Skip to main navigation Skip to search Skip to main content

ResFinder 4.0 for predictions of phenotypes from genotypes

  • Valeria Bortolaia
  • , Rolf S. Kaas
  • , Etienne Ruppe
  • , Marilyn C. Roberts
  • , Stefan Schwarz
  • , Vincent Cattoir
  • , Alain Philippon
  • , Rosa L. Allesoe
  • , Ana Rita Rebelo
  • , Alfred Ferrer Florensa
  • , Linda Fagelhauer
  • , Trinad Chakraborty
  • , Bernd Neumann
  • , Guido Werner
  • , Jennifer K. Bender
  • , Kerstin Stingl
  • , Minh Nguyen
  • , Jasmine Coppens
  • , Basil Britto Xavier
  • , Surbhi Malhotra-Kumar
  • Henrik Westh, Mette Pinholt, Muna F. Anjum, Nicholas A. Duggett, Isabelle Kempf, Suvi Nykäsenoja, Satu Olkkola, Kinga Wieczorek, Ana Amaro, Lurdes Clemente, Joël Mossong, Serge Losch, Catherine Ragimbeau, Ole Lund, Frank M. Aarestrup*
*Corresponding author for this work
  • Université Paris Cité
  • University of Washington
  • Free University of Berlin
  • Rennes University Hospital
  • University of Paris
  • Justus Liebig University Giessen
  • Robert Koch Institute
  • Federal Institute for Risk Assessment
  • University of Antwerp
  • Hvidovre Hospital
  • Animal and Plant Health Agency
  • Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail
  • Finnish Food Authority
  • National Veterinary Research Institute
  • National Institute of Agrarian and Veterinary Research
  • Laboratoire National de Santé
  • Laboratoire de Médecine Vétérinaire de l’Etat

Research output: Contribution to journal › Journal article › Research › peer-review

430 Downloads (Orbit)

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.
Original languageEnglish
JournalJournal of Antimicrobial Chemotherapy
Volume75
Issue number12
Pages (from-to)3491-3500
Number of pages10
ISSN0305-7453
DOIs
Publication statusPublished - 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Fingerprint

Dive into the research topics of 'ResFinder 4.0 for predictions of phenotypes from genotypes'. Together they form a unique fingerprint.

Cite this