# Validation of a representative sampling protocol for studying grape berry microbiota at the plot scale

Gabriela Pinto Miguel, Olivier Rué, Martine Pradal, Evelyne Aguera, Cécile Neuvéglise

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## To cite this version:

Gabriela Pinto Miguel, Olivier Rué, Martine Pradal, Evelyne Aguera, Cécile Neuvéglise. Validation of a representative sampling protocol for studying grape berry microbiota at the plot scale. Journal of Microbiological Methods, 2026, 241, pp.107376. 10.1016/j.mimet.2025.107376. hal-05489027

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# Validation of a representative sampling protocol for studying grape berry microbiota at the plot scale

Gabriela Pinto Miguel<sup>a</sup>, Olivier Rué<sup>b,c</sup>, Martine Pradal<sup>a</sup>, Evelyne Aguera<sup>d</sup>, Cécile Neuvéglise<sup>a,*</sup>

## ARTICLE INFO

### Keywords:

- Grape berries
- Sampling
- Microbiota
- Metabarcoding
- Yeast
- Diversity

## ABSTRACT

In viticulture, a comprehensive understanding of the grape microbiota is crucial, as it influences various aspects of the winemaking process, from vine health and grape development to fermentation dynamics and the final sensory profile of the wine. Monitoring these communities helps optimize vineyard practices and ensure product authenticity. Metagenetics is a fast culture-independent approach that allows identification of the microorganisms present in grape berries' microbiota at the genus or species level. In this context, it is crucial to assess the quality of sampling methods in terms of their representativeness of the diversity of microbial communities on grape berries at the plot level. In this study, the microbiota of the variety Artaban was examined. This variety is resistant to powdery mildew and tolerant to downy mildew. Two sampling methods for studying microbiota were investigated by forming two groups: one that pooled the microbiota of 10 vines, and another that studied the microbiota of each vine separately.

## 1. Introduction

DNA metabarcoding is a method that enables the study of environmental DNA (eDNA) from ecological communities using high-throughput sequencing (HTS) of specific genetic markers. By analyzing DNA extracted from environmental samples, such as grape berry surfaces, air, soil, or leaves, it is possible to identify a wide range of organisms present in a given habitat (Bálint et al., 2018). In a viticultural context, it is crucial to describe and understand the microbiota composition of grape berries. Characterizing these communities provides valuable insights for sustainable viticulture and high-quality winemaking. Spoilage microorganisms responsible for several diseases of the vineyard, such as filamentous fungal pathogens like *Erysiphe necator* and *Botrytis cinerea*, can cause significant damage including harvest loss, reduced yield, altered fermentation, and degradation of wine colour and aromas of wine (Gadoury et al., 2012; Calonnec et al., 2004; Rahman et al., 2024; Kelly et al., 2022). Understanding which microorganisms may be present can help develop monitoring strategies to determine risks, improve plant health strategies, and reduce reliance

on chemical treatments (Vacher et al., 2022; Morales-Cruz et al., 2018). The composition of the grape mycobiota plays a crucial role in the final sensory quality of wine, as fermentative yeasts drive the fermentation process and produce flavour-active metabolites. This is particularly relevant in the case of spontaneous fermentation, also known as indig-enous fermentation, which has gained increasing attention due to growing consumer demand for more natural wines that are perceived as both environmentally friendly and authentic (Griggs et al., 2021; Reiter et al., 2021; Barata et al., 2012). Moreover, several studies have inves-tigated the concept of *terroir*, which refers to how geographic location influences the grape berry microbiota, ultimately leading to distinct wine profiles due to the unique microbial communities associated with each site (Liu et al., 2019; Bokulich et al., 2014; Steenwerth et al., 2021).

In this context, metabarcoding is an effective approach for studying grape microbiota, as it enables high-throughput species identification in a fast, efficient, and culture-independent manner. Current methods for studying the microbial communities on grape berries typically involve randomly collecting and pooling a number of berries from a specific vineyard plot. However, there is a lack of studies investigating the

<footnote><sup>a</sup> SPO, INRAE, Institut Agro, University of Montpellier, 2 Place Pierre Viala, 34060 Montpellier, France</footnote>

<footnote><sup>b</sup> Université Paris-Saclay, INRAE, MaIAGE, 78350 Jouy-en-Josas, France</footnote>

<footnote><sup>c</sup> Université Paris-Saclay, INRAE, BioinfOmics, MIGALE Bioinformatics Facility, 78350 Jouy-en-Josas, France</footnote>

<footnote><sup>d</sup> UE Pech Rouge, INRAE, 11430 Gruissan, France</footnote>

<footnote>* Corresponding author.
E-mail address: cecile.neuveglise@inrae.fr (C. Neuvéglise).</footnote>
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Sampling scheme for vineyard plot number 103 at Les Colombiers, which is located in the Pech Rouge experimental domain in southern France. Each red dot represents a single vine. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

differences in sampling methods used to assess the variability of microbiota within a plot (Martins et al., 2024; Cioch-Skoneczny et al., 2018; Lorenzini and Zapparoli, 2019; Drumonde-Neves et al., 2017). Grape berries represent a highly heterogeneous microenvironment, and microbial composition can significantly vary from one vine to another, or even from one grape to another (Barata et al., 2012; Renouf et al., 2005; Liu et al., 2017). The aim of this study is to investigate whether a pooling method of grapes is representative of the variability of the grape berry microbiota within a single vineyard plot.

## 2. Materials and methods

### 2.1. Sampling

Grapes of the Artaban variety were harvested at the Pech Rouge (PR) experimental unit in Gruissan, in the south of France. This variety has complete resistance to powdery mildew and a high level of resistance to downy mildew (Calonnec et al., 2021). Harvesting took place in September 2024. Two sampling methods were used:

a) The bunches from 10 vines were completely harvested within a vineyard plot consisting of 20 rows of Artaban. Four rows were sampled, with one row left between each one (Fig. 1). Each row had a distance of 2.5 m between them. The distance between each vine sampled within a row was 14 m. Each vine was fully harvested. Only the undamaged grapes were cut individually from the pedicel to produce approximately 1 kg. This was then homogenised and sub-sampled in 500 g of berries. In this study we will refer to this method as "10 vines".

b) After preparing the 10 vines samples weighing 500 g each, between 250 and 300 g of berries from each sample were crushed to obtain juice for the analysis of the physical and chemical properties of the initial must. Then, the remaining berries from each vine sample were pooled to form three additional batches of 500 g, along with three juice samples for initial must characterization. These 500 g samples resulted in a second sampling method, defined in this study as "pooled replicates" (PR).

### 2.2. Initial must characterization

Initial sugar content was determined using a refractometer, while pH

and total acidity were measured by titration following OIV methods (OIV-MA-AS313–15, OIV-MA-AS313–01). Amino nitrogen was quantified using the OPA–FAN method (OIV), and ammoniacal nitrogen was analysed using an enzymatic method (OIV-MA-AS323-02 A).

### 2.3. Microbiota characterization

The subsamples of 500 g of grape berries were rinsed in a 400 mL buffer containing 0.9 % w/v NaCl and Tween and agitated for one hour at 150 rpm on an orbital shaker, BUHLER SM30A (Bühler GmbH, Bodelshausen, Germany) (Setati et al., 2012). The rinse solution was then centrifuged for 20 min at 9600 rpm in 500 mL centrifuge bottles. Around 90 % of the supernatant was removed. The remaining liquid and the pellet were transferred into a 50 mL tube and centrifuged once again for 15 min at 9600 rpm. After removing the supernatant, the resulting pellet was frozen at –20 °C. DNA was extracted from the whole pellet using the PowerSoil Pro kit (Qiagen, France) and was subsequently analysed by amplicon-based DNA metabarcoding with ITS1 as the taxonomic marker. The primers used were ITS1F (5'-CTTGGTCATTTA-GAGGAAGTAA-3') and ITS2 (5'-GCTGCGTTCTTCATCGATGC-3'), which also included the Illumina tail (5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG-3'), and an intermediate sequence of four, six, or eight random nucleotides for forward primers and four, five, or six random nucleotides for reverse primers as previously described (Von Gastrow et al., 2023). The multiplexed Illumina libraries were created with a two-step PCR strategy: a first PCR using the ITS1-specific primers including the Illumina adapter overhang (with 30 cycles), and a second PCR for the incorporation of Illumina dual-indexed adapters (with 12 cycles). Bead purifications were carried out after each PCR. Library sequencing was performed on an Illumina Miseq system.

### 2.4. Bioinformatics and statistical analyses

#### 2.4.1. Data processing and data sequencing

In the processing of metabarcoding data, amplicon sequence variants (ASVs) were constructed from the raw reads using the FROGS v.5.0.0 pipeline (Escudié et al., 2018; Bernard et al., 2021). First, reads containing indeterminate bases (N) were eliminated from the FASTQ files and primers were removed using Cutadapt (Martin, 2011). Next, the reads were denoised using the DADA2 algorithm (Callahan et al., 2016). The R1 and R2 reads were aligned using PEAR (Zhang et al., 2013). The remaining sequences were filtered for length (min 50 bp) before der-eplication. Chimeras were removed with vsearch (Rognes et al., 2016) and low-abundant ASVs (fewer than 100 reads) were discarded. To remove the non-ITS ASVs, ITSx tool (Bengtsson-Palme et al., 2013) was employed and ASVs were taxonomically assigned using UNITE v9.0 and further supplemented with manually curated sequences (Köljalg et al., 2013). With the ITS1 marker, it is frequently impossible to determine unambiguously the species name, especially for fungi such as *Cladosporium* and *Alternaria*. For this reason, a single species name was attributed with the term "Group" in the case of multiple species showing 100 % identity with the ASV sequence. Raw sequences have been deposited at NCBI under the BioProject accession number PRJNA1327833.

#### 2.4.2. Statistical analysis of microbiota diversity and oenological parameters

The statistical analyses were performed using RStudio (version 2024.04.2 + 764). The oenological parameters were analysed using the Mann–Whitney test at a significance level of 0.05, with the *p*-value adjusted using the Bonferroni method. A linear regression was performed to investigate the relationship between the physicochemical parameters of the must and the total weight of the collected bunches for each vine. Additionally, Spearman's rank correlation coefficient was calculated to assess the relationship between each variable and the total weight (the significance threshold was set at *p* < 0.05). The *α*-diversity
<table><caption>**Table 1**
Weight of bunches collected for each of the 10 vines.</caption><thead><tr><td>Sample</td><td>Weight (kg)</td></tr></thead><tbody><tr><td>Vine 1</td><td>2.203</td></tr><tr><td>Vine 2</td><td>3.010</td></tr><tr><td>Vine 3</td><td>3.503</td></tr><tr><td>Vine 4</td><td>2.371</td></tr><tr><td>Vine 5</td><td>1.990</td></tr><tr><td>Vine 6</td><td>3.266</td></tr><tr><td>Vine 7</td><td>1.241</td></tr><tr><td>Vine 8</td><td>2.088</td></tr><tr><td>Vine 9</td><td>3.112</td></tr><tr><td>Vine 10</td><td>3.994</td></tr></tbody></table>

was estimated using four metrics: observed richness, Chao1, Shannon and Simpson indices. Prior to the analysis of diversity, the samples were rarefied to the minimum observed sequencing depth to normalise for sequencing effort. Rarefaction and diversity indices were computed using the ‘phyloseq’ package in R, and statistical comparisons of α-diversity across groups were performed using the Wilcoxon rank-sum test for pairwise comparisons (the significance threshold was set at p <

0.05). Graphs were created using the ggplot2 and ggpubr packages in R.

To estimate the saturation curve of the richness, cumulative α-diversity was estimated for different subsets of n = 1 to 10 randomly selected vine samples, with 10 iterations. From each selected sample, the number of reads was calculated by dividing the total number of reads per sample obtained after rarefaction of the 10 vines samples (79,819 reads) by the number of samples included in the iteration (n). Reads from all selected samples were then pooled, and the observed richness, defined as the number of detected ASVs, was determined.

### 3. Results

The 10 vines of Artaban were fully harvested, producing between 1.241 kg and 3.994 kg of grape bunches (Table 1).

The first step in comparing the two sets of samples was to evaluate the oenological parameters of the must produced from the grapes that had been collected. The 10 vines group exhibited greater variability across all oenological parameters than the PR method (Fig. 2). However, no significant differences were observed between the two groups regardless of the parameters studied. Each PR accurately reflects the diversity of the ten vines. We then tested whether the variability in Brix, pH, and total assimilable nitrogen parameters was linked to the weight

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Boxplot of oenological parameters (nitrogen [ammoniacal, amino and total assimilable], sugar, Brix, probable alcohol, pH and total acidity) for the two sampling methods (10 vines versus the pooled samples). Significant differences were considered when p < 0.05; ns: non-significant.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 3. Graphics of correlation between total grape weight and oenological parameters (pH, assimilable nitrogen and Brix) for the ten individual samples. Significant differences were considered when p < 0,05.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 4. Mycobiota composition for each sample (left panel), and average mycobiota compositions (right panel) for both sampling methods. The 12 most abundant species are listed on the right.</figcaption></figure>

of grapes for each vine. There appears to be no correlation (Fig. 3).

Then, metabarcoding analysis was used to compare the different
microbiota. A total of 1,037,647 reads were obtained for the 13 samples,
with rarefaction occurring at 79,819 reads. The number of ASVs, spe-
cies, genera, families, and reads per sample, as well as α-diversity indices

and rarefaction curves are provided in the supplementary data (tables S1
and S2 and figs. S1 and S2). Analysis of the mycobiota composition
revealed that nine of the twelve most abundant species found on the skin
grapes were filamentous fungi belonging to the genera Aspergillus, Cla-
dosporium, Alternaria, Aureobasidium, Stemphyllum and Epicoccum, as
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Comparison of the abundance (read number) of the eight most abundant species in both sample groups. The “10 vines” data are in red and the pooled samples in blue. ns: non-significant. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Comparison of α-diversity indices (Chao 1, Observed, Shannon and Simpson) for both sampling methods. Significant difference with p < 0.05.</figcaption></figure>

expected for grape berry microbiota (Knight et al., 2020). *Metschnikowia* was the only fermentative yeast observed at this level of abundance. The less abundant species were included in the “Other” category (Fig. 4). The most abundant species found was *Aspergillus carbonarius*. Additional data containing the complete composition of grape microbiota at several taxonomic levels are provided in supplementary figs. S3–S7. The mycobiota composition of each vine sample appeared to be particularly variable in terms of species abundance, although most of the species

observed most frequently were present in all ten vine samples. For example, a high abundance of *Aspergillus carbonarius* was found in Vine_8 (61.54 %), whereas very low abundances were found in Vine_4, Vine_5 and Vine_9 (1 %, 1 % and 2 %, respectively). A similar outcome was observed for *Aureobasidium pullulans*, which was practically absent in the Vine_8 (less than 2 %), but present in higher amounts in all the other vine microbiota. No significant difference (at a significance level of 0.05) was observed between the sample groups (PRs and vines), for
<figure><img src="image_11.png" /><figcaption>**Fig. 7.** Principal coordinates analysis (PCoA) of β-diversity using Bray-Curtis method. Dots represent individual samples (vines or mixes) and crosses represent the centroid.</figcaption></figure>

the 8 most abundant species (Fig. 5). The α-diversity was assessed using the Observed richness, Chao1, Shannon, and Simpson indices. All samples showed consistent Chao1 and Observed values, suggesting a stable and reliable capture of microbial richness (Fig. 6). According to the Chao1 and Observed indices, the method based on PRs exhibited higher

microbial diversity with up to 300 different ASVs. This is likely due to the fact that the PRs combined species present across the ten sampled vines on average, revealing the total diversity through this method. When estimating the rate of cumulative α-diversity, we observed a higher variability in richness when a small number of samples were

<figure><img src="image_12.png" /><figcaption>**Fig. 8.** Curve of cumulative richness for samples ranging from 1 to 10. The curve shows the mean and standard deviation of ten interactions per sample size.</figcaption></figure>
selected, and decreased variability when ten samples were selected. Additionally, observed ASV richness increased globally with the number of combined samples. The curve increases almost exponentially up to 5 samples, after which an inflection is observed, suggesting a slowdown in the accumulation of diversity (Fig. 8, Table S3). The Shannon and Simpson indices, which take into account the abundance of each ASV, did not reveal any difference between the two groups, indicating that the distribution of taxon abundances was similar between groups, with comparable levels of dominance and evenness.

The β-diversity analyses were performed using the Bray–Curtis dissimilarity metric. Principal Coordinates Analysis (PCoA) showed that the centroids are positioned near one another for the two groups, suggesting comparable community profiles (Fig. 7). This visual observation was confirmed by statistical tests. PERMANOVA (adonis2, 9999 permutations) revealed no significant differences between the two groups. The sampling method explained only 7.0 % of the total variance (R² = 0.070, F = 0.83, *p* = 0.396). Similarly, ANOSIM (999 permutations) did not support group separation, yielding a negative R statistic (R = -0.160, *p* = 0.778), which indicates greater variation within groups than between them. Together, these results demonstrate that the mean microbial composition did not differ significantly between the two sampling methods.

## 4. Discussion

The aim of this study was to evaluate the representativeness of a sampling method based on pooling berries from different vines with regard to oenological parameters and microbiota composition. Our data confirmed previous reports of significant variation between vines with regard to oenological parameters, particularly nitrogen and acidity levels, which may be due to agroecological factors such as cultivation practices, amendment type, or phytosanitary treatments (Bramley and Hamilton, 2007; Schorn-García et al., 2023; Bramley, 2005). Pooling grapes from multiple vines smooths out inter-vine variability in physicochemical and oenological traits, providing a representative estimate of the plot-level harvest composition. We attempted to identify the sources of variability in physicochemical parameters but found no correlation with grape yield per vine. Other factors, such as canopy size, soil composition, and water stress, may also contribute to the variability.

The question then was whether pooling grapes would generate a microbiota that was also representative of the entire plot. To address this, we compared the microbiota present on grapes from ten vines with that in a mixture of berries from the same vines. Microbial diversity within vineyard plots is expected, due to microenvironmental factors such as sunlight, humidity, and soil, as well as physiological variations in the plants and interactions between local microorganisms (Knight et al., 2020; Liu and Howell, 2021; Zarraonaindia et al., 2015; Bokulich et al., 2014; Martiniuk et al., 2023). The microbiota of the ten individual vines did not exhibit identical compositions, reflecting the intra-vineyard diversity previously documented in vineyard ecosystems (Griggs et al., 2025). The bioinformatic approach of resampling the vine samples and the reads contained in each sample led to variability that tends to be higher with a low sample number, and to decrease when more samples are selected. These results reflect the influence of sample selection on richness estimates. We sampled a total of 10 vines, but the inherent heterogeneity of intra-vineyard microbiota means that sampling another group of 10 vines might not produce identical results. Previous work by Griggs et al. (2025) indicates that sampling 10–20 individual vines is generally sufficient to capture dominant but not rare taxa, and this limitation should be compensated by increasing the depth of sequencing or sample number. In our study, the curve of cumulative richness (Fig. 8) is not completely saturated, suggesting that the most abundant species have been detected, but not rare taxa. Whether using 10 vines or the PR method, rare taxa are likely underrepresented. It is important to note that, during the formation of the composed mix, all remaining grapes from the ten vines were pooled without precise

individual weighing, although the initial weights of pedicel-cut berries were approximately the same (1 kg). This may have resulted in a slight bias in the representation of each vine sample and influenced the relative abundance of microbial taxa in the pooled samples, particularly rare taxa.

The observed differences in species richness of the two sampling methods between the Chao1 and Observed indices can be explained by the fact that the PR method represents an assemblage of the 10 individual vine samples, resulting in an accumulation of internal diversity. At the β-diversity level, microbial communities exhibit broadly similar compositions and structures across both methods. Therefore, the PR method emerges as a robust strategy for assessing vineyard-scale microbiota composition, due to its capacity to represent the 10 vines.

A high abundance of *Aspergillus carbonarius* was observed across samples, suggesting that, although only visually intact grapes were collected, they may have been secondarily contaminated by nearby diseased berries from the same vine. As this species is typically associated with damaged or decaying grapes, its occurrence indicates microbial dispersal and cross-contamination within vines (Nardi et al., 2021).

To conclude, by averaging out individual fluctuations, pooling appears to smooth intra-plot divergences for the most abundant species present in vineyards, and can thus serve as a reliable approach for studies aiming to characterise the vineyard microbiome more broadly. However, it is important to take into consideration the caution of the analysis of rare species. Moreover, from a practical standpoint, pooled sampling reduces the number of extractions and sequencing runs, which is particularly advantageous in large-scale or longitudinal studies.

### CRediT authorship contribution statement

**Gabriela Pinto Miguel:** Writing – review & editing, Writing – original draft, Software, Investigation, Formal analysis, Data curation.

**Olivier Rué:** Writing – review & editing, Software, Formal analysis, Data curation. **Martine Pradal:** Investigation. **Evelyne Aguera:** Writing – review & editing, Investigation. **Cécile Neuvéglise:** Writing – review & editing, Investigation, Data curation, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

This work was supported through the Occitanie Region's program 'Défi clé Vinid'Occ' run by the University of Montpellier (OenoVard'Occ project, "Studies of the oenological properties of new varieties in Occitania"). G.P.M. would like to thank the French Occitanie region and the INRAE MetaBio metaprogramme for funding her PhD. The authors would like to thank Léane Gesnel and Mélanie Veyret for their technical assistance throughout the project.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.mimet.2025.107376.

### Data availability

Raw sequences have been deposited at NCBI under the BioProject accession number PRJNA1327833.
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