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

In this work, organelle-enriched fractions were obtained from mussel digestive glands and separated into 2-DE gels. Four biological replicates were run per sampling site and experimental group. In total 40 gels were obtained from the field experiment and 24 gels from the laboratory exposure. Automatic spot detection parameters were adjusted so that approximately 400 spots were detected per gel. In general the gels were alike each other, although several high-abundance spots and gel areas were found in particular cases.

Laboratory Exposure

Differences between K, FF and WF samples were analyzed after 2- and 16-day exposures. First, we conducted a PCA analysis using the whole dataset comprised of 468 spots. The results did not give a satisfactory separation of the six groups, and the variance explained by the first two components was of 54%. Therefore, the two-way ANOVA was applied to the whole dataset and 178 spots were separated for which the ANOVA model separated the six groups on a 1% significance level. In other words, there were 178 spots for which the null hypothesis that all groups were equal on a 1% significance level could be rejected. Applying FDR at a 5% rate, a set of 148 spots was selected, about eight of which (5% of 148) were expected to be false discoveries.

Including both 2- and 16-days exposure data in the PCA, a clear separation of groups could not be obtained. Taking the 2- and 16-day exposures separately, only the 16-day exposure data gave a clear separation of groups. Therefore, the 2-days exposure data was excluded, and analysis proceeded with the 16-days data only. It was hypothesized that this data would provide the analysis with a more realistic picture of the mechanisms of response to the pollution at a molecular level. To further improve the separation of the PCA, seven spots that showed high variation within one of the groups, were removed, and a neat separation of the three exposure groups was obtained with the selected 141 spots forming the PES. The first principal component separated the K, WF and FF groups form each other, and the second component separated the WF from the K and FF groups, indicating that the selected PES may be used to classify mussels according to exposure to the different sources of oil under study. The PCA score plots for the 2- and 16-day exposures are shown in Figure 2. At this point one could obtain a different selection of spots with a oneway ANOVA procedure on the 16-day exposure data subset. Although this was a possibility, the exposure groups’ separation with the current selection of PES was satisfactory, and therefore they were kept for the following analyses.

Questions we cannot answer in a qualitative way from the present small experiment are how the mussels are affected by the concentration of oil, the age of the oil, and the amount of time exposed to oil. The reasons for that are the scarce amount of data (12 observations), and the lack of additional data that could be used to validate a potential model with. Hence, we only attempted to find out if there were spots forming a PES that may be used to separate mussels into groups of exposed to oil spill, from unexposed ones.

Field Experiment

The proteome profiles of ten sampling sites in the NW and NE coasts of the Iberian Peninsula were analyzed after two and a half years after the Prestige’s oil spill, and the values of the PES selected by ANOVA, FDR, and PCA recorded.

Therefore, spots in the master gel from the field experiment were matched to the master gel from the laboratory exposure group. Furthermore, the 40 gels from the field experiment were manually checked. If any of the 141 selected spots had not been matched previously, the matching was performed. Vol% values of these 141 spots from each sampling site were plotted in the PCA (Figure 3). It was observed that all the stations were placed closer to the WF group than to the K or FF groups. In particular, following the separation of groups by the first component, several groups were found closer to the FF: three samples from Arrigunaga (Figure 3B), two samples from Gorliz and Mundaka (Figure 3C and D), one sample from Camelle (Figure 3G), all the samples from Caldebarcos (Figure 3H), and three samples from Sao Bartolomeu (Figure 3J). None of the groups was closer to the K group in the first component separation. Moreover, all the groups were closer to the WF following the separation by the second component. It is worth mentioning that mussels for the laboratory experiment were collected from Mundaka in September 2005. Mundaka is considered a relatively clean sampling site (Orbea and Cajaraville, 2006). But our data showed that samples collected in Mundaka in July 2005 were clustered around the samples exposed to the WF. As it was mentioned before, owing to the scarce amount of data, no strong model was obtained in this study, so it could not be concluded whether Mundaka was polluted or not. Nevertheless, with this study it was meant to show that, in the hypothetical case when a strong model was obtained from laboratory exposure experiments, that model could be used to classify the data from field experiments, and thereby, give information about the health status of mussels.

As a conclusion, applying our proteomics approach to the study of mussels exposed to WF and FF, and to non-exposed mussels, these groups were separated by PCA based on a set of spots forming a PES obtained by ANOVA and FDR analyses. In this study, we did not try to obtain a model that can predict sources of fuel oil pollution since our data set was too small, and we did not have an external data set for cross-validating a possible model. But, in the future, that set of 141 spots could be used to build and validate a robust model to use it with classification purposes. As an example of how this model could be used in the future, the same set of protein spots was used to group samples collected at ten sampling sites along the NW and NE coasts of the Iberian Peninsula two and a half years after the Prestige’s oil spill. These samples were grouped closer to the WF, rather than to the K or FF. This application would be valuable for classifying data based on an oil pollution model, but it would not detect other sources of pollution; for that purpose, models for different pollutants or mixtures of them will have to be built based on a combination of univariate and multivariate analyses. These kinds of models would take into account the orchestrated changes among proteins, and not fluctuations in individual proteins, as is the case when univariate analyses alone are applied. We believe that the development and validation of models that can predict sources of pollution based on protein expression signatures will be an important step towards robust methods for marine pollution biomonitoring in the near future. Moreover, these protein expression signatures will not be affected by biotic and abiotic factors as much as single parameter biomarkers could be influenced. The characteristics of the method hereby applied are the simplicity of the experimental procedure, the possibility to high-throughput, the low experimental and ecological (number of samples needed) costs, and the possibility of, at a glance, screening the global response to pollution.

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