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使用16s rRNA基因扩增子测序分析追踪脓毒症进展过程中的细菌DNA模式。

Tracking bacterial DNA patterns in septic progression using 16s rRNA gene amplicon sequencing analysis.

作者信息

Yang Jie, Li Zhuo, Liu Yanan, Guo Shubin

机构信息

Department of Emergency, Beijing Chaoyang Hospital, Capital Medical University No. 8 Worker's Stadium South Road, Chaoyang District, Beijing, China.

Department of Emergency, Beijing Shunyi District Hospital No. 3 Guangming South Street, Shunyi District, Beijing, China.

出版信息

Int J Clin Exp Pathol. 2021 Jun 15;14(6):753-767. eCollection 2021.

Abstract

Bloodstream infections remain prevalent in intensive care units, leading to a public health challenge worldwide. Routine diagnosis is mainly based on blood culture, but the technique is limited by its time-consuming process and relatively low sensitivity. Emerging molecular diagnostic tools, such as 16S metagenomics, have been developed for detecting bacteria in the blood samples of septic patients. Using a collection of 168 blood samples from 96 septic patients, 16S metagenomics method followed by bioinformatics were applied to study bacterial alterations during the pathogenesis of sepsis. Significant taxonomic variations were found between the two survival groups at different therapeutic time points through sequential 16S metagenomics research. The results on the third day during the treatment course were notably distinct among the studied groups. 16S metagenomics approach can bring novel genetic insight about microbiological fluctuations during septic progression, which may be utilized as a complementary prognostic application. Further etiologic and pathophysiologic explorations are needed to fully explain the linkage between clinical outcomes and genetic changes.

摘要

血流感染在重症监护病房中仍然很普遍,给全球公共卫生带来了挑战。常规诊断主要基于血培养,但该技术受其耗时的过程和相对较低的灵敏度所限。新兴的分子诊断工具,如16S宏基因组学,已被开发用于检测脓毒症患者血样中的细菌。利用从96名脓毒症患者收集的168份血样,应用16S宏基因组学方法并结合生物信息学来研究脓毒症发病机制中的细菌变化。通过连续的16S宏基因组学研究,在不同治疗时间点的两个生存组之间发现了显著的分类学差异。治疗过程中第三天的结果在研究组中尤为明显。16S宏基因组学方法可以为脓毒症进展过程中的微生物波动带来新的遗传学见解,这可能被用作一种补充性的预后应用。需要进一步进行病因学和病理生理学探索,以充分解释临床结果与基因变化之间的联系。

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