De Novo Transcriptome Analysis of Differential Functional Gene Expression in Largemouth Bass (Micropterus salmoides) after Challenge with Nocardia seriolae
Department of Veterinary Medicine, College of Veterinary Medicine, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2016, 17(8), 1315; https://doi.org/10.3390/ijms17081315
Submission received: 25 May 2016
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Revised: 2 August 2016
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Accepted: 2 August 2016
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Published: 11 August 2016
(This article belongs to the Special Issue Host-Microbe Interaction)
Abstract
:Largemouth bass (Micropterus salmoides) are common hosts of an epizootic bacterial infection by Nocardia seriolae. We conducted transcriptome profiling of M. salmoides to understand the host immune response to N. seriolae infection, using the Illumina sequencing platform. De novo assembly of paired-end reads yielded 47,881 unigenes, the total length, average length, N50, and GC content of which were 49,734,288, 1038, 1983 bp, and 45.94%, respectively. Annotation was performed by comparison against non-redundant protein sequence (NR), non-redundant nucleotide (NT), Swiss-Prot, Clusters of Orthologous Groups (COG), Kyoto Encyclopaedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Interpro databases, yielding 28,964 (NR: 60.49%), 36,686 (NT: 76.62%), 24,830 (Swissprot: 51.86%), 8913 (COG: 18.61%), 20,329 (KEGG: 42.46%), 835 (GO: 1.74%), and 22,194 (Interpro: 46.35%) unigenes. Additionally, 8913 unigenes were classified into 25 Clusters of Orthologous Groups (KOGs) categories, and 20,329 unigenes were assigned to 244 specific signalling pathways. RNA-Seq by Expectation Maximization (RSEM) and PossionDis were used to determine significantly differentially expressed genes (False Discovery Rate (FDR) < 0.05) and we found that 1384 were upregulated genes and 1542 were downregulated genes, and further confirmed their regulations using reverse transcription quantitative PCR (RT-qPCR). Altogether, these results provide information on immune mechanisms induced during bacterial infection in largemouth bass, which may facilitate the prevention of nocardiosis.
1. Introduction
During intensive aquaculture, fish are always exposed to stressors which may facilitate host infection by opportunistic pathogens existing in the water [1]. Indeed, detecting the invading pathogens depends on the host’s ability to recognize the pathogens [2,3]. Therefore, for rapid elimination of pathogens, fish rely on innate or nonspecific immune responses [1]. Against this background, transcriptome profiling analysis during infection in the host can facilitate genome studies and functional gene identification. However, in fish the broad identification of immune-related genes at the genome or transcriptome levels are limited to a few species [4,5]. Since the genome sequence for many non-model fish species is unknown, the study on immune genes is difficult. Moreover, the introduction of RNA deep sequencing technologies (i.e., Solexa/Illumina RNA-Seq and digital gene expression) have contributed much to the identification of important immune-related genes in fish [6,7,8,9].
In this study, we concentrated on Nocardia seriolae, a Gram-positive, acid-fast bacterium with branched hyphae which causes nocardiosis in cultured marine and freshwater fish in Taiwan, Japan, and China [10,11,12,13,14,15,16]. Nocardia seriolae infections frequently result in considerable economic loss to fish farmers in Taiwan. Recently, after infection with pathogenic microorganisms Aeromonas hydrophila in zebrafish (Danio rerio) [17] and Vibrio anguillarum infection in sole (Cynoglossus semilaevis) [18], the transcriptome profile has been reported. Additional examples of transcriptome profiling analyses include the orange-spotted grouper (Epinephelus coioides) [19], blunt snout bream (Megalobrama amblycephala) [20], Chilean abalone Concholepas (Gastropoda, Muricidae) [21], grass carp (Ctenopharyngodon idella) [22], blowfish or fugu (Takifugu rubripes) [23], large yellow croaker (Larimichthys crocea) [24], and Nile tilapia (Oreochromis niloticus) [25,26]. Several studies have also reported transcriptome profiles for L. crocea in response to immune stimuli, pathogenic infection, or environmental stress [27,28,29]. However, to our knowledge there are no studies related to fish transcriptomes for identification of gene expression profiles in response to Nocardia seriolae infection. In this study, we assembled the transcriptome of largemouth bass (M. salmoides) spleen and compared the gene expression profiles among Nocardia seriolae-infected and control groups to exhibit the molecular fitness mechanisms against bacterial infection and frame a possible strategy to prevent the outbreak of nocardiosis.
2. Results
2.1. Transcriptome Sequence Assembly
Of 47,881 unigenes, 37,712 (78.76%) were annotated using at least one database, including 36,686 (97.27%) in NT, 28,964 (76.80%) in NR, 24,830 (65.81%) in Swiss-Prot, 8913 (23.63%) in KOG, 20,329 (53.90%) in KEGG, 22,194 in Interpro (58.85%), and 835 (2.21%) in GO (Tables S1 and S2).
2.2. Functional Classification
Overall, 8913 (23.63%) annotated putative proteins from COG were grouped into 25 different categories (Figure 1). After filtering the poorly characterised proteins (“general function prediction only” and “function unknown”) based on the number of unigenes, the top three functional clusters were determined to be “replication recombination and modification” (1491, 16.72%), which is followed by “transcription” (1354, 15.19%) and “translation, ribosomal structure, and biogenesis” (1263, 14.17%) (Figure 1).
Furthermore, 37,712 (78.76%) unigenes were assigned to 835 GO terms based on sequence homology and a total of 52 functional groups were clustered into biological process, cellular component, and molecular function (Figure 2). The unigene sequences from molecular function were clustered into 13 different classifications. Further, the largest subcategory within molecular function was “binding”, followed by “catalytic activity” In the biological process, sequences were distributed into 24 classifications. The most represented subcategories were “cellular processes” and “metabolic processes”. “Cell part” and “cell” were the most represented among 13 subcategories within the cellular component category.
Overall, 20,329 (53.90%) sequences had significant matches were allocated to 244 KEGG pathways. Moreover, the highest number of genes categorised from KEGG analysis related to human disease accounted for 9567 (28.10%) genes, with sub-groups from bacterial infectious diseases (1850 genes), viral infectious diseases (1862 genes), and cancer-related genes (1349 genes). Further, 6919 (20.32%) genes were related to organismal systems where the majority of the genes were categorised as immune system-related (2076 genes), followed by endocrine system-related (988 genes), nervous system-related (977 genes), digestive system-related (894 genes), and development-related (778 genes) (Figure 3). Subsequently, metabolism, cellular processes, environmental information processing, and genetic information processing accounted for 5959 (17.50%), 4823 (14.17%), 3514 (10.32%), and 3256 (9.56%) genes, respectively.
2.3. Differentially Expressed Genes after Nocardia seriolae Challenge
A total of 1384 transcript-derived unigenes were upregulated, whereas 1542 genes were downregulated in phosphate buffered saline (PBS) control and bacterial infection groups, respectively (Figure S1). The top 20 enriched pathways are shown in Figure 4, with genes involved in immune-related “Cell adhesion molecule”, “Cytokine receptor interaction”, “Hematopoietic cell lineage”, and “Phagosome” categories being the most significantly enriched. Natural killer cell-mediated cytotoxicity, hematopoietic cell lineage, toll-like receptor signalling, Fc γ R-mediated phagocytosis, antigen processing and presentation, NOD-like receptor signalling, and chemokine signalling (Table S3) were differentially expressed3among immune-related categories. These results suggest an important role for these unigenes during N. seriolae infection in largemouth bass.
The differential expression in immune-related genes were identified from 13 pathways (Table S3) and were mapped to the KEGG database and observed their association among cytokines and their receptors (e.g., IL6, IL8, IL8R, IL4R, IL13RA1, IL12RB2, CXCL12, CXCR4, CCR5), toll-like receptor signalling (TLR) pathways (Figure S2) (e.g., LBP, CASP8, IKK γ, IKK α, IKK β, TRAF6, RIP1, CTSK, TLR3, IFN-αβR, IKKε, STAT1, IRF3, IRF7, p38, TNF α, IL1β, IL12, IL8, RANTES, CD40, CD86, IP10), and T cell receptor signalling (e.g., TCR, CD3, CD4/8, CD28). N. seriolae infection also influenced genes significantly related to transcriptional regulation, including NF-κB signalling (Figure S3) (NEMO, TRIM25, IKBKG, and RIP1), and JAK-STAT signalling (Figure S4) (STAM, STAT1, SOCS, and SHP2). Unigenes representative of genes differentially expressed during bacterial infection are listed in Table 1.
2.4. Differentially Expressed Gene Validation Using Real-Time PCR
We identified immune-related gene sequences that were upregulated from DEG in largemouth bass (Table S4), and evaluated their homology with those from other fish species using the NCBI database. These sequences will be used for our future studies in immune response of largemouth bass to Nocardia seriolae. The expression levels of seven differentially expressed genes related to pathways including TLR, RIG I-like receptors, cytokine-cytokine receptor interaction, natural killer cell mediated cytotoxicity, and antigen processing and presentation (T-cell receptor (TCR)) were evaluated from spleen tissue. The expression levels were largely consistent with the transcriptome profile analyses suggesting that the transcriptome data were reliable (Figure 5).
3. Discussion
In the present study, Illumina sequencing of control and infection treatment groups yielded 47,881 merged unigenes from spleen tissue of largemouth bass (M. salmoides). This study selected the spleen of largemouth bass 24 h after challenge as experimental samples. After challenge with N. seriolae we observed upregulations of many immune-related genes in the largemouth bass. Noticeably, immune-related pro-inflammatory cytokines and signal transduction related genes, including IL-1β, TNF receptor, CXC chemokine, TGF-β, and NF-κB, were the most significantly upregulated transcripts.
After assembly, 47,881 unigenes were generated with an average length of 1038 bp and an N50 of 1983 bp, longer than the sequences achieved in previous studies using a Roche GS FLX 454 system (Basel, Switzerland) with a MIRA assembler [30] or an Illumina/Hiseq-2000 with assembling program SOAP [31]. This difference in sequence quality may be explained by differences in the sampling tissue and de novo assemblers. Since largemouth bass has an absence of a reference genome in the database, the Trinity program used in this study showed better performance compared to other tools in transcriptome assembly [32,33]. In contrast to Trinity, SOAP or MIRA assemblies adopted in previous studies [30,31] have been shown to be more fragmented with high levels of errors in sequencing and polymorphism [33,34]. In this study, the largemouth bass transcriptome yielded 47,881 merged unigenes from the Illumina/Hiseq-2000 RNA-Seq platform compared to 29,682 unigenes from the Roche 454 system and 2139 unigenes from a SMART cDNA library [35].
It is noteworthy that only 37,712 unigenes were annotated from the databases in this study based on sequence similarity; this annotation limitation also exists in other marine organism transcriptomes [36]. This could be explained due to the absence of a genomic database and genomic studies on commercially important aquaculture species [32,37,38,39]. The GO, COG, and KEGG databases used in this study for functional annotation provide valuable information about biological features of largemouth bass challenged by N. seriolae. For example, in the KEGG analysis of 20,329 sequences assigned to 244 KEGG pathways, genetic information processing accounted for 9567 pathways related to pathogen infection (Figure 3). Together, these findings indicate that primary host immune pathways are conserved in largemouth bass which are activated to protect against pathogen infections.
Cytokines are proteins which transfer information among cells to initiate complex intracellular biological processes upon binding to corresponding cell-surface receptors. Moreover, cytokine levels initiate an inflammatory response to bacterial exposure which guides towards leukocyte attraction and activation of antimicrobial pathways [40,41]. Against this background, tumour necrosis factor alpha (TNF-α), which is a first cytokine released during infection activates the downstream expression of other cytokines such as IL-1β and chemokines [42,43]. In the present study, after N. seriolae infection it was observed that different cytokines and cytokine receptor families are upregulated in cytokine–cytokine receptor interaction signalling pathways (Table 1), including chemokine receptors (CXCL10, CXCR3, XCR1, CCL 20, 25, 19, 21, 5, CCR3), hematopoietin receptors (IL11RA IL6R), TNF receptors (SF11B, TNFSF12, SF14, and SF6B), TGF-β receptors (TGFBR2), and IL-1 receptors (IL-1β, IL-18, and IL-1R1). These data indicate that, in the case of largemouth bass in early stages of N. seriolae infection, cytokine–cytokine receptor interaction may represent an important anti-bacterial mechanism.
In the host, pattern-recognition receptors (PRRs) recognise pathogen-associated molecular patterns (PAMPs) to defend against pathogen invasion and activate immune responses through signalling pathways, such as TLRs, RIG-I-like receptors (RLRs), NOD-like receptors (NLRs) [44], and C-type lectin receptors (CLRs) [45,46]. In this study, a total of 29 gene transcripts, which are involved in the TLR signalling pathway, are found to be upregulated, including the fish-specific TLRs (TLR22), and downstream effector molecules, such as LBP, CASP8, IKK α, IKK β, TRAF6, TAK, TBK, IKK, and RANTES. Additionally, we observed downstream effector molecules of cytokines and transcription factors including p38, IRF3, IRF7, STAT1, IL-12, IL-8, CD40, CD86, and IP10. These suggest that TLR mechanisms are conserved from fish to mammals. We observed upregulations in the expression of pro-inflammatory cytokines in our study after N. seriolae infection including IL-1β, IL-8, and TNF-α (Figures S2 and S3). Our results on TNF-α and IL-1β were in agreement with the study on Japanese flounder (Paralichthys olivaceus) in spleen after immersion challenge with N. seriolae, wherein TNF-α and IL-β were upregulated at 24 h post challenge, while CC chemokine downregulated [47]. Moreover, in the case of human monocytes, cytokines induced within 24 h following Gram-positive and Gram-negative bacterial infections [48].
The Janus kinase/signal transducers and activators of transcription (JAK-STAT) pathway initiated due to interleukins, IFNs, and growth factors present in the surrounding microenvironment [49]. Different cytokine receptors are associated with JAK for proliferation, survival, and differentiation in lymphoid cell precursor [50,51], while STAT1 activated upon IFN-γ signalling, resulting in enhanced bacteria killing and protection [48]. In this study, the members of the JAK-STAT, including STAM and Stat1, were upregulated (Figure S4). This can suggest that, the JAK-STAT pathway activated upon N. seriolae infection in largemouth bass, which can further induce other pathways, namely NF-κB signalling, the TGF-β activated SMAD pathway, and apoptosis [52].
4. Materials and Methods
4.1. Animal Maintenance
Healthy largemouth bass (Micropterus salmoides) without pathogen infection weighing 125 ± 10 g were used in this study. The fish were kept in an indoor facility at a constant temperature of 26 °C and fed daily with commercial feed. The experiment was performed two weeks after acclimatisation. Fish were anaesthetised for handling with 2-phenoxyethanol. Approval for the following animal studies was obtained from the Centre for Research Animal Care and Use Committee of the National Pingtung University of Science and Technology under protocol number 101-027, dated 19 March 2012.
4.2. Isolation, Cultivation, and Challenge with Nocardia seriolae
The bacterium N. seriolae was isolated from striped bass and found to be highly virulent in farmed fish [53]. The species was identified by API ZYM and 16S rDNA sequencing, grown in Brain Heart Infusion (BHI) broth for five days at 25 °C, and enumerated prior to the challenge test. Fifteen fish were anaesthetised and injected intraperitoneally with 1.0 × 106 cfu N. seriolae that were suspended in 100 μL phosphate-buffered saline (PBS, pH 7.2). The remaining 15 fish per group received only PBS (pH 7.2) as a control. After the fish were returned to the observation tanks, samples were taken at 24 h post infection (hpi). Three fish each from the challenge (treatment) and control groups (n = 3) were examined. Spleen tissue was dissected and total RNA was isolated.
4.3. Total RNA Extraction, Preparation of cDNA Library, and Sequencing
Total RNA was extracted using TRIzol® reagent (Invitrogen Corp., Carlsbad, CA, USA). RNA integrity was assessed using Agilent Bioanalyzer 2100 system (Agilent Technologies, Palo Alto, CA, USA). A TruSeq™ RNA Sample Preparation Kit (Illumina, Inc., San Diego, CA, USA) was used for cDNA library construction. Further, 40 μg total RNA was used for mRNA isolation using poly-T oligo-attached magnetic beads. First-strand cDNA was synthesized using random hexamer primers and Superscript III (Invitrogen, Carlsbad, CA, USA); this was followed by second-strand cDNA synthesis, end repair, and adaptor ligation. The RNA-Seq library was sequenced on the Illumina HiSeq™ 2000 (Illumina, Inc., San Diego, CA, USA) platform as paired-end reads to 100 bp at Genomics Bioscience Technology Co., Ltd. (Taipei, Taiwan). The transcriptome raw sequencing datasets are available from Sequence Read Archive (SRA) database in NCBI and the accession numbers are SRX1739692 and SRX1738842. All of the information on the assembled unigene sequences and annotations are available from the corresponding authors upon request.
4.4. Filtering of Sequencing Reads
Raw reads were defined as adaptor-polluted reads containing low-quality or unknown base (N) reads; these reads were removed before downstream analyses. Internal software was used to filter reads, removing (1) reads with adaptors; (2) reads in which unknown bases comprised greater than 5% of the read; and (3) low quality reads (defined as the percentage of bases for which quality is less than 10 and greater than 20% in a read). After filtering, the remaining reads were called “Clean Reads” and stored in FASTQ [54] format.
4.5. De Novo Transcriptome Assembly
Trinity [55] was used to perform de novo assembly with clean reads. Next, TIGR Gene Indices clustering tools, or Tgicl, was used to cluster transcripts to unigenes. In the case of two or more samples, Tgicl would be re-executed with each sample’s unigene to obtain the final unigene for downstream analysis. Unigenes were divided into two classes: clusters (CL), comprised of several unigenes with shared similarity greater than 70%, and singletons (Unigenes).
4.6. Functional Unigene Annotation and Classification
For gene annotation, following database were used; NCBI non-redundant protein database [56], gene ontology (GO) [57], Clusters of Orthologous Groups [58], and the Kyoto Encyclopaedia of Genes and Genomes [59] with E-values less than 10−5 using BlastP (Version 2.2.25) [60]. With functional annotation, we selected the region of the unigene that best mapped to functional databases in a priority order of NR, SwissProt, KEGG, and COG as its coding sequence (CDS), and displayed this sequence region from 5’ to 3’ in FASTA format. Unigenes that could not be aligned to any database mentioned above were predicted by ESTScan [61] using Blast-predicted CDS as the model.
4.7. Differentially Expressed Genes
Expression data from two libraries (treatment and control) were determined by mapping to the transcriptome assembly using Bowtie2 software [62,63]. The fragments per kilobase of transcripts per million fragments mapped (FPKM) values were analysed further using RESM [64] to get differentially expressed genes (DEGs) in the spleen between the control and infected groups. Further, to determine the threshold p-value in multiple tests, a false discovery rate (FDR) was used. Furthermore, significant enrichment was calculated when FDR was <0.05 and FPKM values showed at least a two-fold difference between the two samples reads.
4.8. Real-Time Polymerase Chain Reaction
PCR primers were designed based on transcriptome sequences using Primer 2 Plus software (Table 2). cDNA was synthesised from 2 µg of total RNA using 200 U of M-MLV reverse transcriptase (Promega). β-Actin served as internal control and RT-qPCR was performed using iQSYBR Green Supermix (Bio-Rad Laboratories, Hercules, CA, USA), and each sample was run in triplicate. The thermal gradient feature (CFX96, Bio-Rad Laboratories) was used to determine the optimal annealing temperature for all primers. The real-time PCR program used was 95 °C for 3 min, followed by 40 cycles of 95 °C for 15 s, 58 °C for 15 s, and 72 °C for 35 s. Dissociation and melting curves of amplification products were performed and results were analysed using the CFX Manager Software package (Bio-Rad Laboratories). The 2−ΔΔCt method was chosen as the calculation method [65]. The difference in the cycle threshold (Ct) value of the target gene and its housekeeping gene (β-actin), called ΔCt, was calculated using the following equation: ΔΔCt = (ΔCt of bacterial challenge or PBS-injected group for the target gene at each time point) − (ΔCt of the initial control).
4.9. Statistical Analyses
Statistical analyses were performed using SPSS 16.0 software. All data are given as mean ± SD. Significant differences between samples were analysed by one-way analysis of variance (ANOVA), and Duncan’s tests at a significance level of 0.05.
5. Conclusions
This study provides necessary information on differential immune gene transcriptome profiling in largemouth bass (M. salmoides) infected with N. seriolae. Moreover, this transcriptome assembly could be used as a reference for studies related to comparative biology within the genus or family. Of course, we acknowledge that this transcriptome-level response to N. seriolae infections is a preliminary study and larger scale studies are required to further understand the defence mechanisms in largemouth bass.
Supplementary Materials
Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1315/s1.
Acknowledgments
This work was financially supported by National Science Council under grant no. MOST 104-2313-B-020-009-MY3 and NSC 101-2313-B-020-015-MY3 from the Ministry of Science and Technology, Taiwan. The authors thank Genomics Bioscience Technology Co. Ltd. (Taipei, Taiwan) for assistance with transcriptome sequencing. We are grateful to all those who contributed to the development of this research and provided input during the study.
Author Contributions
Omkar Byadgi designed the experiment, conducted, analysed the results and drafted the manuscript, Chi-Wen Chen helped during the experimental design and conduct. Ming-An Tsai reviewed the experimental design and suggested during manuscript draft. Shih-Chu Chen and Pei-Chyi Wang monitored throughout the experimental process and provided substantial contribution towards data analysis and manuscript revision. All authors have read and approved the final version of the manuscript.
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
| DGE | digital gene expression |
| Tgicl | is a pipeline for analysis of large Expressed Sequence Tags (EST) |
| NR | NCBI non-redundant protein |
| NT | NCBI nucleotide |
| COG | Clusters of Orthologous Groups |
| KEGG | Kyoto Encyclopedia of Genes and Genomes |
| ORF | open reading frame |
| KO | KEGG Orthology |
| CDS | coding region of protein |
| INDEL | insertion and deletion |
| HISAT | is a fast and sensitive spliced alignment program for mapping RNA-Seq reads |
| GATK | The Genome Analysis Toolkit |
| FPKM | fragments per kilobase of transcripts per million fragments mapped |
| RESM | is a software package for estimating gene and isoform expression levels from RNA-Seq data |
| FDR | False discovery rate |
| NLR | NOD-like receptors |
| TLR | Toll-like receptors |
| PAMP | Pathogen-associated molecular patterns |
| PRR | Pattern-recognition receptors |
| CLR | C-type lectin receptors |
| SRA | NCBI Sequence Read Archive |
| SSR | Simple Sequence Repeat |
| SNP | single nucleotide polymorphism |
References
- Camp, K.L.; Wolters, W.R.; Rice, C.D. Survivability and immune responses after challenge with Edwardsiella ictaluri in susceptible and resistant families of channel catfish, Ictalurus punctatus. Fish Shellfish Immunol. 2000, 10, 475–487. [Google Scholar] [CrossRef] [PubMed]
- Ellis, A.E. Innate host defense mechanisms of fish against viruses and bacteria. Dev. Comp. Immunol. 2001, 25, 827–839. [Google Scholar] [CrossRef]
- Gomez, G.D.; Balcazar, J.L. A review on the interactions between gut microbiota and innate immunity of fish. FEMS Immunol. Med. Microbiol. 2008, 52, 145–154. [Google Scholar] [CrossRef] [PubMed]
- Hegedus, Z.; Zakrzewska, A.; Agoston, V.C.; Ordas, A.; Racz, P.; Mink, M.; Spaink, H.P.; Meijer, A.H. Deep sequencing of the zebrafish transcriptome response to mycobacterium infection. Mol. Immunol. 2009, 46, 2918–2930. [Google Scholar] [CrossRef] [PubMed]
- Stockhammer, O.W.; Zakrzewska, A.; Hegedus, Z.; Spaink, H.P.; Meijer, A.H. Transcriptome profiling and functional analyses of the zebrafish embryonic innate immune response to Salmonella infection. J. Immunol. 2009, 182, 5641–5653. [Google Scholar] [CrossRef] [PubMed]
- Wang, Z.; Gerstein, M.; Snyder, M. RNA-Seq: A revolutionary tool for transcriptomics. Nat. Rev. Genet. 2009, 10, 57–63. [Google Scholar] [CrossRef] [PubMed]
- Anisimov, S.V. Serial Analysis of Gene Expression (SAGE): 13 years of application in research. Curr. Pharm. Biotechnol. 2008, 9, 338–350. [Google Scholar] [CrossRef] [PubMed]
- Dumrongphol, Y.; Hirota, T.; Kondo, H.; Aoki, T.; Hirono, I. Identification of novel genes in Japanese flounder (Paralichthys olivaceus) head kidney up-regulated after vaccination with Streptococcus iniae formalin-killed cells. Fish Shellfish Immunol. 2009, 26, 197–200. [Google Scholar] [CrossRef] [PubMed]
- Aoki, T.; Hirono, I.; Kondo, H.; Hikima, J.I.; Jung, T.S. Microarray technology is an effective tool for identifying genes related to the aquacultural improvement of Japanese flounder, Paralichthys olivaceus. Comp. Biochem. Phys. 2011, 6, 39–43. [Google Scholar] [CrossRef] [PubMed]
- Kariya, T.; Kubota, S.; Nakamura, Y.; Kira, K. Nocardial infection in cultured yellowtails (Seriola quinqueradiata and S. purpurascens) I. Bacteriological study. Fish. Pathol. 1968, 3, 16–23. (In Japanese) [Google Scholar] [CrossRef]
- Chen, S.C.; Lee, J.L.; Lai, C.C.; Gu, Y.W.; Wang, C.T.; Chang, H.Y.; Tsai, K.H. Nocardiosis in sea bass, Lateolabrax japonicus, in Taiwan. J. Fish. Dis. 2000, 23, 299–307. [Google Scholar] [CrossRef]
- Wang, G.L.; Yuan, S.P.; Jin, S. Nocardiosis in large yellow croaker, Larimichthys crocea (Richardson). J. Fish. Dis. 2005, 28, 339–345. [Google Scholar] [CrossRef] [PubMed]
- Kudo, T.; Hatai, K.; Seino, A. Nocardia seriolae sp. nov. Causing nocardiosis of cultured fish. Int. J. Syst. Bacteriol. 1988, 38, 173–178. [Google Scholar] [CrossRef]
- Huang, S.L.; Lai, K.C.; Su, S.C.; Shei, M.C.; Chen, S.N. Isolation and characterization of the pathogenic bacterium, Nocardia seriolae, from female broodstock of striped mullet (Mugil cephalus). J. Fish. Res. 2004, 12, 61–69. [Google Scholar]
- Wang, P.C.; Chen, S.D.; Tsai, M.A.; Weng, Y.J.; Chu, S.Y.; Chern, R.S.; Chen, S.C. Nocardia seriolae infection in the three striped tigerfish, Terapon jarbua (Forsskal). J. Fish. Dis. 2009, 32, 301–310. [Google Scholar] [CrossRef] [PubMed]
- Wang, G.L.; Xu, Y.J.; Jin, S.; Zhu, J.L.; Yuan, S.P. Nocardiosis in snakehead, Ophiocephalus argus Cantor. Aquaculture 2007, 271, 54–60. [Google Scholar] [CrossRef]
- Lü, A.J.; Hu, X.C.; Wang, Y.; Zhu, A.H.; Shen, L.L.; Tian, J.; Feng, Z.Z.; Feng, Z.J. Skin immune response in the zebrafish, Danio rerio (Hamilton), to Aeromonas hydrophila infection: A transcriptional profiling approach. J. Fish. Dis. 2015, 38, 137–150. [Google Scholar] [CrossRef] [PubMed]
- Zhang, X.; Wang, S.; Chen, S.; Chen, Y.; Liu, Y.; Shao, C.; Wang, Q.; Lu, Y.; Gong, G.; Ding, S.; et al. Transcriptome analysis revealed changes of multiple genes involved in immunity in Cynoglossus semilaevis during Vibrio anguillarum infection. Fish. Shellfish Immunol. 2015, 43, 209–218. [Google Scholar] [CrossRef] [PubMed]
- Huang, Y.; Huang, X.; Yan, Y.; Cai, J.; Ouyang, Z.; Cui, H.; Wang, P.; Qin, Q. Transcriptome analysis of orange-spotted grouper (Epinephelus coioides) spleen in response to Singapore grouper iridiovirus. BMC Genom. 2011, 12, 556. [Google Scholar] [CrossRef] [PubMed]
- Tran, N.T.; Gao, Z.X.; Zhao, H.H.; Yi, S.K.; Chen, B.X.; Zhao, Y.H.; Lin, L.; Liu, X.Q.; Wang, W.M. Transcriptome analysis and microsatellite discovery in the blunt snout bream (Megalobrama amblycephala) after challenge with Aeromonas hydrophila. Fish Shellfish Immunol. 2015, 45, 72–82. [Google Scholar] [CrossRef] [PubMed]
- Cárdenas, L.; Sánchez, R.; Gomez, D.; Fuenzalida, G.; Gallardo-Escárate, C.; Tanguy, A. Transcriptome analysis in Concholepas concholepas (Gastropoda, Muricidae): Mining and characterization of new genomic and molecular markers. Mar. Genom. 2011, 4, 197–205. [Google Scholar] [CrossRef] [PubMed]
- Chen, J.; Li, C.; Huang, R.; Du, F.; Liao, L.; Zhu, Z.; Wang, Y. Transcriptome analysis of head kidney in grass carp and discovery of immune-related genes. BMC Vet. Res. 2012, 8, 108. [Google Scholar] [CrossRef] [PubMed]
- Cui, J.; Wang, H.; Liu, S.; Qiu, X.; Jiang, Z.; Wang, X. Transcriptome analysis of the gill of Takifugu rubripes using Illumina sequencing for discovery of SNPs. Comp. Biochem. Phys. Part D: Genom. Proteom. 2014, 10, 44–51. [Google Scholar] [CrossRef] [PubMed]
- Wang, P.; Wang, J.; Su, Y.Q.; Mao, Y.; Zhang, J.S.; Wu, C.W.; Ke, Q.Z.; Han, K.H.; Zheng, W.Q.; Xu, N.D. Transcriptome analysis of the Larimichthys crocea liver in response to Cryptocaryon Irritans. Fish Shellfish Immunol. 2016, 48, 1–11. [Google Scholar] [CrossRef] [PubMed]
- Zhang, R.; Zhang, L.; Ye, X.; Tian, Y.Y.; Sun, C.F.; Lu, M.X.; Bai, J.J. Transcriptome profiling and digital gene expression analysis of Nile tilapia (Oreochromis niloticus) infected by Streptococcus agalactiae. Mol. Biol. Rep. 2013, 40, 5657–5668. [Google Scholar] [CrossRef] [PubMed]
- Zhu, J.; Li, C.; Ao, Q.; Tan, Y.; Luo, Y.; Guo, Y.; Lan, G.; Jiang, H.; Gan, X. Transcriptomic profiling revealed the signatures of acute immune response in tilapia (Oreochromis niloticus) following Streptococcus iniae challenge. Fish Shellfish Immunol. 2015, 46, 346–353. [Google Scholar] [CrossRef] [PubMed]
- Mu, Y.; Ding, F.; Cui, P.; Ao, J.; Hu, S.; Chen, X. Transcriptome and expression profiling analysis revealed changes of multiple signaling pathways involved in immunity in the large yellow croaker during Aeromonas hydrophila infection. BMC Genom. 2010, 11, 506. [Google Scholar] [CrossRef] [PubMed]
- Yu, S.; Mu, Y.; Ao, J.; Chen, X. Peroxiredoxin IV regulates pro-inflammatory responses in large yellow croaker (Pseudosciaena. crocea) and protects against bacterial challenge. J. Proteome Res. 2010, 9, 1424–1436. [Google Scholar] [CrossRef] [PubMed]
- Ao, J.; Mu, Y.; Xiang, L.X.; Fan, D.D.; Feng, M.; Zhang, S.; Shi, Q.; Zhu, L.Y.; Li, T.; Ding, Y.; et al. Genome Sequencing of the Perciform Fish Larimichthys crocea Provides Insights into Molecular and Genetic Mechanisms of Stress Adaptation. PLoS Genet. 2015, 11, e1005118. [Google Scholar] [CrossRef] [PubMed]
- Kinoshita, S.; Wang, N.; Inoue, H.; Maeyama, K.; Okamoto, K.; Nagai, K.; Kondo, H.; Hirono, I.; Asakawa, S.; Watabe, S. Deep sequencing of ESTs from nacreous and prismatic layer producing tissues and a screen for novel shell formation-related genes in the pearl oyster. PLoS ONE 2011, 6, e21238. [Google Scholar] [CrossRef] [PubMed]
- Zhao, X.; Wang, Q.; Jiao, Y.; Huang, R.; Deng, Y.; Wanh, H.; Du, X. Identification of genes potentially related to biomineralization and immunity by transcriptome analysis of pearl sac in pearl oyster Pinctada martensii. Mar. Biotechnol. 2012, 14, 730–739. [Google Scholar] [CrossRef] [PubMed]
- Zhao, Q.Y.; Wang, Y.; Kong, Y.M.; Luo, D.; Li, X.; Hao, P. Optimizing de novo transcriptome assembly from short-read RNA-Seq data: A comparative study. BMC Bioinform. 2011, 12, S2. [Google Scholar] [CrossRef] [PubMed]
- Vijay, N.; Poelstra, J.W.; Künstner, A.; Wolf, J.B.W. Challenges and strategies in transcriptome assembly and differential gene expression quantification. A comprehensive in-silico assessment of RNA-Seq experiments. Mol. Ecol. 2013, 22, 620–634. [Google Scholar] [CrossRef] [PubMed]
- Zhang, L.; Li, L.; Zhu, Y.; Zhang, G.; Guo, X. Transcriptome analysis reveals a rich gene set related to innate immunity in the eastern oyster (Crassostrea virginica). Mar. Biotechnol. 2014, 16, 17–33. [Google Scholar] [CrossRef] [PubMed]
- Wang, A.; Wang, Y.; Gu, Z.; Li, S.; Shi, Y.; Guo, X. Development of expressed sequence tags from the pearl oyster, Pinctada martensii dunker. Mar. Biotechnol. 2011, 13, 275–283. [Google Scholar] [CrossRef] [PubMed]
- Meyer, E.; Aglyamova, G.V.; Wang, S.; Buchanan-carter, J.; Abrego, D.; Colbourne, J.K.; Willis, B.L.; Matz, M.V. Sequencing and de novo analysis of a coral larval transcriptome using 454 GSFlx. BMC Genom. 2009, 10, 219. [Google Scholar] [CrossRef] [PubMed]
- Chu, S.L.; Weng, C.F.; Hsiao, C.D.; Hwang, P.P.; Chen, Y.C.; Ho, J.M.; Lee, S.J. Profile analysis of expressed sequence tags derived from the ovary of tilapia, Oreochromis mossambicus. Aquaculture 2006, 251, 537–548. [Google Scholar] [CrossRef]
- Dong, B.; Xiang, J.H. Discovery of genes involved in defense/immunity functions in a haemocytes cDNA library from Fenneropenaeus chinensis by ESTs annotation. Aquaculture 2007, 272, 208–215. [Google Scholar] [CrossRef]
- Novaes, E.; Drost, D.R.; Farmerie, W.G.; Pappas, G.J.; Grattapaglia, D.; Sederoff, R.R.; Kirst, M. High-throughput gene and SNP discovery in Eucalyptus grandis, an uncharacterized genome. BMC Genom. 2008, 9, 312. [Google Scholar] [CrossRef] [PubMed]
- Floege, J.; Luscher, B.; Muller-Newen, G. Cytokines and inflammation. Eur. J. Cell Biol. 2012, 91, 427. [Google Scholar] [CrossRef] [PubMed]
- Banyer, J.L.; Hamilton, N.H.R.; Ramshaw, I.A.; Ramsay, A.J. Cytokines in innate and adaptive immunity. Rev. Immunogenet. 2000, 2, 359–373. [Google Scholar] [PubMed]
- Covello, J.M.; Bird, S.; Morrison, R.N.; Battaglene, S.C.; Secombes, C.J.; Nowak, B.F. Cloning and expression analysis of three striped trumpeter (Latris lineata) pro-inflammatory cytokines, TNF-α, IL-1β and IL-8, in response to infection by the ectoparasitic, Chondracanthus goldsmidi. Fish Shellfish Immunol. 2009, 26, 773–786. [Google Scholar] [CrossRef] [PubMed]
- Laing, K.J.; Secombes, C.J. Chemokines. Dev. Comp. Immunol. 2004, 28, 443–460. [Google Scholar] [CrossRef] [PubMed]
- Kawai, T.; Akira, S. The roles of TLRs, RLRs and NLRs in pathogen recognition. Int. Immunol. 2009, 21, 317–337. [Google Scholar] [CrossRef] [PubMed]
- Geijtenbeek, T.B.; Gringhuis, S.I. Signalling through C-type lectin receptors: Shaping immune responses. Nat. Rev. 2009, 9, 465–479. [Google Scholar] [CrossRef] [PubMed]
- Mogensen, T.H. Pathogen recognition and inflammatory signaling in innate immune defenses. Clin. Microbiol. Rev. 2009, 22, 240–273. [Google Scholar] [CrossRef] [PubMed]
- Tanekhy, M.; Matsuda, S.; Itano, T.; Kawakami, H.; Kono, T.; Sakai, M. Expression of cytokine genes in head kidney and spleen cells of Japanese flounder (Paralichthys olivaceus) infected with Nocardia seriolae. Vet. Immunol. Immunopathol. 2010, 134, 178–183. [Google Scholar] [CrossRef] [PubMed]
- Hessle, C.C.; Andersson, B.; Wold, A.E. Gram-positive and Gram-negative bacteria elicit different patterns of pro-inflammatory cytokines in human monocytes. Cytokine 2005, 30, 311–318. [Google Scholar] [CrossRef] [PubMed]
- Haan, C.; Is’harc, H.; Hermanns, H.M.; Schmitz-Van De Leur, H.; Kerr, I.M.; Heinrich, P.C.; Grötzinger, J.; Behrmann, I. Mapping of a region within the N terminus of JAK1 involved in cytokine receptor interaction. J. Biol. Chem. 2001, 276, 37451–37458. [Google Scholar] [CrossRef] [PubMed]
- Flex, E.; Petrangeli, V.; Stella, L.; Chiaretti, S.; Hornakova, T.; Knoops, L.; Ariola, C.; Fodale, V.; Clappier, E.; Paoloni, F.; et al. Somatically acquired JAK1 mutations in adult acute lymphoblastic leukemia. J. Exp. Med. 2008, 205, 751–758. [Google Scholar] [CrossRef] [PubMed]
- Schindler, C.; Plumlee, C. Interferons pen the JAK-STAT pathway. Semin. Cell Dev. Biol. 2008, 19, 311–318. [Google Scholar] [CrossRef] [PubMed]
- Shuai, K.; Liu, B. Regulation of JAK-STAT signalling in the immune system. Nat. Rev. Immunol. 2003, 3, 900–911. [Google Scholar] [CrossRef] [PubMed]
- Shimahara, Y.; Huang, Y.F.; Tsai, M.A.; Wang, P.C.; Yoshida, T.; Lee, J.L.; Chen, S.C. Genotypic and phonotypic analysis of fish pathogen, Nocardia seriolae, isolated in Taiwan. Aquaculture 2009, 294, 165–171. [Google Scholar] [CrossRef]
- Cock, P.; Fields, C.J.; Goto, N.; Heuer, M.L.; Rice, P.M. The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res. 2010, 38, 1767–1771. [Google Scholar] [CrossRef] [PubMed]
- Haas, B.J.; Papanicolaou, A.; Yassour, M.; Grabherr, M.; Blood, P.D.; Bowden, J.; Couger, M.B.; Eccles, D.; Li, B.; Lieber, M.; et al. De novo transcript sequence reconstruction from RNA-Seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 2013, 8, 1494–1512. [Google Scholar] [CrossRef] [PubMed]
- NCBI. Available online: http://www.ncbi.nlm.nih.gov/ (accessed on 22 December 2015).
- Gene Ontology Consortium. Available online: http://www.geneontology.org/ (accessed on 27 December 2015).
- COG. Available online: http://www.ncbi.nlm.nih.gov/COG/ (accessed on 27 December 2015).
- Kyoto Encyclopedia of Genes and Genomes. Available online: http://www.genome.jp/kegg/ (accessed on 28 December 2015).
- Camacho, C.; Coulouris, G.; Avagyan, V.; Ma, N.; Papadopoulos, J.; Bealer, K.; Madden, T.L. BLAST+: Architecture and applications. BMC Bioinform. 2009, 10, 421. [Google Scholar] [CrossRef] [PubMed]
- Iseli, C.; Jongeneel, C.V.; Bucher, P. ESTScan: A program for detecting, evaluating, and reconstructing potential coding regions in EST sequences. In Proceedings of the International Conference on Intelligent Systems for Molecular Biology, San Diego, CA, USA, 23 August 1999; pp. 138–148.
- Bowtie. Available online: http://bowtie-bio.sourceforge.net (accessed on 28 December 2015).
- Langmead, B.; Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 2012, 9, 357–359. [Google Scholar] [CrossRef] [PubMed]
- Li, B.; Dewey, C.N. RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 2011, 12, 323. [Google Scholar] [CrossRef] [PubMed]
- Livak, K.J.; Schmittgen, T.D. Analysis of relative gene expression data using real time quantitative PCR and the 2−∆∆Ct method. Methods 2001, 25, 402–408. [Google Scholar] [CrossRef] [PubMed]
Figure 1.
The cluster of orthologous groups (COG) classification. 8913 (23.63% of the total annotated putative proteins) were grouped into 25 different categories.
Figure 1.
The cluster of orthologous groups (COG) classification. 8913 (23.63% of the total annotated putative proteins) were grouped into 25 different categories.
Figure 2.
Functional distribution of GO annotation.
Figure 3.
KEGG classification of assembled unigenes from control and treated groups. (A) Cellular processes; (B) Environmental information processing; (C) Genetic information processing; (D) Human diseases; (E) Metabolism; and (F) Organismal systems.
Figure 3.
KEGG classification of assembled unigenes from control and treated groups. (A) Cellular processes; (B) Environmental information processing; (C) Genetic information processing; (D) Human diseases; (E) Metabolism; and (F) Organismal systems.
Figure 4.
Scatterplot of the top 20 enriched KEGG pathways. Rich Factor is the ratio of differentially expressed gene numbers annotated in this pathway terms to all gene numbers annotated in this pathway term. q ≤ 0.05 as significantly enriched.
Figure 4.
Scatterplot of the top 20 enriched KEGG pathways. Rich Factor is the ratio of differentially expressed gene numbers annotated in this pathway terms to all gene numbers annotated in this pathway term. q ≤ 0.05 as significantly enriched.
Figure 5.
Comparative gene expression analysis from qPCR and RNA-Seq in spleen from the infected largemouth bass with N. seriolae and compared with those in the control at the 24 h time point. Expression of target genes was normalized to β-actin as a reference gene. Statistically significant differences from control are presented, with * p < 0.05.
Figure 5.
Comparative gene expression analysis from qPCR and RNA-Seq in spleen from the infected largemouth bass with N. seriolae and compared with those in the control at the 24 h time point. Expression of target genes was normalized to β-actin as a reference gene. Statistically significant differences from control are presented, with * p < 0.05.
| Name | Description | Fold Change | Change |
|---|---|---|---|
| RIG I like receptor | |||
| trim25 | Tripartite motif-containing protein 25 | 1.32 | Up |
| dhx58 | ATP-dependent RNA helicase dhx58 | 3.41 | Up |
| ddx3x | ATP-dependent RNA helicase ddx3x | 6.28 | Up |
| ikbke | Inhibitor of nuclear factor κ-B kinase subunit epsilon | 2.54 | Up |
| ikbkg | Inhibitor of nuclear factor κ-B kinase subunit γ | 2.87 | Up |
| irf3 | Interferon regulatory factor 3 | 2.50 | Up |
| irf7 | Interferon regulatory factor 7 | 1.10 | Up |
| casp8 | Caspase 8 | 1.35 | Up |
| casp10 | Caspase 10 | 1.08 | Up |
| ikkβ | Inhibitor of nuclear factor κ-b kinase subunit β | −1.62 | Down |
| traf6 | TNF receptor-associated factor 6 | 3.11 | Up |
| p38 | p38 MAP kinase | −8.64 | Down |
| il-8 | Interleukin-8 | 2.27 | Up |
| ip-10 | Chemokine (c-x-c motif) 10 | 2.32 | Up |
| tnf-α | Tumor necrosis factor superfamily, member 2 | 2.32 | Up |
| il-12 | Interleukin-12a | 3.41 | Up |
| lbp | Lipopolysaccharide-binding protein | 2.94 | Up |
| casp8 | Caspase 8 | 1.35 | Up |
| rip1 | Receptor-interacting serine/threonine-protein kinase 1 | 1.31 | Up |
| ctsk | Cathepsin K | −11.52 | Down |
| tlr-3 | Toll-like receptor 3 | 1.16 | Up |
| ifnar1 | Interferon receptor 1 | 1.67 | Up |
| stat1 | Signal transducer and activator of transcription 1 | 2.38 | Up |
| il1b | Interleukin 1, β | 2.44 | Up |
| rantes | Chemokine (c-c motif) 5 | 2.03 | Up |
| cd40 | Tumor necrosis factor receptor superfamily, member 5 | 1.15 | Up |
| cd86 | cd86 antigen | −1.22 | Down |
| Cytokine-cytokine receptor interaction | |||
| cxcl7 | Platelet basic protein | −1.51 | Down |
| cxcl10 | Chemokine (c-x-c motif) 10 | 2.27 | Up |
| cxcl13 | Chemokine (c-x-c motif) 13 | 2.27 | Up |
| cxcl14 | Chemokine (c-x-c motif) 14 | −2.59 | Down |
| il8rb | Interleukin 8 receptor, β | −1.16 | Down |
| il8ra | interleukin 8 receptor, α | −1.16 | Down |
| cxcr3 | Chemokine (c-x-c receptor) type 3 | 1.13 | Up |
| xcr1 | Chemokine xc receptor 1 | 3.82 | Up |
| ccl20 | Chemokine (c-c motif) 20 | 5.16 | Up |
| ccl25 | Chemokine (c-c motif) 25 | −1.17 | Down |
| ccl19 | Chemokine (c-c motif) 19 | 5.16 | Up |
| ccl21 | Chemokine (c-c motif) 21 | −1.17 | Down |
| ccl5 | Chemokine (c-c motif) 5 | 2.03 | Up |
| ccr3 | Chemokine (c-c receptor) type 3 | 3.82 | Up |
| il6r | Interleukin 6 receptor | −2.11 | Down |
| il11ra | Interleukin 11 receptor α | 4.20 | Up |
| csfr | Colony-stimulating factor receptor (granulocyte) | 1.76 | Up |
| il13ra1 | Interleukin 13 receptor, α-1 | 2.91 | Up |
| il12rb2 | Interleukin 12 receptor, β-2 | 1.76 | Up |
| il23r | Interleukin 23, receptor | 1.76 | Up |
| csf2ra | Granulocyte-macrophage colony-stimulating factor receptor α | 2.91 | Up |
| il1ra | Interleukin 1 receptor, α | 1.15 | Up |
| il21r | Interleukin 21, receptor | −2.67 | Down |
| epor | Erythropoietin receptor | −1.85 | Down |
| ghr | Growth hormone receptor | −9.49 | Down |
| mpl | Thrombopoietin receptor | −1.26 | Down |
| flt1 | FMS-like tyrosine kinase 1 | 1.18 | Up |
| met | Proto-oncogene tyrosine-protein kinase met | −2.17 | Down |
| egf | Epidermal growth factor | −1.07 | Down |
| egfr | Epidermal growth factor receptor | −1.64 | Down |
| csf1r | Macrophage colony-stimulating factor 1 receptor | 1.80 | Up |
| ifnar1 | Interferon receptor, 1 | 1.67 | Up |
| ifnar2 | Interferon receptor, 2 | 1.52 | Up |
| il10ra | Interleukin 10 receptor, α | 4.20 | Up |
| il10rb | Interleukin 10 receptor, β | −1.50 | Down |
| tnfsf11b | Tumor necrosis factor receptor superfamily, member 11B | 1.80 | Up |
| tnfsf12 | Tumor necrosis factor ligand superfamily, member 12 | −1.10 | Down |
| Tnfb | Tumor necrosis factor b (TNF superfamily, member 2) | 2.33 | Up |
| tnfsf14 | Tumor necrosis factor (receptor) superfamily, member 14 | 1.20 | Up |
| tnfsf6b | Tumor necrosis factor (receptor) superfamily, member 6b | 1.80 | Up |
| faslg | Tumor necrosis factor (ligand) superfamily, member 6 | 1.13 | Up |
| cd40 | Tumor necrosis factor (receptor) superfamily, member 5 | 1.15 | Up |
| tnfsf13b | Tumor necrosis factor (ligand) superfamily, member 13B | −1.19 | Down |
| tgfbr2 | TGF-β receptor type-2 | −2.10 | Down |
| Antigen processing and presentation | |||
| psme1 | Proteasome activator subunit 1 | 1.57 | Up |
| hsp70 | Heat shock 70 kDa protein | 4.01 | Up |
| hsp90 | Molecular chaperone HtpG | 2.00 | Up |
| tap1/2 | ATP-binding cassette, subfamily b (MDR/TAP), member 2 | 2.72 | Up |
| tapbp | Tap binding protein (tapasin) | 2.46 | Up |
| pdia3 | Protein disulfide isomerase family a, member 3 | 10.67 | Up |
| mhci | Major histocompatibility complex, class I | 5.19 | Up |
| b2m | β-2-microglobulin | 1.25 | Up |
| mhcii | Major histocompatibility complex, class II | 1.99 | Up |
| ciita | Class II, major histocompatibility complex, transactivator | 1.74 | Up |
| tcr-α | T cell receptor α chain v region | −9.97 | Down |
| Natural Killer Cell Mediated Cytotoxity | |||
| cd48 | cd48 antigen | 2.29 | Up |
| trailr | Tumor necrosis factor (receptor) superfamily, member 10 | −1.02 | Down |
| prf1 | Perforin 1 | 9.91 | Up |
| grb | Granzyme B | −1.74 | Down |
| igg | Immunoglobulin heavy chain g | 5.30 | Up |
| fcγr3 | Low affinity immunoglobulin γ Fc region receptor III | 1.59 | Up |
| fasl | Tumor necrosis factor (ligand) superfamily, member 6 | 1.13 | Up |
| shp-2 | Tyrosine-protein phosphatase non-receptor type 11 | 9.78 | Up |
| dap-12 | Tyro protein tyrosine kinase binding protein | −2.26 | Down |
| vav1 | Guanine nucleotide exchange factor vav | 1.24 | Up |
| 3bp2 | sh3-domain binding protein 2 | 9.78 | Up |
| slp-76 | Lymphocyte cytosolic protein 2 | −3.12 | Down |
| shc1 | SHC-transforming protein 1 | 9.78 | Up |
| can | Serine/threonine-protein phosphatase 2B catalytic subunit | 1.58 | Up |
| pkc | Classical protein kinase c α type | 1.0 | Up |
| Name | Sequence | Target Gene | Application |
|---|---|---|---|
| LMBIL-12 F1Q | TCTTCCATCCTTGTGGTCTTCC | IL-12p40 | qPCR |
| LMBIL-12 R1Q | CAGTTCCAGGTCAAAGTGGTC | ||
| LMBIL-8 F1Q | GAGCCATTTTTCCTGGTGACT | IL-8 | |
| LMBIL-8 R1Q | TCCTCATTGGTGCTGAAAGATC | ||
| LMBIL-1 F1Q | CAAGATGCCTAAGGGACTGGA | IL-1 | |
| LMBIL-1 R1Q | AGGTGAACTTTGCGGTTCTC | ||
| LMBTCR F1Q | ATCATCTTTGGAAGTGGAACC | TCR | |
| LMBTCR R1Q | GATGTTGAAGACGACGGTCTT | ||
| LMBCD40 F1Q | TACAAGTGAAACATGGGGCAAC | CD40 | |
| LMBCD40 R1Q | TGATGAAGAGTCCACCTTACCG | ||
| LMBβ-Actin375F | CCACCACAGCCGAGAGGGAA | β-actin | |
| LMBβ-Actin375R | TCATGGTGGATGGGGCCAGG | ||
| LMBIL-1βF | TTGCCATAGAGAGGTTTA | IL-1β | |
| LMBIL-1βR | ACACTATATGCTCTTCCA | ||
| LMBTNFα-F | CTAGTGAAGAACCAGATTGT | TNF-α | |
| LMBTNFα-R | AGGAGACTCTGAACGATG |
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Byadgi, O.; Chen, C.-W.; Wang, P.-C.; Tsai, M.-A.; Chen, S.-C. De Novo Transcriptome Analysis of Differential Functional Gene Expression in Largemouth Bass (Micropterus salmoides) after Challenge with Nocardia seriolae. Int. J. Mol. Sci. 2016, 17, 1315. https://doi.org/10.3390/ijms17081315
AMA Style
Byadgi O, Chen C-W, Wang P-C, Tsai M-A, Chen S-C. De Novo Transcriptome Analysis of Differential Functional Gene Expression in Largemouth Bass (Micropterus salmoides) after Challenge with Nocardia seriolae. International Journal of Molecular Sciences. 2016; 17(8):1315. https://doi.org/10.3390/ijms17081315
Chicago/Turabian StyleByadgi, Omkar, Chi-Wen Chen, Pei-Chyi Wang, Ming-An Tsai, and Shih-Chu Chen. 2016. "De Novo Transcriptome Analysis of Differential Functional Gene Expression in Largemouth Bass (Micropterus salmoides) after Challenge with Nocardia seriolae" International Journal of Molecular Sciences 17, no. 8: 1315. https://doi.org/10.3390/ijms17081315
APA StyleByadgi, O., Chen, C. -W., Wang, P. -C., Tsai, M. -A., & Chen, S. -C. (2016). De Novo Transcriptome Analysis of Differential Functional Gene Expression in Largemouth Bass (Micropterus salmoides) after Challenge with Nocardia seriolae. International Journal of Molecular Sciences, 17(8), 1315. https://doi.org/10.3390/ijms17081315
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