Identifying Potential Polymicrobial Pathogens Moving Beyond Differential Abundance to Driver Taxa

• Published in: Microbial ecology Vol. 80; no. 2; pp. 447 - 458
• Main Authors: Lu, Jiaqi, Zhang, Xuechen, Qiu, Qiongfen, Chen, Jiong, Xiong, Jinbo
• Format: Journal Article
• Language: English
• Published: New York Springer Science + Business Media 01.08.2020; Springer US; Springer Nature B.V
• Subjects: Abundance; Actinobacteria - isolation & purification; Actinobacteria - physiology; Aetiology; algorithms; amino acid metabolism; Amino acids; Animal diseases; Animals; Aquaculture; Aquatic animals; Bacteria - isolation & purification; Biomedical and Life Sciences; China; Cohorts; Commensals; Data processing; Disease; disease susceptibility; Diseases; Ecology; Energy metabolism; Etiology; feces; Gastrointestinal Microbiome; Geoecology/Natural Processes; health status; HOST MICROBE INTERACTIONS; Identification; Information processing; Intestinal microflora; intestinal microorganisms; Life Sciences; Lipid metabolism; Lipids; Metabolism; Microbial Ecology; Microbiology; Nature Conservation; Pathogens; Penaeidae - microbiology; Polyketides; Probiotics; Profiles; Ruegeria lacuscaerulensis; Shellfish; shrimp; Shrimps; Terpenes; terpenoids; Vibrio - physiology; Vibrio fluvialis; Vibrio tubiashii; Water Quality/Water Pollution; Waterborne diseases; China; Gut microbiota; Driver taxa; Polymicrobial pathogens; Shrimp disease; Primary colonizer
• ISSN: 0095-3628; 1432-184X; 1432-184X
• DOI: 10.1007/s00248-020-01511-y

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.