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BMC Genomics
Open Access
Research article
Profiling of infection specific mRNA transcripts of the European
seabass Dicentrarchus labrax
Elena Sarropoulou*1, Pilar Sepulcre2, Laura Poisa-Beiro3, Victoriano Mulero2,
José Meseguer2, Antonio Figueras3, Beatriz Novoa3, Vasso Terzoglou1,
Richard Reinhardt4, Antonios Magoulas1 and Georgios Kotoulas1
Address: 1Institute of Marine Biology and Genetics, Hellenic Center of Marine Research, PO Box 2214, 710 03 Iraklio, Crete, Greece, 2Department
of Cell Biology and Histology, Faculty of Biology, University of Murcia, 30100 Murcia, Spain, 3Instituto de Investigaciones Marinas, Consejo
Superior de Investigaciones Científicas (CSIC), Eduardo Cabello, 6 36208, Vigo, Spain and 4Max-Planck Institute for Molecular Genetics,
Ihnestrasse 63–73, 14195 Berlin-Dahlem, Germany
Email: Elena Sarropoulou* - sarris@her.hcmr.gr; Pilar Sepulcre - mpsepul@um.es; Laura Poisa-Beiro - laurap@iim.csic.es;
Victoriano Mulero - vmulero@um.es; José Meseguer - meseguer@fcu.um.es; Antonio Figueras - antoniofigueras@iim.csic.es;
Beatriz Novoa - virus@iim.csic.es; Vasso Terzoglou - vassoter@her.hcmr.gr; Richard Reinhardt - rr@molgen.mpg.de;
Antonios Magoulas - magoulas@her.hcmr.gr; Georgios Kotoulas - kotoulas@her.hcmr.gr
* Corresponding author
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.
Published: 10 April 2009
BMC Genomics 2009, 10:157 doi:10.1186/1471-2164-10-157
Received: 11 October 2008
Accepted: 10 April 2009
This article is available from: http://www.biomedcentral.com/1471-2164/10/157
© 2009 Sarropoulou et al; licensee 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.
BMC Genomics 2009, 10:157 http://www.biomedcentral.com/1471-2164/10/157
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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. Con-
sequently, 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 illu-
minating 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), [1-5] as well as in the Atlantic (e.g. Atlantic
halibut Hippoglossus hippoglossus, Salmon Salmo salar) e.g. [6-
13]. These studies focused mainly on non-challenged tissues
in order to obtain a first unigene catalogue. Aquaculture pro-
duction 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 Noda-
virus [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 com-
mercial 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 bac-
teria infection. Additionally we determined in silico differen-
tial 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 Euro-
pean seabass infected with V. anguillarum (liver, spleen, head
kidney, peritoneal exudate, gill and intestine) with perito-
neal exudate, gill and intestine as target organs for V. anguil-
larum 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, stickle-
back, tetraodon and human proteomes were performed.
Genes showing in silico differential expression between
Nodavirus infection and V. anguillarum infection were fur-
ther 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 approx-
imately 0.5 to 2.0 kb. Single pass sequencing was per-
formed 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 car-
ries 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 sys-
tem process" was represented by 79 transcripts.
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 homo-
logues 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 dif-
ferent 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).
Similarity relationships
Figs. 2 and 3 show SimiTri representation of predicted sea-
bass transcripts compared to Danio rerio, Homo sapiens,
Oncorhynchus mykiss, Gasterosteus aculateus and Tetraodon
nigroviridis proteomes. Of 3075 isolated unique tran-
scripts 1040, 1051, 1122 1159, 1103 had Blast hits with a
score > 50 against the H. sapiens, D. rerio, O. mykiss, G. acu-
lateus and T. nigroviridis protein databases respectively.
Expression analysis
The test to compare multiple cDNA libraries with each
other [22] revealed that the genes with the value > 6 of the
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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, fruc-
tose-1,6-biphosphate aldolase A, hepcidin, apolipopro-
tein A1 precursor, ferritin heavy chain and chemokine
receptor 4 transcripts were found in V. anguillarum-
infected tissues, though rarely in Nodavirus-infected tis-
sues (see Additional file 5). Conversely, fructose-1,6-
biphosphate aldolase B and 14 kDa apolipoprotein tran-
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
Summary of GO category Biological process of unique ESTs obtained from seabass cDNA libraries infected with Nodavirus and Vibrio anguillarumFigure 1
Summary of GO category Biological process of unique ESTs obtained from seabass cDNA libraries infected
with Nodavirus and Vibrio anguillarum.
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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
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scripts were frequently observed in Nodavirus infected tis-
sues 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. Tak-
ing 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 pre-
cursor 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 consider-
ably 24 h post-infection in the liver, spleen and head kid-
ney 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).
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 infec-
tion (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 pro-
duction of apoliprotein. Finally, although the mRNA lev-
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
Table 2: Transcripts isolated for the first time in D. labrax and grouped to the GO category" immune system process" (Continued)
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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)Figure 2
Similarity of D. labrax ESTs to the proteomes of Homo sapiens (H), Danio rerio (D), Oryzias latipes (M), Gaster-
osteus 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.
M M
H T H
H
T
ST H
ST
D
ST
H
D
M H
D
T
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els 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).
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 construc-
tion 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 func-
tional 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 devel-
oped to address several scientific issues such as the clon-
ing 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 possibil-
ity of identifying higher expressed transcripts, the percent-
ages 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 charac-
terization of ESTs for components of the immune system,
detection of microsatellite sequences will help in the com-
pletion of quantitative trait locus (QTL) scans currently
being performed. Microsatellite sequences, also called
Simple Sequence Repeats (SSR), are frequent in non-cod-
ing 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
Similarity of D. labrax ESTs to the proteomes of Danio rerio (D), Oryzias latipes (M), Gasterosteus aculateus (ST) and Tetraodon nigroviridis (T)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. acu-
lateus, O. latipes and T. nigroviridis, B: between putative D. labrax peptides and G. aculateus, O. latipes and D. rerio.
A: B:
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Hepicidin precursor expression index of liver, spleen, and head kidney infected with Nodavirus or with V. anguillarum for 4 h and 24 hFigure 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.
Liver
Spleen
Head kidney
0
5000
10000
15000
20000
Control
4h
Control
24h
Noda
4h
Noda
24h
VA
4h
VA
24h
He icidin / b-actin
p
0
0,2
0,4
0,6
0,8
1
Control
4h
Control
24h
Noda
4h
Noda
24h
VA
4h
VA
24h
He
p
icidin / b-actin
0
0,005
0,01
0,015
0,02
0,025
Control
4h
Control
24h
Noda
4h
Noda
24h
VA
4h & 24h
He icidin / b-actin
p
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(A) Ferritin expression index of liver and spleen infected with Nodavirus or with V. anguillarum for 4 h and 24 hFigure 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.
A:
B:
Spleen
Liver
Spleen
0
0,5
1
1,5
2
Control
4h
Control
24h
Noda
4h
Noda
24h
VA
4h
VA
24h
Ferritin/b-actin
0
20
40
60
80
100
120
140
160
Control
4h
Control
24h
Noda
4h
Noda
24h
VA
4h
VA
24h
Ferritin/b-actin
0
0,05
0,1
0,15
0,2
0,25
0,3
Transferrin /b-actin
Control
4h
Control
24h
Noda
4h
Noda
24h
VA
4h
VA
24h
Liver
0
20
40
60
80
100
Control
4h
Control
24h
Noda
4h
Noda
24h
VA
4h
VA
24h
Transferrin /b-actin
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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 hFigure 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.
Liver
0
0,005
0,01
0,015
0,02
0,025
0,03
0,035
0,040
Control
4h
Control
24h
Noda
4h
Noda
24h
VA
4h
VA
24h
Cxcr 4/b-actin
Spleen
0
0,02
0,04
0,06
0,08
0,1
Control
4h
Control
24h
Noda
4h
Noda
24h
VA
4h
VA
24h
Cxcr 4/b-actin
Head kidney
0
0,05
0,1
0,15
0,2
0,25
0,3
0,35
0,4
Control
4h
Control
24h
Noda
4h
Noda
24h
VA
4h&24h
Cxcr 4/b-acti n
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14 KDa apolipoprotein expression index of liver infected with Nodavirus or with V. anguillarum for 4 h and 24 hFigure 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.
Liver
0
100
200
300
400
500
600
700
Control
4h
Control
24h
Noda
4h
Noda
24h
VA
4h
VA
24h
14kDa Apolipoprotein / b-actin
Spleen
0
0,01
0,02
0,03
0,04
0,05
Control
4h
Control
24h
Noda
4h
Noda
24h
VA 4h VA
24h
14kDa A rotein / b-actino
p
oli
pp
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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 hFigure 8
Fructose-1,6-bisphosphatase aldolase B expression index of liver, spleen, and head kidney infected with Noda-
virus 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.
Liver
0
0,5
1
1,5
2
2,5
3
3,5
Control
4h
Control
24h
Noda
4h
Noda
24h
VA
4h
VA
24h
Aldolase B / b-actin
Spleen
0
0,001
0,002
0,003
0,004
0,005
0,006
0,007
0,008
Control
4h
Control
24h
Noda
4h
Noda
24h
VA
4h
VA
24h
Aldolase B / b-actin
0
0,02
0,04
0,06
0,08
0,1
0,12
Control
4h
Control
24h
Noda
4h
Noda
24h
VA
4h & 24h
Aldolase B / b-actin
Head kidney
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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 fre-
quently 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 cat-
egories of biological function (Fig. 9), with three domi-
nant 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.
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 pro-
teins were located in the centre. From separate examina-
tion 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 sea-
bass cytochrome b seems to be more similar to human
cytochrome b than to the tetraodon and medaka cyto-
chrome 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 look-
ing 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 distribu-
tion indicate that sequence variation between the clade
Percomorpha is comparable to that between the clade Per-
comorpha 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
GO categorization of only the EST sequences grouped into the category of immune system processFigure 9
GO categorization of only the EST sequences grouped into the category of immune system process.
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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 sam-
pling 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 con-
sistent 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 robust-
ness of the approach and the small, if any bias, contrib-
uted 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 tran-
scripts including transcripts involved in iron metabolism
such as ferritin and transferrin are also reported as differ-
ential expressed genes in the catfish Ictalurus punctatus and
Ictalurus furcatus infected with the gram negative bacte-
rium Edwardsiella ictaluri [27,28]. One of the main mech-
anisms 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. anguil-
larum 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 alter-
ation of the iron metabolism by the pathogen but rather
to the ability of enzymatically cleaved forms of this pro-
tein to activate fish macrophages [30]. The specific altera-
tion 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 Hepici-
din 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 experi-
ments 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 abun-
dance of transcripts encoding the isoforms A and B glyco-
lytic/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 expres-
sion ratio between the two enzyme isoforms may be used
as a good indicator of the type of infection in the Euro-
pean seabass. Thus, the up-regulation of the B isoform in
the spleen exclusively by V. anguillarum might be consid-
ered 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 expres-
sion 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 infec-
tions, 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 puta-
tive marker for V. anguillarum infection. For assessment of
variability of putative markers further studies looking at
individuals, exposed to other environmental or patho-
genic 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.
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
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anguillarum and Nodavirus. We compared gene expres-
sion of different tissues after viral and pathogenic bacteria
infection. A collection of 3075 unigenes was generated
and candidate microsatellite sequences detected. Further-
more, comparisons of D. labrax transcripts with zebrafish,
human, tetraodon, medaka and stickleback were per-
formed. 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, stickle-
back and zebrafish. Furthermore, in silico analysis of dif-
ferential gene expression between the two infections
based on EST sequences suggests a list of genes with a pre-
sumed function in the immune response of D. labrax
revealing also the importance of looking at "non-classi-
cal" immune host proteins and emphasizing the signifi-
cance 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, fol-
lowed by the confirmation with real-time PCR of poten-
tially 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 iso-
lated from diseased sea bass [from the Instituto Zoopro-
filattico 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 con-
dition as well as peritoneal exudate, gill, intestine from V.
anguillarum infection and brain from Nodavirus infection
were selected and immediately frozen with liquid nitro-
gen. The experiments described comply with the guide-
lines 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 intra-
muscularly with 100 μl of nodavirus suspension in Mini-
mum 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 anes-
thetic (MS-222) overdose and dissected. For the present
study brain, spleen, head kidney and liver were sampled.
For V. anguillarum infection fish were injected intraperito-
neally (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 Nano-
Drop 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 originat-
ing 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 Bio-
science-Clontech, Mountain View, Canada) using the LD
PCR based method. Between 20 and 22 PCR cycles were
performed before size separation of inserts. cDNA frag-
ments > 600 bp were selected and directionally ligated at
the restriction site Sfi1 of the pDNR-lib vector (BD Clon-
tech) 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 con-
structed 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 prod-
ucts 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 sequenc-
ing method and the pDNR FOR (5'-TAAAACGACG-
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GCCAGTA-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 single-
tons 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 sub-
mitted to BLASTX similarity searches [20] against the
zebrafish, tetraodon, stickleback, medaka and human pre-
dicted 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 statis-
tic 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-infec-
tion. 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 Bio-
systems) 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 tech-
nical 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 sea-
bass (data not shown) the expression of the candidate
genes was normalized. The use of β-actin as a suitable ref-
erence gene was also shown in other fish studies [e.g.
[24,25]].
Authors' contributions
ES contributed to EST production, performed analysis and
the conception, design and manuscript writing. PS per-
formed infections with VA and tissue sampling and con-
tributed to RNA extractions. LP performed qPCR
experiments. VM conceived the study, contributed to the
conception and design of the project and also to the man-
uscript writing. JM contributed to the conception and
design of the project. AF contributed to conception and
design of the project and helped to draft the manuscript.
BN conceived qPCR experiments, Nodavirus infections
and tissue sampling and contributed to conception and
design of the project and helped to draft the manuscript.
VT and RR created the cDNA libraries and performed the
sequencing of the clones. AM contributed to conception
and design of the project and helped to draft the manu-
script. GK conceived the study, contributed to the concep-
tion and design of the project and also to the manuscript
writing.
Additional material
Additional file 1
Appendix 1. Catalogue of all unique sequences obtained from all con-
structed cDNA libraries in this study.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-157-S1.zip]
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Page 17 of 18
(page number not for citation purposes)
Acknowledgements
The authors would like to thank Margaret Eleftheriou for carefully proof-
reading the manuscript. This work was supported by the European Com-
mission's 5th Framework Programme WEALTH (Contract No. 501984,
Welfare and health in sustainable aquaculture [WEALTH]).
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Additional file 2
Appendix 2. EST-SSRs identified among EST sequences of all cDNA
libraries constructed in this study.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-157-S2.doc]
Additional file 3
Appendix 3. EST sequences (after clustering of all 3075) grouped into
GO group Immune system process.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-157-S3.xls]
Additional file 4
Appendix 4. A: CDNA sequences of hepicidin precursor showing putative
isoforms B: CDNA showing microsatellite sequ en ce wi th in sili co SS R p ol-
ymorphism after alignment of 8 sequences and C: sequences of cysteine-
rich protein 1-I showing putative alternative splicing polyadenylations.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-157-S4.doc]
Additional file 5
Appendix 5. Calculated R-values for contigs resulting from the EST
sequencing of the cDNA library of spleen, liver and kidney infected with
Nodavirus and Vibrio anguillarum.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-157-S5.xls]
Additional file 6
Appendix 6. The number of genes for a given value or the test statistic R
is plotted as a function of R. The data falling within 1 < R < 6 are decrees
exponential curve, and decreasing exponentially with R. The slope is -
1.081 with significance at the 5% level of 0.013 and is therefore not sig-
nificantly different from -1 at 5% significance. When R > 6, the number
of genes is above this exponential curve.
Click here for file
[http://www.biomedcentral.com/content/supplementary/1471-
2164-10-157-S6.doc]
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