International Journal of
Molecular Sciences
Article
Targeted Blood Plasma Proteomics and Hemostasis Assessment
of Post COVID-19 Patients with Acute Myocardial Infarction
Anna Kalinskaya 1,2, * , Daria Vorobyeva 1,2 , George Rusakovich 2 , Elena Maryukhnich 1,2 ,
Alexandra Anisimova 2 , Oleg Dukhin 2 , Antonina Elizarova 2 , Oxana Ivanova 1,2 , Anna Bugrova 3,4 ,
Alexander Brzhozovskiy 4 , Alexey Kononikhin 4 , Evgeny Nikolaev 4, *,† and Elena Vasilieva 1,2,†
1
2
3
4
*
†
Citation: Kalinskaya, A.; Vorobyeva,
D.; Rusakovich, G.; Maryukhnich, E.;
Anisimova, A.; Dukhin, O.; Elizarova,
A.; Ivanova, O.; Bugrova, A.;
Brzhozovskiy, A.; et al. Targeted
Blood Plasma Proteomics and
Hemostasis Assessment of Post
COVID-19 Patients with Acute
Myocardial Infarction. Int. J. Mol. Sci.
2023, 24, 6523. https://doi.org/
10.3390/ijms24076523
Laboratory of Atherothrombosis, Cardiology Department, A.I. Evdokimov Moscow State University of
Medicine and Dentistry, 127473 Moscow, Russia
I.V. Davydovsky Moscow City Clinical Hospital, Moscow Department of Healthcare, 117463 Moscow, Russia
Emanuel Institute for Biochemical Physics, Russian Academy of Science, 119991 Moscow, Russia
Center for Molecular and Cellular Biology, Skolkovo Institute of Science and Technology,
121205 Moscow, Russia
Correspondence: minutka86@mail.ru (A.K.); e.nikolaev@skoltech.ru (E.N.)
These authors contributed equally to this work.
Abstract: The molecular mechanisms underlying cardiovascular complications after the SARS-CoV-2
infection remain unknown. The goal of our study was to analyze the features of blood coagulation,
platelet aggregation, and plasma proteomics in COVID-19 convalescents with AMI. The study
included 66 AMI patients and 58 healthy volunteers. The groups were divided according to the antiN IgG levels (AMI post-COVID (n = 44), AMI control (n = 22), control post-COVID (n = 31), and control
(n = 27)). All participants underwent rotational thromboelastometry, thrombodynamics, impedance
aggregometry, and blood plasma proteomics analysis. Both AMI groups of patients demonstrated
higher values of clot growth rates, thrombus size and density, as well as the elevated levels of
components of the complement system, proteins modifying the state of endothelium, acute-phase
and procoagulant proteins. In comparison with AMI control, AMI post-COVID patients demonstrated
decreased levels of proteins connected to inflammation and hemostasis (lipopolysaccharide-binding
protein, C4b-binding protein alpha-chain, plasma protease C1 inhibitor, fibrinogen beta-chain, vitamin
K-dependent protein S), and altered correlations between inflammation and fibrinolysis. A new
finding is that AMI post-COVID patients opposite the AMI control group, are characterized by a
less noticeable growth of acute-phase proteins and hemostatic markers that could be explained by
prolonged immune system alteration after COVID-19.
Keywords: post-COVID; SARS-CoV-2; COVID-19; acute myocardial infarction; proteomics
analysis; hemostasis
Academic Editors: Jacek Z. Kubiak
and Malgorzata Kloc
Received: 28 February 2023
Revised: 26 March 2023
1. Introduction
Accepted: 28 March 2023
Hypercoagulation and a high incidence of thrombotic complications in the acute phase
of SARS-CoV-2 infection have been reported [1,2]. Another issue that has come to light
during the pandemic is that of long-term cardiovascular and neurological consequences [3].
In particular, among patients convalescing from COVID-19, a greater risk of developing
myocardial infarction (MI) has been demonstrated [4,5].
Although several possible mechanisms for the development of MI in COVID-19
over the long term (endothelial dysfunction, activation of the blood coagulation system,
dysfunction of the immune system) have been proposed, at present the exact mechanisms
for the development of this phenomenon remain unclear [6,7].
In consideration of the findings and problems mentioned above, the search for specific
mechanisms of cardiovascular complications in the long-term period of COVID-19 is of
great importance. Targeted proteomics is a promising technique to identify key links in
Published: 30 March 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Int. J. Mol. Sci. 2023, 24, 6523. https://doi.org/10.3390/ijms24076523
https://www.mdpi.com/journal/ijms
Int. J. Mol. Sci. 2023, 24, 6523
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the pathogenesis of post-COVID-19 [8,9]. The use of targeted proteomics with a validated
assay of peptide standards and robust instrumentation, such as triple quadrupole, provides
the results which have more potential for further translation in clinical practice. Moreover,
the targeted approach allows the production of consistent results across different instrumental platforms, and thus allows biomarker validation using the exact same methods and
workflows in independent cohorts and laboratories [10,11].
In the current study, we considered the protein and corresponding peptide panel from
the BAK270 MRM assay [12]. The assay is developed for the analysis of potential protein
biomarkers for cardiovascular disease, including 61 FDA-approved biomarkers in blood
plasma. Moreover, the robustness of the current MRM assay for selected blood proteins
was recently demonstrated [13]. We investigated the peculiarities of plasma coagulation,
endogenous fibrinolysis, platelet function, and blood plasma proteomics, including levels
of acute-phase proteins, components of the complement system, pro- and anticoagulant
proteins, and proteins modifying the endothelium state in patients with acute myocardial
infarction (AMI), recovered from SARS-CoV-2 infection.
2. Results
2.1. Clinical Data
The average demographic and clinical characteristics of the groups included in the
study are presented in Table 1. Between patients with AMI, there are no significant differences in clinical data values (Table 1).
Table 1. Comparison of clinical data between study groups. BMI—body mass index, NSAID—nonsteroidal anti-inflammatory drugs. Green: p adj. < 0.1.
AMI Control
AMI Post-COVID
Control
Control
Post-COVID
p adj.
AMI Control vs.
AMI Post-COVID
p adj.
AMI Control vs.
Control
p adj.
AMI Post-COVID
vs. Control
Post-COVID
p adj.
AMI Post-COVID
vs. Control
Male sex, n (%)
22, (73%)
44, (84%)
27, (37%)
31, (26%)
1
1
<0.01
0.01
Age, years
Med [Q1; Q3]
65.5
[56.25; 71.5]
59
[50.75; 72]
48
[43.5; 54.5]
49
[47; 55.5]
0.29
<0.01
<0.01
<0.01
BMI
Med [Q1; Q3]
27.91
[24.9; 30.83]
26.73
[24.39; 30.9]
24.38
[23.13; 26.73]
26.4
[22.87; 29.66]
0.82
0.05
0.55
0.04
Smoking, n (%)
18, (44%)
37, (65%)
25, (16%)
31, (23%)
1
1
0.09
0.03
Arterial
hypertension, n (%)
22, (100%)
44, (86%)
25, (32%)
31, (32%)
1
<0.01
<0.01
<0.01
Diabetes mellitus, n,
(%)
22, (27%)
44, (16%)
25, (0%)
31, (0%)
1
0.96
1
1
History of MI, n (%)
22, (23%)
44, (14%)
25, (0%)
31, (0%)
1
1
1
1
Chronic obstructive
pulmonary disease,
n (%)
22, (5%)
44, (2%)
25, (0%)
31, (0%)
1
1
1
1
Chronic kidney
disease, n (%)
22, (9%)
43, (7%)
25, (0%)
31, (3%)
1
1
1
1
History of PCI, n (%)
22, (18%)
44, (9%)
25, (0%)
31, (0%)
1
1
1
1
Antiplatelet drugs, n
(%)
22, (23%)
41, (17%)
25 *, (12%)
31 *, (3%)
1
1
1
1
Statins, n (%)
22, (27%)
41, (7%)
25, (20%)
31, (3%)
1
1
1
1
ACE
inhibitors/AT-II
blockers, n (%)
21, (62%)
40, (30%)
25, (20%)
31, (16%)
1
0.84
1
1
NSAIDs
19, (0%)
40, (0%)
27, (0%)
31, (0%)
1
1
1
1
SARS-CoV-2
Vaccination, n (%)
22, (55%)
43, (53%)
27, (63%)
31, (81%)
1
1
1
1
History of bleeding,
n (%)
22, (5%)
43, (9%)
25, (4%)
31, (0%)
1
1
1
1
* All antiplatelet drugs were canceled the week before the inclusion in the control groups (see exclusion criteria).
Compared with both control groups, AMI patients were significantly older and characterized by a more frequent history of arterial hypertension; in addition to this, compared with
the control post-COVID group, the AMI post-COVID group was characterized by a higher
prevalence of the male sex and a more frequent prevalence of smoking; compared with the
control group, the AMI control group was characterized by higher values of BMI; compared
with the control group, the AMI post-COVID group was characterized by a higher prevalence
of the male sex, higher values of BMI, a higher prevalence of smoking (Table 1).
Int. J. Mol. Sci. 2023, 24, 6523
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2.2. Laboratory Parameters
Compared with the control group, the AMI control group was characterized by higher
values of hemoglobin, leukocytes, triglycerides, creatinine, INR, ALT, and AST, and by
lower values of high-density lipoproteins (HDL), PT, APTT and AT-III (Table 2).
Table 2. Comparison of laboratory data between study groups. LDL—low-density lipoproteins,
HDL—high-density lipoproteins, APTT—activated partial thromboplastin time, PT Quick—Quick
prothrombin time test, PT—prothrombin time, INR—international normalized ratio, ALT—alanine
aminotransferase, AST—aspartate aminotransferase, AT-III—antithrombin III. Green: p adj. < 0.1.
Data are presented as the median [Q1; Q3].
p adj.
AMI Control vs.
AMI Post-COVID
p adj.
AMI Control vs.
Control
p adj.
AMI Post-COVID
vs. Control
Post-COVID
p adj.
AMI Post-COVID
vs. Control
137
[132.5; 140.5]
0.542
0.078
0.172
0.084
243
[208.5; 273]
0.382
0.702
0.611
0.619
5.39
[4.7; 5.99]
5.25
[4.82; 6.4]
0.84
<0.01
<0.01
<0.01
5.6
[4.89; 6.35]
5.07
[4.57; 5.98]
5.65
[4.91; 6.04]
0.388
0.77
0.92
0.376
3.48
[3.01; 4.08]
2.89
[2.28; 3.7]
3.1
[2.64; 3.46]
0.715
0.668
0.119
0.152
1.1
[0.87; 1.19]
1.07
[0.91; 1.29]
1.67
[1.34; 2.02]
1.79
[1.61; 2.36]
0.909
<0.01
<0.01
<0.01
Triglycerides,
mmol/L
1.85
[1.22; 2.28]
1.72
[1.15; 2.34]
1.02
[0.82; 1.65]
1.02
[0.82; 1.2]
0.889
0.013
<0.01
0.013
Creatinine, µmol/L
102
[91.2; 124.7]
86.5
[79; 106.5]
73
[63; 90.5]
74
[66; 86.5]
0.128
<0.01
<0.01
0.013
APTT, s
25.95
[21.72; 30.42]
27.8
[24.3; 30.65]
30.4
[29.05; 32.75]
32.9
[30.7; 34.6]
0.58
0.036
<0.01
0.036
PT Quick, %
86.7
[76.9; 102.05]
90.3
[77.75; 98.3]
106
[101; 110.5]
102
[97; 112.5]
0.804
<0.01
<0.01
<0.01
PT, s
10.95
[10.6; 12.25]
11.4
[10.6; 11.83]
11.1
[10.85; 11.6]
11.4
[10.75; 11.9]
0.801
0.752
0.95
0.84
INR
1.08
[0.99; 1.15]
1.06
[0.99; 1.15]
0.94
[0.92; 0.98]
0.97
[0.91; 1]
0.711
<0.01
<0.01
<0.01
D-dimer, ng/mL
309
[181.5; 369]
282.5
[187.25; 426]
285
[211.5; 473]
287
[194; 428]
0.979
0.954
0.94
0.92
ALT, IU/L
25.5
[19.2; 36.7]
29.5
[21.5; 39]
14
[12; 24]
17
[12.5; 24]
0.768
0.034
<0.01
<0.01
AST, IU/L
33.5
[24.5; 58.25]
27.5
[21; 49.25]
19
[16; 21]
18
[16.5; 23.5]
0.649
<0.01
<0.01
<0.01
AT-III, %
92.5
[89; 101]
91
[88.75; 96.25]
101
[96; 111.0]
100
[95; 107]
0.663
0.057
<0.01
<0.01
AMI Control
AMI Post-COVID
Control
Control
Post-COVID
Hemoglobin, g/L
143.5
[132.7; 162]
150
[131; 155.2]
133
[122.5; 142.5]
Platelets, ×109 /L
272.5
[228; 317.5]
237.5
[194; 282]
254
[203; 295]
Leukocytes, ×109 /L
9.96
[8.36; 11.68]
10.34
[8.95; 12.1]
Total cholesterol,
mmol/L
4.94
[4.08; 6.39]
LDL, mmol/L
3.01
[2.3; 4.25]
HDL, mmol/L
Compared with the control post-COVID group, the AMI post-COVID group was
characterized by higher values of leukocytes, triglycerides, creatinine, INR, ALT, and AST,
and by lower values of HDL, APTT, PT, and AT-III (Table 2).
Compared with the control group, the AMI post-COVID group was characterized by
higher values of hemoglobin, leukocytes, triglycerides, creatinine, INR, ALT, AST, and by
lower values of HDL, APTT, PT, and AT-III (Table 2).
2.3. Rotational Thromboelastometry
Compared with the AMI control group, the AMI post-COVID group was characterized
by a lower value of mean clot firmness (MCF, mm, 59 [56; 64] vs. 56 [50; 60], p adj. = 0.078).
Compared with the control group, the AMI control group was characterized by a
larger thrombus size (A15, mm, 49 [47; 53] vs. 54 [49; 60], p adj. = 0.064; A20, mm,
52 [49; 56] vs. 57 [53; 61], p adj. = 0.044; A30, mm, 55 [51; 57] vs. 58 [55; 63], p adj. = 0.047)
and maximum clot firmness (MCF, mm, 56 [52; 58] vs. 59 [56; 64], p adj. = 0.044).
Comparisons of other parameters are presented in Table S2.
2.4. Thrombodynamics
Compared with the AMI control group, the AMI post-COVID group was characterized
by a lower value of clot density (D, arb units, 27,381.5 [25,505.75; 31,961.5] vs. 25,461.5
[22,630.75; 27,474.5], p adj. = 0.079).
Compared with the control group, the AMI control group was characterized by a
higher value of clot density (D, arb units, 22,996.5 [21,665.75; 24,478.25] vs. 27,381.5
Int. J. Mol. Sci. 2023, 24, 6523
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[25,505.75; 31,961.5], p adj. < 0.01), and a longer lag time (Tlag, min, 0.95 [0.8; 1] vs.
1.1 [1; 1.12], p adj. = 0.034).
Compared with the control post-COVID group, the AMI post-COVID group was
characterized by higher values of clot growth rate (V, µm/min, 29.2 [26; 32.9] vs. 34.75
[30.96; 38.68], p adj. < 0.01), initial clot growth rate (Vi, µm/min, 52.6 [48.95; 56.45] vs. 63.4
[58.5; 66.38], p adj. < 0.01), stationary clot growth rate (Vst, µm/min, 29 [26; 32.71] vs. 34.7
[30.65; 38.6], p adj. < 0.01), and clot size (CS, µm, 1159 [1044.25; 1246.5] vs. 1384.5 [1245.12;
1450], p adj. < 0.01), and by shorter estimated lysis time (LTE, min, 28.2 [24.6; 36.5] vs. 21.65
[16.68; 31.45], p adj. = 0.073).
Compared with the control group, the AMI post-COVID group was characterized by
higher values of initial clot growth rate (Vi, µm/min, 56.1 [53.08; 57.77] vs. 63.4 [58.5; 66.38],
p adj. < 0.01), and clot density (D, arb units, 22,996.5 [21,665.75; 24,478.25] vs. 25,461.5
[22,630.75; 27,474.5], p adj. = 0.054).
Comparisons of other parameters are presented in Table S2.
2.5. Impedance Aggregometry
There were no significant differences in impedance aggregometry values among
patients with AMI.
Compared with the control group, the AMI control group was characterized by a
lower value of arachidonic-acid-induced platelet aggregation (ASPI, AU × min, 60 [45; 65]
vs. 40 [21; 47], p adj. = 0.054).
Compared with the control post-COVID group, the AMI post-COVID group was
characterized by a lower value of arachidonic-acid-induced platelet aggregation (ASPI,
AU × min, 58 [47.5; 68] vs. 26 [18; 40.75], p adj. < 0.01).
Compared with the control group, the AMI post-COVID group was characterized by a
lower value of arachidonic-acid-induced platelet aggregation (ASPI, AU × min, 60 [45; 65]
vs. 26 [18; 40.75], p adj. < 0.01).
Comparisons of other parameters are presented in Table S2.
2.6. Proteomics
Targeted proteomics analysis for patients in all studied groups revealed 81 proteins that
were reliably measured in all samples, and were connected to one of the processes described
in the Table S3: hemostasis, extracellular matrix (a structural component or modifier),
modification of the state of endothelium, inflammation (except complement system as it
was included in a separate group), complement system, lipid metabolism, calcification, and
steroid hormone transport (a list of proteins in each group is presented in Table S3). These
processes were chosen because of their direct involvement in cardiovascular disease.
Both the AMI control group (compared with the control group) and the AMI postCOVID group (compared with the control and control post-COVID groups) were characterized by elevated levels of acute-phase proteins (lipopolysaccharide-binding protein,
C-reactive protein, haptoglobin), components of the complement system (complement C5,
complement C9, complement factor B, and inhibitory complement factor I), procoagulant
proteins (fibrinogen beta and gamma chains, coagulation factor IX), one protein with proand anticoagulant activities (beta-2-glycoprotein 1), and proteins modifying the state of
endothelium (pigment epithelium-derived factor (PEDF), angiogenin).
Proteins elevated only in the AMI post-COVID group compared with the control
groups included proteins involved in inflammation (attractin, phosphatidylinositol-glycanspecific phospholipase D), proteins of the complement system (complement C1q subcomponent subunit A, complement C4, complement C6, mannan-binding lectin serine protease
2A), and procoagulant proteins (coagulation factors XII and XIII B chain, fibronectin). By
contrast, C4b-binding protein alpha-chain and L-selectin were decreased only in the AMI
post-COVID group.
Proteins elevated only in the AMI control group compared with the control group included acute-phase protein leucine-rich alpha-2-glycoprotein, components of the complement
Int. J. Mol. Sci. 2023, 24, 6523
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system (complement C3, plasma protease C1 inhibitor), the regulator of hemostasis and the
complement system (vitronectin), and anticoagulant vitamin-K-dependent protein S.
Compared with the AMI control group, the AMI post-COVID group was characterized by decreased levels of acute-phase lipopolysaccharide-binding protein, inhibitors of
the complement system (C4b-binding protein alpha-chain, plasma protease C1 inhibitor),
procoagulant fibrinogen beta-chain, anticoagulant vitamin-K-dependent protein S, and PEDF.
The main results of proteomics data comparisons between study groups are presented
in Figure 1. The boxplots include proteins different between AMI control and AMI postCOVID groups: C4b-binding protein alpha-chain, fibrinogen beta-chain, lipopolysaccharidebinding protein, PEDF, plasma protease C1 inhibitor, vitamin K-dependent protein S. Based
on their function, we decided to focus on proteins connected to hemostasis, acute-phase
proteins, and components of the complement system. In the boxplots, we included proteins with these functions if they were different in at least one of the comparisons: AMI
control vs. control, AMI post-COVID vs. control, AMI post-COVID vs. control post-COVID.
One of the main functions of PEDF is negative regulation of angiogenesis, therefore we
also added a protein with angiogenic function, which was also different in the comparisons mentioned above (angiogenin). Full comparisons of other proteins are presented in
Table S4. We tested the influence of gender, age, and BMI on the results obtained. It turned
out that age and gender had no noticeable influence on the results (Spearman’s correlation
coefficient > 0.5 or <−0.5, p adj. < 0.05). BMI correlated with the complement C3, complement factor B and complement factor I levels (Spearman’s correlation coefficient > 0.5,
p adj. < 0.05) (See Table S5).
Proteomics results for C-reactive protein and fibrinogen beta-chain were validated by immunoturbidimetry method and photo-optical detection method, respectively. Detailed protocol and
results of validation are presented in the Supplementary Materials File S1 (Figure S6).
Parameters describing clinical data (age, BMI) were analyzed separately (Table S5). We
also excluded some laboratory data (hemoglobin, platelets, total cholesterol, HDL, LDL, triglycerides, creatinine, AST, ALT), which were previously used for group comparison. We analyzed
correlations between protein levels and parameters of hemostasis. We found correlations
of acute-phase proteins (C-reactive protein, haptoglobin, hemopexin, leucine-rich alpha-2glycoprotein, lipopolysaccharide-binding protein) and components of the complement system
with parameters of blood clotting and/or procoagulant proteins in each group. Anticoagulant
vitamin-K-dependent protein S correlated with the acute-phase proteins and/or components
of the complement system in each group, and with the parameters of blood clotting in the AMI
control and AMI post-COVID groups. The main correlations between the studied parameters are presented in Figure S4. Correlation matrices demonstrated only those parameters of
hemostasis and proteins connected to hemostasis which correlated with proteins connected
to inflammation (our division of proteins into functional groups is presented in Table S3)
and components of the complement system in at least one study group. Additionally, we
included proteins connected to inflammation and components of the complement system if
they correlated with parameters of hemostasis and proteins connected to hemostasis in at least
one study group. The full correlation tables are presented in Tables S6–S9, whereby each table
corresponds to one study group. Full correlation tables include all 81 proteins and all the
parameters of hemostasis analyzed in the study, and a p adjustment was done for this list of
correlations. The control group showed correlations of parameters of fibrinolysis with complement C6, complement C8 alpha-chain, and C4b-binding protein alpha-chain. In the AMI
control group, parameters of fibrinolysis correlated with haptoglobin. Any of these correlations
were absent in the control post-COVID and AMI post-COVID groups (Figure S4). To illustrate
this, we visualized differences in correlations between control and control post-COVID groups,
and between AMI control and AMI post-COVID groups, which included only parameters of
fibrinolysis, inflammatory proteins, and components of the complement system (Figure 2).
Int. J. Mol. Sci. 2023, 24, 6523
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Figure 1. Main comparisons of proteomics data between study groups. Targeted proteomic analysis
was carried out using liquid chromatography–tandem mass spectrometry (LC-MS/MS) with multiplereaction monitoring (MRM). For comparison of several groups, we used the Mann–Whitney rank test
with a continuity correction. In order to overcome errors from multiple comparisons, we performed a
Benjamini–Hochberg FDR correction, with calculation of critical values for each comparison matched
with corresponding p-values; we calculated adjusted p-values and compared them with a critical
ff positive false discovery rate below 10%. Data are presented as the median
value of 0.1 to keep the
[Q1; Q3]. * p adj. < 0.05, ** p adj. < 0.01, *** p adj. < 0.001, ns, not significant. The boxplots include
proteins different between the AMI control and AMI post-COVID groups: C4b-binding protein
alpha-chain, fibrinogen beta-chain, lipopolysaccharide-binding protein, PEDF, plasma protease C1
inhibitor, vitamin-K-dependent protein S. Based on their function, we focused on proteins connected
to hemostasis, acute-phase proteins, and components of the complement system.
In the boxplots, we included proteins with these functions if they were different in at least one of the comparisons:
AMI control vs. control, AMI post-COVID vs. control, AMI post-COVID vs. control post-COVID.
Int. J. Mol. Sci. 2023, 24, 6523
ff
7 of 16
Figure 2. Differences
in correlations between parameters of fibrinolysis, proteins involved in inflamff
mation and components of the complement system.ffDifferences in partial correlations were inferred
ffi
on the basis of p adj. < 0.05, ffi
a magnitude of
a Spearman’s correlation coefficient of at least
0.4, and
−
ffi
a Spearman’s correlation coefficient > 0.5 or <−0.5. In each group,
we calculated the correlation
ffi
coefficients between the parameters, and then compared the obtained coefficients between the groups.
ffi
ff
If there is a statistically significant correlation (p adj. < 0.05) between the parameters in both groups,
and if the correlation coefficients differ by more than 0.4, then
ffisuch a correlation will be displayed in
the figure between the correlating parameters. Red—positive correlation, blue—negative correlation.
Line thickness depends on the Spearman’s correlation coefficient. Parameters of thrombodynamics:
LOT—lysis onset time (min), LP—the rate of lysis progression (%/min), CLT—the clot lysis time
(min), LI—percent of remaining clot density (%), LTE—the expected clot lysis time (min). (A) Correlations present in the AMI control group and absent in the AMI post-COVID group; (B) correlations
present in the AMI post-COVID group and absent in the AMI control group; (C) correlations present
in the control group and absent in the control post-COVID group; (D) correlations present in control
post-COVID group and absent in the control group.
We performed clusterization of parameters of thrombodynamics and rotational thromboelastometry, results of coagulation blood tests, and proteins involved in inflammation
and hemostasis, of components of the complement system, and of two proteins affecting
endothelium (angiogenin, PEDF) (Figure 3 and Figure S5).
In the control group, we found cluster 1 predominantly consisting of components of the
complement system and acute-phase proteins. Closely located clusters 4 and 6 in the control
post-COVID group, and clusters 1 and 5 in the AMI control group, contained a very similar
list of proteins with each other and cluster 1 in the control group. These clusters correspond
Int. J. Mol. Sci. 2023, 24, 6523
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to clusters 1 and 2 in the AMI post-COVID group, which are more remote from each other.
Proteins similar in the listed clusters of the studied groups include acute-phase proteins (Creactive protein, lipopolysaccharide-binding protein), proteins of the complement system
(complement C3, complement factor B, complement factor I), and procoagulant proteins
ff
(fibrinogen
beta-chain, fibrinogen gamma chain, vitronectin). Compared to clusters 1 and
5 in the AMI control group, clusters 1 and 2 in the AMI post-COVID group lack several
components of the complement system, including complement C1q subcomponent subunit
A, mannan-binding lectin serine protease 2A, complement C5, complement C8 alpha-chain,
and plasma protease C1 inhibitor. Remarkably, in the AMI post-COVID group, components
of the complement system formed another cluster (3) with lectin, and classical pathway
proteins and components of the terminal stage of the complement system (complement C1q
subcomponent subunit A, complement C1q subcomponent subunit B, mannan-binding
lectin serine protease 2A, ficolin-2, complement C5, complement C6).
Proteins of which the elevation was similar in the AMI control group (compared to the
control group) and the AMI post-COVID group (compared to the control and control postCOVID groups) were included in three separate clusters in the AMI control group, and 85%
of them were included in clusters 1 and 5. In the AMI post-COVID group, these proteins were
included in four separate clusters, and were predominantly distributed between clusters 1 and
2, and newly formed cluster 3. Proteins elevated only in the AMI control group, compared to
the control group, were all included in clusters 1 and 5. Proteins elevated only in the AMI
post-COVID group were almost evenly distributed between clusters 3, 4, and 5.
Figure 3. Cont.
Int. J. Mol. Sci. 2023, 24, 6523
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Figure 3. Clusterization of parameters of hemostasis, and proteins involved in inflammation and
ff
hemostasis, components of the complement system, and two proteins
affecting the endothelium
(angiogenin, PEDF). We performed clusterization using the k-medoids algorithm and a correlation
matrix used as a matrix of distance. For defining theff number of clusters, we used gap statistics via
bootstrapping with Monte Carlo simulation and different centroids. Parameters of coagulation blood
tests: APTT—activated partial thromboplastin time (s), PT—prothrombin time (s), PTttQuick—Quick
prothrombin time test (%). αParameters of rotational thromboelastometry: CT—clottingtttime (sec),
CFT—clot formation time (s), A10–A30—clot amplitudes at 10–30 min (mm), MCF—maximum
clot firmness (mm), α—angle between the middle axis and theμtangential line to the clotting curve
μ lysis index at 60 min (%), ML—maximum lysis
through μ
the 2 mm amplitude point (◦ ), LI60—clot
μ
(%). Parameters of thrombodynamics: V—clot growth rate (µm/min), Vi—initial clot growth rate
(µm/min), Vst—stationary clot growth rate (µm/min), Tlag—lag time, the delay between the test start
and the clot formation onset (min), CS—clot size (µm), D—clot density (arb units), Tsp—spontaneous
clots formation time (min), LOT—lysis onset time (min), LP—the rate of lysis progression (%/min),
CLT—the clot lysis time (min), LI—percent of remaining clot density (%), LTE—the expected clot
lysis time (min). (A) AMI post-COVID group; (B) AMI control group.
3. Discussion
The main purpose of our study presented here was to identify the peculiarities of the
plasma protein profile and hemostasis in patients with AMI after SARS-CoV-2 infection. At
first, we performed a protein analysis of AMI patients compared with the control groups, in
order to identify the peculiarities of AMI itself. The alterations in AMI patients included elevated plasma levels of proteins connected to inflammation (acute-phase proteins, proteins
of the complement system), prothrombotic proteins, one pro- and anticoagulant protein,
and proteins affecting the endothelial layer (PEDF, angiogenin). Complement factor B and
complement factor I positively correlated with the BMI level that differed between the
study groups. Partially, the observed difference in complement factor B and complement
Int. J. Mol. Sci. 2023, 24, 6523
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factor I levels between AMI and control groups may be explained by this fact. On the other
hand, the high BMI level is a well-known risk factor of the AMI development and can often
accompany the AMI.
Lipopolysaccharide-binding protein and PEDF have previously been shown to be
elevated in COVID-19-associated cardiovascular complications [14]. Elevated procoagulant
protein levels in AMI patients are reported in the literature [15] and are in accordance with
increased thrombus size, density, and growth rate parameters, as measured by thrombodynamics and rotational thromboelastometry. Elevated fibrinogen and coagulation factor
IX levels have been shown to be a risk factor for AMI [16,17]. Acute-phase reaction and
activation of the complement system in AMI patients were also described previously [18].
Moreover, we observed correlations of acute-phase proteins with parameters of
hemostasis and prothrombotic proteins, which were present both in AMI groups and
in the control post-COVID group, indicating the close relationship of inflammation and
hemostasis in these groups of patients. Moreover, we saw correlations of the complement
system proteins with the parameters of blood clotting and procoagulant proteins in all
groups of patients. Interconnections between thrombosis, inflammation, and in particular
complement activation, are described in the literature [19,20]. Both the complement and
the hemostatic systems require proteolytic cleavage reactions for activation, providing a
wide range of potential interactions between them. There is evidence that complement
system components play a role in different stages of hemostasis, both in platelet activation
and coagulation [21,22]. Moreover, the coagulation system has been shown to activate
the complement system [19]. Finally, the complement and coagulation systems both have
several common regulatory proteins [19].
We also revealed elevated levels of the proangiogenic protein angiogenin and the
antiangiogenic PEDF in AMI groups compared with the control groups. Angiogenin
elevation during AMI may be a response to hypoxia [23–25]. Moreover, angiogenin has
been shown to be expressed as an acute-phase protein [26]. PEDF has also been shown
to affect vascular permeability in different directions. On the one hand, it was found to
maintain endothelial tight junctions after AMI [27,28]. On the other hand, PEDF was shown
to increase vascular permeability in human umbilical vein endothelial cells (HUVEC) [29].
In addition, PEDF has cardioprotective effects during AMI [30]. Other actions of PEDF
include anti-inflammatory and antithrombogenic activity, which may be a response to
thrombosis and have beneficial effects [31,32].
The next step was to compare AMI groups, with and without previous SARS-CoV-2
infection. These groups demonstrated a difference in levels of inflammatory mediators. The
AMI post-COVID group had a lower level of lipopolysaccharide-binding protein, and of
the complement system inhibitors, as well as plasma protease C1 inhibitor and C4b-binding
protein alpha-chain. Decreased levels of inflammatory mediators in the AMI post-COVID
group compared to the AMI control group may reflect the chronic inflammation process
in patients after SARS-CoV-2 infection [33], described previously. Because of this constant
chronic inflammation in the AMI post-COVID group, in case of an acute cardiac event,
such as MI, the immune system is not able to produce enough response, which is observed
in AMI control patients.
Alterations in the inflammatory process in AMI post-COVID patients were also shown
from clusterization of proteomics data and parameters of hemostasis. In each group, we
found similar clusters formed by a list of proteins connected to inflammation and thrombosis.
However, in the AMI post-COVID group, these clusters lacked several components of the
complement system, and a unique cluster with the complement system proteins was formed.
We found lower plasma levels of fibrinogen beta-chains in the AMI post-COVID
group in comparison with the AMI control group, corresponding to the lower optical
density and firmness of the clot according to the hemostatic tests. Moreover, the levels
of both antithrombotic vitamin-K-dependent protein S and the inhibitor of its activity,
C4b-binding protein alpha-chains, were also decreased in the AMI post-COVID group,
perhaps as a consequence of impaired clotting activity as a negative feedback loop, or a
Int. J. Mol. Sci. 2023, 24, 6523
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reduced inflammatory response. This is also confirmed by the correlations of vitamin-Kdependent protein S with the proteins involved in clotting and inflammation, especially
in the complement system. The decreased level of PEDF in the AMI post-COVID group,
compared with the AMI control group, may have different interconnections with the
thrombotic event. First, a disrupted axis involving PEDF may be one of the causative
factors of thrombosis due to antithrombogenic and anti-inflammatory actions of PEDF, or
due to modulation of the integrity of the endothelial barrier. Second, it can be a consequence
of impaired inflammatory response and coagulation in patients with a history of SARSCoV-2 and probable bystander of thrombosis.
We also found out that the control and AMI groups without a history of SARS-CoV-2
had correlations of fibrinolysis parameters with components of the complement system
or with acute-phase protein haptoglobin, respectively. It was previously shown in several
studies that the fibrinolytic and complement systems are interrelated. On the one hand,
plasmin is able to activate complement C3 and C5 [34–36]. On the other hand, it can inhibit
activation of the complement system by cleaving complement factors [34]. Complement
system components have also been shown to regulate fibrinolysis via different pathways.
Mannan-binding lectin-associated serine protease 1 intensified fibrinolysis and, on the
other hand, some complement components were shown to increase clot lysis time [37].
Other inflammatory mediators have also been found to be involved in the regulation of
fibrinolysis [38]. In the work we report here, these correlations were absent in the control
post-COVID and AMI post-COVID groups. It is possible that after SARS-CoV-2 infection
the interrelation of the immune system with fibrinolysis is altered.
4. Limitations
Our study has a number of limitations. First, differences in the protein levels measured
by LC-MS/MS with MRM are not equal to differences in the levels of functionally active
proteins. Second, we did not consider the history of selective serotonin reuptake inhibitor
usage that may possibly affect platelet aggregation results. Third, the use of the antiplatelet
drugs during the prehospital stage in the AMI patients may influence the obtained results
in the impedance aggregometry. Finally, the fact of COVID-19 was assessed retrospectively,
which does not allow us to establish the exact date of COVID-19 infection accurately for
each participant.
5. Materials and Methods
5.1. Subjects
The study was performed at I.V. Davydovsky Moscow City Clinical Hospital, from
May to December 2022.
We included 66 patients with AMI and 58 healthy volunteers.
Inclusion criteria for the AMI group: confirmed diagnosis of AMI (ST-elevation myocardial infarction (STEMI) or non-ST-elevation myocardial infarction (non-STEMI)) according to the 4th universal definition of myocardial infarction (2018) [39].
Exclusion criteria for the AMI group: age > 90, acute SARS-CoV-2 infection on admission or less than 1 month before; unconfirmed diagnosis of AMI; active cancer; autoimmune
disease; acute inflammation; anticoagulation therapy.
Inclusion criteria for healthy volunteers: no history of SARS-CoV-2 infection or no
clinically significant symptoms after SARS-CoV-2 infection (post-COVID).
A validated questionnaire was used [40] to exclude the presence of post-COVID symptoms in healthy volunteers. The questionnaire assessed parameters such as dizziness/presyncope, syncope, orthostatic intolerance, palpitation/arrhythmia, dyspnea, fatigue, changes
in blood pressure, depression, insomnia, drowsiness, memory loss, impaired concentration,
nightmares, headache, weight loss, hair loss, loss of smell or taste. Volunteers having fewer
than 5 points in all positions were included.
Exclusion criteria for healthy volunteers: age > 90 or <40, acute SARS-CoV-2 infection
on admission or less than 1 month before, cardiovascular and pulmonary chronic diseases;
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diabetes mellitus; active cancer; autoimmune disease; acute inflammation; history of
thromboembolism; treatment with antithrombotic drugs (some patients in the control group
previously received antiplatelet drugs with no clear indications for them, all antiplatelet
drugs were canceled the week before inclusion in the study).
The patients in each group were divided into two groups: post-COVID and control.
The division of patients was performed in accordance with the level of IgG against Nprotein (anti-N). We measured the titers of anti-N IgG in the serum using the automated
indirect immunoassay ARCHITECT i1000SR analyzer with the compatible reagent kit
(Abbott, Chicago, IL, USA), according to the manufacturer’s standard protocol. Patients
were assigned to the control group if their anti-N IgG levels were less than 1.4. The postCOVID group comprised patients with higher anti-N IgG levels [41]. Finally, we had four
groups: AMI post-COVID (n = 44), AMI control (n = 22), control post-COVID (n = 31), and
control (n = 27).
5.2. Laboratory and Instrumental Analysis
All patients underwent physical examination, standard laboratory tests, including
complete blood count (hemoglobin level, red blood cells (RBC), white blood cells (WBC),
platelet count, etc.), a biochemical blood test (serum creatinine, alanine aminotransferase
(ALT), aspartate aminotransferase (AST), etc.), and coagulation blood tests (D-dimer, activated partial thromboplastin time (APTT), prothrombin time (PT), international normalized
ratio (INR), antithrombin III (AT-III), Quick prothrombin time test (PT Quick). The protocol
is described in more detail in the Supplementary Materials File S1.
In addition to the standard blood testing, all patients underwent thrombodynamics, rotational thromboelastometry (in NATEM mode) to evaluate plasma coagulation,
thrombodynamics in fibrinolysis mode to evaluate endogenous fibrinolysis, impedance
aggregometry to evaluate platelet function, and plasma proteomics analysis.
Venous blood samples were obtained upon admission prior to percutaneous coronary
intervention in the AMI group. The delay from venipuncture to hemostatic testing was
less than 15 min. Whole blood was used for impedance aggregometry and rotational
thromboelastometry, and platelet-free plasma was used for the thrombodynamics study.
The protocol is described in more detail in the Supplementary Materials File S1.
5.3. Rotational Thromboelastometry
The study was performed in NATEM mode on the ROTEM (The Tem Innovations,
GmbH, Germany). We assessed the following parameters: clotting time (CT, s), clot formation
time (CFT, s), clot amplitudes at 10–30 min (A10–A30, mm), maximum clot firmness (MCF,
mm), angle between the middle axis and the tangential line to the clotting curve through the
2 mm amplitude point (α, ◦ ), clot lysis index at 60 min (LI60%), and maximum lysis (ML,%).
The reagents are described in more detail in the Supplementary Materials File S1.
5.4. Thrombodynamics
The study was conducted on a Thrombodynamics Analyzer System T-2 (HemaCore LLC,
Moscow, Russia). The following parameters of clot growth were assessed: clot growth rate (V,
µm/min), initial and stationary clot growth rate (Vi, µm/min; Vst, µm/min), lag time, the
delay between the test start and the clot formation onset (Tlag, min), clot size (CS, µm), clot
density (D, arb units), and spontaneous clots formation time (Tsp, min). An activator with
urokinase was added to induce thrombus lysis and assess the fibrinolysis parameters, such
as lysis onset time (LOT, min), the rate of lysis progression (LP, %/min), the clot lysis time
(CLT, min), percent of remaining clot density (LI, %), and the expected clot lysis time (LTE,
min) [42]. The reagents are described in more detail in the Supplementary Materials File S1.
5.5. Impedance Aggregometry
A Multiplate analyzer (Roche Diagnostics International Ltd., Rotkreuz, Switzerland) was
used to assess impedance aggregometry. We used arachidonic acid (ASPI), adenosine diphos-
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phate (ADP), and thrombin receptor-activated peptide-6 (TRAP-6) for platelet activation. The
test time was 6 min. Platelet aggregation was assessed from the area under the curve (AUC).
The reagents are described in more detail in the Supplementary Materials File S1.
5.6. Targeted Plasma Proteomics
We carried out targeted proteomic analysis using liquid chromatography–tandem mass
spectrometry (LC-MS/MS) with multiple-reaction monitoring (MRM). We used synthetic
stable isotope-labeled internal standard (SIS) and natural (NAT) synthetic proteotypic
peptides for measuring the corresponding 227 proteins in plasma. The selected SIS and
NAT synthetic peptides had been previously validated for use in LC/MRM-MS experiments
for blood plasma [12]. Isotopically labeled peptide standards were synthesized at Skoltech
within the framework of the Megagrant of the Ministry of Science and Education of
Russia, “Next generation proteomics for improvement of personalized medicine and health”
(Agreement # 075-10-2022-090). We generated standard curves using NAT and SIS peptide
standards, with bovine serum albumin (BSA) as a surrogate matrix. All samples were
analyzed in duplicate with high-performance liquid chromatography mass spectrometry
(HPLC-MS), using an ExionLC™ UHPLC system coupled online to a SCIEX QTRAP
6500+ triple-quadrupole mass spectrometer (SCIEX, Toronto, ON, Canada). We carried out
HPLC separation using Zorbax Eclipse Plus RRHD C18 RP-UHPLC (150 × 2.1 mm, i.d.,
1.8 µm particles; Agilent Technologies, Santa Clara, CA, USA) with gradient elution. We
carried out mass-spectrometric measurements using the MRM acquisition method. The
corresponding transition list for MRM experiments with Q1; Q3 masses for each peptide is
available in Table S1. For quantitative analysis of LC-MS/MS raw data, we used the Skyline
Quantitative Analysis software (version 20.2.0.343, University of Washington, Washington,
DC, USA). The protocol is described in more detail in the Supplementary Materials File S1.
The MRM data quality was checked manually in Skyline for all selected proteins/peptides,
and includes the absence of interference peaks and the good quality of the peak shape, and
the ratios of the precursor and product ion. The exemplary MRM data (from Skyline) for
selected proteins are presented in Figures S1–S3. All experimental results from MRM analysis
were uploaded to the PeptideAtlas SRM Experiment Library (PASSEL) and are available via
the link: http://www.peptideatlas.org/PASS/PASS04817 (accessed on 15 March 2023).
5.7. Statistical Analysis
Statistical analysis was performed with R (4.0.5). The expression values obtained in
the present study were in not normally distributed in most cases, according to the Shapiro–
Wilk test, and therefore are represented as medians and interquartile ranges [Q1; Q3]. For
comparison of several groups, we used the Mann–Whitney rank test, with a continuity
correction. For the analysis of categorical parameters, we used a two-tailed Fisher’s exact
test with 2 × 2 frequency tables, and a chi-square test of independence for features that
included more than two categories. In order to overcome errors from multiple comparisons,
we performed a Benjamini–Hochberg FDR correction with calculation of critical values for
each comparison matched with corresponding p-values; we calculated adjusted p-values
and compared them with a critical value of 0.1 to keep the positive false discovery rate
below 10%. For calculation of the Spearman’s coefficient (rho), we used a threshold of p
adjusted < 0.05. To test for differences in partial correlations between the groups within
each interval, the partial correlations were first converted into an intermediary statistic,
using Fisher’s z transformation. We inferred significant differences in partial correlations
on the basis of a p adjusted < 0.05 and differences in the magnitude of correlation of at
least 0.4 and rho > 0.5. We constructed a network of differential correlations for each
group by linking/connecting the parameter with significant differences in partial correlations [43]. We performed clusterization using the k-medoids algorithm and a correlation
matrix used as a matrix of distance. For defining the cluster number, we used gap statistics via bootstrapping with Monte Carlo simulation and different centroids from cluster
Int. J. Mol. Sci. 2023, 24, 6523
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package (version 2.1.4) [44]. The 10 initial random centroids were initially configured, and
300 bootstraps of Monte Carlo were generated.
In 2.1% of the subjects from whom proteomics data were obtained, and 6.7% of the
subjects from whom laboratory test data were obtained, there were no values for various
parameters, so we used the pairwise deletion approach to calculate the correlation coefficients.
The available data sets are sufficient for demonstration of significant differences between
compared groups using the Mann–Whitney rank test, sig. level = 0.1, power = 0.8, and effect
size = 0.9 (which corresponds to ‘large’ effect size) and using a Spearman’s rank correlation,
sig. level = 0.05, power = 0.8, and r = 0.6, based on the smallest compared group [45].
6. Conclusions
Our study demonstrated the elevation of inflammatory protein levels and activation
of coagulation in AMI, which corresponded to previous data. After SARS-CoV-2 infection,
AMI patients had less noticeable growth of acute-phase proteins and markers of hemostasis,
compared with AMI patients without SARS-CoV-2 infection, that could be explained by
prolonged immune system alteration after COVID-19. This fact is a new finding and
requires further research.
Supplementary Materials: The following supporting information can be downloaded at: https://www.
mdpi.com/article/10.3390/ijms24076523/s1. References [11,12,46,47] are cited in Supplementary Materials.
Author Contributions: Conceptualization, A.K. (Anna Kalinskaya) and E.V.; methodology, A.K.
(Anna Kalinskaya), A.A., E.M., A.B. (Anna Bugrova), A.K. (Alexey Kononikhin), A.B. (Alexander
Brzhozovskiy) and G.R.; validation, A.K. (Anna Kalinskaya), D.V., G.R., A.B. (Anna Bugrova), A.K.
(Alexey Kononikhin) and A.B. (Alexander Brzhozovskiy); formal analysis, G.R.; investigation, A.K.
(Anna Kalinskaya), O.D., A.A., D.V., E.M., A.B. (Anna Bugrova), A.K. (Alexey Kononikhin), A.B.
(Alexander Brzhozovskiy) and A.E.; data curation, A.K. (Anna Kalinskaya), O.D., O.I., A.A., A.E. and
E.M.; writing—original draft preparation, A.K. (Anna Kalinskaya), D.V., G.R., A.A., E.M., O.D. and
A.E.; writing—review and editing, A.K. (Anna Kalinskaya), E.N. and E.V.; visualization, G.R. and
D.V.; supervision, E.V. and E.N.; project administration, A.K. (Anna Kalinskaya) and E.V.; funding
acquisition, E.V. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by the grant from the Moscow government (research project
No. 2312-44/22).
Institutional Review Board Statement: The study was conducted in accordance with the Declaration
of Helsinki, and approved by the Moscow City Ethics Committee (protocol code #106, date of approval
12 April 2022).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the
study.
Data Availability Statement: All experimental results from MRM analysis were uploaded to the PeptideAtlas SRM Experiment Library (PASSEL) and are available via the link: http://www.peptideatlas.
org/PASS/PASS04817 (accessed on 15 March 2023).
Acknowledgments: We thank Barry Alpher for assistance in editing and improving the English style.
Conflicts of Interest: The authors declare that the research was conducted in the absence of any
commercial or financial relationships that could be construed as a potential conflict of interest.
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