At the heart of the framework is a simple but consequential idea: enzymes do not merely catalyze reactions in isolation. Microbial-derived enzymes degrade complex macromolecules such as starch, plant cell wall polysaccharides, and proteins, generating fermentable sugars, peptides, amino acids, and other intermediates that support microbial growth and metabolite formation. In doing so, they change which nutrients are available, when they become available, and which microorganisms can access them. Exogenous enzyme supplementation works differently but complementarily, providing targeted modification of food matrices before or during fermentation. The review emphasizes that the functional consequences of both enzyme sources depend not only on catalytic efficiency but also on whether the released substrates can actually be used by the microbial community and funneled into downstream metabolic pathways.

The review draws on concrete examples to illustrate the point. In soybean meal fermentation with Bacillus subtilis strain SBM_1, enhanced solid-state fermentation was associated with a peptide yield of 220.93 milligrams per gram, a 47.74 percent increase relative to natural fermentation, with protease activity ranging from 103.37 to 241.93 units per gram and trypsin inhibitor activity falling by 76.11 percent. Peptides below 3 kilodaltons accounted for 71.64 percent of the final peptide fraction. The authors are careful, however, to note that such observations establish biochemical and process-level effects and do not by themselves demonstrate that protease activity directly restructured the microbial community. Genomic annotation, measured enzyme activity, and ecological function, they stress, are different levels of evidence and should not be conflated.

Genomic studies of lactic acid bacteria feature prominently in the evidence base. Lactiplantibacillus plantarum strain 84-3 was reported to possess an abundant repertoire of carbohydrate-active enzymes, including GH1, GH13, GT2, and GT4 families, together with enrichment of carbohydrate- and butanoate-related functions, supporting a predicted capacity for complex carbohydrate utilization and flavor-associated metabolism. Leuconostoc citreum SH12 contained 193 carbohydrate-metabolism-related genes, including 59 CAZyme genes, and showed experimentally distinguishable carbon source use, efficiently consuming glucose, sucrose, fructose, and maltose while using xylose, arabinose, mannose, and cellobiose less efficiently. The review treats these as strain-level genomic and functional evidence of potential rather than proof of in situ enzyme activity during complex fermentation.

Exogenous enzymes are presented as more than processing aids. In lactic-acetic acid co-fermentation of jujube puree, cellulase pretreatment combined with Lactiplantibacillus plantarum CGMCC 3346 and Acetobacter pasteurianus affected physicochemical characteristics, phytochemical composition, volatile profiles, antioxidant activity, and total phenolic content, which reached 351.89 milligrams of gallic acid equivalents per 100 grams fresh weight under optimized conditions. The authors acknowledge that the relative contributions of cellulase activity and microbial metabolism to individual product characteristics remain difficult to separate. They argue that exogenous enzyme supplementation should be evaluated not only by the extent of hydrolysis but by how much of the enzyme-generated product actually enters subsequent microbial metabolic pathways, and they caution that excessive hydrolysis may cause metabolic imbalance while insufficient modification may fail to overcome nutrient limitations.

The relationship between enzymes and microbes is described as reciprocal. Enzymes convert complex substrates into accessible compounds, while microbial growth and metabolism continuously alter the conditions under which enzymatic reactions occur, through changes in substrate consumption, pH, redox conditions, oxygen availability, and metabolite accumulation. At the regulatory level, the review describes how carbon catabolite repression mediated by CcpA coordinates preferential use of readily metabolizable carbon sources in many Gram-positive fermentative bacteria, while nitrogen-responsive regulators such as CodY couple intracellular amino acid availability to proteolytic, transport, and biosynthetic functions. Acid stress, oxygen limitation, redox imbalance, and quorum-sensing signals can further modify transcriptional programs associated with enzyme production. The authors warn that these regulatory systems are strongly strain- and environment-dependent, and that direct validation in multispecies food fermentation communities remains limited, so mechanisms established in model organisms should not be automatically extrapolated to complex communities.

On the microbial ecology side, the review covers succession, cooperation, competition, and metabolic cross-feeding. Early colonizers often contribute to initial substrate degradation through extracellular enzyme production, while later populations use the resulting metabolites. In yeast–LAB fermentations, yeasts can release amino acids, peptides, and B-group vitamins that may alleviate nutritional limitations of LAB, while LAB-derived lactate can be oxidized into acetate by acetic acid bacteria in mixed fermentations, representing a sequential carbon-transfer pathway. The review is emphatic that such interactions should not be inferred from abundance patterns or metabolite correlations alone; strong evidence for cross-feeding requires mono-culture versus co-culture comparisons, metabolite supplementation or depletion experiments, strain omission and add-back experiments, or stable-isotope tracing. Competition for nutrients, oxygen, micronutrients, and niches occurs simultaneously, and observed fermentation performance generally reflects the balance between complementary metabolism and competitive resource acquisition rather than cooperation alone.

A substantial portion of the review is devoted to how evidence should be weighed. Synthetic microbial communities, or SynComs, offer a rational strategy for function-oriented microbial assembly and improved reproducibility, but the current evidence remains predominantly laboratory-scale, and transferability to complex food matrices containing diverse substrates, spatial heterogeneity, and indigenous microorganisms remains uncertain. Among analytical approaches, metagenomics reveals genetic potential but not active function; transcriptomics and proteomics provide evidence of expression; and metabolomics reflects biochemical consequences. The presence of enzyme-related genes does not necessarily indicate active enzyme production, and transcript abundance does not necessarily reflect protein abundance or catalytic activity. For volatile compounds, the authors recommend integrating absolute quantification, odor thresholds, odor activity values, and sensory evaluation, because compounds showing high relative abundance are not necessarily the main contributors to perceived aroma.

Artificial intelligence receives a measured treatment. Machine-learning approaches have been applied to predict fermentation characteristics and classify community patterns, and the review cites quantitative examples: in rice-wine fermentation, a synergy interval partial least-squares model yielded prediction-set correlation coefficients of 0.8694, 0.8097, and 0.9039 for total sugar, alcohol content, and pH, with corresponding RMSEP values of 0.438, 0.617, and 0.0805. In solid-state fermentation of rapeseed meal, near-infrared spectroscopy combined with interval partial least squares achieved an R2 of 0.9441 and an RMSECV of 0.654 grams per liter for polypeptide prediction using 81 samples. The authors caution that current applications are constrained by small datasets, potential overfitting, insufficient cross-system validation, and limited standardization, and that models should be interpreted according to whether calibration or prediction statistics are reported. They advocate explainable methods such as SHAP analysis and standardized metadata reporting, including community standards such as MIxS.

Looking forward, the review identifies incomplete mechanistic understanding, limited predictability and reproducibility, difficulty translating laboratory results to industrial production, ecological instability of synthetic communities, heterogeneous multi-omics datasets, limited AI interpretability, insufficient real-time monitoring, and regulatory and economic barriers as the main challenges for industrial translation. It proposes precision fermentation in a broad sense, meaning mechanism-guided control of fermentation ecosystems rather than only engineered microbial production of defined molecules. The conclusion is that fermentation performance cannot be explained solely by microbial composition or enzyme activity; it emerges from coordinated interactions among enzyme-mediated resource release, microbial functional traits, ecological relationships, and environmental adaptation. Closing that gap, the authors argue, will require quantitative integration of enzyme activity, microbial functional traits, metabolic flux, and fermentation performance, together with better validation of synthetic consortia and cross-batch benchmark datasets, moving the field from empirical optimization toward more predictable, controllable, and mechanism-guided fermentation design.

Subject of Research: An integrative review of enzyme–microbial interactions in food fermentation, covering molecular mechanisms, microbial ecology, multi-omics and AI approaches, and industrial applications.

Article Title: Integrating enzymology and microbial ecology in food fermentation: Molecular mechanisms, metabolic interactions, and future perspectives

Article References: Hao, J., Zhang, S., Xiong, F., Wu, N., Dabbour, M., Mintah, B. K., Dai, C., Zhang, R., Zhang, Z., Ma, H., & He, R. (2026). Integrating enzymology and microbial ecology in food fermentation: Molecular mechanisms, metabolic interactions, and future perspectives. Current Research in Food Science, Article 101609. https://doi.org/10.1016/j.crfs.2026.101609

Image Credits: AI Generated

DOI: 10.1016/j.crfs.2026.101609

Keywords: food fermentation, enzyme–microbe interactions, microbial ecology, carbohydrate-active enzymes, proteases, metabolic cross-feeding, synthetic microbial communities, multi-omics, metabolomics, machine learning, solid-state fermentation, precision fermentation