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

Shion Hosoda, Suguru Nishijima, Tsukasa Fukunaga, Masahira Hattori, Michiaki Hamada

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Number of pages1
JournalMicrobiome
Volume8
Issue number1
DOIs
Publication statusPublished - 2020 Jun 23

Keywords

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

ASJC Scopus subject areas

  • Microbiology
  • Microbiology (medical)

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