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Kang J, Huang X, Li R, Zhang Y, Chen XX, Han BZ. Deciphering the core microbes and their interactions in spontaneous Baijiu fermentation: A comprehensive review. Food Res Int 2024; 188:114497. [PMID: 38823877 DOI: 10.1016/j.foodres.2024.114497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 06/03/2024]
Abstract
The spontaneous Baijiu fermentation system harbors a complex microbiome that is highly dynamic in time and space and varies depending on the Jiuqu starters and environmental factors. The intricate microbiota presents in the fermentation environment is responsible for carrying out various reactions. These reactions necessitate the interaction among the core microbes to influence the community function, ultimately shaping the distinct Baijiu styles through the process of spontaneous fermentation. Numerous studies have been conducted to enhance our understanding of the diversity, succession, and function of microbial communities with the aim of improving fermentation manipulation. However, a comprehensive and critical assessment of the core microbes and their interaction remains one of the significant challenges in the Baijiu fermentation industry. This paper focuses on the fermentation properties of core microbes. We discuss the state of the art of microbial traceability, highlighting the crucial role of environmental and starter microbiota in the Baijiu brewing microbiome. Also, we discuss the various interactions between microbes in the Baijiu production system and propose a potential conceptual framework that involves constructing predictive network models to simplify and quantify microbial interactions using co-culture models. This approach offers effective strategies for understanding the core microbes and their interactions, thus beneficial for the management of microbiota and the regulation of interactions in Baijiu fermentation processes.
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Affiliation(s)
- Jiamu Kang
- Beijing Laboratory for Food Quality and Safety, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China; Key Laboratory of Precision Nutrition and Food Quality, Department of Nutrition and Health, China Agricultural University, Beijing, China; School of Food Science and Engineering, Hainan University, Haikou, China
| | - Xiaoning Huang
- Beijing Laboratory for Food Quality and Safety, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China; Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Rengshu Li
- Beijing Laboratory for Food Quality and Safety, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China; Key Laboratory of Precision Nutrition and Food Quality, Department of Nutrition and Health, China Agricultural University, Beijing, China
| | - Yuandi Zhang
- Beijing Laboratory for Food Quality and Safety, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China; Key Laboratory of Precision Nutrition and Food Quality, Department of Nutrition and Health, China Agricultural University, Beijing, China
| | - Xiao-Xue Chen
- Beijing Laboratory for Food Quality and Safety, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China; Key Laboratory of Precision Nutrition and Food Quality, Department of Nutrition and Health, China Agricultural University, Beijing, China.
| | - Bei-Zhong Han
- Beijing Laboratory for Food Quality and Safety, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China; Key Laboratory of Precision Nutrition and Food Quality, Department of Nutrition and Health, China Agricultural University, Beijing, China.
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Junker R, Valence F, Mistou MY, Chaillou S, Chiapello H. Integration of metataxonomic data sets into microbial association networks highlights shared bacterial community dynamics in fermented vegetables. Microbiol Spectr 2024; 12:e0031224. [PMID: 38747598 DOI: 10.1128/spectrum.00312-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 03/26/2024] [Indexed: 06/06/2024] Open
Abstract
The management of food fermentation is still largely based on empirical knowledge, as the dynamics of microbial communities and the underlying metabolic networks that produce safe and nutritious products remain beyond our understanding. Although these closed ecosystems contain relatively few taxa, they have not yet been thoroughly characterized with respect to how their microbial communities interact and dynamically evolve. However, with the increased availability of metataxonomic data sets on different fermented vegetables, it is now possible to gain a comprehensive understanding of the microbial relationships that structure plant fermentation. In this study, we applied a network-based approach to the integration of public metataxonomic 16S data sets targeting different fermented vegetables throughout time. Specifically, we aimed to explore, compare, and combine public 16S data sets to identify shared associations between amplicon sequence variants (ASVs) obtained from independent studies. The workflow includes steps for searching and selecting public time-series data sets and constructing association networks of ASVs based on co-abundance metrics. Networks for individual data sets are then integrated into a core network, highlighting significant associations. Microbial communities are identified based on the comparison and clustering of ASV networks using the "stochastic block model" method. When we applied this method to 10 public data sets (including a total of 931 samples) targeting five varieties of vegetables with different sampling times, we found that it was able to shed light on the dynamics of vegetable fermentation by characterizing the processes of community succession among different bacterial assemblages. IMPORTANCE Within the growing body of research on the bacterial communities involved in the fermentation of vegetables, there is particular interest in discovering the species or consortia that drive different fermentation steps. This integrative analysis demonstrates that the reuse and integration of public microbiome data sets can provide new insights into a little-known biotope. Our most important finding is the recurrent but transient appearance, at the beginning of vegetable fermentation, of amplicon sequence variants (ASVs) belonging to Enterobacterales and their associations with ASVs belonging to Lactobacillales. These findings could be applied to the design of new fermented products.
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Affiliation(s)
- Romane Junker
- MaIAGE, INRAE, Université Paris-Saclay, Jouy-en-Josas, France
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Monshizadeh M, Ye Y. Incorporating metabolic activity, taxonomy and community structure to improve microbiome-based predictive models for host phenotype prediction. Gut Microbes 2024; 16:2302076. [PMID: 38214657 PMCID: PMC10793686 DOI: 10.1080/19490976.2024.2302076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 01/02/2024] [Indexed: 01/13/2024] Open
Abstract
We developed MicroKPNN, a prior-knowledge guided interpretable neural network for microbiome-based human host phenotype prediction. The prior knowledge used in MicroKPNN includes the metabolic activities of different bacterial species, phylogenetic relationships, and bacterial community structure, all in a shallow neural network. Application of MicroKPNN to seven gut microbiome datasets (involving five different human diseases including inflammatory bowel disease, type 2 diabetes, liver cirrhosis, colorectal cancer, and obesity) shows that incorporation of the prior knowledge helped improve the microbiome-based host phenotype prediction. MicroKPNN outperformed fully connected neural network-based approaches in all seven cases, with the most improvement of accuracy in the prediction of type 2 diabetes. MicroKPNN outperformed a recently developed deep-learning based approach DeepMicro, which selects the best combination of autoencoder and machine learning approach to make predictions, in all of the seven cases. Importantly, we showed that MicroKPNN provides a way for interpretation of the predictive models. Using importance scores estimated for the hidden nodes, MicroKPNN could provide explanations for prior research findings by highlighting the roles of specific microbiome components in phenotype predictions. In addition, it may suggest potential future research directions for studying the impacts of microbiome on host health and diseases. MicroKPNN is publicly available at https://github.com/mgtools/MicroKPNN.
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Affiliation(s)
- Mahsa Monshizadeh
- Computer Science Department, Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA
| | - Yuzhen Ye
- Computer Science Department, Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA
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Melkonian C, Zorrilla F, Kjærbølling I, Blasche S, Machado D, Junge M, Sørensen KI, Andersen LT, Patil KR, Zeidan AA. Microbial interactions shape cheese flavour formation. Nat Commun 2023; 14:8348. [PMID: 38129392 PMCID: PMC10739706 DOI: 10.1038/s41467-023-41059-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 08/15/2023] [Indexed: 12/23/2023] Open
Abstract
Cheese fermentation and flavour formation are the result of complex biochemical reactions driven by the activity of multiple microorganisms. Here, we studied the roles of microbial interactions in flavour formation in a year-long Cheddar cheese making process, using a commercial starter culture containing Streptococcus thermophilus and Lactococcus strains. By using an experimental strategy whereby certain strains were left out from the starter culture, we show that S. thermophilus has a crucial role in boosting Lactococcus growth and shaping flavour compound profile. Controlled milk fermentations with systematic exclusion of single Lactococcus strains, combined with genomics, genome-scale metabolic modelling, and metatranscriptomics, indicated that S. thermophilus proteolytic activity relieves nitrogen limitation for Lactococcus and boosts de novo nucleotide biosynthesis. While S. thermophilus had large contribution to the flavour profile, Lactococcus cremoris also played a role by limiting diacetyl and acetoin formation, which otherwise results in an off-flavour when in excess. This off-flavour control could be attributed to the metabolic re-routing of citrate by L. cremoris from diacetyl and acetoin towards α-ketoglutarate. Further, closely related Lactococcus lactis strains exhibited different interaction patterns with S. thermophilus, highlighting the significance of strain specificity in cheese making. Our results highlight the crucial roles of competitive and cooperative microbial interactions in shaping cheese flavour profile.
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Affiliation(s)
- Chrats Melkonian
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, 2970, Hørsholm, Denmark.
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, the Netherlands.
- Bioinformatics Group, Wageningen University and Research, Wageningen, the Netherlands.
| | - Francisco Zorrilla
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
| | - Inge Kjærbølling
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, 2970, Hørsholm, Denmark
| | - Sonja Blasche
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117, Heidelberg, Germany
| | - Daniel Machado
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117, Heidelberg, Germany
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology, Trondheim, 7491, Norway
| | - Mette Junge
- Strain Improvement, R&D Food Microbiology, Chr. Hansen A/S, 2970, Hørsholm, Denmark
| | - Kim Ib Sørensen
- Strain Improvement, R&D Food Microbiology, Chr. Hansen A/S, 2970, Hørsholm, Denmark
| | | | - Kiran R Patil
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory (EMBL), Meyerhofstrasse 1, 69117, Heidelberg, Germany
| | - Ahmad A Zeidan
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, 2970, Hørsholm, Denmark.
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Pisaniello A, Handley KM, White WL, Angert ER, Boey JS, Clements KD. Host individual and gut location are more important in gut microbiota community composition than temporal variation in the marine herbivorous fish Kyphosus sydneyanus. BMC Microbiol 2023; 23:275. [PMID: 37773099 PMCID: PMC10540440 DOI: 10.1186/s12866-023-03025-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 09/19/2023] [Indexed: 09/30/2023] Open
Abstract
BACKGROUND Gut microbiota play a key role in the nutrition of many marine herbivorous fishes through hindgut fermentation of seaweed. Gut microbiota composition in the herbivorous fish Kyphosus sydneyanus (family Kyphosidae) varies between individuals and gut sections, raising two questions: (i) is community composition stable over time, especially given seasonal shifts in storage metabolites of dietary brown algae, and (ii) what processes influence community assembly in the hindgut? RESULTS We examined variation in community composition in gut lumen and mucosa samples from three hindgut sections of K. sydneyanus collected at various time points in 2020 and 2021 from reefs near Great Barrier Island, New Zealand. 16S rRNA gene analysis was used to characterize microbial community composition, diversity and estimated density. Differences in community composition between gut sections remained relatively stable over time, with little evidence of temporal variation. Clostridia dominated the proximal hindgut sections and Bacteroidia the most distal section. Differences were detected in microbial composition between lumen and mucosa, especially at genus level. CONCLUSIONS High variation in community composition and estimated bacterial density among individual fish combined with low variation in community composition temporally suggests that initial community assembly involved environmental selection and random sampling/neutral effects. Community stability following colonisation could also be influenced by historical contingency, where early colonizing members of the community may have a selective advantage. The impact of temporal changes in the algae may be limited by the dynamics of substrate depletion along the gut following feeding, i.e. the depletion of storage metabolites in the proximal hindgut. Estimated bacterial density, showed that Bacteroidota has the highest density (copies/mL) in distal-most lumen section V, where SCFA concentrations are highest. Bacteroidota genera Alistipes and Rikenella may play important roles in the breakdown of seaweed into useful compounds for the fish host.
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Affiliation(s)
- Alessandro Pisaniello
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand.
| | - Kim M Handley
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - W Lindsey White
- School of Science, Auckland University of Technology, Private Bag 92006, Auckland, New Zealand
| | - Esther R Angert
- Department of Microbiology, Cornell University, 123 Wing Drive, Ithaca, NY, 14853, USA
| | - Jian Sheng Boey
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - Kendall D Clements
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand.
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Xia Y, Luo H, Wu Z, Zhang W. Microbial diversity in jiuqu and its fermentation features: saccharification, alcohol fermentation and flavors generation. Appl Microbiol Biotechnol 2022; 107:25-41. [DOI: 10.1007/s00253-022-12291-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 12/12/2022]
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A review of methods for the inference and experimental confirmation of microbial association networks in cheese. Int J Food Microbiol 2022; 368:109618. [DOI: 10.1016/j.ijfoodmicro.2022.109618] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 02/21/2022] [Accepted: 03/06/2022] [Indexed: 12/15/2022]
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Jiang N, Wu R, Wu C, Wang R, Wu J, Shi H. Multi-omics approaches to elucidate the role of interactions between microbial communities in cheese flavor and quality. FOOD REVIEWS INTERNATIONAL 2022. [DOI: 10.1080/87559129.2022.2070199] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Nan Jiang
- College of Food Science, Shenyang Agricultural University, Shenyang, P. R. China
| | - Rina Wu
- College of Food Science, Shenyang Agricultural University, Shenyang, P. R. China
- Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang Agricultural University, Shenyang, P. R. China
| | - Chen Wu
- College of Food Science, Shenyang Agricultural University, Shenyang, P. R. China
| | - Ruhong Wang
- College of Food Science, Shenyang Agricultural University, Shenyang, P. R. China
| | - Junrui Wu
- College of Food Science, Shenyang Agricultural University, Shenyang, P. R. China
- Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang Agricultural University, Shenyang, P. R. China
- Shenyang Key Laboratory of Microbial Fermentation Technology Innovation, Shenyang Agricultural University, Shenyang, P. R. China
| | - Haisu Shi
- College of Food Science, Shenyang Agricultural University, Shenyang, P. R. China
- Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang Agricultural University, Shenyang, P. R. China
- Shenyang Key Laboratory of Microbial Fermentation Technology Innovation, Shenyang Agricultural University, Shenyang, P. R. China
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