Comparative transcriptomic and proteomic analysis of yellow shell and black shell pearl oysters, Pinctada fucata martensii

Background The pearl oyster Pinctada fucata martensii (Pfu.), widely cultured in the South China Sea, is a precious source of sea pearls and calcifying materials. A yellow shell variety of Pfu. was obtained after years of artificial breeding. To identify differentially expressed genes between yellow shell and normal black shell pearl oysters, we performed transcriptomic sequencing and proteomic analyses using mantle edge tissues. Results A total of 56,969 unigenes were obtained from transcriptomic, of which 21,610 were annotated, including 385 annotated significant up-regulated genes and 227 significant down-regulated genes in yellow shell oysters (| log2 (fold change) | ≥2 and false discovery rate < 0.001). Tyrosine metabolism, calcium signalling pathway, phototransduction, melanogenesis pathways and rhodopsin related Gene Ontology (GO) terms were enriched with significant differentially expressed genes (DEGs) in transcriptomic. Proteomic sequencing identified 1769 proteins, of which 51 were significantly differentially expressed in yellow shell oysters. Calmodulin, N66 matrix protein, nacre protein and Kazal-type serine protease inhibitor were up-regulated in yellow shell oysters at both mRNA and protein levels, while glycine-rich protein shematrin-2, mantle gene 4, and sulphide: quinone oxidoreductase were down-regulated at two omics levels. Particularly, calmodulin, nacre protein N16.3, mantle gene 4, sulphide: quinone oxidoreductase, tyrosinase-like protein 3, cytochrome P450 3A were confirmed by quantitative real-time PCR. Yellow shell oysters possessed higher total carotenoid content (TCC) compared than black shell oyster based on spectrophotography. Conclusions The yellow phenotype of pearl oysters, characterised by higher total carotenoids content, may reflect differences in retinal and rhodopsin metabolism, melanogenesis, calcium signalling pathway and biomineralisation. These results provide insights for exploring the relationships between calcium regulation, biomineralisation and yellow shell colour pigmentation. Electronic supplementary material The online version of this article (10.1186/s12864-019-5807-x) contains supplementary material, which is available to authorized users.


Background
Shell colour polymorphism, a common qualitative characteristic of shellfish, has been investigated using various chemical and molecular biological methods, but many issues remain unsettled, including the relationship between shell colour and material properties [1]. The shell of the pearl oyster Pinctada fucata martensii (Pfu.) consists of a periostracum layer, a prismatic layer containing calcite, and a nacre layer containing aragonite, from outer to inner layers [2,3]. Calcium carbonate (CaCO 3 ) is a key mineral phase in this highly sophisticated and complex structure, and its synthesis depends on the absorption of calcium. Recent reports revealed that the origin of shell colour in the related species Pinctada margaritifera is associated with biomineralisation of the calcitic layer [4]. Two cDNA suppression subtractive libraries constructed for red-shelled and non-redshelled Pfu. revealed that genes encoding shematrin, mantle protein, and nacrein are related to shell colour [5]. Gong [6] found that an increased concentration of calcium ions can enhance nacrein secretion. Pigments in higher molluscs such as bivalves are thought to be tightly attached to conchiolins (organic matrix proteins) in the shell, similar to gastropods and pulmonarias [7], while the periostracum layer is composed of conchiolins and calcium salts. Calmodulin and calmodulin-like protein, two important proteins in calcium transport and secretion processes, regulate calcite growth and aragonite nucleation in bivalves [8][9][10][11]. Sun et al. [8] found that calmodulin-related protein, adenylate cyclase, and tyrosinase family members are involved in both biomineralisation and melanin biosynthesis in scallop Patinopecten yessoensis. Another study showed that the notch signalling pathway plays a vital role in shell pigmentation in the clam Meretrix meretrix, while calcium signalling may activate this pathway and influence shell colour [12]. Sequential layer-by-layer mineralisation is directed by cells of the mantle edge in Pfu., and pigments, glycoproteins and polysaccharides in the periostracum layer are secreted by the mantle or foot tissue in molluscs [13,14]. Thus, there may be a close relationship between biomineralisation and pigment deposition, with some genes acting as a bridge between these two biological phenomena.
Studies have shown that melanin [7], porphyrins [7,15], bile pigments [16] and carotenoids [17] are the main pigments in mollusc shells [1]. Melanin is a common pigment found in bacteria, plants, fungi and higher animals that performs diverse function related to growth promotion, immune defences, stress resistance, and antioxidation [8,18]. The 3,4-dihydroxyphenylalanine (DOPA) intermediate in melanogenesis is important for periostracum layer sclerotisation, and affects quinone tanning of the periostracum in the bivalve Perna viridis [1,19]. Quinone tanning is believed to be an essential prerequisite for orderly deposition of calcium carbonate crystals [20,21]. Ogimura et al. [22] suggested the black spots on the shells of pearl oysters may be related to melanin, and the melanin pathway may perform a defensive role against pathogen infection and inflammatory reaction. Carotenoids perform similar biological functions to melanin, acting as antioxidants and supporting the immune system [23,24]. Li et al. [25] identified the novel new carotenoid pectenolone in muscle of the Yesso scallop Patinopecten yessoensis. Furthermore, a high total carotenoid content (TCC) can enhance tolerance to high temperatures in Pfu. [26], and total antioxidant capacity (TAC) in the noble scallop Chlamys nobilis [27,28].
Yellow shell colour lines of Pfu. showed significant differences in shell and weight index [29], and can affects growth traits [30] and pearl quality [31]. However, the mechanism of yellow shell formation is not clear. In the present study, comparative transcriptomic and proteomic analysis was performed on mantle edge tissue from yellow shell and black shell Pfu. Differentially expressed genes (DEGs) in the two shell colour phenotypes ( Fig. 1) were identified and characterised by bioinformatics and functional annotation. The findings lay a foundation for investigating the mechanism of yellow shell pigmentation.

Results
TCC values for different tissues from yellow-and blackshelled pearl oysters TCC values ranged from 5.53 to 34.74 μg/g dry weight across seven different tissues from yellow oysters (Y), with the highest value in gonad tissue. TCC values ranged from 1.14 to 40.61 μg/g dry weight across seven different tissues from black oysters (B), with the highest value in digestive gland tissue (Fig. 2). TCC values in gill, foot, heart, adductor muscle, mantle, digestive gland and gonad from group B samples were 1. 14  from group Y were significantly higher than those from B (p < 0.05), and TCC values of heart, mantle and adductor muscle were also higher for group Y samples.

Transcriptome analysis and pathways related to yellow shell colour
In total, 98.05% of raw reads were clean reads in T01 (Black, B) and 98.53% of raw reads were clean reads in T02 (Yellow, Y). Two cDNA libraries were constructed with mantle tissues from Y and B pearl oysters, yielding 18.78 Gb of clean data (NCBI accession number: SRR8357272 for B, SRR8357273 for Y) with Q 30 ≥ 89.82% for Y and Q 30 ≥ 89.19% for B, and a GC content of 43.36% for Y and 44.62% for B. A total of 56,969 unigenes were assembled from Pfu. mantle data, among which 21,610 unigenes were annotated using public databases (Additional file 1). Differential expression analysis identified 385 up-regulated unigenes and 227 down-regulated unigenes in Y compared with B with thresholds of |log 2 fold change (FC) | ≥2 and false discovery rate (FDR) < 0.001 (Table 1, Additional file 2, Fig. 3a).
Among all 21,610 annotated unigenes from pearl oyster mantle edge tissue, the most highly expressed genes are cytochrome c oxidase and glycine-rich protein (GRP). GRPs (c78345.graph_c0, c52407.graph_c0, and c78344.graph_c0) showed higher expressions in B, while the GRP (c52407.graph_c0) was identified as shematrin-2. These highly expressed GRPs showed no significant differences in transcriptome data.

DEPs identified by label-free proteome analysis
Following MaxQuant analysis, peptide sequences were searched against the transcriptome reference dataset, yielding 1769 proteins (Additional file 3), including 24 significant up-regulated proteins and 27 significant down-regulated proteins in Y compared with B (Table 3) . Of these, 1684 proteins were quantified and another 85 proteins were qualitatively analysed using a label-free  qPCR validation of genes related to biomineralisation, calcium regulation, and retinol metabolism Expression of DEGs related to shell colour and biomineralisation was validated by qRT-PCR, as shown in Fig. 6. Three nacre proteins showed significant differential expression in the transcriptomes, and two yielded consistent qRT-PCR validation results (Fig. 6a) . Label-free proteome analysis also revealed higher expression of nacre protein N16.3 in Y. Two out of three shell proteins showed similar transcriptome and qRT-PCR results (Fig. 6b), as did one of the four mantle genes (Fig. 6c), among which mantle gene 4 displayed lower expression in Y according to all three quantification methods. Two calmodulin proteins (c72378.graph_c0 and c50108.graph_c0) showed identical trends in transcriptome, proteome and qRT-PCR validation methods. CYP3A, which was not detected in proteome analysis, was down-regulated in Y according to transcriptome and qRT- PCR results. Tyr-3 showed higher expression in B according to qRT-PCR, but lower expression in B according to transcriptome sequencing. In summary, 12 of 18 (66.7%) selected DEGs showed concordance in transcriptome and qRT-PCR methods.

Analysis of correlations between transcriptome and labelfree proteome data
Global analysis of the association between transcriptome and proteome data was performed, and the Pearson correlation coefficient for the two omics approaches was 0.2775 (Additional file 5), as shown in Fig. 7. The bulk of unigenes were divided in the central part (part 5).
In parts 1, 4 and 7, 217 of 1008 unigenes were downregulated at the protein level regardness of mRNA level. Four of seven unigenes distributed in part 1 were presumed to be extracellular structural proteins. GRP shematrin-2 (c52407.graph_c0), sulphide: quinone oxidoreductase (c76659.graph_c1) and F-type lectin (c65133.graph_c0) in part 7 showed lower expression in Y at two omics levels.  5 Heatmap of significant DEPs based on label-free proteome data. FC (Y/B) ≥1.2 and p < 0.05 served as criteria for significant differences. Green represents lower expression, red represents higher expression, and "unknown" refers to unigenes without annotation.
Analysis of parts 3, 6 and 9 revealed that 346 of 1008 unigenes were up-regulated at the protein level. A total of 30 unigenes in part 3 showed higher expression levels in Y at both mRNA and protein levels, including EF-hand domain protein-like calmodulin and peptidase family protein. These proteins are mostly involved in amino acid transport and metabolism, posttranslational modification, signal transduction mechanisms according to KOG annotation, and some have unknown functions. Three calmodulins (c50108.graph_c0, part 3; c75759.graph_c1, part 6; c72314.graph_c0, part 6) involved in GO terms metarhodopsin inactivation and adaptation of rhodopsin mediated signalling were also up-regulated in the Y proteome. In part 6, a homeobox protein transcript factor that inhibits beta-carotene 15, 15′monooxygenase (BCMO1) activity in human intestine [32] and Kazal-type serine protease inhibitor (c51835.graph_c0) were elevated in the Y proteome. Enzymes such as L-amino-acid oxidase (EC: 1.4.3.2; part 5) and maleylacetoacetate isomerase (EC:5.2.1.2; part 6) in tyrosine metabolism was also higher in the Y proteome.

Discussion
The yellow shell trait in Pfu. Might be influenced by both carotenoids and melanin Shell colour differences in Y and B may be due to the combined effects of carotene and melanin, based on higher TCC values and lower tyrosinase-like protein 3 in the mantle of Y. TCC has been linked to shell colour [27] and immunity [28] in noble scallop Chlamys nobilis. Meanwhile, Zheng et al. [27] found that scallops with orange shell and orange muscle were significantly correlated with higher TCC values (p < 0.05) compared with brown shell organisms. TCC values in different tissues from different mollusc species (μg/g) have been reported previously. TCC in viscera (110 μg/g) of Haliotis discus was higher than that in gonad (62 μg/g) and adductor muscle (2 μg/g), and TCC in viscera (67 μg/g) of Modiolus modiolus difficans was higher than that in gonad (19 μg/g) and adductor muscle (1 μg/g) [34]. TCC in gonad was higher than mantle, adductor muscle and gill in noble scallop, ranging from 0.73 to 59.85 μg/g [27]. Absorption of beta-carotenoids by animals such as molluscs depends on the food chain and carotenoids are believed to be essential for reproduction in marine animals [35]. Thus, high accumulation of carotenoids in the digestive gland and gonads of pearl oysters is consistent with the results of previous studies. Karnaukhov [36] found carotenoids in neurons of the gastropod mollusc Lymnaea stagnalis, and higher TCC values in foot tissue of Y in this study may be related to the pedal ganglion.
Conversion of dietary β-carotene into biologically active products such as retinal and rhodopsin is quite complex. Scavenger receptor proteins family have been proved to function in uptake of carotenoids [32] and Liu et al. [37] has found that down-regulation of scavenger receptor class B-like-3 can decrease blood carotenoid in scallop. Whether the up-regulation of SRCR in Y transcriptome was related to accumulation of caronoid is uncertain. Retinoids was the first carotenoid metabolic transformation in many animal tissues [32,35]. Herein, some DEGs were enriched in retinal metabolism-related pathways including phototransduction, and rhodopsinrelated terms such as metarhodopsin inactivation, according to KEGG and GO enrichment analysis. This may imply different carotenoid-related signalling pathways in B and Y.
Although tyr-3 was up-regulated in Y according to transcriptome data, it was found to be down-regulation in Y by qRT-PCR. Tyrosine metabolism is related to melanogenesis (ko 04916), and tyrosine is a differential metabolite between Y and B (data not shown). High expression of tyr-3, a key enzyme for melanogenesis and colour formation [38], might contribute to accumulation of black pigment in B.
The identified signalling pathways/terms related to carotenoid metabolites and the melanogenesis pathway, together with the TCC results, indicated that yellow shell colour might be due to the combined effects of carotene accumulation and melanin shortage in Y. Further research involving the identification of carotenoids in Pfu. is clearly needed.

Genes involved in calcium regulation might affect melanogenesis in the mantle edge of Pfu
Calcium regulation might be related to melanogenesis (ko04916). Enrichment analysis of significant DEGs illustrated three calmodulin unigenes enriched in melanogenesis, and qPCR validation results followed the same trend as those of transcriptome analysis. Two of them were also identified in label-free proteome analysis with higher expression in Y. Buffey et al. found that the calcium and calmodulin signalling system has an inhibitory influence on melanogenesis in murine B16 melanoma cells [39], and calcium has an inhibitory effect on pre-tyrosinase activity [40].
Paradoxically, we found that CaMK II and calpain were up-regulated in Y at the mRNA level but downregulated at the protein level. Kenji et al. [41] found that calpain plays a positive regulatory role in melanogenesis in mouse B16 melanoma cells. Thus, we predict that lower expression of calpain protein in Y might downregulate melanogenesis, but the exact function of CaMK II in melanogenesis remains elusive. Together, higher Calmodulin and lower calpain might influence melanogenesis in the mantle edge of Pfu. Fig. 7 Correlation of transcriptome and proteome differences between Y and B. The first row represents parts 1,2 and 3. The second row represents parts 4,5 and 6. The third row represents parts 7, 8 and 9 Biomineralisation and calcium regulation may contribute to yellow shell colour pigmentation We predict that yellow pigmentation might be connected to special shell structure and biomineralisationrelated genes. Lemer et al. [4] suggested that biomineralisation is related to pigmentation in Pinctada margaritifera, and some of the identified DEGs also showed differential expression in the proteome.
Many genes related to biomineralisation, such as GRPs, matrix proteins, calmodulins, sushi domains, von Willebrand domains, EGF domains, chitin-binding domains, collagen alpha chain, c-type lectin, ferritin, and others [8,14] were detected in label-free proteome analysis, demonstrating the differential expression profiles of these genes in black-and yellow-shelled oysters. Interestingly, GRPs were highly expressed in the group B transcriptome and proteome, but only significantly at the proteome level. This could indicate the accumulation of shell proteins or matrix proteins. GRP shematrin-2, functioning in mineralisation in Pfu. [2].
GRP and mantle gene 4 were significantly up-regulated in the proteome of group B, while Kazal-type SERP was higher in Y. Wang et al. [42] found that mantle gene 4 in Pfu. can increase mineral deposition. The Kazal-type SERP results was consistent with GO enrichment of DEGs in the main Molecular Function category (Fig. 4), implying different serine-type endopepetide activity in Y and B. SERP was highly expressed in albino phenotype blacklipped pearl oyster Pinctada margaritifera compared with the normal black phenotype [4]. The high level of Kazaltype SERP in yellow oysters might be related to protecting the organic matrix in the shell against exogenous digestive enzymes [14].
In summary, yellow-shell pearl oysters possess higher TCC than wild black-shell oysters. Furthermore, genes related to phototransduction, rhodopsin metabolism, melanogenesis, calcium signalling and biomineralization were differentially expressed in Y and B organisms according to transcriptomic, label-free proteomic, and qRT-PCR analyses. Thus, the yellow shell colour phenotype in Pfu. might be due to contributions from three processes: carotenoid metabolite-related signalling pathways, calcium regulation, and biomineralisation.

Conclusions
A total of 21,610 annotated unigenes were obtained in transcriptomic and 1769 proteins were identified in proteomic. The yellow phenotype of pearl oysters, which possess higher total carotenoids, may be related to phototransduction, retinal and rhodopsin metabolism, tyrosine metabolism, calcium metabolism, melanogenesis and biomineralisation. These results provide insights into exploring the relationships between calcium metabolism, biominesalization and yellow shell colour pigmentation.

Preparation of tissues from pearl oysters
F2 families of Pfu. artificially bred for yellow shell colour by our lab, were established in 2014 and cultured at the Marine Biology Research Station in Daya Bay, Chinese Academy of Sciences (Shenzhen, China). Black shell pearl oysters (B) and yellow shell pearl oysters (Y) aged 1.5 years were randomly sampled from one F2 family (Fig. 1). Tissue samples including digestive gland, gonad, gill, foot, heart, adductor muscle and mantle edge were collected from each oyster individual. Part of each sample was stored with RNA/DNA sample protector (TaKaRa, Japan) for RNA abstraction, another portion was frozen in liquid nitrogen for protein abstraction, and the rest was lyophilised using a vacuum freeze-dryer (ALPHA 1-4 LD plus, Martin Christ, Germany) for TCC determination. Animals used in this study were not endangered and were treated in accordance with regulations.

Total carotenoid extraction and TCC determination
The same tissues from seven yellow shell and seven black shell oysters were mixed and dried in a freezedryer (Martin Christ, Germany) for 20-24 h. The different tissue mixtures were ground with pestles and mortars that sterilised at 180°C in an oven dryer before (Fuma, China). Total carotenoids were extracted according to Yanar's and Zheng's method [27,43]. Each tissue sample in triplicate was homogenized and dissolved in 1.2 ml acetone, and incubated with shaking at 200 rpm/ min for 2 h in a dark room at 25°C. After centrifuging the tissue extracts at 2400 g for 5 min, the supernatant (1 ml) was scanned in an ultraviolet-visible spectrophotometer (TU-1810, Persee, China). Total carotenoid content (TCC) was calculated using an extinction coefficient E (1%,1cm) of 1900 at an absorption wavelength of 480 nm [27,43]  where y is the volume of supernatant (ml) and W is the dry weight of tissue powder (g).
Student's t-tests were applied to compare the mean values of each tissue; p < 0.05 was considered significant (*), and p < 0.01 was considered highly significant (**).
Generation of a reference dataset from Pfu. Mantle transcriptome data for proteomic analysis cDNA libraries construction and Illumina sequencing using hi-Seq 4000 Total RNA was abstracted using a Mollusc RNA kit (Omega, USA) according to the manufacturer's protocol, and then checked with a 1% agarose gel. RNA purity, concentration and integrity were investigated using a NanoPhotometer spectrophotometer (IMPLEN, CA, USA), a Qubit 2.0 Fluorimeter (Life Technologies, CA, USA), and an Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA), respectively.
Total RNA (3 μg per sample, 1 μg per individual) from mantle edges of three yellow shell and black shell pearl oysters was used to construct two libraries using an NEB Next Ultra RNA Library Prep Kit for Illumina (NEB, USA) following the manufacturer's recommendations. Firstly, mRNA was enriched from total RNA with polyT oligo-attached magnetic beads, then broken randomly into fragments with fragmentation buffer. Secondly, first-strand cDNA was then synthesised using random hexamer primer and M-MLV reverse transcriptase. Second-strand cDNA was then obtained using DNA Polymerase I and RNase H. Double-stranded cDNA was purified using AMPure XP beads (Beckman Coulter, Beverly, USA) to select fragments of 150-200 bp. Thirdly, Illumina paired-end adaptors were then ligated to DNA fragments after adenylation of 3'ends, and fragments were incubated with USER Enzyme (NEB) for 15 min at 37°C, followed by 5 min at 95°C. PCR was then performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers and Index (X) Primer. Finally, PCR products were purified and library quality was assessed using an Agilent Bioanalyzer 2100 system. The two cDNA libraries were sequenced using an Illumina Hi-Seq 4000 platform.

De novo assembly and functional annotation
Clean reads were obtained by removing low-quality reads and reads containing adapter and/or poly -N from raw data. Meanwhile, Q 20 , Q 30 , GC content and sequence duplication level of clean reads were calculated. De novo assembly of Pfu. mantle transcripts was conducted with Trinity software [44] with min_kmer_cov set to 2 by default and all other parameters set as default values. Clean reads for each sample were mapped back to the assembled transcript reference and transcript quantification was performed with RSEM (http://deweylab.biostat.wisc.edu/rsem/). Transcripts Per Million (TPM) values based on read counts and transcript lengths were used to evaluate the expression level of each unigene. Transcripts were functionally annotated against Nr database [45], Universal Protein knowledgebase (UniProt) [46], Gene Ontology (GO) [47], Clusters of Orthologous Groups of proteins (COG) [48], euKaryotic Ortholog Groups (KOG) [49], eggNOG4.5 [50], and Kyoto Encyclopedia of Genes and Genomes database (KEGG) databases [51] using BLAST [52] (Evalue ≤1e − 5 ). KEGG Orthology (KO) analysis of unigenes was performed using the KOBAS 2.0 web server (http://kobas.cbi.pku.edu.cn/ [53]). Prediction of coding sequences (CDS) and peptide amino acid sequences of unigene peptides was conducted using TransDecoder software (https://github.com/TransDecoder). After searching for profiles contained in the Pfam database [54] the presence of conserved protein domains was assessed with HMMER 3.0 (http://www.hmmer.org/ [55]). Peptide sequence datasets were then used for protein identification.
Functional enrichment analysis of differentially expressed genes (DEGs) Expression level of unigenes were evaluated by TPM values based on read counts and transcript lengths. Differential expression analysis was conducted using the EBSeq R package with thresholds of |log 2 FC| ≥2 and FDR < 0.001. The resulting FDR values were adjusted based on the posterior probability of being differentially expressed (PPDE). GO and KEGG enrichment analyses were performed with the topGO R package (http://www.bioconductor.org/packages/ 2.11/bioc/html/topGO.html) [56] and KOBAS 2.0 respectively with a hypergeometric test.

Preparation of mantle peptides
Filter-aided sample preparation (FASP Digestion) was performed as described previously [57]. Briefly, 200 μg of protein from each sample was mixed with 30 μl SDT buffer, and the reducing agent DTT and other compounds were removed using UA buffer (8 M Urea, 150 mM TRIS-HCl, pH 8.0) by repeated ultrafiltration with a 10 kDa ultrafiltration tube (Sartorius, Germany). Samples were incubated for 30 min in darkness to block reduced cysteine residues using 100 μl iodoacetamide (IAA). After washing with 100 μl UA buffer three times and 100 μl 25 mM NH 4 HCO 3 buffer twice, the proteins were digested with 4 μg trypsin (Promega, USA) in 40 μl 25 mM NH 4 HCO 3 buffer overnight at 37°C. Finally, the resulting peptides were collected as a filtrate, desalted on C18 cartridges (standard density, bed internal diameter = 7 mm, volume = 3 ml; Sigma), concentrated with vacuum centrifugation (Eppendorf Concentrator Plus, Germany) and reconstituted in 40 μl 0.1% (v/v) formic acid. LC analysis of each peptide mixture was performed on an EASY-nLC System (Thermo Fisher Scientific) in buffer A (0.1% formic acid) with 10 cm capillary columns (internal diameter = 75 μm) containing 3 μm resin (Thermo Fisher Scientific). Peptides were eluted with a linear gradient from 0 to 55% buffer B over 110 min, 55-100% buffer B for 5 min, and 100% buffer B for 5 min at a flow rate of 300 nl/min controlled by IntelliFlow technology.

LC-MS and data analysis
LC-MS analysis of eluates was performed on a Q Exactive mass spectrometer (Proxeon Biosystems, now Thermo Fisher Scientific) coupled to an Easy nLC System for 60 min. The mass spectrometer was operated in positive ion mode. MS data were acquired using a datadependent top 10 mode with dynamic selection of the 10 most intense peaks of each survey scan (300-1800 m/ z) for high collision dissociation (HCD) fragmentation. Survey scans were acquired at a resolution of 70,000 at m/z 200, and the resolution of HCD spectra was set to 17,500 at m/z 200, with an isolation width of 2 m/z. The automatic gain control (AGC) target was set to 3*e 6 , and the maximum inject time was 10 ms. The dynamic exclusion duration was 40.0 s, the normalised collision energy was 30 eV, and the underfill ratio was defined as 0.1%. The instrument was run with peptide recognition mode enabled. MS data were analysed using MaxQuant software version 1.5.3.17 [58]. Proteome-wide quantification with label-free approaches was performed according to the Max-label-free quantification (LFQ) intensity determination and normalisation procedure (Max-LFQ) [59]. Briefly, MaxLFQ algorithms are included within the MaxQuant software suite and MaxLFQ is a generic method for label-free quantification with standard statistical tests of quantification accuracy for each of thousands of quantified proteins (FDR < 0.01). Protein quantification was performed according to the 'unique plus razor peptides' mode and intensity determination was calculated using the full peak volume. All raw files were searched against the CDS sequences of the Pfu. mantle edges transcriptome. The statistical significance of differences between yellow shell and black shell oysters was analysed by t-tests via LFQ values for three samples in each group, and values with FC ≥1.2 and p < 0.05 were considered significant.