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Web-based prediction of antimicrobial resistance in enterococcal clinical isolates by whole-genome sequencing

European Journal of Clinical Microbiology & Infectious Diseases volume 42, pages 67–76 (2023)Cite this article

Abstract

Besides phenotypic antimicrobial susceptibility testing (AST), whole genome sequencing (WGS) is a promising alternative approach for detection of resistance phenotypes. The aim of this study was to investigate the concordance between WGS-based resistance prediction and phenotypic AST results for enterococcal clinical isolates using a user-friendly online tools and databases. A total of 172 clinical isolates (34 E. faecalis, 138 E. faecium) received at the French National Reference Center for enterococci from 2017 to 2020 were included. AST was performed by disc diffusion or MIC determination for 14 antibiotics according to CA-SFM/EUCAST guidelines. The genome of all strains was sequenced using the Illumina technology (MiSeq) with bioinformatic analysis from raw reads using online tools ResFinder 4.1 and LRE-finder 1.0. For both E. faecalis and E. faecium, performances of WGS-based genotype to predict resistant phenotypes were excellent (concordance > 90%), particularly for antibiotics commonly used for treatment of enterococcal infections such as ampicillin, gentamicin, vancomycin, teicoplanin, and linezolid. Note that 100% very major errors were found for quinupristin-dalfopristin, tigecycline, and rifampicin for which resistance mutations are not included in databases. Also, it was not possible to predict phenotype from genotype for daptomycin for the same reason. WGS combined with online tools could be easily used by non-expert clinical microbiologists as a rapid and reliable tool for prediction of phenotypic resistance to first-line antibiotics among enterococci. Nonetheless, some improvements should be made such as the implementation of resistance mutations in the database for some antibiotics.

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Data availability

The genomic datasets generated and analyzed during the current study are available in GenBank as bioproject PRJNA875074.

Code availability

Not applicable.

References

  1. Cattoir V (2022) The multifaceted lifestyle of enterococci: genetic diversity, ecology and risks for public health. Curr Opin Microbiol 65:73–80

    Article  CAS  Google Scholar 

  2. Arias CA, Murray BE (2012) The rise of the Enterococcus: beyond vancomycin resistance. Nat Rev Microbiol 10(4):266–278

    Article  CAS  Google Scholar 

  3. Bender JK, Cattoir V, Hegstad K, Sadowy E, Coque TM, Westh H et al (2018) Update on prevalence and mechanisms of resistance to linezolid, tigecycline and daptomycin in enterococci in Europe: Towards a common nomenclature. Drug Resist Updat 40:25–39

    Article  Google Scholar 

  4. García-Solache M, Rice LB (2019) The Enterococcus: a model of adaptability to its environment. Clin Microbiol Rev 32(2):e00058-e118

    Article  Google Scholar 

  5. Wheat PF (2001) History and development of antimicrobial susceptibility testing methodology. J Antimicrob Chemother 48(Suppl 1):1–4

    Article  CAS  Google Scholar 

  6. Khan ZA, Siddiqui MF, Park S (2019) Current and emerging methods of antibiotic susceptibility testing. Diagnostics 9(2):49

    Article  CAS  Google Scholar 

  7. Stoesser N, Batty EM, Eyre DW, Morgan M, Wyllie DH, Del Ojo EC et al (2013) Predicting antimicrobial susceptibilities for Escherichia coli and Klebsiella pneumoniae isolates using whole genomic sequence data. J Antimicrob Chemother 68(10):2234–2244

    Article  CAS  Google Scholar 

  8. Gordon NC, Price JR, Cole K, Everitt R, Morgan M, Finney J et al (2014) Prediction of Staphylococcus aureus antimicrobial resistance by whole-genome sequencing. J Clin Microbiol 52(4):1182–1191

    Article  CAS  Google Scholar 

  9. Tyson GH, McDermott PF, Li C, Chen Y, Tadesse DA, Mukherjee S et al (2015) WGS accurately predicts antimicrobial resistance in Escherichia coli. J Antimicrob Chemother 70(10):2763–2769

    Article  CAS  Google Scholar 

  10. Bradley P, Gordon NC, Walker TM, Dunn L, Heys S, Huang B et al (2015) Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis. Nat Commun 6(1):10063

    Article  CAS  Google Scholar 

  11. McDermott PF, Tyson GH, Kabera C, Chen Y, Li C, Folster JP et al (2016) Whole-genome sequencing for detecting antimicrobial resistance in nontyphoidal Salmonella. Antimicrob Agents Chemother 60(9):5515–5520

    Article  CAS  Google Scholar 

  12. Ruppé E, Cherkaoui A, Charretier Y, Girard M, Schicklin S, Lazarevic V et al (2020) From genotype to antibiotic susceptibility phenotype in the order Enterobacterales: a clinical perspective. Clin Microbiol Infect 26(5):643

    Article  Google Scholar 

  13. Dahl LG, Joensen KG, Østerlund MT, Kiil K, Nielsen EM (2021) Prediction of antimicrobial resistance in clinical Campylobacter jejuni isolates from whole-genome sequencing data. Eur J Clin Microbiol Infect Dis 40(4):673–682

    Article  CAS  Google Scholar 

  14. Cortes-Lara S, Barrio-Tofiño ED, López-Causapé C, Oliver A, GEMARA-SEIMC/REIPI Pseudomonas study Group (2021) Predicting Pseudomonas aeruginosa susceptibility phenotypes from whole genome sequence resistome analysis. Clin Microbiol Infect 27(11):1631–1637

    Article  CAS  Google Scholar 

  15. Anjum MF, Zankari E, Hasman H (2017) Molecular methods for detection of antimicrobial resistance. Microbiol Spectr 5(6). https://doi.org/10.1128/microbiolspec.ARBA-0011-2017

  16. Su M, Satola SW, Read TD (2019) Genome-based prediction of bacterial antibiotic resistance. J Clin Microbiol 57(3):e01405-e1418

    Article  CAS  Google Scholar 

  17. Papp M, Solymosi N (2022) Review and comparison of antimicrobial resistance gene databases. Antibiot (Basel) 11(3):339

    Article  CAS  Google Scholar 

  18. Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S, Lund O et al (2012) Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother 67(11):2640–2644

    Article  CAS  Google Scholar 

  19. Florensa AF, Kaas RS, Clausen PTLC, Aytan-Aktug D, Aarestrup FM (2022) ResFinder — an open online resource for identification of antimicrobial resistance genes in next-generation sequencing data and prediction of phenotypes from genotypes. Microb Genomics 8(1):000748

    Article  Google Scholar 

  20. Bortolaia V, Kaas RS, Ruppe E, Roberts MC, Schwarz S, Cattoir V et al (2020) ResFinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother 75(12):3491–3500

    Article  CAS  Google Scholar 

  21. Zankari E, Allesøe R, Joensen KG, Cavaco LM, Lund O, Aarestrup FM (2017) PointFinder: a novel web tool for WGS-based detection of antimicrobial resistance associated with chromosomal point mutations in bacterial pathogens. J Antimicrob Chemother 72(10):2764–2768

    Article  CAS  Google Scholar 

  22. Clausen PTLC, Aarestrup FM, Lund O (2018) Rapid and precise alignment of raw reads against redundant databases with KMA. BMC Bioinformatics 19(1):307

    Article  Google Scholar 

  23. Hasman H, Clausen PTLC, Kaya H, Hansen F, Knudsen JD, Wang M et al (2019) LRE-Finder, a Web tool for detection of the 23S rRNA mutations and the optrA, cfr, cfr(B) and poxtA genes encoding linezolid resistance in enterococci from whole-genome sequences. J Antimicrob Chemother 74(6):1473–1476

    Article  CAS  Google Scholar 

  24. Zankari E, Hasman H, Kaas RS, Seyfarth AM, Agersø Y, Lund O et al (2013) Genotyping using whole-genome sequencing is a realistic alternative to surveillance based on phenotypic antimicrobial susceptibility testing. J Antimicrob Chemother 68(4):771–777

    Article  CAS  Google Scholar 

  25. Tyson GH, Sabo JL, Rice-Trujillo C, Hernandez J, McDermott PF (2018) Whole-genome sequencing based characterization of antimicrobial resistance in Enterococcus. Pathog Dis 76(2):fty018

    Article  Google Scholar 

  26. Babiker A, Mustapha MM, Pacey MP, Shutt KA, Ezeonwuka CD, Ohm SL et al (2019) Use of online tools for antimicrobial resistance prediction by whole-genome sequencing in methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant enterococci (VRE). J Glob Antimicrob Resist 19:136–143

    Article  Google Scholar 

  27. Anahtar MN, Bramante JT, Xu J, Desrosiers LA, Paer JM, Rosenberg ES et al (2022) Prediction of antimicrobial resistance in clinical Enterococcus faecium isolates using a rules-based analysis of whole-genome sequences. Antimicrob Agents Chemother 66(1):e01196-e1221

    Article  CAS  Google Scholar 

  28. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS et al (2012) SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol J Comput Mol Cell Biol 19(5):455–477

    Article  CAS  Google Scholar 

  29. Neumann B, Prior K, Bender JK, Harmsen D, Klare I, Fuchs S et al (2019) A core genome multilocus sequence typing scheme for Enterococcus faecalis. J Clin Microbiol 57(3):e01686-e1718

    Article  CAS  Google Scholar 

  30. de Been M, Pinholt M, Top J, Bletz S, Mellmann A, van Schaik W et al (2015) Core genome multilocus sequence typing scheme for high-resolution typing of Enterococcus faecium. J Clin Microbiol 53(12):3788–3797

    Article  Google Scholar 

  31. Letunic I, Bork P (2021) Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res 49(W1):W293–W296

    Article  CAS  Google Scholar 

  32. Singh KV, Weinstock GM, Murray BE (2002) An Enterococcus faecalis ABC homologue (Lsa) is required for the resistance of this species to clindamycin and quinupristin-dalfopristin. Antimicrob Agents Chemother 46(6):1845–1850

    Article  CAS  Google Scholar 

  33. Cattoir V, Isnard C, Cosquer T, Odhiambo A, Bucquet F, Guérin F et al (2015) Genomic analysis of reduced susceptibility to tigecycline in Enterococcus faecium. Antimicrob Agents Chemother 59(1):239–244

    Article  Google Scholar 

  34. Isnard C, Malbruny B, Leclercq R, Cattoir V (2013) Genetic basis for in vitro and in vivo resistance to lincosamides, streptogramins A, and pleuromutilins (LSAP phenotype) in Enterococcus faecium. Antimicrob Agents Chemother 57(9):4463–4469

    Article  CAS  Google Scholar 

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Funding

This work was supported by “Santé Publique France,” the French national public health agency.

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Authors and Affiliations

  1. Service de Bactériologie-Hygiène Hospitalière, CHU de Rennes, Rennes, France

    Malo Penven, Asma Zouari, Sophie Nogues, Anaïs Collet, Maxime Lecourt, François Guerin, Gabriel Auger & Vincent Cattoir

  2. Université de Rennes 1, Inserm U1230, Rennes, France

    Malo Penven & Vincent Cattoir

  3. CNR de La Résistance Aux Antibiotiques (Laboratoire Associé ‘Entérocoques’), Rennes, France

    Asma Zouari, Sophie Nogues, Anaïs Collet, Maxime Lecourt, Aurélien Birer, François Guerin, Gabriel Auger & Vincent Cattoir

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  1. Malo Penven
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  2. Asma Zouari
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  3. Sophie Nogues
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  4. Anaïs Collet
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  9. Vincent Cattoir
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Contributions

Malo Penven and Vincent Cattoir contributed to the study conception and design. Material preparation, data collection, and analysis were performed by all the authors. The first draft of the manuscript was written by Malo Penven and Vincent Cattoir, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Vincent Cattoir.

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Supplementary Information

Figure S1.

Genetic relationship and resistance gene content among 34 E. faecalis clinical isolates. Neighbor-joining phylogenetic tree was constructed from aligned core-genome SNPs analysis based on SNPs and visualized together with ST affiliation (colored strips) and a heatmap for resistance mechanisms (black boxes) with iTOL v5. The scale bar represents 100 SNPs. (PNG 405 kb)

High resolution image (TIF 853 kb)

Figure S2.

Genetic relationship and resistance gene content among 138 E. faecium clinical isolates. Neighbor-joining phylogenetic tree was constructed from aligned core-genome SNPs analysis based on SNPs and visualized together with ST affiliation (colored strips) and a heatmap for resistance mechanisms (black boxes) with iTOL v5. The scale bar represents 1,000 SNPs. (PNG 914 kb)

High resolution image (TIF 2021 kb)

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Penven, M., Zouari, A., Nogues, S. et al. Web-based prediction of antimicrobial resistance in enterococcal clinical isolates by whole-genome sequencing. Eur J Clin Microbiol Infect Dis 42, 67–76 (2023). https://doi.org/10.1007/s10096-022-04527-z

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