Spatiotemporal Profiling of Starch-Degrading Enzymes in Nong-Flavor Daqu: Molecular Markers for Quantitative Quality Evaluation.

Abstract

Nong-flavor (NF) Daqu, a critical fermentation starter for traditional Baijiu, harbors diverse starch-degrading enzymes with poorly characterized functional dynamics. This study transcended traditional quality assessments by developing molecular approaches to dissect starch-hydrolyzing enzyme genes. Specific and degenerate primers targeting glucoamylase, α-amylase, and α-glucosidase genes were designed, and key genes were qualitatively identified with distinct distributions among NF Daqus and unique presences between JXL and HB Daqu. Quantitative PCR revealed six genes with elevated expression in JXL Daqu versus HB Daqu, and which peaked during late fermentation in both Daqus. Metagenomics identified greater enzymatic diversity in HB Daqu. Phylogenetic clustering confirmed evolutionary conservation (GH13/GH15/GH31 families) and specificity of core enzyme genes across both Daqus. Enzymatic assays demonstrated the dominance of saccharification over α-glucosidase activity in both Daqus, with significantly higher α-glucosidase activity in JXL than HB Daqu. Divergent starch degradation strategies emerged: JXL prioritized high enzyme expression/activity, while HB utilized broader gene abundance. Based on Pearson correlation analysis, the saccharification activity showed the highest but weak correlation with α-glucosidase gene_15963 (r = 0.26), and was also positively correlated with the expression of all other enzyme genes except one glucoamylase gene. Meanwhile, α-glucosidase activity was most strongly linked to glucoamylase gene_22243 (r = 0.76), with additional correlations with two α-glucosidase genes being observed. This establishes RNA-based biomarkers for real-time quality control. Our findings decode divergent microbial strategies (JXL: high-expression/high-activity vs. HB: high-diversity) and provide a molecular framework for optimizing starch utilization in Baijiu fermentation. This technology holds potential to enable precision-driven standardization of traditional food production, which would reduce processing waste and enhance resource efficiency.