资源竞争和宿主反馈是肠道微生物群系转变的基础。
Resource Competition and Host Feedbacks Underlie Regime Shifts in Gut Microbiota.
出版信息
Am Nat. 2021 Jul;198(1):1-12. doi: 10.1086/714527. Epub 2021 May 7.
AbstractThe spread of an enteric pathogen in the human gut depends on many interacting factors, including pathogen exposure, diet, host gut environment, and host microbiota, but how these factors jointly influence infection outcomes remains poorly characterized. Here we develop a model of host-mediated resource competition between mutualistic and pathogenic taxa in the gut that aims to explain why similar hosts, exposed to the same pathogen, can have such different infection outcomes. Our model successfully reproduces several empirically observed phenomena related to transitions between healthy and infected states, including (1) the nonlinear relationship between pathogen inoculum size and infection persistence, (2) the elevated risk of chronic infection during or after treatment with broad-spectrum antibiotics, (3) the resolution of gut dysbiosis with fecal microbiota transplants, and (4) the potential protection from infection conferred by probiotics. We then use the model to explore how host-mediated interventions-namely, shifts in the supply rates of electron donors (e.g., dietary fiber) and respiratory electron acceptors (e.g., oxygen)-can potentially be used to direct gut community assembly. Our study demonstrates how resource competition and ecological feedbacks between the host and the gut microbiota can be critical determinants of human health outcomes. We identify several testable model predictions ready for experimental validation.
摘要
肠道病原体的传播取决于许多相互作用的因素,包括病原体暴露、饮食、宿主肠道环境和宿主微生物群,但这些因素如何共同影响感染结果仍知之甚少。在这里,我们开发了一种宿主介导的肠道共生和病原种群间资源竞争模型,旨在解释为什么相似的宿主暴露于相同的病原体时会产生如此不同的感染结果。我们的模型成功再现了与健康和感染状态之间的转变相关的几个经验观察到的现象,包括(1)病原体接种量与感染持续时间之间的非线性关系,(2)广谱抗生素治疗期间或之后慢性感染风险增加,(3)粪便微生物群移植可解决肠道菌群失调,(4)益生菌可提供潜在的抗感染保护。然后,我们使用该模型来探讨宿主介导的干预措施(例如,电子供体(例如膳食纤维)和呼吸电子受体(例如氧气)的供应率的变化)如何可能用于指导肠道群落组装。我们的研究表明,宿主与肠道微生物群之间的资源竞争和生态反馈是决定人类健康结果的关键决定因素。我们确定了几个可用于实验验证的可测试模型预测。