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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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
Subjects
631/114/2390
631/326/2565/2134
639/638/92/1643
692/698/2741/2135
Acid production
Agriculture
Algorithms
Bacteria
Bioinformatics
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Biomedicine
Biotechnology
Fatty acids
Feces - microbiology
Gastrointestinal Microbiome - genetics
Gene clusters
Gene regulation
Genes
Genomes
Gut microbiota
Humans
Intestinal microflora
Life Sciences
Markov chains
Metabolic Networks and Pathways - genetics
Metabolic pathways
Metabolism
Metabolites
Metagenomics
Microbiomes
Microbiota
Microorganisms
Secondary metabolites
Taxa
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Abstract 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.
AbstractList 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.
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.
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.
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 use it to systematically profile gut microbiome metabolism, identifying 19,890 gene clusters in 4,240 high-quality microbial genomes. We find 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 subjects from a Dutch population-based cohort shows that the level of microbiome-derived metabolites in plasma and faeces 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.
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.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.
Author Fu, Jingyuan
Medema, Marnix H.
Pascal Andreu, Victòria
Augustijn, Hannah E.
Dodd, Dylan
Zhernakova, Alexandra
Fischbach, Michael A.
Chen, Lianmin
AuthorAffiliation 5 Department of Microbiology and Immunology, Stanford University, Stanford, USA
6 Chan Zuckerberg Biohub, San Francisco, CA USA
2 Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
4 Department of Bioengineering, Stanford University, Stanford, USA
1 Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
7 Department of Pathology, Stanford University, Stanford, USA
3 Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/36782070$$D View this record in MEDLINE/PubMed
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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
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Snippet The gut microbiota produce hundreds of small molecules, many of which modulate host physiology. Although efforts have been made to identify biosynthetic genes...
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SubjectTerms 631/114/2390
631/326/2565/2134
639/638/92/1643
692/698/2741/2135
Acid production
Agriculture
Algorithms
Bacteria
Bioinformatics
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Biomedicine
Biotechnology
Fatty acids
Feces - microbiology
Gastrointestinal Microbiome - genetics
Gene clusters
Gene regulation
Genes
Genomes
Gut microbiota
Humans
Intestinal microflora
Life Sciences
Markov chains
Metabolic Networks and Pathways - genetics
Metabolic pathways
Metabolism
Metabolites
Metagenomics
Microbiomes
Microbiota
Microorganisms
Secondary metabolites
Taxa
Title gutSMASH predicts specialized primary metabolic pathways from the human gut microbiota
URI https://link.springer.com/article/10.1038/s41587-023-01675-1
https://www.ncbi.nlm.nih.gov/pubmed/36782070
https://www.proquest.com/docview/2875654461
https://www.proquest.com/docview/2776517839
https://pubmed.ncbi.nlm.nih.gov/PMC10423304
Volume 41
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