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Identifying Potential Polymicrobial Pathogens: Moving Beyond Differential Abundance to Driver Taxa

  • Host Microbe Interactions
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Abstract

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.

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Funding

This work was supported by the National Natural Science Foundation of China (31872693), the Natural Science Fund for Distinguished Young Scholars of Zhejiang Province (LR19C030001), the Technology Innovation Team of Ningbo (2015C110018), and the K.C. Wong Magna Fund in Ningbo University.

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Authors and Affiliations

  1. State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Ningbo University, Ningbo, 315211, China

    Jiaqi Lu, Xuechen Zhang, Jiong Chen & Jinbo Xiong

  2. School of Marine Sciences, Ningbo University, Ningbo, 315211, China

    Jiaqi Lu, Xuechen Zhang, Qiongfen Qiu, Jiong Chen & Jinbo Xiong

Authors
  1. Jiaqi Lu
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  2. Xuechen Zhang
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  3. Qiongfen Qiu
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  4. Jiong Chen
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  5. Jinbo Xiong
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Contributions

JC and JX conceived and designed research. JL, XZ, and QQ conducted experiments. JX contributed to analytical tools. JL and JX analyzed the data and wrote the manuscript. All authors read and approved the manuscript.

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Correspondence to Jinbo Xiong.

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Conflict of Interest

The authors declare that they have no conflict of interest.

This article does not contain any studies with human participants performed by any of the authors. All applicable international, national, and/or institutional guidelines for the care and use of shrimp followed the National Institutes of Health Guide for the Care and Use of Laboratory Animals.

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Key points

• Polymicrobial pathogens were identified by distinguishing shrimp age from disease effect.

• Potential pathogens were validated by “driver” feature and diagnosis accuracy.

• Taxa that directly antagonized the candidate pathogens were identified.

• We exemplified the identification of polymicrobial pathogens and the design of antagonized probiotics.

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Lu, J., Zhang, X., Qiu, Q. et al. Identifying Potential Polymicrobial Pathogens: Moving Beyond Differential Abundance to Driver Taxa. Microb Ecol 80, 447–458 (2020). https://doi.org/10.1007/s00248-020-01511-y

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