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一种解析肠道微生物群落抗病原体定植抗性的计算方法。

A computational method to dissect colonization resistance of the gut microbiota against pathogens.

机构信息

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.

College of System Engineering, National University of Defense Technology, Changsha, Hunan 410073, China.

出版信息

Cell Rep Methods. 2023 Sep 25;3(9):100576. doi: 10.1016/j.crmeth.2023.100576. Epub 2023 Aug 29.

Abstract

The mammalian gut microbiome protects the host through colonization resistance (CR) against the incursion of exogenous and often harmful microorganisms, but identifying the exact microbes responsible for the gut microbiota-mediated CR against a particular pathogen remains a challenge. To address this limitation, we developed a computational method: generalized microbe-phenotype triangulation (GMPT). We first systematically validated GMPT using a classical population dynamics model in community ecology and demonstrated its superiority over baseline methods. We then tested GMPT on simulated data generated from the ecological network inferred from a real community (GnotoComplex microflora) and real microbiome data on two mouse studies on Clostridioides difficile infection. We demonstrated GMPT's ability to streamline the discovery of microbes that are potentially responsible for microbiota-mediated CR against pathogens. GMPT holds promise to advance our understanding of CR mechanisms and facilitate the rational design of microbiome-based therapies for preventing and treating enteric infections.

摘要

哺乳动物肠道微生物群通过定植抵抗(CR)保护宿主免受外源微生物的侵袭,这些外源微生物通常是有害的,但确定肠道微生物群介导的 CR 对抗特定病原体的确切微生物仍然是一个挑战。为了解决这一限制,我们开发了一种计算方法:广义微生物-表型三角剖分(GMPT)。我们首先使用群落生态学中的经典种群动态模型系统地验证了 GMPT,并证明了它优于基线方法。然后,我们在从真实群落推断的生态网络生成的模拟数据(GnotoComplex 微生物群)和关于艰难梭菌感染的两项小鼠研究的真实微生物组数据上测试了 GMPT。我们证明了 GMPT 能够简化发现可能对肠道微生物群介导的 CR 对抗病原体负责的微生物的过程。GMPT 有望增进我们对 CR 机制的理解,并促进基于微生物组的疗法的合理设计,以预防和治疗肠道感染。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94c1/10545914/7948c53ff7a2/fx1.jpg

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