Analysis of the Plasma Proteome Associated with ... - ACS Publications

Analysis of the Plasma Proteome Associated with Acute Coronary Syndrome: Does a Permanent Protein Signature Exist in the Plasma of ACS Patients? Veronica M. Darde´,†,‡ Fernando de la Cuesta,‡ Felix Gil Dones,† Gloria Alvarez-Llamas,‡ Maria G. Barderas,† and Fernando Vivanco*,‡,§ Department of Vascular Physiopathology, Hospital Nacional de Paraple´jicos, SESCAM, Toledo, Spain, Department of Immunology, Fundacioń Jimeńez Dıáz, Madrid, Spain, and Department of Biochemistry and Molecular Biology I, Universidad Complutense de Madrid, Spain Received March 5, 2010

Acute coronary syndrome (ACS) is triggered by the occlusion of a coronary artery usually due to the thrombosis caused by an atherosclerotic plaque. The identification of proteins directly involved in the pathophysiological events underlying ACS will enable more precise diagnoses and a more accurate prognosis to be determined. Accordingly, we have performed a longitudinal study of the plasma proteome in ACS patients by 2-DE and DIGE. Plasma samples from patients, healthy controls, and stable coronary artery disease (CAD) patients were immunodepleted of the six most abundant proteins, and they were analyzed in parallel at four different times: 0 (on admission) and after 4, 60, and 180 days. From a total of 1400 spot proteins analyzed, 33 proteins were differentially expressed in ACS patients when compared with control subjects/stable patients. A small group of seven proteins that appear to be altered at admission remain affected for 6 months and also in the stable CAD patients. Interestingly, the maximum number of altered proteins was observed in the stable CAD patients. Some of the proteins identified had been previously associated with ACS whereas others (such as Alpha-1B-glycoprotein, Hakata antigen, Tetranectin, Tropomyosin 4) constitute novel proteins that are altered in this pathology. Keywords: acute coronary syndrome • plasma biomarkers • atherotrombosis • atherosclerosis • myocardial infarction • coronary artery disease • protein signature • DIGE

Introduction Acute coronary syndrome (ACS), like myocardial infarction and unstable angina, represents a major cause of mortality due to ischemic cardiopathy. This syndrome is due to the occlusion of a coronary artery by a thrombus, usually caused by the rupture of the fibrotic capsule of an atherosclerotic plaque.1,2 This fissure allows the lipidic core of the plaque to come into contact with the blood, which is highly thrombogenic.3 The mechanisms leading to plaque thrombosis are currently being hotly pursued, and a clear relationship has been established between the inflammatory activity in the plaque and the occurrence of ACS.4,5 It is now accepted that macrophages within the lesion degrade the extracellular matrix of the fibrotic capsule by secreting proteolytic enzymes that weaken the matrix and facilitate its breaking by hemodynamic forces.6-8 Alternatively, the apoptosis of smooth muscle cells in the atherosclerotic plaques could also play a role in the plaque rupture.9 Since these cells are responsible for the extracellular * To whom correspondence should be addressed. Dr. Fernando Vivanco, Department of Immunology, Fundacioń Jimenez Diaz, Avda. Reyes Catolicos 2, 28040-Madrid, Spain. E-mail: [email protected]. † Hospital Nacional de Paraple´jicos. ‡ Fundacioń Jimeńez Dıáz. § Universidad Complutense de Madrid.

4420 Journal of Proteome Research 2010, 9, 4420–4432 Published on Web 07/05/2010

matrix synthesis, which confers resistance to the capsule, the apoptosis of these cells, along with the proteolytic activity of macrophages in the plaque,10 could trigger its rupture. A major goal of cardiovascular proteomics is to develop noninvasive tests that allow risk prediction of acute coronary events. For this purpose, a proteomic analysis of a significant number of the proteins implicated in atherotrombosis could provide a more integrated insight into the molecular mechanisms involved in ACS.11 The ease with which blood proteins can be sampled makes them a logical choice to search for potential markers of risk. This approach might provide useful diagnostic information and lead to individualized treatments. Hence, the proteomes of both plasma and circulating cells should be studied, especially those involved in atherogenesis such as monocytes and macrophages.12-14 Therefore, proteomic analysis of blood (plasma and circulating cells) can be used to identify candidate proteins that may serve as diagnostic and therapeutic biomarkers of ACS and that can subsequently be validated in large patient cohorts.15 Plasma is one of the richest protein-containing samples, in which abundant resident proteins predominate, such as albumin and immunoglobulins, together with proteins that originate from circulating blood cells and other tissues. It also may carry tissue leakage proteins and, thus, it is expected to contain 10.1021/pr1002017

 2010 American Chemical Society

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Plasma Proteome Associated with ACS

Figure 1. Schematic representation of the protocol to obtain the samples and the subsequent workflow, involving the depletion of highly abundant proteins prior to 2-DE and 2D-DIGE analysis.

information on the physiological status of different parts of the organism.15 For this reason, it is frequently monitored for disease biomarkers and is regularly used for diagnosis. However, extracting useful clinical proteomic data from human plasma has been a major challenge, due to the wide dynamic range of protein concentrations, in the order of 109, that separates the most abundant protein (serum albumin at 30-50 mg/mL) and the least abundant proteins (e.g., IL-6 at 0-5 pg/ mL). In fact, a small group of proteins (albumin, immunoglobulins, haptoglobin, transferrin and R1-antitrypsin) represent 80% of the total protein in plasma. Moreover, many plasma proteins have a similar molecular mass and overall charge, further complicating the analysis of plasma proteome by twodimensional electrophoresis (2-DE).11 Nevertheless, an increasing number of proteomic studies have been published as new methodsaredeveloped,likemultidimensionalchromatography16,17 or the SELDI (surface enhanced laser desorption ionization) technology, which has been effective in detecting candidate proteins in plasma (specially in cancer research)18 despite having been specifically designed to use protein profiles for diagnosis. Furthermore, numerous methods to analyze the plasma proteome by 2-DE or two-dimensional differential gel electrophoresis (2D-DIGE) have been described,19,20 including those that incorporate antibody-based methods to remove abundant proteins.21-27 In the past few years, these methods have become widely accepted and they are frequently used in many proteomic studies involving body fluids.28-34 In the present work, we have specifically removed the six most abundant plasma proteins (albumin, IgG, IgA, haptoglo-

bin, transferrin and antitrypsin) using the Multiple Affinity Removal System (Agilent Technologies) to study the presence of less abundant proteins in the plasma samples from patients with ACS by both 2-DE and 2D-DIGE.

Materials and Methods Sample Collection. Forty patients aged between 40 and 80 years old that were admitted into the Fundacioń Hospital de Alcorcon (Madrid) with non-ST elevation ACS were included in the study. ACS was defined as GIII-IV angina (CCS) with transitory ST depression (>0.05 mV) and/or new T wave inversion (>0.02 mV) on the ECG, and/or positive troponin I. The exclusion criteria were inflammatory or neoplasic disease, coagulation disorders, other significant heart disease except left ventricular hypertrophy secondary to hypertension, chronic treatment except for pre-existing clinical atherosclerosis or its risk factors, normal coronary angiograms, ejection fraction less than 0.45, and having suffered surgical procedures, major traumatisms, thromboembolic events or revascularization procedures in the previous six months. At the moment the diagnosis was made, the patients were asked to participate in the study (Figure 1) and those that accepted signed the informed consent. This study was previously approved by the local Ethical Committee in accordance with institutional guidelines. Twenty healthy volunteers and 10 stable CAD patients who did not differ significantly from the ACS patients in terms of age and sex distribution were also included in the study. Previously we reported13 the proteomic Journal of Proteome Research • Vol. 9, No. 9, 2010 4421

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Table 1. Baseline Characteristics of ACS and Stable CAD Patientsa ACS patients Mean age Sex (male/female) Current smoking Hypertension Diabetes Total cholesterol LDL HDL Triglycerides Body mass index Previous ACS Statin Antithrombotic therapy Ejection fraction NQWMI/Unstable angina Vessel number

60.0 years (50.3-68.5) 67%/33% 39% 44% 22% 215 (194-225) 142 (127-158) 39 (34-44) 159 (129-202) 28.4 (25.3-32.86) 11% 6% 6% 60% (59%-63%) 87.5%/12.5% 0 vessels: 6.7% 1 vessel: 80% 2 vessels: 6.7% 3 vessels: 6.7% Treatment on admission Enoxaparin: 92.9% ASA: 85.8% Clopidogrel: 7.1% ASA+clopidogrel: 7.1%

stable CAD patients 60.0 years (47.5-74,7) 66.7%/33.3% 25% 66% 33% 160.5 (149-198.5) 105 (85.5-141) 42.5 (39.5-53.5) 128 (109-166) 29.3 (25.7-30.1) 100% -

-

a Quantitative data are expressed as the median (interquartile range). Statin and antithrombotic therapy refer to the treatments received by the patients prior to the present event. ACS, acute coronary syndrome; CAD, coronary artery disease; NQWMI, non Q wave myocardial infarction; ASA, acetyl salicylic acid.

analysis of the circulating monocytes in these patients, and here, we extend the study to plasma. The baseline characteristics of the ACS and Stable CAD patients are summarized in Table 1. To obtain the plasma, 28 mL of blood from each patient and healthy volunteer was drawn into EDTA-prepared collection tubes (Venoject, Terumo Europe), centrifuged at 750× g for 10 min at room temperature and kept under -80 °C until use. Depletion of High-Abundance Proteins. The “Multiple Affinity Removal System” (MARS-6, Agilent Technologies) was used to deplete the six most abundant proteins from the plasma samples. Accordingly, 15-20 µL of human plasma from ACS patients, healthy controls and stable CAD patients, was diluted five times with “Buffer A” (Agilent Technologies) and spun through a 0.22 µm spin filter tube at maximum speed (about 16 000× g) in a microfugue for 1 min. Later, the sample was injected into an FPLC System (a¨KTA purifier, Amersham Biosciences) coupled to the MARS-6 column at a flow rate of 0.25 mL/min for 9 min and the flow-through fraction was collected. The bound proteins were then eluted from the column with Buffer B at a flow rate of 1 mL/min for 3.5 min, and subsequently, the column was regenerated with Buffer A for 7.5 min at a flow rate of 1 mL/min. The chromatographic steps were performed according to the manufacturer’s indications, as described previously.26 After several chromatographic cycles, the flow-through fraction aliquots were combined, desalted and equilibrated in 50 mM ammonium bicarbonate and finally quantified by the Bradford method.35 Finally, 250 µg from each aliquot were pipetted into 1.5 mL tubes and lyophilized for 15-18 h. Two-Dimensional Electrophoresis. We performed four 2-D gels for each patient, one at each period of time (0, 4, 60, and 180 days). This longitudinal study facilitates the comparative follow-up of each patient over time, which enables the internal consistency in the data to be defined, as well as enabling the 4422

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different groups of patients to be compared at each time point (see Supporting Information Figure S5a,b). Lyophilized protein samples (250 µg) were diluted to a final volume of 450 µL with rehydration solution containing: 9 M urea, 4% CHAPS, 10 mM Tris, 1.2% (v/v) DeStreak Reagent (Amersham Biosciences) and 1% IPG buffer (pH 3-10NL or 4-7, Amersham Biosciences). IPG strips (24 cm, pH 3-10NL and 4-7) were rehydrated overnight in a reswelling tray (Amersham Biosciences) and after passive rehydration, the strips were placed in Ettan IPGphor ceramic strip holders (Amersham Biosciences) and isoelectric focusing (IEF) was performed in a IPGphor unit (Amersham Biosciences) as described previously.26 Gels (24 × 20 cm) were fixed overnight (5% acetic acid, 30% ethanol) and stained with a Silver Staining Kit (Amersham Biosciences). The protein patterns in the gels were recorded as digitalized images using a GS-800 Calibrated Densitometer (Bio-Rad) and imported to PDQuest gel analysis software (version 8.0.1: BioRad, Hercules, CA) to evaluate, process and analyze the 2-D gels. The intensity of each protein spot in the gel was standardized reporting it as a ratio to the total intensity of all spots present in the gel. The statistical data (mean, SD) corresponding to the mean intensity level of each spot in each group can be found in the Supporting Information (Figures S1 and S2). The coordinates were expressed in relation to a reference point in each 2-D gel. The mean values and CV of the different points (as expression or presence/absence) were calculated using the same software. Normalized data obtained in PDQuest were further analyzed using the SPSS 13.0 statistical software package, which allowed us to perform Student’s t test and ANOVA as described previously.13 Statistical significance was accepted when p < 0.05. 2D-DIGE. Samples were labeled with CyDyes DIGE Fluor minimal dyes (Amersham Biosciences) according to the manufacturer’s indications and as described previously.26 The experimental design included five samples per study group, such that 30 individual samples were labeled with either Cy3 or Cy5. The labeled samples were combined according to the experimental design and loaded onto the same IPG strip. We randomized the assignation of CyDye and the combination of samples in each gel, and one aliquot of the pooled internal standard labeled with Cy2 was included in each gel. 2-DE was performed as previously described.26 After SDS-PAGE, 2D-DIGE gels were scanned using a Typhoon 9400 Variable Mode Imager (Amersham Biosciences) and spot maps were processed, analyzed and compared using the DeCyder Differential Analysis Software version 6.5 (Amersham Biosciences). Spot detection and normalized volume ratio calculations were performed in the Differential In-gel Analysis (DIA) module, while gel-to-gel matching and statistical analysis were performed in the Biological Variation Analysis (BVA) module. The Student’s t test and ANOVA were used to compare the expression of each spot, and statistical significance was accepted when p < 0.05 (see Supporting Information Figures S3a,b and S4). In addition, the Extended Data Analysis (EDA) module of DeCyder was used to perform a principle component analysis (PCA). This statistical method is used to reduce the dimensionality of a multivariate data set, to obtain a simplified plot that displays the principle components explaining most of the variance from the original set and an ellipse delimiting a 95% level of significance. This may serve as an overview of the data set and it usually reveals groups of observations and trends within the data. 2D-DIGE gels were silver stained after image digitalization, to pick differentially expressed spots. Spots

Plasma Proteome Associated with ACS

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Figure 2. Graphical representation of the differences detected in depleted plasma by 2-DE and 2D-DIGE. Numerical data in the graph are detailed in the tables displayed below.

that could not be identified in these gels were taken from preparative gels matched with the 2D-DIGE images. In-Gel Digestion of Proteins and Sample Preparation for Mass Spectrometry. Protein spots from 2-DE gels and 2D-DIGE gels were excised manually and then digested automatically using a Proteineer DP protein digestion station (BrukerDaltonics, Bremen, Germany). The digestion protocol used was that of Shevchenko et al.37 with minor variations. Essentially, the gel plugs were washed with 25 mM ammonium bicarbonate and acetonitrile prior to reduction with 10 mM dithiothreitol in 25 mM ammonium bicarbonate and alkylation with 100 mM iodoacetamide in 50 mM ammonium bicarbonate. Gel pieces were then rinsed with 50 mM ammonium bicarbonate and acetonitrile and dried under a stream of nitrogen. Modified porcine trypsin (sequencing grade; Promega, Madison, WI) was added at a final concentration of 16 ng/µL in 50 mM ammonium bicarbonate to the dried gel pieces and digestion proceeded at 37 °C for 6 h. The reaction was stopped by adding 0.5% trifluoroacetic acid for peptide extraction. Subsequently, 0.4 µL of matrix solution (5 g/L 2,5-dihydroxybenzoic acid in 33% aqueous acetonitrile and 0.1% trifluoroacetic acid) followed by 0.4 µL of the above digestion solution were automatically deposited onto a 400 µm AnchorChip MALDI probe (Bruker-Daltonics) and allowed to dry at room temperature. MALDI-MS (MS/MS) Analysis and Database Searching. MALDI-MS(/MS) data were obtained in an automated analysis loop using an Ultraflex time-of-flight mass spectrometer (BrukerDaltonics) equipped with a LIFT-MS/MS device.38 Spectra were acquired in positive-ion mode at 50 Hz laser frequency and

100 to 1000 individual spectra were averaged. For fragment ion analysis in tandem time-of-flight (TOF) mode, precursors were accelerated to 8 kV and selected in a timed ion gate. Fragment ions generated by laser-induced decomposition of the precursor were further accelerated by 19 kV in the LIFT cell and their masses were analyzed after passing the ion reflector. Automated analysis of mass data was performed using the flexAnalysis software (Bruker-Daltonics). Internal calibration of MALDI-TOF mass spectra was performed using two trypsin autolysis ions with m/z ) 842.510 and m/z ) 2211.105. For MALDI-MS/MS, calibrations were performed with fragment ion spectra obtained for the proton adducts of a peptide mixture covering the 800-3200 m/z region. MALDI-MS and MS/MS data were combined through the BioTools program (Bruker-Daltonics) to search a nonredundant protein database (NCBInr 2006101320070613; ∼4.0-5.2 × 106 entries; National Center for Biotechnology Information, Bethesda, US; or SwissProt 53.2-54.1; ∼2.7-2.8 × 105 entries; Swiss Institute for Bioinformatics, Switzerland) using the Mascot software (Matrix Science, London, UK).39 MALDI MS(/MS) spectra and database search results were manually inspected in detail using the aforementioned programs as well as in-house software (Supporting Information Tables S3 and S4). Immunoblotting. Electrophoresis and protein transfer to nitrocellulose membranes was performed as described previously.13 To ensure equal amounts of plasma samples were loaded onto IPG strips (2-D Western blotting) or onto polyacrylamide gels (regular Western blotting), Ponceau S staining was performed on the transferred membranes. The antibodies Journal of Proteome Research • Vol. 9, No. 9, 2010 4423

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Figure 3. (A) Representative image of a 2-DE silver stained gel of depleted plasma. Protein spots differentially expressed in ACS and CAD patients versus healthy subjects are highlighted and the corresponding spot numbers are displayed. (B) Representative image of a 2D-DIGE gel of depleted plasma. Protein spots differentially expressed in ACS and CAD patients versus healthy subjects are highlighted and the corresponding spot numbers are displayed.

used to probe the membranes were: a mouse monoclonal antihuman apolipoprotein J antibody, mouse monoclonal antihuman Tetranectin antibody, rabbit polyclonal antihuman Hemopexin antiserum and rabbit polyclonal antihuman Alpha1-B-glycoprotein antibody (Abcam); a mouse antihuman betaactin monoclonal antibody (Sigma-Aldrich Co.); a mouse monoclonal antihuman SAA (Biodesign International, Meridian Life Science, Inc.); a rabbit polyclonal anti-TPM4 antiserum (Chemicon International. Inc.); and a monoclonal antibody against the human H-Ficolin/Hakata antigen (Hycult biotechnology b.v.). TrueBlot antimouse and TrueBlot antirabbit secondary antibodies were used (eBiosciences, Inc.).

Results Sample Processing. To increase the sensitivity of detecting less abundant proteins in plasma samples, the six most abundant plasma proteins (albumin, IgG, IgA, haptoglobin, transferrin and antitrypsin) were specifically removed from each sample by affinity chromatography using the “’Multiple Affinity Removal System” (MARS-6, Agilent Technologies) coupled to a FPLC system. This method is very reproducible,26 enabling the flow-through fractions (low abundant proteins) to be combined after several chromatography cycles in order to obtain the amounts of protein necessary for 2-DE. These flow-through fractions were analyzed by both 2-DE and DIGE (Figure 1). We observed that the 2-D gels in the 4-7 pH range are very reproducible and that they contain sufficient spots to study the proteins differentially expressed in depleted plasma from ACS patients when compared to healthy controls. Moreover, the resolution of the spots in the 4-7 pH range is much better than in 3-10NL and thus, we chose this pH range to perform our comparative study. We also found that the silver stained gels 4424


had a better spot resolution in the low-to-medium molecular mass range (from 10 to about 60 kDa), while the 2D-DIGE gels better resolved the spots in the high molecular weight range (between 60 and 120 kDa). The high reproducibility of four different gels from a single sample in addition to that observed in gels from each patient at four different times (see Figure S5, Supporting Information) confirmed the internal consistency of the data. Therefore, we performed a double comparative study, one analyzing silver stained gels and the other one using the 2D-DIGE technology. In this way a wide range of proteins spanning 10-120 kDa could be reliably studied, and overall, 1480 proteins spots were analyzed (703 from 2-DE and 777 from DIGE). 2-DE. Depleted plasma samples from ACS patients obtained on admission, or 4 days, 2 months and 6 months after the acute episode, were compared to depleted samples from healthy volunteers and stable CAD patients. Silver stained 2-D gel images were imported to PDQuest gel analysis software to quantify 703 spots that were finally included in the study. Statistical analysis of the data revealed that the expression of 10 spots was altered on admission (5 of them upregulated and 5 downregulated) with respect to healthy volunteers (Figure 2). Four days after the acute event, similar differences were only detected in 7 of these proteins (4 upregulated and 3 downregulated), although two months later the number of changes again increased to 8 altered spots (5 upregulated and 3 downregulated) and after six months, 12 altered spots were detected (6 upregulated and 6 downregulated). However, the maximum number of differences was detected in plasma samples of stable CAD patients when compared to healthy volunteers, where variations were detected in the expression of 26 spots (20 upregulated and 6 downregulated). Overall, 44 protein spots were identified (Figure 3A) that displayed sig-

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Table 2. Changes in Protein Expression Detected by 2-DE and 2D-DIGE spot (PDQuest)

spot (DeCyder)

ACS day 0

ACS day 4

ACS 2 months

ACS 6 months

STABLE

protein

8 16 17 65 87 90 92 137 145 180 186 199 203 219 225 261 264 281 323 339 367 373 375 383 401 412 425 449 470 480 483 485 488 489 518 527 535 566 573 599 634 665 671 692 694 727 748 773

D U D -

2307 2412 2502 2512 3101 3305 3307 3506 3603 3707 4301 4606 4607 4702 4817 5003 5104 5205 5301

D D U U U U U U U U U U D D D U D D U D -

D U U U U U D U U U U U U D D D U D D D U D D -

D D U U U U -

U U U U U U U U U D U U U U D D D U

U D D U D D D U U U D D U U U U U U U U D D D D D U U D D D U U D D -

U U U U U U U U D D U D D D D U U U U U D U U U D U U

5302 5505 5808 6218 6808 7201 7204 7302 7604 7605 7608 7609 7812 8003 8303 8402 8601 8701 8703 9702

D D U -

U -

U U D U

U D D D U D U

U D U U U U U U U D U D U D -

Complement factor H Hemopexin Complement factor H Complement factor B Gelsolin Alpha-2-macroglobulin Gelsolin Alpha-1-B-glycoprotein Alpha-1-B-glycoprotein Alpha-1-B-glycoprotein Hemopexin Hemopexin Hemopexin Vitronectin Vitronectin Kininogen 1 Beta-2-glycoprotein Antithrombin-III Fibrinogen Gamma Alpha-2-HS-glycoprotein Fibrinogen Gamma Fibrinogen Beta Fibrinogen Gamma Fibrinogen Gamma Fibrinogen Gamma Fibrinogen Gamma Fibrinogen Beta Fibrinogen Gamma Fibrinogen Gamma Apolipoprotein A-IV Apolipoprotein A-IV Apolipoprotein A-IV Beta-Actin Beta-Actin Apolipoprotein J Apolipoprotein J Apolipoprotein J Interalpha-trypsin inhibitor Apolipoprotein E TPM4 Serum amyloid P-component Apolipoprotein A-I Apolipoprotein A-I PRO2222 (HRBP) Tetranectin Transthyretin Transthyretin Hemopexin Fibrinogen Gamma Fibrinogen Gamma Triosephosphate isomerase Zinc-alpha-2-glycoprotein Fibrinogen Gamma Fibrinogen Gamma Fibrinogen Gamma Apolipoprotein A-IV Complement C3 Fibrinogen Gamma Fibrinogen Gamma Hemopexin Vitamin D Binding Protein Fibrinogen Gamma Vitamin D Binding Protein Transthyretin Transthyretin Apolipoprotein A-I Complex-forming glycoprotein HC (Alpha-1-microglobulin) Transthyretin CD5 antigen-like Fibrinogen Gamma Apolipoprotein A-I Fibrinogen Beta Apolipoprotein A-I Apolipoprotein A-I Vitamin D Binding Protein Fibrinogen Beta Fibrinogen Beta Fibrinogen Beta Fibrinogen Beta Fibrinogen Beta Amyloid related serum protein SAA Fibrinogen Beta Hakata antigen Pigment epithelium-derived factor Hemopexin Fibrinogen Beta Hemopexin

708

6719

5501

5305 1303

a

ACS, acute coronary syndrome; STABLE, stable CAD patients; D, downregulated; U, upregulated; -, no variation.

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Figure 4. (A) Principal component analysis of the samples included in the study. The first principal component (PC1) represents 39.5% of the total variance in the analysis, while the second principal component (PC2) represents 18.7% additional variance. Ellipses surrounding related samples are displayed only to emphasize the group distribution in the plot. (B) Functional classification of the differentially expressed proteins identified in the 2-DE analysis. (C) Functional classification of the differentially expressed proteins identified in the 2D-DIGE analysis.

nificant differences in depleted plasma samples from ACS and stable CAD patients when compared with healthy volunteers. These 44 spots were identified by mass spectrometry (Table 2) and they corresponded to 19 different proteins, indicating that some of the spots most likely correspond to isoforms of the same protein. 2D-DIGE. The same depleted plasma samples studied by 2-DE were analyzed by 2D-DIGE, although in this case the range of molecular mass that were well resolved and that could be adequately analyzed lay between 60 and 120 kDa. Gel images were imported to DeCyder Differential Analysis Software, which included 777 spots in the study. Statistical analysis of the data in the DeCyder software showed that 15 spots were differentially expressed at admission (12 upregulated and 3 downregulated) with respect to healthy volunteers (Figure 2) and that four days later the number of differences rose to 29 spots (15 upregulated and 14 downregulated). Two months after ACS we found 18 spots that were altered (11 upregulated and 7 downregulated) and after six months, there were 19 differences (11 upregulated and 8 downregulated). The number of differences between the stable CAD patients and the healthy volunteers was this time the second highest (after ACS patients at day four) with 21 differentially expressed spots, 13 of them upregulated and 8 downregulated. Finally, this analysis revealed that 48 protein spots (Figure 3B) experiments some statistically significant alteration in expression in depleted plasma samples from ACS and stable CAD patients when compared with the healthy control group. The identification of these spots by mass 4426


spectrometry (Table 2) determined that they belonged to 24 different proteins, so we found again differential expression of potential isoforms for certain proteins. Principal component analysis (PCA) is typically used to reduce the complexity of multidimensional data sets, to obtain a clearer overview that may help to discover groups of observations and trends within the data. In our study, the first principal component (PC1) represented 39.5% of the whole variance in the data set, while the second principal component (PC2) depicted an additional 18.7% of the variance (Figure 4A). We observed some association within the samples from each experimental group and we could establish three main regions according to the distribution of these samples in the plot. Briefly, healthy individuals were scattered in the lower part of the plot, stable CAD patients were located in the left area and most ACS patients appeared in the upper-right quadrant. This suggests that the differential expression of the proteins identified in this study might allow us to distinguish between different types of ACS patients and control subjects. Functional Classification of the Proteins Differentially Expressed. The theoretical molecular mass and pI, together with the accession number, sequence coverage according to PMF and the main function of the identified proteins are detailed in Table 3. These proteins may be classified into six different groups according to their functions (Figure 4B and C): (a) Coagulation proteins: Antithrombin-III, Beta-2-glycoprotein, Fibrinogen Beta, Fibrinogen Gamma, Kininogen 1.

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Table 3. Characteristics of the Differentially Expressed Proteins Identified by 2-DE and 2D-DIGE spot spot (PDQuest) (DeCyder)

708

6719

5501

5305 1303

Mascot MW pI coverage accession score (theoretical) (theoretical) (%) number

8 16 17 65 87 90 92 137 145 180 186 199 203 219 225 261 264 281 323 339 367 373 375 383 401 412 425 449 470 480 483 485 488 489 518 527 535 566 573 599 634 665 671 692 694 727 748 773 2307 2412 2502 2512 3101 3305 3307 3506 3603 3707 4301 4606 4607

Complement factor H Hemopexin Complement factor H Complement factor B Gelsolin Alpha-2-macroglobulin Gelsolin Alpha-1-B-glycoprotein Alpha-1-B-glycoprotein Alpha-1-B-glycoprotein Hemopexin Hemopexin Hemopexin Vitronectin Vitronectin Kininogen 1 Beta-2-glycoprotein Antithrombin-III Fibrinogen Gamma Alpha-2-HS-glycoprotein Fibrinogen Gamma Fibrinogen Beta Fibrinogen Gamma Fibrinogen Gamma Fibrinogen Gamma Fibrinogen Gamma Fibrinogen Beta Fibrinogen Gamma Fibrinogen Gamma Apolipoprotein A-IV Apolipoprotein A-IV Apolipoprotein A-IV Beta-Actin Beta-Actin Apolipoprotein J Apolipoprotein J Apolipoprotein J Interalpha-trypsin inhibitor Apolipoprotein E TPM4 Serum amyloid P-component Apolipoprotein A-I Apolipoprotein A-I PRO2222 (HRBP) Tetranectin Transthyretin Transthyretin Hemopexin Fibrinogen Gamma Fibrinogen Gamma Triosephosphate isomerase Zinc-alpha-2-glycoprotein Fibrinogen Gamma Fibrinogen Gamma Fibrinogen Gamma Apolipoprotein A-IV Complement C3 Fibrinogen Gamma Fibrinogen Gamma Hemopexin Vitamin D Binding Protein

207 230 292 150 127 82 143 361 393 262 128 396 352 246 100 148 202 139 160 258 162 518 164 125 246 108 447 140 161 249 412 400 116 177 132 145 213 183 188 306 199 255 431 137 204 159 369 132 117 165 145 134 112 150 143 118 109 162 140 111 141

143654 45916 136448 86847 80578 164600 86043 52479 54809 52479 52385 52385 52385 55069 55069 48936 39584 53025 46823 40098 35497 56577 35497 52106 52106 52106 52106 52106 46823 43358 43358 43358 42218 40536 16267 49342 53031 103489 36246 27570 25485 30759 30759 18208 22921 15991 12835 29068 35497 24337 27095 34079 24 46823 52106 43358 188569 35497 35497 52385 54526

6.23 6.62 5.98 6.67 5.76 6 5.9 5.65 5.58 5.65 6.55 6.55 6.55 5.55 5.55 6.29 8.34 6.32 5.54 5.43 5.86 8.54 5.86 5.37 5.37 5.37 5.37 5.37 5.54 5.22 5.22 5.22 5.24 5.55 5.6 6.27 5.89 6.51 5.65 4.77 6.1 5.56 5.56 4.89 5.52 5.52 5.33 6.45 5.86 6.49 6.9 5.57

17 22 24 7 12 0 6 37 33 28 4 28 19 19 12 22 22 28 29 10 35 40 37 16 31 6 44 23 27 40 56 61 21 44 17 10 19 4 44 58 26 29 44 15 27 18 92 18 22 22 36 18

5.54 5.37 5.22 6.02 5.86 5.86 6.55 5.4

24 25 19 1 35 29 4 25

4702 4817

Fibrinogen Gamma Vitamin D Binding Protein

114 361

52106 52780

5.37 5.17

6 47

5003 5104 5205 5301

Transthyretin Transthyretin Apolipoprotein A-I Complex-forming glycoprotein HC (Alpha-1-microglobulin) Transthyretin CD5 antigen-like Fibrinogen Gamma Apolipoprotein A-I Fibrinogen Beta Apolipoprotein A-I Apolipoprotein A-I Vitamin D Binding Protein

119 169 111 152

15991 4624 30759 20592

5.52 8.18 5.56 5.84

34 59 4 13

108 188 125 177 174 94 100 155

12671 39603 52106 30759 51358 30759 23389 54526

5.26 5.28 5.37 5.56 7.95 5.56 5.55 5.4

33 44 16 14 24 4 26 26

Fibrinogen Beta Fibrinogen Beta Fibrinogen Beta Fibrinogen Beta Fibrinogen Beta Amyloid related serum protein SAA Fibrinogen Beta Hakata antigen Pigment epitheliumderived factor Hemopexin Fibrinogen Beta Hemopexin

167 143 266 130 94 162

52759 56577 40167 52759 51358 11675

8.33 8.54 6.95 8.33 7.95 5.89

24 19 38 20 16 70

157 203 126

28040 33381 46484

8.16 6.2 5.97

14 22 6

165 235 106

52385 38081 45916

6.55 5.84 6.62

17 46 18

5302 5505 5808 6218 6808 7201 7204 7302 7604 7605 7608 7609 7812 8003 8303 8402 8601 8701 8703 9702 a

protein name

function

P08603 P02790 P08603 P00751 P06396 P01023 P06396 P04217 P04217 P04217 P02790 P02790 P02790 P04004 P04004 P01042 P02749 P01008 P02679 P02765 P02679 P02675 P02679 P02679 P02679 P02679 P02679 P02679 P02679 P06727 P06727 P06727 P60709 P60709 P10909 P10909 P10909 Q14624 P02649 P67936 P02743 P02647 P02647 P02753 P05452 P02766 P02766 P02790 P02679 P02679 P02679 P25311 P02679 P02679 P02679 P06727 P01024 P02679 P02679 P02790 P02774

Complement Heme group binding Complement inactivation Complement Cytoskeleton Proteinase inhibitor Cytoskeleton Unknown Unknown Unknown Heme group binding Heme group binding Heme group binding Cell adhesion/Immune response Cell adhesion/Immune response Blood coagulation Blood coagulation Blood coagulation Blood coagulation Acute phase response/Inflammation Blood coagulation Blood coagulation Blood coagulation Blood coagulation Blood coagulation Blood coagulation Blood coagulation Blood coagulation Blood coagulation Lipid metabolism/Lipid transport Lipid metabolism/Lipid transport Lipid metabolism/Lipid transport Cytoskeleton/Cell motility Cytoskeleton/Cell motility Lipid metabolism/Complement activation Lipid metabolism/Complement activation Lipid metabolism/Complement activation Acute phase response Lipid metabolism/Lipid transport Cytoskeleton/Structural constituent of muscle Unfolded protein binding/Acute phase response Lipid metabolism/Lipid transport Lipid metabolism/Lipid transport Retinol (vitA) transport/Transthyretin binding Plasminogen binding Thyroid hormone transporter activity Thyroid hormone transporter activity Heme group binding Blood coagulation Blood coagulation Carbohydrate metabolism Lipid metabolism Blood coagulation Blood coagulation Blood coagulation Lipid metabolism/Lipid transport Complement activation/Inflammatory response Blood coagulation Blood coagulation Heme group binding Carrier activity/Prevention of Actin polymerization P02679 Blood coagulation P02774 Carrier activity/Prevention of Actin polymerization P02766 Thyroid hormone transporter activity P02766 Thyroid hormone transporter activity P02647 Lipid metabolism/Lipid transport P02760 Plasmin and trypsin inhibitor/ Anti-inflammatory response P02766 Thyroid hormone transporter activity O43866 Cellular defense response/Apoptosis inhibition P02679 Blood coagulation P02647 Lipid metabolism/Lipid transport P02675 Blood coagulation P02647 Lipid metabolism/Lipid transport P02647 Lipid metabolism/Lipid transport P02774 Carrier activity/Prevention of Actin polymerization P02675 Blood coagulation P02675 Blood coagulation P02675 Blood coagulation P02675 Blood coagulation P02675 Blood coagulation P02735 Acute phase response/Inflammation/ Lipid metabolism/Coagulation P02675 Blood coagulation O75636 Involved in the serum exerting lectin activity P36955 Inhibition of angiogenesis/Cell proliferation/ Serine-type endopeptidase inhibitor activity P02790 Heme group binding P02675 Blood coagulation P02790 Heme group binding

MW, molecular weight; pI, isoelectric point.


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Figure 5. Verification of the differences observed by 2-DE and 2D-DIGE in Western blots. M, Molecular weight marker. (A) On the left, a band of 32 kDa, consistent with the molecular weight of TPM4, was augmented in plasma from patients on day 4 with respect to healthy volunteers. The same result was observed in the 2-D Western blot, shown on the right. (B) On the left, 1-D Western blot for Apolipoprotein J showed a band of 40 kDa but remarkable differences were not observed. On the right, in the 2-D Western blot differences were evident in the expression of the most acidic isoform (arrows in the figure). (C) On the left, a band of 13 kDa was seen in 1-D Western blot for Transthyretin but like Apolipoprotein J, no differences in expression were detected. In the 2-D Western blot (on the right) a slight difference was found in one isoform (arrows in the figure). (D) On the left, the antihuman Amyloid related serum protein SAA antibody detected an 11 kDa band in patients on day 4 that was almost absent in healthy volunteers. The 2-D Western blot (on the right) confirmed this result. (E) On the left, a band of 42 kDa corresponding to Beta-Actin appeared in the plasma from patients on admission, but it was not observable in the plasma from healthy subjects. On the right, the same result was confirmed in 2-D Western blots. (F) In a 1-D Western blot for Alpha-1-B-glycoprotein, a band of 54 kDa was elevated in the plasma from patients on admission with respect to healthy volunteers. (G) A band of 51 kDa, consistent with Hemopexin molecular weight was augmented in the plasma from patients on day 4. (H) 1-D Western blot for Tetranectin showed a 22 kDa band that was decreased in patients at day 4 with respect to healthy subjects. (I) An Anti-Human Hakata antigen (H-Ficolin) antibody detected a band of 35 kDa in healthy volunteers that was almost imperceptible in stable CAD patients.

(b) Metabolism and/or Lipid transport: Apolipoprotein A-I, Apolipoprotein A-IV, Apolipoprotein E, Apolipoprotein J, Zincalpha-2-glycoprotein. (c) Inflammation and immune response: Alpha-2-HS-glycoprotein, Amyloid related serum protein SAA, CD5 antigen-like, Complement C3, Complement factor B, Complement factor H, Complex-forming glycoprotein HC, Interalpha-trypsin inhibitor, Serum Amyloid P-component (SAP), Vitronectin. (d) Metabolite transport: Hemopexin, PRO2222-Human Retinol Binding Protein (HRBP), Tetranectin, Transthyretin, Vitamin D Binding Protein. (e) Cytoskeleton: Beta-Actin, Gelsolin, Tropomyosin 4 (TPM4). (f) Proteins involved in other activities (miscellaneous proteins): Alpha-1-B-glycoprotein, Alpha-2-macroglobulin, Hakata antigen, Pigment epithelium-derived factor (PEDF), Triosephosphate Isomerase. 4428


Validation by Western Blotting. To validate the differences observed by 2-DE and 2D-DIGE, one-dimensional and twodimensional Western blot were performed to examine representative proteins (Figure 5), where Ponceau S staining confirmed that equal amounts of plasma samples were loaded onto all the membranes (Supporting Information Figure S6). A band with an apparent molecular weight of 32 kDa was detected, consistent with the molecular weight of Tropomyosin 4 (TPM4) (Figure 5A). This band was virtually absent in the plasma of healthy subjects while it was overexpressed in patients at day four, as confirmed in 2-D Western blots in accordance with our previous observations. In the case of Apolipoprotein J, a 40 kDa band corresponding to this protein was evident in Western blots, but there were no differences observed between healthy volunteers and patients in 1-D Western blots (Figure 5B). Nevertheless, in 2-D Western blots the expression of the

Plasma Proteome Associated with ACS most acidic isoform decreased in patients on admission. Similarly, a band of 13 kDa appeared in the Western blot for Transthyretin that did not differ between patients on admission and healthy control subjects (Figure 5C). However, minor modifications were detected in one isoform in 2-D Western blots. By contrast, 1-D Western blots revealed an increase with respect to healthy subjects in the expression of both Amyloid related serum proteins (SAA, Figure 5D) and Beta-Actin (Figure 5E) in patients on day 4 and on admission, respectively. These differences were further confirmed in 2-D Western blots. Likewise, 1D Western blot showed increased expression of Alpha-1-B-glycoprotein (Figure 5F) and Hemopexin (Figure 5G) in patients on admission and after four days, respectively. Finally, the expression of both Tetranectin (Figure 5H) and Hakata antigen (Figure 5I) decreased with respect to the healthy volunteers in patients on day 4 and in stable CAD patients, respectively.

Discussion Although the causes of plaque thrombosis are a fundamental issue in cardiovascular research, the pathophysiology associated with such events remains unclear. Thus, many important factors that could improve ACS prognosis and diagnosis are still unknown. To discover new protein candidates that can be used in disease models, homogeneous cohorts of well phenotyped animals may be particularly informative.40 However, there are no good animal models that permit vulnerability to atherosclerotic plaques to be studied (although the ApoE knockout mice is a good model for human atherogenesis) that reflect the risk of an atherotrombotic event as occurs in humans. Plaque rupture is the nearest event giving rise to an acute attack. However, we do not know of any protein that could indicate the vulnerability or propensity of the plaques to rupture. There has been an increasing interest in applying the analysis of the plasma proteome to the study of ACS, since plasma is not only the most accessible clinical sample for diagnosis but also, it is a protein-rich reservoir of information that contains traces of what has been encountered by the blood during its perfusion throughout the body.15 Here, we have analyzed the plasma proteins of a group of very well clinically characterized ACS patients from whom circulating monocytes were previously described.13 Nevertheless, plasma is one of the most difficult proteomes to characterize, due to the wide dynamic range of concentrations that separate the most abundant proteins and the least common ones.11 To visualize low-abundance proteins in plasma or serum by 2-DE, high-abundant must be removed.41 Different methods to remove albumin have been described over the years,42-44 including antibody-based methods,45,46 and among them, immunoaffinity depletion of multiple high-abundant proteins was found to be more effective when analyzing serum and plasma by 2-DE or 2D-DIGE.21-28 To deplete the highly abundant plasma proteins in this study, we chose the “Agilent Multiple Affinity Removal System” that is based on an affinity column packed with immobilized polyclonal antibody resins to remove albumin, IgG, IgA, haptoglobin, transferrin and antitrypsin in a very specific and reproducible manner.22-28 A total of about 1400 spot proteins over a broad molecular mass range (10-120 kDa) were analyzed, and 33 proteins were differentially expressed in the plasma of ACS patients when compared with that from healthy controls. Although

research articles each of the two techniques used cover different ranges of molecular masses, a group of proteins were identified by both, 2-DE and DIGE (TPM-4, Fibrinogen, Apo-AIV and ApoAI, Hemopexin, Transthyretin, Interalpha-trypsin inhibitor, and Fibrinogen beta). In some cases, proteins of high molecular mass such as Interalpha-trypsin inhibitor (103 kDa) were found as fragments in the low MW range and in others, the low molecular mass monomers are also present in plasma as tetramers, such as transthyretin. A similar situation can be observed with fibrinogens (gamma, beta), Tropomyosin and Apo-AIV and Apo A-I. Analysis of silver stained 2-D gels revealed 44 differentially expressed spots in ACS and stable CAD patients compared with healthy volunteers, and MS identification showed that they corresponded to 19 different proteins. By contrast, 2D-DIGE analysis discovered 48 protein spots whose expression levels changed in ACS and stable CAD patients with respect to healthy individuals and these correspond to 24 different proteins identified by MS. This analysis is further complicated owing to the presence of several isoforms in 8 proteins (γ- and β-fibrinogen, ApoA-I, ApoJ or Clusterin, SAP, Hemopexin, TTR, Tetranectin) suggesting a different isoform balance for these proteins in ACS and stable CAD patients. In the majority of these proteins only one or two isoforms are altered and thus, a detailed study of each individual protein would be necessary. We are currently testing a battery of isoform-specific proteotypic peptides for MRM analysis, which would permit the detection and quantification of isoforms. In this context, it is important to emphasize that 2D Western blots can be used to detect these isoformspecific modifications that would otherwise go unnoticed in 1D Western blots. We have studied a group of ACS patients, following them for 6 months and comparing their plasma proteome over this period with that of stable CAD patients and healthy controls. Noticeably, the differences detected in silver-stained gels are less stable over time (over the full 6 months period) than the differences detected in DIGE gels. This is probably due to the better consistency that the internal standard produces in the DIGE analyses, since some differences detected in DIGE gels also appeared in silver-stained gels but were slightly above the threshold of significance (0.05-0.07) and, thus, were disregarded. Considering all the data together, several relevant consequences can be deduced. First, the number of altered proteins at admission is relatively moderate (14 proteins) when compared with the subsequent periods studied (17-20 proteins), except for two months after ACS (Table 4), which contrasts with the observations in circulating monocytes.13 Seven of these 14 proteins (Hemopexin, Vitamin D binding protein, TTR, γ- and β-fibrinogen, ApoJ and TPM-4) were altered over longer periods of time (up to 6 months), and intriguingly, they remain altered in stable CAD patients (Table 4). In this case, the situation is similar to the behavior of monocytes, in which five proteins remained altered after six months of acute attacks and in stable patients. Surprisingly, the maximum number of proteins altered (versus healthy controls) was observed in stable CAD patients and the reason for this is at present unknown. It is possible that this effect is related to the chronic medication these patients have received over a long period of time. Since plasma collects what cells, tissues and organs release into it, we can assume that it may contain some proteins associated to the long-term treatment in stable CAD patients that should not be present in either ACS patients or healthy subjects. Whereas Journal of Proteome Research • Vol. 9, No. 9, 2010 4429

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Table 4. Overview of the Altered Proteins Identified in This Study ACS day 0 protein

ACS day 4 protein

ACS 2 month protein

ACS 6 month protein

Alpha-2-HS-glycoprotein

Alpha-1-B-glycoprotein Alpha-2-HS-glycoprotein Alpha-2-macroglobulin

Apolipoprotein A-I

Apolipoprotein A-I

Apolipoprotein J

Apolipoprotein J

Alpha-1-B-glycoprotein

Amyloid related serum protein SAA

Alpha-2-HS-glycoprotein Alpha-2-macroglobulin Amyloid related serum protein SAA Antithrombin-III Apolipoprotein A-IV

Apolipoprotein J

Apolipoprotein J

Beta-Actin

Beta-Actin CD5 antigen-like Complement factor B Complement factor H

Complex-forming glycoprotein HC (Alpha-1-microglobulin) Fibrinogen Beta Fibrinogen Beta Fibrinogen Gamma Fibrinogen Gamma Gelsolin Hemopexin

Hemopexin Interalpha-trypsin inhibitor

STABLE CAD protein

Apolipoprotein A-I Apolipoprotein A-IV Apolipoprotein E Apolipoprotein J Beta-2-glycoprotein CD5 antigen-like Complement C3

Complement factor H

Fibrinogen Beta Fibrinogen Gamma Gelsolin

Fibrinogen Beta Fibrinogen Gamma

Hemopexin Hemopexin Interalpha-trypsin inhibitor

Complex-forming glycoprotein HC (Alpha-1-microglobulin) Fibrinogen Beta Fibrinogen Gamma Gelsolin Hakata antigen Hemopexin Kininogen 1

Pigment epitheliumderived factor

TPM4 Transthyretin Triosephosphate isomerase Vitamin D Binding Protein Vitronectin Zinc-alpha-2-glycoprotein

PRO2222 (HRBP) PRO2222 (HRBP) Serum amyloid P-component Tetranectin TPM4 TPM4 Transthyretin Transthyretin Triosephosphate isomerase Vitamin D Binding Protein Vitamin D Binding Protein

PRO2222 (HRBP) Tetranectin TPM4 TPM4 Transthyretin Transthyretin Triosephosphate isomerase Vitamin D Binding Protein Vitamin D Binding Protein Vitronectin

a Proteins that remain permanently altered along the whole study (six months) and in stable CAD patients are highlighted in bold, whereas proteins that are altered in three different groups are in italics.

many altered proteins are common to the ACS groups, some proteins were specific to stable patients alone (Apo A-IV, C3, Hakata antigen, Kininogen I, Apo E, and β2-Glycoprotein). This is reflected in the PCA plot in which stable CAD patients appear to be more similar to each other than to the other ACS group and healthy controls (associating together in the left area of the plot). Finally, there is a close correlation between the functional groups of the altered proteins identified by both techniques (2-DE and 2D-DIGE), which strongly supports the validity of the experimental data. Furthermore, the majority of the proteins altered can be classified into functional groups that are directly associated with the cardiovascular pathology: coagulation, lipid metabolism, inflammation, cytoskeleton, metabolite transport. Additional information and the possible implications of each individual protein in ACS are discussed in the Supporting Information. In this study, some of the proteins identified as potentially relevant to ACS have already been associated with cardiovascular disease (Table 5).47,48 However, they have not yet been specifically related to ACS and CAD. Moreover, altered levels of Alpha-1-B-glycoprotein, Complement factors B and H, Complex-forming glycoprotein HC, Hakata antigen, Interalphatrypsin inhibitor, Serum Amyloid P-component, Tetranectin, Tropomyosin 4, Transthyretin, Vitamin D Binding Protein and Zinc-alpha-2-glycoprotein in ACS patients are novel findings that should receive further attention. Thus, the analysis of the plasma proteome in ACS patients proves to be a useful strategy 4430


to identify candidate markers that may potential be useful to diagnose ACS. The main limitation of this study derives from the gel-based approach we used in combination with the available depletion system. Thus, we could only detect plasma proteins at high and medium concentrations. However, many important molecules are highly abundant such as antibodies, apolipoproteins, protease inhibitors and coagulation factors. Quantitative analysis of these proteins is useful diagnostically to assess the healthy or diseased status of individuals, and thus, it is an essential part of clinical chemistry practice. Many other potential proteins that could be related to ACS in the ng/mL range are not detected by these techniques. Nevertheless, to our knowledge this is the most comprehensive study performed on the plasma proteome in ACS.

Conclusions In this study, we have found 33 proteins differentially expressed in depleted plasma samples from ACS patients and control/stable CAD patients. The number of differentially expressed proteins tended to increase over time, with a small group of proteins that remain altered for a long period of time and in stable patients. The proteins identified were involved in different physiological processes, many of which may play a role in the pathophysiology of atherotrombotic disease. Further validation of these results in larger popula-

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Plasma Proteome Associated with ACS Table 5. List of the Proteins Identified in This Work

a

protein

Anderson

HUPO

Alpha-1-B-glycoprotein Alpha-2-HS-glycoprotein Alpha-2-macroglobulin Amyloid related serum protein SAA Antithrombin-III Apolipoprotein A-I Apolipoprotein A-IV Apolipoprotein E Apolipoprotein J Beta-2-glycoprotein Beta-Actin CD5 antigen-like Complement C3 Complement factor B Complement factor H Complex-forming glycoprotein HC Fibrinogen Beta Fibrinogen Gamma Gelsolin Hakata antigen Hemopexin Interalpha-trypsin inhibitor Kininogen 1 Pigment epithelium-derived factor PRO2222 (HRBP) Serum Amyloid P-component Tetranectin Tropomyosin 4 Transthyretin Triosephosphate Isomerase Vitamin D Binding Protein Vitronectin Zinc-alpha-2-glycoprotein

NO NO YES YES YES YES YES YES YES YES NO NO YES NO NO NO YES YES NO NO YES NO NO NO NO NO NO NO NO NO NO YES NO

NO YES (T8) YES (T4) YES (T1) NO YES (T2) YES (T2) YES (T2) NO YES (T2) YES (T5) YES (T1) NO NO NO NO YES (T2) YES (T2) YES (T3) NO YES (T1) NO YES (T1) YES (T1) YES (T4) NO NO NO NO YES (T1) NO YES(T1) NO

a Table indicates if they were previously included in published reviews of cardiovascular proteomics or not (YES or NO). Candidate biomarkers of cardiovascular disease reviewed by L. Anderson47 are shown in the “Anderson” column. Proteins included in the “Cardiovascular-related proteins identified in human plasma by the HUPO Plasma Proteome Project Pilot Phase”48 are specified in the “HUPO” column; (T1), protein included in Table 1 (Markers of Inflammation and/or Cardiovascular Disease in Plasma); (T2), protein included in Table 2 (Vascular and Coagulation Proteins in Plasma); (T3), protein included in Table 3 (Signaling Proteins in Plasma); (T4), protein included in Table 4 (Growthand Differentiation-Associated Proteins in Plasma); (T5), protein included in Table 5 (Cytoskeletal Proteins in Plasma); (T8), protein included in Table 8 (Heart Failure- and Remodeling-Associated Proteins in Plasma). No proteins from Tables 6 or 7 were found in this study.

tions will prove whether these proteins may serve as novel candidates associated with ACS disease progression. These results might help to enrich our knowledge of the molecular mechanisms involved in ACS and to improve the existing diagnostic tools. Abbreviations: ACS, acute coronary syndrome; CAD, coronary artery disease; CV, coefficient of variation; HRBP, human retinol binding protein; IEF, isoelectric focusing; MRM, multiple reaction monitoring; MS, mass spectrometry; PCA, principal component analysis; SD, standard deviation; TPM4, Tropomyosin 4; 2-DE, two-dimensional electrophoresis; 2D-DIGE, two-dimensional differential gel electrophoresis.

Acknowledgment. We thank Dr. Jesu´s Egido, Dr. Jose´ Tun ˜ on (Fundacioń Jimeńez Diaz, Spain), Dr. Jose´ J. Jimeńez-Nacher, and Dr. Lorenzo Lopez-Bescos (Cardiology Unit, Fundacioń Hospital de Alcorcon, Madrid, Spain) for their collaboration in the early phases of this work. This work was supported by Comunidad de Madrid

(Proteomarkers), Fina Biotech, Sociedad Espan ˜ ola de Aterosclerosis, FIS (PI-080970), Mutua Madrilen ˜ a (20174/ 004), Ministerio de Ciencia (BFU-2005, 08838).

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