JO U RN A L OF P ROTE O M ICS 1 22 ( 20 1 5 ) 4 1 – 54

Available online at www.sciencedirect.com

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Transcriptomic and proteomic analyses of splenic immune mechanisms of rainbow trout (Oncorhynchus mykiss) infected by Aeromonas salmonicida subsp. salmonicida Meng Longa,b , Juan Zhaoc , Tongtong Lia,b , Carolina Tafallad , Qianqian Zhanga,b , Xiehao Wanga,b , Xiaoning Gonga , Zhixin Shenc,⁎, Aihua Lia,⁎ a

Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China University of Chinese Academy of Sciences, Beijing 100049, China c Qinghai Provincial Fishery Environmental Monitoring Center, Xining 810000, China d Centro de Investigación en Sanidad Animal (CISAINIA), Carretera de Algete a El Casar km. 8.1, Valdeolmos, 28130 Madrid, Spain b

AR TIC LE I N FO

ABS TR ACT

Article history:

Furunculosis caused by Aeromonas salmonicida subsp. salmonicida is an epidemic disease

Received 22 January 2015

among salmonids, including rainbow trout (Oncorhynchus mykiss). However, the immune

Accepted 30 March 2015

mechanisms that are elicited in rainbow trout against the invasion of A. salmonicida are not yet fully understood. In this study, we examined the spleen to investigate the immune response of rainbow trout at 3 days post-infection by A. salmonicida at the transcriptome

Keywords:

and proteome levels by using Illumina-seq and iTRAQ methods, respectively. A total of 1036

Rainbow trout

genes and 133 proteins were found to undergo differential expression during the immune

A. salmonicida

response of the spleen against A. salmonicida infection. Gene ontology and KEGG analysis

Furunculosis

were conducted among the differentially expressed genes and proteins, revealing that

Spleen

immune system process and response to stimulus were the top two biological processes,

Transcriptome

and immune system, signaling molecules and interaction, and immune diseases were the

Proteome

differential pathways activated. Correlation analysis of transcriptomic and proteomic results showed 17 proteins (11 upregulated and 6 downregulated) having consistent expression at RNA and protein levels. Moreover, protein–protein interaction analysis showed that diseases, proteasome, aminoacyl-tRNA biosynthesis, and nucleotide metabolism were the main interactions among the consistently expressed proteins. Consequently, these upregulated proteins, namely, ferritin, CD209, IL13Rα1, VDAC2, GIMAP7, PSMA1, and two ANXA11s could be considered as potential biomarkers for rainbow trout immune responses. Biological significance This study provides the first identification of immune markers through an analysis of the differential expression of both genes and their corresponding protein products in the spleen of rainbow trout after infection by A. salmonicida, shedding light on the molecular

⁎ Corresponding authors. Tel.: +86 971 6267378, +86 27 68780053. E-mail addresses: [email protected] (Z. Shen), [email protected] (A. Li).

http://dx.doi.org/10.1016/j.jprot.2015.03.031 1874-3919/© 2015 Elsevier B.V. All rights reserved.

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JO U R N A L OF P ROTE O M ICS 1 22 ( 20 1 5 ) 4 1 – 54

mechanisms triggered in rainbow trout against A. salmonicida infection and providing new molecular targets for further immunological research in fish. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Aeromonas salmonicida subsp. salmonicida is the causative agent of “furunculosis”, one of the major enzootic fish diseases in many parts of the world where susceptible fish are farmed [1]. The mortality caused by A. salmonicida infection can reach as high as 89–90%, resulting in major economic losses in hatcheries [2]. Rainbow trout (Oncorhynchus mykiss) is highly susceptible to A. salmonicida and despite current vaccination protocols against the disease, mortalities continue to occur [2]. Thus, it is meaningful and urgent to uncover the immune mechanism underlying the pathogen-infection process in rainbow trout, providing a good basis for understanding the characteristics of biochemical and physiological properties of the rainbow trout immune response to bacterial infections. The teleost spleen is the main site where immune defense components are released and the defense against microbes takes place, being the major secondary lymphoid organ in the absence of lymph nodes [3]. Besides containing antibody-producing cells and being the major source of immunoglobulins [4,5], the other main tasks of the teleost spleen include the development of B cells, antigen processing, and MHC class II expression [6], important processes to mount an effective adaptive immune response. The rainbow trout spleen has been shown to be actively involved in immune responses against Yersinia ruckeri infection [7]. Over the years, a considerable number of studies have been conducted to reveal the immune mechanisms of rainbow trout against A. salmonicida at both physiological and molecular levels [8–11]. However, a lack of genomic data severely hampers further advancement in terms of revealing specific molecular targets. Recently, an increasing number of studies have focused on the transcriptome of rainbow trout in response to different pathogens. The transcriptome profiling of the gills in two rainbow trout strains provided insights into their distinct immune responses to A. salmonicida [11]. Multiple transcriptome analysis has been performed in rainbow trout in response to other pathogens or pathogenesis-related proteins, such as infectious hematopoietic necrosis virus (IHNV) [12], Tetracapsuloides bryosalmonae (Myxozoa) [13] and non-virion (NV) protein of viral hemorrhagic septicemia virus (VHSV) [14]. Although, transcriptome analysis has provided a new gateway for identifying levels of gene expression and new transcripts and has thus become a powerful tool for species that lack reference genome information, a comparison of mRNA expression levels, protein amounts, and enzymatic activities has revealed low correlations between metabolome and transcriptome, indicating that transcriptome analysis is insufficient to understand protein dynamics or biochemical regulation [15,16]. A more direct correlation is expected for proteins and biological processes [16], making quantitative mass spectrometric (MS) proteomics an attractive approach [17], and a relative quantification approach based on MS results is the ultimate way to go. Moreover, the isobaric tags for relative and absolute quantitation (iTRAQ) are demonstrated to be more reliable and accurate than two-dimensional differential gel electrophoresis

and other proteomic methods in identifying and quantifying proteins [18], and have been increasingly applied to examine differentially expressed proteins in different aquatic animals, such as zebrafish (Danio rerio), marine medaka (Oryzias melastigma) and Chinese mitten crab Eriocheir sinensis, in response to different pathogens, i.e. Aeromonas hydrophila, 2,2′,4,4′-tetrabromodiphenyl ether (BDE-47) and Spiroplasma eriocheiris, in order to identify the pathogen responsive proteins [19–21]. Furthermore, a series of studies in diverse fields was conducted based on the iTRAQ method [22–26]. In recent years, studies by combination of transcriptome and proteome have become more and more popular [17,27–30], especially for non-model organisms and those lacking reference genomes. In most studies, proteome analysis was conducted based on the transcriptome analysis as it was thought that transcriptome deduced proteins could cover almost all of the proteins found in iTRAQ [27]. However, the genetic process in organisms is a complex mechanism, thus, there are still shortages as well as inaccuracy in transcriptome analysis. So it is improper to define proteins solely on transcriptome results. Here, we identified genes and proteins which were obtained from Illumina-seq and iTRAQ searching for likely protein identification in NR, GO, KEGG, KOG, Swissport and Uniprot database, respectively, and focused on the proteins that had consistent expression at transcriptional and translational levels. Consequently, we have identified differentially expressed genes and proteins in the spleen of rainbow trout upon A. salmonicida infection with the aim of clarifying the mechanism of spleen immune response against A. salmonicida in rainbow trout. When a correlation analysis of transcriptome and proteome results was conducted, 17 genes/ proteins showed consistent results. Protein–protein interaction analysis was then employed to reveal the interactions among consistently expressed proteins. The results provide some new and important information on rainbow trout immune responses against bacterial infection.

2. Materials and methods 2.1. Experimental design and sample collection Rainbow trout weighing 50–60 g with a length of 15–16 cm were bought from the rainbow trout basement in Jingzhou, Hubei Province (Central China), and kept in 50 L plastic buckets supplied with aerated water at 14 °C under a 12 h light:12 h dark photoperiod for two weeks. During the infection experiment, the fish were classified into two groups. One group (experimental group) was intraperitoneally injected with 100 μL of A. salmonicida (BG1) suspension culture (6 × 107 CFU/mL), while the other group (control group) was injected with an equal volume of phosphate buffer solution (PBS). Fish (n = 5) were sampled at 3 days post-infection, which was the time of the outbreak of disease (data not shown). The rainbow trout were euthanized using 0.1% MS222 in a separate plastic container,

JO U RN A L OF P ROTE O M ICS 1 22 ( 20 1 5 ) 4 1 – 54

after which the spleens were sampled and washed with PBS to remove blood and fat cells and immediately placed into an RNAlater (Omega) for RNA extraction or frozen in liquid nitrogen overnight and stored at −80 °C for protein extraction. Fish (n = 5) were also sampled from the control group following the same method.

2.2. RNA extraction and sequencing Total RNA was extracted from the spleen of experimental and control groups by using TRIzol reagent (Invitrogen). The transcriptome library was constructed by mixing equal quantities of RNA from five different fish from each group. Enrichment of mRNA, fragment interruption, addition of adapters, size selection, PCR amplification, and RNA sequencing were performed by the staff of Shanghai Hanyu Bio-Tech. Total RNA was quality tested using 2100 Bioanalyzer and digested by DNaseI (Takara, Japan) for 30 min. mRNA purification was then performed using Dynabeads® Oligo (dT) 25 (Life, USA). The cDNA library was constructed using NEBNext® UltraTM RNA Library Prep Kit for Illumina (NEB, USA) and quality controlled by three tests: Qubit quantification, 2% agarose gel electrophoresis, and high-sensitivity DNA chip test. A 10 ng cDNA library was used for cluster generation in cBot by TruSeq PE Cluster Kit (Illumina, USA) before sequencing using Illumina Hiseq™ 2500.

2.3. De novo assembly FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit/) was used to filter out reads with adaptors and low-quality reads from raw data to generate clean data. Contigs were assembled from clean data using Trinity software (http://sourceforge.jp/ projects/sfnet_trinityrna), and non-redundant consensus sequences were then assembled. GetORF was used for gene prediction and for identifying protein-coding sequences from assembled contigs. By using BLASTx alignment (E-value < 10−5) between transcripts and protein databases such as NR, GO, KEGG, KOG, and Swissport, the best-aligned results were used to determine the annotation of transcripts. GoPipe was used to analyze GO annotation, and KEGG pathway annotation was performed using Blastall software against the KEGG database.

2.4. Protein extraction and enzymolysis Spleen tissues were homogenized in 500 μL of STD buffer (4% SDS, 1 mM DTT, and pH 8.0 150 mM TrisHCl) and bathed in boiling water for 5 min. The homogenates were sonicated (10 times at 60 W ultrasonication for 10 s with intervals of 15 s), bathed in boiling water for 5 min, and then centrifuged to obtain the supernatant, which was quantified by the BCA method according to the manufacturer's instructions. Subsequently, 20 μg of protein for each sample was examined by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (not shown). Proteins in the supernatant were kept in −80 °C for further analysis. Total protein (200 μg) taken from each sample was mixed with 200 μL of UA buffer (8 M urea and pH 8.0 of 150 mM TrisHCl) and transferred into a 10 kDa ultrafiltration centrifuge

43

tube to be centrifuged at 14,000 g for 15 min. Afterwards, 200 μL of UA buffer was added to the centrifuge tube, which was centrifuged at 14,000 g for 15 min, after which the filtrate was discarded. Then, 100 μL of Indol Acetic Acid (IAA) (50 mM IAA in UA) was added, centrifuged at 600 rpm for 1 min, stored away from light at room temperature for 30 min, and centrifuged at 14,000 g for 10 min. Afterwards, 100 μL of IAA was added and centrifuged at 14,000 g for 10 min, and this step was repeated twice. Then, 100 μL of dissolution buffer was added and centrifuged at 14,000 g for 10 min, and this step was repeated twice. Finally, 40 μL of trypsin buffer (2 μg trypsin in 40 μL of dissolution buffer) was added and centrifuged at 600 rpm for 1 min, after which it was stored at 37 °C for 16–18 h. The collection tube was changed and then centrifuged at 14,000 g for 10 min to obtain the filtrate. The peptides in the filtrate were quantified under OD280.

2.5. iTRAQ labeling and SCX fractionation Peptide mixtures (100 ug) of the infected and non-infected samples were labeled with iTRAQ reagents 119 and 121 according to the manufacturer's protocol for iTRAQ Reagent-8plex Multiplex Kit (AB SCIEX). The labeled peptide mixtures were pre-classified using strong cation exchange chromatography (SCX) on AKTA Purifier 100 (GE Healthcare). A total of 36 eluted factions were collected and combined into 10 fractions on the basis of the SCX chromatograph chart and then vacuum freeze-dried and desalted by C18 cartridge (Sigma).

2.6. MS analysis Each fraction was resuspended in 10 μL of 0.1% formic acid. The sample was analyzed using an Easy-nLC of nano HPLC system. The column was balanced with 95% buffer A (0.1% formic acid). Then, 10 μL of peptide sample was loaded onto a Thermo Scientific EASY column (2 cm × 100 μm, 5 μm C18) by an auto-sampler. The peptides were eluted onto an analytical Thermo Scientific EASY column (75 μm × 100 mm, 3 μm C18) at a flow rate of 250 nL/min and separated with a 120 min gradient comprising 100 min of 0–35% buffer B (0.1% formic acid, 84% acetonitrile), 8 min of 35–100% buffer B, followed by maintenance at 100% buffer B for 12 min. The separated samples were analyzed by using Q-Exactive (Thermo Finnigan, USA). Positive ion detection mode was used. The precursor ion scanning range was 300–1800 m/z with a full-scan MS resolution of 70,000 (at 200 m/z), a maximum ion accumulation time of 10 ms, and AGC target value of 3e6. The number of scan ranges was 1 with a dynamic exclusion of 40.0 s. The MS2 scan was performed at a resolution of 17,500 (at 200 m/z) and isolation window of 2 m/z with an activation type of high-energy collision-induced dissociation. The number of microscans was 1; the maximum ion accumulation time was 60 ms; the normalized collision energy was 30 eV; and the underfill ratio was 0.1%. Ten fraction profiles were collected after a full scan.

2.7. Data analysis iTRAQ data from three biological replicates was analyzed by Mascot software (V2.3.02, Matrix Science, USA). The search

44

JO U R N A L OF P ROTE O M ICS 1 22 ( 20 1 5 ) 4 1 – 54

was performed using the following settings based on the Salmoniformes Uniprot database which contained 18,122 sequences: MS/MS ion search, trypsin enzyme, two max missed cleavage, monoisotopic mass values, fixed modifications of carbamidomethyl (C), iTRAQ8plex (N-term) and iTRAQ8plex (K), variable modifications of oxidation (M), peptide mass tolerance ± 20 ppm, fragment mass tolerance 0.1 Da and no restriction of protein mass. The identified peptides were filtered based on an FDR (false discovery rate) cut-off of 1%. Proteins containing at least two spectra were used for quantification by Proteome Discoverer 1.4 software (Thermo, USA). Moreover, median normalization was performed in order to normalize the peptide-iTRAQ reporter intensity in which the median value of the ratio of iTRAQ reporters (i.e. 119/121) was adjusted to make sure that it was 1. Protein quantification was based on the total intensity of the assigned peptides using a python script, and the protein ratios were calculated accordingly from these summed values. Student's t-test was performed on the protein ratio, and only proteins with p-value < 0.05 and fold-change > 1.5 were considered as significantly differential proteins. The GO annotation of these proteins was performed in a manner similar to the transcripts.

rainbow trout to A. salmonicida. Briefly, total RNA was extracted from the spleen of five rainbow trout at 3 days post-infection and from the control group using TRIzol (Invitrogen) according to the manufacturer's instructions. The RNA was reverse-transcribed into cDNA with PrimeScript RT reagent kit (TAKARA, Japan). The β-actin gene was selected as a standardization control, and the specific primers used to amplify it and the candidate genes were designed using Primer 5 software (Premier, Canada). All primers are listed in Table 1. qPCR reactions were performed for each rainbow trout sample in triplicate and at a total volume of 20 μL using CFX96™ Real-Time PCR Detection System (Bio-Rad) in 96-microwell plates. Each reaction contained 1 μL of cDNA, 10 μL of SYBR Premix Ex Taq II (TAKARA, Japan), and 0.8 μL of each specific primer (10 μM). The reaction procedure is as follows: initial denaturation at 95 °C for 2 min, followed by 40 cycles of amplification (95 °C for 10 s, 53–57 °C for 30 s, and 72 °C for 30 s) (Table 1).

3. Results 3.1. Illumina sequencing and iTRAQ identification

2.8. RNA–protein correlation analysis Because of the existing of posttranscriptional regulation, pretrans lational events such as alternative splicing or transcription initiation/termination, as well as posttranslational modifications (PTM) such as phosphorylation, acetylation, methylation, ubiquitination, cysteine oxidation, and nitrosylation, the expression levels of mRNA and protein are not always linear correlation. By comparison and correlation analysis of the RNA and protein data, four results could be produced, i.e. genes having similar tendency at the mRNA and protein levels, genes having opposite tendency, genes differentially expressed at mRNA level but not at protein level, and genes differentially expressed at protein level but not at mRNA level. In this study, we focused on the genes or proteins having similar tendency at the mRNA and protein levels, and made analysis among them to filter some useful information for the subsequent analysis of the immune mechanism of rainbow trout against A. salmonicida infection.

2.9. Protein–protein interaction analysis To determine the correlation among consistently expressed proteins, the proteins that had consistent expression at the RNA and protein levels were blasted against Homo sapiens STRING database by using the publicly available program STRING (http://string-db.org/) with the multi-sequences of the identified proteins. Two criteria were applied in the network: confidence (score: 0.15) and no more than five interactors. In this network, nodes are proteins, lines represent the predicted functional associations, and the number of lines represents the strength of predicted functional interactions between proteins.

2.10. Real-time PCR of the selected proteins RT-qPCR (real-time quantitative PCR) was performed for eight genes selected as candidates for the immune defense of

The transcriptome sequencing resulted in a total of 50,791,062 raw reads (24,237,695 and 26,553,367 reads for experimental and control groups, respectively), and 50,278,197 clean reads were generated after cleaning and quality checks with an average length of 100 bp. The raw data were deposited at the NCBI Sequence Read Archive (SRA) (GenBank accession number SRP051567). A total of 84,891 contigs were assembled from the clean data by the Trinity program. The contigs had a mean length of 717 bp and a N50 value of 1328 bp. All of the 84,891 contigs were submitted to the BLASTx top-hit species distribution and taxonomy analysis, among which 22,161 (26%) showed positive hits with a cut-off E value of 10−5. The spleen transcriptome of rainbow trout had the highest number of BLAST hits to Oreochromis niloticus (31.36%), followed by D. rerio (20.59%), Salmo salar (17.54%) and Takifugu rubripes (10.23%). In addition, Tetraodon nigroviridis (4.23%), O. mykiss (4.18%), and Dicentrarchus labrax (2.36%) were also in the top hit list, and the reason for that may be all the five species ahead of O. mykiss having complete genome information in NCBI database, but rainbow trout not. The number of reads mapped to each contig from the experimental and control groups was counted and then converted to reads per kilobase per million mapped (RPKMP) [31]. Finally, the expression abundance differences were calculated using MARS model [32] from DEGseq software package (DEGseq@Bioconductor). A total of 1036 contigs (Supplementary File 1) identified under the 0.1% FDR threshold were considered to be significantly changed. In addition, by searching against the Salmoniformes Uniprot database, 1447 (Supplementary File 2) proteins were identified from a total of 280,025 spectra, 11,005 peptides, and 5089 unique peptides in the proteomic analysis. 133 out of the 1447 proteins showed significantly differential expression under p-value < 0.05 and fold-change > 1.5 (Supplementary File 3). Details of the transcriptome and proteome data are summarized in Table 2.

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Table 1 – The details of the primers used for real-time PCR in the experiment. Gene name CD209-F CD209-R Ferritin-F Ferritin-R IL-13-F IL-13-R VDAC2-F VDAC2-R ADA-F ADA-R ALOX-F ALOX-R NUDK-F NUDK-R ATP5J-F ATP5J-R β-Actin-F β-Actin-R

Primer sequence (5′–3′)

Length (bp)

Temperature (°C)

TACAGTTAGTTACAAGGTTGGGAGA TGGGCATTGTGGTTGTTGAG ACTGGGTGACCAACCTCCG GGGCTACTGGCTTATAGGAACG CTGGACTGAGGAGGAAATATATGGTAAAG TGGGATGTAGAGGTTCTCATTAATGC AAGGATAAAGGTGTAATGGCAGTG CTGGTGTCCGTGTTTGAGGAG CTCGGCAAGTTCGCAGAGTA GGAATGGGTTCCACGTCAG CTGTACAATAACAGCCAGAGCAAG CCTCCGAGACCAGGTGAGTC GGGACGAAGAAAACGGTCA CCTCGGACGCCTGGACT CTTCCTTGCTGAGAATAGGGC TGGATGGGGTCCAGTTGAGT GCTTCTCAGTCTCATTGG ACCGTTCCAGTTGTGTATA

113

55

213

57

229

57

170

57

164

57

196

57

227

57

157

57

98

55

3.2. GO analysis of differentially expressed transcripts and proteins GO, an international standardized gene functional classification system, was used to classify the function of the differentially expressed rainbow trout genes and proteins. All the genes and proteins were classified into three main categories: biological process, cellular component, and molecular function. For the transcripts, 2475 GO terms were assigned to 1036 transcripts, including 1828 and 647 terms for up- and downregulated genes, respectively. Notably, 1 gene could be annotated to more than 1 category, up to as many as 3. Among all the 2475 GO terms, 45% belonged to biological process, 34% to cellular component, and 21% to molecular function. Cellular and metabolic processes were the largest categories in biological processes, accounting for 19% and 15%, respectively. Under the category of cellular components, 28% were related to each of the cell and cell parts and 17% to the organelles. Under the category of molecular function, 53% were related to binding and 23% to catalytic activity. The percentage of the secondary category of GO in the three main categories was the same for up- (Fig. 1) and downregulated transcripts (Fig. 2). Immune system process and response to stimulus were the significantly differential biological processes (p-value < 0.05 and q-value < 0.005) of transcripts according to hypergeometric distribution. Table 2 – Summary of the transcriptome and proteome data in the spleens of rainbow trout. Transcriptome data Raw reads (pair) Clean reads (pair) Average length (bp) Raw data (G) Clean data (G) Contig number Average length (bp) Min length (bp) Max length (bp) N50 length (bp)

50,791,062 50,278,197 100 10.16 10.05 84,891 717 201 21,085 1328

Proteome data Total spectra Peptide number Unique peptide Protein number

280,025 11,005 5089 1447

For the 133 differentially expressed proteins, 46 upregulated proteins were annotated to 144 GO terms, whereas 87 downregulated proteins were assigned to 231 GO terms. One protein could be annotated to different GO terms, ranging from 0 to 13. Nine secondary categories exist in the biological process of upregulated proteins. Among these categories, single-organism process, metabolic process, cellular process, localization, and biological regulation accounted for 25%, 19%, 17%, 15%, and 14%, respectively (Fig. 1). Cellular component had four secondary categories, among which, cell and macromolecular complex were the largest categories, respectively representing 52% and 38%. Binding represented 50% of the molecular function, followed by 19% of each of catalytic activity and transporter activity. Moreover, the composition and percentage of secondary categories in the three main categories for downregulated proteins (Fig. 2) were similar to those of upregulated proteins in general, except for some slight changes.

3.3. Function annotation by KEGG The KEGG database is a knowledge base for the systematic analysis of gene functions, linking genomic information with higher order functional information [19]. To clarify the pathways of transcripts, the KEGG pathway approach was applied. Among the differentially expressed 1036 transcripts resulting from the comparison between experimental versus control groups, 163 genes could be annotated to 585 KEGG pathways. Basically, 6 KEGG pathways were classified: i) human diseases (31%); ii) metabolism (23%); iii) organismal systems (18%); iv) environmental information processing (16%); v) cellular processes (8%); and vi) genetic information processing (4%). Nine secondary pathways were significantly different (p < 0.05) in response to A. salmonicida infection if judged by the p-value on the basis of hypergeometric distribution. Four of the nine pathways (immune system, signaling molecules and interaction, overview, and immune diseases) had p-values < 0.05 after FDR correction (q < 0.05). The immune system pathway consists of 47 subcategories, among which hematopoietic cell lineage and complement

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Fig. 1 – GO categories assigned to the significantly upregulated transcripts and proteins in rainbow trout spleen in response to A. salmonicida. The genes and proteins were categorized on the basis of GO annotation. The proportion of each category displayed is based on (A) biological process, (B) cellular component, and (C) molecular function in percentage. Outer ring, transcript, inner ring, protein.

and coagulation cascades were the two major compositions. In the pathway of signaling molecules and interaction, 71% of the 28 subcategories were linked to cytokine–cytokine receptor interaction and cell adhesion molecules.

3.4. Correlation analysis of RNA and protein expressions To demonstrate the correlation of expression level in protein and mRNA, the differentially expressed proteins obtained in rainbow trout spleen proteome analysis were compared with the mRNAs corresponding to transcriptome analysis. Of the 133 differentially expressed proteins which were from experimental versus control groups, 58 (44%) could be matched to transcripts according to their amino acid sequences (Supplementary File 4). We then determined whether alterations in gene expression were consistent between protein and mRNA data, and found that 17 proteins (29% of the matched proteins) (Table 3) were in the same

direction (either up or down) between mRNA and protein data, although some mRNA changes were not always significant. This observation suggested that only a small proportion (13%) of the differentially expressed proteins showed a positive correlation with their gene expression at the transcript level in the spleen of rainbow trout after A. salmonicida infection. This finding implies the presence of posttranscriptional regulation and posttranslational modifications or other unknown factors in the spleen of infected rainbow trout.

3.5. Protein–protein interaction analysis Proteins in a living cell do not act as single entities but rather form a variety of functional connections with each other, and these connections are fundamental in the cellular processes [33]. To explore the protein interaction networks altered in rainbow trout spleens after A. salmonicida infection, we extracted networks using STRING software. As shown in

JO U RN A L OF P ROTE O M ICS 1 22 ( 20 1 5 ) 4 1 – 54

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Fig. 2 – GO categories assigned to the significant downregulated transcripts and proteins in rainbow trout spleen in response to A. salmonicida. The genes and proteins were categorized on the basis of GO annotation. The proportion of each category displayed is based on (A) biological process, (B) cellular component, and (C) molecular function in percentage. Outer ring, transcript, inner ring, protein.

Fig. 3, 16 consistently expressed proteins were involved in the PPIs, given the two annexins in the 11 upregulated proteins. Five predicted functional partners (WDR77, PSMB1, PSMA2, PSMA3, and PSMA4) were displayed in the network. Abbreviations of the names of the 16 proteins and 5 functional partners are listed in Supplementary File 5. All proteins were in an integrated network, except for CD209-like protein (CD209), plasma glutamate carboxypeptidase (PGCP), and annexin (ANXA11). The interaction between proteins involved in diseases was observed for ATP synthase coupling factor 6 (ATP5J) and voltage-dependent anion channel 2 (VDAC2), and a link between these proteins with proteasome subunit alpha type (PSMA1) and four functional partners, namely, PSMB1, PSMA2, PSMA3, and PSMA4, was revealed. Furthermore, interaction between proteins that participated in aminoacyl-tRNA biosynthesis was observed for the enzymes phenylalanyl-tRNA synthetase beta chain (FARSB), leucyl-tRNA synthetase (LARS), and probable leucyl-tRNA synthatse (LARS2). In the network, upregulated proteins usually interact with downregulated proteins to constitute a large network, such as ATP5J-VDAC2PSMA1-LARS-ADA-IL13Ra1 and GIMAP7-ALOX5-ADA-NME3PRMT5-WDR77.

3.6. Verification of transcriptome and iTRAQ data on selected candidates by qPCR RT-qPCR was used to validate our data. We measured the mRNA expression levels of eight members, including four upregulated proteins [ferritin, CD209, IL-13 receptor alpha-1-a (IL-13Rα1), and VDAC2] responsible for immune responses, as well as four downregulated proteins [arachidonate 5-lipoxigenase (ALOX5), adenosine deaminase (ADA), ATP5J, and nucleoside diphosphate kinase (NME3).] As shown in Fig. 4A, the mRNA expression levels of ferritin, CD209, IL-13Rα1 and VDAC2 were all significantly upregulated at 3 days post-infection (dpi) compared to that of the control group. Among them, CD209 was upregulated with the largest fold-change, followed by IL-13Rα1, ferritin and VDAC2 in turn. On the other hand, Fig. 4B shows that only the expression of ADA was significantly downregulated in response to infection, while the expression of other three genes was just gently decreased. However, the expression of four downregulated genes in qPCR was completely consistent with the results of transcriptome, which indicated that our transcriptome results indeed reflected the relative expression level of each gene in vivo.

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Table 3 – The consistent expression proteins at RNA and protein levels. Uniprot accession number

Unique peptides

MW (kDa)/pI

Fold change in proteins

Up-regulated proteins Q64HY2 2 B5X1W5 1 C1BFT7 1

255 554 262

29.1/6.46 58.7/8.18 29.3/6.11

2.30 1.66 1.65

C1BEL9 B5XFT3

2 1

176 207

20.6/5.76 23/5.67

B5X0Z8

1

589

E6ZCC8

1

387

C0H907 C0PUL6

3 1

1178 420

B5DH06

1

C0H985

p-Value (proteins)

Gene ID

Contig length

Log2 fold change in contigs

p-Value (contigs)

9.85E −06 0.0074 0.0078

comp117852_c0_seq1 comp121566_c6_seq14 comp118323_c1_seq1

1817 3397 425

3.4967 0.8621 0.6182

0 3.39E −160 1.16E −05

1.64 1.64

0.0084 0.0086

comp118831_c0_seq2 comp1036144_c0_seq1

964 220

0.8361 3.1375

0 1

66.3/5.49

1.61

0.0111

comp115284_c0_seq1

2780

0.8698

2.55E −56

45/7.2

1.60

0.0128

comp127061_c0_seq7

3847

0.8404

3.13E −25

133.9/7.9 48/6.84

1.59 1.53

0.0141 0.0239

comp117442_c0_seq1 comp125244_c0_seq2

1729 2815

0.4502 0.0694

3.39E −18 0.2062

283

30.1/8.82

1.52

0.0255

comp122228_c0_seq2

2142

1.0042

1.73E −117

2

504

53.5/7.66

1.52

0.0261

comp97335_c1_seq1

210

0.5525

0.1538

Down-regulated proteins B5X119 1

674

78.2/5.72

0.33

2.06E −07

comp127687_c0_seq3

3633

−0.6298

1.23E −29

C1BG59

2

121

12.9/9.26

0.41

3.05E −05

comp119727_c1_seq4

1192

−0.3686

1.12E −13

B5X976 C0H9P1

1 1

354 912

40.1/5.76 102.9/6.61

0.43 0.47

7.99E −05 0.0004

comp122891_c0_seq2 comp122124_c1_seq2

2119 1789

−1.03196 −0.1434

4.31E −194 0.4449

B5X4A1

1

473

52/6.02

0.47

0.0005

comp117653_c0_seq1

1886

−0.2208

0.0037

B5XE35

1

168

19.3/7.03

0.54

0.0035

comp120697_c0_seq1

1260

−0.29789

0.0012

Protein annotation

CD209-like protein Annexin Proteasome subunit alpha type Ferritin GTPase IMAP family member 7 Phenylalanyl-tRNA synthetase beta chain IL-13 receptor-alpha-1-a precursor Leucyl-tRNA synthetase Arginine N-methyltransferase 5 Voltage-dependent anion channel 2-2 Annexin

Arachidonate 5-lipoxygenase ATP synthase coupling factor 6 Adenosine deaminase Probable leucyl-tRNA synthetase Plasma glutamate carboxypeptidase Nucleoside diphosphate kinase

JO U R N A L OF P ROTE O M ICS 1 22 ( 20 1 5 ) 4 1 – 54

Protein length

JO U RN A L OF P ROTE O M ICS 1 22 ( 20 1 5 ) 4 1 – 54

49

Fig. 3 – The protein–protein interaction network of the 17 consistently expressed proteins was analyzed by String software. In this network, nodes are proteins, lines represent the predicted functional associations, and the number of lines represents the strength of predicted functional interactions between proteins.

4.1.1. Immune-related proteins

proteins. Although none of the classical inflammation or immune response mediators such as interferons, interleukins or cytokines were uncovered, most of them were presented or shown in the transcriptome results. For example, interleukin-8 and interleukin-1β were up-regulated at RNA level with 2.6 and 8.5 fold changes in our study, in addition, complement component 6, CD79, mannose-binding lectin and so on, were also up-regulated, which can be seen in the Supplementary File 1. But they were not up-regulated at protein level more than 1.5 fold change, which may be caused by posttranscriptional regulation and posttranslational modifications or other unknown factors. In this study, we just focused on genes/proteins both up-regulated at RNA and protein levels under our particular standards, and many genes only changed at transcriptional level may be ignored or covered up for subsequent analysis. So our final results and interpretations were just based on those unmodified proteins. Although the iTRAQ approach has advantages in finding more proteins, posttranslationally modified proteins can more easily be identified from 2DE gels, giving a comprehensive view on the actual expression levels of those protein species. Therefore, the iTRAQ approach and two-dimensional gel electrophoresis may be complementary to each other.

Among 11 upregulated proteins, ferritin, CD209, IL-13Rα1, VDAC2, GTPase IMAP family member 7 (GIMAP7), proteasome subunit alpha type (PSMA1), and two ANXA11s are all immune-related

Moreover, both GO and KEGG analysis revealed that the immune system process occurred in the significantly enriched GO term or pathway. Overall, the immune-related proteins

4. Discussion Furunculosis is a common and serious disease of rainbow trout worldwide [1,2]. However, little is known about the molecular mechanisms that mediate the immune response of rainbow trout against A. salmonicida, given that the immune response is a complicated physiological process. The spleen is known to be important for both innate and adaptive immunity. Thus, with the recent advancement of DNA sequencing technology, we report for the first time transcriptomic data from the spleen obtained from rainbow trout infected by A. salmonicida and present the proteomic data by using an iTRAQ approach. Once the transcriptome and proteome results were combined and compared, we were able to provide a comprehensive interpretation and accurate measurement of gene and protein expression in the spleen samples from A. salmonicida-infected rainbow trout for the first time in the field.

4.1. Consistently expressed proteins at RNA and protein levels

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Fig. 4 – (A) Relative mRNA expression levels of the upregulated proteins using real-time PCR analyses related to responses of rainbow trout to A. salmonicida challenge. (B) Relative mRNA expression levels of the downregulated proteins using real-time PCR analyses related to responses of rainbow trout to A. salmonicida challenge. Relative expression levels were calculated according to the 2−△△CT method with β-actin as an internal reference gene and the control group as a calibrator (relative expression = 1). Error bars represent the standard deviation. Significant expression differences were indicated with an asterisk (*p < 0.05).

serve a major function in the anti-infection process against A. salmonicida in rainbow trout. Thus, the immune mechanism deserves further in-depth study. Here, we discuss the functions of these proteins and the correlations among them briefly to provide some clues for the clarification of specific immune mechanisms. Ferritin is widely distributed throughout the living kingdom in a highly conserved conformation, with a molecular structure that is particularly effective in sequestering iron in a soluble, bioavailable, and nontoxic form [34]. In vertebrates, the cytosolic complex consists of two subunits encoded by separate genes, known as ferritin heavy and light chains [35,36]. Many studies reported that the expression level of ferritin was upregulated after infection by pathogens and stimulation of toxin or other factors. After E. sinensis was challenged by the bacteria Listonella anguillarum or Pichia pastoris GS115, the transcriptional levels of two ferritins in the hemocytes were upregulated [37]. The ferritin H mRNA level increased in the rainbow trout ovary after 168 h of treatment with zearalenone [38]. Northern blot analysis and nuclear run-on transcription assay showed that the transcription and accumulation of the ferritin H isoform mRNA were enhanced by cold acclimation in rainbow trout [39]. The transcriptional and translational levels of rainbow trout ferritin

were also upregulated after A. salmonicida challenge in this study, which was consistent with previously obtained results. Furthermore, iron homeostasis has been shown to be involved in many cross-regulatory interactions with the immunological response [40]. Two of the proteins involved in maintaining strict control of free iron are ferritin and transferrin, the regulation of which has been shown to be tightly regulated in sea bass (D. labrax) [41]. In this study, ferritin exhibited an increased expression level in the spleen after A. salmonicida infection, thereby suggesting that in rainbow trout spleen tissue, an increased control of any free iron was maintained as a defense strategy against A. salmonicida. Pathogen-associated molecular patterns (PAMPs) are highly conserved antigens among pathogenic groups and are specifically recognized by membrane proteins that initiate an innate immune response promoting the proliferation and differentiation of specialized cells for host defense. The main receptors of PAMPs are Toll-like receptors (TLRs) and C-type lectin receptors [42]. Dendritic cell-specific intercellular adhesion molecule (ICAM)-3-grabbing non-integrin (DC-SIGN/CD209) is a cell surface C-type lectin expressed on dendritic cells and involved in cell–cell interactions through its capacity to bind ICAM-3 and ICAM-2 [43,44]. CD209 is also capable of binding HIV-1 [45], Ebola virus [46], Mycobacterium tuberculosis [47], Leishmania amastigotes [48], and Candida albicans [49]. Together, these results demonstrate that CD209 is an excellent pathogen-uptake receptor that captures not only viruses and bacteria but also parasites and fungi. The expression level of CD209 was upregulated with the highest fold-change among the upregulated genes at both the RNA and protein levels in our study, suggesting that CD209 plays an important role in the immune defense of rainbow trout against A. salmonicida. Thus, a reasonable hypothesis is that lectin–oligosaccharide interactions are involved in A. salmonicida recognition, which requires further research and confirmation. Macrophages serve a major function in the development of innate and specific immunity, as well as in the resolution of inflammatory processes [50] and have recently been classified into two major subsets, with the M1 population as inflammatory and the M2 subset as anti-inflammatory [50,51]. IL-13Rα1 expression serves as a marker for M2 macrophages and influences their function, which was proposed and proved by Dhakal et al. [52]. M2 macrophages express a functional IL-13Rα1/IL-4Rα heteroreceptor through which both IL-4 and IL-13 ligands stimulate the upregulation of genes characteristic of M2 cells, including Arg1 and DC-SIGN. This finding corroborated well with a previous report, which showed that Arg1 mRNA expression in macrophages was induced by IL-4 and IL-13 [53]. This result is also consistent with that in our study; DC-SIGN/CD209 was upregulated in the spleen of A. salmonicida-infected rainbow trout, which might be induced by the upregulation of IL-13Rα1. VDAC, a class of porin ion channel located on the outer mitochondrial membrane, is important in mitochondrial membrane permeabilization (MMP) and can lead to stress-induced cellular apoptosis and necrosis [54,55]. Various apoptogenic molecules act on a number of mitochondrial receptors, which then trigger MMP loss. This stage is followed by downstream consequences that include the release of cytochrome c and the generation of reactive oxygen species. VDAC acts as a mitochondrial receptor and modulates the open/closed state of the

JO U RN A L OF P ROTE O M ICS 1 22 ( 20 1 5 ) 4 1 – 54

mitochondrial permeability transition pores to induce/prevent mitochondrial cell death. However, the question of how VDAC responds to pathogenic infection has not been widely studied. Some reports have shown the upregulation of VDAC in response to pathogen infection, such as oysters challenged by herpes virus OsHV-1 [56], flounder (Paralichthys olivaceus) infected by Scophthalmus maximus Rhabdovirus [57], and shrimp infected by white spot syndrome virus (WSSV) [58]. A further study on whether the upregulation of VDAC in shrimp was a cell defense mechanism or was induced by WSSV pathogenesis was performed by Wang et al. [59], who found that when the expression of VDAC was silenced, infection was delayed, and the mRNA expression of VDAC was increased following WSSV infection in VDAC knocked-down shrimp. However, all these findings pertained to viral infection. To our knowledge, the upregulation of VDAC at both RNA and protein levels induced by bacterial (A. salmonicida) infection is first reported in our study, and the mechanism underlying this upregulation needs further study. GTPase of the immunity associated protein family (GIMAPs) is a novel protein family of putative small GTPases. GIMAPs are mainly expressed in the cells of the immune system and have been associated with immunological functions, such as thymocyte development, apoptosis of peripheral lymphocytes, and T helper cell differentiation [60]. The family may also provide clues to yet unknown self-defense mechanisms common in higher organisms [61]. GIMAP7 is considered an important gene for anti-apoptotic T cell pathway and T helper (TH) cell differentiation. This gene is located in the endoplasmic reticulum and Golgi, with expression in the spleen, thymus, lymph nodes, and PBL [60,61]. Upregulation of GIMAP7 was presented in the distal intestine of Atlantic salmon (S. salar L.) during the development of soybean meal-induced enteritis [62]. Similarly, GIMAP7 was accumulated in the spleen of rainbow trout after A. salmonicida infection, demonstrating that this gene participates in the immune defense against pathogen invasion. Proteasome is a multicatalytic proteinase complex with a highly ordered ring-shaped 20S core structure. The core structure is composed of 4 rings of 28 non-identical subunits: the 2 end rings are each composed of 7 alpha subunits, and the 2 central rings are each composed of 7 beta subunits. Proteasomes are distributed throughout eukaryotic cells at a high concentration and cleave peptides in an ATP-dependent process. The α-subunit (PSMA1) and the β-subunit (PSMB4) of the 20S proteasome complex were identified as LPS-binding proteins, and the proteasome complex was demonstrated to regulate LPS-induced signal transduction and might be an important therapeutic target in Gram-negative species [63]. The macrophage proteasome was reported to be a key regulator of a second microbial product CpG DNA (unmethylated bacterial DNA)-induced signaling pathway, including the gene expression of TNF-α and other genes involved in the production of inflammatory mediators [64]. PSMA1 was upregulated in rainbow trout following infection with A. salmonicida in this study, which was consistent with pre-existing results. Thus, our hypothesis is that PSMA1 might be involved in the antiinflammatory response of macrophages during the interaction with the pathogen. Annexins also presented different expression levels in the experimental groups. Previous studies reported that annexins had anti-inflammatory properties for bacteria stimuli in host

51

defense [19,65,66], which indicated that annexins might play a role in the immune system. Six different annexins (i.e., A1, A2, A4, A5, A6, and A11) were upregulated in the gills of channel catfish following infection with Edwardsiella ictaluri [66]. Furthermore, annexin A4 was upregulated in fish gill cells that interacted with E. ictaluri [67]. Annexin A13 could induce protective immune response against fish gill fluke Microcotyle sebastis infection [68]. In this study, the ANXA11s were upregulated in rainbow trout infected with A. salmonicida, which may be indicative of an active immune response in the spleen.

4.1.2. Metabolism-related proteins All of the six downregulated proteins were enzymes, and most of them were metabolism-related enzymes, which indicated that the metabolism activities might be suppressed in rainbow trout spleen tissues after infection by A. salmonicida. Among these proteins, ALOX5 is an arachidonic acid metabolism protein that catalyzes the first step in leukotriene biosynthesis and thereby contributes to inflammatory and allergic processes. Thus, the decrease in ALOX5 expression level is an anti-inflammatory reaction to a certain extent. ADA and NDK3 are purine metabolism proteins that both participate in purine nucleotides synthesis as key enzymes. With the decrease in their expression level, the anabolic synthesis of purine nucleotides is blocked, thereby resulting in the interruption of the biosynthesis of nucleic acids and proteins. Along with the suppression of ATP5J, the supply of materials and energy for the reproduction and replication of the bacteria in rainbow trout is also blocked, thus serving as a defense mechanism against A. salmonicida infection. Because the challenge dose for this study was relatively high, which led to an acute toxicity in fish, and the samples were obtained from fish likely to die at 3 dpi, the genes/ proteins identified as up- or down-regulated would most likely be those that were normally expressed in a sustained, long-term manner following infection, rather than specially related to disease resistance. Similarly, multi-immune-related genes were identified as up-regulated in the spleen, liver and head kidney of Atlantic salmon at 13 days (fish began to die) post-exposure to A. salmonicida infected fish [69]. Therefore, work must be aimed at exploring the encoded proteins and their roles in infection and immunity. Moreover, it has been reported in a previous study conducted by Feng et al. [70] that “immune response” was the most enriched GO category in the spleen of Atlantic cod stimulated by formalin-inactivated A. salmonicida, and among the nine identified immune-related genes, ferritin H was verified to be up-regulated, which were completely in accordance with our results; the study by Martin et al. [71] indicated that there were both temporal differences and tissue differences in the transcriptional response to bacterial exposure, which suggested that our results needed further verification in other tissues and at more time points to completely understand their function.

4.2. Protein–protein interaction network revealed the main interactions among the 17 differentially expressed proteins Protein–protein associations have proven to be a useful concept by which to group and organize all protein-coding genes in a genome. The STRING database (http://string-db.

52

JO U R N A L OF P ROTE O M ICS 1 22 ( 20 1 5 ) 4 1 – 54

org/) provides the direct (physical) and indirect (functional) interactions to constitute a larger superset of “functional protein–protein associations” or “functional protein linkages” [72]. Such enriched terms are inferred to describe an important underlying biological process or behavior. In this study, to obtain more information on the biological significance of the differentially expressed proteins in rainbow trout infected with A. salmonicida, the PPI analysis was performed and revealed that 13 out of the 16 proteins interacted with one another to form an integrated network, with diseases, proteasome, aminoacyl-tRNA biosynthesis, and purine metabolism as the main interactions. The results provide valuable information for further studying the functions of these differentially expressed proteins in rainbow trout.

5. Conclusion A total of 17 differentially expressed proteins were filtered by correlation of transcriptome and proteome results. Among these proteins, the class of immune proteins showed the most prominent differences between the experimental and control groups, and their strong increase in the experimental group suggested an important function of immune proteins in the process of anti-infection. From these differentially expressed proteins, candidates for biomarkers of immune defense of rainbow trout against A. salmonicida are revealed. These biomarkers include ferritin, CD209, IL-13Rα1, VDAC2, GIMAP7, PSMA1, and ANXA11s. Overall, the results can provide a significant step forward towards a complete elucidation of the immune relationship between rainbow trout and the pathogen A. salmonicida and can eventually lead to a more precise diagnosis of pathology while providing clues for anti-disease research. Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.jprot.2015.03.031.

Conflict of interest

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

The authors declare that no conflict of interest exists. [14]

Acknowledgment This work was supported by grants from FEBL project (2011FBZ26), Key Laboratory of Plateau Aquatic Organism and Environment of Qinghai Province (KLPA2013-02) and the National Natural Science Foundation of China (nos. 30670112 and 31070112).

[15]

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