Abstract

The intestinal microbiota is considered to be a major reservoir of antibiotic resistance determinants (ARDs) that could potentially be transferred to bacterial pathogens via mobile genetic elements. Yet, this assumption is poorly supported by empirical evidence due to the distant homologies between known ARDs (mostly from culturable bacteria) and ARDs from the intestinal microbiota. Consequently, an accurate census of intestinal ARDs (that is, the intestinal resistome) has not yet been fully determined. For this purpose, we developed and validated an annotation method (called pairwise comparative modelling) on the basis of a three-dimensional structure (homology comparative modelling), leading to the prediction of 6,095 ARDs in a catalogue of 3.9 million proteins from the human intestinal microbiota. We found that the majority of predicted ARDs (pdARDs) were distantly related to known ARDs (mean amino acid identity 29.8%) and found little evidence supporting their transfer between species. According to the composition of their resistome, we were able to cluster subjects from the MetaHIT cohort (n = 663) into six resistotypes that were connected to the previously described enterotypes. Finally, we found that the relative abundance of pdARDs was positively associated with gene richness, but not when subjects were exposed to antibiotics. Altogether, our results indicate that the majority of intestinal microbiota ARDs can be considered intrinsic to the dominant commensal microbiota and that these genes are rarely shared with bacterial pathogens.

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Data availability

The 6,095 pdARDs PDB files, nucleotide and amino acid sequences can be downloaded from http://mgps.eu/Mustard/. The 3.9 million gene catalogue and the MGS database are accessible at https://www.cbs.dtu.dk/projects/CAG/. The reads from the clinical samples generated in this study are available under the accession number PRJEB27799 at the European Nucleotide Archive.

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Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

The authors are grateful to the GENOTOUL (Toulouse, France), GENOUEST (Rennes, France), ABIMS (Roscoff, France), MIGALE (Jouy-en-Josas) and TGCC-GENCI (Institut Curie) calculation clusters. The authors also thank B. Perichon (Institut Pasteur, Paris, France) for providing ARD sequences from Acinetobacter baumannii, P. Siguier (CNRS, Toulouse, France) for helping the search of insertion sequences with ISfinder, J. Guglielmini (Institut Pasteur, Paris, France) for his assistance in finding conjugative elements, S. Volant (Institut Pasteur, Paris, France) for the design of the statistical model in SHAMAN, T. Jové (University of Limoges, France) for his assistance in finding integrons, M. Petitjean (IAME Research Center, Paris, France) for her assistance in bioinformatic analyses, and F. Plaza-Oñate and M. Almeida for their help with MSPs. The project was funded in part by the European Union Seventh Framework Programme (FP7-HEALTH-2011-single-stage) under grant agreement number 282004, EvoTAR. IRYCIS authors acknowledge the European Development Regional Fund ‘A way to achieve Europe’ for co-founding the Spanish R&D National Plan 2012–2019 Work (PI15-0512), CIBER (CIBERESP; CB06/02/0053) and the Government of Madrid (InGeMICS- B2017/BMD-3691). V.F.L. was further funded by a Research Award Grant 2016 of the European Society for Clinical Microbiology and Infectious Diseases.

Author information

Author notes

    • Julien Tap

    Present address: Danone Nutricia Research, Palaiseau, France

  1. These authors contributed equally: Etienne Ruppé, Amine Ghozlane, Julien Tap.

Affiliations

  1. MGP MetaGénoPolis, INRA, Université Paris-Saclay, Jouy en Josas, France

    • Etienne Ruppé
    • , Amine Ghozlane
    • , Julien Tap
    • , Nicolas Pons
    • , Anne-Sophie Alvarez
    • , Nicolas Maziers
    • , Jean-Michel Batto
    • , Hervé M. Blottière
    • , Pierre Léonard
    • , Véronique Léjard
    • , Aline Letur
    • , Florence Levenez
    • , Kevin Weiszer
    • , Florence Haimet
    • , Joël Doré
    • , Sean P. Kennedy
    •  & S. Dusko Ehrlich
  2. IAME, UMR 1137, INSERM, Paris Diderot University, Sorbonne Paris Cité, Paris, France

    • Etienne Ruppé
    • , Victoire de Lastours
    • , Bruno Fantin
    •  & Antoine Andremont
  3. Institut Pasteur - Bioinformatics and Biostatistics Hub - C3BI, USR 3756 IP CNRS, Paris, France

    • Amine Ghozlane
  4. Institut Pasteur - Biomics - CITECH, Paris, France

    • Amine Ghozlane
    •  & Sean P. Kennedy
  5. Centro Nacional de Biotecnología, CSIC, Madrid, Spain

    • Trinidad Cuesta
    • , Sara Hernando-Amado
    • , Irene Clares
    •  & Jose Luís Martínez
  6. Servicio de Microbiología Instituto, Ramón y Cajal de Investigación Sanitaria, Madrid, Spain

    • Teresa M. Coque
    • , Fernando Baquero
    •  & Val F. Lanza
  7. CIBER en Epidemiología y Salud Pública, Madrid, Spain

    • Teresa M. Coque
    • , Fernando Baquero
    •  & Val F. Lanza
  8. Unidad de Resistencia a Antibióticos y Virulencia Bacteriana, Madrid, Spain

    • Teresa M. Coque
    •  & Fernando Baquero
  9. Unit for Cystic Fibrosis, Ramon y Cajal University Hospital, Madrid, Spain

    • Luis Máiz
  10. Internal Medicine Department, Beaujon Hospital, AP-HP, Clichy, France

    • Tiphaine Goulenok
    • , Victoire de Lastours
    • , Nawal Amor
    •  & Bruno Fantin
  11. Bacteriology Laboratory, Bichat-Claude Bernard Hospital, AP-HP, Paris, France

    • Ingrid Wieder
    •  & Antoine Andremont
  12. Department of Medical Microbiology, University Medical Center Utrecht, Utrecht, the Netherlands

    • Willem van Schaik
    • , Malbert Rogers
    • , Xinglin Zhang
    •  & Rob J. L. Willems
  13. Institute of Microbiology and Infection, University of Birmingham, Edgbaston, Birmingham, UK

    • Willem van Schaik
  14. INSERM UMR_S 1134, Paris Diderot University, Sorbonne Paris Cité, Université de la Réunion, Université des Antilles, INTS, GR-Ex, Paris, France

    • Alexandre G. de Brevern
  15. Centre of Host Microbiome Interactions, King’s College, London, UK

    • S. Dusko Ehrlich

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Contributions

E.R., A.G. and J.T. performed the analysis. E.R., A.G., J.T., W.v.S., A.d.B. and S.P.K. wrote the manuscript. A.S.A. and N.M. handled the data management. T.C., S.H.A., I.C. and J.L.M. performed the gene synthesis experiments. J.L.M., T.M.C., V.F.L., F.B., A.d.B., J.D., S.P.K., F.H. and S.D.E. discussed the protocol and results. L.M., T.G., V.d.L., N.A., B.F., I.W., A.A., W.v.S., M.R., X.Z. and R.J.L. recruited the patients and collected the samples. H.B., V.L., A.L. and F.L. handled the wet lab experiments. N.P., P.L. and J.M.B. managed the informatics and the calculation clusters. K.W. and N.P. designed the website (http://mgps.eu/Mustard/).

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Etienne Ruppé.

Supplementary information

  1. Supplementary Information

    Supplementary Figures 1–17, Supplementary Notes, Supplementary References.

  2. Reporting Summary

  3. Supplementary Table 1

    The 6,095 pdARDs that were found in the 3.9 million protein catalogue. PCM score missing values means that the candidate could not be modelled with the negative template, so that the PCM score was considered to be over 50%.

  4. Supplementary Table 2

    Description of the 16 candidates sharing at least 40% amino acid identity with a reference ARD but being not predicted as an ARD by PCM. The TM score represents the degree of correct alignment of the structure generated by PCM and a reference structure (the highest score being 1).

  5. Supplementary Table 3

    Description of the 49 pdARDs found in plasmids and/or phages from GenBank.

  6. Supplementary Table 4

    Description of the 82 pdARDs shared by ≥2 species. Insertion sequences, conjugative elements and integrons were searched the same way as described in the Methods section.

  7. Supplementary Table 5

    Details on the 74 MGS that were found to be differentially abundant between subjects with no recent antibiotic exposure (n = 31) to antibiotics and subjects under chronic exposure to antibiotics (n = 30) using the Wald unpaired test. Padj, adjusted P-value (Benjamini–Hochberg correction).

  8. Supplementary Table 6

    Details on the 133 MGS that were found to be differentially abundant between subjects (n = 10) before and after SDD using the Wald paired test. Padj, adjusted P-value (Benjamini–Hochberg correction).

  9. Supplementary Table 7

    Predictions of ARDs in the functional metagenomics dataset from soils9 by the PCM method.

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DOI

https://doi.org/10.1038/s41564-018-0292-6