ResFinder 4.0 for predictions of phenotypes from genotypes
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)...
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| Published in | Journal of antimicrobial chemotherapy Vol. 75; no. 12; pp. 3491 - 3500 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Oxford University Press
01.12.2020
Oxford University Press (OUP) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0305-7453 1460-2091 1460-2091 |
| DOI | 10.1093/jac/dkaa345 |
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| Abstract | 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. |
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| AbstractList | 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. 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. 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. 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. 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. 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. 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.OBJECTIVESWGS-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.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.METHODSThe 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.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.RESULTSGenotype-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.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.CONCLUSIONSWGS-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. |
| Author | Ragimbeau, Catherine Duggett, Nicholas A Ruppe, Etienne Rebelo, Ana Rita Florensa, Alfred Ferrer Malhotra-Kumar, Surbhi Neumann, Bernd Roberts, Marilyn C Schwarz, Stefan Bender, Jennifer K Pinholt, Mette Fagelhauer, Linda Chakraborty, Trinad Kaas, Rolf S Olkkola, Satu Lund, Ole Nguyen, Minh Allesoe, Rosa L Xavier, Basil Britto Wieczorek, Kinga Amaro, Ana Bortolaia, Valeria Werner, Guido Anjum, Muna F Nykäsenoja, Suvi Losch, Serge Aarestrup, Frank M Clemente, Lurdes Cattoir, Vincent Mossong, Joël Stingl, Kerstin Westh, Henrik Coppens, Jasmine Kempf, Isabelle Philippon, Alain |
| AuthorAffiliation | d9 Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen N, Denmark d19 ANSES, Ploufragan-Plouzané-Niort Laboratory , Ploufragan, France d7 University of Rennes 1, INSERM U1230 , Rennes, France d22 National Institute of Agrarian and Veterinary Research (INIAV), National Reference Laboratory for Animal Health , Oeiras, Portugal d10 Institute of Medical Microbiolgy, Justus Liebig University Giessen , Giessen, Germany d15 Laboratory of Medical Microbiology, Vaccine & Infectious Disease Institute, University of Antwerp , Belgium d2 Université de Paris, IAME, INSERM , Paris, France d5 Rennes University Hospital, Department of Clinical Microbiology , Rennes, France d16 Department of Clinical Microbiology, Hvidovre University Hospital , Hvidovre, Denmark d18 Animal and Plant Health Agency , Addlestone, Surrey, UK d17 Department of Clinical Medicine, University of Copenhagen , Copenhagen, Denmark d23 Laboratoire National de Sant |
| AuthorAffiliation_xml | – name: d4 Institute of Microbiology and Epizootics, Centre for Infection Medicine, Department of Veterinary Medicine, Freie Universität Berlin , Berlin, Germany – name: d6 National Reference Center for Antimicrobial Resistance (lab Enterococci) , Rennes, France – name: d21 National Veterinary Research Institute , Pulawy, Poland – name: d2 Université de Paris, IAME, INSERM , Paris, France – name: d9 Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen N, Denmark – name: d18 Animal and Plant Health Agency , Addlestone, Surrey, UK – name: d22 National Institute of Agrarian and Veterinary Research (INIAV), National Reference Laboratory for Animal Health , Oeiras, Portugal – name: d24 Laboratoire de Médecine Vétérinaire de l'Etat, Veterinary Services Administration , Dudelange, Luxembourg – name: d14 German Federal Institute for Risk Assessment, Department of Biological Safety, National Reference Laboratory for Campylobacter , Berlin, Germany – name: d7 University of Rennes 1, INSERM U1230 , Rennes, France – name: d8 Faculty of Medicine Paris Descartes, Bacteriology , Paris, France – name: d5 Rennes University Hospital, Department of Clinical Microbiology , Rennes, France – name: d12 Institute of Hygiene and Environmental Medicine, Justus Liebig University Giessen , Giessen, Germany – name: d13 Robert Koch Institute, Wernigerode Branch, Department of Infectious Diseases, Division of Nosocomial Pathogens and Antibiotic Resistances , Wernigerode, Germany – name: d1 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 – name: d20 Finnish Food Authority , Helsinki, Finland – name: d15 Laboratory of Medical Microbiology, Vaccine & Infectious Disease Institute, University of Antwerp , Belgium – name: d17 Department of Clinical Medicine, University of Copenhagen , Copenhagen, Denmark – name: d16 Department of Clinical Microbiology, Hvidovre University Hospital , Hvidovre, Denmark – name: d19 ANSES, Ploufragan-Plouzané-Niort Laboratory , Ploufragan, France – name: d3 Department of Environmental and Occupational Health Sciences, University of Washington , Seattle, WA, USA – name: d11 German Center for Infection Research, site Giessen-Marburg-Langen, Justus Liebig University Giessen , Giessen, Germany – name: d23 Laboratoire National de Santé, Epidemiology and Microbial Genomics , Dudelange, Luxembourg – name: d10 Institute of Medical Microbiolgy, Justus Liebig University Giessen , Giessen, Germany |
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Lyngby, Denmark – sequence: 3 givenname: Etienne surname: Ruppe fullname: Ruppe, Etienne organization: Université de Paris, IAME, INSERM, Paris, France – sequence: 4 givenname: Marilyn C surname: Roberts fullname: Roberts, Marilyn C organization: Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA – sequence: 5 givenname: Stefan orcidid: 0000-0002-6303-8212 surname: Schwarz fullname: Schwarz, Stefan organization: Institute of Microbiology and Epizootics, Centre for Infection Medicine, Department of Veterinary Medicine, Freie Universität Berlin, Berlin, Germany – sequence: 6 givenname: Vincent orcidid: 0000-0003-4026-9912 surname: Cattoir fullname: Cattoir, Vincent organization: Rennes University Hospital, Department of Clinical Microbiology, Rennes, France – sequence: 7 givenname: Alain surname: Philippon fullname: Philippon, Alain organization: Faculty of Medicine Paris Descartes, Bacteriology, Paris, France – sequence: 8 givenname: Rosa L surname: Allesoe fullname: Allesoe, Rosa L organization: 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 – sequence: 9 givenname: Ana Rita surname: Rebelo fullname: Rebelo, Ana Rita organization: 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 – sequence: 10 givenname: Alfred Ferrer surname: Florensa fullname: Florensa, Alfred Ferrer organization: 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. 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Lyngby, Denmark – sequence: 35 givenname: Frank M orcidid: 0000-0002-7116-2723 surname: Aarestrup fullname: Aarestrup, Frank M email: fmaa@food.dtu.dk organization: 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 |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32780112$$D View this record in MEDLINE/PubMed https://hal.science/hal-02930733$$DView record in HAL |
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Objectives
WGS-based antimicrobial susceptibility testing (AST) is as reliable as phenotypic AST for several antimicrobial/bacterial species... WGS-based antimicrobial susceptibility testing (AST) is as reliable as phenotypic AST for several antimicrobial/bacterial species combinations. However,... Objectives - WGS-based antimicrobial susceptibility testing (AST) is as reliable as phenotypic AST for several antimicrobial/bacterial species combinations.... |
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| Title | ResFinder 4.0 for predictions of phenotypes from genotypes |
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