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控制食品微生物群落的代谢稳定性以实现稳定的本土白酒发酵。

Controlling metabolic stability of food microbiome for stable indigenous liquor fermentation.

作者信息

Tan Yuwei, Zhu Yang, Wijffels René H, Zhang Hongxia, Scott William T, Xu Yan, Martins Dos Santos Vitor

机构信息

Laboratory of Brewing Microbiology and Applied Enzymology, Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, Wuxi, 214122, Jiangsu, China.

Bioprocess Engineering, Wageningen University and Research, P.O. Box 16, 6700AA, Wageningen, The Netherlands.

出版信息

NPJ Biofilms Microbiomes. 2025 Jul 1;11(1):124. doi: 10.1038/s41522-025-00729-3.


DOI:10.1038/s41522-025-00729-3
PMID:40593731
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12214495/
Abstract

Steering microbial metabolic stability in fermentation is a recurrent goal in microbial food production. Indigenous liquor fermentation typically relies on complex microbiome metabolism, making it difficult to steer fermentation towards consistent high-quality products. Here, we conducted a three-step experiment to identify instability factors and explore ways to steer fermentation stability in lab-scale settings. We found that the metabolic stability of the microbiome fluctuates due to fermentation parameters, dynamic benefit allocation between yeasts and Lactobacilli, and metabolic network redundancy. In addition to parameters control, short-term metabolic stability requires stable microbial benefit allocation, whereas long-term stability requires proper functional redundancy. Rationally setting initial parameters and the microbial inoculation ratio is a practical way to optimize metabolic stability for stable indigenous liquor fermentation. Our study provides new insights into microbiome metabolism control and shows the feasibility of enhancing fermentation stability through appropriate initial conditions, enabling more controlled and efficient microbial food production systems.

摘要

在发酵过程中引导微生物代谢稳定性是微生物食品生产中反复追求的目标。传统白酒发酵通常依赖复杂的微生物群落代谢,这使得难以将发酵引导至始终如一的高质量产品。在此,我们进行了一个三步实验,以识别不稳定因素并探索在实验室规模条件下引导发酵稳定性的方法。我们发现,微生物群落的代谢稳定性会因发酵参数、酵母与乳酸菌之间动态的利益分配以及代谢网络冗余而波动。除了参数控制外,短期代谢稳定性需要稳定的微生物利益分配,而长期稳定性则需要适当的功能冗余。合理设定初始参数和微生物接种比例是优化代谢稳定性以实现稳定的传统白酒发酵的实用方法。我们的研究为微生物群落代谢控制提供了新见解,并展示了通过适当的初始条件提高发酵稳定性的可行性,从而实现更可控、高效的微生物食品生产系统。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21aa/12214495/f34ff6c46cfc/41522_2025_729_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21aa/12214495/21aea632f6f6/41522_2025_729_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21aa/12214495/0de3c5808710/41522_2025_729_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21aa/12214495/6c2ddd8225c6/41522_2025_729_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21aa/12214495/45133ad080e4/41522_2025_729_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21aa/12214495/61cd10635fb9/41522_2025_729_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21aa/12214495/03ef27545d7a/41522_2025_729_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21aa/12214495/3e941f0e2567/41522_2025_729_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21aa/12214495/f34ff6c46cfc/41522_2025_729_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21aa/12214495/21aea632f6f6/41522_2025_729_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21aa/12214495/0de3c5808710/41522_2025_729_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21aa/12214495/6c2ddd8225c6/41522_2025_729_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21aa/12214495/45133ad080e4/41522_2025_729_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21aa/12214495/61cd10635fb9/41522_2025_729_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21aa/12214495/03ef27545d7a/41522_2025_729_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21aa/12214495/3e941f0e2567/41522_2025_729_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21aa/12214495/f34ff6c46cfc/41522_2025_729_Fig8_HTML.jpg

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Controlling metabolic stability of food microbiome for stable indigenous liquor fermentation.

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本文引用的文献

[1]
Microbial interactions shape cheese flavour formation.

Nat Commun. 2023-12-21

[2]
Machine learning and deep learning applications in microbiome research.

ISME Commun. 2022-10-6

[3]
Emergent transcriptional adaption facilitates convergent succession within a synthetic community.

ISME Commun. 2021-9-1

[4]
A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia.

PLoS Comput Biol. 2023-8

[5]
Dynamic genome-scale modeling of Saccharomyces cerevisiae unravels mechanisms for ester formation during alcoholic fermentation.

Biotechnol Bioeng. 2023-7

[6]
Meta-analysis of cheese microbiomes highlights contributions to multiple aspects of quality.

Nat Food. 2020-8

[7]
Secrets of the cheese microbiome.

Nat Food. 2020-8

[8]
Synthetic biology for the engineering of complex wine yeast communities.

Nat Food. 2022-4

[9]
Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics.

Microbiome. 2023-3-29

[10]
Amino Acids Drive the Deterministic Assembly Process of Fungal Community and Affect the Flavor Metabolites in Fermentation.

Microbiol Spectr. 2023-3-21

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