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ResFinder 4.0 for predictions of phenotypes from genotypes

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) determin...

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Autores principales: Bortolaia, Valeria, Kaas, Rolf S, Ruppe, Etienne, Roberts, Marilyn C, Schwarz, Stefan, Cattoir, Vincent, Philippon, Alain, Allesoe, Rosa L, Rebelo, Ana Rita, Florensa, Alfred Ferrer, Fagelhauer, Linda, Chakraborty, Trinad, Neumann, Bernd, Werner, Guido, Bender, Jennifer K, Stingl, Kerstin, Nguyen, Minh, Coppens, Jasmine, Xavier, Basil Britto, Malhotra-Kumar, Surbhi, Westh, Henrik, Pinholt, Mette, Anjum, Muna F, Duggett, Nicholas A, Kempf, Isabelle, Nykäsenoja, Suvi, Olkkola, Satu, Wieczorek, Kinga, Amaro, Ana, Clemente, Lurdes, Mossong, Joël, Losch, Serge, Ragimbeau, Catherine, Lund, Ole, Aarestrup, Frank M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662176/
https://www.ncbi.nlm.nih.gov/pubmed/32780112
http://dx.doi.org/10.1093/jac/dkaa345
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author Bortolaia, Valeria
Kaas, Rolf S
Ruppe, Etienne
Roberts, Marilyn C
Schwarz, Stefan
Cattoir, Vincent
Philippon, Alain
Allesoe, Rosa L
Rebelo, Ana Rita
Florensa, Alfred Ferrer
Fagelhauer, Linda
Chakraborty, Trinad
Neumann, Bernd
Werner, Guido
Bender, Jennifer K
Stingl, Kerstin
Nguyen, Minh
Coppens, Jasmine
Xavier, Basil Britto
Malhotra-Kumar, Surbhi
Westh, Henrik
Pinholt, Mette
Anjum, Muna F
Duggett, Nicholas A
Kempf, Isabelle
Nykäsenoja, Suvi
Olkkola, Satu
Wieczorek, Kinga
Amaro, Ana
Clemente, Lurdes
Mossong, Joël
Losch, Serge
Ragimbeau, Catherine
Lund, Ole
Aarestrup, Frank M
author_facet Bortolaia, Valeria
Kaas, Rolf S
Ruppe, Etienne
Roberts, Marilyn C
Schwarz, Stefan
Cattoir, Vincent
Philippon, Alain
Allesoe, Rosa L
Rebelo, Ana Rita
Florensa, Alfred Ferrer
Fagelhauer, Linda
Chakraborty, Trinad
Neumann, Bernd
Werner, Guido
Bender, Jennifer K
Stingl, Kerstin
Nguyen, Minh
Coppens, Jasmine
Xavier, Basil Britto
Malhotra-Kumar, Surbhi
Westh, Henrik
Pinholt, Mette
Anjum, Muna F
Duggett, Nicholas A
Kempf, Isabelle
Nykäsenoja, Suvi
Olkkola, Satu
Wieczorek, Kinga
Amaro, Ana
Clemente, Lurdes
Mossong, Joël
Losch, Serge
Ragimbeau, Catherine
Lund, Ole
Aarestrup, Frank M
author_sort Bortolaia, Valeria
collection PubMed
description 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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spelling pubmed-76621762020-11-18 ResFinder 4.0 for predictions of phenotypes from genotypes Bortolaia, Valeria Kaas, Rolf S Ruppe, Etienne Roberts, Marilyn C Schwarz, Stefan Cattoir, Vincent Philippon, Alain Allesoe, Rosa L Rebelo, Ana Rita Florensa, Alfred Ferrer Fagelhauer, Linda Chakraborty, Trinad Neumann, Bernd Werner, Guido Bender, Jennifer K Stingl, Kerstin Nguyen, Minh Coppens, Jasmine Xavier, Basil Britto Malhotra-Kumar, Surbhi Westh, Henrik Pinholt, Mette Anjum, Muna F Duggett, Nicholas A Kempf, Isabelle Nykäsenoja, Suvi Olkkola, Satu Wieczorek, Kinga Amaro, Ana Clemente, Lurdes Mossong, Joël Losch, Serge Ragimbeau, Catherine Lund, Ole Aarestrup, Frank M J Antimicrob Chemother Original Research 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. Oxford University Press 2020-08-11 /pmc/articles/PMC7662176/ /pubmed/32780112 http://dx.doi.org/10.1093/jac/dkaa345 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Research
Bortolaia, Valeria
Kaas, Rolf S
Ruppe, Etienne
Roberts, Marilyn C
Schwarz, Stefan
Cattoir, Vincent
Philippon, Alain
Allesoe, Rosa L
Rebelo, Ana Rita
Florensa, Alfred Ferrer
Fagelhauer, Linda
Chakraborty, Trinad
Neumann, Bernd
Werner, Guido
Bender, Jennifer K
Stingl, Kerstin
Nguyen, Minh
Coppens, Jasmine
Xavier, Basil Britto
Malhotra-Kumar, Surbhi
Westh, Henrik
Pinholt, Mette
Anjum, Muna F
Duggett, Nicholas A
Kempf, Isabelle
Nykäsenoja, Suvi
Olkkola, Satu
Wieczorek, Kinga
Amaro, Ana
Clemente, Lurdes
Mossong, Joël
Losch, Serge
Ragimbeau, Catherine
Lund, Ole
Aarestrup, Frank M
ResFinder 4.0 for predictions of phenotypes from genotypes
title ResFinder 4.0 for predictions of phenotypes from genotypes
title_full ResFinder 4.0 for predictions of phenotypes from genotypes
title_fullStr ResFinder 4.0 for predictions of phenotypes from genotypes
title_full_unstemmed ResFinder 4.0 for predictions of phenotypes from genotypes
title_short ResFinder 4.0 for predictions of phenotypes from genotypes
title_sort resfinder 4.0 for predictions of phenotypes from genotypes
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662176/
https://www.ncbi.nlm.nih.gov/pubmed/32780112
http://dx.doi.org/10.1093/jac/dkaa345
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