Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
School of Bioscience, Systems Biology Research Centre, Infection Biology, University of Skövde, Skövde, Sweden.
BMC Infect Dis. 2023 Jan 20;23(1):39. doi: 10.1186/s12879-022-07977-0.
The rapidly growing area of sequencing technologies, and more specifically bacterial whole-genome sequencing, could offer applications in clinical microbiology, including species identification of bacteria, prediction of genetic antibiotic susceptibility and virulence genes simultaneously. To accomplish the aforementioned points, the commercial cloud-based platform, 1928 platform (1928 Diagnostics, Gothenburg, Sweden) was benchmarked against an in-house developed bioinformatic pipeline as well as to reference methods in the clinical laboratory.
Whole-genome sequencing data retrieved from 264 Staphylococcus aureus isolates using the Illumina HiSeq X next-generation sequencing technology was used. The S. aureus isolates were collected during a prospective observational study of community-onset severe sepsis and septic shock in adults at Skaraborg Hospital, in the western region of Sweden. The collected isolates were characterized according to accredited laboratory methods i.e., species identification by MALDI-TOF MS analysis and phenotypic antibiotic susceptibility testing (AST) by following the EUCAST guidelines. Concordance between laboratory methods and bioinformatic tools, as well as concordance between the bioinformatic tools was assessed by calculating the percent of agreement.
There was an overall high agreement between predicted genotypic AST and phenotypic AST results, 98.0% (989/1006, 95% CI 97.3-99.0). Nevertheless, the 1928 platform delivered predicted genotypic AST results with lower very major error rates but somewhat higher major error rates compared to the in-house pipeline. There were differences in processing times i.e., minutes versus hours, where the 1928 platform delivered the results faster. Furthermore, the bioinformatic workflows showed overall 99.4% (1267/1275, 95% CI 98.7-99.7) agreement in genetic prediction of the virulence gene characteristics and overall 97.9% (231/236, 95% CI 95.0-99.2%) agreement in predicting the sequence types (ST) of the S. aureus isolates.
Altogether, the benchmarking disclosed that both bioinformatic workflows are able to deliver results with high accuracy aiding diagnostics of severe infections caused by S. aureus. It also illustrates the need of international agreement on quality control and metrics to facilitate standardization of analytical approaches for whole-genome sequencing based predictions.
测序技术领域的快速发展，特别是细菌全基因组测序，可能在临床微生物学中有应用，包括细菌的种属鉴定、预测遗传抗生素敏感性和毒力基因。为了实现上述目标，对商业云端平台 1928 平台（1928 诊断公司，哥德堡，瑞典）进行了基准测试，与内部开发的生物信息学管道以及临床实验室的参考方法进行了比较。
使用 Illumina HiSeq X 下一代测序技术从 264 株金黄色葡萄球菌分离株中获取全基因组测序数据。这些金黄色葡萄球菌分离株是在瑞典西部的斯卡拉堡医院进行的成人社区获得性严重脓毒症和感染性休克的前瞻性观察性研究中收集的。根据经过认证的实验室方法对收集的分离株进行特征描述，即使用 MALDI-TOF MS 分析进行种属鉴定，以及按照 EUCAST 指南进行表型抗生素敏感性测试（AST）。通过计算一致性百分比来评估实验室方法和生物信息学工具之间的一致性，以及生物信息学工具之间的一致性。
预测的基因型 AST 与表型 AST 结果之间存在总体高度一致，为 98.0%（989/1006，95%CI 97.3-99.0）。然而，与内部管道相比，1928 平台提供的预测基因型 AST 结果的非常大错误率较低，但主要错误率较高。处理时间存在差异，即分钟与小时，1928 平台更快地提供结果。此外，生物信息学工作流程在预测毒力基因特征的遗传方面总体上具有 99.4%（1267/1275，95%CI 98.7-99.7）的一致性，在预测金黄色葡萄球菌分离株的序列类型（ST）方面总体上具有 97.9%（231/236，95%CI 95.0-99.2%）的一致性。
总的来说，基准测试表明，两种生物信息学工作流程都能够提供高度准确的结果，有助于金黄色葡萄球菌引起的严重感染的诊断。它还说明了需要就质量控制和指标达成国际协议，以促进基于全基因组测序的预测分析方法的标准化。
