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. 2026 Mar 28;12:101394. doi: 10.1016/j.crfs.2026.101394

Cross-kingdom metabolic cooperation drives vanillic acid biosynthesis: A spatiotemporal dissection of microbial functional networks in solid-state fermentation

Wenhua Tong a,b,c,⁎,1, Lei Qiao a,1, Ying Yang a,⁎⁎, Xia Li b, Yang Zhang b, Zhijiu Huang b, Huibo Luo a,c, Liming Zhao d, Suyi Zhang b,⁎⁎⁎
PMCID: PMC13062514  PMID: 41972101

Abstract

Microbial self-organization into spatiotemporally structured consortia is key to metabolic specialization in natural environments, yet the principles governing this process in food fermentation are poorly understood. Here, we elucidate how cross-kingdom microbial cooperation drives the biosynthesis of vanillic acid (VA), a critical flavor and bioactive phenolic compound, during the solid-state fermentation of strong-flavor baijiu (SFB). Integrated metagenomic and network analyses across stratified pit layers and fermentation stages revealed a defined three-phase succession model. Early phase (D0-D12) was dominated by filamentous fungi (Aspergillus, Paecilomyces) in upper layers, initiating starch hydrolysis and phenylpropane precursor synthesis (e.g., contributing 22.6% to phenylalanine ammonia-lyase). A transitional bacterial-fungal consortium (Pichia, Klebsiella) then mediated intermediate conversion (D12-D45), with enzymatic hotspots shifting downward. The maturation phase (D45-D85) was defined by the dominance of acidophilic Acetilactobacillus (>80% relative abundance) in the lower layer, which executed the final synthesis steps (contributing 31.5% to caffeic acid O-methyltransferase) and concurrently suppressed vanillic acid degradation via downregulation of vanillate O-demethylase. Network analysis confirmed a spatial metabolic division of labor: fungi specialized in upper-layer lignin deconstruction, while bacteria dominated the completion of phenylpropanoid pathways in the lower layer. Critically, peak VA accumulation (0.375 mg/L at D45) coincided with synchronized enzyme expression across layers, demonstrating active metabolic coordination rather than passive environmental filtering. Our findings establish that functional succession and spatial compartmentalization are fundamental ecological principles enabling efficient biosynthesis in solid-state fermentation, demonstrating that flavor outcomes can be programmed through targeted microbial consortium design.

Keywords: Strong-flavor baijiu, Vanillic acid, Microbial community, Metabolic pathway, Spatiotemporal analysis

Graphical abstract

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Highlights

  • •

    A three-phase microbial succession model drives vanillic acid biosynthesis.

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    Spatial division of labor forms a metabolic assembly line in the pit.

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    Vanillic acid accumulation is enhanced by synthesis and suppressed degradation.

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    Cross-kingdom microbial cooperation partitions the phenylpropane pathway.

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    Functional zoning replaces environmental determinism in fermentation ecology.

1. Introduction

Microbial consortia in solid-state fermentation ecosystems are renowned for their exceptional catalytic capabilities, generating a diverse spectrum of flavor and bioactive compounds. As a quintessential representative of traditional Chinese solid-state fermented distilled liquor (Hong et al., 2023), strong-flavor baijiu (SFB)has gained widespread consumer preference for its distinctive characteristics: “pronounced cellar aroma, mellow texture, harmonious flavor profile, and lingering aftertaste” (Zhang et al., 2025; Zhao et al., 2022). These unique organoleptic properties originate from the complex interactions between microbial communities and metabolic activities during fermentation (Mu et al., 2022). In recent years, with rising living standards, consumer demand for both flavor complexity and health-promoting attributes of baijiu has increased (Chen et al., 2025a; Pan et al., 2023). Studies have confirmed that phenolic compounds not only significantly contribute to the aroma profile but also exhibit antioxidant and antimicrobial activities, thereby enhancing the health attributes of SFB (Di Lorenzo et al., 2021; Silva et al., 2018).

Among these phenolics, vanillic acid (VA) stands out as a pivotal molecule (Khan et al., 2024). It imparts a smooth, vanilla-like aroma that contributes to the complexity of the cellar scent and possesses documented bioactivities, including antioxidant and neuroprotective effects (Lashgari et al., 2023; Osorio-Paz et al., 2023). VA is primarily a microbial metabolite derived from the degradation of lignin and the phenylpropanoid pathway (Gallage and Møller, 2015; Kaur et al., 2022), and it acts as both a direct flavor contributor (at concentrations above its low odor threshold) and a key precursor for downstream aromatic phenolic compounds in SFB. While its presence and importance in SFB are recognized, the fundamental ecological processes governing its biosynthesis within the complex fermentation ecosystem remain largely uncharted.

Current understanding of VA biosynthesis is largely informed by studies using pure microbial cultures (Pseudomonas, Streptomyces, Aspergillus, etc.) under controlled conditions (Tang and Hassan, 2020; Tian et al., 2016). These studies have been instrumental in elucidating the core biochemical pathways (e.g., phenylpropanoid metabolism) and key enzymes (e.g., laccase, vanillin dehydrogenase) (Bugg et al., 2020; Gadhe et al., 2011; Li et al., 2024). However, this reductionist approach inherently overlooks the ecological reality of SFB fermentation—a complex, heterogeneous ecosystem characterized by dense, multi-kingdom communities (Zhang et al., 2020a, 2020b). Crucially, it fails to capture the temporal dynamics and spatial interactions that are hallmarks of such systems.

Consequently, most existing studies of the fermentation process itself have treated the pit as a homogeneous environment or have focused on unidirectional cause-and-effect relationships, often attributing microbial distribution and metabolite formation solely to static physicochemical gradients, such as acidity (Ma et al., 2022; Ji et al., 2023; Tong et al., 2024). This perspective overlooks a fundamental ecological principle: microbial communities are not merely passive respondents to their environment but can actively shape it through metabolic feedback and self-organization. A critical knowledge gap therefore exists regarding how the spatiotemporal succession of microbial communities and their division of metabolic labor collectively orchestrate VA biosynthesis.

We hypothesize that VA biosynthesis is driven by a highly coordinated, self-assembled microbial consortium that exhibits defined temporal succession and spatial division of labor, effectively forming a metabolic “assembly line” distributed across the stratified pit environment. To test this, this study was designed to decode the spatiotemporal dynamics of the microbial metabolic network underlying VA biosynthesis. We established a comprehensive framework integrating stratified sampling (across upper, middle, and lower pit layers), temporal monitoring (across four key fermentation stages from 0 to 85 days), metagenomic sequencing, and physicochemical analysis.

Our final objective was to integrate these findings into a coherent model that explains how orchestrated microbial cooperation, rather than environmental selection alone, drives efficient VA production. This work not only deepens the mechanistic understanding of flavor formation in SFB but also establishes a novel ecological perspective for studying complex solid-state fermentation systems. The insights gained provide a transformative blueprint for precision flavor engineering, suggesting that future optimization strategies should target the design and manipulation of microbial consortia and their successional patterns, rather than merely adjusting bulk environmental parameters.

2. Methods

2.1. Material collection

Zaopei samples were collected from fermentation pits at a renowned SFB distillery located in Yibin, Sichuan Province, China. Stratified sampling was performed at four fermentation stages (Day 0, 12, 45, 85) across three vertical pit layers: upper layer (UL, 3/4 h), middle layer (ML, 1/2 h), and lower layer (LL, 1/4 h), where h represents the total depth of the fermentation pit (Fig. 1A). For each time-layer combination, five subsamples per layer were collected using a quincunx sampling protocol and homogenized into one composite sample (Tong et al., 2023b). Three biological replicates were independently prepared for each layer, resulting in 3 replicates × 4 timepoints × 3 layers = 36 samples. Samples were named following the “D [day]-[layer]" convention (e.g., D12-UL for Day 12 upper layer). Immediately after collection, samples were stored in sterile bags, transported on dry ice, and preserved at −80 °C for subsequent omics analyses. To minimize cross-contamination between layers, separate sterile sampling tools and containers were used for each layer, and the outer 2–3 cm of material at layer boundaries was discarded before collecting representative samples. Samples were taken sequentially from the upper to the lower layer to avoid mixing during collection.

Fig. 1.

Fig. 1

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Dynamic changes in physicochemical properties during strong-flavor baijiu fermentation. (A) Schematic of stratified sampling in fermentation pits (UL: upper layer, 140–160 cm (3/4 h); ML: middle layer, 80–100 cm (1/2 h); LL: lower layer, 20–40 cm (1/4 h), where h represents the total depth of the fermentation pit (90 cm) in this study). Spatiotemporal variations in (B) acidity, (C) vanillic acid content, (D) starch content, (E) reducing sugar content, (F) alcohol concentration, (G) moisture content, and (H) temperature across fermentation stages (D0, D12, D45, D85) and pit layers.

2.2. Physicochemical analysis

Real-time temperature monitoring was implemented using embedded thermal sensors. Moisture content, acidity, and ethanol concentration were determined according to Chinese National Standard DB34/T 2264-2014 (Tan et al., 2019). Starch and reducing sugar levels were quantified using the 3,5-dinitrosalicylic acid (DNS) method with Feline reagent system. All measurements were performed in triplicate (Wang et al., 2024).

2.3. Vanillic acid quantification

After sample crushing, 5 g of the sample was weighed into a 50 mL centrifuge tube, followed by the addition of 10 mL sterile water. The mixture was soaked for 30 min, vortexed for 10 min, and centrifuged at 12,000 r/min for 15 min. The supernatant was then filtered through a 0.22 μm microporous membrane into a sample vial. VA content was determined by HPLC according to a previously reported method (Tong et al., 2023a).

The HPLC system was operated with a mobile phase consisting of 0.1% formic acid (A) and acetonitrile (B) at a ratio of 70:30 (v/v), a flow rate of 1.0 mL/min, a column temperature of 25 °C, and a detection wavelength of 310 nm. The injection volume was 10 μL, and the run time was 8 min. The retention time of VA was approximately 5.6 min. The standard compound was dissolved in methanol for calibration.

2.4. Metagenomic DNA extraction

Genomic DNA samples were extracted using the NEXTFLEX® Rapid DNA-SEQ kit according to the manufacturer's instructions. The quality of extracted genomic DNA was detected by 1% agarose gel electrophoresis, and DNA concentration and purity were determined. Splicing, magnetic bead screening to remove splicing fragments, PCR amplification to enrich library templates, and magnetic bead recovery to recover PCR products are all steps to produce the final library (Zhang et al., 2022). Metagenomic sequencing was performed using Illumina NovaSeg sequencing platform (Shanghai Meiji Biomedical Technology Co., LTD., China).

2.5. Bioinformatics processing

Raw sequencing data were trimmed and filtered using Fastp (v0.23.0) with default quality control parameters. High-quality reads were assembled using MEGAHIT (v1.1.2) across different sequencing depths, with contigs shorter than 300 bp removed from the final assembly. Open reading frames (ORFs) were predicted from assembled contigs using Prodigal (v2.6.3) in metagenomic mode. Genes with nucleotide sequences ≥100 bp were selected and translated into amino acid sequences. Gene clustering was performed using CD-HIT (v4.7) with 95% sequence identity and 90% coverage thresholds to compile a non-redundant gene catalog from each cluster. Gene abundance was assessed by aligning quality-filtered reads to the non-redundant gene catalog using SOAPaligner with 95% sequence identity. Gene abundance in each sample was calculated and normalized using Reads Per Kilobase per Million mapped reads (RPKM). The non-redundant gene set was aligned against the NCBI NR database using DIAMOND (v0.9.10.111) with BLASTP algorithm (e-value ≤1e−5). Taxonomic annotations were obtained through the corresponding taxonomic information database, and species abundance was calculated using corresponding gene abundance data. Abundance profiles were constructed for each taxonomic level (Domain, Kingdom, Phylum, Class, Order, Family, Genus, and Species). Differentially abundant genes between groups were identified using Linear discriminant analysis Effect Size (LEfSe) with log LDA score >2 and p < 0.05, followed by correlation analysis between differential genes and NR annotations. Species-environment and species-species correlations (|Spearman r| >0.6, p < 0.05) were presented as networks using Gephi (v0.10). The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA027621). and are publicly accessible at https://ingdc.cncb.ac.cn/gsa.

2.6. Statistical analysis

Physicochemical parameters were analyzed using Origin software, with data presented as mean ± standard deviation. Microbial community profiles and correlations between physicochemical parameters were evaluated via Origin and R language, including statistical analyses and visualization. Three biological replicates were performed for each sample to ensure reproducibility.

3. Results

3.1. Stratified physicochemical dynamics underpin metabolic zoning

Spatiotemporal physicochemical heterogeneity during Zaopei fermentation delineated distinct functional niches aligned with microbial metabolic division of labor. Starch content decreased markedly during early fermentation (0-45 days) before stabilizing, reflecting initial enzymatic hydrolysis (Fig. 1D) (Wang et al., 2021b). Although starch degradation showed minimal vertical variation, reducing sugar dynamics revealed clear spatial specialization: the UL accumulated significantly higher reducing sugars (peak 2.7% at D12) than ML and LL (p < 0.05), establishing the UL as a primary precursor generation zone (Fig. 1E).

Alcohol production peaked at D45, with UL concentrations consistently exceeding deeper layers, while moisture content increased linearly from 55% to 63% (Fig. 1F and G), supporting UL and ML roles in glycolysis and fermentation (Zhang et al., 2021). A pronounced vertical acidity gradient (UL: 3.0-3.2, ML: 3.4-3.6, LL: 3.7-3.9) distinguished the microniches, with LL acidity 22.6% higher than UL (p < 0.01) (Fig. 1B). This self-generated acidic environment, combined with higher LL temperatures (inter-layer difference ≤7 °C) (Fig. 1H), created a selective niche optimized for acid-tolerant specialists.

Collectively, these parameters outline a compartmentalized fermentation system: the UL functions as a hydrolytic platform, the ML as a metabolic transition zone, and the LL as an acidic biosynthesis chamber conducive to VA stabilization.

3.2. Vanillic acid dynamics reveal sequential microbial biosynthesis

The spatiotemporal heterogeneity of VA concentration throughout fermentation provides direct evidence for a sequential, microbially-driven biosynthesis process (Fig. 1C). The distinct dynamic profiles across the three vertical layers are consistent with a model of metabolic division of labor, rather than a uniform response to bulk environmental conditions.

Initial VA levels at D0 already exhibited a vertical gradient (LL: 0.334 mg/L > UL: 0.308 mg/L), likely reflecting residual substrates from previous batches. The most significant dynamics unfolded in the LL, where VA concentration increased to its fermentation peak of 0.375 mg/L at D45, before declining to 0.305 mg/L by D85. This peak coincided precisely with the metabolic dominance of Acetilactobacillus and the heightened expression of key synthesis enzymes in the LL, as detailed in subsequent sections, identifying this stratum as the primary finishing workshop for VA assembly.

In contrast, the ML displayed a divergent pattern, with VA peaking earlier at D12 (0.347 mg/L) before declining to the lowest final value (0.268 mg/L). This profile suggests the ML acts as a “transformation hub,” where early intermediate conversion is active, but the final stabilization of VA is less efficient compared to the LL. This stratigraphic difference may result from the stratified distribution of microbial communities, substrate accessibility differences, and vertical gradients of the REDOX environment (Qian et al., 2021). The UL maintained relative stability, with concentrations fluctuating within a narrow range, indicative of its role in precursor generation rather than definitive VA synthesis or accumulation.

The convergence of VA concentrations across all three layers by the end of fermentation (D85) indicates the system reached a metabolic steady state. This convergence likely results from the microbial community achieving a functional equilibrium, potentially through the transformation of VA to other aromatic compounds, marking the maturity of the fermentation process. The data collectively demonstrate that VA accumulation is not a uniform process but is spatially and temporally partitioned, underpinned by a highly organized microbial assembly line, and the LL is identified as the primary site for VA biosynthesis and stabilization. An alternative hypothesis that VA is synthesized in the upper/middle layers and passively diffuses to accumulate in the acidic LL is inconsistent with our results. First, the divergent VA dynamic patterns across layers (transient ML peak vs. progressive LL increase) contradict diffusion-driven concentration equilibration, and the high-viscosity, low-water-activity zaopei matrix severely restricts free molecular diffusion (evidenced by persistent vertical physicochemical gradients in Section 3.1). Furthermore, the subsequent enzyme and microbial functional analyses (Section 3.5) confirm that key VA biosynthetic enzymes peak specifically in the LL at D45 with selective downregulation of VA-degrading enzymes, directly supporting active in situ synthesis in the LL rather than passive accumulation.

3.3. Microbial diversity dynamics during fermentation

3.3.1. α and β diversity reveal distinct spatiotemporal succession trajectories

Microbial diversity analysis, based on metagenomic annotations, revealed pronounced spatiotemporal succession patterns that underpin the proposed metabolic division of labor. The dynamics of bacterial and fungal communities, while interconnected, followed distinct trajectories, reflecting their specialized functional roles during fermentation (Wang et al., 2021a).

Bacterial richness (Chao1 index) was highest in the early to mid-stages before declining significantly by D85 (Fig. 2A). The UL consistently supported greater bacterial richness, likely due to more favorable microaerobic conditions for diverse taxa (Tian et al., 2024). In contrast, the bacterial Shannon diversity (Fig. 2B) exhibited a unimodal trend, decreasing most sharply in the LL to a minimum at D85. This decline indicates a process of community specialization, where the high-acidity, high-temperature LL microenvironment selected for a narrower, functionally specific consortium, exemplified by the late-stage dominance of Acetilactobacillus.

Fig. 2.

Fig. 2

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(A) Changes in bacterial Chao1 index, (B) changes in bacterial Shannon index, (C) changes in fungal Chao1 index, (D) Changes in fungal Shannon index, (E) Principal component analysis of bacterial, (F) Principal component analysis of fungi, (G) Bacterial Venn Diagram, (H) Fungal Venn diagram. Note: The abscissa (PC1) and ordinate (PC2) coordinates are used to explain the differences between samples. The same colors are the same group, a point is a sample, and similar samples will cluster together.

Fungal diversity dynamics differed from bacteria, with richness (Chao1, Fig. 2C) and Shannon index (Fig. 2D) increasing significantly during the mid-fermentation stages. This pattern highlights the critical role of fungi in transitional metabolic phases. Notably, late-stage fungal diversity in the LL was significantly lower than in the UL and ML, mirroring the bacterial trend and confirming the deep stratum as a niche for specialized, acid-tolerant microbiota from both kingdoms (Liang et al., 2020).

β-diversity analysis further illuminated these spatiotemporal patterns. Principal coordinate analysis (PCoA) of bacterial communities (Fig. 2E) showed clear separation along the time-associated PC1 axis (51.80% variance), with D0 and D85 forming distinct clusters. The greater dispersion of early-stage samples along PC2 (19.45% variance) underscored initial spatial heterogeneity, which diminished as communities stabilized. Fungal PCoA (Fig. 2F) revealed a similar temporal progression along PC1 (62.65% variance), but with a more pronounced layer-separation in late-stage samples along PC2, emphasizing the stronger influence of spatial niche partitioning on fungal community assembly (Hu et al., 2021).

Venn diagram analysis (Fig. 2G and H) identified a stable core microbiome conserved throughout fermentation (671 bacterial and 3328 fungal species), which likely maintains essential metabolic functions (Jiang et al., 2022). Meanwhile, the enrichment of unique species in specific layers and stages—particularly in the late-stage LL—provides the microbial foundation for the spatial functional zoning observed, with these unique taxa potentially responsible for compartmentalized processes like VA biosynthesis.

Collectively, the diversity dynamics confirm that fermentation progresses through a structured reorganization of the microbial community, where temporal succession and spatial stratification work in concert to establish the functional assembly line for metabolite production.

3.3.2. Microbial community composition exhibits clear spatiotemporal functional stratification

The compositional dynamics of the microbial community provide definitive evidence for the proposed spatiotemporal division of labor. A total of 4581 bacterial and 1341 fungal genera were identified, with the successional patterns of dominant taxa mapping directly onto the metabolic assembly line.

The bacterial community was dominated by Acetilactobacillus, Acetobacter, Weissella, and Clostridium (Fig. 3A). The successional trajectory was striking: Acetilactobacillus progressively increased in relative abundance, culminating in a dominance of >80% in late-stage LL samples, a pattern that was consistently reproduced across all three biological replicates. This is consistent with the report of Acetilactobacillus as a major functional genus in Chinese baijiu (Chen et al., 2025b). This establishes it as the keystone genus of the maturation phase, specializing in the final metabolic steps within the acidic, high-temperature LL niche. The concomitant decline in Shannon diversity in the late-stage LL (Fig. 2B) reflects competitive exclusion under strong environmental filtering rather than a loss of functional capacity—a streamlined, specialist consortium optimized for terminal VA synthesis. At the species level (Fig. 3B), Acetilactobacillus jinshanensis mirrored this trend. Acetobacterium species are known to synthesize acetic acid,a key intermediate in aromatic compound metabolism, possibly contributing to VA biosynthesis through specific metabolic pathways (Qiu et al., 2025). Its absolute dominance in the LL at D45 coincided precisely with the peak in VA concentration, strongly implicating it in the biosynthesis or stabilization of VA. The notable spatial and temporal overlap of Pseudomonas (a known VA producer via lignin degradation) with the VA peak further suggests synergistic interactions within the LL consortium (Li et al., 2024).

Fig. 3.

Fig. 3

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Relative abundance of bacterial communities at the genus (A) and species (B) levels, Relative abundance of fungal communities at the genus (C) and species (D) levels, LEfSe results for bacterial (E) and fungal (F) communities (LDA >4, p < 0.05).

The fungal community exhibited complementary dynamics, dominated by Paecilomyces, Aspergillus, Pichia, and Penicillium (Fig. 3C). Paecilomyces, prevalent in the UL and ML, aligns with its recognized role in hydrolyzing starch and proteins, thereby acting as a primary deconstruction specialist in the upper strata (Li et al., 2023). Aspergillus, reported to produce VA precursors in pure culture (Tang and Hassan, 2020), was enriched in the UL during the early stage (D12), positioning it as an initiator of the phenylpropane flux. Its early enrichment—alongside filamentous fungal like Rasamsonia—linked starch hydrolysis (supplying bacterial sugars) to potential VA precursor synthesis, aligning with its role in early saccharification (Liu et al., 2023). At the species level (Fig. 3D), the abundance of Saccharomyces cerevisiae and Pichia kudriavzevii correlated with ethanol and reducing sugar dynamics, confirming their central role in the alcoholic fermentation that characterizes the mid-fermentation phases.

In summary, the microbial composition demonstrates a highly organized spatiotemporal structure. The early-stage, upper-layer enrichment of hydrolytic fungi (Aspergillus, Paecilomyces) and the late-stage, lower-layer dominance of acidophilic bacteria (Acetilactobacillus) form the foundational pillars of the metabolic assembly line, effectively partitioning the labor of precursor generation and final product synthesis across both time and space.

3.3.3. Functional succession and niche specialization of microbial consortia

LEfSe analysis (LDA >4) further delineated the successional dynamics and functional specialization of microbial consortia across fermentation stages and pit strata (Fig. 3E and F). The microbial community transitioned from heterogeneous, generalist assemblages in the early phase to specialized functional groups in the mid-phase, ultimately forming a stable, cooperative network in the late phase (Zheng et al., 2021).

Temporally, the early-stage community was characterized by taxa such as Enterobacteriaceae and Aspergillus, which functioned as the primary deconstruction consortium, initiating the breakdown of polysaccharides and proteins from raw materials. As fermentation progressed, the mid-stage witnessed the rise of Bacteroidota, Chloroflexi, and Saccharomycetes, forming a transitional transformation consortium crucial for generating organic acids and ester precursors. The late stage was defined by the dominance of Lactobacillaceae, Acetobacteraceae, and Saccharomyces, which constituted the synthesis and maturation consortium responsible for the final assembly and stabilization of characteristic flavor compounds, including VA.

Spatially, each pit layer maintained a distinct microbial identity, demonstrating clear vertical niche partitioning. The LL harbored unique taxa adapted to its anaerobic and acidic microenvironment from the early stage, while the middle and upper layers supported communities thriving under microaerophilic conditions during the mid-phase. By the late stage, these stratified communities established a tightly integrated vertical ecosystem, facilitating cross-layer metabolic exchanges. This structured succession and spatial specialization underscore how microenvironmental heterogeneity drives the assembly of a highly efficient, functionally partitioned microbial network for VA biosynthesis. These results indicate that cross-kingdom cooperation between Aspergillus and Acetilactobacillus is mainly achieved through metabolite cross-feeding and niche modification, which jointly promote the biosynthesis of vanillic acid. Direct experimental validation (e.g., co-culture, isotope tracing) of these cooperative mechanisms will be a key direction for subsequent research to further clarify the metabolic interactions between these core taxa.

3.4. Environmental drivers of microbial assembly and their relationship to VA production

3.4.1. Microbial community succession is coupled with physicochemical dynamics

Redundancy analysis (RDA) elucidated the coupling between microbial community succession and the evolving physicochemical milieu, revealing a temporal progression from substrate-driven to product-influenced assembly. For bacterial communities (Fig. 4A), early-stage (D0) samples clustered with high starch and reducing sugar availability, indicative of a substrate-dependent phase. A progressive shift positioned mid and late-stage samples towards factors like moisture, temperature, ethanol, and ultimately acidity, marking a transition to a phase where microbial metabolism itself reshapes the environment. Fungal communities (Fig. 4B) exhibited a similar temporal trajectory, though with a stronger association of late-stage fungi with the combined effects of ethanol, moisture, and acidity. The closer linkage of bacterial community structure with VA content suggests a more direct role in its biosynthesis, while fungi may operate indirectly by modulating precursors and microenvironments.

Fig. 4.

Fig. 4

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Redundancy analysis (RDA) of bacterial (A) and fungal (B) communities against physicochemical parameters, (C) Mantel test network heatmap depicting correlations between pit layers, microbial communities, and physicochemical factors, Spearman correlation heatmaps between bacteria (D) and fungi (E) and environmental factors at the species level. Note: Positive/negative correlations are indicated by red/blue colors, with intensity reflecting correlation strength (Spearman r), asterisks denote statistical significance (∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001).

The Mantel test network (Fig. 4C) further resolved these relationships into a compartmentalized functional model. The UL microbiota showed strong ties to moisture, starch, and VA, consistent with its role as a precursor generation zone. The ML community correlated broadly with multiple factors (moisture, acidity, starch, reducing sugar, ethanol), reflecting its identity as a transitional metabolic interface. Most notably, the LL microbiota exhibited the strongest and most specific correlation with temperature and VA, solidifying its function as the specialized VA synthesis chamber. This layered model, where VA precursors are generated upstream and final synthesis occurs downstream, is facilitated by cross-layer material exchange. The observed physicochemical interrelationships, such as the negative correlation between moisture and acidity, are thus emergent properties of the spatially organized microbial metabolism, collectively creating the niches that sustain the functional consortium.

3.4.2. Functional partitioning of dominant microorganisms revealed by correlation with physicochemical factors

Spearman correlation analysis delineated the functional roles of dominant microorganisms by linking their abundances with key physicochemical parameters, including VA (Fig. 4D and E). The results revealed a clear temporal and functional specialization between bacterial and fungal consortia.

Bacterial correlations underscored their dominance in late-stage, acidophilic niches. Acetilactobacillus exhibited significant positive correlations with moisture and acidity, and strong negative correlations with starch and reducing sugars, confirming its adaptation to and dominance in the late-fermentation environment (Du et al., 2020). Its weak positive correlation with VA suggests a potential indirect role in its metabolic network. In contrast, Treponema and unclassified_f_Oscillospiraceae showed significant positive correlations with VA, highlighting their specific, though previously overlooked, involvement in VA accumulation. The opposite correlation patterns of early-active genera like Hydrogenophaga compared to Acetilactobacillus further illustrate the temporal shift in bacterial functional dominance.

Fungal correlations highlighted their primary role in early and mid-fermentation processes. Filamentous fungi, including Aspergillus, showed significant negative correlations with moisture and acidity but positive associations with starch and ethanol. This pattern is consistent with their function as early-stage deconstructors, specializing in hydrolyzing macromolecular substrates under less acidic conditions. Pichia's positive correlation with reducing sugars and ethanol aligns with its key role in sugar-to-ethanol conversion. The absence of strong positive correlations between any fungal genus and VA content reinforces the hypothesis that bacteria are the primary executors of VA biosynthesis, though weak associations in Rhizopus and Saccharomyces suggest potential, indirect contributions to precursor supply.

Collectively, these correlation patterns paint a picture of a highly partitioned system: fungi act as the primary initiators of fermentation in the early and middle stages, breaking down complex substrates, while bacteria, particularly acid-tolerant specialists, dominate the later stages, directly or indirectly steering the final steps of flavor compound formation, including VA biosynthesis.

3.5. Metabolic pathway reconstitution and key enzymes in VA synthesis

3.5.1. Reconstruction of VA biosynthetic pathways highlights multi-source precursor supply

The microbial metabolic network for VA biosynthesis was systematically reconstructed, revealing interconnected pathways that supply precursors from both phenylpropanoid metabolism and lignin degradation (Fig. 5A). This redundancy in precursor supply underscores the metabolic robustness of the microbial consortium. Subsequently, the genes involved in VA metabolism (ko00627) were annotated and functional enzymes were predicted (see Supplementary Material).

Fig. 5.

Fig. 5

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(A) Schematic of vanillic acid synthesis via phenylpropane and lignin degradation pathways, including key enzymes, (B) Spatiotemporal dynamics of vanillic acid-related enzyme abundances across pit layers and fermentation stages, Spearman correlation heatmaps between dominant bacteria (C)/fungi (D) and vanillic acid metabolic enzymes.

The core phenylpropanoid pathway initiates from phenylalanine, which is deaminated to trans-cinnamic acid by phenylalanine ammonia-lyase [EC: 4.3.1.24]. Sequential hydroxylation by cinnamate 4-hydroxylase [EC: 1.14.14.91] and coumarate 3-hydroxylase [EC: 1.14.13.21] yields caffeic acid, which is subsequently methylated to ferulic acid by caffeic acid O-methyltransferase [EC: 2.1.1.68]. Ferulic acid is then funneled through parallel routes to vanillin, the immediate precursor to VA. The oxidation of vanillin to VA is primarily catalyzed by vanillin dehydrogenase [EC: 1.2.1.67]. Vanillin oxidase [EC: 1.1.3.38] also participates in vanillin metabolism, facilitating subsequent reactions. An alternative entry point is provided by tyrosine ammonia-lyase [EC: 4.3.1.25], which directly converts tyrosine to p-coumaric acid, enhancing metabolic flexibility under nitrogen-rich conditions (Huo et al., 2020).

Concurrently, in lignin-rich raw materials, the lignin degradation pathway operates as a critical external precursor source. Laccase [EC: 1.10.3.2] and peroxidase [EC: 1.11.1.7] initiate lignin depolymerization, releasing compounds like eugenol, which are enzymatically converted to coniferyl alcohol and subsequently to ferulic acid, thereby feeding into the central phenylpropanoid pathway (Chandrawanshi and Jayapal, 2024).

Finally, a degradation pathway regulates VA accumulation. Following VA formation, vanillate O-demethylase [EC: 1.14.13.82] catalyzes the conversion of VA to protocatechuic acid, which enters the β-ketoadipic pathway. Here, β-ketoadipate CoA-transferase [EC: 2.8.3.6] and β-ketoadipyl-CoA thiolase [EC: 2.3.1.174] degrade protocatechuic acid into acetyl-CoA and succinyl-CoA, which subsequently enter the TCA cycle for energy metabolism (Sgro et al., 2023). The balance between these biosynthetic and degradative routes is crucial for determining the final VA concentration in the Zaopei.

3.5.2. Enzyme dynamics reveal a spatiotemporally segregated metabolic assembly line

The analysis of key enzyme abundances revealed a sophisticated spatiotemporal organization of the VA biosynthetic pathway, characterized by a clear temporal handover and spatial compartmentalization of metabolic tasks (Fig. 5B).

Temporally, VA-related enzymes segregated into two distinct functional groups. The first group, comprising enzymes from foundational pathways like glycolysis and the shikimic acid pathway (e.g., 3-phosphate dehydrogenase [EC: 1.2.1.12]), saw their abundance significantly increase in the late fermentation stage. This indicates that the late-stage microbial community actively provisions the necessary metabolic precursors for VA synthesis. In contrast, the second group—enzymes directly catalyzing VA biosynthesis, such as phenylalanine ammonia-lyase [EC: 4.3.1.24]—were most abundant during the early stage and gradually declined thereafter. This pattern demonstrates that the generation of VA precursors is an early-phase priority, establishing a clear temporal succession from precursor formation to final assembly.

Spatially, the enzymatic machinery was precisely partitioned across the pit strata. In the early stage, the upper and middle layers exhibited high abundances of enzymes like cinnamic acid 4-hydroxylase [EC: 1.14.14.91], confirming their role as the precursor synthesis zone. As fermentation progressed, the LL became the focal point for the final steps, showing elevated abundances of both foundational metabolic enzymes [EC: 1.2.1.12] and key catalytic enzymes like vanillin dehydrogenase [EC: 1.2.1.67], establishing the LL as the dedicated finishing workshop.

Notably, enzymes such as feruloyl-CoA synthetase [EC: 6.2.1.34] maintained stable abundances across time and space, suggesting that the conversion of ferulic acid to vanillin may be a critical, tightly regulated bottleneck in the pathway. Conversely, the significant decrease in vanillate O-demethylase [EC: 1.14.13.82] during mid-to-late fermentation effectively reduced VA degradation, representing a key microbial strategy to promote the net accumulation of VA in the final product. According to the functional gene annotation (Table S1 and S2), this degradation activity in the early phase was primarily attributable to Pseudomonas, Hydrogenophaga, Acinetobacter, and Klebsiella—Proteobacteria that thrive under the relatively neutral, substrate-rich conditions of early fermentation but are progressively outcompeted as acidity rises and the LL environment becomes increasingly selective. Their decline thus coincides with, and contributes to, the net accumulation of VA in the maturation phase.

3.5.3. Metabolic division of labor in VA biosynthesis uncovered by microbial functional contributions

Integrative analysis of metagenomic functional gene abundance and microbial taxonomy elucidated a highly organized metabolic division of labor for VA biosynthesis, with distinct microbial taxa catalyzing specific steps in a spatiotemporally coordinated manner (Fig. 5C and D) (see Supplementary Material).

Acetilactobacillus was identified as the core contributor to the final synthesis steps, providing the highest contribution ratios for two pivotal enzymes: caffeic acid O-methyltransferase (31.5%) and vanillin dehydrogenase (27.3%). This activity was predominantly localized in the LL at D45. Its correlation profile—strong positive associations with downstream enzymes and negative correlations with early-pathway enzymes—confirms its specialized role as the terminal VA assembler within the acidic, mature-phase niche.

The early stages were dominated by Aspergillus, which contributed 22.6% to phenylalanine ammonia-lyase and showed a significant positive correlation with VA during early-mid fermentation (r = 0.63, p < 0.05). Its enrichment in the UL and strong association with lignin-degrading enzymes position it as a key precursor generator, initiating the metabolic flux.

Pichia emerged as a crucial collaborator in the ML, contributing 23.1% to vanillin dehydrogenase. Its positive correlation with this enzyme, coupled with a negative correlation with the degrading enzyme vanillate O-demethylase, suggests a dual strategy of promoting VA synthesis while inhibiting its turnover.

Further specialization was evident: Klebsiella significantly aided the vanillin-to-VA conversion, while Pseudomonas exhibited a high correlation with VA (r = 0.68, p < 0.01) through substantial phenylalanine ammonia-lyase activity, linking its spatial peak in the LL at D45 to metabolic flexibility.

This consortium operates via a defined metabolic assembly line: the early phenylpropanoid pathway is initiated by bacteria like Acetobacter and fungi like Aspergillus; intermediate steps are mediated by fungi like Lichtheimia and bacteria like Bacillus; the final vanillin-to-VA conversion is driven by Klebsiella and Pichia; and further VA metabolism is governed by Pseudomonas and Clostridium. The peak enzyme abundances in the LL at D45, coinciding with maximal VA concentration and Acetilactobacillus dominance, form a coherent “microorganism-enzyme-metabolite” chain, demonstrating that VA homeostasis is achieved through a spatiotemporally partitioned, cross-kingdom cooperative network.

3.6. Network analysis revealed the core microbiota responsible for VA production

3.6.1. Microbial Co-occurrence networks reveal a spatially stratified functional consortium

To dissect the microbial mechanisms underlying VA biosynthesis in SFB fermentation, Spearman correlation analysis was performed on the top 50 bacterial and fungal genera by relative abundance, selecting correlations with |R| > 0.6 and p < 0.05 for network construction (Fig. 6A) (Gao et al., 2022). The network topology revealed a predominance of synergistic interactions, underscoring the collaborative nature of VA biosynthesis.

Fig. 6.

Fig. 6

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(A) Correlation networks between top 50 microorganisms and vanillic acid content, Different nodes represent different genera, node size represents the degree of connectivity of the genus, the same color represents the level of the same phylum, the thickness of the line represents the size of correlation, the red line indicates positive correlation, and the blue line indicates negative correlation. (B) Microbial symbiotic networks in UL, ML, and LL, Node colors represent functional modules; edge thickness indicates correlation strength (Spearman r > 0.6, p < 0.05).

Multiple dominant microorganisms formed strong positive correlations with VA, indicating their roles as functional contributors. Key bacterial taxa, including Lactiplantibacillus, Pseudoxanthomonas, and several unclassified Bacteroidetes, were tightly linked to VA accumulation. Among fungi, Rhizopus, Aspergillus, and Paecilomyces showed significant positive associations, aligning with their hypothesized functions in precursor release through lignin and macromolecule degradation.

Spatial analysis identified the LL as the core functional hub, exhibiting the highest density and strength of microbial-VA correlations. This pattern solidifies the LL's role as the primary synthesis hotspot within the metabolic assembly line. Concurrently, a subset of microorganisms, including specific Hydrogenophaga and Flavobacterium species, displayed significant negative correlations with VA. These negative correlations may reflect distinct biological scenarios: some taxa, such as Hydrogenophaga, likely act as direct VA degraders via the β-ketoadipate pathway; others may compete for shared phenylpropanoid precursors, indirectly reducing VA yield; while certain taxa may simply reflect temporal niche anti-correlation, being abundant under early-stage conditions that are unfavorable for VA accumulation. It should be noted that co-occurrence analysis alone cannot distinguish among these mechanisms, and targeted functional experiments would be required for definitive interpretation.

The resulting model depicts a spatially organized functional consortium: the UL primarily facilitates anaerobic fermentation and initial substrate transformation, the ML acts as a critical interface for intermediate synthesis, while the LL is specialized for the final biosynthesis and stable accumulation of VA. This spatially stratified co-occurrence pattern not only elucidates the microbial interactions underpinning VA synthesis but also identifies potential targets for its targeted regulation.

3.6.2. Stratified symbiotic networks underpin vertical functional zoning in fermentation pits

Analysis of microbial co-occurrence networks across vertical pit layers revealed distinct symbiotic structures that directly reflect the spatial organization of metabolic functions (Fig. 6B). Each layer exhibited unique network architectures corresponding to its specific role in the fermentation process.

The UL network displayed the highest complexity, with nodes distributed among three functional modules (purple 62%, orange 23%, green 15%). This multi-modular structure, characterized by high average degree and clustering coefficient, indicates extensive metabolic cooperation among diverse taxa, consistent with the UL's role as a versatile decomposition center for primary substrate breakdown.

In contrast, the ML network exhibited a near-equal bipartite structure (purple 54%, orange 46%), suggesting the coexistence of two major functional groups with complementary metabolic roles. This dichotomous architecture positions the ML as a critical metabolic interface where transitional processes and material exchange between upper and lower layers are facilitated.

The LL network demonstrated the highest integration, with the two major functional modules showing extensive interconnections. Despite similar node distribution to the ML, the LL network's enhanced connectivity and robustness to random node removal indicate a specialized, stable consortium optimized for late-stage metabolite synthesis. This architecture supports the LL's function as the primary synthesis and stabilization zone for VA and other aromatic compounds.

These network topological features substantiate the concept of vertical functional zoning in SFB fermentation: the UL serves as a diverse decomposition platform, the ML as a transitional exchange region, and the LL as an integrated synthesis chamber. The increasing network integration from upper to lower strata demonstrates how spatial organization of microbial interactions directly enables metabolic specialization and community stability in solid-state fermentation systems.

4. Conclusion

This study demonstrates that VA biosynthesis in SFB fermentation is orchestrated through a highly coordinated microbial metabolic division of labor, rather than being solely driven by physicochemical gradients. Through integrated spatiotemporal analysis, we revealed a defined three-phase microbial succession model: filamentous fungi (Aspergillus, Paecilomyces) initiate precursor synthesis in early stages; bacterial-fungal consortia (Pichia, Klebsiella) mediate intermediate conversion during mid-fermentation; and acidophilic Acetilactobacillus dominates the final synthesis and stabilization phase in LL.

The vertical stratification of the pit environment supports this functional specialization, forming a natural metabolic assembly line where different layers perform distinct roles: the UL specializes in substrate decomposition, the ML facilitates transitional metabolism, and the LL functions as precision synthesis chambers. Crucially, VA accumulation is regulated not only through enhanced biosynthesis but also via strategic suppression of its degradation, as evidenced by the downregulation of vanillate O-demethylase.

These findings establish microbial self-organization and functional compartmentalization as the fundamental principles governing flavor compound biosynthesis, shifting the paradigm from environmental manipulation to microbial community engineering for precision flavor optimization in traditional solid-state fermentation systems. While the specific microbial taxa in this three-phase succession model are unique to strong-flavor baijiu fermentation, its core ecological framework (spatiotemporal functional partitioning of microbial consortia) represents a generalizable principle for stratified solid-state fermentation ecosystems. Future comparative studies across different baijiu flavor types and fermented foods will further validate the universality of this ecological model.

CRediT authorship contribution statement

Wenhua Tong: Methodology, Investigation, Writing-original draft, Project administration, Formal analysis. Lei Qiao: Data curation, Software, Visualization, Formal analysis, Validation. Ying Yang: Investigation, Writing-review & editing. Xia Li: Formal analysis. Yang Zhang: Formal analysis. Zhijiu Huang: Validation. Huibo Luo: Resource, Conceptualization. Liming Zhao: Formal analysis. Suyi Zhang: Resources, Supervision.

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

The Graduate Innovation Fund of Sichuan University of Science and Engineering (NO.Y2024200).

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.crfs.2026.101394.

Contributor Information

Wenhua Tong, Email: tongwh@suse.edu.cn.

Ying Yang, Email: yangying@suse.edu.cn.

Suyi Zhang, Email: xiuluokemo123@163.com.

Appendix A. Supplementary data

The following is/are the supplementary data to this article:

Multimedia component 1
mmc1.docx (39.3KB, docx)

Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

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Associated Data

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Supplementary Materials

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Data Availability Statement

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

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