Department of Medicine, University of Cambridge, Box 157, Addenbrooke's Hospital, Hills Road, Cambridge CB2 0QQ, UK.
Clinical Microbiology and Public Health Laboratory, Public Health England, Cambridge CB2 0QQ, UK.
J Antimicrob Chemother. 2020 May 1;75(5):1117-1122. doi: 10.1093/jac/dkz570.
The genetic prediction of phenotypic antibiotic resistance based on analysis of WGS data is becoming increasingly feasible, but a major barrier to its introduction into routine use is the lack of fully automated interpretation tools. Here, we report the findings of a large evaluation of the Next Gen Diagnostics (NGD) automated bioinformatics analysis tool to predict the phenotypic resistance of MRSA.
MRSA-positive patients were identified in a clinical microbiology laboratory in England between January and November 2018. One MRSA isolate per patient together with all blood culture isolates (total n = 778) were sequenced on the Illumina MiniSeq instrument in batches of 21 clinical MRSA isolates and three controls.
The NGD system activated post-sequencing and processed the sequences to determine susceptible/resistant predictions for 11 antibiotics, taking around 11 minutes to analyse 24 isolates sequenced on a single sequencing run. NGD results were compared with phenotypic susceptibility testing performed by the clinical laboratory using the disc diffusion method and EUCAST breakpoints. Following retesting of discrepant results, concordance between phenotypic results and NGD genetic predictions was 99.69%. Further investigation of 22 isolate genomes associated with persistent discrepancies revealed a range of reasons in 12 cases, but no cause could be found for the remainder. Genetic predictions generated by the NGD tool were compared with predictions generated by an independent research-based informatics approach, which demonstrated an overall concordance between the two methods of 99.97%.
We conclude that the NGD system provides rapid and accurate prediction of the antibiotic susceptibility of MRSA.
基于 WGS 数据分析进行表型抗生素耐药性的基因预测正变得越来越可行，但将其引入常规使用的主要障碍是缺乏完全自动化的解释工具。在此，我们报告了对 Next Gen Diagnostics（NGD）自动化生物信息学分析工具进行的一项大型评估结果，该评估旨在预测耐甲氧西林金黄色葡萄球菌（MRSA）的表型耐药性。
2018 年 1 月至 11 月期间，在英国的一个临床微生物学实验室中鉴定出 MRSA 阳性患者。每位患者选择一个 MRSA 分离株，以及所有血培养分离株（总计 n=778），在 Illumina MiniSeq 仪器上分批测序，每个测序运行 21 个临床 MRSA 分离株和 3 个对照。
NGD 系统在测序后激活，并对序列进行处理，以确定对 11 种抗生素的敏感/耐药预测，分析在单个测序运行中测序的 24 个分离株大约需要 11 分钟。将 NGD 结果与临床实验室使用纸片扩散法和 EUCAST 折点进行的表型药敏试验进行比较。对不一致结果进行重新测试后，表型结果与 NGD 遗传预测之间的一致性为 99.69%。对与持续不一致结果相关的 22 个分离株基因组的进一步调查显示，在 12 个案例中有一系列原因，但其余案例未找到原因。NGD 工具生成的遗传预测与独立研究型信息学方法生成的预测进行了比较，两种方法的总体一致性为 99.97%。
我们得出结论，NGD 系统能够快速准确地预测 MRSA 的抗生素敏感性。
