Introduction

Internationally ranked as the seventh-largest wine producer in 2022, Argentina has a vast area of grapevine crops (International Organization of Vine and Wine, 2023). The diversity of Argentine terroirs is made up of climatically and topographically distinct regions composed of diverse climates, soils, altitudes and native flora and fauna. All of the latter factors contribute to the expression of the unique characteristics of the wines from each geographical location, including the adaptation of specific grapevine cultivars. Terroir is defined by the OIV as being the collective interaction of identifiable physical and biological factors in a given area that, together with the winemaking practices, give the products originating from that area their distinctive characteristics (Resolution OIV/Viti 333/2010). Argentina’s emblematic Malbec (MA) wines and their versatile Cabernet-Sauvignon (CS) varietals are the best example of this concept. Harsh climatic conditions in important national winemaking regions can have an economic impact due to low yield or a low-quality season; this has led the scientific community to carry out research on the subject in order to ultimately help improve the future economic value of the country’s wine industry.

Early microbial studies in grapevine research have found that vine-associated microbial communities are shaped by topography, climatic conditions, soil physicochemical characteristics and agricultural techniques. These communities particularly stimulate the plant productivity, as well as host resistance to pests and diseases (Belda et al., 2017; Bokulich et al., 2016; Zarraonaindia et al., 2015). Additionally, microbial interactions result in the release of aromatic precursors or chemical compounds and secondary metabolites that have been produced in these exchanges, impacting the sensorial attributes of wines (Borneman et al., 2013), and further contributing to the distinct regional characteristics of the terroir expressed in the final product (Liu et al., 2019). Along with other biotic and abiotic factors, the biological characteristics of the soil enable complex metabolic interactions with plant hosts, which contribute significantly to the factors that intrinsically coexist and shape the regional attributes of local wines (Belda et al., 2017; Burns et al., 2016).

In Argentina, research on these grapevine microbial communities and their complex dynamics is scarce. A few studies (mentioned hereafter) have covered areas in the main wine producing regions of Mendoza and San Juan, still leaving a lot of scope for investigation in the other important wine-growing regions of the country; for example, Alto Valle del Rio Negro and the Atlantic coast of Patagonia, and the most north-western province of Salta, where grapevines are grown at > 2000 m asl (National Institute of Viticulture, 2023).

Associations of microorganisms with plants is a widespread phenomenon that comprises mutually beneficial, commensalistic or host-pathogen interactions (Kogel et al., 2006; Newton et al., 2010). Rhizosphere and endosphere communities are enriched by a specific subset of organisms from the soil microbiome (Pieterse et al., 2016). In the context of wine production, the most prevalent microbial populations in soil have a major influence on plant health, soil texture, nutrient cycling and biodiversity (Wang et al., 2024), influencing the quality of the fruit in the vineyard and thus of the produced wine (Liu et al., 2019; Rivas et al., 2021). Grape health (Mueller & Sachs, 2015), grapevine genotype, geographical area and climatic factors all contribute to the differences between microbial populations in the grapevine phyllosphere (Singh et al., 2018). Studies on these biological features have contributed to the understanding of biodiversity patterns influencing microbial populations variations according to geographic features like climate, vintage, and topography in association with the plant genotype and soil edaphic factors (Burns et al., 2016; Gobbi et al., 2022; Griggs et al., 2021; Zarraonaindia et al., 2015).

Numerous studies have shown that the structure of microbial communities in grape berry and grape juice samples are similar, indicating that under ideal conditions the communities present in grapes prior to fermentation can remain relatively stable (Belda et al., 2016; Bokulich et al., 2016; Knight et al., 2015; Zarraonaindia et al., 2015). Meanwhile, the microbial communities associated with roots, root zone and bulk soil are often similar, differing from those of aboveground plant organs (Zarraonaindia et al., 2015). Soil health in vineyards is conditioned by both direct and indirect microbial associations, including the mineralisation of soil organic matter, activation of plant defence mechanisms and production of antibiotics against phytopathogens; together, these affect plant productivity and plant health (Bacon & White, 2016; Sanchez et al., 2009).

The rhizosphere is the region of soil around the plant root whose biological and chemical properties is significantly influenced by root activity. This area is a hotspot for complex biological ultrastructures, and microbial activity is responsible for the highly complex ecosystems found here. The combined populations of filamentous fungi, yeasts and a large subset of prokaryotes constitute part of the grapevine (Darriaut et al., 2022; Xie et al., 2024). The microbiome is also known as the microbiota, which contains microorganisms belonging to different kingdoms, such as Prokaryotes (Bacteria, Archaea), Eukaryotes (e.g., Protozoa, Fungi, and Algae), whose “theatre of activity” comprises microbial structures, metabolites, mobile genetic elements (e.g., transposons, phages and viruses) and relic DNA embedded in a given environment (Berg et al., 2020).

Since the development and availability of Next Generation Sequencing technologies (NGS), research involving V. vinifera grapevine cultivars has increasingly focused on the study of plant-microbe interactions. This approach aims to understand the implications of the microbiome on crop health and the link between grape quality and geographic location. Such technology, in parallel with classical microbial techniques, can help detect the non-culturable fraction of microbes, especially those in complex microbial ecosystems, allowing researchers to fully assess the complexity of the grapevine microbiome (Burns et al., 2016), and to provide better assessment of the use of biological products in the field.

In this context, studies using an NGS approach to characterise regional microbial communities associated with vineyards in Argentina have been conducted over the past decade. Vega-Avila et al. (2015) analysed the diversity of vine-associated prokaryotes in vineyards located in San Juan province, where they found that the changes observed in the bacterial communities could not be explained by variations in the physical and chemical properties of the rhizosphere (Vega-Avila et al., 2015). Through the DNA amplification of the bacterial 16S rRNA and fungal ITS1 regions using the NGS approach, our group analysed the soil prokaryotic and eukaryotic composition of MA and CS grapevine cultivars from two different vineyards located in the San Juan Province of Argentina (Oyuela Aguilar et al., 2020). We showed significant differences in microbial community composition between grape varieties and vineyard locations, Proteobacteria, Firmicutes and Bacteroidetes being the most representative prokaryote groups found. Additionally, Rivas et al. (2022) characterised the microbial communities of MA vineyards recently established in a re-emerging wine region of Argentina (the Southwest Buenos Aires province). They found that Proteobacteria and Actinobacteria were the predominant phyla, in which the order Rhizobiales stood out in the soil and rhizosphere samples (Rivas et al., 2022). Previously, they had studied the variations in soil and wine bacterial diversity in three consecutive vintages, and the effect of climatic conditions on this diversity (Rivas et al., 2021). By demonstrating that soil microbiota is widely adapted to harsh conditions and can withstand even prolonged droughts, they found a core of microorganisms in soil and in wines, belonging to different phyla that are dominant and remain throughout different vintages (Rivas et al., 2021). Recently, Paolinelli et al. (2023) investigated bacterial and fungal communities in arid vineyard soils from two sites in Mendoza (Paolinelli et al., 2023). They found that certain taxonomic groups are linked to the sampling location, which also influences the metabolic pathways present in these soils.

Although NGS has been frequently used to analyse the microbial diversity associated with wine production, comparative studies on how geography, soil and cultivar influence the grapevine microbiome in Argentine vineyards had yet to be performed. Consequently, the aim of this study was to analyse prokaryotic diversity in vineyards of the main Argentine wine-producing regions by carrying out a comparative study of microbial communities in two grape cultivars across four distinct wine-producing regions. To this end, we sampled soils from MA and CS vineyards in Río Negro, Mendoza, San Juan and Salta to evaluate the effect of Argentine geography on prokaryotic diversity. Using Illumina MiSeq 16S rRNA sequencing, we identified microbiological patterns associated with microbial families, locations and soil physicochemical properties. This study provides insights into the microbiological characteristics of vineyards across Argentina, establishing a foundation for understanding prokaryotic role in the diverse microbial terroirs that contribute to the unique qualities of regional Argentine wines.

Materials and methods

1. Soil sampling

Soil samples were collected from ungrafted grapevines of MA and CS cultivars one week prior to the harvest period in 2016. We sampled fourteen vineyards in four wine-producing regions of Argentina: the southern region (Río Negro Province, Patagonia), the western central region (San Juan and Mendoza Provinces), and the northwestern region (Salta Province) (Figure 1A). Information regarding the sampled sites is summarised in Table 1.

In the San Juan (SJ) Province (western central wine region), the samples came from two vineyards located in the Ullum Valley, 6 km from each other: Finca Norte (SJ1) and Finca Arriba (SJ2). In the Mendoza (MZA) Province (also western Central wine region), the samples came from vineyards with vines planted based on a vertical shoot positioning system in Agrelo (Luján de Cuyo); all the vineyards were on loamy-clay soils, and the climate of both San Juan and Mendoza is arid.

Figure 1. Soil physicochemical characteristics of sampled vineyards.

Samples from the Río Negro (RN) Province (southern wine region) were collected from Mainque (RN1) (upper Valley of Río Negro) and Viedma (RN2) (near the Atlantic Ocean), which both have a semi-arid climate. Annual rainfall in Mainque ranges from 120 to 180 mm, and the temperatures range from 30 to 34 °C in summer and from 10 to 14 °C in winter. The climate in Viedma is influenced by its proximity to the Atlantic Ocean: in the summer, maximum temperatures can reach 30 °C, while in winter they drop to between 2 °C and 12 °C. Samples were taken from two plots on with loamy-sandy soils and located 7 m above sea level (masl).

The Salta province (northwestern wine region) has a warm and dry climate. Samples of MA and CS cultivars were collected from three sites in the Calchaquí Valleys (southwest of the province): Molinos (SAL1), Cafayate (SAL2) and Cachi (SAL3). The elevation of the sites varied between 1100 and 1800 masl, that of Molinos being the lowest and Cachi the highest.

Further information regarding location, geographical characteristics, grapevine age, type of crop management (organic or conventional) and inter-row-management (bare ground or presence of other plants) is available in Oyuela Aguilar et al. (2021). Soil and root samples were collected from each vineyard at a depth of 30 cm and a distance of 20–30 cm from the vine trunks of nine vines per plot, covering an area of 49 m². Each plot was situated at least 7 m from the edge of the area. Samples were placed in sterile containers, transported on ice, and stored at −20 °C until analysis. The nine samples of each vineyard were combined from which three biological replicates were formed.

2. Soil physicochemical characteristics analysis

Soil samples were obtained by carefully removing soil from the roots sampled in the vineyards. It was rubbed off gently into a bag, using a scalpel when needed. Rhizosphere soil samples were sieved (pore size 0.5 mm) to eliminate the remaining plant debris. Soil samples were analysed at the Centro Nacional de Investigaciones Agropecuarias (CNIA-INTA) (Buenos Aires, Argentina). Samples were processed as previously described by Oyuela Aguilar et al. (2020). The pH was determined using the potentiometric method and a 1:2.5 distilled water ratio (ISO 10694-Soil quality determination of pH norms). A strong oxidising microscale mixture was used to assess organic carbon (Walkley & Black, 1934), and the mass loss calcination method and the Kjeldahl method were used to assess organic material (Ball, 1964) and organic nitrogen (Bremner, 1960) respectively. Soil phosphorus content was determined using the Bray & Kurtz method (Bray & Kurtz, 1945). Soil texture was analysed according to Kilmer et al. (1949). USDA soil texture diagram was used to determine the soil type in each vineyard, acquired lime, sand and clay content.

3. DNA extraction, library preparation and sequencing

DNA extractions were performed using the FastDNA Spin Kit for Soil (MP Biomedicals, LLC, Solon, OH, USA) from 0.4 g of rhizosphere soil following the supplier’s instructions. The extracted DNA was quantified using a Qubit1 2.0 Fluorometer (Thermo Scientific). DNA purity at 260/280 nm and 260/230 nm absorbance ratios were assessed using a Nanodrop spectrophotometer.

The bacterial diversity was analysed by amplifying the hypervariable V3-V4 region of the 16S rRNA gene using 341F (CCTACGGGNGGCWGCAG) and 806R (GACTACHVGGGTATCTAATCC) primers. A 16S rRNA gene double PCR-step approach was used for Illumina sequencing library preparations (Gobbi et al., 2020). Sequencing was performed on Illumina’s MiSeq platform using the V2 500 cycles reagent kit.

The double amplification was performed as follows. For the initial PCR, a final volume of 25 μL contained a mixture of 12 μL of AccuPrime™ SuperMix II (Thermo Scientific™), 0.5 μL of bovine serum albumin (BSA; final concentration, 0.025 mg/mL), 0.5 μL each of forward and reverse primers (10 μM stock), 1.5 μL of sterile water, and 5 μL of template DNA. This reaction mixture underwent pre-incubation at 95 °C for 2 min, followed by 33 cycles of 95 °C for 15 s, 55 °C for 15 s and 68 °C for 40 s, followed by a final extension at 68 °C for 4 min. Total DNA was quantified using a Qubit® 2.0 fluorometer (Thermo Scientific™).

The second PCR aimed to add indices to each amplicon from the first PCR. A final reaction volume of 28 μL was used, containing 12 μL of AccuPrime™ SuperMix II (Thermo Scientific™), 2 μL of primers with index sequences and Illumina P7 (CAAGCAGAAGACGGCATACGAGAT) and P5 (AATGATACGGCGACCACCGA) adapters, 7 μL of sterile water, and 5 μL of amplicons from the initial PCR. Cycling conditions included denaturation at 98 °C for 1 min, followed by 13 cycles of 98 °C for 10 s, 55 °C for 20 s and 68 °C for 40 s, followed by a final extension at 68 °C for 5 min. In both instances, PCR products were verified via 1.5 % agarose gel electrophoresis.

4. Bioinformatics and statistical analysis

Illumina reads were demultiplexed using bcl2fastq V.2.17.1.14 (Illumina). Adapters were trimmed with Trim Galore v0.4 (https://github.com/FelixKrueger/TrimGalore.git) running cutadapt v1.8.3 (Martin, 2011) and primer sequences were deleted from the 5’ ends of each read using the custom script (https://github.com/padbr/asat/blob/master/strip_degen_primer.py). Raw sequencing data associated with this work was uploaded to the SRA under the BioProject accession number PRJNA1087980.

Data analysis was performed using Shaman platform (https://shaman.pasteur.fr/), following the pipeline described by Volant and collaborators (Volant et al., 2020). Read and operational taxonomic unit (OTU) processing parameters were set with strict quality and annotation thresholds to ensure high-fidelity analyses. For read processing, a Phred quality score cutoff of 20 was applied to trim low-quality ends, with a minimum requirement of 80 % correctly called nucleotides per read and a minimum read length of 50 nucleotides. In the OTU processing stage, reads were dereplicated with a prefix setting, no upper limit on OTU length (maximum OTU length set to 0) and a minimum OTU length of 50 nucleotides. Only sequences with a minimum abundance of 4 were retained at dereplication, and clustering was performed on both strands with a 0.97 similarity threshold. For OTU annotation, both strands were used, and identity thresholds were progressively more stringent across taxonomic levels: a minimum of 0.75 for Kingdom, 0.75–0.785 for Phylum, 0.785–0.82 for Class, 0.82–0.865 for Order, 0.865–0.945 for Family, 0.945–0.98 for Genus, and 0.98–100 for Species annotations. These parameters ensured accurate read trimming, clustering and taxonomic assignment throughout the analysis.

Taxonomic annotation of OTUs was performed using SILVA database (Pruesse et al., 2007) version 138. A weighted non-null normalisation by site and a filtering step for a threshold of a minimal number of 8 samples and 2.4 on the total abundance (in log) was perform prior to data analysis. Richness, Shannon and Inverse Simpson indexes, principal coordinate analysis (PCoA), rarefaction plots, scatterplots, families’ barplot and heatmaps were analysed and plotted using the on-line Shaman platform.

Principal component analysis (PCA), clustering analysis using Euclidean similarity index and Canonical Correspondence Analysis (CCA) were performed using Past software (Hammer et al., 2001). Data were normalised by Standard score ((X-µ)/σ) before analysis (Han et al., 2012).

Linear Discriminant Analysis Effect Size (LEfSe) (Chang et al., 2022) was performed to identify overrepresented OTUs in soil samples. Alpha values for the factorial Kruskal-Walli’s test among classes and for the pairwise Wilcoxon test between subclasses used were 0.05. Taxonomic units above a logarithmic LDA score of 4 were considered as overrepresented and are shown in the graph. LEfSe was run within the Galaxy Metabiome environment (http://mbac.gmu.edu/mbac_wp).

PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) (Douglas et al., 2020)–run within the Galaxy Metabiome environment–was used to predict metabolic pathways in 16sRNA NGS sequencing data. The presence of differential pathways was inferred using LEfSe.

Microbial networks were constructed using the SparCC algorithm (Friedman & Alm, 2012) in the Integrated Network Analysis Pipeline 2.0 (iNAP 2.0. https://inap.denglab.org.cn) (Peng et al., 2024). Network graphs were generated with Cytoscape v3.10.3 (Shannon et al., 2003). The SparCC algorithm was employed to infer microbial associations from compositional data. Pseudo p-values were calculated through 100 permutations, with 20 inference iterations averaged per calculation. To reduce the influence of strongly correlated pairs, 10 exclusion iterations were applied, using a correlation strength exclusion threshold of 0.7. This approach ensured robust estimation of microbial associations. A SparCC correlation matrix was generated and filtered based on a threshold of 0.6. Correlation coefficients with absolute values below this threshold were excluded. Additionally, the SparCC pseudo p-value matrix, derived from the 100 permutations, was filtered at a p-value threshold of 0.05 to retain statistically significant correlations. Network modularisation was performed using the Greedy modularity optimisation method, as implemented in the Galaxy tool (Version 1.0.0). The network matrix, pre-filtered after applying correlation and p-value cutoffs, was analysed to identify modular structures and key hub nodes. The modularity analysis also incorporated Z-P scores to classify module hubs and assess their significance within the microbial association network.

The figures were created using the Inkscape (Inkscape Project, 2020. https://inkscape.org) and GIMP (The GIMP Development Team, 2019. https://www.gimp.org) software packages.

Results

1. Sample collection and soil characterization

Soil physicochemical properties were determined for the fourteen sampled sites (Table S1). Like in any edaphic study on soils, and as a starting point for visualising any variation in the soil composition of different sites, we performed a principal component analysis (PCA). This two-dimensional PCA plot (Figure 1B) clearly distinguished the samples, showing a clear difference between locations. Component 1 differentiates SAL soils from the rest of the sampled sites, while Component 2 discriminates RN2. This analysis showed a clear grouping of SAL and RN2, while samples from the western region (SJ and MZA) are dispersed in quadrants 1 and 4. In contrast to RN2, RN1 is positioned between SJ and MZA, showing a clear distinction between the Patagonian samples.

2. Sequencing results

After performing the 16S rRNA sequencing, we obtained a total of 903,462 amplicons, with a remaining 668,603 after dereplication. Followed by the sequence removal of singletons and chimeras, 14,469 sequences remained for classification into OTUs, resulting in a total of 2,945. Further description of the sequenced samples can be found in Table S2. Although three composite samples were collected for each vine, one sample from the SAL3MA set could not be successfully sequenced (Table 1). Additionally, because the rarefaction curve for sample run.MO.A31_S31_L001.fastq - which belonged to one sequence from the SJ2CS - did not reach saturation, it was excluded from the results (Figure S1), leaving a total of 40 sequences for the final analysis. Samples were normalised per site prior to the statistical analysis.

3. Diversity

Three estimators of α-diversity were used: species (OTUs) richness, Shannon diversity index and the inverse Simpson index. As there were no observed differences between MA and CS indexes (Figure S2), we decided to pool samples in terms of diversity according to the sites.

To evaluate the effect of location on rhizosphere microbial composition, diversity indexes were plotted accordingly (Figure 2). Although the species richness comparison using the non-parametric Kruskal-Walli’s test showed no significant differences in bacterial communities, pairwise-comparisons through a post hoc Mann-Whitney Bonferroni adjusted test showed differences for RN1 with respect to SAL1 and RN2 (Figure 2A). While global comparison given by the Shannon index of Kruskal-Walli’s test showed no significant differences, pair to pair comparisons showed statistically significant differences between RN1 with respect to SJ1, SJ2, MZA, SAL1 and RN2 (Figure 2B). Similar results were obtained for the inverse Simpson index (Figure 2C).

Figure 2. Boxplot of microbial species Richness (S), Shannon (H’) and Inverse Simpson (1/D) Index values.

A PCoA was performed to investigate clustering according to regions (Figure 3 and Figure S3A). We found a clear distinction between SAL and the rest of the regions. Samples from MZA, SJ and RN form a group that behaves as a gradient across Axis 2, with positive values for RN1 and RN2 and negatives for SJ1 and MZA, while SJ2 remained in intermediate values. No differences were found for MA and CS (Figure S3B).

4. Taxonomic identification of 16S rRNA sequencing results

As previously mentioned, 2,945 OTUs were obtained after sequencing. All these OTUs were annotated using SILVA (Pruesse et al., 2007) at the Kingdom level, of which 2,934 (99.63 %) were assigned to the phylum level, 2,864 (97.25 %) to the class level, 2,584 (87.74 %) to the order level, 2,113 (71.54 %) to the family level and 1,273 (43.23 %) to the genus level. Only 514 (17.45 %) of the OTUs could be assigned to the species level.

Figure 3. PCoA of prokaryotic OTUs.

Samples from the different sites were compared to find the most abundant soil microbiota at family level (Figure 4A). Two prokaryotes domains are shown in our results. Regarding the Bacteria Domain, the most abundant Gram-negative microorganisms belonged to the Microscillaceae, Chitinophagaceae, Xanthobacteraceae, Pseudomonadaceae, Sphingomonadaceae, Rhizobiaceae, Vicinamibacteraceae, Steroidobacteraceae, Pedosphaeraceae, and Hyphomicrobiaceae families, while the Bacillaceae family was the most frequently encountered Gram-positive bacteria. Regarding Archaea, the most abundant family was Nitrososphaeraceae.

The Pseudomonadaceae and Bacillaceae families were the most abundant clades in SAL2 and SAL3 (Figure 4B), forming a taxonomical cluster that could be clearly distinguished from the rest of the sites. In contrast, although Bacillaceae was highly represented in SAL1 and RN2, Pseudomonadaceae levels were low in these locations.

Figure 4. Taxonomic analysis of soil samples.

Even though it was possible to determine the main families present in each sampled site, this approach was not useful for determining the organisms differentially found in each soil. To assess any significant shifts in soil microbiological composition, the LEfSe differential abundance method was used to perform class comparisons among samples from different locations (Segata et al., 2011).

Although 256 differential OTUs were determined, only the most significantly shifted (LDA > 4.0) were analysed (Table 2 and Figure S4). Surprisingly, all significantly shifted organisms in RN2 belonged to kingdom Archaea, four of them corresponding to phylum Parvarchaeota. All remaining differential OTUs were assigned to organisms of the kingdom Bacteria. By contrast, RN1 is enriched in Deltaproteobacterias of order Caldilineales and family Cystobacteraceae, Gammaproteobacterias of order Pseudomonadales and Gram-positive bacteria of family Paenibacillaceae.

SAL1 showed a prevalence of individuals of the phylum Firmicutes. In addition to Bacillus badius (also of phylum Firmicutes), SAL3 showed actinobacteria of the family Microccocaceae and a particularly well-represented OTU, which could only be assigned to the kingdom Bacteria. No significantly differential OTUs were identified in SAL2.

Regarding the Cuyo region, SJ1 was found to be enriched in bacteria of the order Chromatinales, the genus Chthoniobacter and the families Rubrobacteraceae and Chitinophageceae. SJ2 was enriched in Deltaproteobacteria, mostly of the order Desulfomonadales, and Betaproteobacteria of the order Rhodocyclales. On the other hand, MZA showed a prevalence of individual bacteria of the phylum Chloroflexi, the order Rhizobiales and the family Chthoniobacteraceae, as well as Gram-positive bacteria, such as Bacillus endophyticus.

PICRUSt2 analysis was carried out to examine any metabolic pathways that were overrepresented in particular locations (Table S3). Pathways that were enriched in SJ2 included PWY_3781, P108_PWY, PWY_6167, PWY_7332 and PWY_6122. SAL3 showed enrichment in pathways such as PWY_7237, PWY_7094, the_ortho_cleavage_PWY, PWY0_1061, HOMOSER_METSYN_PWY, ORNDEG_PWY, PWY_5022, and PWY_6628. Meanwhile, SAL2 was enriched in PWY_1861 and PWY_2941, SAL1 in PWY_6895, RN1 in THISYN_PWY, DENOVOPURINE2_PWY, FASYN_INITIAL_PWY, and P23_PWY, and MZA in PWY-5971.

Interestingly, SJ2 and SAL3 showed an increase in polyamine biosynthesis (P108_PWY and PWY_6628 respectively). Such compounds (e.g., putrescine and spermidine) significantly influence plants by acting as signalling and modulatory molecules in growth and stress responses (Dunn & Becerra-Rivera, 2023). Furthermore, SAL3 showed pathways involved in the degradation of aromatic compounds through meta-cleavage, phenylacetate degradation and ortho cleavage (PWY_7237, PWY_7094, and the_ortho_cleavage_PWY), facilitating the breakdown of complex compounds.

5. Microbial network analysis

Network analysis can identify disproportionately influential species, or keystones, within communities that contribute to the system's resilience to external disturbances. A SparCC-based network was therefore constructed to explore the interactions between the OTUs in the analysed samples. This network was compared to random networks generated using Greedy modularity optimisation and Short random walks. In the comparison of the empirical network with both randomised networks, the values of the Clustering Coefficient, Average Path Distance, Geodesic Efficiency, Centralisation of Stress Centrality, Modularity, Transitivity, Hierarchy and Lubness validated the network generated with our data (Table S4). The empirical network has a significantly higher clustering coefficient than the random networks and larger average distances between nodes. Nodes in the empirical network show high stress centrality, implying that certain species are crucial for connectivity and may act as “hotspots” within the network. The higher transitivity observed in the empirical network suggests a greater tendency for a node's neighbours to also be connected to each other, which is typical in organised networks and less common in random networks.

Figure 5. SparCC OTUs’ network.

The obtained network consisted of 214 nodes (OTUs) grouped into 13 modules (Figure 5A): module 1 with 55 nodes, module 2 with 36 nodes, module 3 with 41 nodes, module 4 with 13 nodes, module 5 with 25 nodes, module 6 with 9 nodes, module 7 with 15 nodes, module 8 with 9 nodes, modules 9 and 10 with 4 nodes each, and modules 11, 12, and 13 with 2 nodes each. The five most abundant modules (1, 2, 3, 5 and 7) represented 80.4 % of the total nodes in the network. These 214 OTUs were assigned to 53 families, with Cytophagaceae, Chitinophagaceae, Bacillaceae and Hyphomicrobiaceae representing 35.5 % of the total taxa. OTU_003 (family Cytophagaceae) was highlighted for its centrality, as it holds the highest values for degree, stress, eigenvector and closeness centrality (Table S5). Z-P analysis showed that the network is composed of 195 peripheral species, 5 module hubs, 13 connector hubs, and only one network hub corresponding to OTU_145 (class Acidobacteria) (Figure 5B and Figure S5A).

6. The soil physicochemical characteristics influences its microbiota

The results of various metagenomics studies have supported the connection between the grape microbiome and factors such as vineyard location, climatic conditions, and other vineyard-related variables (Mezzasalma et al., 2018; Oyuela Aguilar et al., 2020; Zarraonaindia et al., 2015). To assess the possible existence of patterns linking the physicochemical characteristics of the soil and the families of bacteria found, a canonical correspondence analysis (CCA) was performed (Figure 6). Quadrant 1 is characterised by high sand contents, with a high number of bacteria from families such as Pseudomonadaceae, Bacillaceae and Paenibacillaceae. The opposite quadrant (Quadrant 3) is characterised by soils rich in lime, clay, sodium, potassium and carbonate and in bacteria such as Rhizobiaceae, Sphingomonadaceae and Xanthomonadaceae, among others.

This therefore indicates a clear positive correlation between the families of Gram-positive bacteria Bacillaceae and Paenibacillaceae and soils with high sand content (Figure S6A and S6B). Consistent with the results of the CCA, these Gram-positive bacterial families negatively correlated with Xanthomonadaceae (Figure S6C and S6D) and Steroidobacteraceae (Figure S6E and S6F). Thus, the results regarding the Xanthomonadaceae and Steroidobacteraceae families are related to the amount of clay in the soil (Figure S6G and S6H), reinforcing the hypothesis that a soil’s physicochemical characteristics condition its microbiota.

Figure 6. Canonical Correspondence Analysis (CCA) of bacterial communities.

Discussion

Viticulture is a crucial part of Argentina's socioeconomic landscape, evidenced by its substantial wine market and tourism industry (Straffelini et al., 2023). Nonetheless, the sector faces pressing challenges due to climate change and associated extreme weather occurrences. Argentina's vineyards span a lattitude of 22° to 45° south, being primarily situated along the foothills of the Andes Mountain range. Ninety-two percent of the country's vineyards are notably dedicated to grape varieties suitable for wine and must production. While Mendoza’s MA has long been recognised as an iconic Argentine wine, several other regions have also gained prominence for their high-quality soils and the distinctive characteristics of their wines. Topography, climatic conditions and soil features are considered factors that define the microbial communities associated with vines. Together, these physicochemical and biological factors contribute to the development of organoleptic properties of wine produced in each location, defining the regional terroir (Belda et al., 2017; Remenyik et al., 2024; Zarraonaindia et al., 2015). Being a large, geographically and climatologically diverse country, every region in Argentina has its own set of features that imparts special characteristics to its wines. In the present work we studied the prokaryotic microbiota associated with soils in vineyards of four Argentinian wine-producing provinces: Mendoza, San Juan, Salta and Río Negro.

When soil rhizosphere microbiota was analysed through 16S sequences, we observed that the north western samples (SAL1, SAL2 and SAL3) were clearly differentiated from the rest of the sites, probably due to their high sand and low sodium contents, which is distinctive of these soils. In addition, the genus Bacillus was overrepresented in the north western region and clearly correlated with the sand content. The study of α-diversity revealed that RN1 had significantly lower Richness, Shannon and Inverse Simpson index values, which, while clearly a particular feature of this location, could not justify, whether physicochemically or climatologically, the diversity patterns present here. LEfSe analysis allowed us to identify the taxonomical signature of each site. The most distinctive profile was the prevalence of Archaea as a feature that distinguishes RN2 from the rest of the sites, which could be related to its closer proximity to the sea. Compared to the work of Remenyik et al. (2024), we identified a higher number of differential taxa in each studied site, which may be due to the fact that our approach took into account vineyards in different regions (Remenyik et al., 2024).

To study the relationships between OTUs, SparCC-based networks were constructed and validated by comparison with stochastic associations. The obtained network has a strong tendency to form densely connected groups or communities. The geodesic efficiency of the empirical network is lower than that of random networks, indicating that the exchange of information or interactions within the network is more localised. Nodes in the empirical network show high stress centrality, which may mean that certain species are crucial for connectivity and act as “hotspots” within the network. However, the entire network is centred around a particular node: OTU_003 (family Cytophagaceae, Table S5). Such an OTU could represent a keystone species, which are highly influential and significant members of the microbial community. These species play a pivotal role in preserving the community's structure and function, having a greater impact on the ecosystem than other taxa (Banerjee et al., 2018; Röttjers & Faust, 2019).

Regarding the soil’s physicochemical properties, the sand and clay content proved to be a key factor shaping the microbiota. A subset of bacterial families, such as Bacillaceae and Paenibacillaceae, is prone to growing in sandy soils, while Xanthomonadaceae and Steroidobacteraceae families, among others, are more likely to be found in clay-rich soils. The correlation between Bacillus and sand has already been observed: in a study on the influence of abiotic factors on the abundance of microorganism populations in soil, Santos et al. (2022) showed that sand content positively correlated with Bacillus thuringiensis (Santos et al., 2022); similarly, Vu et al. (2022) found that, in one of the studied soils, the genus Bacillus (among others) was well-adapted to sand dune ecosystems and could colonise the roots of various plant species growing in different sand dune areas (Vu et al., 2022). Therefore, the negative correlation between sand- and clay-trophic organisms conditions the soil's microbiota, thereby defining the regional terroir.

The overrepresentation of beneficial bacteria, such as Bacillaceae, Pseudomonadaceae and Rhizobiales, may have positive effects on grapevine health and wine production. Several species within the genus Bacillus have been extensively characterised as beneficial bacteria, functioning as biofertilizers due to their ability to transform plant-unavailable nutrient forms into plant-available ones (commonly referred to as plant growth-promoting rhizobacteria, PGPR). They also suppress the development of plant pathogens and help plants resist both abiotic and biotic stresses, among other benefits (Borriss, 2020; Hashem et al., 2019; Wang et al., 2021). Consistent with this, the network analysis showed that OTUs assigned to the Bacillaceae and Paenibacillaceae families were closely related, with all these OTUs being part of Module 3 (Figure S5B), supporting the hypothesis that certain environmental conditions influence the presence of these families. Moreover, Bacillus was identified as a shared microbial taxon, forming part of a "global core microbiome" that has microbial signatures common to vineyard soils, irrespective of geographical and environmental variability (Gobbi et al., 2022).

Previous work from our group isolated fourteen Bacillus spp. strains: seven from SAL2, two from MZA, four from SJ2 and one from SJ1. These strains were capable of inhibiting phytopathogenic fungi, such as Botrytis cinerea, Alternaria alternata, Fusarium oxysporum, Fusarium gramineraum or Penicillium oxalicum, and of solubilising phosphorus due to their being siderophore producers, and, together with Paenibacillus AMCV14 isolated from SAL, they showed protease, amylase and pectinase activity (Oyuela Aguilar et al., 2021). The fact that this genus was differentially found in such locations in the present study is consistent with the frequency of Bacillus spp. isolations in the SAL region found by Oyuela Aguilar et al. (2021). In addition to the Bacillaceae family, RN1 was found to have a high number of a strain of the family Paenibacillaceae. Pseudomonadaceae was also found in SAL2 and SAL3, but its presence was not relevant in SAL3 and RN2. Meanwhile, the bacteria characteristic of MZA comprised strains of the Rhizobiales order. Together with Bacillus, species of the genus Paenibacillus, Pseudomonas and Rhizobium have also been characterised as PGPRs and are able to inhibit plant pathogen growth (Dorjey et al., 2017; Goswami et al., 2016; Hussain et al., 2020; Shailendra Singh, 2015; Wang et al., 2021). Our research group isolated 3 PGPR strains of Rhizobium spp–two from RN1 and one from SAL2–and 59 strains of Pseudomonas spp. capable of solubilising phosphate and producing siderophores, and which showed protease, amylase, cellulase and/or pectinase activity (Oyuela Aguilar et al., 2021). The high proportion of Pseudomonas spp. isolated by culture techniques can be explained by the ubiquitous presence of this genus (despite the region analysed 16S barcoding), which is characteristic of this bacterial group (Compant et al., 2011; David et al., 2018; Lyng & Kovács, 2023).

The findings reported here can be used to help develop agricultural practices that reduce reliance on chemical inputs while promoting greater efficiency and sustainability, thus improving plant health and soil quality (de Andrade et al., 2023). By inducing systemic resistance, rhizobacteria can activate biological or chemical responses in host plants, enhancing their protection from pathogens (Vacheron et al., 2013). This protection is further supported by competition for space and the secretion of lytic enzymes, such as chitinases, glucanases and proteases, which are the primary hydrolytic enzymes that degrade fungal cell walls (Tariq et al., 2017). Given the numerous benefits that bacteria confer to plants, inoculation with PGPRs has become increasingly popular to improve crop productivity and is now widely recognised as an essential tool for managing plant diseases within the framework of sustainable agriculture (Mohanty et al., 2021). In the light of climate change and the increasing prevalence of crop diseases, modern viticulture faces significant challenges: maintaining the high productivity, quality and environmental sustainability of its products, while ensuring the safety of consumers and workers alike. There is thus a demand for sustainable strategies to reduce fungicide usage and mitigate soil health risks, like copper accumulation. (Pii et al., 2024). Understanding how microbial communities are established and exploring strategies to engineer them represents a promising and forward-thinking approach to optimising the plant microbiome for desired functions.

Conclusions

This study provides valuable insights into the prokaryotic diversity of Argentine vineyard soils, with a particular focus on the rhizosphere microbiota of two grapevine cultivars, MA and CS, across four key wine-producing provinces: Mendoza, San Juan, Río Negro, and Salta. The physicochemical characteristics of the soils, including the sand-to-clay ratio and concentrations of sodium, potassium and carbonates, play a pivotal role in defining regional groups and shaping microbial diversity. Taxonomic assignment and LEfSe analysis identified a unique taxonomic signature for each site, highlighting the prevalence of specific microbial families associated with particular regions. These patterns likely reflect variations in environmental factors, soil properties and vineyard management practices. While the microbial communities associated with the different grapevine regions displayed distinct compositions, a subset of beneficial bacteria–including Bacillus, Paenibacillus, Pseudomonas, and Rhizobium–was consistently identified across all studied locations. These bacteria, recognised as plant growth-promoting rhizobacteria (PGPR), may enhance nutrient availability, confer pathogen resistance and promote overall vine health. The findings underscore the potential of microbial communities to support grapevine health and contribute to wine quality. This comprehensive analysis sheds light on the intricate relationship between soil characteristics, microbial diversity and the concept of terroir in Argentine vineyards. Understanding these interactions is essential for optimising vineyard management practices and ultimately influencing the organoleptic properties of wines produced in different regions.

Acknowledgements

We would like to thank the wineries in Argentina for allowing us to collect samples from their vineyards. We would also like to thank the “MicroWine” team for their support and input. MO carried out her Marie-Curie ITN fellowship at the IBBM- (Instituto de Biotecnología y Biología Molecular) CONICET-UNLP. AMT is a researcher at UNLP; CR and RRW are fellows of the Research Career of CONICET; MFDP and MP are members of the Research Career of CONICET; and LS is a Head Professor of the Universidad Nacional de Quilmes and a member of the Research Career of CIC-PBA.

This study was funded by the European Commission Horizon 2020 programme in the Marie Skłodowska-Curie Innovative-Training-Network “MicroWine” (grant number 643063) and the Agencia Nacional de Promoción Científica y Tecnológica PICT-2017-2833.