A metabarcoding approach for detecting protozoan pathogens in wild oysters from Prince Edward Island, Canada.


Journal

International journal of food microbiology
ISSN: 1879-3460
Titre abrégé: Int J Food Microbiol
Pays: Netherlands
ID NLM: 8412849

Informations de publication

Date de publication:
16 Dec 2021
Historique:
received: 31 03 2021
revised: 30 05 2021
accepted: 14 06 2021
pubmed: 4 7 2021
medline: 17 11 2021
entrez: 3 7 2021
Statut: ppublish

Résumé

Food and waterborne protozoan pathogens including Cryptosporidium parvum, Giardia enterica and Toxoplasma gondii are a global concern for human public health. While all three pathogens have been detected in commercial shellfish, there is currently no standard approach for detecting protozoan parasites in shellfish. Common molecular and microscopic methods are limited in the number of pathogens they can simultaneously detect and are often targeted at one or two of these pathogens. Previously, we developed and validated a novel 18S amplicon-based next-generation sequencing assay for simultaneous detection of Cryptosporidium spp., Giardia spp. and T. gondii in shellfish. In this study, we applied the assay for protozoan pathogen detection in wild oysters from Prince Edward Island (PEI). Oysters were harvested from restricted and prohibited areas, classified by the Canadian government according to fecal coliform counts in surrounding waters, and different fractions (whole tissue homogenate and hemolymph) were analyzed. Protozoan DNA was detected using metabarcoding in 28%, of oysters tested (N = 128), and the pathogen read counts in oyster homogenate were considerably higher than those in hemolymph. Protozoan read count thresholds were established for classifying probable oyster contamination with pathogens to account for low levels of background protozoan reads detected in negative controls. Assay results showed protozoan contamination was not associated with harvesting site classifications, suggesting that using fecal indicators for ensuring food safety may be insufficient. Due to the complex matrix, an oyster DNA reduction step may further improve the pathogen detection sensitivity of the assay. Results from this study affirm that novel metabarcoding is a promising screening tool for detection of protozoan pathogens in shellfish.

Identifiants

pubmed: 34215423
pii: S0168-1605(21)00274-9
doi: 10.1016/j.ijfoodmicro.2021.109315
pii:
doi:

Substances chimiques

DNA, Protozoan 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

109315

Informations de copyright

Copyright © 2021 Elsevier B.V. All rights reserved.

Auteurs

Catherine DeMone (C)

Department of Integrative Biology, University of Guelph, 50 Stone Rd E, Guelph, Ontario N1G 2W1, Canada; Department of Mathematics and Statistics, University of Guelph, 50 Stone Rd E, Guelph, Ontario N1G 2W1, Canada.

J Trenton McClure (J)

Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, 550 University Ave, Charlottetown, Prince Edward Island C1A 4P3, Canada.

Spencer J Greenwood (SJ)

Department of Biomedical Sciences, Atlantic Veterinary College, University of Prince Edward Island, 550 University Ave., Charlottetown, Prince Edward Island C1A 4P3, Canada.

Rebecca Fung (R)

Department of Population Medicine, Ontario Veterinary College, University of Guelph, 50 Stone Rd E, Guelph, Ontario N1G 2W1, Canada.

Mei-Hua Hwang (MH)

Department of Pathobiology, Ontario Veterinary College, University of Guelph, 50 Stone Rd E, Guelph, Ontario N1G 2W1, Canada.

Zeny Feng (Z)

Department of Mathematics and Statistics, University of Guelph, 50 Stone Rd E, Guelph, Ontario N1G 2W1, Canada.

Karen Shapiro (K)

Department of Pathobiology, Ontario Veterinary College, University of Guelph, 50 Stone Rd E, Guelph, Ontario N1G 2W1, Canada; Department of Population Medicine, Ontario Veterinary College, University of Guelph, 50 Stone Rd E, Guelph, Ontario N1G 2W1, Canada; Department of Pathology, Microbiology and Immunology, School of Veterinary Medicine, University of California, Davis, One Shields Ave, Cavis, CA 95616, USA. Electronic address: kshapiro@ucdavis.edu.

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