Fast skeletal muscle transcriptome of the Gilthead sea bream (Sparus aurata) determined by next generation sequencing

  • Daniel Garcia de la serrana1Email author,

    Affiliated with

    • Alicia Estévez2,

      Affiliated with

      • Karl Andree2 and

        Affiliated with

        • Ian A Johnston1Email author

          Affiliated with

          BMC Genomics201213:181

          DOI: 10.1186/1471-2164-13-181

          Received: 7 October 2011

          Accepted: 30 March 2012

          Published: 11 May 2012

          Abstract

          Background

          The gilthead sea bream (Sparus aurata L.) occurs around the Mediterranean and along Eastern Atlantic coasts from Great Britain to Senegal. It is tolerant of a wide range of temperatures and salinities and is often found in brackish coastal lagoons and estuarine areas, particularly early in its life cycle. Gilthead sea bream are extensively cultivated in the Mediterranean with an annual production of 125,000 metric tonnes. Here we present a de novo assembly of the fast skeletal muscle transcriptome of gilthead sea bream using 454 reads and identify gene paralogues, splice variants and microsatellite repeats. An annotated transcriptome of the skeletal muscle will facilitate understanding of the genetic and molecular basis of traits linked to production in this economically important species.

          Results

          Around 2.7 million reads of mRNA sequence data were generated from the fast myotomal of adult fish (~2 kg) and juvenile fish (~0.09 kg) that had been either fed to satiation, fasted for 3-5d or transferred to low (11°C) or high (33°C) temperatures for 3-5d. Newbler v2.5 assembly resulted in 43,461 isotigs >100 bp. The number of sequences annotated by searching protein and gene ontology databases was 10,465. The average coverage of the annotated isotigs was x40 containing 5655 unique gene IDs and 785 full-length cDNAs coding for proteins containing 58–1536 amino acids. The v2.5 assembly was found to be of good quality based on validation using 200 full-length cDNAs from GenBank. Annotated isotigs from the reference transcriptome were attributable to 344 KEGG pathway maps. We identified 26 gene paralogues (20 of them teleost-specific) and 43 splice variants, of which 12 had functional domains missing that were likely to affect their biological function. Many key transcription factors, signaling molecules and structural proteins necessary for myogenesis and muscle growth have been identified. Physiological status affected the number of reads that mapped to isotigs, reflecting changes in gene expression between treatments.

          Conclusions

          We have produced a comprehensive fast skeletal muscle transcriptome for the gilthead sea bream, which will provide a resource for SNP discovery in genes with a large effect on production traits of commercial interest and for expression studies of growth and adaptation.

          Keywords

          Teleost Gene paralogues Splice variants Newbler Roche 454 Myogenesis

          Background

          The gilthead sea bream (Sparus aurata L.) is widely farmed around the Mediterranean with main centres of production in Greece, Turkey, Spain and Italy. This species which is primarily marketed as fresh fish or fillets is also cultivated in the Red Sea, the Persian Gulf, and the Arabian Sea with global production reaching circa 125,000 metric tonnes in 2008 [1]. Gilthead sea bream is a protandrous hermaphrodite that can reach about 70 cm in length and 5 kg body mass. Males become sexually mature after 0.5 kg and by the second year most individuals have become female (>1.5 kg). The axial musculature or fillet is made up of serially arranged myotomes comprising ~65% of body mass containing slow, intermediate and fast muscle fibre types [2]. Fast muscle fibres comprise the bulk of the myotome. The main expansion of fast muscle fibre with growth occurs by a process called mosaic hyperplasia in which myogenic progenitor cells (MPCs) fuse to form new myotubes on the surface of existing muscle fibres giving rise to a mosaic of fibre diameters as the fish matures [36]. MPCs also contribute additional nuclei to the muscle fibre as it expands in length and diameter [5]. In all life history stages, myogenesis involves steps of myoblast proliferation, migration, fusion, terminal differentiation and sarcomere assembly and many of the transcription factors and signaling molecules required for the regulation of these processes have been characterised [7]. In the majority of teleost, mosaic hyperplasia in fast muscle continues until the fish reaches around 40% of its maximum body length [36]. Myogenesis is a highly plastic process in which internal and external signals arising from changing environmental conditions; swimming activity and nutritional inputs are integrated to modify growth patterns [8]. Embryonic temperature regime results in persistent changes in growth patterns in later life affecting the final number and size distribution of muscle fibres in adult fish [5, 6, 9] with potential impacts on flesh quality parameters such as texture [10].

          The application of genomic technologies promises to revolutionise our understanding of the genetic and molecular basis of muscle growth and plasticity in farmed fish species; thereby increasing the efficiency and sustainability of aquaculture production. For example, the discovery of genetic polymorphisms associated with commercially important production traits such as growth rate and flesh quality would form the foundation for marker-assisted selection to produce superior strains for farming. Genomic studies could also enable bioactive nutritional components to be identified in commercial feeds and be used to accelerate the development of more sustainable diets with lower environmental impact. The genome of Atlantic cod (Gadus morhua) has recently been described [11] and several other farmed fish are in the process of being sequenced to draft level including rainbow trout (Oncorhynchus mykiss) [12], Atlantic salmon (Salmo salar) [13] and tilapia (Oreochromis niloticus) [14]. There are also significant genetic resources available for the European sea bass (Dicentrarchus labrax) another important species in Mediterranean aquaculture. For example, Kuhl et al [15, 16] developed a complete BAC-end library from the sea bass and gilthead sea bream genomes using the three-spined stickleback (Gasterosteus aculeatus) genome as a reference for description and annotation. In contrast, there are only 1414 GenBank sequences and 74877 ESTs for the gilthead sea bream (revised on July 2011). These sequencing efforts have allowed the development of microarray platforms for gene expression studies [1719] and sets of microsatellites for selection programs [16]. However, the comparative lack of genetic information is a significant handicap for the development of a serious program for genetic improvement of stocks by marker assistance-selection and for a better understanding of the molecular basis of nutrition, growth, flesh quality, reproduction and disease resistance.

          Next Generation Sequencing (NGS) technologies have the potential to rapidly and cost effectively expand sequence databases for non-model organisms [2022]. In the present study we have used Roche 454 GS FLX titanium sequencing to produce a comprehensive transcriptome of fast skeletal muscle using RNA extracted from adult and juvenile gilthead sea bream subject to different nutritional states and temperatures. The resulting transcriptome with 40-times average coverage was annotated and screened for gene paralogues, alternatively spliced transcripts and microsatellite repeat sequences.

          Results

          Transcriptome assembly

          Four separate cDNA libraries were created from RNA extracted from the fast skeletal muscle of 5 pooled fish per treatment: juveniles (~0.090 kg) which had been either fed to satiation, fasted for 3–5 d (both at 21°C) or acutely transferred to 11 or 33°C over 48 h and maintained for 3-5d with continued feeding. A cDNA library was also created from RNA extracted from the fast skeletal muscle of one adult (~2 kg) fish fed ~3% body mass d-1. 390,000 to 490,000 reads were generated per library giving a total of ~2.7 million sequence reads (Table 1). Reads were deposited in the Sequence Read Archive (SRA) database with the accession number ERP000874 [23]. Sequence reads were assembled using Newbler v2.5 assembler (Roche, 454 Life Sciences). Newbler v2.5 used 42% of the reads to construct 43,461 isotigs over 100 bp ( Additional file 1). The total number of isotigs annotated by Blast2Go was 10,465 (Table 1 and Additional file 2). Details of blast hit and GO distributions for isotigs are provided in Additional file 3 and Additional file 4. Analysis of annotated isotigs revealed the presence of 5655 unique genes transcripts, indicating 46–50% redundancy.
          Table 1

          Number of reads obtained per experimental condition and their respective Newbler assembly results

          Assembler

          Parameters

          Adult

          Fasted

          Juvenile 21°C

          Juvenile 30°C

          Juvenile 11°C

          Total assembly

          Newbler v2.5

          Reads

          351895

          486993

          439734

          459853

          433264

          2711149

           

          Reads assembled

          166460

          239792

          189314

          258331

          200313

          1157833

           

          Singletons

          19809

          24231

          19140

          21125

          24896

          96351

           

          Isotigs

          6502

          9152

          8407

          9922

          9524

          50515

           

          Isotigs over 100 bp

          6254

          8595

          8242

          9554

          9267

          43461

           

          Isotig mean lenght (bp)

          875

          589

          936

          551

          550

          454

           

          N50 (bp)

          1092

          732

          1269

          672

          642

          679

           

          Isotigs Annotated

          2634

          2791

          3031

          3211

          3029

          10465

          Singletons: reads not contained in the final assembly.

          Isotig: contigs consistently connected by a set of reads.

          N50: The value was computed by sorting all contigs from largest to smallest and by determining the minimum set of contigs whose sizes total 50% of the entire transcriptome.

          Assembly validation

          200 full-length cDNAs (80 to 5475 bp) from gilthead sea bream were retrieved from GenBank and blasted against the assembled transcriptome resulting in 80 positive hits suitable for analysis (e-value lower than e-140). Pairwise alignment in ClustalW showed 25% of sequences were identical and 24% differed by only one nucleotide. The proportion of isotigs with more than one nucleotide difference is shown in Figure 1. 85% of differences were mismatches, 7.6% insertions and 7.6% deletions (Figure 1). The coverage of the transcriptome was calculated from a random selection of 200 annotated isotigs and visualized using Tablet software [24]. Coverage for annotated isotigs ranged from 3 to 1,000 times with an average coverage of 40 times.
          http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-181/MediaObjects/12864_2011_4079_Fig1_HTML.jpg
          Figure 1

          (A) Barr chart summarizing the percentage of isotigs showing any change in their sequence compared with 80 NCBI sequences (B) Distribution of the differences between NCBI and transcriptome sequences in categories of mismatch, insertion and deletion. Classification of the discrepancies between sequences was carried out by analysis of the ClustalW alignment.

          Assembly annotation

          Annotated isotigs were attributed to 344 different KEGG pathway maps (see Additional files 5, 6 and 7). In addition, the PI3K/Akt/mTOR pathway and sarcomeric proteins maps were manually constructed to determine the actual representation of components in the transcriptome (Additional file 8 and Figure 2 respectively). In the case of sarcomeric proteins, all major components were shown to be present and isoforms of myosin heavy chain (5 isoforms), actinin (3), tropomyosin (3), actin capping protein (4), myomesin (3), filamin (2), myomezin (2), myosin light chain (2), nebulin (2), myosin binding protein (2), actin (2), titin (2), tropomodulin (3) and troponin C (2) were identified together with potential splice variants of calpain-3 and myopalladin-like (Figure 2). Components of the PI3K/Akt/mTOR pathway also occurred as multiple isoforms including AKT (2), PI3K (2), flotillin (2), integrin β-chain (4) and insulin receptor substrate (IRS) (2). The only PI3K/Akt/mTOR pathway component that was not represented in the transcriptome was the companion of mTOR RAPTOR (Additional file 8).
          http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-181/MediaObjects/12864_2011_4079_Fig2_HTML.jpg
          Figure 2

          Myofibrillar genes represented in the transcriptome mapped onto a reconstruction of a half sarcomere based on published models for filaments and M-line[25]and z-disc structure[26]. Numbers on the right side of the gene name represents isotig length (bp), isotig mean coverage and percentage of identity with the zebrafish orthologue.

          Identification of full-length coding sequences (CDS) and splice variants

          3,000 translated isotigs were manually blasted against the NCBI non-redundant protein (nr) database using blastp. A total of 785 full-length coding sequences were identified. Proteins ranged from 58 to 1536 amino acids (Additional file 9). The 43 genes with splice variants identified among the CDS are summarized in Additional file 10, in all cases one or more exons were predicted to be lost after splicing. Functional domains were identified using InterProScan and 14 splicing events were identified that resulted in some change in domain composition or structure which was predicted to potentially affect their biological function. Because of their biological importance, all 14 genes with a loss of functional domain were verified by PCR, resulting in the experimental confirmation of 12 genes (Table 2).
          Table 2

          Transcripts with functional domains deleted that were experimentally confirmed by PCR

          Isotig annotation

          CDS fraction (%)

          Transcripts coverage

          Orthologs ID

          Number of exons predicted

          Exon deleted

          IPR domain lost

          Function

          Aspartate beta hydroxylase

          37

          4

          ENSTNIT000000003370

          3

          2

          IPR018939

          Authophagic related protein 27 (ATG27)

          Coagulation factor x

          75

          4

          ENSGACT00000011445

          13

          9 and 10

          IPR000294

          GLA domain

                

          IPR000742

          EGF3 domain

          Nucleotide binding

          88

          4

          ENSGACT00000002245

          9

          1 and 2

          IPR000808

          Mrp site

                

          IPR019591

          ATPase like ParA

          Bridging integrator 1

          100

          50

          ENSGACT00000020571

          18

          1 to 10

          IPR004148

          PAR domain

                

          IPR003005

          Amphyphysin

          Paraxonase 2

          87

          5

          ENSTNIG00000018456

          14

          12

          No-IPR

          Signal Peptide

                

          IPR013838

          autoregulation binding site

          Cathepsin H

          75

          4

          ENSTNIG00000022446

          7

          1

          IPR013201

          Proteinase inhibitor, cathepsin propeptide

          Polyadenylate-binding protein- interacting protein 2

          100

          4

          ENSTNIG00000005741

          8

          6

          IPR009818

          Ataxin 2

          Transitional endoplasmic reticulum atpase (cdc48)

          100

          40

          ENSGACG00000018832

          9

          8

          IPR003338

          AAA + Atpase domain

                

          IPR009010

          Aspartate descarboxylase fold

          S-adenosylmethionine decarboxylase

          50

          5

          ENSTNIG00000002751

          6

          5

          IPR018166

          adenosylmethionine descarboxylase

          O-sialoglycoprotein endopeptidase

          62

          4

          ENSGACG00000019496

          6

          5

          IPR017860

          Peptidase M22, Glycopeptidase

          Zinc finger x-chromosomal protein

          30

          4

          ENSGACG00000020679

          2

          1

          IPR007087

          Zinc finger C2H2

          Dead (asp-glu-ala-asp) box polypeptide 1

          57

          6

          ENSGACG00000011162

          13

          8

          IPR000504

          RNA recognition motif domain

          Identification of microsatellite sequences

          The transcriptome was screened for potential microsatellite repeats excluding adenine repetitions, which most likely correspond to polyA tails. Around 750 potential microsatellites were detected in the total isotigs (data available on request from DG). To provide information linked to known sequences, only microsatellites localized in annotated isotigs were further studied. A total of 177 non-redundant microsatellites were identified in annotated isotigs. Dinucleotide repeated motifs were the most abundant, representing 75% of the total, followed by mononucleotide (13%), trinucleotide (11%), tetranucleotide (2%) and pentanucleotide (1%) repeats (Additional file 11). All 177 microsatellite reported in this study were found in predicted UTR regions with 40% of them linked to full-coding sequence genes.

          Identification of gene paralogues

          Translated isotigs from the transcriptome were compared with mouse and teleost proteomes using Inparanoid software producing 3933 positive matches. After removal of false positives and redundant sequences 140 potential paralogues were identified. Phylogenetic analysis confirmed that 26 of these genes were paralogues. 74% of these paralogues (20) were teleost-specific, likely originating from the whole genome duplication event at the base of the teleost radiation (Table 3; paralogues sequences and nwk trees are in Additional file 12 and Additional file 13). Eight Ensembl genes used for the phylogenetical analysis (acethylcholine subunit α-1, carnitine, dysferlin, epithelial factor-3, macroglobulin 2-β, ribosomal protein L5, methylmalonate dehydrogenase and EIF43a) were well annotated and identified as paralogues, but no specific nomenclature was assigned in the database. For two gilthead sea bream paralogues (DUPD1 and FKBP1A) genes from Ensembl were identified as paralogues but not functionally annotated and blastx against the NCBI non-redundant protein database was used to confirm their identity.
          Table 3

          List of paralogues identified in the gilthead sea bream skeletal muscle transcriptome

          Paralogue Gene name

          Gene Function

          Fraction of CDS (%)

          Paralogue1/ paralogue2

          Coverage

          Paralogue1/ Paralogue2

          Paralogues identity (%)

          Nomenclature

          Acethylcoline receptor subunit alpha 1

          Ion-conducting channel

          50/50

          8/12

          85

          Alpha 1.a/1.b

          Adp/atp translocase (Solute carrier family 25, SLC25)

          Catalyzes the exchange of ADP and ATP across the mitochondrial inner membrane

          100/100

          43/87

          92

          SLC25 member 5 and 6

          Calpain small subunit 1

          Calcium-regulated thiol-protease involved in cytoskeletal remodeling

          100/100

          10/11

          79

          Calpain subunit 1a/b

          Carnitine O- acetyltransferase

          Carnitine acetylase is specific for short chain fatty acids

          100/67

          23/7

          67

          Carnitine O-acetyltransferase a1/a2

          Dehydrogenase reductase member 7c

          Putative oxidoreductase

          100/99

          59/32

          60

          DHR7SC-A/DHR7SC-B

          Dysferlin interacting protein 1

          Sarcolemma repair mechanism of both skeletal muscle and cardiomyocytes

          90/90

          39/11

          71

          Dysferlin1a/b

          Epithelial membranse protein 3

          Probably involved in cell proliferation and cell-cell interactions

          100/100

          4/14

          68

          EMP3a/b

          Glioblastoma amplified sequence

          Widely expressed. Most abundant in heart and skeletal muscle

          100/100

          39/10

          80

          Nipsnap2a/b

          High mobility group box 1

          DNA binding proteins that associates with chromatin

          88/60/87

          439/7/9

          60

          HMG1a/b HMG2

          Microglobulin beta-2

          Component of the class I major histocompatibility complex (MHC)

          100/100

          57/90

          60

          B2ma/b

          Myomesin 185 kDa

          Major component of the vertebrate myofibrillar M band

          98/90

          170/54

          65

          Myomesin1a/b

          Serine threonine-protein phosphatase

          Essential for cell division, and participates in muscle contractility and protein synthesis

          50/50

          3/4

          89

          Subunit alpha/gamma

          Solute carrier family 38 member 5

          Sodium-dependent, pyrimidine- and purine-selective. Involved in the homeostasis of endogenous nucleosides

          100/90

          14/5

          73

          Member 5a/b

          Tyrosine 3 monooxigenase

          Enzyme function

          90/100

          5/5

          74

          Ywhab/ywhag2

          Set and mynd domain- containing protein 1

          Acts as a transcriptional repressor. Essential for cardiomyocyte differentiation

          95/97

          154/127

          76

          Smyd1a/b

          Dual specificity phosphatase and pro Isomerase domain containing 1

          Catalyse reaction: Protein tyrosine phosphate + H2O = protein tyrosine + phosphate.

          95/100

          7/7

          51

          DUPD1a/b

          Metalloproteinase inhibitor 2 precursor

          Complexes with metalloproteinases and irreversibly inactivates them

          80/60

          4/4

          62

          TIMP2b/a

          Retinoid x gamma

          Rreceptor for retinoic acid

          100/100

          21/10

          73

          Gamma/beta

          Junctophilin 1

          Contributes to the stabilization of the junctional membrane complexes

          85/87

          7/10

          65

          Junctophilin 1a/b

          60s ribosomal protein l5

          Required for rRNA maturation and formation of the 60 S ribosomal subunits

          100/100

          58/186

          91

          Rpl5a/b

          Trans-2,3-enoyl-CoA reductase

          Reduces trans-2,3-stearoyl-CoA to stearoyl-CoA of long and very long chain fatty acids

          98/98

          11/61

          75

          Trec.a/b

          Methylmalonate- semialdehyde Dehydrogenase

          Plays a role in valine and pyrimidine metabolism. Binds fatty acyl-CoA

          100/100

          26/12

          86

          Aldha1.a/a1.b

          Eukaryotic translation initiation factor 4e type 3

          Its translation stimulation activity is repressed by binding to the complex CYFIP1-FMR1

          100/100

          28/15

          73

          EIF4E3a/b

          Fk506-binding protein 1a

          May play a role in modulation of ryanodine receptor isoform-1 (RYR-1)

          100/100

          52/12

          82

          FKBP1A.1/A.2

          Splicing arginine serine-rich 11

          May function in pre-mRNA splicing.

          95/95

          11/25

          82

          Srf11a/b

          Kelch repeat and btb domain containing 10

          Substrate-specific adapter of an E3 ubiquitin-protein ligase complex

          95/100

          38/15

          53

          Kbtb5/kbtb10

          Transcription related sequences

          Transcription and its regulation is a key component of the cell’s response to its environment and an important target for physiological studies. 320 isotigs were related to transcription, including 218 transcription factors (Table 4). The majority of transcription factors identified were members of the Znf-C2H2 zinc finger sub-family (16.8%) followed by the bZIP (7.8%), beta-scaffold (7.5%), bHLH (6.5%) and general transcription factor (6.2%) families. Homeobox, High Mobility Group, Nuclear Receptors and others families represented less than 6% each of the sequences (Table 4). Over half of the non-transcription factor sequences were identified as co-factors and chromatin-associated proteins (Detailed list in Additional file 14).
          Table 4

          Transcription factor families present in the gilthead sea bream transcriptome

          Transcription Factor Family

          Example of family member

          Number of isotigs

          Percentage of total transcription factors

          ZnF-C2H2

          interleukin enhancer-binding factor 3

          54

          16.8

          Chromatin-associated

          yy1 transcription represor factor

          32

          10.0

          Cofactor

          e1a binding protein p300

          25

          10.9

          Beta-scaffold

          signal transducer and activator of transcription 3

          24

          7.5

          bZIP

          transcription factor jun-d

          25

          7.8

          bHLH

          hypoxia-inducible factor 3 alpha

          21

          6.5

          General transcription factor

          transcription factor 20

          20

          6.2

          Protein-protein interaction

          zinc finger and btb domain containing 33

          16

          5.0

          Homeobox

          six homeobox 1

          14

          4.4

          Others

          bromodomain adjacent to zinc finger 2b

          19

          5.9

          Nuclear hormone receptor

          Peroxisome proliferator-activated receptor alpha

          15

          4.7

          ZnF-Others

          glucocorticoid receptor dna-binding factor 1

          16

          5.0

          High mobility group box

          transcription factor sox-6 isoform 2

          8

          2.5

          Trp-clusters

          interferon regulatory factor 2

          9

          2.8

          Forkhead

          forkhead box o3

          4

          1.2

          TEA

          tea domain family member 3

          3

          0.9

          Dwarfin

          smad family member 2

          3

          0.9

          E2F

          e2f transcription factor 6

          2

          0.6

          Genes were categorized according to the description in Uniprot [27] and TFCONES, Institute of Molecular and Cell Biology [28].

          Znf-C2H2: Zinc finger domain with two conserved cysteines and two histidines co-ordinate a zinc ion.

          bZIP basic leucine zipper.

          bHLH basic helix loop helix transcription domain.

          Trp-clusters: include Interferon regulatory transcription factors and E-twenty six transcription factors.

          TEA transcriptional enhancer factor.

          Partial assemblies and expression analysis

          Since only one adult individual was sequenced expression analysis was restricted to juveniles (n = 5 per treatment). The individual assemblies for each group are summarized in Table 1. All partial transcriptomes were individually investigated to identify the 20 most expressed genes. Five genes were among the top 20 most abundant transcripts in all groups: phosphoglucose isomerase-2, calsequestrin-1, elongation factor 1-alpha, cyclin g1, parvalbumin and adenosine monophosphate deaminase-1 (Additional file 15). Pairwise comparisons of the number of reads that contributed to each isotig were made to provide information on differential gene expression between treatments (Figure 3). The top 10 genes appearing in the ranked list of significant differences between treatments are shown in Table 5 and three examples of the differences in reads mapped for each experimental group are shown in Figure 4.
          http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-181/MediaObjects/12864_2011_4079_Fig3_HTML.jpg
          Figure 3

          Dot plot pairwise comparison of reads contribution to the isotigs formation from each experimental group. Each dot represents a contig with reads from one or both treatments. X vs Y graph illustrate the relation of the number of reads between treatments in function of the region where the dots are placed.

          Table 5

          Expression analysis of libraries showing isotigs where reads from each experimental condition significantly contributed to the assembly

          Condition

          Isotig number

          Gene description

          Orthologue accession number

          e-value

          p-value

          FDR p-value

          Fasted 21°C

          Isotig06049

          Slow myosin heavy chain 2

          CBN81811.1

          0.0

          0

          0

           

          Isotig05152

          Similar to ankyrin 2

          CAM15089.1

          2e-24

          0

          0

           

          Isotig01065

          Calcium binding and coiled coil domain

          AAI17592.1

          1e-57

          0

          0

           

          Isotig30481

          myotubularin-related protein 5

          NP_001038623

          3e-11

          0

          0

           

          Contig01939

          GTPase, IMAP family member 7

          ACO08772.1

          2e-53

          0

          0

           

          Isotig31931

          Myosin, heavy polypeptide 6

          CAX12653.1

          2e-06

          0

          0

           

          Isotig06490

          Jeltraxin

          ACN11240.1

          6e-14

          1,00e-05

          0.004

           

          Isotig30481

          Adenylate kinase 1-2

          ACM41863.1

          5e-33

          0

          0

           

          Isotig07982

          Aurora kinase A-interactinng protein

          ACQ58398

          4e-92

          0

          0

           

          Isotig06835

          Slow Troponin T2

          AAV80376.1

          1e-68

          0

          0

          Fed 21°C

          Contig02082

          Parvalbumin

          ACM41857.1

          0.72

          0

          0

           

          Isotig05339

          VHSV-induced protein

          AEG78384

          1e-15

          1,00E-05

          0.004

           

          Isotig17563

          Putative nuclease HARBI1

          XP_003200346.1

          1e-16

          0

          0

           

          Isotig18811

          Notch 2

          BAA20535.1

          9e-61

          0

          0

           

          Isotig22470

          Phosphatidylinositol N-acetylglucosaminyltransferase

          NP_955461.1

          7e-15

          0

          0

           

          Isotig20332

          Glyceraldehyde 3-dehydrogenase

          XP_9741181.1

          5e-14

          0

          0

           

          Contig01939

          GTPase, IMAP family member 7

          ACO08772.1

          2e-53

          0

          0

           

          Isotig07008

          Ribosomal protein L28

          ACQ58416.1

          5e-59

          2,00E-05

          0.009

           

          Isotig38944

          AMP deaminase-1-like

          XP_003212994.1

          3e-04

          0

          0

           

          Isotig28974

          Myosin light chain 2

          AAX34414.1

          2e-10

          8,00E-05

          0.03

          33°C

          Isotig05428

          Xin actin-binding repeat containing protein 1

          NP_001012377.1

          0.0

          0

          0

           

          Isotig02673

          Clusterin-1

          NP_001117890.1

          3e-139

          0

          0

           

          Isotig02981

          Myosin-6-like isoform 1

          XP_001923213.1

          0.0

          0

          0

           

          Isotig08301

          Heat shock protein 30

          NP_001134440.1

          6e-62

          0

          0

           

          Isotig05469

          Activator of 90kda heat shock protein ATPase homolog 1

          NP_997767.1

          2e-145

          5,00E-05

          0.02

           

          Isotig01745

          Selenoprotein L

          NP_001180385.1

          3e-63

          8.00E-05

          0.03

           

          Isotig07184

          eEF1A2 binding protein

          NP_001133224.1

          1e-104

          0

          0

           

          Isotig03041

          Heat shock protein 4a

          NP_999881.1

          0.0

          5,00E-05

          0.02

           

          Isotig18085

          Srfs18

          CAG06353.1

          1e-45

          1,00E-05

          0.004

           

          Isotig02073

          Heat shock protein 90

          AAQ95586.1

          0.0

          0

          0

          11°C

          Isotig05303

          Zinc binding protein 33A

          XP_694642.3

          5e-61

          0

          0

           

          Isotig01520

          Interferon stimulated gene 15

          BAJ16365.1

          4e-46

          0

          0

           

          Isotig03692

          Receptor transporting protein 3

          ACQ57966.1

          6e-66

          0

          0

           

          Isotig03214

          Nicotinic acetylcholine receptor alpha 1b

          CAG09972.1

          0.0

          0

          0

           

          Isotig05152

          Similar to ankyrin 2

          CAM15089.1

          2e-24

          0

          0

           

          Isotig06087

          G-rich sequence factor 1

          NP_001135339.1

          1e-107

          0

          0

           

          Isotig07416

          Presenilin associated

          ABG81447.1

          6e-45

          1,00E-05

          0.004

           

          Isotig05735

          Ubtf protein

          AAI15119.1

          5e-169

          0

          0

           

          Isotig19061

          rRNA promoter binding protein

          NP_671477

          7e-10

          0

          0

           

          Isotig06639

          C6orf64

          ACO14504.1

          2e-26

          0

          0

          Gene description, orthologue accession number and e-values were obtained by blastx against the NCBI nr database. Both p-values and FDR p-value were calculated by chi-square and FDR statistic using R statistical package [70].

          http://static-content.springer.com/image/art%3A10.1186%2F1471-2164-13-181/MediaObjects/12864_2011_4079_Fig4_HTML.jpg
          Figure 4

          Barr charts summarizing transcripts with significant differences between groups in the number of reads mapped. The groups were as follows: 21°C fed (J), fasted 21°C (F), acutely transferred to 33°C fed (H) and acutely transferred to 11°C (L). All genes represented have been selected from Table 5 and have a FDR ≤ 0.01.

          Discussion

          The number of genes that can be obtained from Next Generation Sequencing is higher for normalised than non-normalised libraries of the kind used in the present study; however, unbiased libraries have the advantage of yielding a higher number of full-length cDNA sequences [29]. The number of annotated isotigs in the present study was 10,465 (24% of the total) corresponding to 5,655 unique genes. The total number of annotated sequences was less than reported in the coral (Millepora arcicornis) transcriptome (17,000) [30], rainbow trout (Oncorhynchus mykiss) (376,238) [31], but similar to that obtained for eel (Anguilla anguilla) (5,530) [32]. However, since our transcriptome was for a single tissue type (fast skeletal muscle) a lower number of unique genes would be expected than for transcriptomes based on sequencing dsDNA libraries from multiple tissues. In addition, previous studies [30, 31, 33] have considered singletons to be a valid source for gene discovery whereas the 96,000 singletons (4805 annotated) obtained in the present study were not included in further analysis.

          Our study is the first report of a skeletal muscle transcriptome in teleost fish and it contained 5655 unique transcripts including over 300 annotated transcripts related to transcription control, 750 microsatellite markers (177 associated with annotated istoigs) and 785 full-length cDNAs. The total number of microsatellites obtained was similar than in previous studies [34]. The transcriptome contained all known components of the sarcomere and the majority of proteins were represented by multiple isoforms even though the starting tissue for library construction comprised a pure population of fast twitch muscle fibres (Figure 2). Multiple isoforms of troponins and myosin light chains have previously been reported in single fish muscle fibres [35]. It is likely that isoforms that are expressed at specific developmental stages [36] or temperatures [37] contribute to the overall diversity of sarcomeric proteins (Figure 2). Previously, only 22 genes with splice variants have been reported in gilthead sea bream based on SANGER sequencing [38]. In the present study, 43 genes with potential splice variants were described, including 12 that affected known functional domains. This is a relatively low discovery rate given that 30% of genes in the three-spine stickleback genome were predicted to occur as multiple transcripts [39]. The reads containing the splice variant regions were analysed (data not show). In the majority of cases, the number of reads containing the deletion was lower than for the unspliced sequences, indicating lower levels of expression. In contrast, for cytochrome c oxidase subunit 4b and c4b binding protein the reads containing the deletion were more abundant and for a few genes, including bridging integrator 1 and cathepsin H, the proportion of splice variants was similar. The physiological effect of the splice variants with altered functional domains was not analysed in the present work and further studies are necessary to evaluate their impact on cell physiology.

          A whole genome duplication occurred in basal teleosts around 300–250 million years ago resulting in duplicate copies of many genes relative to the common ancestor with tetrapods [40]. It was estimated that in the green spotted puffer fish Tetraodon nigriviridis around 15% of the duplicate genes have been retained [40]. Previous transcriptomic studies in Atlantic cod (Gadus morhua) [41], whitefish (Coregonus clupeamorfis) [42] and eel (Anguilla anguilla) [32] have not attempted to identify paralogues. In the present study with over 10,000 transcripts annotated we expected over 400 paralogues, but only 26 could be identified. Differences between expected and the actual number of paralogues found can be explained by three main factors. The first factor is linked to sequence errors in the transcriptome. We found an error rate of 1:200 bp (99.5% accuracy) similar to previous studies [43] with 15.2% of the transcripts having insertions or deletions in their sequence. In our study, paralogue screening was based on translated isotigs, which are dramatically affected by insertions and deletions. This is because any insertion/deletions that are not multiples of three will change the open reading frame of the isotigs or introduce an in-frame stop codon. The second factor resulting in a low rate of paralogue discovery is the short length of some of the translated peptides. The majority of automatically translated isotigs represented less than 50% of the predicted sequence length (with a large number under 20%). Thus potential paralogues with short translated isotigs failed to pass the quality filters and were not considered further. Finally a very small effect will come from the assembly. Many assemblers are designed to tolerate imperfect sequence alignment to avoid missing true joins. This tolerance for error could result in false positive joins that mask polymorphisms, including paralogues [44]. This effect will be small due the divergence of the paralogues retained after the whole genome duplication, but cannot be completely discarded as a possibility.

          Another advantage of using unbiased libraries is that it potentially allows information on gene expression levels to be obtained. The approach used here was to carry out pairwise comparisons between treatments counting the numbers of reads that contributed to isotigs in an assembly derived from the combined treatments (Figure 3; Table 5). The results indicate marked plasticity in gene expression with respect to nutritional status and temperature. In many cases, genes highly ranked for differential abundance between treatments corresponded to the activation of particular pathways. For example, in fed fish, stress chaperones including Hsp90 and Hsp70 and proteins associated with prevention of unfolded protein aggregation, and cytoskeleton structure maintenance was significantly elevated in 33°C compared to 21°C treatments (Figure 3 and 4; Table 5). Heat shock proteins function to increase thermal tolerance following acute exposure to high temperature stress [45]. In contrast, there was no clear pattern of gene expression in the low temperature group that can be specifically associated with treatment. This may result from low temperature inhibiting feeding and inducing a similar depression of protein synthesis and metabolism as observed for fasted fish at higher temperature, thereby masking the specific effects of acute cold stress.

          Food deprivation reduces gene expression of enzymes related with glycolysis in fish liver [46] and muscle [47]. We found a decreased contribution of sequences to isotigs for genes associated with carbohydrate metabolism in fasted relative to fed treatments (Figure 3; Table 5). The fed library was also enriched for Notch-2 which is thought to control myoblast activity and be related to the asymmetric self-renewal of the muscle satellite cells through its inhibitor Numb [48, 49]. It has been suggested that increased Notch expression inhibits differentiation [50] and stimulates myoblast proliferation [49]. The significant increase of Notch-2 expression and other genes related with metabolism (like GAPDH) could be an indication of higher metabolic rates and myoblast activity in this group compared to treatments exposed to stressful conditions. There was evidence for the upregulation of adenylate kinase-1 (AK) in fasted compared to fed libraries. AK acts as a sensor of the energy status of tissues [51]. An increase of some of the adenylate kinase isoforms was also reported in response to the energy imbalance during fasting in rat tissues [52]. We also found up-regulation of three sarcomeric genes (myosin polypeptide 6, slow myosin light chain 2 and slow troponin 2) consistent with shifts in myofibrillar protein isoform composition towards a slow muscle phenotype in fasted fish. Studies in Atlantic salmon also reported an increase in myosin heavy chain and the myosin light chain 2 transcripts with fasting [47].

          Conclusions

          We have produced a detailed fast skeletal muscle transcriptome for the gilthead sea bream, a commercially important aquaculture species in the Mediterranean. The transcriptome contained 5655 unique annotated genes and 785 full-length coding sequences including key transcription factors, signaling molecules and structural proteins involved in myogenesis and growth. Some limitations in the identification of gene paralogues with 454 sequencing were found. In order to facilitate future genomic studies in this species a Blast server has been made available which contains 10, 465 annotated and 35,996 un-annotated isotigs together with ~ 2,700,000 ESTs [53].

          Methods

          Fish

          The juvenile gilthead sea bream (Sparus aurata L.) used in the present study originated from a fish farm brood stock kept at the Institute de Recerca i Tecnologia Agroalimentàries (IRTA) at St Carles de la Ràpita (IRTA-SCR, Spain) and were reared from the larval to juvenile stages according to the standard production procedures of this research facility. After thirteen months, two hundred juvenile gilthead sea bream, weighing 88.1 ± 7.3 g (mean ± SD, n = 35), were selected and maintained in two 400 litre tanks (22.5 kg m−3) in a temperature-controlled seawater re-circulation system (IRTAmarTM) at a mean temperature of 21°C (20.7-21.4°C) and natural photoperiod (13 L:11D). Fish were fed a commercial diet (OptiBreamTM, Skretting; pellet size: 2.6 mm; proximate biochemical composition: 46% protein, 18% fat, 7% ash) at a ration level of 3% (m/m) d−1. An adult female of 2 kg body mass that had been held at ambient temperature (annual range: 10-26°C) and natural photoperiod for several years at IRTA-SCR facilities and fed 3% body mass d-1 was also sampled.

          In order to obtain the widest possible range of expressed transcript sub-sets, fish were exposed to different water temperatures and fasting. Experiments were conducted in 400 litres cylindrical tanks connected to a re-circulation unit in order to maintain constant water temperature and dissolved oxygen over 85% saturation. Fish (n = 5) were transferred from 21°C to 11°C or 33°C over 48 h. During the treatments fish were fed as previously described, however those maintained at 11°C, stopped feeding after their transfer to low temperature. Additionally, another group of fish maintained at 21°C were fasted for 5 days.

          Since transcripts concentration will change over time with treatment fish were sampled at day 3 (n = 2) and day 5 (n = 3) following attainment of the new environmental conditions in order to obtain a broader representation of expressed genes. Fish were sacrificed using an overdose of 1:5,000 (m/v) of bicarbonate-buffered tricaine methanesulphonate (MS222, Sigma, Madrid, Spain) in seawater followed by spinal cord transection. Pure samples of fast skeletal muscle were dissected from dorsal epaxial myotomes at ~ 0.5 fork length (FL) on a pre-chilled glass plate maintained at 0–4°C. Muscle samples were flash frozen in liquid nitrogen and stored at −80°C until further analysis. Fish handling and trials were conducted in September 2009 in accordance with EC Directive 86/609/EEC for animal experimentation.

          RNA extraction and dsDNA synthesis

          RNA was extracted using QIAzol (QIAGEN, Crawley - West Sussex, UK) following the manufacturer’s recommendations. The integrity of the RNA was confirmed by ethidium bromide gel electrophoresis. RNA concentration, 260/280 and 260/230 ratios were evaluated using a NanoDrop 1000 spectrophotometer (Thermo Fischer Scientific, Waltman, MA). All RNA samples extracted had a 260/280 ratio higher than 1.9 and 260/230 above 2.2. Samples from each experimental condition were pooled in equal concentrations and the RNA integrity, concentration and ratios evaluated again. The pooled RNA samples were used for the following steps.

          The dsDNA synthesis was performed using a MINT cDNA synthesis kit (Evrogen, Moscow, Russia) using cDNA synthesis primer described by Meyer et al., 2009 [30] with a broken poly-T to avoid 454 sequencing problems in mono-nucleotide regions (5′-AAGCAGTGGTATCAACGCAGAGTCGCAGTCGGTACTTTTTTCTTTTTTV-3′). For an accurate evaluation of the dsDNA concentration Quati-IT™ PicoGreen® (Invitrogen, Pailey, UK) was used. PicoGreen® fluorescence was detected by a MSPx3000 qPCR machine as previously described [54].

          454 sequencing

          The transcriptome for each physiological condition was determined using Roche 454 GS FLX Titanium pyrosequencing using the service run by Genepool, University of Edinburgh, School of Biological Sciences. Each physiological condition was sequenced using a half 454-plate generating around 390,000–490,000 reads with an average length of 400 bp. Because of a technical problem an initial run of the fasted sampled yielded reads with an average length of only 300 bp and therefore this plate was repeated. Both plates yielded high quality reads and were therefore used in the subsequent global assembly.

          454 assembly and annotation

          Around 2,700,000 reads were used to generate the sea bream transcriptome. For the partial assemblies we used the reads generated from each experimental condition. For the fasted treatment partial assembly reads from the 454 plate that yielded average read lengths (400 bp) were used. Reads were assembled using Newbler 2.5 software (Roche, 454 Life-sciences) which performs well for de novo assembly of 454 transcriptome data [55]. Assemblies were run in a Debain Linux system, IBM x3755 8877, with 8 CPU cores (4 x dual-core AMD Opteron), 64-bit, 2.8GHz processor with 128 Gb of RAM maintained by the University of St Andrews.

          To avoid assembly problems caused by the reads from highly expressed genes we trimed them using the –vs against a fasta file with the available sequences for these genes in gilthead sea bream (adapters and genes sequences used from trimming are in Additional file 16). Isotigs generated by the Newbler software are contigs that are consistently connected by subsets of reads. Isotigs are longer than contigs and were used for the annotation and transcriptome analysis.

          Isotigs were Blasted and annotated using Blast2GO software [56]. Sequences were blasted using Blastx against the NCBI non-redundant protein collection (nr) database with a threshold of 10-3. Annotation was done with an E-value Hit Filter of 10-6 combined with an Annotation Cutoff of 55 and GO weighting of 5. Blast2GO also annotated sequences for functional domains using InterProScan.

          NGS and Sanger sequencing comparisons

          Known sea bream sequences produced by the SANGER sequencing method were downloaded from GenBank [57] and blasted (blastn) against the sea bream transcriptome using a BLAST server [53] generated by the Genepool group. The best hits isotig/GeneBank were aligned using ClustalW [58] to determine the nature and number of differences.

          Pathway annotation

          Successfully annotated isotigs were introduced in the KEGG Automatic Annotation Server (KAAS) [59]. The SBH method, optimized for ESTs annotation, was used against human, chimpanzee, orang-utan, rhesus, mouse, rat, dog, giant panda, cow, pig, horse, opossum, platypus, chicken, clawed frog, zebrafish, fruit fly and nematode pathway databases. For a more detailed reconstruction of the pathway components the PPT-Toolkit-Cell-Biology from motifolio.com was used.

          Identification of full-length cDNAs

          Annotated isotigs were translated to the longest amino acids sequence possible using the ORF translator tool in Blast2GO package (no longer available). Sequences with more than 150 amino acids that started with a methionine or had a methionine in the first 50 amino acids were manually blasted using NCBI Blast server against nr/nt database [60]. Blast results were analysed to confirm that the translated isotig covered, at least 90% of the sequence with best hits and that cover the whole CDS.

          Microsatellite screening

          Isotigs successfully annotated were used for microsatellite repeats search using msatcommander-1.0.2-alpha [61]. An isotig was considered to contain a microsatellite if contain any of the following repeated motifs: at least 10 repeated mononucleotides (other than A), 8 repeated di- or trinucleotides, or 6 repeated tetra-, penta- or hexanucleotid motifs. Their position outside coding sequences was confirmed in those microsatellites linked to annotated isotigs by analysing the translated sequences.

          Identification of splice variants

          For splice variant identification we screened the list of isogroups generated during Newbler assembly. Each isogroup represents a collection of isotigs containing reads that imply connections between the isotigs. Different isotigs from a given isogroup can be used to infer splice variants. Isogroups with non-annotated isotigs were discarded. The screening was focused on detecting splice variants affecting the coding sequence. The isotigs translated sequences from each isogroup were aligned with ClustalW to detect changes in peptide sequence.

          Potential splice variants were filtered a second time by blasting them against the stickleback (Gasterosteus aculeatus) genome where possible, or otherwise the green puffer fish (Tetraodon nigroviridis) genome using the Ensembl webpage BLAT algorithm [62]. Loci positive alignments were retrieved. Splice variants sequences and loci were aligned using the Spidey mRNA/genome analyser [63] to predict changes in the exon composition. Splice variants with potential changes in exon composition were submitted to InterProScan annotation to detect changes in functional domains. Genes with domain annotation that were altered by splicing were experimentally confirmed using conventional PCR.

          Identification of transcription factors (TF)

          For the detection of transcription factors and molecules associated with transcription such as methyl transferases, histone acetyl transferases and others we screened isotigs annotated with GO levels related to transcription: GO:0006355 (regulation of cellular transcription), GO:0003700 (modulate transcription), GO:0003677 (interacts selectively with DNA), GO:0008134 (TF binding), GO:0033276 (protein complex able to transcription regulation), GO:0043425 (basic Helix-Loop-Helix interactive elements), GO:0016563 (any activity required for initiation or upregulation of transcription) and GO:0045941 (any transcription regulator activity). IDs were checked against a Transcription Factor database to confirm a role in transcription regulation and to categorize them into families [64] and against the Uniprot database [28].

          Identification of gene paralogues

          Because no formal software has been developed specifically for paralogue screening in assemblies from Next Generation Sequencing we used an indirect approximation using the translated isotigs. A list of protein sequences of known genes from mouse (Mus musculus) was downloaded using BioMart tool from ESEMBL [27]. We also downloaded a list of known paralogues from different teleost species: Takifugu rubripes Tetraodon Nigroviridis Gasterosteus aculeatus Oryzias latipes and Danio rerio. Comparisons between proteins groups were performed using Inparanoid 4.0 [65]. Comparisons were performed using the gilthead sea bream translated transcriptome against one of the datasets at time. When at least two different isotigs were identified to represent the same transcript matched with a single mouse gene they were consider as potential paralogues. In addition, if two or more teleost known paralogues matched with two different isotigs they were also considered as potential paralogues. Other relations between transcripts can give similar output from Inparanoid and be included as paralogues: redundant transcripts, splice variants, sequence fragments and wrongly translated isotigs by insertions/deletions. Inparanoid output was explored by aligning translated sequences of paralogues against each other using ClustalW. This exploration allowed us to detect and trim these “False positives” from the list of potential paralogues.

          Phylogenetic analysis

          The amino acids sequences of potential paralogues were blasted against the zebrafish (Danio rerio), stickleback (Gasterosteus aculeatus), takifugu (Takifugu rubripes), medaka (Oryzias latipes), green pufferfish (Tetraodon nigroviridis), chicken (Gallus gallus), frog (Xenopus laevis) and human (Homo sapiens) genomes using Essembl [62]. The sequences from the best hits were downloaded. Alignment of the potential paralogues and their orthologues was performed using the GUIDANCE web tool [66]. Only fragments with an alignment confidence score over 0.93 were used for the phylogenetic analysis. The best evolutionary model was estimated for each alignment using MEGA5 software [67]. Maximum Likelihood phylogenetic analysis was constructed, with the best evolutionary model, using the online pipeline from PhylM [68].

          Expression analysis

          Reads from each experimental condition were mapped against the total isotigs from the global assembly using GS Reference Mapper (Roche, 454 Life Sciences). The number of reads per contig from each condition was extracted using the R statistical package [69]. Chi-square statistic was applied to detect significant differences in the number of reads per condition per isotig. Isotigs with less than 10 reads were excluded from the analysis. A FDR correction was applied to all p-values below 0.05. Plot graphs comparing the contribution of reads from each experimental condition to the isotig formation were constructed using R package.

          Declarations

          Acknowledgements

          We would like to thank staff at IRTA for providing Gilthead sea bream for this study, particularly Enric Gisbert and Maria Darias. DNA sequencing was carried out in the GenePool genomics Facility in the University of Edinburgh. We thank GenePool Staff for assistance, especially from Dr Stephen Bridge, with sequencing and bioinformatics advice. Also thank to Dr Paris Vestos for his support in the design of the R scripts. The research was funded by the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 222719 – LIFECYCLE. This work also received funding from the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland) and their support is gratefully acknowledged. MASTS is funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions.

          Authors’ Affiliations

          (1)
          Physiological and Evolutionary Genomics Laboratory, Scottish Oceans Institute, School of Biology, University of St Andrews
          (2)
          Institute for Aquaculture and Food Technology Research (IRTA), Sant Carles de la Ràpita

          References

          1. FAOhttp://​www.​fao.​org/​fishery/​culturedspecies/​Sparus_​aurata/​en
          2. López-Albors O, Gil F, Ramírez-Zarzosa G, Vázquez JM, Latorre R, García-Alcázar A, Arencibia A, Moreno F: Muscle development in Gilthead sea bream (Sparus aurata L.) and sea bass (Dicentrarchus labrax, L.): further histochemical and ultrastructural aspects. Anat Histol Embryol 1998, 27:223–229.PubMedView Article
          3. Rowlerson A, Mascarello F, Radaelli G, Veggetti A: Differentiation and growth of muscle in the fish Sparus aurata (L): II. Hyperplastic and hypertrophic growth of lateral muscle from hatching to adult. J Muscle Res Cell Motil 1995, 16:223–236.PubMedView Article
          4. Weatherley AH, Gill HS, Lobo AF: Recruitment and maximal diameter of axial muscle fibres in teleosts and their relationship to somatic growth and ultimate size. J Fish Biol 1988, 33:851–859.View Article
          5. Johnston IA, Manthri S, Alderson R, Smart A, Campbell P, Nickell D, Robertson B, Paxton CG, Burt ML: Freshwater environment affects growth rate and muscle fibre recruitment in seawater stages of Atlantic salmon (Salmo salar). J Exp Biol 2003, 206:1337–1351.PubMedView Article
          6. Johnston IA, Lee HT, Macqueen DJ, Paranthaman K, Kawashima C, Anwar A, Kinghorn JR, Dalmay T: Embryonic temperature affects muscle fibre recruitment in adult zebrafish: genome-wide changes in gene and microRNA expression associated with the transition from hyperplastic to hypertrophic growth phenotypes. J Exp Biol 2009, 212:1781–1793.PubMedView Article
          7. Johnston IA, Bower NI, Macqueen DJ: Growth and the regulation of myotomal muscle mass in teleost fish. J Exp Biol 2011, 214:1617–1628.PubMedView Article
          8. Johnston IA: Environment and plasticity of myogenesis in teleost fish. J Exp Biol 2006, 209:2249–2264.PubMedView Article
          9. Steinbacher P, Marschallinger J, Obermayer A, Neuhofer A, Sänger AM, Stoiber W: Temperature-dependent modification of muscle precursor cell behaviour is an underlying reason for lasting effects on muscle cellularity and body growth of teleost fish. J Exp Biol 2011, 214:1791–1801.PubMedView Article
          10. Johnston IA, Alderson R, Sandham C, Dingwall A, Mitchell D, Selkirk C, Nickell D, Baker R, Robertson B, Whyte D, Springate J: Muscle fibre density in relation to the colour and texture of smoked Atlantic salmon (Salmo salar L.). Aquaculture 2000, 189:335–349.View Article
          11. Star B, et al.: The genome sequence of Atlantic cod reveals a unique immune system. Nature 2011, 477:207–210.PubMedView Article
          12. INRA Biotechnology Laboratories (http://​locus.​jouy.​inra.​fr/​)
          13. Genomic Research in All Salmon (http://​web.​uvic.​ca/​grasp/​)
          14. Broad Institute (http://​www.​broadinstitute.​org/​)
          15. Kuhl H, Beck A, Wozniak G, Canario AVM, Volckaert FAM, Reinhardt R: The European sea bass Dicentrarchus labrax genome puzzle: comparative BAC-mapping and low coverage shotgun sequencing. BMC Genomics 2010, 11:68.PubMedView Article
          16. Kuhl H, Sarropoulou E, Tine M, Kotoulas G, Magoulas A, Reinhardt R: A comparative BAC map for the gilthead sea bream (Sparus aurata L.). J Biomed Biotechnol 2011, 2011:329025.PubMedView Article
          17. Sarropoulou E, Kotoulas G, Power DM, Geisler R: Gene expression profiling of Gilthead sea bream during early development and detection of stress-related genes by the application of cDNA microarray Technology. Physiol Genomics 2005, 23:182–191.PubMedView Article
          18. Ferraresso S, Vitulo N, Mininni AN, Romualdi C, Cardazzo B, Negrisolo E, Reinhart R, Canario AVM, Patarnello T, Bargelloni L: Development and validation of a gene expression oligo microarray for the gilthead sea bream (Sparus aurata). BMC Genomics 2008, 9:580.PubMedView Article
          19. Calduch-Giner JA, Davey G, Saera-Vila A, Houeix B, Talbot A, Prunet P, Cairns MT, Pérez-Sánchez J: Use of microarray technology to assess the time course of liver stress response after confinement exposure in gilthead sea bream (Sparus aurata L.). BMC Genomics 2010, 11:193.PubMedView Article
          20. Fraser BA, Weadick CJ, Janowitz I, Rodd FH, Hughes KA: Sequencing and characterisation of the guppy (Poecilia reticula) transcriptome. BMC Genomics 2011, 12:202.PubMedView Article
          21. Santure AW, Gratten J, Mossman JA, Sheldon BC, Slate J: Characterisation of the transcriptome of a wild great tit Parus major population by next generation sequencing. BMC Genomics 2011, 12:283.PubMedView Article
          22. Vogel H, Altincicek B, Glöckner G, Vilcinskas A: A comprehensive transcriptome and immune-gene repertoire of the lepidopteran model host Galleria mellonella. BMC Genomics 2011, 12:308.PubMedView Article
          23. Sequence Read Archive (http://​www.​ncbi.​nlm.​nih.​gov/​sra)
          24. Milne I, Bayer M, Cardle L, Shaw P, Stephen G, Wright F, Marshall D: Tablet- next generation sequence assembly visualization. Bioinformatics 2010, 26:401–402.PubMedView Article
          25. Clark KA, McElhinny AS, Beckerle MC, Gregorio CC: Striated muscle cytoarchitecture: an intricate web of form and function. Annu Rev Cell Dev Biol 2002, 18:637–706.PubMedView Article
          26. Hoshijima M: Mechanical stress–strain sensors embedded in cardiac cytoskeleton: Z disk, titin and associated structure. Am J Physiol Heart Circ Physiol 2006, 290:1313–1325.View Article
          27. Ensembl BioMart (http://​www.​ensembl.​org/​biomart/​)
          28. UniProt (http://​www.​uniprot.​org)
          29. Wall PK, Leebens-Mack J, Chanderbali AS, Barakat A, Wolcott A, Liang H, Landherr L, Tomsho LP, Hu Y, Carlson JE, Ma H, Schuster SC, Soltis DE, Soltis PS, Altman N, dePamphilis CW: Comparison of next generation sequencing Technologies for transcriptome characterization. BMC Genomics 2009, 10:347.PubMedView Article
          30. Meyer E, Aglyamova GV, Wang S, Buchanan-Carter J, Abrego D, Colbourne JK, Willis BL, Matz MV: Sequencing and de novo analysis of a coral larval transcriptome using 454 GSFlx. BMC Genomics 2009, 10:219.PubMedView Article
          31. Salem M, Rexroad CE, Wang J, Thorgaard GH, Yao J: Characterization of the rainbow trout transcriptome using Sanger and 454-pyrosequencing approaches. BMC Genomics 2010, 11:564.PubMedView Article
          32. Coppe A, Pujolar JM, Maes GE, Larsen PF, Hansen MM, Bernatchez L, Zane L, Bortolluzzi S: Sequencing, de novo annotation and analysis of the first Anguilla anguilla transcriptome: EelBase opens new perspectives for the study of the critical endangered European eel. BMC Genomics 2010, 11:635.PubMedView Article
          33. Vera JC, Wheat CW, Fescemyer HW, Frilander MJ, Crawford DL, Hanski I, Marden JH: Rapid transcriptome characterization for a nonmodel organism using 454 pyrosequencing. Mol Ecol 2008, 17:1636–1647.PubMedView Article
          34. Vogiatzi E, Lagnel J, Pakaki V, Louro B, Canario AV, Reinhardt R, Kotoulas G, Magoulas A, Tsigenopoulos CS: In silico mining and characterization of simple sequence repeats from gilthead sea bream (Sparus aurata) expressed sequence tags (EST-SSRs); PCR amplification, polymorphism evaluation and multiplexing and cross-species assays. Mar Genomics 2011, 4:83–91.PubMedView Article
          35. Crockford T, Wommack KE, Johnston IA, McAndrew BJ, Mutungi G, Johnson TP: Inter- and intra-specific variation in myosin light chain and troponin I composition in fast muscle fibres from two species of fish (genus Oreochromis) which have different temperature-dependent contractile properties. J Muscle Res Cell Motil 1991, 12:439–446.PubMedView Article
          36. Brooks S, Johnston IA: Influence of development and rearing temperature on the distribution, ultrastructure and myosin sub-unit composition of myotomal muscle fibre types in the plaice, Pleuronectes platessa. Mar Biol 1993, 117:501–513.
          37. Johnston IA, Temple GK: Thermal plasticity of skeletal muscle phenotype in ectothermic vertebrates and its significance for locomotory behaviour. J Exp Biol 2002, 205:2305–2322.PubMed
          38. Louro B, et al.: Gilthead sea bream (Sparus auratus) and European sea bass (Dicentrarchus labrax) expressed sequenced tags: characterization, tissue-specific expression and gene markers. Mar Genomics 2010, 3:179–191.PubMedView Article
          39. Lu J, Peatman E, Wang W, Yang Q, Abernathy J, Wang S, Kucuktas H, Liu Z: Alternative splicing in teleost fish genomes: same-spicies and cross-species analysis and comparisons. Mol Genet Genomics 2010, 283:531–539.PubMedView Article
          40. Jaillon O, et al.: Genome duplication in teleost fish Tetraodon nigroviridis reveals the early vertebrate proto-karyotype. Nature 2004, 431:946–957.PubMedView Article
          41. Johansen SD, Coucheron DH, Andreassen M, Karlsen BO, Furmanek T, Jørgensen TE, Emblem A, Breines R, Nordeide JT, Moum T, Nederbragt AJ, Stenseth NC, Jakobsen KS: Large-scale sequence analyses of Atlantic cod. N Biotechnol 2009, 25:263–271.PubMedView Article
          42. Jeukens J, Renaut S, St-Cyr J, Nolte AW, Bernatchez L: The transcriptomics of sympatric dwarf and normal lake whitefish (Coregonus clupeaformis spp. Salmonidae) divergence as revealed by next-generation sequencing. Mol Ecol 2010, 19:5389–5403.PubMedView Article
          43. Huse SM, Huber JA, Morrison HG, Sogin ML, Welch DM: Accuracy and quality of massively parallel DNA pyrosequencing. Genome Biol 2007, 8:143.View Article
          44. Miller JR, Koren S, Sutton G: Assembly algorithms for next-generation sequencing data. Genomics 2010, 95:315–327.PubMedView Article
          45. Basu N, Todgham AE, Ackerman PA, Bibeau MR, Nakano K, Schulte PM, Iwama GK: Heat shock protein genes and their functional significance in fish. Gene 2002, 95:173–183.View Article
          46. Salem M, Silverstein J, Rexroad CE, Yao J: Effect of starvation on global gene expression and proteolysis in rainbow trout (Oncorhynchus mykiss). BMC Genomics 2007, 8:328.PubMedView Article
          47. Bower NI, Taylor RG, Johnston IA: Phasing on muscle gene expression with fasting-induced recovery growth in Atlantic salmon. Front Zool 2009, 6:19.View Article
          48. Kuang S, Kuroda K, Le Grand F, Rudnicki MA: Asymmetric self-renewal and commitment of satellite stem cells in muscle. Cell 2007, 129:999–1010.PubMedView Article
          49. Buas MF, Kadesch T: Regulation of skeletal myogenesis by Notch. Exp Cell Res 2010, 18:3028–3033.View Article
          50. Conboy IM, Rando TA: The regulation of Notch signaling controls satellite cell activation and cell fate determination in postnatal myogenesis. Dev Cell 2002, 3:397–409.PubMedView Article
          51. Dzeja P, Terzic A: Adenylate kinase and AMP signalling networks: metabolic monitoring, signal communication and body energy sensing. Int J Mol Sci 2009, 10:1729–1772.PubMedView Article
          52. de Lange P, Ragni M, Silvestri E, Moreno M, Schiavo L, Lombardi A, Farina P, Feola A, Goglia F, Lanni A: Combined cDNA array/RT-PCR analysis of gene expression profile in rat gastrocnemius muscle: relation to its adaptive function in energy metabolism during fasting. FASEB J 2004, 18:350–352.PubMed
          53. http://​genepool.​bio.​ed.​ac.​uk/​blast/​DanielGarcia_​SeaBreamTranscri​ptome http://​genepool.​bio.​ed.​ac.​uk/​blast/​DanielGarcia_​SeaBreamTranscri​ptome
          54. Blotta I, Prestinaci F, Mirante S, Cantafora A: Quantitative assay of total dsDNA with PicoGreen reagent and real-time fluorescence detection. Ann Ist Super Sanita 2005, 41:119–123.PubMed
          55. Kumar S, Blaxter ML: Comparing de novo assemblers for 454 transcriptome data. BMC Genomics 2010, 11:571.PubMedView Article
          56. Götz S, García-Gómez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ, Robles M, Talón M, Dopazo J, Conesa A: High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res 2008, 36:3420–3435.PubMedView Article
          57. Genebank (http://​www.​ncbi.​nlm.​nih.​gov/​genbank/​)
          58. EBI ClustalW (http://​www.​ebi.​ac.​uk/​Tools/​msa/​clustalw2/​)
          59. KEGG Automatic Annotation Server (http://​www.​genome.​jp/​tools/​kaas)
          60. NCBI BLAST server (http://​blast.​ncbi.​nlm.​nih.​gov/​Blast.​cgi)
          61. Faircloth BC: msatcommander: detection of microsatellites repeat arrays and automated, locus-specific primer design. Mol Ecol Res 2008, 8:92–94.View Article
          62. Ensembl BLAST server (http://​www.​ensembl.​org/​Multi/​blastview)
          63. Spidey mRNA/genome analyser (http://​www.​ncbi.​nlm.​nih.​gov/​spidey/​)
          64. Transcription factor genes & associated conserve noncoding elements database. http://​tfcones.​fugu-sg.​org/​index.​htm
          65. O’Brien KP, Remm M, Sonnhammer ELL: Inparanoid: A Comprehensive Database of Eukaryotic Orthologs. Nucl Acids Res 2005, 33:476–480. Inparanoid website http://​inparanoid.​sbc.​su.​se/​cgi-bin/​index.​cgi View Article
          66. Penn O, Privman E, Ashkenazy H, Landan G, Graur D, Pupko T: GUIDANDE: a web server for assessing alignment confidence scores. Nucl Acids Res 2010, 38:23–28. Guidence website (http://​guidance.​tau.​ac.​il/​index.​html) View Article
          67. Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S: MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance and maximum parsimony methods. Mol Biol Evol 2011, 28:2731–2739.PubMedView Article
          68. Guindon S, Gascuel O: A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol 2003, 52:696–704. PhyML South France Bioinformatic platform http://​www.​atgc-montpellier.​fr PubMedView Article
          69. R statistical package. http://​www.​R-project.​org

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          © Garcia de la serrana et al.; licensee BioMed Central Ltd. 2012

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