J O U RN A L OF P R O TE O MI CS 75 ( 20 1 1 ) 2 2 9–2 3 6
available at www.sciencedirect.com
www.elsevier.com/locate/jprot
Proteomic analysis of plasma samples from patients with acute
myocardial infarction identifies haptoglobin as a potential
prognostic biomarker
Benjamin Haas a,1 , Tommaso Serchi b,1 , Daniel R. Wagner c , Georges Gilsond ,
Sebastien Planchonb , Jenny Renaut b , Lucien Hoffmannb , Torsten Bohnb,⁎, Yvan Devaux a
a
Laboratory of Cardiovascular Research, Centre de Recherche Public-Santé, Luxembourg, Luxembourg
Department of Environment and Agro-biotechnologies, Centre de Recherche Public-Gabriel Lippmann, Belvaux, Luxembourg
c
Division of Cardiology, Centre Hospitalier, Luxembourg, Luxembourg
d
Laboratory of Biochemistry, Centre Hospitalier, Luxembourg, Luxembourg
b
AR TIC LE I N FO
ABS TR ACT
Article history:
Prognosis of clinical outcome following myocardial infarction is variable and difficult to predict.
Received 24 March 2011
We have analyzed the plasma proteome of thirty patients with acute myocardial infarction to
Accepted 27 June 2011
search for new prognostic biomarkers. Proteomic analyses of blood samples were performed by
Available online 13 July 2011
2-D-DiGE after plasma depletion of albumin and immunoglobulins G. New York Heart
Association (NYHA) class determined at 1-year follow-up was used to identify patients with
Keywords:
heart failure. Principal component analysis and hierarchical clustering of proteomic data
2-D-DiGE
revealed that patients could be separated into 3 groups. The 22 differentially expressed proteins
Plasma proteins
involved in this grouping were identified as haptoglobin (Hp) and respective isoforms. The 3
Myocardial infarction
groups of patients had distinct Hp isoforms: patients from group 1 had the α1–α1, patients from
Heart failure
group 2 the α2–α1, and patients from group 3 the α2–α2 genotype. This classification was also
Biomarkers
associated with different total plasma levels of Hp. The presence of the α2 genotype and low
Haptoglobin
plasma levels of Hp was associated with a higher NYHA class and therefore with a detrimental
functional outcome after myocardial infarction. A plasma level of Hp below 1.4 g/L predicted
the occurrence of heart failure (NYHA 2, 3, 4) at 1-year with 100% sensitivity.
© 2011 Elsevier B.V. All rights reserved.
1.
Introduction
In developed countries, cardiovascular diseases are a leading
cause of morbidity and mortality. They represent a major
public health problem, and their prevalence and costs are
expected to increase considerably over the next few decades
[1]. Myocardial infarction, characterized by the occlusion of a
coronary artery preventing the supply and oxygenation of
cardiac cells, is a main cardiovascular event that sets the stage
for the development of heart failure. The occurrence of heart
failure after myocardial infarction reaches epidemic proportions, affecting 3% of the adult population, with a 5-year
mortality rate of 70% [2]. Although the prognosis of myocardial
infarction patients has been significantly improved with the
⁎ Corresponding author at: Centre de Recherche Public Gabriel Lippmann, 41 rue du Brill, L-4422 Belvaux, Luxembourg. Tel.: + 352 470 261 480;
fax: +352 470 264.
E-mail address: bohn@lippmann.lu (T. Bohn).
1
Both authors contributed equally to this work and are listed in alphabetical order.
1874-3919/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.jprot.2011.06.028
230
J O U RN A L OF P R O TE O MI CS 7 5 (2 0 1 1 ) 2 2 9–2 3 6
use of reperfusion therapies, more than 60% of these patients
still develop heart failure during the 6 years following
myocardial infarction [2,3]. Early and accurate identification
of patients prone to develop heart failure after myocardial
infarction would significantly reduce the incidence of heart
failure as this disease is potentially preventable. However, the
prognostic of heart failure is still difficult to establish and
would certainly benefit from the discovery of new biomarkers.
The development of heart failure after myocardial infarction is consecutive to a complex pathophysiological phenomenon called remodeling. This process mainly affects the
left ventricle and is dictated by alterations in the regulation
of inflammation, turnover of the cardiac extracellular
matrix, angiogenesis, and cell survival. These biological
processes are mediated and controlled by a plethora of
molecules. For instance, during remodeling, the turnover of
the extracellular matrix is accelerated by degradation of
structural components by members of the matrix metalloproteinase family [4]. We and others reported that matrix
metalloproteinase 9 (MMP9) not only contributes to left
ventricular remodeling but also constitutes a prognostic
indicator of cardiac dysfunction in myocardial infarction
patients [5–7].
The complex and multi-facial features of left ventricular
remodeling urged researchers to move from the classical
“single target analysis” to a more global analysis of large sets
of “omics” data to screen, identify and characterize new
potential biomarkers. Combining transcriptomic and interactomic data, we previously identified potential sets of new
biomarkers of left ventricular dysfunction and heart failure
[8–11]. In the present study, we explored the plasma
proteome and, using 2-dimensional difference in-gel electrophoresis, we identified haptoglobin (Hp) as a potential
predictor of outcome after myocardial infarction.
2.
Materials and methods
2.1.
Patients
30 patients enrolled in the Luxembourg acute myocardial
infarction registry were included in this study. Patients were
of Caucasian origin, with no diabetes (fasting blood sugar
level < 126 mg/dL) or prior myocardial infarction. Clinical
characteristics are shown in Table 1. Diagnosis of acute
myocardial infarction was obtained by the presence of chest
pain < 12 h, positive cardiac enzymes and significant STsegment elevation. All patients were treated with primary
percutaneous coronary intervention. Left ventricular ejection
fraction (EF) determined at 4-months and 1-year follow-up by
echocardiography was used to evaluate left ventricular
dysfunction. New York Heart Association (NYHA) class was
used to evaluate heart failure, class 1 meaning no symptoms,
class 2 symptoms with moderate exercise, class 3 symptoms
with mild exercise and class 4 symptoms at rest. All patients
signed a written informed consent and the protocol was
approved by the local ethical committee.
Table 1 – Clinical characteristics of myocardial infarction
patients.
Age, y (mean ± SD)
Male, n (%)
Body mass index (mean ± SD)
Serum markers (mean ± SD)
CPK (units/L)
TnT (ng/mL)
hsCRP (ng/mL)
Cardiovascular history, n (%)
Prior myocardial infarction
CABG
PTCA
Diabetes
Hypertension
Hypercholesterolemia
Tobacco
Medications, n (%)
Beta-blockers
Calcium antagonists
Nitrates
ACE inhibitors
Statins
Angiotensin inhibitors
54
30
26
10
100%
4
2412
6.0
14.8
1642
4.3
14.8
0
1
29
0
8
7
18
0%
3%
97%
0%
27%
23%
60%
29
0
3
20
26
0
87%
0%
10%
67%
87%
0%
All myocardial infarction patients had successful mechanical
reperfusion and stenting of the infarct artery within 12 h of chest
pain onset. All patients received aspirin, clopidogrel, heparin and
abciximab in the presence of large thrombus burden.
ACE: angiotensin converting enzyme; CABG: coronary artery bypass
grafting; CPK: creatine phosphokinase; CRP: C-reactive protein; EF:
ejection fraction; and PTCA: percutaneous transluminal coronary
angioplasty.
2.2.
Plasma collection and albumin/IgG depletion
Plasma samples were collected at presentation using the BD™
P100 blood collection system (Beckton Dickinson, Franklin Lake,
USA). These tubes were coated with spray-dried anticoagulant
(EDTA) and protein stabilizers, allowing the mechanical separation of plasma from blood cells after centrifugation at 2500×g
for 20 min. Collected plasma was depleted of human serum
albumin and immunoglobulin G using the HSA/IgG removal kit
(Sartorius Stedim Biotech, Goettingen, Germany). This depletion
allowed improving the resolution of 2D gels (Figure S1, online
supplement). Protein concentration of depleted plasma was
assessed with a BCA protein assay kit (Pierce Technology,
Rockford, USA) following the manufacturer's instructions.
Samples were stored at −80 °C until analysis.
2.3.
Two-dimensional difference in-gel electrophoresis
(2D-DiGE)
Unless stated otherwise, all materials were from GE Healthcare (Uppsala, Sweden). Albumin and IgG depleted plasma
samples were separated by 2D-DiGE following an adapted
protocol as described by Lasserre et al. [12]. 30 μg of plasma
proteins was used for each sample. Prior to analysis, the pH of
plasma samples was adjusted to 8.5 with 3 M Tris. Plasma
samples were then randomly labeled with either Cy3 or Cy5
dye. A pool of equal volumes of plasma samples from each
subject was generated and used as an internal standard to
J O U RN A L OF P R O TE O MI CS 75 ( 20 1 1 ) 2 2 9–2 3 6
correct for potential uneven loading and electrophoresis
conditions. For each gel, 30 μg of proteins from this pool was
labeled with Cy2 dye. Labeling was achieved by the minimal
labeling process with 240 pmol of dye for 30 min on ice and in
the dark. The labeling reaction was stopped with 1 μl of 10 mM
lysine and incubation was continued for 10 min on ice in the
dark. Then, one Cy3-labeled and one Cy5-labeled experimental sample were combined with the Cy2-labeled internal
standard, and the volume was adjusted to 450 μL by addition
of sample buffer [7 M urea, 2 M thiourea, 0.5% CHAPS (3-[(3cholamidopropyl)dimethylammonio]-1-propanesulfonate)
and traces of bromophenol blue]. 9 μL of Bio-Lyte pH 3–10
ampholyte buffer (Bio-Rad, Nazareth-Eke, Belgium) and 2.7 μl
of destreak reagent were then added to each tube. Samples
were loaded onto an Immobiline DryStrip (24 cm, pH 3–10 nonlinear, BioRad) and incubated overnight at room temperature
to achieve optimal passive rehydration of the strip and loading
of the samples. Proteins were then subjected to isoelectric
focusing carried out on an IPGphor III at 20 °C. Mineral oil was
added on the strips to prevent evaporation. The voltage was
increased stepwise from 30 to 10,000 V during the first 21 h
and then stabilized at 10,000 V for 8 h (about 120 kVh of total
current applied to each strip). Following isoelectric focusing,
strips were equilibrated for 15 min in equilibration buffer
(2Dgel DALT, Gel Company, Tübingen, Germany) containing
urea and 2% DTT, and then for another 15 min in the same
buffer but containing 1% iodoacetamide instead of DTT. After
equilibration, strips were loaded on precast gels (2Dgel DALT
NF 12.5%, Gel Company) for second dimension separation
using an Ettan DALT II (GE Healthcare) system with 0.5 W/gel
for 2 h and then 2.5 W/gel for 14 h at 25 °C.
Gels were scanned using a Typhoon 9400 (GE Healthcare)
scanner with a spatial resolution of 100 μm and analyzed by
the DeCyder 2D Differential Analysis v.7.0 software. Protein
images were produced by excitation at 488 nm, 532 nm, and
633 nm (Cy2, Cy3 and Cy5, respectively) and emission at
520 nm, 610 nm and 670 nm (Cy2, Cy3 and Cy5, respectively).
Produced maps were analyzed by multivariate tests and
grouped on the basis of their common features. Spots
responsible for the classification of patients were selected as
proteins of interest. Selected spots were located on a gel and a
“picking list” was generated. Spot picking, digestion and
loading onto MALDI disposable target plates (MALDI-Tof-Tof
4800, Applied Biosystems, Foster City, CA) was automatically
performed using the Ettan Spot Handling Workstation as
described [13]. Peptide mass fingerprint and MS/MS analyses
were carried out using the Applied Biosystems MALDI-Tof-Tof
4800 Proteomics Analyser. Calibration was carried out with the
peptide mass calibration kit 4700 (Applied Biosystems). Proteins were identified by searching against the SWISSPROT
database (version 20100924 with 519538 sequences) with
“Homo sapiens” as taxonomy, using GPS Explorer Software
v3.6 (Applied Biosystems) including MASCOT (Matrix Science,
www.matrixscience.com, London, UK). All searches were
carried out allowing for a mass window of 150 ppm for the
precursor mass and 0.75 Da for fragment ion masses. The
search parameters allowed for carboxyamidomethylation of
cysteine as fixed modification. Oxidation of methionine and
oxidation of tryptophan (single oxidation, double oxidation
and kynurenin) were set as variable modifications. Proteins
231
with probability-based MOWSE scores (P < 0.01) were considered to be positively identified.
2.4.
Biochemical assays
Creatine phosphokinase (CPK) activity was measured with a
Roche IFCC recommended method on a Cobas c501 instrument (Roche, Prophac, Luxembourg). Troponine T (TnT) was
measured with a 4th generation assay from Roche that was
performed on a Cobas e601 equipment (Roche), 0.01 μg/L being
the lower detection limit and 0.03 μg/L being the TnT
concentration that is reproducibly measured with a coefficient
of variation below 10%. Hp was measured with the Tina-quant
v2 kit from Roche on a Cobas c501 equipment (Roche). This kit
recognizes all three phenotypes of Hp (α1–α1, α1–α2, α2–α2)
and has a lower detection limit of 0.1 g/L. Normal values range
between 0.3 and 2 g/L. MMP9 and tissue inhibitor of matrix
metalloproteinase 1 (TIMP1) were measured by enzyme-linked
immunosorbent assay from R&D Systems (Oxon, UK). MMP9
assay (cat # DMP900) detects both active and pro-MMP9 with a
sensitivity of 0.156 ng/mL. TIMP1 assay (cat # DTM100) has a
sensitivity of 0.08 ng/mL.
2.5.
Statistical analysis
All data were subjected to the Shapiro–Wilk test for normality
between performing comparisons. Comparisons between two
groups were performed by t-tests or Mann–Whitney tests.
One-way ANOVA was used for multiple group comparisons.
Correlations between plasma markers were assessed with the
Pearson test. The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the prognostic
value of single biomarkers. All tests were two-sided and a P
value < 0.05 was considered as significant. Statistical analyses
were performed with the SigmaPlot v11.0 software.
Investigation of clinical factors linked to left ventricular
dysfunction was performed using a linear mixed model with
ejection fraction as the observed (dependent) variable, and
anthropometric data and clinical parameters as the independent variables (fixed or covariate), followed by Bonferroni
post-hoc tests if appropriate.
For proteomic analysis, separated spots were subjected to
principal component analysis (PCA) and hierarchical clustering to
highlight protein patterns and to group samples based on relevant
biological patterns. Analyses were conducted blindly of any
knowledge of patient characteristics. Prior to analysis, the protein
set was filtered for the presence of the protein spot in at least 50%
of the spot maps and for an ANOVA with a P value≤0.05. All
proteomic analyses were carried out using the DeCyder 2D
Differential Analysis program v.7.0. (GE Healthcare).
3.
Results
3.1.
Clinical data and patient follow-up
All 30 patients enrolled in this study were 1-year survivors
after ST-elevation acute myocardial infarction treated by
232
J O U RN A L OF P R O TE O MI CS 7 5 (2 0 1 1 ) 2 2 9–2 3 6
mechanical reperfusion. See Table 1 for clinical characteristics
and medications. Diagnosis was based on ECG changes and
elevation of CPK and TnT. None of them had a history of
myocardial infarction, stroke or diabetes. One fourth of the
population had hypertension and hypercholesterolemia and
60% were smokers.
The mean EF measured at 4-months follow-up with
echocardiography was 51 ± 12%, varying between 40 and 70%
(Table 1). This EF did not significantly change 1 year after
myocardial infarction (51 ± 4%). Following linear mixed model
analysis, among the factors investigated, only smoking habit
was significantly associated with the EF measured at 4months follow-up (P = 0.014). Other parameters, including
age, body mass index, CPK, and CRP (C-reactive protein) had
no significant correlation with EF. At 4-months, 17 patients
were in NYHA class 1, 5 patients in HYNA class 2 and 1 patient
in NYHA class 3. The NYHA class did not change between 4months and 1 year follow-up.
3.2.
Proteomic investigation
After separation, gels were acquired with a Typhoon 9400
instrument (GE Healthcare) and spot detection (mean number
± SD of spot: 2067 ± 213) and matching were performed using
the DeCyder 2D Differential Analysis program v.7.0. As no
grouping of the patients was conducted prior to proteomic
analyses, results were analyzed in a blinded manner using
multivariate tests (PCA and hierarchical clustering). PCA
clearly showed that samples could be divided into 3 groups
(Fig. 1A). The cumulative variance of 90% was reached with
component 2. Group 2 and group 3 were relatively close to
each other, while group 1 was slightly more separated from
the other two groups. The same classification was obtained
following hierarchical clustering, where the 3 groups formed
single clusters, with groups 1 and 2 being closer to each other
than group 3 (Fig. 1B). The analyses revealed that 22
differentially expressed proteins were involved in this classification (Fig. 1B). The spots corresponding to these 22 proteins
were used to create a picking list. Mass spectrometry
identified all of these spots as Hp and respective isoforms
(Table 2). Due to the high homology of Hp isoforms, it was
difficult to distinguish them based on mass spectrometry
alone. For this reason, identification of each patient's Hp
isoform was further derived on the basis of the molecular
weight of the isoforms: 9 kDa for Hp α1, 17 kDa for Hp α2 and
40 kDa for Hp β [14]. Attribution of the picked spots to their
respective isoforms is shown in Fig. 2.
3.3.
Haptoglobin isoforms and Hp plasma levels
The grouping of patients by PCA and hierarchical clustering
was clearly associated with Hp isoforms: patients from group 1
had the α1–α1 genotype, patients from group 2 the α2–α1
genotype and patients from group 3 the α2–α2 genotype. This
Fig. 1 – Grouping of acute MI patients. Plasma samples from the 30 patients of the test cohort were processed by 2D-DiGE.
Principal component analysis (A) and hierarchical clustering (B) identified 3 groups of patients. In PCA, the cumulative variance
of 90% was reached with component 2.
Table 2 – List of 22 proteins responsible for the classification of patients into 3 distinct groups.
Gene name
1005
1006
1035
1036
1038
1623
1626
1627
1631
1632
1633
1637
1639
1641
1642
1651
1656
1661
1877
1886
HPT_HUMAN
HPT_HUMAN
HPT_HUMAN
HPT_HUMAN
HPT_HUMAN
HPT_HUMAN
HPT_HUMAN
HPT_HUMAN
HPT_HUMAN
HPT_HUMAN
HPT_HUMAN
HPT_HUMAN
HPT_HUMAN
HPT_HUMAN
HPT_HUMAN
HPT_HUMAN
HPT_HUMAN
HPT_HUMAN
HPT_HUMAN
HPTR_HUMAN
1890
2507
HPT_HUMAN
HPT_HUMAN
Protein
name
Haptoglobin
Haptoglobin
Haptoglobin
Haptoglobin
Haptoglobin
Haptoglobin
Haptoglobin
Haptoglobin
Haptoglobin
Haptoglobin
Haptoglobin
Haptoglobin
Haptoglobin
Haptoglobin
Haptoglobin
Haptoglobin
Haptoglobin
Haptoglobin
Haptoglobin
Haptoglobin
related
protein
Haptoglobin
Haptoglobin
Haptoglobin
isoform
Hp
Hp
Hp
Hp
Hp
Hp
Hp
Hp
Hp
Hp
Hp
Hp
Hp
Hp
Hp
Hp
Hp
Hp
Hp
Hp
Hp α1
Hp β
β
β
β
β
β
α2
α2
α2
α2
α2
α2
α2
α2
α2
α2
α2
α2
α2
α1
α1
Acc
no.
Theor. Theor. MOWSE Identification Queries
Mr
pI
score
P-value
matched
Seq
cov
%
Av ratio
G2 vs G1
45861
45861
45861
45861
45861
45861
45861
45861
45861
45861
45861
45861
45861
45861
45861
45861
45861
45861
45861
39518
6.13
6.13
6.13
6.13
6.13
6.13
6.13
6.13
6.13
6.13
6.13
6.13
6.13
6.13
6.13
6.13
6.13
6.13
6.13
6.63
465
448
353
392
405
245
359
155
103
139
238
388
504
266
328
308
292
254
270
136
30
27
32
28
27
15
18
15
10
16
15
17
18
15
17
16
16
14
17
9
47%
35%
33%
42%
40%
23%
27%
25%
23%
25%
23%
27%
23%
23%
27%
33%
23%
23%
28%
21%
− 1.91
− 1.61
1.87
1.86
2.19
2.13
8.27
2.57
2.2
20.04
5.72
8.79
13.56
2.24
3.94
4.51
5.4
3.5
− 2.84
− 1.76
8.14E-03
0.0176
8.39E-04
1.41E-03
1.50E-03
0.0133
4.68E-07
9.36E-04
8.59E-03
1.22E-09
2.36E-08
3.29E-08
5.83E-10
2.24E-03
1.89E-04
3.49E-06
2.24E-06
9.73E-06
2.96E-03
0.0102
−3.43
−2.83
4.82
4.61
4.66
3.65
13.25
6.72
4.51
32.81
14.27
14.84
25.86
2.79
5.96
5.28
8.47
7.22
−14
−5.91
1.63E-06
3.60E-06
3.04E-05
1.76E-04
3.77E-04
1.52E-04
7.54E-13
1.89E-08
7.60E-06
1.75E-12
3.12E-11
7.40E-11
2.62E-13
1.17E-03
9.31E-06
2.98E-07
1.50E-08
3.10E-08
3.78E-08
8.24E-10
− 1.8
− 1.76
2.57
2.47
2.12
1.71
1.6
2.61
2.05
1.64
2.5
1.69
1.91
1.24
1.51
1.17
1.57
2.06
− 4.93
− 3.36
3.10E-03
5.95E-03
1.63E-03
3.14E-03
0.0307
0.0276
4.86E-03
1.00E-05
1.05E-03
7.47E-03
9.86E-06
2.25E-03
1.04E-03
0.491
0.0535
0.43
0.0238
4.29E-04
8.00E-06
4.11E-07
P00738 45861
P00738 45861
6.13
6.13
72
278
9
18
14%
27%
− 2.19
2.08
2.85E-04
6.96E-04
−3.33
3.82
8.15E-05
9.70E-04
− 1.52
1.84
0.037
0.0665
P00738
P00738
P00738
P00738
P00738
P00738
P00738
P00738
P00738
P00738
P00738
P00738
P00738
P00738
P00738
P00738
P00738
P00738
P00738
P00739
6.40E-43
3.20E-41
1.00E-31
1.30E-35
6.40E-37
6.40E-21
2.60E-32
6.40E-12
1.00E-06
2.60E-10
3.20E-20
3.20E-35
8.10E-47
5.10E-23
3.20E-29
3.20E-27
1.30E-25
8.10E-22
2.00E-23
5.10E-10
0.0012
3.20E-24
t-Test
Av ratio
t-Test
Av ratio
t-Test
G2 vs G1 G3 vs G1 G3 vs G1 G3 vs G2 G3 vs G2
J O U RN A L OF P R O TE O MI CS 75 ( 20 1 1 ) 2 2 9–2 3 6
Spot
no.
Each protein is reported with the gene name, the protein name, the attribution to the specific Hp isoform, Swiss-prot accession number, the theoretical mass and isoelectric point, the MOWSE score of
the identification, the P-value of the identification, number of queries matched with mass finger print, percentage of sequence coverage and the fold change for each group pair, with the respective
P-value following t-tests.
233
234
J O U RN A L OF P R O TE O MI CS 7 5 (2 0 1 1 ) 2 2 9–2 3 6
Fig. 3 – Plasma Hp levels in 30 acute MI patients.
(A) Frequency plot. The dotted line indicates the 2 g/L
threshold for abnormally high Hb values. (B) The 3 groups of
patients of the test cohort showed different Hp plasma levels.
Means ± 95% confidence intervals are shown.
3.4.
Association between Hp and clinical outcome
Outcome after myocardial infarction was evaluated by the
NYHA class. We observed a distinct distribution of NYHA
classes among the 3 groups: all patients from group 1 were in
NYHA class 1, 50% of patients of group 2 were in NYHA class 2,
and 10% of patients from group 3 were in NYHA classes 2 and
3. No patients were in NYHA class 4 (Fig. 4A). ROC curve
analysis revealed an overall modest ability (AUC = 0.63) of Hp
to predict the occurrence of heart failure (NYHA score > 2).
Interestingly, a Hp level < 1.4 g/L predicted heart failure with a
sensitivity of 100% (Fig. 4B). Therefore, both low levels of Hp
and presence of the α2 isoform appear to be associated with a
worse functional outcome after myocardial infarction.
4.
Fig. 2 – Pictures of 2D-DiGE gels from plasma samples of acute
MI patients. (A) Representative gel of a patient of group 1
showing the presence of α1 isoforms. (B) Representative gel
of a patient of group 2 showing the presence of α1 and α2
isoforms. (C) Representative gel of a patient of group 3
showing the presence of α2 isoforms.
grouping was however not statistically significantly associated with infarct severity and myocardial damage (as assessed
by CPK and TnT levels); left ventricular function (as assessed
by ejection fraction); markers of extracellular matrix turnover
(MMP9, TIMP1); nor with markers of inflammation (WBC
counts and CRP levels).
We investigated whether Hp plasma levels were different
between the 3 groups. Mean level of Hp was 1.52 g/L. 4
patients had a Hp level above the upper limit of normal
(2 g/L, Fig. 3A). Patients from the 3 groups determined by
PCA and hierarchical clustering had distinct levels of Hp,
group 1 having the highest level and group 3 the lowest level
(Fig. 3B).
Discussion
In this study, we tested the hypothesis that the plasma
proteome is a source of prognostic biomarkers after acute
myocardial infarction. The main finding of this proteomic
Fig. 4 – Association between Hp and NYHA score.
(A) Distribution of patients from the 3 groups in NYHA
classes. All patients of group 1 were in NYHA class 1, half of
patients of group 2 were in NYHA class 2, and 10% of patients
from group 3 were in NYHA classes 2 and 3. No patient was in
NYHA class 4. (B) Receiver operating characteristic curve and
area under the ROC curve (AUC) showing that Hp is an overall
modest predictor of 1-year NYHA class.
J O U RN A L OF P R O TE O MI CS 75 ( 20 1 1 ) 2 2 9–2 3 6
investigation is the identification of Hp as a potential predictor
of outcome following acute myocardial infarction in humans.
Major advances in proteomic technologies achieved during
the last few decades attracted researchers from a broad range
of biomedical fields including the cardiovascular field [15]. The
usefulness of studying the plasma proteome to identify
biomarkers and therapeutic targets of cardiac dysfunction
has been reported [16–18]. Proteomic analysis of plasma
proteins allows the study of primary effectors of cellular
function during pathogenesis [16–18]. For instance, proteomic
analyses demonstrated that several proteins were present at
high concentration in oxidized form in the plasma of heart
failure patients [19]. However, the number of proteomic
studies dedicated to biomarker discovery in heart failure is
comparatively still limited.
The acute phase protein Hp has been described in all
mammalian species. Human Hp is composed of four subunits
(2 α subunits and 2 β subunits) linked by disulfide bridges [14].
In humans, but not in all other species such as in mice, there
exists a functional polymorphism in the gene encoding for the
α subunits, which can generate two possible isoforms: α1 and
α2 [20]. The two alleles have different molecular weights
(9 kDa for α1 and 17 kDa for α2), and different binding affinity
for free hemoglobin (α1 higher than α2) [21]. In humans, 3
genotypes are observed: α1–α1, α2–α1 and α2–α2, with an
expected distribution in the European population of approx.
15%, 48% and 37%, respectively [22,23]. In our cohort, the
distribution was as follows: 23%, 37% and 40% for α1–α1, α2–α1
and α2–α2 genotypes, respectively. Hp expression is known to
be modulated during chronic and acute inflammation [21].
Thus, Hp has been associated with several diseases involving
an inflammatory component, especially type II diabetes [24].
Accordingly, there is a strong relationship between Hp
genotype and the outcome of patients with pre-existing
diabetes [25]. Furthermore, in patients with type II diabetes,
the risk of developing cardiovascular disease is dependent on
the Hp genotype, and is highest for Hp α2–α2, moderately high
for Hp α1–α2 and lowest for Hp α1–α1 [26]. In diabetic mice, the
Hp isoform was associated with cardiac remodeling and
mortality after myocardial infarction [27]. This observation
in mice is consistent with the present study in humans. In
addition, our study suggests that the association between Hp
isoforms and outcome after myocardial infarction could also
be valid in non-diabetic patients.
Analysis of proteomic results by PCA and hierarchical
clustering, followed by mass spectrometry identification of
candidate proteins, revealed that the 30 patients enrolled in this
study could be distinguished through their Hp genotpye. We
observed that the distribution of Hp genotype was not
associated with infarct severity, expression of markers of
extracellular matrix turnover, or mortality. However, we found
a correlation between Hp genotype and NYHA class at 1-year
follow-up. In addition, our results suggest that not only the type
of Hp isoform but also total Hp plasma levels correlate with the
occurrence of heart failure after myocardial infarction. The
ability of Hp to scavenge free hemoglobin is not the same for the
two isoforms, with Hp-α1 being more efficient than Hp-α2 [21].
Thus, the expression of the genotype α2–α2, together with the
reduced levels of Hp detected in patients belonging to group 3, as
highlighted by the PCA analysis, could result in increased
235
cardiac tissue damage during myocardial infarction and would
thus be associated with a worse outcome.
Haptoglobin is an independent prognostic marker in
several diseases. A large clinical study by Holme et al. has
shown that elevated Hp plasma levels can be used to predict
the risk of cardiac disease [28]. This observation appears
contradictory to the results reported in our study, where low
levels of Hp measured at the time of presentation in acute
myocardial infarction patients predicted the development of
heart failure as attested by NYHA class. However, the study by
Holme did not specifically address the prognostic value of Hp
in patients with acute myocardial infarction, but rather
considered Hp as a risk factor for developing cardiovascular
diseases in a population of healthy volunteers. Our study
suggests for the first time that that Hp genotype and levels at
the moment of the myocardial infarction may be prognostic
biomarkers of heart failure following acute myocardial
infarction. It can be speculated from these data that Hp is
detrimental when elevated in healthy volunteers but may be
beneficial after acute myocardial infarction.
In addition to its potential value as a biomarker, Hp can be
assumed not to merely be a passive bystander of disease. This
may be related to its main function, which is the binding and
scavenging of free hemoglobin. Hemoglobin binds and transports oxygen to the tissues. However, hemoglobin is highly
toxic when present in a free state, unbound within the
erythrocytes, e.g. in the blood plasma [29]. Owing to its
lipophilic nature, hemoglobin has the potential to disrupt
cell membrane bilayers and, as iron is present in its prostatic
group, can lead to the formation of reactive oxygen species
and to tissue damage [30]. The ability of Hp to bind free
hemoglobin and to form a complex that is rapidly captured by
monocytes through the CD163 receptor and degraded in the
spleen and liver, thus contributes to reduce the potential of
free hemoglobin to trigger oxidative tissue damage [31]. In
addition, Hp modulates the inflammatory response [21] and
may therefore contribute to the inflammatory component of
left ventricular remodeling. Another potential functional role
of Hp in left ventricular remodeling may be its ability to
interact with MMP9 and MMP2 [32], which are of primary
importance for matrix degradation and tissue remodeling.
This proof-of-concept study has some limitations, the
main coming from the low number of patients enrolled.
Also, the NYHA class may not be the best indicator of heart
failure since it may be subject to bias from both patient and
clinician. More objective indicators of left ventricular dysfunction will have to be considered in future validation studies.
These include for instance the change of left ventricular
volumes between discharge and follow-up. Furthermore,
assessment of cardiac function with magnetic resonance
imaging technique will certainly be of added informative
value compared with the traditional echocardiography technique used in the present study.
5.
Conclusions
In conclusion, proteomic analysis of plasma proteins identified haptoglobin as potential prognostic biomarker after acute
236
J O U RN A L OF P R O TE O MI CS 7 5 (2 0 1 1 ) 2 2 9–2 3 6
myocardial infarction. This observation has to be studied in
larger populations for further validation. If confirmed, this
biomarker, which can easily be measured, may become
clinically important for the prognosis and care of infarction
subjects.
Acknowledgments
We thank Malou Gloesener, Loredana Jacobs, Céline Jeanty,
Christelle Nicolas, Bernadette Leners and Laurent Quennery
for expert technical assistance. The help of Celine Leclercq
for proteomic analyses is acknowledged. This study was
funded by the National Research Fund of Luxembourg (grant
# C08/BM/08). B.H. was recipient of a fellowship from the
National Research Fund of Luxemburg (fellowship # TR-PhD
BFR 08-082).
Appendix A. Supplementary data
Supplementary data to this article can be found online at
doi:10.1016/j.jprot.2011.06.028.
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