Pilot Evaluation of a Fully Automated Bioinformatics System for Analysis of Methicillin-Resistant Staphylococcus aureus Genomes and Detection of Outbreaks
Pilot Evaluation of a Fully Automated Bioinformatics System for Analysis of Methicillin-Resistant Staphylococcus aureus Genomes and Detection of Outbreaks
复制标题
DOI：
10.1128/jcm.00858-19
复制
发表时间：
2019-11-01
影响因子：
9.4
通讯作者：
Peacock, Sharon J.
中科院分区：
文献类型：
--
作者：
Brown, Nicholas M.;Blane, Beth;Peacock, Sharon J.
结合细菌测序和流行病学信息的基因组监测将成为疫情检测的黄金标准，但由于缺乏自动解释工具，其临床翻译受到阻碍。我们进行了一项前瞻性试点研究，以评估使用下一代诊断（NGD）自动化生物信息学系统对耐甲氧西林金黄色葡萄球菌（MRSA）基因组的分析。2018年，英国临床微生物实验室在2周内鉴定出17例未选择的MRSA阳性患者，并在Illumina MiniSeq仪器上对每例1株MRSA分离物进行测序。NGD系统在测序和处理fastq文件夹后自动激活，以确定物种、多位点序列类型、mec基因的存在、抗生素敏感性预测以及基于参考MRSA基因组的遗传相关性和对核心基因组单核苷酸多态性的检测。NGD系统需要90 s来自动分析每次运行的数据，并自动显示结果。使用基于研究的方法对相同的数据进行独立分析。两种分析方法在菌种（金黄色葡萄球菌）、mecA检测、序列型分配和耐药遗传决定因素检测方面完全一致。两种分析方法都根据相关性确定了两个MRSA聚类，其中一个包含3例病例，这些病例与与糖尿病患者护理相关的诊所和病房有关。我们的结论是，在这项试点研究中，NGD系统提供了快速准确的数据，可以支持感染控制实践。
Genomic surveillance that combines bacterial sequencing and epidemiological information will become the gold standard for outbreak detection, but its clinical translation is hampered by the lack of automated interpretation tools. We performed a prospective pilot study to evaluate the analysis of methicillin-resistant Staphylococcus aureus (MRSA) genomes using the Next Gen Diagnostics (NGD) automated bioinformatics system. Seventeen unselected MRSA-positive patients were identified in a clinical microbiology laboratory in England over a period of 2 weeks in 2018, and 1 MRSA isolate per case was sequenced on the Illumina MiniSeq instrument. The NGD system automatically activated after sequencing and processed fastq folders to determine species, multilocus sequence type, the presence of a mec gene, antibiotic susceptibility predictions, and genetic relatedness based on mapping to a reference MRSA genome and detection of pairwise core genome single-nucleotide polymorphisms. The NGD system required 90 s per sample to automatically analyze data from each run, the results of which were automatically displayed. The same data were independently analyzed using a research-based approach. There was full concordance between the two analysis methods regarding species (S. aureus), detection of mecA, sequence type assignment, and detection of genetic determinants of resistance. Both analysis methods identified two MRSA clusters based on relatedness, one of which contained 3 cases that were involved in an outbreak linked to a clinic and ward associated with diabetic patient care. We conclude that, in this pilot study, the NGD system provided rapid and accurate data that could support infection control practices.