieved, meeting these established criteria. The dataset included 16S rRNA amplicon genomes from grapevine bulk soil, rhizosphere soil, phyllosphere (wood and leaf samples), and carposphere (fruits and berries).

## 2.2. Analysis of global grapevine cultivars and their geographic origins

A comprehensive metadata analysis was conducted on 219 grapevine cultivars to determine their countries of origin. Data was also retrieved from databases such as the Vitis International Variety Catalogue (VIVC) for *V. vinifera* cultivars, accessible at https://www.vivc.de/, to gather details across various countries.

### 2.2.1. Data access and SRA accession retrieval

To effectively integrate metadata, we linked SRA accessions to their corresponding metadata, focusing on grape variety, geographical location, sample type, and vineyard conditions. A major challenge was ensuring standardized and detailed metadata across diverse countries and sample types for meaningful comparisons.

To address this, we implemented a structured workflow. Using the SRA Toolkit (https://metasra.biostat.wisc.edu/supportpages/sequence-data-download.html), we retrieved raw sequencing data, while Entrez Direct was used to programmatically link SRA accessions to their respective BioSamples and BioProjects, creating a direct connection between raw data and study information (Supplementary Table S1).

For comprehensive metadata retrieval, we utilized the ENA API to obtain structured metadata in TSV and JSON formats, providing detailed insights into sample characteristics and project parameters. XML parsing techniques (Supplementary Figure S1) were applied to extract specific attributes from BioSample XML files. A complete list of SRA accession numbers used in this study is available in Supplementary Table S2, enabling direct access to raw sequencing data.

SRA run accessions were obtained using batch downloading techniques via the command line. The SRA Toolkit, including the prefetch package, was employed for data retrieval. Paired-end sequences corresponding to 378 SRA accessions of 16S rRNA amplicon datasets were acquired (Supplementary Table S2). The Fasterq-dump package was used to convert the SRA data to the fastq format [29,30]. Paired-read sequences were merged using the Galaxy pipeline (http://hannonlab.cshl.edu/fastx_toolkit).

### 2.2.2. Quality control of the SRA metadata

Quality control for paired-end sequences was performed using FastQC Version: 0.11.9 (https://github.com/angelovangel/nxf-fastqc) via the command line (Supplementary Data S1). Trimming, when necessary was carried out using fastp Version: 0.20.1 (https://bioconda.github.io/recipes/fastp/README.html) to ensure data integrity (Supplementary Data S2). After quality control, 754 out of 756 SRA accessions (377 paired-end reads) were retained. Scripts for SRA accessions, FastQC, and fastp processing are available in Supplementary data S1.
### 2.2.3. Taxonomic assignment

Taxonomic classification and metagenome analyses were performed using Kraken2 [31] with the standard Kraken2 database for bacterial taxa. Results were visualized using the Pavian R package v1.2.1, providing a comprehensive overview of taxonomic composition [32,33]. The biome convert tool (Supplementary data S1) was used to convert Kraken reports into biome categories to facilitate downstream analysis and interpretation [34,35].

### 2.2.4. Taxonomic diversity, variability and bacterial composition across cultivars and sample types

The coefficient of variation (CV) was used to evaluate the variability of taxa. Prevalence analysis was also performed to explore the relationship between taxa prevalence and their average abundance on a log scale [36]. The total number of classified reads for each sample was calculated using the sample_summs() function in the phyloseq R package and stored as metadata in sample_data (https://rdr.io/bioc/phyloseq/).

To investigate relationships between sequencing depth and taxonomic composition across grapevine varieties, we employed correlation heatmap analysis. The phyloseq object was first filtered to remove low abundance taxa, retaining only those present in at least 60 % of samples using the taxa_filter function. We then utilized the variable_correlation_heatmap function to generate heatmaps depicting Pearson correlations between classified read counts and relative abundances at the phylum and class level.

Alpha diversity indices were calculated to assess microbial diversity and richness. Sequencing depth was measured, and the head lengths of each sample were documented. Microbial richness was compared across different geographical regions and between treated and untreated vineyard samples [37]. To conduct a more comprehensive analysis of alpha diversity, two key aspects were considered. Non-phylogenetic alpha diversity was used to evaluate microbial richness and evenness using indices such as Inverse Simpson, Gini-Simpson, Shannon, and Coverage. Additionally, statistical comparisons were performed using t-tests, adjusted for multiple comparisons with the Bonferroni correction method, to identify differences in microbial diversity across sample types, grapevine cultivars, and vineyard conditions [38]. Furthermore, phylogenetic diversity analysis was conducted to evaluate the evolutionary relationships among microbial taxa across grapevine-associated environments, including bulk soil, phyllosphere, rhizosphere, and carposphere [39]. For statistical comparisons, the Kruskal-Wallis test was applied, followed by post-hoc pairwise tests to determine significant differences [40].

Microbial community composition was analyzed using heat tree matrices and heatmaps across sample types, grapevine cultivars, vineyard conditions, and origins at different taxonomic levels. The heat tree matrix visualized relative abundance, while heatmaps highlighted taxonomic variations in plant parts and grapevine varieties, illustrating microbial diversity patterns [41]. Unweighted UniFrac analysis was performed to assess the presence or absence of microbial taxa across sample types based on sequencing depth [42,43]. In contrast, Weighted UniFrac analysis, conducted via principal coordinate analysis (PCoA), evaluated microbial abundance patterns across sample types [43]. Additionally, beta diversity analyses using NMDS with Bray-Curtis dissimilarity metrics visualized microbial community structure at the population level across sample types and countries [44]. Differential abundance of microbial taxa across grapevine varieties, sample types, countries, and vineyard treatments were analyzed using a linear model on log2-transformed data with a minimum prevalence threshold. Statistically significant differences in taxa across these groups were identified using FDR-adjusted p-values with a threshold of 0.05 [44,45].

### 2.3. Statistical analysis

The statistical analysis involved several approaches to evaluate the microbial diversity and community structure across various factors. The

coefficient of variation (CV) of microbial taxa was visualized using a scatter plot with a logarithmic transformation on the x-axis to interpret the variability among taxa within the Phyloseq object effectively. Alpha diversity was assessed by comparing the distributions of various diversity indices across sample types, grapevine varieties, vineyard condition, and country using boxplots in ggplot2. Pairwise comparisons were performed with stat_compare Means() function to identify significant differences in microbial community structure. T-test was conducted to evaluate differences in microbial diversity metrics across sample types, grapevine varieties, vineyards, and countries. For microbial community composition, the heat tree analysis was conducted to visualize the abundance of microbial taxa across all grapevine varieties using the Phyloseq package in R, specifically utilizing the heat_tree() function to represent taxa abundance in a comprehensive format. Beta diversity analyses were conducted to measure dissimilarities in microbial communities across samples, utilizing metrics such as Bray-Curtis dissimilarity and UniFrac distances. A PERMANOVA analysis (Supplementary Data S3) was performed to assess dissimilarities in microbial community composition across different sample types, grapevine cultivars, vineyard conditions, and countries. To identify significant differences in microbial taxa across conditions, differential abundance analysis was conducted using the Phyloseq package in R Version: 4.4.0. The Taxatree_label() function were employed to label taxa based on statistical significance, focusing on p-values less than 0.05 for taxonomic ranks of Phylum and Genus. Scripts for the statistical analysis used are available in the Supplementary data S1.

## 3. Results

### 3.1. Global analysis and species classification of grapevine cultivars

The analysis of 219 grapevine cultivars highlighted their global distribution and identified regions with significant varietal diversity (Supplementary Table S3). The findings reveal that France, Italy, and Spain exhibit notably higher grapevine diversity compared to other regions (Fig. 1A). Cultivars were classified into four primary species groups: *V. vinifera*, *V. rupestris* Scheele, *V. labrusca*, and *Vitis* interspecific crossings (Fig. 1B). The majority of cultivars, particularly in France, Italy, and the USA, belonged to *V. vinifera*. While species such as *Vitis* interspecific crossings and *V. labrusca* are less common, they remain significant in regions like Turkey, Canada, and the USA. This classification underscores the global predominance of *Vitis vinifera* while also revealing regional variations in species distribution.

The variability of the grapevine microbiome across cultivars and plant parts was conducted using a subset of 12 grapevine cultivars sampled from six countries. These cultivars were selected based on strict eligibility criteria to mitigate selection bias, thereby ensuring robust and representative results. Geographical mapping using Simple Map provided additional spatial context, visualizing the distribution of the selected grapevine cultivars (Supplementary Figure S2).

### 3.2. Variation in microbial taxa abundance and prevalence across grapevine samples

Of the 378 samples analyzed, 377 successfully passed quality control, retaining 100 % of reads after filtering. The dataset demonstrated high base quality, with Q20 and Q30 values of 98.7 % and 95.6 %, respectively, and a GC content of 56.2 %. One sample (ERR11006541_1) was excluded due to a high proportion of poor-quality reads (Supplementary Data S2). The read counts varied significantly across samples, with a wide range from 0 to 809,517 reads. Most samples exhibited moderate to high read counts, with a notable proportion exceeding 50,000 reads. A few samples had exceptionally low read counts, which may indicate sequencing artifacts or low microbial load (Supplementary data S4).

Taxonomic variability was assessed using the CV. The analysis revealed that taxa with lower average abundance exhibit higher CV
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Grapevine species and varieties by country. (A) a detailed visualization of 219 grapevine varieties, showcasing their frequency and diverse national origins. (B) the distribution of grapevine species across various countries.</figcaption></figure>

values, indicating greater variability across samples. In contrast, more
abundance taxa, such as Actinobacteria, Proteobacteria, and Firmicutes,
displayed lower CV values, reflecting their stable and consistent pres-
ence within the microbial community (Supplementary Figure S3A). This
pattern suggests that more abundant phyla are less variable and
contribute significantly to the core microbiome, while rarer taxa show
higher fluctuations, likely due to their niche-specific distribution. To
further examine the relationship between prevalence and abundance,
taxa prevalence was analyzed against average count abundance on a
logarithmic scale to identify distribution patterns. The results
(Supplementary Figure S3B) demonstrate that Actinobacteria, Proteo-
bacteria, and Firmicutes not only exhibit high average abundance but
also high prevalence, highlighting their dominance and widespread
across samples. In contrast, taxa with lower average abundance, such as
Thermotogae and Synergistetes, showed low prevalence, suggesting that
these taxa are limited to specific samples and may occupy more specified
ecological niches. The correlation between classified reads (absolute
detection) and relative abundances (pro-portional abundance) of taxa
was analyzed using Pearson correlation, allowing the identification of
significant patterns across different taxonomic levels. At the class level,
Firmicutes, Actinobacteria, and Proteobacteria exhibited strong positive
correlations in the carposphere and rhizosphere, suggesting their stable
presence in plant-associated environments (Supplementary Figure S4A).
Conversely, correlations in the soil and phyllosphere were more vari-
able, indicating diverse microbial adaptations. At the family level,
Bacillaceae, Rhizobiaceae, and Micrococcaceae showed significant as-
sociations, particularly in phyllosphere and carposphere compartments,
while the rhizosphere lacked significant correlation (Supplementary

Figure S4B).

3.3. *Significant variability in microbial diversity across cultivars, sample types, and vineyard practices*

Heat tree analysis and alpha diversity indices were used to investi-
gate microbial community composition and diversity across sample
types, grapevine cultivars, vineyard conditions, and countries
(Supplementary Figure S5A, B, C, and D). The analysis revealed Acti-
nobacteria and Proteobacteria as the dominant phyla within grapevine
cultivars. Across countries, Actinobacteria, Proteobacteria, and Firmi-
cutes emerged as the top abundant phyla. Notably, Firmicutes showed
increased abundance under treated vineyard conditions, highlighting
the influence of environmental factors and agricultural practices on
microbial composition in vineyards.

Microbial richness exhibited significant variability across sample
types, cultivars, origins, and vineyard treatments. For example, Sau-
vignon Blanc and Pinot Noir exhibited higher microbial richness, mainly
dominated by Acidobacteria and Chloroflexi (Fig. 2A).

Region-specific differences in microbial richness were also observed, with countries like Australia, France, and New Zealand hosting a more diverse array of taxa, including Actinobacteria, Proteobacteria, and Firmicutes, and Bacteroidetes. Meanwhile, Austria displayed lower microbial richness, predominantly composed of Proteobacteria (Fig. 2 B). A comparison of treated and untreated vineyard samples revealed that untreated vineyards exhibited greater microbial diversity across a broader range of taxa, including some that were less abundant or absent in treated conditions. This pattern suggests that vineyard treatments
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Microbial taxa richness illustrates the distribution of dominant taxa across grapevine cultivars (A), countries (B), vineyard conditions (C), and sample types (D), highlighting their prevalence within grapevine ecosystems.</figcaption></figure>

may selectively favor certain microbial taxa, such as Firmicutes, while reducing overall microbial diversity by limiting more susceptible taxa (Fig. 2 C).

Microbial richness also varied significantly among plant parts. Soil and phyllosphere (wood and leaves) samples exhibited the highest richness, with substantial contributions from Actinobacteria, Proteobacteria, and Firmicutes. In contrast, the rhizosphere exhibited lower richness compared to both bulk soil and the phyllosphere. Despite these differences, all sample types displayed significant contributions from Actinobacteria, Proteobacteria, and Firmicutes, reflecting the complexity of microbial ecosystems in grapevine-associated environments. Carosphere samples (flowers and berries), however, exhibited reduced richness, with Proteobacteria and Firmicutes being the most dominant taxa, suggesting a more specialized microbiome in these aerial parts (Fig. 2 D).

Phylogenetic diversity analysis, which considers evolutionary relationships among microbial taxa, revealed substantial variability across grapevine-associated environments. Among grapevine cultivars, Pinot Noir and Cabernet Sauvignon exhibited the highest phylogenetic diversity, while Blauer Wildbacher and C.C. Gmelin show the lowest, highlighting the broad range of microbial diversity among cultivars. Geographical comparisons (Fig. 3A) revealed that Australia, France, and New Zealand had the highest phylogenetic diversity, while Austria and Italy display lower diversity, underscoring the strong regional influence on microbial community structure. Untreated vineyards exhibited

notably higher microbial diversity compared to treated ones, emphasizing the impact of agricultural interventions on reducing microbial complexity (Fig. 3B). Among sample types (Fig. 3C), bulk soil exhibited the highest phylogenetic diversity, followed by the phyllosphere (wood and leaves), rhizosphere soil, and the carosphere (flowers and fruits), which exhibited the lowest diversity. Statistical analysis confirmed high significant differences (p < 0.0001) across all comparisons, indicating the distinct microbial diversity profiles associated with these grapevine environments.

### 3.4. *Distinct patterns of non-phylogenetic alpha diversity across grapevine cultivars and plant parts*

Non-phylogenetic alpha diversity analyses were performed to assess species richness and evenness within specific communities, without considering their evolutionary relatedness. The result (Fig. 4A) revealed distinct patterns across various diversity indices, including Inverse Simpson, Gini-Simpson, Shannon, and Coverage indices. According to the Inverse Simpson index, Cabernet Sauvignon and Pinot Noir exhibited the highest microbial diversity, whereas Sangiovese and Sauvignon Blanc showed the lowest diversity. Similarly, the Gini-Simpson index highlighted high variability for Cabernet Sauvignon and Pinot Noir, while Sangiovese and Sauvignon Blanc showed consistently lower diversity scores. The Shannon index further corroborated these findings, identifying Cabernet Sauvignon and Pinot Noir as having greater species
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Phylogenetic alpha diversity across countries (A), vineyard conditions (B), and sample types (C). Panels (A–C) show significant pairwise comparisons (*p* < 0.001) revealing differences in microbial composition within grapevine ecosystems.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Non-phylogenetic alpha diversity, integrating Simpson, Shannon, and coverage indices within grapevine communities across diverse varieties (A), plant parts (phyllo-sphere, soil, rhizosphere, and carposphere) (B), origins (C), and vineyard practices (D), (*p* < 0.001).</figcaption></figure>
richness and evenness compared to other cultivars. Collectively, these results underscore significant variability in microbial diversity across grapevine cultivars, with Cabernet Sauvignon and Pinot Noir consistently showing the highest diversity across multiple metrics.

Microbial diversity also varied significantly across plant parts (Fig. 4B). The phyllosphere, soil, and rhizosphere consistently displayed higher microbial diversity compared to the carposphere, which harbored a more specialized and less diverse microbiome. The Inverse Simpson, Shannon, and Gini-Simpson indices all demonstrated higher diversity in the phyllosphere, soil, and rhizosphere. In the contrast, the carposphere showed a more specialized and less diverse microbiome. The coverage index further supported these observations, showing moderate to high microbial diversity in the phyllosphere, soil, and rhizosphere, but comparatively lower diversity in the carposphere. Alpha diversity varied across countries (Fig. 4C). Australia and France exhibited the highest diversity, while Italy and Austria showed the lowest diversity across all metrics. The Coverage index showed the highest diversity in Australia and France, while Italy showed the lowest coverage. Gini-Simpson values were uniformly high across all geographic comparisons, reflecting stable evenness in community composition across regions. In terms of vineyard management, untreated vineyards consistently displayed higher microbial diversity

compared to treated vineyards (Fig. 4D). This trend was evident in the Inverse Simpson and Shannon indices, suggesting that agricultural treatments may reduce microbial diversity by favoring specific taxa and suppressing others.

To further validate the observed difference in alpha diversity, statistical comparisons were conducted. The results revealed significant variation in microbial diversity among sample types (F(3, 83.14) = 194.02, p < 0.01). Pairwise comparisons revealed that the carposphere exhibited the lowest diversity, while the soil, phyllosphere, and rhizosphere displayed significantly higher diversity levels. These analysis underscores the soil, rhizosphere and phyllosphere, as rich and diverse microbial niches, highlighting the significant impact of environmental and biological factors on microbial community composition in grapevine ecosystems (Fig. 5A). Microbial diversity also differed between fungicide-treated and untreated vineyards (t(250.9) = 9.18). This result indicates that fungicide applications have a marked effect on microbial diversity within the vineyard environment, potentially reducing overall microbial richness and evenness (Fig. 5 B). Additionally, microbial diversity showed significantly variation across countries (F(5, 100.75) = 153.97, p < 0.01). This variation underscores the influence of agroecological conditions specific to each country, which shape the structure and composition of microbial communities (Fig. 5

A

<figure><img src="image_9.png" /></figure>

C

<figure><img src="image_10.png" /></figure>

B

<figure><img src="image_11.png" /></figure>

D

<figure><img src="image_12.png" /><figcaption>**Fig. 5.** T-test statistics for alpha diversity measures (chao1 index), highlighting significant differences within the grapevine microbial community across sample types (A), vineyard conditions (B), countries (C), and cultivars (D) (p < 0.001). Pairwise comparisons using Games-Howell test bars indicate significant differences.</figcaption></figure>
C). Finally, significant differences in microbial diversity were observed across grapevine varieties (F(11, 18.46) = 26.96, p < 0.01). Pairwise tests highlighted significant differences between specific varieties, reflecting the unique contribution of genetic and phenotypic traits to the diversity and structure of associated microbial communities (Fig. 5 D).

### 3.5. Dissimilarities in microbial communities between varieties sample type

To assess the presence or absence of microbiome taxa, Unweighted Unifrac analysis was employed, accounting for sequencing depth and its influence on the detection of unique OTU. The UniFrac-based PCoA analysis revealed distinct microbial patterns across various factors. When grouping by sample type, comparison of country origin and vineyard management practices highlighted the carposphere as significantly distinct from the phyllosphere, rhizosphere, and soil in both Weighted and Unweighted analyses, highlighting its unique microbial composition (Fig. 6A, B).

The NMDS (Non-metric Multidimensional Scaling) Bray-Curtis distance metrics results further supported these findings, revealing clear patterns of microbial composition variability. Sample type emerged as the main variable influencing microbial community variation across grapevine varieties, countries, and vineyard practices, with clear clustering patterns observed for the carposphere, phyllosphere, rhizosphere, and soil. Among these, soil exhibited the highest diverse microbial community, highlighting its ecological complexity compared to other sample types (Fig. 7A). Microbial community structures also differed distinctly across grapevine varieties within each sample type. Notable clustering was observed in Pinot Noir, Cabernet Sauvignon, and Sauvignon Blanc, which exhibited higher community compared to other varieties (Fig. 7B). Additionally, microbial communities varied significantly across countries based on sample type, with Australia, France, and New Zealand showing more pronounced clustering and higher community diversity (Fig. 7C). Distinct differences in microbial community composition were notably observed between treated and untreated vineyards, with untreated vineyards generally exhibiting slightly higher microbial density across all sample type, grapevine varieties, and origin (Fig. 7D).

### 3.6. Differential abundance of microbial taxa in grapevine plant part

Differentially abundance analysis of microbial taxa was conducted using log2-transformed compositional data at the genus level, with a

minimum prevalence threshold of 10% to maintain better statistical modelling. A linear model was applied to test differential abundance at various taxonomic levels, and p-values were adjusted for multiple comparisons using the false discovery rate (FDR) methods. This analysis identified specific taxa driving microbial compositions differences across plant compartments. In the soil and phyllosphere, microbial variation was primarily influenced by Proteobacteria, Acidobacteria, Actinobacteria, and Firmicutes. In the rhizosphere, Proteobacteria and Acidobacteria were the dominant contributors to variation, while in the carposphere, Firmicutes and Actinobacteria played a major role in shaping microbial compositions (Supplementary Figure S6).

## 4. Discussion

This meta-analysis examined microbial communities associated with twelve grapevine cultivars across six countries, covering four agroecological niches: soil, rhizosphere, phyllosphere, and carposphere. Using data from 16S rRNA amplicon metagenomic sequences of 756 accessions, the study elucidated microbial composition, abundance patterns, and the ecological dynamics of the grapevine microbiome. Classification of samples by niche provided critical insights into microbial colonization patterns, emphasizing the significance of niche-specific dynamics in shaping microbial community structure [20,46,47]. Furthermore, this meta-analysis emphasizes the importance of metadata-driven studies in uncovering the roles of microbial taxa by considering multiple influencing factors [48–50]. Highlight that, while previous studies have typically focused on single-factor analyses, this study adopts a more comprehensive approach by integrating various factors influencing microbial diversity [51,52].

### 4.1. Variability in taxonomic composition and abundance

Taxonomic variability was evaluated using the Coefficient of Variation, a common metric for assessing dispersion in OTU abundance data [36,53,54]. Proteobacteria consistently exhibited the highest relative mean abundance across all samples, followed by Actinobacteria, highlighting their ecological significance within the grapevine microbiome. These results are in accordance with previous studies, such as Zarraonaindia et al. [55,56] which reported Proteobacteria as the dominant phylum in grape-associated microbiomes, including grapes, leaves, and flowers. Similarly, Proteobacteria has been identified as the most abundance group in endophytic bacterial community, followed by Actinobacteriota [1].

<figure><img src="image_13.png" /><figcaption>**Fig. 6.** Unweighted Unifrac (A) and Weighted Unifrac (B) analysis of significant dissimilarities in microbial communities across different sample types (p < 0.05).</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>**Fig. 7.** Visualisation of community of grapevine microbiota using metrics of NMDS (Non-metric Multidimensional Scaling) and Bray-Curtis dissimilarity in sample types (A), variety (B), country (C) and vineyard (D).</figcaption></figure>

Sequencing depth was a key factor in assessing alpha diversity, with read counts ranging from 0 to 809,517 per sample, illustrating substantial variability. Low-count samples may require further investigation to assess potential biases or technical limitations [57]. Filtering and normalization may be necessary to ensure accurate downstream analysis and biological interpretation.

This variation reinforces the representativeness of the sampling strategy in capturing the broad microbial diversity within the grapevine microbiome. Consequently, these observations validate the robustness of alpha diversity analysis and their application to grapevine microbiomes [56,58,59]. Future research should investigate how sequencing depth and normalization techniques influence the assessment of microbial diversity in grapevine microbiomes and their implications for plant-microbe interactions.

## 4.2. Grape cultivars influence bacteria diversity across plant parts

Microbial richness analyses revealed cultivar-specific influences on bacteria diversity across four plant parts: soil, rhizosphere, phyllosphere, and carposphere. Cultivars with higher microbial abundance exhibited greater influence on bacterial diversity, while others displayed reduced diversity. Notably, the soil and phyllosphere exhibited the highest diversity, reflecting the complexity of these environments [60,

61]. The rhizosphere also contributed substantially to microbial richness, while the carposphere (fruits and berries) exhibited reduced diversity [62]. These findings highlight the role of cultivars and plant parts in shaping microbial communities [63]. Non-phylogenetic alpha diversity metrics, including Simpson, and coverage indices, were employed to assess microbial richness and evenness. This comprehensive analysis illuminated taxonomic distributions across twelve grapevine cultivars and four plant parts. Niem et al. [64] used similar methods to compare bacterial diversity across locations and symptomatic grapevines. High species richness and evenness were observed in Cabernet Sauvignon and Pinot Noir cultivars, while lower diversity was found in Sangiovese and Sauvignon Blanc, indicating the influence of cultivars on bacterial diversity. Across plant parts, the phyllosphere, the soil, and rhizosphere exhibited higher diversity, whereas the carposphere displayed a more specialized and less diverse microbiome. As reported by Willis [65], the structure of ecological communities is determined by alpha diversity metrics through quantifying richness, evenness, or a combinations, providing insights into microbiota assembly dynamics.

Non-phylogenetic alpha diversity analysis showed significant differences in microbial diversity within grapevine cultivars across various sample types (p < 0.001). These findings align with previous studies investigating grapevine compartments [66], the biogeography patterns
of grapevine microbiota [67], and cultivar-specific microbiomes [24]. Phylogenetic alpha diversity metrics were also analyzed to explore the evolutionary relationships and divergence within microbial communities associated with twelve grapevine cultivars and their respective plant parts. Pairwise analyses revealed significant differences in microbial composition across the soil, rhizosphere, phyllosphere, and carposphere. Metrics such as Faith's Phylogenetic Diversity (PD) were calculated to examine bacterial and fungal communities across grapevine ecological niches, including soil, roots, leaves, and berries, as reported by Leinonen et al. [68]. This analysis provides a comprehensive understanding of the evolutionary divergence and relatedness of microbial taxa colonizing different parts of grapevine cultivars.

Heat tree analysis further elucidated the distribution of microbial taxa, with Actinobacteria and Proteobacteria identified as the dominant phyla within grapevine cultivars. The relative abundances of these taxa varied by sample types, with Actinobacteria, Proteobacteria, and Firmicutes consistently emerging as the top abundant phyla across plant parts. These findings align with previous studies [20,66], which similarly reported the predominance of Proteobacteria and Actinobacteria in grapevine-associated microbial communities. The dominance of these taxa across cultivars and plant parts highlights their ecological and potential agricultural applications. Future research may explore how cultivar-specific microbial diversity patterns across different plant parts contribute to grapevine resilience, disease resistance, and overall vineyard ecosystem health. Additionally, investigating the functional roles of dominant phyla like Actinobacteria and Proteobacteria could develop targeted strategies for improving vine health and wine production.

### 4.3. Geographic location and vineyard practices shape bacterial diversity in grapevine microbiomes

Non-phylogenetic alpha diversity analyses showed significant differences in microbial diversity influenced by geographic location and vineyard management practices (p < 0.001). These results align with prior findings highlighting the regional microbial signature of grapevines [69] and the impact of vineyard management practices [70].

Similarly, studies by Liu et al. [71] and Martiniuk et al. [72] underscores the role of both geographic and management practices in shaping fungal community structures, contributing to microbial diversity and the distinctiveness of wines.

Microbial richness varied significantly across countries, with regions such as New Zealand, Australia, and France hosting a more diverse array of microbial taxa compared to Austria, which exhibited lower richness. This geographic influence on grapevine-associated microbial communities aligns with studies by Wei et al. [73], who indicated that environmental factors and geographic distance significantly influence microbial community dissimilarity in vineyards. Cooler and wetter regions were associated with lower microbial richness but higher dominance of specific taxa.

A comparison between treated and untreated vineyard samples revealed that untreated vineyards exhibited greater microbial diversity, including some that were less abundant or absent in treated conditions. This pattern suggests that treatments may selectively favor certain microbial taxa while potentially reducing overall microbial diversity by limiting more susceptible taxa. Statistical comparison confirmed significant difference in microbial diversity between fungicide-treated and untreated vineyards (p < 0.01). This findings align with studies by Cordero-Bueso et al. [74] and Liu et al. [75], which demonstrated that untreated vineyards exhibit a greater microbial diversity, with organic practices fostering a more diverse yeast community compared to conventional treatments, which often favor specific taxa and reduce overall microbial richness.

Geographic analysis further underscored the substantial role of location on microbial diversity, with significant differences observed across countries (p < 0.01). These findings underscore the influence of agroecological conditions along with vineyard management in shaping

microbial community diversity. Chu et al. [76] similarly emphasizes that geographic location significantly influences soil microbial community composition and diversity, interestingly illustrate the importance of agroecological conditions in shaping microbial biogeography in vineyards.

Heat tree analysis of phylogenetic microbial diversity across countries and vineyard management conditions identified Actinobacteria, Proteobacteria, and Firmicutes as the top abundant phyla. Research by Darriaut et al. [77] confirms that these taxa are among the most abundant bacterial phyla in vineyard soils indicating their significance in microbial dynamics. Untreated vineyards displayed higher abundance of these phyla, while Firmicutes were more dominant in treated vineyard conditions. Previous studies by Burns et al. [78] and Kovács et al. [79] align with these findings, demonstrating that vineyard management practices and geographic origin significantly influence soil bacterial diversity and community structure in grapevine ecosystems. Ongoing research should explore how geographic location and vineyard management practices shape microbial diversity and community structures in grapevine ecosystems, potentially influencing wine characteristics and vineyard resilience to environmental stressors. Additionally, studies may investigate how specific vineyard treatments impact microbial taxa abundance and diversity, aiming to develop sustainable practices that maintain beneficial microbial communities while addressing viticultural challenges.

### 4.4. Influence of sample type, geography, and vineyard practices on bacterial diversity

Unweighted and Weighted UniFrac analyses were employed to examine both qualitative (presence/absence) and quantitative (abundance) differences in microbial taxa across grapevine samples. Unweighted Unifrac assessed beta diversity, focusing on the presence or absence of microbial taxa, revealing significant similarities across grapevine sample types, such as soil, rhizosphere, phyllosphere, and carposphere [49]. The primary focus of this analysis was to assess the presence or absence of microbial taxa without weighting their abundance, which provides the microbial community with qualitative insights [49]. These findings align with those reported by Swift et al. [80], which noted distinct microbial communities associated with different grapevine ecosystems, such as soil, roots, leaves, and berries.

Weighted Unifrac, which incorporates the relative abundance of taxa, provided complementary insights into microbial community composition. This analysis identified significant variation in grapevine microbiome composition among the four sample types, with p = 0.003 in multivariate dispersion across the four sample types. Previous studies, including Bokulich et al. [67], have shown that factors such as cultivar, season, and climate significantly influence microbial abundance patterns in wine grapes from different regions.

NMDS ordination further indicated that geographical locations (e.g. countries) of grapevine cultivars played a key role in shaping microbial diversity. These findings support the notion that environmental and geographic factors strongly influence microbial community assembly. Prior studies by Bokulich et al. (2014) and Marasco et al., (2018) have similarly highlighted the role of environmental and plant-associated factors, such as grape cultivar, vintage and rootstock genotypes, in shaping microbial community structure. Additionally, our results emphasized the importance of cultivar-specific effects on microbiome diversity, indicating the potential for tailoring viticultural practices to specific grapevine varieties and regional conditions. Notably, no significant differences were found between soil and rhizosphere sample types, consistent with Zarraonaindia et al. [55], who attributed the overlap to the close physical proximity of these zones and the exchange of microorganisms. Differential abundance analysis revealed significant variations in microbial taxa across grapevine cultivars and plant parts. Proteobacteria, a key driver of variation in the soil and phyllosphere, was also prevalent in the rhizosphere. This finding corroborates
Zarraonaindia *et al.*, (2015), who attributed its prevalence across grapevine compartments to its functional roles in nutrient cycling and plant growth promotion. Similarly, Acidobacteria played a key driver in soil and rhizosphere, reflecting its adaptation to nutrient-poor environments, as noted by Hamaoka et al. [59]; Zarraonaindia and Gilbert [81]. In the carnosphere and phyllosphere, Actinobacteria and Firmicutes were identified as key taxa, well-known for their roles in plant defense and organic matter decomposition [82–84]. These findings are consistent with studies by Mezzasalma et al. [58], Vitulo et al. [85], which demonstrated their compartment-specific abundance and functional contributions of these taxa to plant health. Additionally, Firmicutes enrichment in the carnosphere aligns with observations by Swift et al. [80], who linked its presence to enhanced stress tolerance and pathogen resistance. Further studies should investigate how microbial diversity across grapevine compartments contributes to understanding the functional roles of key taxa in nutrient cycling, plant defense, and stress tolerance. Additionally, exploring the influence of environmental factors, geographic location, and cultivar-specific effects on microbial community assembly could guide tailored viticultural practices that enhance plant health and optimize wine production.

## 5. Conclusion

This meta-analysis provides a comprehensive examination of grapevine microbiomes across twelve cultivars from six production countries, identifying key factors shaping bacterial communities. These factors include grapevine ecology, vineyard management practices, and geographical variations. The study highlights the dominance of Actinobacteria and Proteobacteria in grapevine associated-environments, suggesting their ecological adaptation to this niche and their potential applications in sustainable viticulture management. The analysis reveals that microbial assemblages are significantly influenced by complex interactions between grapevine cultivars and environmental factors. By offering a broad understanding of microbial diversity across grapevine varieties, plant parts, and geographical regions, this meta-analysis underscores the critical role of microbiomes in grapevine ecology and their impact on plant health and productivity. However, it is crucial to recognize the methodological biases inherent in amplicon metagenomics analysis, which depend heavily on tool selection and database quality. Public sequence databases like GenBank and Silva exhibit significant gaps in data availability, with approximately 40 % of genomic data either unavailable or unusable due to formatting errors, labeling inconsistencies, or incomplete metadata [86,87]. Bulk submissions from sequencing centers and outdated entries exacerbate genomic misclassifications [88]. Larger databases like NCBI improve classification accuracy but require significant computational resources [89]. Tools like Kraken2, Silva, and EzBioCloud each offer distinct advantages but come with biases: Kraken2 excels in speed and precision but relies heavily on database completeness, while Silva provides broad phylogenetic coverage at the cost of resolution due to infrequent updates, and EzBioCloud offers superior taxonomy accuracy but with limitations in novel taxa detection due to its smaller database size [88,89]. Therefore, tool selection must balance resolution, coverage, and computational efficiency based on the study's goals.

The Galaxy platform offers accessible and reproducible workflows for microbiome analysis, integrating tools like PEAR for paired-end read merging, but it faces challenges in computational scalability and workflow customization [88,89]. Visualization tools like Pavian and KRONA provide effective analysis options, yet biases toward sample types may still impact results [33,90]. Phyloseq and MicroViz integrations enable streamlined diversity analysis, though they may also exhibit sample-type bias [91].

Despite these challenges, future research should focus on improving database curation, addressing metadata inconsistencies, and developing integrated analytical pipelines for more accurate taxonomic classification and visualization. Hybrid methods that combine classification and

visualization, along with scalable computational solutions, will be essential to managing the growing volume of metagenomic data.

Additionally, the study emphasizes the importance of depositing sequence datasets into publicly accessible databases to facilitate ongoing research and enable robust meta-analyses of metagenomic data. This practice supports a deeper understanding of grapevine microbiomes and promotes advancements in viticultural science and management strategies. Future research may explore how complex interactions between grapevine cultivars, environmental factors, and microbial communities contribute to plant health, productivity, and wine characteristics across different geographical regions. Large-scale, multi-site studies could investigate how leveraging dominant bacterial taxa in grapevine microbiomes might revolutionize sustainable viticulture practices and enhance ecosystem resilience in the face of climate change.

## Funding

This work was supported by the University of Reims Champagne-Ardenne and financially supported by the Evervigne project, funded by Bpifrance and the French Government as part of France 2030.

## CRediT authorship contribution statement

**Daniel Legesse:** Writing – original draft, Investigation, Formal analysis, Conceptualization.
**Qassim Esmaeel:** Writing – review & editing, Validation, Supervision, Resources, Investigation.
**Yassine Bouhouch:** contributed to the statistical analysis.
**Cédric Jacquard:** Resources.
**Lisa Sanchez:** Supervision, review & editing.
**Essaid Ait-Barka:** Supervision, review & editing.

## Declaration of Competing Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## Acknowledgements

We acknowledge the University of Reims Champagne-Ardenne for their support of this work.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.cpb.2025.100478.

## Data availability

All data supporting the findings of this study are available within the paper and within its supplementary materials published online.

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