Journal of Biomedicine and BiotechnologyVolume 2011 (2011), Article ID 920763, 10 pagesdoi:10.1155/2011/920763

Research Article

Quantitative Proteomics Analysis by Isobaric Tags for Relative and Absolute Quantitation Identified Lumican as a Potential Marker for Acute Aortic Dissection

Guorong Gu,¹ Weizhong Cheng,¹ Chenling Yao,¹ Jun Yin,¹ Chaoyang Tong,¹ Andrew Rao,² Lawrence Yen,² Matthew Ku,² and Jianyu Rao²

¹Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai 200032, China²Department of Pathology and Laboratory Medicine, UCLA School of Medicine, Los Angeles, CA 90095, USA

Received 6 September 2011; Revised 27 October 2011; Accepted 27 October 2011

Academic Editor: Saulius Butenas

Copyright © 2011 Guorong Gu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Acute aortic dissection (AAD) is a serious vascular disease. Currently the diagnosis relies on clinical and radiological means whereas serum biomarkers are lacking. The purpose of this study was to identify potential serum biomarkers for AAD using isobaric tags for relative and absolute quantitation (iTRAQ) approach. A total of 120 serum samples were collected from three groups: AAD patients (), patients with acute myocardial infarction (AMI, ), and healthy volunteers (), whereas the first 10 samples from each group were used for iTRAQ analysis. Using iTRAQ approach, a total of 174 proteins were identified as significantly different between AAD patients and healthy subjects. Among them, forty-six proteins increased more than twofold, full-scale analysis using serum sample for the entire 120 subjects demonstrated that Lumican level was significantly increased relative to control and AMI samples. Further, Lumican level correlated with time from onset to admission in AAD but not AMI samples. Using iTRAQ approach, our study showed that Lumican may be a potential AAD-related serum marker that may assist the diagnosis of AAD.

1. Introduction

Acute aortic dissection (AAD) has become a treatable disease due to recent advances in new therapeutic approaches for the management of heart and arterial diseases; however, development of quick and economic diagnostic methods remains a challenge. Variability in disease presentation often obscures diagnoses, and imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and esophagus ultrasound remain prohibitive due to cost and availability. Aortic dissection remains a frequent target of medicolegal litigations with accusations of failure to diagnose against treating physicians and hospitals [1]. Some progress in the biochemical diagnosis of AAD has been made in the last decade [2, 3]; several acute phase proteins and coagulation parameters were identified to increase in AAD patients, but these are nonspecific biomarkers for AAD as they may be also aberrantly expressed in other disease conditions such as acute myocardial infarction (AMI).

Recently a quantitative proteomic assay, isobaric tags for relative and absolute quantitation (iTRAQ), has been developed and utilized to identify biomarkers for various disease conditions [4, 5]. This chemical labeling method involves the stable incorporation of isotopes into an amine tagging reagent, which can then be reliably detected by mass spectrometry, thereby permitting comparative quantitation of various proteins in a multiplex manner. It has been suggested to be suitable for the discovery of biomarkers in a wide range of body fluids and tissues, including serum and plasma [5, 6]. With this method, we expect to find the potential biomarkers which are released from the disruption of the aortic media and can provide sufficient specificity and longer time window for the diagnosis of AAD.

2. Materials and Methods

2.1. Samples

The study included a total of 30 healthy individuals and 90 patients (60 AAD, 30 AMI). All the patients were selected in a consecutive manner from the period of July 2009 to November 2011 from Fudan University affiliated Zhongshan Hospital (Shanghai, China). iTRAQ analysis was performed for the first twenty patients (10 AAD and 10 AMI) and ten healthy individuals. All patients presented within 72 hours after an episode of chest and/or back pain lasting 5 minutes or more. The diagnosis of AAD was confirmed by computed tomographic arteriography (CTA). The AMI patient was confirmed by electrocardiography (ECG) and cardiac troponin T (cTNT) tests. All patients gave their informed consent for the study. The protocol was approved by the Ethics Committee of Zhongshan Hospital.

For each study subject, whole blood samples were immediately collected in BD Vacutainer SST tubes (BD Diagnostics, Plymouth, UK) after admission and centrifuged at 4000 rpm for 10 min at room temperature. The serum was frozen and stored in aliquots at −80°C until analysis.

2.2. Serum C-Reactive Protein and Myoglobin Test

Vitros 5.1 FS automatic biochemistry analyzer (Johnson & Johnson; Calif, USA) was used for serum C-reactive protein (CRP) test, and Cobas e411 immunoassay analyzer (Roche; Mannheim, Germany) was used for the serum myoglobin (Myo) test. The results were then interpreted in accordance with that tested by the International Federation of Clinical Chemistry (IFCC) recommended method. Analyses were performed immediately after the centrifugation of whole blood samples.

2.3. iTRAQ Sample Preparation: Strong Cation Exchange (SCX) Chromatography

iTRAQ reagents were purchased from Applied Biosystems (Foster City, USA). Fourteen interfering highly abundant proteins from serum samples were removed using Agilent multiple affinity removal liquid chromatography (LC) column-Human 14 (MARS) (shimadzu, Kyoto, Japan). One hundred micrograms of each extract were precipitated using acetone at −20°C and suspended in 20 μL of dissolution buffer (Applied Biosystems, Foster City, USA). After reduction and alkylation, each sample was digested with trypsin (w(trypsin) : w(protein) = 1 : 20) at 37°C overnight. The tryptic peptides were labeled with the iTRAQ reagents as follows: normal controls group was labeled with iTRAQ 113, AMI group was labeled with iTRAQ 114, and AAD group was labeled with iTRAQ 115. The peptides were pooled and desalted with Sep-Pak Vac C18 (Waters, Milford, USA). The peptide mixture was diluted with buffer A containing 10 mM KH₂PO₄ in 25% acetonitrile (ACN) at pH 2.6. The peptides were fractionated by 20AD high-performance liquid chromatography (HPLC) system (Shimadzu; Kyoto, Japan) equipped with a polysulfoethyl A column (2.1 mm × 100 mm, 5u, 200 A; The Nest Group, Southborough, Mass). The composition of buffer B was 350 mM KCl, 10 mM KH₂PO₄, and 25% ACN at pH 2.6. Separation was performed using a linear binary gradient of 0–80% buffer B in buffer A at a flow rate of 200 μL/min for 60 min. The fractions were combined into 20 groups.

2.4. LC-MS Analysis

Each SCX fraction was dried down by the rotary vacuum concentrator, dissolved in buffer C (0.1% formic acid, 5% ACN, 95% water), and analyzed on Qstar XL (Applied Biosystems; Foster City, USA). The HPLC gradient was 5–35% buffer D (95% ACN, 0.1% formic acid) in buffer C at a flow rate of 300 nL/min for 70 min. Analysis survey scans were acquired MS from m/z 400–1800 with up to 4 precursors selected for MS/MS from m/z 100–2000.

2.5. The Confirmative ELISA Analysis for Lumican

The confirmative ELISA analysis for Lumican was performed using the kits from CUSABIO BIOTECH CO, following manufacture’s recommendation (CUSABIO BIOTECH CO., LTD., Wuhan, China).

2.6. Data Analysis

All statistical analyses were performed in SPSS 12.0 (SPSS Inc. Chicago, USA). Results were presented as Mean ± SD. A comparative analysis of multiple groups was performed with a one-way-ANOVA or Mann-Whitney/Kruskal-Wallis Test. Statistical significance was defined as . Peptide and protein identification was performed by searching the MS/MS spectra against the SwissProt database using the local Protein Pilot 2.0.1 software. Only peptides identified with confidence interval values of no less than 95% (Unused ProtScore >1.3) were used for protein identification compilation and subsequent quantitation calculation. Fold changes of >2 or <0.5 were set as cut-off values to designate significant differences in protein expression among the AAD group and the normal control group.

2.7. PANTHER Analysis

The PANTHER database was used to elucidate cellular components, biological processes, and the molecular functions associated with each individual protein (http://www.pantherdb.org/).

3. Results

3.1. Clinical Features of Study Subjects

The clinical features of the AAD patients, AMI patients, and normal controls are summarized in Table 1. There were no differences in age distribution and sex composition among the three groups involved either for ELISA analysis () ( and 0.378, resp.,) or iTRAQ analysis ( and 0.873, resp.). There was no differences in the time from onset to admission between AAD and AMI group either ().

Table 1: Clinical characteristics in three groups.

AAD
AMI
Normal controls
value

60
30
30

ELISA test ()
Age (Mean ± SD)

0.351a

Gender, (%), male
30 (50)
17 (56.67)
16 (53.33)
0.378b

Admission after onset hours (Mean ± SD)

/
0.776c

Type A (%)
31 (51.67)
/
/

Type B (%)
29 (48.33)
/
/

Marfan (%)
7 (11.67)
/
/

10
10
10

iTRAQ test ()
Age (Mean ± SD)

0.241a

Gender, (%), male
6 (60)
6 (60)
5 (50)
0.873b

Admission after onset hours (Mean ± SD)

/
0.363c

Type A (%)
5 (50)
/
/

Type B (%)
5 (50)
/
/

Marfan (%)
2 (20)
/
/

a One-way-ANOVA; bChi-square Test; cMann-Whitney Test.

3.2. Functional Classification of Identified Proteins by iTRAQ

A total of 174 proteins with confidence interval values of no less than 95% were identified (Unused ProtScore > 1.3). However, after manually rechecking the MS/MS data thoroughly peak by peak, 155 proteins (89.08%) had a relative quantitation of one or more peptides. Fifteen proteins had no quantifiable peptides that could be ascertained, and four proteins had peptides with confidence interval values that were less than 95%.

In total, 174 proteins were sorted using the PANTHER classification system, which sorts the proteins into respective categories based on their molecular functions. The major groups include: signaling molecules (13%), enzyme modulators (12%), transfer/carrier proteins (11%), and proteases (10%). Other groups include: structural proteins (1%), cell adhesion molecules (1%), cytoskeletal proteins (3%), extracellular matrix proteins (2%), and cell junction proteins (1%).

As a way to cross-check the reliability of quantitation of iTRAQ reagent, serum CRP and Myo levels were assessed using both conventional biochemical and immunoassay tests and iTRAQ analysis on the same specimens. With biochemical and immunoassay analysis, CRP was 41.31 ± 32.76 mg/mL and Myo was 66.42 ± 81.23 mg/mL in AAD group, while in normal controls, the former was 5.88 ± 1.42 and the latter was 32.07 ± 14.14 mg/mL. CRP and Myo levels of AAD patients were 7.03-fold and 2.07-fold higher, respectively, than normal controls. Using iTRAQ, the AAD/normal controls ratios of CRP and Myo were similar at 9.12-fold (Table 2) and 1.47-fold. The ratios of CRP and Myo among three groups were similar with either biochemical and immunoassay or iTRAQ analysis, confirming the reliability of iTRAQ analysis.

Table 2: List of the increased (>2-fold) protein targets identified and their corresponding class, associated biological process, and cellular component.

N
Unuseda
Peptidesb
Accession #
Name
Biological process
Cellular component
Protein class
AAD/CON ratio
AAD/AMI ratio

1
2.7
1
Q13509
Tubulin beta-3 chain
Cellular component morphogenesis
Cytoskelelton
Cytoskeletal protein/tubulin
39.8406
0.0711

2
2
1
P01600
Ig kappa chain V-I region Hau

Unclassified
31.3480
3.0760

3
6.67
3
P02743
Serum amyloid P-component
Response to stress

Defense/immunity protein/antibacterial response
24.4499
9.1241

4
13.53
10
P05546
Heparin cofactor 2
Protein metabolic process

Enzyme modulator
19.7628
11.2740

5
17.67
9
P36955
Pigment epithelium-derived factor
Protein metabolic process

Enzyme modulator
19.7628
9.8135

6
6.24
4
P05543
Thyroxine-binding globulin
Protein metabolic process

Enzyme modulator
12.7065
3.7665

7
12.63
18
P01834
Ig kappa chain C region
Response to stimulus
Immunoglobulin complex
Defense/immunity protein
11.6959
1.8031

8
55.14
43
P02751
Fibronectin
Blood coagulation
Extracellular matrix
Transfer/carrier protein
11.5875
3.7327

9
35.94
29
P01011
Alpha-1-antichymotrypsin
Protein metabolic process

Enzyme modulator
10.5708
8.0906

10
2.22
1
P02741
C-reactive protein
Response to stress

Defense/immunity protein
9.1241
5.9701

11
2
1
Q9UK55
Protein Z-dependent protease inhibitor
Protein metabolic process

Enzyme modulator
8.7873
2.4888

12
100.78
51
P04114
Apolipoprotein B-100
Lipid metabolic process

Transfer/carrier protein
8.6281
2.8050

13
9.19
4
P35858
Insulin-like growth factor-binding protein complex acid labile chain
Cell-cell adhesion
Extracellular matrix
Receptor
8.4746
4.0177

14
47.88
42
P19827
Inter-alpha-trypsin inhibitor heavy chain H1
Protein metabolic process

Enzyme modulator
5.8072
5.6497

15
106.72
89
P00450
Ceruloplasmin
Blood coagulation
Extracellular matrix
Transporter
5.1046
9.6339

16
37.56
23
P04196
Histidine-rich glycoprotein
Blood coagulation

Unclassified
4.6125
3.2206

17
4.15
2
P08571
Monocyte differentiation antigen CD14
Immune system process

Receptor
4.0933
2.4888

18
2.72
1
Q96KN2
Beta-Ala-His dipeptidase
Protein metabolic process

Protease
4.0933
1.9231

19
31.66
20
P01871
Ig mu chain C region
Response to stimulus

Defense/immunity protein
3.8023
2.3552

20
12.12
6
P51884
Lumican
Cell-cell adhesion
Extracellular matrix
Receptor
3.6311
1.2942

21
2.71
1
P00740
Coagulation factor IX
Blood coagulation

Protease
3.4037
2.0137

22
6.27
3
P08185
Corticosteroid-binding globulin
Protein metabolic process

Enzyme modulator
3.2808
5.4945

23
2.58
21
P00739
Haptoglobin-related protein

Unclassified
3.2206
1.7538

24
4.98
4
P04003
C4b-binding protein alpha chain
Blood coagulation

Transfer/carrier protein
3.1626
5.0582

25
6.02
6
P02745
Complement C1q subcomponent subunit A
Response to stimulus

Transfer/carrier protein
3.1046
3.6982

26
5.83
3
P22792
Carboxypeptidase N subunit 2
Cell adhesion
Extracellular matrix
Receptor
3.0202
1.3805

27
15.99
11
P05155
Plasma protease C1 inhibitor
Protein metabolic process

Enzyme modulator
2.9647
1.2824

28
2.02
2
P01742
Ig heavy chain V-I region EU

Unclassified
2.8580
1.1695

29
2
1
P09486
SPARC
Cell-cell signaling

Transfer/carrier protein
2.7042
0.7312

30
18.06
9
P02760
Protein AMBP
Blood coagulation

Enzyme modulator
2.6062
3.5651

31
8.99
5
P12259
Coagulation factor V
Blood coagulation
Extracellular matrix
Transporter
2.6062
3.2206

32
8.31
6
P27169
Serum paraoxonase/arylesterase 1
Immune system process

Oxidoreductase
2.5349
5.6497

33
2.32
1
P19320
Vascular cell adhesion protein 1
Cell-cell adhesion

Defense/immunity protein
2.3121
2.1478

34
2.01
1
O00187
Mannan-binding lectin serine protease 2
Response to stimulus

Protease
2.3121
1.5277

35
43.19
29
P02749
Beta-2-glycoprotein 1
Blood coagulation

Transfer/carrier protein
2.2287
7.1124

36
3.52
2
P11226
Mannose-binding protein C
Response to stimulus

Defense/immunity protein
2.2080
2.6062

37
87.93
67
P00738
Haptoglobin
Blood coagulation

Protease
2.1678
3.5651

38
5.7
4
Q13790
Apolipoprotein F
Lipid metabolic process

Transporter
2.1478
3.9448

39
65.42
41
P02790
Hemopexin
Vitamin transport

Transfer/carrier protein
2.1478
3.9078

40
2.96
1
P20851
C4b-binding protein beta chain
Blood coagulation

Transfer/carrier protein
2.1281
4.8309

41
2
1
P61769
Beta-2-microglobulin
Response to stimulus

Defense/immunity protein
2.1088
2.1281

42
20.86
10
P02748
Complement component C9
Response to stimulus

Receptor
2.1088
0.6252

43
6.96
3
P07477
Trypsin-1
Protein metabolic process

Protease
2.1088
0.3436

44
13.64
7
P05156
Complement factor I
Response to stimulus

Protease
2.0325
0.9120

45
15.17
10
P01842
Ig lambda chain C regions
Response to stimulus
Immunoglobulin complex
Defense/immunity protein
2.0137
2.1678

46
4.29
1
P35030
Trypsin-3
Protein metabolic process

Protease
2.0137
1.3931

a Unused > 1.3 means at least 95% confidence; bnumber of peptides with 95% confidence; AAD: acute aortic dissection; AMI: acute myocardial infarction; CON: normal controls.

3.3. Proteins with Over Twofold Differential Expression

A total of 155 proteins had a relative quantitation difference for AAD patients compared with the normal control group of which 46 proteins increased more than twofold (Table 2), while 36 proteins decreased more than twofold among the AAD patients (Table 3). Among the identified proteins with increased levels in AAD, there were a number of acute phase reactants (CRP, Beta-2-microglobulin, Complement factor I), blood coagulation marker (Haptoglobin, Coagulation factor V, Coagulation factor IX), and cellular components (Lumican, Tubulin beta-3 chain, Fibronectin). However when compared to AMI patients, 14 of the 46 protein showed less than 2-fold increase, including complement component 9, complement factor1, Plasma protein C1 inhibitor, and Ig Kappa chain C-region (Table 2). Interestingly, some acute phase proteins such as CRP remains on the list as it showed the differential expression between the two conditions.

Table 3: List of the decreased (<0.5 folds) protein targets identified and their corresponding class, associated biological process, and cellular component.

N
Unuseda
Peptidesb
Accession #
Name
Biological process
Cellular component
Protein classification
AAD/CON ratio
AAD/AMI ratio

1
19.31
17
P02775
Platelet basic protein
Blood coagulation

Transfer/carrier protein
0.0209
0.0398

2
44.6
31
P02671
Fibrinogen alpha chain
Blood coagulation
Extracellular matrix
Transfer/carrier protein
0.0370
0.0203

3
19.82
30
P02656
Apolipoprotein C-III
Lipid metabolic process

Transporter
0.0570
0.0240

4
5.7
5
P01717
Ig lambda chain V-IV region Hil

Unclassified
0.0655
0.0679

5
8.45
4
P02768
Serum albumin
Transport

Transfer/carrier protein
0.0724
0.8472

6
3.22
1
P04264
Keratin, type II cytoskeletal 1
Cellular component morphogenesis
Cytoskelelton
Structural protein
0.0780
0.9639

7
2.05
1
P04070
Vitamin K-dependent protein C
Blood coagulation

Protease
0.0794
0.0441

8
34.98
27
P02652
Apolipoprotein A-II
Lipid metabolic process

Transporter
0.0991
0.1067

9
20.3
10
P02654
Apolipoprotein C-I
Lipid metabolic process

Transporter
0.1159
0.0847

10
94.94
108
P02765
Alpha-2-HS-glycoprotein
Protein metabolic process
Extracellular matrix
Extracellular matrix protein
0.1180
0.1472

11
39.07
25
P01008
Antithrombin-III
Protein metabolic process

Enzyme modulator
0.1202
0.0973

12
72.69
57
P00734
Prothrombin
Blood coagulation

Enzyme modulator
0.1225
0.2051

13
6.88
3
P27918
Properdin
Response to stimulus

Unclassified
0.1419
0.1905

14
6.31
4
P69905
Hemoglobin subunit alpha
Blood circulation

Transfer/carrier protein
0.1500
0.2312

15
8.97
15
Q03591
Complement factor H-related protein 1
Blood coagulation

Transfer/carrier protein
0.1706
0.0991

16
27.03
22
P10909
Clusterin
Apoptosis

Unclassified
0.1905
0.1076

17
2
1
Q15942
Zyxin
Cellular component morphogenesis

Enzyme modulator
0.1923
0.2109

18
20.4
26
P01024
Complement C3
Protein metabolic process

Transfer/carrier protein
0.2070
0.1406

19
7.43
3
P17936
Insulin-like growth factor-binding protein 3
Cell-matrix adhesion

Unclassified
0.2089
0.3162

20
2.23
2
P55290
Cadherin-13
Cell-cell adhesion
Cell junction
Receptor
0.2188
0.1660

21
1.41
1
P13598
Intercellular adhesion molecule 2
Cell-cell adhesion

Transfer/carrier protein
0.2355
0.1600

22
96.24
64
P00751
Complement factor B
Blood coagulation

Transfer/carrier protein
0.2466
0.2729

23
28.82
16
P09871
Complement C1s subcomponent
Blood coagulation

Protease
0.2606
0.4169

24
13.74
9
P01019
Angiotensinogen
Protein metabolic process

Enzyme modulator
0.2630
0.5058

25
95.34
74
P19823
Inter-alpha-trypsin inhibitor heavy chain H2
Protein metabolic process

Enzyme modulator
0.2805
0.5649

26
5.15
2
P18065
Insulin-like growth factor-binding protein 2
Cell-matrix adhesion

Unclassified
0.2884
0.3281

27
16.84
10
P07996
Thrombospondin-1
Blood coagulation
Extracellular matrix
Transfer/carrier protein
0.3404
0.8317

28
5.31
3
P26927
Hepatocyte growth factor-like protein
Blood coagulation

Transfer/carrier protein
0.3436
0.3436

29
7.85
4
O14791
Apolipoprotein L1
Lipid metabolic process

Transporter
0.3908
0.2831

30
94.76
57
P06727
Apolipoprotein A-IV
Lipid metabolic process

Transporter
0.4018
0.1837

31
14.1
9
P35527
Keratin, type I cytoskeletal 9
Cellular component morphogenesis

Structural protein
0.4055
0.7244

32
9.48
6
P00746
Complement factor D
Blood coagulation

Protease
0.4571
0.6668

33
48.09
24
P02649
Apolipoprotein E
Lipid metabolic process

Transporter
0.4656
0.2051

34
38.8
32
P02735
Serum amyloid A protein
Immune system process

Transporter
0.4742
0.0319

35
9.46
5
P10720
Platelet factor 4 variant
Blood coagulation

Transfer/carrier protein
0.4742
0.5105

36
53.31
29
P02774
Vitamin D-binding protein
Transport

Transfer/carrier protein
0.4831
2.2287

a Unused > 1.3 means at least 95% confidence; bnumber of peptides with 95% confidence; AAD: acute aortic dissection; AMI: acute myocardial infarction; CON: normal control.

Among proteins with decreased expression in AAD patients compared with normal controls, there were a number of molecules involved in protein metabolism (Inter-alpha-trypsin inhibitor heavy chain H2, Alpha-2-HS-glycoprotein), lipid metabolic process (Apolipoprotein A-IV, Apolipoprotein E, Apolipoprotein C-I), blood coagulation marker (Fibrinogen alpha chain, Prothrombin), and cellular components (Alpha-2-HS-glycoprotein, thrombospondin-1 (TSP-1)). When compared to AMI patients, 8 of 36 proteins did not reach the 2-fold differential expression (Table 3).

3.4. The ELISA Analysis of Serum Concentrations of Lumican

Based on the iTRAQ findings above we selected two targets, Fibronectin and Lumican, the protein markers potentially associated with vascular injury, for the validation using ELISA method. At the initial analysis using 10 AAD and 10 normal samples we found that statistical significant difference between AAD and normal individual was seen for Lumican but not Fibronectin (data not shown). Therefore, we carried a full validation study only for Lumican, using the entire 120 samples collected (see Table 1). A statistical significant difference between AAD, AMI, and normal individual was seen in serum concentrations of Lumican (2.66 ± 4.58 ng/mL in AAD group, 0.69 ± 0.34 ng/mL in AMI group, and 0.85 ± 0.53 ng/mL in normal control, ). The difference for AAD and AMI also reached statistical significance () suggesting the specificity of this marker for AAD (Figure 1). We further analyzed the correlation between Lumican levels with time from onset of symptoms to admission. As shown in Figure 2, a correlation was seen in AAD group (, ) but not in AMI group (, ), further confirming the specificity of Lumican as a marker for AAD.

Figure 1: Lumican levels were significant difference between AAD, AMI, and normal individuals (Mean ± SEM; ).

Figure 2: Lumican levels were correlation with time from onset to admission in AAD group (), but not in AMI group ().

4. Discussion

iTRAQ analysis is recently been used as a potentially more effective biomarker discovery method than traditional proteomic methods. The high reproducibility optimizes this technique for embarking on “fishing-expeditions” as an initial screening for potential useful biomarkers [6–8]. As a means of internal validation, the iTRAQ method was compared with CRP biochemical assay and Myo immunoassay. In our study there were no significant differences in the serum levels determined by the different methods. Thus, the iTRAQ method we employed appears in this preliminary analysis to be suitable for the detection of relevant proteins.

To identify differentially expressed proteins, in many studies, the cut-off points were set at 20% to 50% average variance [7, 9, 10]. However, such approaches may result in finding markers with low specificity [2, 3]. We therefore appropriated to increase the cut-off point at 100% variance in serum levels of candidate proteins between AAD patients and normal subjects. Thus, only twofold changes below or above normal controls were considered significant. In our study, total of 155 proteins had a relative difference between AAD patients and healthy volunteers. Therefore, with higher specificity, these candidate proteins are more likely to be potential biomarkers for AAD.

In the group of significantly increased proteins, there were numerous acute phase reactants, such as Beta-2-microglobulin (P61769), which could be indicative of an increased inflammatory response among AAD patients. CRP (P02741), a protein found to be elevated in patients who presented with symptoms or rupture of AAD and abdominal aortic aneurysm, was also identified using iTRAQ [11, 12]. CRP is a nonspecific biomarker associated with AAD and a predictor for long-term adverse events [13], and it can be used to monitor evolution of false lumen thrombosis [14]. Unfortunately, CRP is also produced in coronary plaques [15], acute myocardial infarction [16], and so forth. The elevations of these acute phase reactants represent a generalized reaction to vascular injury, and as such, they are nonspecific biomarkers. In addition, many proteins identified are associated with blood coagulation and fibrinolytic system. Among which, ten had increased serum levels (e.g., P00450-Ceruloplasmin, P02751-Fibronectin, P00738-Haptoglobin…), and twelve had decreased serum levels (e.g., P02671-Fibrinogen alpha chain, P00751-Complement factor B, P00734-Prothrombin…). The pathophysiological mechanism for the appearance of these proteins may be explained by the release of tissue factors from the dissected aortic wall then the activation of the extrinsic coagulation system [17–19]. In addition, platelets can be activated by injuries to the vessel wall, activation of the coagulation cascade, or by activating factors released from stimulated endothelial cells and platelets (e.g., ADP, thromboxane, von Willebrand Factor). It also has been found that platelet functions were affected secondary to acute massive consumption coagulopathy in the false lumen in AAD patient [20, 21].

In the past few years, extracellular matrix (ECM) components of vessel walls such as elastin have been shown to be elevated in aortic dissections; however, such increases were less than twofold [3]. Our study found 9 extracellular matrix component proteins with greater than twofold differences, among these are Carboxypeptidase (P22792), Lumican (P51884), Fibronectin (P02751), Ceruloplasmin (P00450), and Thrombospondin-1 (TSP-1, P07996). Fibronectin is a polymorphic and multifunctional glycoprotein that plays wide-ranging roles in tissue injury [22–25]. TSP-1, which is an extracellular protein that participates in cell-to-cell and cell-to-matrix communication, can stimulate or inhibit the migration of vascular smooth muscle cells or endothelial cells. It has been known as a plasma marker of peripheral arterial disease [26].

Lumican is distributed in interstitial collagenous matrices throughout the body. In coronary arteries ischemic lesion, it is overexpressed by vascular smooth muscle cells (VSMCs) [27] and also synthesized in aortic smooth muscle cells [28]. In iTRAQ analysis, serum Lumican levels in patients with AAD were 1.29-fold and 3.63-fold higher than in patients with AMI and normal controls, respectively. It is interesting to note that with iTRAQ analysis, the level of difference between AAD and AMI for Lumican is less than that of Fibronectin (Table 2), yet the initial validation using ELISA method showed that only Lumican was significantly increased in AAD and AMI samples. While there may be variety reasons to explain the variations of the findings between the two methods, it highlights the importance of validation in biomarker studies. The finding that Lumican expression correlated with the time from onset to admission only in AAD but not in AMI sample further confirmed the specificity of this protein in association with AAD.

Proteomic approach provides an exciting platform to identify clinically useful protein biomarkers. As an initial step our study identified potential candidate protein biomarkers in the serum of AAD patients with the iTRAQ technique. However, the ultimate development of biomarkers which provide sufficient sensitivity or specificity for the diagnosis of AAD will require multiple validations and clinical testing, which may include nonprotein markers. Nevertheless our findings provide preliminary list of candidate biomarkers that should be further validated, either alone or in combination.

5. Conclusion

In this paper, we found that iTRAQ technique is a suitable approach for the detection of the new potential protein markers in the serum of AAD patients. Using iTRAQ approach, our study identified that Lumican may be a potentially interesting new serum marker of AAD, and upon further validation this marker may assist the clinical diagnosis of AAD.

Acknowledgments

This study was supported by Shanghai Committee of Science and Technology (114119a9000). The authors also thank the Department of Chemistry and the Institute of Biomedical Science, Fudan University, Peoples Republic of China, for providing support for the project.

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