Transcriptomic responses to Marteilia sydneyi infection in the Sydney rock oyster Saccostrea glomerata

Transcriptomic responses to Marteilia sydneyi infection in the Sydney rock oyster Saccostrea glomerata | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Transcriptomic responses to Marteilia sydneyi infection in the Sydney rock oyster Saccostrea glomerata View ORCID Profile Nikolina Nenadic , View ORCID Profile Ido Bar , View ORCID Profile Carmel McDougall doi: https://doi.org/10.1101/2025.02.02.636094 Nikolina Nenadic a Centre for Planetary Health and Food Security, Griffith University , Nathan, Queensland, 4111, Australia b School of Environment and Science, Griffith University , Nathan, Queensland, 4111, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nikolina Nenadic For correspondence: nikolina.nenadic{at}griffithuni.edu.au cm107{at}st-andrews.ac.uk Ido Bar a Centre for Planetary Health and Food Security, Griffith University , Nathan, Queensland, 4111, Australia b School of Environment and Science, Griffith University , Nathan, Queensland, 4111, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ido Bar Carmel McDougall b School of Environment and Science, Griffith University , Nathan, Queensland, 4111, Australia c Scottish Oceans Institute, University of St. Andrews , St. Andrews, Scotland, KY16 8LB, U.K Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Carmel McDougall For correspondence: nikolina.nenadic{at}griffithuni.edu.au cm107{at}st-andrews.ac.uk Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Marteilia sydneyi , an ascetosporean parasite, is the causative agent of Queensland Unknown (QX) disease in Saccostrea glomerata . QX disease outbreaks often lead to high mortality rates and considerable population losses. Investigating host/parasite interactions at a molecular level is imperative to better understand how S. glomerata mounts immune defences, and to explore whether M. sydneyi evades host responses. This study aims to investigate S. glomerata’s response to M. sydneyi infection through differential gene expression analysis to uncover immune mechanisms and potential markers for resistance. RNA sequencing and differential gene expression analysis revealed widespread transcriptional changes between infected and non-infected oysters. Genes encoding proteins involved in pathogen recognition and immune response signalling, such as galectin-4-like and G-protein coupled receptors, were significantly differentially expressed in S. glomerata infected with M. sydneyi , suggesting involvement in the host’s immune responses. Moreover, the upregulation of cytochrome P450 family genes indicate increases in the host’s detoxification processes and metabolic pathways, a possible response to infection-induced stress. However, extracellular superoxide dismutase, a gene previously implicated in the oxidative stress response to pathogens, was significantly downregulated, suggesting potential suppression of oxidative burst defence mechanisms. These results reveal the complex nature of S. glomerata’s response to M. sydneyi infection, and possible suppression or evasion of host defences by the parasite. The study also identifies multiple genes that likely play crucial roles in the molecular responses and defence mechanisms of S. glomerata to M. sydneyi infection. The identification of these genes provides potential target genes for future studies and possible biomarkers for breeding QX - resistant oyster lines. Highlights M. sydneyi infection induces a large transcriptional response in S. glomerata Upregulation of pathogen recognition genes, including galectins, occurs in infected tissues. Lack of differential expression of apoptosis genes suggests other immune strategies. G-protein coupled receptor pathways were upregulated, indicating species-specific responses. Results suggest possible suppression of host immune responses by the parasite. 1. Background Oysters play a vital role in both marine ecosystems and global aquaculture, contributing significantly to coastal economies and food production [ 1 – 4 ]. However, the sustainability of oyster aquaculture is increasingly threatened by recurrent outbreaks of parasite-induced diseases [ 5 ]. There are a range of different parasites that infect oysters from several different taxonomic groups. A group of prevalent oyster parasites with substantial impact on oyster populations is Ascetosporea, which includes Mikrocytos mackini (parasitic in Magallana gigas, Crassostrea virginica, Ostrea edulis and Ostrea lurida ), Haplosporidium nelsoni (parasitic in C. virginica ), Marteilia refringens, and Bonamia ostreae (both primarily parasitic in O. edulis) [ 6 , 7 , 9 – 11 ] Like all other invertebrates, oysters lack an antibody-based adaptive immune system and rely on the innate immune system for defence against pathogens [ 12 , 13 ]. The innate immune system in oysters relies on specific receptors that are able to recognise broad pathogen-associated molecular patterns (PAMPs) and activate humoral and cellular responses [ 12 , 14 ]. The humoral response involves the release of antimicrobial proteins and enzymes into the hemolymph where cellular responses are mediated by hemocytes (the primary defence cells in oysters) at the source of infection [ 15 , 16 ]. Hemocytes are involved in several innate immune functions such as encapsulation, the production of reactive oxygen species (ROS), and phagocytosis [ 14 ]. Molecular studies have shown that parasite infections trigger a range of innate immune responses in oysters, often leading to changes in the expression of genes involved in oxidative stress regulation, immune signalling, and pathogen recognition [ 15 , 17 , 18 ] [ 13 , 17 , 18 ]. For example, genes encoding antioxidant enzymes, protease inhibitors, and pattern recognition proteins have been associated with response to infection and immune regulation [ 12 , 13 , 15 ]. Despite advances in understanding oyster immunity, knowledge gaps remain regarding whether responses to parasites differ from bacterial or viral infections, and the extent of cross-species similarities in immune strategies. Addressing these gaps is essential for improving oyster disease management and selective breeding efforts. Oyster aquaculture has been a vital industry in Australia since European settlement, with Saccostrea glomerata (the Sydney rock oyster) being a key species [ 21 ], currently contributing AUD $102 million annually to the national economy [ 22 ]. S. glomerata populations have suffered significant declines due to a parasite-induced disease, Queensland Unknown (QX) [ 23 ]. Since the 1960s QX disease has been responsible for severe losses in wild and farmed S. glomerata populations, with up to 95% overall mortality [ 24 ], and many consider QX to be the biggest threat to the S. glomerata wild populations and aquaculture industry [ 23 , 25 , 26 ]. QX is caused by the protozoan parasite Marteilia sydneyi , which enters the oyster through its gills and labial palps, systematically spreading throughout the oyster until it establishes itself within the digestive tubule epithelium [ 27 ]. It is there that the sporonts contained in the established sporangiosorae undergo further proliferation, starving and killing the host [ 28 ]. In response to significant mortality the industry has turned to selective breeding for disease resistance [ 29 ], and it was found that QX-resistant bred S. glomerata exhibited stronger immune responses against QX infection compared to wild-type oysters [ 19 ]. Previous studies on S. glomerata have identified key molecular responses associated with resistance to QX disease, providing insights into the genetic basis of immunity. QX-resistant oysters have demonstrated increased constitutive phenoloxidase activity in their hemolymph and hemocytes, along with larger and more active immune cells [ 19 ]. These findings suggest that elevated phenoloxidase activity and a robust phagocytic response play a vital role in defence against M. sydneyi infection. Additionally, gene expression analyses have revealed differential baseline levels of key immune-related genes in disease-resistant oysters compared to wild populations. Notably, extracellular superoxide dismutase (EcSOD) exhibits higher baseline expression in resistant oysters, while peroxiredoxin 6 (Prx6) and interferon-inhibiting cytokine factor (IK) showed lower expression levels [ 30 ]. These expression patterns are hypothesized to enhance the production of hydrogen peroxide during respiratory bursts, which is crucial for neutralizing pathogens [ 30 ]. Despite the economic and ecological significance of QX disease to the Australian oyster industry, the molecular responses of S. glomerata to M. sydneyi infection remain poorly understood, likely due to the inability to culture the parasite or perform controlled challenge experiments. In contrast, oyster responses to parasite infection in northern hemisphere species have been better studied (e.g., O. edulis and M. gigas responses to the rhizarian parasite B. ostreae ), which has led to the identification of key immune genes and pathways involved in host defence [ 17 , 18 , 31 , 32 ]. This knowledge gap underscores the importance of investigating the host responses of S. glomerata to M. sydneyi during QX infection. In this study we conducted a comprehensive transcriptomic analysis to investigate the molecular responses of S. glomerata to M. sydneyi infection. Using RNA sequencing, differential gene expression analysis, and functional annotation, we identified key genes and pathways involved in the oyster’s immune response to QX disease. By comparing gene expression profiles between infected and uninfected oysters, we aimed to gain insights into the molecular strategies employed by the host to combat QX disease. 2. Materials and Methods 2.1 Sample collection 20 S. glomerata oysters (sourced from wild-caught spat) were collected from the Pimpama River, Queensland ( Fig. 1 ) during a QX outbreak event. Oysters were approximately 2.5 years old and had been in the held in the river for a minimum of 3 months. Once collected oysters were held in an aerated aquarium containing natural seawater from the oyster lease at 25°C for 3 days prior to processing and were fed approximately 1 mL of Shellfish Diet 1800™ (Reed Mariculture) daily. Download figure Open in new tab Fig. 1. Oyster collection site located on the western side of Woogoompah Island, - 27.813664, 153.402702, in Moreton Bay, QLD. Satellite image © Queensland Globe, State of Queensland, CC BY 4.0. 2.2 Oyster sampling and DNA extractions Each oyster was shucked and the digestive tissue was dissected and split into three samples. The first piece was stored in RNAlater (Sigma) at 4°C for 24 hours and then at -20°C for long-term storage. The second piece was stored in 80% ethanol at 4°C for DNA extractions, and the third was immediately stored at -80°C for subsequent histological examinations. DNA extractions were performed from ethanol-stored samples using the Qiagen DNeasy Blood and Tissue Kit™ following the protocol provided. A variation was made to the protocol; 30 µL of DNAse/RNAse free water was used in the final DNA elution step instead of the suggested 200 µL of Buffer AE. DNA yield was assessed using the Qubit Fluorometer and the DNA broad range kit (Thermo Fisher Scientific), following the provided protocol. The extracted DNA was diluted to 50 ng/µL with nuclease-free water. 2.3 Diagnosis of infection 2.3.1 PCR Samples were assessed for presence of M. sydneyi infection using the Leg1 and Pro2 primers that yield an expected product size of 195 bp [ 33 ]. PCR amplifications were performed in 20 µL reactions containing: 2 µL of 10x ThermoPol 10X buffer, 1 µL of each primer at 10 µM, 0.4 µL dNTP solution mix at 10 mM, 0.2 µL Taq DNA polymerase (all PCR components from New England Biolabs), and 1 µL of sample DNA at 50 ng/µL. The following PCR thermoprofile was run on a thermal cycler: 94°C for 2 minutes followed by 30 cycles of 94°C for 30 seconds, 55°C for 30 seconds and 68°C for 1 minute with a final extension of 68°C for 10 minutes and incubation at 14°C. PCR amplicons were visualised on a 2.5% TAE agarose gel. 2.3.2 Histology Histological screening was performed to confirm infection in samples that tested positive for M. sydneyi infection via PCR. The protocol outlined in Adlard and Worthington Wilmer [ 34 ] was followed with the variation of using digestive tissue that was frozen at -80°C then thawed prior to smearing. Initial tests showed that this provided identical results to the analysis of fresh tissue (Supplementary Fig. 1). Thawed tissue was blotted on microscope slides and left to dry for 5 minutes. The Hemacolour Rapid Staining of Blood smear kit (Sigma-Aldrich) was used; slides were submerged in solution 1 for 5 seconds, solution 2 for 3 seconds, solution 3 for 2 seconds and solution 4 for 20 seconds, with hand mixing. Slides were left for up to 15 minutes to dry, covered with 100 µL of DPX mounting solution and a coverslip, and left in a dark space to set for 24 hours. Slides were inspected under a microscope for presence of M. sydneyi cells. 2.4 RNA Extraction and Sequencing Three samples highly infected with M. sydneyi and three non-infected samples were chosen for transcriptome sequencing. Samples were chosen based on PCR amplicon brightness when visualised on an agarose gel and number of sporonts visible upon histological examination. RNA was extracted from 0.5 cm 2 of the excised digestive tissue stored in RNAlater at -20°C. Extractions were carried out using the standard protocol for RNA extraction using Trizol™ with the following modifications: 200 µL of Trizol™ was added to digestive tissue. The samples were incubated at 65°C for five minutes with vortexing every 2 minutes, the remaining 400 µL of Trizol™ was then added and vortexed for 15 seconds. 20 µL of 1-bromo-3-chloropropane was added to initiate phase separation. For precipitation, 0.5 µL of 20 mg/mL glycogen was added to the transferred supernatant and mixed before the addition of 100 µL of ice-cold isopropanol. The resulting pellet was resuspended in 6 µL of nuclease-free water. RNA yield was assessed using the Qubit Fluorometer RNA broad-range kit following the manufacturer’s instructions. RNA quality was assessed by electrophoresis on a 1% TBE gel. Extracted RNA was sent to Macrogen, Korea for quality control (via Tapestation), library preparation and transcriptome sequencing. Individual libraries were created for each sample using the Truseq stranded mRNA kit (Illumina) and were sequenced on a NovaSeq 6000, generating 150 bp paired-end reads. 2.5 RNA-Seq Data Analysis 2.5.1 De Novo Transcriptome Assembly Paired-end RNA-Seq reads were error-corrected with Rcorrector (v1.5.0, [ 40 ]) and any unfixable reads were removed. A de novo transcriptome was assembled and annotated from the error-corrected reads with TransPi (v1.1.0, [ 41 ]), a pipeline implemented in the Nextflow scientific workflow system [ 42 ]. In brief, TransPi performs a complete transcriptome assembly, basic annotation and assessment pipeline using well-established methods, including: Trimming of low-quality bases and removal of sequencing adaptors with Fastp [ 43 ]; Ribosomal RNA (rRNA) removal matching against the SILVA rRNA database [ 44 ]; Transcriptome assembly using a range of tools, including Trinity, SPAdes, Trans-ABySS and SOAPdenovo-Trans, with a range of k-mers [ 45 , 46 ]; Collapsing redundant transcripts with EvidentialGene ; Annotation of the assemblies against UniProt databases; Building a Trinotate database for each assembly and generating an annotation. Since TransPi is not actively maintained, several issues were encountered in the installation and running of the pipeline in newer Nextflow versions and container systems (Apptainer). A modified version of TransPi was therefore used (available at https://github.com/IdoBar/TransPi ), refer to https://idobar.github.io/QX_bioinfo_analysis/ for detailed description of the bioinformatics analysis, including specific details and code snippets of the modifications made to the pipeline. Assembly completeness was determined using BUSCO [ 47 ] using the Metazoa database (Metazoa_Odb10) to assess the presence of highly-conserved single copy orthologs. The quality and coverage of the transcriptomic data was further assessed using TransRate [ 48 ]. 2.5.2 Transcriptome Annotation The transcriptome assembly was annotated initially by TransPi, using homology searches (BLAST [ 49 ] and DIAMOND, [ 50 ]) against the Uniprot_Sprot protein database, as well as using HMM search against the Pfam database of protein families. In addition, signal peptides, ribosomal genes and transmembrane topology were predicted with SignalP v4.1[ 51 ], Rnammer and TMHMM [ 52 ], respectively [ 53 ]. 2.5.3 Gene Ontology and Functional Annotation In addition to the annotations performed with TransPi, the assembled transcripts were annotated against the non-redundant nucleotide database of the NCBI database (nt) using BLASTn (v2.16.0) homology search to achieve more accurate species-specific annotations. To reduce computation time, we used nf-blast , a custom Nextflow workflow that splits the input transcriptome and processes them in parallel on the HPC cluster. The same tool was used to annotate the predicted proteins derived from the transcriptome using DIAMOND (v2.1.9) against the NCBI non-redundant protein database (nr). The predicted proteins were functionally annotated with InterProScan v5.66-98.0 [ 54 ] to assign protein families, motifs and ontologies to assist with transcript-to-gene annotation. Gene Ontology (GO) annotation terms were extracted from the InterProScan result tables. 2.5.4 Differential Gene Expression Analysis Data processing for transcript count generation were performed on Galaxy Australia [ 55 ]. The trimmed and corrected RNA-Seq reads that were used to assemble the transcriptome were then aligned back to the transcriptome using BWA-MEM2 (v2.2.1+galaxy1[ 56 ]) using default parameters. Transcript counts were generated from the alignments by featureCounts (v2.0.8+galaxy0[ 57 ]). Quality-trimming and alignment statistics were aggregated into a single report with MultiQC (v1.27+galaxy0[ 58 ]). The count tables were imported into R and DESeq2 (v1.42.1[ 59 ]) was used for differential gene expression analysis. Count data was imported into a DESeqDataSet object, and normalisation was performed using the default method in DESeq2. The design matrix included infection status as a factor, and differentially expressed genes were identified using the Wald test. The resulting p-value was adjusted to account for multiple testing using the Benjamin-Hochberg algorithm [ 60 ] to control the false discovery rate (FDR). Genes with an absolute log2 fold change (|log 2 FC|) > 2 and an adjusted p-value ≤ 0.05 were considered as significantly differentially expressed. GO term enrichment analysis was performed using the clusterProfiler R package (v4.10.1[ 61 ]) on the identified differentially expressed genes. However, due to the poor protein annotation rates for molluscs (17%, see Results section 3.1 ), no GO terms were found to be enriched and therefore the GO terms were summarised to identify the most frequent terms in the biological process (BP) and molecular function (MF) categories. Lollipop plots visualising the most frequent annotations in all differentially expressed genes were generated using ggplot2. 2.5.5 Data processing and visualisation RNA-Seq data analysis and transcriptome assembly were performed on ‘Gowonda’, the Griffith University High Performance Computing cluster and ‘Bunya’, the University of Queensland High Performance Computing cluster. Downstream analyses were performed in the R environment for statistical computing (v4.1) [ 35 , 36 ]. Various R packages from the tidyverse (v2.0.0) [ 37 ] along with the janitor package (v2.2.0) [ 38 ] were used for data cleaning, structuring, summarising and visualising. All code utilised for the analysis is available on Zenodo at https://doi.org/10.5281/zenodo.14564721 [ 39 ] (see Data Availability section). A Principal Component Analysis (PCA) plot was generated to assess sample clustering based on normalised expression data. A heatmap of the top 500 differentially expressed genes was created using the pheatmap package (v1.0.12[ 62 ]), with hierarchical clustering applied to both genes and samples. A volcano plot depicting the relationship between fold change and statistical significance was generated using the EnhancedVolcano package (v1.13.2[ 63 ]). 3. Results and Discussion 3.1 Confirmation of M. sydneyi infection in samples The presence and absence of M. sydneyi infection in S. glomerata samples was confirmed via PCR and histological tissue smears. PCR results were assessed based on amplicon brightness ( Fig. 2A ), as a previous correlation was observed between band intensity and the number of M. sydneyi cells in infected tissue ( Fig.2B ). Download figure Open in new tab Fig. 2. M. sydneyi infection diagnosis. (A) Gel electrophoresis of PCR amplicons generated using the Leg1 and Pro2 primers on S. glomerata digestive gland DNA. A 100bp ladder was used to aid visualization. Lanes 1-3: uninfected S. glomerata individuals 1, 2, and 3, respectively. Lanes 6, 9 and 10: M. sydneyi infected S. glomerata individuals 1, 2, and 3, respectively. Lanes 4,5,7,8: uninfected S. glomerata individuals that were not included for sequencing. +: positive control of S. glomerata confirmed to have M. sydneyi infection via PCR, histology and sequencing. -: no template control. (B) S. glomerata digestive gland histology tissue smears on individuals chosen for transcriptomic sequencing, scale bars = 50µm. 1-3: uninfected S. glomerata individuals 1, 2, and 3, respectively. 4-6: M. sydneyi infected S. glomerata individuals 1, 2, and 3, respectively. Black arrows indicate presporulating sporangiosori. 3.2 Transcriptome assembly and differential gene expression RNA sequencing generated 35-39 million paired-end reads per sample, with read duplication rate ranging between 25% - 38%. On average, 94% of the reads per sample (equivalent to 9.9 million bases) had a phred score of over Q30 ( Table 1 ). The assembled transcriptome of the S. glomerata digestive gland produced a total of 216,914 transcripts, with an N50 value of 1,235 bp, indicating the median contig length of the assembly’s longest 50% of the bases. The N10 value, representing the length above which 10% of the total assembled bases are found, was 3,713 bp, highlighting the presence of longer, well-assembled contigs. The assembly had a median contig length of 460 bp, and the overall GC content of the transcriptome was 36.8%, reflecting the nucleotide composition. View this table: View inline View popup Download powerpoint Table 1. General statistics from sequencing results and the alignment of raw reads against the S. glomerata transcriptome generated in this study. The BUSCO analysis of the de novo transcriptome assembly for S. glomerata digestive gland indicated a high level of completeness, with 95.3% of the expected core genes identified ( Table 2 ). A small fraction (1.9%) of the genes were found to be fragmented, and 4.7% were missing from the assembly, indicating minimal loss of expected gene content. 74.3% of the BUSCO genes were duplicated, suggesting the transcriptome assembly is likely to contain some level of redundancy and duplication due to assembly and sequencing inaccuracies. Overall, the results indicate that the transcriptome assembly is of high quality, with good representation of the gene content. View this table: View inline View popup Download powerpoint Table 1. BUSCO completeness scores for the de novo S. glomerata transcriptome assembly. View this table: View inline View popup Download powerpoint Table 2: Summary of differentially expressed genes between infected and uninfected S. glomerata samples Alignment of the reads to the assembled transcriptome revealed that the uninfected samples had a high percentage of reads that aligned to the reference S. glomerata digestive transcriptome (approximately 98.9%), while infected samples had much lower alignment rates, indicating the presence of parasite-derived reads. Infected samples 1 and 2 had 59.4% and 50.5% of reads align to the reference genome, whereas infected sample 3 showed a higher alignment rate of 82.4%, falling between the uninfected and other infected samples. Although all three samples appeared to have similar levels of infection based on PCR results and histology tissue smears ( Fig. 2 ), the distribution of M. sydneyi cells throughout infected digestive gland tissue could vary. Therefore, the tissue sampled from infected sample 3 for RNA extraction may have had a lower parasite load than initially thought. Assessment of GC content in the different samples suggests this may be the case (Supplementary Fig. 2). 3.3 Differential gene expression Of the total 216,914 transcripts analysed, 2,200 were found to be differentially expressed between infected and uninfected samples, emphasizing the substantial and wide-ranging molecular responses to infection (Table 3 and Fig. 3C ). There were clear patterns of expression between infected and uninfected S. glomerata individuals, demonstrating the substantial impact M. sydneyi infection has on S. glomerata gene expression profiles in the digestive tissues ( Fig. 3A ). This distinction was further supported by the PCA plot, where the samples from the two experimental groups clustered with a clear separation between infected and uninfected samples ( Fig. 3B ). Download figure Open in new tab Fig. 3. Differential gene expression in infected and uninfected S. glomerata . (A) Heatmap of the top 500 differentially expressed genes in M. sydneyi infected S. glomerata digestive tissue. (B) PCA plot of infected and un-infected samples. (C) Volcano plot highlighting the log2fold change and p-value of differentially expressed genes. NS (non-significant) indicates genes that had an absolute log 2 FC less than 2 in differential gene expression analysis. Transcripts indicated in green had an absolute log 2 FC greater than 2, and transcripts indicated in red had both an absolute log 2 FC greater than 2 and an adjusted p-value less than 0.05 (considered significant in our study). Previous studies have shown that transcriptional responses to pathogens can be large, and that they are dependent on the stage of infection. For example, a study on M. gigas infected with Vibrio alginolyticus identified 1,543 DEGs at 6 hours post-infection, increasing to 2,483 DEGs at 48 hours [ 64 ], demonstrating that our findings align with prior observations and are within a comparable range. The high number of DEGs found in our study could be attributed to the severity and progress of M. sydneyi infection in the oysters, with substantial tissue damage and animals possibly close to mortality. Pathogen-induced stress and tissue damage during later stages of infection likely amplify DEGs as the host attempts to counteract infection and repair cellular damage. 3.4 Gene ontology classifications Only 17% of the differentially expressed transcripts were successfully annotated (Table 3). Annotation rates in non-model organisms such as molluscs, including S. glomerata , are often limited due to the scarcity of genomic resources, highlighting the need for further functional characterisation efforts in this taxonomic group [ 65 , 66 ]. The DEGs were analysed by GO term classification to explore whether certain biological processes or molecular functions are associated with M. sydneyi infection. GO biological processes that included more than four differentially expressed genes were visualised and are shown in Fig. 4A . The GO biological process analysis revealed a variety of responses associated with infection in oysters. These included genes involved in the activation of inflammatory responses, indicating the presence of a continued immune reaction despite the late stage of infection. Xenobiotic metabolic processes were observed to have four DEG within the group, suggesting that detoxification processes may play a vital role as a defence mechanism against infection-induced stress ( Fig. 4A ). Additionally, the presence of genes related to serine-type endopeptidase activity (5 DEG) and oxidoreductase activity (16 DEG) suggests involvement in proteolysis and redox processes ( Fig. 4B ). This suggests that QX infection induces protein degradation, turnover, and alterations in redox balance, which are a likely part of the host’s response to cellular stress. These changes may represent a defensive mechanism or processes involved in immune regulation. Upregulation of genes linked to cell matrix adhesion and nuclear envelope organisation in infected tissues likely reflects the poor condition of the infected digestive tissues ( Fig. 2B , 3 ). This indicates that the infection impacts cellular integrity and signalling, disrupting normal cellular functions and structure [ 67 , 68 ]. Download figure Open in new tab Fig. 4. Gene ontology annotations of genes that are differentially expressed in response to M. sydneyi infection in S. glomerata . (A) GO biological process terms associated with four or more DEGs. (B) GO molecular function terms associated with four or more DEGS. A similar approach was applied to GO molecular functions, focusing on categories containing a minimum of four differentially expressed genes. Fig. 4B highlights the involvement of transcription and chromatin-related activities, including terms such as DNA-binding transcription factor activity, chromatin binding, and DNA binding, supporting our observation that the infection causes widespread changes in gene regulation [ 69 , 70 ]. 3.5 Functional Annotation of Differentially Expressed Genes Despite the low annotation rates, some interesting differentially expressed gene were identified that appear relevant to S. glomerata’s response to M. sydneyi infection. The annotated genes with the largest transcriptional changes in S. glomerata infected with M. sydneyi are shown in Fig. 5 and include galectin, cytochrome P450, ecSOD, fibrinogen-related proteins, and G-coupled receptors. The magnitude of differential gene expression likely reflects the complex interaction between host immune defences, potential evasion strategies employed by the parasite, and the significant tissue damage observed during infection. Host immune defences may activate specific pathways to target and neutralize the parasite, while the parasite simultaneously employ evasion mechanisms, such as suppressing immune signalling, altering host cell processes, or avoiding recognition altogether. Several of these highly differentially expressed genes have also been identified in previous studies focusing on transcriptomic responses in bivalves to parasitic infections. Among these genes, notable examples include galectins, cytochrome P450 family genes and extracellular superoxide dismutase (ecSOD). These genes represent diverse functional categories. The identification of the same response genes in different bivalves infected with different parasites suggests that the host response pathway may be highly conserved across different taxa. Download figure Open in new tab Fig. 5. Annotated differentially expressed genes that had a minimum log 2 fold change of 5 or -5 between infected and uninfected S. glomerata digestive gland tissue. Yellow bars indicate up-regulated genes and blue bars indicate down-regulated genes in S.glomerata infected with M. sydneyi . 3.5.1 Galectin Galectins are a family of lectins distinguished by their specific binding of β- galactosides and their conserved sequence motif within the carbohydrate recognition domain [ 72 ]. In S. glomerata several predicted galectins are upregulated during M. sydneyi infection ( Fig. 5 ). Similar findings in other bivalve species (including O. edulis, M. gigas, and Ruditapes philippinarum ) have indicated that galectins are involved in pathogen recognition, regulation of immune cell activity, and binding to specific surface glycans on micro-organisms that facilitates immune responses [ 11 , 31 , 73 ]. For example, galectins identified in C. virginica have been shown to target and bind to external glycans on bacteria and parasites [ 74 , 75 ]. This suggests that the upregulation of galectin-4-like in S. glomerata during M. sydneyi infection may enhance the oyster’s ability to identify and respond to the parasite by promoting surface glycan binding of the parasite to host immune cells, potentially regulating downstream immune signalling pathways. 3.5.2 Cytochrome P450 Cytochrome P450 (CYP450) represents a diverse superfamily of hemoproteins found in all biological kingdoms, characterised by a heme cofactor that facilitates a wide range of catalytic functions [ 76 – 78 ]. CYP450 enzymes are involved in a range of physical processes such as the metabolism of endogenous and xenobiotic compounds, the breakdown of environmental toxins, and hormone biosynthesis [ 76 – 78 ]. They mediate various reactions, including hydroxylation, peroxidation, epoxidation and reduction, contributing to detoxification and cellular protection mechanisms [ 77 ]. In some bivalves, CYP450 family members are known for their involvement in cellular protection mechanisms and post-phagocytosis degradation, most notably oxidative metabolism [ 79 ]. The presence of multiple CYP450 family members within both upregulated and downregulated DEGs suggests a complex regulatory response to M. sydneyi infection. There was a significant up-regulation of some CYP450 family members, with a logfold change of approximately 6.85 ( Fig. 5 ), congruent with the activation of enhanced metabolic or detoxification activities as a possible response to infection [ 80 ]. This expression pattern aligns with results seen in other bivalve studies investigating host-pathogen interactions, in which CYP450 superfamily genes were linked to oxidative metabolism and detoxification during infection. For example, in Mercenaria mercenaria CYP450 was significantly upregulated following Quahog parasite unknown (QPX) infection, where it was proposed that the gene plays a role in detoxifying damaging compounds produced during the initial immune response [ 81 ]. Upregulation of CYP450 was associated with initial stages of B. ostreae infection in O. edulis , potentially also as a response to oxidative stress [ 79 ]. However, our study revealed that three of the CYP450 family member genes were observed to be downregulated, indicating possible suppression of the metabolic and detoxification processes they are involved in. This could be a means to conserve energy or avoid excessive oxidative damage. The varying expression levels of CYP450 genes suggests they have multifaceted roles in S. glomerata’s defence against M. sydneyi . This likely reflects the host’s approach to balancing immune defence, metabolic adaption and oxidative stress. 3.5.3 Extracellular superoxide dismutase In bivalves, ROS is utilised as a key defence mechanism during phagocytosis. ecSOD is a key enzyme involved in this process, converting superoxide anions to hydrogen peroxide, an anti-parasitic compound [ 82 , 83 ]. Previous research has shown elevated expression of ecSOD genes in disease-resistant S. glomerata and O. edulis [ 31 , 82 ]. It was also recently shown that S. glomerata possesses two copies of the ecSOD gene, ecSODa and ecSODb (with ecSODa corresponding to the previously investigated S. glomerata ecSOD; the role of ecSODb is unknown) [ 84 ]. Prior studies on ecSODa expression in S. glomerata revealed the gene to be non-inducible upon bacterial infection with Vibrio anguillarum , indicating that expression of ecSOD is not modulated in response to infection [ 30 , 85 ]. In this study, we observed a significant downregulation of all isoforms of both ecSOD genes in S. glomerata infected with M. sydneyi ( Fig. 5 , 6 ). This could imply a weakened oxidative stress response to infection, potentially compromising the host’s ability to manage ROS, or it may reflect the host’s efforts to moderate immune responses by reducing ROS production, potentially minimizing self-inflicted oxidative damage to tissues and cells. The moderation of ROS production may serve to preserve energy and resources during prolonged immune responses, allowing the host to allocate resources more efficiently to other defence mechanisms [ 86 , 87 ]. Conversely, it could indicate the parasite’s successful suppression of host defences, allowing it to evade ROS-mediated destruction [ 79 ]. Parasite suppression of host responses has been proposed in B. ostreae infection in M. gigas and O. edulis, where a downregulation of ecSOD during infection was associated with reduced phagocytic activity, potentially increasing the parasite’s chances of survival [ 11 , 18 , 79 ]. Similarly, M. sydneyi infection in S. glomerata has been linked to the suppression of the phenoloxidase cascade, a critical immune pathway involved in pathogen encapsulation and melanisation [ 19 , 24 , 88 ]. This suppression is also associated with reduced phagocytic activity, further facilitating parasite persistence within the host [ 19 , 24 ]. These findings suggest that immune suppression mechanisms employed by the parasite may play a role in disease progression and host susceptibility. Download figure Open in new tab Fig. 6. Gene expression of ecSOD transcripts in M. sydneyi infected (QX) and uninfected (control) S. glomerata tissue. There are multiple isoforms of ecSODa and ecSODb within the transcriptome assembly; all isoforms show downregulation in infected tissue. (A) Normalised counts of S. glomerata ecSODa transcripts. (B) Normalised counts of S. glomerata ecSODb transcripts. Previous studies observed a downregulation of Prx6 in selectively bred disease resistant oysters, alongside an upregulation of ecSOD, suggesting a coordinated oxidative stress response [ 82 ]. Our findings suggest that Prx6 expression may be reduced in S. glomerata infected with M. sydneyi , as indicated by an apparent decrease in normalised Prx6 counts in infected samples compared to uninfected samples ( Fig. 7 ). However, this decrease was not statistically significant in DGE analyses, making it unclear whether Prx6 suppression plays a major role in the host response to infection. Given that ecSOD plays a key role in ROS detoxification, its suppression in infected oysters could impair the host’s ability to regulate oxidative stress, potentially making them more susceptible to tissue damage and pathogen persistence. If the expression of Prx6 is also reduced, this could further compromise the oxidative stress response, weakening host defences against M. sydneyi infection. Download figure Open in new tab Fig. 7. Gene expression of Prx6 transcripts in M. sydneyi infected (QX) and uninfected (control) S. glomerata tissue . There are three isoforms of Prx6 within the transcriptome assembly; all isoforms show downregulation in infected tissue. 3.5.4 Fibrinogen-related protein family Fibrinogen-related proteins (FREPs) are proteins that contain a fibrinogen-related domain (FReD). They are pattern recognition receptors with vital roles in invertebrate innate immune responses [ 89 , 90 ]. The gene family is highly diverse and encodes multiple protein types that are typically upregulated following immune stimulation [ 91 , 92 ]. FREPs are able to bind to pathogen cells and precipitate parasite antigens [ 92 ]. Upregulation of FREPs in QPX resistant M. mercenaria clams led to the gene family being recognised as a potential marker for resistance due to its ability to increase defence responses against pathogens [ 71 ]. In contrast with previous findings, Fibrinogen/Tenascin was downregulated in S. glomerata infected with M. sydneyi ( Fig. 5 ), suggesting a potential disruption in immune processes. This suppression could indicate the parasite’s interference with the host’s immune pathways, reducing the host’s ability to effectively detect and respond to the parasite. The downregulation of this gene in the present study could suggest an evasion strategy by M. sydneyi to undermine the hosts immune response. 3.5.5 G-protein coupled receptors G-protein coupled receptors (GPCRs) are essential for a multitude of immune processes, including the activation of transcription factors that regulate downstream inflammatory responses, such as NF-κB, CREB, and STAT3 [ 93 , 94 ]. Several GPCRs were upregulated in S. glomerata tissue infected with M. sydneyi ( Fig. 5 ). The most significant upregulation is seen in GPCR family 1 member with a log2fold change of 9.45. Other GPCR genes also display significant upregulation, with log2fold change values ranging from 5.31 to 6.04. The significant upregulation of multiple GPCRs in the host indicates that GPCRs play an active role in mediating the host’s immune response signalling. The upregulation seen could suggest an enhanced immune surveillance mechanism in oysters, as GPCRs assist in the migration and activation of immune cells at sites of infection [ 18 ]. Recent studies focusing on host-pathogen interactions between M. gigas and ostreid herpes virus, and M. mercenaria infection with QPX, found that GPCRs were associated with defence responses [ 83 , 95 ]. Upregulation of these receptors may reflect the oyster’s attempt at boosting immune signalling pathways and managing infection-induced stress. While the upregulation of GPCRs suggests an attempt by the host to enhance immune surveillance and immune cell activation, the parasite may counteract these efforts by suppressing downstream signalling pathways, leading to an overall weakened immune response. Studies investigating host/pathogen interactions between B. ostreae and M. gigas revealed downregulation of GPCRs in lightly infected oysters, with further diminishment with heavier infection [ 96 , 97 ]. This pattern highlights the parasite’s active suppression of these pathways as a strategy to evade host immune responses. Overall, the results underline the potential role of GPCRs in regulating oyster defence mechanisms and the importance of understanding host-pathogen molecular responses. 3.5.6 The role of apoptosis in the S. glomerata - M. sydneyi interaction Interestingly, apoptosis or necrosis-related genes (such as p53, Bcl-2 proteins, or caspases), were not found to be differentially expressed in our analysis. The stable expression of these genes suggests that programmed cell death may not be a prominent immune response at this stage of the infection or a significant feature in the host’s immune response to M. sydneyi infection. Nevertheless, studies indicate that immune challenges can induce apoptosis through both pre-existing pathways (caspase activation) and changes in gene expression, particularly under prolonged or complex stress conditions [ 98 , 99 ]. As mentioned, previous research on C. virginica infected with P. marinus has demonstrated the parasite’s ability to survive within host hemocytes by modulated apoptotic pathways, ultimately facilitating infection persistence [ 8 , 100 ]. Specifically, P. marinus has been shown to upregulate antioxidant enzymes such as superoxide dismutase (SOD) and Prx6, which help suppress the production of ROS and delay apoptosis in host cells [ 101 , 102 ]. This strategy allows the parasite to evade host defences and continue intracellular replication. Apoptotic responses in oyster hemocytes exposed to P. marinus have been observed to follow a dynamic pattern, with an early increase in apoptosis followed by suppression over time [ 8 , 100 ]. This temporal regulation indicates that the parasite may actively manipulate apoptotic pathways to establish infection. The intrinsic mitochondrial apoptosis pathway, which involved key regulators such as Bcl-2, plays a significant role in this process by potentially inhibiting cytochrome-c release and delaying cell death [ 103 , 104 ]. Studies have further revealed that anti-apoptotic genes are upregulated in virulent cultures of P. marinus, suggesting that the parasite utilises these mechanisms to suppress apoptosis and enhance survival in the host [ 105 , 106 ]. In contrast, our findings imply that M. sydneyi may employ a different strategy to evade host immune responses, potentially bypassing the need to regulate apoptosis at the transcriptional level. The absence of significant changes in apoptosis-related gene expression suggests that apoptosis may not be a critical component of the immune response to M. sydneyi , or that the response occurs through non-transcriptional modifications. Further investigation, including protein-level analysis and temporal studies are needed to better understand whether apoptosis plays a role at earlier or later stages of infection, or in specific tissues, as our comparisons were limited to a specific time point. 3.5.7 Conserved molecular responses to parasite infection in bivalves The findings of this study highlight potential shared defence strategies across bivalve species, suggesting that fundamental immune responses may be conserved across a range of pathogens rather than being specific to ascetosporean parasites. Comparing the results of this study with that of O. edulis infected with B. ostreae reveals the possibility of shared defence strategies between the O. edulis and S. glomerata , including the central role of glycan-binding proteins such as FREPs and lectins in pathogen recognition [ 18 ]. Glycan-binding proteins have been reported in bivalve immune systems as key components of non-self-recognition, mediating interactions with pathogen glycans and activating downstream immune pathways. In M. gigas, lectins were observed to bind to PAMPs and play a significant role in immune surveillance against multiple pathogens, underscoring their conserved function across bivalve species [ 12 ]. Oxidative stress regulation also appears to be a conserved immune mechanism across bivalves, with a downregulation of ecSOD observed in both O. edulis and S. glomerata [ 79 ]. This down regulation may reflect a parasite-driven suppression of oxidative stress pathways to evade host immune responses, or a host-mediated response to limit further tissue damage from excessive ROS. Previous investigations on M. galloprovincialis during bacterial infections emphasized the dual role of oxidative stress pathways in balancing pathogen elimination and host tissue preservation. The increased activity of antioxidant enzymes, such as catalase and GST, reflects an active response by the digestive gland to manage the heightened production of ROS triggered by bacterial challenges [ 107 ]. Specifically, infections with Vibrio splendidus and/or Vibrio anguillarum have been shown to enhance the activities of these enzymes, highlighting their necessity in mitigating oxidative damage and preserving cellular integrity whilst supporting immune defence responses [ 107 ]. The similarities in oxidative stress regulation across different bivalve-pathogen interactions suggest that these mechanisms represent a fundamental aspect of molluscan immunity. Species-specific differences in immune responses were evident in the structural responses and signalling pathways observed between M. gigas and S. glomerata , suggesting potential adaptations to distinct pathogenic or environmental pressures, Previous studies noted that structural responses involving extracellular matrix remodelling were notable in M. gigas, suggesting an essential role in maintaining tissue integrity and facilitating immune cell migration during infection [ 108 ]. In contrast, S. glomerata demonstrated significant upregulation of GPCR pathways, which are known to mediate immune signalling. 4. Conclusions This study highlights the significant molecular changes that S. glomerata undergoes when infected with M. sydneyi , including notable alterations in cellular defence mechanisms, oxidative stress management, and immune response genes. The upregulation of genes such as galectin-4-like and multiple GPCRs suggests their involvement in immune signalling and pathogen recognition, potentially reflecting the host’s attempt to increase or initiate immune surveillance strategies. Conversely, the significant changes in the expression of CYP450 family genes, ecSOD, FREPs and GPCRs imply that M. sydneyi may employ molecular strategies to suppress the host’s oxidative burst defences, thereby weakening the oyster’s ability to combat infection. These findings not only broaden our understanding of S. glomerata’s immune responses but also identify key genes that could serve as biomarkers for breeding disease-resistant oysters and provide valuable targets for investigating the molecular basis of oyster immunity in future studies. Comparative insights with other bivalves not only enhance our understanding of shared immune strategies but also reveal species-specific adaptation that could inform aquaculture practices and disease management. Future research should further explore the host-pathogen interactions and molecular mechanisms underpinning M. sydneyi infections, as these could reveal that strategies the parasite utilises to manipulate host immune responses for successful infection. Author contributions: CRediT N. N.: Conceptualisation, formal analysis, investigation, methodology, visualisation, writing – original draft, writing – review and editing. I. B.: Conceptualisation, investigation, methodology, supervision, writing – review and editing. C. M.: Conceptualisation, investigation, methodology, supervision, writing – review and editing. Conflict of interest statement The authors declare that they have no conflict of interest. Funding sources This research was funded by HDR candidate support funding from Griffith University to N. Nenadic, I. Bar, and C. McDougall. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Data availability The data used in the study are available on NCBI under BioProjectID: PRJNA1173877. http://www.ncbi.nlm.nih.gov/bioproject/1173877 . All bioinformatic analysis code is available on GitHub at https://idobar.github.io/QX_bioinfo_analysis/ . All downstream analysis code is available on Zenodo at https://doi.org/10.5281/zenodo.14564721 . Figure Legends Supplementary Figure 1. Histology tissue smears of S. glomerata digestive gland tissue infected with M. sydneyi . Black arrows indicated presporulating sporangiosori of M. sydneyi . (A) S. glomerata digestive gland tissue frozen at -80°C then thawed prior to tissue smears, visualization at 1000x magnification. (B) S. glomerata digestive gland fresh tissue smears, taken immediately after dissection, visualized at 400x magnification. Supplementary Figure 2. GC content per sequence sample (forward and reverse), generated by MultiQC. Green indicates M. sydneyi positive samples (infected 1 and 2), orange represents M. sydneyi infected sample 3, and red represents uninfected samples 1 and 2. The peak for infected sample 3 falls between infected and uninfected samples, suggesting that the sample may have had a lower parasite load than infected samples 1 and 2. Acknowledgements We thank T Prowse and A Prowse from the Queensland Oyster Company for providing us with oysters from their lease. We thank T Prowse, A Prowse, M Richardson and M Stefanek for support with sample and data collection. We thank K Roper for the development of the altered standard RNA extraction using Trizol or Tri-reagent protocol utilised for our RNA extractions. Computing resources for the bioinformatic analyses were kindly provided by Griffith University’s Gowonda HPC cluster and the University of Queensland’s Bunya HPC cluster. Footnotes http://www.ncbi.nlm.nih.gov/bioproject/1173877 List of abbreviations QX Queensland Unknown PAMPs pathogen associate molecular patterns ROS reactive oxygen species MSX multinucleated sphere unknown EcSOD extracellular superoxide dismutase Prx6 peroxiredoxin 6 IK interferon-Inhibiting Cytokine Factor SOD superoxide dismutase TIMP tissue inhibitor of metalloproteinase ECM extracellular matrix FREPs fibrinogen-related proteins C1qDCs complement C1q domain-containing proteins CRD carbohydrate recognition domain CYP450 Cytochrome P450 QPX Quahog parasite unknown GPCR G-protein couple receptor References 1. ↵ van der Schatte Olivier A , Jones L , Vay LL , Christie M , Wilson J , Malham SK . A global review of the ecosystem services provided by bivalve aquaculture . Reviews in Aquaculture . 2020 ; 12 : 3 – 25 . 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Share Transcriptomic responses to Marteilia sydneyi infection in the Sydney rock oyster Saccostrea glomerata Nikolina Nenadic , Ido Bar , Carmel McDougall bioRxiv 2025.02.02.636094; doi: https://doi.org/10.1101/2025.02.02.636094 Share This Article: Copy Citation Tools Transcriptomic responses to Marteilia sydneyi infection in the Sydney rock oyster Saccostrea glomerata Nikolina Nenadic , Ido Bar , Carmel McDougall bioRxiv 2025.02.02.636094; doi: https://doi.org/10.1101/2025.02.02.636094 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Zoology Subject Areas All Articles Animal Behavior and Cognition (7624) Biochemistry (17650) Bioengineering (13871) Bioinformatics (41882) Biophysics (21424) Cancer Biology (18566) Cell Biology (25461) Clinical Trials (138) Developmental Biology (13365) Ecology (19867) Epidemiology (2067) Evolutionary Biology (24290) Genetics (15590) Genomics (22476) Immunology (17713) Microbiology (40331) Molecular Biology (17148) Neuroscience (88477) Paleontology (666) Pathology (2828) Pharmacology and Toxicology (4816) Physiology (7635) Plant Biology (15114) Scientific Communication and Education (2044) Synthetic Biology (4286) Systems Biology (9815) Zoology (2268)