CoChat
Sign Up
Cited by 3,373Antibiotic Resistance in BacteriaBacterial Identification and Susceptibility TestingAntibiotic Use and Resistance

ResFinder 4.0 for predictions of phenotypes from genotypes

Open access
Bortolaia, Valeria, Kaas, Rolf S, Ruppe, Etienne, Roberts, Marilyn C, Schwarz, Stefan, Cattoir, Vincent, Philippon, Alain, Allesoe, Rosa L +27 more·2020·Journal of Antimicrobial Chemotherapy
DOI 10.1093/jac/dkaa345PMC PMC7662176
Open this paper in CoChat — free accountOpen PDF

Abstract

Objectives: WGS-based antimicrobial susceptibility testing (AST) is as reliable as phenotypic AST for several antimicrobial/bacterial species combinations. However, routine use of WGS-based AST is hindered by the need for bioinformatics skills and knowledge of antimicrobial resistance (AMR) determinants to operate the vast majority of tools developed to date. By leveraging on ResFinder and PointFinder, two freely accessible tools that can also assist users without bioinformatics skills, we aimed at increasing their speed and providing an easily interpretable antibiogram as output. Methods: The ResFinder code was re-written to process raw reads and use Kmer-based alignment. The existing ResFinder and PointFinder databases were revised and expanded. Additional databases were developed including a genotype-to-phenotype key associating each AMR determinant with a phenotype at the antimicrobial compound level, and species-specific panels for in silico antibiograms. ResFinder 4.0 was validated using Escherichia coli (n = 584), Salmonella spp. (n = 1081), Campylobacter jejuni (n = 239), Enterococcus faecium (n = 106), Enterococcus faecalis (n = 50) and Staphylococcus aureus (n = 163) exhibiting different AST profiles, and from different human and animal sources and geographical origins. Results: Genotype–phenotype concordance was ≥95% for 46/51 and 25/32 of the antimicrobial/species combinations evaluated for Gram-negative and Gram-positive bacteria, respectively. When genotype–phenotype concordance was <95%, discrepancies were mainly linked to criteria for interpretation of phenotypic tests and suboptimal sequence quality, and not to ResFinder 4.0 performance. Conclusions: WGS-based AST using ResFinder 4.0 provides in silico antibiograms as reliable as those obtained by phenotypic AST at least for the bacterial species/antimicrobial agents of major public health relevance considered.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/jac/dkaa345

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

No references could be resolved yet.

Research works printed but not linked: 42

  1. [1]Wheat PF. History and development of antimicrobial susceptibility testing methodology. J Antimicrob Chemother 2001; 48 Suppl 1: 1– 4. 11420332
  2. [2]Pedersen SK, Wagenaar JA, Vigre H et al Proficiency of WHO global foodborne infections network external quality assurance system participants in identification and susceptibility testing of thermotolerant Campylobacter spp. from 2003 to 2012. J Clin Microbiol 2018; 56: e01066– 18. 30209182 PMC6204681
  3. [3]Hendriksen RS, Seyfarth AM, Jensen AB et al Results of use of WHO Global Salm-Surv external quality assurance system for antimicrobial susceptibility testing of Salmonella isolates from 2000 to 2007. J Clin Microbiol 2009; 47: 79– 85. 19020068 PMC2620846
  4. [6]Thaker MN, Kalan L, Waglechner N et al Vancomycin-variable enterococci can give rise to constitutive resistance during antibiotic therapy. Antimicrob Agents Chemother 2015; 59: 1405– 10. 25512425 PMC4325790
  5. [7]Acar JF, Courvalin P, Chabbert YA. Methicillin-resistant staphylococcemia: bacteriological failure of treatment with cephalosporins. Antimicrob Agents Chemother 1970; 10: 280– 5. 4939735
  6. [8]Mouton JW. Soup with or without meatballs: impact of nutritional factors on the MIC, kill-rates and growth-rates. Eur J Pharm Sci 2018; 125: 23– 7. 30218696
  7. [9]Courvalin P. Genotypic approach to the study of bacterial resistance to antibiotics. Antimicrob Agents Chemother 1991; 35: 1019– 23. 1929240 PMC284279
  8. [10]Pfaller MA. Molecular approaches to diagnosing and managing infectious diseases: practicality and costs. Emerg Infect Dis 2001; 7: 312– 18. 11294731 PMC2631730
  9. [12]Hendriksen RS, Bortolaia V, Tate H et al Using genomics to track global antimicrobial resistance. Front Public Health 2019; 7: 242. 31552211 PMC6737581
  10. [13]Rigden DJ, Fernandez XM. The 27th annual Nucleic Acids Research database issue and molecular biology database collection. Nucleic Acids Res 2020; 48: D1– 8. 31906604 PMC6943072
  11. [14]Panunzi LG. sraX: a novel comprehensive resistome analysis tool. Front Microbiol 2020; 11: 52. 32117104 PMC7025521
  12. [15]Zankari E, Hasman H, Cosentino S et al Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother 2012; 67: 2640– 4. 22782487 PMC3468078
  13. [16]Zankari E, Allesøe R, Joensen KG et al PointFinder: a novel web tool for WGS-based detection of antimicrobial resistance associated with chromosomal point mutations in bacterial pathogens. J Antimicrob Chemother 2017; 72: 2764– 8. 29091202 PMC5890747
  14. [17]Johnsen CH, Clausen P, Aarestrup FM et al Improved resistance prediction in Mycobacterium tuberculosis by better handling of insertions and deletions, premature stop codons, and filtering of non-informative sites. Front Microbiol 2019; 10: 2464. 31736907 PMC6834686
  15. [18]Naas T, Oueslati S, Bonnin RA et al β-lactamase database (BLDB)—structure and function. J Enzyme Inhib Med Chem 2017; 32: 917– 19. 28719998 PMC6445328
  16. [20]Clausen P, Aarestrup FM, Lund O. Rapid and precise alignment of raw reads against redundant databases with KMA. BMC Bioinformatics 2018; 19: 307. 30157759 PMC6116485
  17. [21]Bankevich A, Nurk S, Antipov D et al SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 2012; 19: 455– 77. 22506599 PMC3342519
  18. [22]Kaas RS, Leekitcharoenphon P, Aarestrup FM et al Solving the problem of comparing whole bacterial genomes across different sequencing platforms. PLoS One 2014; 9: e104984. 25110940 PMC4128722
  19. [23]McDermott PF, Tyson GH, Kabera C et al Whole-genome sequencing for detecting antimicrobial resistance in nontyphoidal Salmonella. Antimicrob Agents Chemother 2016; 60: 5515– 20. 27381390 PMC4997858
  20. [24]CLSI Methods for Dilution Antimicrobial Susceptibility Tests for Bacteria That Grow Aerobically—Eleventh Edition: M07. 2018.
  21. [25]Zankari E, Hasman H, Kaas RS et al Genotyping using whole-genome sequencing is a realistic alternative to surveillance based on phenotypic antimicrobial susceptibility testing. J Antimicrob Chemother 2013; 68: 771– 7. 23233485
  22. [26]Stubberfield E, AbuOun M, Sayers E et al Use of whole genome sequencing of commensal Escherichia coli in pigs for antimicrobial resistance surveillance, United Kingdom, 2018. Euro Surveill 2019; 24: 1900136. PMC6918588 31847943
  23. [27]Hasman H, Clausen P, Kaya H et al 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 2019; 74: 1473– 6. 30863844
  24. [28]Schmitz FJ, Petridou J, Jagusch H et al Molecular characterization of ketolide-resistant erm(A)-carrying Staphylococcus aureus isolates selected in vitro by telithromycin, ABT-773, quinupristin and clindamycin. J Antimicrob Chemother 2002; 49: 611– 17. 11909834
  25. [29]Robicsek A, Jacoby G, Hooper DC. The worldwide emergence of plasmid-mediated quinolone resistance. Lancet Infect Dis 2006; 6: 629– 40. 17008172
  26. [30]Kime L, Randall CP, Banda FI et al Transient silencing of antibiotic resistance by mutation represents a significant potential source of unanticipated therapeutic failure. mBio 2019; 10: e01755– 19. 31662453 PMC6819657
  27. [31]Mouton JW, Meletiadis J, Voss A et al Variation of MIC measurements: the contribution of strain and laboratory variability to measurement precision—authors’ response. J Antimicrob Chemother 2019; 74: 1761– 2. 30989229
  28. [32]Davies TJ, Stoesser N, Sheppard AE et al Reconciling the potentially irreconcilable? Genotypic and phenotypic amoxicillin–clavulanate resistance in Escherichia coli. Antimicrob Agents Chemother 2020; 64: e02026– 19. 32205351 PMC7269502
  29. [33]Wallmann J, Böttner A, Goossens L et al Results of an interlaboratory test on antimicrobial susceptibility testing of bacteria from animals by broth microdilution. Int J Antimicrob Agents 2006; 27: 482– 90. 16697559
  30. [34]Barry AL, Braun LE. Reader error in determining minimal inhibitory concentrations with microdilution susceptibility test panels. J Clin Microbiol 1981; 13: 228– 30. 7462416 PMC273758
  31. [35]Mouton JW, Muller AE, Canton R et al MIC-based dose adjustment: facts and fables. J Antimicrob Chemother 2018; 73: 564– 8. 29216348
  32. [36]Bader JC, Lakota EA, Andes DR et al Time for precision: a world without susceptibility breakpoints. Open Forum Infect Dis 2018; 5: ofy282. 30581880 PMC6297858
  33. [37]Ruppé E, Schrenzel J. Messages from the third International Conference on Clinical Metagenomics (ICCMg3). Microbes Infect 2019; 21: 273– 7. 30836173
  34. [38]Ellington MJ, Ekelund O, Aarestrup FM et al The role of whole genome sequencing in antimicrobial susceptibility testing of bacteria: report from the EUCAST Subcommittee. Clin Microbiol Infect 2017; 23: 2– 22. 27890457
  35. [39]Rossen JWA, Friedrich AW, Moran-Gilad J; ESCMID Study Group for Genomic and Molecular Diagnostics (ESGMD). Practical issues in implementing whole-genome-sequencing in routine diagnostic microbiology. Clin Microbiol Infect 2018; 24: 355– 60. 29117578
  36. [40]Sonda T, Kumburu H, van Zwetselaar M et al Molecular epidemiology of virulence and antimicrobial resistance determinants in Klebsiella pneumoniae from hospitalised patients in Kilimanjaro, Tanzania. Eur J Clin Microbiol Infect Dis 2018; 37: 1901– 14. 30030694
  37. [41]Doyle RM, O’Sullivan DM, Aller SD et al Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: an inter-laboratory study. Microb Genom 2020; 6: e000335. PMC7067211 32048983
  38. [42]Aytan-Aktug D, Clausen P, Bortolaia V et al Prediction of acquired antimicrobial resistance for multiple bacterial species using neural networks. mSystems 2020; 5: e00774– 19. 31964771 PMC6977075
  39. [44]Duggett NA, Randall LP, Horton RA et al Molecular epidemiology of isolates with multiple mcr plasmids from a pig farm in Great Britain: the effects of colistin withdrawal in the short and long term. J Antimicrob Chemother 2018; 73: 3025– 33. 30124905
  40. [46]Neumann B, Prior K, Bender JK et al A core genome multilocus sequence typing scheme for Enterococcus faecalis. J Clin Microbiol 2019; 57: e01686– 18. 30651394 PMC6425188
  41. [47]Bender J, Fleige C, Lange D et al Rapid emergence of highly variable and transferable oxazolidinone and phenicol resistance gene optrA in German Enterococcus spp. clinical isolates. Int J Antimicrob Agents 2018; 52: 819– 27. 30236952
  42. [48]AbuOun M, O’Connor HM, Stubberfield EJ et al Characterizing antimicrobial resistant Escherichia coli and associated risk factors in a cross-sectional study of pig farms in Great Britain. Front Microbiol 2020; 11: 861. 32523560 PMC7261845

Cited by

Sorted by citations

Authors
Bortolaia, V., Kaas, R. S., Ruppe, E., Roberts, M. C., Schwarz, S., Cattoir, V., Philippon, A., Allesoe, R. L., Rebelo, A. R., Florensa, A. F., Fagelhauer, L., Chakraborty, T., Neumann, B., Werner, G., Bender, J. K., Stingl, K., Nguyen, M., Coppens, J., Xavier, B. B., Malhotra-Kumar, S., Westh, H., Pinholt, M., Anjum, M. F., Duggett, N. A., Kempf, I., Nykäsenoja, S., Olkkola, S., Wieczorek, K., Amaro, A., Clemente, L., Mossong, J., Losch, S., Ragimbeau, C., Lund, O., & Aarestrup, F. M.
Affiliations
  • Technical University of Denmark (DK)
  • Université de Paris, IAME, INSERM, Paris, France
  • University of Washington (US)
  • Institute of Microbiology and Epizootics, Centre for Infection Medicine, Department of Veterinary Medicine, Freie Universität Berlin, Berlin, Germany
  • Inserm (FR)
  • Université Paris Descartes (FR)
  • Technical University of Denmark, National Food Institute, European Union Reference Laboratory for Antimicrobial Resistance, WHO Collaborating Centre for Antimicrobial Resistance in Foodborne Pathogens and Genomics, FAO Reference Laboratory for Antimicrobial Resistance, Kgs. Lyngby, Denmark
  • Justus-Liebig-Universität Gießen (DE)
  • Robert Koch Institute (DE)
  • German Federal Institute for Risk Assessment (BfR) (DE)
  • University of Antwerp (BE)
  • Laboratory of Medical Microbiology, Vaccine & Infectious Disease Institute, University of Antwerp, Belgium
  • University of Copenhagen (DK)
  • Hvidovre Hospital (DK)
  • Animal and Plant Health Agency (GB)
  • Agence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du Travail (FR)
  • Finnish Food Authority (FI)
  • National Veterinary Research Institute (PL)
  • Instituto Nacional de Investigação Agrária e Veterinária (PT)
  • Laboratoire National de Santé (LU)
  • Laboratoire de Médecine Vétérinaire de l'Etat, Veterinary Services Administration, Dudelange, Luxembourg
Corresponding author
Aarestrup, Frank M
ORCID
Venue
Journal of Antimicrobial Chemotherapy
Publisher
Oxford University Press (OUP)
Published
2020-08-11
Volume
75
Issue
12
Pages
3491-3500
ISSN
  • 0305-7453 (ISSN-L)
  • 1460-2091
DOI
10.1093/jac/dkaa345
PMC
PMC7662176
OpenAlex
W3048398081
Cited by
3,373
References
0
Citations by year
2026: 673 · 2025: 683 · 2024: 594 · 2023: 539 · 2022: 538 · 2021: 318 · 2020: 25 · 2019: 1 · 2012: 1
FWCI
118.92
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Antimicrobial Chemotherapy (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by-nc)
  • Publication server of the Robert Koch Institute (Robert Kock Institute) (submittedVersion, cc-by-nc)
  • HAL (submittedVersion, cc-by-nc)
  • Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, cc-by-nc)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
Funders
  • European Union Horizon 2020 (643476)
  • Novo Nordisk Foundation (NNF16OC0021856)
  • Global Surveillance of Antimicrobial Resistance
  • German Center of Infection Research
  • Zoonoses Network ‘ESBL
  • German Federal Ministry of Education and Research (8000 701-3, 01KI1313G, TI06.001, 8032808811, 8032808818, 8032808820)
  • BMBF (03ZZ0815A)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Medicine › Antibiotic Resistance in Bacteria
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Bacterial Identification and Susceptibility Testing
  • Life Sciences › Immunology and Microbiology › Applied Microbiology and Biotechnology › Antibiotic Use and Resistance
Keywords
antimicrobial resistance, antimicrobial susceptibility testing, whole genome sequencing, genotype-phenotype concordance, antimicrobial resistance determinants, Escherichia coli, Salmonella, Campylobacter jejuni, Enterococcus faecium, Enterococcus faecalis, Staphylococcus aureus
MeSH terms
Animals, Anti-Bacterial Agents, Genotype, Humans, Microbial Sensitivity Tests, Phenotype, Drug Resistance, Bacterial

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review