# Long-term exposure of mice to sodium fluoride and to a mixture of endocrine disruptors causes tooth enamel and retina abnormalities associated with changes in hepatic metabolism

Emilie Picard, Fatima Domenica Elisa de Palma, Sophia Loiodice, Vincent Carbonnier, Seiki Achiedo, Noor Mimoun, Diluxe Mutale, Sophie Doublier, Sylvère Durand, Mélanie Bourgin, et al.

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## To cite this version:

Emilie Picard, Fatima Domenica Elisa de Palma, Sophia Loiodice, Vincent Carbonnier, Seiki Achiedo, et al.. Long-term exposure of mice to sodium fluoride and to a mixture of endocrine disruptors causes tooth enamel and retina abnormalities associated with changes in hepatic metabolism. Environment International, inPress, pp.109861. 10.1016/j.envint.2025.109861. hal-05325164

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1 Long-term exposure of mice to sodium fluoride and to a mixture of endocrine disruptors

2 causes tooth enamel and retina abnormalities associated with changes in hepatic

3 **metabolism**

4 Emilie Picard¹*, Fatima Domenica Elisa De Palma²⁻⁴*, Sophia Loiodice⁵&, Vincent

5 Carbonnier²,³&, Seiki Achiedo¹, Noor Mimoun⁵, Diluxe Mutale⁶, Sophie Doublier⁵,⁷, Sylvère

6 Durand²,³, Mélanie Bourgin²,³, Fanny Aprahamian²,³, Pierre Cordier⁸, Louise Gutter¹, Katia

7 Jedeon⁶, Marie-Christine Naud¹, Philippe Noirez⁹,¹⁰,¹¹, Jean-Marc Ricort⁵,⁶,¹², Lotfi

8 Slimani⁶, Guido Kroemer²,³,⁷, Francine Behar-Cohen¹,¹³, Maria Chiara Maiuri²⁻⁴#, Sylvie

9 Babajko⁵,⁶#

10

11 ¹Team « Physiopathology of ocular diseases: therapeutic innovations », Centre de Recherche des Cordeliers, UMRS1138, Institut National de la Santé Et Recherche Médicale (INSERM), Université Paris Cité, Sorbonne Université, Paris, France.

12 ²Team « Metabolism, Cancer & Immunity », Équipe labellisée par la Ligue contre le cancer, Centre de Recherche des Cordeliers, UMRS1138, INSERM, Université Paris Cité, Sorbonne Université, Paris, France.

13 ³Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Villejuif, France.

14 ⁴Department of Molecular Medicine and Medical Biotechnologies, University of Napoli Federico II, Napoli, Italy.

15 ⁵Team « Molecular Oral Physiopathology », Centre de Recherche des Cordeliers, UMRS1138, INSERM, Université Paris Cité, Sorbonne Université, Paris, France.

16 ⁶Team « Oro-facial Pathophysiology and Regeneration », UMR 1333 Oral Health, Université Paris Cité, INSERM, Université Sorbonne Paris Nord, Montrouge, France.

17 ⁷Institut du Cancer Paris CARPEM, Hôpital Européen Georges Pompidou, Assistance Publique Hôpitaux de Paris (AP-HP), Paris, France.

18 ⁸Oniris, LabOniris, École Nationale Vétérinaire, Agroalimentaire et de l'Alimentation Nantes-Atlantique, Nantes, France.

19 ⁹Laboratoire Performance, Santé, Métrologie, Société (PSMS), Université de Reims Champagne-Ardenne, Reims, France.

20 ¹⁰Département des Sciences de l'activité physique, Université du Québec à Montréal, Canada.

21 ¹¹School of Sport Science, University of Jordan, Amman, Jordan.
32 ¹²Ecole Normale Supérieure Paris-Saclay, Université Paris-Saclay, Gif-sur-Yvette, France.

33 ¹³Cochin Hospital, Assistance Publique Hôpitaux de Paris (AP-HP), Paris, France.

34

35 * Equal contribution

36 & Equal contribution

37 # Co-last authors

38 ° Corresponding author: Sylvie Babajko, UMR 1333 Oral Health, Université Paris Cité, 1 rue

39 Maurice Arnoux, 92120 Montrouge, France - sylvie.babajko@inserm.fr

40
41 **ABSTRACT**

42 Exposure to environmental pollutants may contribute to various health disorders, often
43 manifesting after a prolonged period of latency. The identification of early markers is, therefore,
44 of paramount importance.

45 To achieve this objective, realistic models of environmental exposure were set up. Whereas
46 most studies consider one substance or mixture with one target organ, here, in addition to the
47 liver, two easily accessible sensitive organs, the eyes and teeth, were analyzed as good potential
48 candidates to be original markers of environmental exposures.

49 C57BL/6J mice were exposed to either sodium fluoride or a mixture of 15 endocrine-disrupting
50 substances, or both, in their drinking water from the time of conception until 1 or 10 months
51 after birth. The doses of these substances were chosen to be close to the tolerable daily intake.
52 All mice in test groups presented lower weight gain than controls with the most significant
53 differences observed in females 30 days after birth. Alterations in the eye retina were detected
54 as early as 1 month of age and considerably worsened by 10 months. Dental defects were
55 observed in all test groups at the last time point. At that age, metabolic disruptions were
56 identified through molecular metabolomic and RNAseq analyses, while histological analyses
57 showed no significant defects in liver.

58 This study shows that chronic exposure to widespread toxicants resulted in hepatic metabolic
59 disorders. However, the most evident histological and clinical signs were observed in eyes and
60 teeth. These easily accessible stigmata may be used as early markers of low-dose exposure to
61 environmental toxicants.

62

63 **1. INTRODUCTION**

64 **1.1 Chemicals with endocrine-disrupting activities in daily environment**
The exposome is currently the focus of much interest, due to its relation to a number of significant issues, including human health, climate change, biodiversity and, more generally, the preservation of environmental quality (Maitre et al., 2022). The environment is continually evolving with considerable changes in recent decades that have had an impact on health and well-being of many living organisms (Kress and Stine, 2017). It is imperative to develop markers of exposure to environmental toxicants in view of facilitating early diagnosis in individuals susceptible to developing pathologies related to the exposome (Braun and Sears, 2021). Despite the absence of precise data regarding the total quantity of chemicals, it is estimated that hundreds of novel chemicals are introduced annually, resulting in an average 300-fold increase in the number and quantity of chemicals released into the environment over the past 50 years (European Environment Agency, 2023; Steinhäuser and Große Ophoff, 2025). A host of studies have documented significant contamination of human populations and wildlife by multiple anthropogenic chemicals (European Environment Agency, 2013). Among thousands of active chemicals present in active and passive environment, some are also categorized as endocrine-disruptors (EDs). According to the World Health Organization's definition (2012), EDs are chemical compounds that can interfere with the endocrine system and cause deleterious health effects to organisms or even their descendants. Of note, many EDs exhibit non-monotonic dose responses which complicate the understanding of their mechanisms of action (Vandenberg et al., 2023). Relations between exposure to EDs and an increased prevalence of chronic metabolic and cardio-vascular diseases, neurodevelopmental delays, cancers and behavior and fertility troubles, have been reported (Caporale et al., 2022; Del Río Barrera et al., 2025). The EDs exposures in the United States are associated with over 90,000 annual deaths, alongside pronounced health disparities (Trasande and Sargis, 2024). On average, more than 30 anthropogenic chemicals are detectable in American women, 15 among which are ubiquitous, including in pregnant women (Woodruff et al., 2011; Fini et al., 2017).
A 2024 cohort study further indicates that prenatal exposure to a complex mixture of EDs is associated with poorer metabolic health in childhood, potentially elevating future risk of metabolic syndrome (Güil-Oumraitet al, 2024). Although certain xenobiotics have been investigated for their individual actions on specific endocrine axes, few studies have addressed their combined, or ‘cocktail’, effects. This lack of experimental data is striking given mounting evidence that combinations of substances that individually have no adverse effect but can produce significant effects when tested as a mixture (Axelstad et al., 2014; Delfosse et al., 2015; Caporale et al., 2022).

## 1.2 Health effects of EDs

Most of xenobiotics are metabolized by the liver and eliminated by the kidneys justifying the multiple experimental and observational data reported on these two detoxifying organs whose disruptions cause metabolic disorders (Le Magueresse-Battistoni, 2021; Bernal et al., 2022; Lința et al., 2024). However, most, if not all, cells can be targeted by environmental toxicants leading to functional disorders and chronic pathologies with age. Thus, developmental enamel defects including dental fluorosis and molar incisor hypomineralization (MIH), that result from chronic exposures to environmental contaminants present in fetal and early postnatal life (when teeth are developing), are public health concerns because of their high prevalence (Lopes et al., 2021; Kühnisch and Fresen, 2024). Dental fluorosis is caused by an excessive intake of fluoride, naturally present in groundwaters used for drinking and cooking in some regions, or added in tap water and dental care products to prevent caries (Rasool et al., 2018; Saad et al., 2022). Despite a less clear MIH etiology (Lopes et al., 2021), increasing prevalence and severity reported in recent studies (Irigoyen-Camacho et al., 2021) underlie the contribution of environmental conditions (Babajko et al., 2025), including new life-habits, infections and environmental toxicants such as EDs (Jedeon et al., 2013; Bui et al., 2022).
The eye and more specifically the retina are considered to be an extension of the central nervous system (CNS), but much more accessible, like the teeth with which they share some common developmental pathological features (Gong et al., 2009). Chemicals and toxicants are known to affect the visual system (Fox, 2015), and occupational or environmental exposure to EDs may increase the risk of retinal alterations, and even retinal diseases (Li M et al., 2021; Thompson et al., 2022). The retina is particularly sensitive and vulnerable to prenatal (and especially embryonic) chemical exposure which can result in immediate or delayed effects (Thompson et al., 2022). However, there is still little literature on the effects of chronic exposure to low-doses of EDs as well as their cocktail effects on ocular health disruption.

## 1.3 Sodium fluoride (NaF) as a potential ED

NaF is widely present in our daily environment in dental care products to prevent dental decay (Saad et al., 2022), in some drinking waters, in tea and in some food naturally containing fluoride. Experimental studies demonstrate NaF may be neurotoxic (Niu et al., 2018) raising concerns about its widespread use. Moreover, it has recently been proposed for classification as an endocrine disruptor and reproductive toxicant under the EU REACH regulation (European Chemicals Agency, 2022), reflecting growing regulatory concern about its potential to interfere with hormonal systems (Skórka-Majewicz et al., 2020). In fact, several studies report that NaF disrupts endocrine function across multiple biological systems: it alters thyroid hormone levels, impairs steroidogenesis, and adversely affects metabolic endpoints such as glucose tolerance (García-Montalvo et al., 2009; Orta Yilmaz et al., 2018; Skórka-Majewicz et al., 2020; Li W et al. 2023; Talebi et al., 2025). The common endocrine targets of NaF and other EDs raise the question of their combined effects on neuronal cells such as those of the retina through a common ED action or not, as it is the case in ameloblasts (Jedeon et al., 2016; Le et al., 2017).

## 1.4 Aim of the study
Whereas observational studies are unable to demonstrate the effect of a single xenobiotic on human populations, and most of experimental studies report the action of a single suspected toxicant on a single organ, the present study aimed to report the combined long-term chronic effects of NaF with a mixture of 15 chemicals with ED activity (Table S1), mimicking realistic environmental conditions, on two easily accessible oro-facial organs, dental enamel and retina, as well as on the liver as the main target of xenobiotics. Based on previous data showing developmental enamel and retina defects in rodents exposed to EDs (Jedeon et al., 2016; Bui et al., 2022; Picard et al., 2022), we aimed here to characterize these defects that may be used as non-invasive markers of exposure to environmental toxicants with detailed untargeted omics profiling of hepatic gene expression as the reference organ.

148

## 2. MATERIALS AND METHODS

### 2.1 Chemicals and mixtures

The following chemicals (purity >98%) were purchased from Sigma-Aldrich (Saint-Quentin Fallavier, France): bisphenol-A (BPA), triclosan (TCS), benzophenone-3 (BP3), sodium perchlorate (NaClO4), 4-4'-dichlorodiphenyldichloroethylen (4,4'-DDE), hexa-chlorobenzene (HCB), dibutylphtalate (DBP), diethylhexylphtalate (DEHP), 2,2',4,4',5,5'-PCB, 2,2',4,4',5,5'-hexachlorobiphenyl (PCB-153), 2-naphtol (2-NAPL), perfluorooctanesulfonic acid (PFOS), perfluorooctanoic acid (PFOA), methylmercury chloride (CH3ClHg), lead chloride (PbCl2), sodium fluoride (NaF), dimethyl sulfoxide (DMSO), acetone, acetonitrile, isopropanol, methanol, chloroform, acetic acid, formic acid, dibutylamine acetate concentrate (DBAA), methoxyamine hydrochloride, N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA), pyridine. Decabromodiphenyl ether (BDE-209) was purchased from LGC Standards (Dr. Ehrenstorfer, Molsheim, France). All chemicals were dissolved at 10⁻¹ M in DMSO, with the exception of HCB (10⁻² M in acetone), BDE-209 (10⁻³ M in DMSO) and NaClO₄ (10⁻¹ M in
163 Milli-Q water) and NaF (from Sigma-Aldrich (Saint-Quentin Fallavier, France) (0.5 M in Milli-Q water). These solutions were aliquoted and stored at -80 °C until use with exception of NaF
164 that is prepared freshly before the use. The chemical mixture (BPA, TCS, BP-3, 4-4'-DDE,
165 HCB, NaClO₄, DEHP, DBP, BDE-209, PCB-153, 2-NAPL, PFOS, PFOA, CH3ClHg, PbCl2),
166 named in this study as "ED mix", was prepared at 10,000-fold concentration by mixing
167 appropriate volumes of stocks as described by Fini and co-authors (2017), aliquoted and stored
168 at -80 °C. The ED mixture was diluted in drinking water and used to adjust the level of exposure
169 to substances to the tolerable dose intake (TDI) of BPA and DEHP (established for humans by
170 the EFSA) based on previously published proportions (Fini et al 2017) (Table 1).
171

172 NaF was diluted in drinking water at 1 mM (equivalent to 20 ppm fluoride), an exposure level
173 2- to 5-times lower than the generally reported dose causing dental fluorosis in mice (Houari et
174 al., 2019; Kao et al., 2021). Given that rodents need to ingest much higher doses of fluoride in
175 the drinking water (10-20 times) as compared to humans (Mullenix et al., 1995), the
176 concentrations used here are lower than those measured in some areas of the USA (11.2 to 15.9
177 mg/L) (National Research Council, 2006). The United States Environmental Protection Agency
178 (US EPA, 1987) fixed the NOAEL for NaF at 0.06 mg/kg/day and EFSA proposed a close
179 Tolerable Upper Intake Level at 0.1 mg/kg/day for children (EFSA, 2006).
180

180

## 2.2 Animal housing, treatments and sample collections

182 Studies were performed in accordance with the French and European legal requirements
183 (2010/63/UE) and were approved by the local Ethical Committee (#24913-
184 2020032317149116v3). Mice were housed in specific pathogen-free conditions, in nest-
185 enriched polysulfone cages, maintained at 22°C / 50% humidity and under a 12:12 h light-dark
186 cycle, fed a standard diet (A03-10; Safe-diets), adhering to the ARRIVE guidelines, as
187 previously described (Bui et al., 2022; Houari et al., 2019). The drinking water was obtained
from tap water treated by the Avoidy Science system (Edstrom, Waterford, USA) through a
sequential treatment of 2 filtrations (1 micron sediment filter and activated carbon filter) and a
reverse osmosis pump passage. The 6-week-old female and male WT C57BL/6J mice were
obtained from Envigo France (Gannat, France) with a total mice required for the study pre-
determined based on previous studies (Jedeon et al., 2013; Bui et al., 2022). A total of four
groups of randomly distributed mice were set up: control group (no exposure to the substances,
exposure limited to 0.01% DMSO, used as a solvent), ED group (exposure to a mixture of 15
EDs; Table 1), 1 mM NaF group (20 ppm), and the ED mix + 1 mM NaF group (named ED
NaF). All the animals were orally exposed via drinking water 15 days before the mating,
females were exposed during the gestation and lactation (sacrificed after weaning of pups), and
the pups were exposed until the sacrifice at 30 days for developmental and early events and 10
months after birth for long-term effects and aging (details in the Supplementary file). No
remarkable difference in water intake was observed between groups. Weight of all animals was
measured throughout the experiment at least once a week. Fat and lean mass as well as the free
body fluid were measured by magnetic resonance imaging (Bruker) at 3/4- and 6/7-months after
birth. At the end of the treatment, animals were euthanized using cervical dislocation and
immediately processed for the collection of biological samples (Figure 1A). All clinical and
histological analyses were independently reproduced by at least two investigators and double-
blinded analyzed.

207

208 **2.3 Micro X-ray computed tomography (µCT) for enamel analysis**

209 Hemi-mandibles were fixed in 4% paraformaldehyde (PFA) for further structural analyses. All
210 incisors were independently examined by two investigators (SL & DM). Incisors were imaged
211 by µCT (Quantum FX Caliper, Life Sciences, Perkin Elmer, Waltham, MA). The acquisition
212 parameters were a 10×10 mm field of view, 20 µm voxel size, 90 kV, 160 µA, 360° rotation,
and 3 min exposure time for the X-ray source. Then, 360° projections were reconstructed using
RigakuSW (Caliper) software into a 512×512×512 voxel matrix. Stacks of images were
oriented for choosing similar ROIs across samples, distinguishing secretory and maturation
stages based on anatomical landmarks 38, processed using DataViewer software (Skyscan,
release 1.5.2.4, Kontich, Belgium), and finally analyzed using the CT-Analyser software
(Skyscan, release 1.13.5.1, Kontich, Belgium). Image densities were scaled in Hounsfield Units
(HU) during acquisition and converted to mineral density (mgHA.cm⁻³) during post-processing.
Quantitative analysis focused on three 500-µm-thick ROIs located under each molar, Molar 1
(M1), M2 and M3. A threshold was applied to separate enamel from other tissues and mineral
density calculated for each ROI.

223

2.4 Eye Analysis

2.4.1 Spectral-domain optical coherence tomography (SD-OCT)

For SD-OCT, mice were anesthetized using intraperitoneal injection of 15:5 mix of 10%
ketamine (Virbac®, Carros, France) and 2% xylazine (Bayer, Lyon, Fr) (100 µL/10 g), pupils
were dilated with one drop of tropicamide (Mydriaticum® 2 mg/0.4 ml, Théa, Clermont-
Ferrand, France) and vertical B-scans of the retinas of both eyes were acquired longitudinally
at 3- and 9-months, at the optic nerve and at the superior and inferior poles of the eye, using a
spectral domain OCT (SD-OCT) (Micron 3, Phoenix Research Labs, Pleasanton, CA, USA) at
3- and 9-month-old on the same mice. Neural retina thicknesses were measured at 500 and
1,000 µm from the optic nerve at both poles using Fiji software, and averaging to give a single
value per eye.

2.4.2 Histology and outer nuclear layer thickness measurement

Oriented ocular globes were collected and prepared as described in the Supplemental Materials.

Eyes sections (10µm at the optic nerve head) were observed on a Leitz Aristoplan microscope
238

and photographed with a Leica camera. Entire eye pictures were obtained by merging pictures taken at 10X, and neural retina thicknesses were measured using Fiji across the whole retina, considering the inferior pole to 0 from -1,800µm of optic nerve and superior pole to 0 from 1,800 µm of optic nerve.

239

240

241

242

The thickness of the retinal layers was measured on 25X pictures photographed every 340 µm using ImageJ software by delimiting each layer with a wand tracing tool and modulating tolerance as function as all the concerned layer was delimited. The area of the delimited layer was reported to the length giving a mean thickness for the retinal section. Three pictures were measured and average to give a single value per pole.

243

244

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246

247

## 2.5 Metabolomic analysis

248

249

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254

255

Collected tissues were weighted (30 mg) and prepared as described in the Supplemental Materials. Then, as described previously (Viltard et al., 2019) and in the Supplemental Materials, we performed a widely-targeted analysis of intracellular metabolites by gas chromatography (GC) coupled to a triple quadrupole (QQQ) mass spectrometer (TCA Cycle), untargeted analysis of nucleotides and cofactors by ion pairing ultra-high performance liquid chromatography (UHPLC) coupled to a Triple Quadrupole (QQQ) mass spectrometer and pseudo-targeted analysis of intracellular metabolites by UHPLC coupled to a Q-Exactive mass spectrometer (Reversed phase acetonitrile method).

256

## 2.6 Total RNA extraction and RNA-sequencing (RNAseq)

257

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261

262

Total RNA was isolated from livers using a silica membrane following manufacturer's instructions (RNeasy Mini Kit, Qiagen, Courtaboeuf Les Ulis, France). RNA purity was assessed using the NanodropTM spectrophotometer and RNA integrity was measured using the Agilent 2100 Bioanalyzer System. One microgram of total RNA, quantified using a Qubit fluorometer (Thermo Fisher Scientific, Illkirch, France) was used for mRNA sequencing that has been performed by iGenSeq core facility (ICM, Paris, France).
263

## 2.7 Statistics

264

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268

All analyses were realized by R (V4.3 (https://www.R-project.org/)) with tidyverse (10.21105/joss.01686), a R package. Overall, plots were generated with ggplot2 (https://ggplot2.tidyverse.org). Volcano plots were generated using EnhancedVolcano (10.18129/B9.bioc.EnhancedVolcano) as described on the corresponding website (Blighe et al., 2025).

269

### 2.7.1 RNAseq analysis

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281

RNAseq raw data were aligned on the murine genome, mm10, with HISAT2. Sam files that we obtain were counted by HTSeq-count. Count files were analyzed with DESeq2 R package (V4.3). Differential expression analysis was performed using the DESeq2 R package. Genes with a p-value ≤ 0.05 found by DESeq2 and an absolute log2 fold change (|log2(FC)|) ≥ log2(1.5) were considered as significantly differentially expressed (DE). Our threshold is based on the non-adjusted p-value due to existence of really sensitive comparisons in which we would like capture a broader range of potential differentially expressed genes. DE genes were used for generating Venn diagram plots. Enrichment analysis, including Gene Ontology (GO), KEGG (Kyoto Encyclopedia of Genes and Genomes) and REACTOME, was performed on DE genes (|log2(FC)| ≥ log2(1.5), p-value ≤ 0.05, and baseMean > 10) using gprofiler2, enrichplot and Clusterprofiler R package. Most enriched (p-value < 0.05) pathways were visualized as barplots using GraphPad (v10). All RNAseq data are available at NCBI GEO portal (ID: GSE289561).

282

### 2.7.2 Mouse weight

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287

Mouse weights were monitored during experimentation and compared within the following age groups: 0-30, 31-60, 61-110, 0-110, 111-220, 221-320 days. All treated groups were compared, in any age groups, by a Kruskall-Wallis test and for all tests with a p value score below the threshold of 0.05. We pursue the analysis with a Dunn's test, from the R package dunn.test (https://CRAN.R-project.org/package=dunn.test), with Bonferroni's correction.
288 **2.7.3 Metabolomic analysis**

289 In order to identify relevant metabolites, area means of groups were tested with Tuckey's multi
290 comparison test. To compare metabolite changes between two conditions, volcano plots
291 associate log2 fold change (based on log2 area mean) with -log10 p-values (Mann-Whitney
292 test). We selected metabolites with significant p value (volcano plot realized with
293 EnhancedVolcano - 10.18129/B9.bioc.EnhancedVolcano). We used met4j
294 (https://github.com/MetExplore/met4j) for find the shortest metabolic pathways in the graph
295 iMM1865 (https://doi.org/10.1038/s41598-020-63235-w). These subgraphs are represented in
296 the MetExplore visualisation tool (https://doi.org/10.1093/nar/gky301).

297 **2.7.4 Eyes**

298 For paired neural retina thickness on SD-OCT pictures, Kruskall-Wallis test was performed and
299 pursued by a paired pairwise Wilcoxon-Mann-Whitney test.

300 For eye analysis, the Kruskall-Wallis test was performed and pursued by a Dunn's test with
301 Benjamini-Hochberg correction were performed when groups were compared together.

302 For area under the curve analysis, One-way ANOVA tests was performed and pursued by a
303 Dunnett's test when compared to control group, or by Sidak's tests when exposed groups were
304 compared together.

305 Normal distribution of data was confirmed using the Shapiro-Wilk test. p value ≤ 0.05 was
306 considered statistically significant.

307 **2.7.5 Teeth**

308 Data were analyzed by Anova test, all significant tests with a p value threshold of 0.05, was
309 followed by a post-hoc Tukey's HSD test.

310

311 **3. RESULTS**

312 **3.1 Effects of chronic administration of NaF and/or ED mixture (mix) on mice**
The present study was designed to investigate the long-term effects of chronic exposures to NaF and to a low-dose ED mixture (Table 1, Table S1), alone or in combination, on male and female mice by reproducing realistic environmental conditions. Males and females were exposed to these treatments (NaF, ED, ED NaF) two weeks before mating, and throughout the gestation and lactation for females. After weaning, the pups were directly exposed to the same doses of substances for 10 months (Figure 1A). During this treatment period, the body weight was monitored (Figure 1B-I and Supplementary Figure 1A-F). Overall, a decrease in body weight was observed in all test groups in both adult males and females compared to controls, particularly from day 0 to postnatal day (PND) 110 (Figure 1C, 1G, and Supplementary Figure 1B, 1C, 1E, and 1F). Further stratification by age revealed that the highest differences between groups were recorded in females from PND 31 to 110 (Supplementary Figure 1A-C). Of note, there was no significant difference in the weight of the pups up to PND 30 (Supplementary Figure 1A and 1D), indicating no general toxicity of treatments during fetal and early post-natal life. From PND 30 onward, the difference in body weight gain in NaF groups compared to controls remained significant throughout life both in males and females. However, when NaF was combined with the ED mix (ED NaF groups), there was no significant difference anymore compared to controls after PND 111, neither in males nor in females (Figure 1D-E and 1H-I). In females (PND 111 to PND 320), a significant difference in weight was observed between the group exposed to NaF alone and the group exposed to the ED NaF combination, suggesting that the ED mix influenced weight gain in NaF-exposed females or mitigated the effects of NaF. These observations were supported by the absence of a significant weight difference between animals exposed to the ED mix alone and those exposed to the ED NaF combination over the same period. The monitoring of fat and lean relative mass by MRI in 3-4 to 6-7-months-old mice showed a significant increase in fat mass of males exposed to the ED mix alone or in combination with NaF compared to NaF alone (Supplementary Figure 1M and 1P). Conversely,
338 in 6-7-month-old mice, the lean mass was significantly lower in males exposed to ED mix
339 alone or in combination with NaF compared to NaF alone (Supplementary Figure 1Q). Despite
340 a similar tendency, there was no significant difference in fat/lean/fluid mass in females due to
341 high data dispersion (Supplementary Figure 1G-L).

342
343 **3.2 Effects of chronic exposures to the ED mixture alone and in combination with NaF on**
344 **teeth**

345 Macroscopic clinical analysis of the incisors of 10-month-old female mice showed differences
346 in color (Figure 2A and 2B) and erupted length (Figure 2C) in the different groups. Compared
347 to controls, erupted incisors of mice exposed to 1 mM NaF were opaque and whiter, which is a
348 sign of dental fluorosis (Figure 2A), as well as shorter (Figure 2C). Similarly, incisors of ED
349 NaF treated-mice were whiter than those of mice exposed to ED mix alone. Conversely, incisors
350 of animals exposed to ED mix were darker than those of controls and those of ED NaF group
351 showing a dark line absent in all mice exposed to NaF alone (Figure 2A and 2B). Incisors of 1-
352 month-old mice from any group were free of defects when clinically observed. Quantification
353 by µCT of enamel mineral density and volume of the growing incisor and molars showed a
354 decrease in mineral density in incisors of mice exposed to NaF alone or in combination with
355 ED mix corresponding to moderate dental fluorosis (Figure 2D-G). There was no significant
356 difference in enamel density nor enamel volume between the groups exposed to the EDs and
357 the controls. When analyzing molars, we did not detect any difference between groups in either
358 enamel volume or in mineral density suggesting no developmental effects. A detailed
359 qualitative analysis of each tooth showed less white enamel in the growing incisor with areas
360 of enamel hypomineralization corresponding to larger alterations in the ED NaF group than
361 NaF alone (Figure 2D), also observed in the frontal sections under the second root of the first
362 molar (Figure 2E). Overall, these data revealed major dental defects when female mice were
363 exposed to NaF alone or in combination with the ED mixture. However, chronic exposure to
364 the low-dose ED mixture alone was capable of generating macroscopic defects in enamel color
365 as well.

366
367 **3.3 Structural analysis of eyes of mice exposed to the ED mixture and NaF, alone or in combination**
368

369 *In vivo* longitudinal imaging of the retina was performed on 3- and 9-month-old mice using an
370 SD-OCT, which allows to follow retinal morphology in a non-invasive manner. Gross
371 anomalies of the retina were not observed in any of the groups at either age (Figure 3A). To go
372 further, the thickness of the neural retina, the light-sensitive layer, was measured on vertical B-
373 scan pictures taken at the optic nerve level from the superior to the inferior poles of the eye
374 (Figure 3B). A slight, but non-significant, decrease in neural retina thickness was observed
375 between 3 and 9 months in control and NaF groups (1.94% for control and 2.56% for NaF),
376 which is consistent with the age-related decrease in the retinal thickness observed in C57BL/6
377 mice (Ferdous et al., 2021). In the ED group, we observed a highly significant decrease in neural
378 retina thickness with aging (Figure 3B, p=0.00391). Mice exposed to ED NaF failed to show
379 an additional age-related decrease in neural retinal thickness (Figure 3B). Statistical analysis
380 by group of age shows a significant decrease in neural retinal thickness at 3 months only for
381 mice exposed to ED NaF compared to control group, and a significant decrease in neural retinal
382 thickness at 9 months only for mice exposed to ED compared to control and ED NaF groups
383 (Supplementary Figure 2A).

384 To provide a detailed overview of the morphological changes, resin sections were performed at
385 the end of eye maturation, at 1 month of age (PND 30) and at the advanced age of 10 months
386 (Figure 3C). No major changes of eye histology were observed in the 4 groups. The thicknesses
387 of the neural retina and of the complex formed by the retinal pigmented epithelium (RPE),
388 choroid and sclera were quantified on sections passing through the optic nerve (Figure 3D-E).
389 At 1 month of age, the neural retina thickness was the lowest in the ED NaF group (Figure 3D
and Supplementary Figure 2B). At 10 months of age, the neural retina thickness in the NaF and ED groups was significantly thinner than in the control and ED NaF groups (Figure 3D and Supplementary Figure 2B). The thickness of RPE/choroid/sclera complex was not different between groups at 1 month, whereas it was significantly thinner in all test groups than in the control group at 10 months (Figure 3E and Supplementary Figure 2C). Evaluation of the axial length, using A-scan ultrasonography, in 9-month-old-mice did not show any difference between the 4 groups (Supplementary Figure 2D-E).

To thoroughly examine the histological alterations, each retinal layer was analyzed using high magnification images obtained from eye resin sections from 1- and 10-month-old mice (Figure 4). At 1 month, the inner nuclear layer (INL) and inner plexiform layer (IPL), corresponding to the second level of light message transmission, exhibited significantly increased thickness in the NaF mice compared with the control group. The layers formed by retinal cell nuclei, the outer nuclear layer (ONL) and the inner nuclear layer (INL), were thicker in the NaF group than in the ED and ED NaF groups (Figure 4A and 4C). At 10 months, a substantial reduction in thickness was observed across the majority of layers in the NaF and ED groups when compared to the control group (Figure 4B and 4D). The ED NaF groups exhibited a moderate reduction in thickness. The thicknesses of the choroid and the RPE were significantly decreased in the NaF group compared with the controls, while the ONL was thinner in all groups. The INL presented a significant decrease in thickness in mice exposed to NaF or ED mix compared to controls and ED NaF group. The outer and inner plexiform layers (OPL and IPL), the synaptic layers, did not show any significant differences in the three test groups of exposed mice compared to control mice. Overall, these data indicate that perinatal exposure to a mixture of toxicants results in a moderate alteration of retinal histology whereas chronic exposure has been observed to affect all layers of the retina.
415

**3.4 Impact of NaF and ED mixture chronic administration on mouse metabolism**

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The liver is the main organ responsible for detoxification of naturally occurring endogenous toxins, as well as exogenous environmental chemicals. Currently, hepatotoxicity of long-term chronic exposure to low-dose mixtures of environmental toxicants is under investigated in female rodents (Le Magueresse-Battistoni, 2021). In this context, we examined the effect(s) of a chronic administration of widespread toxicants on the livers collected from the same 10-month-old female mice used in the aforementioned tooth and eye analyses. Histological analysis of the livers did not show any specific morphological differences among the treatments; tissues mostly showed incidental and/or age-related changes (Supplementary Figure 3A-D). Subsequently, to obtain an unbiased metabolic profile of the different treatment groups, an untargeted UHPLC-Orbitrap metabolomic analysis was performed on the liver extracts. We identified a total of 254 metabolites whose levels were altered upon chronic exposure to NaF, ED, and ED NaF compared to the controls (heatmap in Figure 5A). In a general overview, the pattern of metabolites observed in mice chronically exposed to NaF, ED or ED NaF was very different from the controls and among the treatments (Figure 5B-D, Supplementary Figure 3E-G). Notably, exposure to NaF upregulated oxidative and inflammatory stress-related metabolites, including anthranilic acid and phosphatidylcholine acyl-alkyl ethers (PCae) (Figure 5B). Chronic exposure to the ED mix altered liver energetic metabolism by increasing the levels of the central energy metabolites nicotinamide adenine dinucleotide phosphate (NADPH) which is also relevant to the pentose phosphate pathway (Fuentes-Lemus et al., 2023) and acetyl-CoA which is also a major inhibitor of autophagy (Pietrocola et al., 2015; Sciarretta et al. 2021) (Figure 5C). In the ED NaF group, the metabolic signature of the ED mix persisted, with higher levels of NADPH and acetyl-CoA than in the controls, and additional alterations appeared (Figure 5D). Thus, chronic exposure to NaF ED increased bioactive lipid metabolites
439 associated with inflammation and oxidative stress, including 8-hydroxyeicosatetraenoic acid
440 (8-HETE), malonyl-CoA and glyceraldehyde-3-phosphate (G3P) (Figure 5D).

441 Using a metabolic database and pathway analysis tool (MetExploreViz) (Cottret et al., 2018),
442 the individual metabolites were assigned to the corresponding metabolic pathways and the most
443 strongly affected signaling pathways were identified (Supplementary Figures 4-6). This
444 analysis revealed distinct metabolite clusters, that were either unique or overlapped between
445 the groups. Notably, three main metabolic pathways were modulated across the 3 conditions:
446 fatty acid oxidation, glycolysis/gluconeogenesis, and the pentose phosphate pathway
447 (Supplementary Figures 4-6). This indicates that the treatments cause liver dysfunction.
448 Moreover, hippurate metabolism was impacted by both the ED NaF and ED treatments
449 (Supplementary Figures 5 and 6). Recent evidence suggests that intrahepatic levels of
450 microbiome-derived hippurate correlate with improvements of steatotic liver disease (Deslande
451 et al., 2025). Focusing on the individual metabolic signatures, nine singular pathways out of the
452 17 emerged in the NaF condition vs control, such as beta-alanine metabolism, cytochrome
453 metabolism, and glutathione metabolism (Supplementary Figure 4). Methionine/cysteine
454 metabolism and purine catabolism appeared to be exclusively altered when comparing ED
455 treatment to control group (Supplementary Figures 5). Additionally, the metabolism of arginine
456 and proline, together with fructose and mannose, was specifically perturbed only in the ED NaF
457 compared to the control group (Supplementary Figures 6). Indeed, altered fructose metabolism
458 in the liver is implicated in many metabolic diseases (Febbraio and Karin, 2021). Overall, these
459 results suggest that chronic exposure to the mixture of low-dose toxicants altered the
460 metabolism within the liver through the modulation of several metabolites, mainly involved in
461 pentose phosphate pathway, as well as amino acid and lipid metabolism.

462

463 **3.5 Liver transcriptomic profile of mice exposed to an ED mixture and/or NaF**
We next investigated potential hepatic transcriptomic changes caused by chronic exposure to ED mix and/or NaF. To this aim, we performed an RNA-sequencing on the livers of the same female mice than those used for the metabolomic experiments (Supplementary Tables S2-S4 and Figure 6). We identified a total of 559, 817, and 654 genes differentially expressed (|log2FC| ≥ 0.58, p < 0.05) between control and NaF, ED, ED NaF groups, respectively (Supplementary Tables S2-S4, and Figure 6A-C). More precisely, out of the 559 genes identified in the NaF group, 98 were highly (|log2FC| ≥ 1.5, p < 0.05) upregulated and 89 downregulated (Supplementary Table S2). Among the 817 genes differentially expressed in the ED group, 146 were highly (|log2FC| ≥ 1.5, p < 0.05) upregulated and 97 downregulated (Supplementary Table S3). Concerning the ED NaF group, out of 654 differentially expressed genes, 48% showed a significantly (log2FC ≥ -1.5, p < 0.05) decreased level of expression (Supplementary Table S4). Remarkably, among the top 10 downregulated genes in the group exposed to the ED NaF, many detoxifying enzymes emerged, including *Glutathione S-transferase A4* (*Gsta4*, log2FC = -3.5, p = 0.001), *2-hydroxy acid oxidase-2* (*Hao2*, log2FC = -3.2, p = 0.005), and the *Cytochrome P3A57* (*Cyp3a57*, log2FC = -3.1, p = 0.01) (Supplementary Table S4). Looking at the most significantly upregulated genes in ED NaF group, we identified many genes involved in development and cancer, such as such as the gene *Homeobox B6* (*Hoxb6*), and *Zinc Finger and BTB Domain Containing 14* (*Zbtb14*) (Supplementary Tables S3 and S4). To obtain more insights into the biological processes and pathways associated with exposures to ED mix and/or NaF in the liver, we performed gene ontology (GO) annotation, Kyoto Encyclopedia of Gene and Genomes (KEGG) and REACTOME enrichment analyses on sets of genes showing a significant different expression (|log2FC| ≥ 0.585, p-value < 0.05) in the different comparisons (Supplementary Table S4-S10 and Figure 6D-F). Interestingly, when comparing NaF and control group, we only found genes enriched in two pathways, including “metabolic pathway” and “hepatocellular carcinoma”
489 (Figure 6D, Supplementary Table S5). ED and ED NaF exposures revealed overlapping
490 pathways, such as “metabolic pathways”, “metabolism of proteins”, “ribosome”, and
491 “complement/coagulation cascade” (Figure 6E and 6F, Supplementary Tables S7-S10).
492 Additionally, the most highly repressed genes in ED NaF group were assigned to “cholesterol
493 metabolism”, “immune system” and “cellular response to stress” (Supplementary Table S10).
494 Finally, we investigated the unique transcriptomic signature of exposure to the ED mix and NaF
495 in the liver. Hence, we looked for overlapping genes (|log2FC| ≥ 1.5, baseMean ≥ 10, p < 0.05)
496 between the 3 test groups and identified 45 common genes, 21 being decreased and 23 increased
497 (Figure 6G, Supplementary Table S11). Many of them have been already described as
498 particularly (de)regulated in liver diseases. Among the most differentially expressed genes,
499 *Actb*, *Atp6v1h*, *Hspa5* and *Smarcb1* were decreased, whereas *Igf2r*, *Lyrm4* and *Shld1* were
500 increased (Supplementary Tables S7-S10). Functional annotation analyses conducted on the 45
501 genes, recognized 2 GO terms in the molecular function category, including “unfolded protein
502 binding” and “Tat protein binding” (Figure 6H, Supplementary Table S11). In addition, 2
503 KEGG and 4 REACTOME pathways were also identified (Figure 6H, Supplementary Table
504 S10). Particularly, a cluster of 3 genes (i.e., *ribosomal protein S5* (*Rps5*), 24 (*Rps24*), and 26
505 (*Rps26*)) were shared among the 4 enriched REACTOME pathways (Figure 6H, Supplementary
506 Table S12).

507 Overall, we conclude that the long-term chronic exposure to NaF, ED mix and ED NaF
508 (de)regulate the expression of genes, including some coding for detoxifying enzymes,
509 associated with metabolic pathways in the liver.
510

## 4. DISCUSSION

512 To date, most published studies characterizing health effects due to long-term exposure to low-
513 dose environmental chemicals have focused on a single substance and a single organ. In our
514

study, four realistic environmental conditions were generated to evaluate the long-term effects
of a mixture of 15 chemicals with known ED activity (Fini et al., 2017), combined or not with
NaF, all worldwide prevalent environmental toxicants.

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Fluoride ion has been reported for its properties to prevent caries due to its extra- and
intracellular actions on dental tissues. Recently, exposure to low-dose NaF has also been
associated with lower scores in cognitive function tests in children exposed during early infancy
 irr 520
es 521
controversies within the scientific community (Guichon et al., 2024). This has triggered intense
debates and controversies within the scientific community (Guichon et al., 2024).

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Here, we report that mice exposed to NaF from fetal life to adulthood had a lower weight gain
than controls recorded from PND30 with the difference in body weight gain that remained
significant throughout life in males and females. Interestingly, ED mixture has slight but
significant effects on weight in young adults only (PND31 to PND110). However, from
PND111, the weight of mice exposed to the NaF combined with the ED mix was not
significantly different from that of controls. Based on previous studies reporting NaF and ED
effects in the liver, we investigated hepatic molecular signatures in females with weight gain
effects that were more pronounced than in males.

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Despite the absence of specific histological lesions, the liver exhibited significant perturbations
at both the metabolic and transcriptomic levels across all treatment groups. NaF and/or ED mix
affected oxidative stress-related and energy metabolism in the liver by altering the levels of
several metabolites (e.g., anthranilic acid and 2-oxoglutaric acid for NaF, and acetyl-CoA and
NADPH levels for ED mix). This indicates a liver stress response to chronic low-dose exposure.

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It has been demonstrated that fluoride inhibits glycolysis via glycolytic enzymes (e.g., enolase)
(Qin et al., 2006; Mikesh and Bruns, 2008; Strunecka and Strunecky, 2020). This inhibition
blocks the conversion of intermediate sugars downstream in glycolysis, leading to the

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539 accumulation of glycolytic intermediates, including xylulose-5-phosphate, which may be
540 redirected into the polyol pathway. NaF further alters carbohydrate and energy metabolism,
541 promoting alternative routes such as the pentose phosphate and polyol pathways.

542 Elevated NADPH levels alter the NADP/NADPH ratio, affecting cellular redox balance and
543 energy metabolism (Xiao et al., 2018). Acetyl-CoA, a known autophagy inhibitor (Pietrocola
544 et al., 2015), may impair this hepatoprotective mechanism (Motiño et al., 2022) under ED mix
545 treatment, increasing cytotoxicity. In the ED NaF group, alteration of bioactive lipids (i.e., 8-
546 HETE, malonyl-CoA, G3P), which are associated with inflammation and oxidative stress,
547 suggests a metabolic shift to lipogenesis in the liver. These findings hint to additional effects
548 of ED and NaF in modulating liver metabolism, and also in amplifying the risk of metabolic
549 dysfunction and hepatic toxicity. In addition, *in silico* pathway analysis performed on the
550 significantly deregulated metabolites across the 3 different groups, identified critical metabolic
551 pathways, including amino acid and lipid metabolism, fatty acid oxidation,
552 glycolysis/gluconeogenesis, and the pentose phosphate pathway, supporting the notion that
553 chronic exposure to toxicants alters liver metabolic homeostasis.

554 Transcriptomic analysis of liver tissues revealed unique and shared gene signatures between
555 the treatment groups. NaF exposure induced *Wee1* overexpression and *Eif3m* downregulation,
556 genes respectively implicated in tumorigenesis (Ghelli Luserna di Rorà et al., 2020) and liver
557 homeostasis, with *Eif3m* ablation being lethal in adult mice (Zeng et al., 2013). The ED mix
558 decreased *Rad50*, essential for DNA repair and genomic stability (Kissling et al., 2022), and
559 increased *Aldh1l2*, a regulator of CoA-dependent metabolism (Krupenko et al., 2020),
560 corroborating the metabolomic data. Combined ED NaF exposure further downregulated
561 detoxification-related genes (e.g., *Gsta4*, *Cyp3a57*, *Hao2*), whose loss is associated with
562 oxidative stress and hepatocellular damage (Park et al., 2019; Dwivedi et al., 2006; Shearn et
563 al., 2015; Mattu et al., 2016; Campone et al., 2009; Burk and Wojnowski, 2004). Notably, this
group also upregulated oncogenic and fibrogenic markers (*Hoxb6*, *Zbtb14*) (Li and Jiang, 2020; Chen et al., 2023).

In line with metabolomics results, pathway enrichment analysis identified the “metabolic pathways” as significantly enriched in the 3 treatment groups. Notably, the ED NaF group exhibited unique pathways including "cholesterol metabolism," "immune system" and "cellular response to stress," reflecting a multifaceted disruption of liver homeostasis.

Comparative analysis identified 45 overlapping genes as a shared transcriptomic signature across all treatment groups. Upregulated genes (*Igf2r*, *Lyrm4*, *Shld1*) were associated with tumorigenesis, whereas downregulated genes (*Hspa5*, *Actb*, *Atp6v1h*, *Smarcb1*) included *Hspa5*, a key regulator of protein folding, whose reduction may promote ER stress. Several genes (e.g. *Fmo3*, *Acot12*) linked to liver disease were consistently upregulated (Rudraiah et al., 2014). Moreover, evidence indicated *Acot12* as a key regulator of acetyl-CoA metabolism in hepatocellular carcinoma (Lu et al., 2019). This is in accordance with our metabolomic data, demonstrating that acetyl-CoA increased after treatment with ED alone or in combination with NaF. In addition, functional annotations of 45 genes highlighted enrichment for “unfolded protein binding” and ribosomal genes (*Rps5*, *Rps24*, *Rps26*), with *Rps24* and *Rps26* implicated in hepatocellular carcinoma, energy metabolism, and stress responses (Li et al., 2023; Havkin-Solomon et al., 2023).

The observed downregulation of detoxifying enzymes and the enrichment of pathways related to protein metabolism, oxidative stress, and immune response suggest that a chronic exposure to low-dose toxicants compromises liver detoxification and homeostatic processes. These results are consistent with previous studies, although NaF may exert variegated metabolic effects depending on the dose and the duration of exposure (Pereira et al., 2018). The involvement of pathways related to metabolic disorders and carcinogenesis further supports the
588 notion that such exposures significantly increase the risk of metabolic liver dysfunction despite
589 the absence of obvious morphological or histological liver defects.

590 Extrahepatic effects, in teeth and eyes, were clinically observable in 10-month-old mice and
591 detected as early as one month in some cases.

592 The enamel of the incisors of C57BL/6 mice treated with NaF presented moderate fluorosis, in
593 accordance with previous studies (DenBesten and Li, 2011; Everett, 2011), with lower mineral
594 density and lower erupted size due to increased erosion. Incisors of mice treated with ED
595 mixture presented a more pigmented enamel compared to the control group. The enamel
596 pigmentation defects due to NaF have been related to disrupted iron accumulation in
597 ameloblasts and rodent enamel (Houari et al., 2019). In ED NaF-treated mice, additive enamel
598 defects are consistent with previously observed enamel defects in rats exposed to both NaF and
599 BPA (Jedeon et al., 2016), an established pro-estrogenic ED (Jedeon et al., 2013; Li H et al.,
600 2021). It could be assumed that mechanisms similar to those highlighted in liver may occur in
601 dental epithelial cells, ameloblasts, which are responsible for enamel synthesis, especially those
602 linked to glycolysis and oxidative stress already reported in fluorotic experimental models
603 (Nagendra et al., 2021; Kumar et al., 2024).

604 Our results indicate that enamel organ seems to be more sensitive to ED and NaF than liver.
605 The main reason that may explain this difference is that there is no detoxification mechanism
606 in dental cells, meaning that xenobiotics that enter the cells are not excluded and can reach their
607 target genes at lower doses. In addition, as rodent ameloblasts express high levels of steroid
608 receptors, hormones and environmental toxicants with endocrine disrupting activity may
609 perturb their function in enamel synthesis (Houari et al., 2016).

610 The sensitivity of the eye and especially the retina to environmental chemicals has been
611 recognized for a long time (Grant and Schuman, 1993). However, research on their effects on
612 the eye and retina have recently emerged and studies remained limited.
No evident alterations in retinal histology were observed in treated groups at either time point.
To investigate more deeply, we analyzed each retinal layer at the end of development and during
the onset of age-related changes (Ferdous et al., 2021). Retinal development begins
embryonically and continues postnatally until neuronal and macroglial cell differentiation and
synapse maturation are complete (Miesfeld and Brown, 2019). In 1-month-old mice, only NaF
exposure affected secondary neuronal layers (INL, IPL), consistent with chick embryo data
showing NaF-induced thickening of nuclear and plexiform layers (Suleman et al., 2023). This
effect may involve Notch1 signaling (Du et al., 2024), essential for maintaining retinal
progenitors (Dvoriantchikova et al., 2015). By contrast, the ED mix, alone or combined with
NaF, did not alter retinal thickness at this stage.
With the aging of the retina, increased oxidative stress and inflammation, mitochondrial
dysfunction, and accumulation of damage affect neuronal viability and function, and retinal
thickness decreases with age in mice (Subhi et al., 2016). In addition, retinal thickness is
correlated to cognitive decline (Kim et al., 2022), suggesting that retina could serve as a
biomarker of CNS alterations. However, whether retinal thickness reflects lifelong exposure to
environmental factors has not been yet demonstrated in humans, although a number of
environmental and occupational toxicants are known to be neurotoxic (Fox, 2015). In the
present study, we found that the thickness of nearly all retinal layers was reduced, particularly
in the outer nuclear layers in 10 months-old mice. This could be the consequence of the fact
that EDs and NaF induce apoptosis, oxidative stress and inflammation in neurons (Gopnar et
al., 2023; Costa and Cairrao, 2024; Kumar et al., 2024). Therefore, it seems that the
photoreceptors have a heightened sensitivity compared to other retinal neurons, given their
vulnerability to stress. Moreover, certain EDs, including heavy metals, can cross the blood-
retinal barrier and accumulate in ocular tissues, particularly the RPE and choroid (Erie et al.,
2005), which are critical for retinal survival, one being the vascular bed, the other serving as a
638 blood-retinal barrier and feeder retinal cells. We found that chronic exposures to environmental  
639 toxicants reduced the thickness of these layers in all treatment groups, probably impairing  
640 nutrient and oxygen delivery and compromising photoreceptor viability. Given that ocular  
641 changes often precede CNS manifestations, the eye provides a sensitive and accessible model  
642 for assessing long-term effects of realistic ED mixtures (Chalkias et al., 2021; Ma et al., 2022).

643 Our results indicate that chronic exposure to toxicants affects retinal histology, which can serve  
644 as a marker to study the impact of ED exposure on the (much less accessible) CNS. Whether  
645 retinal thickness, longitudinally measured non-invasively by SD-OCT, could reflect  
646 environmental exposure in humans remains to be demonstrated. Further studies are required to  
647 understand the consequences of the observed retinal histology alterations on visual functionality  
648 and the possible development of age-related ocular diseases.

649 This study has several limitations. First, because exposure began during the fetal period and  
650 continued throughout life, it is unclear whether the adult phenotypes reflect cumulative lifelong  
651 exposure or early developmental perturbations with delayed onset. Distinguishing these  
652 mechanisms is critical, as developmental reprogramming may irreversibly predispose tissues to  
653 dysfunction, whereas chronic exposure may induce progressive damage. Future studies using  
654 temporally restricted or stage-specific exposures are needed to clarify the relative contributions  
655 of developmental versus lifelong effects. Second, internal concentrations of administered  
656 toxicants or their metabolites in blood, urine, feces as our approach focused on long-term  
657 morpho-functional outcomes rather than absorption or distribution kinetics. The absence of  
658 individual quantitative analyses restricts the possibility of correlating observed morpho-  
659 functional or omics alterations with definitive internal or target organ exposure. While our  
660 findings provide evidence of target organ effects following chronic low-dose exposure,  
661 additional studies with direct internal exposure assessment during time (via mass spectrometry
662 when feasible in rodents) will be informative to fully demonstrate accumulation of substances
663 and their metabolites in teeth and eyes and possibly to identify direct target cells.

664 Finally, functional assessments, such as behavioral or visual tests, were not performed, limiting
665 the interpretation of whether observed structural and molecular changes result in measurable
666 physiological impairments.

667 In conclusion, this study provides novel evidence that eyes and teeth are particularly sensitive
668 to chronic low-dose exposure to EDs and NaF compared to the liver. By uncovering the
669 molecular mechanisms underlying dental and retinal alterations, we highlight these organs as
670 accessible and quantifiable markers of environmental toxicity. Importantly, enamel defects and
671 retinal thinning represent complementary easily detectable, non-invasive markers that could
672 serve as early indicators of systemic toxicant exposure, with potential diagnostic value for
673 monitoring populations at risk. Thanks to the specificity of these two organs, enamel defects
674 reflect perinatal health conditions and retinal stigmata considered as early marker of exposure
675 to toxicants. These findings not only advance mechanistic understanding but also open avenues
676 for translational applications in public health surveillance and preventive strategies.

677

678 **5. Glossary**

679 BPA: bisphenol A, ED: endocrine disruptor; EFSA: European Food Safety Agency; KEGG:
680 kyoto encyclopedia of genes and genomes; NaF: sodium fluoride; NOAEL: non-observed
681 adverse effect level; TDI: tolerable daily intake.

682

683 **6. Acknowledgments**

684 The study was supported by ANSES (ILEDET – N°2019/1/230), ANR (ANR-24-AF-DStress;
685 ANR-18-IDEX-0001), INSERM, Université Paris Cité and Sorbonne Université. The authors
686 thank the CRC core facilities for the technical support.
687

688

7. Author contributions: CRediT

689

Emilie Picard: Conceptualization, Data curation, Formal analysis, Funding acquisition,

690

Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing –

691

original draft, Writing – review and editing

692

Fatima Domenica Elisa De Palma: Data curation, Formal analysis, Funding acquisition,

693

Investigation, Methodology, Resources, Validation, Visualization, Writing – review and editing

694

Sophia Loiodice: Data curation, Formal analysis, Investigation, Methodology, Resources,

695

Validation, Visualization, Writing – review and editing

696

Vincent Carbonnier: Data curation, Formal analysis, Investigation, Methodology, Validation,

697

Visualization, Writing – review and editing

698

Seiki Achiedo: Investigation, Methodology, Writing – review and editing

699

Noor Mimoun: Investigation, Methodology, Writing – review and editing

700

Diluxe Mutale: Investigation, Methodology, Writing – review and editing

701

Sophie Doublier: Investigation, Methodology, Writing – review and editing

702

Sylvère Durand: Investigation, Methodology, Writing – review and editing

703

Mélanie Bourgin: Investigation, Methodology, Writing – review and editing

704

Fanny Aprahamian: Investigation, Methodology, Writing – review and editing

705

Pierre Cordier: Investigation, Methodology, Writing – review and editing

706

Louise Gutter: Investigation, Methodology, Writing – review and editing

707

Katia Jedeon: Formal analysis, Investigation, Methodology, Writing – review and editing

708

Marie-Christine Naud: Formal analysis, Investigation, Methodology, Resources, Writing –

709

review and editing

710

Philippe Noirez: Investigation, Methodology, Writing – review and editing

711

Jean-Marc Ricort: Investigation, Methodology, Writing – review and editing

712

Lotfi Slimani: Investigation, Methodology, Writing – review and editing
713 Guido Kroemer: Funding acquisition, Supervision, Writing – review and editing

714 Francine Behar-Cohen: Conceptualization, Funding acquisition, Supervision, Writing – review and editing

716 Maria Chiara Maiuri: Conceptualization, Formal analysis, Investigation, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing

719 Sylvie Babajko: Conceptualization, Funding acquisition, Investigation, Project administration, Supervision, Writing – original draft, Writing – review and editing

721 All authors contributed to writing the paper and approved the final version.

722

723 **8. Funding sources**

724 SB, EP, FBC & MCM were sustained by Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES) (ILEDET – N°2019/1/230).

726 SB and DM are supported by Agence Nationale de la Recherche (ANR-24-AF-DStress)

727 GK is supported by the Ligue contre le Cancer (équipe labellisée); Agence National de la Recherche (ANR – AMMICa US23/CNRS UMS3655); Association pour la recherche sur le cancer (ARC); Cancéropôle Ile-de-France; Fondation pour la Recherche Médicale (FRM); a donation by Elior; Equipex Onco-Pheno-Screen; European Joint Programme on Rare Diseases (EJPRD); Gustave Roussy Odyssea, the European Union Horizon 2020 Projects Oncobiome and CRIMSON (grant agreement No. 101016923); Fondation Carrefour; Institut National du Cancer (INCa); Institut Universitaire de France; LabEx Immuno-Oncology (ANR-18-IDEX-0001); a Cancer Research ASPIRE Award from the Mark Foundation; the RHU Immunolife; Seerave Foundation; SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE); and SIRIC Cancer Research and Personalized Medicine (CARPEM).
738

739

## 9. Declaration of competing financial interests (CFI):

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GK has been holding research contracts with Daiichi Sankyo, Kaleido, Lytix Pharma, PharmaMar, Osasuna Therapeutics, Samsara Therapeutics, Sanofi, Sutro, Tollys, and Vascage.

GK is on the Board of Directors of the Bristol Myers Squibb Foundation France. GK is a scientific co-founder of everImmune, Osasuna Therapeutics, Samsara Therapeutics and Therafast Bio. GK is in the scientific advisory boards of Centenara Labs (formerly Rejuveron Life Sciences), Hevolution, and Institut Servier. GK is the inventor of patents covering therapeutic targeting of ACBP/DBI, aging, cancer, cystic fibrosis and metabolic disorders.

GK's wife, Laurence Zitvogel, has held research contracts with Glaxo Smyth Kline, Incyte, Lytix, Kaleido, Innovate Pharma, Daiichi Sankyo, Pilege, Merus, Transgene, 9 m, Tusk and Roche, was on the Board of Directors of Transgene, is a cofounder of everImmune, and holds patents covering the treatment of cancer and the therapeutic manipulation of the microbiota. GK's brother, Romano Kroemer, was an employee of Sanofi and now consults for Boehringer-Ingelheim. The funders had no role in the design of the study; in the writing of the manuscript, or in the decision to publish the results.

754

All other authors declare they have no conflicts of interest related to this work to disclose.

755
756 **10. REFERENCES**

757

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**Table 1:** The fifteen chemicals of the mixture, their chemical family and dose of exposure based on the earlier published mixture for proportions and the tolerated dose intake established by EFSA for DEHP and BPA for the levels of exposure. The tolerable daily intake (TDI) for each substance was also reported or the reference dose (RfD) or the tolerable weekly intake (TWI) with the reglementary agencies and dates of publications. The TDI (and RfD) may vary depending on the considered end-points. The lowest available values are reported here. ND: not determined.

<table><thead><tr><th>Molecule - CAS n°</th><th>Family</th><th>dose of exposure µg/kg/day</th><th>Reported TDI, TWI or RfD µg/kg/day</th></tr></thead><tbody><tr><td>Bisphenol A (BPA)_80-05-07</td><td>Phenol</td><td>0.90</td><td>4 (until 2023)<br>0.2 ng/kg/d (EFSA 2023)</td></tr><tr><td>Triclosan (TCS), dichlorophenoxyphenol_3380-34-5</td><td>Phenol</td><td>40.50</td><td>ND</td></tr><tr><td>Benzophenone-3 (BP-3), Oxybenzone_131_57_7</td><td>Phenol</td><td>39.20</td><td>10 (for BP-3 and 4-hydroxybenzophenone group) (EFSA 2009)</td></tr><tr><td>Dibutyl phthalate (DBP)_84-74-2</td><td>Phthalate</td><td>133.6</td><td>10 (EFSA 2019)</td></tr><tr><td>di-2-ethylhexylphthalate (DEHP)_117-81-7</td><td>Phthalate</td><td>39.00</td><td>50 (EFSA 2019)</td></tr><tr><td>Hexachlorobenzen (HCB)_118-74-1</td><td>Organochlorine pesticide</td><td>0.05</td><td>0.17 (WHO 1995, EFSA 2006)</td></tr><tr><td>Dichlorodiphenyldichloroethylene (4-4' DDE, p-p' DDE metabolite DDT), _72-55-9</td><td>Organochlorine pesticide</td><td>0.42</td><td>10 (WHO 2017)</td></tr><tr><td>Perfluorooctanoic acid (PFOA)_335-67-1</td><td>Perfluorinated compound</td><td>3.60</td><td>0.004 (TWI, EFSA 2020)</td></tr><tr><td>Perfluorooctanyl sulfonate (PFOS)_1763-23-1</td><td>Perfluorinated compound</td><td>9.33</td><td>0.004 (TWI, EFSA 2020)</td></tr><tr><td>2-Naphthol_135-19-3</td><td></td><td>1.44</td><td>ND</td></tr><tr><td>Sodium perchlorate monohydrate (NaClO4-) _7791-07-3</td><td>Poly aromatic hydroxylated compound (PAH)</td><td>0.73</td><td>1.4 (EFSA 2024)</td></tr><tr><td>Decabromodiphenylether (BDE 209)_1163-19-5</td><td>Polybrominated compound (PBDE)</td><td>1.21</td><td>7 (RfD EPA 2008)</td></tr><tr><td>2,2',4,4',5,5'-Hexachlorbiphenyl (PCB-153)_35065-27-1</td><td>Polychlorinated compound (PCB)</td><td>1.44</td><td>0.01 (Norwegian Scientific Committee for Food Safety, 2008)</td></tr><tr><td>Methyl mercury (MeHg)_115-09-3</td><td>Heavy Metal</td><td>6.30</td><td>1.3 (TWI, EFSA 2012)</td></tr><tr><td>Lead (II) Chloride (PbCl2)_7758-95-4</td><td>Heavy Metal</td><td>0.29</td><td>0.5 (RfD EPA 1993)</td></tr></tbody></table>
1206 FIGURE CAPTIONS

1207 **Figure 1. Effects of chronic administration of NaF and/or ED mixture on mice.**

1208 (A) Schematic diagram of the in vivo experimental plan. A total of four treatments groups were set up: Control group (no exposure to the substances, exposure limited to 0.01% dimethyl sulfoxide, DMSO, used as a solvent); NaF (exposure to 1 mM NaF); ED (exposure to a mixture of 15 EDs; Table 1); and ED NaF (exposure to the combination of the ED mixture + 1 mM NaF group. All the animals were orally exposed via drinking water 15 days before the mating, females were exposed during the gestation and lactation (sacrificed after weaning of pups), and the pups were exposed until the sacrifice at 30 days and 10 months after birth. Weights of all females (B-E) and males (F-I) mice were measured at least once a week for the duration of the experiment at 0-320 postnatal days (PND) (B and F), analyzed with Kruskall-Wallis test, pursued by a Dunn's test with Bonferroni correction for PND 0-110 (C and G), PND 111-220 (D and H) and PND 221-320 (E and I). Abbreviations: ED, endocrine disruptors; NaF, sodium fluoride.
<figure><img src="image_3.png" /><figcaption>Fig. 1</figcaption></figure>

<caption>Figure 2: Effects of chronic exposure to NaF and/or ED mixture on tooth development and mineralization</caption>

(A-B) Photographs of typical mouse incisors in each group. (C) Boxplots represent measurements of the erupted part of mouse incisors (Anova test, followed by a post-hoc Tukey's HSD test, n= 8 for controls, n = 6 for NaF, n = 10 for ED and n = 11 for ED NaF groups). (D) Longitudinal view of representative mandibles of each group analyzed by µCT. Enamel, the most mineralized tissue is also the dense (white). (E) Frontal view of the same mandibles showing the incisor under the second root of the first molar. (F and G) Boxplots represent measurements of enamel density (F) and volumes (G) in incisors (Anova test, followed by a post-hoc Tukey's HSD test, n= 8 for controls, n = 6 for NaF, n = 8 for ED and n = 7 for ED NaF groups). Abbreviations: ED, endocrine disruptors; NaF, sodium fluoride.
<figure><img src="image_4.png" /><figcaption>Fig. 2</figcaption></figure>

<caption>**Figure 3.** Ocular investigations in mice exposed to endocrine disruptors.</caption>

(A) Representative Spectral Domain-Optical coherence tomography photographs of the superior pole of control mice (Control) and mice exposed to either sodium fluoride at 1 mM (NaF), a mix of endocrine disruptor (ED) or a cocktail of 1 mM NaF and the mix of EDs (ED NaF) and examined at 3- and 9-month-old. Scale bars: 100 µm. (B) Boxplots represent the measurements of the neural retina thickness (Kruskall-Wallis test, pursued by a paired pairwise Wilcoxon-Mann-Whitney test; n = 8 for control and ED NaF groups; n = 10 for NaF and ED).

(C) Representative histological photographs of eye sections from control mice and mice exposed to either NaF, ED or ED NaF combination obtained at 1- and 10-month-old. Scale bars: 500 µm. Insert: higher magnification of the retina delimited neural retina and the complex formed by the retinal pigmented epithelium (R), choroid (C) and sclera (S) (RCS). (D-E)
Measurement, at 1- and 10-months of age, of the thickness of the neural retina (D) and the RCS complex (E) across the overall eye at each side of the optic nerve. Areas under the curves (AUC) were used for the one-way ANOVA test, pursued by a Dunnett's post-test; * p < 0.05, ** p < 0.01, **** p < 0.000. The star colors reflect the p values for groups compared to control group (for more details see Fig Suppl 2 B-C). Values are means ± SEM. For 1-month-old mice, n = 5 for all groups and for 10-month-old, n = 5 for control, n = 6 for NaF, n = 6 for ED, and n = 8 for ED NaF. Abbreviations: ED, endocrine disruptors; NaF, sodium fluoride.

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

<figure><img src="image_6.png" /><figcaption>Fig. 3</figcaption></figure>

1252

1253

<caption>Figure 4. Retinal morphometric analysis in mice exposed to endocrine disruptors.</caption>
(A-B) Representative photographs of histological retinal sections of control and exposed mice at 1- and 10-month-old. Scale bars: 100µm. (C-D) Boxplots represent variations of the thickness of the choroid, the retinal pigmented epithelium (RPE), outer nuclear layer (ONL), the outer plexiform layer (OPL), the inner nuclear layer (INL), and the inner plexiform layer (IPL) (Kruskall-Wallis test, pursued by a Dunn's test with Benjamini-Hochberg correction; n= 4-7). For 1-month-old mice, n = 5 for control, NaF and ED NaF, and n = 4 for ED and for 10-month-old, n = 4 for control, n = 5 for NaF and ED, and n = 7 for ED NaF. Abbreviations: ED, endocrine disruptors; NaF, sodium fluoride.

<figure><img src="image_7.png" /><figcaption>Figure 5. Metabolomic profiles of mouse liver tissue exposed to endocrine disruptors.</figcaption></figure>

(A) The mass spectrometric metabolomic profile of female mice livers treated for 10 months with water containing the solvent (Control), NaF, ED and ED NaF were performed (n = 6
1266 animals per treatment group). Metabolites analyzed are arranged in a heatmap, as indicated by the color scales. (B-D) Volcano plots displaying the significant (|log2FC| ≥ 0.58, p-value < 1267 0.05) differentially expressed metabolites between liver tissues after treatment with NaF (B), 1268 ED (C) and ED NaF (D) compared to the control group. Abbreviations: ED, endocrine 1269 disruptors; NaF, sodium fluoride. 1270

<figure><img src="image_8.png" /><figcaption>Fig. 5</figcaption></figure>

1271

1272 **Figure 6.** Transcriptomic characterization of endocrine disruptors in female murine liver tissues. (A-C) Volcano plots illustrating the significant (|log2FC| ≥ 0.58, p-value < 0.05) differentially expressed (DE) genes between liver tissues after treatment with NaF (A), ED (B) or ED NaF (C) compared to the control group. See **Supplementary Tables S2-S4** for complementary data. (D-F) Bar plots showing KEGG and/or REACTOME pathway enrichment analyses performed on genes DE between control and NaF samples (D), control and ED (E), and control and ED NaF (F) specimens. Colored bars indicated the number of enriched genes (Number of genes) associated with significant (p < 0.05) pathways. The top 6
pathways are shown. Enriched pathways in common between the different comparisons (panels
D-F) are highlighted in bold. Complementary data are shown in **Supplementary Tables S5- S10. (G)** Venn diagram displaying overlapping genes of NaF vs control, ED vs control and ED
Functional annotation and enrichment analyses using GO,
KEGG and REACTOME pathways of the 45 overlapping genes between the 3 groups (panel
See **Supplementary Tables S11** for complementary data. (G). Abbreviations: ED,
endocrine disruptors; FC, fold change; GO, gene ontology; GO-MF, gene ontology molecular
function term; KEGG, Kyoto Encyclopedia of Gene and Genomes; NaF, sodium fluoride.
<figure><img src="image_9.png" /><figcaption>Figure 6</figcaption></figure>

1288
1289
1290