Identifying Potential Polymicrobial Pathogens: Moving Beyond Differential Abundance to Driver Taxa
Identifying Potential Polymicrobial Pathogens: Moving Beyond Differential Abundance to Driver Taxa
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识别潜在的多种微生物病原体：超越差异丰度到驱动类群
DOI：
10.1007/s00248-020-01511-y
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发表时间：
2020-04-19
影响因子：
3.6
通讯作者：
Xiong, Jinbo
中科院分区：
文献类型：
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作者：
Lu, Jiaqi;Zhang, Xuechen;Xiong, Jinbo
目前已认识到，水生动物的某些疾病是由多种微生物病原体感染引起的。因此，“一种病原体，一种疾病”的传统观点可能会误导多种病原体的识别，这反过来又阻碍了益生菌的设计。为了解决这一差距，我们探讨了多微生物病原体的基础上的起源和时间增加丰度超过虾白色粪便综合征（WFS）的进展。OTU70848河流弧菌、OTU35090溶珊瑚弧菌和OTU28721管氏弧菌被鉴定为主要定殖者，其丰度仅在最终显示疾病体征的个体中增加，但在相同时间段内在健康受试者中稳定。值得注意的是，随机森林模型显示，三个主要殖民者的配置文件贡献了91.4%的虾健康状况的诊断准确性。此外，NetShift分析量化了三种主要定殖者是从健康虾到WFS虾的肠道微生物群落中的重要“驱动者”。由于这些原因，主要定植者是导致WFS恶化的潜在病原体。通过这种逻辑，我们进一步确定了健康个体中的一些“驱动”细菌，如OUT 50531 Demequina sediminicola和OTU_74495 Ruegeria lacuscaerulensis，它们直接拮抗三种主要定殖菌。预测的功能途径涉及的能量代谢，遗传信息处理，萜类和聚酮代谢，脂肪和氨基酸代谢显着降低，在患病的虾与健康的队列相比，在一致的知识，这些功能途径的衰减增加虾对病原体感染的敏感性。总的来说，我们提供了一个生态框架，推断多微生物病原体和设计拮抗益生菌量化其变化的“驱动程序”的功能，密切联系虾WFS进展。该方法可推广到其他水生动物疾病的病因学研究。
It is now recognized that some diseases of aquatic animals are attributed to polymicrobial pathogens infection. Thus, the traditional view of "one pathogen, one disease" might mislead the identification of multiple pathogens, which in turn impedes the design of probiotics. To address this gap, we explored polymicrobial pathogens based on the origin and timing of increased abundance over shrimp white feces syndrome (WFS) progression. OTU70848 Vibrio fluvialis, OTU35090 V. coralliilyticus, and OTU28721 V. tubiashii were identified as the primary colonizers, whose abundances increased only in individuals that eventually showed disease signs but were stable in healthy subjects over the same timeframe. Notably, the random Forest model revealed that the profiles of the three primary colonizers contributed an overall 91.4% of diagnosing accuracy of shrimp health status. Additionally, NetShift analysis quantified that the three primary colonizers were important "drivers" in the gut microbiotas from healthy to WFS shrimp. For these reasons, the primary colonizers were potential pathogens that contributed to the exacerbation of WFS. By this logic, we further identified a few "drivers" commensals in healthy individuals, such as OUT50531 Demequina sediminicola and OTU_74495 Ruegeria lacuscaerulensis, which directly antagonized the three primary colonizers. The predicted functional pathways involved in energy metabolism, genetic information processing, terpenoids and polyketides metabolism, lipid and amino acid metabolism significantly decreased in diseased shrimp compared with those in healthy cohorts, in concordant with the knowledge that the attenuations of these functional pathways increase shrimp sensitivity to pathogen infection. Collectively, we provide an ecological framework for inferring polymicrobial pathogens and designing antagonized probiotics by quantifying their changed "driver" feature that intimately links shrimp WFS progression. This approach might generalize to the exploring disease etiology for other aquatic animals.