Differential basal expression of immune genes confers Crassostrea gigas resistance to Pacific Oyster Mortality Syndrome

doi:10.21203/rs.2.16448/v1

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Differential basal expression of immune genes confers Crassostrea gigas resistance to Pacific Oyster Mortality Syndrome

https://doi.org/10.21203/rs.2.16448/v1

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published 20 Jan, 2020

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Background As a major threat to the oyster industry, Pacific Oyster Mortality Syndrome (POMS) is a polymicrobial disease affecting the main oyster species farmed across the world. POMS affects oyster juveniles and became panzootic this last decade, but POMS resistance in some oyster genotypes has emerged. While we know some genetic loci associated with resistance, the underlying mechanisms remained uncharacterized. So, we developed a comparative transcriptomic approach using basal gene expression profiles between different oyster biparental families with contrasted phenotypes when confronted to POMS (resistant or susceptible).

Results We showed that POMS resistant oysters show differential expression of genes involved in stress responses, protein modifications, maintenance of DNA integrity and repair, and immune and antiviral pathways. We found similarities and clear differences among different molecular pathways in the different resistant families. These results suggest that the resistance process is polygenic and partially varies according to the oyster genotype.

Conclusions We found differences in basal expression levels of genes related to TLR-NFκB, JAK-STAT and STING-RLR pathways. These differences could explain the best antiviral response, as well as the robustness of resistant oysters when confronted to POMS. As some of these genes represent valuable candidates for selective breeding, we propose future studies should further examine their function.

Epigenetics & Genomics

Pacific oyster

oyster disease

resistance

OsHV-1

antiviral molecular pathways

invertebrate immunity

Originally from Asia, the Pacific oyster (Crassostrea gigas) has been introduced to numerous countries throughout the world (Canada, USA, Australia, New-Zealand, Chile, Mexico, Argentina, Brazil, South Africa, Namibia and in numerous European countries including France) during the 20th century and has become the main oyster species farmed worldwide [1]. For decades, C. gigas has suffered from mortalities, but the severity of these outbreaks has dramatically increased since 2008. These outbreaks mainly affect juvenile stages, decimating up to 100% of young oysters in French farms [2]. In recent years, this mortality syndrome, designated Pacific oyster mortality syndrome (POMS), has became panzootic, being observed in all coastal regions of France and numerous other countries worldwide [3]. Today, POMS consequences are dramatic and represents a significant threat for the global oyster industry [2]. Research efforts have revealed a series of factors contributing to the disease, including infectious agents interacting with seawater temperature and oyster genetics [2, 4–9]. Recently, holistic molecular approaches revealed the mechanism of POMS. These studies showed that an infection by the Ostreid herpesvirus (OsHV–1 μVar) is the critical step in the infectious process leading to an immune-compromised state by altering hemocyte physiology. This first process is followed by a microbiota destabilization which “opens the door” to bacterial pathogens (e.g. vibrios) that target hemocyte to induce their lysis. The infectious process is completed with subsequent bacteraemia, which is the ultimate step inducing oyster death.

However, some oysters are disease-resistant to POMS. Genetic studies on oyster resistance revealed a significant additive genetic component for survival during OsHV–1 infection [4, 10, 11]. Over the past decade, many genomic resources have been developed like a reference genome [12] and SNP arrays [13]. These resources enabled investigating the genetic architecture of C. gigas resistance to OsHV–1 infection [14]. In this last study, a population of oyster juveniles was experimentally challenged with OsHV–1 and genotyped using a high density linkage map constructed for the Pacific oyster. The genome-wide association developed suggested a polygenic nature of resistance to OsHV–1 and highlighted region of linkage group 6 containing a significant QTL affecting host resistance [14]. Several SNPs showing an association with survival and/or viral load were located in several genes encoding a RAN Binding Protein 9, a Coronin and an actin motor protein Myo10. However, their involvement in the resistance process remains obscured. A recent transcriptomic approach developed on different oyster biparental families displaying contrasted susceptibilities to POMS showed that the early induction of genes involved in antiviral defense is a hallmark of resistant families [15]. However, the genetic components responsible for this early induction remain unidentified.

To fill this gap in knowledge and to identify putative transcriptomic determinants associated to POMS resistance, we compared the basal transcriptomes of six biparental families of oysters displaying contrasted susceptibilities to the disease (3 resistant and 3 susceptible). Here, we showed that genes involved in stress response, protein modifications, maintenance of DNA integrity and repair, and immune and antiviral pathways are differentially expressed in resistant oysters.

Infectious environments select oysters resistant to POMS

In 2015, oyster broodstocks were collected in two distinct geographic areas (Brittany-Atlantic coast and Gulf of lion- Mediterranean coast) and two sampling sites (farming, high biomass and non-farming, low biomass) from each area (Fig. 1).. These broodstocks were used to produce 12 biparental families (3 families from each origin and sampling sites). Three additional biparental families were produced from broodstocks originating from a mass selection program for higher survival for POMS [16] (Fig. 1).. These 15 oyster families were subjected to four infectious challenges performed with two infectious environments (Atlantic and Mediterranean) and two experimental procedures (mesocosm and field infections) (Fig. 2 and Table 1)..

High variability in percentages of mortality, ranging from 1% to 100%, was observed among families (Table 1).. Family F15 showed the most susceptibility with a mortality rate higher than 97% for any infection trial. In contrast, Family F21 showed the highest resistance whatever the infection trial. Taking into account the 15 families and the 2 infectious environments, the percentage of mortalities observed in the field were not significantly different than those obtained in mesocosm conditions (Mann-Whitney test, p = 0.06). Overall, disease susceptibility was quite similar for the different families in the different infection trials (Table 1).. Mortalities observed for the 15 families were slightly more important for the Atlantic infectious environment than for those from the Mediterranean infectious environment (Mann Whitney, p = 0.02) (Table 1)..

As expected, the 3 families (F21, F23 and F28) produced from broodstocks coming from the mass selection program [16] displayed low percentages of mortality (Fig. 3 and 4).. Similarly, families obtained from wild oysters sampled in a farming area and putatively submitted to disease selection were also more resistant than those from non-farming areas ((Mann Whitney, p = 0.018), Fig. 4)..

The 6 families, retained for the subsequent comparative transcriptomics, were the three best oyster families for POMS resistance (F21, F23 and F48 renamed R_F21, R_F23 and R_F48, respectively) and the three worst (F11, F14 and F15, renamed S_F11, S_F14 and S_F15, respectively) (Fig. 3)..

Stress and immune functions are enriched in the basal transcriptome of resistant oysters

To identify putative transcriptomic determinants associated with POMS resistance, we compared the basal transcriptome profiles of the 6 selected families (R_F21, R_F23, R_F48, S_F11, S_F14 and S_F15), maintained in the same hatchery conditions and without disease challenge. We sequenced a total of 36 RNA-seq libraries (6 families, 2 independent experiments and 3 replicates for each experiment). Sequencing yielded between 30.1 and 39.3 million Illumina single reads per sample of which 70.1–74.9% mapped to the C. gigas V9 reference genome.

From these RNA-seq data, we compared the basal transcriptome of each resistant family to the three susceptible families using DEseq (DEseq p-value < 0.05). The differentially expressed genes (DEGs) common between each comparison were retained for further analysis. This strategy identified 3304, 2711 and 3259 DEGs modulated in the same way (up- or down-represented for the three comparisons) in R_F21, R_F23 and R_F48, respectively (Fig. 5A).. Among these DEGs, (i) 299 were differentially expressed by the three resistant families, (ii) 924 were differentially expressed by both R_F21 and R_F23, (iii) 261 were differentially expressed by both R_F23 and R_F48, and (iv) 308 were differentially expressed by both R_F21 and R_F48 (Fig. 5A and Additional file 1). The remaining 1773, 1227 and 2391 DEGs displayed a specific differential expression in R_F21, R_F23 and R_F48, respectively (Fig. 5A and Additional file 2, 3 and 4).

To determine the enriched functions, we used a gene ontology (GO) enrichment analysis. As the mechanisms underlying resistance can be specific or shared by oyster families, we performed the enrichment analysis on DEGs for each resistant family separately and also on DEGs shared by at least two of the three resistant families. First, the analyses performed on DEGs of each resistant family separately showed a limited number of enriched functions (7, 6 and 5 for R_F21, R_F23 and R_F48, respectively; Fig. 5B, 5C and 5D, respectively). Interestingly, four functional categories (“defense response to other organism”, “response to external stimulus”, “defense response” and “response to stress”) were enriched for the 3 resistant families. It is noteworthy that R_F21 and R_F23 shared two additional enriched categories (“receptor-mediated endocytosis” and “protein modification by small protein conjugation or removal”). Finally, a functional category related to the “actin polymerization and depolarization” showed enrichment in R_F48 only, whilea functional category related to “ubiquitin-dependent protein catabolic process” showed enrichment in R_F21 only. Second, the GO enrichment analysis on DEGs shared by at least two of the three resistant families (1 792 DEGs, Additional file 1) revealed five enriched functional categories (Fig. 5E):: “defense response to other organism”, “response to external stimulus”, “defense response”, “response to stress” and “protein modification by small protein conjugation or removal”. DEGs falling in these five enriched categories (374 genes) are shown in Additional file 1 (CGI indicated in yellow).

Resistant oysters differentially express common and specific immune genes

To further delineate the molecular mechanisms underlying POMS resistance shared by the different resistant families, we first analysed the 374 DEGs belonging to the above identified functions and shared by at least two of the three resistant families (CGI indicated in yellow in Additional file 1). Among these genes, we found members of large multigene families known to be involved (i) in stress response like heat shock proteins (HSP) and glutathione S-transferases, (ii) in protein modifications like ubiquitin ligases and Tripartite Motif containing proteins (TRIM), (iii) in maintenance of DNA integrity and repair like Poly [ADP-ribose] polymerases (PARP), nucleases and helicases, (iv) in PAMP (Microbe Associated Molecular Pattern) recognition (PRR) like C1q domain containing proteins, lectins, scavenger receptors (SR), Fibrinogen domain containing proteins, hemagglutinins and (v) in antiviral defense like IFI44 proteins. We also identified a series of genes putatively involved in antiviral defense and signaling (TLR-NF-κB, JAK-STAT and RLR-STING pathways, Fig. 6A).. A putative endosomal Toll-like receptor 13 was overexpressed in the 3 resistant families. A dsRNA-specific adenosine deaminase (ADAR) was over-represented in R_F21 and R_F23. A stimulator of interferon genes (STING) is under-represented in R_F23 and R_F48.

We then analysed DEGs in only one resistant family and belonged to the enriched functions from Figure 5 (CGI indicated in yellow in Additional file 2, 3 and 4).. This analysis highlighted specific processes associated with resistance in each resistant genotype. These genes represented 371, 251 and 315 DEGs in R_F21, R_F23 and R_F48, respectively. In these specific sets of DEGs, we again found several genes belonging to the same large multigene families reported above (HSP, glutathione S-transferases, ubiquitin ligases, TRIM, PARP, nucleases, helicases, PRR and IFI44). In addition, several genes involved in antiviral and signaling pathways were also found differentially expressed in each resistant family specifically (TLR-NF-κB, JAK-STAT and RLR-STING pathways, Fig. 6B).. Transcripts corresponding to a Toll-like receptor (TLR), 2 myeloid differentiation primary response 88 (MyD88), a TNF receptor-associated factor (TRAF), a deoxynucleoside triphosphate triphosphohydrolase (SAMHD1), and 2 stimulator of interferon genes (STING) were differentially represented in the R_F21family. Transcripts corresponding to 2 TLRs, a MyD88, a TRAF, a 2’,5’- oligoadenylate synthase (2’,5’-OAS) and an interferon regulatory factor (IRF), were differentially represented in the R_F23family. However, in these 2 families, the majority (9/13) of these DEGs was under-represented in comparison with susceptible oysters (Fig. 6B).. In contrast, the majority (10/11) of DEGs in the R_F48 family was over-represented in this resistant family. They corresponded to an interleukin–1 receptor-associated kinase (IRAK), a NF-κB p105 subunit, 2 Serine threonine- kinases TBK1, a signal transducer and transcription activator (STAT), 2 suppressors of cytokine signaling (SOCS), a STING and 2 IRFs (Fig. 6B).. Only a 2’,5’- oligoadenylate synthase (OAS) appeared under-represented in R_F48.

Fifteen oyster families were produced and phenotyped using mesocosm and field infections. No significant differences in terms of mortality between mesocosm and field infections were evidenced. This result suggested that the disease developed in mesocosm experiments accurately reproduces the disease in the natural environment with the same outcomes. The mortalities observed with the Atlantic infectious environment were slightly higher than those obtained with the Mediterranean infectious environment. This difference can be explained first by some differences in the pathogenesis process playing in these two distinct geographic areas. Future studies will investigate this specific question. A second explanation can be the age difference of oysters submitted to these two infectious environments. Indeed, the oysters submitted to the Mediterranean infections were 2 months older. As resistance to the disease increases with age [4, 18], we cannot exclude that older oysters were slightly less susceptible. As expected, higher survival rates occurred in oyster families produced from broodstocks coming from a breeding program using mass selection [16] or from broodstocks recruited in farming areas compared to those from broodstocks recruited in non-farming areas. This last result shows that the selective pressure exerted by the infectious environment during the first years after recruitment is sufficient to select oysters more resistant to POMS. This is in agreement with the significant genetic basis to increase the resistance to OsHV–1 in C. gigas [19, 20]. In contrast, wild oysters collected in non-farming areas still showed high susceptibility to POMS suggesting either the absence of disease pressure or a much lower disease pressure than in farming area as previously shown [21].

In order to identify the transcriptomic determinants of POMS resistance, we selected the three more resistant (R) and the three more susceptible (S) oyster families and compared their basal transcriptome when they were maintained in the same controlled conditions and without exposure to the disease. Two R families came from broodstocks selected through “mass selection” (R_F21 and R_F23), and one R family came from broodstocks recruited in a farming area and selected through “natural selection” (R_F48). The three S families (S_F11, S_F14 and S_F15) were produced from broodstocks recruited in non-farming areas. Our transcriptomic analysis revealed a clear modulation and enrichment of genes belonging to functions related to defense, stress and protein modifications among the resistant and susceptible families. A series of genes related to these functional categories belong to large multigene families like Tripartite Motif containing proteins (TRIM), ubiquitin ligases, IFI44, heat shock proteins, glutathione S-transferases, proteins involved in maintenance of DNA integrity and repair like Poly [ADP-ribose] polymerases (PARP), nucleases and helicases, C1q domain containing proteins, lectins, scavenger receptors, fibrinogen domain containing proteins and hemagglutinins. Other genes involved in immune and antiviral pathways (TLR-NF-κB, JAK-STAT and RLR-STING) were also found to be modulated in our study. Such differences in basal expression of immune genes have been already described in resistant and susceptible cultivars of litchi, apple or soybean to pathogens [22–24]. Variations of immune status between human populations have also been described at genetic and epigenetic levels, and these changes modulate several key regulators of innate immunity [25, 26]. These differences in immune status likely arise from the different selected pressures experienced that impact the host response to pathogens, especially in African populations, which develop a strong inflammatory response compared to European populations [25]. Even if the genetic determinism of POMS resistance is clear [4, 10, 11], we cannot exclude that the difference in expression reported in our study can be controlled by epigenetic mechanisms known to influence gene expression in invertebrates [27]. This possibility shall be investigated in future studies.

As R_F21, R_F23 and R_F48 can control virus replication in oyster tissues [15], we focussed particularly in this discussion on genes belonging to pathways clearly involved in oyster antiviral defense [28]. Several of the multigene families identified are potentially implicated in antiviral immunity. TRIM, one of the most represented multigene family in our sets of DEGs (one third), can target viral proteins for ubiquitination, in association with ubiquitin ligases (also identified here), to inhibit viral replication and induce RLR-STING and TLR-NF-κB signaling pathways, which contribute to antiviral defense [29]. IFI44 multigene family are interferon-alfa inducible proteins, which are associated with infection of several viruses and can affect viral replication [30]. Some other genes like PARP could also participate to the defense against viral infection [31]. As TLR-NF-κB, JAK-STAT and RLR-STING pathways are 3 conserved pathways crucial to mount an efficient antiviral response [28], we made a particular focus on the DEGs belonging to these pathways. Only transcripts corresponding to one gene belonging to these pathways is overrepresented in the 3 resistant families. It corresponded to an endosomal Toll-like Receptor displaying similarities to the TLR 13, which can act as a sensor of viral and bacterial RNA in the TLR-NF-κB signalling pathway [32, 33]. This gene is particularly interesting because its sensor function could explain how these three resistant family may detect the viral infection earlier to mount a more rapid and efficient antiviral response, which is a common feature of these three resistant family [15]. Another gene, a dsRNA-specific adenosine deaminase (ADAR), displayed an over-representation of its transcripts in R_F21 and R_F23. ADAR can act on viral development in the host via its connection with the JAK-STAT signaling pathway [34]. As this same antiviral role was recently proposed in C. gigas [35], TLR 13 and ADAR represent good candidates whose function should be examined in future studies. In addition, a series of genes corresponding to the TLR-NF-κB, JAK-STAT and RLR-STING pathways were differentially expressed only in one family. Overall, we obtained very different results for this set of DEGs between families produced from “mass selection” (R_F21 and R_F23) and “natural selection” (R_F48) broodstocks. Indeed, most transcripts corresponding to these pathways were over-represented in the R_F48family. They correspond to IRAK, NF-kB and TBK1 (TLR-NF-κB pathway), STAT and SOCS (JAK-STAT pathway) and IRFs and STING (RLR-STING pathway) genes. For the two selected families produced from “mass selection” (R_F21 and R_F23), genes belonging to these molecular pathways were also identified. The majority of the corresponding transcripts were under-represented (2 TLRs, a MyD88, 2 TRAF, 2 STING, a 2’,5’-OAS and an IRF), while a minor part of them were over-represented (a TLR, 2 MyD88, and a SAMHD1). Thus, the molecular phenotype of the R_F21 and R_F23families differs in part from that of the family R_F48. R_F21 and R_F23 families are derived from a four-generation selection program conducted in natural environment showing a significant positive response to selection with a gain of resistance/survival that accumulated over the generations [16]. Consequently, the R_F21 and R_F23 families might develop mechanisms of resistance and a genetic architecture for this trait that are significantly different by comparison with the R_F48family whose genitors were confronted to a single POMS outbreak. We propose this rationale could explain the differences in the transcriptomic phenotype observed.

Overall, our results show that the selection process in these different oyster families has impacted their molecular phenotype in numerous molecular pathways, particularly for genes involved in functions related to antiviral immunity and maintenance of DNA integrity and repair. These modifications could participate in improving their fitness when confronted to a viral infection. Several identified DEGs were modulated in at least 2 disease resistant families, but most of them were differentially expressed in only a single family. Taken together, these results suggest that resistance mechanisms can vary at least partially among genotypes and that they are probably complex (multigenic). This is in agreement with a recent study of Gutierrez and collaborators [14], which suggested a polygenic nature of oyster resistance to OsHV–1.

A previous study demonstrated that POMS resistant oyster families present a more rapid antiviral response compared to susceptible oyster families [15]. This rapid antiviral response of resistant oysters blocks replication of the herpes virus OsHV–1 and prevents subsequent bacteraemia by opportunistic bacterial pathogens [15]. Here, we found differences in basal expression levels of genes related to immunity suggesting different immune status between resistant and susceptible oysters. These expression differences occurred for genes that mediate stress responses, protein modifications, maintenance of DNA integrity and repair, and immune and antiviral pathways, including sensors. These differences could explain the early antiviral response, as well as the robustness of resistant oysters when confronted to POMS. Further studies will determine the function of these promising candidates and uncover the link between these expression differences and disease resistance. Such functional studies must precede identifying valuable candidates for future successful selective breeding.

Production of biparental oyster families. Four wild stocks of Crassostrea gigas were sampled (collected in field by ourselves) in farming as well as in non-farming areas in two regions (French Mediterranean and Atlantic coasts) (Fig. 1).. In addition, a fifth stock used selected oysters for their higher resistance to the infection by OsHV–1 [16]. From each stock, three bi-parental families were produced as previously described [15]. The 15 oyster families were maintained under controlled biosecured conditions to ascertain that no oyster pathogens would interfere with further experiments. The “pathogen-free” status of the animals was confirmed by i) the absence of OsHV–1 DNA detection by qPCR and ii) a low Vibrio presence (<10 cfu/g tissue) determined by isolation on selective culture medium (thiosulfate-citrate-bile salts-sucrose agar, TCBS) [7]. Oysters were observed to remain free of any abnormal mortality throughout the larvae until the beginning of the experimental and field infections.

Mesocosm infections. The mesocosm infection protocol consists of cohabitation in controlled conditions between C. gigas oysters carrying the POMS disease (“donors”) and “pathogen-free” C. gigas oysters (“recipients”) [18]. A first experimental infection used donors previously exposed to the infectious environment of Atlantic origin. The donors were “pathogen-free” oysters (mixture of 116-day-old oysters from the 15 families, 17,700 g with a mean individual weight of 1.1 g) were first deployed in a farming area (Logonna Daoulas, lat 48.335263, long –4.317922) during the infectious period until the first mortalities occurred (0.01%). Then, donor oysters were transferred back to the laboratory and placed in contact with “pathogen-free” recipient oysters in a controlled environment (Fig. 2).. The experiment was conducted using the same biomass (1,120 g) of donors in cohabitation in 15 independent tanks (500 l), each containing one of the 15 families (recipient oysters with a mean individual weight of 1.1 g) which were previously acclimatized in these structures for two weeks. The Atlantic experimental infection began on 17 July 2015 and ended on 31 July 2015. Similarly, a second experimental infection used donors previously exposed to the infectious environment of Mediterranean origin in a farming area (Thau lagoon, lat 43.418736, long 3.622620), except that donors deployed were a mixture of 176-day-old oysters from the 15 families (26,500 g with a mean individual weight of 1.7 g) and that the biomass of donors and the biomass of recipients in each tank was 1,760 g each (recipient oysters with a mean individual weight of 1.73 g). The Mediterranean experimental infection began on 21 September 2015 and ended on 6 October 2015.

Field infections. Concomitantly to the mesocosm infections, the survival rates of the 15 oysters families (n = 100 per family) were also recorded in oyster farms in the two infectious environments where were deployed the donors (Fig. 2, Logonna Daoulas in Atlantic area and Thau lagoon in Mediterranean area). The Atlantic field infection began on July 17^th 2015 and ended on August 3^rd 2015 while it was on September 21^st 2015 and October 6^th 2015 for the Mediterranean.

Statistical analyses. For Mesocosm and field infections, statistical data analysis was conducted in GraphPad Prism (V6.0) for Windows (GraphPad Software, La Jolla, USA). For all analysis, statistical significance was set at p < 0.05. We performed non-parametric Kruskal-Wallis tests to compare mortalities. When Kruskal-Wallis tests were significant, we computed pairwise comparisons using Mann-Whitney U t-test.

Oyster transcriptome analyses. Before experimental infection, 10 oysters in triplicate were randomly sampled from each family without blinding protocols. The shell was removed, and pools of 10 oysters were flash frozen in liquid nitrogen. Oyster pools (10 individuals per pool) were ground in liquid nitrogen in 50-ml stainless steel bowls with 20-mm-diameter grinding balls (Retsch MM400 mill). The obtained powders (stored at –80°C) were then used for extracting RNA. RNA was extracted from powdered oysters using the Direct-Zol RNA Miniprep kit (Proteigene) according to the manufacturer’s protocol. RNA concentration and purity were checked using a Qubit® Fluorometer (Thermo Scientific), and their integrity was analysed by capillary electrophoresis on a BioAnalyzer 2100 (Agilent). RNA-seq library construction and sequencing were performed by the Bioenvironment platform (Perpignan, France). All data treatments were carried out under a local galaxy instance [36]. Phred scores were checked using the Fastq-X toolkit [37] and were higher than 26 over 90% of the read length for all the sequences. All the reads were thus kept for subsequent analyses. Mapping to the C. gigas reference genome (assembly version V9 [38]) was performed using RNAstar (Galaxy Version 2.4.0d–2 [39]). The HTSeq-count was used to count the number of reads overlapping annotated genes (mode Union) (Galaxy Version v0.6.1) [40]. Finally, the differential gene expression levels were analysed with the DESeq2 R package[41]. Fold changes between each resistant and susceptible oysters were considered significant when the adjusted p-value (Padj) for multiple testing with the Benjamini-Hochberg procedure, which controls the false discovery rate (FDR), was <0.05.

Gene ontology annotation and enrichment analysis. To work with current functional annotations of the C. gigas gene set, we performed a de novo functionalannotation. Blastx comparison against the NR database was performed for the 28,027 genes annotated in the genome, with a maximum number of target hits of 20 and a minimum e-value of 0.001. XML blast result files were loaded onto Blast2GO [42] for GO mapping and annotation with the b2g_sep13 version of the B2G database. These results were used as inputs for GO enrichment analysis, which was performed using adaptive clustering and a rank-based statistical test (Mann-Whitney U-test combined with adaptive clustering). The R and Perl scripts used [43] can be downloaded [https://github.com/z0on/GO_MWU]. The following parameters were used for adaptive clustering: largest = 0.5; smallest = 5; clusterCutHeight = 0.25. For the continuous value characterization of each gene in the dataset, we used a strategy aiming to take into account both the level of expression and the significance of the differential expression. To combine these two factors, the log2 fold change was attributed to genes that were significantly differentially expressed (adjusted p < 0.05),while a zero was attributed to the others. A category was considered enriched with a FDR < 1%.

POMS: pacific oyster mortality syndrome

QTL: quantitative trait loci

TLR: Toll-like receptor

NFκB: Nuclear Factor Kappa B

JAK: signal transducers and activators of transcription

STAT: signal transducer and transcription activator

STING: stimulator of interferon genes

RLR: RIG-I-like receptors

RIG-I: retinoic acid-inducible gene I

DEGs: differentially expressed genes

TRIM: Tripartite Motif containing proteins

HSP: heat shock proteins

CGI: Crassostrea gigas identifier

ADAR: dsRNA-specific adenosine deaminase

SR: scavenger receptors

IFI44: Interferon Induced Protein 44

MyD88: myeloid differentiation primary response 88

TNF: Tumor Necrosis Factor

TRAF: TNF receptor-associated factor

SAMHD1: deoxynucleoside triphosphate triphosphohydrolase

2’,5’-OAS: 2’,5’- oligoadenylate synthase

IRF: interferon regulatory factor

IRAK: interleukin–1 receptor-associated kinase

TBK1: Serine/Threonine-Protein Kinase

SOCS: suppressors of cytokine signaling

PARP: Poly [ADP-ribose] polymerases

Ethics approval and consent to participate

All the experiments were conducted according to the ethics committee of Ifremer. Oyster genitors were sampling in field and sites for sampling were accessible on foot or by boat. No permit was required to harvest oysters. A very small fraction of the oysters (< 0.1% in every site) was collected without disturbance. Except for the production of biparental families of oysters, the study did not used samples collected from the field.

Consent for publication

The manuscript has been seen and approved by all of the authors.

Availability of data and materials

RNA-seq data have been made available through the SRA database (BioProject accession number PRJNA423079 with SRA accession SRP130264). Complementary information is available from the corresponding authors on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

The present study was supported by the ANR project DECIPHER (ANR–14-CE19–0023), by the ANR project DECICOMP and by Ifremer, CNRS, Université de Montpellier and Université de Perpignan via Domitia.

Authors’ contributions

J. D.L, A. L., B. P., C. M., V. P., L. D., C. M., J. M.E, P. H., F. L., M. L., Y. G. and G. M. performed oyster experiments. J. D. L., A. L., V. P., P. L. S., C. M., J. V. D., J. F. A., Y. G. and G. M. performed RNA-seq analyses. J. D. L., B. P. and G. M. designed experiments. J. D. L., B. P., Y. G. and G. M. interpreted results. J. D. L., Y. G. and G. M. wrote the paper. All the authors have revised and approved the manuscript submission.

Acknowledgements

We warmly thank the staff of the Ifremer site of Argenton (LPI, PFOM) and Sète (LER), and the Comité Régional de Conchyliculture de Méditerranée (CRCM) for technical support in the collection of the oyster genitors and reproduction. We also thank Vangeli Geshkovski, Maxime Lafont, Eve Toulza and Delphine Destoumieux-Garzón for technical support or fruitful discussions and the Bio-Environnement platform of the University Perpignan Via Domitia for support in NGS library preparation and sequencing. The authors are grateful also to Philippe Clair from the qPHD platform/Montpellier genomix for RNA quality control and useful advice. This work, through the use of the GENSEQ platform (http://www.labex-cemeb.org/fr/genomique-environnementale–2) from the labEx CeMEB, benefited from the support of the National Research Agency under the “Investissements d’avenir” program (reference ANR–10-LABX–04–01). We also thank the “Bio-Environnement” technological UPVD platform (Région Occitanie, CPER 2007–2013 Technoviv, CPER 2015–2020 Technoviv2) for access to the sequencing platform. This study is set within the framework of the “Laboratoires d’Excellence (LABEX)" TULIP (ANR‐10‐LABX‐41). We thank Life Science Editors for editorial assistance.

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Table 1 : Oysters broodstock origin and families susceptibility to the mesocosm and field infection trials.

Oyster Family	Broodstock origin			Mortality (%)
	Geographic area	Sampling site	Geographical coordinate	Atlantic mesoscosm infection	Atlantic field infection	Mediterranean mesocosm infection	Mediterranean field infection
F1	Atlantic	Farming area	Logonna Daoulas (lat 48.335263 - long - 4.317922)	98	99	75	89
F2	Atlantic	Farming area	Logonna Daoulas (lat 48.335263 - long - 4.317922)	68	100	70	88
F9	Atlantic	Farming area	Logonna Daoulas (lat 48.335263 - long - 4.317922)	56	95	14	44
F11	Atlantic	Non farming area	Dellec (lat 48.353970, long - 4.566123)	99	100	90	93
F14	Atlantic	Non farming area	Dellec (lat 48.353970, long - 4.566123)	96	100	94	94
F15	Atlantic	Non farming area	Dellec (lat 48.353970, long - 4.566123)	100	100	97	98
F21	Breeding program	Breeding program	Charente Maritime- La Tremblade (lat 45.781741, long - 1.121910)	3	2	1	7
F23	Breeding program	Breeding program	Charente Maritime- La Tremblade (lat 45.781741, long - 1.121910)	12	24	12	11
F28	Breeding program	Breeding program	Charente Maritime- La Tremblade (lat 45.781741, long - 1.121910)	32	38	18	28
F32	Mediterranean	Non farming area	Vidourle (lat 43.553906, long 4.095175)	56	98	78	89
F33	Mediterranean	Non farming area	Vidourle (lat 43.553906, long 4.095175)	48	92	39	59
F37	Mediterranean	Non farming area	Vidourle (lat 43.553906, long 4.095175)	30	95	2	30
F42	Mediterranean	Farming area	Thau lagoon (lat 43.418736, long 3.622620)	37	96	44	65
F44	Mediterranean	Farming area	Thau lagoon (lat 43.418736, long 3.622620)	40	86	2	10
F48	Mediterranean	Farming area	Thau lagoon (lat 43.418736, long 3.622620)	18	40	7	12

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Differential basal expression of immune genes confers Crassostrea gigas resistance to Pacific Oyster Mortality Syndrome

Status:

Journal Publication

Version 1

Abstract

Figures

Background

results

Infectious environments select oysters resistant to POMS

Stress and immune functions are enriched in the basal transcriptome of resistant oysters

Resistant oysters differentially express common and specific immune genes

Discussion

conclusions

methods

abbreviations

POMS: pacific oyster mortality syndrome

QTL: quantitative trait loci

TLR: Toll-like receptor

NFκB: Nuclear Factor Kappa B

JAK: signal transducers and activators of transcription

STAT: signal transducer and transcription activator

STING: stimulator of interferon genes

RLR: RIG-I-like receptors

RIG-I: retinoic acid-inducible gene I

DEGs: differentially expressed genes

TRIM: Tripartite Motif containing proteins

HSP: heat shock proteins

CGI: Crassostrea gigas identifier

ADAR: dsRNA-specific adenosine deaminase

SR: scavenger receptors

IFI44: Interferon Induced Protein 44

MyD88: myeloid differentiation primary response 88

TNF: Tumor Necrosis Factor

TRAF: TNF receptor-associated factor

SAMHD1: deoxynucleoside triphosphate triphosphohydrolase

2’,5’-OAS: 2’,5’- oligoadenylate synthase

IRF: interferon regulatory factor

IRAK: interleukin–1 receptor-associated kinase

TBK1: Serine/Threonine-Protein Kinase

SOCS: suppressors of cytokine signaling

PARP: Poly [ADP-ribose] polymerases

declarations

Ethics approval and consent to participate

Consent for publication

The manuscript has been seen and approved by all of the authors.

Availability of data and materials

Competing interests

The authors declare that they have no competing interests.

Funding

The present study was supported by the ANR project DECIPHER (ANR–14-CE19–0023), by the ANR project DECICOMP and by Ifremer, CNRS, Université de Montpellier and Université de Perpignan via Domitia.

Authors’ contributions

Acknowledgements

references

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3.Paul-Pont I, Dhand NK, Whittington RJ: Influence of husbandry practices on OsHV–1 associated mortality of Pacific oysters Crassostrea gigas. . Aquaculture 2013, 412:202–214.

5.Le Roux F, Wegner KM, Polz MF: Oysters and Vibrios as a Model for Disease Dynamics in Wild Animals. Trends Microbiol 2016, 24(7):568–580.

12.Zhang G, Fang X, Guo X, Li L, Luo R, Xu F, Yang P, Zhang L, Wang X, Qi H et al: The oyster genome reveals stress adaptation and complexity of shell formation. Nature 2012, 490(7418):49–54.

18.Petton B, Boudry P, Alunno-Bruscia M, Pernet F: Factors influencing disease-induced mortality of Pacific oysters Crassostrea gigas. Aquaculture Environment Interactions 2015, 6(3):205–222.

19.Dégremont L: Evidence of herpesvirus (OsHV–1) resistance in juvenile Crassostrea gigas selected for high resistance to the summer mortality phenomenon. Aquaculture 2011, 317(1–4):94–98.

26.Sanz J, Randolph HE, Barreiro LB: Genetic and evolutionary determinants of human population variation in immune responses. Current Opinion in Genetics & Development 2018, 53:28–35.

27.Cosseau C, Wolkenhauer O, Padalino G, Geyer KK, Hoffmann KF, Grunau C: (Epi)genetic Inheritance in Schistosoma mansoni: A Systems Approach. Trends Parasitol 2017, 33(4):285–294.

28.Green TJ, Raftos D, Speck P, Montagnani C: Antiviral immunity in marine molluscs. J Gen Virol 2015, 96(9):2471–2482.

29.Wei Y, Chen S, Wang M, Cheng A: Tripartite motif-containing proteins precisely and positively affect host antiviral immune response. Scand J Immunol 2018, 87(6):e12669.

30.Power D, Santoso N, Dieringer M, Yu J, Huang H, Simpson S, Seth I, Miao H, Zhu J: IFI44 suppresses HIV–1 LTR promoter activity and facilitates its latency. Virology 2015, 481:142–150.

31.Daugherty MD, Young JM, Kerns JA, Malik HS: Rapid evolution of PARP genes suggests a broad role for ADP-ribosylation in host-virus conflicts. PLoS genetics 2014, 10(5):e1004403.

33.Shi Z, Cai Z, Sanchez A, Zhang T, Wen S, Wang J, Yang J, Fu S, Zhang D: A novel Toll-like receptor that recognizes vesicular stomatitis virus. J Biol Chem 2011, 286(6):4517–4524.

34.Samuel CE: Adenosine deaminases acting on RNA (ADARs) are both antiviral and proviral. Virology 2011, 411(2):180–193.

36.Goecks J, Nekrutenko A, Taylor J: Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol 2010, 11(8):R86.

37.Blankenberg D, Gordon A, Von Kuster G, Coraor N, Taylor J, Nekrutenko A, Galaxy T: Manipulation of FASTQ data with Galaxy. Bioinformatics 2010, 26(14):1783–1785.

38.Zhang G, Fang X, Guo X, Li L, Luo R, Xu F, Yang P, Zhang L, Wang X, Qi H et al: The oyster genome reveals stress adaptation and complexity of shell formation. Nature 2012, 490(7418):49–54.

39.Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR: STAR: ultrafast universal RNA-seq aligner. Bioinformatics 2013, 29(1):15–21.

40.Anders S, Pyl PT, Huber W: HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 2015, 31(2):166–169.

41.Love MI, Huber W, Anders S: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014, 15(12):550.

43.Wright RM, Aglyamova GV, Meyer E, Matz MV: Gene expression associated with white syndromes in a reef building coral, Acropora hyacinthus. BMC Genomics 2015, 16:371.

tables

Supplementary Files

Status:

Journal Publication

Version 1

19.Dégremont L: Evidence of herpesvirus (OsHV–1) resistance in juvenile Crassostrea gigas* selected for high resistance to the summer mortality phenomenon.* Aquaculture 2011, 317(1–4):94–98.