Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Prediction of the intestinal resistome by a three-dimensional structure-based method

Nature Microbiology volume 4, pages 112–123 (2019)Cite this article

Subjects

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.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Illustration of the concept of PCM with a class A β-lactamase.
Fig. 2: MGEs and pdARDs.
Fig. 3: Association between resistotypes, enterotypes, MGS and pdARDs profiles in the 663 individuals from the MetaHIT cohort.
Fig. 4: Dynamics of the pdARDs under various exposures to antibiotics.

Similar content being viewed by others

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.

References

  1. United Nations High-Level Meeting on Antimicrobial Resistance (WHO, UN, 2016).

  2. Ghosh, T. S., Gupta, S. S., Nair, G. B. & Mande, S. S. In silico analysis of antibiotic resistance genes in the gut microflora of individuals from diverse geographies and age-groups. PLoS ONE 8, e83823 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Hu, Y. et al. Metagenome-wide analysis of antibiotic resistance genes in a large cohort of human gut microbiota. Nat. Commun. 4, 2151 (2013).

    Article  PubMed  Google Scholar 

  4. Forslund, K. et al. Country-specific antibiotic use practices impact the human gut resistome. Genome Res. 23, 1163–1169 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Sommer, M. O. A., Dantas, G. & Church, G. M. Functional characterization of the antibiotic resistance reservoir in the human microflora. Science 325, 1128–1131 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Moore, A. M. et al. Pediatric fecal microbiota harbor diverse and novel antibiotic resistance genes. PLoS ONE 8, e78822 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Gupta, S. K. et al. ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrob. Agents Chemother. 58, 212–220 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  8. McArthur, A. G. et al. The comprehensive antibiotic resistance database. Antimicrob. Agents Chemother. 57, 3348–3357 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Zankari, E. et al. Identification of acquired antimicrobial resistance genes. J. Antimicrob. Chemother. 67, 2640–2644 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Arango-Argoty, G. et al. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome 6, 23 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Gibson, M. K., Forsberg, K. J. & Dantas, G. Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology. ISME J. 9, 207–216 (2015).

    Article  CAS  PubMed  Google Scholar 

  12. Wright, G. D. The antibiotic resistome: the nexus of chemical and genetic diversity. Nat. Rev. Microbiol. 5, 175–186 (2007).

    Article  CAS  PubMed  Google Scholar 

  13. Salyers, A. A., Gupta, A. & Wang, Y. Human intestinal bacteria as reservoirs for antibiotic resistance genes. Trends Microbiol. 12, 412–416 (2004).

    Article  CAS  PubMed  Google Scholar 

  14. Ghosh, S., Sadowsky, M. J., Roberts, M. C., Gralnick, J. A. & LaPara, T. M. Sphingobacterium sp. strain PM2-P1-29 harbours a functional tet(X) gene encoding for the degradation of tetracycline. J. Appl. Microbiol. 106, 1336–1342 (2009).

    Article  CAS  PubMed  Google Scholar 

  15. Stinear, T. P., Olden, D. C., Johnson, P. D., Davies, J. K. & Grayson, M. L. Enterococcal vanB resistance locus in anaerobic bacteria in human faeces. Lancet 357, 855–856 (2001).

    Article  CAS  PubMed  Google Scholar 

  16. Penders, J., Stobberingh, E. E., Savelkoul, P. H. M. & Wolffs, P. F. G. The human microbiome as a reservoir of antimicrobial resistance. Front. Microbiol. 4, 87 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Zhang, Y. & Skolnick, J. TM-Align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res. 33, 2302–2309 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Forsberg, K. J. et al. Bacterial phylogeny structures soil resistomes across habitats. Nature 509, 612–616 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Nielsen, H. B. et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat. Biotechnol. 32, 822–828 (2014).

    Article  CAS  PubMed  Google Scholar 

  20. Goossens, H., Ferech, M., Van der Stichele, R. & Elseviers, M. & ESAC Project Group. Outpatient antibiotic use in Europe and association with resistance: a cross-national database study. Lancet 365, 579–587 (2005).

    Article  PubMed  Google Scholar 

  21. Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    Article  CAS  PubMed  Google Scholar 

  22. Berglund, F. et al. Identification of 76 novel B1 metallo-β-lactamases through large-scale screening of genomic and metagenomic data. Microbiome 5, 134 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Everard, A. et al. Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity. Proc. Natl Acad. Sci. USA 110, 9066–9071 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sokol, H. et al. Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients. Proc. Natl Acad. Sci. USA 105, 16731–16736 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Leski, T. A. et al. Multidrug-resistant tet(X)-containing hospital isolates in Sierra Leone. Int. J. Antimicrob. Agents 42, 83–86 (2013).

    Article  CAS  PubMed  Google Scholar 

  26. Plaza Oñate, F. et al. MSPminer: abundance-based reconstitution of microbial pan-genomes from shotgun metagenomic data. Bioinformatics https://doi.org/10.1093/bioinformatics/bty830 (2018).

  27. Li, J. et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834–841 (2014).

    Article  CAS  PubMed  Google Scholar 

  28. Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. de Smet, A. M. G. A. et al. Decontamination of the digestive tract and oropharynx in ICU patients. N. Engl. J. Med. 360, 20–31 (2009).

    Article  PubMed  Google Scholar 

  30. van Schaik, W. The human gut resistome. Philos. Trans. R. Soc. Lond. B Biol. Sci. 370, (2015).

  31. Le Chatelier, E. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).

    Article  PubMed  Google Scholar 

  32. Pehrsson, E. C. et al. Interconnected microbiomes and resistomes in low-income human habitats. Nature 533, 212–216 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Léonard, F., Andremont, A., Leclerq, B., Labia, R. & Tancrède, C. Use of β- lactamase-producing anaerobes to prevent ceftriaxone from degrading intestinal resistance to colonization. J. Infect. Dis. 160, 274–280 (1989).

    Article  PubMed  Google Scholar 

  34. Bilinski, J. et al. Fecal microbiota transplantation in patients with blood disorders inhibits gut colonization with antibiotic-resistant bacteria: results of a prospective, single-center study. Clin. Infect. Dis. 65, 364–370 (2017).

    Article  CAS  PubMed  Google Scholar 

  35. Lupo, A., Coyne, S. & Berendonk, T. U. Origin and evolution of antibiotic resistance: the common mechanisms of emergence and spread in water bodies. Front. Microbiol. 3, 18 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Lagier, J.-C. et al. Culture of previously uncultured members of the human gut microbiota by culturomics. Nat. Microbiol. 1, 16203 (2016).

    Article  CAS  PubMed  Google Scholar 

  37. Illergård, K., Ardell, D. H. & Elofsson, A. Structure is three to ten times more conserved than sequence—a study of structural response in protein cores. Proteins 77, 499–508 (2009).

    Article  PubMed  Google Scholar 

  38. Baquero, F., Tedim, A. P. & Coque, T. M. Antibiotic resistance shaping multi-level population biology of bacteria. Front. Microbiol. 4, 15 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Martínez, J. L., Coque, T. M. & Baquero, F. What is a resistance gene? Ranking risk in resistomes. Nat. Rev. Microbiol. 13, 116–123 (2015).

    Article  PubMed  Google Scholar 

  40. Van Boeckel, T. P. et al. Global antibiotic consumption 2000 to 2010: an analysis of national pharmaceutical sales data. Lancet. Infect. Dis. 14, 742–750 (2014).

    Article  PubMed  Google Scholar 

  41. Allen, H. K., Moe, L. A., Rodbumrer, J., Gaarder, A. & Handelsman, J. Functional metagenomics reveals diverse β- lactamases in a remote Alaskan soil. ISME J. 3, 243–251 (2009).

    Article  CAS  PubMed  Google Scholar 

  42. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Powell, S. et al. eggNOGv3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges. Nucleic Acids Res. 40, D284–D289 (2012).

    Article  CAS  PubMed  Google Scholar 

  45. Liu, Y.-Y. et al. Emergence of plasmid-mediated colistin resistance mechanism MCR-1 in animals and human beings in China: a microbiological and molecular biological study. Lancet Infect. Dis. 16, 161–168 (2016).

    Article  PubMed  Google Scholar 

  46. Kultima, J. R. et al. MOCAT: a metagenomics assembly and gene prediction toolkit. PLoS ONE 7, e47656 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Finn, R. D., Clements, J. & Eddy, S. R. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 39, W29–W37 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Pearson, W. R. & Lipman, D. J. Improved tools for biological sequence comparison. Proc. Natl Acad. Sci. USA 85, 2444–2448 (1988).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Jones, D. T. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292, 195–202 (1999).

    Article  CAS  PubMed  Google Scholar 

  52. Sali, A. & Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).

    Article  CAS  PubMed  Google Scholar 

  53. Wallner, B. & Elofsson, A. Can correct protein models be identified?. Protein Sci. 12, 1073–1086 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Wiederstein, M. & Sippl, M. J. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 35, W407–W410 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Ortiz, A. R., Strauss, C. E. M. & Olmea, O. MAMMOTH (matching molecular models obtained from theory): an automated method for model comparison. Protein Sci. Publ. Protein Soc. 11, 2606–2621 (2002).

    Article  CAS  Google Scholar 

  56. Tibshirani, R. Regression shrinkage and selection via the lasso. J R. Stat. Soc B 58, 267–288 (1996).

    Google Scholar 

  57. Fan, R. E., Chang, K. W., Hsieh, C. J., Wang, X. R. & Lin, C. J. A library for large linear classification. JMLR 9, 1871–1874 .

  58. Siguier, P., Perochon, J., Lestrade, L., Mahillon, J. & Chandler, M. ISfinder: the reference centre for bacterial insertion sequences. Nucleic Acids Res. 34, D32–D36 (2006).

    Article  CAS  PubMed  Google Scholar 

  59. Guglielmini, J., Quintais, L., Garcillán-Barcia, M. P., de la Cruz, F. & Rocha, E. P. C. The repertoire of ICE in prokaryotes underscores the unity, diversity, and ubiquity of conjugation. PLoS Genet. 7, e1002222 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Cury, J., Jové, T., Touchon, M., Néron, B. & Rocha, E. P. Identification and analysis of integrons and cassette arrays in bacterial genomes. Nucleic Acids Res. 44, 4539–4550 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Holmes, I., Harris, K. & Quince, C. Dirichlet multinomial mixtures: generative models for microbial metagenomics. PLoS ONE 7, e30126 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Ding, T. & Schloss, P. D. Dynamics and associations of microbial community types across the human body. Nature 509, 357–360 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Dray, S. & Legendre, P. Testing the species traits-environment relationships: the fourth-corner problem revisited. Ecology 89, 3400–3412 (2008).

    Article  PubMed  Google Scholar 

  64. Godon, J. J., Zumstein, E., Dabert, P., Habouzit, F. & Moletta, R. Molecular microbial diversity of an anaerobic digestor as determined by small-subunit rDNA sequence analysis. Appl. Environ. Microbiol. 63, 2802–2813 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Suau, A. et al. Direct analysis of genes encoding 16S rRNA from complex communities reveals many novel molecular species within the human gut. Appl. Environ. Microbiol. 65, 4799–4807 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Pons, N. et al. METEOR—A Platform for Quantitative Metagenomic Profiling of Complex Ecosystems (JOBIM Montpellier, 2010); http://www.jobim2010.fr/sites/default/files/presentations/27Pons.pdf

  67. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Quereda, J. J. et al. Bacteriocin from epidemic Listeria strains alters the host intestinal microbiota to favor infection. Proc. Natl Acad. Sci. USA 113, 5706–5711 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Robert, P. & Escoufier, Y. A unifying tool for linear multivariate statistical methods: the RV-coefficient. Appl. Stat. 25, 257 (1976).

    Article  Google Scholar 

Download references

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

  1. Julien Tap

    Present address: Danone Nutricia Research, Palaiseau, France

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

Authors and 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

Authors
  1. Etienne Ruppé
    View author publications

    Search author on:PubMed Google Scholar

  2. Amine Ghozlane
    View author publications

    Search author on:PubMed Google Scholar

  3. Julien Tap
    View author publications

    Search author on:PubMed Google Scholar

  4. Nicolas Pons
    View author publications

    Search author on:PubMed Google Scholar

  5. Anne-Sophie Alvarez
    View author publications

    Search author on:PubMed Google Scholar

  6. Nicolas Maziers
    View author publications

    Search author on:PubMed Google Scholar

  7. Trinidad Cuesta
    View author publications

    Search author on:PubMed Google Scholar

  8. Sara Hernando-Amado
    View author publications

    Search author on:PubMed Google Scholar

  9. Irene Clares
    View author publications

    Search author on:PubMed Google Scholar

  10. Jose Luís Martínez
    View author publications

    Search author on:PubMed Google Scholar

  11. Teresa M. Coque
    View author publications

    Search author on:PubMed Google Scholar

  12. Fernando Baquero
    View author publications

    Search author on:PubMed Google Scholar

  13. Val F. Lanza
    View author publications

    Search author on:PubMed Google Scholar

  14. Luis Máiz
    View author publications

    Search author on:PubMed Google Scholar

  15. Tiphaine Goulenok
    View author publications

    Search author on:PubMed Google Scholar

  16. Victoire de Lastours
    View author publications

    Search author on:PubMed Google Scholar

  17. Nawal Amor
    View author publications

    Search author on:PubMed Google Scholar

  18. Bruno Fantin
    View author publications

    Search author on:PubMed Google Scholar

  19. Ingrid Wieder
    View author publications

    Search author on:PubMed Google Scholar

  20. Antoine Andremont
    View author publications

    Search author on:PubMed Google Scholar

  21. Willem van Schaik
    View author publications

    Search author on:PubMed Google Scholar

  22. Malbert Rogers
    View author publications

    Search author on:PubMed Google Scholar

  23. Xinglin Zhang
    View author publications

    Search author on:PubMed Google Scholar

  24. Rob J. L. Willems
    View author publications

    Search author on:PubMed Google Scholar

  25. Alexandre G. de Brevern
    View author publications

    Search author on:PubMed Google Scholar

  26. Jean-Michel Batto
    View author publications

    Search author on:PubMed Google Scholar

  27. Hervé M. Blottière
    View author publications

    Search author on:PubMed Google Scholar

  28. Pierre Léonard
    View author publications

    Search author on:PubMed Google Scholar

  29. Véronique Léjard
    View author publications

    Search author on:PubMed Google Scholar

  30. Aline Letur
    View author publications

    Search author on:PubMed Google Scholar

  31. Florence Levenez
    View author publications

    Search author on:PubMed Google Scholar

  32. Kevin Weiszer
    View author publications

    Search author on:PubMed Google Scholar

  33. Florence Haimet
    View author publications

    Search author on:PubMed Google Scholar

  34. Joël Doré
    View author publications

    Search author on:PubMed Google Scholar

  35. Sean P. Kennedy
    View author publications

    Search author on:PubMed Google Scholar

  36. S. Dusko Ehrlich
    View author publications

    Search author on:PubMed Google Scholar

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/).

Corresponding author

Correspondence to Etienne Ruppé.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Supplementary Information

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

Reporting Summary

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%.

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).

Supplementary Table 3

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

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.

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).

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).

Supplementary Table 7

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ruppé, E., Ghozlane, A., Tap, J. et al. Prediction of the intestinal resistome by a three-dimensional structure-based method. Nat Microbiol 4, 112–123 (2019). https://doi.org/10.1038/s41564-018-0292-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

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

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing