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Prediction of antimicrobial resistance in clinical Campylobacter jejuni isolates from whole-genome sequencing data.

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.

SUBMITTER: Dahl LG

PROVIDER: S-EPMC7979593 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The increasing prevalence of antimicrobial resistance (AMR) in Campylobacter spp. is a global concern. This study evaluated the use of whole-genome sequencing (WGS) to predict AMR in Campylobacter jejuni and C. coli. A panel of 271 isolates recovered from Canadian poultry was used to compare AMR genotype to antimicrobial susceptibility testing (AST) results (azithromycin, ciprofloxacin, erythromycin, gentamicin, tetracycline, florfenicol, nalidixic acid, telithromycin, and clindamycin). The presence of antibiotic resistance genes (ARGs) was determined for each isolate using five computational approaches to evaluate the effect of: ARG screening software, input data (i.e., raw reads, draft genome assemblies), genome coverage and genome assembly software. Overall, concordance between the genotype and phenotype was influenced by the computational pipelines, level of genome coverage and the type of ARG but not by input data. For example, three of the pipelines showed a 99% agreement between detection of a tet(O) gene and tetracycline resistance, whereas agreement between the detection of tet(O) and TET resistance was 98 and 93% for two pipelines. Overall, higher levels of genome coverage were needed to reliably detect some ARGs; for example, at 15X coverage a tet(O) gene was detected in >70% of the genomes, compared to <60% of the genomes for bla(OXA). No genes associated with florfenicol or gentamicin resistance were found in the set of strains included in this study, consistent with AST results. Macrolide and fluoroquinolone resistance was associated 100% with mutations in the 23S rRNA (A2075G) and gyrA (T86I) genes, respectively. A lower association between a A2075G 23S rRNA gene mutation and resistance to clindamycin and telithromycin (92.8 and 78.6%, respectively) was found. While WGS is an effective approach to predicting AMR in Campylobacter, this study demonstrated the impact that computational pipelines, genome coverage and the genes can have on the reliable identification of an AMR genotype.

Project 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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Prediction of antimicrobial resistance in clinical Campylobacter jejuni isolates from whole-genome sequencing data.

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