Sanchez Gerardo J, Cuypers Lize, Laenen Lies, Májek Peter, Lagrou Katrien, Desmet Stefanie
Laboratory of Clinical Microbiology, KU Leuven, Department of Microbiology, Immunology and Transplantation, Leuven, Flanders, Belgium.
Department of Laboratory Medicine, National Reference Centre for Invasive Pneumococci, University Hospitals Leuven, Leuven, Flanders, Belgium.
J Clin Microbiol. 2025 Feb 19;63(2):e0107924. doi: 10.1128/jcm.01079-24. Epub 2024 Dec 31.
Determination of antimicrobial resistance (AMR) in pneumococcal isolates is important for surveillance purposes and in a clinical context. Antimicrobial susceptibility testing (AST) of pneumococci is complicated by the need for exact minimal inhibitory concentrations (MICs) of beta-lactam antibiotics. Two next-generation sequencing (NGS) analysis tools have implemented the prediction of AMR in their analysis workflow, including the prediction of MICs: Pathogenwatch (https://pathogen.watch/) and AREScloud (OpGen). The performance of these tools in comparison to phenotypic AST following EUCAST guidelines is unknown. A total of 538 isolates were used to compare both tools with phenotypic AST for penicillin, amoxicillin, cefotaxime/ceftriaxone, erythromycin, trimethoprim-sulfamethoxazole, and tetracycline. Disk diffusion was performed for all isolates, and broth microdilution was performed for isolates with reduced beta-lactam susceptibility. Demultiplexed FASTQ files from Illumina sequencing, covering the whole genome of pneumococci, were used as input for the NGS tools. Categorical agreement (CA), major error (ME), and very major error (VME) rates were calculated. For beta-lactam antibiotics, CA was high (>94%) associated with none or only one ME and VME (<1%). For erythromycin and tetracycline, CA was >93% for predictions by AREScloud, while for Pathogenwatch, this ranged around 88%. For trimethoprim-sulfamethoxazole, CA was for both tools <86%. High VME rates were observed for erythromycin and tetracycline, higher for Pathogenwatch (53.6% and 47.0%, respectively) compared to AREScloud (14.3% and 19.1%, respectively). Both tools performed excellently despite the complexity of predicting beta-lactam resistance in pneumococci. Further optimization and validation are needed for non-beta-lactams since high (very) major error rates were observed.
肺炎球菌分离株中抗菌药物耐药性（AMR）的测定对于监测目的和临床情况都很重要。肺炎球菌的抗菌药物敏感性测试（AST）因需要准确测定β-内酰胺类抗生素的最低抑菌浓度（MIC）而变得复杂。两种新一代测序（NGS）分析工具在其分析工作流程中实现了AMR预测，包括MIC预测：Pathogenwatch（https://pathogen.watch/）和AREScloud（OpGen）。与遵循欧洲抗菌药物敏感性试验委员会（EUCAST）指南的表型AST相比，这些工具的性能尚不清楚。总共使用了538株分离株，将这两种工具与青霉素、阿莫西林、头孢噻肟/头孢曲松、红霉素、甲氧苄啶-磺胺甲恶唑和四环素的表型AST进行比较。对所有分离株进行纸片扩散法检测，对β-内酰胺敏感性降低的分离株进行肉汤微量稀释法检测。来自Illumina测序的解复用FASTQ文件覆盖肺炎球菌的全基因组，用作NGS工具的输入。计算分类一致性（CA）、主要错误（ME）和非常主要错误（VME）率。对于β-内酰胺类抗生素，CA很高（>94%），伴有无或仅有一个ME和VME（<1%）。对于红霉素和四环素，AREScloud预测的CA>93%，而Pathogenwatch的CA约为88%。对于甲氧苄啶-磺胺甲恶唑，两种工具的CA均<86%。红霉素和四环素的VME率较高，Pathogenwatch的VME率更高（分别为53.6%和47.0%），而AREScloud的VME率分别为14.3%和19.1%。尽管预测肺炎球菌β-内酰胺耐药性很复杂，但两种工具的表现都很出色。由于观察到较高的（非常）主要错误率，非β-内酰胺类药物需要进一步优化和验证。
