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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