Conzemius Rick, Bergman Yehudit, Májek Peter, Beisken Stephan, Lewis Shawna, Jacobs Emily B, Tamma Pranita D, Simner Patricia J
Ares Genetics GmbH, Vienna, Austria.
Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Front Microbiol. 2022 Aug 8;13:973605. doi: 10.3389/fmicb.2022.973605. eCollection 2022.
Whole-genome sequencing (WGS) enables the molecular characterization of bacterial pathogens. We compared the accuracy of the Illumina and Oxford Nanopore Technologies (ONT) sequencing platforms for the determination of AMR classes and antimicrobial susceptibility testing (AST) among 181 clinical isolates. Sequencing reads for each isolate were uploaded to AREScloud (Ares Genetics) to determine the presence of AMR markers and the predicted WGS-AST profile. The profiles of both sequencing platforms were compared to broth microdilution (BMD) AST. Isolates were delineated by resistance to third-generation cephalosporins and carbapenems as well as the presence of AMR markers to determine clinically relevant AMR classes. The overall categorical agreement (CA) was 90% (Illumina) and 88% (ONT) across all antimicrobials, 96% for the prediction of resistance to third-generation cephalosporins for both platforms, and 94% (Illumina) and 91% (ONT) for the prediction of resistance to carbapenems. Carbapenem resistance was overestimated on ONT with a major error of 16%. Sensitivity for the detection of carbapenemases, extended-spectrum β-lactamases, and plasmid-mediated genes was 98, 95, and 70% by ONT compared to the Illumina dataset as the reference. Our results highlight the potential of the ONT platform's use in clinical microbiology laboratories. When combined with robust bioinformatics methods, WGS-AST predictions may be a future approach to guide effective antimicrobial decision-making.
全基因组测序(WGS)能够对细菌病原体进行分子特征分析。我们比较了Illumina和牛津纳米孔技术(ONT)测序平台在181株临床分离株中确定抗菌药物耐药(AMR)类别和抗菌药物敏感性测试(AST)的准确性。将每个分离株的测序读数上传到AREScloud(Ares Genetics)以确定AMR标记的存在和预测的WGS-AST谱。将两个测序平台的谱与肉汤微量稀释(BMD)AST进行比较。通过对第三代头孢菌素和碳青霉烯类的耐药性以及AMR标记的存在来划分分离株,以确定临床相关的AMR类别。所有抗菌药物的总体分类一致性(CA)在Illumina平台为90%,ONT平台为88%;两个平台对第三代头孢菌素耐药性预测的CA为96%;对碳青霉烯类耐药性预测的CA,Illumina平台为94%,ONT平台为91%。ONT平台对碳青霉烯类耐药性存在高估,主要错误率为16%。以Illumina数据集为参考,ONT检测碳青霉烯酶、超广谱β-内酰胺酶和质粒介导基因的敏感性分别为98%、95%和70%。我们的结果突出了ONT平台在临床微生物实验室应用的潜力。当与强大的生物信息学方法相结合时,WGS-AST预测可能成为指导有效抗菌决策的未来方法。