Composition Divergence and Synergistic Mechanisms in Microbial Communities During Multi-Varietal Wine Co-Fermentation

Abstract

The bacterial microbial community composition during wine fermentation is a key contributor to wine quality and flavor. However, studies on the regulatory effects of different grape varieties and co-fermentation processes on the microbial community structure and their synergistic mechanisms remain limited. In this study, Cabernet Sauvignon (CS) was subjected to single-variety fermentation and used as the base wine for co-fermentation with three other grape varieties—Marselan (CSMN), Merlot (CSMT), and Cabernet Gernischt (CSCG)—to systematically compare the differences in the microbial community composition and their effects on the production of metabolic compounds. The results showed that, compared with single-variety fermentation, co-fermentation significantly increased the α-diversity of microbial communities (the Shannon index increased) and exhibited significant differences in β-diversity (PERMANOVA analysis, R² = 0.421, p < 0.001). A neutral model analysis indicated that co-fermentation had a significant impact on microbial community assembly mechanisms, with the contribution of neutral processes to community assembly increasing from 45.5% (in the CSCG process) to 62.3% (in the CSMT process). A microbial co-occurrence network analysis revealed that co-fermentation enhanced the network complexity of microbial communities and strengthened the synergistic interactions between microbial taxa. A metabolic compound analysis revealed that co-fermentation significantly enhanced the production of key aroma compounds, resulting in increased concentrations of isoamyl acetate, ethyl hexanoate, linalool, and geraniol. These findings highlight the differences in microbial communities and their synergistic mechanisms among co-fermented grape varieties, providing theoretical guidance and practical insights for optimizing co-fermentation processes and improving wine quality.

1. Introduction

Wine is one of the most popular fermented beverages worldwide, with its complex flavors and quality largely dependent on the involvement of microbial communities during fermentation [1]. These microorganisms not only convert the sugars in grapes into alcohol but also produce a series of secondary metabolites, such as esters, higher alcohols, and terpenes, which impart unique aromas and flavor characteristics to the wine [2]. Although fungi play a critical role in mixed fermentations of wine, the quality and flavor of wine are also significantly influenced by bacterial microbial communities during the fermentation process [3]. The role of yeasts in fermentation has been extensively studied and is well understood, especially with regard to yeasts management techniques such as inoculation, which are now well established, consistent, and predictable. In contrast, the diversity and unpredictability of bacteria are significantly greater than those of fungi, and bacteria also make substantial and indispensable contributions to the formation of metabolites during fermentation. Therefore, there is a pressing need for systematic research on the roles of bacteria in fermentation, particularly in the context of mixed-culture fermentation processes. Due to the complexity of grape varieties, fermentation techniques, and environmental factors, the microbial communities and their synergistic interactions in different winemaking processes remain incompletely understood. This is particularly true for the microbial ecology of co-fermentation processes, which has yet to be systematically investigated.

The co-fermentation process is an important technique in winemaking, where grapefruits or juices from different varieties are mixed and fermented together [4,5]. This approach can balance the taste of wine, optimize flavor characteristics, and introduce a greater aromatic complexity. The chemical properties of different grape varieties, such as the sugar content, total acidity, pH, phenolic compounds, and volatile compounds, exert significant selective effects on microorganisms, thereby influencing the ecological niche distribution and metabolic functions of microbial communities during fermentation [6]. For instance, grape varieties rich in phenolic compounds may inhibit the growth of certain microorganisms while promoting the proliferation of others, whereas differences in the sugar content and acidity may regulate the metabolic pathways of yeasts and lactic acid bacteria (LAB) [1,7]. As a result, the microbial community structure and functional characteristics in co-fermentation are likely to differ significantly from those in single-variety fermentation. Synergistic interactions among microbial communities are crucial in determining wine quality [8]. During fermentation, various microbial populations may interact through mechanisms such as metabolic complementation, signaling, or direct competition. For example, Saccharomyces cerevisiae, the dominant microorganism in alcoholic fermentation, is significantly influenced by non-Saccharomyces yeasts (e.g., Hanseniaspora, Pichia) and bacteria (e.g., LAB and acetic acid bacteria) [6,9]. LAB, such as Lactobacillus and Oenococcus, often regulate wine acidity through malolactic fermentation and produce volatile flavor compounds, forming metabolic complementation relationships with yeasts [10]. Moreover, the niche competition between non-Saccharomyces yeasts and LAB in the early stages of fermentation can greatly affect the final microbial community structure and the composition of metabolic products [11]. Therefore, investigating how microbial communities in co-fermentation synergistically metabolize and regulate the production of key flavor compounds has significant theoretical and practical value.

Although previous studies have shown that the fermentation process of single-variety wines exhibits specific microbial community dynamics, the synergistic effects of different grape varieties in co-fermentation and their impact on microbial communities remain insufficiently studied [12]. During co-fermentation, the α-diversity of microbial communities (e.g., species richness and evenness) and β-diversity (community structural differences) may be significantly regulated by the chemical properties of the grape varieties [13]. For example, Cabernet Gernischt, which is rich in phenolic compounds and exhibits higher acidity, may result in microbial communities with lower diversity and higher species specificity [14]. In contrast, Merlot, with relatively lower acidity, may support a more complex microbial ecosystem [15]. Furthermore, the co-fermentation of different grape varieties may enhance the synergistic interactions among microbial communities through nutritional complementation or chemical property balance, thereby optimizing the production of metabolic compounds.

Taken together, this study uses Cabernet Sauvignon as the base wine and investigates co-fermentation with three grape varieties—Marselan, Merlot, and Cabernet Gernischt—to systematically analyze the regulatory effects of the co-fermentation process on the microbial community structure, dynamic changes, and functional characteristics. By integrating 16S rRNA sequencing technology with a gas chromatography–mass spectrometry (GC-MS) analysis, combined with microbial co-occurrence network construction and a neutral model analysis, this study aims to reveal the differences in microbial communities and their synergistic mechanisms among different co-fermented grape varieties. The main objectives of this research include the following: (1) comparing the diversity and structural characteristics of microbial communities in different co-fermentation processes; (2) exploring the synergistic metabolic mechanisms of microbial communities among co-fermented grape varieties and their effects on the production of key flavor compounds; and (3) elucidating the driving effects of grape variety chemical properties on microbial community assembly processes. This study provides new scientific insights for optimizing co-fermentation processes and improving wine quality.

2. Materials and Methods

2.1. Sample Collection

Samples were collected from a wine fermentation facility at a commercial winery in Yantai, China (121.17° E, 37.57° N). The grapes used in this study included Cabernet Sauvignon (CS), Cabernet Sauvignon blended with Marselan (CSMN), Cabernet Sauvignon blended with Merlot (CSMT), and Cabernet Sauvignon blended with Cabernet Gernischt (CSCG), all sourced from commercial suppliers. These particular varieties were selected due to their strong market demand and widespread popularity among consumers rather than their local cultivation in the Yantai region. The harvest occurred between 20 August and 1 September 2024, during which the local climate in Yantai featured minimum and maximum daily temperatures of 19 °C and 29 °C, a total precipitation of approximately 28 mm, a relative humidity ranging from 38% to 54%, and an average of 7.4 h of daily sunshine, providing favorable conditions for grape ripening. After harvest, the grapes were destemmed and crushed. Single-variety fermentations were carried out with 100% Cabernet Sauvignon must, while for each co-fermentation group, Cabernet Sauvignon and the respective blending variety were mixed at a 1:1 volume ratio. All fermentations were conducted in duplicate in 10 L stainless steel tanks under identical conditions: a fermentation temperature of 25 °C, an initial sugar content of 23.0–24.0 °Brix, and a sulfur dioxide addition of 50 mg/L. Commercial Saccharomyces cerevisiae yeast was used for inoculation when required. The fermentation process was monitored daily and considered complete when the residual sugar content dropped below 2 g/L. Upon completion, the wines were racked and stored at 15 °C prior to further analysis. The physicochemical indicators of the wines obtained through the four fermentation processes are shown in Table S1. From each tank, three composite samples were collected, resulting in a total of 30 mL per sample. Of this, 10 mL was used for the determination of volatile organic compounds, and 20 mL was used for DNA extraction.

2.2. Determination of Volatile Organic Compounds

The volatile organic compounds (VOCs) in the three co-fermentation processes, including esters (isoamyl acetate and ethyl hexanoate), higher alcohols (phenylethanol and isoamyl alcohol), and terpenes (linalool and geraniol), were analyzed. The wine samples were centrifuged at 4000× g for 10 min to remove suspended particles. A 10 mL aliquot of the clarified supernatant was transferred into a 20 mL headspace vial. Internal standards (final concentration: 10 mg/L) were added to each sample for quantification. For the extraction of volatile compounds, a liquid–liquid extraction method was applied using dichloromethane as the solvent. Specifically, 10 mL of the wine sample was mixed with 2 mL of dichloromethane and vigorously vortexed for 5 min. The mixture was then allowed to settle, and the organic layer was carefully collected and dried over anhydrous sodium sulfate. The extract was concentrated to 1 mL under a gentle nitrogen stream and transferred to a gas chromatograph (GC-2014, Shimadzu, Kyoto, Japan) equipped with a packed column (FFAP capillary column, 30 m × 0.25 mm × 0.25 μm) and a flame ionization detector (FID) for analysis [16]. Calibration curves were prepared using standard solutions of isoamyl acetate and ethyl hexanoate at concentrations ranging from 0.1 to 50 mg/L. The concentrations of the compounds in the samples were calculated based on the ratio of the target compound peak area to the internal standard peak area. The analysis conditions for higher alcohols were the same as those for esters. The terpenes in the samples were analyzed using gas chromatography–mass spectrometry (GC-MS). After extraction and concentration using the aforementioned liquid–liquid extraction method, 10 mL of the sample was introduced into a gas chromatography–mass spectrometer (7890B GC system, Agilent, Santa Clara, CA, USA). Standard calibration curves for linalool and geraniol were prepared, and the concentrations of the target compounds in the samples were calculated based on the peak areas and the standard curves [15].

2.3. Extraction of DNA

For each co-fermentation process, triplicate 20 mL wine samples were pooled and filtered through a 0.22 μm sterile membrane filter to capture the microorganisms present in the samples. DNA was extracted from the 0.22 μm membrane using a DNeasy PowerWater Kit (QIAGEN, Hilden, Germany) according to the manufacturer’s default protocol. The microbial composition of the selected samples was analyzed by Illumina sequencing of the V3-V4 hypervariable region of the bacterial 16S rRNA gene. The targeted region was amplified using a thermocycler PCR system (GeneAmp 9700, Thermo Fisher Scientific Inc., Waltham, MA, USA) with primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′), which were synthesized by BGI (Shenzhen, China) [2]. The resulting PCR products were extracted from 2% agarose gels, purified using an AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA), and quantified with QuantiFluor™ (ST, ProMag Industries, Phoenix, AZ, USA) following the manufacturer’s instructions. Sequencing was performed on an Illumina MiSeq PE300 platform at Majorbio Inc. (Shanghai, China). The relevant data were uploaded to the NCBI database under accession number SRR15725716-SRR15725807.

2.4. Bioinformatics Analysis

Amplicon data were processed and analyzed using USEARCH (version 11.0.667) [17]. OTUs (Operational Taxonomic Units) were obtained through the unoise3 algorithm, followed by the taxonomic annotation of OTUs using sintax with the RDP V18 database. The alpha variety was obtained using the R package Vegan [18]. An lefse analysis of communities was performed using the R package microeco [18]. The Neutral Community Model (NCM) was employed to assess the potential influence of stochastic processes on microbial community assembly [19]. In this model, the R² parameter served as an indicator of the goodness of fit to the neutral model. Bootstrap resampling with 1000 iterations was performed to calculate fit statistics, establishing 95% confidence intervals. During NCM fitting, each dataset was categorized into three groups: taxa with occurrence frequencies above the 95% confidence interval of NCM predictions (upper interval), below the 95% confidence interval (lower interval), and within the confidence interval (neutral interval). The NCM fitting was implemented using the “MicEco” package (https://github.com/Russel88/MicEco) in R. To compare the microbial community structure differences across ecosystems, microbial ecological networks (MENs) were constructed using ggClusterNet (version 0.1.0) with the following parameter settings: “N = 0, r = 0.8, p = 0.05, method = spearman” [20]. Multiple topological parameters of the networks, including the number of edges (L), number of nodes (n), relative modularity (RM), and network vulnerability (Vul), were calculated using ggClusterNet (version 0.1.0). The LEfSe (Linear Discriminant Analysis Effect Size) was employed to identify statistically significant taxa that differentiated microbial communities across distinct biological conditions. Detailed computational methods for network parameters have been comprehensively described in previous publications. Detailed analysis data can be found in Tables S2–S9.

3. Results and Discussion

3.1. Microbial Community Structure in Single Fermentation and Different Co-Fermentation Processes

The differences in the microbial community composition across the single fermentation and three co-fermentation processes were analyzed, as shown in Figure 1. Figure 1a shows an Upset plot depicting the distribution of OTUs among four fermentation groups: CS, CSCG, CSMT, and CSMN. The largest intersection, containing 367 OTUs, is shared exclusively among all three co-fermentation groups (CSCG, CSMT, and CSMN), notably excluding CS. This finding highlights that co-fermentation, regardless of the specific grape variety combination, tends to foster a highly similar and stable core microbial community distinct from that of single-variety fermentation. The second and third largest intersections (210 and 184 OTUs, respectively) are shared among CS with two of the co-fermentation groups (CSCG and CSMT; CS and CSMN with CSCG), indicating that a substantial part of the CS microbiota overlaps with that present under some—but not all—co-fermentation conditions. This suggests that certain microbial taxa are robust under fermentation processes, while others may be specifically selected or inhibited depending on the grape variety interactions. The remaining intersections show progressively smaller OTU overlaps among other combinations of groups, and the number of unique OTUs in each group is remarkably low (six in CS, CSCG, and CSMT; three in CSMN). This pattern suggests that the vast majority of microbial diversity is not group-specific but rather widely shared across fermentation types. The low number of strictly unique OTUs implies that the core microbiome is highly resilient to changes in the fermentation strategy and that only a very small fraction of the community is uniquely shaped by individual grape combinations. Overall, these results indicate that co-fermentation processes, despite involving different partner grape varieties, drive the assembly of a large and largely consistent core microbiome that is distinct from that of single-variety fermentation. Meanwhile, most OTUs are shared among multiple groups, underscoring the ecological stability and overlap of wine fermentation microbiota under varying fermentation practices [21,22]. Figure 1b,c present the taxonomic composition of the microbial communities at the phylum and genus levels, respectively, across the four fermentation groups (CS, CSCG, CSMT, and CSMN). At the phylum level (Figure 1b), the microbial community of the CS group is dominated by Pseudomonadota, with Bacillota comprising a smaller fraction. In contrast, all three co-fermentation groups (CSCG, CSMT, and CSMN) show a marked increase in the relative abundance of Bacillota, which becomes the predominant phylum, while the relative abundance of Pseudomonadota decreases correspondingly. Additionally, minor phyla such as Actinomycetota and Bacteroidota are more prominent in the co-fermentation groups than in CS. This shift suggests that co-fermentation, regardless of the grape variety combination, promotes the proliferation of Bacillota, likely due to synergistic metabolic environments or broader nutrient spectra provided by mixed grape musts. The enrichment of Bacillota, which includes many lactic acid bacteria (LAB), is especially relevant for wine fermentation, as these taxa are closely associated with malolactic fermentation, acid metabolism, and wine flavor development [23]. At the genus level (Figure 1c), the CS group is overwhelmingly dominated by the genus Pantoea (within Pseudomonadota), with only a limited representation of lactic acid bacteria such as Weissella or Lactiplantibacillus. In contrast, the co-fermentation groups (particularly CSCG and CSMT) show a substantial increase in the diversity and abundance of LAB genera, including Weissella, Pediococcus, Lactiplantibacillus, and Lactococcus. The relative dominance of these LAB genera in the co-fermentation groups highlights the effect of mixed grape musts in promoting beneficial microbial populations, which can enhance fermentation robustness, microbial stability, and desirable sensory attributes in the final product. Notably, the CSCG group exhibits the highest genus-level evenness and LAB abundance, likely reflecting the specific chemical properties of Cabernet Gernischt that favor LAB growth [24]. The phylum- and genus-level data together demonstrate that co-fermentation not only increases microbial diversity but also selectively enriches beneficial LAB populations. These changes are expected to have positive implications for fermentation kinetics, malolactic fermentation, and the sensory complexity of the resulting wines. The results underscore the importance of fermentation strategy and grape variety selection in shaping the microbial ecology of wine fermentation, providing a basis for the targeted manipulation of microbial communities to improve wine quality. Figure 1d displays the results of the LEfSe (Linear Discriminant Analysis Effect Size) analysis, highlighting the specific bacterial taxa that are differentially abundant among the three co-fermentation groups (CSMN, CSCG, and CSMT). Each bar represents a taxon with a significant LDA score, indicating its discriminatory power for a particular group. The results reveal pronounced group-specific microbial signatures. In the CSMN group, taxa such as Pantoea, Aeromonas, and Kosakonia (mainly within Pseudomonadota and Enterobacteriaceae) are highly enriched, suggesting that Marselan co-fermentation favors the proliferation of certain facultative and environmental bacteria, possibly due to its unique polyphenol or nutrient profile [25]. The CSCG group is characterized by a strong enrichment of lactic acid bacteria (LAB), including Weissella, Pediococcus, and Lactiplantibacillus, as well as Lactococcus and Levilactobacillus. The dominance of these LAB taxa is consistent with the higher overall LAB abundance observed in Figure 1c, and it likely reflects the specific chemical environment created by Cabernet Gernischt, which appears to promote LAB growth and ecological competitiveness. This is beneficial for wine fermentation, as LAB are essential for malolactic fermentation and contribute to wine stability and flavor complexity. In the CSMT group, taxa such as Staphylococcus, Macrococcus, and Bacillus (within Bacillota), as well as Moraxella and Acinetobacter (within Pseudomonadota), are discriminative. The presence of these genera suggests that Merlot co-fermentation may provide ecological niches for both Gram-positive and Gram-negative bacteria, contributing to a more diverse but distinct microbial consortium. Notably, some genera enriched in CSMT, such as Staphylococcus and Bacillus, are known for their roles in complex metabolic interactions and may influence the production of aroma-active compounds [26]. Based on the UpSet plot analysis, which demonstrates that single-variety fermentations harbor a limited bacterial community while co-fermentation significantly increases microbial diversity, we infer that the co-fermentation of Cabernet Gernischt and Cabernet Sauvignon creates a more selective metabolic environment that strongly influences the composition and diversity of the microbial community. The ability to shape the microbial consortium through co-fermentation provides opportunities for the targeted modulation of wine fermentation processes, potentially improving wine quality, stability, and sensory properties.

Overall, Figure 1 demonstrates that different co-fermentation processes significantly alter the composition and structure of microbial communities during wine fermentation. Each co-fermentation group (CSMN, CSMT, and CSCG) develops a distinct microbiota shaped by grape variety interactions and the fermentation environment. Co-fermentation generally promotes the enrichment of lactic acid bacteria and increases microbial diversity, especially in CSCG, while CSMN is characterized by acetic acid and environmental bacteria. These findings highlight the important regulatory role of grape variety combinations in shaping fermentation microbiota, providing a basis for optimizing fermentation strategies and improving wine quality.

3.2. Relationship Between Volatile Organic Compounds and Microbial Communities in Different Fermentation Processes

To explore the relationship between volatile organic compounds (VOCs) and microbial communities in different fermentation processes, the levels of esters (isoamyl acetate and ethyl hexanoate), higher alcohols (isoamyl alcohol and phenethyl alcohol), and terpenes (linalool and geraniol) were measured, along with their correlations with the dominant microbial communities, as shown in Figure 2a–c. Figure 2a shows the distribution of the two main esters (isoamyl acetate and ethyl hexanoate). The total ester content is the highest in CSMT (35.98 ± 2.93 mg/L and 16.26 ± 2.15 mg/L), followed by in CSMN (22.36 ± 3.78 mg/L and 19.38 ± 1.69 mg/L) and CSCG (18.39 ± 2.29 mg/L and 23.35 ± 3.23 mg/L), and it is the lowest in CS (10.56 ± 1.85 mg/L and 8.98 ± 1.12 mg/L). The contents of isoamyl acetate and ethyl hexanoate, two key esters contributing to the fruity aroma, are significantly higher in the CSMT group compared to in the other groups. The CS group exhibits the lowest ester concentrations, while CSMN and CSCG show intermediate levels. The RDA analysis below further demonstrates a strong positive correlation between Levilactobacillus and ester production, particularly ethyl hexanoate, with the CSMT samples clustering closely with these variables. This suggests that the enrichment of certain lactic acid bacteria (LAB) in CSMT may promote ester biosynthesis, thereby enhancing the wine’s fruity and floral aromatic qualities [27]. Figure 2b illustrates the distribution of two major higher alcohols (phenethyl alcohol and isoamyl alcohol). The total higher alcohol content is the highest in CSMN (37.79 ± 2.13 mg/L and 41.58 ± 4.98 mg/L), followed by in CSMT (40.26 ± 4.22 mg/L and 19.32 ± 2.32 mg/L) and CS (27.26 ± 2.18 mg/L and 21.62 ± 1.98 mg/L), and it is the lowest in CSCG (16.78 ± 1.45 mg/L and 15.58 ± 1.87 mg/L). This suggests that the total content of higher alcohols (isoamyl alcohol and phenethyl alcohol) is the highest in the CSMN group, followed by in CSMT, with CS and CSCG displaying lower concentrations. The RDA results indicate that isoamyl alcohol is positively associated with genera such as Pantoea and Aeromonas, which are more abundant in CSMN. In contrast, phenethyl alcohol and some LAB genera show a closer relationship in other groups. Elevated higher alcohols can contribute to complexity but may also impart undesirable sensory effects if present in excess, suggesting that CSMN fermentation may require careful control to balance aroma and quality [28]. Figure 2c shows the distribution of two major terpenes (linalool and geraniol). The total terpene content is the highest in CSMT (2.33 ± 0.68 mg/L and 1.05 ± 0.10 mg/L), followed by in CSMN (1.32 ± 0.36 mg/L and 0.32 ± 0.04 mg/L) and CSCG (0.98 ± 0.12 mg/L and 0.45 ± 0.05 mg/L), and it is the lowest in CS (0.65 ± 0.12 mg/L and 0.33 ± 0.04 mg/L). Terpene compounds (linalool and geraniol), which impart floral and citrus notes, are the most abundant in the CSMT group, with CSMN and CSCG exhibiting moderate levels and CS exhibiting the lowest. The RDA plot reveals that Lactiplantibacillus is positively correlated with geraniol, and Levilactobacillus is positively correlated with linalool, again emphasizing the role of LAB in enhancing terpene profiles. Notably, the CSMT samples are closely associated with both terpenes and the corresponding LAB genera, indicating that co-fermentation with Merlot may create favorable conditions for the accumulation of desirable terpenes [29]. These findings reflect the varied contributions of microbial communities to terpene biosynthesis. The distribution of these three types of VOCs and the RDA analysis collectively show that the CSMT process exhibits a significant advantage in the production of esters, alcohols, and terpenes. This could be attributed to the chemical composition of Merlot grapes (e.g., the sugar and acid contents) and their synergistic interaction with Cabernet Sauvignon. The CSMN process stands out in higher alcohol production, indicating that Marselan grapes may be more conducive to enhancing wine complexity. In contrast, the CSCG process generally exhibits lower VOC levels, which may be attributed to the chemical properties of Cabernet Gernischt or the composition of its microbial community, potentially limiting certain metabolic pathways. The RDA analysis further reveals the influence of specific microbial communities on VOC production. For example, Levilactobacillus and Lactiplantibacillus significantly promote the formation of esters and terpenes, while Weissella and Pantoea are closely associated with the production of isoamyl acetate and isoamyl alcohol [30]. These results demonstrate that different co-fermentation strategies not only shape the microbial community but also modulate the synthesis of key aroma compounds via specific microbial–metabolite interactions. Co-fermentation, especially with CSMT, enriches LAB genera that are closely linked to higher concentrations of esters and terpenes, enhancing the wine’s aromatic complexity and sensory appeal. In contrast, CSMN is more associated with higher alcohols, likely due to the dominance of environmental and acetic acid bacteria. This highlights the importance of grape variety selection and microbial management in controlling wine aroma profiles and optimizing product quality.

3.3. Differences in and Distribution of Microbial Communities Across Different Co-Fermentation Processes

As shown in Figure 3a, the microbial community structures of the three groups (CSMN, CSMT, and CSCG) are clearly separated, with CSCG being the most distant from the other two groups. This indicates that CSCG exhibits greater divergence in the microbial community structure. While there is some overlap between CSMN and CSMT, the two groups still show distinct separation, suggesting that Marselan and Merlot grapes have different selective effects on the microbial communities when co-fermented with Cabernet Sauvignon. This separation may be attributed to the different chemical properties of the grape varieties, such as sugars, acids, and phenolic compounds, which exert ecological selection on the microbial communities. As shown in Figure 3b, the distribution of the sample points for the three processes is consistent with the PCoA results. The sample points for CSCG are more tightly clustered compared to those for the other two groups, indicating higher internal consistency in the microbial community structure in the CSCG process [31]. This may reflect the strong selective pressure exerted by the chemical composition of Cabernet Gernischt grapes on the microbial communities. In contrast, the sample points for CSMN and CSMT are more widely dispersed, particularly for CSMT, indicating higher microbial community diversity between the samples in these processes. This diversity may result from the greater heterogeneity in the microbial sources or fermentation environments associated with Marselan and Merlot grapes.

α-diversity is used to describe the species richness and evenness within microbial communities. The diversity of microbial communities in the three co-fermentation processes was evaluated using six indices (the Richness, Shannon, Chao1, ACE, Simpson, and InvSimpson indices), as shown in Figure 4. According to the Richness, Chao1, and ACE indices, the species richness in CSMN and CSCG was higher, while CSMT exhibited a significantly lower richness compared to the other two groups. This indicates that the CSMN and CSCG co-fermentation processes are more conducive to maintaining a higher number of microbial species, whereas the chemical properties in CSMT may have restricted the growth of certain microorganisms. The high richness observed in CSCG could be attributed to the complex microbial sources on the skin of Cabernet Gernischt grapes and their abundant phenolic compounds. In contrast, the lower richness in CSMT may be due to the lower acidity or sugar content of Merlot grapes, which exerts stronger selective pressure on the microbial community. The Shannon index indicates that the microbial community diversity was the highest in CSCG, followed by in CSMN, and it was the lowest in CSMT. This suggests that the co-fermentation of Cabernet Gernischt and Cabernet Sauvignon (CSCG) facilitates the formation of a more uniform and balanced microbial community structure. The Simpson and InvSimpson indices further support this conclusion, showing that CSCG had the highest evenness and diversity, while CSMT exhibited a relatively low evenness. This may be because certain lactic acid bacteria (e.g., Lactobacillus) dominated the microbial community in the CSMT process, resulting in a reduced community evenness.

3.4. Ecological Characteristics of Core Microbial Communities in Three Co-Fermentation Processes

Figure 5 illustrates the neutral assembly mechanisms of the microbial communities and their interactions, with the top panel representing the neutral model analysis and the bottom panel showing the microbial co-occurrence networks. For CSMN, the model fit (R² = 0.231) was relatively low, indicating that the assembly process of the microbial communities was weakly driven by neutral processes (random dispersal). The pie chart shows that 57.7% of the microbes fell within the predicted range of the model (neutral taxa), while the rest were classified as dominant (high abundance) or rare (low abundance) taxa. This suggests that, in the CSMN process, certain microbial communities may be strongly influenced by the specific chemical environment of the grape variety, limiting the impact of neutral processes. In the co-occurrence network, Bacillota and Pseudomonadota were the dominant phyla, indicating that these microbes played a major role during fermentation under the co-fermentation conditions of Marselan and Cabernet Sauvignon. For CSMT, the model fit (R² = 0.339) was higher than that for CSMN and CSCG, suggesting that the assembly of microbial communities was closer to neutral processes. The pie chart shows that 62.3% of the microbes fell within the predicted range of the model, suggesting that, in the CSMT process, neutral mechanisms such as random dispersal may play a relatively larger role in microbial community assembly, while selective pressures from the grape variety may be less pronounced compared to in the other fermentation types studied. However, further comparative analysis would be needed to confirm this trend. The proportions of dominant and rare taxa were smaller, suggesting a more even microbial community in this process. While Bacillota and Pseudomonadota remained the dominant phyla, the relative proportions of Actinomycetota and Bacteroidota increased compared to in CSMN, indicating that the fermentation environment of Merlot grapes may have provided suitable niches for these specific microbes. For CSCG, the model fit (R² = 0.202) was the lowest among all groups. This result suggests that the assembly of microbial communities in the CSCG process may be more strongly influenced by selective pressures associated with the specific chemical properties of the grape must, with a reduced contribution from neutral processes. In particular, the pie chart shows that 45.5% of the microbes fell within the predicted range of the model, while the proportions of rare and dominant taxa increased significantly. These findings imply that characteristics such as a potentially higher phenolic compound content and a more acidic environment in the Cabernet Gernischt blend could play a role in shaping a more specialized microbial community. However, given the lack of a parallel analysis of the single-variety CS fermentation, we cannot fully exclude the possibility that similar effects might occur in the base variety fermentation. Therefore, the specific contribution of co-fermentation and selection by phenolic compounds requires further investigation in future studies [5,32]. While Bacillota and Pseudomonadota remained the dominant phyla, the proportions of Bacteroidota and Actinomycetota increased further compared to in both CSMN and CSMT, indicating that the unique environment of Cabernet Gernischt grapes provided more ecological opportunities for these phyla. From the results of the neutral model and co-occurrence networks, it is evident that the influence of neutral assembly mechanisms varied among the processes: CSMT was the most influenced by neutral assembly, suggesting that its microbial community formation was primarily driven by random dispersal, while CSCG was the least influenced by neutral processes, with the microbial communities primarily shaped by the specific chemical properties of the grape variety. CSMN fell in between, being influenced by both neutral processes and selective pressures. In terms of microbial community network characteristics, CSCG had the most complex microbial network with the highest modularity, indicating that the chemical properties of Cabernet Gernischt significantly promoted microbial interactions and module differentiation. CSMT had the simplest network, with fewer interactions among microbial species, likely due to the relatively uniform fermentation environment of Merlot grapes. CSMN had a larger network but lower modularity, suggesting more evenly distributed microbial interactions [33]. These findings demonstrate that different co-fermentation processes indirectly regulate microbial ecology during fermentation by influencing microbial assembly mechanisms and network characteristics, providing a microbiological basis for optimizing co-fermentation processes.

4. Conclusions

This study systematically analyzed the regulatory effects and synergistic mechanisms of co-fermented grape varieties on microbial communities. The results show that co-fermentation significantly influenced the structure, function, and metabolic characteristics of the microbial communities. The α-diversity of the microbial communities significantly increased under co-fermentation conditions, particularly in the CSMT process. The chemical properties of the grape varieties were identified as the main driving factors affecting microbial community assembly. In the CSCG process, the high concentration of phenolic compounds strongly selected specific microbial taxa, reducing the contribution of neutral processes to community assembly to 45.5%. In contrast, the CSMT process had the highest contribution of neutral processes (62.3%), indicating significant differences in the regulatory effects of different co-fermentation processes on microbial assembly mechanisms. A co-occurrence network analysis revealed that co-fermentation significantly enhanced the complexity and cooperative interactions within the microbial communities. Among the processes, the microbial network connectivity was the highest in the CSMN process, indicating particularly close ecological interactions among microbes under these conditions. A metabolite analysis further confirmed the positive impact of co-fermentation on the aromatic characteristics of wine. The concentrations of isoamyl acetate and ethyl hexanoate significantly increased, while the levels of linalool and geraniol were also notably elevated, endowing co-fermented wines with more complex and enriched aromatic profiles. This study comprehensively elucidated, from the perspectives of microbial ecology and metabolism, the differences in and synergistic mechanisms of microbial communities across co-fermented grape varieties. It provides new theoretical insights for optimizing co-fermentation processes and establishes a scientific foundation for the precise regulation of wine flavor.