Abstract

To minimize the impact of antibiotics, gut microorganisms harbour and exchange antibiotics resistance genes, collectively called their resistome. Using shotgun sequencing-based metagenomics, we analysed the partial eradication and subsequent regrowth of the gut microbiota in 12 healthy men over a 6-month period following a 4-day intervention with a cocktail of 3 last-resort antibiotics: meropenem, gentamicin and vancomycin. Initial changes included blooms of enterobacteria and other pathobionts, such as Enterococcus faecalis and Fusobacterium nucleatum, and the depletion of Bifidobacterium species and butyrate producers. The gut microbiota of the subjects recovered to near-baseline composition within 1.5 months, although 9 common species, which were present in all subjects before the treatment, remained undetectable in most of the subjects after 180 days. Species that harbour β-lactam resistance genes were positively selected for during and after the intervention. Harbouring glycopeptide or aminoglycoside resistance genes increased the odds of de novo colonization, however, the former also decreased the odds of survival. Compositional changes under antibiotic intervention in vivo matched results from in vitro susceptibility tests. Despite a mild yet long-lasting imprint following antibiotics exposure, the gut microbiota of healthy young adults are resilient to a short-term broad-spectrum antibiotics intervention and their antibiotics resistance gene carriage modulates their recovery processes.

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Data availability

The high-quality reads have been deposited in the European Nucleotide Archive with accession number ERP022986. Relative abundances of taxa and functional features can be downloaded at http://arumugamlab.sund.ku.dk/SuppData/Palleja_et_al_2018_ABX/.

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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

This work was funded by an international alliance grant from The Novo Nordisk Foundation Center for Basic Metabolic Research, which is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (grant no. NNF10CC1016515). Our work was also funded by the TARGET research initiative (Danish Strategic Research Council [0603–00484B]), the Danish Diabetes Academy supported by the Novo Nordisk Foundation, the Danish Council for Independent Research (Medical Sciences), and the Danish Diabetes Association. S.K.F. was funded by FP7 METACARDIS HEALTH-F4-2012-305312.

Author information

Author notes

  1. These authors contributed equally: Albert Palleja, Kristian H. Mikkelsen, Sofia K. Forslund.

Affiliations

  1. Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    • Albert Palleja
    • , Alireza Kashani
    • , Kristine H. Allin
    • , Trine Nielsen
    • , Tue H. Hansen
    • , Paul Theodor Pyl
    • , Torben Hansen
    • , Filip K. Knop
    • , Manimozhiyan Arumugam
    •  & Oluf Pedersen
  2. Clinical-Microbiomics A/S, Copenhagen, Denmark

    • Albert Palleja
    •  & Henrik Bjorn Nielsen
  3. Center for Diabetes Research, Gentofte Hospital, University of Copenhagen, Hellerup, Denmark

    • Kristian H. Mikkelsen
    • , Morten F. Nielsen
    • , Tina Vilsbøll
    •  & Filip K. Knop
  4. Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin and Max Delbruck Center for Molecular Medicine, Berlin, Germany

    • Sofia K. Forslund
  5. Max Delbruck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany

    • Sofia K. Forslund
    •  & Peer Bork
  6. Charité-Universitätsmedizin Berlin , Freie Universität Berlin Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany

    • Sofia K. Forslund
  7. Berlin Institute of Health, Berlin, Germany

    • Sofia K. Forslund
  8. Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany

    • Sofia K. Forslund
    • , Luis Pedro Coelho
    • , Athanasios Typas
    •  & Peer Bork
  9. Danish Diabetes Academy, Odense, Denmark

    • Alireza Kashani
  10. Department of Clinical Epidemiology, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark

    • Kristine H. Allin
  11. BGI-Shenzhen, Shenzhen, China

    • Suisha Liang
    • , Qiang Feng
    • , Chenchen Zhang
    • , Huanming Yang
    •  & Jian Wang
  12. China National GeneBank, BGI-Shenzhen, Shenzhen, China

    • Suisha Liang
    • , Qiang Feng
    • , Chenchen Zhang
    • , Huanming Yang
    •  & Jian Wang
  13. James D. Watson Institute of Genome Sciences, Hangzhou, China

    • Huanming Yang
    •  & Jian Wang
  14. Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany

    • Athanasios Typas
  15. Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, Heidelberg, Germany

    • Peer Bork
  16. Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany

    • Peer Bork
  17. iCarbonX, Shenzhen, China

    • Jun Wang
  18. Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China

    • Jun Wang
  19. Department of Biology, University of Copenhagen, Copenhagen, Denmark

    • Jun Wang
  20. State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Avenida Wai Long, Taipa Macau, China

    • Jun Wang
  21. Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark

    • Torben Hansen
  22. Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    • Filip K. Knop

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Contributions

O.P., T.H. and F.K.K. devised the study protocol. M.F.N. participated in the protocol design and application and in the participant recruitment and selection. K.H.M. performed sample collections and carried out patient phenotyping. K.H.A. supervised the microbial DNA extraction. S.L., C.Z., J.W., Q.F. and H.Y. performed shotgun metagenomics sequencing and taxonomic profiling. P.T.P., L.P.C. and M.A. estimated IGC gene profiles. H.B.N. generated the MGS groups based on IGC. A.P., S.K.F. and A.K. designed and performed the data analysis. M.A., T.H., P.B. and O.P. supervised the data analysis. A.P., S.K.F., K.H.M. and M.A. wrote the paper. K.H.A., T.N., T.H.H., A.K., H.B.N., J.W., A.T., P.B., T.V., F.K.K., T.H. and O.P. revised the paper. All authors contributed to data interpretation, discussion and editing of the paper. All authors read and approved the final manuscript.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Filip K. Knop or Manimozhiyan Arumugam or Oluf Pedersen.

Supplementary information

  1. Supplementary Information

    Supplementary Figures 1–12, legends for Supplementary Tables and Supplementary Datasets.

  2. Reporting Summary

  3. Supplementary Table 1

    Comparison between taxa and KEGG function abundances at baseline (D0) and at subsequent time points (D8, D42, D180) using a two-sided Wilcoxon signed-rank test.

  4. Supplementary Table 2

    Sample information and read quality control statistics.

  5. Supplementary Table 3

    Predicted and manually curated gene assignments (taken from GenBank) for 3 well-characterized species such as Salmonella typhimurium, Enterobacter cloacae and Escherichia coli.

  6. Supplementary Table 4

    Species that differentially changed their abundance (two-sided Wilcoxon signed-rank test) following antibiotic treatment (from Supplementary Table 1) contrasted with their extent of relative growth inhibition from Maier et al.38.

  7. Supplementary Table 5

    Extent of enrichment (significantly higher number of genes) of ARGs for the drugs used in this study (and multidrug efflux pumps) in species enriched under intervention versus not (from Supplementary Table 1).

  8. Supplementary Table 6

    Gene-level differentially abundant ARGs under intervention, relative to their significantly differential prevalence in genomes in enriched versus depleted species among those differentially abundant under intervention (from Supplementary Table 1).

  9. Supplementary Dataset 1

    Associated data for Figure 4.

  10. Supplementary Dataset 2

    Associated data for Figure 5.

  11. Supplementary Dataset 3

    Associated data for Supplementary Figure 6.

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DOI

https://doi.org/10.1038/s41564-018-0257-9