Ares Genetics GmbH, Vienna, Austria.
Center for Integrative Bioinformatics Vienna, Max Perutz Laboratories, University of Vienna and Medical University of Vienna, Vienna, Austria.
J Clin Microbiol. 2020 Jun 24;58(7). doi: 10.1128/JCM.00273-20.
Whole-genome sequencing (WGS) is now routinely performed in clinical microbiology laboratories to assess isolate relatedness. With appropriately developed analytics, the same data can be used for prediction of antimicrobial susceptibility. We assessed WGS data for identification using open-source tools and antibiotic susceptibility testing (AST) prediction using ARESdb compared to matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) identification and broth microdilution phenotypic susceptibility testing on clinical isolates from a multicenter clinical trial of the FDA-cleared Unyvero lower respiratory tract infection (LRTI) application (Curetis). For the trial, more than 2,000 patient samples were collected from intensive care units across nine hospitals and tested for LRTI. The isolate subset used in this study included 620 clinical isolates originating from 455 LRTI culture-positive patient samples. Isolates were sequenced using the Illumina Nextera XT protocol and FASTQ files with raw reads uploaded to the ARESdb cloud platform (ares-genetics.cloud; released for research use in 2020). The platform combines Ares Genetics' proprietary database ARESdb with state-of-the-art bioinformatics tools and curated public data. For identification, WGS showed 99 and 93% concordance with MALDI-TOF MS at the genus and species levels, respectively. WGS-predicted susceptibility showed 89% categorical agreement with phenotypic susceptibility across a total of 129 species-compound pairs analyzed, with categorical agreement exceeding 90% in 78 species-compound pairs and reaching 100% in 32. Results of this study add to the growing body of literature showing that, with improvement of analytics, WGS data could be used to predict antimicrobial susceptibility.
全基因组测序（WGS）现在已在临床微生物学实验室中常规进行，以评估分离株的相关性。通过适当开发的分析方法，相同的数据可用于预测抗菌药物敏感性。我们评估了使用开源工具进行鉴定的 WGS 数据，以及与基质辅助激光解吸电离飞行时间质谱（MALDI-TOF MS）鉴定和肉汤微量稀释表型药敏试验相比，使用 ARESdb 预测抗生素药敏性的结果，这些数据来自 FDA 批准的 Unyvero 下呼吸道感染（LRTI）应用（Curetis）多中心临床试验的临床分离株。在该试验中，从 9 家医院的重症监护病房收集了 2000 多个患者样本，并对 LRTI 进行了测试。本研究中使用的分离株子集包括来自 455 个 LRTI 培养阳性患者样本的 620 个临床分离株。使用 Illumina Nextera XT 方案对分离株进行测序，并将原始读数的 FASTQ 文件上传到 ARESdb 云平台（ares-genetics.cloud；于 2020 年发布供研究使用）。该平台将 Ares Genetics 专有的 ARESdb 数据库与最先进的生物信息学工具和经过整理的公共数据相结合。在鉴定方面，WGS 在属和种水平上与 MALDI-TOF MS 的一致性分别为 99%和 93%。在总共分析的 129 对种-化合物对中，WGS 预测的敏感性与表型敏感性的类别一致性为 89%，在 78 对种-化合物对中类别一致性超过 90%，在 32 对中达到 100%。这项研究的结果增加了越来越多的文献，表明通过分析方法的改进，WGS 数据可用于预测抗菌药物敏感性。
