Proteomic analysis highlights the immune responses of the hepatopancreas against Hematodinium infection in Portunus trituberculatus

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Proteomic analysis highlights the immune responses of the hepatopancreas against Hematodinium infection in Portunus trituberculatus Meng Lia,b, Jinfeng Wanga,b,c, Qian Huanga,b,c, Caiwen Lia,b,c,d,

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a

CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China c University of Chinese Academy of Sciences, Beijing 100049, China d Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Crustacean Parasite iTRAQ labeling Proteomic profile Host-parasite interaction

The parasitic dinoflagellate Hematodinium is considered an important pathogen of economically important marine crustaceans and has been reported from many wild and cultured species. While limited studies have been conducted to reveal the host-parasite interaction in crustaceans, the underlying molecular mechanisms between Hematodinium and its crustacean hosts are scarcely known. We conducted a comprehensive study to investigate the proteomic responses to Hematodinium infection in the hepatopancreas of Portunus trituberculatus using an iTRAQ-based quantitative proteomic technology. A total of 905 identified proteins including 392 differentially expressed proteins (DEPs) were subjected to GO, COG and KEGG-pathway enrichment analysis, with sixteen DEPs further validated by quantitative real-time PCR. Hematodinium parasites resulted in immune-suppressive and adverse effects on affected hosts, thorough inhibition of the important pattern recognition receptors (Clectin, SR class B, and Toll)-mediated immune responses, regulation of the complement and coagulation pathway, dysregulation of important cell adhesion molecules and extracellular matrix, and imbalance of the cellular redox homeostasis in the hepatopancreas of affected crabs. Moreover, the lysosomes pathway was dysregulated seriously in the hepatopancreas of P. trituberculatus post Hematodinium challenge. The results provided evidences on how the Hematodinium parasite overcame the innate immunity of P. trituberculatus and caused pathological alteration in affected tissues Biological significance: The manuscript presented the first iTRAQ-based proteomic study of the host-parasite interaction between an important marine crustacean and the parasitic dinoflagellate Hematodinium. The manuscript reported the key pathways and proteins involved in the host-parasite interactions. The major findings will contribute to the better understanding of the molecular mechanism of the particular host-parasite interaction, as wells as the pathogenic process in susceptible tissues of affected crustacean hosts.

1. Introduction The Chinese swimming crab (Portunus trituberculatus) is one of the most important commercial species distributed widely in East Asia. It accounts for a subtantial proportion of crab aquaculture in China, with the total production over 125,000 tons in 2016 [1]. However, the sustainability and development of the crab aquaculture industry was recently threatened by epidemic diseases caused by a variety of pathogens (e.g. bacteria, viruses, fungi, parasites) [2–4]. Among those pathogens, the parasitic dinoflagellate Hematodinium perezi has significantly impacted the sustainable aquaculture of P. trituberculatus, and resulted in significant economic loss since 2004 [4–6]. Over the last decade, Hematodinium epidemics has not only affected the wild

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population of commercial valuable crustacean species [7,8], but also the sustainable aquaculture of several crustacean species [4,9]. Therefore, in an attempt to better understand and work towards a strategy aimed at mitigating the effects of this pathogen in aquaculture facilities, we first have to further investigate the crustacean host response to the parasite. Like other invertebrates, crustaceans lack an adaptive immune system and rely on their innate immunity comprised of the humoral and cellular systems to defend against invading pathogens [10,11]. With the rapid development of molecular techniques, omics studies (e.g. transcriptomics, proteomics) have been widely applied to explore the hostpathogen interactions in different crustacean species against various pathogens (e.g. bacteria, viruses and parasites) [12–17]. Compared to

Corresponding author at: Key Lab of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China. E-mail address: [email protected] (C. Li).

https://doi.org/10.1016/j.jprot.2018.11.012 Received 24 May 2018; Received in revised form 15 October 2018; Accepted 16 November 2018 1874-3919/ © 2018 Elsevier B.V. All rights reserved.

Please cite this article as: Li, M., Journal of Proteomics, https://doi.org/10.1016/j.jprot.2018.11.012

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experimental period, crabs were fed once a day (at night) with clam meat and food residues were removed daily. Half of water was renewed once every two days to ensure the water quality.

conventional genomics, proteomics provides comprehensive interpretation and is able to describe the molecular responses more directly [18]. Lately, with the advantage of identifying and quantifying more proteins with the labeled peptides by sensitive mass spectrometers, the isobaric tags for relative and absolute quantification (iTRAQ)-based quantitative proteomics became a more reliable approach than other conventional proteomics techniques [19]. Thus, in recent years, iTRAQbased proteomics technique has been applied to explore the host-pathogen interactions in vertebrates [20] and invertebrates [21,22], as well as in crustaceans [14–16,23,24]. The iTRAQ-based proteomics has been gradually applied to reveal crustacean immune responses to bacterial or viral pathogens. For example, the immunological responses of Eriocheir sinensis, Macrobrachium rosenbergii and Procambarus clakii against Spiroplasma eriocheiris infection were investigated via the iTRAQ-based proteomic technique [14,15,23]. Jeswin et al. [16] carried out an in vitro study to explore the host-virus interaction between the red claw crayfish Cherax quadricarinatus and the white spot syndrome virus (WSSV) using the iTRAQ-based proteomics method. Sun et al. [24] applied the iTRAQbased proteomic technique to study the immune responses of the mud crab Scylla. Paramamosain in response to WSSV or Vibrio alginolyticus infection. A large number of immune-related proteins/pathways have been identified in crustaceans by depicting molecular and proteomics profiles before and after the bacterial or viral challenges with the iTRAQ-based quantitative proteomics approach [14–16,23,24]. Whereas, the immune response of the crustaceans against parasitic infections has not yet been investigated by the proteomics analysis. The hepatopancreas is known to play an important role in the innate immune response of crustaceans [17,25–27]. In our previous studies, we investigated the early transcriptional response (8 days) of the hepatopancreas in Portunus trituberculatus against Hematodinium parasites by gene expression analysis and found several critical immunological molecules that participated in the host response against parasite challenge [28]. However, the immune reactions in crustacean hepatopancreas against Hematodinium parasites have not been fully uncovered. In the present study, we investigated the protein expression in P. trituberculatus challenged with Hematodinium at a timescale of 16 days to further explore the immune responses of crab hepatopancreas to the Hematodinium parasites, thereby to provide better understanding on the molecular mechanisms of the complicated host-parasite interaction between the crustacean host and Hematodinium parasite.

2.2. Protein preparation Six out of the ten collected crab hepatopancreas samples at each time point with confirmed Hematodinium infection at 8 d and 16 d were individually ground into powder in liquid nitrogen, extracted with Lysis buffer (7 M Urea, 2 M Thiourea, 4% CHAPS, 40 mM Tris-HCl, pH 8.5) containing 1 mM PMSF and 2 mM EDTA (final concentration) for 5 min, then DTT was added with a final concentration of 10 mM. After sonication (200 W) for 15 min and centrifuged (4 °C, 30,000 g) for 15 min, the supernatant was mixed well with 5-fold volume of chilled acetone containing 10% (v/v) TCA and incubated at −20 °C overnight. The mixture was centrifuged for 15 min (4 °C, 30,000 g), and the precipitate was washed with chilled acetone three times. The pellet was air-dried and then dissolved in lysis buffer (7 M Urea, 2 M Thiourea, 4% NP40, 20 mM Tris-HCl, pH = 8.5). The pellet was subsequently sonicated (200 W) for 15 min and centrifuged for 15 min (4 °C, 30,000 g) to collect the supernatant. To reduce disulfide bonds in proteins of the supernatant, 10 mM DTT was added and incubated at 56 °C for 1 h. Subsequently, 55 mM IAM was added and incubated for 1 h in the darkroom to block cysteines. The supernatant was mixed well with 5fold volume of chilled acetone for 2 h at −20 °C and then centrifuged (4 °C, 30,000 g) for 15 min. The pellet was air-dried for 5 min, dissolved in 500 μL of 0.5 M TEAB (Applied Biosystems, USA), and sonicated at 200 W for 15 min. Finally, all samples were centrifuged (4 °C, 30,000 g) for 15 min. The resulting supernatant was removed and quantified (using a 2-D Quant Kit, GE Healthcare, USA) prior to storage at −80 °C until further analysis. 2.3. iTRAQ Labeling and SCX fractionation Total protein (100 μg) of each sample solution was digested with Trypsin Gold (Promega, USA) with the ratio of protein: trypsin = 30:1 at 37 °C for 16 h. After digestion, peptides were dried by vacuum centrifugation. Peptides were reconstituted in 0.5 M TEAB and processed according to the manufacture's protocols for 8-plex iTRAQ reagent (Applied Biosystems, USA). Protein samples from four time points (0 d, 4 d, 8 d and 16 d) were labeled with the iTRAQ reagents 116, 117, 118 and 119, respectively, and incubated at room temperature for 2 h. The labeled peptide mixtures were pooled and dried by vacuum centrifugation. After that, the labeled samples were subjected to strong cation exchange (SCX) chromatography using a LC-20AB HPLC Pump system (Shimadzu, Japan). The iTRAQ-labeled peptide mixtures were reconstituted with 4 mL buffer A (25 mM NaH2PO4 in 25% ACN, pH = 2.7) and loaded onto a 4.6 × 250 mm Ultremex SCX column containing 5 μm particles (Phenomenex, USA). The peptides were eluted at a flow rate of 1 mL/min with a gradient of buffer A for 10 min, 5-60% buffer B (25 mM NaH2PO4, 1 M KCl in 25% ACN, pH = 2.7) for 27 min, and 60-100% buffer B for 1 min. The system was then maintained at 100% buffer B for 1 min before equilibrating with buffer A for 10 min prior to the next injection. Elution was monitored by measuring the absorbance at 214 nm, and fractions were collected every 1 min. The eluted peptides were pooled into 20 fractions, desalted with a Strata X C18 column (Phenomenex, USA) and vacuum-dried.

2. Materials and methods 2.1. Experimental animals and sample collection Fifty healthy Portunus trituberculatus (140 ± 10 g) were purchased from local aquaculture farms (Jiaonan, Shandong Province, China). The crabs were screened for the presence of Hematodinium infection using both microscopic and molecular methods as described in Stentiford & Shields [8] and Small et al. [29]. In addition, those crabs were screened for bacterial infections by microscopic observation. The healthy crabs (free of Hematodinium and bacterial infections) and the Hematodiniuminfected crabs were held separately in two independent aerated recycling seawater systems (salinity 30 ppt, temperature 23 ± 0.5 °C, ammonia: 0-0.3 ppm, nitrite: 0-0.6 ppm, pH: 7.4-8.2). The healthy crabs were acclimatized for one week prior to laboratory experiments. Hematodinium parasites were isolated from one heavily infected crab immediately before the challenge experiment, as described in Small et al. [30]. The crabs in this experiment (n = 40) received an inoculum of 100 μL Nephrops saline [31] containing 105Hematodinium trophonts, at the juncture (sterilized with 70% ethanol) between the basis and ischium of the 5th walking leg. Ten crabs were randomly sacrificed at time points of 0, 4, 8, and 16 days (d) post inoculation. Hepatopancreas tissues (0.3 - 0.5 g) were removed and immediately frozen in liquid nitrogen, and stored at −80 °C until further processing. During the

2.4. LC-ESI-MS/MS analysis based on Triple TOF 5600 Each fraction was resuspended in buffer A (5% ACN, 0.1% FA) and centrifuged at 20,000 g for 10 min, the final concentration of peptide was approximately 0.5 g/μL on average, and 10 μL supernatant was loaded on a LC-20 AD nano-HPLC (Shimadzu, Japan) by the autosampler onto a 2 cm C18 trap column. Then, the peptides were eluted onto a 10 cm analytical C18 column (inner diameter 75 μm) packed in2

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house. The samples were loaded at 8 μL/min for 4 min, then a 35 min gradient was run at 300 nL/min starting from 2 to 35% buffer B (95% ACN, 0.1% FA), followed by 5 min linear gradient to 60%, followed by 2 min linear gradient to 80% (maintenance at 80% buffer B for 4 min), and finally returned to 5% in 1 min. Data acquisition was performed with a TripleTOF 5600 System fitted with a Nanospray III source (AB SCIEX, USA) and a pulled quartz tip as the emitter (New Objectives, USA). Data was acquired using an ion spray voltage of 2.5 kV, curtain gas of 30 psi, nebulizer gas of 15 psi, and an interface heater temperature of 150 °C. The MS was operated with a RP of greater than or equal to 30, 000 FWHM for TOF MS scans. For IDA, survey scans were acquired in 250 ms and as many as 30 product ion scans were collected if exceeding a threshold of 120 counts per second (counts/s). Total cycle time was fixed to 3.3 s and the Q2 transmission window was 100 Da for 100%. Four time bins were summed for each scan at a pulse frequency of 11 kHz through monitoring of the 40 GHz multichannel TDC detector with a four-anode channel detect ion. A sweeping collision energy setting of 35 ± 5 eV coupled with iTRAQ adjust rolling collision energy was applied to all precursor ions for collision-induced dissociation. Dynamic exclusion was set for 1/2 of peak width (15 s), and then the precursor was refreshed off the exclusion list.

Table 1 qRT-PCR primers used in the present study.

2.5. Proteomic data analysis Raw data files were converted into MGF files by the Proteome Discoverer 1.2 (Thermo, USA), and the MGF file were searched. Proteins were identified by using Mascot search engine in version 2.3.02 (Matrix Science, UK). The parameters were set as follows: Gln> pyro-Glu (N-term Q), oxidation (M), deamidated (NQ) were defined as potential variable modifications, and Carbamidomethyl (C), iTRAQ8plex (N-term), and iTRAQ8plex (K) were as fixed modifications. The charge states of peptides were set to +2 and + 3. The iTRAQ data were analyzed by MASCOT 2.3.02 software, then protein identification was performed by using P. trituberculatus transcriptome database (35, 222 sequences). To reduce the probability of false peptide identification, only peptides with significance scores (≥ 20) at the 99% confidence interval by a Mascot probability analysis (greater than “identity”) were counted as identified. Each confidently identified protein contains at least one unique peptide. And proteins consisting of at least two unique peptides were subjected to protein quantitation. The quantitative protein ratios were weighted and normalized by the median ratio in Mascot. The ratios with both P-values < .05 and fold changes of > 1.2 were considered as significant. Functional annotations of the proteins were conducted using Blast2GO program against the non-redundant protein database (NR; NCBI). The KEGG database (http://www.genome.jp/kegg/) and the COG database (http://www. ncbi.nlm.nih.gov/COG/) were used to classify and group these identified proteins. The protein-protein interaction (PPI) networks and functional relations of the differentially expressed proteins were analyzed by STRING Version 10.5 (http://string-db.org).

Forward (F)/reverse (R) primers

Sequences from 5′to 3′

SR-qRT-F SR-qRT-R C-lectin-qRT-F C-lectin-qRT-R Toll-qRT-F Toll-qRT-R GILT-qRT-F GILT-qRT-R Cathepsin C-qRT-F Cathepsin C-qRT-R Chymotrypsin-qRT-F Chymotrypsin-qRT-R Tetraspanin-qRT-F Tetraspanin-qRT-R Trypsin-qRT-F Trypsin-qRT-R A2M-qRT-F A2M-qRT-R α-spectrin-qRT-F α-spectrin-qRT-R CAT-qRT-F CAT-qRT-R Calnexin-qRT-F Calnexin-qRT-R MnSOD-qRT-F MnSOD-qRT-R HSP70-qRT-F HSP70-qRT-R Hemolectin-qRT-F Hemolectin-qRT-R Integrin-qRT-F Integrin-qRT-R β-actin-qRT-F β-actin-qRT-R EF1α–qRT-F EF1α–qRT-R GAPDH- qRT-F GAPDH- qRT-R

GGCACCTGACGAGAAGAAACA CTCAAACCACAGCATAGGCAG AAATCATACTGGCTTGGGGC GAAAGAAGAATGCGGGGCT CATTGAGGACAGCCACAGGAC TGGTAGAGAGGTACAGCTTGAGTTC CGGTGTATTACGAGACGCTGT TGGCAAACGAAGATGTAGGAA CAACACAGAATCAGCGGCAG CCTCCAGCATCACTTCCTCAT CCGTGAGGTCCTCTCGCAT CAAAGCCAGCACCGTCCA CCCGAGGCTCAAGCAAAG CAAGCACAGGTAACAGCAAGG ACGGCACCACCTTTACCAAC TCACTCACCACGGGCACTG GGGAAGGGTGTGCGTTG AGGCTCTGATGCGTTTGGT AATTCAGGCCCGCAACCA TCATACCCCAGTGCTCGCAG ATTTGAGGGACCCAAGGAGA GTTCACCAATCGTTGCCGT CTTATTGGTGACCGAGGGC GTGCAGCTTGTAGTCGTTTCC AGGAGAACCCACAGGAGACATT CACACATCCAAACCCAGCAG GACATTGTTCTTGTGGGAGGC TCAGGGTTTATGGACTTGTTCAG GTATGTTTCCACTTGCTACTGCT CTAAGGGATAATCTGATGGGTTG GGTGGAGCCCAATGATGAGA TTGATGTTCTTTTCCTCTGCCA TCACACACTGTCCCCATCTACG ACCACGCTCGGTCAGGATTTTC ACATCACCCTCTTTGACGCC TGTGCCAATGCCACCGAT ACTACGAAGAGGTCTGGGGAAT CAATCAACACTACCACACTGCG

six dilution points measured in triplicate. The AE value was determined by the eq. E = 10 (−1/slope) [32]. The transcripts of target genes were normalized by the geometric mean of three endogenous reference genes including β-actin, glyceraldehyde-3-phosphate dehydrogenase (gapdh) and elongation factor 1-alpha (ef1a) according to the MIQE guidelines [33]. The specific qRT-PCR primers for this study were listed in Table 1. Data were shown as the mean ± standard deviation (SD) and were subjected to the one-way analysis of variance (ANOVA), followed by Duncan's multiple range test using the SPSS Statistics software (version 20.0, SPSS, USA). Differences were considered statistically significant at the level of P-values < .05. 3. Results 3.1. Protein profiling

2.6. Quantitative Real Time PCR (qRT-PCR) All MS/MS spectra were processed using Mascot software. A total number of 296,164 spectra were detected, including 9481 unique spectra (Fig. 1). In addition, 905 proteins were identified and presented with the distribution of sequence coverage (Fig. 2). Among those proteins, 560 proteins were annotated into the three major ontologies by GO enrichment analysis, including biological processes, molecular functions, and cellular components (Fig. 3). The biological process ontology includes reproduction, immune system process, metabolic process, cellular process, reproductive process, biological adhesion, signaling, multicellular organismal process, developmental process, growth, locomotion, single-organism process, etc. The molecular function ontology includes protein binding transcription factor activity, nucleic acid binding transcription factor activity, catalytic activity,

Quantitative Real Time PCR (qRT-PCR) was performed with a Rotor-Gene Q 2plex HRM thermocycler (QIAGEN, Germany). Reactions (triplicate for each sample) were carried out in a 25 μL mixture composed of 12.5 μL SYBR Premix Ex Taq II (2×) (TaKaRa, Japan), 0.5 μL for each primer (10 μM), 1 μL RT reaction solution and additional double deionized water (10.5 μL). The reaction mixture was initially denatured at 95 °C for 30 s, followed by 40 cycles of denaturation at 95 °C for 5 s and annealing at 60 °C for 30 s. Melt curve analysis was performed at the end of each PCR thermal profile to assess the specificity of amplification. The amplification efficiency (AE) of qRT-PCR was calculated from the given slopes in Rotor-gene Real-Time PCR System Manager software by a 10-fold diluted cDNA sample series, with 3

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groups in the present study (Fig. 4). The KEGG pathway enrichment analysis indicated that the identified proteins were mainly involved in metabolism, transport and catabolism, carbohydrate metabolism, digestive system, cell communication, immune system, amino acid metabolism, lipid metabolism, translation, endocrine system, signaling molecules and interaction, etc. (Fig. 5). 3.2. iTRAQ quantification Using a 1.2-fold increase or 0.83-fold decrease in protein expression as the benchmark for significant physiological change, there are 186 (91), 160 (67), and 119 (75) significantly up-regulated (down-regulated) proteins identified at the time points of 4 d, 8 d, and 16 d respectively, in comparison to the time point of 0 d (Fig. 6). Furthermore, 392 differentially expressed proteins (DEPs) were reliably quantified by iTRAQ analysis, including 256 up-regulated proteins, 130 down-regulated proteins, and 6 DEPs with fluctuant changes during the course of Hematodinium infection (Fig. 7). Fig. 1. Summary of the proteome profile in P. trituberculatus hepatopancreas by iTRAQ. Total spectra are the secondary mass spectrums, and spectra are the secondary mass spectrums after quality control. Unique Peptide is the identified peptides which belong only to a group of proteins, and protein is identified by Mascot 2.3.02 software.

3.3. GO enrichment analysis of the DEPs To further elucidate the biological changes in the hepatopancreas of P. trituberculatus challenged by Hematodinium, biological processes were analyzed by the GO enrichment analysis of the DEPs at different time points of 4 d, 8 d, and 16 d after the parasitic challenge (Fig. 8). The dominant GO terms at 4 d after the parasitic challenge were “glucose metabolic process”, “monosaccharide metabolic process”, “hexose metabolic process”, “carbohydrate metabolic process”, “single-organism carbohydrate metabolic process”, “generation of precursor metabolites and energy”, “gluconeogenesis”, “hexose biosynthetic process”, “glucose catabolic process”, “response to abiotic stimulus”, etc. The dominant GO terms at 8 d after the parasitic challenge were “one-carbon metabolic process”, “regulation of intracellular transport”, “response to abiotic stimulus”, “anatomical structure homeostasis”, “amino-acid betaine metabolic process”, “serine family amino acid metabolic process”, “regulation of ion transport”, “muscle cell homeostasis”, “response to hypoxia”, “cysteine metabolic process”, etc. While, the dominant GO terms at 16 d after the parasitic challenge were “cell activation”, “positive regulation of protein processing”, “regulation of protein transport”, “negative regulation of fertilization”, “regulation of establishment of protein localization”, “negative regulation of reproductive process”, “negative regulation of metabolic process”, “positive regulation of biological process”, “reactive oxygen species metabolic process”, “translational initiation”, etc.

Fig. 2. Distribution of the protein sequence coverage. The percent (%) of the identified proteins with different protein sequence coverage were shown.

3.4. Immune related KEGG pathways of the DEPs After the parasitic challenge, KEGG pathway analysis of the DEPs at the time point of 4 d, 8 d, and 16 d were carried out, respectively (Fig. 9). The immune related KEGG pathways at 4 d after Hematodinium challenge were “Cell adhesion molecules (CAMs)”, “Lysosome”, “Tight junction”, “ECM-receptor interaction”, “Complement and coagulation cascades”, “MAPK signaling pathway”, and “Antigen processing and presentation”. The immune related KEGG pathways at 8 d after Hematodinium challenge were “Complement and coagulation cascades”, “Metabolism of xenobiotics by cytochrome P450”, “ECM-receptor interaction”, “Phagosome”, “Focal adhesion”, “Tight junction”, “Lysosome”, “Antigen processing and presentation” and “MAPK signaling pathway”. The immune related KEGG pathways at 16 d after Hematodinium challenge were “Antigen processing and presentation”, “Lysosome”, “MAPK signaling pathway”, “Peroxisome”, “Cell adhesion molecules (CAMs)”, “Toll-like receptor signaling pathway”, “Phagosome”, “Tight junction”, “ECM-receptor interaction”, and “Focal adhesion”. The immune related KEGG pathways as well as their associated DEPs in hepatopancreas after the parasitic challenge were further summarized in Table 2, in which the significantly up-regulated/down-regulated

receptor activity, structural molecule activity, transporter activity, binding, electron carrier activity, antioxidant activity, enzyme regulator activity, translation regulator activity, and molecular transducer activity. And the cell component ontology includes extracellular region, cell, membrane, cell junction, extracellular matrix, membrane-enclosed lumen, macromolecular complex, organelle, extracellular region part, organelle part, membrane part, synapse part, cell part, and synapse. In addition, 605 proteins were mapped and classified into 22 different COG categories based on the Clusters of Orthologous Groups (COG) database (Fig. 4). Among the COG categories, the R category “General function prediction only”, O category “posttranslational modification, protein turnover, chaperones”, C category “Energy production and conversion”, G category “Carbohydrate transport and metabolism”, J category “Translation, ribosomal structure and biogenesis”, E category “Amino acid transport and metabolism”, I category “Lipid transport and metabolism”, Z category “Cytoskeleton”, Q category “Secondary metabolites biosynthesis, transport and catabolism”, and T category “Signal transduction mechanisms” are the predominant COG 4

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Fig. 3. Gene ontology analysis of the identified proteins in hepatopancreas. These proteins were classified into different categories based on three major ontologies including molecular function, biological process and cellular component.

in the connected nodes of the PPI network.

proteins identified with known physiologic or immunologic roles and their corresponding iTRAQ ratios were also presented.

3.6. Validation of the proteomics data by qRT-PCR analysis 3.5. Protein-protein interaction (PPI) of the immune-related DEPs In order to validate the iTRAQ results, the mRNA transcript levels of sixteen identified DEPs, including eight significantly down-regulated proteins (scavenger receptor class B, C-lectin, Toll, gamma-interferoninducible lysosomal thiol reductase, cathepsin C, chymotrypsin, tetraspanin, trypsin) and eight significantly up-regulated proteins (alpha 2macroglobulin, α-spectrin, catalase, calnexin, cytosolic manganese superoxide dismutase, heat shock protein 70, hemolectin, integrin) were further validated by qRT-PCR assays (Fig. 11). The transcripts of SR, Clectin, Toll, GILT, cathepsin C, and chymotrypsin were significantly

The protein-protein interaction (PPI) networks and functional relations of the immune-related DEPs were analyzed by STRING Version 10.5 (http://string-db.org) as shown in Fig. 10. We extracted a network of 24 immune-related DEPs from the STRING database with the default settings; these DEPs were functionally associated with at least one other putative protein. And the different colored lines between the proteins exhibit various types of interaction evidence. Several proteins, such as HSP70, CAT, Cathepsin D, alpha spectrin, etc., were found to be located

Fig. 4. Cluster of orthologous groups (COG) classification of the identified proteins in hepatopancreas. 5

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Fig. 5. KEGG pathway classification of the identified proteins in hepatopancreas.

Fig. 6. Comparative analysis of the identified protein levels at different time points of 4 d (Hp4d), 8 d (Hp8d) and 16 d (Hp16d) post Hematodinium challenge compared to the time point 0 d (Hp0d). The red dots represent the significantly up-regulated proteins, while the green dots represent the significantly down-regulated proteins. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Hematodinium challenge. The transcripts of hemolectin were significantly up-regulated at 4 d (1.86-fold, P < .05), while reduced significantly at 16 d (0.50-fold, P < .05) post the Hematodinium challenge. The qRT-PCR results were in accordance with the iTRAQ-based proteomics data. The consistency between the qRT-PCR results and the proteomics data reflected the reliability of the iTRAQ-based proteomics results in the present study.

decreased at 4 d (0.42-fold, 0.15-fold, 0.25-fold, 0.47-fold, 0.42-fold, 0.45-fold) (P < .05), 8 d (0.34-fold, 0.22-fold, 0.16-fold, 0.60-fold, 0.57-fold, 0.52-fold) (P < .05), and 16 d (0.57-fold, 0.64-fold, 0.37fold, 0.31-fold, 0.65-fold, 0.32-fold) (P < .05) post the Hematodinium challenge. The transcripts of trypsin were significantly reduced at 4 d (0.45-fold, P < .05) and 8 d (0.56-fold, P < .05) post the Hematodinium challenge. While, the transcripts of tetraspanin were not significantly altered during the Hematodinium challenge. The transcripts of A2M and HSP70 were significantly increased at 4 d (6.82-fold, 4.43fold) (P < .05) and 8 d (2.76-fold, 2.77-fold) (P < .05) post the Hematodinium challenge. The transcripts of α-spectrin, catalase, calnexin, MnSOD, and integrin were enhanced significantly at 4 d (9.49-fold, 4.02-fold, 6.17-fold, 2.77-fold, 3.84-fold) (P < .05), 8 d (3.84-fold, 2.83-fold, 7.19-fold, 3.34-fold, 5.09-fold) (P < .05), and 16 d (2.89fold, 2.16-fold, 2.11-fold, 2.70-fold, 2.24-fold) (P < .05) post the

4. Discussion Hepatopancreas has been considered as an important organ involved in innate immunity of crustaceans [34–36], besides its critical physiological functions (e.g. synthesis of digestive enzymes, absorption and storage of nutrients) [37,38]. In the present study, we firstly applied the iTRAQ-based proteomics approach to explore the proteomics 6

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[39,41,43]. In the present study, all of the three important PRRs (CLT, SR class B and TLR) involved in immune pathways of phagosome and TLR signaling pathway were significantly inhibited in P. trituberculatus hepatopancreas, suggesting that Hematodinium can interfere with the host's innate immunity by inhibiting the PRRs-mediated immune responses. 4.2. Regulation of the complement and coagulation pathway The complement and coagulation pathways consisting of serine proteases cascades are involved in barrier immunity and collaborative host defense against the invasive and infectious pathogens [50]. Recent studies suggest that complement and coagulation cascades pathways perform important functions in the immune responses of Eriocheir sinensis against Spiroplasma eriocheiris infection [51] and M. japonicas against WSSV infection [17]. In addition, Yin et al. [52] indicated that the complement and coagulation cascade pathways plays an important role in the immune response of Larimichthys crocea against a ciliate ectoparasite Cryptocaryon irritans. In the present study, the complement and coagulation pathway was obviously affected, and there were six relevant proteins differentially expressed in the hepatopancreas of P. trituberculatus challenged with Hematodinium. Among the six DEPs, an alpha 2-macroglobulin (A2M) was identified and significantly upregulated in the hepatopancreas of affected crabs. A2M participates in regulating the activation process of the proPO system, it is one of the crucial components of the crustacean innate immunity defending against various invading pathogens via the melanization reaction [11,53]. In shrimps, the increased expression of A2M could inhibit the activation of proPO system via inhibition of serine protease activity [26,54,55]. Thus, the significant induction of the A2M in hepatopancreas of Hematodinium-challenged P. trituberculatus reflected its immune-suppressive effects on crustacean hosts by inhibiting the activation of proPO system.

Fig. 7. Venn diagram of the altered hepatopancreas proteome profiles between different time-points post Hematodinium challenge. The numbers of the unique and common differentially expressed proteins (DEPs) between the time points are indicated.

profiles in the hepatopancreas of Portunus trituberculatus against Hematodinium parasites, and various proteins and biological processes had been significantly altered in the hepatopancreas tissues of Hematodinium-affected crab hosts. There were a total of 905 proteins including 392 differentially expressed proteins (DEPs) identified and subjected to GO, KEGG and COG enrichment analysis. Further proteomics analysis indicated that the Hematodinium parasite caused series of negative impacts to the hosts' immune responses in the hepatopancreas of the affected crabs, through inhibition of pathogen recognition receptors, regulation of the complement and coagulation pathway, distraction of the cell adhesion molecules (CAMs) and ECM-receptor pathway, imbalance of the cellular redox homeostasis, dysregulation of the lysosomes, etc.

4.3. Distraction of the cell adhesion molecules (CAMs) and ECM-receptor pathway The cell adhesion molecules (CAMs) pathway was obviously influenced with seven DEPs identified in the hepatopancreas post the Hematoidnium challenge. Cell adhesion molecules (CAMs) perform important functions in maintaining tissue integrity and mediating migration of immune cells [56,57]. In invertebrates, CAMs are essentially involved in innate immunity such as phagocytosis, nodulation, and encapsulation [58,59]. In the present study, protein levels of the three important CAMs (integrin, integrin beta subunit and fascilin 2-like protein) were significantly induced in the hepatopancreas of P. trituberculatus challenged with Hematodinium. Integrin proteins are a large family of conserved cell surface receptors that could mediate cell to pathogen interactions, particularly it can be exploited as viral receptors in host-virus interaction and facilitate the virus entry [39,60–62]. Thereby, the significant alterations of the CAMs in the present study suggested that the CAMs (e.g. integrins) were importantly involved in the host-pathogen interaction between the P. trituberculatus and Hematodinium parasites through the CAMs pathway. Besides, hepatopancreas is the major sources of immune molecules involved in the innate immunity of crustaceans, such as lectins, hemocyanin, antibacterial/ antiviral proteins and nitric oxide [12,34,63]. The damage of the crustacean hepatopancreas tissue by Hematodinium parasites likely affected the innate immunity in the affected crustacean hosts. In addition, the extracellular matrix (ECM)-receptor pathway was overtly affected, with four DEPs identified in the hepatopancreas tissues after the Hematodinium challenge. The ECM is an important structure essential for a normal tissue homeostasis [64]. The ECM components constantly interact with epithelial cells by serving as ligands (e.g. laiminins, fibrinogens) to transmit signals to regulate cellular adhesion, proliferation, apoptosis, survival and differentiation [65].

4.1. Inhibition of pattern recognition receptors (PRRs) The phagosome and TLR signaling pathways were importantly involved in the crustacean immunity against pathogenic infections [39,40]. In the present study, the phagosome and TLR signaling pathways were influenced remarkably in the hepatopancreas by the Hematodinium parasites, with twelve DEPs identified in the two important immune pathways. Among the DEPs, three important pattern recognition receptors (PRRs), including the C-lectin (CLT), scavenge receptor (SR) and toll-like receptor (TLR) were identified and differentially expressed in P. trituberculatus hepatopancreas post Hematodinium challenge in this study. As important PRRs, CLT, SR, and TLR could trigger important immune responses to invading pathogens by recognizing various PAMPs on pathogens [41–45]. CLTs could act as important PRRs to activate the immune responses of shrimp hepatopancreas against bacterial and viral infection [46–48]. The SR class B could promote phagocytosis process and transcriptional up-regulation of the antimicrobial peptides (AMPs) in the immune responses of Marsupenaeus japonicas against bacterial infection [49]. Recently, Li et al. [40] proposed the immunosuppressive effects of Hematodinium parasites by inhibiting the TLR transcripts in P. trituberculatus. The crustacean innate immune responses were generally initiated via the recognition of pathogen associated molecular patterns (PAMPs) by the important PRRs 7

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Fig. 8. Analysis of the temporal biological processes in hepatopancreas after Hematodinium challenge through the GO enrichment analysis of DEPs at each time point of 4 d, 8 d and 16 d.

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Fig. 9. KEGG pathway classification of the DEPs in hepatopancreas at various time points of 4 d (A), 8 d (B), and 16 d (C) post the Hematodinium challenge.

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Table 2 The KEGG immune related pathways and their associated differentially expressed proteins identified in hepatopancreas by iTRAQ. Fold changes were calculated in comparison with the time point of 0 d. Bold numbers represent the significant fold changes (P < .05). KEGG ID

Protein_ID

Description

Ratio (4d VS 0d)

Ratio (8d VS 0d)

Ratio (16d VS 0d)

Antigen processing and presentation

comp10890_c0_seq1_pep1

Gamma-interferon-inducible lysosomal thiol reductase Heat shock protein 70 Protein-disulfide isomerase Cathepsin L Calreticulin precursor Legumain Calnexin Legumain-like Uncharacterized peptidase C1-like protein Cathepsin B Hypothetical protein LOAG_08691 Midline fasciclin Fascilin 2-like protein Integrin Hypothetical protein AND_15691 Integrin beta subunit Golgi apparatus protein Trypsin Putative serine proteinase Alpha 2-macroglobulin Colostrum trypsin inhibitor-like Zonadhesin-like Hemolectin Hypothetical protein BRAFLDRAFT_126389 Fibrinogen-related protein 6 Oplophorus-luciferin 2-monooxygenase Laminin B2-like Vinculin, putative RhoA Tetraspanin Predicted protein Cathepsin C Hypothetical protein Lysosomal alpha-mannosidase Ecdysteroid-regulated protein Tetraspanin-like protein CD9 Cathepsin A-like protein PREDICTED: nucleobindin-2 PREDICTED: beta-glucuronidase-like Cathepsin D Clathrin heavy chain Niemann-Pick C1 protein-like Hyaluronidase-like D-Glucosyl-N-acylsphingosine glucohydrolase beta-Galactosidase-1-like protein 3-like alpha-L-fucosidase GM11849 Niemann Pick type C2 alpha-N-acetylgalactosaminidase-like beta-Hexosaminidase alpha chain precursor beta-N-acetylglucosaminidase beta-Galactosidase Acid ceramidase-like Serine collagenase 1 precursor Hypothetical protein LOAG_00074 Chymotrypsin-like proteinase Ras-related protein Rap-1b precursor PREDICTED: filamin-C-like Isocitrate dehydrogenase Catalase Medium-chain specific acyl-CoA dehydrogenase NADP-dependent isocitrate dehydrogenase Unnamed protein product Cytosolic manganese superoxide dismutase Aldehyde oxidase 2-like Mitochondrial manganese superoxide dismutase

0.55

0.68

0.51

3.04 2.74 0.70 3.61 0.63 2.46 0.97 1.76 0.66 3.01 1.67 1.14 2.65 1.80 1.38 19.20 0.60 0.59 3.15 1.83 3.43 1.51 0.15 1.24 0.72 5.24 3.83 1.80 0.74 0.89 0.52 0.41 0.40 0.55 2.10 1.43 1.17 0.53 0.84 1.80 0.40 0.90 0.77 0.54 0.89 0.56 0.54 0.57 0.59 0.73 0.33 0.62 0.43 2.26 0.33 3.03 1.70 3.08 10.13 1.94 3.97 1.86 1.74 0.65 0.79

2.00 4.57 0.91 3.48 0.61 2.74 1.39 1.40 0.49 2.08 2.09 1.28 2.67 2.13 1.81 5.28 0.79 0.79 1.71 3.02 1.78 1.04 0.17 3.11 0.88 5.25 3.56 1.32 0.61 0.99 0.67 0.27 0.49 1.49 2.13 1.25 1.80 0.79 0.76 1.06 1.16 0.59 1.21 0.70 0.70 0.86 0.60 0.48 0.68 0.57 0.75 0.65 0.52 2.12 0.36 3.64 1.15 2.92 3.29 1.74 1.41 1.88 1.54 0.64 0.84

1.42 1.52 0.55 1.28 0.74 1.49 1.10 2.23 0.61 1.59 2.04 1.80 1.66 1.32 1.48 1.69 0.92 0.87 1.52 1.47 1.14 0.90 0.34 0.37 0.70 2.61 2.50 2.22 0.58 0.73 0.75 0.31 0.64 0.59 1.73 0.86 1.35 0.56 0.85 1.09 0.47 0.93 1.02 1.54 0.72 0.77 0.46 0.53 0.48 0.61 0.64 0.89 0.41 2.19 0.44 2.03 1.44 1.41 1.53 2.18 1.66 1.83 1.61 0.64 0.69

Cell adhesion molecules (CAMs)

Complement and coagulation cascades

ECM-receptor interaction

Focal adhesion Lysosome

MAPK signaling pathway

Peroxisome

comp12855_c0_seq1_pep1 comp14011_c0_seq1_pep1 comp15528_c0_seq1_pep1 comp18231_c0_seq1_pep1 comp18252_c0_seq1_pep1 comp18420_c0_seq1_pep1 comp18846_c0_seq1_pep1 comp19137_c0_seq2_pep1 comp19274_c0_seq14_pep1 comp15967_c0_seq1_pep1 comp17002_c0_seq1_pep1 comp18341_c0_seq1_pep1 comp18665_c1_seq1_pep1 comp19315_c0_seq1_pep1 comp22023_c0_seq1_pep1 comp64855_c0_seq1_pep1 comp11750_c0_seq1_pep1 comp15341_c0_seq1_pep1 comp18979_c0_seq1_pep1 comp19080_c0_seq2_pep1 comp50919_c0_seq1_pep1 comp5471_c0_seq1_pep1 comp6633_c0_seq1_pep1 comp18570_c0_seq2_pep1 comp23365_c0_seq1_pep1 comp44926_c0_seq1_pep1 comp18302_c0_seq1_pep1 comp19551_c0_seq1_pep1 comp5516_c0_seq1_pep1 comp5520_c0_seq1_pep1 comp12290_c0_seq1_pep1 comp12987_c0_seq1_pep1 comp13834_c0_seq1_pep1 comp14293_c0_seq1_pep1 comp15196_c0_seq1_pep1 comp15350_c0_seq1_pep1 comp16334_c0_seq1_pep1 comp17067_c0_seq1_pep1 comp18241_c0_seq1_pep1 comp18296_c0_seq1_pep1 comp18365_c0_seq1_pep1 comp18620_c0_seq1_pep1 comp18801_c0_seq1_pep1 comp18832_c0_seq1_pep1 comp18948_c0_seq1_pep1 comp18999_c0_seq1_pep1 comp23274_c0_seq1_pep1 comp23602_c0_seq1_pep1 comp31569_c0_seq1_pep1 comp49932_c0_seq1_pep1 comp66078_c0_seq1_pep1 comp84040_c0_seq1_pep1 comp3828_c0_seq2_pep1 comp6896_c0_seq1_pep1 comp18676_c0_seq1_pep2 comp18791_c0_seq1_pep1 comp19324_c0_seq1_pep1 comp12311_c0_seq1_pep1 comp15361_c0_seq1_pep1 comp17526_c0_seq1_pep1 comp18590_c0_seq1_pep1 comp18928_c1_seq2_pep1 comp19876_c0_seq1_pep1 comp25347_c0_seq1_pep1 comp27900_c0_seq1_pep1

(continued on next page)

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Table 2 (continued) KEGG ID

Protein_ID

Description

Ratio (4d VS 0d)

Ratio (8d VS 0d)

Ratio (16d VS 0d)

Phagosome

comp5600_c0_seq1_pep1 comp13432_c0_seq1_pep1 comp16574_c0_seq1_pep1 comp16777_c0_seq1_pep1 comp18816_c0_seq1_pep1 comp19272_c0_seq1_pep1 comp19344_c0_seq1_pep1 comp19448_c0_seq1_pep1 comp5222_c0_seq1_pep1 comp5419_c0_seq1_pep1 comp13514_c0_seq2_pep1 comp16900_c0_seq1_pep1 comp18358_c0_seq1_pep1 comp18833_c0_seq2_pep1 comp19312_c0_seq10_pep1 comp21377_c0_seq1_pep1 comp6813_c0_seq1_pep1 comp18235_c0_seq1_pep1

Mannose-binding protein Hypothetical protein CAPTEDRAFT_172504 V-H-ATPase subunit A GM11995 Scavenger receptor class B V-type ATPase A subunit isoform 2 Actin C-type lectin Spectrin beta chain Plastin-1 GL18785 alpha-Actinin, sarcomeric-like isoform 1 Serine/threonine-protein phosphatase 2A Hypothetical protein DAPPUDRAFT_218819 AGAP007523-PB Alpha spectrin Leucine-rich alpha-2-glycoprotein-like isoform 2 Toll-like receptor

1.21 2.40 5.41 5.28 0.52 3.65 5.73 0.56 3.64 2.61 2.83 2.75 3.63 3.26 2.16 4.90 0.38 0.68

0.81 1.49 1.90 1.62 0.52 1.24 3.07 0.87 1.44 2.17 1.57 1.39 1.88 2.52 1.31 1.68 0.98 0.52

1.38 1.07 1.65 2.07 0.65 1.20 3.60 0.58 1.37 1.12 2.09 1.99 2.04 1.48 1.45 1.79 0.76 0.78

Tight junction

Toll-like receptor signaling pathway

dysregulation of the peroxisome homeostasis were generally associated with various diseases [66]. During the process of pathogenic infection, invading pathogens usually break down the host cellular redox homeostasis which results in oxidative damage to host cells (e.g. DNA and protein damage) [26,67]. In addition, a recent study suggested the important roles of peroxisomes in the innate immunity against the viral infection (Sendai virus) by the quantitative proteomics-based analysis [68]. The peroxisomes pathway was also seriously disturbed in the hepatopancreas after the Hematodinium challenge in the present study, and eight DEPs were identified, including three critical antioxidant enzymes (CAT, cytosolic MnSOD, mitochrondrial MnSOD). A recent study showed that both the cytoplasmic and mitochondrial MnSOD participated in the immune responses of the crab E. sinensis against the bacterial and fungal challenges [69]. As indicated, the peroxisome pathway plays an important role in the immune responses of hepatopancreas to Hematodinium parasites, and serious imbalance of this pathway caused by invading parasites could damage the host's redox status and lead to subsequent pathological changes. 4.5. Dysregulation of the lysosomes pathway by the Hematodinium parasites The lysosomes perform critical functions in multiple physiological processes such as signal transduction, cellular adaptation, and immunity [70]. In the present study, the lysosomes pathway was seriously influenced in the hepatopancreas of P. trituberculatus after the Hematodinium challenge, and twenty-four DEPs were identified, including three lysosomal proteases (cathepsin A, C, and D). The lysosomal proteases cathepsins played important roles in the innate and adaptive immunity of the vertebrates [71–73]. While in crustaceans, only a few of cathepsins (e.g. cathepsin B, C, and L) had been identified and participated in the crustacean innate immunity against bacterial or viral infection [74–78]. The significant alteration of the three lysosomal cathepsins implied their involvement in the immune responses of the hepatopancreas to the parasitic challenge. In addition to the cathepsins, a total of ten glycosidase enzymes (α-mannosidase, β-glucuronidase, hyaluronidase, D-glucosyl-N-acylsphingosine glucohydrolase, β-galactosidase, α-L-fucosidase, α-N-acetylgalactosaminidase, β-hexosaminidase, β-N-acetylglucosaminidase, and β-galactosidase) were significantly affected in hepatopancreas of P. trituberculatus after the Hematodinium challenge. Dysregulation of these catabolic enzymes may lead to abnormal cellular metabolism of the substances and energy. Furthermore, maintenance of the lysosome integrity and homeostasis is vital for cell viability and normal physiological processes [70]. Dysfunction of the lysosomes contributes to the pathogenesis of various

Fig. 10. Protein-protein interaction networks of immune related DEPs by using STRING Version 10.5. The DEPs were associated functionally with at least one other protein. Lines with different colours between proteins show the different types of interaction evidence.

Dysregulation of ECM components is associated with pathological conditions and can promote disease progression [64]. In the present, three DEPs (integrin, laiminin B2-like protein and fibrinogen-related protein 6) involved in interaction between the epithelial cell and ECM thorough the ECM-receptor interaction pathway were significantly induced in the hepatopancreas of P. trituberculatus post Hematodinium challenge. The results imply that Hematodinium parasites could break down tissue homeostasis and further result in pathological changes as well as inevitable immune damage via dysregulation of ECM-receptor pathway, which may in turn benefit the survival and proliferation of the parasites in crustacean hosts. 4.4. Imbalance of cellular redox homeostasis Peroxisomes are a major source of ROS in cells that contribute to cellular redox homeostasis [66]. Normal cells must maintain peroxisome homeostasis to prevent the excessive production of ROS, thus 11

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Fig. 11. Comparison of transcriptional analysis and iTRAQ-based proteomic results for sixteen DEPs. The fold changes in transcripts of the sixteen DEPs in hepatopancreas of P. trituberculatus challenged with Hematodinium were detected by qRT-PCR. The qRT-PCR and the iTRAQ-based proteomic results are represented with the bar and line chart, respectively. Transcriptional changes with statistical significances are indicated with asterisks compared to the time point of 0 d (P < .05).

Conflict of interest

diseases (e.g. lysosomal storage diseases and cancer) [79,80]. Thus, the dysregulation of the lysosomes pathway in hepatopancreas was largely involved in the pathogenic process of the Hematodinium infection in crustacean host. In summary, a systematic proteomic study on the host-parasite interaction between Hematodinium and P. trituberculatus was conducted for the first time. A large number of differentially expressed proteins in the hepatopancreas of P. trituberculatus post the parasitic challenge were identified and analyzed functionally. As indicated by the proteomic profiles and the quantitative analysis, the invasion of Hematodinium parasites impaired the innate immune system in the hepatopancreas of P. trituberculatus via dysregulation of numerous important immunological proteins and pathways. The results depicted the mechanism on how the Hematodinium parasite overcame the innate immune defense system in the hepatopanceas of P. trituberculatus and resulted in subsequent pathological changes in the affected tissues, and will contribute to better understanding on the molecular mechanisms underlying the host–parasite interaction between susceptible hosts and Hematodinium parasites.

There is no conflict of interest exiting in the submission of this manuscript, and all the authors listed have approved the manuscript for submission and subsequent publication. This work contains original data that has not been previously published, or being considered for publication elsewhere in whole or in part. Acknowledgements We thanked Dr. Hamish J. Small from Virginia Institute of Marine Science (USA) for his kind assistance to improve the manuscript. This study was financially supported by the General Program (grant no. 41676102) of the National Natural Science Foundation of China (NSFC), CPSF-CAS Joint Foundation for Excellent Postdoctoral Fellows (grant no. 2016LH0034), and China Postdoctoral Science Foundation (grant no. 2016M600561). References [1] Fisheries Bureau of Agriculture Ministry of China, China Fisheries Yearbook, China Agriculture Press, Beijing, 2017.

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