Transcriptome and expression profiling analysis revealed changes of multiple signaling pathways involved in immunity in the large yellow croaker during Aeromonas hydrophila infection
- Yinnan Mu†1,
- Feng Ding†2,
- Peng Cui2,
- Jingqun Ao1,
- Songnian Hu2Email author and
- Xinhua Chen1Email author
© Mu et al; licensee BioMed Central Ltd. 2010
Received: 13 May 2010
Accepted: 22 September 2010
Published: 22 September 2010
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.
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.
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.
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 . 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 .
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 , Atlantic salmon , and orange-spotted grouper , as well as microarrays in adult zebrafish , rainbow trout , blue catfish , medaka, and Xiphophorus maculates . However, microarrays are limited by background and cross-hybridization problems and only measure the relative abundance of transcripts. Moreover, only predefined sequences are detected . EST sequencing techniques have limitations in the depth of the transcriptome that can be sampled .
Recent rapid developments of high-throughput deep sequencing technologies have provided an unprecedented increase in transcriptome data . 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 . Nevertheless, there are still important gaps in the knowledge of numerous immune mechanisms, and the available information varies according to the fish species .
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.
Transcriptome profile of the large yellow croaker (Pseudosciaena crocea)
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.
GO function annotation results of 8216 unigenes
intracellular organelle part
structural constituent of ribosome
nucleoside-triphosphatase regulator activity
translation factor activity, nucleic acid binding
RNA polymerase II transcription factor activity
metal cluster binding
small protein activating enzyme activity
primary metabolic process
cellular metabolic process
macromolecule metabolic process
nitrogen compound metabolic process
establishment of localization
establishment of protein localization
establishment of localization in cell
regulation of biological quality
cellular component assembly
response to chemical stimulus
macromolecular complex subunit organization
alcohol metabolic process
protein complex biogenesis
cellular macromolecular complex subunit organization
immune system development
ribonucleoprotein complex biogenesis
organophosphate metabolic process
positive regulation of metabolic process
establishment of RNA localization
pigment metabolic process
regulation of cellular component biogenesis
Statistically significant KEGG classifications of large yellow croaker genes
Pathways in cancer
Ubiquitin mediated proteolysis
MAPK signaling pathway
Regulation of actin cytoskeleton
Insulin signaling pathway
Neurotrophin signaling pathway
Chemokine signaling pathway
T cell receptor signaling pathway
Leukocyte transendothelial migration
B cell receptor signaling pathway
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.
Representative genes significantly differentially expressed after A. hydrophila infection
Immunity related genes
Toll-like receptor 1
Toll-like receptor 2
Toll-like receptor 3
Toll-like receptor 22
Interleukin 2 receptor, gamma b
Interleukin 4 receptor
Interleukin 6 receptor
CC chemokine ligand 4
CC chemokine receptor type 1
CC chemokine receptor family-like
cytokine receptor-like factor 3
TNF, alpha-induced protein 8-like protein 1
TNF superfamily, member 10 like 2
C-type lectin receptor
Immunoglobulin binding protein 1
High affinity immunoglobulin gammaFc receptor I precursor
RAD23 homolog B
caspase9 apoptosis-related cysteine protease
Heat shock protein HSP 90-alpha 1
Growth arrest and DNA-damage-inducible, beta
Glutathione peroxidase 1b
Glutathione peroxidase 4b
Non-homologous end-joining factor 1
Ubiquitin-conjugating enzyme E2N-like
Transcription regulator activity
NF-kB 2 inhibitor, epsilon
Janus kinase 1
Jun D proto-oncogene
X-box binding protein 1
Cellular tumor antigen p53
CCAAT/enhancer binding protein alpha
PDZ and LIM domain 1
Aryl hydrocarbon receptor 2B
interferon regulatory factor
interferon regulatory factor 4
interferon regulatory factor 9
Myc-associated factor X
Retinoic acid receptor gamma
LIM domain-binding protein 4
Chromosome segregation 1-like
protein phosphatase 1, regulatory subunit 10
protein phosphatase 1, catalytic subunit alpha
General transcription factor IIH, polypeptide 2
General transcription factor IIH, polypeptide 3
General transcription factor IIF, polypeptide 2
General transcription factor IIE, polypeptide 2, beta
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 . 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
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 . 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.
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 . 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 . 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 . 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 . 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 . 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 .
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 , cell differentiation, and cell death . Modulation of MAPK activity in the common periwinkle in response to Escherichia coli-derived LPS has been studied . 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 . 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 . 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 . 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 , 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.
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 . 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.
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 . 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 . 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.
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 .
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 . GenMAPP 2.0 was used to show differences in expression in the different pathways .
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 . Each sample was first normalized for the amount of template added by comparison with the abundance of β-actin mRNA .
The work was supported by grants from the Nation '863' Project (2006AA10A402 and 2007AA091406) and National Natural Science Foundation of China (30871925 and 31001131).
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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 ServerGoogle Scholar
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Orme I: Adaptive immunity to mycobacteria. Curr Opin Microbiol. 2004, 7 (1): 58-61. 10.1016/j.mib.2003.11.002.PubMedView ArticleGoogle Scholar
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
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.