ORIGINAL ARTICLE

Platelet proteome in healthy volunteers who smoke

ANNA DELLA CORTE¹, CHIARA TAMBURRELLI², MARILENA CRESCENTE², LUCIA GIORDANO¹, MARCO D'IMPERIO¹, MICHELA DI MICHELE¹, MARIA BENEDETTA DONATI¹,², GIOVANNI DE GAETANO¹,², DOMENICO ROTILIO¹, & CHIARA CERLETTI²

¹Laboratory of Analytical Techniques and Proteomics, Research Laboratories, Catholic University, Campobasso, Italy and ²Laboratory of Cell Biology and Pharmacology of Thrombosis, Research Laboratories, Catholic University, Campobasso, Italy

Abstract

Smoking accelerates atherosclerosis and is a well-known risk factor for acute cardiovascular complications; however, the mechanisms of these effects have not been completely clarified. Recently developed proteomic approaches may offer new clues when combined with well-established functional tests. Platelet proteome of healthy smokers and non-smokers was resolved by two-dimensional difference gel electrophoresis, compared by Decyder software and identified by mass spectrometry analysis (nano-LC-MS/MS). In smokers, three proteins (Factor XIII-A subunit, platelet glycoprotein IIb and beta-actin) were significantly up-regulated, whereas WDR1 protein and chaperonine HSP60 were down-regulated. Furthermore, the highest scored network derived by Ingenuity Pathway Analysis using the modulated proteins as input showed the involvement of several proteins to be related to inflammation and apoptosis. Platelet function tests and the levels of markers of platelet and leukocyte activation were not different in smokers vs. non-smoker subjects. The platelet proteomic approach confirms that cigarette smoking triggers several inflammatory reactions and may help clarify some of the molecular mechanisms of smoke effect on cellular systems relevant for vascular integrity and human health.

**Keywords:** Smoke, platelet proteome, DIGE, pathway analysis, factor XIII, mass spectrometry

Introduction

Cigarette smoking is a well-known risk factor for cardiovascular disease with a great impact on mortality [1], as it induces an imbalance among different hemostatic molecules in circulating blood or at the vessel-wall interface leading to a hypercoagulable state [2, 3]. Disturbance of platelet function is one of the mechanisms involved in the smoke-attributed cardiovascular risk [4]. Moreover, smoking and atherosclerosis have been associated with increased levels of inflammatory markers and of leukocyte counts [5, 6], both contributing to thrombosis either directly or through leukocyte/platelet interactions [7, 8].

Although reported in several papers [9, 10], the effect of smoking on platelet function is still controversial, possibly due to heterogeneity in platelet function tests used [11–17].

Proteomic and interactomic profile approaches applied to platelets have allowed novel insights into

molecular mechanisms associated to the pathophysiology of cardiovascular disease and to antiplatelet drug response [18–20]. Many factors, including cellular signaling, mitochondria dysfunctions and production of reactive oxygen species, and interactions between different pathways and cellular components are involved in these processes. With the advent of new technologies to study biological systems, bioinformatic methods help to create complex pathways connecting individual genes, proteins, and related processes. Interactomics permits the simultaneous visualization of the interactions of all the molecules in a cell [21, 22].

Differential expression of proteins between platelets from smokers and non-smokers, as studied by proteomics and interactomics, could provide new information on the molecular mechanisms of this cardiovascular risk factor and new hypotheses of potential targets to be experimentally proved.
The main objective of this study was to compare proteomic profiles of platelets from smokers and non-smokers subjects, in relation to both functional tests and markers of platelet and leukocyte interaction.

## Materials and methods

### Chemicals and materials

Chemicals were obtained from Sigma (St Louis, MO, USA). Immobilized pH gradient (IPG) buffers, IPG strips, electrophoresis apparatus, cyanine dyes Cy2, Cy3, and Cy5, and electrophoresis reagents were purchased from GE Healthcare (Uppsala, Sweden). The matrix for MALDI-TOF (matrix-assisted laser desorption/ionization-time of flight) was from Sigma, and the calibration kit was from Applied Biosystems (Foster City, CA, USA). Monoclonal antibodies for flow cytometry were from Instrumentational Laboratory (Milan, Italy). Antibodies against Factor XIII (FXIII) and pan-actin were purchased from Sigma. All chemicals and biochemicals used were of analytical grade.

### Recruitment of subjects

Venous blood was collected into 3.8% sodium citrate from 32 Caucasian, 25-47 years old, 16 women, apparently healthy volunteers, based on hematochemical values which were within the normal ranges; all donors denied to have taken any medication in the previous 2 weeks. Half of these subjects (eight men and eight women) were current smokers (they have smoked for 3 years at least 10 cigarettes/day). The study was approved by the Ethical Committee of the Catholic University and run accordingly to the Helsinki declaration; each subject signed an informed consent and answered a questionnaire about personal data, smoking, and life habits. No differences between smokers and non-smokers subjects were recorded concerning gender, age, weight, height, dietary, or lifestyle habits, such as physical exercise, alcohol drinking, etc.

### Platelet preparation for proteomics

Blood was collected in the morning before first daily cigarette smoking. Washed platelet suspensions were prepared as previously described [23]. Platelets were resuspended in Hepes Tyrode buffer and counted both by an automatic analyser (Ac Tdiff 2, Beckman Coulter) and contrast phase microscopy. Leukocyte contamination was excluded by flow cytometric analysis of CD45 positive events, which were virtually absent in all preparations tested; platelet activation during washing procedure was measured by the expression of P-selectin, detected by flow cytometry,

<table><caption>Table I. Experimental design of 2-D DIGE experiment.</caption><thead><tr><th>Gel</th><th>Cy3</th><th>Cy5</th><th>Cy2</th></tr></thead><tbody><tr><td>1</td><td>NSF-I</td><td>SM-I</td><td>Pooled standard</td></tr><tr><td>2</td><td>NSF-I</td><td>SF-I</td><td>Pooled standard</td></tr><tr><td>3</td><td>NSM-I</td><td>SF-I</td><td>Pooled standard</td></tr><tr><td>4</td><td>NSM-I</td><td>SM-I</td><td>Pooled standard</td></tr><tr><td>5</td><td>SM-II</td><td>NSF-II</td><td>Pooled standard</td></tr><tr><td>6</td><td>SF-II</td><td>NSF-II</td><td>Pooled standard</td></tr><tr><td>7</td><td>SF-II</td><td>NSM-II</td><td>Pooled standard</td></tr><tr><td>8</td><td>SM-II</td><td>NSM-II</td><td>Pooled standard</td></tr></tbody></table>

Notes: Each gel contained the pooled standard and two other samples. The pooled internal standard comprises equal amount of proteins from each of the 16 samples (two non-smoker females (NSF), two non-smoker males (NSM), two smoker females (SF), and two smoker males (SM). I and II indicate the first and second blood collection at 2 months of distance).

and was always below 10% of total platelet preparation. Immediately after, the platelet suspension was aliquoted and centrifuged at 10,000 rpm for 10 min and the pellet stored at -80°C for proteomic analysis.

### Proteome analysis

DIGE study design and labeling of platelet samples. Sixteen platelet pellets (eight smokers and eight non-smokers) were studied, derived from two platelet samples, each collected at a time distance of 2 months, from eight different volunteers. Proteins were extracted from platelets as described [24] and separated on eight two-dimensional difference gel electrophoresis (2-D DIGE) gels. According to a cross-over design (Table I), each gel contained the proteins from two samples (a smoker and a non-smoker), labeled with Cy3 and Cy5 and an internal standard, constituted by equal amounts of proteins from each sample of the whole experiment, labeled with Cy2 dye. Briefly, 50 µg of proteins from smoker, non-smoker platelets, or internal standard were labeled by the addition of 400 pmol of the appropriate Cy-dye in DMF, for 30 min on ice in the dark, and then quenched by incubation with 10 mM lysine for 10 min on ice. The labeled protein extracts were pooled and immediately used for electrophoresis. The isoelectric focusing, the reduction and alkyl-ation, and the second dimension separation were performed as described [25].

Gel image acquisition and image analysis. Fluorescently labeled proteins were visualized using the Typhoon Trio variable mode imager system from Amersham Biosciences/GE Healthcare. Cy2, Cy3, and Cy5 images were scanned using a 488, 532, and 633 nm laser, respectively.
The Photo Multiplier Tube (PMT), value for all scanned images was between 490 and 570 V. All gels were scanned at 100 µm resolution and, prior to analysis, gel images were cropped using the ImageQuant TL software program (GE Healthcare, Uppsala, Sweden). Gel analysis was performed with the DeCyder V6.5 2-D analysis software package for DIGE (Amersham Biosciences/GE Healthcare). The DeCyder differential in-gel analysis (DIA) module was used for pairwise comparisons of each smoker and non-smoker sample to the internal standard present in each gel and for calculation of normalized spot volume/protein abundance. The spots from each gel were simultaneously detected and the number of spots for co-detection was set to 2500. A filter was run on the original spots to exclude artifacts before performing statistical analyses, therefore reducing the number of differentially expressed spots.

To reduce the intrinsic variability associated to the different samples, more stringent criteria were chosen: only proteins present at least in two of the triplicate gel images were considered for statistical analysis; moreover, spots of interest were manually verified.

The 24-spot maps corresponding to the eight gels were used to calculate average abundance and paired Student's *t*-test *p*-values for each protein across the eight gels. This was done using the DeCyder biological variation analysis module (BVA) and Cy3:Cy2 and Cy5:Cy2 ratios for each individual protein.

Matching was carried out in the BVA module after first landmarking 25 proteins on every gel relative to the master map. Matching was then corrected and improved manually where necessary. Finally, extended data analysis (EDA) module was carried out for statistical analyses: *T*-test (*p* < 0.05) was performed on the normalized abundances of matched spots to compare similarity among the two groups according to expression patterns, by applying the false discovery rate correction method [26]. The data were shown graphically by hierarchical clustering and principal component analysis (PCA).

Fluorescently labeled proteins were visualized using the Typhoon Trio variable mode imager system from Amersham Biosciences/GE Healthcare. Cy2, Cy3, and Cy5 images were scanned using a 488, 532, and 633 nm laser, respectively.

The data were shown graphically by hierarchical clustering and PCA.

*In-gel digestion and mass spectrometry.* For spot picking, gels were fixed in 50% methanol/7% acetic acid for 1 h followed by silver staining [27]. Spots of interest were excised, reduced to small pieces, and digested with 20 ng/µL trypsin (Sigma) [28].

The proteolytic peptides were extracted with 50% acetonitrile (ACN)/0.1% trifluoroacetic acid (TFA) and dried in a vacuum centrifuge. For MALDI-TOF analysis, digested peptides were redissolved in 50% ACN/0.1% TFA, and mixed 1:1 with 10 mg/mL α-cyano 4-hydroxy 9-cinnamic acid in 70% ACN and 0.1% TFA, before being spotted onto a stainless steel MALDI target plate. For LC-MS/MS analysis, peptides were solubilized in 2% ACN and 0.1% formic acid.

*Protein MS identification.* Proteins were identified by MALDI-TOF and LC-MS/MS analysis. Peptide mass fingerprinting (PMF) was performed on a Voyager-DE STR MALDI-TOF mass spectrometer (MALDI-TOF MS) (Applied Biosystem-MDS Sciex, Toronto, Canada), as previously reported [29, 30]. The MS/MS analysis of peptides generated by in-gel digestion was carried out using a nano-LC-ESI-Q-TOF instrument (Q-STAR Elite, Applied Biosystem). High-performance liquid chromatography was performed on a LC Packings Ultimate 3000 nanoflow system (Dionex, Sunnyvale, CA), as described by Di Michele et al. [28]; the chromatographic conditions and mass parameters were adjusted as follows: buffer A (2%ACN, 0.1% formic acid) and buffer B (2% water, 0.1% formic acid in ACN) with the gradient of 8–50% buffer B in 40 min, 50–80% in 10 min and 80% for the next 10 min, finally at 8% B in 10 min to equilibrate the system. For each TOF MS scan (350–1300 m/z), the two most intense doubly or triply charged ions were selected for the enhanced product ion scans (110–1600 m/z). The ion spray voltage was 1.4 kV, the curtain gas was set to 15 and the declustering potential was 80 V. Mascot software V1.6 b20 (Matrix Science, London) was employed for protein database searching. The searches were performed using the NCBI database and the following standard parameters: Homo sapiens; tryptic digest with a maximum of one missed cleavage; carboxyamidomethylation of cysteine; partial methionine oxidation; and a mass tolerance of 100 ppm. For MS/MS data, the searches were performed with the following additional criteria: maximal tolerance for MS/MS data of 0.3 Da and searching peptide charges of 2+ and 3+. For mass fingerprint experiments, a Mascot score of 64, corresponding to *p* < 0.05, was chosen as the cut-off for a significant hit. Protein identifications by MS/MS were considered when Mascot search results gave at least two unique consensus peptides, each with peptide probability scores at/or exceeding 20.

*Western blot analysis of FXIII.* Equal amounts (5 and 10 µg) of protein extracts from smoker and non-smoker platelets were run on 10% SDS-PAGE
(sodium dodecyl sulfate polyacrylamide gel electro-phoresis) gels and transferred onto nitrocellulose membranes. The Western blot protocol was previously described by Di Michele et al. [28]: briefly, the membranes were incubated with an anti-FXIII monoclonal antibody (1:800) overnight at 4°C, washed with Tris-buffered saline Tween-20 (TBST) and incubated 2 h with goat anti-mouse IgG conjugated with horseradish peroxidase (1:3000) at room temperature. Pan-actin was used as internal loading control of protein concentration in the extracts, detecting all actin isoforms, being the entity of changes of the specific isoform negligible in respect to the total amount of actin. The bands were detected using the enhanced luminescence kit ECL+ (Amersham Pharmacia Biotech, Arlington Heights, IL).

**Network analysis.** To examine the relationships between the regulated proteins identified by 2-D DIGE, an in silico protein network was created based on known protein interactions derived from the biomedical literature [20]. The analysis was performed by Ingenuity Pathway Analysis (IPA; Ingenuity Systems, Mountain View, CA; www.ingenuity.com). The interactions used for the network comprise direct relationships that describe physical (binding) and functional (such as phosphorylation and proteolysis) interactions between proteins. IPA computes a score for each possible network according to the fit of that network to the inputted proteins. The score is calculated as the negative base -10 logarithm of the *p*-value that indicates the probability of the inputted proteins to be found together in a given network as a result of random chance. Networks with scores of 10 or higher (negative log of the *p*-value) have a high confidence of not being generated by random chance alone.

### Cell function tests

**PFA-100 (collagen/ADP cartridge) closure time.** Platelet Function Analyzer (PFA)-100® (Dade Behring, Siemens, Germany) closure time (CT) was measured on citrated whole blood, as previously described [31, 32], using collagen/ADP (ADP, adenosine diphosphate) cartridges.

**Markers of platelet and leukocyte activation in whole blood.** Markers of platelet and leukocyte activation and interaction were detected by flow cytometry in citrated whole blood, both in basal condition and upon in vitro activation by collagen/ADP (2 µg/mL/ 5 µM final concentrations, respectively) [33].

Samples were labeled with specific antibodies for the expression of activated fibrinogen receptor (PAC-1) and P-selectin on platelets, and of CD11b

(the integrin beta-chain) and Mac-1 (the activated form of CD11b/CD18) on leukocytes; platelet-leukocyte mixed aggregates were quantified by double fluorescence positivity. A Coulter EPICS XL flow cytometer (Beckman Coulter), daily checked by the acquisition of Flow-Check™ Fluorospheres was used. For each measurement, 10,000 events were analyzed. Platelets and leukocytes were defined by morphological characteristics, using forward light scatter vs. side light scatter intensity dot-plot representation and by CD42b or CD61 and CD45 positivity, respectively.

Data, expressed as percent of cells positive for each specific antigen or for the other cell specific antigen (mixed aggregates), are reported as means ± SEM, statistical analysis of differences was performed by Student *t*-test. Mean fluorescence intensity (MFI) of CD61 was detected on platelet surface to evaluate GpIIIa expression.

### Results

#### Differential proteomic analysis in platelets from smokers and non-smokers

Expression information for all individual proteins in the hierarchical clustering analysis (grouped into horizontal bars in the expression matrix and related via a similar dendrogram on the left) are reported in Figure 1a. The resulting heat map clearly classified the 16 samples into two major clusters. In particular, the dendrogram separated into two main branches, smokers, and non-smokers. Hierarchical clustering of individual samples is shown on top and clustering of individual proteins is shown on the left, with relative expression values displayed as an expression matrix (heat map) using a relative scale ranging from -0.3 (green) to +0.3 (red); the value 0.3 corresponds to a variation in decrease or increase of the protein expression of twofold change.

The PCA, used to reduce the complexity of the proteomic data and to examine global trends in protein expression in platelets from smokers and non-smokers (Figure 1b), shows that samples were grouped based on the variance of their protein expression. The majority of the variance (48.2%) in the protein expression between smokers and non-smokers was captured by the first principal component, with additional 19.9% of the variance by the second component.

Both PCA and hierarchical clustering indicated a complete and clear separation of the two groups, showing high reproducibility between the replicate DIGE samples obtained from two blood collections from each subject performed 2 months apart. Comparisons between gels from smokers and non-smokers revealed that 10 protein spots varied in a
<figure><img src="image_1.png" /><figcaption>Figure 1. Multivariate analysis of DIGE/MS results. (a) Hierarchical clustering of the eight independent samples based on the global expression patterns of modulated proteins. Hierarchical clustering of individual samples is shown on top and clustering of individual proteins shown on the left, with relative expression values displayed as an expression matrix (heat map) using a relative scale ranging from -0.3 (green) to +0.3 (red). (b) Scatter plot of smoker volunteers (red) and non-smoker volunteers (blue). PCA reduces the dimensionality of a multidimensional analysis, and displays the two principal components that can distinguish between the two largest sources of variation within the dataset. PCA discretely clustered the eight individual Cy3- and Cy5-labeled DIGE expression maps into smoker volunteers and non-smoker volunteers.</figcaption></figure>

statistically significant way ($p < 0.05$). Spots of interest were manually verified to have the protein characteristic Gaussian three-dimensional profile, while spots with volume close to the background or

with undefined shape were eliminated, as well as precipitated dye, dust particles, or bubbles. It is worth mentioning that statistics were calculated on all spots, while manual verification was only used for
<figure><img src="image_2.png" /><figcaption>Figure 2. Two-dimensional DIGE map. (a) The map contains the five spots differentially expressed and detected in human platelet proteome (smokers against non-smokers). After labeling, proteins were separated on 13 cm, pH 3–10 linear strips in the first dimension, and on a 10% polyacrylamide SDS PAGE in the second dimension. (b) Three-dimensional representation of the degree of differential expression of the platelet protein spot 319, in smoker and non-smoker subjects.</figcaption></figure>

spots to be identified. Only eight spots were excised and in-gel digested with trypsin. Of these spots, five were identified by MALDI-TOF MS and LC-MS/MS: the modulated proteins are reported in Figure 2a. The DIA shows pairwise variations in smokers and non-smokers of spot 319 (Figure 2b), then identified as FXIII-A subunit.

### Identification of differentially expressed platelet proteins

Protein identities are listed in Table II. The five different identifications according to accession numbers, the peptide sequences and scores, the charge of identified peptides, the values of fold change, and other protein details are reported. The spots 319,

633, and 209 were assessed both by PMF and peptide sequencing, while the spot 413 and 417 were identified only by LC-ESI-MS/MS maybe because too faint.

### Validation of FXIII by Western blotting

Western blotting and densitometric quantitation of FXIII in platelets from smokers and non-smokers confirmed the trends obtained with DIGE data, as the intensity of FXIII in smokers was higher than in non-smokers (Figure 3). The use of pan-actin for protein amount normalization, detecting all actin isoforms, and the increase of a beta-actin variant in smokers could not affect the amount of FXIII-A in smokers, but could rather underestimate it.

### Pathway analysis

To examine the relationship between the platelet proteome of smoker and non-smoker volunteers, pathway analysis was performed using IPA software using the proteins detected in the proteomic analysis as input. The proteins reported in Table II were analyzed in a systematic way using known protein-protein interactions reported to determine the most significant interaction networks, global pathways, and functions, ranked by score. From the analysis, the cluster “cellular development, lipid metabolism, and small molecule biochemistry” was generated and the protein dataset is reported in Table III. The cluster with the highest score consists of a network of 35 proteins, five of which were identified by DIGE-based proteomics and 29 recognized to be related because of their reported interactions with the proteins identified by DIGE. In Figure 4, a graphical representation of the highest scoring network obtained from all identified proteins is shown. Nodes represent proteins: shaded features depict proteins identified in this study, whereas unshaded features depict additional members of these networks and pathways that were not detected by DIGE/MS. Node shapes indicate different functions: enzymes, transcription regulators, nuclear receptors, cytokines, transporter, and other. Protein-protein associations are indicated by edges containing single lines, whereas proteins that act upon another protein (controlling their expression) are indicated by arrows. Continuous or dotted lines indicate direct or indirect protein interactions, respectively.

### Platelet function and platelet and leukocyte activation markers

The results reported in Table IV show no significant difference between smokers and non-smokers in any cell function tests studied. The number of individual data was sufficient to show differences of 20% or
<table><caption>Table II. List of proteins identified using the MALDI-TOF and the nano-LC-ESI-Q-TOF.</caption><thead><tr><th colspan="11">MS/MS</th></tr><tr><th rowspan="2">DIGE code</th><th rowspan="2">Protein identification</th><th rowspan="2">Accession</th><th rowspan="2">Molecular weight/ Isoelectric point (Mr/pI)</th><th rowspan="2">Mr/pI (theoretical)</th><th rowspan="2">Protein score</th><th rowspan="2">Matched peptides</th><th rowspan="2">Peptide sequences (peptide charges)</th><th colspan="3">PMF</th><th colspan="3">Fold-change</th></tr><tr><th>Matched peptides</th><th>Sequence coverage</th><th>Average ratio</th><th>Up-down regulation</th><th>p-Value</th></tr></thead><tbody><tr><td>319</td><td>FXIII a subunit</td><td>gi|182309</td><td>94 915/5.16</td><td>83 744/5.75</td><td>410</td><td>13</td><td>AVPPNNSNAAEDDLPTVELQGV- VPR (+3); GVNLQEFLNVTSVHLFK (+3); GTYIPVIVSELOSGK (+2); VEYVIGR (+2); LSIQSSPK (+2); CLGIPAR (+2); CGPASVQAIK(+2); DGTHVVENVDATHIGK (+3); LIASMSSDSLR (+2); SNVDMDFEVENAVLGK (+2); ETFDVTLEPLSFK (+2); STVLТИРРИК (+2); LALEFTALMYGAK (+2); YEYQPFAGK (+2); YAPSGFYIASGDVSGK (+2); VFASLPQVER(+2); VFASLPQVER(+2); IAVVGEGR (+2); VINSVDIK (+2); YTSLMLR Oxidation (M) (+2); AGFAGDDAPR (+2); GYSFTTTAER (+2); IIAPPER Oxidation (M) (+2); EITALAPSTMK (+2)</td><td>22</td><td>27%</td><td>1.18</td><td>↑</td><td>0.0397</td><td></td><td></td></tr><tr><td>413</td><td>WDR1 protein</td><td>gi|3420181</td><td>77 123/6.08</td><td>58 604/6.41</td><td>164</td><td>6</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>633</td><td>Beta-actin variant [Homo sapiens]</td><td>gi|62897625</td><td>52 726/5.46</td><td>42 080/5.37</td><td>153</td><td>4</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

(continued)
<table><caption>Table II. Continued.</caption><thead><tr><th colspan="10">MS/MS</th></tr><tr><th rowspan="2">DIGE code</th><th rowspan="2">Protein identification</th><th rowspan="2">Accession</th><th rowspan="2">Molecular weight/ Isoelectric point (Mr/pI) (experimental)</th><th rowspan="2">Mr/pI (theorethical)</th><th rowspan="2">Protein score</th><th rowspan="2">Matched peptides</th><th rowspan="2">Peptide sequences (peptide charges)</th><th colspan="3">PMF</th><th colspan="3">Fold-change</th></tr><tr><th>Matched peptides</th><th>Sequence coverage</th><th>Average ratio</th><th>Up-down regulation</th><th>p-Value</th></tr></thead><tbody><tr><td>209</td><td>Platelet membrane glycoprotein IIb</td><td>gi|190068</td><td>114 317/4.03</td><td>114 387/5.21</td><td>255</td><td>19</td><td>VAIVVGAPR (+2); AEAQVELR (+2); SCVLQPTK (+2); IVLLDVPVR (+2); VYLFLQPR (+2); ALSNVEGFER (+2); NVGSQTLQTFK (+2); EQNSLDSWGPK (+2); VVLCELGNPMK + Oxidation (M) (+2); FGSAIAPLGDLDR (+2); GQVLVFLGQSEGLR (+2); AEGGQCPSSLFDLR (+2); TPVGSCFLAQPESGR (+2); ASVQLLLVQDSLNPAVK (+2); GNSFPASLVVAAEEGER (+2); GPHALGAPSLLLTGTQLYGR (+3); TLGPSQEETGGVFLCPWR (+3); SRPSQVLDSPFPTGSAFGFSLR (+3); GAVDIDDNGYPDLIVGAYGA-NQVAVYR (+3); VGLQVVAVK (+2); LSDGVAVLK (+2); VGGTSDEVNEK (+2); NAGVEGSLIVEK (+2); IPAMTIAK Oxidation (M) (+2); VTDALNATR (+2)</td><td>25</td><td>24%</td><td>1.15</td><td>↑</td><td>0.0064</td><td></td><td></td></tr><tr><td>417</td><td>Chaperonin (HSP60)</td><td>gi|306890</td><td>75 853/4.36</td><td>61 160/5.70</td><td>88</td><td>6</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>Figure 3. FXIII validation by Western blotting. The 2-D DIGE results relative to FXIII are validated by Western blotting followed by densitometric analysis; the ratios of the volume of FXIII band over the volume of actin band are calculated and normalized for µg of protein. The histograms report means and SD of the densitometric analyses of three different blots (not shown).</figcaption></figure>

greater in smokers vs. non-smokers (power = 80%;
alpha = 0.05, two-tailed).

Discussion

The main message of this study is that proteomics
offers new tools for the investigation of the pro-
thrombotic condition induced by cigarette smoking.
Five proteins were found to be either significantly
up- or down-regulated in platelets from smoking
subjects as compared to non-smokers. In particular,
coagulation FXIII-A subunit, beta-actin, and platelet
GpIIb were up-regulated by 18%, 18%, and 15%,
respectively, in smokers compared to non-smokers,
while WD repeat domain 1 (WDR1) protein and
heat shock protein 60 (HSP60) were down-regulated
by 19% and 13%, respectively. PCA and hierarchical
clustering carried out on the basis of protein abun-
dances of each sample indicate a complete and clear
separation of smoker from non-smoker group.

FXIII is a pro-transglutaminase, transformed into
a transglutaminase upon activation, that catalyzes
crosslink formation in fibrin clots and protects them
from fibrinolysis. Two forms of FXIII are known: an
extracellular plasmatic one, associated with fibrino-
gen, consisting of two types of subunits (A and B),
and an intracellular form, localized in platelets,
monocytes, and monocyte-derived macrophages,
consisting of subunit A only [34]. FXIII in platelets
is activated under conditions of sustained calcium
flux through a non-proteolytic process; its main
protein substrates include components of the
clotting-fibrinolytic system, adhesive, and contractile
proteins; it plays an important role in the cytoskel-
eton remodeling and reorganization of activated
platelets [34]; translocation of intraplatelet FXIII
to platelet periphery, observed upon platelet

activation, depends on polymerization of cytoskele-
ton proteins [35].

Our finding of increased FXIII in platelets from
smokers is in line with previous observations of FXIII
relationship with platelet function and smoke [36].
A potential role of FXIII-A has been recently
reported supporting platelet adhesion at the site of
vascular damage in association with other thrombo-
genic matrix proteins [37]. In a study on healthy
subjects, both plasma FXIII-A levels and activity
were increased in smokers, hypertensive, and elderly
people [38], suggesting a role for elevated FXIII in
vascular risk. However, the results of studies on
association of the risk of coronary artery disease and
FXIII levels or activity, are up to now inconclusive
[reviewed in 39].

Two studies on clot formation and stability in
smokers reported shorter onset and increased rate of
fibrin formation, development of greater fibrin clot
strength [40], lower fibrin clot permeability and
longer clot lysis time [41]. In a cross-sectional cohort
study [42], D-dimer plasma levels were lower in
smoker as compared to non-smoker women, sug-
gesting a reduced fibrinolytic activity in the former
group [43].

A modulatory role of FXIII on platelet adhesion to
endothelial cells was mediated by the activated
GpIIbIIIa receptor [44], which plays a central role
in platelet aggregation, contributing to the binding of
platelets to soluble or surface-immobilized FXIII.
The fibrinogen receptor and its over-expression may
be responsible for platelet hyperactivity. FXIII has
also been associated to endothelial cell proliferation
and migration and inhibition of apoptosis, suggesting
a role in tissue repair and remodeling [45].

Increased levels of beta-actin found in smokers,
consistently with FXIII levels, suggest that platelet
cytoskeleton rearrangement may occur, compatible
with an activation state, though undetectable by
presently available *in vitro* platelet function tests.

In our study, the significantly increased expression
of GpIIb, the beta chain of the fibrinogen receptor
(GpIIbIIIa), found by proteomics in platelets from
smokers, was not paralleled by platelet surface GpIIIa
(CD61) measured by flow cytometry, similarly to
findings from a previous report [13]. This is not
surprising, since the surface expression of this glyco-
protein only accounts for a part of the total protein
[46]. Changes in the integrin signaling have been
shown in platelets of patients with cystic fibrosis,
supporting a pro-inflammatory role of platelets [47].

As far as the two down-regulated platelet proteins
are concerned, WDR1 protein, through the actin
depolymerization factor, mediates actin dynamics,
promotes the rapid turnover of filament networks
and enhances the rate of actin filament cleavage [48];
the HSP60 belongs to the “chaperonine” family,
<table><caption>Table III. IPA (protein dataset).</caption><thead><tr><th rowspan="2">ID</th><th colspan="4">Molecules in network</th><th rowspan="2">Focus molecules</th><th rowspan="2">Top functions Cellular development, lipid metabolism, and small molecule biochemistry</th></tr><tr><th>ACTB, ASAH1, BCL9, CAP1, CRAT, CTNNB1, CXADR, D-glucose, DKK3, EMD, F13A1, Fascin, GREM1, GSTA4, HOMER3, HSPD1, ICAM3, IFI6, IGF2BP1, IL-6, ITGA2B (includes EG:3674), KIFC1, LIMA1, MYO7A, NSMAF, PBSN, REG1A, RETNLB (includes EG:84666), SCGB2A2 (includes EG:4250), SCIN, SENP2, SKIV2L, SYNE2, TFF3, WDR1</th><th>Score</th><th>Focus molecules</th></tr><tr><th>Symbol</th><th>Entrez gene name</th><th>GI number</th><th>Location<sup>a</sup></th><th>Type(s)<sup>b</sup></th><th>Biomarker application(s)<sup>c</sup></th><th>Drug(s)</th><th>Entrez gene ID for human</th></tr></thead><tbody><tr><td>ACTB</td><td>Actin, beta</td><td>62 897 625</td><td>C</td><td>O</td><td></td><td></td><td>60</td></tr><tr><td>ASAH1</td><td>N-acylsphingosine amidohydro-lase (acid ceramidase) 1</td><td>–</td><td>C</td><td>E</td><td>UA</td><td></td><td>427</td></tr><tr><td>BCL9</td><td>B-cell CLL/lymphoma 9</td><td>–</td><td>N</td><td>O</td><td></td><td></td><td>607</td></tr><tr><td>CAP1</td><td>CAP, adenylate cyclase-associated protein 1 (yeast)</td><td>–</td><td>PM</td><td>O</td><td></td><td></td><td>10 487</td></tr><tr><td>CRAT</td><td>Carnitine acetyltransferase</td><td>–</td><td>C</td><td>E</td><td></td><td></td><td>1384</td></tr><tr><td>CTNNB1</td><td>Catenin (cadherin-associated protein), beta 1, 88 kDa</td><td>–</td><td>N</td><td>TRR</td><td>D; DSP; E; P</td><td></td><td>1499</td></tr><tr><td>CXADR</td><td>Coxsackie virus and adenovirus receptor</td><td>–</td><td>PM</td><td>TMR</td><td></td><td></td><td>1525</td></tr><tr><td>D-glucose</td><td>–</td><td>–</td><td>Unk</td><td>CEM</td><td>E; S</td><td></td><td>27 122</td></tr><tr><td>DKK3</td><td>Dickkopf homolog 3 (Xenopus laevis)</td><td>–</td><td>ES</td><td>O</td><td></td><td></td><td></td></tr><tr><td>EMD</td><td>Emerin</td><td>–</td><td>N</td><td>O</td><td></td><td></td><td>2010</td></tr><tr><td>F13A1</td><td>Coagulation FXIII, A1 polypeptide</td><td>182 837</td><td>ES</td><td>E</td><td></td><td></td><td>2162</td></tr><tr><td>Fascin</td><td>–</td><td>–</td><td>Unk</td><td>G</td><td></td><td></td><td></td></tr><tr><td>GREM1</td><td>Gremlin 1, cysteine knot super-family, homolog (Xenopus laevis)</td><td>–</td><td>ES</td><td>O</td><td>UA</td><td></td><td>26 585</td></tr><tr><td>GSTA4</td><td>Glutathione S-transferase alpha 4</td><td>–</td><td>C</td><td>E</td><td></td><td></td><td>2941</td></tr><tr><td>HOMER3</td><td>Homer homolog 3 (drosophila)</td><td>–</td><td>PM</td><td>O</td><td></td><td></td><td>9454</td></tr><tr><td>HSPD1</td><td>Heat shock 60 kDa protein 1 (chaperonin)</td><td>306 890</td><td>C</td><td>E</td><td></td><td></td><td>3329</td></tr></tbody></table>
<table><tbody><tr><td>ICAM3</td><td>Intercellular adhesion molecule 3</td><td>-</td><td>PM</td><td>O</td><td>3385</td></tr><tr><td>IF16</td><td>Interferon, alpha-inducible protein 6</td><td>-</td><td>C</td><td>O</td><td>2537</td></tr><tr><td>IGF2BP1</td><td>Insulin-like growth factor 2 mRNA binding protein 1</td><td>-</td><td>C</td><td>TLR</td><td>10 642</td></tr><tr><td>IL-6</td><td>Interleukin 6 (interferon, beta 2)</td><td>-</td><td>ES</td><td>C</td><td>D; DSP; E; P; S; UA</td><td>3569</td></tr><tr><td>ITGA2B (includes EG:3674)</td><td>Integrin, alpha 2b (platelet glycoprotein IIb/IIIa complex, antigen CD41)</td><td>119 571 979</td><td>PM</td><td>TMR</td><td>Abciximab, TP 9201, eptifibatide, tirofiban</td><td>3674</td></tr><tr><td>KIFC1</td><td>Kinesin family member C1</td><td>-</td><td>N</td><td>O</td><td>3833</td></tr><tr><td>LIMA1</td><td>LIM domain and actin binding 1</td><td>-</td><td>C</td><td>O</td><td>51 474</td></tr><tr><td>MYO7A</td><td>Myosin VIIA</td><td>-</td><td>C</td><td>E</td><td>4647</td></tr><tr><td>NSMAF</td><td>Neutral sphingomyelinase (N-SMase) activation associated factor</td><td>-</td><td>C</td><td>O</td><td>8439</td></tr><tr><td>PBSN</td><td>Probasin</td><td>-</td><td>ES</td><td>T</td><td>5967</td></tr><tr><td>REG1A</td><td>Regenerating islet-derived 1 alpha</td><td>-</td><td>ES</td><td>GF</td><td>84 666</td></tr><tr><td>RETNLBP (includes EG:84666)</td><td>Resistin-like beta</td><td>-</td><td>ES</td><td>O</td><td>4250</td></tr><tr><td>SCGB2A2 (includes EG:4250)</td><td>Secretoglobin, family 2A, member 2</td><td>-</td><td>ES</td><td>O</td><td>85 477</td></tr><tr><td>SCIN</td><td>Scinderin</td><td>-</td><td>C</td><td>O</td><td>59 343</td></tr><tr><td>SENP2</td><td>SUMO1/sentrin/SMT3 specific peptidase 2</td><td>-</td><td>N</td><td>P</td><td>23 224</td></tr><tr><td>SKIV2L</td><td>Superkiller viralicidic activity 2-like (Saccharomyces cerevisiae)</td><td>-</td><td>C</td><td>TLR</td><td>6499</td></tr><tr><td>SYNE2</td><td>Spectrin repeat containing, nuclear envelope 2</td><td>-</td><td>N</td><td>O</td><td>7033</td></tr><tr><td>TFF3</td><td>Trefoil factor 3 (intestinal)</td><td>-</td><td>ES</td><td>O</td><td>9948</td></tr><tr><td>WDR1</td><td>WD repeat domain 1</td><td>3 420 181</td><td>ES</td><td>O</td><td>D; UA</td></tr></tbody></table>

Notes: <sup>a</sup>C, cytoplasm; ES, extracellular space; N, nucleus; PM, plasma membrane; and Unk, unknown; <sup>b</sup>CEM, chemical endogenous mammalian; C, cytokine; E, enzyme; G, group; GF, growth factor; O, other; P, peptidase; TRR, transcription regulator; TLR, translation regulator; TMR, transmembrane receptor; and T, transporter; <sup>c</sup>D, diagnosis; DSP, disease progression; E, efficacy; P, prognosis; S, safety; UA, unspecified application.
<figure><img src="image_4.png" /><figcaption>Figure 4. Biological pathways associated with modulated proteins identified in smoker platelets and non-smoker platelets by DIGE/MS analysis. From the analysis, the cluster “cellular development, lipid metabolism, and small molecule biochemistry” was generated. IPA software (www.ingenuity.com) was used to map identified proteins and associated proteins based on known protein-protein interactions, mRNA expression studies and other biochemical interactions established in the literature. See molecules in network in Table III.</figcaption></figure>

<table><caption>Table IV. Platelet and leukocyte activation and interaction in smoker and non-smoker subjects.</caption><thead><tr><th rowspan="2">Platelet function<br>PFA-100 CT</th><th colspan="2">Smokers</th><th colspan="2">Non-smokers</th></tr><tr><th colspan="2">88 ± 7</th><th colspan="2">87 ± 4</th></tr><tr><th></th><th>Basal</th><th>Collagen/ADP</th><th>Basal</th><th>Collagen/ADP</th></tr></thead><tbody><tr><td>PAC-1</td><td>7.3 ± 1.5</td><td>43.9 ± 3.8</td><td>6.4 ± 1.2</td><td>36.3 ± 2.9</td></tr><tr><td>P-selectin</td><td>0.9 ± 0.1</td><td>34.9 ± 4.6</td><td>0.9 ± 0.1</td><td>34.8 ± 4.0</td></tr><tr><td>Platelet mixed aggregates with<br>PMN leukocytes</td><td>4.6 ± 0.7</td><td>15.8 ± 4.2</td><td>7.2 ± 1.7</td><td>14.2 ± 3.6</td></tr><tr><td>Monocytes</td><td>6.0 ± 1.0</td><td>11.9 ± 3.7</td><td>6.5 ± 1.2</td><td>9.3 ± 2.3</td></tr><tr><td>Leukocyte function:</td><td></td><td></td><td></td><td></td></tr><tr><td>CD11b PMN</td><td>40.2 ± 3.3</td><td>86.2 ± 2.6</td><td>48.0 ± 4.0</td><td>88.1 ± 1.8</td></tr><tr><td>CD11b monocyte</td><td>33.4 ± 5.2</td><td>71.1 ± 4.0</td><td>36.3 ± 4.1</td><td>74.2 ± 4.3</td></tr><tr><td>MAC-1 PMN</td><td>8.9 ± 1.1</td><td>74.2 ± 5.1</td><td>9.5 ± 1.4</td><td>77.7 ± 3.0</td></tr><tr><td>MAC-1 monocyte</td><td>6.7 ± 1.2</td><td>60.3 ± 5.3</td><td>7.6 ± 1.1</td><td>66.2 ± 3.6</td></tr></tbody></table>

Notes: Means ± SEM, n = 16 subjects for each group. PFA (100) CT is reported in seconds. Flow cytometric data are reported as percent of cells positive for each specific antigen or for the other cell specific antigen (mixed aggregates).
constituted by proteins with a high degree of evolu-
tionary conservation, crucial for protein conforma-
tion [49]. In fact, HSPs through interaction with cell
proteins regulate their final folding, necessary for
optimal functionality. They are present in blood
platelets and act as signaling scaffold, helping regu-
late platelet adhesion and spreading *via* modulation
of protein phosphatase activity [50]. These proteins,
synthesized mainly in response to cell stress condi-
tions, such as temperature increase, infections, or
free oxygen radicals, play a fundamental role in cell
function and protection, minimizing damage and
preventing apoptosis [51].

We speculate that down-expression of HSP60 in
smokers could depend on chronic exposure to smoke
stress condition, a finding somehow in keeping with
that of Mateos-Cáceres et al. [19], who found, within
other proteins, two isotypes of the HSP60 signifi-
cantly down-expressed in platelets from aspirin-
resistant as compared to aspirin-sensitive cardiovas-
cular patients. Whether the decreased expression of
HSP60 is the result of cell damage or of a feedback
response remains to be established.

As the variations in protein expression observed
could also be the result of changes affecting less
expressed and non-detectable proteins, a pathway
analysis was performed to include these proteins.
IPA showed that the pathway significantly associated
with the highest number of changing proteins was
related to inflammation and apoptosis. IPA analysis
revealed a direct relation among beta-actin, HSP60,
and beta-catenin, and an indirect relation among
GpIIb, IL-6, HSP60, beta-catenin, and FXIII. All
these proteins converged to or diverged from two
crucial nodes represented by IL-6 and beta-catenin.
IL-6 is a cytokine involved in the regulation of the
acute-phase response to injury and infection [52].

HSP60, usually present in association with cyto-
skeletal frameworks, is synthesized in response to
environmental stress and mediates IL-6 production
by stimulation of inflammatory cells such as macro-
phages [53].

IL-6 indirectly modulates the level of beta-actin,
while HSP60 is directly associated to beta-catenin, a
pivotal component of the Wnt pathway, recently
reported to be present and to regulate platelet
function [54]. IPA analysis shows a relation between
beta-catenin and FXIII, suggesting that FXIII level
modulation *via* beta-catenin-Wnt pathway should be
further investigated.

Study considerations and limitations

This study, performed on a relatively limited number
of healthy subjects exposed, or not, to cigarette
smoke, shows a modulation of platelet proteome in

the smoking condition. The extent of changes is
quantitatively limited, possibly due to the cut-off of
smoking habit selected for inclusion in the study, in
addition to the young age range of the volunteers.
Notwithstanding this, it was possible to detect
significant up- or down-regulation of selected pro-
teins, due to the DIGE methodology employed.
Moreover, the use of a pI range between 3 and 10
allowed a widespread analysis of the proteome, which
could represent a starting point for a deeper detailed
study of selected proteins.

Our data show – apparently for the first time – a
significant effect of cigarette smoking on the platelet
proteome. The proteomic approach allowed to detect
platelet changes apparently unrelated to current
platelet function tests. We believe this is an impor-
tant finding suggesting that lifestyle factors, such as
cigarette smoking, must be taken into account in
human proteomic investigations, since they can
contribute to inter-individual variability.

Our results are in agreement with the notion that
cigarette smoking triggers several inflammatory reac-
tions, as most of the proteins affected by smoking can
be traced back as responding to an inflammatory
stimulus.

In conclusion, this study suggests a proteomic
approach should be used to further clarify some of
the molecular mechanisms of smoke effect on cellu-
lar systems relevant for vascular integrity and human
health.

Acknowledgments

The authors thank Dr Walter Coletta for subject
recruitment and Dr Maria Barisciano for platelet
preparation. This study was partially supported by
MIUR (Programma Triennale Ricerca, decreto
1588) and EU 6FP (FLORA Project, contract no.
CT-2005-007130).

Conflicts of interest

No conflict of interest to declare.

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