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一与多:多微生物群落与囊性纤维化气道。

One versus Many: Polymicrobial Communities and the Cystic Fibrosis Airway.

机构信息

Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA.

Department of Chemical Engineering, University of Massachusetts, Amherst, Massachusetts, USA.

出版信息

mBio. 2021 Mar 16;12(2):e00006-21. doi: 10.1128/mBio.00006-21.

Abstract

Culture-independent studies have revealed that chronic lung infections in persons with cystic fibrosis (pwCF) are rarely limited to one microbial species. Interactions among bacterial members of these polymicrobial communities in the airways of pwCF have been reported to modulate clinically relevant phenotypes. Furthermore, it is clear that a single polymicrobial community in the context of CF airway infections cannot explain the diversity of clinical outcomes. While large 16S rRNA gene-based studies have allowed us to gain insight into the microbial composition and predicted functional capacities of communities found in the CF lung, here we argue that approaches can help build clinically relevant models of polymicrobial communities that can in turn be used to experimentally test and validate computationally generated hypotheses. Furthermore, we posit that combining computational and experimental approaches will enhance our understanding of mechanisms that drive microbial community function and identify new therapeutics to target polymicrobial infections.

摘要

非培养研究表明,囊性纤维化(CF)患者的慢性肺部感染很少局限于一种微生物物种。已经报道了这些多微生物群落中细菌成员之间的相互作用会调节与临床相关的表型。此外,显然,CF 气道感染背景下的单一多微生物群落并不能解释临床结果的多样性。虽然基于 16S rRNA 基因的大型研究使我们能够深入了解 CF 肺部发现的群落的微生物组成和预测功能能力,但在这里我们认为,多组学方法可以帮助构建与临床相关的多微生物群落模型,从而可以用于实验测试和验证计算生成的假设。此外,我们假设将计算和实验方法相结合将增强我们对驱动微生物群落功能的机制的理解,并确定针对多微生物感染的新疗法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b02b/8092191/17be5de99c18/mBio.00006-21-f0001.jpg

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