Integrating molecular and ecological approaches to identify potential polymicrobial pathogens over a shrimp disease progression
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It is now recognized that some gut diseases attribute to polymicrobial pathogens infections. Thus, traditional isolation of single pathogen from disease subjects could bias the identification of causal agents. To fill this gap, using Illumina sequencing of the bacterial 16S rRNA gene, we explored the dynamics of gut bacterial communities over a shrimp disease progression. The results showed significant differences in the gut bacterial communities between healthy and diseased shrimp. Potential pathogens were inferred by a local pathogens database, of which two OTUs (affiliated with Vibrio tubiashii and Vibrio harveyi) exhibited significantly higher abundances in diseased shrimp as compared to healthy subjects. The two OTUs cumulatively contributed 64.5% dissimilarity in the gut microbiotas between shrimp health status. Notably, the random Forest model depicted that profiles of the two OTUs contributed 78.5% predicted accuracy of shrimp health status. Removal of the two OTUs from co-occurrence networks led to network fragmentation, suggesting their gatekeeper features. For these evidences, the two OTUs were inferred as candidate pathogens. Three virulence genes (bca, tlpA, and fdeC) that were coded by the two candidate pathogens were inferred by a virulence factor database, which were enriched significantly (P < 0.05 in the three cases, as validated by qPCR) in diseased shrimp as compared to healthy ones. The two candidate pathogens were repressed by Flavobacteriaceae, Garvieae, and Photobacrerium species in healthy shrimp, while these interactions shifted into synergy in disease cohorts. Collectively, our findings offer a frame to identify potential polymicrobial pathogen infections from an ecological perspective.
KeywordsShrimp gut microbiota Health status Random Forest model Virulence gene Co-occurrence network
This work was supported by the Project of Science and Technology Department of Ningbo (2017C10044), the Zhejiang Province Public Welfare Technology Application Research Project (2016C32063), and the K.C. Wong Magna Fund in Ningbo University.
Compliance with ethical standards
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 animals were followed the National Institutes of Health Guide for the Care and Use of Laboratory Animals.
Conflict of interest
The authors declare that they have no conflict of interest.
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