gutSMASH predicts specialized primary metabolic pathways from the human gut microbiota

The gut microbiota produce hundreds of small molecules, many of which modulate host physiology. Although efforts have been made to identify biosynthetic genes for secondary metabolites, the chemical output of the gut microbiome consists predominantly of primary metabolites. Here we introduce the gut...

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Bibliographic Details
Published inNature biotechnology Vol. 41; no. 10; pp. 1416 - 1423
Main Authors Pascal Andreu, Victòria, Augustijn, Hannah E., Chen, Lianmin, Zhernakova, Alexandra, Fu, Jingyuan, Fischbach, Michael A., Dodd, Dylan, Medema, Marnix H.
Format Journal Article
LanguageEnglish
Published New York Nature Publishing Group US 01.10.2023
Nature Publishing Group
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Summary:The gut microbiota produce hundreds of small molecules, many of which modulate host physiology. Although efforts have been made to identify biosynthetic genes for secondary metabolites, the chemical output of the gut microbiome consists predominantly of primary metabolites. Here we introduce the gutSMASH algorithm for identification of primary metabolic gene clusters, and we used it to systematically profile gut microbiome metabolism, identifying 19,890 gene clusters in 4,240 high-quality microbial genomes. We found marked differences in pathway distribution among phyla, reflecting distinct strategies for energy capture. These data explain taxonomic differences in short-chain fatty acid production and suggest a characteristic metabolic niche for each taxon. Analysis of 1,135 individuals from a Dutch population-based cohort shows that the level of microbiome-derived metabolites in plasma and feces is almost completely uncorrelated with the metagenomic abundance of corresponding metabolic genes, indicating a crucial role for pathway-specific gene regulation and metabolite flux. This work is a starting point for understanding differences in how bacterial taxa contribute to the chemistry of the microbiome. Taxon-specific primary metabolic pathways are identified using profile hidden Markov models.
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Author Contributions Statement
M.A.F. and M.H.M. initially conceived the project, with modifications and extensions introduced on the advice of V.P.A., A.Z., J.F. and D.D. The gutSMASH software was developed and used to analyze genomic data by V.P.A., with input from M.H.M., D.D. and M.A.F. Analysis of metagenomic and metatranscriptomics data was performed by H.E.A., V.P.A. and L.C. Correlations with metabolomic data were performed by L.C. M.H.M., D.D. and M.A.F. coordinated and supervised the study as a whole, and A.Z. and J.F. coordinated and supervised analysis of cohort data. All authors contributed to data interpretation. V.P.A., M.A.F., D.D. and M.H.M. drafted the initial manuscript with input from the other authors. All authors read and contributed to the final manuscript.
Contributed equally
ISSN:1087-0156
1546-1696
1546-1696
DOI:10.1038/s41587-023-01675-1