Open Access

Transcriptome and expression profiling analysis revealed changes of multiple signaling pathways involved in immunity in the large yellow croaker during Aeromonas hydrophila infection

Contributed equally
BMC Genomics201011:506

DOI: 10.1186/1471-2164-11-506

Received: 13 May 2010

Accepted: 22 September 2010

Published: 22 September 2010

Abstract

Background

The large yellow croaker (Pseudosciaena crocea) is an economically important marine fish in China suffering from severe outbreaks of infectious disease caused by marine bacteria such as Aeromonas hydrophila (A. hydrophila), resulting in great economic losses. However, the mechanisms involved in the immune response of this fish to bacterial infection are not fully understood. To understand the molecular mechanisms underlying the immune response to such pathogenic bacteria, we used high-throughput deep sequencing technology to investigate the transcriptome and comparative expression profiles of the large yellow croaker infected with A. hydrophila.

Results

A total of 13,611,340 reads were obtained and assembled into 26,313 scaffolds in transcriptional responses of the A. hydrophila-infected large yellow croaker. Via annotation to the NCBI database, we obtained 8216 identified unigenes. In total, 5590 (68%) unigenes were classified into Gene Ontology, and 3094 unigenes were found in 20 KEGG categories. These genes included representatives from almost all functional categories. By using Solexa/Illumina's DeepSAGE, 1996 differentially expressed genes (P value < 0.05) were detected in comparative analysis of the expression profiles between A. hydrophila-infected fish and control fish, including 727 remarkably upregulated genes and 489 remarkably downregulated genes. Dramatic differences were observed in genes involved in the inflammatory response. Bacterial infection affected the gene expression of many components of signaling cascades, including the Toll-like receptor, JAK-STAT, and MAPK pathways. Genes encoding factors involved in T cell receptor (TCR) signaling were also revealed to be regulated by infection in these fish.

Conclusion

Based on our results, we conclude that the inflammatory response may play an important role in the early stages of infection. The signaling cascades such as the Toll-like receptor, JAK-STAT, and MAPK pathways are regulated by A. hydrophila infection. Interestingly, genes encoding factors involved in TCR signaling were revealed to be downregulated by infection, indicating that TCR signaling was suppressed at this early period. These results revealed changes of multiple signaling pathways involved in immunity during A. hydrophila infection, which will facilitate our comprehensive understanding of the mechanisms involved in the immune response to bacterial infection in the large yellow croaker.

Background

The large yellow croaker (Pseudosciaena crocea) is an economically important marine fish in China, with an annual yield that exceeds any other single netcage-farmed marine species. However, recent rapid development of the large yellow croaker farming industry has led to increasingly severe outbreaks of infectious disease caused by marine bacteria such as Aeromonas hydrophila (A. hydrophila), resulting in great economic losses [1]. However, little is known about the molecular mechanisms underlying the immune response to such pathogenic bacteria in this fish species, thereby hindering the establishment of effective measures in disease control [2].

Cellular identity and function are determined by the transcriptome or the complete repertoire of expressed RNA transcripts. Transcriptome profiling is a powerful method for assessing the relative importance of gene products in any chosen cell, tissue, organism, or condition. During the last few years, several methods have been used to study the fish transcriptome, including ESTs in channel catfish [3], Atlantic salmon [4], and orange-spotted grouper [5], as well as microarrays in adult zebrafish [6], rainbow trout [7], blue catfish [8], medaka, and Xiphophorus maculates [9]. However, microarrays are limited by background and cross-hybridization problems and only measure the relative abundance of transcripts. Moreover, only predefined sequences are detected [10]. EST sequencing techniques have limitations in the depth of the transcriptome that can be sampled [11].

Recent rapid developments of high-throughput deep sequencing technologies have provided an unprecedented increase in transcriptome data [12]. These next-generation sequencing platforms, such as the Solexa/Illumina Genome Analyzer and ABI/SOLiD Gene Sequencer, can sequence in parallel massive amounts of DNA molecules derived directly from mRNA, producing millions or even billions of high-quality short reads [13, 14]. DeepSAGE is a tag sequencing method on the Illumina high-throughput sequencing platform that is analogous to LongSAGE [15, 16]. Compared to LongSAGE, DeepSAGE provides much more sensitive and cost-efficient gene expression profiling [15, 16]. By using this technology, some progress has recently been made in the characterization of the immune mechanisms and pathways in zebrafish [17]. Nevertheless, there are still important gaps in the knowledge of numerous immune mechanisms, and the available information varies according to the fish species [18].

Here, the large yellow croaker was used as a model to investigate the host response to A. hydrophila infection. First, a transcriptome library was constructed from spleen isolated from A. hydrophila-infected fish. Deep sequencing was accomplished using the Solexa/Illumina sequencing technology. Using the SOAP de novo transcriptome assembly software, we ultimately obtained a transcriptome database containing 8216 identified unigenes. Quantitative gene expression analysis was performed using DeepSAGE technology. Tags identified from normal and bacteria-infected fish were mapped to the transcriptome database above for comparative analysis. A reference set of significantly upregulated and downregulated immune-related genes was compiled.

Results

Transcriptome profile of the large yellow croaker (Pseudosciaena crocea)

To better understand the molecular mechanisms of the large yellow croaker immune system, we constructed a Solexa cDNA library from the spleen of fish infected with A. hydrophila. High-throughput paired-end sequencing yielded a total of 13,611,340 reads. Of these, 901,200 reads containing more than five consecutive bases with a quality < 13 were removed. The remaining 12,710,140 high-quality reads were assembled into 26,313 scaffolds by using the SOAP de novo software, with a maximum scaffold length of 7585 bp. The length statistics of all scaffolds are presented in Figure 1.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-11-506/MediaObjects/12864_2010_Article_3100_Fig1_HTML.jpg
Figure 1

Length statistics of scaffolds obtained from the large yellow croaker transcriptome library. The length distributions of the transcriptome library. Sequences with lengths of 500-1000 bp were most abundant, making up 47% of the scaffolds.

Scaffold annotation was achieved through BLASTN similarity searches against the zebrafish RefSeq mRNA database (version danRer5). This analysis revealed that 10,502 of the 26,313 scaffolds (40%) shared homology with zebrafish genes when a cutoff E-value of 1e-05 was used. Scaffolds were clustered if two or more query sequences were annotated to the same zebrafish gene. Ultimately, 5715 unigenes were obtained. Scaffolds that did not display any similarity to zebrafish genes were further searched against the nonredundant (nr) database, and 2501 unigenes were obtained after clustering. In total, 8216 unigenes were identified in the transcriptome of the large yellow croaker (Additional file 1, Table S1). The remaining 13,102 scaffolds failed to match proteins in the nr database and therefore represented potentially novel genes.

Gene ontology (GO) analysis of these genes was performed using the web-based Database for Annotation, Visualization, and Integrated Discovery (DAVID) [19, 20]. Among the 8216 unigenes, DAVID had functional annotation for 5590 genes. The DAVID functional annotation analysis for GO (level 2) is summarized in Table 1. Sequences with GO terms corresponding to the "cellular component" group fell into 14 subcategories, "molecular function" into 16 subcategories, and "biological process" into 31 subcategories. The largest subcategory found in the "cellular component" group was 'cell part,' which comprised 98.8% of the genes in this subcategory. In the "molecular function" and "biological process" categories, "nucleotide binding" and "primary metabolic process" were the most abundant GO terms, making up 22.4% and 50.2% of each subcategory, respectively.
Table 1

GO function annotation results of 8216 unigenes

Term

GO ID

Description

Gene No.

%*

P Value

CC_2

GO:0044464

cell part

2650

98.84

0.000013

CC_2

GO:0005622

intracellular

2041

76.13

0.000000

CC_2

GO:0044424

intracellular part

1707

63.67

0.000000

CC_2

GO:0043229

intracellular organelle

1351

50.39

0.000000

CC_2

GO:0043227

membrane-bounded organelle

1103

41.14

0.000000

CC_2

GO:0044446

intracellular organelle part

466

17.38

0.000000

CC_2

GO:0044422

organelle part

466

17.38

0.000000

CC_2

GO:0043234

protein complex

410

15.29

0.000000

CC_2

GO:0043228

non-membrane-bounded organelle

366

13.65

0.000000

CC_2

GO:0030529

ribonucleoprotein complex

210

7.83

0.000000

CC_2

GO:0031090

organelle membrane

137

5.11

0.001153

CC_2

GO:0043233

organelle lumen

135

5.04

0.000000

CC_2

GO:0031967

organelle envelope

98

3.66

0.000398

CC_2

GO:0012505

endomembrane system

86

3.21

0.021749

MF_2

GO:0000166

nucleotide binding

827

22.39

0.000000

MF_2

GO:0016787

hydrolase activity

674

18.25

0.000000

MF_2

GO:0016740

transferase activity

606

16.4

0.000001

MF_2

GO:0001882

nucleoside binding

506

13.7

0.000000

MF_2

GO:0016874

ligase activity

122

3.3

0.000001

MF_2

GO:0003735

structural constituent of ribosome

118

3.19

0.000000

MF_2

GO:0048037

cofactor binding

96

2.6

0.001411

MF_2

GO:0060589

nucleoside-triphosphatase regulator activity

77

2.08

0.001763

MF_2

GO:0008135

translation factor activity, nucleic acid binding

60

1.62

0.000000

MF_2

GO:0016853

isomerase activity

58

1.57

0.000002

MF_2

GO:0003702

RNA polymerase II transcription factor activity

32

0.87

0.000002

MF_2

GO:0051540

metal cluster binding

24

0.65

0.026354

MF_2

GO:0008430

selenium binding

16

0.43

0.031351

MF_2

GO:0019825

oxygen binding

11

0.3

0.022460

MF_2

GO:0004601

peroxidase activity

9

0.24

0.072047

MF_2

GO:0008641

small protein activating enzyme activity

5

0.14

0.071494

BP_2

GO:0044238

primary metabolic process

1571

50.18

0.000000

BP_2

GO:0044237

cellular metabolic process

1466

46.82

0.000000

BP_2

GO:0043170

macromolecule metabolic process

1271

40.59

0.000000

BP_2

GO:0009058

biosynthetic process

637

20.34

0.000000

BP_2

GO:0006807

nitrogen compound metabolic process

608

19.42

0.000020

BP_2

GO:0051234

establishment of localization

533

17.02

0.051495

BP_2

GO:0006810

transport

528

16.86

0.055745

BP_2

GO:0009056

catabolic process

235

7.51

0.000000

BP_2

GO:0033036

macromolecule localization

214

6.83

0.000000

BP_2

GO:0045184

establishment of protein localization

178

5.69

0.000000

BP_2

GO:0006996

organelle organization

154

4.92

0.031676

BP_2

GO:0051641

cellular localization

139

4.44

0.000000

BP_2

GO:0051649

establishment of localization in cell

132

4.22

0.000000

BP_2

GO:0065008

regulation of biological quality

124

3.96

0.055589

BP_2

GO:0022607

cellular component assembly

107

3.42

0.001415

BP_2

GO:0042221

response to chemical stimulus

88

2.81

0.067111

BP_2

GO:0043933

macromolecular complex subunit organization

84

2.68

0.000005

BP_2

GO:0016192

vesicle-mediated transport

75

2.4

0.000639

BP_2

GO:0006066

alcohol metabolic process

64

2.04

0.022528

BP_2

GO:0019725

cellular homeostasis

63

2.01

0.000479

BP_2

GO:0070271

protein complex biogenesis

58

1.85

0.000005

BP_2

GO:0034621

cellular macromolecular complex subunit organization

58

1.85

0.003498

BP_2

GO:0002520

immune system development

54

1.72

0.000019

BP_2

GO:0051301

cell division

45

1.44

0.019598

BP_2

GO:0022613

ribonucleoprotein complex biogenesis

38

1.21

0.000011

BP_2

GO:0019637

organophosphate metabolic process

38

1.21

0.029496

BP_2

GO:0009893

positive regulation of metabolic process

37

1.18

0.060638

BP_2

GO:0006413

translational initiation

25

0.8

0.000041

BP_2

GO:0051236

establishment of RNA localization

15

0.48

0.017466

BP_2

GO:0042440

pigment metabolic process

14

0.45

0.004606

BP_2

GO:0044087

regulation of cellular component biogenesis

13

0.42

0.008675

*, indicates the percentage of genes in the specific subcategory from each of the three GO ontologies.

To identify the biological pathways that are active in the large yellow croaker, we mapped the 8216 genes to canonical signaling pathways found in the Kyoto Encyclopedia of Genes and Genomes (KEGG). A total of 3094 genes of the large yellow croaker transcriptome were mapped to KEGG, and 20 statistically remarkable categories (P value < 0.05) are listed in Table 2. The mitogen-activated protein kinase (MAPK) signaling pathway, neurotrophin signaling pathway, and chemokine signaling pathway were identified as statistically significant. In fact, 47 genes were found to be related to the MAPK pathway. Other major immune pathways, such as those mediated by the T cell receptor (TCR) and B cell receptor (BCR), were also statistically significant.
Table 2

Statistically significant KEGG classifications of large yellow croaker genes

Category

Gene No.

%*

P value

Huntington's disease

81

2.62

0.000000

Ribosome

70

2.26

0.000000

Pathways in cancer

70

2.26

0.000020

Oxidative phosphorylation

69

2.23

0.000000

Alzheimer's disease

67

2.17

0.000000

Parkinson's disease

62

2

0.000000

Ubiquitin mediated proteolysis

54

1.75

0.000000

Lysosome

54

1.75

0.000000

Purine metabolism

51

1.65

0.000192

MAPK signaling pathway

47

1.52

0.027690

Regulation of actin cytoskeleton

47

1.52

0.000330

Focal adhesion

43

1.39

0.000870

Pyrimidine metabolism

37

1.2

0.000079

Insulin signaling pathway

35

1.13

0.000092

Neurotrophin signaling pathway

35

1.13

0.000014

Chemokine signaling pathway

34

1.1

0.041450

Proteasome

32

1.03

0.000000

T cell receptor signaling pathway

29

0.94

0.000161

Leukocyte transendothelial migration

29

0.94

0.000788

B cell receptor signaling pathway

27

0.87

0.000001

*, indicates the percentage of genes in each pathway from 3094 genes mapped to KEGG.

Global changes in gene expression upon A. hydrophila infection

To characterize the immune response of the large yellow croaker to bacterial infection, two DeepSAGE libraries were constructed using mRNA from spleens injected with A. hydrophila or 0.9% NaCl. After removal of the low-quality tags, adaptor tags, and one copynumber tag, a total of 4,841,402 and 5,395,715 clean tags were obtained from the two libraries with 100,107 and 108,572 unique nucleotide sequences, respectively (Additional file 2, Table S2). Subsequently, the tag sequences from the infected and control libraries were mapped to the transcriptome database described above. Approximately 50% of the tags matched sequences in the transcriptome, while 39% could be identified unequivocally by unique tag mapping (Additional file 3, Table S3). A total of 1996 differentially expressed genes (P value < 0.05) were found (Additional file 4, Table S4), including 1133 upregulated genes and 863 downregulated genes, in the spleen of fish infected with A. hydrophila. Particularly, 727 genes were upregulated at least 1.5-fold, including 208 genes that were unique to the infected library, while 489 genes were downregulated at least 1.5-fold, including 182 genes uniquely expressed in the control library.

To achieve a functional annotation of the infection-responsive genes, GO classifications were assigned to the 1996 differentially expressed genes by using DAVID (Additional file 5, Table S5). GO analysis indicated that bacterial infection up- and downregulated genes involved in immunity, transcription, translation regulations, and biological regulation.

Some significantly differentially expressed genes in expression profiles using GO classifications are shown in Table 3. The immune-related genes were enriched in GO terms "response to chemical stimulus" and "immune system development." Relative quantitative real-time PCR analysis was also performed to confirm the differentially expression genes. These genes were mapped to KEGG and found to be associated with the Toll-like receptor (TLR) signaling pathway (Figure 2). This group included TLR genes (e.g., TLR1, TLR2, TLR3, and TLR22), cytokine genes (e.g., TNF-α, IL-1β, and IL-8), and chemokine and chemokine receptor genes (e.g., CCL-4, CCL-c25v, CCR-1, CCR-12.3). Additionally, apoptosis-related genes, including Casp9 and Fas, as well as those involved in antioxidant activity such as Prdx1, Prdx2, Gpx1b, and Gpx4b were discovered. Genes involved in B cell and T cell development, such as Blnk and CD3ζ/d, were also found to be differentially expressed (Table 3). The B cell linker protein (Blnk), also known as SLP-65, is essential for normal B cell development by influencing the BCR signaling pathway [21]. The TCR/CD3ζ complex mediates antigen recognition and T cell stimulation, with CD3ζ/d playing a pivotal role in this process [22].
Table 3

Representative genes significantly differentially expressed after A. hydrophila infection

Gene Name

Accession NO.

Describe

Fold

P value

Immunity related genes

TLR1

P79800

Toll-like receptor 1

18/0

0.000001

TLR2

NM_212812

Toll-like receptor 2

0/94

0

TLR3

BAD01045

Toll-like receptor 3

4/0

0

TLR22

NM_001128675

Toll-like receptor 22

-2.5

0

IL-1β

gb|AAP33156.1|

Interleukin-1β

+17.9

0

IL-8

XP_695462

Interleukin-8

+20.8

0.000007

IL-2rgb

NM_001123050

Interleukin 2 receptor, gamma b

+2.3

0.001956

IL-4r

NM_001013282

Interleukin 4 receptor

+1.4

0.000015

IL-6r

NM_001114318

Interleukin 6 receptor

+2.2

0.000023

CCL-4

CAO78735.1

CC chemokine ligand 4

+2.75

0

CCL-c25v

NM_001115103

Chemokine CCL-C25v

0/34

0

CCr-1

ref|NP_001028030.1

CC chemokine receptor type 1

+9.6

0

CCr-12.3

NM_001045027

CC chemokine receptor family-like

+4.4

0

Crlf-3

ref|NP_001167401.1

cytokine receptor-like factor 3

65/0

0

TNFaip8

NM_200332

TNF, alpha-induced protein 8-like protein 1

-2.2

0.043671

TNFsf10l2

NM_001002593

TNF superfamily, member 10 like 2

-1.3

0.00616

Jfmip1a

dbj|BAC10650.1|

MIP1alpha

-2.5

0.000505

Cklr

gb|AAP58737.1|

C-type lectin receptor

+1.7

0

Blnk

NM_212838

B-cell linker

-1.6

0

zgc:55347

NM_213522

Immunoglobulin binding protein 1

4/0

0.026366

Fcgr1

gb|ACN10126.1|

High affinity immunoglobulin gammaFc receptor I precursor

+2.6

0.049003

CD3g/d

ref|NP_001033072.1

CD3 gamma/delta

-1.9

0

Rad23b

NM_200564

RAD23 homolog B

+2.4

0.018151

Fas

ref|NP_001075464.

Fas

4/0

0.026366

Casp9

NM_001007404

caspase9 apoptosis-related cysteine protease

+5.8

0.000013

Was

emb|CAQ15295.1|

Wiskott-Aldrich syndrome

-3.3

0.017699

Tpsn

NM_130974

Tapasin

+2.3

0

Lipf

NM_213404

Lipf protein

+2.4

0

Hsp90a.1

NM_131328

Heat shock protein HSP 90-alpha 1

+1.2

0.048885

Gadd45al

NM_200576

Growth arrest and DNA-damage-inducible, beta

+1.7

0

Prdx1

NM_001013471

Peroxiredoxin 1

+5.8

0

Prdx2

NM_001002468

Peroxiredoxin 2

-1.5

0

Glrx5

NM_213021

Glutaredoxin 5

+1.3

0.015063

Gpx1b

NM_001004634

Glutathione peroxidase 1b

+1.4

0

Gpx4b

NM_001030070

Glutathione peroxidase 4b

-1.4

0

zgc:85657

NM_214749

Non-homologous end-joining factor 1

-1.4

0.02618

Mpx

NM_212779

Myeloid-specific peroxidase

-1.9

0.02238

Ube2nl

NM_200342

Ubiquitin-conjugating enzyme E2N-like

+2

0

Transcription regulator activity

NF-kB2

NM_001001840

NF-kB, p49/p100

-1.7

0

NF-kBie

NM_001080089

NF-kB 2 inhibitor, epsilon

+1.7

0

Jak1

NM_131073

Janus kinase 1

+6.1

0.003023

Stat1

ref|NP_001117126.1|

STAT1 alpha

+3.9

0.027891

Jun

NM_199987

c-Jun

0/5

0.02144

Jund

NM_001128342

Jun D proto-oncogene

+1.5

0.012807

Xbp1

NM_131874

X-box binding protein 1

0/6

0.011301

Smad9

NM_001004014

Smad9

-3.6

0.031597

Slp-1

gb|AAC41262.1|

Transcription factor

0/4

0.040678

Srf

gb|AAH50480.1|

Srf protein

-1.5

0.006007

Tp53

NM_131327

Cellular tumor antigen p53

-1.2

0.031928

Cebpa

NM_131885

CCAAT/enhancer binding protein alpha

11/0

0.00014

Pdlim1

NM_001017870

PDZ and LIM domain 1

+2.1

0.000003

Ahr2b

ref|NP_001033052.1|

Aryl hydrocarbon receptor 2B

+2.3

0.00573

IRF

dbj|BAA83468.1|

interferon regulatory factor

+2.9

0

IRF4

NM_001122710

interferon regulatory factor 4

+1.2

0.040115

IRF9

NM_205710

interferon regulatory factor 9

+2.3

0

Max

NM_131220

Myc-associated factor X

+1.3

0.003228

Rargb

NM_001083310

Retinoic acid receptor gamma

13/0

0.000031

Ldb1a

NM_131313

LIM domain-binding protein 4

+1.6

0.0016

Cse1l

NM_201450

Chromosome segregation 1-like

-1.2

0.045136

Ppp1r10

NM_212568

protein phosphatase 1, regulatory subunit 10

-1.2

0.00723

Ppp1caa

NM_214811

protein phosphatase 1, catalytic subunit alpha

-3.9

0.007564

Gtf2h2

NM_201581

General transcription factor IIH, polypeptide 2

4/0

0.026366

Gtf2h3

NM_001002564

General transcription factor IIH, polypeptide 3

+2.5

0.000295

Gf2f2

NM_001017832

General transcription factor IIF, polypeptide 2

+1.4

0.021645

Gtf2e2

NM_212731

General transcription factor IIE, polypeptide 2, beta

+31.9

0

Limitations of all differentially expressed genes are based on P < 0.05. A P value < 0.05 indicated that the gene was significantly altered after bacterial challenge. The absolute value of "Fold" means the magnitude of up- or downregulation for each gene/homolog after bacterial challenge; "+" indicates upregulation, "-" indicates downregulation, and "0" indicates the gene was not found in one library. "Accession NO" is GenBank identifiers for the conformable reference sequences.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-11-506/MediaObjects/12864_2010_Article_3100_Fig2_HTML.jpg
Figure 2

Gene list involved in TLR pathway generated by KEGG. Red indicates significantly increased expression; pink, not remarkably increased expression; blue, significantly decreased expression; cyan, not remarkably decreased expression; and gray, unchanged expression. White denotes genes that were not identified in the expression profile analysis.

Many genes in the transcription regulation group were upregulated by A. hydrophila infection. This group includes genes encoding NF-κB2, NF-κBie, IRF9, IRF11, Jund, Jak1, Stat1, Cebpa, and Cebpb (Table 3). NF-κB is a transcription factor involved in regulating a large number of genes, especially cytokine genes [23]. Jak1 and Stat1 are components of the JAK-STAT signaling pathway. The remaining genes were represented by GO terms such as cellular component, binding, catalytic activity, structural molecular activity, and growth. These biological functions and pathways have not been associated directly with a particular immune-related event. Meanwhile, a number of uniquely expressed genes were hypothetical proteins, and future identification of these genes and their function may provide new insights into the immune response to A. hydrophila infection.

GenMAPP analysis reveals genes involved in TCR and MAPK signaling

To further explore the immune response profiles induced by A. hydrophila infection to the level of a single pathway, we performed a map-based pathway analysis by using the GenMAPP software package http://www.genmapp.org/. In our study, 4004 Mus musculus homologs were used to create the GenMAPP. Mus musculus homologs were identified by searching the 8216 unigenes against the zebrafish RefSeq data downloaded from the UCSC website http://genome.ucsc.edu/ and then the database of HomoloGene at the NCBI http://www.ncbi.nlm.nih.gov. GenMAPP analysis was performed to identify genes involved in the MAPK pathway (Figure 3). In total, seven genes were identified as highly upregulated upon infection, Casp9, Prkcb1, Hspa5, Radd45a, Dusp7, Rac1, and Casp1. Contrarily, four genes were highly downregulated in response to A. hydrophila infection, Map3k12, Crkl, Jun, and Raf1 (Additional file 6, Table S6). We also used GenMAPP to analyze genes involved in TCR signaling. T cell activation, a key event in adaptive immunity, promotes a variety of signaling cascades that ultimately lead to cytokine production, cell survival, proliferation, and differentiation [24]. The resultant map (Figure 4) revealed eight remarkably downregulated genes (Was, Lyn, Ptpn6, Ctnnb1, Itk, Crkl, Jun, and Ripk2) and seven remarkably upregulated genes (Khdrbs1, Scap2, Vasp, Pik3r2, Cebpb, Zap70, and Cbl) involved in TCR signaling after A. hydrophila infection (Additional file 7, Table S7).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-11-506/MediaObjects/12864_2010_Article_3100_Fig3_HTML.jpg
Figure 3

The MAPK signaling pathways generated by GenMAPP. The map-based pathway was made using the GenMAPP software package. A total of 4004 Mus musculus homologs were used to create the GenMAPP. Mus musculus homologs were identified by searching the 8216 unigenes against the zebrafish RefSeq data downloaded from the UCSC website and then the database of HomoloGene at the NCBI. Red indicates significantly increased expression; pink, not remarkably increased expression; blue, significantly decreased expression; cyan, not remarkably decreased expression; and gray, unchanged expression. White denotes genes that were not identified in the expression profile analysis.

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Figure 4

The TCR signaling pathways generated by GenMAPP. The map-based pathway was made using the GenMAPP software package. A total of 4004 Mus musculus homologs were used to create the GenMAPP. Mus musculus homologs were identified by searching the 8216 unigenes against the zebrafish RefSeq data downloaded from the UCSC website and then the database of HomoloGene at the NCBI. Red indicates significantly increased expression; pink, not remarkably increased expression; blue, significantly decreased expression; cyan, not remarkably decreased expression; and gray, unchanged expression. White denotes genes that were not identified in the expression profile analysis.

Discussion

At present, molecular studies on the immune response to pathogens in the large yellow croaker are still rare. To increase our knowledge of host responses to bacterial infection, we firstly analyzed the transcriptome profile of the fish after A. hydrophila infection. Bioinformatic analysis of RNA-seq data should involve mapping of short reads to the genome [17]. However, genome and transcriptome resources for most vertebrate species have not yet been obtained, including the large yellow croaker. We analyzed the transcriptome of the large yellow croaker in advance and obtained a mass of sequence information. Then quantitative gene expression profile analysis was performed, and the tags were mapped to obtained transcriptome database. In the set of highly differentially expressed genes, a number of genes were reported to be involved in immunity and signal transduction, encoding receptors, cytokines, innate defense molecules, enzymes, signal transducers, transcription factors, and other functional proteins.

The innate immune system represents an efficient first line of defense against invading microbial pathogens. TLRs signal the presence of pathogens and elicit an innate immune response. This process has been reported in zebrafish infected with Mycobacterium marinum[25, 26]. Our data revealed 35 genes involved in TLR cascades in the transcriptome of infected large yellow croaker and 29 differentially expressed genes in expression profiles (Figure 2). TLR1 and TLR2 function together to recognize lipopeptides with a triacylated N-terminal cysteine. TLR1 is only mildly expressed in T. nigroviridis tissues and slightly upregulated in the spleens of LPS-injected fish [27]. Our data demonstrated that TLR1 was upregulated while TLR2 was downregulated at 24 h after A. hydrophila infection (Figure 5A). This result was partly consistent with that reported by Baoprasertkul et al., in which TLR2 expression in the spleens of channel and blue catfish was downregulated initially but upregulated 1 day postinfection with Edwardsiella ictaluri[28]. Bacterial infection has also been shown to induce TLR3 mRNA expression in zebrafish and channel catfish, as well as in channel-blue backcross hybrids following infection with E. tarda and E. ictaluri[25, 29]. In our study, TLR3 expression was also upregulated 22.5-fold postinfection (Figure 5A), suggesting that this receptor might be involved in the immune response to bacterial infection in fish in addition to recognizing double-stranded RNA as in mammals. TLR22 is a fish-specific member of this family [30] that has also been found in the large yellow croaker. Recently, TLR22 was found located on the pufferfish cell surface recognizing long dsRNA sequences, whereas mammalian nucleic acid-sensing TLRs are localized in endosomes or the ER of myeloid cells, indicating that TLR22 may be a functional substitute for mammalian TLR3 that monitors for infections by double-stranded RNA viruses [25]. TLR22 was downregulated in the expression profile, implying that TLR22 was suppressed in the early period of A. hydrophila infection. Taken together, these results indicate that TLRs are regulated by various components of Gram-negative bacteria, suggesting that multiple TLR-mediated signaling cascades may simultaneously be involved in immune response to bacterial infection.
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Figure 5

The expression analysis of selected genes from the expression profile by relative quantitative real-time PCR. Total RNA was extracted from spleens of fish infected with A. hydrophila or injected with 0.9% NaCl. Real-time PCR was used to validate gene expression changes in the TLR pathway (A), cytokines (B), the MAPK signaling pathway (C), and the TCR signaling pathway (D). Increases and decreases in relative levels of transcripts with respect to the control β-actin gene are shown. For each gene, the black bar indicates the gene expression ratio of fish injected with 0.9% NaCl and is defined as 1; the grey bar indicates the expression ratio of fish infected with A. hydrophila, with associated standard error bars. Statistical significance of the relative expression ratio is indicated (*, P < 0.01).

In our study, A. hydrophila infection led to a dramatic increase in the expression of proinflammatory cytokines such as IL-1β, IL-8, and TNF-α (Table 3). Studies have reported that these cytokines are induced within 24 h in human monocytes following Gram-positive and Gram-negative bacterial infection [31]. IL-1β is considered the prototypic multifunctional cytokine that affects nearly all cell types, either alone or in combination with other cytokines response to infection, injury, or immunologic challenge [32]. IL-8 is a proinflammatory CXC chemokine that has been shown to be regulated by a number of different stimuli including inflammatory signals (e.g., TNF-α, IL-β), chemical and environmental stresses, and steroid hormones [33]. Here, upregulation of these cytokines was observed by real-time PCR (Figure 5B), which is consistent with the observed findings in DeepSAGE. Therefore, the upregulation of these proinflammatory cytokines strongly suggests that the proinflammatory response may represent an important antibacterial mechanism at the early phase of infection.

The JAK-STAT pathway is initiated in response to cytokines, such as interleukins and IFNs, and growth factors present in the surrounding microenvironment [34]. Jak1 is a cytoplasmic tyrosine kinase that noncovalently associates with a variety of cytokine receptors and plays a nonredundant role in lymphoid cell precursor proliferation, survival, and differentiation [35, 36]. STAT1, after activation by IFN-γ signaling, leads to the activation of peritoneal macrophages, resulting in enhanced bacteria killing and protection against lethal levels of Listeria monocytogenes infection in mice [23]. Genes encoding JAK-STAT pathway members, including Jak1 and Stat1, were found to be upregulated in our study (Table 3), suggesting that the JAK-STAT pathway may be affected by bacterial infection, which may result in changes in other cross-talk biological processes, such as NF-κB signaling pathway, TGF-β-activated SMAD pathway, and apoptosis [37].

Another signaling pathway affected by bacterial infection in the large yellow croaker was the MAPK cascade. This pathway has been demonstrated to regulate the expression of genes involved in the immune response to pathogens [38], cell differentiation, and cell death [39]. Modulation of MAPK activity in the common periwinkle in response to Escherichia coli-derived LPS has been studied [40]. Some key MAPK-related genes were identified in our transcriptome, including Casp9, Rac1, Gadd45α, and Dusp7 (Additional file 6, Table S6). Quantitative PCR analysis confirmed the differential expression of Casp9 and Dusp7 (Figure 5C). The Rho family GTPase Rac1 has been implicated in the control of the p38 MAPK signaling pathway by controlling β1 integrin. As shown in humans, dominant-negative Rac1 completely inhibits β1 integrin-induced p38 MAPK activation, whereas wild-type Rac1 overexpression causes a slight increase in β1 integrin-induced p38 MAPK activation [41]. Dual-specificity phosphatases including Dusp7 are a subset of protein tyrosine phosphatases, many of which dephosphorylate threonine and tyrosine residues on MAPKs and hence are also referred to as MAPK phosphatases (MKPs). The regulated expression and activity of DUSP family members in different cells and tissues control MAPK intensity and duration to determine the type of physiological response [42, 43]. Therefore, the identified changes in gene expression in the large yellow croaker may facilitate the activation of the MAPK pathway and protect hosts against A. hydrophila infection.

Adaptive immunity is the process that leads to specific host resistance to infection [44]. T cells orchestrate responses against such foreign pathogens as viruses and bacteria. TCR and its downstream signaling cascades play a key role in these events. Here, we identified TCR pathway-related genes that were downregulated at 24 h after A. hydrophila infection. This complex process is shown in Figure 4, and genes expressed differentially are listed in Additional file 7, Table S7. Lyn, Itk, Was, Ptpn6, and Jun expression was downregulated, implying that the TCR signaling pathway may be suppressed in the early period (24 h) following bacterial infection. Studies have shown that a fine balance exists between a positive signal that initiates TCR cascade and a negative signal that controls the threshold, extent, and termination of TCR activation [45]. Several protein tyrosine phosphatases (PTPs) have been shown to function as negative regulators of the TCR signaling pathway by dephosphorylating activated signaling molecules [46, 47]. Here, expression of Ptpn6, a member of the PTP family [48], was downregulated (Figure 5D), suggesting that although the TCR signaling pathway was suppressed by A. hydrophila, the host began to downregulate the expression of the PTPs to antagonize the repression. Clearly, there is a need for further studies to elucidate the precise roles of the PTP family members in the TCR signaling pathway in fish.

Conclusions

Several recent studies have exploited novel high-throughput deep sequencing technology as a new method to advance further understanding of the mechanism of fish defense against infection [17]. We used the A. hydrophila-infected large yellow croaker as a model to study the immune response of fish to bacterial infection. Our analysis of the transcriptome and gene expression in A. hydrophila-infected large yellow croaker revealed changes in multiple signaling pathways involved in immunity in the large yellow croaker. The multiple TLR-mediated signaling cascades may be involved in early response to bacterial infection, causing the production of proinflammatory cytokines, chemokines, and other cytokines, which may result in the inflammatory response and affect other signal pathways such as JAK-STAT and MAPK. However, the TCR signaling pathway, a pivotal process in cellular immunity, was suppressed in the early period of A. hydrophila infection. The immune-related genes and signaling pathways involved in bacterial infection were identified and thereby provided valuable leads for further investigations into the immune response of fish.

Methods

Fish and infection experiments

Large yellow croakers (mean weight, 200 g) were purchased from a mariculture farm in Lianjian, Fuzhou, China. The fish were maintained at 25°C in aerated water tanks with a flow-through seawater supply. After 7 days of acclimation, these fish were used for the infection experiments. Twenty fish were injected intramuscularly with A. hydrophila at a dose of 1 × 108 cfu/200 g (This dose was chosen based on previous unpublished data) of fish. The strain of A. hydrophila (PPD 134/91) used in our manuscript was kindly provided by professor Xuanxian Peng [49]. A second group of 20 fish was injected with sterilized 0.9% NaCl at a dose of 0.2 ml/200 g of fish as a control [50]. The spleen tissues sampled at 12 h after infection with A. hydrophila were used for transcriptome analysis. The spleen tissues sampled at 24 h after injections with A. hydrophila or 0.9% NaCl were used for gene expression profiling analysis. All experiments were conducted in Third Institute of Oceanography, SOA, China. The protocols used meet the "Regulations for the Administration of Affairs Concerning Experimental Animals" established by the Fujian Provincial Department of Science and Technology on the Use and Care of Animals.

RNA isolation

Total RNA was extracted from 50 to 100 mg of tissue with TRIZOL® Reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer's instructions. The RNA samples were incubated for 30 min at 37°C with 10 units of DNaseI (Takara, Dalian, China) to remove residual genomic DNA. The quality and quantity of the purified RNA were determined by measuring the absorbance at 260 nm/280 nm (A260/A280) using a Nanodrop® ND-1000 spectrophotometer (LabTech, Holliston, MA, USA). The samples had an average RIN value of 8.9 according to Labon-chip analysis using the 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA).

Library preparation and sequencing

First, to survey the gene expression profile in the large yellow croaker and obtain longer transcript sequences for better annotation of the transcriptome, we constructed the entire library using the Mate Pair Library Preparation Kit. Then, to investigate the dynamics of gene expression after infection with A. hydrophila, we performed two tag-library preparations using the DeepSAGE: Tag Profiling for Nla III Sample Prep Kit from Illumina according to the manufacturer's instructions.

To better assemble the entire transcriptome de novo, a paired-end sequencing strategy was used for sequencing. A fragment sequencing strategy was used to sequence the tags. The data has been submitted to NCBI, and the accession number is SRA010789.13.

Assembly of transcripts and annotation

Transcripts were assembled using the SOAP de novo software http://soap.genomics.org.cn/soapdenovo.html. As a result, 26,313 scaffolds were generated. To annotate these scaffolds, we first aligned them by using the zebrafish RefSeq mRNA database. The remaining non-annotated scaffolds were further aligned to the nr database. The annotated scaffolds were clustered and designated as unigenes when two or more query sequences were annotated to the same gene. The assembled contigs were used as a reference for annotating the DeepSAGE tags. GO and KEGG gene function were performed using DAVID [19].

Identification of differentially expressed genes

Gene expression was measured by counting tags from normal and bacteria-infected fish and normalized to the total high-quality reads. High-throughput sequencing was performed using the Solexa/Illumina Genome Analyzer. To investigate differences in gene expression profiles, we analyzed genes between both libraries using the IDEG6 modeling methods [51]. GenMAPP 2.0 was used to show differences in expression in the different pathways [52].

Quantitative real-time PCR

Quantitative real-time PCR was performed using the ABI Prism 7500 Detection System (Applied Biosystems, Foster City, CA, USA) with SYBR Green as the fluorescent dye according to the manufacturer's protocol (Takara). First-strand cDNA was synthesized from 2 μg of total RNA as described above and used as a template for real-time PCR with specific primers (Additional file 8, Table S8). Real-time PCR was performed in a total volume of 20 μl, and cycling conditions were 95°C for 5 min, followed by 40 cycles of 94°C for 5 s, 55°C for 20 s, and 72°C for 20 s. All reactions were performed in biological triplicates, and the results were expressed relative to the expression levels of β-actin in each sample by using the 2ΔΔCT method [53]. Each sample was first normalized for the amount of template added by comparison with the abundance of β-actin mRNA [54].

Notes

Declarations

Acknowledgements

The work was supported by grants from the Nation '863' Project (2006AA10A402 and 2007AA091406) and National Natural Science Foundation of China (30871925 and 31001131).

Authors’ Affiliations

(1)
Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, State Oceanic Administration
(2)
The CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences

References

  1. Wu ZP, Wang SY: Experimental Study on Immune Protection of Trivalent Vaccine to Common Bacterial Diseases of Pseudosciaena crocea. J Xiamen Univ (Natural Sci). 2004, 43: 115-118.Google Scholar
  2. Zheng W, Liu G, Ao J, Chen X: Expression analysis of immune-relevant genes in the spleen of large yellow croaker (Pseudosciaena crocea) stimulated with poly I:C. Fish Shellfish Immunol. 2006, 21 (4): 414-430. 10.1016/j.fsi.2006.01.006.PubMedView ArticleGoogle Scholar
  3. Cao D, Kocabas A, Ju Z, Karsi A, Li P, Patterson A, Liu Z: Transcriptome of channel catfish (Ictalurus punctatus): initial analysis of genes and expression profiles of the head kidney. Anim Genet. 2001, 32 (4): 169-188. 10.1046/j.1365-2052.2001.00753.x.PubMedView ArticleGoogle Scholar
  4. Martin SA, Caplice NC, Davey GC, Powell R: EST-based identification of genes expressed in the liver of adult Atlantic salmon (Salmo salar). Biochem Biophys Res Commun. 2002, 293 (1): 578-585. 10.1016/S0006-291X(02)00263-2.PubMedView ArticleGoogle Scholar
  5. Shiue YL, Wang LH, Chao TY, Lin CH, Tsai CL: EST-based identification of genes expressed in the hypothalamus of adult tilapia, Oreochromis mossambicus. Biochem Biophys Res Commun. 2004, 316 (2): 523-527. 10.1016/j.bbrc.2004.02.079.PubMedView ArticleGoogle Scholar
  6. Meijer AH, Verbeek FJ, Salas-Vidal E, Corredor-Adamez M, Bussman J, van der Sar AM, Otto GW, Geisler R, Spaink HP: Transcriptome profiling of adult zebrafish at the late stage of chronic tuberculosis due to Mycobacterium marinum infection. Mol Immunol. 2005, 42 (10): 1185-1203. 10.1016/j.molimm.2004.11.014.PubMedView ArticleGoogle Scholar
  7. Bayne CJ, Gerwick L, Wheeler PA, Thorgaard GH: Transcriptome profiles of livers and kidneys from three rainbow trout (Oncorhynchus mykiss) clonal lines distinguish stocks from three allopatric populations. Comparative Biochemistry and Physiology. 2006, 1: 396-403.PubMedGoogle Scholar
  8. Peatman E, Terhune J, Baoprasertkul P, Xu P, Nandi S, Wang S, Somridhivej B, Kucuktas H, Li P, Dunham R: Microarray analysis of gene expression in the blue catfish liver reveals early activation of the MHC class I pathway after infection with Edwardsiella ictaluri. Mol Immunol. 2008, 45 (2): 553-566. 10.1016/j.molimm.2007.05.012.PubMedView ArticleGoogle Scholar
  9. Boswell MG, Wells MC, Kirk LM, Ju Z, Zhang Z, Booth RE, Walter RB: Comparison of gene expression responses to hypoxia in viviparous (Xiphophorus) and oviparous (Oryzias) fishes using a medaka microarray. Comp Biochem Physiol C Toxicol Pharmacol. 2009, 149 (2): 258-265. 10.1016/j.cbpc.2008.11.005.PubMedView ArticleGoogle Scholar
  10. t Hoen PA, Ariyurek Y, Thygesen HH, Vreugdenhil E, Vossen RH, de Menezes RX, Boer JM, van Ommen GJ, den Dunnen JT: Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms. Nucleic Acids Res. 2008, 36 (21): e141-10.1093/nar/gkn705.View ArticleGoogle Scholar
  11. Hanriot L, Keime C, Gay N, Faure C, Dossat C, Wincker P, Scote-Blachon C, Peyron C, Gandrillon O: A combination of LongSAGE with Solexa sequencing is well suited to explore the depth and the complexity of transcriptome. BMC Genomics. 2008, 9: 418-10.1186/1471-2164-9-418.PubMed CentralPubMedView ArticleGoogle Scholar
  12. Han X, Wu X, Chung WY, Li T, Nekrutenko A, Altman NS, Chen G, Ma H: Transcriptome of embryonic and neonatal mouse cortex by high-throughput RNA sequencing. Proc Natl Acad Sci USA. 2009, 106 (31): 12741-12746. 10.1073/pnas.0902417106.PubMed CentralPubMedView ArticleGoogle Scholar
  13. Morozova O, Hirst M, Marra MA: Applications of new sequencing technologies for transcriptome analysis. Annu Rev Genomics Hum Genet. 2009, 10: 135-151. 10.1146/annurev-genom-082908-145957.PubMedView ArticleGoogle Scholar
  14. Morozova O, Marra MA: Applications of next-generation sequencing technologies in functional genomics. Genomics. 2008, 92 (5): 255-264. 10.1016/j.ygeno.2008.07.001.PubMedView ArticleGoogle Scholar
  15. Nielsen KL, Hogh AL, Emmersen J: DeepSAGE--digital transcriptomics with high sensitivity, simple experimental protocol and multiplexing of samples. Nucleic Acids Res. 2006, 34 (19): e133-10.1093/nar/gkl714.PubMed CentralPubMedView ArticleGoogle Scholar
  16. Morrissy AS, Morin RD, Delaney A, Zeng T, McDonald H, Jones S, Zhao Y, Hirst M, Marra MA: Next-generation tag sequencing for cancer gene expression profiling. Genome Res. 2009, 19 (10): 1825-1835. 10.1101/gr.094482.109.PubMed CentralPubMedView ArticleGoogle Scholar
  17. Hegedus Z, Zakrzewska A, Agoston VC, Ordas A, Racz P, Mink M, Spaink HP, Meijer AH: Deep sequencing of the zebrafish transcriptome response to mycobacterium infection. Mol Immunol. 2009, 46 (15): 2918-2930. 10.1016/j.molimm.2009.07.002.PubMedView ArticleGoogle Scholar
  18. Alvarez-Pellitero P: Fish immunity and parasite infections: from innate immunity to immunoprophylactic prospects. Vet Immunol Immunopathol. 2008, 126 (3-4): 171-198. 10.1016/j.vetimm.2008.07.013.PubMedView ArticleGoogle Scholar
  19. Huang da W, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009, 4 (1): 44-57. 10.1038/nprot.2008.211.PubMedView ArticleGoogle Scholar
  20. Huang da W, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, Guo Y, Stephens R, Baseler MW, Lane HC: DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 2007, W169-175. 10.1093/nar/gkm415. 35 Web Server
  21. Kelly ME, Chan AC: Regulation of B cell function by linker proteins. Curr Opin Immunol. 2000, 12 (3): 267-275. 10.1016/S0952-7915(00)00086-8.PubMedView ArticleGoogle Scholar
  22. Januchowski R, Jagodzinski PP: Effect of 5-azacytidine and procainamide on CD3-zeta chain expression in Jurkat T cells. Biomed Pharmacother. 2005, 59 (3): 122-126. 10.1016/j.biopha.2004.07.002.PubMedView ArticleGoogle Scholar
  23. Schindler C, Plumlee C: Inteferons pen the JAK-STAT pathway. Semin Cell Dev Biol. 2008, 19 (4): 311-318. 10.1016/j.semcdb.2008.08.010.PubMed CentralPubMedView ArticleGoogle Scholar
  24. Smith-Garvin JE, Koretzky GA, Jordan MS: T cell activation. Annu Rev Immunol. 2009, 27: 591-619. 10.1146/annurev.immunol.021908.132706.PubMed CentralPubMedView ArticleGoogle Scholar
  25. Rebl A, Goldammer T, Seyfert HM: Toll-like receptor signaling in bony fish. Vet Immunol Immunopathol. 2010, 134 (3-4): 139-50. 10.1016/j.vetimm.2009.09.021.PubMedView ArticleGoogle Scholar
  26. Lin B, Chen S, Cao Z, Lin Y, Mo D, Zhang H, Gu J, Dong M, Liu Z, Xu A: Acute phase response in zebrafish upon Aeromonas salmonicida and Staphylococcus aureus infection: striking similarities and obvious differences with mammals. Mol Immunol. 2007, 44 (4): 295-301. 10.1016/j.molimm.2006.03.001.PubMedView ArticleGoogle Scholar
  27. Chang MX, Nie P: RNAi suppression of zebrafish peptidoglycan recognition protein 6 (zfPGRP6) mediated differentially expressed genes involved in Toll-like receptor signaling pathway and caused increased susceptibility to Flavobacterium columnare. Vet Immunol Immunopathol. 2008, 124 (3-4): 295-301. 10.1016/j.vetimm.2008.04.003.PubMedView ArticleGoogle Scholar
  28. Baoprasertkul P, Peatman E, Abernathy J, Liu Z: Structural characterisation, expression analysis of Toll-like receptor 2 gene from catfish. Fish Shellfish Immunol. 2007, 22: 418-426. 10.1016/j.fsi.2006.04.005.PubMedView ArticleGoogle Scholar
  29. Bilodeau AL, Waldbieser GC: Activation of TLR3 and TLR5 in channel catfish exposed to virulent Edwardsiella ictaluri. Dev Comp Immunol. 2005, 29 (8): 713-721. 10.1016/j.dci.2004.12.002.PubMedView ArticleGoogle Scholar
  30. Jault C, Pichon L, Chluba J: Toll-like receptor gene family and TIR-domain adapters in Danio rerio. Mol Immunol. 2004, 40 (11): 759-771. 10.1016/j.molimm.2003.10.001.PubMedView ArticleGoogle Scholar
  31. Hessle CC, Andersson B, Wold AE: Gram-positive and Gram-negative bacteria elicit different patterns of pro-inflammatory cytokines in human monocytes. Cytokine. 2005, 30 (6): 311-318. 10.1016/j.cyto.2004.05.008.PubMedView ArticleGoogle Scholar
  32. Church LD, Cook GP, McDermott MF: Primer: inflammasomes and interleukin 1beta in inflammatory disorders. Nat Clin Pract Rheumatol. 2008, 4 (1): 34-42. 10.1038/ncprheum0681.PubMedView ArticleGoogle Scholar
  33. Waugh DJ, Wilson C: The interleukin-8 pathway in cancer. Clin Cancer Res. 2008, 14 (21): 6735-6741. 10.1158/1078-0432.CCR-07-4843.PubMedView ArticleGoogle Scholar
  34. Pear WS, Aster JC: T cell acute lymphoblastic leukemia/lymphoma: a human cancer commonly associated with aberrant NOTCH1 signaling. Curr Opin Hematol. 2004, 11 (6): 426-433. 10.1097/01.moh.0000143965.90813.70.PubMedView ArticleGoogle Scholar
  35. Haan C, Is'harc H, Hermanns HM, Schmitz-Van De Leur H, Kerr IM, Heinrich PC, Grotzinger J, Behrmann I: Mapping of a region within the N terminus of Jak1 involved in cytokine receptor interaction. J Biol Chem. 2001, 276 (40): 37451-37458. 10.1074/jbc.M106135200.PubMedView ArticleGoogle Scholar
  36. Flex E, Petrangeli V, Stella L, Chiaretti S, Hornakova T, Knoops L, Ariola C, Fodale V, Clappier E, Paoloni F: Somatically acquired JAK1 mutations in adult acute lymphoblastic leukemia. J Exp Med. 2008, 205 (4): 751-758. 10.1084/jem.20072182.PubMed CentralPubMedView ArticleGoogle Scholar
  37. Shuai K, Liu B: Regulation of JAK-STAT signalling in the immune system. Nat Rev Immunol. 2003, 3 (11): 900-911. 10.1038/nri1226.PubMedView ArticleGoogle Scholar
  38. Tarrega C, Pulido R: A one-step method to identify MAP kinase residues involved in inactivation by tyrosine- and dual-specificity protein phosphatases. Anal Biochem. 2009, 394 (1): 81-86. 10.1016/j.ab.2009.07.006.PubMedView ArticleGoogle Scholar
  39. Hrstka R, Stulik J, Vojtesek B: The role of MAPK signal pathways during Francisella tularensis LVS infection-induced apoptosis in murine macrophages. Microbes Infect. 2005, 7 (4): 619-625.PubMedView ArticleGoogle Scholar
  40. Iakovleva NV, Gorbushin AM, Storey KB: Modulation of mitogen-activated protein kinases (MAPK) activity in response to different immune stimuli in haemocytes of the common periwinkle Littorina littorea. Fish Shellfish Immunol. 2006, 21 (3): 315-324. 10.1016/j.fsi.2005.12.008.PubMedView ArticleGoogle Scholar
  41. Mainiero F, Soriani A, Strippoli R, Jacobelli J, Gismondi A, Piccoli M, Frati L, Santoni A: RAC1/P38 MAPK signaling pathway controls beta1 integrin-induced interleukin-8 production in human natural killer cells. Immunity. 2000, 12 (1): 7-16. 10.1016/S1074-7613(00)80154-5.PubMedView ArticleGoogle Scholar
  42. Jeffrey KL, Camps M, Rommel C, Mackay CR: Targeting dual-specificity phosphatases: manipulating MAP kinase signalling and immune responses. Nat Rev Drug Discov. 2007, 6 (5): 391-403. 10.1038/nrd2289.PubMedView ArticleGoogle Scholar
  43. Owens DM, Keyse SM: Differential regulation of MAP kinase signalling by dual-specificity protein phosphatases. Oncogene. 2007, 26 (22): 3203-3213. 10.1038/sj.onc.1210412.PubMedView ArticleGoogle Scholar
  44. Orme I: Adaptive immunity to mycobacteria. Curr Opin Microbiol. 2004, 7 (1): 58-61. 10.1016/j.mib.2003.11.002.PubMedView ArticleGoogle Scholar
  45. Qian D, Weiss A: T cell antigen receptor signal transduction. Curr Opin Cell Biol. 1997, 9 (2): 205-212. 10.1016/S0955-0674(97)80064-6.PubMedView ArticleGoogle Scholar
  46. Mustelin T, Rahmouni S, Bottini N, Alonso A: Role of protein tyrosine phosphatases in T cell activation. Immunol Rev. 2003, 191: 139-147. 10.1034/j.1600-065X.2003.00014.x.PubMedView ArticleGoogle Scholar
  47. Mohi MG, Neel BG: The role of Shp2 (PTPN11) in cancer. Curr Opin Genet Dev. 2007, 17 (1): 23-30. 10.1016/j.gde.2006.12.011.PubMedView ArticleGoogle Scholar
  48. Alonso A, Sasin J, Bottini N, Friedberg I, Osterman A, Godzik A, Hunter T, Dixon J, Mustelin T: Protein tyrosine phosphatases in the human genome. Cell. 2004, 117 (6): 699-711. 10.1016/j.cell.2004.05.018.PubMedView ArticleGoogle Scholar
  49. Peng X, Zhang J, Wang S, Lin Z, Zhang W: Immuno-capture PCR for detection of Aeromonas hydrophila. J Microbiol Methods. 2002, 49 (3): 335-338. 10.1016/S0167-7012(02)00010-6.PubMedView ArticleGoogle Scholar
  50. Wan X, Chen X: Molecular cloning and expression analysis of a CXC chemokine gene from large yellow croaker Pseudosciaena crocea. Vet Immunol Immunopathol. 2009, 127 (1-2): 156-161. 10.1016/j.vetimm.2008.09.009.PubMedView ArticleGoogle Scholar
  51. Romualdi C, Bortoluzzi S, D'Alessi F, Danieli GA: IDEG6: a web tool for detection of differentially expressed genes in multiple tag sampling experiments. Physiol Genomics. 2003, 12 (2): 159-162.PubMedView ArticleGoogle Scholar
  52. Salomonis N, Hanspers K, Zambon AC, Vranizan K, Lawlor SC, Dahlquist KD, Doniger SW, Stuart J, Conklin BR, Pico AR: GenMAPP 2: new features and resources for pathway analysis. BMC Bioinformatics. 2007, 8: 217-10.1186/1471-2105-8-217.PubMed CentralPubMedView ArticleGoogle Scholar
  53. Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001, 25 (4): 402-408. 10.1006/meth.2001.1262.PubMedView ArticleGoogle Scholar
  54. Yu S, Ao J, Chen X: Molecular characterization and expression analysis of MHC class II alpha and beta genes in large yellow croaker (Pseudosciaena crocea). Mol Biol Rep. 2010, 37 (3): 1295-1307. 10.1007/s11033-009-9504-8.PubMedView ArticleGoogle Scholar

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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.