# Twenty exposure concentrations, zero dose-response for subcellular biomarkers: Unravelling this paradox in cadmium and copper exposed Dreissena polymorpha

Manon Bain, Simon Devin, Marie Zaffino, Sandrine Pain-Devin

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Manon Bain, Simon Devin, Marie Zaffino, Sandrine Pain-Devin. Twenty exposure concentrations, zero dose-response for subcellular biomarkers: Unravelling this paradox in cadmium and copper exposed Dreissena polymorpha. Journal of Hazardous Materials Advances, 2025, 20, pp.100922. 10.1016/j.hazadv.2025.100922. hal-05356763

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# Twenty exposure concentrations, zero dose-response for subcellular biomarkers: Unravelling this paradox in cadmium and copper exposed *Dreissena polymorpha*

Manon Bain<sup>a,b,*</sup> <span style="font-size: 2em; vertical-align: middle;">ID</span>, Simon Devin<sup>a,b</sup>, Marie Zaffino<sup>a</sup>, Sandrine Pain-Devin<sup>a,b</sup>

## ARTICLE INFO

### Keywords:

- Metals

- Mussels

- Multi-biomarker

- Dose-response

- Bioaccumulation

## ABSTRACT

The assessment of aquatic ecosystem quality under the Water Framework Directive suffers from a gap between chemical measurements and observed effects at the community level, with a critical lack of direct measurements of organism health. Biomarkers, situated at intermediate levels, could bridge this gap but cause-effect relationships remain poorly understood, limiting their operationality. This study implements an innovative experimental protocol with 20 exposure concentrations in order to answer 4 questions: (i) Is there a dose-response relationship for both subcellular biomarkers and tissue concentrations? (ii) Does exposure duration influence the response of biomarkers? (iii) Does response pattern of biomarkers differ between cadmium and copper? The results obtained and used to answer the first three questions gave rise to another one: (iv) How did the inter-individual variability and statistical power of our experimental design influence our ability to detect biological effects? *Dreissena polymorpha* was exposed to cadmium and copper for 4 and 8 days, with measurement of 8 biomarkers (SOD, GPx, TAC, GST, CSP3, LOOH, ETS, LDH) and tissue metal concentrations. Results reveal a paradox: while dose-response relationships were confirmed for bioaccumulation and mortality, subcellular biomarkers showed no dose-response relationships. Exposure duration had no influence, but correlation networks revealed different biomarker response patterns between cadmium and copper. Prospective analysis demonstrated that a sample size of 6–10 individuals per group would optimise statistical detection. This study reports on the difficulties associated with applying biomarkers under controlled conditions, provides data on the model species *Dreissena polymorpha* and contributes to the debate on the operationality of biomarkers.

## 1. Introduction

The Water Framework Directive (WFD, 2000/60/EC) establishes a framework for community policy applicable to all waters in the European Union. The water bodies concerned are surface waters, transitional waters, coastal waters, artificial and heavily modified surface waters, and groundwater. The main objective of the WFD is to achieve a good ecological status for all waters by 2027. To assess the status of these water bodies, it requires to monitor both chemical and ecological status. Chemical status is assessed through measurement of concentrations of 45 priority substances (like metals, pesticides, polycyclic aromatic hydrocarbons, etc.) in water, sediment, and biota. Ecological status is evaluated using biological quality elements such as benthic macro-invertebrate communities, fish assemblages, phytoplankton, and

macrophytes, as well as physico-chemical parameters supporting these communities. While both approaches are essential, they have several limitations (Milinkovitch et al., 2019; Lam, 2009; Bartell, 2006). Chemical approaches only target a list of 45 priority substances considered to be of concern for human health and/or freshwater communities' integrity. Furthermore, while the toxicity of individual substances is taken into account in the monitoring of priority pollutants, their combined toxic effects remain overlooked. The ecological approaches indicate the state of health of populations/communities after impacts have already occurred, but with only poor knowledge on the roles of contaminants and on the biological pathways involved in the degradation observed. Tools based on the measurement of more direct biological effects appear to be good candidates for palliating this lack of predictivity (Xuereb et al., 2023; Martinez-Haro et al., 2015; Serafim

<footnote><sup>a</sup> Université de Lorraine, CNRS, LIEC, F-57000 Metz, France</footnote>

<footnote><sup>b</sup> LTSER France, Zone Atelier du Bassin de la Moselle, F-57000 Metz, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: manonbain@gmail.com (M. Bain).</footnote>
et al., 2011). Indeed, biomarkers, measured from molecular to individual scales, occupy intermediate positions in the biological organisation hierarchy and should theoretically bridge the gap between environmental contamination and effects at higher organisational levels (population, community) (Bashnin et al., 2019; Wernersson et al., 2015; Devin et al., 2014). However, they are still not integrated either into regulatory policies or into routine biomonitoring programs (Sanchez and Porcher, 2009) and are even, for some authors, unusable as they stand (Garric et al., 2010; Forbes et al., 2006). The main obstacles limiting the operatibility of biomarkers are the lack of knowledge concerning (i) the temporal dynamics of the response, (ii) the influence of biotic and abiotic factors and (iii) the link between pressure/exposure and biological effects.

First, few studies have truly focused on the temporal dynamics of biomarkers. Yet as Wu et al. (2005) indicate, without knowing the temporal dynamics of biomarker responses, i.e., the initial induction time (time required for a statistically significant change to be observable), maximal time (time required for the induced response to reach a plateau), the adaptation time (time required for a maximally induced response to return to baseline levels during continuous exposure to chemical stress) and the recovery time (time required for an induced response to return to background level after depuration), it is likely to lead to misleading conclusions. Two problems arise from this (Devin et al., 2014; Calow, 1996): false negatives, where no response is detected despite exposure (adaptation mechanisms, inadequate sampling in relation to the temporal dynamics of the response, physiological compensation), and false positives, where beneficial adaptive responses or those linked to confounding factors are interpreted as adverse effects. The challenge is to strike a balance between responsiveness and robustness of interpretation, and this requires a better understanding of the causal links between exposure and effect (Xuereb et al., 2023; Martinez-Haro et al., 2015).

Next, the influence of biotic and abiotic factors is well documented in crustaceans, fish, and bivalves. These organisms constitute privileged models for methodological studies and biomonitoring programs using biomarkers due to their relevant ecological and biological characteristics, but also because of their economic interest as a food resource. The most studied abiotic factors are temperature, which influences enzymatic activity and general metabolism (Attig et al., 2014; Sokolova and Lannig, 2008), and ion concentrations (Mg²⁺ and Ca²⁺), which can interfere with trace metal absorption and toxicity (Wojtal-Frankiewicz et al., 2017; Bourgeault et al., 2012). Concerning biotic factors, the most documented are intrinsic such as sex, which can influence the response via hormonal variations, while reproductive cycle (Marchand et al., 2019; Navarro et al., 2011), size and age of organisms affect metabolic and detoxification capacities (Potet, 2017; Acs et al., 2016).

Finally, the interpretation of biomarker responses focuses mainly on observing changes rather than assessing the health status of organisms. This approach presupposes knowledge of the natural range of variation in responses, as well as the amplitude of responses induced by chemical stress. The main difficulties are (i) distinguishing a beneficial or neutral response from a harmful one, (ii) differentiating natural variations from those induced by chemical stress and (iii) avoiding the need to use controls (Hagger et al., 2008). To overcome these difficulties, it is necessary to define the natural range of variation of biomarkers (basal levels considered to be the range of values observable in a healthy individual), as well as the range of response in stressful situations (inhibition and/or induction thresholds beyond which we can consider that the health status of organisms is altered) (Xuereb et al., 2023). Then, building such a reference system for interpreting biomarker responses based on the health status of organisms is a promising method.

Several studies have established, in a context of active biomonitoring, basal, inhibition and/or induction threshold values for model species such as *Gammarus fossarum* (Leprêtre et al., 2022), *Dreissena polymorpha* (Leprêtre et al., 2022) et *Gasterosteus aculeatus* (Marchand et al., 2019), using the 95th percentile method. This

methodological approach is based on the hypothesis that the natural variability of a biomarker in an organism evolving in a “healthy” environment, combined with background noise, follows a gaussian distribution (Besse et al., 2013). However, it provides no information on the cause-effect relationship, the shape of the relationship or the intrinsic sensitivity of the biomarker. Furthermore, the threshold values defined correspond to statistical thresholds specific to the population studied and they cannot be used for other populations from another geographical area.

Dose-response approaches offer the possibility of characterising the relationship between the intensity of exposure and the amplitude of the biological response. The use of a wide concentration gradient, coupled with the assessment of the temporal kinetics of responses, makes it possible to identify effect thresholds more precisely, to characterise the sensitivity of biomarkers and to provide an improved understanding of the biological mechanisms involved in the response to chemical stress (Hook et al., 2014). Furthermore, such an experimental approach may be conducted for each population of a model species, what may provide a specific interpretation grid adapted to the local needs. This dose-response approach is therefore complementary to previously developed statistical methods based on percentiles and helps in improving the interpretation of biomarkers in environmental biomonitoring.

To test the usefulness of a dose-response experimental design, we targeted a model species widely used in aquatic ecotoxicology (*Dreissena polymorpha*) and two trace metal models with distinct modes of action (cadmium and copper). An innovative design with 20 exposure concentrations was applied over two exposure durations (4 and 8 days). The underlying objectives are defined around 4 questions: (i) Is there a dose-response relationship for both subcellular biomarkers and tissue concentrations? (ii) Does exposure duration influence the response of biomarkers? (iii) Does response pattern of biomarkers differ between cadmium and copper? The results obtained and used to answer the first three questions gave rise to another one: (iv) How did the inter-individual variability and statistical power of our experimental design influence our ability to detect biological effects? Such comprehensive dose-response studies with high concentration resolution remain rare in the biomarker literature, particularly for subcellular endpoints (Colas and Le Faucheur, 2024).

The zebra mussel *Dreissena polymorpha* (ZM) is a sentinel species regularly used in active (Bourgeault et al., 2010; Bervoets et al., 2005) and passive (Palais et al., 2012; Binelli et al., 2006) biomonitoring contexts and in laboratory experiments (Yancheva et al., 2018; Potet et al., 2016; Ivanković et al., 2010). Its biological and ecological characteristics (Mackie and Schloesser, 1996; Claudi and Mackie, 1994) make it particularly suitable for these kinds of studies. Its sedentary nature, its high filtration and bioaccumulation capacity make it an effective integrator of local environmental contamination. Its tolerance to handling and transplantation facilitate its use in the laboratory or in the field. In addition, the significant accumulation of knowledge concerning confounding factors on the response of its biomarkers (Devin et al., 2023; Louis, 2020; Potet, 2017; Wojtal-Frankiewicz et al., 2017; Minguez et al., 2011) makes it an appropriate model for assessing their operatibility.

Cadmium and copper were selected as model contaminants based on several complementary criteria. First, these metals are of high regulatory relevance, being classified as priority substances (Cd) or specific pollutants of ecological status (Cu) under the WFD (Dendiev et al., 2022). Second, they are prevalent contaminants in European aquatic ecosystems, making them ecologically relevant choices. Third, their status as widely-used model metals in aquatic ecotoxicology provides an extensive literature background on biomarker responses (Bultelle et al., 2024; Le Saux et al., 2020), facilitating interpretation and comparison of our results. Most importantly, cadmium and copper present fundamentally contrasting toxicity mechanisms, making them ideal candidates for testing whether metals with distinct modes of action generate different
biomarker response patterns. Although both induce oxidative stress, their biological behavior differs markedly (Le Saux et al., 2020; Stohs and Bagchi, 1995). Cadmium, a non-essential metal, is characterised by effects linked to membrane dysfunction (lipid peroxidation, DNA damage, impairment of mitochondrial functions). Conversely, copper, although essential at low concentrations, becomes toxic at higher concentrations through disruption of ionic homeostasis and enzymatic processes.

By combining this high-resolution experimental design with a well-characterized sentinel species and metals with contrasting toxicity mechanisms, this study provides a robust framework for characterising dose-response relationships.

## 2. Material and methods

### 2.1. Mussel sampling and acclimation

Zebra mussels were collected in January 2024 from the Moselle River at Blénod-lès-Pont-à-Mousson (France, E06°04'20'' N48°52'08''). After sampling, specimens were immediately transported to the laboratory in their water of origin.

Upon arrival, 150 mussels were distributed into continuously aerated 15 L aquaria under natural photoperiod for a 96 h acclimation period. During this phase, mussels received no food and were kept at field temperature using thermostatically controlled water baths. Site water was progressively substituted with spring water at a daily rate of one-third (Cristaline®, composition: Ca²⁺: 106; Mg²⁺: 4.2; Na⁺: 3.5; K⁺: 1.5; HCO₃⁻: 272; SO₄²⁻: 50; Cl⁻: 3.8; F⁻: 0.9; SiO₂: 8.3; NO₃⁻: < 1 mg·L⁻¹).

### 2.2. Exposure treatments

Following the acclimation phase, the mussels were individually placed in beakers containing 200 mL of exposure solution. Cadmium and copper exposure experiments were carried out two weeks apart, using identical protocols. Exposure periods lasted 4 and 8 days. Each experiment comprised 20 test concentrations including control, with three replicate samples per concentration and exposure duration (n = 60 per exposure duration). Cadmium concentrations were 0 - 10 - 50 - 100 - 150 - 200 - 250 - 300 - 350 - 400 - 450 - 500 - 550 - 600 - 650 - 700 - 750 - 800 - 850 - 900 µg·L⁻¹, and for copper, 0 - 10 - 25 - 50 - 75 - 100 - 125 - 150 - 175 - 200 - 225 - 250 - 275 - 300 - 325 - 350 - 375 - 400 - 425 - 450 µg·L⁻¹. Standard solutions of cadmium (Product no: 86,668) and copper (Product no: 86,673) at 1000 mg·L⁻¹ acidified to 2 % with nitric acid were purchased from VWR. For each experiment, a stock solution was prepared, at 200 mg·L⁻¹ for cadmium and at 100 mg·L⁻¹ for copper. The test concentrations were obtained by dilution of the stock solution. Solution media were prepared with Cristaline® water and renewed every 24 h to maintain a constant concentration of oxygen and metal in the beaker. After 24 h, 15 mL of exposure media were sampled from each condition and were used for determination of cadmium and copper concentrations respectively. During exposure, beaker water was not aerated to limit metal adsorption on glass walls and dispersion outside the beaker. For each experiment, mortality was monitored each day and dead organisms were removed from the beaker.

The exposure durations (4 and 8 days) were selected according to methodological criteria and ecotoxicological works detailed in our previous study (Bain et al., 2025). The test concentrations were selected on the basis of previous works in *Dreissena polymorpha* (Berezina et al., 2024; Louis et al., 2021; Le Guernic et al., 2020; 2019; Navarro et al., 2011; Faria et al., 2009). They encompass a large spectrum, from environmental levels to sub-lethal ones, spanning regulatory thresholds including industrial discharge limits (25 µg·L⁻¹ for cadmium, 150 µg·L⁻¹ for copper), and drinking water standards (5 µg·L⁻¹ for cadmium, 2000 µg·L⁻¹ for copper). The wide range of tested concentrations goes from environmental ones to intentionally high ones, because we intended to explore the response of biomarker at concentrations close to those that

could cause death. To ensure that zebra mussels sampled in the field may present measurable response to test metals, we checked that they were not pre-exposed to high concentrations in their site of origin: according to French monitoring databases (Naïades and AERM), environmental concentrations measured along the Moselle River near our sampling location between January 2023 and January 2025 show cadmium levels below 0.01 µg·L⁻¹ and copper concentrations between 0.8 and 2.1 µg·L⁻¹, all of these concentrations being lower than the lowest we tested.

### 2.3. Post-exposure analyses

#### 2.3.1. Dissections and condition index

At the end of the experiment (4 and 8 days), individual shell measurements (length, width, height in mm) were recorded using a calliper. After opening the shell, the soft tissues, excluding the byssus, were weighed. Individual condition indices were calculated using the relationship between soft tissue mass and shell volume (Guerlet et al. 2010):

$$ \text{Condition index (mg·mm}^{-3}\text{)} = \frac{\text{Wet weight}}{\text{Lenght x Width x Height}} $$

In order to limit the influence of the body size on biomarker response, we checked that condition indices and biometric parameters were not significantly different between all the treatment groups within each experiment (p-value > 0.05).

The following organs were dissected: (i) the digestive gland was recovered and immediately frozen at -80 °C for subsequent biomarker assay and (ii) the remaining soft tissues were stored at -80 °C for the determination of tissue metal concentrations.

#### 2.3.2. Biomarker measurements

Digestive glands were thawed and processed using a multi-step protocol. First, tissues were homogenised in phosphate buffer (50 mM, pH 7.6) supplemented with protease inhibitors (1 mM phenyl-methylsulfonyl fluoride (PMSF) and 1 mM L-serine borate at 8/1 (v/w) ratio). Homogenisation was then performed using an electric ball crusher (RETSCH MM400) at 30 Hz for 30 s. An initial centrifugation for 5 min at 250 x g at 4 °C obtained the crushed fraction. After recovery of the volumes required for assaying the first set of biomarkers, the remaining volume of supernatant underwent a second high-speed centrifugation (20 000 x g, 50 min, 4 °C) to obtain the cytosolic fraction, used for assaying the second set of biomarkers.

Biomarker responses were measured using two automated spectrophotometers (Konelab 20-XTi and Gallery plus, ThermoScientific). The advantage of these two measuring instruments is that they can work on microvolumes, allowing us to measure ten biomarkers on a single digestive gland. This makes it possible to observe inter-individual variability, link each biomarker response to a single individual, and analyse the relationship between different biomarkers in greater detail. The digestive gland was the targeted organ because it is a major site of enzymatic activity, but also for the biotransformation of xenobiotics (Viarengo et al., 2007; Livingstone et al., 1992). The crushed extract was used to determine lipid hydroperoxide concentrations (LOOH), the activity of the electron transport system (ETS) and protein concentrations (protB). The cytosolic fraction was used to measure the activity of glutathione-S-transferase (GST), superoxide dismutase (SOD), glutathione peroxidase (GPx), lactate dehydrogenase (LDH), caspase-3 (CSP3), the total antioxidant capacity (TAC), and protein concentrations (protFC). The protocols used to measure biomarker response are detailed by Garaud et al., 2016 except for SOD and CSP3. SOD activity was determined using the WST-1 formazan-based method, whose production is inversely proportional to SOD activity. SOD activity is calculated using a standard range produced with recombinant human SOD. Caspase-3 activity was measured using CSP3 fluorescent substrate DEVD-AFC on a microplate spectrofluorimeter (SAFAS Xenius) as detailed in Potet et al. (2018). Protein quantification in both fractions utilised the pyrogallol red colorimetric method with bovine serum
albumin standards (ThermoScientific, Ref981831).

### 2.3.3. Quantification of metal concentration in exposure media and soft tissue

Water samples taken at 0 and 24 h exposure were acidified to 1 % with HNO₃ before analysis. These time points correspond to the beginning and end of each 24 h exposure cycle (before media renewal). Metal quantification was performed by atomic absorption spectrophotometry, with the analytical method chosen according to concentration levels: flame (FAAS; Thermofisher ICE 3000) was used for high concentrations, while oven (GFAAS; Varian SPectrAA 800) was used for trace determinations. Quality assurance included the use of certified reference materials: SPS-WW1 batch n°133 for FAAS analyses and SPS-W1 batch n°117 for GFAAS analyses. Results are expressed in µg.L⁻¹.

Soft tissue samples were lyophilised prior to mineralisation. For mineralisation, cadmium-exposed tissues were digested using 400 µL of HNO₃ combined with 100 µL of H₂O₂, while copper-exposed samples were digested using 600 µL of HNO₃. Cadmium concentrations were determined by FAAS or GFAAS based on tissue loading levels, while copper analysis used inductively coupled plasma-optical emission spectroscopy (Agilent ICP OES 5800). The certified reference material used was TORT-3. Metal concentrations in tissues are expressed in µg.g dry weight⁻¹.

## 2.4. Statistical analysis

All statistical and multivariate analyses and all graphical representations were performed using R studio software (R development Core Team, version 4.5.0).

### 2.4.1. Assessment of dose-response relationships

Dose-response modelling was carried out on R studio using the DRomics tool (package ‘DRomics’) (Larras et al., 2018). On the one hand, the biomarker response was represented as a function of metal concentrations measured in the tissue and in the exposure media. On the other hand, the tissue concentrations of cadmium and copper were represented as a function of the concentrations measured in the exposure media. For each case, quadratic trend test was used to detect potential linear or biphasic dose-response relationships. As the number of individuals per experimental group was small, the statistical differences between these groups were tested using Kruskal-Wallis for biomarker response and metal tissue concentrations. The threshold of significance considered is p-value < 0.05. If the differences were significant, a Wilcoxon test was performed.

### 2.4.2. The influence of exposure duration

In order to test the influence of exposure duration on biomarker response, two MANOVA were performed to compare 4 and 8 days of cadmium exposure and then 4 and 8 days of copper exposure.

### 2.4.3. Correlation networks based on the response of biomarkers to metals

In order to test the influence of the ‘contaminant’ factor on biomarker response, two MANOVA were performed to compare the responses obtained after cadmium and copper exposure, one with the data set obtained after 4 days and the second with that obtained after 8 days.

Correlation networks were constructed to visualise relationships between biomarkers for each metal by combining the 4- and 8-days exposure data, as no significant differences were observed between exposure durations. All exposure concentrations were pooled regardless of the number of individuals per concentration, as the objective was to examine inter-biomarker correlations. Data were centred and scaled to ensure equal contribution of each biomarker to the correlation matrix. Correlation networks were generated using the ‘qgraph’ package in R. Correlation significance was calculated with the ‘rcorr’ function from the ‘Hmisc’ package, using Spearman correlation. Only correlations above |0.2| were represented. In the graphical representation, line

thickness reflects correlation strength, solid lines indicate positive correlations and dashed lines indicate negative correlations and colours correspond to significance levels.

### 2.4.4. Integration of inter-individual variability: IBR tools

The Integrated Biomarker Response (IBR) is a tool initially developed by Beliaeff & Burgeot (2002) that has been continuously improved for better application (Catteau et al., 2023, 2022; Devin et al., 2014; Sanchez et al., 2013). IBR-type tools aim to represent the overall response of biomarkers, by providing both a graphical synthesis (radar diagram) and a numerical value integrating all the biomarker responses simultaneously. Such synthetic tools make the interpretation of biomarker responses easier in the context of environmental management.

The IBRv2i used in this work is an IBR variant developed by Mattos et al. (2023), whose main advantages are the consideration of inter-individual variability of biomarkers, the elimination of the need to select the direction of biomarker response variation a priori, and the possibility to weight the importance of a biomarker according to the experimenter’s choice. Concretely, for each individual, an IBRv2i value is obtained corresponding to the sum of individual variation coefficients of biomarkers. For example, for an individual on which 8 biomarkers are measured, the IBRv2i will correspond to the sum of these 8 individual coefficients of variation calculated according to the Mattos et al. (2023) method.

### 2.4.5. Assessment of statistical power and prospective approach

To assess the statistical detection capacity of our experimental design and optimise the design of future studies, we calculated the minimum detectable differences (MDD) for each biomarker. The power.t.test() function on Rstudio with standard parameters (α = 0.05, power = 80 %, two-tailed test) was used. The MDD were calculated for three-sample size per concentration (n = 3, the real sample size, n = 6 and n = 10, hypothetical sample size) using pooled standard deviation observed in our study. This makes it possible to integrate the natural variability of the controls and the variations in the variability induced by exposure to metals. This approach allows statistical detectability thresholds to be defined: effects beyond ±MDD relative to the control mean are statistically detectable, while changes below these thresholds remain in the non-detection zone. The MDD was calculated using the following formula: $(t_{α/2} + t_ρ) \times sd \text{ pooled} \times \sqrt{\frac{1}{n_1} + \frac{1}{n_2}}$ with n₁ corresponding to the number of individuals in the control group and n₂ the number of individuals in the exposed group. The sd pooled was calculated using the

following formula: $\sqrt{\frac{((n_1 - 1) \times sd_1^2 + (n_2 - 1) \times sd_2^2)}{(n_1 + n_2 - 2)}}$ with sd₁ the standard deviation of the control group and sd₂ the standard deviation of the exposed group. The significance threshold ($t_{α/2}$) and statistical power ($t_ρ$) were set at 1.96 and 0.8 respectively.

## 3. Results

### 3.1. Contrasting dose-response relationships

#### 3.1.1. Concentrations in the exposure media and in the soft tissues of mussels

Analysis of cadmium and copper concentrations in exposure media reveals a positive correlation with nominal concentrations (R² between 0.90 and 0.99; p-value < 0.01) (Fig. 1; supp. Data A). This linear relationship is statistically validated by the quadratic trend tests applied (p-value < 0.01). The narrowness of the confidence intervals around the slopes of the regression lines (Fig. 1) attests to the robustness of the experiment. In addition, slope values close to 1 (Fig. 1) indicate little loss of cadmium and copper by adsorption to the glass walls of the beaker and the mussel shells.
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Simple linear regression between measured copper (A) and cadmium (B) and nominal concentrations (µg.L⁻¹) in the water after 24 h of exposure (n = 1 to 3). For each regression, all the model parameters are shown on the graph (equation, correlation coefficient (r²), p-value (p), F-statistic (F), degree of freedom (df) and slope confidence interval (IC slope)).</figcaption></figure>

The metal concentration in the soft tissue of mussels showed a dose-dependent relationship for all experiments except for copper exposure after 4 days of exposure (Fig. 2). These relationships were tested using a quadratic test showing a significant fit of the model to the data (p-value < 0.01). The exponential models used here follow this formula: $y = d + b \left( \exp\left(\frac{x}{e}\right) - 1 \right)$. The parameters ‘b’ and ‘e’ are shape parameters

and ‘d’ is the mean signal in the control condition (i.e. baseline level). The interpretation of the variation in these parameters is as follows: if ‘e’ is <0, the dose-response curve will be increasing if ‘b’ is greater than 0 and decreasing if ‘b’ is <0, with no asymptote for high doses (Larras et al., 2018).

The absolute value of ‘e’ is higher for cadmium (621 at 4 days and 426 at 8 days) than for copper (7.9 at 8 days), reflecting a greater increase in tissue concentration for copper. Unlike tissue concentrations of copper, which reaches a plateau very quickly, tissue concentration of cadmium continues to increase even at high exposure concentrations after 8 days. This suggests that the two metals have different accumulation and regulation mechanisms. For cadmium, the ‘b’ value was higher at 8 days (118) than 4 days (89), suggesting a potentially greater accumulation capacity after 8 days. The similar ‘d’ between both exposure durations indicates comparable baseline levels. Detailed

statistics are available in the supp. Data B.

### 3.1.2. Mortality patterns

The analysis of mortality data reveals that independently of the contaminant (supp. Data C): (i) the increase in exposure concentration induced an increase in mortality, reaching 100 % for the highest concentrations and (ii) mortality is greater at 8 days than at 4 days of exposure. In addition, marked differences in toxicity between cadmium and copper are highlighted. The results reveal that copper exerts a more pronounced toxicity than cadmium on dreissenids, resulting in 100 % mortality from 250 µg.L⁻¹ after 8 days of exposure.

### 3.1.3. Biomarker responses

The strength of our experimental methodology lies in the simultaneous measurement of the eight biomarkers and metal concentrations in the tissue of each individual mussel, allowing us to link biomarker responses and tissue metal concentrations within the same organism. Thus, biomarker responses were plotted as a function of metal tissue concentrations (Fig. 3) and as a function of measured concentrations in the exposure media (supp. Data D). For both contaminants and all exposure durations, no significant differences between the experimental groups, including control, were found (supp. Data E). No clear effect of exposure could be demonstrated and a strong inter-individual variability

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Tissue concentrations of metal (µg.g dry weight⁻¹) and measured exposure concentrations (µg.L⁻¹) for cadmium and copper (n = 1 to 3) after 4 days or 8 days of exposure. The equation of the model is shown on the graph.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Biomarker responses after 4 days or 8 days of exposure to cadmium and copper were plotted as a function of tissue concentrations. Acronyms: GST (glutathione-S-transferase), SOD (superoxide dismutase), GPx (glutathione peroxidase), TAC (total antioxidant capacity), LOOH (lipid hydroperoxide), CSP3 (caspase 3), ETS (electron transport system) and LDH (lactate dehydrogenase).</figcaption></figure>
in both control and exposed organisms was demonstrated.

## 3.2. Influence of exposure duration on biomarker patterns

The effect of exposure duration was tested for each contaminant separately. The MANOVA results for cadmium ($p = 0.12$, $df = 55$, $df_{res} = 62$, $F = 1.71$) and for copper ($p = 0.28$, $df = 36$, $df_{res} = 43$, $F = 1.30$) indicate that the biomarker response does not vary between the two exposure durations.

## 3.3. Cadmium vs copper: distinct correlation networks

The effect of the 'contaminant' factor was tested using a MANOVA by comparing the responses between cadmium and copper after 4 days ($p = 7.175^{-11}$, $df = 61$, $df_{res} = 68$, $F = 13.24$) and 8 days ($p = 1.03^{-4}$, $df = 30$, $df_{res} = 37$, $F = 6.16$) of exposure. In both cases, cadmium and copper appear to generate different responses.

Correlation networks were built to explore these differences in biomarker responses of individuals exposed to cadmium and to copper (Fig. 4). Cadmium exposure generates a fragmented network with few significant connections between biomarkers, suggesting decoupled response pathways. The strongest connection observed is ETS-CSP3 ($p < 0.01$), while LOOH appears isolated from the main network. Conversely, copper exposure produces a dense and interconnected network, with TAC as a central biomarker significantly connected to most other biomarkers. The strongest connection is between TAC and ETS, while LOOH again appears to be the most isolated biomarker (the one with the weakest connections). This difference in connectivity suggests that copper, an essential metal, and cadmium, a non-essential metal, follow different regulation pathways.

## 3.4. Does inter-individual variability compromise biomarker response detection? An integrative approach using IBRV2i

Details of individual biomarker variations and the IBRV2i value calculated for each individual are available in the supp. Data F. The analysis of covariance (ANCOVA) performed on IBRV2i values (dependent variable) with exposure duration and exposure concentration (independent variables) reveals for cadmium a significant effect of exposure duration on IBRV2i values ($p = 0.002$, $df = 1$, $df_{res} = 87$, $F = 9.95$) as well as a significant interaction between exposure duration and concentrations ($p = 0.01$, $df = 1$, $df_{res} = 87$, $F = 7.03$). Conversely, copper shows only a significant effect of exposure concentrations on IBRV2i values ($p = 0.03$, $df = 1$, $df_{res} = 48$, $F = 4.77$) without temporal

effect or significant interaction. However, the marked imbalance in sample sizes between the two exposure durations, caused by significant mortality at 8 days (particularly for copper), constitutes a major bias likely to artificially amplify statistical differences. Moreover, even if statistical analysis reveals significant effects, these results do not translate into any clear and interpretable response pattern (Fig. 5). The apparently random distribution of IBRV2i values, coupled with the imbalance in sample sizes between treatments, suggests that these statistical significances reflect more the methodological limitations of the approach than actual clear and distinct response patterns depending on the concentration gradient and exposure duration. Although IBRV2i is particularly adapted for integrating inter-individual variability, our results demonstrate that the defence mechanisms implemented vary considerably from one individual to another, regardless of the intensity of the applied pressure (Fig. 5).

## 3.5. Experimental design limitations: prospective analysis of statistical power

To evaluate the statistical detection capacity of our experimental design and optimise the design of future studies, we calculated the minimum detectable differences (MDD) for each biomarker by testing three sample sizes: $n = 3$ (corresponding to our current design), $n = 6$ (doubling the number of replicates) and $n = 10$ (approaching the sample sizes usable in an active biomonitoring context). This prospective approach uses the variability observed in our study to define the statistical detectability thresholds: effects exceeding $\pm$MDD relative to the control mean are statistically detectable, while changes below these thresholds remain in the non-detection zone. The graphs show these three thresholds for each contaminant and each exposure duration, as well as mean of the control and the raw biological responses in exposed individuals (supp. Data G). To facilitate interpretation of these results, Table 1 represents the number of individuals exceeding the thresholds ($n = 3, 6$ and $10$).

The increase in sample size improves the statistical detection capacity. With $n = 3$, very few threshold exceedances are observed, while with $n = 6$ and $n = 10$, the number of individuals exceeding the thresholds increases for most biomarkers. This relationship reveals that our experimental design with $n = 3$ per group, despite 20 exposure concentrations, limits the statistical detection of biological effects.

Our results show a better ability to detect variations in certain biomarkers. ETS, SOD, GPx and CSP3 present the best potential for detection of variations with numerous individuals exceeding the thresholds, particularly at $n = 10$. Conversely, biomarkers like LOOH or TAC show

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Correlation networks for cadmium and copper for the eight biomarkers studied. The interpretation guide is as follows: line thickness reflects correlation strength; solid lines indicate positive correlations and dashed lines indicate negative correlations and colours correspond to significance levels.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 5. Boxplot represented IBRv2i values for each contaminant and each exposure duration.</figcaption></figure>

<caption>**Table 1**</caption>

Number of individuals exceeding the thresholds set at *n* = 3, *n* = 6 and *n* = 10 individuals per exposure condition for cadmium and copper after 4 and 8 days of exposure.

<table><thead><tr><th rowspan="2"></th><th colspan="6">Copper</th><th colspan="6">Cadmium</th></tr><tr><th colspan="3">4 days</th><th colspan="3">8 days</th><th colspan="3">4 days</th><th colspan="3">8 days</th></tr></thead><tbody><tr><td>Thresholds</td><td>3</td><td>6</td><td>10</td><td>3</td><td>6</td><td>10</td><td>3</td><td>6</td><td>10</td><td>3</td><td>6</td><td>10</td></tr><tr><td>GST</td><td>0</td><td>3</td><td>5</td><td>0</td><td>2</td><td>4</td><td>0</td><td>5</td><td>11</td><td>0</td><td>5</td><td>6</td></tr><tr><td>SOD</td><td>1</td><td>12</td><td>16</td><td>0</td><td>0</td><td>4</td><td>0</td><td>8</td><td>11</td><td>2</td><td>3</td><td>5</td></tr><tr><td>GPx</td><td>0</td><td>4</td><td>7</td><td>0</td><td>1</td><td>2</td><td>1</td><td>10</td><td>17</td><td>0</td><td>3</td><td>7</td></tr><tr><td>TAC</td><td>0</td><td>5</td><td>9</td><td>0</td><td>1</td><td>2</td><td>1</td><td>4</td><td>5</td><td>1</td><td>3</td><td>6</td></tr><tr><td>LOOH</td><td>2</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>2</td><td>3</td><td>5</td><td>1</td><td>2</td><td>0</td></tr><tr><td>CSP3</td><td>0</td><td>2</td><td>9</td><td>0</td><td>3</td><td>8</td><td>3</td><td>5</td><td>6</td><td>0</td><td>18</td><td>23</td></tr><tr><td>ETS</td><td>5</td><td>13</td><td>23</td><td>1</td><td>0</td><td>3</td><td>1</td><td>12</td><td>17</td><td>0</td><td>1</td><td>9</td></tr><tr><td>LDH</td><td>0</td><td>4</td><td>7</td><td>0</td><td>1</td><td>0</td><td>1</td><td>4</td><td>8</td><td>1</td><td>6</td><td>11</td></tr></tbody></table>

few exceedances even with *n* = 10, suggesting either low sensitivity to treatments, or excessive inter-individual variability masking the effects.

Detection patterns vary according to the metal and exposure duration. For cadmium at 4 days, GPx shows the best potential (17 individuals exceeding the *n* = 10 threshold), while at 8 days, CSP3 emerges (23 individuals) against 7 individuals for GPx. For copper, SOD and ETS prove very reactive at 4 days (16 and 23 individuals exceeding the *n* = 10 threshold), but at 8 days, increased mortality limits the number of available data, with very few threshold exceedances, making interpretation delicate. This analysis highlights that the temporal variability of biomarker responses must not be neglected, since responses observed only at 4 or 8 days could lead to completely different conclusions, potentially resulting in erroneous interpretations on the utility or sensitivity of a given biomarker. These results highlight on one hand the importance of multi-temporal approaches in biomarker evaluation, and on the other hand suggest that a sample size of 6 to 10 individuals per group would constitute an optimal compromise between statistical efficiency and technical and ethical considerations, while accounting for the survival biases observed.

## 4. Discussion

The discussion of our results is structured around the four central observations that this study sought to elucidate: (i) the absence of dose-response relationships for biomarkers despite metal accumulation and dose-dependent mortality, (ii) the influence of exposure duration on biomarker responses, (iii) distinct response patterns between cadmium and copper and (iv) the influence of inter-individual variability on experimental design.

The metal concentrations measured in soft tissue of *Dreissena*

*polymorpha* under experimental conditions similar to ours and reported in the literature are highly variable: they are either much lower (Berezina et al., 2024), higher (Lecoeur et al., 2004) or close (Louis et al., 2019) to the concentrations measured in our study. Although this illustrates the variability in bioaccumulation levels, it shows that our results remain consistent with other data. Furthermore, we confirmed a dose- and time-dependent accumulation of cadmium and copper in soft tissues of dreissenids. The differences in their accumulation pattern may be explained by their distinct physiological status. Copper, as an essential metal, is subject to strictly regulated homeostasis in order to maintain intracellular concentrations within a physiologically optimal range (Le et al., 2022; 2021; Bourgeault, 2010; Burkhead et al., 2009). This active regulation explains the rapid plateau achievement observed in our study, reflecting saturation of storage capacities and activation of compensatory excretion mechanisms. Conversely, cadmium being a non-essential metal, it has no dedicated transport system and enters cells by mimicking essential metals, in particular via calcium channels and zinc transporters (Bultelle et al., 2024; Le Saux et al., 2020; Stohs and Bagchi, 1995). Regardless of the process, cadmium and copper were accumulated in soft tissue, as it was expected. However, despite our powerful experimental design allowing the measurement of metal concentration and the measurement of biomarker responses directly on the same organism, we could not detect any dose-response relationships for the subcellular biomarkers. This is all the more surprising given that cadmium and copper were chosen precisely because they do produce well-documented toxic effects under exposure conditions similar to ours (exposure time, concentration range and temperature).

These two metals are known to generate oxidative stress through the production of reactive oxygen species (ROS), either directly for copper (Fenton reactions), or indirectly for cadmium through disruption of
cellular homeostasis and interference with essential metal functions (Bultelle et al., 2024; Le Saux et al., 2020; Stohs and Bagchi, 1995). Cadmium, for example, shares common chemical properties with certain divalent ions such as zinc, calcium and copper, which enables it on one hand to cross biological membranes and accumulate in the organism, and on the other hand to substitute for essential ions at enzyme active sites such as SOD, thus disrupting their function (Bultelle et al., 2024). Beyond effects linked to ROS overproduction, negative effects are demonstrated at metabolic, mitochondrial and cytoskeletal levels as well as on antioxidant (CAT, GPx, SOD) and antitoxic (GST) enzymes (Bultelle et al., 2024; Le Saux et al., 2020; Stohs and Bagchi, 1995). However, our results reveal neither decrease nor increase in antioxidant (SOD, GPx) and antitoxic (GST) enzymatic activities nor variation in total antioxidant capacity (TAC), which mainly integrates non-enzymatic defences. These observations find echo in the literature which reports similar response patterns in *Dreissena polymorpha*: minimal or absent effects on subcellular biomarkers (Louis et al., 2019; Ekelund et al., 2020; Navarro et al., 2011; Faria et al., 2009) while more integrative physiological biomarkers reveal deleterious effects, such as reduction in filtration rates (Kraak et al., 1992) and alteration of oxygen consumption (Rao and Khan, 2000).

Despite the absence of dose-response relationships, analysis of correlation networks between biomarkers reveals distinct patterns according to the metal tested, consistent with their different mechanisms of action. The fragmented network observed for cadmium, characterised by sparse connections, suggests targeted and selective cellular responses, while the dense and integrated copper network, with TAC as central biomarker, indicates coordinated mobilisation of multiple defence systems. These observations find echo in recent omics approaches. Proteomic analysis by Bultelle et al. (2024) shows that cadmium exposure at 100 µg·L⁻¹ for 96 h in *Dreissena polymorpha* significantly modifies the abundance of 88 proteins, revealing alterations affecting sulphur and glutathione metabolism, calcium signalling, cytoskeleton dynamics, energy production and membrane processes. This study also demonstrates complex interconnections between biological pathways: intracellular free cadmium accumulation stimulates ROS production, which by interacting with reduced glutathione, compromises antioxidant defences and amplifies cellular damage, while consequent mitochondrial lesions can trigger apoptotic pathways. Similarly, the transcriptomic study by Ekelund et al. (2022 & 2020) in *Anodonta anatina* exposed to copper reveals differential gene responses according to organ, but contrary to cadmium, establishing mechanistic links between biomarkers proves more complex for copper, reflecting the multiplicity of regulatory pathways for this essential metal. These omics data show that biological disturbances are present at the molecular level, but their translation to higher scales remains complex. Indeed, transcriptomic modifications require numerous steps (translation, post-translational modifications, activity regulation) before manifesting as functional proteins, while protein abundance variations do not necessarily imply detectable enzymatic activity modifications by the biomarkers using in this study.

The absence of subcellular biomarker response could result not only from biological mechanisms masking effects (excessive variability, compensation, saturation), but also from inadequate experimental features, such as exposure duration which might not correspond to the optimal temporal window for detecting subcellular responses. The choice of exposure durations of 4 and 8 days was based on dual justification. On one hand, they are standard in aquatic ecotoxicology and allow observation of effects on integrative physiological biomarkers (filtration rate, oxygen consumption), apical parameters (mortality) or molecular ones (Berezina et al., 2024; Bultelle et al., 2024; Ekelund et al., 2022; Rao and Khan, 2000). On the other hand, methodological constraints impose this temporal limit since without feeding, 8 days represents the threshold beyond which starvation could significantly bias responses (Schäfer et al., 2012). Although these time scales are commonly used in aquatic ecotoxicology, it would be a mistake to

conclude that there is no effect because there is no response at a given moment, when the temporal dynamics of the response are unknown (Wu et al., 2005). Indeed, our study presents a case of false negatives: no significant variations in biomarker response yet significant dose-dependent mortality. The literature demonstrates that biomarker responses can follow complex temporal patterns. Some biomarkers, particularly antioxidant enzymes like superoxide dismutase and catalase, may exhibit rapid induction within the first hours or days of metal exposure, followed by a return to baseline levels or even inhibition during prolonged exposure due to adaptive or compensatory mechanisms (Fokina et al., 2020; Geret et al., 2002). Conversely, other biomarkers may show delayed responses appearing only after extended exposure periods beyond 8 days, particularly those related to chronic oxidative damage or energy metabolism perturbations (Louis et al., 2019; Tsangaris et al., 2008). By testing only two time points (4 and 8 days), our experimental design may have missed early transient responses (<4 days) or delayed responses (>8 days), potentially contributing to the absence of significant biomarker variations observed.

The mortality observed in our study reveals marked differences in toxicity between cadmium and copper. Our results showed that copper exerts more pronounced toxicity than cadmium on dreissenids, what is explained by their distinct mechanisms of action. This mortality induces selection bias, also called survivor bias. Only the most tolerant individuals survive at high concentrations, particularly for copper, creating a subsample that no longer represents the original sample average sensitivity. This bias potentially masks the sought dose-response relationships: individuals analysed for biomarkers at high concentrations constitute a resistant subsample whose biological responses may be atypical. Thus, the observed absence of dose-response relationships could partly result from this artificial selection of the most tolerant individuals. This tolerance can be explained in several ways: on one hand, defence and detoxification systems could be intrinsically more efficient or less sensitive to exposure variations in these surviving individuals; on the other hand, a behavioural response to stress observed in bivalves is valve closure thus limiting exchanges with the external environment and reducing real exposure to contaminants (Borcherding, 1994). It should also be considered that beyond differences in defence system efficiency, some organisms can be naturally more resistant than others under identical conditions, even with similar defence levels. The existence of putative survivor bias in such experimental contexts emphasises the complexity of interpreting biological responses and reveals the need to systematically report mortality data during controlled exposure conditions, data that are still too rarely available in the literature (Bain et al., 2025).

The development of multi-biomarker approaches leads in more complex data analysis and interpretation, while their integration into regulations policies requires simplified tools that can be easily used by non-specialists. For this purpose, many authors have developed integrative statistical tools that are easy to interpret, providing a single value for a given condition. The Integrated Biomarker Response (IBR) is one of these tools. However, in our study, the IBRv2i values showed no difference between concentrations and exposure durations. This observation is consistent with two recent studies conducted on oysters under controlled conditions, which also found no significant difference between the IBRv2i values calculated for control and exposure conditions (Paiva et al., 2024; Saldaña-Serrano et al., 2025). Paradoxically, our ANCOVA analysis reveal a contradiction: statistical significances emerge without revealing biologically coherent response patterns when directly observing IBRv2i values. This discordance between statistical significance and biological interpretation emphasises the importance of critical analysis of statistical results (Ahmed and Butt, 2025; Willigenburg and Poolman, 2023), particularly when methodological biases (sample size imbalances due to mortality) or excessive biological variability can mask or artificially amplify detected effects. This situation highlights the necessity of adopting a cautious approach in interpreting statistical tools, prioritising examination of raw data alongside statistical tests.
Our design with 20 exposure concentrations represents an innovative approach and, to our knowledge, such a design has never been tested on biomarkers in bivalves. This methodological originality is confirmed by the analysis of Colas & Le Faucheur (2024) covering 156 articles about dose-response exposure, which reveals that only 12 % use an experimental design with between 7 and 15 concentrations, our approach largely exceeding these standards. This experimental strategy aimed to finely characterise dose-response relationships and precisely identify biomarker effect thresholds. However, despite this fine resolution in terms of concentration gradient, no effect could be detected, raising the question of this approach's effectiveness. The choice to favour a large number of doses was made at the expense of the number of replicates per concentration (n = 3), creating an unfavourable compromise for statistical power. Our prospective analyses based on minimal detectable differences (MDD) calculation reveal that with 6 individuals per concentration, effects could potentially have been detected for some of the measured biomarkers. However, these MDDs being calculated from this study's entire dataset, these projections remain to be validated experimentally. Indeed, given the strong inter-individual variability observed, it remains uncertain that even with n = 10, effects would have been statistically detectable (Willigenburg and Poolman, 2023; Bendtsen, 2020).

## 5. Conclusion

Our study illustrates a case of false-negativity, characterised by the absence of a dose-response relationship for subcellular biomarkers despite evidence of toxic effects (dose-dependent mortality) and proven metal accumulation in soft tissues. The innovative use of a 20 concentrations experimental design failed to detect the expected sublethal effects. In addition, the duration of exposure did not influence the biomarker response, and different response patterns were observed between cadmium and copper. Analysis of inter-individual variability using the integrative tool IBRv2i shows no difference between the responses of control organisms and those of exposed organisms, and highlights the need to adopt a cautious approach when interpreting statistical tools, favouring examination of raw data alongside statistical tests.

These results highlight the lack of knowledge about the temporal dynamics of biological responses and the causal links between exposure and effect, making it difficult to establish threshold values. The number of factors that can influence biomarker response further complicates the construction of a reference system for coding biological responses in terms of environmental quality, and raises questions about its applicability to all individuals of the same species (Bain et al., 2025; Pain-Devin et al., 2014; Xuereb et al., 2023).

Our prospective analysis highlights the need to find a compromise between technical and methodological limitations and statistical power (Ahmed and Butt, 2025). This study demonstrates the difficulties of applying biomarkers under controlled conditions, quantifies inter-individual variability, provides data on the model species *Dreissena polymorpha* and contributes to the debate on the operatibility of biomarkers. We also show that so-called 'negative' results are just as informative as those deemed 'positive', enriching our understanding of methodological limitations and helping to improve future protocols.

## Funding

This work was supported by ZAM (Zone Atelier Moselle, Lorraine) and SIREna doctoral school grant (Sciences et Ingénierie des Ressources Naturelles).

## CRediT authorship contribution statement

**Manon Bain:** Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis,

Conceptualization. **Simon Devin:** Writing – review & editing, Writing – original draft, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization. **Marie Zaffino:** Methodology, Investigation. **Sandrine Pain-Devin:** Writing – review & editing, Writing – original draft, Supervision, Project administration, Investigation, Funding acquisition, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

This work was partly done with resources from the Pôle de compétences en biologie environnementale, Pôle de compétences en chimie analytique environnementale, Pôle de compétences en train-instrumentation, ANATELO, LIEC laboratory, UMR 7360 CNRS – Université de Lorraine and the LiecOscope, LIEC laboratory, UMR 7360 CNRS – Université de Lorraine. This work was also partly supported by Eurométropole de Metz and CPER RENETE 2020–2027. The authors would like to thank Amandine ZAHM for her valuable help in mussel sampling. We would also like to thank Valérie GONCALVES for her valuable help in metal analyses. And finally, we thank Bénédicte SOHM, Claire CAILLARD and Chloé DE VERNISY for their valuable help in biomarker analyses.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.hazadv.2025.100922.

## Data availability

Data will be made available on request.

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