Revealing the microbial assemblage structure in the human gut microbiome using latent Dirichlet allocation.

Bayesian model Enterotype Human gut microbiome Latent Dirichlet allocation Machine learning Metagenomics Microbial assemblage

Journal

Microbiome
ISSN: 2049-2618
Titre abrégé: Microbiome
Pays: England
ID NLM: 101615147

Informations de publication

Date de publication:
23 06 2020
Historique:
received: 08 05 2018
accepted: 13 05 2020
entrez: 25 6 2020
pubmed: 25 6 2020
medline: 18 3 2021
Statut: epublish

Résumé

The human gut microbiome has been suggested to affect human health and thus has received considerable attention. To clarify the structure of the human gut microbiome, clustering methods are frequently applied to human gut taxonomic profiles. Enterotypes, i.e., clusters of individuals with similar microbiome composition, are well-studied and characterized. However, only a few detailed studies on assemblages, i.e., clusters of co-occurring bacterial taxa, have been conducted. Particularly, the relationship between the enterotype and assemblage is not well-understood. In this study, we detected gut microbiome assemblages using a latent Dirichlet allocation (LDA) method. We applied LDA to a large-scale human gut metagenome dataset and found that a 4-assemblage LDA model could represent relationships between enterotypes and assemblages with high interpretability. This model indicated that each individual tends to have several assemblages, three of which corresponded to the three classically recognized enterotypes. Conversely, the fourth assemblage corresponded to no enterotypes and emerged in all enterotypes. Interestingly, the dominant genera of this assemblage (Clostridium, Eubacterium, Faecalibacterium, Roseburia, Coprococcus, and Butyrivibrio) included butyrate-producing species such as Faecalibacterium prausnitzii. Indeed, the fourth assemblage significantly positively correlated with three butyrate-producing functions. We conducted an assemblage analysis on a large-scale human gut metagenome dataset using LDA. The present study revealed that there is an enterotype-independent assemblage. Video Abstract.

Sections du résumé

BACKGROUND
The human gut microbiome has been suggested to affect human health and thus has received considerable attention. To clarify the structure of the human gut microbiome, clustering methods are frequently applied to human gut taxonomic profiles. Enterotypes, i.e., clusters of individuals with similar microbiome composition, are well-studied and characterized. However, only a few detailed studies on assemblages, i.e., clusters of co-occurring bacterial taxa, have been conducted. Particularly, the relationship between the enterotype and assemblage is not well-understood.
RESULTS
In this study, we detected gut microbiome assemblages using a latent Dirichlet allocation (LDA) method. We applied LDA to a large-scale human gut metagenome dataset and found that a 4-assemblage LDA model could represent relationships between enterotypes and assemblages with high interpretability. This model indicated that each individual tends to have several assemblages, three of which corresponded to the three classically recognized enterotypes. Conversely, the fourth assemblage corresponded to no enterotypes and emerged in all enterotypes. Interestingly, the dominant genera of this assemblage (Clostridium, Eubacterium, Faecalibacterium, Roseburia, Coprococcus, and Butyrivibrio) included butyrate-producing species such as Faecalibacterium prausnitzii. Indeed, the fourth assemblage significantly positively correlated with three butyrate-producing functions.
CONCLUSIONS
We conducted an assemblage analysis on a large-scale human gut metagenome dataset using LDA. The present study revealed that there is an enterotype-independent assemblage. Video Abstract.

Identifiants

pubmed: 32576288
doi: 10.1186/s40168-020-00864-3
pii: 10.1186/s40168-020-00864-3
pmc: PMC7313204
doi:

Substances chimiques

Butyrates 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't Video-Audio Media

Langues

eng

Sous-ensembles de citation

IM

Pagination

95

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Auteurs

Shion Hosoda (S)

Graduate School of Advanced Science and Engineering, Waseda University, 55N-06-10, 3-4-1, Okubo Shinjuku-ku, Tokyo, 169-8555, Japan.
Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan.

Suguru Nishijima (S)

Graduate School of Advanced Science and Engineering, Waseda University, 55N-06-10, 3-4-1, Okubo Shinjuku-ku, Tokyo, 169-8555, Japan.
Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan.
Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.

Tsukasa Fukunaga (T)

Graduate School of Advanced Science and Engineering, Waseda University, 55N-06-10, 3-4-1, Okubo Shinjuku-ku, Tokyo, 169-8555, Japan.
Department of Computer Science, Graduate School of Information Science and Engineering, The University of Tokyo, Tokyo, Japan.

Masahira Hattori (M)

Graduate School of Advanced Science and Engineering, Waseda University, 55N-06-10, 3-4-1, Okubo Shinjuku-ku, Tokyo, 169-8555, Japan.
Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.
RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan.

Michiaki Hamada (M)

Graduate School of Advanced Science and Engineering, Waseda University, 55N-06-10, 3-4-1, Okubo Shinjuku-ku, Tokyo, 169-8555, Japan. mhamada@waseda.jp.
Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan. mhamada@waseda.jp.
Graduate School of Medicine, Nippon Medical School, Tokyo, Japan. mhamada@waseda.jp.
Center for Data Science, Waseda University, Tokyo, Japan. mhamada@waseda.jp.

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