# Deciphering the molecular mechanisms of oyster resistance to Pacific Oyster Mortality Syndrome (POMS) disease induced by high temperatures

Léo Duperret, Alejandro Valdivieso, Emily Kunselman, Bruno Petton, Benjamin Morga, Julien de Lorgeril, Fabrice Pernet, Lionel Dégremont, Nicole Faury, Jean-François Allienne, et al.

► To cite this version:

Léo Duperret, Alejandro Valdivieso, Emily Kunselman, Bruno Petton, Benjamin Morga, et al. Deciphering the molecular mechanisms of oyster resistance to Pacific Oyster Mortality Syndrome (POMS) disease induced by high temperatures. Science of the Total Environment, 2025, 994, pp.180026. 10.1016/j.scitotenv.2025.180026. hal-05338576

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# Deciphering the molecular mechanisms of oyster resistance to Pacific Oyster Mortality Syndrome (POMS) disease induced by high temperatures

Léo Duperret<sup>a</sup>, Alejandro Valdivieso<sup>b</sup>, Emily Kunselman<sup>c</sup>, Bruno Petton<sup>d</sup>, Benjamin Morga<sup>e</sup>,

Julien de Lorgeril<sup>f</sup>, Fabrice Pernet<sup>d</sup>, Lionel Degremont<sup>e</sup>, Nicole Faury<sup>e</sup>,

Jean-François Allienne<sup>a</sup>, Juliette Pouzadoux<sup>a</sup>, Océane Romatif<sup>a</sup>, Gaelle Courtay<sup>a</sup>,

Cristián J. Monaco<sup>g</sup>, Eve Toulza<sup>a</sup>, Lizenn Delisle<sup>h</sup>, Jeremie Vidal-Dupiol<sup>b</sup>, Arnaud Lagorce<sup>a, *,</sup>

Guillaume Mitta<sup>g, **</sup>

## HIGHLIGHTS

* High temperature prevents oyster mortalities induced by POMS disease.

* Integrative omics provide deeper insights into the underlying mechanisms.

* 30 °C induces metabolic shift, increases immune capacities, and reduces POMS susceptibility.

## GRAPHICAL ABSTRACT

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

## ARTICLE INFO

Editor: Jay Gan

**Keywords:**

Pacific oyster

High temperature

Integrative omics

## ABSTRACT

Climate change and rising temperatures are frequently cited as key factors in the emergence of diseases. While the increase in temperature can alter host immunity, influence pathogen virulence, and change the geographic distribution of vectors and their associated pathogens, few studies have investigated the impact of temperature variations on the molecular mechanisms controlling disease permissiveness. The present study addresses this question on a panzootic and polymicrobial disease, the Pacific Oyster Mortality Syndrome (POMS). POMS, initiated by the herpesvirus OsHV-1 μVar, affects juveniles of *Magallana gigas*, which is the most widely cultured

<footnote><sup>a</sup> IHPE, Univ Perpignan Via Domitia, CNRS, IFREMER, Univ Montpellier, Perpignan, France</footnote>

<footnote><sup>b</sup> IHPE, Univ Montpellier, CNRS, IFREMER, Univ Perpignan Via Domitia, Montpellier, France</footnote>

<footnote><sup>c</sup> Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA</footnote>

<footnote><sup>d</sup> Ifremer, LEMAR UMR 6539, UBO/CNRS/IRD/Ifremer, Argenton-en-Landunvez, France</footnote>

<footnote><sup>e</sup> Ifremer, ASIM, La Tremblade, France</footnote>

<footnote><sup>f</sup> Ifremer, IRD, Université de la Nouvelle-Calédonie, Université de La Réunion, ENTROPIE, Nouméa, Nouvelle-Calédonie, France</footnote>

<footnote><sup>g</sup> Ifremer, IRD, Institut de Louis-Malarde, Université Polynésie française, UMR SECOPOL, Taravao, Tahiti, French Polynesia</footnote>

<footnote><sup>h</sup> Cawthron Institute, Nelson, New Zealand</footnote>

<footnote>* Correspondence to: A. Lagorce, IHPE, Université de Perpignan, Via Domitia - 58, Avenue Paul Alduy Bâtiment, R - 66860 Perpignan Cedex, France.</footnote>

<footnote>** Correspondence to: G. Mitta, Centre Ifremer du Pacifique, Vairao- BP 49 - 98725 Vairao, Tahiti, French Polynesia.</footnote>

<footnote>E-mail addresses: arnaud.lagorce@univ-perp.fr (A. Lagorce), Guillaume.Mitta@ifremer.fr (G. Mitta).</footnote>
oyster species in the world. In our study, two full-sib families were exposed to the disease under permissive (23 °C) and non-permissive (30 °C) conditions. Using an integrative multi-omics approach, we demonstrate that high temperature has a dual effect on oysters (1) inducing a metabolic reprogramming, creating a sub-optimal metabolic environment for viral infection and thereby limiting POMS development, and (2) enhancing the host's antiviral immune capabilities, both at a baseline level and in response to infection. Overall, these responses triggered at elevated temperature improve oyster survival against POMS. Our study showed that temperature exerts complex effects on host-pathogen interactions; and molecular-level mechanistic approaches are crucial to thoroughly understand and accurately assess how temperature changes can influence epidemiological risk.

# 1. Introduction

Climate change has numerous effects on marine species and ecosystems, including species migration (Chaudhary et al., 2021; Occhi-pinti-Ambrogi, 2007), biological invasions and increased disease occurrences (Combe et al., 2023; Harvell et al., 2002; Rowley et al., 2024). Considering diseases, temperature impacts host-pathogen interactions by changing host distribution or susceptibility, while also affecting the geographical range, virulence, and/or replication rates of pathogens (Altizer et al., 2013; Chapin et al., 1998; Combe et al., 2023; Eissa and Zaki, 2011; Harvell et al., 2002; Hickling et al., 2006; Kurane, 2010; Okon et al., 2023; Petton et al., 2023; Rowley et al., 2024). As a consequence, a global increase in epizootics has been reported these last decades, especially in marine ectotherm species (Harvell et al., 2002; Tracy et al., 2019).

The main etiological agents of these epizooties are bacteria and viruses. The link between rising temperatures and bacterial pathogens emergence has been clearly demonstrated (Baker-Austin et al., 2013; Rowley et al., 2024) and Vibrio species are a prime example of this phenomenon. This genus can be found as commensal, mutualistic, opportunistic or pathogenic in marine organisms (Sanches-Fernandes et al., 2022). Warmer temperatures favor the transition from a commensal to a pathogenic state resulting in diseases called vibriosis. This has been documented in Vibrio alginolyticus for the white shrimp Litopenaeus vannamei (Cheng et al., 2005), Vibrio coralliilyticus for the coral Pocillopora damicornis (Rozenblat and Rosenberg, 2004; Vidal-Dupiol et al., 2011), Vibrio harveyi for the abalone Haliotis tuberculata (Travers et al., 2009) and Vibrio aestuarianus for the oyster Magallana gigas (Azéma et al., 2017a). Similarly, for marine viruses, a 1 °C increase in seawater temperature has been associated with higher host mortalities when shrimps are infected by the White Spot Syndrome Virus (WSSD), carps by the Cyprinid HerpesVirus-3 (CyHV-3), or when cupped oysters are infected by the Ostreid HerpesVirus-1 µVar (OsHV-1 µVar) (Combe et al., 2023). In addition to the rising emergence of pathogens, elevated temperatures modulate host immunity in a species dependent manner, affecting host-pathogen interactions (Bailey et al., 2017; Rowley et al., 2024).

Rising seawater temperatures represent a significant threat to a broad range of marine species, including those exploited in mariculture (FAO, 2022). Mollusk mariculture is no exception to this rule and the Pacific cupped oyster (Magallana gigas), one of the most prominent oyster species produced worldwide (5,450,000 t in 2022) (FAO, 2022) is severely affected by the Pacific Oyster Mortality Syndrome (POMS) (FAO, 2022; Garcia et al., 2011a; Segarra et al., 2010). POMS is initiated by a variant of the Ostreid herpesvirus OsHV-1, termed OsHV-1 µVar (Segarra et al., 2010). This disease affects oyster juveniles and has now become panzootic, impacting the oyster industry all around the world (Burge et al., 2021; Dundon et al., 2011; Garcia et al., 2011b; Gittenberger et al., 2016; Jenkins et al., 2013; Keeling et al., 2014; Peeler et al., 2012; Pernet et al., 2012, 2014; Renault et al., 2012; Roque et al., 2012; Segarra et al., 2010). POMS has been characterized as a polymicrobial disease (de Lorgeril et al., 2018b), initiated by a primary viral infection triggering an immunocompromised state followed by a secondary bacterial infection caused by a consortium of bacteria composed of opportunistic pathogens including Vibrio species (Clerissi et al., 2023; de

Lorgeril et al., 2018b; Delisle et al., 2022; Kunselman et al., 2024; Oyanedel et al., 2023; Rubio et al., 2019).

POMS has been shown to be influenced by a series of factors, including (epi)genetics (Azéma et al., 2017a; de Lorgeril et al., 2020, 2018b; Dégremont et al., 2015; Divilov et al., 2019; Gawra et al., 2023; Gutierrez et al., 2018), food availability (Pernet et al., 2019; Petton et al., 2021, 2023), oyster age (Azéma et al., 2017a; Dégremont, 2013; Pernet et al., 2012; Petton et al., 2015a) and temperature. The permissible window for this disease identified in Europe is between 16 °C to 24 °C in natura (Delisle et al., 2018, 2020; Fleury et al., 2020; Pernet et al., 2015; Petton et al., 2013). Several studies have corroborated the influence of temperature on POMS disease in various regions, including the South Pacific (de Kantzow et al., 2016), and the United States of America (Burge et al., 2006; Friedman et al., 2005; Kunselman et al., 2024). Although the role of temperature in POMS outbreaks is well-established, the precise molecular mechanisms through which it modulates disease permissiveness are still under investigation. In which studies, authors have shown that elevated temperature (29 °C) reduces oyster susceptibility to POMS by modulating host transcriptomic responses, notably through the regulation of genes involved in immune function and the upregulation of apoptosis, synaptic signaling, protein metabolism, and stress-related pathways (Delisle et al., 2020; Zhu et al., 2016), without affecting OsHV-1 infectivity or virulence (Delisle et al., 2018). Furthermore, elevated temperatures, even in the absence of pathogens, have been shown to increase hemocyte counts and activate stress- and immune-related pathways, including heat shock proteins (HSPs), apoptosis, and pattern recognition receptors (PRRs) (Scanes et al., 2023; L. Wang et al., 2018; Yang et al., 2017; Zhang et al., 2015). These responses could illustrate a functional cross-talk between abiotic and biotic stress responses, mediated by a high degree of pleiotropy among stress- and immune-related genes (X. Guo et al., 2015). This supports the hypothesis that elevated temperature could not only limit viral progression but also enhance the oyster's stress and immune response.

The aim of the present study is to investigate the underlying molecular mechanisms of how elevated temperature inhibits POMS pathogenesis. To address this objective, we conducted a comparative and integrative approach in two full-sib oyster families acclimated to either permissive (23 °C) or non-permissive (30 °C) temperatures, before exposure to POMS disease. We found that at elevated temperature, disease development is affected by a modification of both immunity and metabolism.

# 2. Materials and methods

## 2.1. Production of biparental oyster families

Two full-sib families of *Magallana gigas*, named F11 and F14, and highly susceptible to POMS (mortality >90 %; see de Lorgeril et al., 2018b for more details), were reproduced at the Ifremer facilities in Argenton on 11th May 2020, producing the F11N and F14R families, respectively. In addition, a third full-sib family, named H2D, was produced in this study. This family was derived by reproducing the previously established H2 lineage, which has been used in earlier research on POMS susceptibility and was shown to exhibit high susceptibility to the
disease (Azéma et al., 2017a, 2017b). The larvae, and then spat, were raised and maintained under biosecure conditions at the Ifremer hatchery in Bouin (France) as previously described (de Lorgeril et al., 2018b). At 3 months old, F11N, F14R and H2D families were transferred to the Ifremer experimental facilities in Argenton (France), and were maintained at 23 °C with an *ad libitum* diet comprised of a mixture of *Tisochrysis lutea* and *Skeletonema costatum* at equivalent biomass (50/50) at 1500 µm³·µL⁻¹ (Rico-Villa et al., 2006). A “pathogen-free” status for these three families was confirmed by verifying the absence of OsHV-1 µVar DNA in the tissue of live oysters, and ensuring that the concentration of Vibrio was below 10 colony-forming units (CFU) per milligram of tissue (Petton et al., 2015b). These three families are called hereafter “recipients”.

## 2.2. Production of the viral suspension for the experimental infection

A viral suspension at a concentration of 2.2·10⁵ genomic copies·µL⁻¹ was prepared from OsHV-1 µVar-infected oyster tissue homogenate collected in July 2020, following the protocol of (Schikorski et al., 2011). This concentration corresponds to the standard dose commonly used for OsHV-1 µVar infection *via* cohabitation (de Lorgeril et al., 2018b; Gawra et al., 2023; Valdivieso et al., 2025).

## 2.3. Experimental infection in mesocosm

To investigate the effects of elevated temperature on oysters’ permissiveness to POMS, we exposed three families to OsHV-1 µVar at 23 °C (“permissive” condition) and 30 °C (“non-permissive” condition) on the 25th August 2020, when the oysters were 3 months old (Pernet et al., 2012; Petton et al., 2013). To do so, oysters were distributed

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** High temperature strongly induces resistance to POMS disease in three genetically independent full-sib oyster families.</figcaption></figure>

A) Graphical representation of the mesocosm experimental design: (1) Three full-sib oyster families F11N, F14R and H2D (3-month-old juveniles), fed *ad libitum*, hereafter referred to as recipient oysters, were acclimated for 16 days at two temperatures: 23 °C (permissive condition) and 30 °C (non-permissive condition) to POMS disease. (2) Pathogen-free oysters used as diseased donors were injected at 23 °C with a OsHV-1 µVar suspension. (3) Twenty-four hours post-injection, donor oysters were transferred to tanks containing acclimated recipient oysters to allow infection by cohabitation. (4) Pools of 10 recipient oysters per temperature and family were sampled during the first 72 hours post-cohabitation (hpc). The experiment was realized in triplicate.

B–D) Kaplan-Meier survival curves of the F11N (B), F14R (C) and H2D (D) families. Mortality monitoring stopped after 48 h without any recorded death. Mortality rates were higher at 23 °C with 100 %, 75.5 % and 27.1 % against 11.7 %, 3.4 % and 1.4 % at 30 °C in F11N, F14R and H2D family, respectively. The letter adjacent to each curve represents significant differences between the two temperatures (Mantel-Cox log-rank test, *P* < 0.05).
among six 400 L tanks. Each tank contained 383 individuals from the F11N family (total weight of 157.2 grams, g), 400 individuals from the F14R family (204.4 g), and 400 individuals from the H2D family (211.5 g). Three tanks were maintained at 23 °C while the three others were subjected to a water temperature increase of 1 °C per day up to 30 °C. Oysters were then acclimated to these temperatures for 16 days, while being fed *ad libitum* with a mixture of *Tisochrysis lutea* and *Skeletonema costatum* (50/50 biomass equivalence) at 1500 µm³·µL⁻¹ (Fig. 1A). Phytoplankton concentration was assessed twice daily at the tank outlet using a Beckman Coulter Counter Multisizer 3 particle counter with a 100 µm aperture tube, to ensure consistent homogenization throughout the entire tank. Each tank was outfitted with thermometers, including a WTW MULTIPARAMETRE MultiLine® 3630 IDS, a Dotsman P750 with a PT100 sensor, and an iButton® DS1922L-F5 thermochron®, to maintain appropriate temperature throughout the experiment. Additionally, pH, salinity, and dissolved oxygen levels were monitored using the WTW MULTIPARAMETRE MultiLine® 3630 IDS. During the 16-day acclimation period, water was renewed daily, and the oysters were rinsed with freshwater to eliminate any sedimented feces. No mortalities were observed during the acclimation period, confirming that the experiment was performed within the physiological range of the species.

To mimic the natural transmission of POMS disease, we used a protocol of cohabitation between donor oysters, which receive injection of an OsHV-1 µVar suspension, and the F14R and F11N recipient oysters (Schikorski et al., 2011). The weight ratio between donor and recipient oysters was 1:1. Donor oysters were 5 months old, and came from two mass spawnings: the first used broodstock originating from the Rade de Brest (non-infectious area, Dellec, France) which produced highly POMS-susceptible individuals (>90 %) (de Lorgeril et al., 2018b). The second used standardized oyster spat with an expected susceptibility to POMS of 50–60 % (Petton et al., 2013). The use of the two donors aimed to maximize OsHV-1 µVar production.

Twenty hours before the cohabitation, donor oysters were anaesthetized with hexahydrate MgCl₂ (ACROS, 50 g·L⁻¹, 200 g of oysters·L⁻¹) for 2 h (Suquet et al., 2009). Then, 50 µL of the OsHV-1 µVar viral suspension was injected into their adductor muscle using a 26-gauge needle inserted with a multi-dispensing hand pipette (Schikorski et al., 2011). Following injection, donor oysters were maintained at 23 °C for 18 h to allow intensive viral replication, and then transferred into each experimental tank containing the recipient oysters. This marked the beginning of the experimental infection by cohabitation. Seawater flow was stopped to create an optimal infectious environment until 125 hours post-cohabitation (hpc) at 23 °C and 96 hpc at 30 °C. This difference is to reflect the thermally dependent physiological time in ectothermic organisms, both corresponding to ~2880 degree-hours. While seawater flow stopped, oxygen flow was maintained to ensure proper homogenization in the tank.

A pool of ten live recipient oysters per tank (hereafter referred to as “sample”) of each family were then sampled at 0, 3, 6, 12, 24, 48 and 72 hpc. Oysters were shucked and fleshes were instantly flash-frozen in liquid nitrogen and stored at -80 °C. Mortality was monitored twice daily in each tank and dead oysters (recipients and donors) were removed upon detection. Mortality monitoring ceased when no deaths were observed among the recipients in any of the tanks for 48 consecutive hours.

Survival curves (Kaplan-Meier) were fitted to evaluate the impact of the temperature treatment on the mortality rate induced by the infection on each family (Surv and survfit functions, {survival} package v3.5-7) (Therneau, 2015). The survival probabilities (Y-axis) were plotted as a function of the product of time and temperature (X-axis). A statistical log-rank test was performed to compare conditions (survdiff function, {survival}). Statistical analyses were conducted in R v4.2.3. Results were considered significant when *P*-value was <0.05.

## 2.4. DNA and RNA extraction

Samples were ground in 50-mL stainless steel bowls filled with liquid nitrogen, and suited with 20-mm diameter grinding balls (Retsch MM400 mill) (de Lorgeril et al., 2018b). Ground tissues were stored at -80 °C. DNA extraction was performed using the Genomic DNA tissue kit from Macherey-Nagel following the manufacturer's protocol and DNA was stored at -20 °C. DNA concentration, purity and quality were checked with a NanoDrop One spectrophotometer (Thermo Scientific) and 1 % agarose electrophoresis gel. RNA extraction was performed using the Direct-zol RNA Miniprep kit (Zymo Research) according to the manufacturer's protocol. RNA concentration was assessed with a NanoDrop One spectrophotometer (Thermo Scientific) and RNA integrity was measured with the BioAnalyzer 2100 (Agilent). RNA was stored at -80 °C.

## 2.5. Viral and bacterial load quantification and analysis

Quantification of OsHV-1 µVar and total bacteria was performed by qPCR (quantitative Polymerase Chain Reaction) using a Stratagene Mx3005P Real-Time thermocycler. The total qPCR reaction volume was 20 µL consisting of 5 µL DNA (5 ng·µL⁻¹), 2 µL of each primer at the final concentration of 550 nM (Eurogentec SA), 1 µL of distilled water and 10 µL of Brilliant III Ultra-Fast SYBR® Green PCA Master Mix (Agilent). Virus-specific primer pairs targeted a region of the OsHV-1 µVar genome predicted to encode a DNA polymerase catalytic subunit (ORF100, AY509253): Forward: 5'-ATTGATGATGTGGATAATCTGTG-3' and Reverse: 5'-GGTAAATACCATTGGTCTTGTTCC-3' (Pépin et al., 2008; Webb et al., 2007). The total bacteria specific primer pairs were 341F-5'-CCTACGGGNGGCWGCAG-3' and 805R-5'-GACTACHVGGGTATC-TAATCC-3', targeting the variable V3-V4 loops of the 16S rRNA gene for bacterial communities (Klindworth et al., 2013). The amplification program was: 3 min at 95 °C, 40 cycles of amplification at 95 °C for 5 s, and 20 s at 60 °C. A subsequent melting curve of the amplicon was performed to verify the specificity of the amplification. Absolute quantification of viral DNA copies was estimated by comparing the observed Cq (quantification cycles) values to a standard curve of the DP amplification product cloned into the pCR4-TOPO vector. For total bacteria, we used the relative quantification calculated by the 2⁻⁰⁻⁰Cq method (Pfaffl, 2001) with the measured Cq of one reference gene (Cg-actin, GenBank: AF026063).

Normality and homogeneity of variance for viral DNA and RNA quantity were evaluated by a Shapiro test (shapiro.test function, {stats} v4.3.2) and the Levene's test (leveneTest function, {car} v3.1-2) (Fox et al., 2012), respectively. The student-t-test was used to test the differences in viral DNA and RNA quantity between the temperature treatments before the onset of mortality (72 hpc) (t.test, {stats} v4.3.2). For bacterial load, Kruskal-Wallis tests (kruskal.test function, {stats} v4.3.2) were conducted on the whole infection time period, with a Dunn's test for multiple comparisons (dunn.test, {dunn.test} v1.3.5 (Dinno, 2024)) corrected by the Benjamini-Hochberg adjustment. Statistical analyses were conducted in R v4.2.3. Results were considered significant when *P*-value was <0.05.

## 2.6. Bacterial community analysis with 16S metabarcoding

Bacterial communities were analyzed by metabarcoding. The 84 libraries (2 families, 2 temperatures, 7 time-points, in triplicate) were obtained by targeting the V3-V4 region of the 16S rRNA with the Illumina two-step PCR protocol and primers 341F-5'-CCTACGGGNGGCWGCAG-3' and 805R-5'-GACTACHVGGGTATC-TAATCC-3' (Klindworth et al., 2013). PCR products were normalized with SequalPrep plates (Thermofischer) and paired-end sequenced (2 × 250 base pairs, bp) on an Illumina MiSeq sequencer at the Bioenvironment Platform (University of Perpignan, France).
## 2.6.1. Preprocessing

SAMBA pipeline (v3.0.1) was used to process the sequences and generate the Amplicon Sequence Variants (ASV) using the default parameters developed by SeBiMER (Ifremer's Bioinformatic Core Facility) (Cormier et al., 2021). SAMBA pipeline is an open-source and modular workflow developed by SeBiMER, Ifremer's Bioinformatics Core Facility, for processing eDNA metabarcoding data. Built on the NextFlow workflow manager (Di Tommaso et al., 2017), SAMBA integrates various bioinformatics tools, with core processes relying primarily on QIIME 2 (version 2020.2) (Bolyen et al., 2019), a next-generation platform for microbiome analysis. Amplicon Sequence Variants (ASVs) were inferred and denoised using the DADA2 algorithm (version 1.14) (Callahan et al., 2016). Taxonomic classification of ASVs was conducted using a Bayesian classifier trained on the Silva database (version 138), implemented through the QIIME feature-classifier plugin (Quast et al., 2013; Wang et al., 2007). A filtering process was applied to remove ASVs that did not appear at least once in two different replicates from the same sampling time point and condition. Downstream analyses were performed by constructing a phyloseq object ({phyloseq} v1.46.0) (McMurdie and Holmes, 2013). Data were normalized according to the Trimmed Mean of M values (TMM) to correct the effective library sizes (normalize function, {microbial} v0.0.22) (Guo and Guo, 2021).

## 2.6.2. β-Diversity and differential representation

To evaluate the β-diversity, a Bray-Curtis dissimilarity matrix was computed (distance function, {phyloseq} v1.46.0) on the merged triplicates (merge Samples function, {phyloseq} v1.46.0). Results were plotted in a Principal Coordinate Analysis (PCoA) ordination (trajectoryPCoA function, {ecotraj} v0.1.1) (De Cáceres et al., 2019; Sturbois et al., 2021). A Permutational Multivariate Analysis of Variance with 999 permutations (PERMANOVA) (adonis2 function, {vegan} v2.6-4) (Oksanen, 2012) was performed to compare communities between temperature treatments. Differential representation analysis based on the negative binomial distribution (DESeq function, {DESeq2} v1.42.0) (Love et al., 2014) was carried out on bacterial communities throughout the infection period (0 hpc being the control group). Statistical results were corrected for false discovery rates using the Benjamini-Hochberg procedure (Haynes, 2013).

## 2.7. Transcriptomic analysis by RNA-seq

Eighty-four RNA-seq libraries (2 families, 2 temperatures, 7 time-points, in triplicate) were constructed with the NEBNext Ultra™ DNA Library Prep Kit for Illumina (NEB) and single-read sequenced (75 bp) on an Illumina Nextseq 550 instrument at the Bio-Environment Platform.

### 2.7.1. Preprocessing

Bioinformatic analyses were performed using a Galaxy instance environment (Goecks et al., 2010). After quality check of raw reads with FastQC (v0.7.1) (Andrews, 2010), the adaptors and reads with a Phred score < 30 and a length < 35 bp were trimmed with Trim Galore! (v0.6.3) (Krueger, 2012). Trimmed reads were mapped to the M. gigas genome (cgigas_uk_roslin_v1, Assembly: GCA902806645.1) (Peñaloza et al., 2021) using RNA Star (v2.6.0b-1) (Dobin et al., 2013) with default parameters. Counts per annotated gene were obtained with HTSeq-count (v0.9.1) (Anders et al., 2015) under the union mode, feature type as gene, ID attribute as ID and stranded reverse parameters. The raw matrix counts data were transformed with the variance stabilizing transformation function (VST) ({DESeq2} v2.11.40.7) (Love et al., 2014) to remove the differences induced by the sequencing depth.

Trimmed reads were also mapped to the OsHV-1 µVar genome to analyze the viral transcriptional activity (KY242785.1) (Burioli et al., 2017) with Bowtie2 (v2.3.4.3) (Langmead et al., 2009; Langmead and Salzberg, 2012). Counts per open reading frame (ORF) were obtained with HTSeq-count and the annotation GFF3 file, under the union mode,

stranded reverse, feature type as CDS and ID attribute. The same normalization technique implemented by de Lorgeril (2018b) was used here to correct for the size of the ORF and the sequencing depth: $C = C_{ORF}/S_{ORF}$.

The viral raw counts per ORF ($C_{ORF}/S_{ORF}$) were first divided by the ORF's size ($S_{ORF}$) and then multiplied by the ratio between the average reads for all libraries ($\bar{L}$) and the total number of reads of the specific library ($L_i$).

### 2.7.2. Global transcriptome analysis

Euclidian distances were computed to assess transcriptomic differences between samples across the infection process in all temperature conditions (distance function, {phyloseq}). To identify changes in gene expression, differential gene expression analysis was conducted using DESeq2 with a False Discovery Rate (FDR) < 0.05 for significant Differentially Expressed Genes (DEG). To detect temperature-induced changes ("Temperature treatment") in gene expression, the group exposed to 23 °C was compared to the group exposed to 30 °C at 0 hpc (reference) in each family. For the infection-induced changes ("Infection treatment"), each sampling time point (3, 6, 12, 24, 48 and 72 hpc) was compared to 0 hpc (reference) within the same temperature treatment and family.

### 2.7.3. Weighted Gene Correlation Network Analysis (WGCNA)

Weighted gene co-expression networks were constructed using the WGCNA package (v1.72-5) (Langfelder and Horvath, 2008) on the transcriptomic data. A pre-filtering step was conducted by selecting genes having at least 15 raw counts in 75 % of the samples, followed by VST transformation. Unsupervised networks were constructed with the parameters: maxBlockSize = 25000, networkType and TOMtype = signed, mergeCutHeight = 0.25 and minModuleSize = 100 and a soft-power threshold (sft) specific to each comparison to construct a scale-free topology network. A sft of 21.5 was applied for the "Temperature treatment", while sfts of 14 and of 16 were used for the "Infection treatment" at 23 °C and 30 °C, respectively. The different modules containing co-expressed genes were then correlated (Pearson correlation) to Temperature or Infection treatments. Genes identified in modules significantly ($P < 0.05$) correlated with Temperature or Infection treatment were selected for further analysis.

### 2.7.4. Functional annotation

In order to perform functional analysis with the most up-to-date dataset, we computed our own functional annotation (Table S1) of the genes present in the M. gigas reference genome (see Supplementary Materials and Methods, for details) (Peñaloza et al., 2021).

### 2.7.5. Gene Ontology and enrichment factor

For Gene Ontology enrichment analysis (GO-terms), we used the GO_MWU package (github.com/z0on/GO_MWU) (Wright et al., 2015) at the Biological Process level (BP). Genes used were either extracted from DESeq2 analysis, or from WGCNA modules correlated with the Temperature or Infection treatments. DESeq2 was performed to extract immunome information for each family (see below), from which the log2FC was used as a continuous response variable. WGCNA was performed to analyze the impact of Temperature and Infection treatments on the whole transcriptome regardless of family, from which we used the kME (Module Membership) of the genes identified in specific modules (see Section 2.7.3), whereas a "0" was attributed to the others (Wright et al., 2015).

The identification of BP GO-terms was performed with the parameters: largest = 0.5, smallest = 10, clusterCutHeight = 0.25. To those parameters, the Module = TRUE and Alternative="g" were added for the WGCNA analysis only. Changes in BP GO-terms were significant when the P-value corrected by the Benjamini-Hochberg procedure was
<0.05.

REVIGO treemap tool (Supek et al., 2011) was used to cluster significantly enriched GO-terms of the BP root under a representative function based on the SimRel semantic similarity measure and the Uniprot database. An aggregation value of 0.7 was used to remove the redundancy between GO-terms.

An analysis with specific focus on the immune genes identified in *M. gigas* (retrieved from de Lorgeril et al., 2018b), termed the “immunome”, was performed. After the GO-term enrichment analysis, an enrichment index for significant immunome-associated GO-terms was calculated. It is calculated as the ratio of the number of DEGs involved in the significant BP ($G_{DESeq2}$) to the total number of genes within that BP ($G_{BP}$) as follows: $E = r \times \frac{G_{DESeq2}}{G_{BP}}$ (de Lorgeril et al., 2018b). The ratio ($r$) was positively or negatively signed according to whether it was over- or under-represented. All enrichment analyses were plotted on heatmaps (p heatmap function, {p heatmap} v1.0.12) (Kolde and Kolde, 2015) with Euclidean-distance clustering on the GO-terms.

## 2.8. Targeted metabolomic analysis

The metabolomic analysis was performed on 48 samples (2 families, 2 temperatures, 4 time-points (0, 3, 6 and 12 hpc), in triplicate). Samples were sent to the Montpellier Alliance for Metabolomics and Metabolism Analysis (MAMMA) platform (BioCampus Montpellier, France) to perform targeted metabolites analyses with four different foci: Global Metabolites (GM), Nucleotides analysis, Poly-Unsaturated Fatty Acids and derivatives (PUFAs), and Krebs cycle (see Supplementary Materials and Methods, for details, and Tables S2–S5).

### 2.8.1. Preprocessing

For each metabolite detected from the GM, Nucleotides, PUFAs and Krebs cycle, the values obtained were peak area counts. These values were normalized based on their respective ISTD using the {MetaboAnalystR} package, (v4.0.0) (Chong and Xia, 2018). A filtering process was performed to correct for the potential detection biases based on the Interquantile Range (IQR), which filtered out 10 % of the data. Finally, a Quantile normalization followed by a Logarithmic transformation (Base 10) was applied. The normalized data were then used for further analyses.

### 2.8.2. Weighted (Metabolic) Correlation Network Analysis (WMCNA)

Similar to the transcriptomic data, WMCNA analysis was used for the metabolomic data (Pei et al., 2017). Networks were computed with the same parameters as used for transcriptomics, except minModuleSize was set to 5 and sft to 21 for the Temperature treatments and sfts was set to 13 and 11 for the Infection treatments at 23 °C and 30 °C, respectively. Modules obtained were correlated with the Temperature and Infection treatment. The metabolites identified in the modules as significantly (*P* < 0.05) correlated to these treatments were selected and their abundances (as normalized peak area counts) were plotted in a heatmap using a Euclidean-distances.

### 2.8.3. Pathway enrichment analysis

Pathway enrichment analysis was performed upon the detection of differentially represented metabolites. Sub-networks of the *M. gigas* genome were extracted from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (Kanehisa and Goto, 2000) and constructed through the diffusion algorithm in FELLA (runDiffusion function, {FELLA} v1.22.0) (Picart-Armada et al., 2023). Input metabolites introduced a unitary flow that propagated through the intermediate entities from KEGG (reactions, enzymes and modules) until the detection of pathways, subsequently corrected by a normalization through a z-score (approx = “normality”).

## 3. Results

### 3.1. Temperatures strongly reduce mortality rate caused by POMS disease in the three full-sib oyster families

Three susceptible full-sib families (F11N, F14R and H2D) were exposed to the Pacific Oyster Mortality Syndrome (POMS) in permissive (23 °C) and non-permissive (30 °C) conditions (Fig. 1A). In all families, mortality rates were significantly higher at permissive than non-permissive temperatures (Mantel-Cox log-rank test, *P*-value <0.0001 for the three families, Fig. 1B–D). For the permissive condition (23 °C), mortality rates were 100 %, 75.5 % and 27.1 %, while it was only 11.7 %, 3.4 % and 1.4 % in the non-permissive condition (30 °C), for F11N, F14R and H2D, respectively (Fig. 1B–D). The mortality dynamics also differed, occurring earlier at 23 °C in both families. These results confirmed the key role of temperature in influencing and controlling POMS disease development in susceptible oysters. Although all three families showed similar survival with respect to temperature, we selected only two—F11N and F14R—for molecular analyses, as they displayed the most contrasting phenotypes between permissive and non-permissive conditions.

### 3.2. High temperature prevents typical POMS disease development

To assess whether a typical POMS pathogenesis process (de Lorgeril et al., 2018b) occurred in both conditions and families (23 °C and 30 °C; F11N and F14R), we measured key indicators of POMS dynamics, including viral and bacterial load, viral transcriptional activity, and changes in the bacterial community (Fig. 2). Increases in viral DNA load and viral transcriptional activity were detected at both temperatures, indicating active viral infection in all conditions and both families (Fig. 2A–B). Nevertheless, these increases were significantly higher in the permissive condition (23 °C) when compared to the non-permissive condition (30 °C) at 72hpc (DNA *t*-test, *P*-value = 3 x 10⁻⁴ and 0.008 for F11N and F14R, respectively; RNA *t*-test, *P*-value = 0.02 for both F11N and F14R) (Fig. 2A–B). However, no specific viral genes could be reliably associated with this effect, likely due to the limited functional annotation and the absence of a defined temporal gene classification during OsHV-1 µVar infection.

The metabarcoding analysis performed on 16S rRNA revealed a clear change in the bacterial communities in the permissive condition (23 °C) (Fig. 2C), whereas the bacterial community was more stable in the non-permissive condition (30 °C) (Fig. 2C). From the 84 libraries sequenced, 2,049,723 raw sequence reads were obtained, mapping to 22,426 ASVs. Post-filtering, the dataset contained 1162 non-zero ASVs corresponding to 907,071 sequences. A sufficient sequencing depth was confirmed for each library by species richness rarefaction curves (Fig. S1). To monitor the bacterial changes during the infection for both temperatures and families, a PCoA based on β-diversity was computed (Fig. 2C). The principal drivers of bacterial community changes, represented by the first and the second PCoA axis, were temperature and infection (Fig. 2C). Additionally, the bacterial communities exhibited significantly different patterns of modification over time for each temperature treatment, independent of family (PERMANOVA, *P*-value = 0.001). The changes in the bacterial β-diversity occurred after 24 hpc at 23 °C in both families (Fig. 2C). There was a marked increase per family of ~60 and ~100 differentially represented ASVs at 48 hpc and 72 hpc when compared to 0 hpc, respectively, using DESeq2 analysis (Fig. S2A). Some genera identified during this dysbiosis (e.g., Amphitea, Marinomonas, Pseudoalteromonas, Vibrio) have also been reported in previous studies (Clerissi et al., 2023; de Lorgeril et al., 2018b; Delisle et al., 2022; Kunselman et al., 2024; Lokmer and Mathias Wegner, 2015; Oyanedel et al., 2023) and were identified in both F11N and F14R (Fig. S2B&C and Table S6). In contrast, DESeq2 analysis showed that bacterial communities remained stable throughout the infection at 30 °C with only ~10 and ~30 ASVs differentially represented (DESeq2 analysis) at 48 and 72
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Pathobiota analysis of POMS reveals reduced replication of OsHV-1 µVar and the absence of microbiota dysbiosis when exposed to high temperature in two full-sibling oyster families.</figcaption></figure>

A–B) Absolute quantification of OsHV-1 µVar DNA load determined by qPCR analysis (viral genomic units per ng of oyster DNA represented in solid lines, filled symbols), and viral replication activity estimated by the total number of RNA-seq counts mapped to the OsHV-1 µVar genome (dotted lines, hollow symbols) in F11N (A) and F14R (B) families. The lowercase letters indicate the difference in OsHV-1 µVar DNA load (t-test, P-value = 3 x 10⁻⁴ and 0.008 for F11N and F14R, respectively), while uppercase letters indicate the difference in OsHV-1 µVar RNA activity (t-test, P-value = 0.02 for both F11N and F14R). Values are log₁₀(x + 1) transformed. Dots and Squares represent a pool of 10 oysters, while the lines represent the mean of the three replicates per sampling time.

C) Microbiota changes due to temperature and the infection process, represented by a Principal Coordinate Analysis (PCoA) obtained with the Bray-Curtis dissimilarity matrix. Points represent the mean of triplicates per condition and time point. Green and blue shades represent 23 °C and 30 °C, respectively; light and dark colors distinguish F11N and F14R. D) Relative quantification of total bacteria measured by qPCR at 23 °C (dots) and 30 °C (squares). Higher bacterial load was observed under permissive conditions (23 °C). Light and dark colors represent F11N and F14R families, respectively.

hpc, respectively, relative to the beginning of the experiment (0 hpc) (Fig. S2D&E and Table S7).

In addition to the observed bacterial dysbiosis, total bacterial abundance significantly increased over the course of the infection under the permissive condition (23 °C) (Kruskal-Wallis test, P-value = 0.0077 and 0.0134 for F11N and F14R families, respectively). This bacterial load increase was significantly different between 72 hpc and 3 hpc in both families (Dunn test, P-value = 0.01 and 0.03 for F11N and F14R, respectively), while bacterial load remained low and unchanged in the non-permissive condition (30 °C) (Kruskal-Wallis test, P-value = 0.053 and 0.16 for F11N and F14R families, respectively). Compared with 0 hpc at 23 °C, the bacterial load at 72 hpc was 17- and 5-fold higher in F11N and F14R families, respectively (Fig. 2D). This corresponds to the bacteremia already exhibited in previous studies (Clerissi et al., 2023; de Lorgeril et al., 2018b).

To conclude on the disease development, the permissive condition (23 °C) was characterized by high mortality rates regardless of the family, and a typical pathogenesis process as seen in previous studies (primary infection by OsHV-1 µVar followed by a dysbiosis and a

secondary bacterial infection) (Clerissi et al., 2023; de Lorgeril et al., 2018b). On the contrary, the pathogenesis process was blocked in the non-permissive condition (30 °C) with lower mortality rates, lower viral loads, a more stable bacterial community and the absence of a subsequent bacteremia in both oyster families.

### 3.3. *High temperature disrupts energy metabolism, limiting the viral infection*

To identify the mechanisms underlying differences in permissiveness to POMS, comparative transcriptomic approaches were computed before and after (different kinetic times) POMS exposure. Out of the 84 libraries sequenced, a mean number of 27.5 million reads per sample was obtained. One sample (F11N at 23 °C, 48 hpc, replicate #2), was discarded because of the low percentage of reads mapping to the oyster genome (21 % vs. an average of 82 % for the other samples). The PCoA was computed to get a general view of the influence of temperature and infection at the transcriptomic level (Fig. S3A&B). Temperature and infection were the principal drivers of transcriptional differences, with a
total of 67 % and 79 % of the total variance explained by these two axes in F11N and F14R families, respectively.

We first compared the constitutive differences between 23 °C and 30 °C for both families before POMS exposure (at 0 hpc). Genes identified in modules significantly correlated with the Temperature treatment were selected using WGCNA (Fig. S4A). We identified 7027 and 4099 genes correlated with 23 °C and 30 °C, respectively. These genes were subjected to an enrichment analysis that revealed 167 and 298 significant BP GO-terms that were associated with 23 °C and 30 °C, respectively (Fig. S4B&C and Table S8). From all these BP GO-terms, 48 % (23 °C) and 33 % (30 °C) were involved in metabolic processes, e.g., cellular amino acid metabolic process (GO:0006520), galactose (GO:0006012) and glutamate metabolic process (GO:0006536), cellular modified amino acid metabolic process (GO:0006575), macromolecule catabolic process (GO:0009057), tricarboxylic acid cycle (GO:0006099). This transcriptomic analysis revealed that the permissiveness to POMS could be linked to metabolic changes. We therefore assessed the metabolic status of acclimated oysters using a targeted LC/MS and GC/MS metabolomic analysis.

Using a WMCNA approach, we identified metabolites correlated with the temperature treatment (Figs. 3A & S5) and these were retained for metabolic pathway enrichment analysis. We identified 10 pathways explaining metabolomic differences between the two temperatures (Fig. 3B). These pathways were mainly involved in the metabolism of amino acid, cofactors and vitamins with the alanine, aspartate and glutamate metabolism (crg00250), the glycine, serine and threonine metabolism (crg00260), the arginine and proline metabolism (crg00330), the cysteine and methionine metabolism (crg00270) and the pantothenate and CoA biosynthesis (crg00770).

Then, we decided to integrate our two omic approaches. For this, we selected genes that were i) differentially expressed between the two temperatures (Fig. S4A and Table S8) as well as ii) involved in the pathways identified in our metabolomic approach (Fig. 3B and Table S9). We identified 265 unique DEGs correlated with 23 °C, and 118 unique DEGs correlated with 30 °C that belong to these pathways (Fig. 3C). Among the 5 pathways mentioned above (crg00250, crg00260, crg00330, crg00270 and crg00770), 60 genes were over-represented at 23 °C; they were mainly involved in the production of pyruvate and Coenzyme A, CoA (Fig. 4). These two metabolites serve as essential substrates for initiating the Krebs cycle, which generates reducing equivalents like NADH and FADH₂. These latter coenzymes are crucial for driving oxidative phosphorylation (OXPHOS), and thus ATP production under aerobic conditions. Conversely, only 14 genes from the same 5 pathways were over-represented at 30 °C, and 9 related metabolites (pantothenate, alanine, choline, dimethylglycine, sarcosine, NAD⁺, succinate, FAD and fumarate) were over-represented (Figs. 3A & 4). This accumulation of metabolites observed at 30 °C indicates the dysregulation of the associated pathway: the accumulation of pantothenate and the down-representation of genes involved in its consumption for CoA production at 30 °C reveal a disruption in the pantothenate and CoA biosynthesis pathway. Simultaneously, the overrepresentation of NAD⁺, FAD, fumarate and succinate, concomitantly to the under-representation of genes involved in the production of Krebs cycle intermediates (pyruvate, oxaloacetate and CoA) reflected the dysfunction of the Krebs cycle and associated pathways. These results suggest a modification in the energy production at 30 °C due to a disruption of the classical aerobic metabolism: glycolysis - Krebs cycle and OXPHOS.

### 3.4. Transcriptomic analysis revealed that oysters have better immune capabilities at non-permissive temperature

#### 3.4.1. Genes involved in antiviral immunity were constitutively more expressed at 30 °C

The differential transcriptomic analysis we performed after acclimation at both temperatures (0 hpc) highlighted the effect of

temperature change on immunity (Fig. S4B&C). From the BP GO-terms differentially represented between the two temperatures, 8 % (23 °C) and 18 % (30 °C) were involved in immune functions. To better characterize this effect of temperature on oyster immunity we focused our differential gene expression analysis on the immunome that was characterized in a previous study (de Lorgeril et al., 2018b).

The comparison of the immunome of the non-permissive (30 °C) and the permissive (23 °C) conditions revealed that both families displayed an over-expression of genes involved in antiviral functions at 30 °C. Enrichment analysis revealed GO-terms that are common to both families (response to virus, GO:0009615) and others exclusively identified in the F11N family (cytoplasmic pattern recognition receptor signaling pathway in response to virus, GO:0039528) or in the F14R family (intracellular signal transduction, GO:0035556) (Fig. 5). From these BP GO-terms, we identified DEGs involved in classical antiviral pathways that were either commonly over-represented at 30 °C in both families or specifically in one family (Table S10). They were involved in JAK/STAT pathway (e.g., IFI27 and SOCS5), RLR/STING pathway (e.g., STING), NF-κB pathway (e.g., TRAF7 and MIB2) or corresponded to antiviral effectors (e.g., SAMHD-1 and ADAR). We also identified DEGs encoding proteins involved in apoptosis and autophagy (e.g., DRAM2 and MKRN1) and in DNA damage response (e.g., RAD18/51/54) (Table S10). These results suggest that acclimation to 30 °C modified the oyster immunome, notably enhancing baseline antiviral defenses, which may confer an advantage during subsequent exposure to POMS.

#### 3.4.2. An earlier and more efficient antiviral response reduces the viral infection at 30 °C

After the characterization of transcriptomic differences due to temperature acclimation, we studied the influence of temperature on the capacity of the two oyster families to develop a transcriptomic response to a POMS exposure (Figs. 6A&B and S3). After POMS exposure, we observed a strong transcriptomic reprogramming, peaking after 12 hpc in the permissive condition (23 °C) with 9626 and 9423 DEGs representing 31.3 % and 30.7 % of the whole transcriptome in F11N and F14R, respectively (Fig. 6A&B). This shift occurring concomitantly with the maximum viral activity (Fig. 6A&B) was reported in a previous study (de Lorgeril et al., 2018b).

During the early phase of the infection (first 12 h), previously shown to be critical for surviving POMS exposure (de Lorgeril et al., 2018b), no viral activity was recorded at 30 °C, whereas viral replication was clearly initiated at 23 °C (Fig. 6A&B). On the other hand, significant changes occurred in the oyster transcriptome for both temperatures during the first 12 h (Fig. 6A&B). We therefore decided to investigate the genes putatively involved in blocking the disease during this early phase. WGCNA was used to identify co-expressed genes correlated with the Infection treatment during the first 12 h of exposure for both families in the two temperatures. Only genes belonging to modules positively correlated with the Infection treatment were selected (Rho value >0) (Fig. S6A&B and Table S11). The 3124 and 1373 genes identified at 23 °C and 30 °C, respectively, were submitted to an enrichment analysis giving 68 and 47 BP GO-terms that were differentially represented. Among them, 42 % and 36 % were involved in immunity for the 23 °C and 30 °C conditions, respectively (e.g., response to virus, GO:0009615; intracellular signal transduction, GO:0035556; immune response, GO:0006955) (Fig. S6C&D).

Based on these results, we focused our next transcriptomic analysis on the immunome to study the immune components that are over-represented during this early phase of the infection. This last analysis revealed that the antiviral response was significantly induced at 30 °C but repressed at 23 °C (Fig. 6C). Both families shared the following induced antiviral BP GO-terms at 30 °C: cytoplasmic pattern recognition receptor signaling pathway in response to virus (GO:0039528), pattern recognition receptor signaling pathway (GO:0002221), defense response to virus (GO:0051607) and cellular response to virus (GO:0098586). Response to virus (GO:0009615) was only over-
A

<figure><img src="image_7.png" /></figure>

Family
Temperature

Methionine sulfoxide

15-HEPE

5-HEPE

DHA

13-HDoHE

16-HDoHE

Uridine

L-Cystathionine

Tryptophan

9-HOTrE

Phenylalanine

Stearidonic Acid

Cystine

Proline

Creatinine

Glutamine

Eicosapentaenoic Acid

18-HEPE

4-HDoHE

PGI2

Hypoxanthine

Lysine

NAD

Fumarate

Succinate

Valine

Choline

Anthranilic Acid

Cytidine monophosphate

Dimethylglycine

FAD

2-Aminobutyric acid

Uric acid

Adenosine 3',5'-cyclic monophosphate

Cytosine

S-Adenosylhomocysteine

Ophthalmic acid

Histamine

Pantothenate

Guanosine

Alanine

Sarcosine

B

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

Family

<figure><img src="image_9.png" /></figure>

Temperature

<figure><img src="image_10.png" /></figure>

C

<figure><img src="image_11.png" /></figure>

(caption on next page)
<caption>**Fig. 3.** Transcriptomic and metabolomic integration reveals a temperature-driven metabolic reprogramming in oysters.</caption>

A) Heatmap of the 42 metabolites differentially associated with the Temperature treatment (between 23 and 30 °C 0 hpc), as determined by an unsupervised network (WMCNA).
B) Metabolic pathways differentially represented between 23 °C and 30 °C at 0 hpc based on FELLA. This identification was obtained using the metabolites associated with the Temperature treatment. Enrichment significance is shown by the p score, a statistically-adjusted prioritisation score. The differential pathways were primarily related to global metabolic processes, highlighting the impact of temperature on overall metabolism.
C) Heatmap integrating transcriptomic and metabolomic data. Genes associated with the temperature treatment (identified by WGCNA at 0 hpc) and mapped to the metabolic pathways revealed in (B) are shown. This integrative analysis demonstrates the interconnection between gene expression and metabolite abundance in temperature-induced metabolic pathways.

The color intensity represents the abundance of one metabolite/gene in a specific temperature condition: red shade refer to higher abundances, yellow shade to lower abundances. The green color represents 23 °C, the dark blue 30 °C, the brown F11N and the light blue F14R family.

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /><figcaption>**Fig. 4.** High temperature (30 °C) induces major metabolic reprogramming towards a partial anaerobiosis.</figcaption></figure>

Metabolic map integrating transcriptomic and metabolomic data, illustrating the interconnection of five significantly altered pathways (out of ten identified) in response to temperature. Metabolites with higher abundance at 30 °C are shown in blue boxes, while those at 23 °C are shown in green boxes. Genes with higher expression at 30 °C (0 hpc) are labeled in blue, and those at 23 °C are in green. Solid arrows represent direct metabolic conversions, while dashed arrows indicate indirect metabolic links.

The two metabolites in bold are those initiating the Krebs cycle. At the bottom is represented the electron transport chain followed by oxidative phosphorylation (OXPHOS) as a link with the production of cellular energy and the Krebs cycle in a classical aerobic state. The accumulation of specific metabolites associated with the Krebs cycle at 30 °C, along with the downregulation of numerous genes related to Krebs cycle precursors compared to 23 °C, suggests a disruption of aerobic metabolism at elevated temperature.

represented in the F11N family, while intracellular receptor signaling pathway (GO:0030522) was only over-represented in the F14R. Among the genes involved in these functions, we identified DEGs that had an earlier induction and/or a higher expression level at 30 °C when compared to 23 °C during the infection kinetics. These DEGs were involved in antiviral pathways (e.g., JAK/STAT, RLR/STING, effectors) (Table S12).

On the contrary at 23 °C, we observed over-represented BP GO-terms such as: regulation of apoptotic process (GO:0042981), regulation of cell death (GO:0010941) and regulation of programmed cell death (GO:0043067) with DEGs encoding inhibitors of apoptotic proteins (IAPs) (e.g., induction of apoptosis 2 inhibitor, putative inhibitor of apoptosis, repression of apoptosis-inducing factor 1) (Table S13). As previously shown, IAPs were highly induced in the susceptible oysters facilitating the success of the viral infection (de Lorgeril et al., 2018b).

These results demonstrated an early and efficient antiviral response at 30 °C, successfully reducing viral infection, while a typical pattern of pathogenesis involving IAPs was observed at 23 °C (de Lorgeril et al., 2018b).

## 4. Discussion

The aim of this study was to unravel the molecular mechanisms controlling the host *M. gigas* permissiveness to POMS disease. For this, we developed a comparative approach between permissive (23 °C) and non-permissive (30 °C) thermal conditions. We first confirmed that oysters displayed a typical pathogenesis process in the permissive condition (23 °C), with an increased viral activity during the first phase of the disease followed by bacterial dysbiosis involving a consortium of opportunistic bacteria known to colonize oysters and trigger bacteremia
<figure><img src="image_14.png" /><figcaption>**Fig. 5.** Temperature shapes the oyster immune profile, with constitutive enhancement of antiviral responses at 30 °C. Heatmap showing significantly enriched biological processes (BP) from the oyster immunome at 30 °C compared to 23 °C, at 0 hour post-cohabitation (hpc), in both oyster families. Enrichment analysis was performed using GO_MWU based on differentially expressed genes related to immune functions. The enrichment score is represented as the ratio between the number of DEGs associated with a given BP and the total number of genes annotated for that function. Positive values (yellow shades) indicate processes over-represented at 30 °C, while negative values (blue shades) indicate under-represented processes at this temperature. Black tiles represent biological processes that were not significantly enriched in the comparison. This analysis highlights a clear impact of the temperature on the immunome profile in oysters with a particular constitutive over-representation of antiviral immune functions in both families exposed at 30 °C, supporting the hypothesis that high temperature enhances basal immunity towards viral infection.</figcaption></figure>

leading to oyster death. This process was depicted in previous studies (Clerissi et al., 2023; de Lorgeril et al., 2018a, 2018b; Delisle et al., 2022; Kunselman et al., 2024; Lokmer and Mathias Wegner, 2015; Oyanedel et al., 2023). On the contrary, for the 30 °C non-permissive condition, we showed that the primary and secondary infections were hindered, resulting in lower mortalities. Previous research has highlighted the impact of elevated temperature on POMS permissiveness (Delisle et al., 2020). In the same study, a transcriptomic approach revealed the modulation of genes involved in immune and metabolic processes by temperature elevation. Specifically, genes related to metabolism, growth, and cell development were downregulated at 29 °C compared to 21 °C (Delisle et al., 2020). Although based solely on transcriptomic data, these findings led to the hypothesis explored in the present study—namely, that both immune function and metabolism play key roles in resistance to POMS. In the present study, we studied the mechanisms underlying these different susceptibilities to the disease through comparative and integrative transcriptomic and metabolomic

approaches between the two temperature conditions. Our results showed that exposing oysters at a temperature above the threshold of POMS permissiveness reduces viral infection and prevents further dysbiosis and bacteremia. This coincides with significant changes in energetic metabolism and immune capabilities making the host an inappropriate ecological niche for POMS disease development.

## 4.1. *High temperature creates a sub-optimal metabolic environment for efficient viral infection*

Under optimal thermal conditions, organisms display an aerobic metabolism characterized by the production of cellular energy (ATP) through the efficient but slow tryptic pathway: glycolysis - Krebs cycle - oxidative phosphorylation (OXPHOS) (Palsson-Mcdermott and O'Neill, 2013). In ectotherms, temperature increase has been associated with an acceleration of the cellular processes, leading to higher energy demands for maintenance (Ivanina et al., 2013; Jiang et al., 2020; Meistertzheim
A

F11N

<figure><img src="image_15.png" /></figure>

B

F14R

<figure><img src="image_16.png" /></figure>

C

F11N

<figure><img src="image_17.png" /></figure>

F14R

<figure><img src="image_18.png" /></figure>

(caption on next page)
<caption>**Fig. 6.** An earlier antiviral response and a blocking of viral replication occur at 30 °C.</caption>

A–B) Temporal analysis of viral transcriptomic activity combined with the oyster transcriptomic response during infection. For each family (F11N, A; F14R, B) viral activity (solid line, log scale: total RNA-seq reads mapped to the OsHV-1 µVar genome, previously shown in Fig. 1D–E) is plotted alongside the number of differentially expressed genes (DEGs; bars) at each time point relative to 0 hpc.
During the early phase of infection (up to 12 hour post-cohabitation, hpc), 8.3 % and 11.2 % of the transcriptome were differentially expressed at 23 °C, compared to 10 % and 3.3 % at 30 °C for F11N and F14R, respectively. At 12 hpc, viral transcription remained undetectable at 30 °C, while it increased by an average of 0.3 log at 23 °C. During the late phase (after 12 hpc), corresponding to active viral replication (2.5 log at 23 °C), DEGs represented ~30 % of the transcriptome at 23 °C, vs ~18 % at 30 °C. This suggests that temperature modulates the timing and magnitude of the host transcriptional response. The early phase (0–12 hpc) was therefore analyzed in detail to uncover the molecular mechanisms that would explain the viral regulation at 30 °C.
C) Heatmap focusing on the Biological Processes (BP) GO-terms significantly enriched in the immune network during the early phase of infection (0–12 hpc), compared between temperatures in the two families (GO_MWU). The enrichment ratio was calculated as in Fig. 4. These findings indicate that the early and coordinated immune response observed at 30 °C may restrict viral progression, whereas such a response is lacking at 23 °C.

et al., 2007; Sokolova, 2013; Sokolova et al., 2012). When temperature approaches the thermal tolerance limit, aerobic metabolism declines and switches to anaerobic to sustain the high energy demands (Sokolova, 2013). This was seen in bivalves subjected to high temperatures, both in mussels (Delorme et al., 2021) and in oysters (Ghaffari et al., 2019). After 16 days of acclimation at 30 °C, we show in the present study that *M. gigas* displays a metabolic reprogramming.

We identified ten differentially represented pathways between the two temperature conditions (23 °C and 30 °C). At 30 °C, we saw a disruption of the aerobic metabolism with the accumulation of anaerobic end-products (succinate and fumarate). The shift to anaerobic metabolism leading to the accumulation of succinate rather than lactate has been previously reported in marine invertebrates such as oysters and shrimp (Hammen, 1969; Meng et al., 2018). The demonstration of this metabolic shift was strengthened by integrating our omics data as we saw a concomitant under-representation of genes involved in the production of Krebs cycle's precursors (pyruvate and CoA) and the overrepresentation of a gene encoding the hypoxia-inducible factor alpha (HIFα) known to fine-tune the balance between aerobic and anaerobic metabolism at 30 °C (Table S8). HIFα is degraded under normoxic conditions but stabilized by the transport of anaerobic end-products from the mitochondria to the cytosol (King et al., 2006). Activation of the hypoxia-inducible transcription factor (HIF) by HIFα is then responsible for the over-activation of the glycolysis pathway and the repression of oxidative phosphorylation (OXPHOS) (King et al., 2006; Selak et al., 2005; Semenza, 2010). In addition, we observed a higher nutrient uptake at 30 °C, marked by the under-representation of glutamine and the over-expression of two genes (G6200 and G3271, Table S8) involved in glutaminolysis (Pant et al., 2021). Glutaminolysis allows the generation of Krebs cycle intermediates in the absence of pyruvate through glutamine (Girdhar et al., 2021). This step is handled by genes encoding a glutaminase (G6200) and a glutamate dehydrogenase (G3271), facilitating the conversion of glutamine to α-ketoglutaric acid. Consequently, the disruption of OXPHOS activity, the increase of glycolysis with the production of anaerobic end-products, and glutaminolysis at 30 °C illustrated a rewiring of the energy metabolism towards a Warburg-like effect (Dolezal et al., 2019; Pant et al., 2021; Su et al., 2014; Warburg et al., 1924). The Warburg effect is adopted by cancer cells to promote their growth, their proliferation and their survival (Liberti and Locasale, 2016; Pant et al., 2021). Indeed, glycolysis is a sub-optimal but faster way to produce energy. In our experiment, this faster ATP production at 30 °C is set up to sustain the increasing energetic needs of oysters due to the physiological acceleration induced by the increase in temperature at 30 °C which is far higher than the thermal optimum for *M. gigas* growth, estimated at 20 °C (Ivanina et al., 2013; Jiang et al., 2020; Meistertzheim et al., 2007; Sokolova et al., 2012; Wiltshire, 2007). This Warburg-like effect indicates a shift in metabolism caused by an accelerated metabolic rate. Such a shift could influence viral replication by altering the availability of cellular resources needed to overcome the oyster's antiviral defenses (Fig. 7).

It is important to note that the effects of temperature observed under controlled conditions may not directly translate to intertidal environments. In our study, oysters were acclimated for 16 days before

infection, eliminating short-term fluctuations typical of intertidal zones. However, a recent study by Corporeau et al. (2022) suggests that intertidal oysters exposed to intense solar radiation during summer can reach body temperatures exceeding the permissive range for OsHV-1 µVar infection (16–24 °C), potentially reducing susceptibility to the virus.

## 4.2. High temperature favors constitutive antiviral immunity

Acclimation at high temperature induced a clear transcriptomic reprogramming. We showed that 36 % of the total oyster transcriptome was differentially expressed between the two temperature conditions (30 °C and 23 °C) during acclimation and a large proportion of these genes were involved in immune processes. Elevated temperatures have been shown to induce an immunosuppression state in some marine species (Rowley et al., 2024), while in oysters (Delisle et al., 2020; Kim et al., 2017; Meistertzheim et al., 2007; Rahman et al., 2019; Yang et al., 2017; Zhu et al., 2016) and other marine invertebrates (Brener-Raffalli et al., 2022; Eliachar et al., 2022; Jing et al., 2023; Mydlarz et al., 2006; Rahman et al., 2019), high temperatures have been associated with an enhancement of immunity. As identified in the present work, the oyster's immunome showed an over-representation of antiviral functions at 30 °C with genes involved in antiviral pathways (e.g., JAK/STAT, RLR/STING, antiviral effectors) (Green et al., 2015; Green and Speck, 2018). We also identified over-expressed genes encoding proteins involved in DNA damage response and repair. While the constitutive over-expression of genes involved in antiviral pathways or genes encoding antiviral effectors can limit viral infection, the expression of genes involved in DNA damage response and repair could participate in maintaining homeostasis of cells confronted with viral infection (Luftig, 2014; Parthasarathy et al., 2024). Indeed, viruses are obligate intracellular pathogens relying on host cells to replicate. Thus, by entering host cells, viruses provoke a DNA damage response, which is consistent with double-stranded DNA herpesvirus infections (Luftig, 2014). The stress response induced by high temperature increases antiviral immune capabilities, thereby reducing the host's susceptibility to viral infection (Fig. 7).

## 4.3. High temperature favors a faster antiviral response in oysters exposed to POMS

The primary step of POMS disease is the viral infection by OsHV-1 µVar (de Lorgeril et al., 2018b; Petton et al., 2021). It is well known that the early induction of antiviral genes within the first 12 h of the infectious process is crucial to combat POMS disease. This phenomenon was characterized in resistant oyster genotypes, enabling them to effectively counter the infectious process, while the susceptible oysters respond later and inefficiently (de Lorgeril et al., 2018b). Indeed, the dynamics of the host's response to a pathogen is critical in determining the success, or failure, of controlling pathogen proliferation and preventing the progression of the pathogenesis process (Alizon et al., 2011). The rapid induction of antiviral immunity in resistant genotypes prevents POMS pathogenesis by hindering the virus proliferation and the
A 23°C_Permissive condition

<figure><img src="image_19.png" /></figure>

B 30°C_Non permissive condition

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

(caption on next page)
**Fig. 7.** Schematic overview of molecular mechanisms underlying POMS disease progression under permissive (23 °C) and non-permissive (30 °C) temperature conditions.

A) At 23 °C (permissive condition), thermal acclimation leads to the classical progression of POMS disease. During infection, OsHV-1 µVar targets hemocytes (2), compromising their physiological integrity and immune function (3). This immunocompromised state impairs the oyster's ability to control its microbiota, leading to dysbiosis (4) and subsequent fatal secondary bacterial infections (6).

B) At 30 °C (non-permissive condition), thermal acclimation promotes a sustained enhancement of antiviral functions and induces a metabolic shift towards a Warburg-like profile (0). This reprogramming may disrupt the virus' ability to efficiently exploit host cellular energy for replication. The elevated baseline expression of antiviral genes, combined with an effective immune response upon infection (2), contributes to rapid viral clearance. The absence of immune dysfunction (3) enables the oyster to maintain microbiota homeostasis (4), thereby preventing dysbiosis and the secondary bacterial infections that typically lead to mortality under permissive conditions (6).

subsequent secondary bacterial infection. In the present study, we showed that viral activity was reduced at 30 °C and this occurred during the first 12 h following infection. We also showed at 30 °C a clear and rapid induction of a series of genes involved in antiviral pathways such as JAK/STAT, RLR/STING, and NF-κB and a series of antiviral effectors already reported in different studies (Fig. 7). Indeed, the JAK/STAT pathway is known to transcribe hundreds of interferon-stimulated genes (ISGs), which play key roles in antiviral defense by targeting and inhibiting cellular processes essential for viral replication and spread (Green and Speck, 2018; Schoggins and Rice, 2011). The RLR/STING pathway, a component of the interferon (IFN) response, is also recognized for its antiviral function (Agius et al., 2020; Green et al., 2015; Li et al., 2022; Ma et al., 2024). Together, these pathways are central to the antiviral immune response in oysters during POMS outbreaks (Agius et al., 2020; de Lorgeril et al., 2018b; Gawra et al., 2023; Green et al., 2015; Green and Speck, 2018; Guo et al., 2015). On the contrary, at 23 °C, genes involved in antiviral immunity are either constitutively less expressed or their expression is delayed, facilitating POMS disease development. The enhanced antiviral immune response observed at elevated temperatures in oysters is not unique to this species. Similar temperature-dependent immune mechanisms have been reported in other marine invertebrates, such as shrimp. For instance, when infected with White Spot Syndrome Virus (WSSV), shrimp exhibit a behavior known as “behavioral fever,” migrating towards warmer waters where higher temperatures are associated with reduced mortality (Rakhshaninejad et al., 2023; Rakus et al., 2017). This protective effect is mediated by a temperature-induced immune response in which elevated temperatures stimulate HSP70 expression. HSP70, in turn, activates the Toll4-Dorsal signaling pathway, leading to the upregulation of antimicrobial peptides and enhanced viral clearance (Xiao et al., 2024). More broadly, elevated temperatures are known to modulate immune responses in a species-dependent manner (Rowley et al., 2024). Heat stress can activate key immune pathways—including those involving heat shock proteins (HSPs), apoptosis, NF-κB, and TNF signaling, DNA repair—as shown in mussels (Wei et al., 2025), cuttlefish (Wang et al., 2023; Zhao et al., 2025), crabs (Yin et al., 2025), among others.

## 5. Conclusion

Taken together, our results showed that high temperature (30 °C) corresponded to an inappropriate ecological niche for POMS development. At this high temperature, the metabolism shifted towards an anaerobic metabolism to sustain the substantial energetic needs induced by such a temperature. This metabolic reprogramming creates conditions that are unfavorable for robust viral replication, limiting the virus's ability to overcome the oyster's antiviral immune defenses. At the same time, this high temperature induced a generalized stress response, with the up-regulation of several biological processes including antiviral pathways. When the virus interacts with the oyster at a high temperature, it may encounter a host cellular environment that is less able to meet its energetic needs for successful development, coupled with an environment more hostile to the virus due to the higher antiviral activity. It would be interesting to investigate the consequences of a potential antiviral priming conferred by the high temperature during the

acclimation. A constitutive protection against viral infection due to 16 days at 30 °C might be a novel idea to tackle POMS economic challenges in the oyster farming industry.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2025.180026.

## CRediT authorship contribution statement

**Léo Duperret:** Writing – review & editing, Writing – original draft, Methodology, Investigation. **Alejandro Valdivieso:** Writing – review & editing, Writing – original draft, Investigation. **Emily Kunselman:** Writing – review & editing, Investigation. **Bruno Petton:** Writing – review & editing, Methodology, Conceptualization. **Benjamin Morga:** Writing – review & editing, Methodology, Investigation. **Julien de Lorgeril:** Writing – review & editing, Conceptualization. **Fabrice Permet:** Writing – review & editing, Methodology. **Lionel Degremont:** Writing – review & editing, Methodology. **Nicole Faury:** Writing – review & editing, Methodology. **Jean-François Allienne:** Writing – review & editing, Methodology. **Juliette Pouzadoux:** Writing – review & editing, Methodology. **Océane Romatif:** Writing – review & editing, Methodology. **Gaelle Courtay:** Writing – review & editing, Methodology. **Cristián J. Monaco:** Writing – review & editing. **Eve Toulza:** Writing – review & editing, Writing – original draft, Supervision, Methodology, Investigation. **Lizenn Delisle:** Writing – review & editing. **Jeremie Vidal-Dupiol:** Writing – review & editing, Writing – original draft, Supervision, Methodology, Investigation, Conceptualization. **Arnaud Lagorce:** Writing – review & editing, Writing – original draft, Supervision, Methodology, Investigation. **Guillaume Mitta:** Writing – review & editing, Writing – original draft, Supervision, Methodology, Investigation, Funding acquisition, Conceptualization.

## Declaration of competing interest

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

## Acknowledgments

We would like to thank the staff of the Ifremer experimental platforms at Argenton and Bouin for providing experimental facilities and the production of the biological material used in this study. We thank Bruno Chollet, Louis Boismorand, Charlotte Corporeau, Mathias Huber, Marianne Alunno-Bruscia and Moussa Diagne for assistance in the experimental design and procedure. We are also grateful to Margot Doberva and Michèle Laudié from the Bio-Environment platform (UPVD, Région Occitanie, CPER 2007–2013 Technoviv, CPER 2015–2020 Technoviv2) for technical support in library preparation and sequencing. We thank the Montpellier Alliance for Metabolomics and Metabolism Analysis (MAMMA) platform (BioCampus Montpellier) for technical support in the metabolomic analysis. We would like to thank Jean-Michel Escoubas for his knowledge on the virus. We thank the bioinformatic service of Ifremer (SeBiMER) for their help in bioinformatics. This study is set within the framework of the “Laboratoire d’Excellence (LABEX)” TULIP (ANR-10-LABX-41). The present study
was supported by the DECICOMP project (ANR-19-CE20-0004) and the MAHEWA project (France 2030, ANR-23-POCE-0001).

## Data availability

RNA-seq data and amplicon sequences for microbiota analyses have been made available through the ENA database under Bioproject DECICOMP: PRJEB86647. RNA-seq data were submitted under accession numbers PRJEB86646 for the 30 °C condition and PRJEB86525 for the 23 °C condition. Amplicon sequences for microbiota analyses are available under accession numbers PRJEB86594 for the 30 °C condition and PRJEB86519 for the 23 °C condition. Other data analyzed during this study are included in this submitted article and its supplementary information files. Complementary information is available from the corresponding authors on reasonable request.

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