Literature DB >> 32276974

Chinese Liquor Fermentation: Identification of Key Flavor-Producing Lactobacillus spp. by Quantitative Profiling with Indigenous Internal Standards.

Rubing Du1, Qun Wu2, Yan Xu2.   

Abstract

Identifying the functional microbes in spontaneous food fermentation is important for improving food quality. To identify the key flavor producers in Chinese liquor fermentation, we propose a novel quantitative microbiome profiling method that uses indigenous internal standards to normalize high-throughput amplicon sequencing results. We screened Lactobacillus acetotolerans and Lactobacillus jinshani as indigenous internal standards based on their high distribution frequencies and relative abundances. After determining the absolute abundance of indigenous internal standards using quantitative PCR with species-specific primers, the liquor-fermented bacterial community and its dynamics were better characterized by internal standards normalization. Based on quantitative microbiome profiling, we identified that Lactobacillus was a key flavor producer correlated with eight flavor compounds. Metatranscriptomic analysis indicated that Lactobacillus was active in transcribing genes involving the biosynthesis of flavor compounds and their precursors. This work has developed a novel and extensible absolute quantification method for microbiota that will alleviate concerns in the statistical analyses based on relative microbiome profiling, and shed insights into the function of Lactobacillus in food fermentation. It can potentially be applied to other microbial ecology studies.IMPORTANCE In this study, we developed a novel strategy using indigenous internal standards to normalize the high-throughput amplicon sequencing results. We chose two Lactobacillus species as indigenous internal standards and characterized the absolute abundance of the bacterial community. Further, we identified Lactobacillus as the key flavor producer using quantitative microbiome profiling combined with multivariate statistics and metatranscriptomic analysis. This work developed a novel strategy for absolute quantitative abundance analysis of microbiota and expanded our understanding of the role of Lactobacillus in food fermentation.
Copyright © 2020 American Society for Microbiology.

Entities:  

Keywords:  Chinese liquor; indigenous internal standards; key flavor producer; microbiota; quantitative microbiome profiling

Year:  2020        PMID: 32276974     DOI: 10.1128/AEM.00456-20

Source DB:  PubMed          Journal:  Appl Environ Microbiol        ISSN: 0099-2240            Impact factor:   4.792


  5 in total

1.  Metatranscriptomics Unravel Composition, Drivers, and Functions of the Active Microorganisms in Light-Flavor Liquor Fermentation.

Authors:  Yuanyuan Pan; Ying Wang; Wenjun Hao; Chengbao Duan; Shiyuan Wang; Jinwang Wei; Gang Liu
Journal:  Microbiol Spectr       Date:  2022-05-31

2.  Constructing a Defined Starter for Multispecies Vinegar Fermentation via Evaluation of the Vitality and Dominance of Functional Microbes in an Autochthonous Starter.

Authors:  Ting Huang; Zhen-Ming Lu; Ming-Ye Peng; Li-Juan Chai; Xiao-Juan Zhang; Jin-Song Shi; Qi Li; Zheng-Hong Xu
Journal:  Appl Environ Microbiol       Date:  2021-11-24       Impact factor: 5.005

3.  Bacterial Diversity and Lactic Acid Bacteria with High Alcohol Tolerance in the Fermented Grains of Soy Sauce Aroma Type Baijiu in North China.

Authors:  Jiali Wang; Chengshun Lu; Qiang Xu; Zhongyuan Li; Yajian Song; Sa Zhou; Tongcun Zhang; Xuegang Luo
Journal:  Foods       Date:  2022-06-17

4.  Effects of Different Daqu on Microbial Community Domestication and Metabolites in Nongxiang Baijiu Brewing Microecosystem.

Authors:  Fengjiao Mao; Jun Huang; Rongqing Zhou; Hui Qin; Suyi Zhang; Xiaobo Cai; Chuanfeng Qiu
Journal:  Front Microbiol       Date:  2022-06-30       Impact factor: 6.064

5.  Metabolic Features of Ganjang (a Korean Traditional Soy Sauce) Fermentation Revealed by Genome-Centered Metatranscriptomics.

Authors:  Byung Hee Chun; Dong Min Han; Hyung Min Kim; Dongbin Park; Da Min Jeong; Hyun Ah Kang; Che Ok Jeon
Journal:  mSystems       Date:  2021-08-03       Impact factor: 6.496

  5 in total

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