Fish Physiology and Biochemistry

, Volume 40, Issue 1, pp 311–322 | Cite as

Proteomic profiling of sea bass muscle by two-dimensional gel electrophoresis and tandem mass spectrometry

  • Genciana Terova
  • Salvatore Pisanu
  • Tonina Roggio
  • Elena Preziosa
  • Marco Saroglia
  • Maria Filippa Addis
Article

Abstract

In this study, the proteome profile of European sea bass (Dicentrarchus labrax) muscle was analyzed using two-dimensional electrophoresis (2-DE) and tandem mass spectrometry with the aim of providing a more detailed characterization of its specific protein expression profile. A highly populated and well-resolved 2-DE map of the sea bass muscle tissue was generated, and the corresponding protein identity was provided for a total of 49 abundant protein spots. Upon Ingenuity Pathway Analysis, the proteins mapped in the sea bass muscle profile were mostly related to glycolysis and to the muscle myofibril structure, together with other biological activities crucial to fish muscle metabolism and contraction, and therefore to fish locomotor performance. The data presented in this work provide important and novel information on the sea bass muscle tissue-specific protein expression, which can be useful for future studies aimed to improve seafood traceability, food safety/risk management and authentication analysis. This work is also important for understanding the proteome map of the sea bass toward establishing the animal as a potential model for muscular studies.

Keywords

European sea bass Proteome MS/MS analysis 2-DE Muscle Aquaculture 

Introduction

Aquaculture is becoming an increasingly important source of fish and shellfish available for human consumption (Zhou et al. 2012). The growing demand for fish has opened new markets and has increased the circulation and distribution of fish products, but, at the same time, has caused the development of fraudulent practices, which damage the health of consumers and cause economic loss to the community, as well as to companies. In this context, food traceability’s main drivers are food safety/risk management and authentication to prevent fraudulent labeling and to certify the origin of products in the market. To prevent economic frauds and health hazards, the European Union establishes that fish products can enter the commercial circuit only if the commercial name, method of production and capture area are clearly indicated on the label (Civera 2003). Traceability regulations have the potential to reduce fraud issues, and the information reported on labels can potentially decrease product adulteration as well as provide indications on quality of the seafood product through all stages of production, processing and distribution.

As a consequence of this, the development of robust analytical techniques aimed at achieving a precise identification and characterization of fish species in both raw and processed fish material is gaining increasing importance (Piñeiro et al. 1999). Along with nucleic acid techniques, such as DNA-based tags developed for species identification in seafood, PCR-fragment size determination (Perez et al. 2004), PCR–RFLP (restriction fragment length polymorphism) (Perez et al. 2005) and direct sequencing of target DNA fragments (Toffoli et al. 2008), proteomic technologies are proving to be powerful tools in this area (Martinez and Friis 2004). Most of the proteomic studies in fish have been carried out in model organism such as zebrafish (Danio rerio), but the application of proteomic technologies to cultured species with commercial interest is gradually increasing (Fornè et al. 2010).

Two very recent reports of our group (Terova et al. 2011; Addis et al. 2012) exploited proteomic techniques (2D-DIGE followed by MS/MS identification of differential protein spots) to assess the effects of postmortem storage temperature and slaughter methods in the muscle of European sea bass (Dicentrarchus labrax) which is a species of great interest for the Mediterranean aquaculture.

Muscle proteomics may be applied to the comprehensive biochemical profiling of developing and maturing fish muscle, as well as the analysis of contractile tissues undergoing physiological adaptations seen during hypoxia or other environmental stress conditions. Muscle proteins involved in body mass variation can also be investigated in fish (Reddish et al. 2008; Schiavone et al. 2008), in order to identify intact and/or proteolytic fragments of muscle-specific gene products that are involved in fish muscle growth.

Accordingly, here, we report a detailed proteome map of sea bass skeletal muscle tissue obtained by 2-D electrophoresis (2-DE) and tandem mass spectrometry (MS/MS) analysis. In addition, a study on the predicted biochemical pathway components carried out by means of Ingenuity Pathway Analysis is reported.

Materials and methods

Samples

European sea bass used in the experiment were maintained for 1 month before slaughter, in three tanks of 1 m3, connected to a sea water recirculation system with 40 fish/tank. At the start of the experiment and after 2 days of starvation, 3 fish (average body weight 710 ± 157.87 g) were removed from water and killed by asphyxia/hypothermia (immersion in ice-cold water at a fish/ice ratio 2:1). Fragments of the epiaxial muscular quadrant were then taken, frozen at −80 °C and stored at this temperature until the molecular biology analysis. All procedures were approved by the Animal Care Committee of the University of Insubria and conducted according to the guidelines of the Italian Committee on Animal Care.

Protein extraction

Proteins were extracted from frozen muscle tissues using a TissueLyser mechanical homogenizer (Qiagen). For extraction, a small portion (50 mg) of muscle was minced with a sterile scalpel, placed in a 2-mL Eppendorf safe-lock tube, and then immersed in lysis buffer (8 M urea, 2 % CHAPS, and 0.5 % IPG buffer) at a 5 % w/v ratio. Three cycles of 1.5 min at 30 cycles/s in the TissueLyser were employed for each sample. All extracts were then clarified for 15 min at 12,000 × g at 4 °C, quantified by the Bradford method, tested for quality and quantity by Sodium Dodecyl Sulphate–PolyAcrylamide Gel Electrophoresis (SDS–PAGE) and then stored at −80 °C until analysis.

2D gel electrophoresis

First-dimension IsoElectric Focusing (IEF) was performed using 24-cm precast IPG strips (pH 3 to 11, nonlinear [NL]). Of protein preparation, 500 μg was applied onto the strips by passive rehydration overnight at room temperature. All strips (three experimental replicates per sample) were run together in a IPGphor equipped with the Ettan™ IPGphor™3 loading manifold (GE Healthcare). The strips were focused at 20 °C for a total of about 90,000 Vh. After IEF, the strips were sequentially incubated in a freshly prepared solution of 1 % dithiothreitol and 2 % iodoacetamide in 50 mM Tris–HCl (pH 8.8), 6 M urea, 20 % glycerol and 2 % SDS for 10 min. The second-dimension SDS–PAGE was conducted on 8 to 16.5 % polyacrylamide gradient gels, using an Ettan™ DALTtwelve electrophoresis system, for 30 min at 5 W/gel and then for 5 h at 17 W/gel, at 25 °C.

Protein identification

After electrophoresis, the gel slab was fixed in 50 % methanol, and 10 % acetic acid in water for 30 min. It was then washed for 15 min with 5 % methanol in water and additionally for 15 min for 3 times with water to remove the remaining acid. The gel was sensitized by 15-min incubation in 120 mg/L sodium thiosulfate, and it was then rinsed with three changes of distilled water for 30 s each. After rinsing, the gel was submerged in chilled 2 g/L silver nitrate solution and incubated for about 25 min. After incubation, the silver nitrate was discarded, and the gel slab was washed with three changes of distilled water for 1 min each and then developed in 30 g/L sodium carbonate (37 % formaldehyde in water and 0.2 % sodium thiosulfate) with intensive shaking. After the desired intensity of staining was achieved, the reaction was stopped with 14 g/L of EDTA (ethylenediaminetetraacetic acid, anhydrous, Sigma) and was stored in distilled water at 4 °C until analyzed.

Image digitalization and analysis

Stained gels were digitalized with an image scanner (GE Healthcare), and images were processed with ImageMaster Platinum 6.0. A differential analysis was first carried out among replicate gels in order to estimate the repeatability of the muscle tissue map, and the different fish maps were then subjected to the ImageMaster analysis workflow. All the software steps were manually verified in order to eliminate artifact, split and missed spots. For gel analysis, percentage volume values of spots were used, intended as arbitrary units assigned by the software. Mean, mean squared deviation (MSD) and CV values were then obtained for each match, as described previously (Addis et al. 2010). Isoelectric point and molecular weight values were validated by calibration with internal standards as described previously (Terova et al. 2011; Addis et al. 2012).

Spot picking and in situ tryptic digestion

For protein identification, preparative 2D-PAGE gels were set up by overnight rehydration loading of 500 μg of protein extract into 3–11 NL 24-cm IPG strips. Strips were then focused and subjected to second-dimension electrophoresis as described above. After electrophoresis, the gel slab was subjected to mass compatible silver staining according to Chevallet et al. (2006). Visible protein spots of interest were manually excised from the gels, destained with 15 mM K3Fe(CN)6 in 50 mM Na2S2O3, washed with water and then stored in acetonitrile. The spots were then subjected to an overnight tryptic digestion at 37 °C in 50 mM (NH4)HCO3, pH 8.0, by using between 40 and 100 ng of trypsin, depending on spot intensity. Peptide mixtures were then collected by extraction with acetonitrile, followed by centrifugation. Peptides were subsequently acidified with 20 % TFA, dried in SpeedVac® (Eppendorf), resuspended in formic acid 0.2 % and stored at −20 °C.

Tandem mass spectrometry analysis

LC–MS/MS analysis was performed on a XCT Ultra 6340 ion trap equipped with a 1,200 HPLC system and a chip cube (Agilent Technologies, Palo Alto, CA). After loading, samples were concentrated and desalted at 4 μl/min on a 40-nL enrichment column (75 μm × 43 mM, Agilent Technologies chip), with 0.2 % formic acid. Peptides were then fractionated on a C18 reverse-phase capillary column at flow rate of 300 nl/min, with a linear gradient of eluent B (0.2 % formic acid in 95 % acetonitrile) in A (0.2 % formic acid in 2 % acetonitrile) from 3 to 60 % in 20 min. ESI parameters were as follows: Capillary voltage 1,730 V; dry gas (N2), 5.00 L/min; dry temperature, 325 °C; trap drive, 100; skimmer 30 V; lens 1, −5.00 V; octopole RF amplitude, 200 Vpp; capillary exit, 90 V. The ion trap mass spectrometer was operated in positive ion mode. Trap ICC smart target was 300,000 units, and maximal accumulation time was 100 ms. MS/MS was operated at a fragmentation amplitude of 1.3 V, and threshold ABS was 6,000 units. Scan speed was 8,100 uma/sec in MS and 26,000 uma/sec in MS/MS scans. Peptide analysis was performed by scanning from m/z 250 to m/z 2,200 in AutoMS (n) precursor selection mode of the three most intense ions (fragmentation mass range from 100 to 2,200 m/z). Dynamic exclusion was used to acquire a more complete survey of the peptides by automatic recognition and temporary exclusion (0.15 min) of ions from which definitive mass spectral data had previously acquired. Data Analysis software was used to analyze MS/MS spectra and to generate a peak list which was introduced in the in-house Mascot MS/MS ion search software (Version 2.3, Matrix Science) for protein identification in NCBI database using the Chordata taxonomy. Search parameters were: peptide tolerance 300 ppm, MS/MS tolerance 0.6 Da, charge state +2, +3 and +4, enzyme trypsin, allowing 2 missed cleavage. Protein identifications were accepted when false discovery rate (FDR) of peptide-spectra matches (PSM) was equal or less than 1 %, with at least 2 unique peptides.

Data and network pathway analysis

The list of protein identifications was imported in the online software package IPA (Ingenuity Systems, Redwood City, CA), and network analyses were performed by substituting UniProt IDs with the UniProt ID (Uniprot Consortium 2008) for the closest human protein equivalent in order to enable the best exploitation of the knowledge-based IPA software, version 12718793, updated May 2012.

In the IPA software, a numerical value called score is used to rank networks according to their degree of relevance to the network eligible molecules in the dataset. The score takes into account the number of network eligible molecules in the network and its size, as well as the total number of network eligible molecules analyzed and the total number of molecules in the Ingenuity Knowledge Base that could potentially be included in networks. The network score is based on the hyper geometric distribution and is calculated with the right-tailed Fisher’s Exact Test.

The significance value (p value) associated with functional analysis in the IPA software is a measure of the likelihood that the association between a set of functional analysis genes of the experiment and a given process or pathway is due to random chance. The smaller the p value, the less likely that the association is random and the more significant the association. In general, p values less than 0.05 indicate a statistically significant, non-random association.

The p value associated with a biological process or pathway annotation is a measure of its statistical significance with respect to the functions/pathways/lists eligible molecules for the dataset and a reference set of molecules (which define the molecules that could possibly have been functions/pathways/lists eligible). The p value is calculated with the right-tailed Fisher’s Exact Test. In this test, only overrepresented functions or pathways, those that have more functions/pathways/lists eligible molecules than expected by chance, are significant. Underrepresented functions or pathways (‘left-tailed’ p values) which have significantly fewer molecules than expected by chance are not shown.

Results and discussion

MS/MS and data analysis

The 2-DE reference map of sea bass (D. labrax) muscle is presented in Fig. 1. 2-DE enabled the separation of proteins over the entire pH 3–11 range and comprised proteins between 10 and 250 kDa. Results of the statistical analysis carried out among the different individuals examined in this study are reported in Table 1. The spot abundance values were highly comparable among all fish, when considering that the average CV was 0.24 and that only two spots had CVs slightly higher than 0.5 (spots 6 and 23 with CVs of 0.53 and 0.54, respectively).
Fig. 1

Two-dimensional gel reference map of sea bass Dicentrarchus labrax skeletal muscle. 2-DE was performed using a pH range of 3–11 in the first dimension. The protein loading was 500 μg and the gel was stained using the mass compatible silver staining procedure. Forty-nine spots were identified and analyzed for mass spectrometry (MS/MS)

Table 1

List of the 49 protein identities from the European sea bass muscle based on mass spectrometry (MS/MS) analysis

Spot nr.

Identified protein

ID

Symbol

Species

Location

Biological function

Score

QM

% Vol

MSD

CV

pI/Mr Theoretical (kDa)

pI/Mr Observed (kDa)

1.

Myosin heavy chain

Q90339

MHC

C. carpio

Cytoplasm, thick filament

Muscle contraction

285

26

0.53

0.01

0.02

5.57/222.32

5.80/191

2.

Myosin-binding protein C

Q90688

MYBPC3

G. gallus

Thick filament

Cell adhesion

87

2

0.42

0.16

0.39

5.96/143.05

5.98/142

3.

Glycogen phosphorylase

P11217

PYGM

H. sapiens

Cytoplasm

Carbohydrate metabolism, glycogen metabolism

498

17

0.16

0.06

0.35

6.57/97.49

7.26/107

4.

AMP deaminase 3

Q01432

AMPD3

H. sapiens

Cytoplasm

Nucleotide metabolism

138

9

0.05

0.01

0.20

6.51/89.27

7.06/101

5.

6-phosphofructokinase

Q0IIG5

PFKM

B. taurus

Cytoplasm

Glycolysis

227

8

0.06

0.01

0.14

8.56/86.09

8.02/93

6.

Myosin heavy chain

P13538

MHC

G. gallus

Cytoplasm, thick filament

Muscle contraction

40

2

0.50

0.27

0.53

6.05/223.98

6.05/96

7.

Myosin heavy chain

P13538

MHC

G. gallus

Cytoplasm, thick filament

Muscle contraction

40

2

0.06

0.03

0.49

5.63/223.98

6.16/88

8.

Myosin heavy chain

P13538

MHC

G. gallus

Cytoplasm, thick filament

Muscle contraction

49

2

0.10

0.01

0.13

5.63/223.98

6.28/85

9.

Heat shock 70 kDa protein

P08106

HSPA2

G. gallus

Cell surface

Stress response

216

11

0.11

0.01

0.10

5.53/69.94

5.33/86

10.

Desmin

O62654

DES

B. taurus

Cytoplasm, intermediate filament

Muscle protein

84

3

0.06

0.00

0.01

5.21/53.56

5.53/61

11.

Desmin

Q05AI8

desmb

D. rerio

Cytoplasm, intermediate filament

Muscle protein

85

6

0.13

0.02

0.15

5.36/54.30

5.62/61

12.

Glycerol-3-phosphate Dehydrogenase

Q5XIZ6

gpd1 l

D. rerio

Cytoplasm

Glycolysis (oxidoreductase)

135

7

0.12

0.00

0.02

5.45/38.94

5.73/61

13.

Phosphoglucomutase-1

Q08DP0

PGM1

B. taurus

Cytoplasm

Glycolysis (isomerase)

582

18

0.25

0.04

0.15

6.36/61.84

7.04/66

14.

Pyruvate kinase isozyme

P00548

PKM2

G. gallus

Cytoplasm

Glycolysis (kinase, transferase)

546

33

0.07

0.01

0.20

7.29/58.43

7.63/70

15.

Pyruvate kinase

Q8QGU8

PKM2

T. rubripes

Cytoplasm

Glycolysis (Kinase Transferase)

294

12

0.10

0.04

0.41

7.96/58.57

7.90/71

16.

Alpha-enolase

Q9XSJ4

ENO1

B. taurus

Cell membrane, cytoplasm

Glycolysis (lyase)

320

17

0.95

0.06

0.06

6.63/47.64

6.70/55

17.

Elongation factor 1α

P68103

EEF1A1

B. taurus

Cytoplasm, nucleus

Protein biosynthesis

295

27

0.12

0.05

0.39

9.10/50.45

8.21/60

18.

Elongation factor 1α

P13549

eef1as

X. laevis

Cytoplasm, nucleus

Protein biosynthesis

202

15

0.14

0.01

0.07

9.10/50.52

6.81/41

19.

Malate dehydrogenase

Q5ZME2

MDH1

G. gallus

Cytoplasm

Tricarboxylic acid cycle (oxidoreductase)

124

5

0.13

0.04

0.33

6.92/36.75

6.57/42

20.

Fructose-bisphosphate aldolase A

P04075

ALDOA

H. sapiens

Cytoplasm

Glycolysis (lyase)

58

3

0.24

0.02

0.07

8.30/39.85

7.76/46

21.

Glyceraldehyde-3-phosphate dehydrogenase

Q5R2J2

GAPDH

T. sinensis

Cytoplasm, nucleus

Apoptosis, Glycolysis (oxidoreductase)

251

11

0.30

0.11

0.36

8.70/36.05

7.84/46

22.

Phosphoglycerate mutase 2

Q32KV0

PGAM2

B. taurus

Cytoplasm

Glycolysis (hydrolase, isomerase)

56

3

0.64

0.08

0.13

8.99/28.84

8.23/35

23.

Triosephosphate isomerase B

Q90XG0

tpi1b

D. rerio

Cytoplasm

Glycolysis (isomerase)

392

13

0.20

0.11

0.54

6.45/27.10

8.10/34.5

24.

Troponin I

P48788

TNNI2

H. sapiens

Cytoplasm, nucleus

Muscle protein

95

7

1.71

0.67

0.39

8.87/21.50

8.14/29

25.

Nucleoside diphosphate kinase B (fragments)

P85292

NME2

M. magellanicus

Nucleus

Glycolysis (kinase, transferase)

406

13

2.12

0.90

0.43

5.70/14.28

7.17/15

26.

Nucleoside diphosphate kinase B (fragments)

P85292

NME2

M. magellanicus

Nucleus

Glycolysis (kinase, transferase)

265

4

0.48

0.12

0.26

5.70/14.28

6.64/15

27.

Nucleoside diphosphate kinase B (fragments)

P85292

NME2

M. magellanicus

Nucleus

Glycolysis (kinase transferase)

345

10

0.03

0.00

0.07

5.70/14.28

6.36/13.9

28.

Nucleoside diphosphate kinase B (fragments)

Q2EN76

NME2

S. scrofa

Nucleus

Glycolysis (kinase transferase)

84

2

0.11

0.03

0.31

7.77/17.28

5.89/15

29.

Eukaryotic translation initiation factor 5A-1

Q6EWQ7

EIF5A

B. taurus

Cytoplasm, endoplasmic reticulum, membrane, nuclear pore complex, nucleus

Protein biosynthesis, protein transport, translocation, transport, mRNA transport

55

2

0.13

0.02

0.12

5.08/17.05

5.40/19

30.

Lactoylglutathione lyase

Q9CPU0

Glo1

M. musculus

Cytoplasm

Glycolysis (Lyase)

42

5

0.15

0.03

0.21

5.24/20.97

4.92/26

31.

Alpha-enolase

P06733

ENO1

H. sapiens

Cell membrane, cytoplasm, nucleus

Glycolysis (lyase)

651

23

0.45

0.08

0.17

7.01/47.48

7.11/56

32.

Alpha-enolase

P19140

ENO1

A. platyrhynchos

Cell membrane, cytoplasm, nucleus

Glycolysis (lyase)

921

27

0.23

0.06

0.26

6.37/47.61

6.45/56

33.

Triosephosphate isomerase B

Q90XG0

tpi1b

D. rerio

Cytoplasm

Glycolysis (isomerase)

1,430

41

1.00

0.35

0.35

6.45/27.10

7.73/26

34.

Adenylate kinase isoenzyme 1

P12115

ak1

C. carpio

Cytoplasm

Glycolysis (kinase, transferase)

426

39

0.31

0.11

0.35

6.64/21.53

6.92/25

35.

F1 ATP synthase beta subunit

C1J0J0

ATP5E

G. seta

Cytoplasm

ATP synthesis, hydrogen ion transport, rule base, ion transport, transporter

372

14

0.05

0.01

0.18

5.15/53.95

5.11/52

36.

Actin

Q7T2J3

ACT

C. carpio

Cytoplasm

ATP binding

940

47

12.74

4.12

0.32

5.22/42.28

5.39/44

37.

Tropomyosin α-1 chain

P84335

TPM1

L. aurata

Cytoplasm, cytoskeleton

Actin binding

957

37

2.97

0.51

0.17

4.69/32.77

4.66/40

38.

Myosin light chain 1

Q90W41

mlc1

S. japonicus

Cytoplasm, thick filament

Muscle contraction

375

13

1.14

0.27

0.23

4.70/21.34

4.52/26

39.

Myosin regulatory light chain 2

P02609

MYLPF

G. gallus

Cytoplasm

Muscle protein

129

6

4.11

1.03

0.25

4.77/18.94

4.57/19

40.

Myosin light chain 2

Q9IB25

mlc2

T. trachurus

Cytoplasm

Muscle protein

803

31

2.08

0.34

0.16

4.71/19.14

3.91/17

41.

Parvalbumin

C0LEL4

PVALB

B. saida

Cytoplasm

Calcium ion binding

108

3

3.48

0.46

0.13

4.46/11.68

4.35/15

42.

Parvalbumin

B6UV97

PVALB

H. molitrix

Cytoplasm

Calcium ion binding

189

7

0.50

0.10

0.19

4.46/11.62

3.98/14

43.

Muscle-type creatine kinase

C7ASM1

CKM

S. chuatsi

Cytoplasm

Glycolysis (kinase, transferase)

685

29

1.91

0.64

0.33

6.41/43.12

7.24/47

44.

Creatine kinase M-type

C1BIK3

CKM

O. mordax

Cytoplasm

Glycolysis (kinase, transferase)

286

13

3.32

0.52

0.16

6.32/42.99

6.90/46

45.

Glyceraldehde-3-phosphate dehydrogenase

Q5I1Z5

GAPDH

D. labrax

Cytoplasm

Glycolysis (oxidoreductase)

449

24

4.77

0.77

0.16

8.56/36.04

8.01/40

46.

Fructose-bisphosphate aldolase A

Q803Q7

aldoaa

D. rerio

Cytoplasm

Glycolysis (lyase)

568

34

6.18

1.92

0.31

8.50/40.09

7.97/45

47.

Lactate dehydrogenase-A

Q7T3M3

LDH-A

C. caudalis

Cytoplasm

Glycolysis (oxidoreductase)

131

5

0.32

0.08

0.26

6.92/36.39

7.42/39

48.

Capping protein (actin filament) muscle Z-line beta

B5DFX6

Capzb

S. salar

Cytoplasm

Actin cytoskeleton organization

121

6

0.06

0.01

0.23

5.39/31.20

5.49/37

49.

Phosphoglycerate mutase 2

Q32KV0

PGAM2

B. taurus

Cytoplasm

Glycolysis (hydrolase isomerase)

56

3

0.21

0.06

0.31

8.20/8.99

8.20/36

The protein identities are listed according to their accession number obtained from the UniProt database. Protein identification details are reported. Spot Nr. refers to the spot numbering in Fig. 1

ID: UniProt identifier, Score: Score assigned by the Mascot search engine to the protein identification, QM: Queries matched: number of queries matched in the protein identification, %Vol: Percent volume of each spot normalized on the total protein abundance, MSD: Mean squared deviation of the percent spot volumes among all samples, CV: Coefficient of variation of the spot volumes among all samples

The most abundant single spot of the isoelectric series was excised for identification, as described previously (Terova et al. 2011; Addis et al. 2012), leading to 49 protein identities for a total of 33 non-redundant proteins (Table 1). The proteins identified covered molecular masses (Mr) ranging from 13.9 to 193 kDa, and isoelectric points (pI) ranging from 3.91 to 8.23. Approximately 72 % of the total proteins identified in sea bass muscle tissue were acidic, having theoretical pI values ranging from 4 to 7, whereas 28 % of the total proteins identified were basic, having theoretical pI values ranging from 7 to 9 (Fig. 2b). In general, there was a good correlation between the observed and theoretical Mr values of the identified proteins (Table 1).
Fig. 2

Pie chart distribution of protein identities based on a function of the proteins b location of the proteins

The 49 protein identities obtained were classified according to their cellular localization and biological function. Over half (60 %) of the proteins were found to be localized in the cytoplasm, 16 % were localized in the nucleus and 14 % in the filaments (Fig. 2d). About 51 % of the identified proteins were enzymes involved in glycolytic processes (Fig. 2a), 25 % were structural proteins, 7 % were binding proteins, such as calcium ion-binding proteins and ATP-binding proteins, and a low percentage were transport and biosynthesis proteins (Fig. 2a). Among the enzymes, the kinases–transferases were the most frequently identified functional protein class. Indeed, 20 % of the total identified proteins were categorized in this category which included kinases, aldolases, dehydrogenases, isomerases, mutases and enolases. Six proteins (11 %) belonged to the enolase family. Five proteins (9 %) were classified as dehydrogenases, and other five ones (9 %) were classified as isomerases. A series of glycolytic enzymes were identified.

In addition to sarcoplasmic proteins, myofibrillar or structural proteins made up a significant group of the proteins identified in sea bass muscle. A total of six different myofibrillar proteins were detected. Other proteins, such as elongation factor 1-alpha, parvalbumin eukaryotic translation initiation factor 5a-1 and heat shock 70 kDa protein (Table 1), were identified in the muscle tissue map.

Network pathway analysis

Analysis of the muscle proteins for biological pathways based on the Ingenuity Pathways Knowledge Base software (Ingenuity® Systems; www.ingenuity.com) selected 33 protein identities out of the 49 identified (Table 1). To these proteins, several others were associated by the knowledge-based software to the networks, and the sum of interactions among these proteins is graphically illustrated in Fig. 3. Three protein networks were identified with high significance: organ morphology, skeletal and muscular system development and function, and carbohydrate metabolism (1); cell-to-cell signaling and interaction, cell-mediated immune response and cellular movement (2); and cell morphology, developmental disorder and ophthalmic disease (3) (Table 2). Fifteen protein identities were found to be involved in network 1 (Fig. 3a; Table 1); eleven in network 2 (Fig. 3b; Table 1) and seven in network 3 (Fig. 3c; Table 1).
Fig. 3

Top networks obtained by Ingenuity Pathway Analysis of all sea bass muscle proteins examined in this study (Ingenuity® Systems; www.ingenuity.com). Three protein networks were obtained with the following associated network functions: a organ morphology, skeletal and muscular system development and function, carbohydrate metabolism; b cell-to-cell signaling and interaction; c cellular movement, cell morphology. Shaded proteins are those used for analysis; white proteins are those identified by the software as significantly associated to the network according to the knowledge-based attribution

Table 2

Results of the Ingenuity Pathway Analysis concerning the top networks, biological functions and canonical pathways obtained upon analysis of all the sea bass muscle protein identities considered in this study

Top networks

Associated network functions

Score

 Organ morphology, skeletal and muscular system development and function, carbohydrate metabolism

37

 Cell-to-cell signaling and interaction

25

 Cellular movement, cell morphology

14

Top biological functions

Name

p value

Molecules

Molecular and cellular functions

 Carbohydrate metabolism

1.08E−11–4.88E−02

14

 Nucleic acid metabolism

6.43E−07–2.15E−02

14

 Small molecule metabolism

6.43E−07–4.88E−02

21

 Cell signaling

6.82E−05–3.63E−02

7

 Molecular transport

6.82E−05–4.88E−02

13

Physiological system development and function

 Organ morphology

8.25E−10–3.21E−02

16

 Skeletal and muscular system development and function

8.25E−10–4.67E−02

16

 Cardiovascular system development and function

3.35E−06–5.00E−02

9

 Tissue development

9.54E−05–5.00E−02

10

 Tissue morphology

6.85E−04–3.42E−02

4

Top canonical pathways

 Name

p value

Ratio

 Glycolysis/gluconeogenesis

1.49E−13

9/130 (0.069)

 Calcium signaling

1.96E−06

6/210 (0.029)

 Pyruvate metabolism

1.53E−05

4/139 (0.029)

 Pentose phosphate pathway

3.64E−05

3/80 (0.038)

 Inositol metabolism

6.82E−05

2/18 (0.111)

The top biological functions identified both in terms of molecular and cellular functions and of physiological functions were consistent with the tissue under examination. Indeed, the highest score was attributed to carbohydrate metabolism with 14 protein identities (p value 1.08E−11–4.88E−02) (Table 1, 2), followed by nucleic acid metabolism (p value 6.43E−07–2.15E−02) and small molecule metabolism (p value 6.43E−07–4.88E−02), and by Molecular Transport (p value 6.82E−05–4.88E−02) (Table 1, 2). Moreover, the highest number of identified proteins was found in small molecule metabolism.

Concerning Physiological System Development and Function, organ morphology (p value 8.25E−10–3.21E−02), strictly followed by skeletal and muscular development and function (p value 8.25E−10–4.67E−02), showed the highest significance, consistently with the presence of protein constituents of the muscle tissue contractile units. In addition, these two categories show a large number of proteins identified (Table 1, 2). Still, cardiovascular system development and function (p value 3.35E−06–5.00E−02) was also identified due to the extreme similarity between the two striated muscles. Finally, tissue development and tissue morphology were also found as significant (p value 9.54E−05–5.00E−02 and 6.85–04–3.42E−02, respectively) (Table 1, 2).

In keeping with these observations, the five top canonical pathways were glycolysis/gluconeogenesis, calcium signaling, pyruvate metabolism, pentose phosphate pathway and inositol metabolism.

In conclusion, in this study, we reported the 2-DE proteome profile of normal adult European sea bass muscle and identified 49 different proteins involved in various biological processes and functions. This study may serve as an initial framework for the European sea bass muscle proteome database. The work accomplished may also set the ground for studies aimed to develop novel molecular methods and protocols for seafood authentication, monitoring of growth dynamics, traceability and quality of cultured European sea bass. This work is also important for understanding the proteome map of the sea bass toward establishing the animal as a potential model for muscular and various other developmental studies.

Notes

Acknowledgments

This work has been funded under the ARRAINA project N°288925: Advanced Research Initiatives for Nutrition and Aquaculture. The views expressed in this work are the sole responsibility of the authors and do not necessary reflect the views of the European Commission.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Genciana Terova
    • 1
    • 2
  • Salvatore Pisanu
    • 3
  • Tonina Roggio
    • 3
  • Elena Preziosa
    • 1
  • Marco Saroglia
    • 1
    • 2
  • Maria Filippa Addis
    • 3
  1. 1.Department of Biotechnology and Life Sciences (DBSV)University of InsubriaVareseItaly
  2. 2.Inter-University Centre for Research in Protein Biotechnologies “The Protein Factory”Polytechnic University of MilanVareseItaly
  3. 3.Proteomics LaboratoryPorto Conte Ricerche SrlAlgheroItaly

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