Scottish E. coli O157/STEC Reference Laboratory (SERL), Royal Infirmary of Edinburgh, Edinburgh, Scotland
Gastrointestinal Bacteria Reference Unit (GBRU), Reference Microbiology Services, National Infection Service, Public Health England, London, United Kingdom.
J Clin Microbiol. 2018 Feb 22;56(3). doi: 10.1128/JCM.01388-17. Print 2018 Mar.
Whole-genome sequencing (WGS) is rapidly becoming the method of choice for outbreak investigations and public health surveillance of microbial pathogens. The combination of improved cluster resolution and prediction of resistance and virulence phenotypes provided by a single tool is extremely advantageous. However, the data produced are complex, and standard bioinformatics pipelines are required to translate the output into easily interpreted epidemiologically relevant information for public health action. The main aim of this study was to validate the implementation of WGS at the Scottish O157/STEC Reference Laboratory (SERL) using the Public Health England (PHE) bioinformatics pipeline to produce standardized data to enable interlaboratory comparison of results generated at two national reference laboratories. In addition, we evaluated the BioNumerics whole-genome multilocus sequence typing (wgMLST) and genotyping plug-in tools using the same data set. A panel of 150 well-characterized isolates of Shiga toxin-producing (STEC) that had been sequenced and analyzed at PHE using the PHE pipeline and database (SnapperDB) was assembled to provide identification and typing data, including serotype (O:H type), sequence type (ST), virulence genes ( and Shiga toxin [] subtype), and a single-nucleotide polymorphism (SNP) address. To validate the implementation of sequencing at the SERL, DNA was reextracted from the isolates and sequenced and analyzed using the PHE pipeline, which had been installed at the SERL; the output was then compared with the PHE data. The results showed a very high correlation between the data, ranging from 93% to 100%, suggesting that the standardization of WGS between our reference laboratories is possible. We also found excellent correlation between the results obtained using the PHE pipeline and BioNumerics, except for the detection of and when these subtypes are both carried by strains.
全基因组测序（WGS）正在迅速成为暴发调查和微生物病原体公共卫生监测的首选方法。单一工具提供的改进聚类分辨率和耐药性及毒力表型预测的结合具有极大的优势。然而，产生的数据非常复杂，需要标准的生物信息学流程将输出转化为易于解释的公共卫生行动相关的流行病学信息。本研究的主要目的是使用英国公共卫生署（PHE）生物信息学流程验证苏格兰 O157/STEC 参考实验室（SERL）实施 WGS 的情况，以生成标准化数据，从而能够比较两个国家参考实验室生成的结果。此外，我们使用相同的数据集评估了 BioNumerics 全基因组多位点序列分型（wgMLST）和基因分型插件工具。组装了一组 150 个经过充分表征的产志贺毒素（STEC）分离株，这些分离株已经在 PHE 进行了测序和分析，使用的是 PHE 流程和数据库（SnapperDB），以提供鉴定和分型数据，包括血清型（O:H 型）、序列型（ST）、毒力基因（和志贺毒素 []亚型）和单核苷酸多态性（SNP）地址。为了验证 SERL 测序的实施情况，从分离株中重新提取 DNA 并使用已在 SERL 安装的 PHE 流程进行测序和分析，然后将输出与 PHE 数据进行比较。结果表明，数据之间具有非常高的相关性，范围从 93%到 100%，表明我们的参考实验室之间进行 WGS 标准化是可能的。我们还发现 PHE 流程和 BioNumerics 获得的结果之间存在极好的相关性，除了在两种亚型均由菌株携带时检测到 和 。
