医院暴发常见病原菌检测核心基因组多位点测序分型管道的比较。

IF 5.4 2区 医学 Q1 MICROBIOLOGY
Heather L Glasgow, Ying Zheng, Jessica N Brazelton, Li Tang, Randall T Hayden
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引用次数: 0

摘要

基于微生物全基因组测序(WGS)的方法在传染病暴发调查或监测中已经取代了传统的基因组相关性分析方法。已提出采用核心基因组多位点序列分型(cgMLST)分析WGS,以便进行高分辨率的标准化菌株比较和纵向疫情监测。我们比较了三种商业cgMLST软件管道,即Ridom SeqSphere+、1928 Diagnostics平台和Ares Genetics ARESdb,用于鉴定255株常见细菌病原体的相关(聚集)菌株,包括鲍曼不动杆菌、大肠杆菌、粪肠球菌、屎肠球菌、肺炎克雷伯菌、铜绿假单胞菌、金黄色葡萄球菌和粘质沙雷菌。分离株以前被确定为与从同一患者或不同患者收集的至少一种其他分离株聚集在一起。SeqSphere+使用建议阈值区分聚类和非聚类分离对的一致性在1928平台为100%,在ARESdb总体上为99.5%,在ARESdb中,同一患者聚类、不同患者聚类和不同患者非聚类分离对的一致性分别为91.8%、96.1%和100%。ARESdb在同一患者聚集性分离对(平均[标准差,SD], 7.6[7.17], 1.18[1.56], 1[1.59])和不同患者聚集性分离对(平均[SD], 8.34[4.31], 3.61[2.26], 3.91[2.67])中的等位基因距离均显著大于SeqSphere+和1928 (P < 0.0001),但在非聚集性分离对中则无显著差异。在足够样本量的所有物种中,ARESdb中聚类分离物对的等位基因距离均显著增大。使用商业管道的CgMLST分析可能会产生不同的等位基因距离,但使用建议的聚类阈值来确定菌株之间的亲缘关系时显示出一致性。微生物遗传相关性分析通常用于调查由同一种病原体引起的感染的不同患者之间的疑似疫情,并越来越多地用于疫情监测,以发现患者之间未被怀疑的与医疗保健相关的传播事件,使感染预防和控制专家能够进行早期干预,以防止进一步传播。在这里,我们比较了三种用于细菌亲缘性分析的商业软件工具,它们执行基因对基因的比较,以确定几种常见病原体的亲缘程度。这些软件工具有可能使临床实验室在不需要内部生物信息学专家的情况下,为感染控制目的进行快速和常规的分析。本研究评估了这些软件工具中的三个的可比性,同时提出了一个比较分析管道评估的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of core genome multi-locus sequencing typing pipelines for hospital outbreak detection of common bacterial pathogens.

Microbial whole genome sequencing (WGS)-based methods have replaced conventional methods for genomic relatedness analysis in the investigation of or surveillance for infectious outbreaks. Analysis of WGS by core genome multi-locus sequence typing (cgMLST) has been proposed for standardized strain comparisons at high resolution and for longitudinal outbreak surveillance. We compared three commercial cgMLST software pipelines, Ridom SeqSphere+, 1928 Diagnostics' platform, and Ares Genetics ARESdb, for the identification of related (clustered) strains among 255 isolates of common bacterial pathogens, including Acinetobacter baumannii, Escherichia coli, Enterococcus faecalis, Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, and Serratia marcescens. Isolates were previously identified as clustered with at least one other isolate collected from the same patient or different patients. Concordance with SeqSphere+ for differentiating clustered from non-clustered isolate pairs using suggested thresholds was 100% for the 1928 platform and 99.5% for ARESdb overall and 91.8%, 96.1%, and 100% among same-patient clustered, different-patient clustered, and different-patient non-clustered isolate pairs, respectively, in ARESdb. ARESdb showed significantly greater allelic distances than SeqSphere+ and 1928 among same-patient clustered isolate pairs (mean [standard deviation, SD], 7.6 [7.17], 1.18 [1.56], and 1 [1.59]) and different-patient clustered isolate pairs (mean [SD], 8.34 [4.31], 3.61 [2.26], and 3.91 [2.67]) (P < 0.0001), but not among non-clustered isolate pairs. For all species analyzed with sufficient sample size, allelic distances of clustered isolate pairs were significantly higher in ARESdb. CgMLST analysis using commercial pipelines may result in different allelic distances but showed concordance using suggested clustering thresholds to determine relatedness among strains.IMPORTANCEMicrobial genetic relatedness analysis is commonly used to investigate suspected outbreaks among different patients with infections caused by the same species of pathogen and, increasingly, for outbreak surveillance to uncover unsuspected healthcare-associated transmission events among patients, enabling early intervention by infection prevention and control specialists to prevent further spread. Here, we compared three commercial software tools for bacterial relatedness analysis, which perform gene-by-gene comparisons to determine the degree of relatedness for several species of common pathogens. Such software tools potentially allow clinical laboratories to perform rapid and routine analysis for infection control purposes without the need for in-house bioinformatic expertize. This study evaluates the comparability of three of these software tools, while presenting a model for comparative analytic pipeline evaluation.

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来源期刊
Journal of Clinical Microbiology
Journal of Clinical Microbiology 医学-微生物学
CiteScore
17.10
自引率
4.30%
发文量
347
审稿时长
3 months
期刊介绍: The Journal of Clinical Microbiology® disseminates the latest research concerning the laboratory diagnosis of human and animal infections, along with the laboratory's role in epidemiology and the management of infectious diseases.
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