# First study on the determination of baseline biomarkers in Mimachlamys varia for an intra-port environmental biomonitoring in French Atlantic coastline (La Rochelle)

Marine Barbarin, Frédéric Muttin, Hélène Thomas

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## To cite this version:

Marine Barbarin, Frédéric Muttin, Hélène Thomas. First study on the determination of baseline biomarkers in Mimachlamys varia for an intra-port environmental biomonitoring in French Atlantic coastline (La Rochelle). Marine Pollution Bulletin, 2022, 182, pp.113979. 10.1016/j.marpolbul.2022.113979. hal-03936292

HAL Id: hal-03936292

https://univ-rochelle.hal.science/hal-03936292v1

Submitted on 25 Jul 2025

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1 **Research paper**

2 **Title**

3 First study on the determination of baseline biomarkers in *Mimachlamys varia* for an intra-port

4 environmental biomonitoring in French Atlantic coastline (La Rochelle)

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22 **Authors:**

23 Marine Barbarin ¹, Frédéric Muttin ², Hélène Thomas ¹

24 **Affiliations:**

25 ¹ Littoral Environnement et Sociétés (LIENSs), UMR 7266, CNRS-Université de La Rochelle, 2 rue
26 Olympe de Gouges, F-17042 La Rochelle Cedex 01, France.

27 ² École d'ingénieurs généralistes (EIGSI), 26 Rue François de Vaux de Foletier, F-17041 La Rochelle
28 Cedex 01, France.

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30 **\# Corresponding author:** M. Barbarin

31 Email: marine.barbarin1@univ-lr.fr

32 Phone: +33 (0)6 59 47 07 74

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<table><thead><tr><td>Author name</td><td>Laboratory affiliation</td><td>Email address</td></tr></thead><tbody><tr><td>Marine Barbarin#</td><td>LIENSs</td><td>marine.barbarinwalker@gmail.com</td></tr><tr><td>Frédéric Muttin</td><td>EIGSI</td><td>frederic.muttin@eigsi.fr</td></tr><tr><td>Hélène Thomas</td><td>LIENSs</td><td>helene.thomas@univ-lr.fr</td></tr></tbody></table>
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# 1. Abstract

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The characterization of organic and inorganic environmental pollution in coastal ecosystems, such as port areas, is complex and difficult to carry out due to the effect of environmental variables, as well as anthropic activities. For this study, the objective was to define a statistical method, taking into account the confounding factors influence, to define reference values for biomarkers in the black scallop (*Mimachlamys varia*). Thus, for five biomarkers (SOD (Superoxide Dismutase), GST (Glutathion-S Transferase), MDA (Malondialdehyde), AChE (Acetylcholinesterase) and LAC (Laccase)), reference data could be described for individuals placed on sites more or less strongly impacted by specific environmental contaminations in port areas for more than two years, which had never been done before. All these results enabled us to calibrate and validate our approach in terms of active biomonitoring for the evaluation of a good ecological status of the environment of a port located on the French Atlantic coast.

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**Keywords:** bivalve, biomarkers, environmental quality, baseline, biomonitoring
Nowadays, the identification of environmental impacts caused by human activities is an important issue for the preservation of marine ecosystems. Indeed, these ecosystems abound in important resources for the various species that find refuge there, with breeding grounds or food sources for example. Shellfish farming areas can also be found in the vicinity of these ecosystems, with significant oyster and mussel production, particularly in the Pertuis Charentais (Charente-Maritime, France). In order to preserve these ecosystems, governmental measures have been put in place with different laws including the WFD (Water Framework Directive, 2000/60/EC) and the MSFD (Marine Strategy Framework Directive, 2008/56/EC), as well as the establishment of marine protected areas in order to preserve these environments to the maximum. The Pertuis Charentais have been considered as a protected maritime area by the OSPAR convention (Convention for the Protection of the Marine Environment of the North-East Atlantic) since 2012 and have been classified as a Natura 2000 area since 2014. This classification allows to increase the environmental preservation of the Pertuis Charentais. They are recognized as home to many natural habitats and protected species. The Pertuis are also a migratory corridor for some species, as well as a breeding area. However, these channels are subject to various sources of anthropogenic pollution, such as pesticides and antifouling for example, resulting from numerous activities including trade, tourism and agriculture.

Numerous biomonitoring programs are being set up to address these environmental / environmental protection issues, including multi-biomarker approaches and the study of environmental stressors and chemical contamination. However, it is important to take into account the influence of confounding factors in the interpretation of the results, in particular the physicochemical parameters of the water such as salinity and temperature, as well as the biometric data of individuals (Amiard-Triquet et al., 2015; Barrick et al., 2018; Kalman et al., 2010; Fossi Tankoua et al., 2012). In addition, the evolution of the life cycle of individuals, in particular during the reproduction period, can also influence biomarker responses according to the different sentinel species studied (Benito et al., 2019; Sheehan and Power, 1999). Finally, pollutants, whether organic or inorganic, can also have an effect on the evolution of biomarker responses (Amiard and Amiard-Triquet, 2008; Barrick et al., 2018).
In order to counteract these different variabilities as much as possible, it is important to
establish a reliable and reproducible experimental protocol in order to be able to carry out statistical
analyses under good conditions with the standardization of data and the implementation of a predictive
model. Other studies have already been carried out on this subject, in particular using a method
developed by the OSPAR Commission and ICES (The International Council For the Exploration of
the Sea). For this approach, it is recommended that individuals be sampled at a reference site over
several periods/seasons in order to record the evolution of confounding factors with the measurement
of selected biomarkers. This method thus facilitates the identification of contaminated sites (OSPAR
Commission, 2013; Amiard-Triquet et al., 2015). One example of the application of this approach is to
set the 90th percentile of the data (about two standard deviations from the mean) as a threshold value
(OSPAR Commission, 2013). This is effective for confounders with many data sets from the reference
site. However, it may be less effective for biomarkers as it does not take into account the impact of
these factors on the natural variability of biomarkers and may mask potential contamination (Amiard-
Triquet et al., 2015, Barrick et al., 2016). Another approach was used for acetylcholinesterase (AChE)
in blue mussel *Mytilus edulis* with the objective of characterising the influence of environmental
conditions on the activity of this biomarker. This allowed the establishment of a baseline data set over
a period of three seasonal cycles (OSPAR Commission, 2013; Bocquené et al., 2004). In this study, it
was shown that seasonal variations, or hydrocarbon concentrations, can be associated with a 30%
decrease in activity. The AChE was thus defined as being 30% below the 90th percentile, indicating a
first definition of contamination thresholds. However, these results also demonstrate the need to use
tools to identify anthropogenic and natural variations. Another study chose to look at the impacts of
temperature on acetylcholinesterase values using a statistical model (Burgeot et al., 2010). This
approach is based on a linear regression model and k-means clustering in order to define the level of
natural variation expected as a function of temperature. It thus allows the influence of this
confounding factor on the variations of the biomarker to be taken into account in order to establish
reference values as well as possible. However, it does not allow the relationship between biotic and
abiotic variables to be formally characterized (Johnson and Rodgers, 2005). Indeed, it is necessary to
consider the relationships between confounding factors in order to more effectively describe the
influence of natural conditions on biomarker responses (Barrick et al., 2016).

In addition to properly accounting for confounding factors in the statistical model, it is
important to choose the study model carefully. The model must be able to incorporate and withstand
environmental variation in its organism (Hanson, 2011). The black scallop *Mimachlamys varia*,
described as a sentinel species in the literature and with a high capacity to bioaccumulate
contaminants, therefore seemed to be a good study model for this approach (Butler et al., 1971;
Breitwieser et al., 2016; Bustamante and Miramand, 2004; Bustamante and Miramand, 2005; Minchin
et al., 1987). Several biomarkers have been used in previous studies on this biological model including
Superoxide Dismutase (SOD), Glutathione-S Transferase (GST), Malondialdehyde (MDA) and
Laccase (LAC) (Breitwieser et al., 2020a; Breitwieser et al., 2017; Breitwieser et al., 2016). SOD and
GST allow us to assess oxidative stress in individuals, MDA allows us to observe the formation of the
lipid peroxidation phenomenon in cell membranes, and LAC indicates the presence of an immune
response in the individuals' body. These four biomarkers allow us to have an overall view of the state
of health of the individuals, and the results obtained during the previous studies were very satisfactory.
It was therefore decided to keep these four biomarkers, as well as the AChE presented previously, for
the next study.

The objective of this study was to establish reference values through a predictive model for the
black scallop *Mimachlamys varia* within the framework of the various studies carried out in the
Pertuis Charentais, and particularly in the port areas of La Rochelle between 2019 and 2021. The
interest for the ports with this approach is to obtain additional information on the state of health of
their port basins, to improve their environmental monitoring and to obtain additional data that can be
integrated into the files for the environmental standards of the ports. For this purpose, several
confounding factors were chosen to take the best possible account of environmental variations:
temperature and salinity. The weight of the digestive glands, the organs where the biomarkers were
measured, was also taken into account.
Three study sites were used in the marina of Les Minimes (La Rochelle, Region Nouvelle-Aquitaine). These sites were chosen following previous studies carried out within the marina as being exposed to potential anthropogenic contamination: careening station (CAR), fuel station (NFS), storm water outfall (PLU) (Breitwieser et al., 2020b; Breitwieser et al., 2020a; Breitwieser et al., 2017). The reference site selected for this study is a former oyster farm, in a semi-open environment, and not subject to port activities (Marsh, CNRS site, L'Houmeau). It has already been used in a previous study as a reference site (Breitwieser et al., 2020). The biological model used for this study is the same as that used in previous publications: the black scallop *Mimachlamys varia*. An active biomonitoring method was used with caging. For this, individuals were fished at the Loix site (Ile de Ré, less subject to port influences, (Breitwieser et al., 2018; Breitwieser et al., 2017) before the experiments. They were caged in the reference site (Marsh) for a minimum of 15 days to acclimatize before being transplanted to the study site in the port. On each day of the experiment (D0, D07, D21, D30), the individuals were counted in order to assess mortality, then seven individuals per site were taken (the D0 sample was taken only at the Marsh reference site) and placed in liquid nitrogen in order to preserve enzymatic activities. Low mortality (less than 10%) at both sites was observed during the study. The individuals were then placed at -80°C before being prepared for biochemical analysis. Once in the laboratory, the individuals were processed in a pool to save time and money (the seven individuals recovered from each sample become one sample). The results of a previous study comparing pooled and individual data showed that few differences were observed between the two methods (Breitwieser et al., 2018). All analyses were then performed on these pools, i.e. on the average weight of the digestive glands of the seven individuals in each sample. The method is presented in Fig. 1. Averages of the results obtained for the Marsh reference site (for digestive gland weight, temperature and salinity) are presented in Table 1.

For each day of sampling, the physicochemical parameters of the water (Temperature (°C) and Salinity (PSU)) were measured using a HANNA probe (multiparameter HI9829). The probe was calibrated before each sampling period (Table 1). All biochemical assays (SOD, GST, MDA, AChE and LAC) were performed using the same instrument: SpectroStar Nano with specific absorbance for
each type of biomarker. The assays were carried out on digestive glands pools (one pool (n=7 individuals) per site per date) of black scallop (*M. varia*) from the sites (reference site: D0, D07, D21 and D30; study sites: D07, D21 and D30). Data were obtained in U.mg protein for SOD, GST, AChE and LAC. Data obtained for MDA were in µM.mg protein. Total protein concentrations were determined using the BCA kit methodology (Bicinchononic Acid Kit, Sigma Aldrich). The BCA kit contains bovine serum albumin (BSA) as a standard and involves the reduction of alkaline Cu(II) proteins by proteins (Smith et al., 1985) at an absorbance of 562 nm. Superoxide dismutase (SOD) activity plays an important role in the oxidative stress response (Paoletti et al., 1986) and has been used in previous studies (Breitwieser et al., 2017). The specific activity of glutathione S-transferase (GST) plays a major role in the detoxification of xenobiotics. Total GST activity (GST being a group of enzymes) was determined according to the Sigma kit method (CS0410-1 KT) (Breitwieser et al., 2017). In addition to SOD, oxidative stress was assessed by lipid peroxidation by quantifying malondialdehyde (MDA), a chemical metabolite of cellular lipid breakdown. The concentration of MDA was measured using an MDA analysis kit (Oxis International) (Breitwieser et al., 2017; Milinkovitch et al., 2015). AChE is an enzyme involved in the neurotransmitter mechanism and is responsible for the hydrolysis of acetylcholine to choline and acetic acid. Its inhibition is directly related to the mechanism of toxic action of organophosphorus and pesticides. Some studies have focused on the inhibition of cholinesterase activity as a diagnostic tool for organophosphorus pesticide contamination in birds (Cairns et al., 1991; Driver et al., 1991) or in fish (Galgani et al., 1992; Zinkl et al., 1987), but few studies have been carried out on marine invertebrates, which are widely used as indicators in pollution biomonitoring programs (Day and Scott, 1990). AChE activity (U.mg protein-1) was measured using the AcetylCholinEsterase Assay Kit (Colorimetric) ab138871 (Abcam©). The phenoloxidase (Laccase) activity reveals an alteration of the bivalve immune system. Its enzymatic activity is sensitive to contaminants (Luna-Acosta et al., 2010b; Luna-Acosta et al., 2010a) and plays a crucial role in the immune defense mechanism of marine invertebrates. Its quantification has been performed in previous studies (Breitwieser et al., 2017).
Statistical analysis was performed using R software (RStudio, version 1.4.1103) and Excel 2013. The data exploration was organized by seasons (7), sites (4) and sampling days (4). For all variables (five biomarkers (SOD, GST, MDA, AChE, LAC), two physicochemical water parameters (Salinity, Temperature) and one biometric parameter (wet weight of digestive gland)) the homogeneity of variances was assessed using Fligner-Killeen tests. Normality was also tested using a Shapiro-Wilk test. With the data obtained at the Marsh reference site, several tests were then carried out to determine the most appropriate linear regression equation for each biomarker, taking into account each of the confounding factors (Press et al., 2013; Saporta 2006; Venables and Ripley, 2013). Thus, stable polynomial regression formulae were obtained for each biomarker:

$$201 \quad \textbf{SOD (U.mg of protein}^{-1}\textbf{)} = 342.86 - 1066.55 (\text{weight d.glands}) + 562.90 (\text{weight d.glands})^2 + 1.87 (\text{temperature}) + 5.86 (\text{salinity})$$

$$203 \quad R^2 = 0.68$$

$$204 \quad \textbf{GST (U. mg of protein}^{-1}\textbf{)} = 3.34 - 9.29 (\text{weight d.glands}) + 9.73 (\text{weight d.glands})^2 - 2.96 (\text{weight d.glands})^3 - 0.05 (\text{temperature}) + 0.05 (\text{salinity})$$

$$206 \quad R^2 = 0.15$$

$$207 \quad \textbf{MDA (µM.mg of protein}^{-1}\textbf{)} = 5.57 + 111.77 (\text{weight d.glands}) - 144.77 (\text{weight d.glands})^2 + 52.33 (\text{weight d.glands})^3 - 0.17 (\text{temperature}) - 0.56 (\text{salinity})$$

$$209 \quad R^2 = 0.47$$

$$210 \quad \textbf{AChE (U. mg of protein}^{-1}\textbf{)} = -5283.75 + 65.80 (\text{weight d.glands}) - 105.17 (\text{weight d.glands})^2 + 38.98 (\text{weight d.glands})^3 - 0.55 (\text{temperature}) + 544.98 (\text{salinity}) - 18.39 (\text{salinity})^2 + 0.20 (\text{salinity})^3$$

$$213 \quad R^2 = 0.53$$

$$214 \quad \textbf{LAC (U. mg of protein}^{-1}\textbf{)} = 178.80 + 28.63 (\text{weight d.glands}) - 40.61 (\text{weight d.glands})^2 + 15.75 (\text{weight d.glands})^3 - 2.87 (\text{temperature}) + 0.24 (\text{temperature})^2$$
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$$ -0.01 (\text{temperature})^3 - 19.27 (\text{salinity}) + 0.71 (\text{salinity})^2 - 0.01 (\text{salinity})^3 $$

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$$ R^2 = 0.40 $$

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With these equations obtained from the reference site data, it was possible to calculate, for each biomarker, the predicted values for the reference site, as well as the residuals. The latter are the difference between the measured and predicted values. The residuals, were classified into three groups for each biomarker using the k-means clustering method (Barrick et al., 2016; Devin et al. 2014). To do this, the R software classifies the residuals into three groups and calculates the mean of each group. Then, it reclassifies the residuals according to their proximity to the means and recalculates them. It performs the operation 25 times according to the selected method. The resulting clusters (the means of each cluster) were then used to determine, for each biomarker, the expected ranges of variation according to environmental conditions. The results are presented in Table 2. For the study site results, the equations for each biomarker were used to calculate the predicted values, thus obtaining two values per site for each biomarker: the measured value and the predicted value.

Graphs with the different indicator levels are presented in Fig. 2 for each biomarker. The average biomarker activity/concentration of the Marsh reference site is represented by the black line. The interpretation of results, especially for multi-biomarker analyses of the health status of individuals (enzymatic and/or protein), is always a more or less complex statistical analysis trying to take into account a maximum of variables. Consequently, errors of interpretation can occur and distort the analysis of the data. It therefore seemed essential to take into account the influence of the environment where the different study sites are located, together with the confounding factors for the analysis of the results. Among them, temperature and salinity seemed to be the most relevant and are generally the most used in the literature (Almeida et al., 2022; Chahouri et al., 2022; Zhenhua et al., 2021). Furthermore, the biometry of individuals is also an important element, as it allows to take into account the life stage of individuals. For this reason, the weight of the digestive glands, the organs where the biomarkers were measured, was chosen.
For SOD (Fig. 2A), the values measured at the CAR, PLU and NFS sites are very close to the data observed at the Marsh reference site. Thus, no contamination seems to be observed at the study sites and the samples can be qualified as healthy. Some of the values predicted by the model, especially in 2019 and 2020 for the CAR site, are very close to the measured values. This reflects a good prediction of the statistical model for these two periods, whereas in 2021 all predicted values show sub-lethal results expected with the different confounding factors measured. The same differences are found for the PLU and NFS sites in 2020. For GST (Fig. 2B), the measured and predicted results are close to the reference site for Winter - 20 for all sites. In Autumn 19, 20 and 21, the model predicted healthy individuals, consistent with the values observed in Autumn 19 and Autumn 21 (except for PLU in Autumn 19). In contrast, the values observed at the CAR and NFS study sites in Autumn 20 are at the limit of sublethal effects in individuals, as well as for the Winter 21 period. Conversely, the model predicted lethal effects in individuals in Spring 21, whereas the activity observed at the CAR and PLU study sites is identical to that at the Marsh reference site. For MDA (Fig. 2C), the values measured at the three study sites for the periods Autumn 19, 20, 21 and Winter 21 showed healthy individuals compared to the Marsh reference site. In contrast, the model indicates that sublethal effects were observed in Winter 20 and Spring 20 (only for the CAR site), and lethal effects in Spring 21. For the latter period, the predictive model indicated healthy individuals with the measured confounding factors. Conversely, in Autumn 19 and Autumn 21, lethal effects were expected. For AChE (Fig. 2D), the values obtained at the study sites are very close to those of the Marsh reference site, except for Spring 20 where lethal effects were observed according to the model for the CAR and NFS sites. In Autumn 19 and Autumn 21, the model predicted a healthy state for the individuals, while sublethal effects were observed. In Spring 21, lethal effects were predicted by the model, as well as in Spring 20. For the LAC (Fig. 2E), only the results observed in Autumn 19, 20 and 21 are close to the values obtained at the Marsh reference site. For all other sites, sublethal or lethal effects were measured by the model, consistent with the predicted values obtained, except for Winter 21.
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The different results seem to suggest that the statistical model works rather well. Indeed, for most of the data observed here, the values predicted by the model and the values obtained during the analyses are relatively consistent. However, for several data, the model failed to make good predictions, particularly in 2021 in the spring and autumn periods for almost all biomarkers. Further differences were found in Autumn 19 for MDA, AChE and LAC, and in Spring 21 for AChE. There are several possibilities to explain these inconsistencies. Incorporating confounding factors into the data analysis is essential to support differences between study sites, but the choice of confounding factors to include in the analysis can influence the results. Seasonal variations, such as temperature or salinity, are an important consideration in the analysis of results. The data may change to a greater or lesser extent from one year to the next. These two parameters are among the most commonly used confounding factors in the literature (Amiard-Triquet et al., 2015; Kalman et al., 2010, Mouneyrac MPB 2018, Fossi Tankoua et al., 2012). It also seems interesting to take into account the age and reproductive cycle of individuals. They can also provide answers on the bioaccumulation capacities and adaptation to environmental variations of individuals. On the other hand, the addition of several confounding factors can also lead to the appearance of covariance, requiring statistical studies beforehand (Barrick et al., 2018). Secondly, carrying out this approach over a season does not seem relevant. As previously mentioned, many seasonal variations could be observed, depending on the period, the year but also depending on the biomarkers. It therefore seems essential to carry out these approaches over the long term in order to take the best possible account of environmental variations.

The objective of this statistical approach for the black scallop *Mimachlamys varia* was also to provide port areas with answers on the state of health of their environment and to complete their own environmental and quality approach in order to meet environmental standards. The implementation of this statistical approach should provide a better understanding of the analysis of biomarkers on the bivalve *Mimachlamys varia* and, in the long term, this approach could be applied to other bivalve species such as oysters or mussels. This study, as well as those to come, could be integrated into the La Rochelle Ports Center (the first in France) set up this year. The aim is to introduce the public to the port world as a whole, both in terms of port activities, the blue economy, tourism and the environment.
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Our approach to identifying the reference value of the *M. varia* scallop fits perfectly into this project.

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Once in place, multi-site, multi-species and multi-biomarker analyses over the long term will enable

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the creation of an environmental database on the port species of La Rochelle, but also on other

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territories.

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## Funding and acknowledgements

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Mr. Barbarin has benefited from a CIFRE doctoral grant, as well as financial support from the La

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Rochelle marina and the La Rochelle commercial port in the framework of his collaboration with La

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Rochelle University. This study is a contribution to the QUALIPERTUIS project. The LIENSs

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laboratory would like to thank the general management of the marina of La Rochelle for giving access

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to the marina basins. The authors would also like to thank the people working on the logistic platforms

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of the LIENSs laboratory (Elementary Analysis and Data: Integration, Structuring and Analysis). The

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authors would like to thank V. Huet and A. Fontanaud, as well as the entire marina team.

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<figure><img src="image_3.png" /><figcaption>Figure 1: Schematic representation of the method of sampling and preparation of analyzes. CAR: careening station, PLU: storm water outfall, NFS: fuel station</figcaption></figure>

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**Figure 2:** Graphical representations of health indicators for SOD (A), GST (B), MDA (C), AChE (D) and LAC (E). For each graph, the measured and model-predicted values for each study site are represented by circles and triangles respectively (calculations of the predicted values from the equations in the article). Environmental variations (k-means method) are defined by dark green and light green bands (healthy individuals), yellow bands (expected sublethal effects) and light red bands (expected lethal effects). The measured values for the reference site Marsh are represented on each graph by a black line.

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SOD *M. varia* (pool)

<figure><img src="image_4.png" /></figure>

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GST *M. varia* (pool)

<figure><img src="image_5.png" /></figure>

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MDA *M. varia* (pool)

<figure><img src="image_6.png" /></figure>

AChE *M. varia* (pool)

<figure><img src="image_7.png" /></figure>
LAC *M. varia* (pool)

<figure><img src="image_8.png" /></figure>