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In Denmark, antimicrobial resistance (AMR) in pigs has been monitored since 1995 by phenotypic approaches using the same indicator bacteria. Emerging methodologies, such as metagenomics, may allow novel surveillance ways.


This study aimed to assess the relevance of indicator bacteria ( and ) for AMR surveillance in pigs, and the utility of metagenomics.


We collated existing data on AMR and antimicrobial use (AMU) from the Danish surveillance programme and performed metagenomics sequencing on caecal samples that had been collected/stored through the programme during 1999–2004 and 2015–2018. We compared phenotypic and metagenomics results regarding AMR, and the correlation of both with AMU.


Via the relative abundance of AMR genes, metagenomics allowed to rank these genes as well as the AMRs they contributed to, by their level of occurrence. Across the two study periods, resistance to aminoglycosides, macrolides, tetracycline, and beta-lactams appeared prominent, while resistance to fosfomycin and quinolones appeared low. In 2015–2018 sulfonamide resistance shifted from a low occurrence category to an intermediate one. Resistance to glycopeptides consistently decreased during the entire study period. Outcomes of both phenotypic and metagenomics approaches appeared to positively correlate with AMU. Metagenomics further allowed to identify multiple time-lagged correlations between AMU and AMR, the most evident being that increased macrolide use in sow/piglets or fatteners led to increased macrolide resistance with a lag of 3–6 months.


We validated the long-term usefulness of indicator bacteria and showed that metagenomics is a promising approach for AMR surveillance.


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  1. Aarestrup FM. The livestock reservoir for antimicrobial resistance: a personal view on changing patterns of risks, effects of interventions and the way forward. Philos Trans R Soc Lond B Biol Sci. 2015;370(1670):20140085.  https://doi.org/10.1098/rstb.2014.0085  PMID: 25918442 
  2. Woolhouse M, Ward M, van Bunnik B, Farrar J. Antimicrobial resistance in humans, livestock and the wider environment. Philos Trans R Soc Lond B Biol Sci. 2015;370(1670):20140083.  https://doi.org/10.1098/rstb.2014.0083  PMID: 25918441 
  3. Aarestrup FM, Bager F, Jensen NE, Madsen M, Meyling A, Wegener HC. 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). Acta Pathol Microbiol Scand Suppl. 1998;106(7-12):745-70.  https://doi.org/10.1111/j.1699-0463.1998.tb00222.x  PMID: 9744762 
  4. Hammerum AM, Heuer OE, Emborg HD, Bagger-Skjøt L, Jensen VF, Rogues AM, et al. Danish integrated antimicrobial resistance monitoring and research program. Emerg Infect Dis. 2007;13(11):1632-9.  https://doi.org/10.3201/eid1311.070421  PMID: 18217544 
  5. Ferreira JP, Staerk K. Antimicrobial resistance and antimicrobial use animal monitoring policies in Europe: Where are we? J Public Health Policy. 2017;38(2):185-202.  https://doi.org/10.1057/s41271-017-0067-y  PMID: 28533531 
  6. Hesp A, Veldman K, van der Goot J, Mevius D, van Schaik G. Monitoring antimicrobial resistance trends in commensal Escherichia coli from livestock, the Netherlands, 1998 to 2016. Euro Surveill. 2019;24(25):1800438.  https://doi.org/10.2807/1560-7917.ES.2019.24.25.1800438  PMID: 31241037 
  7. Hesp A, Ter Braak C, van der Goot J, Veldman K, van Schaik G, Mevius D. Antimicrobial resistance clusters in commensal Escherichia coli from livestock. Zoonoses Public Health. 2021;68(3):194-202.  https://doi.org/10.1111/zph.12805  PMID: 33455079 
  8. Munk P, Andersen VD, de Knegt L, Jensen MS, Knudsen BE, Lukjancenko O, et al. A sampling and metagenomic sequencing-based methodology for monitoring antimicrobial resistance in swine herds. J Antimicrob Chemother. 2017;72(2):385-92.  https://doi.org/10.1093/jac/dkw415  PMID: 28115502 
  9. Andersen VD, DE Knegt LV, Munk P, Jensen MS, Agersø Y, Aarestrup FM, et al. The association between measurements of antimicrobial use and resistance in the faeces microbiota of finisher batches. Epidemiol Infect. 2017;145(13):2827-37.  https://doi.org/10.1017/S0950268817001285  PMID: 28651652 
  10. Munk P, Knudsen BE, Lukjancenko O, Duarte ASR, Van Gompel L, Luiken REC, et al. , EFFORT Group. Abundance and diversity of the faecal resistome in slaughter pigs and broilers in nine European countries. Nat Microbiol. 2018;3(8):898-908.  https://doi.org/10.1038/s41564-018-0192-9  PMID: 30038308 
  11. Andersen VD, Munk P, de Knegt LV, Jensen MS, Aarestrup FM, Vigre H. Validation of the register-based lifetime antimicrobial usage measurement for finisher batches based on comparison with recorded antimicrobial usage at farm level. Epidemiol Infect. 2018;146(4):515-23.  https://doi.org/10.1017/S0950268818000134  PMID: 29409561 
  12. Aarestrup FM, Bager F, Jensen NE, Madsen M, Meyling A, Wegener HC. Surveillance of antimicrobial resistance in bacteria isolated from food animals to antimicrobial growth promoters and related therapeutic agents in Denmark. Acta Pathol Microbiol Scand Suppl. 1998;106(1-6):606-22.  https://doi.org/10.1111/j.1699-0463.1998.tb01391.x  PMID: 9725794 
  13. Bager F, Aarestrup FM, Jensen NE, Madsen M, Meyling A, Wegener HC. Design of a system for monitoring antimicrobial resistance in pathogenic, zoonotic and indicator bacteria from food animals. Acta Vet Scand Suppl. 1999;92:77-86. PMID: 10783720 
  14. Knudsen BE, Bergmark L, Munk P, Lukjancenko O, Priemé A, Aarestrup FM, et al. Impact of Sample Type and DNA Isolation Procedure on Genomic Inference of Microbiome Composition. mSystems. 2016;1(5):e00095-16.  https://doi.org/10.1128/mSystems.00095-16  PMID: 27822556 
  15. Hendriksen RS, Munk P, Njage P, van Bunnik B, McNally L, Lukjancenko O, et al. , Global Sewage Surveillance project consortium. Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage. Nat Commun. 2019;10(1):1124.  https://doi.org/10.1038/s41467-019-08853-3  PMID: 30850636 
  16. Bortolaia V, Kaas RS, Ruppe E, Roberts MC, Schwarz S, Cattoir V, et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother. 2020;75(12):3491-500.  https://doi.org/10.1093/jac/dkaa345  PMID: 32780112 
  17. Aitchison J. The Statistical Analysis of Compositional Data. Chapman and Hall, London, 1986; 416 p.
  18. van den Boogaart KG, Tolosana-Delgado R, Bren M. compositional: Compositional Data Analysis. 2020. https://CRAN.R-project.org/package=compositions
  19. Kolde R. pheatmap: Pretty Heatmaps. 2019. https://CRAN.R-project.org/package=pheatmap
  20. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2019. https://www.R-project.org/
  21. Oksanen J. vegan: Community Ecology Package. 2019. https://CRAN.R-project.org/package=vegan
  22. Bougeard S, Dray S. Supervised Multiblock Analysis in R with the ade4 Package. J Stat Softw. 2018;86(1):1-17.  https://doi.org/10.18637/jss.v086.i01 
  23. Fox J, Weisberg S. An {R} Companion to Applied Regression, Third Edition. Thousand Oaks CA: Sage. 2019. https://socialsciences.mcmaster.ca/jfox/Books/Companion/
  24. Sarrazin S, Joosten P, Van Gompel L, Luiken REC, Mevius DJ, Wagenaar JA, et al. , EFFORT consortium. Quantitative and qualitative analysis of antimicrobial usage patterns in 180 selected farrow-to-finish pig farms from nine European countries based on single batch and purchase data. J Antimicrob Chemother. 2019;74(3):807-16.  https://doi.org/10.1093/jac/dky503  PMID: 30544242 
  25. Agunos A, Gow SP, Léger DF, Deckert AE, Carson CA, Bosman AL, et al. Antimicrobial Use Indices-The Value of Reporting Antimicrobial Use in Multiple Ways Using Data From Canadian Broiler Chicken and Turkey Farms. Front Vet Sci. 2020;7:567872.  https://doi.org/10.3389/fvets.2020.567872  PMID: 33195547 
  26. Aarestrup FM, Seyfarth AM, Emborg HD, Pedersen K, Hendriksen RS, 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. 2001;45(7):2054-9.  https://doi.org/10.1128/AAC.45.7.2054-2059.2001  PMID: 11408222 
  27. Bronzwaer SL, Cars O, Buchholz U, Mölstad S, Goettsch W, Veldhuijzen IK, et al. , European Antimicrobial Resistance Surveillance System. A European study on the relationship between antimicrobial use and antimicrobial resistance. Emerg Infect Dis. 2002;8(3):278-82.  https://doi.org/10.3201/eid0803.010192  PMID: 11927025 
  28. Munk P, Brinch C, Møller FD, Petersen TN, Hendriksen RS, Seyfarth AM, et al. , Global Sewage Surveillance Consortium. Genomic analysis of sewage from 101 countries reveals global landscape of antimicrobial resistance. Nat Commun. 2022;13(1):7251.  https://doi.org/10.1038/s41467-022-34312-7  PMID: 36456547 
  29. Commission Implementing Decision (EU) 2020/1729 of 17 November 2020 on the monitoring and reporting of antimicrobial resistance in zoonotic and commensal bacteria (OJ L 387, 19.11.2020, p. 8-21).
  30. Duarte ASR, Röder T, Van Gompel L, Petersen TN, Hansen RB, Hansen IM, et al. Metagenomics-Based Approach to Source-Attribution of Antimicrobial Resistance Determinants - Identification of Reservoir Resistome Signatures. Front Microbiol. 2021;11:601407.  https://doi.org/10.3389/fmicb.2020.601407  PMID: 33519742 
  31. Likotrafiti E, Oniciuc EA, Prieto M, Santos JA, López S, Alvarez-Ordóñez A, Host institution: Department of Food Hygiene and Technology, Institute of Food Science and Technology, Universidad de León, León, Spain. Risk assessment of antimicrobial resistance along the food chain through culture-independent methodologies. EFSA J. 2018;16(Suppl 1):e160811. PMID: 32626061 

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