State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Ningbo University, Ningbo, 315211, China.
School of Marine Sciences, Ningbo University, Ningbo, 315211, China.
Microb Ecol. 2020 Aug;80(2):447-458. doi: 10.1007/s00248-020-01511-y. Epub 2020 Apr 19.
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.
现在人们认识到，一些水生动物疾病是由多种微生物病原体感染引起的。因此，传统的“一种病原体，一种疾病”的观点可能会误导对多种病原体的识别，从而阻碍益生菌的设计。为了解决这一差距，我们基于虾白便综合征（WFS）进展过程中丰度增加的起源和时间，探索了多微生物病原体。OTU70848 弗氏弧菌、OTU35090 珊瑚弧菌和 OTU28721 鳗弧菌被鉴定为主要定植菌，其丰度仅在最终表现出疾病迹象的个体中增加，但在同一时间段内稳定在健康个体中。值得注意的是，随机森林模型显示，这三种主要定植菌的特征对虾健康状况的诊断准确率达到了 91.4%。此外，NetShift 分析量化了这三种主要定植菌是从健康虾到 WFS 虾肠道微生物群中重要的“驱动因素”。基于这些原因，主要定植菌是导致 WFS 恶化的潜在病原体。按照这个逻辑，我们进一步在健康个体中发现了一些“驱动因素”共生菌，例如 OUT50531 深海栖黏土杆菌和 OTU_74495 蓝湖栖滑液菌，它们直接拮抗这三种主要定植菌。与知识一致的是，与健康队列相比，患病虾的能量代谢、遗传信息处理、萜类和聚酮代谢、脂质和氨基酸代谢相关的预测功能途径显著减少，这与这些功能途径的衰减增加了虾对病原体感染的敏感性。总的来说，我们通过量化与虾 WFS 进展密切相关的它们变化的“驱动因素”特征，提供了一种推断多微生物病原体和设计拮抗益生菌的生态框架。这种方法可能会推广到其他水生动物疾病病因的探索。
