代谢组
生物
微生物群
计算生物学
代谢组学
数据科学
进化生物学
生物信息学
计算机科学
作者
Wei Shi,Li‐Juan Chai,Guan-Yu Fang,Jun-Lan Mei,Zhen‐Ming Lu,Xiao‐Juan Zhang,Xiao Chen,Songtao Wang,Caihong Shen,Jin‐Song Shi,Zhenghong Xu
标识
DOI:10.1016/j.foodres.2022.111298
摘要
High-temperature Daqu, usually used as a fermentation starter for sauce-flavor Baijiu production, plays an essential role in the yield and flavor quality of Baijiu. The environmental heterogeneity of different locations in the workshop during fermentation led to the final production of Daqu with three different types (i.e., white, yellow, and black Daqu). How to use these three types of Daqu in Baijiu production mainly depends on the workers’ experience so far. Here, we aimed to reveal the potential functions of different types of Daqu by comparing enzyme activity, volatile metabolites, and microbiota characteristics. White_Qu exhibited the highest liquefaction and saccharification enzyme activities, while the highest neutral proteinase and cellulase enzyme activities were detected in black_Qu. The total volatile content of yellow_Qu and black_Qu was roughly double that of white_Qu, and multivariate analysis revealed distinct volatile dissimilarities across different types of Daqu. Significant differences in bacterial and fungal community structures, assembly patterns, and potential functional profiles were discovered among different types of Daqu. At the genus level, Oceanobacillus and Thermomyces dominated the white_Qu microbiota, and the abundant microbes in yellow_Qu and black_Qu were scattered in Kroppenstedtia and Thermoascus. Bacterial and fungal communities were dominated by deterministic and stochastic assembly processes, respectively, suggesting that bacteria may be more affected by abiotic environmental factors and species interaction than fungi. Co-occurrence network analysis showed positive correlations characterized Daqu microbial networks, and network topological features indicated stronger interactions between bacterial taxa compared with fungal community. The Spearman correlation analysis revealed that four bacterial genera (Kroppenstedtia, Virgibacillus, Scopulibacillus, and Staphylococcus) and two fungal genera (Thermoascus and Aspergillus) exhibited positive correlations with almost all of the abundant volatiles. This work reveals that spatially varying environments lead to the microbiome and metabolome heterogeneity of high-temperature Daqu in the same workshop.
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