Transcriptome analysis of head kidney in grass carp and discovery of immune-related genes

  • Jin Chen1, 2,

    Affiliated with

    • Cai Li1, 2,

      Affiliated with

      • Rong Huang1,

        Affiliated with

        • Fukuan Du1, 2,

          Affiliated with

          • Lanjie Liao1,

            Affiliated with

            • Zuoyan Zhu1 and

              Affiliated with

              • Yaping Wang1Email author

                Affiliated with

                BMC Veterinary Research20128:108

                DOI: 10.1186/1746-6148-8-108

                Received: 12 February 2012

                Accepted: 18 June 2012

                Published: 9 July 2012

                Abstract

                Background

                Grass carp (Ctenopharyngodon idella) is one of the most economically important freshwater fish, but its production is often affected by diseases that cause serious economic losses. To date, no good breeding varieties have been obtained using the oriented cultivation technique. The ability to identify disease resistance genes in grass carp is important to cultivate disease-resistant varieties of grass carp.

                Results

                In this study, we constructed a non-normalized cDNA library of head kidney in grass carp, and, after clustering and assembly, we obtained 3,027 high-quality unigenes. Solexa sequencing was used to generate sequence tags from the transcriptomes of the head kidney in grass carp before and after grass carp reovirus (GCRV) infection. After processing, we obtained 22,144 tags that were differentially expressed by more than 2-fold between the uninfected and infected groups. 679 of the differentially expressed tags (3.1%) mapped to 483 of the unigenes (16.0%). The up-regulated and down-regulated unigenes were annotated using gene ontology terms; 16 were annotated as immune-related and 42 were of unknown function having no matches to any of the sequences in the databases that were used in the similarity searches. Semi-quantitative RT-PCR revealed four unknown unigenes that showed significant responses to the viral infection. Based on domain structure predictions, one of these sequences was found to encode a protein that contained two transmembrane domains and, therefore, may be a transmembrane protein. Here, we proposed that this novel unigene may encode a virus receptor or a protein that mediates the immune signalling pathway at the cell surface.

                Conclusion

                This study enriches the molecular basis data of grass carp and further confirms that, based on fish tissue-specific EST databases, transcriptome analysis is an effective route to discover novel functional genes.

                Keywords

                Grass carp Head kidney cDNA EST Immune-related gene

                Background

                Grass carp (Ctenopharyngodon idella) is one of the most important freshwater fish, with fast growth, low cost of breeding, and delicious meat. It is widely distributed in China's major river systems. Grass carp is a farmed species that is easily affected by diseases induced by viruses and bacteria; this can cause tremendous economic losses. To date, no excellent breeding varieties have been obtained by the oriented cultivation technique. Because of the long breeding cycle (4–5 years), a hybrid breeding strategy is not feasible. Further, because of the lack of understanding of the genetic background of grass carp, no molecular breeding technology has been applied. The discovery of economically important trait-related genes and their functional study may help to establish a molecular breeding technology system in the fish.

                ESTs (expressed sequence tags) are partial cDNA sequences obtained after sequencing the ends of random cDNA clones. ESTs were first used in 1991 as an effective new method to discover human genes. Using EST sequences, unknown genomes could be explored at a relatively low cost [1]. With the development of DNA sequencing technology, the cost of sequencing is becoming lower, and the application of large-scale EST sequencing combined with bioinformatics tools for analyzing data is being widely used in different species to find novel genes, for genome annotation, for the identification of gene structure and expression, and in the development of type I molecular markers [2]. In fish, large scale EST sequencing was used in channel catfish (Ictalurus punctatus) [3], common carp (Cyprinus carpio) [4], and zebrafish (Danio rerio) [5].

                In recent years, high-throughput data analysis methods have gradually improved and the genomes of many kinds of fishes have been studied. The fishes that have been studied include zebrafish [6] and fugu [7], as model organisms, and the commercial fishes such as Atlantic salmon [8], sea bass [9, 10], rainbow trout [11], Atlantic halibut [12], bluefin tuna [13], turbot [14, 15], and Senegal sole fish [16]. In contrast, the molecular biology of grass carp is relatively unknown; currently, there are only 6,915 grass carp ESTs in NCBI’s dbEST database. Most functional genomic research on economically important fish is focused mainly on the development of molecular markers, genetic map construction and gene interval mapping, and other basic data accumulation. Research into gene function and its application to breeding is still in the initial stages.

                Head kidney is an important immune organ in teleost fish; its role is equivalent to mammalian bone marrow [17]. Head kidney contains a large number of T and B lymphocytes, macrophages and granulocytes that are the basis upon which specific and non-specific immunity is acquired.

                In this study, we constructed a non-normalized cDNA library for the head kidney of grass carp and obtained 3,027 unigenes including 221 genes of unknown function. We compared the head kidney expression profiles of grass carp infected with grass carp reovirus (GCRV) with normal controls and obtained 22,144 differential expressed tags. Based on a comparison of the differential expressed tags and potential genes with unknown function in the cDNA library, and by identifying gene expression response to GCRV and predicting protein structure, we discovered a novel immune-related gene. This study provides a method for the discovery of novel genes, and reveals the function and the network regulation mechanism of immune-related genes. The results provide a theoretical foundation for molecular design breeding in grass carp.

                Methods

                RNA extraction and construction of the cDNA library

                Total RNA was extracted from the head kidney of healthy adult grass carp using Trizol reagent (Invitrogen, Carlsbad, CA, USA). The mRNA was isolated using the Oligotex mRNA Kit (QIAGEN, Hilden, Germany). Full length cDNA was synthesized by the CreatorTM SMARTTM cDNA Library Construction Kit (Clontech, CA, USA) following the method described previously [18]. cDNA segments longer than 1 kb were isolated by electrophoresis, then ligated into pDNR-LIB vector (Clontech) and used to transform competent E. coli DH5α cells. After growing the colony for 12 hours on an LB plate containing chloramphenicol, the cDNA library was constructed by selecting mono-clones from the 96-well plate. Ethical approval for the work was obtained from Expert Committee of Biomedical Ethics, Institute of Hydrobiology of the Chinese Academy of Sciences. The Reference number obtained was Y12202-1-303.

                DNA sequencing and processing of the EST sequences

                10,464 clones were randomly selected from 109 96-well plates. After extracting the recombinant plasmids, 5’ terminal sequencing was performed using the T7 universal primer (T7: 5′-TAATACGACTCACTATAGGG-3′; Tm = 53.2 °C).

                An optimal peak chart was obtained by processing the raw sequence data with basecalling. Next, FASTA format sequences (raw ESTs) were obtained by processing the optimal peak chart using the Phrap program [19] with the Q20 standard. We used crossmatch (Smith and Green, unpublished observations) to remove the pDNR-LIB vector sequences and after excluding EST sequences that were less than 100 bp long, we obtained a cleaned EST data set. Clustering of the cleaned ESTs was performed using UIcluster [20]. The UIcluster sequences were assembled using the Phrap program to build a unigene data set for the ESTs from the head kidney of grass carp.

                BLAST searches, GO functional classification and KEGG pathway analysis

                We used the NCBI BLAST server [21] to identify sequences that were similar to the sequences in the NCBI nucleotide sequence database (Nt), the protein sequence database (Nr) [22] and the Swissprot database [23] using BLASTN, and BLASTX [24]. Using the EST sequence with the highest homology as a guide, we set the threshold E-value to E < 1e-6.

                We used the BLASTX search results from the Swissprot database and the Blast2GO tool [25] to assign GO functional classification to the unigene sequences. Blast2GO parameters were set as follows: E-Value-Hit-Filter < 1e-6; annotation cutOff = 55; other parameters remained at the default values.

                KAAS [26] was used to assign the unigene ESTs to pathways based on KEGG Orthology (KO) [27]. Unigenes were mapped to the corresponding KEGG pathways using the comparison method of bi-directional best hit.

                GCRV infection of grass carp and preparation of RNA sample

                The GCRV-873 strain was provided by the Gaobo biotechnology company (Wuhan, China). One-year-old grass carp with an average weight of 180–210 g were intraperitoneally injected with 150–200 μL GCRV, a dosage of approximately 106 TCID50 kg-1 body weight. The injected grass carp were raised in clean tanks at 28°C. Three infected grass carp with typical hemorrhage symptoms (infected group, n = 3) and three uninfected grass carp (healthy control group, n = 3) were selected at 5d after infection for further study. Total RNA was extracted from the head kidney of both groups using Trizol reagent. cDNA was obtained after reverse transcription and used for Solexa sequencing.

                Three-month-old grass carp with an average weight of 30–60 g were intraperitoneally injected with 50–80 μL GCRV, a dosage of approximately 106 TCID50 kg-1 body weight; fish in the control group were injected with same amount of saline. The grass carp were raised in clean tanks at 28°C. At 1d, 2d, 3d, 4d, 5d after infection ten GCRV-infected carp were selected for further study (n = 10). Ten uninfected fish were selected from the control group at 0d (n = 10). The whole fish was immediately used for RNA isolation. cDNA was obtained after reverse transcription and used for the detection of gene expression.

                Solexa sequencing and expression profile analysis

                The NlaIII and MmeI digestion method [28] was used to build a 21-bp cDNA tag library of the two groups (one-year-old), the control group and the GCRV-infected group. The tags in the two libraries end with different Illumina adapter sequences. The raw sequencing read length was 35 bp. The Solexa sequencing was performed by the Beijing Genomics Institute (BGI, Shenzhen, China).

                The raw sequence data was processed through basecalling, the adapter and low quality sequences were removed, and cleaned 21-bp tags were obtained. We converted the cleaned tag number into the standard (relative) number of transcripts per million (TPM), and calculated the logarithm of TPM for each of the cleaned tags from the control and GCRV-infected groups. We used a dual limit of P <0.01 and FPR (false positive rate) <0.01, to find cleaned tags with log2Ratio ≥ 1 or log2Ratio ≤ −1 [29]. The selected tags have differential expression levels of more than 2-fold in both groups. We then compared the differential expressed tags with the unigenes from the cDNA library using SeqMap [30]; mismatch was set to 0, and sense and antisense strands were considered in the mapping.

                Semi-quantitative RT-PCR and RACE cloning

                Total RNA was used to synthesize the first strand cDNA. Upstream and downstream primers (Table 1) were designed based on the unigene sequences. β-actin (primers, β-actin-F and β-actin-R) was used as the internal reference. PCR and electrophoresis was used to detect the change of expression level.
                Table 1

                Primers used for semi-quantitative RT-PCR and RACE

                Primer

                Sequence (5′ to 3′)

                Application

                291-F1

                ATGTGGGTGATAGTTGGTTTACAAT

                Expression study

                291-R1

                GTAATTTCAGAAGCACAGTTGAGAG

                Expression study

                357-F1

                CTATCGCATGATTGCCTACTCAGACT

                Expression study

                357-R1

                ACAACATTTTCCATCTCAATCTCAG

                Expression study

                788-F1

                GGTCTTAACGGAGAGAAGTGCGA

                Expression study

                788-R1

                GACTCTTCCGGCACGTAACT

                Expression study

                153-F1

                CCAGCATCACAGTGTTCAGGCAG

                Expression study

                153-R1

                AGTGTGTAGTTGTGTTCACCCTCC

                Expression study

                β-actin-F

                CAGATCATGTTTGAGACC

                Expression study

                β-actin-R

                ATTGCCAATGGTGATGAC

                Expression study

                291-F2

                CTCTCAACTGTGCTTCTGAAATTAC

                3’ RACE PCR

                291-R2

                ATTGTAAACCAACTATCACCCACAT

                5’ RACE PCR

                357-F2

                GGTATGATTATGACTAAAGCAGGAC

                3’ RACE PCR

                357-R2

                GTCCTGCTTTAGTCATAATCATACC

                5’ RACE PCR

                788-F2

                AGTTACGTGCCGGAAGAGTC

                3’ RACE PCR

                788-R2

                TCGCACTTCTCTCCGTTAAGAC

                5’ RACE PCR

                153-F2

                GGAGGGTGAACACAACTACACACT

                3’ RACE PCR

                153-R2

                CTGCCTGAACACTGTGATGCTGG

                5’ RACE PCR

                3' and 5' RACE was performed using the BD SMART RACE cDNA Amplification Kit (Clontech) according to the manufacturer’s instructions. Upstream and downstream primers used in the 3' and 5' RACE were designed based on the EST sequences (Table 1). Full length cDNA sequences of each gene were assembled using the 3' and 5' terminal sequences.

                Results

                Head kidney cDNA library of grass carp

                The storage capacity of the original library was 6 × 105, in the form of the E. coli DH5α cells that were stored on the 532 96-well plates in a total of 51,072 clones. One hundred randomly selected clones were used for further study. The PCR test results showed that the size of inserts was between 1–3 kilobases, the library reorganization was 97.85% and the no-load rate was 2.15%.

                EST sequence analysis

                10,464 EST clones were sequenced, and 10,282 FASTA sequences (raw ESTs) with an average read length of 470 bp were obtained. After removing the vector and sequences less than 100 bp long, 7,918 cleaned ESTs (accession no. JK847435-JK855352) were obtained. After clustering and assembly, we obtained 3,027 unigene EST sequences, 802 (26.5%) of which were contigs and 2,225 (73.5%) of which were singletons; the library redundancy was 61.78%. Most genes in the library exhibited low-level expression, only a small number of genes exhibited high-abundance expression. The number of low expression unigenes, the singletons, was 2,225 (73.5%); the number of medium expression unigenes, those containing 2–5 ESTs was 641 (21.2%); and the number of high expression unigenes, those that contained six or more ESTs, was 161 (5.3%). Only 23 unigenes contained more than 20 ESTs. The average length of the unigenes was 431 bp and 77.33% of the unigenes were 300–500 bp long (Figure 1).
                http://static-content.springer.com/image/art%3A10.1186%2F1746-6148-8-108/MediaObjects/12917_2012_456_Fig1_HTML.jpg
                Figure 1

                Length distribution of the assembled EST unigenes. The abscissa indicates the length of the unigenes, the ordinate indicates the number of unigenes.

                BLAST searches and GO functional classification

                The 3,027 unigenes were used as queries in BLAST searches of the NCBI nucleotide and protein sequence databases and the Swissprot database. 2,713 unigenes (89.6%) matched sequences in the nucleotide sequence database, 2,162 unigenes (71.4%) matched sequences in protein sequence database and 1,845 unigenes (61.0%) matched sequences in the Swissprot database. In all, 2,806 unigenes (92.7%) matched sequences in at least one of the three databases; the remaining 221 unigenes (7.3%) were not found (E-value <1e-6) in any of the three databases and may be novel gene sequences.

                Using the gene ontology (GO) classification, we successfully assigned functional annotations to 1,323 of the unigene sequences. In the GO biological process ontology, three terms accounted for the largest proportion of unigenes, they were cellular process, metabolic process and biological regulation; in the GO molecular function ontology, the three most commonly occurring terms were binding, catalytic activity and structural molecule activity; and in the GO cellular component ontology, cell, cell part and organelle were the terms that occurred most frequently (Table 2). Of the 1,323 GO-annotated unigenes, 53 were immune system process-related genes (Table 3), 4 were response to virus, and 9 were response to bacterium process-related genes (Tables 4 and 5). Some unigenes were assigned multiple functions. Not all of the unigenes could be mapped to the lower level GO terms.
                Table 2

                GO functional classification of the unigene data set

                 

                GO term

                Number of unigenes

                %

                Biological Process

                cellular process

                899

                68.0

                 

                metabolic process

                672

                50.8

                 

                biological regulation

                379

                28.6

                 

                regulation of biological process

                356

                26.9

                 

                localization

                250

                18.9

                 

                establishment of localization

                232

                17.5

                 

                developmental process

                135

                10.2

                 

                response to stimulus

                126

                9.5

                 

                multicellular organismal process

                100

                7.6

                 

                positive regulation of biological process

                80

                6.0

                 

                anatomical structure formation

                71

                5.4

                 

                negative regulation of biological process

                66

                5.0

                 

                immune system process

                53

                4.0

                 

                multi-organism process

                22

                1.7

                 

                growth

                18

                1.4

                 

                biological adhesion

                14

                1.1

                 

                locomotion

                14

                1.1

                 

                reproduction

                14

                1.1

                 

                reproductive process

                14

                1.1

                 

                viral reproduction

                4

                0.3

                Cellular Component

                cell part

                1084

                81.9

                 

                cell

                1084

                81.9

                 

                organelle

                733

                55.4

                 

                macromolecular complex

                402

                30.4

                 

                organelle part

                384

                29.0

                 

                membrane-enclosed lumen

                140

                10.6

                 

                envelope

                80

                6.0

                 

                extracellular region

                32

                2.4

                 

                extracellular region part

                12

                0.9

                 

                synapse

                6

                0.5

                 

                synapse part

                4

                0.3

                Molecular Function

                binding

                770

                58.2

                 

                catalytic activity

                440

                33.3

                 

                structural molecule activity

                77

                5.8

                 

                transporter activity

                70

                5.3

                 

                transcription regulator activity

                46

                3.5

                 

                molecular transducer activity

                46

                3.5

                 

                enzyme regulator activity

                30

                2.3

                 

                translation regulator activity

                29

                2.2

                 

                electron carrier activity

                11

                0.8

                 

                antioxidant activity

                5

                0.4

                Table 3

                Unigenes annotated with the GO term immune system process

                Sequence name

                Sequence description

                Hit AC

                Clustered EST

                Cluster1088

                fucolectin

                Q7SIC1

                1

                Cluster1225

                endoplasmic reticulum aminopeptidase 1

                Q9NZ08

                1

                Cluster1249

                transcription factor sp2

                Q02086

                1

                Cluster1357

                complement c3

                P98093

                1

                Cluster1410

                b-cell lymphoma 6 protein homolog

                P41183

                1

                Cluster1474

                matrix metalloproteinase-9

                P14780

                1

                Cluster1562

                serine threonine-protein phosphatase subunit

                P30153

                1

                Cluster1638

                inosine-5 -monophosphate dehydrogenase 2

                Q3SWY3

                1

                Cluster1667

                chemokine-like factor

                Q9UBR5

                1

                Cluster1692

                60 kda heat shock mitochondrial

                Q5ZL72

                1

                Cluster1821

                transcription elongation factor

                Q4KLL0

                1

                Cluster1865

                serine threonine-protein kinase tbk1

                Q9WUN2

                1

                Cluster1872

                dedicator of cytokinesis protein 2

                Q92608

                1

                Cluster1891

                complement c3

                P98093

                1

                Cluster1908

                interferon regulatory factor 4

                Q64287

                1

                Cluster2109

                sh2 domain-containing protein 1a

                B2RZ59

                1

                Cluster2173

                bisphosphate phosphodiesterase gamma-2

                Q8CIH5

                1

                Cluster2189

                ig heavy chain v-iii region cam

                P01768

                2

                Cluster2214

                complement c3

                P12387

                2

                Cluster2253

                calreticulin

                P18418

                2

                Cluster2255

                ap-2 complex subunit sigma-1

                P62744

                2

                Cluster2335

                myosin-if

                O00160

                2

                Cluster2337

                adenylate kinase mitochondrial

                Q1L8L9

                2

                Cluster2342

                40s ribosomal protein s14

                P62263

                2

                Cluster2345

                apoptotic chromatin condensation inducer

                Q9UKV3

                2

                Cluster244

                MHC I-related gene protein

                Q95460

                1

                Cluster2440

                ubiquitin thioesterase otub1

                Q96FW1

                2

                Cluster2466

                nf-kappa-b inhibitor alpha

                P25963

                2

                Cluster2474

                toll-interacting protein

                A2RUW1

                2

                Cluster2602

                moesin

                P26038

                3

                Cluster2612

                beta-2-microglobulin

                Q04475

                3

                Cluster265

                myosin-9

                P14105

                1

                Cluster2659

                proteasome maturation protein

                Q3SZV5

                3

                Cluster2663

                apoptotic chromatin condensation inducer

                Q9UKV3

                3

                Cluster2706

                cd81 antigen

                P35762

                3

                Cluster2717

                complement -binding mitochondrial

                Q07021

                4

                Cluster2828

                integrin alpha-l

                P24063

                6

                Cluster2869

                moesin

                P26038

                8

                Cluster2872

                beta-2-microglobulin

                O42197

                8

                Cluster2877

                c-x-c chemokine receptor type 4

                P61072

                8

                Cluster2908

                fucolectin

                Q7SIC1

                12

                Cluster311

                proteasome subunit beta type-9

                Q8UW64

                1

                Cluster33

                inosine-5 -monophosphate dehydrogenase 1

                P20839

                1

                Cluster490

                paired box protein pax-5

                Q02548

                1

                Cluster493

                nucleosome assembly protein 1-like 1-a

                Q4U0Y4

                1

                Cluster588

                cysteine-rich protein 2

                Q9DCT8

                1

                Cluster634

                interleukin enhancer-binding factor 2 homolog

                Q6NZ06

                1

                Cluster668

                zinc finger e-box-binding homeobox 1

                P36197

                1

                Cluster780

                kinase catalytic subunit delta isoform

                O35904

                1

                Cluster789

                cd81 antigen

                P35762

                1

                Cluster812

                interferon regulatory factor 1

                P15314

                1

                Cluster937

                high mobility group protein b3

                Q32L31

                1

                Cluster999

                aminoacyl trna synthetase protein

                Q12904

                1

                Table 4

                Unigenes annotated with the GO term response to virus

                Sequence name

                Sequence description

                Hit AC

                Clustered EST

                Cluster2255

                ap-2 complex subunit sigma-1

                P62744

                2

                Cluster2287

                interferon-induced gtp-binding protein

                Q8JH68

                2

                Cluster2379

                40s ribosomal protein s15a

                P62244

                2

                Cluster2877

                c-x-c chemokine receptor type 4

                P61072

                8

                Table 5

                Unigenes annotated with the GO term response to bacterium

                Sequence name

                Sequence description

                Hit AC

                Clustered EST

                Cluster12

                histone h2a

                P02264

                1

                Cluster1225

                endoplasmic reticulum aminopeptidase 1

                Q9NZ08

                1

                Cluster1269

                lysozyme c

                P85045

                1

                Cluster1910

                akirin-2

                Q25C79

                1

                Cluster2173

                phosphatidylinositol phosphodiesterase gamma-2

                Q8CIH5

                1

                Cluster2335

                myosin-if

                O00160

                2

                Cluster2543

                histone h1

                P06350

                2

                Cluster2861

                histone h1

                P06350

                7

                Cluster566

                histone h1

                P06350

                1

                KEGG pathway analysis

                A total of 989 of the 3,027 were assigned a KEGG ontology (KO) annotation; they were mapped to 201 KEGG pathways. Three most frequently occurring KEGG pathways were ribosome, oxidative phosphorylation, and proteasome. 68 unigenes mapped to immune-related pathways including leukocyte transendothelial migration, antigen processing and presentation, chemokine signalling pathway, and T cell receptor signalling pathway (Table 6). We found that 28 unigenes from head kidney in grass carp have been reported to be involved in the following pathways, Toll-like receptor signalling pathway, RIG-I-like receptor signalling pathway and the NOD-like receptor signalling pathway (Table 7).
                Table 6

                The most represented KEGG pathways in the unigene data set

                Pathway

                Mapping genes

                Categories

                Ribosome

                60

                Genetic Information Processing

                Oxidative phosphorylation

                53

                Metabolism

                Proteasome

                32

                Genetic Information Processing

                Spliceosome

                31

                Genetic Information Processing

                Lysosome

                28

                Cellular Processes

                Purine metabolism

                25

                Metabolism

                Endocytosis

                24

                Cellular Processes

                Regulation of actin cytoskeleton

                24

                Cellular Processes

                Cell cycle

                19

                Cellular Processes

                Leukocyte transendothelial migration

                18

                Organismal Systems

                Pyrimidine metabolism

                17

                Metabolism

                MAPK signalling pathway

                17

                Environmental Information Processing

                Antigen processing and presentation

                17

                Organismal Systems

                Chemokine signalling pathway

                17

                Organismal Systems

                Tight junction

                16

                Cellular Processes

                T cell receptor signalling pathway

                16

                Organismal Systems

                Table 7

                Mapping genes in fish primary non-specific immune pathways

                Pathway

                Mapping genes

                Containing ESTs

                Toll-like receptor signalling pathway

                8

                16

                RIG-I-like receptor signalling pathway

                11

                20

                NOD-like receptor signalling pathway

                9

                17

                Expression profiling analysis

                By Solexa sequencing, we obtained 7,696,804 and 6,136,889 raw tags from the transcriptomes of head kidney tissue from grass carp before and after GCRV infection, respectively. After removing low quality sequences, adapter sequences and single copy sequence the cleaned tag numbers were 7,188,005 and 5,724,526, respectively. The final numbers of non-redundant distinct tags were 152,826 and 105,653 before and after GCRV infection, respectively. All tags were submitted to SRA at NCBI under the accession no. SRA052520.2. Of the distinct tags, 22,144 were differentially expressed by more than 2-fold between the GCRV-infected and uninfected groups.

                These 22,144 differentially expressed tags mapped to 3,027 unigenes using SeqMap [30]. Of the differentially expressed tags, 679 (3.1%) mapped to 483 differentially expressed unigenes (16.0%); 145 of the unigenes were up-regulated genes, 307 were down-regulated genes. The remaining 31 unigenes mapped to tags that exhibited both up and down regulation, and so these unigenes were not included in the statistics. The up- and down-regulated genes were mainly annotated with the GO terms, genetic information processing, metabolism, and cellular processes and 16 unigenes were annotated with the GO term immune-related (Table 8). We found 54 tags that mapped onto 42 of the 221 unknown unigenes. These are potentially infection related novel genes; 15 of them were up-regulated between the GCRV-infected and uninfected groups, and 27 were down-regulated genes (Table 9).
                Table 8

                Differentially expressed unigenes annotated as immune-related

                Sequence name

                Description

                log2Ratio (VP/CP)

                Up-Down

                cichka_Cluster2189.seq. Contig1

                ig heavy chain v-iii region cam

                9.552669098

                Up

                cichka_Cluster2214.seq. Contig1

                complement c3

                −1.234417227

                Down

                cichka_Cluster2335.seq. Contig1

                myosin-if

                −1.616395009

                Down

                cichka_Cluster2337.seq. Contig1

                adenylate kinase mitochondrial

                −2.622261042

                Down

                cichka_Cluster2612.seq. Contig1

                beta-2-microglobulin

                14.96510786

                Up

                cichka_Cluster2717.seq. Contig1

                complement -binding mitochondrial

                2.831849484

                Up

                cichka_Cluster2828.seq. Contig1

                integrin alpha-l

                −3.476196501

                Down

                cichka_Cluster2872.seq. Contig1

                beta-2-microglobulin

                −2.257387843

                Down

                cichka_Cluster2877.seq. Contig1

                c-x-c chemokine receptor type 4

                −2.941536738

                Down

                cichka_Cluster2908.seq. Contig1

                fucolectin

                −3.57091306

                Down

                cichka_Cluster2379.seq. Contig1

                40s ribosomal protein s15a

                −2.133495724

                Down

                cichka_Cluster1269

                lysozyme c

                −5.60930435

                Down

                cichka_Cluster634

                interleukin enhancer-binding factor 2 homolog

                −8.383704292

                Down

                cichka_Cluster812

                interferon regulatory factor 1

                2.652601218

                Up

                cichka_Cluster1474

                matrix metalloproteinase-9

                −5.851050959

                Down

                cichka_Cluster1667

                chemokine-like factor

                −8.189824559

                Down

                Table 9

                Potentially novel differentially expressed unigenes

                Sequence name

                log2 Ratio(VP/CP)

                Up-Down

                cichka_Cluster1

                −1.30897451703681

                Down

                cichka_Cluster1004

                −8.18982455888002

                Down

                cichka_Cluster1074

                −4.68008319087111

                Down

                cichka_Cluster1080

                1.5772610962369

                Up

                cichka_Cluster1095

                1.70760741456741

                Up

                cichka_Cluster1139

                −3.01282922395069

                Down

                cichka_Cluster1321

                −1.17687776208408

                Down

                cichka_Cluster1418

                2.57740490960702

                Up

                cichka_Cluster144

                −2.37056287013824

                Down

                cichka_Cluster1502

                −2.69938241135805

                Down

                cichka_Cluster153

                9.00842862207058

                Up

                cichka_Cluster155

                −4.35320513151951

                Down

                cichka_Cluster1567

                −3.23219204494701

                Down

                cichka_Cluster1689

                −1.24599865006401

                Down

                cichka_Cluster18

                −8.55458885167764

                Down

                cichka_Cluster1830

                5.67155018571725

                Up

                cichka_Cluster1847

                −1.8154025874359

                Down

                cichka_Cluster19

                −7.4998458870832

                Down

                cichka_Cluster1931

                1.44222232860508

                Up

                cichka_Cluster2016

                2.84923580318831

                Up

                cichka_Cluster2063

                −3.29278174922784

                Down

                cichka_Cluster219

                8.44708322620965

                Up

                cichka_Cluster2432.seq. Contig1

                −1.12271915825313

                Down

                cichka_Cluster2506.seq. Contig1

                −2.26096007759593

                Down

                cichka_Cluster2646.seq. Contig1

                −8.18982455888002

                Down

                cichka_Cluster2651.seq. Contig1

                −8.32192809488736

                Down

                cichka_Cluster2765.seq. Contig1

                −8.38370429247405

                Down

                cichka_Cluster291

                3.07771266869725

                Up

                cichka_Cluster2966.seq. Contig1

                −5.46317402032312

                Down

                cichka_Cluster317

                −2.29418310440446

                Down

                cichka_Cluster357

                3.48529281620541

                Up

                cichka_Cluster468

                −1.41853954357293

                Down

                cichka_Cluster482

                1.43096228428556

                Up

                cichka_Cluster559

                7.82654848729092

                Up

                cichka_Cluster613

                −1.71345884128158

                Down

                cichka_Cluster619

                −3.62148837674627

                Down

                cichka_Cluster625

                1.46068016483455

                Up

                cichka_Cluster751

                1.83711846346595

                Up

                cichka_Cluster788

                1.13154390971446

                Up

                cichka_Cluster790

                −5.47619650111671

                Down

                cichka_Cluster837

                −2.25442127552909

                Down

                cichka_Cluster891

                −1.33599920243744

                Down

                Cloning and expression regulation analysis of the novel genes

                Using semi-quantitative RT-PCR, we examined the gene expression changes of the 42 potentially novel unigenes that were detected in the head kidney after viral infection. By comparing the 1, 2, 3, 4, and 5 day post-infection samples and the samples from the control group, we found four unigenes that showed a significant response to the viral infection: cichka_Cluster153 and cichka_Cluster291 were up-regulated in days 1 and 2 post-infection after which their expressions returned to the starting level; cichka_Cluster357 and cichka_Cluster788 were up-regulated in days 1 and 2 post-infection, and the increased expression levels were maintained till day 5 (Figure 2).
                http://static-content.springer.com/image/art%3A10.1186%2F1746-6148-8-108/MediaObjects/12917_2012_456_Fig2_HTML.jpg
                Figure 2

                RT-PCR verification of the novel infection-related genes. M, maker; 0, non-infected tissue; 1, 1 day after infection; 2, 2 days after infection; 3, 3 days after infection; 4, 4 days after infection;5, 5 days after infection; N, the negative control.

                The full-length cDNA sequences of these four unigenes were 2,057 bp (cichka_Cluster291, JQ412736), 2,288 bp (cichka_Cluster357, JQ412737), 1,044 bp (cichka_Cluster788, JQ412738) and 1,387 bp (cichka_Cluster153, JQ412739) encoding polypeptides of 586, 322, 142 and 155 amino acids, respectively. BLAST searches revealed that cichka_Cluster291 can encode a protein that is similar to the vertebrate endonuclease domain containing protein, cichka_Cluster357 can encode a protein that is similar to the vertebrate ankyrin repeat domain 10 protein, cichka_Cluster788 can encode a protein that is similar to the CST complex subunit TEN1; for the cichka_Cluster153 encoded protein, no similar sequences were found in the databases that we searched, suggesting that cichka_Cluster153 may represent a novel gene in grass carp. We used the SMART server [31] to predict the domain structure of the 42 novel unigenes and found that 83.02% of them contained the endonuclease domain 1 that is found in proteins that are involved in the apoptosis pathway, and 35.22% contained the ankyrin repeat domain that is present in proteins that are involved in pathways that include the B cell receptor signalling pathway, the T cell receptor signalling pathway, and the apoptosis pathway. The cichka_Cluster788 unigene contained no obvious structural domains; the cichka_Cluster153 encoded protein contained two transmembrane domains and may be a transmembrane protein.

                Discussion

                Currently, there are about 6,915 sequences of grass carp in the public databases. This situation does not reflect the extremely important breeding position of grass carp. In this study, we built a head kidney non-normalized cDNA library of healthy grass carp and obtained 3,027 unigene EST sequences. This library greatly enriches the available genomic data for grass carp and lays an important foundation for the discovery of novel genes and for their functional investigation.

                GO analysis revealed that the annotated unigenes were mainly related to genes involved in basic biological processes such as cellular process (25.5%), metabolic process (19.1%) and biological regulation (10.8%). This functional distribution is similar to the EST distributions reported earlier in the head kidney of zebrafish [32] and sea bass [10].

                Of the unigenes that were similar to immune-related genes, 66 unigenes were annotated as associated with the immune process; 53 were related to the immune system process, 4 were annotated as response to virus, and 9 were related to response to bacteria. Among the 989 unigenes that were assigned KO annotations, 68 were mapped to immune-related pathways that included leukocyte transendothelial migration, antigen processing and presentation, chemokine signalling pathway and T cell receptor signalling pathway. By examining the literature, we found that 28 of the unigenes in grass carp head kidney were related to fish genes that were reported to be involved in the Toll-like receptor signalling pathway, the RIG-I-like receptor signalling pathway and the NOD-like receptor signalling pathway. Clearly, head kidney tissue plays an important role in immune processes in fish. EST databases of head kidney tissue are likely to become important resources in which immune-related genes can be identified.

                In the 3,027 unigene library of head kidney in grass carp, 7.3% (221) failed to match any of the sequences in the three public databases that were searched. Of the 10 unigenes that were the most highly expressed in grass carp head kidney, 9 were unknown sequences (Table 10). This could be partly because sequence data for fish is still very scarce, and partly because fish head kidney tissue may contain tissue-specific or species-specific genes. EST databases can be important resources for identifying unknown genes in fish [3335]. In recent years, the fish transcriptome has been used to study the regulation of gene expression. Pardo et al (2008) conducted a comparative study of turbot expression profiles in the main immune tissue before and after pathogen infection to find genes that were related to immune response and disease resistance [36]. Chini et al (2008) carried out a comparative study of reproductive development-related tissues in bluefin tuna using transcriptome research methods to explore the molecular mechanism of gonadal development and maturity split [13]. Indeed, comparative transcriptome analysis can be used, not only to investigate the mechanisms of expression and regulation of known genes, but also as an effective means to find important and novel function-related genes.
                Table 10

                Ten most highly expressed unigenes in the head kidney of healthy grass carp

                Sequence name

                ORF length

                Clustered ESTs

                Description

                Cluster2971

                159

                1114

                Unknown

                Cluster2970

                267

                251

                hybrid granulin

                Cluster2969

                132

                166

                hypothetical 18 K protein

                Cluster2968

                282

                109

                Unknown

                Cluster2967

                273

                123

                Unknown

                Cluster2966

                267

                78

                hypothetical protein

                Cluster2965

                132

                85

                Unknown

                Cluster2964

                0

                83

                Unknown

                Cluster2963

                279

                63

                Unknown

                Cluster2962

                108

                55

                Unknown

                Conclusion

                We carried out a comparative analysis to find differences in the Solexa expression profiles of head kidney in grass carp before and after infection, and identified 42 unigenes of unknown function that showed differential expression in response to the pathogen. After RT-PCR validation of the cDNA and gene structure analysis, we found a potentially novel immune-related gene. Based on its response to viral infection and the prediction that it might encode a membrane protein, we speculate that this novel gene may encode a virus receptor or a protein that mediates the immune signalling pathway at the cell surface. We intend to further investigate the function of this gene in a future study. Our findings confirm that fish tissue-specific EST databases combined with comparative transcriptome analysis are effective tools that can direct the discovery of novel functional genes.

                Declarations

                Acknowledgements

                The research was financially supported by the Innovation Project of the Chinese Academy of Sciences (KSCX2-EW-N-004-3), the National Key Basic Research Program (2009CB118701), and the Autonomous Project of State Key Laboratory of Freshwater Ecology and Biotechnology (2011FBZ18).

                Authors’ Affiliations

                (1)
                State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences
                (2)
                Graduate School of Chinese Academy of Sciences

                References

                1. Adams MD, Kelley JM, Gocayne JD, Dubnick M, Polymeropoulos MH, Xiao H, Merril CR, Wu A, Olde B, Moreno RF, Kerlavage AR, Richard Mccombie W, Craig Venter J: Complementary DNA sequencing: expressed sequence tags and human genome project. Science 1991, 252:1651–1656.PubMedView Article
                2. Nagaraj SH, Gasser RB, Ranganathan S: A hitchhiker's guide to expressed sequence tag (EST) analysis. Brief Bioinform 2007, 8:6–21.PubMedView Article
                3. Karsi A, Li P, Dunham R, Liu ZJ: Transcriptional activities in the pituitaries of channel catfish before and after induced ovulation by injection of carp pituitary extract as revealed by expressed sequence tag analysis. J Mol Endocrinol 1998, 21:121–129.PubMedView Article
                4. Savan R, Sakai M: Analysis of expressed sequence tags (EST) obtained from common carp, Cyprinus carpio L ., head kidney cells after stimulation by two mitogens, lipopolysaccharide and concanavalin-A. Comp Biochem Physiol B Biochem Mol Biol 2002, 131:71–82.PubMedView Article
                5. Zeng S, Gong Z: Expressed sequence tag analysis of expression profiles of zebrafish testis and ovary. Gene 2002, 294:45–53.PubMedView Article
                6. Lo J, Lee S, Xu M, Liu F, Ruan H, Eun A, He Y, Ma W, Wang W, Wen Z, Peng J: 15,000 Unique Zebrafish EST Clusters and Their Future Use in Microarray for Profiling Gene Expression Patterns During Embryogenesis. Genome Res 2003, 13:455–466.PubMedView Article
                7. Clark MC, Edwards YJK, Peterson D, Clifton SW, Thompson AJ, Sasaki M, Suzuki Y, Kikuchi K, Watabe S, Kawakami K, Sugano S, Elgar G, Johnson SL: Fugu ESTs: New Resources for Transcription Analysis and Genome Annotation. Genome Res 2003, 13:2747–2753.PubMedView Article
                8. Adzhubei AA, Vlasova AV, Hagen-Larsen H, Ruden TA, Laerdahl JK, Høyheim B: Annotated Expressed Sequence Tags (ESTs) from pre-smolt Atlantic salmon ( Salmo salar ) in a searchable data resource. BMC Genomics 2007, 8:209.PubMedView Article
                9. Chini V, Rimoldi S, Terova G, Saroglia M, Rossi F, Bernardini G, Gornati R: EST-based identification of genes expressed in the liver of adult seabass ( Dicentrarchus labrax, L. ). Gene 2006, 376:102–106.PubMedView Article
                10. Sarropoulou E, Sepulcre P, Poisa-Beiro L, Mulero V, Meseguer J, Figueras A, Novoa B, Terzoglou V, Reinhardt R, Magoulas A, Kotoulas G: Profiling of infection specific mRNA transcripts of the European seabass Dicentrarchus labrax. BMC Genomics 2009, 10:157.PubMedView Article
                11. Govoroun M, Gac FL, Guiguen Y: Generation of a large scale repertoire of Expressed Sequence Tags (ESTs) from normalised rainbow trout cDNA libraries. BMC Genomics 2006, 7:196.PubMedView Article
                12. Douglas SE, Knickle LC, Kimball J, Reith ME: Comprehensive EST analysis of Atlantic halibut ( Hippoglossus hippoglossus ), a commercially relevant aquaculture species. BMC Genomics 2007, 8:144.PubMedView Article
                13. Chini V, Cattaneo AG, Rossi F, Bernardini G, Terova G, Saroglia M, Gornati R: Genes expressed in blue fin tuna ( Thunnus thynnus ) liver and gonads. Gene 2008, 410:207–213.PubMedView Article
                14. Pardo BG, Fernández C, Millán A, Bouza C, Vázquez-López A, Vera M, Alvarez-Dios JA, Calaza M, Gómez-Tato A, Vázquez M, Cabaleiro S, Magariños B, Lemos ML, Leiro JM, Martínez P: Expressed sequence tags (ESTs) from immune tissues of turbot ( Scophthalmus maximus ) challenged with pathogens. BMC Vet Res 2008, 4:7.View Article
                15. Park KC, Osborne JA, Tsoi SCM, Brown LL, Johnson SC: Expressed sequence tags analysis of Atlantic halibut ( Hippoglossus hippoglossus ) liver, kidney and spleen tissues following vaccination against Vibrio anguillarum and Aeromonas salmonicida. Fish Shellfish Immunol 2005, 18:393–415.PubMedView Article
                16. Cerdà J, Mercadé J, Lozano JJ, Manchado M, Tingaud-Sequeira A, Astola A, Infante C, Halm S, Viñas J, Castellana B, Asensio E, Cañavate P, Martínez-Rodríguez G, Piferrer F, Planas JV, Prat F, Yúfera M, Durany O, Subirada F, Rosell E, Maes T: Genomic resources for a commercial flatfish, the Senegalese sole ( Solea senegalensis ): EST sequencing, oligo microarray design, and development of the Soleamold bioinformatic platform. BMC Genomics 2008, 9:508.PubMedView Article
                17. Press CML, Evensen O: The morphology of the immune system in teleost fishes. Fish S hellfish Immunol 1999, 9:309–318.View Article
                18. Zhu YY, Machleder EM, Chenchik A, Li R, Siebert PD: Reverse transcriptase template switching: a SMART approach for full-length cDNA library construction. Biotechniques 2001, 30:892–897.PubMed
                19. Ewing B, Hillier L, Wendl MC, Green P: Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res 1998, 8:175–185.
                20. Trivedi N, Bishof J, Davis S, Pedretti K, Scheetz TE, Braun TA, Roberts CA, Robinson NL, Sheffield VC, Bento Soares M, Casavant TL: Parallel creation of non-redundant gene indices from partial mRNA transcripts. Future Gen Comp Sys 2002, 18:863–870.View Article
                21. BLAST. http://​blast.​ncbi.​nlm.​nih.​gov/​Blast.​cgi
                22. Benson DA, Boguski MS, Lipman DJ, Ostell J, Ouellette BF, Rapp BA, Wheeler DL: GenBank. Nucleic Acids Res 1999, 27:12–17.PubMedView Article
                23. Boeckmann B, Bairoch A, Apweiler R, Blatter M, Estreicher A, Gasteiger E, Martin MJ, Michoud K, O'Donovan C, Phan I, Pilbout S, Schneider M: The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res 2003, 31:365–370.PubMedView Article
                24. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic Local Alignment Search Tool. J Mol Biol 1990, 215:403–410.PubMed
                25. Conesa A, Goetz S, Garcia JM, Terol J, Talon M, Robles M: Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 2005, 21:3674–3676.PubMedView Article
                26. Moriya Y, Itoh M, Okuda S, Yoshizawa A, Kanehisa M: KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res 2007, 35:W182-W185.PubMedView Article
                27. Kanehisa M, Goto S: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000, 28:27–30.PubMedView Article
                28. Sorana Morrissy A, 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, 19:1825–1835.PubMedView Article
                29. Audic S, Claverie JM: The significance of digital gene expression profiles. Genome Res 1997, 7:986–995.PubMed
                30. Jiang H, Wong WH: SeqMap: mapping massive amount of oligonucleotides to the genome. Bioinformatics 2008, 24:2395–2396.PubMedView Article
                31. SMART. http://​smart.​embl-heidelberg.​de/​
                32. Song HD, Sun XJ, Deng M, Zhang GW, Zhou Y, Wu XY, Sheng Y, Chen Y, Ruan Z, Jiang CL, Fan HY, Zon LI, Kanki JP, Liu TX, Look AT, Chen Z: Hematopoietic gene expression profile in zebrafish kidney marrow. Proc Natl Acad Sci USA 2004, 101:16240–16245.PubMedView Article
                33. Yang AF, Zhou ZC, He CB, Hu JJ, Chen Z, Gao XG, Dong Y, Jiang B, Liu WD, Guan XY, Wang XY: Analysis of expressed sequence tags from body wall, intestine and respiratory tree of sea cucumber ( Apostichopus japonicus ). Aquaculture 2009, 296:193–199.View Article
                34. Chen SL, Xu MY, Hu SN, Li L: Analysis of immune-relevant genes expressed in red sea bream ( Chrysophrys major ) spleen. Aquaculture 2004, 240:115–130.View Article
                35. 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, 32:169–188.PubMedView Article
                36. Pardo GB, Fernández C, Millán A, Bouza C, Vázquez-López A, Vera M, Alvarez-Dios AJ, Calaza M, Gómez-Tato A, Vázquez M, Cabaleiro S, Magariños B, Lemos LM, Leiro MJ, Martínez P: Expressed sequence tags (ESTs) from immune tissues of turbot ( Scophthalmus maximus ) challenged with pathogens. BMC Vet Res 2008, 4:37.PubMedView Article

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                © Chen et al.; licensee BioMed Central Ltd. 2012

                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.

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