Abstract

Species of Perkinsus are responsible for high mortalities of bivalve molluscs world-wide. Techniques to accurately estimate parasites in tissues are required to improve understanding of perkinsosis. This study quantifies the number and tissue distribution of Perkinsus marinus in Crassostrea virginica by modern stereology and immunohistochemistry. Mean total number of trophozoites were (mean +/- SE) 11.80 +/- 3.91 million and 11.55 +/- 3.88 million for the optical disector and optical fractionator methods, respectively. The mean empirical error between both stereological approaches was 3.8 +/- 1.0%. Trophozoites were detected intracellularly in the following tissues: intestine (30.1%), Leydig tissue (21.3%), hemocytes (14.9%), digestive gland (11.4%), gills (6.1%), connective tissues (5.7%), gonads (4.1%), palps (2.2%), muscle (1.9%), mantle connective (0.8%), pericardium (0.7%), mantle epithelium (0.1%), and heart (0.1%). The remaining 0.6% were found extracellularly. Percentages of trophozoite stages were (mean +/- SE): large, log-phase trophonts, i.e., signet rings, 97.0 +/- 1.2%; meronts, 2.0 +/- 0.9%; clusters of small, log-phase trophonts, i.e., merozoites, 1.0 +/- 0.5%. Levels of infection in hemocytes and Leydig tissue were representative of total parasite intensity. These techniques are a powerful tool to follow parasite distribution and invasion, and to further explore mechanisms of Perkinsus spp. pathogenesis in bivalves.

Highlights

  • Numerical Quantification of Perkinsus marinus in the American Oyster Crassostrea virginica (Gmelin, 1791) (Mollusca: Bivalvia) by Modern Stereology

  • The pathology caused by Perkinsus spp., generally known as perkinsosis, is characterized by infiltration of hemocytes into infected tissues, followed by chronic wasting, impairment of growth, and eventual mortality of the shellfish by emaciation (Ford and Tripp, 1996)

  • Present techniques to quantify Perkinsus spp. include (1) visual detection of parasite stages in tissue smears (Stein and Mackin, 1957) and tissue sections (Dungan and Roberson, 1993); (2) assays based on counting hynospores in Ray's fluid thiogly collate medium (RFTM)-incubated tissues from the whole animal (Choi et al 1989; Bushek et al, 1994; Fisher and Oliver, 1996), tissue subsamples (Ray, 1952), and hemolymph sampling (Gauthier and Fisher, 1990; Nickens et al, 2002); and (3) molecular biology techniques based on detection of DNA or RNA by polymerase chain reaction (PCR; Marsh et al, 1995; Robledo et al, 1998; Yarnall et al, 2000; Audemard et al, 2004; Elandalloussi et al, 2004)

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Summary

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Follow this and additional works at: https://digitalcommons.uri.edu/favs_facpubs. Remacha-Trivino, A., Borsay-Horowitz, D., Dungan, C., Gual-Arnau, X., Gomez-Leon, J., Vilamil, L., & Gomez-Chiarri, M. (2008). Numerical Quantification of Perkinsus marinus in the American Oyster Crassostrea virginica (Gmelin, 1791) (Mollusca: Bivalvia) by Modern Stereology. Modern stereological methods can be defined as a collection of strongly based mathematical procedures aimed to quantify geometrical properties of target objects without assumptions concerning inherent characteristics of these objects, like size, shape, or distribution. These techniques stem from the concept of accuracy as an additive property of unbiasness and precision. The present study aims to quantify the number and tissue distribution of different stages of the protozoan parasite P. marinus in a natural population of Crassostrea virginica by the application of modern stereology and immunohistochemistry. The information provided by these techniques will be useful to follow parasite distribution and progression in experimental and natural infections, and to further explore mechanisms of pathogenesis

Initialprocessing of samples
Extraction of the meat
Estimation of the volume of the meat
Estimation of the total number of trophozoites
Estimation of trophozoite stages within different tissues
Practical implementation
Correlations and allometrlc models
Numerical example
Number of parasites and parasite stages
DISCUSSION
Findings
Correlations and allometric models

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