# Tissue concentration, mortality, and response of subcellular biomarkers in Dreissena polymorpha exposed to nickel: absence of dose-response relationships for biomarkers despite dose-dependent toxicity

Manon Bain, Sandrine Pain-Devin, Bénédicte Sohm, Chloé de Vernisy, Simon Devin

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Manon Bain, Sandrine Pain-Devin, Bénédicte Sohm, Chloé de Vernisy, Simon Devin. Tissue concentration, mortality, and response of subcellular biomarkers in Dreissena polymorpha exposed to nickel: absence of dose-response relationships for biomarkers despite dose-dependent toxicity. Aquatic Toxicology, 2025, 287, pp.107517. 10.1016/j.aquatox.2025.107517. hal-05225743

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# Tissue concentration, mortality, and response of subcellular biomarkers in *Dreissena polymorpha* exposed to nickel: absence of dose-response relationships for biomarkers despite dose-dependent toxicity

Manon Bain<sup>a,b,*</sup> <u>&#x2193;</u>, Sandrine Pain-Devin<sup>a,b</sup>, Bénédicte Sohm<sup>a</sup>, Chloé De Vernisy<sup>a,b</sup>, Simon Devin<sup>a,b,*</sup>

## ARTICLE INFO

### Keywords:

Nickel

*Dreissena polymorpha*

Multi-biomarker

Dose-response

## ABSTRACT

Biomarkers are biological responses that inform about stress or toxic effects on sentinel species and they are widely used in environmental monitoring. However, interpreting many biomarkers remains a challenge at present, before we can move towards codifying their responses in terms of environmental quality. The aim of our study was to bring a better perspective to this scientific issue by defining threshold values based on subcellular biochemical response, that would enable to conclude on the health of the entire organism. To define such threshold values, we tried to establish dose-response relationships between biomarker responses and increasing concentrations of a well-known contaminant. *Dreissena polymorpha* was exposed to between 6 and 20 concentrations of nickel in order to identify potential dose-response relationships. A set of 10 biomarkers representative of antioxidant and antitoxic defences (SOD, GPx, GST, TAC), energetic metabolism (ETS, LDH) and cellular damage (LOOH, CSP3) were measured, as well as internal nickel concentrations. The experiment was conducted at three different periods in May 2023, in July 2023 and in January 2024 to consider the seasonal effect and it was performed with mussels originating from two different populations to see if the results were repeatable. Surprisingly, whatever the season and whatever the population, no dose-response relationship was found for the biomarkers measured, despite a dose-dependent increase in mortality and tissue concentrations. There was a high inter-individual variability both in control and exposure treatments, making it impossible to decipher a specific response pattern. These results did not enable threshold values to be defined as initially planned, since it seems that each mussel finds its own way of dealing with contaminant-induced stress.

## 1. Introduction

At present, there is no consensus within the scientific community on the definition of a biomarker (McCarty and Munkittrick, 1996; Nordberg et al., 2009; Schlenk, 1999). However, biomarkers are often defined as biochemical or physiological changes that can be observed or measured individually from the molecular to the behavioural scale and that indicate a past or present exposure to environmental pollutants (Lagadic et al., 1997). Although biomarkers are considered to be early warning tools and are complementary to the traditional approaches imposed by the Water Framework Directive (DCE, 2000/60/CE) in the European Union (Martinez-Haro et al., 2015; Milinkovitch et al., 2019; Wernersson et al., 2015), their routine use in national or international freshwater

monitoring programs is very limited (Sanchez and Porcher, 2009). While some authors insist on the usefulness of biomarkers in biomonitoring contexts (Besse et al., 2012; Geffard et al., 2021; Guerlet et al., 2010; Serafim et al., 2011) or for risk assessment of chemical products (Hagger et al., 2008), others emphasise the associated disadvantages (Forbes et al., 2006; McCarty and Munkittrick, 1996). One of the main obstacles is the lack of knowledge about the baseline level and the natural variability of the responses studied, also known as the reference problem (Lagadic, 1998).

Active biomonitoring can partly resolve this problem, as by definition it involves taking organisms from a source population and then caging them at different sites over a determined period (Bervoets et al., 2005; Bourgeault et al., 2010; Catteau et al., 2022; Palais et al., 2012).

<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 authors.</footnote>

<footnote>E-mail addresses: manonbain@gmail.com (M. Bain), simon.devin@univ-lorraine.fr (S. Devin).</footnote>
This approach limits biological variability, as the organisms belong to a
single population originating from the same geographical site, which
implies that the organisms have at least the same contamination history.
In addition, it is possible to calibrate individuals by selecting sex and/or
a specific size class, thereby controlling some of the main confounding
factors (Benito et al., 2019; Geffard et al., 2010; Marchand et al., 2019;
Viarengo et al., 2007). Various studies have attempted to define
threshold values in the context of active biomonitoring by defining the
range of natural variations in biological responses in *Gammarus fossarum*
(Besse et al., 2013; Geffard et al., 2019; Leprètre et al., 2022), *Dreissena*
*polymorpha* (Leprètre et al., 2022), *Gasterosteus aculeatus* (Marchand
et al., 2019), *Hediste diversicolor* (Barrick et al., 2016). These threshold
values are specific to source populations that were used. They cannot be
used for other populations of the same species, as there are
inter-population differences, as previously demonstrated for *Dreissena*
*polymorpha*, for example (Wojtal-Frankiewicz et al., 2017; Pain-Devin
et al., 2014). As a matter of fact, it means that threshold values should be
defined for each source population that may be used for active
biomonitoring.

Despite these efforts to refine the framework for the use of bio-
markers, it is still difficult to determine whether the responses measured
are linked to chemical stressors or fall within the range of natural var-
iations recorded in healthy individuals. The response of biomarkers
being dependent on a large number of biotic and abiotic parameters, it
requires preliminary laboratory studies to calibrate their variation
(Hook et al., 2014; Wu et al., 2005). In addition, the link between
observed effects and exposure is still unclear (Wu et al., 2005). Finally,
the absence of a conceptual framework for interpreting biomarkers has
led to the implementation of comparative approaches to analyze and
exploit biomarker results. Biomarker responses are compared with
controls in laboratory experiments or with a reference population in
field biomonitoring. In this situation, it is supposed that the control or
reference are considered "healthy". In the absence of knowledge
regarding the range of variation in a healthy individual coming from a
given population, it is difficult to define the baseline and the thresholds
indicating a real alteration of certain physiological processes. In this
context, our main aim is to calibrate biomarker responses under
controlled laboratory conditions by establishing dose-response curves to
a model contaminant. Then, each obtained biomarker value can be
related to the state of health of mussels exposed to the model contami-
nant, what should enable comparisons with other laboratory or field
studies. To achieve this goal, the sentinel species *Dreissena polymorpha*
was exposed to nickel in order to identify possible dose-response re-
lationships planned to be used as the reference framework for
interpretation.

*Dreissena polymorpha* is a freshwater mussel often used in active
biomonitoring as an indicator of the presence and/or toxicity of one or
more contaminants in industrial, agricultural and/or urban exposure
contexts (Contardo-Jara et al., 2009; Guerlet et al., 2007; Kraak et al.,
1991; Marie et al., 2006). The use of mussels is facilitated by charac-
teristics such as their sedentary nature, their tolerance to handling and
transplantation, and their high filtration and bioaccumulation rates. In
addition, the influence of some abiotic and biotic factors on the response
of biomarkers in *Dreissena polymorpha* was previously studied: repro-
ductive cycle (Louis et al., 2021), physicochemical parameters of the
water (Wojtal-Frankiewicz et al., 2017), size (Potet, 2017), parasitism
(Minguez et al., 2011), season and spatial variation in responses (Devin
et al., 2023) or population origin (Pain-Devin et al., 2014).

Nickel is an essential metal naturally present in the environment (around 9 % of the earth's crust), but its main contribution to aquatic environments comes from human activities using it in the manufacture of nickel-cadmium batteries, electrical components or stainless steel and alloys for example (Eisler, 1998). The toxic effects of nickel on human health and aquatic organisms are known: it is a pro-oxidant (Stohs and Bagchi, 1995) that leads to the formation of reactive oxygen species resulting in lipid peroxidation (Attig et al., 2014; Potet et al., 2018),

DNA damage and cytotoxic effects (Stohs and Bagchi, 1995), modifi-
cations in fatty acid and lipid content (Fokina, 2023; Fokina et al.,
2020), modification in filtration rate (Stuijfzand et al., 1995; Yancheva,
2017) and it is also a human carcinogen (Denkhaus and Salnikow,
2002). Ni was used here as a model contaminant in order to study
dose-response relationships based on what is already known about its
effects.

This study reports the results of laboratory experiments carried out to
explore the response of biomarkers along an increasing gradient of Ni
concentrations (from 20 to 1200 µL⁻¹). A series of biomarkers involved
in (i) antioxidant and antitoxic defences (glutathione-S-transferase
(GST), superoxide dismutase (SOD), glutathione peroxidase (GPx) and
total antioxidant capacity (TAC)), (ii) energy metabolism (lactate de-
hydrogenase (LDH), electron transport system (ETS) and protein con-
centrations) and (iii) the occurrence of cellular damage (lipid
hydroperoxide (LOOH) and caspase-3 (CSP3)) were measured after 4 or
8 days. Tissue nickel concentrations were also measured after 4 and 8
days of exposure. Experiments were carried out in different seasons
(winter and summer) and by sampling mussels in two different source
populations in order to test effects of these known confounding factors
on biomarker response.

## 2. Material and methods

2.1. Mussel sampling

Organisms were collected in May 2023 and in January 2024 in the
Moselle River, respectively at the entrance of the Canal de Jouy at Jouy-
aux-arches (France, E06°04'43'' N49°03'58'') and upstream of the lock
in Blénod-lès-Pont-à-Mousson (France, E06°04'20'' N48°52'08''). These
two locations are considered to shelter the same population. A second
population was sampled in July 2023 from the Lac du der (France,
E04°45'40'' N48°34'44''). Site water temperature was 15 °C in May and
in July 2023 (summer) and 5 °C in January 2024 (winter). After sam-
pling, mussels were quickly transferred to the laboratory in field water.

## 2.2. Acclimation

Two types of acclimations were implemented during this study: (i) short acclimation corresponding to standard 96h acclimation period at field water temperature, without feeding before the experiment and using the same population (Jouy-aux-Arches) for May 2023 and January 2024 and (ii) long acclimation corresponding to several weeks of laboratory acclimation (SEBIO laboratory facilities with courtesy of A. Gef-fard) designed to allow biomarkers to return to baseline levels, tested on the population of Lac du Der (July 2023). Throughout the acclimation period in May 2023 and January 2024, respectively 150 and 200 mus- sels were placed in 15 L aquaria continuously aerated under natural photoperiod conditions. Aquaria were maintained in water baths allowing constant temperature control. The site water was gradually replaced by spring water (Cristaline®, 1/3 per day; 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).

## 2.3. Exposure treatments

The choice of 4 and 8-day exposure durations to metals in freshwater
mussels is based on several ecotoxicological and methodological con-
siderations. These exposure times are often tested in the literature,
which allows inter-study comparisons of the different parameters
measured such as LT50 or biomarkers (Fokina et al., 2020; Yancheva,
2017; Tsangaris et al., 2008). A 4-day exposure allows the detection of
early biomarker responses following metallic stress, a period during
which cellular defence mechanisms are rapidly activated. This duration
is particularly relevant as we have important data on dreissenid pop-
ulations exposed to nickel for 96h thanks to the work of Potet (2017).
The 8-day exposure represents an intermediate temporal window allowing the evaluation of the persistence or amplification of biological effects observed at 4 days, whilst respecting an important methodolog-ical constraint: without feeding, 8 days appears to be the limit for starvation not to significantly affect biomarker response (Schäfer et al., 2012). This temporal approach is validated by previous studies which have demonstrated that at similar exposure times and concentrations, significant effects on biomarker response and on filtration rate have been evidenced in *Dreissena polymorpha* (Yancheva, 2017) and *Anodonta cygnea* (Fokina et al., 2020).

After acclimation, mussels were exposed during 4 days in May and July 2023 or during 4 and 8 days in January 2024. Each beaker con-taining one mussel was filled with 200 mL of exposure media. For ex-periments carried out in May and in July 2023, there were 6 treatments including control (5 replicates per treatment, i.e., 30 organisms): 0 – 20 – 200 – 400 – 800 – 1200 µg.L⁻¹. For the January 2024 experiment, there were 20 treatments including control (3 replicates per treatment and per exposure duration, i.e., 60 organisms per exposure duration): 0 – 20 – 100 – 200 – 300 – 400 – 450 – 500 – 550 – 600 – 650 – 700 – 750 – 800 – 850 – 900 – 950 – 1000 – 1100 – 1200 µg.L⁻¹. Ni chloride (Cl₂Ni-6H₂O; CAS: 7791-20-0) was purchased from VWR. The concentrations tested were obtained by dilution of a 250 mg.L⁻¹ stock solution acidified to 1 % with nitric acid. During exposure, beaker water was not aerated to limit metal adsorption on glass walls and dispersion outside the beaker. So-lution media were prepared with Cristaline® water and renewed every 24h to maintain a constant concentration of oxygen and metal in the beaker. After 24h, 15 mL of exposure media was taken from each con-dition, for determination of Ni concentrations. For each experiment, mortality was monitored each day and dead organisms were removed from the experiment. The concentrations tested were selected based on previous studies carried out on *Dreissena polymorpha* originating from the same (Potet et al., 2016, 2018) or other populations (Louis et al., 2021; Minguez et al., 2011; Stuijzfand et al., 1995; Yancheva, 2017) as those used in this study. We have selected a wide range of exposure concentrations, from those that are representative of environmental situations to those that are not, but which allow us to study biomarker responses occurring at doses close to lethal.

The biometric parameters (length, width and height of shell, in mm) were checked to ensure that the pool of mussels was homogeneous be-tween concentrations before the experiment was set up and thus to limit the influence of the size on the response of the biomarkers. No signifi-cant differences were found between biometric parameters assessed in the groups constituted for each experiment (p-value > 0.05).

## 2.4. Dissections and condition index

At the end of exposure, the biometric parameters (length, width and height of shell, in mm) were measured a second time and soft tissues were weighed (without byssus, in mg) to calculate an individual con-dition index using the following formula (Guerlet et al., 2010):

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

No significant differences were found between condition indices parameters assessed in the groups constituted for each experiment (p-value > 0.05).

The rest of soft tissue was stored at -80 °C for further measurement of metal tissue concentrations, except for May 2023. A small part of the gonad was removed and frozen at -80 °C to further sex the mussels. The digestive gland of each mussel was dissected, frozen at -80 °C and used for biomarker measurements.

## 2.5. Concentrations in water

Prior to assay, each sample was acidified to 1 % with HNO₃. Ni concentrations were determined by atomic absorption

spectrophotometry in flame (FAAS; Thermofisher ICE 3000) or in oven (GFAAS; Varian SPectrAA 800), depending on the metal concentration. The certified reference material used for FAAS is SPS-WW1 batch n°133 and for GFAAS is SPS-W1 batch n°117. Results are expressed in µg.L⁻¹.

## 2.6. Concentrations in soft tissue

Prior to assay, each sample was lyophilized. Depending on the experiment, different mineralization methods were used: calcination or digestion in 400 µL HNO₃ and 100 µL H₂O₂. Ni concentrations were assayed by FAAS or GFAAS, depending on internal metal concentration. The certified reference material used is TORT-3. Results are expressed in µg.g⁻¹ of dry weight tissue. Concentrations in soft tissues were assessed in July 2023 and January 2024.

## 2.7. Sex determination

Gonads were defrosted and spread on a slide for observation under an unfiltered optic microscope. Sex identification was carried out by recognizing oocytes (or oogonia, depending on the reproductive period) in female and spermatozoa (or spermatogonia) in males.

## 2.8. Biomarker measurements

Biomarker responses were measured using two automated spec-trophometers (Konelab 20-XTi and Gallery plus, ThermoScientific) that enable to work with microvolume. Then, all the biomarker measure-ments can be performed on a single digestive gland. This makes it possible to observe individual variability and to link every measurement carried out on the same individual. The digestive gland was the targeted organ because it is a major site of enzymatic activity, but also of xeno-biotics biotransformation (Livingstone et al., 1992; Viarengo et al., 2007).

Samples were defrosted and crushed in phosphate buffer (50 mM, pH 7.6 supplemented with 1 mM phenylmethylsulfonyl fluoride (PMSF) and 1 mM L-serine borate as protease inhibitors at 8/1 (v/w) ratio) for 30 s at 30 Hz on an electric ball crusher (RETSCH MM400). Homogenates were centrifuged for 5 min at 250 x g at 4 °C. The supernatants recovered constituted the crushed fraction. One part of the crushed fraction was immediately used to measure lipid hydroperoxide concentrations (LOOH), the activity of the electron transport system (ETS) and protein concentrations (protB). The other part was centrifuged a second time at 20 000 x g for 50 min at 4 °C. Then, the recovered supernatants corre-sponding to the cytosolic fraction were used to measure the activity of glutathione-S-transferase (GST), superoxide dismutase (SOD), gluta-thione peroxidase (GPx), lactate dehydrogenase (LDH), caspase-3 (CSP3), the total antioxidant capacity (TAC), and protein concentra-tions (protFC). Proteins were measured on both the crushed and cyto-solic fractions by the Pyrogallol red colorimetric method using sCal (ThermoScientific, Ref981831) prepared from bovine serum as refer-ence. The protocols used to measure biomarker response are detailed by Garaud et al., 2016 except for SOD and CSP3. The SOD activity was assessed using the WST-1 method adapted to *Dreissena polymorpha* and to the spectrophotometer analyzer (Ukeda et al., 1997). The SOD method is based on the assay of formazan, whose production is inversely proportional to SOD activity. A standard range with recombinant human SOD is used to calculate the SOD activity as human SOD equivalent. CSP3 activity was measured by using a CSP3 fluorescent substrate (DEVD-AFC) and a microplate spectrofluorimeter (SAFAS Xenius, Monaco) as detailed in Potet et al., 2018.

## 2.9. Statistical analysis

Statistical analyses were carried out using R studio software (R Core Team (2024), version 4.3.3). First, mortality results were used to esti-mate the LC50 for each experiment using the MOSAIC web interface
(Charles et al., 2018). Linear regressions were built to relate nominal and measured nickel concentrations in exposure media. Dose-response modelling for (i) nickel concentrations in tissues (ii) biomarker values in response to exposure concentrations and (iii) biomarker values in relation to nickel concentrations in tissues were carried out using the DRomics tool on R studio (Larras et al., 2018). To select significant responses to dose, a quadratic test was performed with DRomics, that is used to identify linear or biphasic dose-response relationships.

Then, differences between experimental groups for biomarker responses and nickel concentrations in mussel soft tissues were tested using ANOVA or Kruskal-Wallis depending on normality (Shapiro-Wilk test) and homoscedasticity (Levene test). A threshold of *p* < 0.05 was considered a significant difference. Post hoc tests (Tukey or Wilcoxon tests) were performed when the differences were significant. Differences between control and Ni-exposed groups were tested using Dunnett's test. In order to test for a significant effect of season, population typology or duration of exposure on the whole set of biomarker responses, multivariate analyses of variance (MANOVA) were performed. Datasets from May 2023 and January 2024 were considered to assess seasonal effects: these two experiments use the same population (Moselle River) with strictly identical pre-treatment and exposure protocols, but conducted in different seasons (summer vs. winter) with different exposure temperatures (15 °C vs. 5 °C), thus allowing isolation of the effect of seasonality on biomarker responses. Datasets from May 2023 and January 2024 (Moselle River population) were considered to assess a seasonal effect, datasets from May 2023 and July 2023 (Moselle River and Der populations collected at the same water temperature) were considered to assess for a population effect and dataset from January 2024 was considered to assess for an exposure duration effect. Principal component analyses (PCA) were used to describe inter-population, inter-season, inter-exposure duration and inter-individual variability in biomarker responses.

# 3. Results

## 3.1. Mortality

For the 3 experiments, a dose-dependent relationship was identified: the mortality increased as the concentration of exposure increased (Supp. data A). Even though our experimental procedures were not designed for this purpose, we calculated LC50 values in order to dispose of a comparison tool. In May 2023 the mortality was higher than in the other experiments, resulting in lower LC50 (Table 1). The 4-day LC50 differed according to season and population typology. On the one hand, for the same geographic population but a different season, the 4-day LC50 was lower in summer (May 2023, 15 °C, short acclimation) than in winter (January 2024, 5 °C, short acclimation). On the other hand, the LC50 was different when the season was the same but the population typology was different, as the 4-day LC50 was lower in May 2023 (Moselle, 15 °C, short acclimation) than in July 2023 (Lac du der, 15 °C, long acclimation). Finally, the increase in exposure duration led to a decrease in LC50 values (4 vs. 8 days in January 2024).

<table><caption>**Table 1**
LC50 after 4 or 8 days of exposure (µg.L⁻¹) with associated confidence interval for the three experiments.</caption><thead><tr><th rowspan="2"></th><th>May 2023</th><th>July 2023</th><th colspan="2">January 2024</th></tr><tr><th>4 days</th><th>4 days</th><th>4 days</th><th>8 days</th></tr></thead><tbody><tr><td>LC50 (µg.L<sup>-1</sup>)</td><td>773.9</td><td>1111</td><td>1071</td><td>799.1</td></tr><tr><td>Confidence interval</td><td>557.8 - 1020</td><td>711.2 - 2633</td><td>897.6 - 1840</td><td>685.3 - 969</td></tr></tbody></table>

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

Nickel concentrations measured in exposure media were positively correlated with nominal concentrations (R² between 0.989 and 0.999; p-values < 0.01) (Table 2; supp. data B) and a linear increasing dose-dependent relationship was demonstrated by quadratic trend tests for the 3 experiments (p-value < 0.01). The confidence intervals of the slopes of the linear regressions for each experiment are narrow around the mean and are close to 1 (slope between 0.864 and 0.975) (supp. data B), indicating respectively good reproducibility of the experiment and low loss of nickel by adsorption on the shell or glass of the beaker.

For concentrations measured in the soft tissues, a linear increasing dose-dependent relationship was demonstrated by quadratic trend tests for July 2023 (p-value < 0.01) and January 2024 for both the 4-day (p-value < 0.01) and the 8-day (p-value < 0.01) exposure (supp. data C). Significant differences with the control were observed for 400 and 800 µg.L⁻¹ in July 2023, for 500, 550 and 800 µg.L⁻¹ after a 4-day exposure and for 550, 750 and 850 µg.L⁻¹ after a 8-day exposure in January 2024 (Table 3).

## 3.3. Biomarker responses

Biomarker responses and results of associated statistical tests were detailed in the supp. data D. For all the nickel exposure experiments, all the exposure durations tested and all the biomarkers measured, no statistical differences could be demonstrated. This result is the same even when the response is observed as a function of: (i) the measured exposure concentrations (Fig. 1), (ii) the nominal exposure concentrations and (iii) the nickel concentrations measured in the tissues. No significant differences were observed between the responses of the control organisms and those of the organisms exposed to nickel, or between each Ni exposure treatment. In addition, quadratic trend tests did not give significant results indicating the absence of a dose-response relationship. A high inter-individual variability was observed in both control and

<table><caption>**Table 2**
Nominal concentrations measured after 24h of exposure to nickel for each experiment. Data are expressed as mean ± SD in µg.L⁻¹ (n = 1 to 5).</caption><thead><tr><th rowspan="2">[Ni] nominal<br>(µg.L<sup>-1</sup>)</th><th>May 2023</th><th>July 2023</th><th colspan="2">January 2024</th></tr><tr><th colspan="2">24 hours</th><th colspan="2">24 hours</th></tr><tr><th></th><th>[Ni] measured (µg.L<sup>-1</sup>)</th><th></th><th>[Ni] nominal<br>(µg.L<sup>-1</sup>)</th><th>[Ni] measured<br>(µg.L<sup>-1</sup>)</th></tr></thead><tbody><tr><td>0</td><td>0.7 ± 1.4</td><td>0.33 ±<br>NA</td><td>0</td><td>0 ± 0</td></tr><tr><td>20</td><td>14.5 ± 2</td><td>11.7 ±<br>NA</td><td>20</td><td>14.8 ± 0.9</td></tr><tr><td>200</td><td>206.4 ±<br>11.9</td><td>170.5 ±<br>NA</td><td>100</td><td>125.4 ± 5.2</td></tr><tr><td>400</td><td>379.8 ±<br>30.1</td><td>336.5 ±<br>NA</td><td>200</td><td>240.4 ± 16</td></tr><tr><td>800</td><td>761.8 ±<br>26.9</td><td>674 ± NA</td><td>300</td><td>318.1 ± 13.1</td></tr><tr><td>1200</td><td>1179.2 ±<br>23.6</td><td>1040.5 ±<br>NA</td><td>400</td><td>416.6 ± 12.5</td></tr><tr><td></td><td></td><td></td><td>450</td><td>448.7 ± 32.7</td></tr><tr><td></td><td></td><td></td><td>500</td><td>508.5 ± 9.4</td></tr><tr><td></td><td></td><td></td><td>550</td><td>544.6 ± 10.7</td></tr><tr><td></td><td></td><td></td><td>600</td><td>607.6 ± 19.6</td></tr><tr><td></td><td></td><td></td><td>650</td><td>670.4 ± 47.9</td></tr><tr><td></td><td></td><td></td><td>700</td><td>700.5 ± 10</td></tr><tr><td></td><td></td><td></td><td>750</td><td>740.5 ± 14.6</td></tr><tr><td></td><td></td><td></td><td>800</td><td>785.8 ± 23.3</td></tr><tr><td></td><td></td><td></td><td>850</td><td>842 ± 18.9</td></tr><tr><td></td><td></td><td></td><td>900</td><td>881.6 ± 69.4</td></tr><tr><td></td><td></td><td></td><td>950</td><td>930.2 ± 18.6</td></tr><tr><td></td><td></td><td></td><td>1000</td><td>998.5 ± 40.5</td></tr><tr><td></td><td></td><td></td><td>1100</td><td>1039.7 ± 16</td></tr><tr><td></td><td></td><td></td><td>1200</td><td>1098 ± 69.4</td></tr></tbody></table>
<caption>**Table 3**</caption>

Tissue concentrations measured in the rest of the mussel soft tissues after 4 or 8 days of exposure to nickel for each exposure treatment and each experiment. Data are expressed as mean ± SD in µg.g dry weight⁻¹ (n = 1 to 5). In conditions where mortality is 100%, the values are replaced by NA and in conditions where n is <3 the SD is replaced by NA. Differences from the control are indicated by an asterisk (*) and NS mean no difference between the experimental conditions. The absence of * or NS indicates the absence of statistical testing (n < 3).

<table><thead><tr><th rowspan="2">[Ni] in water (µg.L<sup>-1</sup>)</th><th colspan="1">July 2023</th><th colspan="2">January 2024</th></tr><tr><th>4 days<br>Ni in soft mussel tissues (µg/g dw<sup>-1</sup>)</th><th>[Ni] in water (µg.L<sup>-1</sup>)</th><th>4 days<br>Ni in soft mussel tissues (µg/g dw<sup>-1</sup>)</th></tr></thead><tbody><tr><td>0</td><td>2.1 ± 0.3<sup>NS</sup></td><td>0</td><td>1.6 ± 0.2<sup>NS</sup></td><td>2 ± 0.2<sup>NS</sup></td></tr><tr><td>20</td><td>3.4 ± 0.8<sup>NS</sup></td><td>20</td><td>2.1 ± 0.4<sup>NS</sup></td><td>3.3 ± 0.4<sup>NS</sup></td></tr><tr><td>200</td><td>8.4 ± 7.3<sup>NS</sup></td><td>100</td><td>4.6 ± 0.4<sup>NS</sup></td><td>5.5 ± 1.5<sup>NS</sup></td></tr><tr><td>400</td><td>13.2 ± 7<sup>*</sup></td><td>200</td><td>9.2 ± 4.6<sup>NS</sup></td><td>7.1 ± 1.3<sup>NS</sup></td></tr><tr><td>800</td><td>25.4 ± 1.8<sup>*</sup></td><td>300</td><td>7.4 ± 2.3<sup>NS</sup></td><td>9.8 ± 5.2<sup>NS</sup></td></tr><tr><td>1200</td><td>26.9 ± NA</td><td>400</td><td>9.4 ± 2.3<sup>NS</sup></td><td>13.2 ± 2<sup>NS</sup></td></tr><tr><td></td><td></td><td>450</td><td>11.7 ± 3<sup>NS</sup></td><td>18.5 ± 9.9<sup>NS</sup></td></tr><tr><td></td><td></td><td>500</td><td>21.6 ± 10.7<sup>*</sup></td><td>13.7 ± 0.4<sup>NS</sup></td></tr><tr><td></td><td></td><td>550</td><td>15.2 ± 7.3<sup>*</sup></td><td>30.1 ± 17<sup>*</sup></td></tr><tr><td></td><td></td><td>600</td><td>13.5 ± 3.2<sup>NS</sup></td><td>32.8 ± NA</td></tr><tr><td></td><td></td><td>650</td><td>10 ± NA</td><td>26.2 ± NA</td></tr><tr><td></td><td></td><td>700</td><td>12.4 ± NA</td><td>29.6 ± NA</td></tr><tr><td></td><td></td><td>750</td><td>17.2 ± 5<sup>NS</sup></td><td>26 ± 4.9<sup>*</sup></td></tr><tr><td></td><td></td><td>800</td><td>17.4 ± 9.5<sup>*</sup></td><td>NA</td></tr><tr><td></td><td></td><td>850</td><td>17.9 ± NA</td><td>28.9 ± 22.3<sup>*</sup></td></tr><tr><td></td><td></td><td>900</td><td>39.7 ± NA</td><td>50.8 ± NA</td></tr><tr><td></td><td></td><td>950</td><td>18.7 ± 2.6</td><td>18.6 ± NA</td></tr><tr><td></td><td></td><td>1000</td><td>33.2 ± NA</td><td>NA</td></tr><tr><td></td><td></td><td>1100</td><td>28.1 ± NA</td><td>NA</td></tr><tr><td></td><td></td><td>1200</td><td>25.7 ± NA</td><td>31.2 ± NA</td></tr></tbody></table>

exposed organisms (Fig. 1). There was no clear effect of exposure on biomarker responses.

### 3.3.1. Effect of population typology

The effect of the population typology was tested by comparing May 2023 (15 °C, Moselle, short acclimation) and July 2023 (15 °C, Lac du Der, long acclimation). The MANOVA indicated an effect of population typology on biomarker response ($p = 1.561^{-11}$, df = 1, $df_{res} = 27$, F = 49.793).

A PCA was carried out using all the data from May 2023 and July 2023 (Fig. 2). The first axis was positively influenced by LDH, TAC, CSP3 and negatively by LOOH concentrations and ETS. The second axis was positively influenced by GPx, GST and SOD and negatively by LOOH concentrations and CSP3. The population typology explained 62.2 % of the overall data variability. The two populations were separated by the first axis. July 2023 was characterised on the one hand by higher values of antioxidant and anti-toxic defence (GST, SOD, GPx) and anaerobic metabolism (LDH) than May 2023 and on the other, by lower cell damage (LOOH, CSP3) than May 2023.

### 3.3.2. Effect of season

The effect of the season was tested by comparing the response of the same geographic population but sampled at two different seasons: summer (May 2023, 15 °C, Moselle, short acclimation) and winter (January 2024, 5 °C, Moselle, short acclimation). The MANOVA indicated an effect of season on biomarker response ($p = 6.963^{-10}$, df = 1, $df_{res} = 26$, F = 36.03)

A PCA carried out on data from May 2023 and January 2024 revealed a different pattern of responses between winter and summer (Fig. 3). The first axis was positively influenced by TAC and CSP3 and negatively by GST. The second axis was positively influenced by SOD and GPx, and negatively by LOOH concentrations, ETS and LDH. The season explained 61.8 % of the overall data variability. Summer (May 2023) showed higher LOOH concentrations, anaerobic metabolism (LDH), non-enzymatic antioxidant capacity (TAC) and mitochondrial activity (ETS) compared to winter (January 2024). Winter showed higher antioxidant activity (SOD, GPx) than summer.

### 3.3.3. Effect of exposure duration

The effect of exposure duration was tested by comparing 4 and 8-days exposure during the experiment conducted in January 2024. The MANOVA results ($p = 0.12$, df = 1, $df_{res} = 72$, F = 1.70) showed that there was no effect of exposure duration on biomarker responses, supported by the PCA unable to reveal specific response patterns (Fig. 4).

## 4. Discussion

The main finding of this study is the absence of a dose-response relationship between biomarkers and nickel exposure despite its significant dose-dependent accumulation in mussels soft tissues. Although somewhat surprising, the present results are robust because they were obtained independently in several experiments involving different populations, at different times of the year (and therefore of the reproductive cycle), and according to experimental protocols allowing the testing of up to 20 doses. The absence of significant differences among biomarker responses, which we interpret here as an absence of a dose-response relationship, is considered to be a constant dose-response in the meta-analysis of Colas & Le Faucheur published in 2024. This analysis included 156 articles reporting results on (i) biomarkers of defence and/or toxic effects, (ii) whole organisms, (iii) laboratory experiments, (iv) the toxicity of (in-)organic contaminants and (v) exposure to at least 4 doses, including controls. From these 156 articles, 3176 observations on biomarker responses were extracted, of which 411 (i.e., 13 %) were classified as constant dose-response relationships. The results obtained in our study fall into this category. In the Colas & Le Faucheur analysis, it was also noted that of the 156 articles selected, only 12 % presented experimental designs with a number of doses of between 7 and 15, but never more. However, our approach of testing up to 20 doses of exposure, which is actually quite rare, did not lead to better detection of significant differences in biomarker responses in *Dreissena polymorpha*. In the previously cited meta-analysis, only 3 articles out of the 156 selected corresponded to dose-response exposures to NiCl₂ or Ni, and of these 3 articles, 2 showed either constant dose-response relationships (no significant differences between the concentrations tested) or responses for which it was not possible to define trends (neither biphasic nor monotonic) (Akpiri et al., 2017; Wang and Wang, 2010). Two other studies carried out by Potet et al. (2018 and 2016) did not observe any differences in biomarker responses when *Dreissena polymorpha* were exposed to 0–50–500 µg.L⁻¹ NiCl₂ for 96h and/or 192h. Other studies have highlighted a point difference in some of the parameters measured for a given concentration and/or exposure time (Fokina et al., 2020; Minguez et al., 2011; Tsangaris et al., 2008).

This experimental strategy (20 doses) was aimed at finely characterising dose-response relationships and precisely identifying biomarker effect thresholds. However, despite this fine resolution in terms of
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Biomarker responses after 4 days or 8 days of exposure to nickel. Biomarker responses were plotted as a function of measured nickel concentration. 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>
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** PCA representing inter-population variability in responses to biomarkers. F1F2 factorial design based on the table of individuals x biomarker responses (50×8) from the May 2023 and July 2023 experiments.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** PCA representing inter-season variability in biomarker responses. F1F2 factorial design based on the table of individuals x biomarker responses (40×8) from the May 2023 and January 2024 experiments.</figcaption></figure>

concentration gradient, no effect could be detected, raising the question of the effectiveness of this approach. 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. This is an essential consideration for future experimental designs on dose-response, where we will seek to optimise the balance between statistical power, methodological constraints and ethical considerations.

The lack of dose-response relationship in biomarker responses is surprising regarding the toxic effects of Ni reported in numerous reviews

(Le Saux et al., 2020; Stohs and Bagchi, 1995) or studies. Nickel is known to cause overproduction of reactive oxygen species (ROS), which can lead to (i) damage to the mitochondrial respiratory chain, which may lead to an abnormal increase in apoptosis by affecting cytochrome c release in mitochondria (Genchi et al., 2020); (ii) increased lipid peroxidation, leading to cell damage (Attig et al., 2010; Das et al., 2020; Minguez et al., 2011; Yancheva, 2017); (iii) destabilization of lysosomal membranes (Yancheva, 2017); (iv) a decrease in antioxidant defences such as SOD, GPx or CAT caused by an overflow of these defences (Das et al., 2020; Stohs and Bagchi, 1995); (v) overexpression of stress
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** PCA representing inter-exposure time variability in biomarker responses. F1F2 factorial design based on the table of individuals x biomarker responses (92×8) from the January 2024 experiment.</figcaption></figure>

proteins (MTs, HSP) (Attig et al., 2010, 2014) and (vi) disruption of energy demand (Tsangaris et al., 2008).

The absence of biomarker responses may reflect specific cellular mechanisms of nickel detoxification that bypass classical oxidative stress pathways. In mussels, metal tolerance is also based on non-enzymatic processes that control intracellular metal concentration. On one hand, Shi & Wang (2004) demonstrated that metallic storage patterns vary between species and metals, it is therefore possible that nickel is preferentially sequestered in metabolically inactive compartments (granules, lysosomes) without triggering oxidative stress responses. On the other hand, other cellular ligands such as MTs (Attig et al., 2010 and 2014) or vitamin C (Das et al., 2020) may explain this absence of oxidative stress response by maintaining cellular homeostasis without requiring the activation of biomarkers. In our study, the non-enzymatic capacity of an organism to neutralize free radicals (TAC) was measured, but no significant variation was observed for this biomarker.

Our results also suggest a disconnection between nickel accumulation and cellular toxicity in *Dreissena polymorpha* that is difficult to explain. Several hypotheses can be advanced: (i) nickel does not preferentially bind to cellular components that could interfere with the enzymatic functions measured by our biomarkers or (ii) below a certain cellular concentration, nickel can be efficiently managed without detectable stress responses and above this threshold, cellular systems are overwhelmed leading directly to mortality without intermediate activation of biomarkers.

The contradiction between the observation of individual deaths and the absence of cellular damage could result from systemic physiological dysfunction rather than generalised cellular damage detectable by the subcellular biomarkers measured in our study. Firstly, nickel could disrupt osmoregulatory and respiratory functions by inhibiting Na⁺/K⁺-ATPase activity in the gill epithelia or by interfering with ion channels, leading to hydroelectrolytic imbalance and filtration disruption without massive cellular damage (Pane et al., 2003; Stuijfzand et al., 1995). Secondly, nickel can specifically target mitochondrial function by directly inhibiting respiratory complexes I and III through substitution of essential metals (iron, copper) in enzyme active sites, leading to decreased ATP production and energy deficiency without triggering classic oxidative stress responses (Genchi et al., 2020; Bénit et al., 2019).

Thirdly, nickel could alter membrane integrity and fluidity by directly interacting with phospholipids or membrane proteins, disrupting cellular transport and signalling without inducing lipid peroxidation (Fokina et al., 2020, 2023). These hypotheses suggest that future investigations should focus on histopathological examination of gill tissues, lipidomic analyses to assess changes in membrane composition, mitochondrial function assays (respiratory complex activities, ATP production), and physiological measurements of osmoregulatory capacity to better understand the mechanistic basis of nickel toxicity in this species. Such approaches would provide insights into why *Dreissena polymorpha* exhibits an apparent “all-or-nothing” response to nickel exposure, where individuals maintain cellular homeostasis until critical thresholds are exceeded, leading to rapid mortality.

The role of antioxidant defence systems is to deal with ROS. However, our results did not reveal any significant reactions from these systems. Similarly for freshwater bivalves, only a few studies have shown that antioxidant defence systems respond to during nickel exposure (Potet et al., 2018; Attig et al., 2010). Most of the time, only physiological measurements displayed significant responses, such as the filtration rate, which increases with nickel concentrations (Stuijfzand et al., 1995; Yancheva, 2017), or the scope for growth, which decreases with the concentration (Tsangaris et al., 2008).

In our study, the lack of a dose-response relationship brings us back to the criticisms that have raised concerning the use of biomarkers (Forbes et al., 2006; Lam and Gray, 2003; Schlenk, 1999; Vasseur and Cossu-Leguille, 2003). Getting false negatives is one of the risks, as our work shows that exposed organisms did not respond differently from controls. Previously cited authors explained this false negative as (i) levels of contaminants insufficient to induce responses or provoke effects, or (ii) defences and/or repair mechanisms sufficient to compensate for damages. These hypothesis do not explain our results because we clearly observed a dose-dependent increase of nickel concentrations in tissue and in mortality, indicating a real exposure and a real toxic effect of nickel.

Information on mortality is often lacking in the literature, which makes it difficult to compare our results with other studies. Of 15 articles dealing with laboratory exposure of freshwater or marine macro-invertebrates to nickel, only 5 reported mortality: $LT50 = 193.6h \pm 85.2$
during the exposure of *Dreissena polymorpha* to NiCl₂ at a concentration of 2500 µg.L⁻¹ at 12 °C (Potet et al., 2018); no mortality when *Dreissena polymorpha* was exposed to NiCl₂ at 12 °C for 72h at the following concentrations 0, 250, 500, 750 et 1000 µg.L⁻¹ (Yancheva, 2017); no mortality when *Dreissena polymorpha* was exposed to NiCl₂ at 12 and 17 °C for 96h at the following concentrations : 0, 20 et 500 µg.L⁻¹ (Potet et al., 2016); LC50 48h = 1633 ± 27.8 µg.L⁻¹ when *Daphnia magna* was exposed to Ni at 20 °C (Traudt et al., 2016) and LC50 48h = 1068 µg.L⁻¹ when daphnids neonates were exposed to NiSO₄ at 20.5 °C (Pane et al., 2003). The LC50 in our study were similar or lower than in the above studies, with the LC50 measured in January 2024 (LC50 4 days = 1071 µ.L⁻¹) tended to be higher than in May 2023 (LC50 4 days = 773.9 µ.L⁻¹) indicating a seasonal effect. Thus, the LC50 differences can be explained by a different energetic allocation according to the reproductive status: in winter (January 2024; 5 °C), organisms are in reproductive rest and more energy can potentially be allocated to defence, preventing mussels from toxic effects and reducing the risk of mortality, whereas in summer (May 2023; 15 °C) metabolic demand increases with rising temperatures and the transition to the pre-spawning and spawning phases, leading to an energy allocation in favour of reproduction rather than defence, and then reducing the survival (Devin et al., 2023; Faria et al., 2014; Potet, 2017).

When comparing the values for Ni tissue concentrations obtained in this study with those obtained on *Dreissena polymorpha* or *Anodonta cygnea* during exposure to nickel at similar durations and concentrations, we find that they are either close (Potet et al., 2016; Fokina, 2023) or lower (Potet et al., 2018; Stuijfzand et al., 1995). Although we found that mussels accumulated nickel in their tissues in a dose-dependent manner, no responses indicating activation of anti-toxic or anti-oxidant defences, or responses indicating potential cellular damage were observed. However, the subcellular biomarkers we have used are the intermediate level of investigation between internal concentration and mortality. On the one hand, mortality is an apical and integrative response, but it is too late and it cannot tell us what the individual died of. On the other hand, internal contaminant concentrations tell us about accumulation and therefore about internal exposure but it gives no information on the potential resulting effects, or on the life expectancy of exposed organisms.

Subcellular biomarkers are considered to be good tools for establishing the link between internal exposure and the toxic response at the organism level. Indeed, they can reveal the activation of detoxification pathways or also their potential exceeding then leading to toxicity mechanisms (Lomartire et al., 2021). Several studies have shown a positive correlation between measurements of contaminants (Ni, Cu, TBT) in the tissues or organs of freshwater (*Corbicula fluminea*, *Dreissena polymorpha* et *Dreissena r. bugensis*) or marine (*Mytilus galloprovincialis*) bivalves and subcellular responses such as GST, CAT, MDA (Attig et al., 2010), MTs (Attig et al., 2010; Marie et al., 2006). In our study, it appears that the mussels showed an "all-or-nothing" type response, as if there existed an accumulation threshold below which individuals are capable of managing damage, and above which all processes are saturated and the mussels die. According to this hypothesis, the expected relationship between tissue concentration and biomarkers would be an "all-or-nothing" response pattern rather than a gradual dose-response relationship: biomarker values are stable below the critical bio-accumulation threshold, then death occurs directly when the threshold is exceeded, without an intermediate phase of biomarker activation. However, we were unable to test this hypothesis for several methodological reasons. On one hand, to measure biomarker levels measured in organisms that are already dead are not relevant and we do not have tools to predict the life expectancy of an organism sacrificed to measure biomarkers. On the other hand, biomarker measurement methods are destructive (organism sacrifice), which makes impossible the temporal monitoring of physiological parameters of the same individual during exposure. However, testing the accumulation threshold hypothesis would require continuous physiological monitoring of biomarkers in

individual organisms during exposure, allowing detection of the exact moment of crossing the hypothetical threshold. Our current experimental design, based on sampling at fixed times (4 and 8 days), does not allow observation of this dynamic process. These methodological constraints - survival bias and destructive nature of analyses - fundamentally complicate interpretation and limit our capacity to test the threshold hypothesis. Although our analysis of individual tissue concentrations versus biomarker responses did not reveal clear patterns, this probably reflects these limitations rather than invalidation of the hypothesis itself.

Finally, concerning the effect of the population typology observed at the same season (15 °C in May 2023, Moselle River, short acclimation and 15 °C in July 2023, Lac du Der, long acclimation), our results showed that the Lac du Der population exhibited high levels of defences and low levels of cell damages, and showed the opposite for the Moselle population. It should be noted that in addition to having higher defence levels than the Moselle population, the Der population also had a higher LC50, both information leading to conclude that mussels from the Der population are more tolerant than mussels from Moselle population. As they have been sampled from two distinct geographical locations and have received two different pre-exposure treatments, the question of the specific origin of the difference cannot be answered. Whatever the origin of this difference, it concerns the constitutive level of biomarkers that may differ from one type of population to the other, especially because of distinct life history (including contaminant exposure history) (Lomartire et al., 2021; Wojtal-Frankiewicz et al., 2017; Bartell, 2006). However, even if their constitutive levels are different, neither population was able to show a significant dose-response relationship between biomarker responses and exposure concentrations.

The lack of response of subcellular biomarkers that we observed raises a number of questions, in particular that of the adaptation time of the biological response. Adaptation time is defined as the time required for a maximally induced biological response to return to background levels during continuous exposure to chemical stress (Wu et al., 2005). Shouldn't this adaptation time be considered shorter than 4 days? This question has not yet been clearly answered and need to be explored in greater depth to better understand the response of biomarkers. The inter-individual variability observed in both exposed and unexposed organisms may also explain the absence of a dose-response relationship despite very contrasting nickel exposure conditions. Despite this, it gives relevant information about the physiology of each individual but also about the differences in tolerance/sensitivity that can be observed within the same population. Trying to control the variability of biological responses by calibrating organisms (size, sex, etc.) can lead to the selection of a sub-sample within a natural population and therefore not fully represent the responses in a population (Barrick et al., 2016). Moreover, as Devin et al. (2014) explained, inter-individual variability should be considered as a demographic response in itself without seeking to neglect individuals described as "outliers", since this proportion of individuals may also form the basis of a future population rebuilt after a stress episode.

## 5. Conclusion

The aim of our study was to establish a dose-response relationship in order to determine threshold values for interpreting the state of health of organisms. Our results did not allow us to achieve this objective conclusively but reinforced the idea that the interpretation of biomarkers remains a major challenge today. The main results of this study are: (i) the absence of a dose-response relationship for subcellular biomarkers despite proven toxic effects (dose-dependent mortality) and dose-dependent tissue accumulation of nickel, (ii) a seasonal effect, (iii) no effect of exposure time (0, 4 and 8 days) and (iv) a population typology effect related to geographical location and/or different pretreatment. The results obtained in this study are relative to the tested populations and are therefore not generalised to all populations of
dreissenids. Although the study of the quantitative relationship between exposure and effects (dose-response) has been recommended for many years to improve the understanding of the modulations of biological responses, very few studies have addressed this issue. To improve our understanding of biological responses and also our ability to perform meta-analysis, we recommend to provide at least information about exposure conditions (contaminant concentrations and temperature in exposure media) and about exposed organisms (biometric data, mortality, tissue concentrations of contaminants). In addition, it would be interesting to add other measurements on an intermediate scale, in particular by using more integrative biomarkers such as filtration rate. Lastly, inter-individual variability is an obstacle that we often try to reduce and which does not make our results any easier to understand, but is nonetheless important.

### CRediT authorship contribution statement

**Manon Bain:** Writing – review & editing, Writing – original draft, Software, Methodology, Formal analysis, Conceptualization.
**Sandrine Pain-Devin:** Writing – review & editing, Validation, Supervision, Project administration, Investigation, Funding acquisition, Conceptualization.
**Bénédicte Sohm:** Methodology, Validation, Writing – review & editing.
**Chloë De Vernisy:** Investigation, Writing – review & editing.
**Simon Devin:** Writing – review & editing, Validation, Supervision, Project administration, Investigation, Funding acquisition, Formal analysis, 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 terrain-instrumentation, ANATELO, LIEC laboratory, UMR 7360 CNRS – Université de Lorraine and the LiecOscope, LIEC laboratory, UMR 7360 CNRS – Université de Lorraine. The authors would like to thank Alain GEFFARD (UMR-I 02 SEBIO INERIS-URCA-ULH, Reims) for his kind help in providing mussels from SEBIO facilities for the experiment carried out in July 2023 and Amandine ZAHM for her valuable help in mussel sampling. We would also like to thank Maximilien BEURET and Valérie GONCALVES for their valuable help in metal analyses. And finally, we thank Marie ZAFFINO, Claire CAILLARD, Nawal ROCHDI and Anne-Fleur WOLFF for their valuable help in biomarker analyses.

### Fundings

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

### Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.aquatox.2025.107517.

### Data availability

Data will be made available on request.

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