Resource Competition and Host Feedbacks Underlie Regime Shifts in Gut Microbiota
Resource Competition and Host Feedbacks Underlie Regime Shifts in Gut Microbiota
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DOI：
10.1086/714527
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发表时间：
2021-07-01
影响因子：
2.9
通讯作者：
Litchman, Elena
中科院分区：
文献类型：
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作者：
Guittar, John;Koffel, Thomas;Litchman, Elena
肠道病原体在人类肠道中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的研究仍不清楚。在这里，我们建立了一个宿主介导的资源竞争模型，在肠道中互惠和致病分类群之间，旨在解释为什么暴露于相同病原体的相似宿主可以有如此不同的感染结果。我们的模型成功地再现了几个与健康和感染状态之间过渡相关的经验观察现象，包括：(1)病原体接种量与感染持续性之间的非线性关系，(2)广谱抗生素治疗期间或之后慢性感染的风险增加，(3)粪便微生物群移植解决肠道生态失调，以及(4)益生菌对感染的潜在保护作用。然后，我们使用该模型来探索宿主介导的干预-即电子供体（如膳食纤维）和呼吸电子受体（如氧气）供应率的变化-如何潜在地用于指导肠道群落组装。我们的研究证明了宿主和肠道微生物群之间的资源竞争和生态反馈如何成为人类健康结果的关键决定因素。我们确定了几个可测试的模型预测，准备进行实验验证。
The 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.