# Synergistic pathogenicity between Herpesvirus and Vibrio aestuarianus in killing juvenile oysters during spring in natural conditions

Arthur Cousson, Marie-Charlotte Cheutin, Fabien Rieuvilleneuve, Cécile Roques, Juliette Pouzadoux, Florian Voron, Sébastien Mas, Hervé Violette, Elise Hatey, Patrice Got, et al.

► To cite this version:

Arthur Cousson, Marie-Charlotte Cheutin, Fabien Rieuvilleneuve, Cécile Roques, Juliette Pouzadoux, et al.. Synergistic pathogenicity between Herpesvirus and Vibrio aestuarianus in killing juvenile oysters during spring in natural conditions. Science of the Total Environment, 2025, 1003, pp.180675. 10.1016/j.scitotenv.2025.180675. hal-05349107

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# Synergistic pathogenicity between Herpesvirus and Vibrio aestuarianus in killing juvenile oysters during spring in natural conditions

Arthur Cousson<sup>a,d,* ,1 ID</sup>, Marie-Charlotte Cheutin<sup>c,1 ID</sup>, Fabien Rieu villeneuve<sup>a</sup>, Cécile Roques<sup>a</sup>, Juliette Pouzadoux<sup>e</sup>, Florian Voron<sup>f</sup>, Sébastien Mas<sup>f</sup>, Hervé Violette<sup>b</sup>, Elise Hatey<sup>a</sup>, Patrice Got<sup>a</sup>, Romain Gros<sup>a</sup>, Sébastien Duperron<sup>g ID</sup>, Yannick Gueguen<sup>b ID</sup>, Marie-Agnès Travers<sup>e,1 ID</sup>, Audrey Caro<sup>a,* ,1 ID</sup>

<figure><img src="image_3.png" /></figure>

## HIGHLIGHTS

* Environmental factors, as seawater temperature, drive juvenile oyster mortality.

* Oyster microbiota varies seasonally and is dominated by Mycoplasmataceae in spring.

* Vibrio aestuarianus benefits from OsHV-1 to promote juvenile oyster mortality.

* Oysters' pathogens could circulate through seawater in the lagoon between juveniles and adults.

## GRAPHICAL ABSTRACT

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

## ARTICLE INFO

**Keywords:**

Pacific oyster

Dysbiosis

Microbiota

Ostreid herpesvirus 1

Vibrio aestuarianus

Environmental connectivity

## ABSTRACT

Young oysters of Pacific oyster *Magallana (Crassostrea) gigas* are regularly impacted by summer mortalities since 2008, caused by the infection by the Ostreid herpesvirus 1 (OsHV-1) when seawater temperature increases in summer. Adult oysters rather suffer from a vibriosis caused by the bacterial primary pathogen *Vibrio aestuarianus francensis*. Although these diseases are now well described, little is known about their contribution and possible synergy in natural mortality events of juvenile oysters. Using an *in-situ* approach conducted in the Thau lagoon, we investigated the potential implication of *Vibrio aestuarianus* in the juvenile oyster's mortality, as well as the virus OsHV-1, throughout a large sampling period from winter to late summer, after the mortality event. We further determined their respective effects on the microbial abundance and diversity on alive and moribund

<footnote><sup>a</sup> MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, Montpellier, France</footnote>

<footnote><sup>b</sup> MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, Sète, France</footnote>

<footnote><sup>c</sup> Institut de Recherche sur la Biologie de l'Insecte, UMR 7261, CNRS, Univ Tours, Tours, France</footnote>

<footnote><sup>d</sup> Eco&Sol s, Univ Montpellier, CIRAD, INRAE, IRD, L'Institut Agro, Montpellier, France</footnote>

<footnote><sup>e</sup> IHPE, Univ Montpellier, CNRS, Ifremer, Univ Perpignan Via Domitia, Montpellier, France</footnote>

<footnote><sup>f</sup> MEDiterranean Platform for Marine Ecosystem Experimental Research (MEDIMEER), OSU OREME, Univ Montpellier, CNRS, IRD, INRAE, Sète, France</footnote>

<footnote><sup>g</sup> UMR 7245 MCAM, Muséum National d'Histoire Naturelle, CNRS, Paris, France</footnote>

<footnote>* Corresponding authors at: MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, Montpellier, France.
E-mail addresses: arthur.cousson@ird.fr (A. Cousson), audrey.caro@umontpellier.fr (A. Caro).</footnote>

<footnote n="1">¹ These co-authors contributed equally.</footnote>
oysters. We demonstrated that microbiomes are dynamic throughout seasons and tend to become more similar in spring with a dominance of Mycoplasmataceae in alive oysters. Surprisingly, our data highlighted the prevalence and high loads of *Vibrio aestuarianus* in all moribund oysters. In contrast with the literature, we found that the microbial communities of moribund juvenile oysters were significantly influenced by both *Vibrio aestuarianus* and the virus OsHV-1. The moribund bacterial indicators were detected in abundance during mortalities in the surrounding environment of oyster farms and further (700 m far). These results highlight the connectivity between the different compartments of the lagoon and raise questions about the potential spread of the primary pathogen *Vibrio aestuarianus* from adult to juvenile oysters in farming areas located in lagoons.

# 1. Introduction

The Pacific oyster (formerly *Crassostrea gigas* but now *Magallana gigas*) is one of the most intensively cultured species worldwide generating significant socio-economic value in coastal communities (FAO, 2018). However, this industry has been confronted over the past 20 years to recurrent mortality events, so-called Pacific Oyster Mortality Syndrome (POMS) (Destoumieux-Garzón et al., 2020; Pernet et al., 2016). The underlying mechanisms causing POMS are complex and result from several synergistic factors. Among them, the environmental parameters such as temperature (Barbosa Solomieu et al., 2015; Go et al., 2017), the oyster genetics, the age (Azéma et al., 2017), the energetic reserves (Pernet et al., 2014), the farming practices (Pernet et al., 2012), the microbiota composition (Delisle et al., 2022) are known to be involved to various degrees. Typically, it is now well established that elevated temperatures preceding summer affect the host immune system (Cheng et al., 2004) and its microbiota (Liu et al., 2023). As climate change was recognized as major cause of mortality in bivalves because of microbial diseases (Infante-Villamil et al., 2021), a growing interest is emerging to understand the role of the microbiota in the disease progression, particularly in the culture of the Pacific oyster (Delisle et al., 2022; Dittmann et al., 2017; Paillard et al., 2022).

Typically, the rising temperatures are known to trigger the onset of the lethal Ostreid herpesvirus 1 μvar (OsHV-1). This virus exhibits a lytic and a latent stage that might be reactivated to the lytic stage (Divilov et al., 2024). During latency, OsHV-1 remains in a dormant stage in the oyster, making it hardly detectable, which has important consequences during oysters transfer moving between oyster farms (Pernet et al., 2016). The reactivation and the intense replication of the virus in oyster hemocytes during thermal elevation (i.e., 17 °C-24 °C, (Renault et al., 1994) dampens the host immune system and allows massive colonization by opportunistic bacteria, leading to bacteraemia, and ultimately death of the oyster (de Lorgeril et al., 2018a; de Lorgeril et al., 2018b; Segarra et al., 2010). Whereas OsHV-1 has been recognized as the causative infectious agent of POMS, especially in spat and juveniles (Barbosa Solomieu et al., 2015; Dégremont et al., 2016; Hick et al., 2018), members of the genus *Vibrio* are also considered as major aetiological agents responsible for other diseases (King et al., 2019; Petton et al., 2015; Petton et al., 2021). Among them, *V. aestuarianus* subsp. *francensis* is of major concern because of its induced mortalities in oyster adults that are strikingly increasing since 2012 (Lupo et al., 2020; Mesnil et al., 2023). Several studies demonstrated the relationships between mortalities associated with *V. aestuarianus* and water temperature, with lower mortality rates in winter (Parizadeh et al., 2018). So far, the ecology of the associated pathogens such as *V. aestuarianus* is scarcely documented as it is rarely detected in the environment, whether in seawater or sediment (Parizadeh et al., 2018). Some experimental studies showed that it might be associated with phytoplankton (Vezzulli et al., 2015) enhancing its uptake by oysters (Doni et al., 2023). Yet, this bacterium has been mainly isolated from unhealthy or moribund adult oysters collected during summer mortalities (Garnier et al., 2007) while its ability to persist in healthy oysters during winter has been demonstrated (Parizadeh et al., 2018).

The potential involvement of *Vibrio aestuarianus* in juvenile mortality is of growing interest for several reasons. Unlike adult oysters,

*V. aestuarianus* has a limited virulence for spat and juveniles when inoculated alone (Azéma et al., 2016). However, experimental cohabitation studies using both *Vibrio aestuarianus* and the virus OsHV-1 showed a striking increase of mortality rate, especially on selected triploïds oysters in comparison to diploïds. Since triploid oysters are preferentially grown in Thau lagoon and that *Vibrio aestuarianus* was experimentally virulent for juveniles in presence of OsHV-1, we hypothesize that the closeness of rearing structures between adults and spat/juveniles in Thau lagoon favors the spread of *Vibrio aestuarianus* from adults to juveniles. Also, because of the putative presence of the virus in a latent stage (Pernet et al., 2016), we expect that the mutual presence of both pathogens induces a microbial disequilibrium associated with a severe mortality event similar to described POMS phenomenon (Clerissi et al., 2023; Petton et al., 2015; Richard et al., 2021).

In this study, we developed an *in-situ* approach in the Thau lagoon, a French mediterranean oyster-farming site that represents 10 % of the French oyster production, between February and August 2021. During this whole period, that begun from the spat installation, we tested 1) the relation between environmental parameters and mortality rate. We collected and characterized the oyster-associated microbiota to investigate 2) its natural variation in alive oysters throughout the period, and 3) we analyzed its response during the mortality event, comparing the microbiota of alive and moribund oysters, and testing their relation with pathogen loads OsHV-1 and *V. aestuarianus*). Finally, 4) we searched for bacterial indicators strongly correlated with moribund oysters and investigated their occurrence in the surrounding environment (i.e., seawater and sediment) as well as in an eelgrass site located 700 m away to analyze the potential connectivity existing between environmental reservoirs of the lagoon.

# 2. Material and methods

## 2.1. Juvenile oyster's mortality survey and sample collection

Our study involved the environmental and microbial monitoring associated with Pacific oyster juveniles *Magallana gigas* in a farming area of the Thau Mediterranean lagoon, located in the south of France (N 43°26,628' E 3°38,696'). The monitoring began one week after the deployment of two sets of 1200 juveniles (T6 mm) in four different pearl nets suspended along two ropes placed one meter next to each other. To prevent oysters from silting and facilitate access for maintenance and harvesting from the surface, ropes are located two meters above the sediment and one meter from the surface. The juvenile oyster's survey was monitored between 23rd February and 27th August (2021). For each survey, "moribund oysters" were considered as empty shells and oyster slightly opened that could not close their valve after a manual pressure exerted on them (Richard et al., 2019). As sampling frequency was not regular over the survey, the count of moribund oysters was related to the number of days separating each sampling date. We thus defined the mortality ratio as the number of moribund oysters related to the total number of oysters planted, normalized per week, for each sampling date. Moribund oysters were counted and removed from both ropes at each survey. Oyster samples from a single rope were used for the microbiota collection. We randomly conserved 8 to 9 of the removed moribund individuals, and we additionally collected 8 to 9 alive
individuals, at each sampling date. The second rope was used to verify that the mortality ratio measured was not influenced by the collection of alive specimens (Negative Binomial Generalized Linear Model, Likelihood ratio $\chi^2 = 9.34 \times 10^{-4}$, $R^2 = 3.59 \times 10^{-5}$, $P = 0.976$). Upon recovery, oysters were immediately kept on ice and the flesh tissue was collected then stored at -80 °C until DNA extraction. Additionally, we collected the environmental parameters associated with the surface seawater and the sediment from the farming area but also from an eelgrass zone located at 700 m away. We overall collected a total of 90 samples of seawater, 89 samples of sediment and 79 alive oysters in the period running from February 23rd to August 27th 2021, and 24 moribund oysters between June 3rd and June 17th 2021, corresponding to the mortality period (see details of the sampling protocols in Supplemental Material). The genomic DNA of each individual sample was extracted using the NucleoSpin® Tissue extraction kit (Macherey-Nagel™, Düren, Germany) according to the manufacturer protocol (details in Supplemental Material). The extraction step involved homogenization of the oyster flesh tissue in a sterile NaCl solution (30 g.L⁻¹) (flesh weight/volume: 10 %) or the half of the polycarbonate membrane of the seawater filtration (corresponding to 100 mL) and 0.2 g of sediment.

## 2.2. Bacterial and pathogen quantification

Ostreid herpesvirus I (OsHV-1), Vibrio aestuarianus and total amount of bacteria in the juvenile oysters (i.e., alive and moribund) and in the environmental samples (i.e., seawater and sediment) were quantified using quantitative PCR (qPCR) approach. Briefly, DNA extracts were calibrated at a concentration of 40 ng.µL⁻¹ (oyster samples), diluted 10× (sediment samples) or undiluted (seawater samples). The qPCRs were realized on the LightCycler® 480 system and consisted of a final volume of 1.5 µL using the SYBR Green I Master® (Roche™, Bäle, Switzerland) mix with the primer pairs DPF (5'-ATTGATGATGTGGA-TAATCTGTG-3')/DPR (5'-GGTAAATACCATTGGTCTTGTTCC-3') (Seg arra et al., 2010) for OsHV-1, DNAj-F (5'-GTATGAAATTTTAACT-GACCCACAA-3')/DNAjR (5'-CAATTTCTTTCGAACAACCAC-3') (Saulni er et al., 2009) for V. aestuarianus and 341F (5'-CCTACGGNGGCWG-CAG3')/805R (5'-GACTACHVGGGTATCTAATCC-3') (Klindworth et al., 2012) for bacteria. Absolute quantification was carried out using the standard curves generated by the amplification products cloned into the pCR2.1-TOPO plasmid with the OsHVDPF and OsHVDPR inserts for the OsHV-1 copies, or from genomic extractions of known concentrations of V. aestuarianus or bacterial 16S copies. The qPCR program involved an enzymatic activation at 95 °C for 3 min, followed by 40 cycles of denaturation (95 °C, 10 s), annealing (60 °C, 20 s) and elongation (72 °C, 25 s). The accuracy of the qPCR was confirmed by analysis of the melting curves. All quantifications were performed in triplicate for each sample in independent runs.

## 2.3. 16S metabarcoding and bioinformatic process

The V3-V4 region of the 16S rDNA gene was amplified using the primer set 341F/805R (Klindworth et al., 2012) coupled with platform-specific linkers. For the amplification, we used the Taq Polymerase Phusion® High-Fidelity PCR Master Mix with GC buffer (Qiagen, Hilder, Germany) in the following PCR steps: an initial denaturation for 30 s at 98 °C, followed by 30 amplification cycles of denaturation (98 °C, 10 s), annealing (55 °C, 30 s), extension (72 °C, 30 s), and ended by a final extension step of 10 min at 72 °C. The primer specificity and the amplification success were checked by electrophoresis migration. Several blank (molecular H₂O) extractions were included at each step of the process. Samples were PCR amplified in triplicate and equally pooled for a final product of 30 µL that was sequenced with 2x250bp Illumina MiSeq technology at the Bio-Environnement platform (University of Perpignan, France). The obtained libraries were processed through FROGS 3.2 pipeline (Escudié et al., 2017) on Galaxy platform. Briefly, the demultiplexed reads were trimmed, filtered and merged

using a quality threshold allowing a mismatch rate of 0.1 and a minimum size of 420 bp. Obtained sequences were clustered into Operational Taxonomic Units (OTUs) using the swarm method (Mahé et al., 2014) and chimeric sequences were identified and removed with vsearch v2.17.0 (Rognes et al., 2016). Taxonomic assignment was conducted using the SILVA reference database (release 138.1) (Quast et al., 2013) and the OTUs multiple alignment was produced with the MAFFT program (Katoh and Standley, 2013). The OTUs table and the additional sample-associated metadata were transformed into a phyloseq object using the phyloseq package v1.42.0 (McMurdie and Holmes, 2013). H₂O blank represented only 3461 out of a total of 6.10⁶ sequences (5.10⁻⁴ as proportion), accounting for 46 distinct OTUs. Each OTU observed in blank sample was removed from the total dataset. An additional step of filtration was proceeded to exclude Eukarya and organelles sequences (i. e., Mitochondria and Chloroplast). Finally, we obtained a final dataset consisting of 4,050 different OTUs in oysters, 3,337 in seawater and 5,749 in sediment (3,365 to 32,976 OTUs per sample, Fig. S1).

## 2.4. Diversity metrics

Alpha and beta diversity of the alive juvenile oysters sampled from February 23rd to August 27th 2021 were computed to analyze the effect of the seasonal variation on microbiotas. Observed richness and Shannon entropy were calculated after rarefying all samples to a threshold of 5,035 OTUs/sample. This corresponds to the threshold at which rarefaction curves reach their stationary phases (Fig. S1) with an average Good's coverage of 79.19 % ± 31.06. This step involved the removal of one alive oyster sample from May 27th that contained fewer reads than the threshold. Beta diversity was then computed between pairs of alive oyster microbiomes using the Jaccard (OTUs presence/absence) and Bray-Curtis (accounting for abundances) distances. The community homogeneity (i.e., microbial dispersion) within each sampling date was measured for each metric using the function *betadisper* from the package *vegan* v2.6.4. The pairwise dissimilarities between oysters were projected into the first two axes of a Principal Coordinates Analysis (PCoA). The effects of the pathogenic loads of OsHV-1 and V. aestuarianus were then tested by reiterating the calculation of alpha and beta diversity metrics on alive and moribund oysters sampled on June 3rd, 10th and 17th 2021.

## 2.5. Statistical analyses

All statistical analyses were conducted on the R software v4.4.0 (Team, 2024). First, we investigated the correlation between the juvenile oyster mortality and environmental variables and pathogens' loads. To do that, we performed a series of paired Spearman correlations to test the significant correlations between the abiotic parameters and oyster mortality. Then, to test the effects of the pathogen loads on the bacterial composition, we performed a generalized linear mixed model (GLMM) using Template Model Builder (TMB) following a beta-binomial model with the glmmTMB v1.1.9 package (Brooks et al., 2017) and an analysis of variance (ANOVA) with car v3.1.1 package (Fox and Weisberg, 2019). In this model, the composition of the bacterial microbiota was coded as a response variable, and the OsHV-1 and the V. aestuarianus log concentrations (from qPCR data) (discrete), and their interaction as explanatory variables. To control for the batch effect of the sampling date, we entered the date as random effect. Second, we analyzed the natural variation of the alive oyster microbiota throughout seasons. For the bacterial abundance in oysters, the alpha diversity and the microbial homogeneity (i.e., dispersion), we used a series of five linear mixed models (LMER) with the lme4 v1.1–35 package (Bates et al., 2015) followed by an ANOVA. In these models, we entered either the bacterial abundance, the observed richness, the Shannon entropy, the Jaccard or the Bray-Curtis dispersion values as response variables (log-transformed), and the date (categorical) as explanatory variable. Because observed richness can be considered as counts, we specifically used a
negative binomial mixed model. When significant, we conducted post-hoc pairwise comparisons between dates, by calculating the estimated marginal means with the *emmeans* package v1.8.0. The R² of each model was calculated with the *MuMln* package v1.47.1. Regarding the beta diversity of microbial composition between samples, we used two permutational analyses of variances (PERMANOVAs) in which we entered either the Jaccard or the Bray-Curtis distance matrix as response variables and the date as explanatory variable. When significant, we conducted post-hoc pairwise comparisons between dates with the *pair-wiseAdonis* v0.4.1. Third, we analyzed the effects of the loads of OsHV-1 and *V. aestuarianus* on the microbiota. Given that *Vibrio aestuarianus* is part of the bacterial community of moribund oysters in particular, we are aware that these two variables are not independent. We have chosen to test the effect of this pathogen on the bacterial community composition by considering the *Vibrio aestuarianus* load from qPCR data versus a community of OTUs (two different types of variables). To minimize the relative abundance of *V. aestuarianus* OTU in the bacterial community, we rather considered Jaccard indices for the description of beta diversity. As *V. aestuarianus* covaried with the host status, we did not enter the status, which resulted from the effect of both pathogens including the virus, in the following models. In these models, the response variables were the microbial diversity metrics associated with the alive and moribund oysters of June 3rd, 10th and 17th 2021, and we entered the load of *V. aestuarianus* (discrete), the load of OsHV-1 (discrete) and their interaction, as explanatory variables. The sampling date was entered as a random effect. We identified the microbial indicators of alive or moribund oysters by performing a STAMP analysis based on the OTU compositions (Parks et al., 2014). Finally, to locate these microbial indicators in the environment, we subset these taxa from the seawater and sediment datasets and illustrated their abundances in a heatmap. All model assumptions were checked using the *DHARMa* package. Results regarding the observed richness and Bray-Curtis will figure in the Supplementary Material.

## 3. Results

### 3.1. Juvenile oyster's mortality response to environmental change and pathogen loads

We investigated the physico-chemical and microbial parameters before, during and after the mortality period. Sea surface temperatures (SST) increased by 6.6 °C in two weeks, from May 25th 2021 to June

10th 2021, of which 3.6 °C occurred within one week (from June 3rd to 10th); this increased in temperature preceded the oyster mortality peak that reached 25.9 % mortality on June 10th (Fig. 1). The mortality ratio was positively correlated with *Chlorophyll c* (*Chlc*), nanoplankton, particulate organic nitrogen (PON) and particulate organic carbon (POC) concentrations measured in seawater (0.89 ≥ R² ≥ 0.73, all p-values <0.05) (Table S1; Fig. S2&S3).

We also tested whether known pathogens (OsHV-1 virus, a juvenile-specific pathogen, and *Vibrio aestuarianus*, a primary pathogen for adults) may have contributed to oysters' mortality. With this objective in mind, we analyzed 79 alive oysters collected from February to August and we screened 24 moribund oysters in June. High amounts of *V. aestuarianus* DNA were quantified in moribund specimens, particularly on oysters sampled the 10th and 17th June (>10⁷ genomic units/ 2.5 ng of total DNA in 100 % of the individuals) (Fig. 2C). *V. aestuarianus* DNA was also detected at high level (almost 10⁶ genomic units/ ml) in seawater at the end of the mortality period (June 17th) (Fig. 2A). We overall detected the DNA of the virus only on the 3rd June with high concentration in moribund oysters, reaching 5.10⁵ genomic units OsHV-1/2.5 ng total DNA (Fig. 2D). The health status (i.e., alive or moribund) of oysters was significantly explained by the load of *V. aestuarianus* (Likelihood ratio χ² = 4.17, P = 0.041) that was three-fold more important in moribund oysters (Fig. 2C). By contrast, the concentration of OsHV-1 did not explain the oyster status considering the whole mortality period (χ² = 1.06, P = 0.304, Fig. 2D).

### 3.2. Composition of juvenile oyster's microbiome

We investigated the taxonomic composition of microbiotas associated with alive juvenile oysters collected from winter to summer and moribund collected in June. Briefly, their microbiota spanned 40 proprietary phyla and consisted mainly of Firmicutes (39.1 % of the reads, Fig. 3), Proteobacteria (28.6 %) and Bacteroidota (13.7 %). The OTUs were distributed among 232 families of which Mycoplasmataceae (Phylum Firmicutes - 34.3 %), Vibrionaceae (Phylum Proteobacteria - 8.7 %), Prolixibacteraceae (Phylum Bacteroidota - 5.5 %) and Terasakiellaceae (Phylum Proteobacteria - 5.1 %) were the most abundant. These abundances and those of other families depended on the sampling season. For example, Terasakiellaceae, Anaplasmataceae and Vibrionaceae (Phylum Proteobacteria) dominated the microbiome of oysters sampled in winter (14.8 %, 11.2 % and 10.3 %, respectively). In contrast, Mycoplasmataceae largely dominated the microbiome of alive

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** – Relation between temperature and juvenile oyster mortality. Sea surface temperature (°C, red curve) across the sampling period with the mortality ratio (number of moribund oysters related to the total number of oysters planted) normalized per week (%) illustrated by the red barplots measured for each sampling date. The mortality period is indicated in grey with dates written in bold.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** – Relation between mortality and pathogen loads in seawater of the farming area and oyster flesh. Pathogens concentrations (Log) and prevalence for V. aestuarianus and OsHV-1 measured by qPCR in (A, B) seawater and in (C, D) the flesh of alive (green) and moribund (blue) juvenile oysters over the sampling period. The mortality period is indicated in grey with dates written in bold. Pathogens were not detected in the sediment.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** – Composition of the juvenile oyster microbiome. Individual microbiome (taxonomic rank: Family) for the top 30 most abundant OTUs. Specimens are ordered by sampling date, from February 23rd to August 27th, with alive oysters on the top panels and moribund oysters on the bottom part. The primary pathogen Vibrio aestuarianus is specifically identified, isolated from other Vibrionaceae.</figcaption></figure>

oysters sampled in spring (51.7 %) while they only represented 10.7 % of the reads in moribund oyster, the latter displaying high abundances of Vibrionaceae, of which 11.8 % was V. aestuarianus, and Prolixibacteraceae (16.1 %). Finally, the oysters sampled in summer were dominated by Mycoplasmataceae (30.3 %) and Spirochaetaceae (Phylum Spirochaetota - 14.3 %) (Fig. 3).

### 3.3. Temporal dynamic of alive oyster's microbiome

The qPCR-estimated bacterial abundance of alive oysters varied slightly but significantly through time (Fig. 4A; $\chi^2 = 35.33$, $R^2 = 0.245$, $P < 0.001$, Table 1). Highest abundance was measured on February 23rd and June 10th (mortality peak) and the lowest on July 13rd ($P_{adj} < 0.001$). The alpha diversity of the alive juvenile oyster microbiome also significantly varied over the sampling period (Fig. 4B; Shannon index; $\chi^2 = 3.99$, $R^2 = 0.293$; $P < 0.001$, Table 1), with a net decrease from winter to spring and then an increase in summer (Fig. 4B, FigureS4A). The decrease of the alpha diversity in spring was associated with a significant change in the microbial variability between dates (Table 1; Fig. 4C; Fig. S3B-C; $\chi^2 = 4.19$, $R^2 = 0.324$, $P < 0.001$), meaning a decrease of the beta dispersion between oyster microbiomes collected in spring compared with those of winter (23rd February versus dates from
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** – Temporal variation of bacterial abundance and microbial diversity in alive oysters. (A) Bacterial abundance (copy/µL) measured by qPCR of the 16 rRNA-encoding bacterial gene (B) Alpha diversity is expressed by the Shannon index. (C, D) Beta diversity is represented according to the Bray-Curtis distance of each sample to the centroid (Dispersion, C) and through a two-dimensional PCoA between microbiome sample pairs (D). Microbiomes are colored by sampling date (i.e., from February 23rd to August 27th 2021) and shaped according to their sampling season (where winter dates are in square, spring in circles and summer samples in triangle). Boxplots illustrate the median by the middle bar, and the interquartile range by the box, with whiskers representing 1.5-fold the interquartile range. Significant grouping contrasts are indicated by the above letters (P ≤ 0.05). Dates corresponding to the mortality period (3rd, 10th and 17th June) appear in bold.</figcaption></figure>

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

– Responses of the juvenile oyster's microbiome across the whole sampling period (grey) and to pathogens' loads during the mortality episode (i.e., the OsHV-1 and V. aestuarianus loads, white). Effect of sampling date and of pathogens' loads were tested independently on bacterial abundance, alpha diversity and dispersion responses through linear mixed models followed by an analysis of variances (ANOVA), and community dissimilarities response with Permutational ANOVA (PERMANOVA). As interactions between pathogen's loads were not significant (all P ≥ 0.098), these models were simplified. Significant p-values are indicated in bold.

<table><thead><tr><th rowspan="3">Model</th><th rowspan="3"></th><th rowspan="3">Bacterial abundance</th><th colspan="2">Alpha diversity</th><th colspan="4">Beta diversity</th></tr><tr><th colspan="2">Richness</th><th colspan="2">Dispersion</th><th colspan="2">Community dissimilarities</th></tr><tr><th>Shannon</th><th>Jaccard</th><th>Bray-Curtis</th><th>Jaccard</th><th>Bray-Curtis</th></tr></thead><tbody><tr><td rowspan="3">Sampling date</td><td>χ<sup>2</sup> (or F)</td><td>35.33</td><td>23.94</td><td>3.99</td><td>4.19</td><td>4.64</td><td>4.19</td><td>6.51</td></tr><tr><td>R<sup>2</sup></td><td>0.245</td><td>0.232</td><td>0.293</td><td>0.300</td><td>0.322</td><td>0.324</td><td>0.426</td></tr><tr><td>P</td><td><strong>0.001</strong></td><td><strong>0.002</strong></td><td><strong>0.001</strong></td><td><strong>0.001</strong></td><td><strong>0.001</strong></td><td><strong>0.001</strong></td><td><strong>0.001</strong></td></tr><tr><td rowspan="3">OsHV-1 load</td><td>χ<sup>2</sup> (or F)</td><td>2.17</td><td>1.49</td><td>16.93</td><td>1.91</td><td>1.60</td><td>4.7</td><td>7.45</td></tr><tr><td>R<sup>2</sup></td><td>0.17</td><td>0</td><td>0.25</td><td>0.05</td><td>0.04</td><td>0.07</td><td>0.1</td></tr><tr><td>P</td><td>0.13</td><td>0.22</td><td><strong>0.001</strong></td><td>0.17</td><td>0.20</td><td><strong>0.001</strong></td><td><strong>0.001</strong></td></tr><tr><td rowspan="3">V. aestuarianus load</td><td>χ<sup>2</sup> (or F)</td><td>157.78</td><td>37.19</td><td>1.11</td><td>28.50</td><td>29.98</td><td>10.4</td><td>18.5</td></tr><tr><td>R<sup>2</sup></td><td>0.77</td><td>0.47</td><td>0.05</td><td>0.38</td><td>0.39</td><td>0.17</td><td>0.25</td></tr><tr><td>P</td><td><strong>0.001</strong></td><td><strong>0.001</strong></td><td>0.29</td><td><strong>0.001</strong></td><td><strong>0.001</strong></td><td><strong>0.001</strong></td><td><strong>0.001</strong></td></tr><tr><td rowspan="3">Interaction</td><td>χ<sup>2</sup> (or F)</td><td>0.13</td><td>0.57</td><td>1.41</td><td>2.26</td><td>2.74</td><td>1.01</td><td>0.97</td></tr><tr><td>R<sup>2</sup></td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>0.02</td><td>0.01</td></tr><tr><td>P</td><td>0.72</td><td>0.45</td><td>0.23</td><td>0.13</td><td>0.10</td><td>0.35</td><td>0.36</td></tr></tbody></table>

May 20th to July 13th, P<sub>adj</sub> ≤ 0.043) (Fig. S3B-C). Additionally, the composition of bacterial microbiota significantly varied throughout sampling dates (PERMANOVAs, F<sub>8</sub> = 6.51, R<sup>2</sup> = 0.426, P = 0.001, Table 1). All date-to-date pairwise comparisons displaying significant differences, except comparisons between May 20th and 27th and between May 20th and June 3rd that were not different (Pairwise PERMANOVAs P<sub>adj</sub> ≥ 0.057, Fig. S4). These results are illustrated on the two first axes of the PCoA that showed a clear separation between winter oysters versus spring and summer oysters on the first axis (20.8 % variance explained, Fig. 4D). The distinction between spring and summer oysters' microbiomes is illustrated along the second axis (14.3 % variance explained, Fig. 4D).

### 3.4. OsHV-1 and V. aestuarianus effects on the juvenile oyster's microbiota

During the mortality period, the bacterial abundances in oysters, measured by qPCR was significantly explained by V. aestuarianus load (χ<sup>2</sup><sub>1</sub> = 157.78, R<sup>2</sup> = 0.77, P = 0.001) but not by OsHV-1 load (χ<sup>2</sup><sub>1</sub> = 2.17, R<sup>2</sup> = 0.17, P = 0.13), without interaction between both pathogens (Table 1). Bacterial abundance (Log) was significantly higher in moribund oysters compared to alive (Fig. 5A).

Regarding alpha diversity, Shannon index was significantly affected by OsHV-1 load (χ<sup>2</sup><sub>1</sub> = 16.93, R<sup>2</sup> = 0.25, P = 0.001) (Table 1) whereas Observed richness (number of OTUs) was affected by V. aestuarianus
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** – Microbial diversity associated with alive (green surrounded circles) and moribund juvenile (blue surrounded circles) oysters in relation to pathogen loads during the mortality episode. (A) Total bacterial abundance (in log) measured by qPCR of the 16 rRNA-encoding gene (B) Alpha diversity is expressed by the Shannon index. (C, D) Beta diversity is estimated using Jaccard distance of each sample to the centroid (Dispersion) (C) and through a two-dimensional PCoA between microbiota sample pairs (D). Microbiotas are colored according to their host health status (external circle, blue = moribund; green = alive oysters), sized according to the *V. aestuarianus* loads and filled according to the OsHV-1 loads, all quantified by specific qPCR in the host. Boxplots illustrate the median by the median bar, and the interquartile range by the box, with whiskers representing the 1.5-fold the interquartile range. \*\*\* *P* ≤ 0.001, \*\* *P* ≤ 0.01 and NS. non-significant.</figcaption></figure>

load ($\chi_1^2$ = 37.19, $R^2$ = 0.47, P = 0.001) (Table 1), without any interaction between both pathogens (Table 1). However, based on Shannon index or Observed richness, no significant difference was observed between alive and moribund oysters (Fig. 5B and Figure 5SA).

Finally, in term of beta diversity, *V. aestuarianus* load affected the microbial variability (i.e., dispersion) (Jaccard: $\chi_1^2$ = 28.5, $R^2$ = 0.38, P = 0.001; Bray-Curtis: $\chi_1^2$ = 29.98, $R^2$ = 0.39, P = 0.001) (Table 1), with a significantly higher dispersion of microbiota in moribund compared to alive oysters (Fig. 5C, Fig. S5B). The beta diversity in terms of microbial assemblies between samples was also significantly influenced by both *V. aestuarianus* (Jaccard: $\chi_1^2$ = 10.5, $R^2$ = 0.17, P = 0.001; Bray-Curtis: $\chi_1^2$ = 18.5, $R^2$ = 0.25, P = 0.001) and by the concentration of the OsHV-1 (Jaccard: $\chi_1^2$= 4.7, $R^2$ = 0.07, P = 0.001; Bray-Curtis: $\chi_1^2$ = 7.45, $R^2$ = 0.1, P = 0.001) (Table 1), without any interaction between both pathogens. The independent effects of both pathogens were well illustrated in the PCoAs where the first axis (24.2 % of the variance) segregates samples according to *V. aestuarianus* concentration while the second axis

(6.9 %) separates moribund oysters according to the concentration of the OsHV-1 (Fig. 5D), overall prevalent in the oysters sampled on June 3rd 2021 (Fig. 2D).

Specifically, according to the STAMP analysis, eight OTUs were found to be significantly associated but in different proportion between alive (2 OTUs) and moribund (6 OTUs) juvenile oysters sampled in June. In particular, the microbial family of Mycoplasmataceae OTU2 was the first indicator, two-fold more abundant in alive oysters compared to moribund ($P_{adj} < 0.001$) despite being detected in 20 of the 26 moribund juvenile oysters. The Genus *Aestuariispira* sp. OTU10 (Phylum Proteobacteria, Family Terasakiellaceae) was the second indicator of the alive group. In contrast, *Vibrio aestuarianus* OTU11, the *Roseimarinus* OTU8 (Prolixibacteraceae), JTB215 OTU42 (Clostridia), the other members of the Genus *Vibrio* OTU40, the Marinifilaceae OTU64 and the *Posidionibacter* OTU99 (Arcobacteraceae) were significantly more abundant in moribund oysters. Among them, *V. aestuarianus* OTU11 and *Roseimarinus* OTU8 were the most overrepresented by being 10-fold
more abundant in moribund than in alive oysters ($P_{adj} \le 0.001$) (Fig. 6). These eight OTUs represent indicators of the health status of oysters.

### 3.5. Distribution of the microbial indicators in the environment

The indicators of oyster's health status were also detected in the other environmental compartments of the lagoon. Overall, they were dominant in the seawater of the oyster farming area during the mortality period. Mycoplasmataceae OTU2, the most abundant indicator in alive specimens, was found in the surrounding seawater, reaching 1.6% of the planktonic community reads on June 10th (mortality peak). It was also found in the sediment of the oyster farming area all over the sampling period (1.5% of the reads on May 27th), but also in the sediment of the eelgrass area at lower proportion (≤ 0.003 %). Vibrio aestuarianus OTU11, predominant and highly abundant in moribund oysters (previously shown in Fig. 2), was found exclusively in the seawater of oyster farm (up to 0.0026 %) at the mortality peak (June 10th) and one week later (June 17th) (up to 0.0003 %) in some replicates (Fig. 7). It doesn't persist in the seawater beyond the mortality period and was never detected in the sediment. For other indicators of moribund oysters (Roseimarinus OTU8, JTB215 OTU42, Vibrio OTU40 and the Marinifilaceae OTU64), they were found in high proportion in the seawater of the farming area on June 10th and 17th, as well as after the mortality period until August 27th (from 0.024 to 1.563% relative abundance) (Fig. 7). In the seawater of the eelgrass area, we overall found the Vibrio OTU40 across the sampling period (with relative abundance ranging from 0 on February 23rd to 0.39% on June 17th and July 13th), but also more punctually Roseimarinus OTU8 and Clostridia JTB215 OTU42 (Fig. 7).

## 4. Discussion

Thanks to sampling collection of abiotic parameters and microbiomes before, during and after the mortality event, our results highlight the precursor role of regularly increasing water temperatures associated with the effect of the bacteria Vibrio aestuarianus and the OsHV-1 virus in a polymicrobial outbreak of juvenile mortalities. Juvenile oyster microbiota compositions naturally tend towards homogenization during the mortality event, characterized by a dominance of

Mycoplasmataceae, in accordance with the Anna Karenina principle (Zaneveld et al., 2017). This mortality period is characterized by a brief and early participation of OsHV-1, known to disturb the immune system and microbial balance. The microbial dysbiosis observed in moribund individuals features massive colonization by several bacteria, including as unexpected in juveniles, the adult oyster pathogen V. aestuarianus.

### 4.1. The dual effect of temperature and pathogens in mortalities

The episode of juveniles' mortality during 2021 in Thau lagoon was brief (2 weeks), moderate, comparable in terms of intensity to that of 2015 (Richard et al., 2019), but very late (June) compared with the ten previous years (Lagarde et al., 2025). Just before the oyster juvenile mortalities (peak of 25.9% reached on June 10th), the temperature increased by a striking 6.6 °C within two weeks. Fast and high temperature increase was considered in the literature as a major risk factor that can promote oyster infectious diseases (Fleury et al., 2020; Whittington et al., 2019). The increase of some environmental parameters (Chlc, Nanoplankton, POC, PON) correlated with mortality may correspond to the consequence of leaking of flesh moribund oysters (Richard et al., 2017) and subsequent mineralization in the microbial loop (Richard et al., 2019). This mineralization was beneficial for nanoplankton growth, identified as diatoms from chlc at the mortality peak i. e., on June 10th (Fig. S2). However, our experimental *in situ* setup did not allow to disentangle the causative role of each environmental parameter involved in oyster mortality since several factors did covary. As environmental factors and phytoplankton production are known to compromise the oyster immune response, these environmental parameters may be implicated in mortalities (Dégremont et al., 2010), while aetiological agents are not necessarily involved (Go et al., 2017).

In the mortality event observed in this study, the health status of juvenile oysters was not explained by the concentrations of OsHV-1 because significant concentrations of the virus were only observed in moribund animals on a single date (i.e., June 3rd 2021) at the onset of the mortality event. The rare presence of OsHV-1 in oysters and their undetectability in seawater at the mortality peak and latter was probably due to excessive temperatures. The upper limit of the temperature window at which OsHV-1 remains active is 24 °C (Pernet et al., 2012), but SST reached up to 26 °C, largely exceeding this threshold. Still, the

<figure><img src="image_10.png" /><figcaption>**Fig. 6.** – Most discriminant bacterial OTUs between alive (green) and moribund (blue) oysters. Microbial indicators resulting from the STAMP analysis (Difference in mean proportions ≥2 %) in the 26 alive (marked with *) and 24 moribund oysters sampled during the mortality period (i.e., June 3rd, 10th and 17th 2021). The prevalence is given in bold (left panel). All p-values are adjusted for multiple testing with the Bonferroni correction.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Fig. 7.** – Temporal and spatial distribution of the microbial indicators associated with alive (marked with *) and moribund juvenile oysters. Heatmap of the relative abundances of the microbial indicators resulting from the STAMP analysis (effect size ≥2%) between the alive and the moribund oysters, in the different environmental compartments (i.e., seawater and sediment) sampled near the sampling site (i.e., oyster farming area) or in an eelgrass area located 700 m away from the farm. The distribution of each microbial indicator is given throughout the sampling period, where the sampling date corresponding to the mortality period are indicated in bold (i.e., June 3rd, 10th and 17th 2021). Increased intensity of blue represents increased relative abundance.</figcaption></figure>

presence of the virus at the onset of the event has probably induced immuno-deficiency (de Lorgeril et al., 2018a, 2018b) that favored, under thermal stress conditions, the establishment of this newly detected primary pathogen in juveniles: Vibrio aestuarianus. The *in situ* involvement of V. aestuarianus in the pathogenesis of juveniles was somewhat unexpected; this species was generally associated with *in situ* chronic mortalities of adults (Barbosa Solomieu et al., 2015; Garnier et al., 2007; Travers et al., 2015) but rarely detected in young oysters (Lasa et al., 2019; Travers et al., 2015). Conversely, the herpesvirus was described as lethal in juveniles but not in adults (Fleury et al., 2020; Renault et al., 1994; Segarra et al., 2010). We assume that the co-occurrence of the virus (at least during the onset of mortality) and V. aestuarianus in the population may play a synergistic role, enhanced by the thermal stress that precedes the mortality event. This synergistic effect between Vibrio aestuarianus and OsHV-1 in oysters was already evident in experimental dual exposure experiences, but within a very specific developmental window corresponding to the transition between juvenile and adult stages, i.e. at 1 year old (Azéma et al., 2016; Azéma et al., 2017). Our results, in accordance with previous literature, strengthens the idea that V. aestuarianus virulence might overall depend on the oyster susceptibility, linked to physiological status or to other pathogen, such as the virus OsHV-1, known to induce an immunodeficiency (Clerissi et al., 2020; de Lorgeril et al., 2018b). This study also brings new insights on oyster pathogens, suggesting that V. aestuarianus could strongly contribute to the *in situ* mortality of juveniles in addition to that of adult oysters; consequently, V. aestuarianus host range could be larger than expected (Azéma et al., 2017). In the highly connected environment of the Thau lagoon, it cannot be excluded that disease episodes affecting adult oysters and caused by Vibrio aestuarianus may have contributed to an increased representation of V. aestuarianus within the microbial community structure of the bay, although no major mortality events were reported in adult oysters during the study year.

## 4.2. Oyster microbiome is dynamic throughout season and responds to mortality events

The bacterial abundance associated with alive juveniles tended to increase slightly in late spring during mortalities, in comparison with the week preceding the events. This bacterial colonization went hand in hand with the decrease of the alpha diversity during the mortality event (i.e., June 3rd and 17th) in comparison with microbiomes associated with winter (i.e., February 23rd) and late summer (July 13th) oysters. Bacterial abundance increasing and alpha diversity decreasing were already interpreted as signs of dysbiosis and declining health caused by the proliferation of a few OTUs (Lokmer and Wegner, 2015; Lokmer et al., 2016; Pathirana et al., 2019). Regarding beta diversity, the bacterial communities associated with oysters changed throughout the survey with distinct microbiotas between oysters collected in winter, spring and summer, as already observed (Akter et al., 2023). Our results contradict previous studies that demonstrated that oyster microbiota was stable through time thanks to host regulation of symbionts (Fu et al., 2024). Our study highlighted dominant bacterial families in alive oysters for each season: Terasakiellaceae, Anaplasmataceae and Vibrionaceae in winter (14.8 %, 11.2 % and 10.3 % respectively), Mycoplasmataceae in spring (51.7 %) and Mycoplasmataceae (30.3 %) and Spirochaetaceae (14.3 %) in summer. The two latest families frequently dominated the microbial community of healthy oysters (King et al., 2019; Lasa et al., 2019; Lokmer et al., 2016). Members of Mycoplasmataceae, commonly found in marine animals, are described as core members in digestive systems of oysters and clams (Clerissi et al., 2020; Offret et al., 2023). As described in our results, the increasing abundance of Mycoplasmatacea in oysters as seawater warms up was reported by several studies (Akter et al., 2023; Pathirana et al., 2022; Wegner et al., 2013). A growing interest for this clade is emerging in the literature because of its potential beneficial role for the host. Members of this
family have been found able to synthesis vitamins, degrade poly-
saccharides, detoxify ammonia and recycle carbon (Fu et al., 2024;
Offret et al., 2023). Additionally, a recent study demonstrated that
*Mycoplasma* sp. associated with the eastern oyster (C. virginica) exhibited
a limited poor set of metabolic functions reinforcing the hypothesis of a
longstanding symbiosis between this bacteria and its host (Pimentel
et al., 2021). However, the beneficial role of Mycoplasmatacea, a very
diverse group, for the host is controversial as some *Mycoplasma* species
were associated with disorders in fish and marine bivalves (Paillard
et al., 2004). Similarly, members of Spirochaetaceae family are
described in the core microbiota of numerous marine invertebrates such
as oysters, abalone and corals, with potential beneficial functions for the
host fitness (Fu et al., 2024; King et al., 2020; Van De Water et al., 2016).
Further investigations should be carried out to understand the role of
these abundant symbionts in the host fitness and resistance to patho-
gens. The changes in the associated microbial community are likely due
to changing sea surface temperature, that are known to be a dominant
factor shaping oyster microbiome at daily scales (Arromrak et al., 2024)
and throughout seasons (Liu et al., 2023).

Additionally, to the seasonal changes in the microbiota of alive
oysters during the mortality event, we observed a bacterial shift between
alive and moribund oysters. Particularly, we observed an increase in
bacterial abundance and higher microbiota dispersion in moribund
oysters, likely reflecting the dysbiosis occurring because of viral infec-
tion. Indeed, the infected oysters become more susceptible to bacterial
colonization as a consequence of the weakness of the oyster's cellular
defenses (Petton et al., 2021), leading to death of oysters (de Lorgeril
et al., 2018a; de Lorgeril et al., 2018b). While our *in situ* experimental
design did not allow to investigate a direct cooperation between the
virus and *V.aestuarianus* in juvenile oysters, our data still suggest that a
sequential role of these pathogens in oysters mortality events. Further
crossed-controlled experiments should be made to disentangle their role
and their potential synergy on juvenile mortalities. Bacterial commu-
nities in moribund oysters are characterized by high relative abundance
of a few OTUs: the potential pathogen *Vibrio aestuarianus*, *Roseimarinus*
OTU8, *Clostridia* JTB215 OTU42, *Vibrio* sp. OTU40, *Marinifilaceae*
OTU64, *Poseidonibacter* sp. OTU99, compared to alive oysters. Such
bacterial communities composed of a few dominant OTUs, aside with
the virus, was referred as the pathobiota by Lasa et al. (2019). In our
study, most of the pathobiota OTUs are resident bacteria from alive
oysters as already mentioned by Clerissi et al. (2023), that benefit from
the immunocompromised state in moribund oysters. We revealed a
bacterial composition in moribund oysters partly comparable to those
described in the literature during POMS events (Clerissi et al., 2023;
Lasa et al., 2019; Le Roux et al., 2016; Lokmer and Wegner, 2015;
Lokmer et al., 2016; Richard et al., 2021). In alive oysters, only two taxa,
Mycoplasmataceae OTU8 and Terasakiellaceae *Aestuariuspira* OTU10,
were dominant during the mortality period. In moribund oysters, the
genera *Vibrio* was frequently cited in the pathobiota of immunode-
pressed oysters (de Lorgeril et al., 2018b), but we could demonstrate in
our study that the *Vibrio* fraction was mainly attributed to
*V. aestuarianus*, pathogen for adult oysters. Their prevalence in all
moribund oysters, their concentration in flesh and its effect on the
microbiota composition strongly suggest about its responsibility in the
pathogenesis and mortality of juveniles as evocated by Lasa et al.
(2019). The shift in the dysbiosis between alive and moribund oysters
coming from resident OTUs is likely due to the host's inability to regulate
the proliferation of these opportunistic and resident bacteria. This re-
inforces the idea that juvenile oyster mortality arises from host sus-
ceptibility caused by environmental change, such as elevated
temperature, in addition to immunosuppression by the OsHV-1 that
enhances the microbiota dysbiosis (de Lorgeril et al., 2018b; Richard
et al., 2021) and can promote development of pathogens such as *Vibrio
aestuarianus*. As we briefly detected the virus in this *in situ* mortality
event, we do not consider this mortality episode as a typical POMS (with
a systematic detection of the virus expected in all moribund oysters). We

rather hypothesize a succession of a first viral and bacterial co-infection,
followed by a vibriosis, with *V. aestuarianus* clearly suspected as the
causal agent in the vibriosis. The association of this microbial dynamic
and juvenile mortality is very different from what is usually described in
the literature regarding the POMS, during which *Vibrio* are always
prevalent (Petton et al., 2021).

4.3. *Environmental reservoirs of opportunistic bacteria*

As filter feeding animals, oysters are very sensitive to free-living
microbes that may easily transit and colonize the individuals. Here,
we found that Mycoplasmataceae OTU2 could be from environmental
origin, as we found it abundant in the surrounding seawater and sedi-
ment of oyster farming area but also in the sediment of the eelgrass area.
Seemingly, the moribund bacterial indicators *Roseimarinus* OUT8 and
*Poseidonibacter* OTU99 have been detected before the mortality event in
the seawater of the oyster farming area and in the eelgrass zone, sug-
gesting that these compartments could be potential reservoirs of these
bacteria. In addition, the mortality event was followed by an increase of
numerous moribund indicators including *Vibrio aestuarianus*, in
seawater of the farming area and other moribund oysters detected
further in eelgrass area. As *V. aestuarianus* presents a strengthened
virulence through losses from infected oyster (Parizadeh et al., 2018),
we can wonder about the easy spread of the disease from moribund to
healthy oysters in a confined environment such as the Thau lagoon
(Pernet et al., 2012; Richard et al., 2021). The potential transfer of
*V. aestuarianus* and other members of the pathobiota *via* seawater inside
oysters' beds and between different oyster farming sites in a same lagoon
during mortality events needs future investigation.

5. Conclusion

Since 2008, juvenile oysters face severe mortality events triggered by
rapidly elevating water temperature in late spring. Results from this
study confirmed that *in situ* mortalities in juvenile Pacific oyster from the
Thau lagoon were concomitant to the elevated temperature and to
bacterial dysbiosis probably facilitated by the quick, but transient,
infection of the Ostreid-herpesvirus 1. Dysbiosis resulted in massive
colonization by opportunistic bacteria and development of another
known pathogen, *V. aestuarianus*. These opportunistic and primary
bacterial pathogens, naturally present in marine animals including
oysters, occur in the seawater and sediment from the farming area, but
also in an eelgrass zone located at 700 m apart the farming site. While
some of them (i.e., Mycoplasmataceae OTU2, *Aestuasiira* OTU10,
*Roseimarinus* OTU8, *Poseidonibacter* OTU99) were detected before the
mortality event, others were probably released by oyster death in the
surrounding seawater. Understanding the role of these bacteria and their
transmission throughout the lagoon is necessary to understand their role
in the polymicrobial outbreak occurring in oyster farming after elevated
temperature but also to anticipate potential transmission disease to the
rest of Thau lagoon.

CRediT authorship contribution statement

**Arthur Cousson:** Writing – original draft, Visualization, Validation, Resources, Methodology, Investigation, Formal analysis. **Marie-Charlotte Cheutin:** Writing – original draft, Visualization, Validation, Data curation, Formal analysis, Writing – review & editing. **Fabien Rieu-villeneuve:** Resources, Methodology, Investigation, Formal analysis. **Cécile Roques:** Investigation, Formal analysis. **Juliette Pouzadoux:** Methodology, Formal analysis. **Florian Voron:** Methodology, Formal analysis. **Sébastien Mas:** Validation, Resources, Methodology, Investigation, Funding acquisition, Formal analysis. **Hervé Violette:** Methodology, Investigation. **Elise Hatey:** Methodology, Formal analysis. **Patrice Got:** Investigation. **Romain Gros:** Investigation. **Sébastien Duperron:** Writing – review & editing, Visualization, Validation,
Supervision, Resources, Conceptualization. **Yannick Gueguen:** Writing – original draft, Visualization, Validation, Supervision, Funding acquisition, Conceptualization. **Marie-Agnès Travers:** Writing – review & editing, Writing – original draft, Visualization, Validation, Resources, Methodology, Formal analysis. **Audrey Caro:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

## Ethical approval

All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.

## 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.

## Acknowledgments

This work was funded by the Cemeb labex to support the Microlag project (2020–2022).

We are also grateful to MARBEC (Marine Biodiversity Exploitation and Conservation) laboratory for recurrent annual grant.

We would like to thank the MARBEC DAL (Dispositif d'Analyses en laboratoire) of which the CEMEB Microbrix Platform for their technical support, the Bio-Environnement platform of Perpignan (especially Eve Toulza and Margot Doberva), the OSU-OREME platform (Observatoire de recherche Montpellierain de l'Environnement).

We would like to thanks Julien De Lorgeril for its implication in the initial conception of the project.

This study is set within the framework of the « Laboratoire d'Excellence (LabEx) » TULIP (ANR-10-LABX-41).

The illustrations of the Fig. 2 are from https://www.biorender.com.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2025.180675.

## Data availability

Supplemental material, raw metadata file and R scripts are deposited on the Zenodo platform https://doi.org/10.5281/zenodo.17094627.

Raw sequencing data have been deposited in NCBI under BioProject accession number: PRJNA1319951 (http://www.ncbi.nlm.nih.gov/bioproject/1319951).

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