Abstract

Antimicrobial resistance (AMR) in bacteria and associated human morbidity and mortality is increasing. The use of antimicrobials in livestock selects for AMR that can subsequently be transferred to humans. This flow of AMR between reservoirs demands surveillance in livestock and in humans. We quantified and characterized the acquired resistance gene pools (resistomes) of 181 pig and 178 poultry farms from nine European countries, sequencing more than 5,000 Gb of DNA using shotgun metagenomics. We quantified acquired AMR using the ResFinder database and a second database constructed for this study, consisting of AMR genes identified through screening environmental DNA. The pig and poultry resistomes were very different in abundance and composition. There was a significant country effect on the resistomes, more so in pigs than in poultry. We found higher AMR loads in pigs, whereas poultry resistomes were more diverse. We detected several recently described, critical AMR genes, including mcr-1 and optrA, the abundance of which differed both between host species and between countries. We found that the total acquired AMR level was associated with the overall country-specific antimicrobial usage in livestock and that countries with comparable usage patterns had similar resistomes. However, functionally determined AMR genes were not associated with total drug use.

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References

  1. 1.

    Antimicrobial Resistance Global Report on Surveillance 2014 (WHO, 2014).

  2. 2.

    Aarestrup, F. M. The livestock reservoir for antimicrobial resistance: a personal view on changing patterns of risks, effects of interventions and the way forward. Phil. Trans. R. Soc. B 370, 20140085 (2015).

  3. 3.

    Chantziaras, I., Boyen, F., Callens, B. & Dewulf, J. Correlation between veterinary antimicrobial use and antimicrobial resistance in food-producing animals: a report on seven countries. J. Antimicrob. Chemother. 69, 827–834 (2014).

  4. 4.

    Dunlop, R. H. et al. Associations among antimicrobial drug treatments and antimicrobial resistance of fecal Escherichia coli of swine on 34 farrow-to-finish farms in Ontario, Canada. Prev. Vet. Med. 34, 283–305 (1998).

  5. 5.

    Aarestrup, F. M., Seyfarth, A. M., Emborg, H., Pedersen, K. & Bager, F. Effect of abolishment of the use of antimicrobial agents for growth promotion on occurrence of antimicrobial resistance in fecal enterococci from food animals in Denmark. Antimicrob. Agents Chemother. 45, 2054–2059 (2001).

  6. 6.

    Agersø, Y. & Aarestrup, F. M. Voluntary ban on cephalosporin use in Danish pig production has effectively reduced extended-spectrum cephalosporinase-producing Escherichia coli in slaughter pigs. J. Antimicrob. Chemother. 68, 569–572 (2013).

  7. 7.

    Dutil, L. et al. Ceftiofur resistance in Salmonella enterica serovar Heidelberg from chicken meat and humans, Canada. Emerg. Infect. Dis. 16, 48–54 (2010).

  8. 8.

    Dorado-García, A. et al. Quantitative assessment of antimicrobial resistance in livestock during the course of a nationwide antimicrobial use reduction in the Netherlands. J. Antimicrob. Chemother. 71, 3607–3619 (2016).

  9. 9.

    Aarestrup, F. M. et al. Resistance to antimicrobial agents used for animal therapy in pathogenic-, zoonotic- and indicator bacteria isolated from different food animals in Denmark: a baseline study for the Danish Integrated Antimicrobial Resistance Monitoring Programme (DANMAP). APMIS 106, 745–770 (1998).

  10. 10.

    European Food Safety Authority Harmonised monitoring of antimicrobial resistance in Salmonella and Campylobacter isolates from food animals in the European Union. Clin. Microbiol. Infect. 14, 522–533 (2008).

  11. 11.

    McEwan, S. A., Aarestrup, F. M. & Jordan, D. in Antimicrobial Resistance in Bacteria of Animal Origin (ed. Aarestrup, F. M.) 397–413 (ASM Press, Washington DC, 2006).

  12. 12.

    Veterinary Medicines Division European Medicines Agency, European Surveillance of Veterinary Antimicrobial Consumption, 2016 (EMA, 2016).

  13. 13.

    European Centre for Disease Prevention and Control, European Food Safety Authority & European Medicines Agency ECDC/EFSA/EMA first joint report on the integrated analysis of the consumption of antimicrobial agents and occurrence of antimicrobial resistance in bacteria from humans and food-producing animals. EFSA J. 13, 4006 (2015).

  14. 14.

    European Food Safety Authority Technical specifications on the harmonised monitoring and reporting of antimicrobial resistance in Salmonella, Campylobacter and indicator Escherichia coli and Enterococcus spp. bacteria transmitted through food. EFSA J. 10, 64 (2012).

  15. 15.

    Nordahl Petersen, T. et al. Meta-genomic analysis of toilet waste from long distance flights; a step towards global surveillance of infectious diseases and antimicrobial resistance. Sci. Rep. 5, 11444 (2015).

  16. 16.

    Xiao, L. et al. A reference gene catalogue of the pig gut microbiome. Nat. Microbiol. 1, 16161 (2016).

  17. 17.

    Munk, P. et al. A sampling and metagenomic sequencing-based methodology for monitoring antimicrobial resistance in swine herds. J. Antimicrob. Chemother. 72, 385–392 (2016).

  18. 18.

    Hasman, H. et al. Detection of mcr-1 encoding plasmid-mediated colistin-resistant Escherichia coli isolates from human bloodstream infection and imported chicken meat, Denmark 2015. Euro Suveill. 20, 30085 (2015).

  19. 19.

    Wang, Y. et al. A novel gene, optrA, that confers transferable resistance to oxazolidinones and phenicols and its presence in Enterococcus faecalis and Enterococcus faecium of human and animal origin. J. Antimicrob. Chemother. 70, 2182–2190 (2015).

  20. 20.

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

  21. 21.

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

  22. 22.

    Martínez, J. L., Coque, T. M. & Baquero, F. What is a resistance gene? Ranking risk in resistomes. Nat. Rev. Microbiol. 13, 116–123 (2014).

  23. 23.

    Knudsen, B. E. et al. Impact of sample type and DNA isolation procedure on genomic inference of microbiome composition. mSystems 1, e00095-16 (2016).

  24. 24.

    Bowers, R. M. et al. Impact of library preparation protocols and template quantity on the metagenomic reconstruction of a mock microbial community. BMC Genomics 16, 856 (2015).

  25. 25.

    Hammerum, A. M. et al. Characterization of extended-spectrum β-lactamase (ESBL)-producing Escherichia coli obtained from Danish pigs, pig farmers and their families from farms with high or no consumption of third- or fourth-generation cephalosporins. J. Antimicrob. Chemother. 69, 2650–2657 (2014).

  26. 26.

    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).

  27. 27.

    Anantharaman, K. et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat. Commun. 7, 13219 (2016).

  28. 28.

    Ma, L. et al. Metagenomic assembly reveals hosts of antibiotic resistance genes and the shared resistome in pig, chicken, and human feces. Environ. Sci. Technol. 50, 420–427 (2016).

  29. 29.

    BBduk v39.92 (Bushnell, B., 2018); https://sourceforge.net/projects/bbmap/

  30. 30.

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

  31. 31.

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

  32. 32.

    Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

  33. 33.

    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).

  34. 34.

    Pehrsson, E. C., Forsberg, K. J., Gibson, M. K., Ahmadi, S. & Dantas, G. Novel resistance functions uncovered using functional metagenomic investigations of resistance reservoirs. Front. Microbiol. 4, 145 (2013).

  35. 35.

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

  36. 36.

    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).

  37. 37.

    Legendre, P. & Gallagher, E. D. Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280 (2001).

  38. 38.

    Timmerman, T. et al. Quantification and evaluation of antimicrobial drug use in group treatments for fattening pigs in Belgium. Prev. Vet. Med. 74, 251–263 (2006).

  39. 39.

    Oksanen, J. et al. vegan: Community Ecology Package v2.4-1 (CRAN, 2016); https://CRAN.R-project.org/package=vegan

  40. 40.

    Pielou, E. C. The measurement of diversity in different types of biological collections. J. Theor. Biol. 13, 131–144 (1966).

  41. 41.

    Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).

  42. 42.

    Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics 23, 127–128 (2007).

  43. 43.

    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, NY, 2009).

  44. 44.

    Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

  45. 45.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

  46. 46.

    McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).

  47. 47.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).

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Acknowledgements

We thank all of the anonymous pig and poultry herd owners who agreed to participate in the study and especially everyone involved in sampling and laboratory work: M. Schlepers (Belgium); T. Ivanova, N. Cholakov, E. Gurova-Mehmedova and K. Penchev (Bulgaria); F. Nienhaus (Germany); C. L. Nielsen, P. Ryt-Hansen, B. Hølstad, B. Rasmussen and K. Nielsen (Denmark); C. Jenna, D. Virginie, E. Florent, E. Eric, S. Le Bouquin, L. Denis and T. Rodolphe (France); M. Gherpelli, M. Pegoraro and V. Carfora (Italy); D. de Vries (the Netherlands); B. Gawlik, D. Krasucka and A. Hoszowski (Poland). The EFFORT project (www.effort-against-amr.eu) and the work presented here is supported by the European Union, FP7-KBBE-2013-7, grant agreement 613754.

Author information

Author notes

  1. A list of participants and their affiliations appears at the end of the paper.

Affiliations

  1. Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Kongens Lyngby, Denmark

    • Patrick Munk
    • , Berith Elkær Knudsen
    • , Oksana Lukjancenko
    • , Ana Sofia Ribeiro Duarte
    • , Thomas Nordahl Petersen
    • , Ole Lund
    • , Tine Hald
    • , Sünje Johanna Pamp
    • , Håkan Vigre
    •  & Frank M. Aarestrup
  2. Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands

    • Liese Van Gompel
    • , Roosmarijn E. C. Luiken
    • , Lidwien A. M. Smit
    • , Heike Schmitt
    • , Alejandro Dorado Garcia
    • , Alex Bossers
    • , Haitske Graveland
    •  & Dick Heederik
  3. Intomics A/S. Diplomvej 377, Kongens Lyngby, Denmark

    • Rasmus Borup Hansen
  4. Wageningen Bioveterinary Research, Lelystad, the Netherlands

    • Alex Bossers
    • , Alieda van Essen
    • , Helmut W. Saatkamp
    • , Jaap A. Wagenaar
    •  & Dik Mevius
  5. Genomic Research Laboratory, Hôpitaux Universitaires de Genève, Geneva, Switzerland

    • Etienne Ruppé
  6. Department of Infectious Diseases and Immunology, Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands

    • Jaap A. Wagenaar
    •  & Dik Mevius
  7. Department of Animal Health and Health Surveillance Center (VISAVET), Complutense University of Madrid, Madrid, Spain

    • Bruno Gonzalez-Zorn
    •  & Gabriel Moyano
  8. Fougeres Laboratory, French Agency for Food, Environmental and Occupational Health & Safety, Fougères, France

    • Pascal Sanders
    • , Claire Chauvin
    •  & Julie David
  9. Istituto Zooprofilattico Sperimentale del Lazio e della Toscana, Department of General Diagnostics, National Reference Laboratory for Antimicrobial Resistance, Rome, Italy

    • Antonio Battisti
    •  & Andrea Caprioli
  10. Department of Reproduction, Obstetrics and Herd Health, Ghent University, Merelbeke, Belgium

    • Jeroen Dewulf
  11. University of Veterinary Medicine Hannover, Bakum, Germany

    • Thomas Blaha
    • , Katharina Wadepohl
    •  & Maximiliane Brandt
  12. National Veterinary Research Institute, Pulawy, Poland

    • Dariusz Wasyl
    • , Magdalena Skarzyńska
    •  & Magdalena Zajac
  13. National Diagnostic Research Veterinary Institute, Sofia, Bulgaria

    • Hristo Daskalov
  14. SAFOSO AG, Liebefeld, Switzerland

    • Katharina D. C. Stärk

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Consortia

  1. EFFORT Group

Contributions

F.M.A., D.H., J.A.W., T.H., D.M. and the EFFORT group designed the study. F.M.A. and B.E.K. detailed the sampling and sequencing. A.S.R.D. and the EFFORT group carried out the sampling. B.E.K. and S.J.P. conducted the DNA purification and, with P.M., organized the sequencing. R.B.H., O.Lund. and T.N.P. created the read-mapping pipeline. E.R. created the FRD. P.M., O.Lukjacenko., R.E.C.L., L.V.G., L.A.M.S., H.S., A.B., A.D.G. and H.V. carried out the bioinformatics and statistical analysis. P.M. created the figures and drafted the manuscript. All authors helped review, edit and complete the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Frank M. Aarestrup.

Supplementary information

  1. Supplementary Information

    Supplementary Figures 1–17, Supplementary Table legends and Supplementary Methods.

  2. Reporting Summary

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    Supplementary Tables 1–11.

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DOI

https://doi.org/10.1038/s41564-018-0192-9