Proteomic Analysis Associated with the Immune Response in Hemocytes of Portunus trituberculatus Challenged with Vibrio parahaemolyticus

Abstract

Vibrio parahaemolyticus belongs to an expanding group of aquatic pathogens that are widely distributed in aquatic environments. This species is a lethal pathogen for a number of economically important marine crabs. However, studies exploring host–vibrio interactions between V. parahaemolyticus and crabs are scarce, and therefore, the underlying molecular mechanisms are unclear. Herein, we performed a comprehensive proteomic analysis to investigate the immune response of Portunus trituberculatus hemocytes to V. parahaemolyticus infection. A total of 4433 proteins were identified using isobaric tags for relative and absolute quantitation (iTRAQ), and 526 differentially expressed proteins (DEPs) were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, with six DEPs further subjected to quantitative real-time PCR. Several identified DEPs were found to be mainly involved in the immune defense of the crustacean, such as a hemocyanin subunit, C-type lectin, α-2-macroglobulin, Cu/Zn-superoxide dismutase, and heat shock protein 70, playing a key role in the response to V. parahaemolyticus infection. Moreover, many immune-related KEGG pathways were markedly altered, such as cell adhesion molecules, complement and coagulation cascades, and phagosomes. Our results provide insights into how V. parahaemolyticus overcomes the innate immunity of P. trituberculatus to induce pathological alterations in affected tissues. We report the first iTRAQ-based proteomic analysis and highlight the key pathways and proteins involved in the host–vibrio interactions between P. trituberculatus and V. parahaemolyticus. These findings should enhance our understanding of the molecular mechanisms underlying such interactions.

1. Introduction

As an important economic species in Asia-Pacific nations, Portunus trituberculatus has become a major aquaculture species in the coastal areas of China due to its fast growth rate and delicious taste, with its production reaching 105,283 tons in 2021 [1]. However, the stability of crab aquaculture was recently restricted by epidemic diseases caused by diverse pathogens [2,3]. Notably, Vibrio parahaemolyticus has had a significant impact on the sustainable aquaculture of P. trituberculatus, causing considerable economic losses since 2006 [4,5]. Therefore, for better cognition and to develop strategies to overcome this issue, further investigations on the response of P. trituberculatus against pathogens are needed. P. trituberculatus is reported to mainly rely on innate immunity capabilities, including humoral and cellular immune systems, to defend against invading pathogens, which is similar to other invertebrates [6,7]. Previous studies have reported that hemocytes are essential for the survival of crabs in the case of systemic infection and are the main targets during infections caused by V. parahaemolyticus [8].

Many approaches have been applied to study the immune mechanism of crustaceans, such as suppression subtractive hybridization, a simple gene investigation, high-throughput expressed sequence tag analysis, and proteomics [9,10,11,12]. Proteomics places additional value to the interpretation of biological functions inferred to facilitate the elucidation of molecular responses more directly comparing to conventional genomics [13]. Isobaric tags for relative and absolute quantitation (iTRAQ) have become a widely used method [12,14,15]. This technique uses four isobaric amine-specific tags and can help identify and quantify more proteins than most sensitive mass spectrometers.

iTRAQ has been applied to analyze proteomic profiles pre- and post-pathogen infection [16]. Moreover, iTRAQ-based proteomics has been used to study crustacean immune responses to bacterial or viral pathogens. For example, the immunological responses of Procambarus clarkii, Cherax quadricarinatus, Eriocheir sinensis, Macrobrachium rosenbergii, and Scylla paramamosain against Spiroplasma eriocheiris or WSSV infection have been investigated using iTRAQ-based proteomics [12,14,17,18,19]. Such studies have made us aware that iTRAQ-based proteomics could be a reliable and accurate method to investigate the immune relationship between pathogens and crustaceans.

A proteomic analysis to elucidate the immune response of P. trituberculatus to V. parahaemolyticus has not yet been reported. Our previous gene expression analysis helped us identify some genes that play important roles in the response of P. trituberculatus to a V. parahaemolyticus challenge [8,20]. The pertinent immune response remains to be explored. Here, we used proteomics to investigate the immune response of P. trituberculatus hemocytes to V. parahaemolyticus infection. Our findings should contribute to further understanding of the molecular mechanism underlying the immune response of crustaceans to bacterial pathogens.

2. Material and Methods

2.1. Statement of Ethics

All experiments were performed in accordance with the Guidelines for the Care and Use of Laboratory Animals in China. This study was approved by the Institutional Animal Care and Use Committee (IACUC) of the Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences (Qingdao, China), under approval number YSFRI2022026.

2.2. Experimental Animals and Sample Collection

We captured 120 healthy P. trituberculatus (38.6 g ± 2 g) specimens from an aquaculture pond. In the indoor closed seawater tank (1000 L water, temperature 22 °C, salinity 30 ppt), the crabs were reared for 7 days, and then, the test treatment was carried out. After 7 days, 60 crabs were injected with 100 μL of V. parahaemolyticus (2.6 × 10⁷ CFU/mL) according to a previous study [8,20,21]. The injection site was the juncture of the swimming foot. At 0 h, 36 h, and 72 h post-inoculation, hemolymph was extracted from 3 randomly chosen crabs, with the 0 h sample as the control group. The hemolymph samples were centrifuged at 700× g for 10 min at 4 °C. Next, the hemocytes were washed 3 times with 1× PBS to remove hemocyanin and other impurities, frozen in liquid nitrogen, and stored at −80 °C. During the experimental period, the crabs were fed clam meat.

2.3. Protein Preparation

The hemocytes were disrupted in lysis buffer (with enzyme inhibitors). The mixtures were centrifuged and mixed with 5 volumes of cold acetone after removing the supernatant. The mixture was subsequently re-centrifuged, and the obtained pellets were dissolved. Next, dithiothreitol and iodoacetamide were added and incubated successively. Subsequently, 5 volumes of cold acetone were added, and the solution was incubated at –20 °C for 2 h. After centrifugation, the pellet was dissolved. The protein concentration was determined, and the solution was stored at −80 °C.

2.4. iTRAQ Labeling and Reversed-Phase (RP) Fractionation

The total protein (100 μg) in each sample was digested for 4 h at 37 °C with Trypsin Gold (Promega, Madison, WI, USA). Subsequently, Trypsin Gold was re-added and the duration of digestion was 8 h. Formic acid was mixed with the digested sample, the pH was adjusted under 3, and the solution was centrifuged at 12,000× g for 5 min at room temperature. The supernatant was slowly loaded to the C18 desalting column, washed with washing buffer (0.1% formic acid, 3% acetonitrile) 3 times, and then eluted by some elution buffer (0.1% formic acid, 70% acetonitrile). The eluents of each sample were collected and lyophilized. Peptides were then reconstituted in 20 μL of 1 Mtriethylammoniumbicarbonate. iTRAQ labeling of the peptide samples was performed using an iTRAQ Reagent 8Plex Kit (AB Sciex, Foster City, CA, USA). Next, the isobaric-tag-labeled peptides were pooled and dried by vacuum centrifugation. The iTRAQ-labeled peptides were then fractionated using a C18 column (Waters BEH C18, 4.6 × 250 mm, 5 μm) on a RIGOL L3000 HPLC system, and the column oven temperature was set to 45 °C. The eluates were monitored at UV 214 nm, collected for a tube per minute, and combined into 10 fractions finally. All fractions were dried under vacuum and then reconstituted in 0.1% (v/v) formic acid (FA) in water.

2.5. Liquid Chromatography–Electrospray Ionization–Tandem Mass Spectrometry (LC-ESI-MS/MS)

UHPLC-MS/MS analyses were performed using an EASY-nLCTM 1200 UHPLC system (Thermo Fisher, Bremen, Germany) coupled with a Q Exactive^TM HF-X (Thermo Fisher, Germany) in Novogene Bioinformatics Technology Co., Ltd.(Beijing, China). Peptides were separated on a Reprosil-Pur 120 C18-AQ analytical column (15 cm × 150 μm, 1.9 μm) using a 60 min linear gradient from 5% to 100% eluent B (0.1% FA in 80% acetonitrile (ACN)) in eluent A (0.1% FA in H₂O) at a flow rate of 600 nL/min. The solvent gradient was as follows: 5–10% B, 2 min; 10–30% B, 49 min; 30–50% B, 2 min; 50–90% B, 2 min; and 90–100% B, 5 min.

The separated peptides were analyzed using a Q Exactive^TM HF-X mass spectrometer under data-dependent acquisition (DDA) mode, with an ion source of Nanospray Flex™ (ESI), a spray voltage of 2.1 kV, and an ion transport capillary temperature of 320 °C. The full scan ranged from m/z 407 to 1500 with a resolution of 60,000 (at m/z 200), the automatic gain control (AGC) target value was 3 × 10⁶, and the maximum ion injection time was 20 ms. The top 40 precursors of the highest abundance in the full scan were selected and fragmented by higher-energy collisional dissociation (HCD) and analyzed by MS/MS, where the resolution was 15,000 (at m/z 200), the automatic gain control (AGC) target value was 5 × 10⁴, the maximum ion injection time was 45 ms, the normalized collision energy was set to 32%, the intensity threshold was 2.2 × 10⁴, and the dynamic exclusion parameter was 20 s.

2.6. Data Analyses

All resulting spectra from LC-MS/MS were searched against the reference proteome generated by genome sequencing using Proteome Discoverer 2.2. The retrieved results were filtered using Proteome Discoverer v. 2.2 peptide-spectrum matches (PSMs), with 95% confidence intervals. Proteins with at least one unique peptide fragment were considered reliable. The reliable PSMs and proteins were verified with other reliable proteins. Peptide fragments and proteins with false discovery rates (FDRs) of >5% were excluded. The protein quantitation results were statistically analyzed using a t-test. Differentially expressed proteins (DEPs) were determined by the t-test when there was a significant change (p < 0.05) in expression levels (fold-change ≥ 1.2).

2.7. Quantitative Real-Time PCR (qRT-PCR)

qRT-PCR was used to evaluate the correlation between mRNA expression and protein abundance. Primers are listed in

Table 1. qRT-PCR primers used in this study.

Forward (F)/Reverse (R) Primers	Sequences from 5′ to 3′
Ribosomal protein L10-qRT-F	GGTGAAGAACTGCGGCAAGGA
Ribosomal protein L10-qRT-R	GCATCCCCGTTTGGAGCCTAT
Scavenger receptor class B-qRT-F	AATGGAGCCAAGCCCGACCT
Scavenger receptor class B-qRT-R	TCGTCCCAAACCCACCACCT
Hemocyanin subunit 3-qRT-F	GCCAAACAGGTTCCTCATTCCC
Hemocyanin subunit 3-qRT-R	GCAACCCTTCAACGGCAGCA
C type lectin containing domain protein-qRT-F	AGAGGCGAACAATGGCGAGTG
C type lectin containing domain protein-qRT-R	TTAGACAGCGGTGACGCAGAGG
Clip domain serine proteinase 2-qRT-F	GCTGGACCACACTGAAGGGGAT
Clip domain serine proteinase 2-qRT-R	ATGCCACATCTGGGAGGCTGCT
Serine protease-qRT-F	TAACCTGCTGCTGAGTGCCTACC
Serine protease-qRT-R	TCCACCCTCGTCACCATTACAAG
β-actin-F	CGAAACCTTCAACACTCCCG
β-actin-R	GGGACAGTGTGTGAAACGCC

. Total RNA was extracted from hematocrit samples using TRIzol reagent, and cDNA was synthesized using a PrimeScript™ RT reagent kit with gDNA Eraser (Perfect Real Time; TaKaRa, Japan) according to the manufacturer’s instructions. PCR was performed on an ABI 7500 RT-PCR instrument using a 20 µL reaction system. The procedures and methods of qRT-PCR were the same as used by Gao et al. [22].

3. Results

3.1. Protein Profiling

A total of 924,144 spectra were detected, including 195,272 unique spectra. A total of 4433 proteins were identified and distributed in different ranges of peptide sequence coverage (Figure 1; Table S1). Among them, 1857 proteins were annotated into three major ontologies—biological processes, molecular functions, and cellular components—by Gene Ontology (GO) enrichment analyses (Table S2). Biological processes had 0.74% of proteins involved in immune system process terms (Figure 2). In addition, 2125 proteins were mapped and classified into 24 different cluster of orthologous group (COG) categories. In the COG category, 9 proteins were annotated to the V category “defense mechanisms” (Figure 3; Table S3). Of the identified proteins, 3498 were annotated with 301 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, of which 2.89% were involved in the phagosome pathway (Table S4)

3.2. iTRAQ Quantification

Using a 1.2-fold increase or decrease in protein expression as a benchmark for physiologically significant changes, we found that 214 (93) and 303 (78) proteins were significantly downregulated (upregulated) at 36 h and 72 h, respectively, between the experimental and control groups. Moreover, in the experimental group, 55 (33) proteins were significantly downregulated (upregulated) at 36 h in comparison to 72 h (Figure 4).

iTRAQ analysis helped in the reliable identification and quantification of 526 differentially expressed proteins (DEPs): 143 were upregulated, and 419 were downregulated. Moreover, 36 DEPs with fluctuant changes were identified (Figure 5).

3.3. GO Enrichment Analysis

In the GO enrichment analysis of DEPs between the experimental and control groups at 36 h after the V. parahaemolyticus challenge, 126 significantly enriched GO terms (p < 0.05) were identified, including 96, 19, and 11 terms associated with biological processes, cellular components, and molecular functions, respectively. In the GO enrichment analysis of DEPs between the experimental and control groups at 72 h after the V. parahaemolyticus challenge, 71 significantly enriched GO terms (p < 0.05) were identified, including 50, 10, and 11 terms associated with biological processes, cellular components, and molecular functions, respectively (Table S5).

3.4. Immune-Related KEGG Pathways

The KEGG pathway analysis of DEPs at 36 h and 72 h after the V. parahaemolyticus challenge was performed. At 36 h after the V. parahaemolyticus challenge, the KEGG pathway analysis revealed altered immune pathways, including cell adhesion molecules, complement and coagulation cascades, phagosomes, lysosomes, tight junctions, ECM–receptor interactions, the MAPK signaling pathway, metabolism of xenobiotics by cytochrome P450, focal adhesion, peroxisomes, the B cell receptor signaling pathway, Toll-like receptor signaling, TNF signaling, and the T cell receptor signaling pathway. Similarly, at 72 h after the V. parahaemolyticus challenge, the KEGG pathway analysis revealed altered immune pathways, including complement and coagulation cascades, cell adhesion molecules, focal adhesion, ECM–receptor interactions, phagosomes, lysosomes, tight junctions, the MAPK signaling pathway, antigen processing and presentation, the B cell receptor signaling pathway, the metabolism of xenobiotics by cytochrome P450, the Toll-like receptor signaling pathway, peroxisomes, TNF signaling, and the T cell receptor signaling pathway”. Further details are provided in

Table 2. KEGG immune-related pathways and their associated differentially expressed proteins identified in hemocytes by iTRAQ. Fold-changes were calculated in comparison with the control group.

KEGG_ID	Protein_ID	Description	Ratio (36 h vs. Control)	Ratio (72 h vs. Control)
Cell adhesion molecules (CAMs)	comp38016_c0 comp42008_c1 comp44017_c0 comp56776_c0 comp54701_c0 comp52938_c0 comp23347_c0 comp62976_c0 comp48402_c0 comp132136_c0 comp43324_c0 comp463272_c0 comp53019_c0 comp39439_c0 comp47792_c0 comp16281_c0 comp41102_c0 comp44493_c0	Tyrosine-protein phosphatase 10D Nidogen-1 E2C1D1_HARSA Teneurin-3 Type-2 ice-structuring protein Q9GV46_9EUCA oxygenase Laminin subunit beta-1 Techylectin-5B Transforming growth factor–beta-induced protein ig-h3 Integrin alpha-8 Ficolin-1 Plexin-A1 Motile sperm domain-containing protein 2 Laminin subunit gamma-1 MAM and LDL-receptor class A domain-containing protein C10orf112 Ryncolin-2 Basement membrane-specific heparan sulfate proteoglycan core protein von Willebrand factor D and EGF domain-containing fasciclin-2	0.68 0.57 1.28 1.68 2.23 0.77 0.60 0.58 0.83 1.78 1.35 0.70 0.74 0.80 0.98 0.91 0.98 0.88	0.71 0.49 1.04 1.15 0.45 0.60 0.48 0.53 0.84 1.36 1.22 0.51 0.56 0.82 1.21 0.60 0.83 0.76
Complement and coagulation cascades	comp41102_c0 comp23347_c0 comp48863_c0 comp49374_c1 comp39018_c0	von Willebrand factor D and EGF domain-containing protein Techylectin-5B Alpha-2-macroglobulin Alpha-2-macroglobulin-like protein 1 Serine proteinase stubble	0.98 0.60 1.54 1.22 1.27	0.83 0.48 1.31 1.25 1.33
Phagosomes	comp53019_c0 comp50028_c0 comp57849_c0 comp42008_c0 comp47792_c0 comp26865_c0 comp51455_c0 comp16281_c0 comp41102_c0 comp50625_c0 comp26085_c0 comp32356_c0 comp52938_c0 comp23347_c0 comp62976_c0 comp50837_c0 comp55957_c0 comp49730_c0 comp44017_c0 comp56776_c0 comp48402_c0 comp30556_c0	Laminin subunit gamma-1 Early endosome antigen 1 Tubulin alpha chain Nidogen-1 Ryncolin-2 Vesicle-trafficking protein SEC22b-B Cytoplasmic dynein 1 light intermediate chain 2 Basement membrane-specific heparan sulfate proteoglycan core protein von Willebrand factor D and EGF domain-containing protein Cdc42 homolog Tubulin alpha-3 chain Pulmonary surfactant-associated protein D Laminin subunit beta-1 Techylectin-5B Transforming growth factor–beta-induced protein ig-h3 Protein croquemort Tubulin alpha-2 chain Tubulin alpha chain E2C1D1_HARSA teneurin-3 C-type lectin protein Integrin alpha-8 Syntaxin-12	0.74 0.88 0.84 0.57 0.98 0.78 0.78 0.91 0.98 0.62 1.11 1.17 0.77 0.60 0.58 0.71 0.83 0.80 1.28 1.68 0.83 0.82	0.56 0.84 0.79 0.49 1.21 0.75 0.75 0.60 0.83 0.76 1.22 1.73 0.60 0.48 0.53 0.83 0.95 0.88 1.04 1.15 0.84 0.86
Lysosomes	comp30813_c0 comp49562_c0 comp4076_c0 comp31475_c0 comp48159_c0 comp170042_c0 comp40157_c0 comp30098_c0 comp60626_c0 comp109451_c0	AP-3 complex subunit delta-1 ADP-ribosylation factor-binding protein GGA1 AP-3 complex subunit delta-1 AP-1 complex subunit gamma-1 AP-3 complex subunit beta-2 Chitooligosaccharidolytic beta-N-acetylglucosaminidase AP-1 complex subunit sigma-2 E9GTP3_DAPPU putative uncharacterized protein AP-1 complex subunit beta-1 ADP-ribosylation factor-binding protein GGA3	0.86 0.77 0.88 0.83 0.87 1.54 0.76 0.82 0.81 0.73	0.79 0.77 0.77 0.87 0.83 1.35 0.88 0.84 0.80 0.79
Tight junctions	comp53821_c0 comp43480_c0 comp21124_c0 comp17686_c0 comp434052_c0 comp36045_c0 comp54789_c0 comp283514_c0 comp37064_c0 comp54333_c0	Spectrin beta chain Lethal(2) giant larvae protein homolog 2 FACT complex subunit spt16 Exocyst complex component 3 Myosin regulatory light chain 2 MICAL-like protein 2 Myosin heavy chain MAGUK p55 subfamily member 7 Symplekin Myosin heavy chain	0.86 0.88 0.82 0.87 0.79 0.87 0.93 0.81 0.79 0.80	0.81 0.74 0.82 0.74 1.35 0.78 1.39 0.77 0.79 1.12
ECM–receptor interactions	comp53019_c0 comp42008_c1 comp39439_c0 comp47792_c0 comp16281_c0 comp41102_c0 comp47172_c0 comp52938_c0 comp23347_c0 comp62976_c0 comp44017_c0 comp48402_c0	Laminin subunit gamma-1 Nidogen-1 MAM and LDL-receptor class A domain-containing protein C10orf112 Ryncolin-2 Basement membrane-specific heparan sulfate proteoglycan core protein von Willebrand factor D and EGF domain-containing protein Laminin subunit alpha Laminin subunit beta-1 Techylectin-5B Transforming growth factor–beta-induced protein ig-h3 E2C1D1_HARSA Teneurin-3 Integrin alpha-8	0.74 0.57 0.80 0.98 0.91 0.98 0.73 0.77 0.60 0.58 1.28 0.83	0.56 0.49 0.82 1.21 0.60 0.83 0.54 0.60 0.48 0.53 1.04 0.84
MAPK signaling pathway	comp50940_c0 comp41423_c0 comp50628_c0 comp44493_c0 comp53980_c0 comp50625_c0 comp51079_c0	Caspase-3 Mitogen-activated protein kinase ERK-A Serine/threonine-protein kinase 3 Fasciclin-2 JNK-interacting protein 3 Cdc42 homolog Serine/threonine-protein kinase 3	0.86 1.20 0.83 0.88 0.82 0.62 0.79	0.79 1.21 0.78 0.76 0.81 0.76 0.80
Metabolism of xenobiotics by cytochrome P450	comp40890_c0 comp25721_c0	UDP-glucuronosyltransferase 2B13 Glutathione S-transferase Mu 2	1.23 0.67	1.17 0.60
Focal adhesion	comp53019_c0 comp42008_c1 comp53679_c0 comp39439_c0 comp47792_c0 comp434052_c0 comp41423_c0 comp16281_c0 comp41102_c0 comp50625_c0 comp47172_c0 comp51983_c0 comp36045_c0 comp37473_c0 comp52938_c0 comp23347_c0 comp40576_c0 comp62976_c0 comp44017_c0 comp52761_c0 comp48402_c0	Laminin subunit gamma-1 Nidogen-1 Vascular endothelial growth factor receptor 1 MAM and LDL-receptor class A domain-containing protein C10orf112 Ryncolin-2 Myosin regulatory light chain 2 Mitogen-activated protein kinase ERK-A Basement membrane-specific heparan sulfate proteoglycan core protein von Willebrand factor D and EGF domain-containing protein Cdc42 homolog Laminin subunit alpha Dedicator of cytokinesis protein 2 MICAL-like protein 2 Guanine nucleotide-releasing factor 2 Laminin subunit beta-1 Techylectin-5B Talin-2 Transforming growth factor–beta-induced protein ig-h3 E2C1D1_HARSA Teneurin-3 Platelet-derived growth factor receptor alpha Integrin alpha-8	0.74 0.57 0.67 0.80 0.98 0.79 1.20 0.91 0.98 0.62 0.73 0.84 0.87 0.86 0.77 0.60 0.88 0.58 1.28 0.76 0.83	0.56 0.49 0.63 0.82 1.21 1.35 1.21 0.60 0.83 0.76 0.54 0.80 0.78 0.83 0.60 0.48 0.82 0.53 1.04 0.83 0.84
Peroxisomes	comp96590_c0 comp52167_c0 comp40945_c0	ATP-binding cassette subfamily D member 3 Superoxide dismutase [Cu-Zn] Superoxide dismutase [Cu-Zn]	0.66 1.36 1.23	0.60 1.07 1.51
Antigen processing and presentation	comp7092_c0	Heat shock 70 kDa protein cognate 3	0.89	0.78
Toll-like receptor signaling pathway	comp41423_c0 comp50625_c0	Mitogen-activated protein kinase ERK-A Cdc42 homolog	1.20 0.62	1.21 0.76
T cell receptor signaling pathway	comp48402_c0 comp41423_c0	Integrin alpha-8 Mitogen-activated protein kinase ERK-A	0.83 1.20	0.84 1.21
B cell receptor signaling pathway	comp41423_c0, comp50625_c0	Mitogen-activated protein kinase ERK-A Cdc42 homolog	1.20 0.62	1.21 0.76
TNF signaling pathway	comp50940_c0 comp41423_c0 comp44944_c0	Caspase-3 Mitogen-activated protein kinase ERK-A Dynamin-1-like protein	0.86 1.20 0.85	0.79 1.21 0.81

and Figure 6.

3.5. Validation of Proteomic Data by qRT-PCR

To confirm our iTRAQ results, the mRNA transcript levels of six immune-related DEPs, including two significantly downregulated (ribosomal protein L10, scavenger receptor class B) and four significantly upregulated (hemocyanin subunit 3, C-type lectin containing a domain protein, clip domain serine proteinase 2, serine protease) proteins at 36 h after the V. parahaemolyticus challenge, were validated by qRT-PCR (Figure 7). The mRNA transcript levels of most genes showed a consistent alteration trend with the corresponding proteins, but a disparity was noted between the mRNA transcript levels and protein abundance of C-type lectin containing a domain protein at 72 h after the V. parahaemolyticus challenge.

4. Discussion

In recent years, V. parahaemolyticus has had a considerable impact on the sustainable aquaculture of P. trituberculatus [4,5], Scylla serrate [23,24], and Penaeus vannamei [25]. V. parahaemolyticus infection can lead to major economic losses; therefore, elucidating the mechanisms underlying the pathogenesis of this infection has become pivotal for achieving sustainable crustacean production. iTRAQ has emerged as a highly efficient, reliable, and widely used method for protein identification as well as quantification. In our previous studies, the injected V. parahaemolyticus could establish an infection of P. trituberculatus, which could cause crab death and tissues lesions [8,20,26].

Herein, we used an iTRAQ-based approach to analyze the immune responses of P. trituberculatus hemocytes to V. parahaemolyticus infection. We could identify a total of 4433 proteins; 526 DEPs were subjected to GO enrichment and KEGG pathway analyses. Our findings indicated that V. parahaemolyticus considerably alters the host immune system, for example, by affecting immune-related KEGG pathways, such as CAMs, focal adhesion, and ECM-receptor interaction.

We believe that the DEPs identified by us play a pivotal role in the immune response of P. trituberculatus hemocytes to V. parahaemolyticus infection. For example, hemocyanin is an immunoglobulin superfamily molecule, which is one of the important host factors against pathogenic invasion [27,28]. More recent reports have indicated that hemocyanin plays a key role in the immune defense mechanisms of shrimp. In addition, hemocyanin isolated from L. vannamei and Penaeus monodon reportedly shows antiviral and antibacterial properties [29,30]. In this study, the expression level of hemocyanin in response to V. parahaemolyticus infection was upregulated, which is consistent with previously reported results [31,32,33]. In invertebrates, C-type lectins recognize and eliminate pathogens efficiently, which play an important role in the innate immunity system [34]. A study reported that under acute ammonia stress, the expression level of C-type lectins is significantly affected in the hepatopancreas of shrimp [35]; this result is in line with our observations. We also found that α-2-macroglobulin (A2M) and serine proteinase stubble are significantly upregulated in response to V. parahaemolyticus infection. A2M is a key component of the crustacean innate immunity system and plays an important role in defending against invading pathogens; melanization through the prophenol oxidase (proPO) cascade is one of the many innate immune mechanisms. Briefly, the proPO system is a principal component of the humoral immune response in crustaceans and can be activated even by little amounts of microbial components [7,36]. On infecting Fenneropenaeus chinensis with WSSV or a Vibrio species, the increased expression level of A2M inhibited the activation of the proPO system by inhibiting the serine protease activity [37]. Similar results were reported after subjecting P. trituberculatus hepatopancreas to a Hematodinium challenge [38], and we observed similar results in this study, too.

The Toll-like receptor signaling pathways and phagosomes are key in protecting crustaceans against pathogenic infections [38,39,40]. Herein, the phagosome and Toll-like receptor signaling pathways were affected in response to V. parahaemolyticus, with 24 DEPs identified in the two pathways. The complement and coagulation cascades pathways have been reported to play an important role in the immune response of P. trituberculatus to Hematodinium infection [38], Larimichthys crocea to Cryptocaryon irritans [41], and E. sinensis to S. eriocheiris [42]. In this study, the complement and coagulation cascades pathways were remarkably affected, with five relevant proteins being differentially expressed in the hemocytes of P. trituberculatus, including A2M and serine proteinase stubble. CAMs play an important role in mediating the migration of immune cells and maintaining tissue integrity [43,44,45]. The CAM pathway was also influenced (18 DEPs identified) in response to the V. parahaemolyticus challenge. The protein levels of some important CAMs (integrin α-8, laminin subunit β-1 and γ-1, and fasciclin-2) were significantly induced. In addition, the ECM–receptor interactions, focal adhesion, lysosomes, tight junctions, and MAPK signaling pathway were considerably affected(19, 21, 10, 10, and 10 DEPs identified, respectively) in response to V. parahaemolyticus infection. The metabolism of xenobiotics by cytochrome P450, peroxisomes, antigen processing and presentation, T and B cell receptor signaling pathways, and the TNF signaling pathway were all found to be significantly affected, too. To summarize, our results indicate that these pathways play an important role in the immune response of P. trituberculatus to V. parahaemolyticus infection

5. Conclusions

To the best of our knowledge, this is the first comprehensive proteomic analysis to highlight the key proteins and pathways involved in the host–vibrio interaction between P. trituberculatus and V. parahaemolyticus. The results of the study will enhance our understanding of the molecular mechanisms underlying this interaction.