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2020 年春季意大利医院获得性细菌病原体的泛病原体深度测序:一项前瞻性队列研究。

Pan-pathogen deep sequencing of nosocomial bacterial pathogens in Italy in spring 2020: a prospective cohort study.

机构信息

Department of Biostatistics, Faculty of Medicine, University of Oslo, Oslo, Norway.

Department of Biostatistics, Faculty of Medicine, University of Oslo, Oslo, Norway.

出版信息

Lancet Microbe. 2024 Oct;5(10):100890. doi: 10.1016/S2666-5247(24)00113-7. Epub 2024 Aug 20.

Abstract

BACKGROUND

Nosocomial infections pose a considerable risk to patients who are susceptible, and this is particularly acute in intensive care units when hospital-associated bacteria are endemic. During the first wave of the COVID-19 pandemic, the surge of patients presented a significant obstacle to the effectiveness of infection control measures. We aimed to assess the risks and extent of nosocomial pathogen transmission under a high patient burden by designing a novel bacterial pan-pathogen deep-sequencing approach that could be integrated with standard clinical surveillance and diagnostics workflows.

METHODS

We did a prospective cohort study in a region of northern Italy that was severely affected by the first wave of the COVID-19 pandemic. Inpatients on both ordinary and intensive care unit (ICU) wards at the San Matteo hospital, Pavia were sampled on multiple occasions to identify bacterial pathogens from respiratory, nasal, and rectal samples. Diagnostic samples collected between April 7 and May 10, 2020 were cultured on six different selective media designed to enrich for Acinetobacter baumannii, Escherichia coli, Enterococcus faecium, Enterococcus faecalis, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, and Streptococcus pneumoniae, and DNA from each plate with positive growth was deep sequenced en masse. We used mSWEEP and mGEMS to bin sequencing reads by sequence cluster for each species, followed by mapping with snippy to generate high quality alignments. Antimicrobial resistance genes were detected by use of ARIBA and CARD. Estimates of hospital transmission were obtained from pairwise bacterial single nucleotide polymorphism distances, partitioned by within-patient and between-patient samples. Finally, we compared the accuracy of our binned Acinetobacter baumannii genomes with those obtained by single colony whole-genome sequencing of isolates from the same hospital.

FINDINGS

We recruited patients from March 1 to May 7, 2020. The pathogen population among the patients was large and diverse, with 2148 species detections overall among the 2418 sequenced samples from the 256 patients. In total, 55 sequence clusters from key pathogen species were detected at least five times. The antimicrobial resistance gene prevalence was correspondingly high, with key carbapenemase and extended spectrum ß-lactamase genes detected in at least 50 (40%) of 125 patients in ICUs. Using high-resolution mapping to infer transmission, we established that hospital transmission was likely to be a significant mode of acquisition for each of the pathogen species. Finally, comparison with single colony Acinetobacter baumannii genomes showed that the resolution offered by deep sequencing was equivalent to single-colony sequencing, with the additional benefit of detection of co-colonisation of highly similar strains.

INTERPRETATION

Our study shows that a culture-based deep-sequencing approach is a possible route towards improving future pathogen surveillance and infection control at hospitals. Future studies should be designed to directly compare the accuracy, cost, and feasibility of culture-based deep sequencing with single colony whole-genome sequencing on a range of bacterial species.

FUNDING

Wellcome Trust, European Research Council, Academy of Finland Flagship program, Trond Mohn Foundation, and Research Council of Norway.

摘要

背景

医院感染对易感患者构成相当大的风险,尤其是在医院相关细菌流行的重症监护病房中更为严重。在 COVID-19 大流行的第一波期间,患者的激增对感染控制措施的有效性构成了重大障碍。我们旨在通过设计一种新的细菌泛病原体深度测序方法来评估在高患者负担下医院病原体传播的风险和程度,该方法可以与标准的临床监测和诊断工作流程相结合。

方法

我们在意大利北部一个受到 COVID-19 大流行第一波严重影响的地区进行了一项前瞻性队列研究。在帕维亚的圣马泰奥医院的普通病房和重症监护病房(ICU)的住院患者多次采样,从呼吸道、鼻腔和直肠样本中鉴定细菌病原体。在 2020 年 4 月 7 日至 5 月 10 日之间采集的诊断样本在六种不同的选择性培养基上进行培养,这些培养基旨在富集鲍曼不动杆菌、大肠杆菌、粪肠球菌、屎肠球菌、肺炎克雷伯菌、铜绿假单胞菌、金黄色葡萄球菌和肺炎链球菌,每个有阳性生长的平板上的 DNA 被大规模深度测序。我们使用 mSWEEP 和 mGEMS 通过序列簇对每个物种的测序reads 进行分类,然后使用 snippy 进行映射,以生成高质量的比对。使用 ARIBA 和 CARD 检测抗生素耐药基因。通过比较患者内和患者间样本的细菌单核苷酸多态性距离,获得医院传播的估计值。最后,我们将我们的分类鲍曼不动杆菌基因组与来自同一医院的分离株的单菌落全基因组测序的结果进行了比较。

结果

我们于 2020 年 3 月 1 日至 5 月 7 日招募了患者。患者的病原体群体庞大且多样,256 名患者的 2418 个测序样本中共有 2148 种物种检测到。总体而言,至少有 55 个关键病原体物种的序列簇被检测到至少 5 次。相应地,抗生素耐药基因的流行率也很高,在 ICU 中至少有 50%(125 名患者中的 40%)的患者检测到关键碳青霉烯酶和扩展谱β-内酰胺酶基因。使用高分辨率映射来推断传播,我们确定医院传播很可能是每种病原体的主要获得途径。最后,与单菌落鲍曼不动杆菌基因组的比较表明,深度测序提供的分辨率与单菌落测序相当,并且具有检测高度相似菌株共同定植的额外优势。

解释

我们的研究表明,基于培养的深度测序方法可能是改善未来医院病原体监测和感染控制的一种途径。未来的研究应旨在直接比较基于培养的深度测序与单菌落全基因组测序在一系列细菌物种上的准确性、成本和可行性。

资助

惠康信托基金会、欧洲研究理事会、芬兰科学院旗舰项目、特隆赫姆莫恩基金会和挪威研究理事会。

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