Molluscan shellfish biomarker study of the Quebec, Canada, Saguenay Fjord with the soft-shell clam, Mya arenaria

Biomonitoring of Gulf of Patras, N. Peloponnesus, Greece. Application of a biomarker suite including evaluation of translation efficiency in Mytilus galloprovincialis cells

Specimens of Mytilus galloprovincialis were placed in bow nets and immersed at 3-10 m depth in a clean coastal region (reference area), Itea, and two marine stations along Gulf of Patras, N. Peloponnesus, Greece. One site is near the estuaries of the Glafkos River, which are influenced by local industrial and urban sources (Station 1); the second site, Agios Vasilios, has no evident organic pollution but is enriched in metals, particularly zinc (Station 2). One month after immersion, digestive glands were removed from the mussels and tested for lysosomal membrane stability, metallothionein content, and translational efficiency of ribosomes. In addition, gill cells were isolated and their micronuclei content was determined. Compared with the reference samples, mussels transplanted to Gulf of Patras showed a significant increased lysosomal membrane permeability and metallothionein content, reduced polysome levels, and increased chromosomal damage in relation to the contamination burden of each sampling area. Also, runoff ribosomes from mussels transplanted to Gulf of Patras (that is, ribosomes stripped of endogenous messengers and peptidyl- or/and aminoacyl-tRNAs) were less efficient at initiating protein synthesis in an in vitro-translation system than those prepared from reference samples. The whole set of data suggests that the degree of Gulf of Patras pollution differs between different sites and depends on the proximity of urban sewage and industrial outfalls. In addition, our results emphasize the importance of protein synthesis regulation as a component of the cellular stress response.

Application of rough sets analysis to identify polluted aquatic sites based on a battery of biomarkers: a comparison with classical methods

The evaluation of toxicological effects at the cellular and molecular levels in organisms are often used to determine sites subjected to contamination problems that pose a threat to the long-term survival of organisms. However, the integration of multiple measurements on the health status of organisms into a model for site discrimination is challenging. This study compares two discrimination methods which are based on rule inference: rough sets (RS) analysis and classification trees (CT) with classical multivariate discriminant analysis (DA). Site classification was attempted with six biomarkers of effects: metallothionein levels, lipid peroxidation, DNA damage, levels of lipophosphoproteins (i.e., vitellins), phagocytosis activity and haemocyte cell viability on clam (Mya arenaria) populations from the Saguenay River fjord (Quebec, Canada). Rule based methods have the advantage of complete independence from data distribution constraints in contrast to the classification methods from multivariate analysis that are more commonly used in ecotoxicology. Results show that RS and CT gave better classifications than DA because they do not require strong distributional assumptions. Moreover, RS provided classification rules that could identify the most important biomarker(s) for site discrimination. RS and CT were shown to be simple and efficient methods for classifying multivariable ecotoxicological data. This methodology would be especially useful when freedom from distributional assumptions is required.