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WGS-Based Prediction and Analysis of Antimicrobial Resistance in Campylobacter jejuni Isolates From Israel

Rapid developments in the field of whole genome sequencing (WGS) make in silico antimicrobial resistance (AMR) a target within reach. Campylobacter jejuni is a leading cause of foodborne infections in Israel with increasing rates of resistance. We applied WGS analysis to study the prevalence and gen...

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Autores principales: Rokney, Assaf, Valinsky, Lea, Vranckx, Katleen, Feldman, Noa, Agmon, Vered, Moran-Gilad, Jacob, Weinberger, Miriam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7438411/
https://www.ncbi.nlm.nih.gov/pubmed/32903472
http://dx.doi.org/10.3389/fcimb.2020.00365
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author Rokney, Assaf
Valinsky, Lea
Vranckx, Katleen
Feldman, Noa
Agmon, Vered
Moran-Gilad, Jacob
Weinberger, Miriam
author_facet Rokney, Assaf
Valinsky, Lea
Vranckx, Katleen
Feldman, Noa
Agmon, Vered
Moran-Gilad, Jacob
Weinberger, Miriam
author_sort Rokney, Assaf
collection PubMed
description Rapid developments in the field of whole genome sequencing (WGS) make in silico antimicrobial resistance (AMR) a target within reach. Campylobacter jejuni is a leading cause of foodborne infections in Israel with increasing rates of resistance. We applied WGS analysis to study the prevalence and genetic basis of AMR in 263 C. jejuni human and veterinary representative isolates retrieved from a national collection during 2003–2012. We evaluated the prediction of phenotypic AMR from genomic data. Genomes were screened by the NCBI AMRFinderPlus and the BioNumerics tools for acquired AMR genes and point mutations. The results were compared to phenotypic resistance determined by broth microdilution. The most prevalent resistant determinants were the multi-drug efflux transporter gene cmeABC (100%), the tetracycline resistance tet(O) gene (82.1%), the quinolone resistance gyrA T861 point mutation (75.7%), and the aadE streptomycin resistance gene. A variety of 12 known β lactam resistance genes (bla(OXA) variants) were detected in 241 (92%) isolates, the most prevalent being bla(OXA−193), bla(OXA−461), and bla(OXA−580) (56, 16, and 7%, respectively). Other aminoglycoside resistance genes and the macrolide resistance point mutation were rare (<1%). The overall correlation rate between WGS-based genotypic prediction and phenotypic resistance was 98.8%, sensitivity, specificity, positive, and negative predictive values being 98.0, 99.3, 99.1, and 98.5%, respectively. wgMLST-based phylogeny indicated a high level of clonality and clustering among the studied isolates. Closely related isolates that were part of a genetic cluster (single linkage distance ≤ 15 alleles) based on wgMLST phylogeny mostly shared a homogenous AMR determinant profile. This was observed in 18 of 20 (90.0%) clusters within clonal complex-21, suggesting clonal expansion of resistant isolates. Strong association to lineage was noted for the aadE gene and the various bla(OXA) genes. High resistance rates to tetracycline and quinolones and a low resistance rate to macrolides were detected among the Israeli C. jejuni isolates. While a high genotypic-phenotypic correlation was found, some resistance phenotypes could not be predicted by the presence of AMR determinants, and particularly not the level of resistance. WGS-based prediction of antimicrobial resistance in C. jejuni requires further optimization in order to integrate this approach in the routine workflow of public health laboratories for foodborne surveillance.
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spelling pubmed-74384112020-09-03 WGS-Based Prediction and Analysis of Antimicrobial Resistance in Campylobacter jejuni Isolates From Israel Rokney, Assaf Valinsky, Lea Vranckx, Katleen Feldman, Noa Agmon, Vered Moran-Gilad, Jacob Weinberger, Miriam Front Cell Infect Microbiol Cellular and Infection Microbiology Rapid developments in the field of whole genome sequencing (WGS) make in silico antimicrobial resistance (AMR) a target within reach. Campylobacter jejuni is a leading cause of foodborne infections in Israel with increasing rates of resistance. We applied WGS analysis to study the prevalence and genetic basis of AMR in 263 C. jejuni human and veterinary representative isolates retrieved from a national collection during 2003–2012. We evaluated the prediction of phenotypic AMR from genomic data. Genomes were screened by the NCBI AMRFinderPlus and the BioNumerics tools for acquired AMR genes and point mutations. The results were compared to phenotypic resistance determined by broth microdilution. The most prevalent resistant determinants were the multi-drug efflux transporter gene cmeABC (100%), the tetracycline resistance tet(O) gene (82.1%), the quinolone resistance gyrA T861 point mutation (75.7%), and the aadE streptomycin resistance gene. A variety of 12 known β lactam resistance genes (bla(OXA) variants) were detected in 241 (92%) isolates, the most prevalent being bla(OXA−193), bla(OXA−461), and bla(OXA−580) (56, 16, and 7%, respectively). Other aminoglycoside resistance genes and the macrolide resistance point mutation were rare (<1%). The overall correlation rate between WGS-based genotypic prediction and phenotypic resistance was 98.8%, sensitivity, specificity, positive, and negative predictive values being 98.0, 99.3, 99.1, and 98.5%, respectively. wgMLST-based phylogeny indicated a high level of clonality and clustering among the studied isolates. Closely related isolates that were part of a genetic cluster (single linkage distance ≤ 15 alleles) based on wgMLST phylogeny mostly shared a homogenous AMR determinant profile. This was observed in 18 of 20 (90.0%) clusters within clonal complex-21, suggesting clonal expansion of resistant isolates. Strong association to lineage was noted for the aadE gene and the various bla(OXA) genes. High resistance rates to tetracycline and quinolones and a low resistance rate to macrolides were detected among the Israeli C. jejuni isolates. While a high genotypic-phenotypic correlation was found, some resistance phenotypes could not be predicted by the presence of AMR determinants, and particularly not the level of resistance. WGS-based prediction of antimicrobial resistance in C. jejuni requires further optimization in order to integrate this approach in the routine workflow of public health laboratories for foodborne surveillance. Frontiers Media S.A. 2020-08-13 /pmc/articles/PMC7438411/ /pubmed/32903472 http://dx.doi.org/10.3389/fcimb.2020.00365 Text en Copyright © 2020 Rokney, Valinsky, Vranckx, Feldman, Agmon, Moran-Gilad and Weinberger. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cellular and Infection Microbiology
Rokney, Assaf
Valinsky, Lea
Vranckx, Katleen
Feldman, Noa
Agmon, Vered
Moran-Gilad, Jacob
Weinberger, Miriam
WGS-Based Prediction and Analysis of Antimicrobial Resistance in Campylobacter jejuni Isolates From Israel
title WGS-Based Prediction and Analysis of Antimicrobial Resistance in Campylobacter jejuni Isolates From Israel
title_full WGS-Based Prediction and Analysis of Antimicrobial Resistance in Campylobacter jejuni Isolates From Israel
title_fullStr WGS-Based Prediction and Analysis of Antimicrobial Resistance in Campylobacter jejuni Isolates From Israel
title_full_unstemmed WGS-Based Prediction and Analysis of Antimicrobial Resistance in Campylobacter jejuni Isolates From Israel
title_short WGS-Based Prediction and Analysis of Antimicrobial Resistance in Campylobacter jejuni Isolates From Israel
title_sort wgs-based prediction and analysis of antimicrobial resistance in campylobacter jejuni isolates from israel
topic Cellular and Infection Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7438411/
https://www.ncbi.nlm.nih.gov/pubmed/32903472
http://dx.doi.org/10.3389/fcimb.2020.00365
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