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Biomonitoring programs that use mussels to assess the water quality around the …

Biology Articles » Hydrobiology » Proteomic Analysis of Mussels Exposed to Fresh and Weathered Prestige’s Oil » Introduction

- Proteomic Analysis of Mussels Exposed to Fresh and Weathered Prestige’s Oil

Coastal ecosystems are often exposed to diverse sources of pollution such as oil spills, and this has detrimental effects in the biota. The high concern about pollution’s effects in the environment apprizes the value of marine biomonitoring programs. The biological effects of the 1989 Exxon Valdez oil spill at different trophic levels have been reported in the years following the disaster. The conclusions from those investigations have already been reviewed somewhere else (Harwell and Gentile, 2006).

Many of the biomonitoring programs use mussels as bioindicators of pollution due to their wide distribution, sessile nature, and filter feeding mechanism (Widdows and Donkin, 1992). Mussels have a reduced biotransformation capacity and can accumulate several xenobiotic compounds that can severely affect metabolic homeostasis. The bioaccumulation capacity is useful in biomonitoring programs because it can show the actual pollution levels in that environment, and can lead to biomagnification of pollutants higher up in the food chain. Therefore, levels of different cellular and molecular biomarkers can be measured in mussels in order to obtain a picture of their health status (Cajaraville et al., 2000, Guerlet et al., 2007; Zorita et al., 2007).

The Prestige tanker’s accidental oil spill (November 2002, 42°12.5’N, 12°3’W) resulted in more than 60,000 tons heavy fuel oil overspreading Galician and Bay of Biscay waters in the following months (Albaiges et al., 2006). It has been reported that a year after the Prestige oil spill, the incidence of natural oil weathering processes (by evaporation, dissolution, biodegradation, and photo-oxidation) was low, and mainly enhanced in oil stranded on the shoreline (Diez et al., 2007). In the mentioned study, 17% of the analyzed samples did not match the Prestige oil fingerprint, and half of these corresponded to a common spill. These results emphasize the need of tools to distinguish the effects that different sources of pollution can have in biota.

Proteomics methods that allow biological data classification and characterization by univariate and multivariate analyses have already been recommended and applied previously (Meleth et al., 2005; Chich et al., 2007; Karp and Lilley, 2007; Karp et al., 2007). In environmental 2-DE proteomics of mussel, Student t-test, analysis of variance (ANOVA), principal components analysis (PCA), and hierarchical clustering have been applied to obtain protein expression signatures specific to pollutants, and to a gradient of pollution, but no classification models were built (Apraiz et al., 2006; Mi et al., 2007; Amelina et al., 2007). Monsinjon et al. reported a classification model based on protein peaks obtained by ProteinChip© array technology and surface-enhanced laser desorption/ionization time-of-flight (SELDITOF)- mass spectrometry (MS), but because of the criteria to guard against overfitting, classification was not successful (Monsinjon et al., 2006). The use of protein expression signatures (PES) to build up statistically verified models that could classify samples exposed to different sources of pollution, could become a powerful tool for biomonitoring programs in the future.

Therefore, a laboratory experiment was set where mussels, Mytilus galloprovincialis, were exposed to fresh and weathered Prestige-like fuel oil for two and sixteen days. A control group was kept in parallel. Mussel digestive glands were subjected to a simple cellular prefractionation and liquid chromatography (LC) coupled with two-dimensional electrophoresis (2-DE) method previously developed by our group, and that has been successful in separating four stations along a pollution gradient around the harbor of Gothenburg (Amelina et al., 2007). Here, we performed ANOVA, and false discovery rate (FDR) procedures to extract protein spots composing a PES that were further analyzed by principal components analysis (PCA). These spots were successful in separating the exposed groups. Furthermore, samples from ten sampling sites along the Galician (NW) and Bay of Biscay (NE) coasts were also processed by LC coupled with 2-DE, and we showed how the previously obtained PES could be used to classify the sampling sites.

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