Prediction of antimicrobial resistance in clinicalCampylobacter jejuniisolates from whole-genome sequencing data

Campylobacter jejuniis recognised as the leading cause of bacterial gastroenteritis in industrialised countries. Although the majority ofCampylobacterinfections are self-limiting, antimicrobial treatment is necessary in severe cases. Therefore, the development of antimicrobial resistance (AMR) inCampylobacteris 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 inC. jejunifrom whole-genome sequencing data. A total of 516 clinicalC. jejuniisolates 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 includingtet(O),tet(O/32/O),ant(6)-Ia,aph(2 '')-If,blaOXA,aph(3 ')-III, andcatas well as mutations in three genes:gyrA,23S rRNA, andrpsL. 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.