Recovery of gut microbiota of healthy adults following antibiotic exposure

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Gut microbial diversity recovers after a broad-spectrum antibiotic intervention.
Fig. 2: Gut microbial composition dramatically changes after broad-spectrum antibiotic intervention but recovers progressively.
Fig. 3: Microbial antibiotic resistance potential changes after broad-spectrum antibiotic intervention.
Fig. 4: Resistance gene carriage impacts the odds of microbial survival and colonization.
Fig. 5: Microbial virulence factors are enriched immediately after antibiotic treatment.

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/.

References

  1. 1.

    Lynch, S. V. & Pedersen, O. The human intestinal microbiome in health and disease. N. Engl. J. Med. 375, 2369–2379 (2016).

    CAS  Google Scholar 

  2. 2.

    Manges, A. R. et al. Comparative metagenomic study of alterations to the intestinal microbiota and risk of nosocomial Clostridum difficile-associated disease. J. Infect. Dis. 202, 1877–1884 (2010).

    Article  Google Scholar 

  3. 3.

    Ley, R. E. et al. Obesity alters gut microbial ecology. Proc. Natl Acad. Sci. USA 102, 11070–11075 (2005).

    CAS  Article  Google Scholar 

  4. 4.

    Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006).

    Article  Google Scholar 

  5. 5.

    Forslund, K. et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262–266 (2015).

    CAS  Article  Google Scholar 

  6. 6.

    Karlsson, F. H. et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013).

    CAS  Article  Google Scholar 

  7. 7.

    Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012).

    CAS  Article  Google Scholar 

  8. 8.

    Craven, M. et al. Inflammation drives dysbiosis and bacterial invasion in murine models of ileal Crohn’s disease. PLoS ONE 7, e41594 (2012).

    CAS  Article  Google Scholar 

  9. 9.

    Hsiao, E. Y. et al. Microbiota modulate behavioral and physiological abnormalities associated with neurodevelopmental disorders. Cell 155, 1451–1463 (2013).

  10. 10.

    Konig, J. et al. Consensus report: faecal microbiota transfer—clinical applications and procedures. Aliment. Pharmacol. Ther. 45, 222–239 (2017).

    CAS  Article  Google Scholar 

  11. 11.

    Bindels, L. B., Delzenne, N. M., Cani, P. D. & Walter, J. Towards a more comprehensive concept for prebiotics. Nat. Rev. Gastroenterol. Hepatol. 12, 303–310 (2015).

  12. 12.

    Hemarajata, P. & Versalovic, J. Effects of probiotics on gut microbiota: mechanisms of intestinal immunomodulation and neuromodulation. Therap. Adv. Gastroenterol. 6, 39–51 (2013).

  13. 13.

    Mikkelsen, K. H., Allin, K. H. & Knop, F. K. Effect of antibiotics on gut microbiota, glucose metabolism and body weight regulation: a review of the literature. Diabetes Obes. Metab. 18, 444–453 (2016).

    CAS  Article  Google Scholar 

  14. 14.

    Hollis, A. & Ahmed, Z. Preserving antibiotics, rationally. N. Engl. J. Med. 369, 2474–2476 (2013).

    CAS  Article  Google Scholar 

  15. 15.

    Nobel, Y. R. et al. Metabolic and metagenomic outcomes from early-life pulsed antibiotic treatment. Nat. Commun. 6, 7486 (2015).

    Article  Google Scholar 

  16. 16.

    Cho, I. & Blaser, M. J. The human microbiome: at the interface of health and disease. Nat. Rev. Genet. 13, 260–270 (2012).

    CAS  Article  Google Scholar 

  17. 17.

    Frohlich, E. E. et al. Cognitive impairment by antibiotic-induced gut dysbiosis: analysis of gut microbiota-brain communication. Brain Behav. Immun. 56, 140–155 (2016).

    Article  Google Scholar 

  18. 18.

    Korpela, K. et al. Intestinal microbiome is related to lifetime antibiotic use in Finnish pre-school children. Nat. Commun. 7, 10410 (2016).

    CAS  Article  Google Scholar 

  19. 19.

    Bailey, L. C. et al. Association of antibiotics in infancy with early childhood obesity. JAMA Pediatr. 168, 1063–1069 (2014).

    Article  Google Scholar 

  20. 20.

    Saari, A., Virta, L. J., Sankilampi, U., Dunkel, L. & Saxen, H. Antibiotic exposure in infancy and risk of being overweight in the first 24 months of life. Pediatrics 135, 617–626 (2015).

    Article  Google Scholar 

  21. 21.

    Shaw, S. Y., Blanchard, J. F. & Bernstein, C. N. Association between the use of antibiotics and new diagnoses of Crohn’s disease and ulcerative colitis. Am. J. Gastroenterol. 106, 2133–2142 (2011).

  22. 22.

    Trasande, L. et al. Infant antibiotic exposures and early-life body mass. Int. J. Obes. 37, 16–23 (2013).

    CAS  Article  Google Scholar 

  23. 23.

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

    CAS  Article  Google Scholar 

  24. 24.

    van Schaik, W. The human gut resistome. Phil. Trans. R. Soc. B 370, 20140087 (2015).

    Article  Google Scholar 

  25. 25.

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

    CAS  Article  Google Scholar 

  26. 26.

    Rashid, M. U. et al. Determining the long-term effect of antibiotic administration on the human normal intestinal microbiota using culture and pyrosequencing methods. Clin. Infect. Dis. 60, S77–S84 (2015).

    CAS  Article  Google Scholar 

  27. 27.

    Zaura, E. et al. Same exposure but two radically different responses to antibiotics: resilience of the salivary microbiome versus long-term microbial shifts in feces. mBio 6, e01693-15 (2015).

    Article  Google Scholar 

  28. 28.

    Reijnders, D. et al. Effects of gut microbiota manipulation by antibiotics on host metabolism in obese humans: a randomized double-blind placebo-controlled trial. Cell Metab. 24, 63–74 (2016).

    CAS  Article  Google Scholar 

  29. 29.

    Abeles, S. R. et al. Microbial diversity in individuals and their household contacts following typical antibiotic courses. Microbiome 4, 39 (2016).

    Article  Google Scholar 

  30. 30.

    Raymond, F. et al. The initial state of the human gut microbiome determines its reshaping by antibiotics. ISME J. 10, 707–720 (2016).

    CAS  Article  Google Scholar 

  31. 31.

    Mikkelsen, K. H. et al. Effect of antibiotics on gut microbiota, gut hormones and glucose metabolism. PLoS ONE 10, e0142352 (2015).

    Article  Google Scholar 

  32. 32.

    Houben, A. J. et al. Selective decontamination of the oropharynx and the digestive tract, and antimicrobial resistance: a 4 year ecological study in 38 intensive care units in the Netherlands. J. Antimicrob. Chemother. 69, 797–804 (2014).

    CAS  Article  Google Scholar 

  33. 33.

    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).

    CAS  Article  Google Scholar 

  34. 34.

    Sunagawa, S. et al. Metagenomic species profiling using universal phylogenetic marker genes. Nat. Methods 10, 1196–1199 (2013).

    CAS  Article  Google Scholar 

  35. 35.

    Consortium, H. M. P. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

    Article  Google Scholar 

  36. 36.

    Voigt, A. Y. et al. Temporal and technical variability of human gut metagenomes. Genome Biol. 16, 73 (2015).

    Article  Google Scholar 

  37. 37.

    Talukdar, P. K., Olguin-Araneda, V., Alnoman, M., Paredes-Sabja, D. & Sarker, M. R. Updates on the sporulation process in Clostridium species. Res. Microbiol. 166, 225–235 (2015).

    Article  Google Scholar 

  38. 38.

    Maier, L. et al. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature 555, 623–628 (2018).

    CAS  Article  Google Scholar 

  39. 39.

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

    CAS  Article  Google Scholar 

  40. 40.

    Kultima, J. R. et al. MOCAT2: a metagenomic assembly, annotation and profiling framework. Bioinformatics 32, 2520–2523 (2016).

    CAS  Article  Google Scholar 

  41. 41.

    Jia, B. et al. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res. 45, D566–D573 (2017).

    CAS  Article  Google Scholar 

  42. 42.

    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).

    CAS  Article  Google Scholar 

  43. 43.

    Queenan, A. M. & Bush, K. Carbapenemases: the versatile β-lactamases. Clin. Microbiol. Rev. 20, 440–458 (2007).

    CAS  Article  Google Scholar 

  44. 44.

    Werner, G., Strommenger, B. & Witte, W. Acquired vancomycin resistance in clinically relevant pathogens. Future Microbiol. 3, 547–562 (2008).

    CAS  Article  Google Scholar 

  45. 45.

    Damier-Piolle, L., Magnet, S., Brémont, S., Lambert, T. & Courvalin, P. AdeIJK, a resistance-nodulation-cell division pump effluxing multiple antibiotics in Acinetobacter baumannii. Antimicrob. Agents Chemother. 52, 557–562 (2008).

    CAS  Article  Google Scholar 

  46. 46.

    Hou, P. F., Chen, X. Y., Yan, G. F., Wang, Y. P. & Ying, C. M. Study of the correlation of imipenem resistance with efflux pumps AdeABC, AdeIJK, AdeDE and AbeM in clinical isolates of Acinetobacter baumannii. Chemotherapy 58, 152–158 (2012).

    CAS  Article  Google Scholar 

  47. 47.

    Kanehisa, M. et al. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42, D199–D205 (2014).

    CAS  Article  Google Scholar 

  48. 48.

    Pu, Y. et al. Enhanced efflux activity facilitates drug tolerance in dormant bacterial cells. Mol. Cell 62, 284–294 (2016).

    CAS  Article  Google Scholar 

  49. 49.

    Winter, S. E. et al. Gut inflammation provides a respiratory electron acceptor for Salmonella. Nature 467, 426–429 (2010).

    CAS  Article  Google Scholar 

  50. 50.

    Winter, S. E. et al. Host-derived nitrate boosts growth of E. coli in the inflamed gut. Science 339, 708–711 (2013).

    CAS  Article  Google Scholar 

  51. 51.

    Rivera-Chávez, F. et al. Depletion of butyrate-producing Clostridia from the gut microbiota drives an aerobic luminal expansion of Salmonella. Cell Host Microbe 19, 443–454 (2016).

    Article  Google Scholar 

  52. 52.

    Zhu, W. et al. Precision editing of the gut microbiota ameliorates colitis. Nature 553, 208–211 (2018).

    CAS  Article  Google Scholar 

  53. 53.

    Driscoll, T. et al. Integration and visualization of host-pathogen data related to infectious diseases. Bioinformatics 27, 2279–2287 (2011).

    CAS  Article  Google Scholar 

  54. 54.

    Rutherford, S. T. & Bassler, B. L. Bacterial quorum sensing: its role in virulence and possibilities for its control. Cold Spring Harb. Perspect. Med . 2, a012427 (2012).

    Article  Google Scholar 

  55. 55.

    Buffie, C. G. & Pamer, E. G. Microbiota-mediated colonization resistance against intestinal pathogens. Nat. Rev. Immunol. 13, 790–801 (2013).

    CAS  Article  Google Scholar 

  56. 56.

    Kassam, Z., Lee, C. H., Yuan, Y. & Hunt, R. H. Fecal microbiota transplantation for Clostridium difficile infection: systematic review and meta-analysis. Am. J. Gastroenterol. 108, 500–508 (2013).

  57. 57.

    Finegold, S. M. et al. Gastrointestinal microflora studies in late-onset autism. Clin. Infect. Dis. 35, S6–S16 (2002).

    Article  Google Scholar 

  58. 58.

    Song, Y., Liu, C. & Finegold, S. M. Real-time PCR quantitation of Clostridia in feces of autistic children. Appl. Environ. Microbiol. 70, 6459–6465 (2004).

  59. 59.

    Luna, R. A. et al. Distinct microbiome-neuroimmune signatures correlate with functional abdominal pain in children with autism spectrum disorder. Cell. Mol. Gastroenterol. Hepatol. 3, 218–230 (2017).

    Article  Google Scholar 

  60. 60.

    Ren, Z. et al. Intestinal microbial variation may predict early acute rejection after liver transplantation in rats. Transplantation 98, 844–852 (2014).

    CAS  Article  Google Scholar 

  61. 61.

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

    Article  Google Scholar 

  62. 62.

    Rao, S., Kupfer, Y., Pagala, M., Chapnick, E. & Tessler, S. Systemic absorption of oral vancomycin in patients with Clostridium difficile infection. Scand. J. Infect. Dis. 43, 386–388 (2011).

    CAS  Article  Google Scholar 

  63. 63.

    Rohrbaugh, T. M., Anolik, R., August, C. S., Serota, F. T. & Koch, P. A. Absorption of oral aminoglycosides following bone marrow transplantation. Cancer 53, 1502–1506 (1984).

    CAS  Article  Google Scholar 

  64. 64.

    Craig, W. A. The pharmacology of meropenem, a new carbapenem antibiotic. Clin. Infect. Dis. 24, S266–S275 (1997).

    CAS  Article  Google Scholar 

  65. 65.

    IHMS_SOP 07 V2: Standard Operating Procedure for Fecal Samples DNA Extraction Protocol H INRA (IHMS, 2015); http://www.microbiome-standards.org/index.php?id=254

  66. 66.

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

    Article  Google Scholar 

  67. 67.

    Yutin, N. & Galperin, M. Y. A genomic update on clostridial phylogeny: Gram-negative spore formers and other misplaced clostridia. Environ. Microbiol. 15, 2631–2641 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  Article  Google Scholar 

  69. 69.

    Liu, B. & Pop, M. ARDB—antibiotic resistance genes database. Nucleic Acids Res. 37, D443–D447 (2009).

    CAS  Article  Google Scholar 

  70. 70.

    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    CAS  Article  Google Scholar 

  71. 71.

    Lovell, D., Pawlowsky-Glahn, V., Egozcue, J. J., Marguerat, S. & Bähler, J. Proportionality: a valid alternative to correlation for relative data. PLoS Comput. Biol. 11, e1004075 (2015).

    Article  Google Scholar 

  72. 72.

    Weiss, S. et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5, 27 (2017).

    Article  Google Scholar 

  73. 73.

    Aitchison, J. The statistical analysis of compositional data. J. R. Stat. Soc. Ser. B 44, 139–177 (1982).

    Google Scholar 

  74. 74.

    Palleja, A. et al. Roux-en-Y gastric bypass surgery of morbidly obese patients induces swift and persistent changes of the individual gut microbiota. Genome Med. 8, 67 (2016).

    Article  Google Scholar 

Download references

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

Affiliations

Authors

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.

Corresponding authors

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

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

Reporting Summary

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.

Supplementary Table 2

Sample information and read quality control statistics.

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.

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.

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).

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).

Supplementary Dataset 1

Associated data for Figure 4.

Supplementary Dataset 2

Associated data for Figure 5.

Supplementary Dataset 3

Associated data for Supplementary Figure 6.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Palleja, A., Mikkelsen, K.H., Forslund, S.K. et al. Recovery of gut microbiota of healthy adults following antibiotic exposure. Nat Microbiol 3, 1255–1265 (2018). https://doi.org/10.1038/s41564-018-0257-9

Download citation

Further reading