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Data availability

The LifeLines DEEP cohort raw metagenomic sequencing data, metabolome data and human phenotypes (that is, age and sex) used for the analysis presented in this study are available at the European Genome-phenome Archive under accession EGAS00001001704. Taxonomic assignments of bacteria were performed according to Genome Taxonomy Database release 95 (https://gtdb.ecogenomic.org/). Lists of accessions of genome assemblies used are available in Supplementary Tables 3 and 4. iHMP multi-omics data were downloaded from https://ibdmdb.org. Raw sequence data of the iHMP are available from the National Center for Biotechnology Informationʼs Sequence Read Archive via BioProject PRJNA398089; metatranscriptome data are available through Gene Expression Omnibus series accession number GSE111889; and metabolomics data are available at the Metabolomics Workbench (http://www.metabolomicsworkbench.org; Project ID PR000639). Source data are provided with this paper.

Code availability

The gutSMASH source code is available freely under an open-source AGPL-3.0 license from https://github.com/victoriapascal/gutsmash/.

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Acknowledgements

This work was supported by the Chan Zuckerberg Biohub (M.A.F.); DARPA awards HR0011-15-C-0084 and HR0112020030 (M.A.F.); National Institutes of Health (NIH) awards R01 DK101674, DP1 DK113598 and P01 HL147823 (to M.A.F.); the Leducq Foundation; and a European Research Council (ERC) Starting Grant (948770-DECIPHER to M.H.M.). A.Z. is supported by ERC Starting Grant 715772; Netherlands Organization for Scientific Research NWO-VIDI grant 016.178.056; Netherlands Heart Foundation CVON grant 2018-27; and NWO Gravitation grant ExposomeNL 024.004.017. J.F. is supported by the ERC Consolidator Grant (grant agreement no. 101001678); NWO-VICI grant VI.C.202.022; Dutch Heart Foundation IN-CONTROL (CVON2018-27); the Netherlands Organ-on-Chip Initiative; and the NWO Gravitation Project (024.003.001), funded by the Ministry of Education, Culture and Science of the government of The Netherlands. L.C. is supported by a Foundation de Cock-Hadders grant (20:20-13) and a joint fellowship from the University Medical Centre Groningen and the China Scholarship Council (CSC201708320268). D.D. was supported by NIH awards K08 DK110335, R35 GM142873 and R01 AT011396.

Author information

Author notes

1. These authors contributed equally: Hannah E. Augustijn, Lianmin Chen.

Authors and Affiliations

1. Bioinformatics Group, Wageningen University, Wageningen, The Netherlands

   Victòria Pascal Andreu, Hannah E. Augustijn & Marnix H. Medema

2. Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

   Hannah E. Augustijn, Lianmin Chen, Alexandra Zhernakova & Jingyuan Fu

3. Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

   Lianmin Chen & Jingyuan Fu

4. Changzhou Medical Center, Nanjing Medical University, Changzhou, China

   Lianmin Chen

5. Department of Cardiology, Nanjing Medical University, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China

   Lianmin Chen

6. Department of Bioengineering, Stanford University, Stanford, CA, USA

   Michael A. Fischbach

7. Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA

   Michael A. Fischbach & Dylan Dodd

8. Chan Zuckerberg Biohub, San Francisco, CA, USA

   Michael A. Fischbach

9. Department of Pathology, Stanford University, Stanford, CA, USA

   Dylan Dodd

Contributions

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.

Corresponding authors

Correspondence to
Michael A. Fischbach, Dylan Dodd or Marnix H. Medema.

Ethics declarations

Competing interests

M.A.F. is a co-founder and director of Federation Bio, a co-founder of Revolution Medicines and a member of the scientific advisory board of NGM Biopharmaceuticals. D.D. is a co-founder of Federation Bio. M.H.M. is a co-founder of Design Pharmaceuticals and a member of the scientific advisory board of Hexagon Bio. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Pathway prevalence using different core coverage thresholds.

Pathway prevalence was computed by assessing the number of reads (per sample) mapping to known gene clusters at a certain core coverage cutoff. The figure illustrates how the pathway prevalence gradually changes when increasing the core coverage cutoff from 10 to 80%.

Source data

Extended Data Fig. 2 Limited correlation of genetic pathway abundance with metabolites abundance in blood plasma.

This figure shows correlation plots for additional metabolites not shown in Fig. 4a. Spearman correlation (two sided with rho and empirical P value are reported) is used to check the relationship between pathway abundances and metabolite levels after adjusting for age, sex and read depth. n = 1054 biologically independent samples.

Source data

Extended Data Fig. 3 Network of putative non-redundant MGCs predicted by gutSMASH.

From all the unknown predicted MGCs, a redundancy filtering of 0.9 sequence similarity was applied using MMseqs2. From each cluster, two representatives were picked, and all representatives were used as input for BiG-SCAPE using the default cutoffs. The network contains 2,921 nodes and 7,474 edges. The MGCs have been classified into four different categories based on the key enzyme classes they code for. The GR (glycyl-radical) category is composed of MGCs that include pyruvate formate-lyase (PFL-like) and/or glycyl radical (Gly_radical), OD (oxidative decarboxylation) involves MGCs with at least one of the following Pfam domains: pyruvate ferredoxin/flavodoxin oxidoreductase (POR), pyruvate flavodoxin/ferredoxin oxidoreductase, thiamine diP-bdg (POR_N), pyruvate:ferredoxin oxidoreductase core domain II (PFOR_II) and thiamine pyrophosphate enzyme, C-terminal TPP binding domain (TPP_enzyme_C). The flavoenzymes category is a combination of MGCs harbouring at least one of the custom-made BaiCD and BaiH pHMMs. HGD-D-related MGCs, as the name states, include enzymes matching any of the 2-hydroxyglutaryl-CoA dehydratase, D-component (HGD-D)-related pHMM domains.

Source data

Extended Data Fig. 4 Subset of unknown MGCs predicted by gutSMASH manually picked.

The network/nodes present in the left side of the figure represent the subnetwork extracted from the complete network in Extended Data Fig. 3. The arrows have been coloured-coded based on the Pfam domains found in the protein-coding sequences and the functional annotations of these proteins.

Source data

Supplementary information

Source data

Source Data Fig. 2

Raw pathway abundance across most representative genera of bacteria in the human gut, absolute counts of genomes harboring genes and MGCs corresponding to the main acetate-producing pathways, summarized at phylum level (also found in Supplementary Tables 5 and 6).

Source Data Fig. 3

Pathway prevalence and abundance counts across 1,135 human microbiome samples (also found in Supplementary Tables 7 and 8).

Source Data Fig. 4

Multi-omics correlation data.

Source Data Extended Data Fig. 1

Pathway prevalence values of the 41 pathways across 1,135 human microbiomes using different BiG-MAP mapping coverage threshold values (also found in Supplementary Table 7).

Source Data Extended Data Fig. 2

Correlations between different metabolites and the MGC abundance of those pathways that have the same end product.

Source Data Extended Data Figs. 3 and 4

Sequence similarity network (raw data, can be imported into Cytoscape) of putatively novel types of MGCs identified by gutSMASH general rules.

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Pascal Andreu, V., Augustijn, H.E., Chen, L. et al. gutSMASH predicts specialized primary metabolic pathways from the human gut microbiota.
Nat Biotechnol (2023). https://doi.org/10.1038/s41587-023-01675-1

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• Received: 02 March 2021

• Accepted: 10 January 2023

• Published: 13 February 2023

• DOI: https://doi.org/10.1038/s41587-023-01675-1

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