Quantitative prediction of shrimp disease incidence via the profiles of gut eukaryotic microbiota
One common notion is emerging that gut eukaryotes are commensal or beneficial, rather than detrimental. To date, however, surprisingly few studies have been taken to discern the factors that govern the assembly of gut eukaryotes, despite growing interest in the dysbiosis of gut microbiota-disease relationship. Herein, we firstly explored how the gut eukaryotic microbiotas were assembled over shrimp postlarval to adult stages and a disease progression. The gut eukaryotic communities changed markedly as healthy shrimp aged, and converged toward an adult-microbiota configuration. However, the adult-like stability was distorted by disease exacerbation. A null model untangled that the deterministic processes that governed the gut eukaryotic assembly tended to be more important over healthy shrimp development, whereas this trend was inverted as the disease progressed. After ruling out the baseline of gut eukaryotes over shrimp ages, we identified disease-discriminatory taxa (species level afforded the highest accuracy of prediction) that characteristic of shrimp health status. The profiles of these taxa contributed an overall 92.4% accuracy in predicting shrimp health status. Notably, this model can accurately diagnose the onset of shrimp disease. Interspecies interaction analysis depicted how the disease-discriminatory taxa interacted with one another in sustaining shrimp health. Taken together, our findings offer novel insights into the underlying ecological processes that govern the assembly of gut eukaryotes over shrimp postlarval to adult stages and a disease progression. Intriguingly, the established model can quantitatively and accurately predict the incidences of shrimp disease.
KeywordsGut eukaryotic community Null model Interspecies interaction Disease-discriminatory taxa Disease incidence
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.
- Bledsoe JW, Peterson BC, Swanson KS, Small BC (2016) Ontogenetic characterization of the intestinal microbiota of channel catfish through 16S rRNA gene sequencing reveals insights on temporal shifts and the influence of environmental microbes. PLoS One 11(11):e0166379CrossRefPubMedPubMedCentralGoogle Scholar
- Cornejo-Granados F, Lopez-Zavala AA, Gallardo-Becerra L, Mendoza-Vargas A, Sánchez F, Vichido R, Brieba LG, Viana MT, Sotelo-Mundo RR, Ochoa-Leyva A (2017) Microbiome of Pacific Whiteleg shrimp reveals differential bacterial community composition between wild, aquacultured and AHPND/EMS outbreak conditions. Sci Rep 7(1):11783CrossRefPubMedPubMedCentralGoogle Scholar
- Gomathi V, Saravanakumar K, Kathiresan K (2013) Production of polyunsaturated fatty acid (DHA) by mangrove-derived Aplanochytrium sp. Afr J Microbiol Res 7(13):1098–1103Google Scholar
- Gutiérrez-Salazar GJ, Molina-Garza ZJ, Hernández-Acosta M, García-Salas JA, Mercado-Hernández R, Galaviz-Silva L (2011) Pathogens in Pacific white shrimp (Litopenaeus vannamei Boone, 1931) and their relationship with physicochemical parameters in three different culture systems in Tamaulipas, Mexico. Aquaculture 321(1):34–40CrossRefGoogle Scholar
- Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18–22Google Scholar
- Parfrey LW, Walters WA, Lauber CL, Clemente JC, Berg-Lyons D, Teiling C, Kodira C, Mohiuddin M, Brunelle J, Driscoll M (2014) Communities of microbial eukaryotes in the mammalian gut within the context of environmental eukaryotic diversity. Front Microbiol 5:298CrossRefPubMedPubMedCentralGoogle Scholar
- Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41(1):590–596Google Scholar
- R Core Team (2015) A language and environment for statistical computing. Vienna, Austria: The R Foundation for Statistical Computing. ISBN: 3-900051-07-0. http://www.R-project.org/Google Scholar
- Refaeilzadeh P, Tang L, Liu H (2009) Cross-validation encyclopedia of database systems. Springer, pp 532–538Google Scholar
- Stecher B, Chaffron S, Käppeli R, Hapfelmeier S, Freedrich S, Weber TC, Kirundi J, Suar M, McCoy KD, von Mering C, Macpherson AJ, Hardt WD (2010) Like will to like: abundances of closely related species can predict susceptibility to intestinal colonization by pathogenic and commensal bacteria. PLoS Pathog 6(1):e1000711CrossRefPubMedPubMedCentralGoogle Scholar
- Tourtip S, Wongtripop S, Stentiford GD, Bateman KS, Sriurairatana S, Chavadej J, Sritunyalucksana K, Withyachumnarnkul B (2009) Enterocytozoon hepatopenaei sp. nov. (Microsporida: Enterocytozoonidae), a parasite of the black tiger shrimp Penaeus monodon (Decapoda: Penaeidae): fine structure and phylogenetic relationships. J Invertebr Pathol 102(1):21–29CrossRefPubMedGoogle Scholar