Transcriptome signatures in common carp spleen in response to Aeromonas hydrophila infection

Fish & Shellfish Immunology 57 (2016) 41e48

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Transcriptome signatures in common carp spleen in response to Aeromonas hydrophila infection Yanliang Jiang a, 1, Shuaisheng Feng a, b, 1, Songhao Zhang a, Hong Liu d, Jianxin Feng e, Xidong Mu f, Xiaowen Sun a, Peng Xu a, c, * a

CAFS Key Laboratory of Aquatic Genomics and Beijing Key Laboratory of Fishery Biotechnology, Centre for Applied Aquatic Genomics, Chinese Academy of Fishery Sciences, Beijing, 100141, China College of Life Sciences, Shanghai Ocean University, Shanghai, 201306, China c Fujian Collaborative Innovation Center for Exploitation and Utilization of Marine Biological Resources, Xiamen University, Xiamen, 361102, China d College of Fisheries, Key Lab of Freshwater Animal Breeding, Ministry of Agriculture, Huazhong Agricultural University, Wuhan, 430070, China e Henan Academy of Fishery Sciences, Zhengzhou, 450044, China f Pearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Laboratory of Tropical & Subtropical Fishery Resource Application & Cultivation, Ministry of Agriculture, Guangzhou, 510380, China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 15 April 2016 Received in revised form 26 July 2016 Accepted 7 August 2016 Available online 10 August 2016

The common carp is an important aquaculture species that is worldwide distributed. Nowadays, intensive rearing in aquaculture increases the susceptibility of fish to various pathogens such as Aeromonas hydrophila, which has caused severe damage to carp production. However, systematic analysis on the host response of common carp against A. hydrophila is less studied. In order to better understand the common carp immune response process against bacteria at the global gene expression level, we examined transcriptional profiles of the common carp spleen at three timepoints following experimental infection with A. hydrophila. A total of 545 million 125-bp paired end reads were generated, and all trimmed clean reads were mapped onto the common carp whole genome sequence. Comparison of the transcriptomes between the treatment and control group fish revealed 2900 unigenes with significantly differential expression, including 732, 936, 928 genes up-regulated, and 248, 475, 700 genes downregulated at 4 h, 12 h, 24 h post infection respectively. The captured significantly differentially expressed genes are mainly involved in the pathways including junction/adhesion, pathogen recognition, cell surface receptor signaling, and immune system process/defense response. Our study will provide fundamental information on molecular mechanism underlying the immune response of teleost against bacterial infection and might suggest strategies for selection of resistant strains of common carp in aquaculture. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Transcriptome Common carp Aeromonas hydrophila Bacterial infection

1. Introduction The common carp, one of the most important cyprinid species, is widely cultured in more than 100 countries. The annual production of common carp is approximately 3.7 million metric tons worthy of $5.31 billion worldwide, accounting for 10% of freshwater aquaculture production [1]. Nowadays, however, intensive rearing

* Corresponding author. Fujian Collaborative Innovation Center for Exploitation and Utilization of Marine Biological Resources, Xiamen University, Xiamen, 361102, China. E-mail address: [email protected] (P. Xu). 1 Equal contributors. http://dx.doi.org/10.1016/j.fsi.2016.08.013 1050-4648/© 2016 Elsevier Ltd. All rights reserved.

in aquaculture causes environmental stress to fish, which results in increasing susceptibility to various pathogens. One of the most common and frequently encountered bacterial pathogen in freshwater aquaculture is Aeromonas hydrophila, causing severe damage to carp production [2,3]. A. hydrophila is a Gram-negative bacterium widely present in freshwater habitats, and associates with several fish diseases such as hemorrhagic septicemia and dropsy, in many species of freshwater fishes. Due to the significant economic losses to common carp industry, many efforts have been made on the immune response to the infection of A. hydrophila, but mainly focus on the effect of different dietary supplementation to strengthen the immune ability against pathogen. For instance, elevated dietary

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Y. Jiang et al. / Fish & Shellfish Immunology 57 (2016) 41e48

arginine for juvenile Jian carp (Cyprinus carpio var. Jian) significantly influenced the head kidney and spleen weights, erythrocyte and leukocyte counts, hemagglutination titre, serum lysozyme activity, IgM concentration, C3 and C4 content, as well as the mRNA expression of several immune-related genes, therefore could improve humoral and cellular immunity and increase the survival rate following A. hydrophila infection [4]. Similar studies had been conducted on the effects of other dietary supplementations against infection of A. hydrophila on common carp, including astaxanthin [5], dietary Bacillus coagulans, Bacillus licheniformis and Paenibacillus polymyxa supplementation [6], carotenoids [7], dietary choline [8], Beta-glucan [9], dietary isoleucine [10], dietary methionine hydroxy analogue [11], conjugated linoleic acid [12]. Their results showed that those dietary supplementations could enhance resistance against A. hydrophila by increasing the weight of immune-related tissues, or increasing the number of immunerelated cells, or regulating the expression of immune-related genes. However, molecular genetics underlying the immune response to bacterial infection in common carp are less studied. Only a few studies reported single genes that might be involving in the disease resistance. For instance, Duan et al. [13] examined the expression level of TLR5M in various organs of common carp after A. hydrophila infection, and observed that its expression was significantly increased in kidney and spleen. Other genes such as MHC [14], NCCRP-1 [15] were also associated with resistance to A. hydrophila. To our knowledge, so far no studies on common carp have systematically examined the global gene expression and pathways triggered following A. hydrophila infection. In order to better understand the common carp immune response process against bacteria at the transcriptomic level, here in this study, we examined transcriptome profiles of the common carp spleen at three timepoints following experimental infection with A. hydrophila. With the available common carp whole genome sequences [1], we mapped all clean reads back to the reference genome, and captured significantly differentially expressed genes that were mainly involved in the pathways including junction/ adhesion, pathogen recognition, cell surface receptor signaling, and immune system process/defense response. Deeply look into the molecular process of the immune response against bacterial infection will advance our knowledge of teleost immunology and suggest strategies for selection of resistant strains of common carp. 2. Materials and methods 2.1. Bacteria challenge and sample collection All sampling procedures involving the handling and treatment during this study were approved by the Animal Care and Use committee of the Centre for Applied Aquatic Genomics at the Chinese Academy of Fishery Sciences prior to initiation. Six-monthold common carp of both sexes were reared at the hatchery of the Henan Academy of Fishery Sciences, Henan, China. All fishes were maintained in the laboratory at around 25
C and acclimatized for two weeks before experimental use. A. hydrophila bacteria, provided by the College of Fisheries, Key Lab of Freshwater Animal Breeding, Huazhong Agricultural University, were isolated from diseased fish in Dongxi Lake (Wuhan, China). Several fish were experimentally infected with an isolate, and bacteria were re-isolated from a single symptomatic fish and confirmed visually and biochemically to be A. hydrophila. A single colony was inoculated into LB broth and grown for 16 h at 28
C. The concentration of the bacteria was determined using colony forming unit (CFU) per mL by plating 10 ml of 10-fold serial dilutions onto LB agar plates. The final bacteria concentration used for challenge was 1  108 CFU/ml.

300 specimens of common carp (180 ± 25 g and 15 ± 3 cm) were randomly divided into 3 control groups (4 h, 12 h, 24 h) and 3 treatment groups (4 h, 12 h, 24 h). Treatment group were injected with A. hydrophila cultured in LB broth (100 ml per fish), while control group with only sterilized LB broth. Spleen tissues were collected at 4 h, 12 h, and 24 h timepoints post challenge. At each time point, 15 fish from each group were randomly selected and divided into 3 replicates (5 fish per replicate). The fish were euthanized with tricaine methanesulfonate (MS-222) before tissues were collected. Spleen tissues were immediately submerged into 10 ml RNAlater™ (Ambion, USA), following the manufacturer's protocol. Tissues were stored at 80
C until RNA extraction. 2.2. RNA isolation and Illumina sequencing Samples were homogenized with sterilized mortar and pestle in the presence of liquid nitrogen to a fine powder. Total RNA was isolated from tissue powder using the RNeasy Plus Mini Kit (Qiagen, USA) following the manufacturer's instruction. RNA quality was verified using a Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA). Triplicates of RNA from treatment and control groups at each timepoint were used for sequencing library construction, respectively. The sequencing was carried out by BerryGenomics Company (Beijing, China) on Illumina Hiseq2000 platform. 2.3. Differential expression analysis The common carp whole genome sequence and annotation were obtained from the CarpBase database (http://www.carpbase. org/). All raw sequencing reads were trimmed by removing adaptor sequences, ambiguous nucleotides, low quality sequences (Q < 20) and short reads (length below 30 bp) using CLC Genomics Workbench (CLC bio, Aarhus, Denmark). Clean reads from treatment groups and control groups were aligned to the common carp genome using TopHat [16]. Considering the high sequence similarities among duplicated genes in common carp genome might led to multiple alignment of sequence reads, only unique aligned reads were used to estimate digital gene expression. After running TopHat, the resulting alignment files were used to generate a transcriptome assembly for treatment group and control group by using Cufflinks, and then merged together using Cuffmerge utility in the Cufflinks package [17]. Cuffdiff was used to calculate the expression level of each transcript in each sample and to test the statistical significance of observed changes [17]. Transcripts with fold change values larger than 2 and P values lower than 0.05 were included in subsequent analyses as the differentially expressed genes. 2.4. Gene ontology enrichment analysis Statistical analysis for overrepresentation of Gene Ontology (GO) terms in sets of differentially expressed genes was performed using a web-based program WEGO [18], with default settings. The study set represented the GO terms of all identified differentially expressed genes, while the population set represented the whole reference gene set. The Pearson Chi-Square test is applied to examine significant relationships between study and population gene sets. 2.5. Quantitative real-time PCR validation In order to validate the reliability of the high throughput sequencing data, quantitative real-time PCR (qRT-PCR) analysis procedure was conducted on 15 representative differentially

Y. Jiang et al. / Fish & Shellfish Immunology 57 (2016) 41e48

expressed genes at each timepoint, following established protocols with slight modifications [19]. Briefly, the first-strand cDNA was synthesized using the SuperScript® III RT kit (Invitrogen) according to the manufacturer's instructions, and diluted to 100 ng/ml, following the qRT-PCR on an ABI PRISM 7500 Real-Time Detection System (Life Technologies) using TransStart Top Green qPCR Supermix kit (Transgen Biotech, Beijing, China). A total volume of 15 ml qRT-PCR reaction mixture containing 100 ng cDNA were performed with thermal cycling conditions as following: an initial denaturation at 95
C for 10 min, followed by 40 cycles of denaturation at 95
C for 15 s and annealing/extension at 60
C for 1 min. Melting curve was generated with an additional temperature-ramping step from 95
C to 65
C. All reactions were conducted in triplicates and included negative controls with no template. Expression differences between groups were assessed for statistical significance using a randomization test in REST software [20]. All primer sequences are listed in Table S1. The house-keeping gene b-actin was used as an internal reference. The expression levels of genes were normalized to the levels of b-actin in the same sample. 3. Results 3.1. Bacteria challenge The artificial challenge experiment with A. hydrophila showed initial mortality of infected fish beginning at 12 h after bacteria injection and a final cumulative mortality of 53.3% at 48 h post infection. No control fish manifested symptoms of A. hydrophila, and randomly selected control fish were confirmed to be negative for A. hydrophila. Dying fish manifested external clinical signs associated with A. hydrophila including exophthalmia, petechial hemorrhages, and redness in the eyes.

43

library generated more than 23 million reads. All raw sequencing reads were then removed ambiguous nucleotides, low-quality sequences (Q < 20) and short reads (length<30 bp). The remaining 536 million high-quality reads (98.3%) were used for further analysis. All raw sequence data were deposited in NCBI Sequence Read Archive (SRA) under accession number SRP072018. 3.3. Transcriptome assembly based on common carp genome All 536 million high-quality clean data were aligned to common carp genome with TopHat [16]. Combining the merged alignments of all samples with the reference annotation of common carp, 107,039 transcripts were constructed with Cufflinks, which covered 49,436 common carp genes. Compared with the reference annotation, of all assembled transcripts, 49,951 were complete matched intron chain, 36,729 were shared at least one splice junction with reference transcript, and the remaining were intergenic transcripts. 3.4. Identification and analysis of differentially expressed genes Based on the criteria of fold changeS2 and p-value&0.05, a total of 2900 unigenes showed significantly differential expression in spleen at least one timepoint following A. hydrophila infection (Table S2). As shown in Table 2, there are 980 genes differentially expressed at 4 h post infection comparing to control, 1411 genes differentially expressed at 12 h post infection comparing to control, and 1628 genes differentially expressed at 24 h post infection comparing to control. At all three timepoints, significantly upregulated genes were more than down-regulated. For instance, almost three times as many differentially expressed genes were upregulated than were down-regulated at 4 h, and twice the number of genes were up-regulated than were down-regulated at 12 h. Similar number of genes were up-regulated at 12 h (936) and 24 h (928).

3.2. Sequencing of short expressed reads from common carp spleen Illumina-based high throughput transcriptome sequencing was performed on spleen samples from both control group and treatment group infected with A. hydrophila at three timepoints (4 h, 12 h, and 24 h post infection). Three duplicates samples were sequenced for each group at each timepoint. As shown in Table 1, a total of 545 million 125-bp paired end reads were generated. Each

Table 2 Significantly differentially expressed genes in spleen of common carp at different time points post A. hydrophila challenge.

Up-regulated Down-regulated Total Total unigenes

4h

12 h

24 h

732 248 980 2900

936 475 1411

928 700 1628

Table 1 Summary of sequencing results of A. hydrophila challenge sample from common carp spleen.

Treatment 4h

12 h

24 h

Control 4h

12 h

24 h

No. of reads

No. of reads after trimming

Percentage retained

Average length after trimming(bp)

Replicate Replicate Replicate Replicate Replicate Replicate Replicate Replicate Replicate

1 2 3 1 2 3 1 2 3

27,495,684 33,190,074 48,250,346 38,597,534 23,697,762 29,167,164 26,707,840 26,526,312 25,216,156

27,010,734 32,550,376 47,332,866 37,795,177 23,388,538 28,652,374 26,281,717 26,053,848 24,721,252

98.2% 98.1% 98.1% 97.9% 98.7% 98.2% 98.4% 98.2% 98.0%

118 117 118 118 118 118 118 118 117

Replicate Replicate Replicate Replicate Replicate Replicate Replicate Replicate Replicate

1 2 3 1 2 3 1 2 3

32,450,728 26,272,420 33,581,016 24,830,742 28,767,564 31,123,186 31,591,512 26,801,282 31,109,444

31,988,631 25,905,559 33,113,627 24,512,899 28,364,973 30,700,418 31,002,885 26,322,852 30,509,140

98.6% 98.6% 98.6% 98.7% 98.6% 98.6% 98.1% 98.2% 98.1%

118 118 118 119 118 119 118 118 118

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3.5. Gene ontology enrichment and pathway analysis Based on the likely functions, we attempted to categorize the 2900 differentially expressed genes by using gene annotation, gene ontology enrichment, and KEGG pathway analysis. All differentially expressed genes were classified into different cellular, biological and functional gene ontology categories according InterProScan [21]. Together with the GO annotation of the common carp genome sequences, GO enrichment analysis using WEGO revealed that 28 significantly GO terms at the 3rd GO level were over-represented with p value < 0.05. Ten higher level of GO terms were retained as informative for further pathway analysis (Table 3), including immune response (GO:0006955), oxidation reduction (GO:0055114), response to external stimulus (GO:0009605), regulation of biological process (GO:0050789), oxidoreductase activity (GO:0016491), signal transducer activity (GO:0004871), cell communication (GO:0007154), hydrolase activity (GO:0016787), cofactor binding (GO:0048037), and response to stress (GO:0006950). Based on GO enrichment analysis, KEGG pathway analysis and literature searches, representative key genes were grouped into 4 categories, including junction/adhesion, pathogen recognition, cell surface receptor signaling, and immune system process/defense response (Table 4). 3.6. Validation of differentially expressed genes using quantitative real-time PCR In order to validate the differentially expressed genes identified by comparative transcriptome analysis, we randomly selected 15 genes from each timepoint (4 h, 12 h, and 24 h post infection) for qRT-PCR confirmation of differential expression. 3 biological replicates sample pools (n ¼ 5 for each pool) from control group and A. hydrophila infected group at different timepoints were used for qRT-PCR. As shown in Fig. 1, all tested genes were in agreement with the results of the comparative transcriptome analysis with only slight differences in expression levels, indicating that there was no consistent bias in the expression patterns for either method. Melting-curve analysis showed that a single product was amplified, indicating that the reference assembly was largely accurate and that it did not contain a large number of chimeric transcripts. 4. Discussion A. hydrophila is an important pathogen which infects a range of vertebrates including humans, reptiles, and fishes [22]. The everchanging genotype and phenotype of the species has complicated the study of disease, thus make it difficult to develop broad

Table 3 Selected summary of GO term enrichment result of differentially expressed genes in common carp following A. hydrophila challenge. The “Count ratio” column indicates study count ratio/population count ratio; the study count ratio is the ratio of genes associated with the GO term in the study set to all genes in the study set, and the population count ratio is the ratio of genes associated with the GO term in the population set to all genes in the population set. GO ID

GO term

P value

Count ratio(%)

GO:0006955 GO:0055114 GO:0009605 GO:0050789 GO:0016491 GO:0004871 GO:0007154 GO:0016787 GO:0048037 GO:0006950

Immune response Oxidation reduction Response to external stimulus Regulation of biological process Oxidoreductase activity Signal transducer activity Cell communication Hydrolase activity Cofactor binding Response to stress

0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.005 0.006 0.007

1.5/0.5 4.1/2.2 0.8/0.2 20.8/17.5 4.8/2.7 8.3/6.2 14.7/12.2 13.8/11.8 1.3/0.8 1.5/0.9

treatment solutions. Acute outbreaks of A. hydrophila have caused huge economic damages in common carp industry. During the last decades, a lot of efforts have been focused on the potential effects of dietary elements to enhance the disease resistance of fish [2e5], however, A. hydrophila remains one of the primary pathogen in commercial industry. It is believed that, one of the effective approaches to lessen the impact of the disease is to select lines or strains of fish with higher resistance to A. hydrophila. However, the knowledge of genetic variation in host susceptibility or the molecular mechanism underlying pathogen resistance is limited. Accordingly, in this study we conducted the global measurement of transcriptomic response to A. hydrophila infection in common carp spleen to understand the underlying mechanism of pathogen-host interaction and downstream immune process triggered by the bacterial infection. Comparative transcriptome analysis via high throughput sequencing technologies has been accepted as a robust approach to assess transcriptional response to different challenging conditions [23,24]. To estimate digital gene expression, the common carp whole genome sequences and annotations were used as reference. Clean reads from treatment groups and control groups were aligned to the common carp genome using TopHat. It is reported that the common carp, comparing to most of teleost, went through additional round of whole genome duplication [1], which led to an increase of duplicated genes. Considering the high sequence similarities among duplicated genes in common carp genome might led to multiple alignment of sequence reads, only unique aligned reads were used for analysis. The unique alignment of reads, on one hand, could improve the accuracy of the digital gene expression estimation. On the other hand, the orthologous identified in our result could provide transciptomic evidences for the presence of duplicated genes in common carp genome. We identified 2900 unigenes which were differentially expressed at three timepoints following infection, including genes with potential roles in facilitating pathogen adhesion and the concomitant host immune response. Those candidate genes will provide fundamental genetic resources for examining individual variation in host defense mechanism against A. hydrophila infection, or comparing susceptibility variation among different lines/strains/families of fish. A total of 2900 differentially expressed genes with significant fold change in at least one timepoint were identified in spleen of common carp following A. hydrophila infection. Based on GO annotation and manual reannotation through literature search of studies in vertebrate model species, we attempted to categorize all differentially expressed genes into broad functional categories. As discussed below, several important processes or pathways were highlighted, which were likely involved in mediating the common carp response to A. hydrophila infection. Adhesion/junctional modification. Cell adhesions generate an essential barrier that protects organisms from external aggression such as microbial infection. The first stage of bacterial infection process is, attaching to a cell surface. Bacterial pathogens usually require adhesion to host cells or modification of host cellular junction to gain access for invasion [25]. Dysregulation of components of cell-cell junction was observed. A bunch of genes were significantly differentially expressed (Table 4), including cadherins, claudins, integrins, aquaporins, adherens junction associated protein, intercellular adhesion molecule, vascular cell adhesion protein. Claudins are important functional and structural components of tight junctions, which are cell-cell contact structures forming tissue barriers and pores to prevent uncontrolled passage of molecules and ions through the intercellular space [26]. Due to its importance in immune response, claudin gene family has been widely studied in mammals [27] and teleost such as fugu [28], zebrafish [29], catfish [30], and showed that, the barrier formed by

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Table 4 Key genes differentially expressed in spleen post bacterial challenge. Bold values indicate that the gene is significantly changed compared to control. Gene name

Adhesion/junctional modification Adherens junction-associated protein 1 Aquaporin-11 Aquaporin-8 Cadherin-11 Cadherin-23 Cadherin-6 Cell adhesion molecule 4 Claudin-4 Claudin-5 Integrin alpha-1 Integrin alpha-2 Integrin alpha-3 Integrin alpha-5 Integrin alpha-7 Integrin beta-1 Integrin beta-3 Integrin beta-5 Integrin beta-7 Intercellular adhesion molecule 1 Neural-cadherin Vascular cell adhesion protein 1 Pathogen recognition Toll-like receptor 4 Toll-like receptor 21 Macrophage mannose receptor 1 NLRC3 Peptidoglycan-recognition protein SC2 Serotransferrin-1 Cell surface receptor signaling Activin a receptor, type ib Activin receptor iib Activin receptor iia Disintegrin and metalloproteinase domain-containing protein 17 Disintegrin and metalloproteinase domain-containing protein 28 Disintegrin and metalloproteinase domain-containing protein 8 Adenosine receptor A2b Bone morphogenetic protein 1 Bone morphogenetic protein 2 Bone morphogenetic protein 7 cd82 antigen, a cd9 antigen (p24) a DAb2 dab2 interacting protein b Ephrin a1a Keratinocyte growth factor Fibroblast growth factor receptor 2 Frizzled homolog 10 Frizzled homolog 2 Frizzled homolog 7a G-protein coupled receptor 55 G-protein coupled receptor 64 G-protein coupled receptor 84 Glutamate receptor 4 Proheparin-binding EGF-like growth factor Hepatocyte growth factor Protein jagged-1b Low density lipoprotein receptor adapter protein 1 Induced myeloid leukemia cell differentiation protein Mcl-1 homolog Notch homolog 2 Neurotrophin-3 Plexin b2a Semaphorin 3aa Semaphorin 3ab Semaphorin 3f Semaphorin 4a Semaphorin 4b Semaphorin5a Semaphorin 7a Secreted frizzled-related protein 1 shc transforming protein 2 slit homolog 2

Carp gene ID

4h

12 h

24 h

FC

FPKM

FC

FPKM

FC

FPKM

cycg007751 cycg049603 cycg015191 cycg001553 cycg011376 cycg005262 cycg014219 cycg002553 cycg009816 cycg025214 cycg025213 cycg010790 cycg010501 cycg046888 cycg041904 cycg002224 cycg008660 cycg049747 cycg030634 cycg014340 cycg040062

0.58 1.17 0.39 1.03 3.94 1.97 1.57 2.66 1.43 0.17 2.64 1.16 2.43 0.51 ¡3.58 1.90 ¡2.10 3.76 2.34 3.25 2.18

5.28 1.02 3.96 6.35 1.62 6.12 2.42 37.68 43.84 9.44 12.05 42.77 2.13 78.59 18.73 3.28 3.81 15.92 25.77 1.06 10.94

2.51 0.53 0.68 ¡3.54 3.91 ¡3.87 2.40 0.64 2.17 0.92 1.98 ¡3.14 2.06 ¡2.97 7.91 1.25 ¡3.21 0.52 0.50 0.52 2.18

9.20 2.65 2.60 1.22 2.21 2.35 6.60 3.74 52.83 5.32 7.32 4.09 2.76 16.92 7.91 4.11 3.11 2.16 3.77 27.63 21.12

2.26 3.23 2.46 1.31 5.88 2.63 2.71 1.42 0.93 ¡2.64 3.27 ¡2.88 2.69 5.00 4.28 4.11 1.50 0.23 0.28 4.03 1.31

29.74 10.63 7.44 2.87 2.23 9.78 4.49 11.00 41.33 1.54 9.67 3.76 2.28 4.11 4.67 20.68 6.14 2.42 3.00 1.33 6.85

cycg052035 cycg015475 cycg048376 cycg024828 cycg034342 cycg004391

3.18 3.26 1.67 2.09 6.38 ¡3.76

8.67 15.45 41.08 61.67 119.74 17.17

2.30 3.33 2.29 4.80 6.35 3.03

5.67 4.53 71.58 12.01 75.11 34.93

1.81 0.28 1.60 2.31 4.05 0.18

2.02 4.68 109.51 7.13 45.29 45.12

cycg035465 cycg046180 cycg047996 cycg012784 cycg004677 cycg011803 cycg035962 cycg021499 cycg022536 cycg034073 cycg032873 cycg025215 cycg004159 cycg028208 cycg040999 cycg017072 cycg030338 cycg007650 cycg018685 cycg051609 cycg035139 cycg022295 cycg027373 cycg044123 cycg015126 cycg020295 cycg001342 cycg006810 cycg043629 cycg007486 cycg003239 cycg040316 cycg050184 cycg025202 cycg010185 cycg034514 cycg017413 cycg026488 cycg038544 cycg004665 cycg046435 cycg031918

2.97 0.81 2.16 1.17 2.51 3.45 2.20 2.29 1.03 1.53 0.49 1.02 2.91 2.41 1.65 3.16 0.17 2.46 1.14 0.18 2.56 1.23 7.70 1.01 2.00 1.58 0.73 2.29 2.12 1.64 3.78 1.90 1.69 2.85 1.82 2.95 1.70 3.11 0.15 0.36 2.13 7.14

1.60 470.42 24.39 17.77 73.93 299.97 32.31 12.56 5.19 25.78 42.34 170.05 63.55 2.31 8.46 7.80 4.75 7.60 2.57 74.29 22.02 4.19 1255.96 116.53 22.58 7.56 9.02 4.99 241.09 5.19 6.39 14.25 5.13 47.61 9.26 3.33 11.77 5.00 42.41 2.52 19.69 4.72

1.44 ¡6.15 2.73 2.37 3.33 4.11 1.46 3.64 2.39 ¡2.30 0.58 1.90 3.92 1.97 2.05 4.14 1.44 2.58 2.26 ¡2.95 2.68 1.25 6.83 7 2.12 4.01 ¡2.37 2.90 3.52 ¡3.14 ¡3.10 2.91 2.51 3.85 1.77 2.51 3.63 4.56 ¡2.48 1.32 3.58 1.76

1.11 22.51 19.97 22.08 149.14 324.72 29.25 19.45 7.06 7.03 40.99 378.80 79.68 2.76 10.02 7.46 1.92 18.86 1.93 30.66 24.05 5.94 908.09 9.26 36.45 28.52 5.54 6.56 434.59 2.91 6.71 21.67 2.51 60.80 6.01 2.23 22.04 8.92 5.60 2.88 20.66 2.97

3.33 ¡7.13 2.57 1.30 5.20 5.22 1.60 2.48 1.23 4.48 ¡2.03 2.42 3.74 3.35 1.22 3.50 ¡3.45 4.23 ¡2.24 5.33 1.50 3.68 6.29 ¡3.17 2.68 3.47 1.49 1.76 0.47 3.58 ¡3.53 1.47 2.20 3.78 2.55 0.33 1.67 3.29 0.55 2.69 2.66 0.79

1.07 4.57 27.21 25.36 140.95 361.73 20.93 37.21 5.28 3.87 23.85 571.52 49.25 4.62 12.31 3.83 1.73 26.83 2.18 2.90 9.82 11.74 749.94 28.70 31.40 20.80 7.39 4.12 261.84 1.09 3.77 12.22 4.64 48.88 7.13 1.02 15.55 4.97 7.75 11.65 32.21 2.49

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Table 4 (continued ) Gene name

mad homolog 1 Signal transducer and activator of transcription 1 Signal transducer and activator of transcription 3 Signal transducer and activator of transcription 4 Transforming growth factor beta-1 Immune system process/defense response B-cell receptor CD22 B-cell linker C-C chemokine receptor type 9 C-C motif chemokine 2 C-C motif chemokine 21 Chemokine-like receptor 1 C-X-C chemokine receptor type 3 C-X-C chemokine receptor type 4 C-X-C motif chemokine 11 Major histocompatibility complex class I-related gene protein Complement C3 Complement C7 Complement factor B Colony stimulating factor 3 receptor (granulocyte) Cathepsin L Ferritin, middle subunit Ferritin, heavy subunit Heat shock 70 kDa protein 1 Heat shock 70 kDa protein 13 Heat shock 70 kDa protein 4 Heat shock 70 kDa protein Heat shock protein HSP 90-alpha 1 Heat shock protein HSP 90-beta Immunoglobulin-like domain-containing receptor 2 Immune-responsive gene 1 protein homolog Interleukin-1 beta Interleukin-1 receptor type 1 Interleukin-1 receptor type 2 Interleukin-10 Interleukin-10 receptor subunit beta Interleukin-12 receptor subunit beta-2 Interleukin-2 receptor subunit beta Interleukin-6 Interleukin-6 receptor subunit beta Interleukin-8 Interferon regulatory factor 1 Interferon regulatory factor 4 Interferon regulatory factor 7 Interferon regulatory factor 8 Protein jagunal homolog 1-B C-type lectin domain family 4 member C Intelectin-1b Matrix metalloproteinase-9 Nuclear factor NF-kappa-B p100 subunit Nitric oxide synthase, inducible Vitronectin ccaat/enhancer binding protein (c/ebp), beta

Carp gene ID

4h

12 h

24 h

FC

FPKM

FC

FPKM

FC

FPKM

cycg030606 cycg019809 cycg021835 cycg044857 cycg029208

2.34 1.82 1.53 1.02 2.12

15.16 53.23 44.21 36.16 95.01

0.80 3.26 2.26 2.04 1.15

5.29 74.80 47.17 47.50 68.07

0.08 1.74 0.96 0.15 0.36

7.72 37.68 37.85 28.25 76.21

cycg007048 cycg027781 cycg001378 cycg050305 cycg009358 cycg052199 cycg043813 cycg028192 cycg037173 cycg030330 cycg021523 cycg020729 cycg031057 cycg014132 cycg016214 cycg002422 cycg024047 cycg030029 cycg043900 cycg016308 cycg002727 cycg046112 cycg029371 cycg008738 cycg008940 cycg049297 cycg010992 cycg010656 cycg035130 cycg002318 cycg002501 cycg000413 cycg030295 cycg009458 cycg000117 cycg027425 cycg044156 cycg037550 cycg035229 cycg050984 cycg029235 cycg012673 cycg013911 cycg021196 cycg013101 cycg038870 cycg038165

2.71 4.91 1.38 2.33 0.68 1.99 2.56 1.77 2.06 3.29 2.11 2.23 ¡3.87 1.41 3.27 2.18 1.25 0.64 2.41 0.96 0.47 0.46 0.08 0.66 9.05 8.59 2.92 3.51 4.39 1.63 1.64 1.63 6.34 2.07 4.61 2.63 1.50 2.29 2.55 0.47 1.74 1.80 0.74 2.18 7.48 ¡5.61 2.46

42.28 292.00 87.32 103.04 4.83 511.18 17.87 12.27 360.53 249.31 4.48 15.12 4.07 13.06 314.56 2778.1 5.12 5.42 6.06 18.65 14.63 5.18 2.85 4.72 1179.4 1391.5 43.53 35.70 4.80 40.80 42.05 22.64 274.89 45.05 190.20 1090.2 23.59 35.56 27.43 8.49 1.47 32.30 525.63 51.50 455.96 3.06 853.29

2.40 4.49 2.14 1.17 4.41 1.26 0.29 2.42 1.92 0.18 5.68 5.92 0.07 1.82 0.39 0.86 3.17 3.46 1.77 1.92 2.59 1.58 1.18 0.83 5.66 8.53 3.41 4.47 3.01 2.11 2.55 2.17 4.00 3.05 1.65 2.19 2.42 3.26 1.31 0.21 2.44 3.54 1.62 1.09 8.26 0.22 3.48

29.10 342.71 86.35 27.77 25.32 191.28 3.83 26.28 31.97 70.46 25.06 126.86 4.89 17.07 44.96 1322.8 9.86 18.67 4.76 24.21 16.84 7.01 5.47 6.39 569.1 1993.4 53.35 32.24 39.39 47.90 52.59 19.84 154.03 101.34 90.09 822.66 29.83 24.49 11.94 20.01 1.78 135.00 242.08 26.11 758.12 2.16 1080.2

2.82 4.02 1.19 1.23 3.18 3.40 0.19 1.40 ¡3.44 0.18 5.08 4.27 2.38 2.25 0.17 0.37 2.62 1.46 2.65 2.11 1.53 2.30 3.19 2.53 4.40 7.20 1.55 3.57 1.62 1.27 1.32 0.31 8.01 2.45 2.46 0.39 0.87 0.83 0.54 2.02 3.08 4.31 2.21 0.45 5.70 4.63 3.42

29.27 289.83 92.26 2.77 14.91 134.63 1.98 17.03 5.41 73.79 47.46 115.78 3.15 17.13 28.80 1161.4 23.60 16.99 5.43 22.63 14.58 11.57 8.20 2.83 180.1 1423.6 26.50 60.68 11.89 39.81 35.60 11.32 180.42 64.84 68.14 246.82 15.39 8.64 7.80 31.66 2.29 344.99 1294.8 14.56 238.56 1.63 1054.2

claudins and other transmembrane proteins can protect the organisms from pathogens, however, some pathogens can affect the structure of tight junctions and influence the synthesis of claudins. Syakuri et al. [9] reported that claudin genes in common carp were involved in intestinal inflammatory response post A. hydrophila infection. Our observations showed that the expressions of claudin genes were significantly regulated in common carp spleen as well, and confirmed the function of claudin genes against bacterial infection (Table 4). Pathogen recognition. The innate immune system, which constitutes the first line of host defense during infection, plays important roles in the early recognition of invading pathogens [31]. This process relies on recognition of evolutionarily conserved structures on pathogens, termed pathogen-associated molecular patterns (PAMPs), through a limited number of pattern recognition

receptors (PRRs). In this category, several putative PRR molecules including Toll-like receptors (TLR), Macrophage mannose receptor, NOD-Like Receptor C3 (NLRC3), Peptidoglycan recognition protein (PGRP) were significantly up regulated, especially at the early timepoint. Those genes have dramatically expanded in teleost species, indicating their important roles on maintaining homeostasis during evolution. Toll-like receptors (TLRs) are a PRR family that has been extensively studied. TLR 4 is able to recognize the bacterial lipopolysaccharide (LPS) and results great functional role against gram negative bacteria [32], while TLR 21 can recognize CpG-ODN and its activation required endosomal acidification [33,34]. It was found in common carp that TLR 4 was mainly expressed in immune organs such as spleen, head kidney, and gut [10]. Following bacterial infection, both TLR4 and TLR21 were observed up-regulated at 4 h post infection, suggesting the

Y. Jiang et al. / Fish & Shellfish Immunology 57 (2016) 41e48

Fig. 1. Comparison of gene expression patterns obtained using comparative transcriptome analysis and qRT-PCR. Fold changes are expressed as the ratio of digital gene expression in common carp spleen after A. hydrophila challenge to the control group, as normalized with b-actin gene. Gene abbreviations are: BLINK, B-cell linker protein; IL1B, Interleukin-1 beta; CASP3, Caspase-3; FRIH, Ferritin, heavy subunit; IRG1, Immune-responsive gene 1 protein homolog; XCT, Cystine/glutamate transporter; IL8, Interleukin-8; NLRC3, Protein NLRC3; BMP1, Bone morphogenetic protein 1; TNNC2, Troponin C, skeletal muscle; APJA, Apelin receptor A; PODN, Podocan; WNT2B, Protein Wnt-2b; XCR1, Chemokine XC receptor 1; TCF21, Transcription factor 21; MX2, Interferon-induced GTP-binding protein Mx2; IL6RB, Interleukin-6 receptor subunit beta; PIGR, Polymeric immunoglobulin receptor; CO7, Complement component C7; MX1, Interferon-induced GTP-binding protein Mx1; TLR21, Toll-like receptor 21; GBP1, Interferon-induced guanylate-binding protein 1; IL1R2, Interleukin-1 receptor type 2.

expression patterns were consistent with their functions. Further studies are clearly needed on the function of other pathogenrecognition molecules to understand the pathogen recognition mechanisms in teleost. Cell surface receptor signaling. Cell surface receptor signaling is a series of molecular signals initiated by activation of cell surface receptors. Cell surface receptors are specialized integral membrane proteins that can receive or bind to extracellular molecules such as cytokines, cell adhesion molecules, hormones, and allow communication between the cell and the outside world. Extracellular molecules react with the cell surface receptors to trigger a cascading of intracellular chemical or physiological responses. Our results showed a bunch of genes in this category exhibited significantly different expression pattern after bacterial infection, including G-protein coupled receptors, enzyme-linked receptors such as fibroblast growth factor receptor, ion channel linked receptor such as glutamate receptor (Table 4), indicating that the signal transduction on cell surface was affected by invasion, and a series of molecular signals initiated by the binding of an extracellular ligand to a receptor on the surface of the target cell could activate, perpetuate, or inhibit an immune response. Immune system process/defense response. A number of genes encoding innate immune related proteins were differentially expressed in common carp spleen in the early timepoints after

47

A. hydrophila infection (Table 4). Innate immune is a fundamental defense mechanism in fish. This response is divided into physical barriers and cellular and humoral immune response, and modulated by various immunological parameters such as chemokines, interleukin, interferons, complement factors. Chemokines and chemokine receptors are important components of innate immunity and involved in stimulating the recruitment, activation, and adhesion of cells to sites of infection or injury [35]. Most of chemokine and chemokine receptors we identified in this study were up-regulated after A. hydrophila challenge, with exception of chemokine receptor 7, chemokine 11 and 14 down-regulated at either one tested timepoint or all three timepoints. We speculated that the different expression patterns were caused by their distinct role in immune process, but further functional analysis of common carp chemokine families are required to provide firm answers. Chemokine receptor 3 has been reported in common carp that it is highly expressed in immune-related tissues and corroborated a predominantly immune erelated function [36]. This study consistent with our result, that CXCR3 was observed significantly up-regulated immediately following bacterial infection (at 4 h). Interleukins and interferons belong to a large class of protein known as cytokines, which are especially important in immune system, by triggering the protective defenses of the immune system that help eradicate pathogens [37]. Same as we anticipated, interleukins and interferons were largely up-regulated in common carp spleen after challenge. Numerous interleukin and interferon genes have been identified in fish species and extensively studied. For instance, interleukin-10 has been cloned in several fish species, and its expression was induced following pathogen infection [38e43]. Consistent with our observation, the expression of interleukin-10 was induced during the first 12 h post challenge. Iron is an essential element in the establishment of infection by many pathogens. The availability of iron in the tissue fluids is determined by several genes, such transferrin and ferritin, which therefore were also involved in response to infection. It is interesting that the iron transporter transferrin was not found among differentially expressed genes, but iron storage protein ferritin was significantly up-regulated following pathogen infection (Table 4). It could have been caused by transient nature of up-regulation that was not reflected in the selected timepoints of this study. In summary, we conducted a comparative analysis of common carp spleen transcriptome files following bacteria A. hydrophila challenge. There were 2900 unique genes detected that were differentially expressed. Further annotation, GO enrichment and pathway analysis indicated that there are four significant categories of pathways involving in the host immune response to bacterial infection. These results provide us with a valuable basis for understanding of the molecular mechanisms of teleost immunity. Acknowledgements This study was supported by grants from the National Natural Science Foundation of China (No. 31422057), Special Scientific Research Funds for Central Non-profit Institutes of Chinese Academy of Fishery Sciences (2014C011), and open funds from Key Laboratory of Tropical & Subtropical Fishery Resource Application and Cultivation, Ministry of Agriculture, China. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.fsi.2016.08.013.

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