||Minier C1, Moore M2, Galgani Francois1, Claisse Didier1
||1 : IFREMER, Dept Biogeochim & Ecotoxicol, F-44311 Nantes, France.
2 : Plymouth Marine Lab, Plymouth PL1 3DH, Devon, England.
||Marine Ecology Progress Series (0171-8630) (Inter-Research), 2006-09 , Vol. 322 , P. 143-154
|WOS© Times Cited
||Crassostrea gigas, Mytilus galloprovincialis, Mytilus edulis, Biomonitoring, P glycoprotein, MXR
||Multixenobiotic resistance (MXR) is a membrane-transport mechanism that allows organisms to exclude many compounds from their cells and tissues. It is thus a first line of defence against a variety of toxic compounds. Since mussels and oysters possess MXR proteins, an analysis of the expression level of these membrane-transporters has been conducted in relation to their body burden of some major environmental contaminants. Mussels Mytilus edulis and M. galloprovincialis and the oyster Crassostrea gigas were sampled from a total of 43 sites along the French coasts. High expression levels were found in animals from the major French estuaries (Seine, Loire and Gironde), at a few sites in Brittany and in nearly all sites from the Mediterranean mainland coasts. Multivariate analysis of the data for both species of blue mussel did show significant differences between groups of samples. Results indicated that expression of MXR protein was strongly associated with contaminant concentrations in mussels, and that polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) were directly correlated with MXR protein concentration. However, multivariate analysis of oysters, which were collected in less-contaminated sites, at least for organic pollutants, did not show any significant differences between MXR protein expression and contaminants. Although the results do not infer a causal linkage between mussel MXR protein, PAHs and PCBs, since many other chemical contaminants are also present at some sites, they do show clearly that MXR protein expression can be used as an indicator of pollutant exposure in blue mussels. The findings also highlight the need to use alternative analytical methods for the interpretation of complex environmental data, and that non-parametric multivariate statistical methods are appropriate for this task.