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Prediction of antimicrobial resistance in clinical Campylobacter jejuni isolates from whole-genome sequencing data.
Dahl, Louise Gade; Joensen, Katrine Grimstrup; Østerlund, Mark Thomas; Kiil, Kristoffer; Nielsen, Eva Møller.
Afiliación
  • Dahl LG; Department of Bacteria, Parasites & Fungi, Statens Serum Institut, Copenhagen, Denmark.
  • Joensen KG; Department of Bacteria, Parasites & Fungi, Statens Serum Institut, Copenhagen, Denmark.
  • Østerlund MT; Department of Bacteria, Parasites & Fungi, Statens Serum Institut, Copenhagen, Denmark.
  • Kiil K; Department of Bacteria, Parasites & Fungi, Statens Serum Institut, Copenhagen, Denmark.
  • Nielsen EM; Department of Bacteria, Parasites & Fungi, Statens Serum Institut, Copenhagen, Denmark. emn@ssi.dk.
Eur J Clin Microbiol Infect Dis ; 40(4): 673-682, 2021 Apr.
Article en En | MEDLINE | ID: mdl-32974772
ABSTRACT
Campylobacter jejuni is recognised as the leading cause of bacterial gastroenteritis in industrialised countries. Although the majority of Campylobacter infections are self-limiting, antimicrobial treatment is necessary in severe cases. Therefore, the development of antimicrobial resistance (AMR) in Campylobacter is a growing public health challenge and surveillance of AMR is important for bacterial disease control. The aim of this study was to predict antimicrobial resistance in C. jejuni from whole-genome sequencing data. A total of 516 clinical C. jejuni isolates collected between 2014 and 2017 were subjected to WGS. Resistance phenotypes were determined by standard broth dilution, categorising isolates as either susceptible or resistant based on epidemiological cutoffs for six antimicrobials ciprofloxacin, nalidixic acid, erythromycin, gentamicin, streptomycin, and tetracycline. Resistance genotypes were identified using an in-house database containing reference genes with known point mutations and the presence of resistance genes was determined using the ResFinder database and four bioinformatical methods (modified KMA, ABRicate, ARIBA, and ResFinder Batch Upload). We identified seven resistance genes including tet(O), tet(O/32/O), ant(6)-Ia, aph(2″)-If, blaOXA, aph(3')-III, and cat as well as mutations in three genes gyrA, 23S rRNA, and rpsL. There was a high correlation between phenotypic resistance and the presence of known resistance genes and/or point mutations. A correlation above 98% was seen for all antimicrobials except streptomycin with a correlation of 92%. In conclusion, we found that WGS can predict antimicrobial resistance with a high degree of accuracy and have the potential to be a powerful tool for AMR surveillance.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Campylobacter jejuni / Genoma Bacteriano / Farmacorresistencia Bacteriana / Antibacterianos Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Eur J Clin Microbiol Infect Dis Asunto de la revista: DOENCAS TRANSMISSIVEIS / MICROBIOLOGIA Año: 2021 Tipo del documento: Article País de afiliación: Dinamarca

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Campylobacter jejuni / Genoma Bacteriano / Farmacorresistencia Bacteriana / Antibacterianos Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Eur J Clin Microbiol Infect Dis Asunto de la revista: DOENCAS TRANSMISSIVEIS / MICROBIOLOGIA Año: 2021 Tipo del documento: Article País de afiliación: Dinamarca