Open Access

Profiling of infection specific mRNA transcripts of the European seabass Dicentrarchus labrax

  • Elena Sarropoulou1Email author,
  • Pilar Sepulcre2,
  • Laura Poisa-Beiro3,
  • Victoriano Mulero2,
  • José Meseguer2,
  • Antonio Figueras3,
  • Beatriz Novoa3,
  • Vasso Terzoglou1,
  • Richard Reinhardt4,
  • Antonios Magoulas1 and
  • Georgios Kotoulas1
BMC Genomics200910:157

DOI: 10.1186/1471-2164-10-157

Received: 11 October 2008

Accepted: 10 April 2009

Published: 10 April 2009

Abstract

Background

The European seabass (Dicentrarchus labrax), one of the most extensively cultured species in European aquaculture productions, is, along with the gilthead sea bream (Sparus aurata), a prospective model species for the Perciformes which includes several other commercially important species. Massive mortalities may be caused by bacterial or viral infections in intensive aquaculture production. Revealing transcripts involved in immune response and studying their relative expression enhances the understanding of the immune response mechanism and consequently also the creation of vaccines. The analysis of expressed sequence tags (EST) is an efficient and easy approach for gene discovery, comparative genomics and for examining gene expression in specific tissues in a qualitative and quantitative way.

Results

Here we describe the construction, analysis and comparison of a total of ten cDNA libraries, six from different tissues infected with V. anguillarum (liver, spleen, head kidney, gill, peritoneal exudates and intestine) and four cDNA libraries from different tissues infected with Nodavirus (liver, spleen, head kidney and brain). In total 9605 sequences representing 3075 (32%) unique sequences (set of sequences obtained after clustering) were obtained and analysed. Among the sequences several immune-related proteins were identified for the first time in the order of Perciformes as well as in Teleostei.

Conclusion

The present study provides new information to the Gene Index of seabass. It gives a unigene set that will make a significant contribution to functional genomic studies and to studies of differential gene expression in relation to the immune system. In addition some of the potentially interesting genes identified by in silico analysis and confirmed by real-time PCR are putative biomarkers for bacterial and viral infections in fish.

Background

The European seabass Dicentrarchus labrax is one of the most extensively aquacultured fish species in the Mediterranean, resulting in steadily increasing pressure on producers. Consequently, it is important to acquire new techniques and knowledge in order to improve aquaculture practices. Detailed information concerning growth, health, disease resistance and flesh quality benefit from the molecular as well as from the physiological point of view can provide illuminating new findings leading to improved aquaculture techniques. Several efforts have been made up till now to enrich the genomic resources in aquaculture production in the Mediterranean (chiefly for the gilthead sea bream Sparus aurata and for the European seabass Dicentrarchus labrax), e.g. Marine Genomics Europe (Network of Excellence) (CT-2003-505403), [15] as well as in the Atlantic (e.g. Atlantic halibut Hippoglossus hippoglossus, Salmon Salmo salar) e.g. [613]. These studies focused mainly on non-challenged tissues in order to obtain a first unigene catalogue. Aquaculture production however is affected by viral and pathogenic bacteria, particularly in respect of D. labrax which has been shown to be the species most sensitive to pathogenic bacteria such as Vibrio anguillarum [14] and to viral infections such as Nodavirus [15, 16]. There are several commercial vaccines which provide protection against infection from V. anguillarum although the mechanism of immune response still remains unknown. Nodavirus can cause massive mortalities [17] and cannot be controlled so far because the production of commercial vaccines here is still in its infancy. In the present study we have generated a collection of EST sequences from tissues of European seabass infected with V. anguillarum and Nodavirus. Within this collection we were able to isolate immune relevant genes, and have gone on to compare gene expression in different tissues after viral and pathogenic bacteria infection. Additionally we determined in silico differential expression between the two infections. In this context the construction and analysis of a total of ten cDNA libraries are described; six cDNA libraries were from tissues of the European seabass infected with V. anguillarum (liver, spleen, head kidney, peritoneal exudate, gill and intestine) with peritoneal exudate, gill and intestine as target organs for V. anguillarum infections, and four cDNA libraries were from tissues of the European seabass infected with Nodavirus (liver, spleen, head kidney and brain) with the brain as target organ of the virus. Comparisons between the predicted European seabass peptide data set and the zebrafish, medaka, stickleback, tetraodon and human proteomes were performed. Genes showing in silico differential expression between Nodavirus infection and V. anguillarum infection were further analysed by real-time PCR.

Results

Summary of ESTs from the cDNA libraries infected with Nodavirus and V. anguillarum

The amplified libraries contained insert size from approximately 0.5 to 2.0 kb. Single pass sequencing was performed resulting in 9605 high quality sequences. All sequences were submitted to the EST database (dbEST http://www.ncbi.nlm.nih.gov/projects/dbEST/ with the accession numbers FK939975 – FK944381, FL484477 – FL488763 and FL501471 – FL502381. A set of 3075 unique sequences was generated. Among the unique sequences (3075) [see Additional file 1] 371 [12%, see Additional file 2] sequences contained Simple Sequence Repeats (SSR). Cluster analyses performed for each library separately (Table 1a and Table 1b) revealed redundancy rates which varied from 72% (28% unique sequences) in intestine cDNA library infected with V. anguillarum to 37% (63% unique sequences) in spleen cDNA library infected with V. anguillarum (Table 1a). The set of unique EST sequences was annotated with Blast2GO which carries out BLASTX searches and attempts to assign function and GO classification. Out of the 3075 unique EST sequences submitted to GO2Blast for annotation and GO classification, 1521 sequences fell into 14 categories of biological process function at GO annotation level 2 (Fig. 1), where two categories, cellular process and metabolic process, were predominant. The category "immune system process" was represented by 79 transcripts.
Table 1

Summary of sequences derived of cDNA libraries of D. labrax tissue infected with V. anguillarum (a) and Nodavirus (b)

A

Tissue

singletons

contigs

unique

total sequences

% of unique sequences

Liver

503

190

693

2140

32.38

Spleen

326

38

364

651

63.14

Kidney

266

66

332

911

36.24

Peritoneal exudate

386

103

489

827

59.13

Gill

92

15

107

343

31.19

Intestine

88

4

92

326

28.22

B

Tissue

singletons

contigs

unique

total sequences

% of unique sequences

Liver

253

127

380

1126

33.75

Spleen

698

118

816

1284

63.55

Brain

617

41

658

1099

59.87

Kidney

321

55

376

934

40.26

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-157/MediaObjects/12864_2008_Article_2041_Fig1_HTML.jpg
Figure 1

Summary of GO category Biological process of unique ESTs obtained from seabass cDNA libraries infected with Nodavirus and Vibrio anguillarum.

EST matches with known function

Out of the 3075 EST sequences, 1246 (~ 41%) had a positive hit after submission to BLASTX database search. Among those EST sequences with a known function, 128 homologues were found to be involved in the immune response and 79 of these were grouped into the GO category "immune system process". The remaining 49 transcripts were manually determined to be involved in the immune response (see Additional file 3). Immune related transcripts isolated for the first time for seabass amounted to 115 (Table 2). Among transcripts of interest, the transcript encoding for an important antimicrobial protein, hepicidin, was isolated. Aligning EST sequences grouped into one contig can provide additional data. In the case of hepcidin it is probable that different isoforms are grouped together. Alignments of other cDNA sequences either showed alternative polyadenylation or they showed in silico polymorphism of microsatellite DNA as for instance the transcript coding for cysteine-rich protein 1-I (see Additional file 4).
Table 2

Transcripts isolated for the first time in D. labrax and grouped to the GO category" immune system process"

Contig ID

BLASTX Hit

Accession number

Contig_265, Contig_3

alpha globin

Q9PVM4 BAA86218

Contig_681

alpha-1-microglobulin bikunin precursor

CAA45294

Contig_3061

b-cell leukemia lymphoma 6

XP_001340785

Contig_1838

bcl2 adenovirus e1b 19 kda interacting protein 3

NP_001012245, AAR83676

Contig_276

beta-2 microglobulin

ABB60035, ABB60037

Contig_773

beta-2 microglobulin precursor

AAC64994

Contig_2731

blood thirsty

AAX12162

Contig_2210

blood thirsty

XP_699830

Contig_1617

c1 inhibitor

NP_001117851, CAD58653

Contig_936

cathepsin s

AAQ01147

Contig_2779

cc chemokine

AAY79324

Contig_616

ccaat enhancer binding protein (c ebp)beta

BAB40971

Contig_1810

cd59-like protein 2

NP_001117969, AAT94063

Contig_1441

cell division cycle 42

NP_956159, AAH48035, AAH75761, AAX20139, CAM56524, AAI64988

Contig_2676

chemokine (c-c motif) ligand 13

BAC20610

Contig_2058

chemokine (c-c motif) ligand 21b

AAT52146, ABA54959

Contig_241

chemokine (c-c motif) ligand 25

ABC69050

Contig_2858

chemokine (c-x-c motif) ligand 12b (stromal cell-derived factor 1)

NP_840092, AAN64414, AAS92649, AAI09418

Contig_524

chemokine (c-x-c motif) ligand 9

ABC69049

Contig_627

chemokine (c-x-c motif) receptor 4

ABP48751

Contig_525, Contig_1672

chemokine cxc-like protein

ABC69049

Contig_983

complement c4-2

CAD45003

Contig_2306

complement component 1 q subcomponent

ABV57766

Contig_513

complement component 1 qb chain

XP_001110783

Contig_986

complement component 5

BAC23058

Contig_1044

complement component 7

BAA88899

Contig_2558

complement component alpha polypeptide

NP_001118096, CAH6548

Contig_1413

complement component c3

BAA88901

Contig_1114

complement component c4

CAD45003

Contig_2843

complement component c5-1

BAC23057

Contig_2534, Contig_2600

complement component c9

P79755, AAC60288

Contig_1499

complement component factor h

NP_001117882, CAF25505

Contig_1496

complement component gamma polypeptide

NP_001117880, CAF22027

Contig_927

complement component1, q gamma polypeptide

XP_544508

Contig_2432

complement component beta subunit

Q9PVW7, BAA86877

Contig_2605

complement component q subcomponent binding protein

EDM05067

Contig_1536

complement component r subcomponent

AAR20889

Contig_868

complement factor b

CAD21938

Contig_1607

complement factor d preproprotein

XP_001117186

Contig_2013

complement factor h

NP_001117876, CAF05664, CAF05665

Contig_1481

complement factor h-related 1

AAA92556

Contig_1375

cornichon homolog

O35372, AAC15828

Contig_1198

c-reactive protein

NP_999009, O19062 BAA21473

Contig_1657

c-type lectin

BAE45333

Contig_1596

cu zn superoxide dismutase

AAW29025

Contig_395

deah (asp-glu-ala-his) box polypeptide 16

NP_956318, AAH45393, AAI65206

Contig_2814

ets-1 transcript variant ets-1 delta(iii-vi)

AAY19514

Contig_626

ferritin heavy chain

NP_001117129, P49946, AAB34575

Contig_280

fth1 protein

CAL92185

Contig_2392

g-protein couplededg6

NP_001112363

Contig_2662

heat shock 10 kda protein 1 (chaperonin 10)

AAV37068

Contig_2975

heat shock 70 kda protein 4

AAH65970

Contig_2939

heme oxygenase1

ABL74501

Contig_275

hemoglobin alpha chain

CAP69820

Contig_731

Hephaestin

NP_579838

Contig_1713

hypoxanthine phosphoribosyltransferase 1

NP_001002056, AAH71336

Contig_1745, Contig_1876

integrin beta 2

BAB39130, NP_990582, CAA50671

Contig_2452

interleukin 18 receptor accessory protein

XP_001371334

Contig_2261

interleukin 1 type i

XP_416914

Contig_1718

interleukin 1 type ii

NP_001015713, AAH89644

Contig_1506

interleukin 2 receptor gamma chain

CAJ38407

Contig_1835

interleukin enhancer binding factor 3

AAH47175

Contig_966

interleukin-1 receptor type ii

ABP99035

Contig_852

interleukin-1 receptor type ii

CAL30143

Contig_2196

loc559360 protein

AAI51869

Contig_2044

macrophage migration inhibitory factor

ABG54276

Contig_1348

major histocompatibility complex class i a chain

BAD13369

Contig_17

mflj00348 protein

BAD90390

Contig_889

mhc class i alpha antigen

ABB04088

Contig_1349

mhc class i antigen

BAD13366

Contig_2797

mitochondrial ribosomal protein s18b

NP_001106610, AAI52129, AAI55448

Contig_3008

natural resistance-associated macrophage protein

AAG31225

Contig_576

neurofibromatosis 1

AAD15839

Contig_1716

novel protein vertebrate complement component 3

NP_001116778, CAQ13357

Contig_1327

otuubiquitin aldehyde binding 1

NP_001002500, AAH76301

Contig_2661

Proteasome activator subunit 1 (pa28 alpha)

ABE60902, ABK41199

Contig_1948

protein kinase alpha

AAI51472

Contig_2319

protein tyrosinereceptorc

XP_547374

Contig_2009

purinergic receptorg-protein13

XP_001516794

Contig_1715

rhamnose binding lectin

NP_001117668, BAA92256

Contig_934

ribosomal protein s19

P61155, AAP20214

Contig_2680

sam domain- and hd domain-containing protein 1

XP_001097562

Contig_684

serum amyloid p-component

P12246 AAA40093, CAA34774, AAH61125, AAY88178, BAE25796, BAE38344, EDL39002

Contig_2165

sffv proviral integration 1

NP_035485

Contig_1473

sh2 containing inositol-5-phosphatase

XP_687502

Contig_2675

skin mucus lectin

BAD90686

Contig_840

strawberry notch homolog 2

EDL31603

Contig_1540

tnf superfamily member 14

NP_001118039, ABC84585

Contig_469

transcription factor 3 isoform cra_b

NP_571169, CAA54305

Contig_2299

transforming growth beta receptor ii (70 80 kda)

XP_534237

Contig_2692

trypsin 10

BAF76146

Contig_1814

vascular endothelial growth factor

NP_001038320, AAY89335

Contig_1660

x-box binding protein 1

AAQ08005

Similarity relationships

Figs. 2 and 3 show SimiTri representation of predicted seabass transcripts compared to Danio rerio, Homo sapiens, Oncorhynchus mykiss, Gasterosteus aculateus and Tetraodon nigroviridis proteomes. Of 3075 isolated unique transcripts 1040, 1051, 1122 1159, 1103 had Blast hits with a score > 50 against the H. sapiens, D. rerio, O. mykiss, G. aculateus and T. nigroviridis protein databases respectively.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-157/MediaObjects/12864_2008_Article_2041_Fig2_HTML.jpg
Figure 2

Similarity of D. labrax ESTs to the proteomes of Homo sapiens (H), Danio rerio (D), Oryzias latipes (M), Gasterosteus aculateus (ST) and Tetraodon nigroviridis (T). SimiTri plots show the graphical similarity i) between putative D. labrax peptides and H. sapiens, O. latipes and T. nigroviridis, cytochrome b is circled in red ii) between putative D. labrax peptides and H. sapiens, O. latipes and G. aculateus, iii) between putative D. labrax peptides and H. sapiens, D. rerio and T. nigroviridis, iv) between putative D. labrax peptides and H. sapiens, D. rerio, G. aculateus, v) between putative D. labrax peptides and H. sapiens, D. rerio, O. latipes, as well as vi) between putative D. labrax peptides and H. sapiens, D. rerio, T. nigroviridis.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-157/MediaObjects/12864_2008_Article_2041_Fig3_HTML.jpg
Figure 3

Similarity of D. labrax ESTs to the proteomes of Danio rerio (D), Oryzias latipes (M), Gasterosteus aculateus (ST) and Tetraodon nigroviridis (T). SimiTri plots show the graphical similarity A: between putative D. labrax peptides and G. aculateus, O. latipes and T. nigroviridis, B: between putative D. labrax peptides and G. aculateus, O. latipes and D. rerio.

Expression analysis

The test to compare multiple cDNA libraries with each other [22] revealed that the genes with the value > 6 of the test statistic R can be confidently considered as genes with true variation with the slope of 1.081 and are therefore not significantly different from -1 at the 5% level (see Additional file 6). The hits with R > 6 are in total 109 out of 2234 contigs resulting from EST sequences of liver, spleen and head kidney infected with Nodavirus and V. anguillarum. The list of the 109 transcripts with R > 6 and their putative homologues are shown in Additional file 5. It is interesting to note that although most transcripts were abundantly expressed in both bacterial and viral infected tissues, not all of them could be considered as specific markers of a specific infection. For example, fructose-1,6-biphosphate aldolase A, hepcidin, apolipoprotein A1 precursor, ferritin heavy chain and chemokine receptor 4 transcripts were found in V. anguillarum-infected tissues, though rarely in Nodavirus-infected tissues (see Additional file 5). Conversely, fructose-1,6-biphosphate aldolase B and 14 kDa apolipoprotein transcripts were frequently observed in Nodavirus infected tissues compared with V. anguillarum-infected tissues. The above results were further validated by determining the expression of putative markers for each infection in key tissues using real-time PCR. Here also control tissues were included in order to determine the expression of untreated fish. The real-time PCR confirmed the results obtained with the in silico analysis for selected genes. Taking into account the fold inductions of the real-time PCR experiments the correlations between in silico and qPCR are uniform. For instance the transcript for hepicidin precursor revealed in silico (R = 298.16) high expression only in liver tissues infected with V. anguillarum. The real-time PCR results show higher expression in all three tissues infected with V. anguillarum. However fold induction in liver is 20,000 times more than in spleen tissue, therefore theoretically 20,000 more cDNA clones had to be sequenced to obtain the sequence for hepicidin precursor in spleen infected with V. anguillarum. This correlation of high fold induction with in silico results can be observed for each transcript examined in this study. Thus, while the mRNA levels of hepcidin were found to increase considerably 24 h post-infection in the liver, spleen and head kidney of V. anguillarum-infected fish, they increased only slightly in Nodavirus-infected fish (Fig. 4). Notably, although the mRNA levels of transferrin and ferritin, both involved in iron metabolism with spleen and liver as the two main organs, increased in the liver after infection with both pathogens, they increased only in the spleen of V. anguillarum-infected animals (Figs. 5a and 5b).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-157/MediaObjects/12864_2008_Article_2041_Fig4_HTML.jpg
Figure 4

Hepicidin precursor expression index of liver, spleen, and head kidney infected with Nodavirus or with V. anguillarum for 4 h and 24 h. Infection for 4 h and 24 h of head kidney with V. anguillarum is pooled as not enough material was available. Each bar represents the mean of two technical duplicates of cDNA originating out of three individuals pooled prior to RNA extraction.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-157/MediaObjects/12864_2008_Article_2041_Fig5_HTML.jpg
Figure 5

(A) Ferritin expression index of liver and spleen infected with Nodavirus or with V. anguillarum for 4 h and 24 h. (B) Transferrin expression index of liver and spleen infected with Nodavirus or with V. anguillarum for 4 h and 24 h. Each bar represents the mean of two technical duplicates of cDNA originating out of three individuals pooled prior to RNA extraction.

The mRNA levels of the chemokine receptor 4 were not affected or were slightly reduced in the head kidney and spleen of Nodavirus-infected fish but were considerably increased in these two tissues after V. anguillarum infection (Fig. 6). On the other hand, the mRNA levels of the 14 kDa apolipoprotein increased in the fish livers infected with both pathogens, but at 4 h and 24 h post-infection in the case of the Nodavirus and at 4 h post-infection in the case of V. anguillarum (Fig. 7). Here the expression in the liver is studied, as the liver is the major organ in the production of apoliprotein. Finally, although the mRNA levels of fructose-1,6-bisphosphate aldolase B decreased in the liver and head kidney following infection with both pathogens, they increased at 24 h post-infection in the spleen of V. anguillarum-infected fish (Fig. 8).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-157/MediaObjects/12864_2008_Article_2041_Fig6_HTML.jpg
Figure 6

Chemokine (c-x-c motif) receptor 4 (Cxcr 4) expression index of liver, spleen, and head kidney infected with Nodavirus or with V. anguillarum for 4 h and 24 h. Infection for 4 h and 24 h of head kidney with V. anguillarum is pooled as not enough material was available. Each bar represents the mean of two technical duplicates of cDNA originating out of three individuals pooled prior to RNA extraction. Between the two technical replicates of Noda 4 h and of VA 24 h a greater variation was detected. The values of the two replicates of Noda 4 h are 0.014 and 0.00095 and the values of the two replicates of VA 24 h are 0.03 and 0.009.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-157/MediaObjects/12864_2008_Article_2041_Fig7_HTML.jpg
Figure 7

14 KDa apolipoprotein expression index of liver infected with Nodavirus or with V. anguillarum for 4 h and 24 h. Each bar represents the mean of two technical duplicates of cDNA originating out of three individuals pooled prior to RNA extraction.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-157/MediaObjects/12864_2008_Article_2041_Fig8_HTML.jpg
Figure 8

Fructose-1,6-bisphosphatase aldolase B expression index of liver, spleen, and head kidney infected with Nodavirus or with V. anguillarum for 4 h and 24 h. Infection for 4 h and 24 h of head kidney with V. anguillarum is pooled as not enough material was available. Each bar represents the mean of two technical of cDNA originating out of three individuals pooled prior to RNA extraction.

Discussion

Although viral and bacterial infections are among the key challenges in fish aquaculture, nevertheless today the immune response of fish against V. anguillarum and Nodavirus remains largely unknown. Identification of genes involved in the immune response as well as the detection of differentially expressed genes between the two infections can make a significant contribution to future research leading to a better understanding of the biological system of immune response after fish infection. In the present study ten cDNA libraries, six from tissues infected with V. anguillarum and four from tissues infected with Nodavirus were analysed. Analysis of EST sequences coming from infected tissues will enhance the construction of an immune specific microarray chip containing already known transcripts involved in immune-related biological processes, such as the immune response as well as transcripts for which no annotation is available so far. Furthermore, transcripts indicating a higher expression level in one of the infections can be taken for future functional studies at RNA or DNA level as well as at protein level.

Over the past 30 years cDNA cloning for gene discovery and transcriptome analysis has become a very important molecular technique. Various techniques have been developed to address several scientific issues such as the cloning of rare transcripts, the construction of libraries with a wider cloning range, etc. (for review [26]). Construction of non-normalized libraries in the present study gave a first insight into the tissue-specific manner of transcript abundance according to their origin. Besides the possibility of identifying higher expressed transcripts, the percentages of unique sequences can also be assessed. In this study the redundancy of the cDNA libraries of liver, spleen and kidney infected with Nodavirus and V. anguillarum was in agreement with all three tissues (~33%, ~63% and ~38% respectively). This result is in line with other cDNA libraries of various fish species where the redundancy ranges between 40% and 60% depending on the tissues of origin [e.g. [12, 13]]. Besides the identification and characterization of ESTs for components of the immune system, detection of microsatellite sequences will help in the completion of quantitative trait locus (QTL) scans currently being performed. Microsatellite sequences, also called Simple Sequence Repeats (SSR), are frequent in non-coding regions and are used as molecular markers. Detection of SSR within ESTs (exonic microsatellites or EST-SSRs) presents a shortcut to obtaining microsatellite markers. Since EST-SSRs are exonic they have two advantages over intergenic microsatellites. First, it is expected that their flanking regions are more conserved, so that the primers can be used even in related species, and second, it is assumed that they are in strong linkage disequilibrium with functionally important sites. Therefore they are frequently used in population genomics or in mapping of genes of economic significance identified as candidate markers for QTL and/or quantitative trait nucleotide (QTN). For EST similarity search in the present study a homologue of a known gene is defined as a cDNA whose similarity to a gene of any other organism in the database exceeds a certain fixed threshold. The identification of orthologues is outside the scope of this study. In total 1246 (41%) were assigned to a known transcript, with 79 (6%) categorized to the GO category "immune system process". Separate examination of these 79 transcripts by GO annotation reveals their involvement in 11 other categories of biological function (Fig. 9), with three dominant categories of response to stimulus, cellular process, and biological regulation. This collection should provide the base material for further research into understanding the immune response of European seabass as well as for the isolation of putative biomarkers.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-10-157/MediaObjects/12864_2008_Article_2041_Fig9_HTML.jpg
Figure 9

GO categorization of only the EST sequences grouped into the category of immune system process.

Similarity relationships

Comparison of predicted seabass genes compared to the genomes of zebrafish, medaka, tetraodon, stickleback and human (Fig. 2) showed that the majority of putative proteins were located in the centre. From separate examination of the different triads a bias towards the top and right sections is revealed. This bias is not unexpected as seabass is more closely related to medaka, zebrafish, stickleback and tetraodon. However it is worth noting, that the seabass cytochrome b seems to be more similar to human cytochrome b than to the tetraodon and medaka cytochrome b as shown in Fig. 2. This was not the case with stickleback and zebrafish cytochrome b. Interestingly, results from comparisons of putative proteins of the Atlantic halibut (Hippoglossus hippoglossus) with the human, zebrafish and tetraodon protein database showed that the halibut cytochrome c oxidase subunit 3 (Cox3) is more similar to human COX3 than the zebrafish and tetraodon Cox3 [13]. Comparison of predicted proteins with only the protein database of fish genomes shows a slight bias towards medaka and stickleback looking at the triad medaka, stickleback and tetraodon (Fig. 3A) and again a slight bias towards medaka and stickleback looking at the triad stickleback, medaka and zebrafish (Fig. 3B). These results give a first insight towards the evolution of immune related genes as the relatively equal distribution indicate that sequence variation between the clade Percomorpha is comparable to that between the clade Percomorpha and Ostariophysi.

Expression analysis

For in silico expression analysis transcript appearing more than once in the cDNA libraries were selected and their relative abundance were submitted to expression analysis after Stekel et al. [22]. Validation of in silico analysis was performed by qPCR. Individual variation may be masked in this approach as pooling strategy was chosen for qPCR experiments. The differential expressed transcripts detected in the present study can be further put forward for analysis of individual expression pattern. Nonetheless in order to study individual expression pattern the sampling frame has to be extended. In the present study the pooling strategy for qPCR was chosen in order to show cross-method consistency. However since results are consistent between the two approaches, influence of between individuals variability in response to infection has been addressed to some extent. In addition total RNA for qPCR analysis was extracted out of different individuals than the once used for cDNA library construction and patterns appear to be consistent between the different samples for all the selected candidate genes, which reflect the robustness of the approach and the small, if any bias, contributed by individual outliers. In silico expression analysis revealed a number of genes for R > 6 that are considerably above the exponential curve (see Additional file 6). Genes with R > 6 can be considered as significant and thus are candidate genes for further studies. Several of those transcripts including transcripts involved in iron metabolism such as ferritin and transferrin are also reported as differential expressed genes in the catfish Ictalurus punctatus and Ictalurus furcatus infected with the gram negative bacterium Edwardsiella ictaluri [27, 28]. One of the main mechanisms whereby gram-negative bacteria pathogens like V. anguillarum obtain iron is the use of free heme or heme proteins from the host tissues [29]. The heme uptake mechanisms are considered to contribute to V. anguillarum virulence in fish [29]. However, it is surprising that Nodavirus infection also resulted in the up-regulation of transferrin and ferritin expression, especially within 24 h of infection. The abundance of transferrin transcripts in Nodavirus-infected tissues may not be related to the alteration of the iron metabolism by the pathogen but rather to the ability of enzymatically cleaved forms of this protein to activate fish macrophages [30]. The specific alteration of iron metabolism by V. anguillarum infection is also supported by the higher abundance of transcripts coding for hepcidin, a major homeostatic regulator of iron metabolism [31], and for α and β chains of hemoglobin in V. anguillarum- than Nodavirus-infected livers (255 clones vs. 1 clone, respectively). The expression of Hepicidin after bacterial infection has been shown in seabass [32] as well as in several other fish species like the striped bass [33], the red sea bream [34], the catfish [35, 36], the Atlantic halibut [37], the zebrafish [38], the rainbow trout [39] and the perch [40]. In this study the qPCR experiments confirmed the up-regulation of hepicidin in D. labrax after infection with V. anguillarum and showed in addition to this, that the expression of hepcidin might be considered as an excellent marker of bacterial infections, since it was up-regulated in all examined tissues of V. anguillarum-infected fish but unaffected in Nodavirus-infected tissues. Another interesting observation of the in silico gene expression analysis is the differential abundance of transcripts encoding the isoforms A and B glycolytic/gluconeogenic enzyme fructose-1,6-biphopshate aldolase in bacterial and viral infected tissues. Although the role played by this enzyme in the outcome of these infections is difficult to anticipate due to its dual role in glucose metabolism, these results suggest that the expression ratio between the two enzyme isoforms may be used as a good indicator of the type of infection in the European seabass. Thus, the up-regulation of the B isoform in the spleen exclusively by V. anguillarum might be considered another potential marker for this bacterial infection. Similarly, apolipoprotein A1 and 14 kDa apolipoprotein, two major components of high density lipoproteins (HDL) and synthesized in the fish liver [41], also show a differential expression in the liver of fish infected with V. anguillarum and Nodavirus following the time course and, therefore, they also may be good candidate indicators of the fish health status and/or the type of infection. The real-time PCR confirmed observations of in silico expression analysis and also revealed that the expression of the 14 kDa apolipoprotein and aldolase B in the spleen is an appropriate marker of Nodavirus and V. anguillarum infections, respectively. Previous studies in carp and medaka have also shown the involvement of apolipoproteins in the immune response [42, 43]. Finally, the differential expression of one of the clear immune-related genes, the chemokine receptor 4, was also found to be a good putative marker for V. anguillarum infection. For assessment of variability of putative markers further studies looking at individuals, exposed to other environmental or pathogenic conditions are needed to exclude possible biological variability caused by infections.

Conclusion

In this study we generated a collection of EST sequences from tissues of the European seabass infected with V. anguillarum and Nodavirus. We compared gene expression of different tissues after viral and pathogenic bacteria infection. A collection of 3075 unigenes was generated and candidate microsatellite sequences detected. Furthermore, comparisons of D. labrax transcripts with zebrafish, human, tetraodon, medaka and stickleback were performed. The majority of putative proteins were located in the centre with a bias towards the right sections, with D. labrax as expected being more closely related to the other fish species than to human. Comparison of putative D. labrax proteins was also performed among fish species. In this case a slight bias towards stickleback and medaka was observed when comparing medaka, stickleback and tetraodon and a slight bias towards stickleback and medaka was observed when comparing medaka, stickleback and zebrafish. Furthermore, in silico analysis of differential gene expression between the two infections based on EST sequences suggests a list of genes with a presumed function in the immune response of D. labrax revealing also the importance of looking at "non-classical" immune host proteins and emphasizing the significance of EST sequences generated from cDNA libraries of infected fish tissues. In addition, we show the power of sequencing cDNA sequences for expression analysis by performing real-time PCR experiments for transcripts with high, medium and low R-value. In view of new and high throughput sequence techniques detection of differential expression by measuring in silico the abundance of each transcript will enhance significantly the era of functional genomics. Furthermore in silico analysis in this study, followed by the confirmation with real-time PCR of potentially interested genes, has revealed some of them as potential biomarkers for bacterial and viral infections in fish.

Methods

Experimental condition and tissues collection

Two infections, one with Nodavirus strain 475-9/99 isolated from diseased sea bass [from the Instituto Zooprofilattico Sperimentale delle Venezie (Italy) [16]] and one with V. anguillarum strain R-82 (serogoup 01) [from the University of Santiago (Spain) [14]] were performed with seabass as previously described [14, 16]. Tissues were taken 4 and 24 h post-infection. Three tissue types (spleen, liver and head kidney) of each experimental condition as well as peritoneal exudate, gill, intestine from V. anguillarum infection and brain from Nodavirus infection were selected and immediately frozen with liquid nitrogen. The experiments described comply with the guidelines of the European Union Council (86/609/EU) for the use of laboratory animals and have been approved by the Bioethical Committee of the University of Murcia (Spain) and the CSIC National Committee on Bioethics.

In brief; For Nodavirus infection fish were injected intramuscularly with 100 μl of nodavirus suspension in Minimum Essential Medium (MEM) (5.9 × 106 TCID50 ml-1) and placed at 25°C. Mock-infected control fish were injected with the medium alone, and maintained under the same experimental conditions. Three fish from each experimental and control groups were sampled 4 and 24 hours post-infection. Animals were sacrificed by anesthetic (MS-222) overdose and dissected. For the present study brain, spleen, head kidney and liver were sampled. For V. anguillarum infection fish were injected intraperitoneally (i.p.) with 1 ml of phosphate-buffered saline (PBS) alone or containing either 2 × 106 live or 108 formalin-killed V. anguillarum R82 cells (serogroup 01). Under these experimental conditions, about half of the fish were moribund at 24 h post-infection and all of them died within 48 h post-infection. Head kidney (bone marrow equivalent of fish) and peritoneal exudate cells were obtained 4 h and 24 h after bacterial challenge.

RNA extraction

Total RNA was extracted using the NucleoSplin RNA II extraction kit (Machinery Nagel, Dueren, Germany). RNA quality was checked on EtBr stained agarose gels and RNA concentrations and purity were measured using a NanoDrop spectrophotometer. For library construction equal amounts of total RNA extracted out of infected tissues (4 h and 24 h) were pooled. For qPCR experiments total RNA was freshly extracted out of infected tissues originating from three different individuals pooled prior to RNA extraction (liver, spleen and head kidney) with 4 h and 24 h post-infection.

cDNA library construction

All libraries were constructed from total RNA using the Creator SMART cDNA library construction kit (BD Bioscience-Clontech, Mountain View, Canada) using the LD PCR based method. Between 20 and 22 PCR cycles were performed before size separation of inserts. cDNA fragments > 600 bp were selected and directionally ligated at the restriction site Sfi1 of the pDNR-lib vector (BD Clontech) or the pal 32 vector. Plasmids were transformed into E. coli strain DH10B (Invitrogen) by electroporation. The libraries were tested for the presence and the size of insert by PCR using two primer pairs. For the libraries constructed with pal 32 vector, the primer pair pal 32 FOR: 5'-CTCGGGAAGCGCGCCATT-3' and pal 32, REV: 5'-TAATACGACTCACTATAGGGC-3' were used. For the libraries constructed with pDNR-lib vector pDNR FOR: 5'-TAAAACGACGGCCAGTA-3' pDNR REV: 5'-GAAACAGCTATGACCATGTTC-3' were used. The products were run on an EtBr stained agarose gel.

DNA sequencing

After plasmid preparation, dideoxy-temination DNA cycle sequencing was performed using the BigDye 3.1 sequencing method and the pDNR FOR (5'-TAAAACGACGGCCAGTA-3') primer. The sequences were run on an ABI 3730 XL sequencer at MPI Molecular Genetics, Berlin.

Sequence analysis

The raw sequence reads were quality-trimmed and vector- and poly-A-clipped using PREGAP4 [18]. Clustering (grouping of clones related to one another by sequence homology) was performed using the software SeqManII (DNAstar Inc.). After clustering the term 'contig' is used to describe the sequence obtained from one cluster (the sequences of a cluster can be collapsed into a single, non-redundant sequence) and the term 'singleton' describes sequences appearing only once in the entire dataset. The set of sequences obtained by merging contigs and singletons are named as unique sequences.

Simple Sequence Repeats (SSR) in EST sequences

In silico mining for repeat motifs within the obtained unique sequences was perfomed with the programme Msatfinder http://www.genomics.ceh.ac.uk/msatfinder/[19].

Homology search and GO annotation

Gene Ontology (GO) category (Biological process) was assigned after BLASTX search of 3075 unique EST sequences using BLAST2GO. Threshold cutoff was at E-value 1e-3 and the alignment length of 33 amino acids (aa).

Similarity relationships

The unique sequences from all seabass libraries were submitted to BLASTX similarity searches [20] against the zebrafish, tetraodon, stickleback, medaka and human predicted proteomes (downloadable from http://www.ensembl.org/index.html). For each database the highest BLAST scores (bit score values) in excess of 50 were retained. Relative similarities between triads were visualized as a triangular plot generated by the SimiTri software [21].

Expression analysis

In silico

All sequences of each cDNA library were submitted to BLASTX and BLASTN searches [20]. Transcripts appearing more than once in the cDNA libraries were selected for in silico expression analysis after Stekel et al. [22]. In brief, this method allows the comparison of gene expression in any number of libraries in order to identify differential expressed genes. The method uses a single statistical test to describe the extent to which a gene is differentially expressed between libraries by a log likelihood ratio statistic and tends asymptotically to a χ2 distribution [22]. For real-time PCR experiments transcripts with high, medium and low R-value were selected.

Real-time PCR

Gene expression was assessed by real-time PCR (qPCR) in spleen, head kidney and liver at 4 h and 24 h post-infection. RNAs out of three animals pooled prior to RNA extraction were isolated as described above and were used to obtain cDNA by the Superscript II Reverse Transcriptase and oligo (dT)12–18 primer (Invitrogen) following the manufacturer's instructions. Quantitative PCR assays were performed using the 7300 real-time PCR System (Applied Biosystems) with specific primers (Table 3). Each primer (0.5 μl; 10 μM) and the cDNA template (1 μl) were mixed with 12.5 μl of SYBR green PCR master mix (Applied Biosystems) in a final volume of 25 μl. The standard cycling conditions were 95°C for 10 min. followed by 40 cycles of 95°C 15 s and 60°C 1 min. For all reactions two technical duplicates were performed. The comparative CT method (2-ΔΔ CT method) was used to determine the expression level of analysed genes [23]. After evaluation of β-actin as a suitable reference gene for this study in seabass (data not shown) the expression of the candidate genes was normalized. The use of β-actin as a suitable reference gene was also shown in other fish studies [e.g. [24, 25]].
Table 3

Real-time primer sequences

Name

F/R

Sequence 5'-3'

β-actin

Forward

GTGCGTGACATCAAGGAGAA

β-actin

Reverse

GCTGGAAGGTGGACAGAGAG

Apoliprot

Forward

ATACGTCCTGGCACTGATCC

Apoliprot

Reverse

AGCCTGACCTTGCTCACTGT

Chemokin-R4

Forward

TCAAAACGATGACGGACAAG

Chemokin-R4

Reverse

ACACGCTGCTGTACAGGTTG

Transferrin

Forward

CTGGGAAGTGTGGTCTGGTT

Transferrin

Reverse

CAAGACCTCTTGCCCTTCAG

Ferritin_HC

Forward

ATGCACAAGCTCTGCTCTGA

Ferritin_HC

Reverse

TTTGCCCAGGGTGTGTTTAT

Hepcidin-Prec

Forward

CCAGTCACTGAGGTGCAAGA

Hepcidin-Prec

Reverse

TCAGAACCTGCAGCAGACAC

Aldolase-B

Forward

TGACATTGCTCAGAGGATCG

Aldolase-B

Reverse

AGTTGGACATGGAGGGACTG

Declarations

Acknowledgements

The authors would like to thank Margaret Eleftheriou for carefully proofreading the manuscript. This work was supported by the European Commission's 5th Framework Programme WEALTH (Contract No. 501984, Welfare and health in sustainable aquaculture [WEALTH]).

Authors’ Affiliations

(1)
Institute of Marine Biology and Genetics, Hellenic Center of Marine Research
(2)
Department of Cell Biology and Histology, Faculty of Biology, University of Murcia
(3)
Instituto de Investigaciones Marinas, Consejo Superior de Investigaciones Científicas (CSIC)
(4)
Max-Planck Institute for Molecular Genetics

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

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