Stress and stability: applying the Anna Karenina principle to animal microbiomes
Stress and stability: applying the Anna Karenina principle to animal microbiomes
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DOI：
10.1038/nmicrobiol.2017.121
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发表时间：
2017-09-01
影响因子：
28.3
通讯作者：
Thurber, Rebecca Vega
中科院分区：
文献类型：
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作者：
Zaneveld, Jesse R.;McMinds, Ryan;Thurber, Rebecca Vega
迄今为止研究的所有动物都与微生物的共生群落有关。这些动物微生物群通常在正常生理功能和疾病易感性中发挥重要作用;预测它们对扰动的反应是微生物学的一个重要挑战。大多数微生物群动力学研究测试的模式，其中扰动改变动物微生物群从健康到生态失调的稳定状态。在这里，我们考虑一个补充的替代方案：由许多扰动引起的微生物变化是随机的，因此导致从稳定到不稳定的社区状态的过渡。其结果是动物微生物组的“安娜卡列尼娜原理”，其中微生物个体在微生物群落组成上比健康个体差异更大-类似于列夫托尔斯泰的格言“所有幸福的家庭看起来都一样;每个不幸的家庭都以自己的方式不幸”。我们认为，安娜卡列尼娜效应是一种常见的和重要的反应，动物微生物组的压力，降低宿主或其微生物组的能力，以调节社区组成。从暴露于高于平均温度的濒危珊瑚表面到艾滋病毒/艾滋病患者的肺部，都发现了与安娜卡列尼娜效应一致的模式。然而，尽管这些模式明显普遍存在，但它们很容易被一些常见的工作流程遗漏或丢弃，因此可能报告不足。现在，大量的研究已经确定了这些模式在不同系统中的存在，严格的测试，密集的时间序列数据集和改进的随机建模将有助于探索它们对从个性化医疗到宿主微生物共生进化理论等主题的重要性。
All animals studied to date are associated with symbiotic communities of microorganisms. These animal microbiotas often play important roles in normal physiological function and susceptibility to disease; predicting their responses to perturbation represents an essential challenge for microbiology. Most studies of microbiome dynamics test for patterns in which perturbation shifts animal microbiomes from a healthy to a dysbiotic stable state. Here, we consider a complementary alternative: that the microbiological changes induced by many perturbations are stochastic, and therefore lead to transitions from stable to unstable community states. The result is an 'Anna Karenina principle' for animal microbiomes, in which dysbiotic individuals vary more in microbial community composition than healthy individuals-paralleling Leo Tolstoy's dictum that "all happy families look alike; each unhappy family is unhappy in its own way". We argue that Anna Karenina effects are a common and important response of animal microbiomes to stressors that reduce the ability of the host or its microbiome to regulate community composition. Patterns consistent with Anna Karenina effects have been found in systems ranging from the surface of threatened corals exposed to above-average temperatures, to the lungs of patients suffering from HIV/AIDs. However, despite their apparent ubiquity, these patterns are easily missed or discarded by some common workflows, and therefore probably underreported. Now that a substantial body of research has established the existence of these patterns in diverse systems, rigorous testing, intensive time-series datasets and improved stochastic modelling will help to explore their importance for topics ranging from personalized medicine to theories of the evolution of host-microorganism symbioses.