APPENDIX
METHODS
Population
We enrolled 20 patients admitted to our Coronary Care Unit with a diagnosis of non-ST-elevation
myocardial infarction (NSTEMI). We defined NSTEMI as detection of the rise and fall of cardiac
troponin T (cTnT) and at least one of the following: angina, ST-segment depression, or T-wave
inversion. As control groups, we enrolled 20 patients with chronic stable angina (SA) admitted to
our cardiovascular ward to undergo coronary angiography because of severe symptoms (CCS class
III or IV) and/or high-risk abnormalities on noninvasive testing, and 20 individuals aged >50 years
at intermediate risk for cardiovascular diseases, without a previous history and/or current symptoms
or signs of ischemic heart disease (controls).
Patients enrolled in the SA group had symptoms of stable effort angina lasting more than 12
months, angiographically confirmed coronary artery disease, no previous acute coronary events, and
no overt ischemic episodes during the previous 48 hours.
We screened controls in our outpatients clinic among subjects at intermediate risk for
cardiovascular diseases. Controls never had symptoms of ischemic heart disease. To exclude intercurrent signs of ischemic heart disease, a complete cardiovascular screening was performed,
including a standard 12-lead EKG, a treadmill EKG stress test, an echocardiogram, and an echocolor Doppler of carotid arteries.
We excluded patients with: 1) age >80 years; 2) evidence of inflammatory or infectious
diseases, malignancies, immunologic, or hematological disorders; 3) allergic disorders; 4) ejection
fraction <40%; 5) treatment with anti-inflammatory drugs other than low-dose aspirin. In all
patients, we evaluated demographic data, classical cardiovascular risk factors, history of previous
ACS, previous coronary revascularization procedures, ventricular function, and medical treatment.
All NSTEMI and SA patients underwent coronary angiography; in NSTEMI, coronary angiography
was performed within 72 hours after admission; in-hospital revascularization procedures were
recorded. After 1-year follow-up, we re-assessed 10 NSTEMI patients who had not experienced any
recurrence of acute coronary events and 10 SA patients free of any symptom, matched for age,
gender, and treatment.
All patients gave their written informed consent. The Ethics Committee of the Catholic
University of Rome approved the study.
Characteristics of the study population are reported in Online Table 1.
Blood sampling
We drew venous blood samples at the time of patient enrollment. In ACS, we collected venous
blood samples within 24 hours from symptoms onset (11.5 ± 5.7 h). We analyzed total and
differential white blood cell counts with a Bayer H*3-hematology analyzer using automated
cytochemistry in flow. Coded serum samples were stored at 70°C and analyzed for high-sensitivity
C-reactive protein (hs-CRP) in a single batch at the end of the study by laboratory staff unaware of
the clinical data. In ACS patients, we determined serum cTnT at the time of hospital admission as
routine measurement. All categorization and management of patients were independent of these
results.
Measurements of hs-CRP and cTnT
We measured hs-CRP using a high-sensitivity latex-enhanced immunonephelometric assay
(Latex/BN II, Dade Behring, Marburg, Germany). The working range of the assay was 0.175 to
1100 mg/L, and the coefficient of variation was <5%. The median normal value for hs-CRP was 0.8
mg/L, with 90% of normal values <3 mg/L. We measured cTnT by the third-generation cTnT assay
on an Elecsys 2010 (Roche Diagnostics, Mannheim, Germany). The minimum detectable
concentration was 0.01 ng/mL (99th percentile in healthy individuals). cTnT was <0.01 in all
controls and SA patients.
Phosphoflow analysis
We obtained peripheral blood mononuclear cells (PBMCs) from whole blood samples by standard
gradient centrifugation over Ficoll-Hypaque (GE Healthcare Bio-Sciences, Piscataway, New
Jersey). PBMCs were starved overnight with RPMI 1640 1% FCS at concentration of 1×106
cells/ml. Then, 1×106 PBMCs/ml were stimulated with anti-CD3/CD28 mAb (1 μg/ml each) for
TCR stimulation; goat anti-mouse IgG (BD Bioscience, Mountain View, California) was added for
induction of cross-linking. We started stimulation by transferring the cells to 37°C water bath. After
5ʹ, PBMCs were fixed and permeabilized in FIX/PERM buffer (eBioscience, San Diego, California)
and finally stained for CD4 FITC (Beckman Coulter, Brea, California) and for one of the following:
Zap70-PE (pY-292), ZAP70-PE (pY-319) or CREB-PE (pS-133) (all, BD Bioscience, Mountain
View California). To analyze the early phosphotyrosine levels, PBMCs were stained with CD4 PECy5 and phosphotyrosine pY100 Alexa Fluor 488 (Beckman Coulter, Brea California). Cells were
stained in PBS 5% FCS. Before staining, mouse normal Ig (Invitrogen, Carlsbard California) was
added to neutralize the goat anti-mouse IgG used to cross-link the anti-CD3/CD28 mAb and to
reduce the non-specific binding.
At least 100,000 events were acquired. Nonspecific staining with isotype-matched control
mAb was <1%; the intra- and inter-assay variability was <10%. We conducted FACS analysis with
FC 500 (Beckman Coulter, Brea, California) and we analyzed the data with Kaluza software
(Beckman Coulter, Brea, California).
Cell cultures and immunophenotypic analysis
We purified CD4+ T cells from PBMCs by sorting with CD4+ magnetic beads (Miltenyi Biotec,
Auburn, California). We assessed the purity of cell preparations by cytofluorometric staining. Cells
were cultured in RPMI 1640 medium supplemented with heat-inactivated 10% LPS-screened FCS,
2 mM L-glutamine, 100 U/ml penicillin, 100 μg/ml streptomycin (all from Lonza, Basil,
Switzerland). To assess the TCR-induced Treg generation, cells were stimulated with plate bound
anti-CD3 mAb and CD28 mAb (1 μg/ml each) in the presence or absence of okadaic acid (2 nM);
goat anti-mouse IgG (BD Bioscience, Mountain View, California) was added for induction of crosslinking. After six days, cells were fixed and permeabilized in FIX/PERM buffer (eBioscience San
Diego, California) and stained with anti CD4 FITC (Beckman Coulter, Brea, CA), Foxp3 PE
(eBioscience San Diego, California), CD25 PE-Cy5 and CD127 PE-Cy7 (Beckman Coulter, Brea,
CA).
For PTPN22 expression, PBMCs were stained with CD4+ PE-Cy5 (Beckman Coulter, Brea,
California), PTPN22 Rabbit (Abcam, Cambridge, United Kingdom) and Alexa Fluor Mouse antiRabbit 488 (Invitrogen, Paisley, United Kingdom). We assessed PTPN22 relative mean
fluorescence intensity (MFI) by dividing PTPN22 MFI with isotype control MFI.
Immunofluorescence microscopy
To analyze CREB intranuclear localization, cells were stimulated for 5ʹ, fixed and permeabilized as
described above, blocked in PBS 20% FCS for 20ʹ and stained with Mouse-pCREB (S-133) (Santa
Cruz Biotechnology, Dallas, Texas) and antibody labeled with Alexa Fluor Rabbit Anti-Mouse 488
(Invitrogen, Paisley, United Kingdom). All images were captured by Leica DFC 420C and analyzed
by LAS software (Leica Microsystems, Heerbrugg, Switzerland). Data are presented as MFI means
± SEM with respect to untreated sample, from a minimum of 50 cells.
Quantitative Real-Time PCR Analysis
We extracted total RNA from PBMC using the RNeasy Plus Mini Kit (Qiagen GmbH, Hilden,
Germany) according to the indications provided by the company. A small aliquot of total RNA
obtained (1 μL) was subjected to qualitative and quantitative control by using the microdrop
(Thermo Fisher Scientific, Waltham, Massachusetts). We determined the qualitative and
quantitative assessments of the individual samples using dedicated software. Total RNA was
reverse-transcribed into cDNA by using iScript RT (Bio-Rad, Hercules, California). SYBR Green
gene expression assays were performed in triplicate according to the manufacturer’s instructions
using the iQ SYBR Green Supermix (Bio-Rad, Hercules, California) and the iQ5 Multicolor RealTime PCR Detection System (Bio-Rad, Hercules, California). For this purpose, we used the
following pairs of primers: for PTPN22: 5ʹ-AACAATATGAACTGGTCTACAAT' and 3ʹTGCTTGGAGAGTGTGATT; for Foxp3: 5ʹ-GAGAGGTCTGCGGCTTCCAC and 3ʹGGGCATCGGGTCCTTGTCC; for IL-2: 5ʹ- CTCTGGAGGAAGTGCTAA and 3ʹTGTTGTTTCAGATCCCTTTAG; for IL-10: 5ʹ- CAAGCCTTGTCTGAGATG and 3ʹTTCACAGGGAAGAAATCG; for TFG-β: 5ʹ-GAATGAGCGCCCGGTGTCCC and 3ʹTCCAAGCCAGCGACGCAGTG. Expression levels were normalized to beta 2 microglobulin: 5ʹAGGACTGGTCTTTCTATCTCTTGT and 3ʹ-ACCTCCATGATGCTGCTTACA. We calculate
relative gene expression, presented as percentage of the relevant baseline, using the 2-∆∆CT
(comparative threshold) method.
Chromatin immunoprecipitation (ChIP) assays
We performed ChIP assay on CD4+ isolated T-cells (stimulated or not with anti-CD3/anti-CD28
crosslink) with EZ magna-ChIP kit from Millipore (Billerica, Massachusetts) according to the
manufacturer’s instructions. CREB immunoprecipitations were carried out with 4 µg of anti-PCREB Ser-133. Purified anti-rabbit IgG was used as a non-specific precipitator and control (Merck
Millipore, Darmstadt, Germany). The final DNA pellets were resuspended in ultra-pure H2O (Life
Technologies, Carlsbad, California) and subjected to quantitative real-time PCR using the following
primers: 5’- AAGAGGGATTTCACCTACAT and 3’- GAACAAGAGATGCAATTTATACTG for
IL-2; 5’ CTAAGGTGACTGCCTAAGT and 3’- GTTCTCATTCGCGTGTTC for IL-10; 5’GGTATCTGCCCTCTTCTC and 3’- TTTCTGACTGGGTTTCTCA for Foxp3; 5’GGCCCACGAGACCTCTGAGACA and 3’-GCCTTGGCGCGTGTCCTAATCT for c-Fos. We
normalized data dividing qPCR signals from the ChIP samples by the qPCR signals derived from
the input sample. Results were expressed as means ± SEM with respect to untreated sample.
Transfection
To silence PTPN22, we transfected isolated CD4+ T-cells with Accell Human SMARTpool
PTPN22 (3 μg) or negative control siRNA by using Nucleofector Kit (Amaxa, Lonza, Walkersville,
Maryland). We used a combination of 4 siRNA. The sense strands were:
UCAGGACUCUAAAAGUUAA; UCUUUAUCACUGAAUUCUC;
CCUUUGACUUUAGGACUUC; CUCUAAACACCAAAUACGU (Thermo Fisher Scientific,
Waltham, Massachusetts). Forty-eight hours after transfection, cells were harvested and PTPN22
expression was quantified as describe above. Cells were assayed for their response to CD3/CD28
stimulation.
Statistical Analysis
The continuous variables that were normally distributed, as assessed by the Shapiro-Wilk test, were
described as mean ± SEM, and analyzed with parametric tests. For comparisons among 3 groups,
we used 1-way ANOVA with Bonferroni correction. For multiple pairwise comparisons, we used 2way ANOVA for repeated measures, with Bonferroni correction. To compare two different groups,
we used an unpaired t test. To compare the means of two related samples within groups, we used a
paired-samples t test. For correlations, we used the Pearson's test. hs-CRP values that were nonnormally distributed were described as median and range, and compared with Kruskal-Wallis nonparametric ANOVA, with the Dunn’s test for comparisons among groups. Proportions were
compared using the chi-square test. A two-tailed P value <0.05 was considered statistically
significant. Statistical analysis was performed with GraphPad Prism version 5.00 for Windows
(GraphPad Software, San Diego, California) and SPSS 18.0 software (SPSS Inc., Chicago, Illinois).
We planned to use multivariate logistic regression analysis to individuate the variables
independently associated with PTPN22 expression and ZAP-70 Y292 and CREB phosphorylation.
An exploratory univariate analysis was performed to individuate the variables with a value of P ≤ 0.1
necessary to include them in the multivariate model, with age and sex as confounding variables.
None of the variables considered passed the univariate analysis; thus the multivariate logistic
regression analysis was not performed (Online Table 4).
Online Table 1. Clinical characteristics of the study population
Controls
SA
ACS
(n = 20)
(n = 20)
(n = 20)
P value
12/8
14/6
15/5
0.60
61.3 ± 11.4
66.1 ± 10.9
62.3 ± 11.1
0.19
NA
NA
11.5 ± 5.7
NA
Hypercholesterolemia, n (%)
12 (60)
13 (65)
11 (55)
0.82
Hypertension, n (%)
10 (50)
17 (85)
14 (70)
0.06
Smoke, n (%)
6 (30)
10 (50)
13 (65)
0.09
Family history of IHD, n (%)
5 (25)
6 (30)
9 (45)
0.39
Obesity, n (%)
3 (15)
3 (15)
1 (5)
0.54
Diabetes, n (%)
2 (10)
5 (25)
4 (20)
0.47
ACS, n (%)
NA
NA
4 (20)
NA
Previous PCI/CABG, n (%)
NA
NA
1 (5)
NA
6 (30)
13 (65)
15 (75)
<0.001*†
Sex (M/F)
Age (mean ± SD)
Time (h) of blood sampling from
symptom onset (mean ± SD)
Risk factors
History
Medications (at the time of blood
sampling)
Aspirin, n (%)
ACE-inhibitors/ARBs, n (%)
9 (45)
12 (60)
13 (65)
0.43
Statins, n (%)
7 (35)
11 (55)
9 (45)
0.46
β-blockers, n (%)
5 (25)
7 (35)
6 (30)
0.80
Oral antidiabetic drugs, n (%)
1 (5)
3 (15)
2 (10)
0.59
Low molecular weight heparin, n
1 (5)
1 (5)
2 (10)
0.77
Ticlopidine/clopidogrel, n (%)
0
3 (15)
4 (20)
0.13
Insulin, n (%)
0
1 (5)
1 (5)
0.61
0
0
20 (100)
NA
56 ± 10
53 ± 9
51 ± 11
0.50
Multi-vessel disease, n (%)
NA
8 (40)
9 (45)
0.76
PCI/CABG for the index event, n
NA
18 (90)
17 (85)
0.64
Total Cholesterol (mg/dL)
178 ± 51
168 ± 45
165 ± 38
0.34
LDL (mg/dL)
112 ± 39
96 ± 46
99 ± 29
0.15
HDL (mg/dL)
47 ± 13
48 ± 13
43 ± 7
0.25
Triglycerides (mg/dL)
130 ± 81
121 ± 57
129 ± 66
0.82
2 ± 0.5
2.1 ± 0.7
2.3 ± 1.2
0.47
(%)
In-hospital management
cTnT > 0.01 ng/mL, n (%)
LVEF (mean ± SD)
(%)
Laboratory assay (mean ± SD)
Lymphocyte count (109/L)
hs-CRP (mg/L), median (range)
0.9
0.6
6.1
(0.3–4.2)
(0.4-9.5)
(0.6-55.5)
<0.001‡
Online Table 2. Biological parameters.
Controls
SA
ACS
P by ANOVA
2ʹ
4.457± 1.333
4.256± 1.188
3.196± 0.960
<0.001*
5ʹ
3.271± 0.960
3.264± 0.698
2.493± 1.049
0.011†
10ʹ
4.457± 0.517
4.457± 0.486
4.457± 0.602
0.025†
2ʹ
4.372± 1.138
4.732± 0.906
4.471± 1.299
0.71
5ʹ
3.167± 0.855
3.571± 0.765
4.127± 0.663
0.06
10ʹ
2.087± 0.450
2.339± 0.315
2.182± 0.552
0.75
2ʹ
2.579± 0.852
2.039± 0.856
1.463± 0.403
<0.001‡
5ʹ
2.566± 0.807
2.215± 0.694
1.669± 0.458
<0.001‡
1.369± 0.301
1.216± 0.173
1.294± 0.388
0.261
Phosphoflow analysis
(n = 20 per group)
pZap70 (Y-292)
pZap70 (Y-319)
pCREB (S-133)
10ʹ
pTyr (Y100)
5ʹ
2.471± 0.720
2.329± 0.746
4.443± 1.271
<0.001*
1.684± 0.464
1.767± 0.391
2.388± 0.435
0.072
RT-qPCR
6.457± 5.143
7.412± 5.701
16.11± 9.499
0.001§
MFI
15.88± 5.781
16.15± 6.218
25.40± 4.402
<0.001*
12.90 ± 3.982
12.30 ± 4.064
6.760 ± 1.528
<0.001*
IL-2
27.90 ± 9.291
14.87 ± 10.25
10.71 ± 10.15
0.012
IL-10
21.74 ± 2.246
10.50 ± 9.070
5.157 ± 5.384
0.042
Foxp3
12.01 ± 9.311
9.672 ± 6.829
8.081 ± 6.103
0.766
10ʹ
PTPN22 expression
Phenotypic analysis
(n = 10 for each group)
% Treg/CD4+ T-cells
ChIP assay
(n = 5 for each group)
c-Fos
7.901 ± 11.41
8.768 ± 7.835
7.173 ± 8.100
0.972
Variables are presented as mean ± SD. 1-way ANOVA with Bonferroni correction was used for comparisons among the 3 groups.
*P < 0.001 ACS vs SA and controls, †P < 0.001 ACS vs SA; P < 0.05 ACS vs controls; ‡P < 0.001 ACS vs controls; §P < 0.01 ACS vs SA; P <
0.001 ACS vs controls; ║P < 0.05 ACS vs controls.
Online Table 3. Biological parameters of follow-up.
SA
ACS
(n = 10)
(n = 10)
P by t test
2ʹ
3.320 ± 1.075
2.066 ± 0.607
0.007
5ʹ
2.532 ± 0.815
1.508 ± 0.594
0.004
10ʹ
1.691 ± 0.679
1.358 ± 0.306
0.058
2ʹ
2.140 ± 0.461
3.121 ± 0.861
0.009
5ʹ
1.709 ± 0.303
3.281 ± 0.791
0.001
10ʹ
1.485 ± 0.280
2.850 ± 0.403
<0.001
5ʹ
2.524 ± 0.630
4.219 ± 577
<0.001
10ʹ
1.764 ± 0.483
2.118 ± 0.402
0.091
RT-qPCR
7.647 ± 3.182
14.37 ± 3.837
<0.001
MFI
15.60 ± 4.402
28.10 ± 8.01
<0.001
12.02 ± 3.848
8.76 ± 2.270
0.031
Phosphoflow analysis
pZap70 (Y-292)
pCREB (S-133)
pTyr (Y100)
PTPN22 expression
% Treg/CD4+ T cells
Variables are presented as mean ± SD. Comparisons between groups were done by t tests.
Online Table 4. Predictors of PTPN22 mRNA expression.
Univariate Analysis
Multivariate Analysis
Odds Ratio
Age
Odds Ratio
(95% CI)
P
(95% CI)
P
1.02
0.073
1.00
0.21
(0.96–1.07)
Sex
0.57
(0.94–1.07)
0.92
(0.18–1.77)
NSTEMI as
8.01
index event
(3.59–12.42)
Family History
of IHD
Hypertension
1.38
0.52
(0.20–2.78)
0.001
7.973
0.002
(3–12.9)
0.042
(0.46–4.15)
1.3
0.76
1.7
0.092
(0.65–6.04)
0.46
(0.27–6.19 )
Hypercholesterol
emia
Smoke
0.5
0.23
(0.16–1.57)
1.35
0.25
(0.33–5.42)
Diabetes
0.87
0.18
(0.27–2.75)
NSTEMI = non-ST-elevation myocardial infarction; IHD = ischemic heart disease.
Online Figure 1
Analysis of PTPN22 mRNA and protein expression levels, TCR-induced Treg generation,
and protein phosphorylation between baseline and one year follow-up in SA and NSTEMI
patients. The figure shows the changes over time for individual patients in each group. Data
are presented as single data points. For multiple pairwise comparisons, we used 2-way
ANOVA for repeated measures, with Bonferroni correction. *PTPN22 mRNA and protein
expression levels P<0.001 NSTEMI baseline vs SA baseline and NSTEMI FU vs SA FU;
†TCR-induced Treg generation: P<0.05 NSTEMI baseline vs SA baseline and NSTEMI FU
vs SA FU; ‡ZAP-70 Y292 phosphorylation after 2’ and 5’ of TCR stimulation: P<0.01
NSTEMI baseline vs SA baseline and NSTEMI FU vs SA FU; §CREB phosphorylation after
2-5-10’ of TCR stimulation: P<0.01 NSTEMI FU vs NSTEMI baseline; NSTEMI FU vs SA
baseline; and NSTEMI FU vs SA FU. See Figures 2 and 3 in the main article for a
description of experimental conditions.