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ORIGINAL RESEARCH
published: 11 November 2021
doi: 10.3389/fcvm.2021.779073
Frontiers in Cardiovascular Medicine | www.frontiersin.org 1November 2021 | Volume 8 | Article 779073
Edited by:
Ioannis Mitroulis,
Democritus University of
Thrace, Greece
Reviewed by:
Claudia Maria Radu,
University of Padua, Italy
Elena Campello,
University of Padua, Italy
*Correspondence:
Maria Zellner
maria.zellner@meduniwien.ac.at
Specialty section:
This article was submitted to
Thrombosis,
a section of the journal
Frontiers in Cardiovascular Medicine
Received: 17 September 2021
Accepted: 22 October 2021
Published: 11 November 2021
Citation:
Ercan H, Schrottmaier WC, Pirabe A,
Schmuckenschlager A, Pereyra D,
Santol J, Pawelka E, Traugott MT,
Schörgenhofer C, Seitz T, Karolyi M,
Yang J-W, Jilma B, Zoufaly A,
Assinger A and Zellner M (2021)
Platelet Phenotype Analysis of
COVID-19 Patients Reveals
Progressive Changes in the Activation
of Integrin αIIbβ3, F13A1, the
SARS-CoV-2 Target EIF4A1 and
Annexin A5.
Front. Cardiovasc. Med. 8:779073.
doi: 10.3389/fcvm.2021.779073
Platelet Phenotype Analysis of
COVID-19 Patients Reveals
Progressive Changes in the
Activation of Integrin αIIbβ3, F13A1,
the SARS-CoV-2 Target EIF4A1 and
Annexin A5
Huriye Ercan 1, Waltraud Cornelia Schrottmaier 1, Anita Pirabe 1,
Anna Schmuckenschlager 1, David Pereyra 1,2 , Jonas Santol 1,2 , Erich Pawelka 3,
Marianna T. Traugott 3, Christian Schörgenhofer 4, Tamara Seitz 3, Mario Karolyi 3,
Jae-Won Yang 5, Bernd Jilma 4, Alexander Zoufaly 3, Alice Assinger 1and Maria Zellner 1
*
1Center for Physiology and Pharmacology, Institute of Vascular Biology and Thrombosis Research, Medical University of
Vienna, Vienna, Austria, 2Division of Visceral Surgery, Department of General Surgery, General Hospital Vienna, Medical
University of Vienna, Vienna, Austria, 3Department of Medicine IV, Clinic Favoriten, Vienna, Austria, 4Department of Clinical
Pharmacology, Medical University of Vienna, General Hospital Vienna, Vienna, Austria, 5Center for Physiology and
Pharmacology, Institute of Pharmacology, Medical University of Vienna, Vienna, Austria
Background: The fatal consequences of an infection with severe acute respiratory
syndrome coronavirus 2 are not only caused by severe pneumonia, but also by
thrombosis. Platelets are important regulators of thrombosis, but their involvement in the
pathogenesis of COVID-19 is largely unknown. The aim of this study was to determine
their functional and biochemical profile in patients with COVID-19 in dependence of
mortality within 5-days after hospitalization.
Methods: The COVID-19-related platelet phenotype was examined by analyzing their
basal activation state via integrin αIIbβ3 activation using flow cytometry and the proteome
by unbiased two-dimensional differential in-gel fluorescence electrophoresis. In total we
monitored 98 surviving and 12 non-surviving COVID-19 patients over 5 days of hospital
stay and compared them to healthy controls (n=12).
Results: Over the observation period the level of basal αIIbβ3 activation on platelets
from non-surviving COVID-19 patients decreased compared to survivors. In line with
this finding, proteomic analysis revealed a decrease in the total amount of integrin αIIb
(ITGA2B), a subunit of αIIbβ3, in COVID-19 patients compared to healthy controls;
the decline was even more pronounced for the non-survivors. Consumption of the
fibrin-stabilizing factor coagulation factor XIIIA (F13A1) was higher in platelets from
COVID-19 patients and tended to be higher in non-survivors; plasma concentrations
of the latter also differed significantly. Depending on COVID-19 disease status and
mortality, increased amounts of annexin A5 (ANXA5), eukaryotic initiation factor 4A-I
(EIF4A1), and transaldolase (TALDO1) were found in the platelet proteome and also
correlated with the nasopharyngeal viral load. Dysregulation of these proteins may play
Ercan et al. Longitudinal Phenotyping of COVID-19 Platelets
a role for virus replication. ANXA5 has also been identified as an autoantigen of the
antiphospholipid syndrome, which is common in COVID-19 patients. Finally, the levels of
two different protein disulfide isomerases, P4HB and PDIA6, which support thrombosis,
were increased in the platelets of COVID-19 patients.
Conclusion: Platelets from COVID-19 patients showed significant changes in the
activation phenotype, in the processing of the final coagulation factor F13A1 and
the phospholipid-binding protein ANXA5 compared to healthy subjects. Additionally,
these results demonstrate specific alterations in platelets during COVID-19, which are
significantly linked to fatal outcome.
Keywords: COVID-19, thrombosis, platelets, integrin αIIbβ3, coagulation factor XIII (FXIII, F13A1), antiphospholipid
syndrome, annexin A5, eukaryotic initiation factor (EIF4A1)
GRAPHICAL ABSTRACT |
INTRODUCTION
Coronavirus disease 2019 (COVID-19), caused by severe acute
respiratory syndrome corona virus 2(SARS-CoV-2) infection, is
characterized by variable clinical features and degrees of severity,
ranging from asymptomatic, mild influenza-like symptoms
to life-threatening respiratory distress, and multiple organ
failure (1–3). Innate immune responses against SARS-CoV-2
lead to activation of the coagulation cascade (4), with presence
of microthrombi not only in pulmonary tissue of deceased
COVID-19 patients, but also in distant organs like heart
and kidney (5–7). Hence, COVID-19 shows features of an
immuno-thrombotic disease (8–10), with clinical thrombosis
incidences reaching up to 40% among COVID-19 patients
(11–14), particularly in critically ill patients requiring intensive
care (15–17). Even with controlled thromboprophylaxis,
Frontiers in Cardiovascular Medicine | www.frontiersin.org 2November 2021 | Volume 8 | Article 779073
Ercan et al. Longitudinal Phenotyping of COVID-19 Platelets
VTE occurred in 27% of COVID-19 patients in the medical
ward unit and 76% in the intensive care units (18). Notably,
thromboembolisms were observed both at arterial and venous
sites indicating a derangement of both platelet mediated and
plasmatic coagulation (12,19–22).
The role of platelets in COVID-19 is still incompletely
understood. Thrombocytopenia was found to be associated
with disease severity (23–26) and platelet apoptosis observed
in COVID-19 patients requiring intensive care unit treatment
(27). In line with these findings, severely ill COVID-19
patients display elevated markers of platelet activation (9,
26,28–30) and elevated plasma levels of platelet activation
markers (31,32).
It is also noteworthy that platelet exhaustion and hypo-
responsiveness of platelets have been observed in COVID-19
patients. Platelets of COVID-19 patients show hypo-reactivity
in response to in-vitro stimulation (9,29,30,32,33), indicating
prior platelet hyper-activation and resulting hypo-responsiveness
in COVID-19 patients (34). However, little is known about more
detailed molecular changes in platelets in the context of SARS-
CoV-2 infection and the course of the disease. Therefore, the
aim of this study was to decipher specific changes in platelet
function associated with COVID-19 as well as between COVID-
19 survivors and non-survivors. We analyzed platelet activation
status by means of flow cytometry and platelet proteome using
2D-DIGE technology over a period of 5 days in a cohort of
hospitalized COVID-19 patients.
MATERIALS AND METHODS
Study Design
In total 110 patients with COVID-19 (98 survivors and 12
non-survivors) admitted to the central COVID-19 hospital
Clinic Favoriten, Vienna, Austria, between April and November
2020 were included in this study. Flow cytometry analysis was
performed on the first 97 patients enrolled and platelet proteome
analysis was performed on the following 13 patients. Blood
was taken upon study entry (day 0), on day two to three
(day 2–3) and on day four to five (day 4–5) after enrollment.
Outcome data was available for all patients at the time of
analysis. All patients gave written informed consent and the
study was conducted in accordance with the Declaration of
Helsinki. The collection of data was part of the ACOVACT
study (ClinicalTrials.gov: NCT04351724) approved by the local
ethics committee (EK1315/2020). This study was approved by
the Ethics Committee of the Medical University of Vienna in
accordance with the Declaration of Helsinki (EK1548/2020).
Patient demographics including comorbidities and use of
medication were recorded. Routine laboratory analysis was
performed upon admission and every second day afterwards.
Nasopharyngeal swabs and quantitative polymerase chain
reaction (qPCR) for SARS-CoV-2 were performed according to
the Charité protocol (35). Peripheral vein blood was also collected
from 12 SARS-CoV-2 negative healthy volunteers recruited
among the research staff of the institute (median age, 61 years;
age range, 44–63; 58% male; Supplementary Table 1).
Blood Collection, Washed Platelet, and
Plasma Isolation
For platelet isolation, blood was drawn from an antecubital vein
into 3.5 mL vacuum tubes containing 0.129 mM trisodium citrate
as anticoagulant (Greiner Bio-One, Kremsmünster, Austria).
To obtain platelet rich plasma (PRP), two 1 mL aliquots of
citrated blood in 1.5 mL tubes were centrifuged [8 min, 67 g,
room temperature (RT)] and the supernatant PRP was pooled
into a fresh 1.5 mL tube. Platelets were pelleted (2 min, 2,000 g,
RT) in the presence of 0.8 µM PGI2(Sigma-Aldrich, St. Louis,
MO, USA) and washed once in phosphate-buffered saline (w/o:
Ca2+and Mg2+) containing PGI2(0.8 µM).The supernatant was
carefully discarded and the platelet pellet was frozen at −80◦C
until further processing.
For plasma preparation citrated blood was centrifuged
(10 min, 1,000 g, 4◦C) to separate the cellular fraction and
the plasma supernatant, which was subsequently cleared of
debris by a second centrifugation step (10 min, 10,000 g) and
stored at −80◦C.
Flow Cytometric Platelet Analysis
Citrated whole blood obtained at day 0, day 2–3, and day 4–5 was
stained with PerCP-labeled anti-CD42b (1:75, Biolegend) and
FITC-labeled PAC-1 antibodies (1:40, BD Biosciences) for 20 min
at RT in the dark. Platelets were fixed and erythrocytes lysed by
addition of 1-step Fix/Lyse solution (eBioscience). Samples were
diluted with PBS and analyzed using a Cytoflex S cytometer and
CytExpert 2.4 software (both Beckman Coulter). Platelets were
specifically gated by the specific signal from the antibody against
CD42b (glycoprotein Ib—receptor for von Willebrand factor)
with a PerCP-labeled anti-CD42b (1:75, Biolegend). Accordingly
only flow cytometry singlet events in the size and granularity
(FSC and SSC) of platelets and positive for the CD42b signal were
gated for PAC-1 binding. PAC-1 binding (FITC-labeled PAC-1
antibody; 1:40, BD Biosciences) was then quantified as % positive
of CD42 +events. Gate location for PAC-1 was confirmed with
activated platelets (Supplementary Figure 1).
Platelet Preparation for Two-Dimensional
Fluorescence Differential Gel
Electrophoresis (2D-DIGE) Analysis
The frozen platelet protein pellets were resolubilized in urea-
sample buffer (7 M urea, 2M thiourea, 4% CHAPS, 20 mM Tris-
HCl pH 8.68) and incubated for 1 h at RT under agitation (800
rpm). Protein quantitation of individual samples was done in
duplicate with a Coomassie brilliant blue protein assay kit (Pierce,
Thermo Scientific, Rockford, IL, USA). The internal standard (IS)
was prepared by pooling equal protein amounts of all included
samples. Platelet protein samples and IS were aliquoted and
stored at −80◦C.
Platelet Proteome Analysis by 2D-DIGE
Proteins were labeled with fluorescent cyanine dyes (5 pmol
of CyDyes per µg of protein; Cytivia, Hoegaarden, Belgium)
according to our previous publication (36). The IS was always
labeled using Cy2, while Cy3 and Cy5 were used alternately for
Frontiers in Cardiovascular Medicine | www.frontiersin.org 3November 2021 | Volume 8 | Article 779073
Ercan et al. Longitudinal Phenotyping of COVID-19 Platelets
study samples. Briefly, IPG-Dry-Strips (24 cm, pH 4–7, Cytivia,
Hoegaarden, Belgium) were rehydrated for 11 h with 450 µL
rehydration buffer (7 M urea, 2 M thiourea, 70 mM DTT, 0.5%
pH 4–7 ampholyte; Serva, Heidelberg, Germany) mixed with a
total of 30 µg (2 ×10 µg sample +1×10 µg IS) of alternatively
Cy-labeled sample. Isoelectric focusing (IEF) was performed on a
Protean I12 IEF unit (Biorad, Hercules, California,) until 30 kVh
was reached.
After IEF, the strips were first equilibrated with gentle shaking
in 12.5 mL of equilibration buffer 1 (1% DT T, 50 mM Tris-HCl
pH 8.68, 6 M Urea, 30% glycerol and 2% SDS) for 20 min followed
by incubation in equilibration buffer 2 (2.5% iodoacetamide,
50 mM Tris-HCl pH 8.68, 6 M Urea, 30% glycerol and 2% SDS)
for 15 min. The IPG-strips were transferred on 11.5% acrylamide
gel (26 ×20 cm, 1 mm gel) and sealed with low melting agarose
sealing solution (375 mM Tris-HCl pH 8.68, 1% SDS, 0.5%
agarose). SDS-PAGE was performed using an Ettan DALTsix
electrophoresis chamber (GE Healthcare, Uppsala, Sweden)
under the following conditions: 35 V for 1 h, 50 V for 1.5 h and
finally 110 V for 16.5 h at 10◦C.
2D-DIGE Image Analysis
For protein spot detection, 2D-DIGE gels were scanned at
489, 550, and 649 nm corresponding to the three different
excitation wavelengths of the CyDyes and imaged with a
resolution of 100 µm using a Typhoon 9410 Scanner (GE
Healthcare, Uppsala, Sweden). Gel images were analyzed via
the DeCyderTM software (version 7.2, GE Healthcare, Uppsala,
Sweden). Spots were matched to a master 2D-DIGE gel (a
representative pH 4-7 platelet protein map of the IS images).
On average, 400 protein spots were matched manually to
the master gel using the DeCyderTM software. Afterwards an
automatic spot match was used which achieved an average
of 2100 matched spots per gel. Detailed information about
the image analysis was published by Winkler et al. (37).
The standardized abundance (SA) of every protein spot was
calculated by the DeCyderTM software with two normalization
steps. Since we only carried out one washing step for platelet
isolation due to the COVID-19-related safety measures, we
included an additional normalization step using a geometric
mean of eight low biological variable platelet proteins (YWHAE,
YWHAZ, YWHAH, TPM4, ATP5F1B, GNB1, GRB2, PRDX6;
Supplementary Table 2;Supplementary Figures 2,3), which we
previously identified in the proteomic database of washed
platelets (36) and gel-filtrated platelets (38). This normalization
step ensured that the respective plasma contamination does not
affect the exact quantification of the platelet proteins.
Protein Identification via Mass
Spectrometry
For MS-based identifications, 250 µg unlabelled proteins were
separated by the same 2D-DIGE equipment that was used for the
fluorescently-labeled samples described samples above. Proteins
were visualized by MS-compatible silver staining (39). Protein
spots of interest were excised manually from the gels, de-
stained, disulfide was reduced and afterwards derivatized with
iodoacetamide and the proteins were tryptically digested. An
electrospray ionization (ESI)-quadrupole-time-of-flight (QTOF;
Compact, Bruker) coupled with an Ultimate 3000 nano-HPLC
system (Dionex) was used for LC-MS/MS data acquisition.
A PepMap100 C-18 trap column (300 µm×5 mm) and
PepMap100 C-18 analytic column (75 µm×250 mm) were
used for reverse phase (RP) chromatographic separation with
a flow rate of 500 nl/min. The two buffers used for the
RP chromatography were 0.1% formic acid/water and 0.08%
formic acid/80% acetonitrile/water with gradient condition for
90 min. Eluted peptides were then directly sprayed into the
mass spectrometer and the MS/MS spectra were interpreted
with the Mascot search engine (version 2.7.0, Matrix Science,
London, UK) against Swissprot database (564,277 sequences,
released in January 2021) and the taxonomy was restricted to
homo sapiens (human; 20,397 sequences). The search parameters
were used with a mass tolerance of 10 ppm and an MS/MS
tolerance of 0.1 Da. Carbamidomethylation (Cys), oxidation
(Met), phosphorylation (Ser, Thr, and Tyr), acetylation (Lys and
N-term), and deamidation (Asn and Gln) were allowed with
2 missing cleavage sites. The Mascot cut-off score was set to
15 and proteins identified with two or more peptides were
considered (40).
One and Two-Dimensional Western Blot
Analysis
For one-dimensional Western blot (1-D WB), a total of 12 µg
platelet protein were mixed with a sample buffer (150 mM Tris-
HCl pH 8.68, 7.5% SDS, 37.5% glycerol, bromine phenol blue,
125 mM DTT) to obtain a final volume of 20 µL. Samples were
boiled for 4 min at 95◦C and centrifuged for 3 min at 20,000 g.
Thereafter, the samples were separated in a 11.5% SDS gel (50V,
20 min and 100 V, 150min) and blotted (75 V, 120 min) on a
polyvinylidene difluoride membrane (PVDF; FluoroTrans R
W,
Pall, East Hills, NY, USA).
For two-dimensional Western blot (2-D WB) analysis, 30
µg Cy2-labeld platelet proteins were separated by IEF on a
24 cm pH 4–7 IPG strip as described for 2D-DIGE gels, and
subsequently transferred onto a PVDF membrane (75 V, 90 min).
The membranes were blocked with 5% non-fat dry milk (BioRad,
Hercules, CA, USA) in 1x PBS containing 0.3% Tween-20 (PBS-
T) over night at 4◦C under gentle shaking. Membranes were
washed (PBS-T, 3x 5 min) and incubated with monoclonal anti-
Factor F13A1 antibody (1:250 in PBS-T containing 3% non-fat
dry milk; ab1834; Abcam, USA) for 2 h at RT (180 rpm). After
washing (PBS-T, 3x 5 min), the membranes were incubated with
a horseradish peroxidase (HRP)- conjugated secondary antibody
(1:20,000 in PBS-T containing 3% non-fat dry milk) for 1.5 h
in the dark at RT (65 rpm). Membranes were washed again (2x
5 min in PBS-T, 1x 5 min in PBS) and the HRP signal was detected
using an Enhanced Chemiluminescent substrate (FluorChem R
HD2, Alpha Innotech, CA, USA).
Measurement of Haemostatic Biomarkers
in Plasma
F13A1 and D-dimer were assessed using LEGENDplex Human
Fibrinolysis Panel Kit (BioLeged) according to manufacturer’s
Frontiers in Cardiovascular Medicine | www.frontiersin.org 4November 2021 | Volume 8 | Article 779073
Ercan et al. Longitudinal Phenotyping of COVID-19 Platelets
instruction, measured on a Cytoflex S cytometer and
analyzed by LEGENDplex v8.0 software (BioLegend). This
multiplex bead-based assay works with beads of differential
size and internal fluorescence intensities. Each bead set
is conjugated with a specific antibody on its surface and
serves as the capture beads for that particular analyte. As
with the ELISA system, the specific analyte is then made
detectable for flow cytometry with the respective specific
detection antibody.
Biological Pathway Analysis
To get an initial insight into the biological function of the newly
revealed COVID-19-related platelet proteins, a protein-protein
interaction network analysis was performed. The data source
was the protein query of the STRING database (Version 11.0b)
(41), with the following settings (active interaction sources:
experiments and databases; score =0.4; maximal additional
interactors =0). For the functional enrichment, the Gene
Ontology Biological Processes and KEEG pathway analyses
were used for the PPI networks with a specific color for each
biological process and KEGG pathway. The STRING Version
11.0b was used.
Statistics
For explorative statistical analysis, only 2D-DIGE protein image
spots were included which could be matched by the IS spot map
with more than 95% of all 2-D platelet proteome maps of this
study. This quality selection limits the resulting protein spots to
420 out of an average of 2100 spot events matched with the master
gel. One-way analysis of variance (ANOVA) was calculated
for these 420 reliably matched spots between the five study
groups (COVID-19 survivors day 0, COVID-19 non-survivors
day 0, COVID-19 survivors day 4–5, COVID-19 non-survivors
day 4–5, and healthy controls). Significant differences between
control group and COVID-19 patients and between patients with
different outcome were analyzed by planned contrasts analysis
in SPSS Statistics 25 (SPSS Inc, Chicago, USA). Graphs were
created with GraphPad Prism 7 (GraphPad Software, Inc. San
Diego California, USA).
RESULTS
Patient Characteristics of the Two Study
Cohorts
To determine platelet-specific differences between COVID-19
survivors, non-survivors and healthy controls we included 89
surviving and 8 non-surviving COVID-19 patients for flow
cytometric analysis of basal platelet activation (study cohort I)
(Tables 1A,2A). For the platelet proteomics analysis, 9 additional
surviving and 4 non-surviving COVID-19 patients as well as 12
healthy controls were included (study cohort II) (Tables 1B,2B).
Detailed characteristics of the COVID-19 patient demographics
are presented in Tables 1,2. The median age of the healthy
controls was 61 years (Demographics: Supplementary Table 1).
A Drop in Basal Platelet Activation Level in
Non-surviving COVID-19 Patients
In the early stages of severe COVID-19, increased basal
platelet activation has been demonstrated (28,29). We
determined dynamic changes of basal platelet activation
during hospitalization. For this purpose, we examined the
platelets on study day 0 and after 4–5 days after inclusion in
the study in patients who died with COVID-19 (n=8) in
comparison with survivors (n=89). Basal platelet activation was
determined by measuring the percentage of the activated integrin
αIIbβ3 (CD41/CD61) complex on the membrane surface by flow
cytometry. We focused on the glycoprotein αIIbβ3 rather than
CD62P as activation marker since CD62P as activation marker
is prone to time-dependent shedding. The activation-dependent
upregulation of CD62P from the alpha granules, which is widely
used as degranulation marker, can also be shed from the surface
in the case of very strong platelet activation and thus become less
again on the surface of platelets. Moreover, the quantification
of the activation-dependent conformational change of the
glycoprotein αIIbβ3 provides also the link between platelet
activation and fibrinogen binding and thus platelet aggregation.
On the day of study entry, day 0, no significant difference
in integrin αIIbβ3 activation was observed between survivors
and non-survivors among COVID-19 patients. At days 2–3
and days 4–5, however, a significant decrease in the activated
integrin αIIbβ3 complex was detected in non-surviving COVID-
19 patients (Figure 1). This apparent decrease in the basal
platelet activation state in COVID-19 patients corresponds to
a contradicting platelet phenotype, which, however, is often
observed in diseases with an increasing incidence of thrombotic
and fatal courses. For example exhausted platelets are described
in patients with chronic obstructive pulmonary disease (42),
sepsis (43) acute stroke (44), and cancer types with high risk of
venous thromboembolism (45). Due to the continuous activation
of the platelets in these conditions, exhaustion, or hypo-reactivity
of the platelets is assumed. An alternative and non-mutually
exclusive explanation is that activated platelets do not circulate
but are rapidly removed from the circulation (46). More precise
dynamic changes in the biochemical processes of such “hypo-
reactive” platelets in the circulation are largely unknown in these
different diseases with a high risk of thrombosis, as in our current
COVID-19 patients.
Outcome-Related Alterations in the
Platelet Proteome of Patients With
COVID-19 With Comparison to
Healthy Controls
To gain a deeper insight into the biochemical changes of
platelets in COVID-19 and to determine differences between
survivors and non-survivors, we examined the platelet proteomes
of 9 surviving and 4 non-surviving COVID-19 patients
and compared these with 12 healthy controls (Figure 2,
Supplementary Figure 2;Table 1 study cohort II). Similar to
basal αIIbβ3 activation, the platelet proteome of COVID-19
patients was determined on study day 0 and after 4–5 days using
2D-DIGE analysis in the pH range 4–7 (Figure 3). After applying
Frontiers in Cardiovascular Medicine | www.frontiersin.org 5November 2021 | Volume 8 | Article 779073
Ercan et al. Longitudinal Phenotyping of COVID-19 Platelets
TABLE 1 | Patient demographics flow cytometric study cohort I (A) and proteomics study cohort II (B).
A: Study cohort I
Missing data All
(n=97)
Survivors
(n=89)
Non-survivors
(n=8)
Parameter n n (%)
Median (IQR)
n(%)
Median (IQR)
n(%)
Median (IQR)
P-value*
Sex – 0.355
Male 63 (65) 59 (66) 4 (50)
Female 34 (35) 30 (34) 4 (50)
Age (years) – 61 (49–77) 59 (69–73) 83 (79–86) <0.001
Comorbidities
Current smoker 37 5 (8.3) 5 (8.6) 0 (0.0) 0.665
Obesity (BMI >25) 21 56 (73.7) 53 (73.6) 3 (75.0) 0.951
Diabetes type II – 25 (25.8) 20 (22.5) 5 (62.5) 0.013
Hypertension 1 54 (56.3) 46 (52.3) 8 (100.0) 0.009
Cardiovascular disease (any) – 26 (26.8) 20 (22.5) 6 (75.0) 0.001
Coronary heart disease – 13 (13.4) 9 (10.1) 4 (50.0) 0.002
Chronic heart failure – 9 (9.3) 6 (6.7) 3 (37.5) 0.004
Atrial fibrillation – 11 (11.3) 8 (9.0) 3 (37.5) 0.015
Peripheral arterial disease – 4 (4.1) 2 (2.2) 2 (25.0) 0.002
Chronic obstructive pulmonary disease – 10 (10.3) 10 (11.2) 0 (0.0) 0.317
Asthma – 5 (5.2) 4 (4.5) 1 (12.5) 0.337
Hypo-/Hyperthyroidism 1 9 (9.4) 8 (9.1) 1 (12.5) 0.752
Chronic renal insufficiency – 13 (13.4) 11 (12.4) 2 (25.0) 0.315
Chronic liver disease 1 4 (4.2) 3 (3.4) 1 (14.3) 0.164
Malignancy – 8 (8.2) 8 (9.0) 0 (0.0) 0.376
Medication (anti-platelet/anticoagulation)
Anti-platelet therapy – 15 (15.5) 11 (12.4) 4 (50.0) 0.005
Anticoagulation therapy – 94 (96.9) 86 (96.6) 8 (100.0) 0.598
COVID-19 classification at admission†– 0.304
Asymptomatic/mild 15 (15.5) 14 (15.7) 1 (12.5)
Moderate 46 (47.4) 44 (49.4) 2 (25.0)
Severe 29 (29.9) 25 (28.1) 4 (50.0)
Critical 7 (7.2) 6 (6.7) 1 (12.5)
Clinical characteristics
Total hospitalization (days) – 17 (9–23) 17 (9–24) 10 (6–10) 0.012
Invasive ventilation – 12 (12.4) 9 (10.1) 3 (37.5) 0.024
B: Study cohort II
Missing data All
(n=13)
Survivors
(n=9)
Non-survivors
(n=4)
Parameter n n (%)
Median (IQR)
n(%)
Median (IQR)
n(%)
Median (IQR)
P-value*
Sex 1>0.999
Male 9 (69) 6 (67) 3 (75)
Female 4 (31) 3 (33) 1 (25)
Age (years) 1 75 (74–84) 73 (67–82) 81 (79–84) 0.328
Comorbidities
Current smoker 1 1 (8.3) 1 (12.5) 0 (0.0) 0.460
Obesity (BMI >25) 3 7 (70.0) 6 (100.0) 1 (25.0) 0.011
Diabetes type II 1 4 (33.3) 3 (37.5) 1 (25.0) 0.665
Hypertension 1 9 (75.0) 6 (75.0) 3 (75.0) >0.999
Cardiovascular disease (any) 1 6 (50.0) 3 (37.5) 3 (75.0) 0.221
Coronary heart disease 1 2 (16.7) 1 (12.5) 1 (25.0) 0.584
(Continued)
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Ercan et al. Longitudinal Phenotyping of COVID-19 Platelets
TABLE 1 | Continued
Missing data All
(n=13)
Survivors
(n=9)
Non-survivors
(n=4)
Parameter n n (%)
Median (IQR)
n(%)
Median (IQR)
n(%)
Median (IQR)
P-value*
Chronic heart failure 1 2 (16.7) 1 (12.5) 1 (25.0) 0.584
Atrial fibrillation 1 3 (25.0) 2 (25.0) 1 (25.0) >0.999
Peripheral arterial disease 1 1 (8.3) 1 (12.5) 0 (0.0) 0.460
Chronic obstructive pulmonary disease 1 2 (16.7) 2 (25.0) 0 (0.0) 0.273
Asthma 2 0 (0.0) 0 (0.0) 0 (0.0)
Hypo-/Hyperthyroidism 1 3 (25.0) 2 (25.0) 1 (25.0) >0.999
Chronic renal insufficiency 1 1 (8.3) 0 (0.0) 1 (25.0) 0.140
Chronic liver disease 1 1 (8.3) 1 (12.5) 0 (0.0) 0.460
Malignancy 1 4 (33.3) 3 (37.5) 1 (25.0) 0.665
Medication (anti-platelet/anticoagulation)
Anti-platelet therapy 1 3 (25.0) 2 (25.0) 1 (25.0) >0.999
Anticoagulation therapy 1 12 (100.0) 8 (100.0) 4 (100.0)
COVID-19 classification at admission†2 0.449
Asymptomatic/mild 1 (9.1) 1 (12.5) 0 (0.0)
Moderate 9 (81.8) 6 (75.0) 3 (100.0)
Severe 1 (9.1) 1 (12.5) 0 (0.0)
Critical 0 (0.0) 0 (0.0) 0 (0.0)
Clinical characteristics
Total hospitalization (days) 1 16 (12–19) 16 (14–18) 15 (10–19) 0.666
Invasive ventilation 1 0 (0.0) 0 (0.0) 0 (0.0)
*p<0.05. Nominal variables were compared using the Chi-square test, metric variables were compared using T-test.
†COVID-19 classification according to the guidelines issued by the World Health Organization in mild (fever <38◦C, no dyspnea, no pneumonia), moderate (fever, respiratory symptoms,
pneumonia), severe (respiratory distress with respiratory rate ≥30 per minute, oxygen saturation <93% at rest) and critical (respiratory failure with requirement of mechanical ventilation,
requirement of ICU).
BMI: body mass index; IQR: interquartile range.
Statistically significant changes are highlighted in bold.
FIGURE 1 | Basal platelet activation in COVID-19 patients. Activation of
integrin αIIbβ3 complex on platelets from COVID-19 patients, detected by
PAC-1 antibody binding. Basal platelet activation was specified as % binding
of the FITC-labeled PAC-1 antibody and depicted as mean ±95% confidence
interval (CI).
selection criteria for the quality of the comparable protein spots
(defined in the Materials and Methods section) on the 2D-DIGE
gels, a total of 420 protein spots were included in the exploratory
statistical data analysis.
Significant alterations in platelet protein levels between
COVID-19 survivors and non-survivors at day 0 and day 4–5 and
relative to healthy controls were filtered out by one-way ANOVA,
revealing 44 significantly changed protein spots. To determine
COVID-19-related platelet protein changes, a planned contrast
analysis was carried out between all COVID-19 patients on
day 0 and healthy controls. These hypothesis-directed statistical
tests limited the number to 14 significantly altered COVID-
19-related platelet proteins shown in Figure 2 and given in
Table 3. The following Figures (Figures 3B,C,4A,5A,C,E,6A,B)
show these platelet protein alterations for the individual patients
in comparison to healthy controls. The consistency of these
COVID-19-dependent protein courses is also documented in a
non-survivor on day 7.
Unexpectedly, when comparing patients to healthy controls
this unbiased proteomics analysis revealed the strongest COVID-
19-related influence on the total amount of integrin αIIb
(ITGA2B; CD41; spot 413: FC =0.72, p=3.32−8) part of the
platelet integrin αIIbβ3 complex, which was highly significantly
decreased (Figure 2,Table 3).
ITGA2B is a multiply glycosylated protein in platelets and is
therefore visible in the 2-D proteome map as a protein chain
with 10 protein spots (proteoforms) with different isoelectric
Frontiers in Cardiovascular Medicine | www.frontiersin.org 7November 2021 | Volume 8 | Article 779073
Ercan et al. Longitudinal Phenotyping of COVID-19 Platelets
TABLE 2 | Laboratory findings at admission flow cytometric study cohort I (A) and proteomics cohort II (B).
A: Study cohort I
Missing data All
(n=97)
Survivors
(n=89)
Non-survivors
(n=8)
Parameter nMedian (IQR) Median (IQR) Median (IQR) P-value*
Hemoglobin (g/dL) 3 13.1
(12.1–14.6)
13.2
(12.3–14.7)
11.9
(11.1–13.3)
0.093
Red blood cell count (×1012/L) 3 4.5
(4.1–5.0)
4.6
(4.1–5.0)
4.0
(3.5–4.4)
0.043
Platelet count (×109/L) 3 205
(140–226)
200
(137–226)
261
(183–283)
0.348
Leukocyte count (×109/L) 3 6.0
(4.0–7.2)
6.0
(3.9–7.2)
6.8
(5.0–7.4)
0.494
Lymphocyte count (×109/L) 7 1.0
(0.6–1.2)
1.0
(0.7–1.2)
0.8
(0.6–0.8)
0.148
Neutrophil count (×109/L) 7 4.7
(3.0-5.9)
4.6
(3.0–5.8)
5.5
(3.8–6.1)
0.492
Monocyte count (×109/L) 7 0.4
(0.2–0.5)
0.4
(0.2–0.4)
0.5
(0.3–0.6)
0.078
Eosinophil count (×109/L) 7 0.03
(0.00–0.03)
0.03
(0.00–0.03)
0.02
(0.01–0.03)
0.834
Basophil count (×109/L) 7 0.03
(0.01–0.03)
0.03
(0.01–0.04)
0.02
(0.01–0.02)
0.208
C–reactive protein (mg/L) 3 65.8
(31.5–82.6)
65.4
(29.2–82.6)
69.6
(35.6–88.7)
0.982
D–dimer (mg/dL) 15 0.8
(0.5–1.5)
0.8
(0.5–1.0)
1.7
(0.7–1.9)
0.219
Prothrombin time (%) 6 97.8
(89.7–109.3)
98.0
(89.7–110.8)
95.9
(91.6–102.5)
0.453
International normalized ratio 6 1.1
(1.0–1.1)
1.1
(1.0–1.1)
1.0
(1.0–1.1)
0.352
Activated partial thromboplastin time (s) 10 33.8
(29.4–37.1)
33.8
(29.2–37.0)
34.2
(30.3–37.9)
0.533
B: Study cohort II
Study cohort II Missing data All
(n=13)
Survivors
(n=9)
Non-survivors
(n=4)
Parameter nMedian (IQR) Median (IQR) Median (IQR) P-value*
Hemoglobin (g/dL) 1 13.3
(12.2–14.4)
13.2
(12.9–14.2)
13.4
(12.2–14.5)
0.885
Red blood cell count (×1012/L) 1 4.4
(4.1–4.7)
4.4
(4.3–4.6)
4.5
(4.0–4.9)
0.765
Platelet count (×109/L) 1 214
(184–238)
223
(201–238)
196
(178–200)
0.299
Leukocyte count (×109/L) 1 8.4
(4.9–10.6)
8.9
(5.6–10.6)
8.1
(4.4–10.5)
0.781
Lymphocyte count (×109/L) 4 0.9
(0.5–1.1)
1.0
(0.6–1.4)
0.6
(0.5–0.7)
0.331
Neutrophil count (×109/L) 4 6.7
(2.7–7.8)
7.7
(3.8–10.9)
4.7
(3.2–5.7)
0.352
Monocyte count (×109/L) 4 0.4
(0.4–0.5)
0.4
(0.4–0.5)
0.3
(0.2–0.3)
0.181
Eosinophil count (×109/L) 4 0.05
(0.01–0.06)
0.07
(0.02–0.08)
0.02
(0.02–0.03)
0.289
Basophil count (×109/L) 4 0.08
(0.05–0.10)
0.09
(0.07–0.11)
0.05
(0.04–0.05)
0.083
(Continued)
Frontiers in Cardiovascular Medicine | www.frontiersin.org 8November 2021 | Volume 8 | Article 779073
Ercan et al. Longitudinal Phenotyping of COVID-19 Platelets
TABLE 2 | Continued
Study cohort II Missing data All
(n=13)
Survivors
(n=9)
Non-survivors
(n=4)
Parameter nMedian (IQR) Median (IQR) Median (IQR) P-value*
C–reactive protein (mg/L) 1 117.1
(76.9–154.5)
111.5
(63.1–168.0)
128.4
(123.7–148.1)
0.933
D–dimer (mg/dL) 5 1.3
(1.1–1.8)
1.1
(0.9–1.4)
1.8
(1.2–2.5)
0.762
Prothrombin time (%) 3 93.5
(79.1–106.7)
91.8
(79.1–104.1)
95.9
(86.7–106.5)
0.724
International normalized ratio 4 1.1
(1.0–1.2)
1.1
(1.0–1.2)
1.1
(1.0–1.1)
0.841
Activated partial thromboplastin time (s) 3 32.7
(30.2–35.8)
32.5
(29.9–35.8)
32.9
(30.8–33.4)
0.914
*p<0.05. Metric variables were compared using T- test or Mann-Whitney test; IQR, interquartile range.
Statistically significant changes are highlighted in bold.
FIGURE 2 | 2D-DIGE-based proteome analysis of platelets from COVID-19 patients compared to controls. Representative 2D-DIGE image of protein spots with
significant alterations in COVID-19 patients compared to controls (see Table 3). Platelet protein extracts were separated according to the isoelectric point (pI) in the
pH 4–7 range and the molecular weight (MW). Protein spots identified by MS are circled and labeled with their corresponding gene name and spot numbers. Detailed
descriptions of the highlighted proteins are listed in Table 3.
points (pI), which reflect different degrees of glycosylation. The
very strict qualitative selection criteria used included only two of
these ITGA2B proteoforms (Figure 3A) in the statistical analyses,
spot 403 (Figure 3B) and 413 (Figure 3C), which represent the
two most abundant forms of this integrin (Figure 3A). The
other ITGA2B proteoforms were generally similarly regulated
Frontiers in Cardiovascular Medicine | www.frontiersin.org 9November 2021 | Volume 8 | Article 779073
Ercan et al. Longitudinal Phenotyping of COVID-19 Platelets
FIGURE 3 | COVID-19 and mortality-dependent course of the abundance of integrin αIIb in platelets. (A) Illustration of the 2-D profile of the integrin αIIb (ITGA2B or
CD41) spot chain from the 2D-DIGE analysis. (B,C) Protein levels of the ITGA2B proteoforms (spot 403 and 413). Scatter dot plot and time course of COVID-19
patients of ITGA2B standardized abundance (SA) of the platelet protein spots quantified by 2D-DIGE (healthy controls: n=12; COVID-19 survivors: n=9; COVID-19
non-survivors: n=4). Protein levels were depicted as single values and mean. 2D-DIGE, two-dimensional differential in-gel electrophoresis.
in COVID-19 patients, but data did not reach a reproducibility
of 95%.
An additional planned contrast analysis of the time course
of ITGA2B levels in platelets further showed a significant
decrease of ITGA2B over time in non-surviving COVID-19
patients (Figures 3B,C), which fits to the decrease in the activated
integrin-αIIbβ3 complex observed in non-survivors by flow
cytometry (Figure 1).
A STRING protein network analysis
(Supplementary Figure 4) showed that ITGA2B together
with coagulation factor XIIIA (F13A1), annexin A5 (ANXA5)
and calmodulin (CALM1)—all significantly altered in COVID-
19 patients relative to healthy donors (Table 3)—significantly
enriched the biological process “platelet degranulation” (p
=0.0243), thereby also confirming the recent bioinformatic
meta-assessment results of several previous clinical proteomics
studies of COVID-19 patients demonstrating increased
platelet degranulation (47). F13A1 is represented in our 2D-
DIGE platelet protein map by three 83 kDa proteoforms (pI
5.85 to pI 6.05), though only one of them was significantly
changed in COVID-19 patients relative to healthy controls
(Table 3,Figures 4A,B). This proteoform with the pI 5.65 was
significantly decreased in COVID-19 patients (FC =0.58; p
=0.0002). As the last factor in the coagulation cascade, this
transglutaminase catalysis the irreversible cross-linking of
fibrin and thus ensures the formation of a stable thrombus.
In a previous platelet proteomics study of patients with lung
cancer, we could show an accelerated inactivation of F13A1
via an elevated amount of a 55 kDa fragment of F13A1 (36).
Also in the current study, the COVID-19-dependent, significant
reduction of the F13A1 proteoform with pI 5.65 could be caused
by its increased breakdown. However, due to the rigorous
access-restrictions of personnel to COVID-19 samples, optimal
sample preparation was not possible during bio banking work
and platelets were only washed once. This limitation led to a
higher level of plasma proteins in the platelet proteome than
in our previous studies. Consequently, we could not detect
the 55 kDa inactivation product of F13A1, as its spot area was
overlaid by spots of the plasma protein SERPINA1. Nevertheless,
a 2-D Western blot analysis of the internal standard sample
(pool of all proteomics study samples) detected this particular
55 kDa F13A1 fragment immunologically, demonstrating
F13A1 degradation (Figure 4B). A 1-D Western blot analysis
further showed that this 55 kDa F13A1 fragment was clearly
detectable in platelets of COVID-19 patients regardless of
the outcome, but not in the healthy controls (Figure 4C). An
increased consumption of F13A1 linked to a decrease in the
F13A1 concentration in the plasma has already been described
earlier in various thrombotic diseases, such as acute deep vein
thrombosis (48). Similarly, the F13A1 concentration in the
plasma of COVID-19 non-survivors was significantly reduced
compared to the surviving COVID-19 patients (FC =0.71; p
=0.011) (Figure 4D), even though the total F13A1 amount in
platelets (sum of all 83 kDa proteoform spots) was not found
to be different (data not shown). Statistically, this decrease in
F13A1 in plasma was higher significant than the increase in the
degradation product of cross-linked fibrin, D-dimer (FC =1.81;
p=0.126, Figure 4E).
ANXA5, the major annexin in human platelets, was
significantly increased in COVID-19 patients compared to
healthy controls (FC =1.26; p=0.007). This platelet
protein showed the highest association with mortality among
the identified significantly altered proteins (Table 3) with a
significantly increased amount in the deceased compared to
surviving COVID-19 patients (FC =1.58; p=0.040) on day 0
and even more so on day 4–5 (FC =2.12; p=0.001; Figure 5A).
It has been shown that ANXA5 of an influenza virus-infected
cell can be incorporated into the virus particle (49). In the
current study, a correlation of ANXA5 with the SARS-CoV-2
exposure of COVID-19 patients was found (rp=0.677; p=
0.002; n=18; Figure 5B). The virus load was quantified by
means of a nasopharyngeal swab (Supplementary Figure 5).
For the correlations of the viral load with the respective
amount of the platelet protein, the different points in time
were combined.
Transaldolase (TALDO1), an enzyme of the carbohydrate
metabolism, was also significantly increased in COVID-
19 patients compared to the healthy controls, however
levels declined over time in survivors almost to levels
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Ercan et al. Longitudinal Phenotyping of COVID-19 Platelets
TABLE 3 | 2D-DIGE-identified proteome alterations in platelets from COVID-19 patients compared to healthy controls.
All COVID-19 patients/Healthy controls Non-survivors/Survivors Day 4–5/Day 0
Day 0 Day 0 Day 4-5 Survivors Non-survivors
Spot number Protein name Uni-Prot
number
Gene name MW [kDa] pI P-value of
One-way ANOVA
FC P-value FC P-value FC P-value FC P-value FC P-value
413 Integrin αIIb P08514 ITGA2B 113 4.80 0.0001 0.72 3.32E-08 1.02 0.417 0.88 0.367 0.89 0.411 0.88 0.052
403 Integrin αIIb P08514 ITGA2B 113 4.50 0.0005 0.67 1.34E-05 1.15 0.213 0.92 0.350 0.92 0.694 0.92 0.022
2160 Transaldolase P37837 TALDO1 37 6.36 0.0002 1.47 2.78E-06 0.88 0.345 1.26 0.068 0.75 0.016 1.26 0.733
2288 Annexin A5 P08758 ANXA5 35 4.93 0.0005 1.26 0.007429 1.58 0.040 2.12 0.001 1.24 0.055 2.12 0.078
1602 Protein disulfide-isomerase A6 Q15084 PDIA6 48 4.95 0.0006 1.40 0.001516 1.12 0.343 1.17 0.095 0.86 0.067 1.17 0.333
827 Coagulationfactor XIIIA P00488 F13A1 83 5.65 0.0007 0.58 0.000157 0.61 0.220 0.78 0.122 0.68 0.217 0.78 0.777
2493 Platelet-activating factor acetylhydrolase
IB subunit α2
P68402 PAFAH1B2 25 5.57 0.0012 1.79 0.000225 0.86 0.864 1.96 0.029 0.60 0.028 1.96 0.256
2116 β-parvin Q9HBI1 PARVB 35 6.25 0.0029 1.34 0.000139 0.99 0.565 0.98 0.484 0.91 0.946 0.98 0.332
1748 α-enolase P06733 ENO1 47 7.01 0.0036 0.65 0.000281 1.02 0.779 1.09 0.240 0.99 0.751 1.09 0.937
2235 α-soluble NSF attachment protein
(SNAP-α)
P54920 NAPA 33 5.23 0.0055 1.41 0.001080 0.98 0.750 1.01 0.918 0.81 0.316 1.01 0.359
1753 Eukaryotic initiation factor 4A-I P60842 EIF4A1 44 5.32 0.0055 1.33 0.038561 0.97 0.191 1.41 0.019 0.74 0.009 1.41 0.481
2127 F-actin-capping protein subunit α-2 P47755 CAPZA2 33 5.57 0.0057 1.56 0.000448 0.77 0.446 1.33 0.326 0.74 0.088 1.33 0.510
3047 Calmodulin P0DP23 CALM1 16 4.09 0.0078 1.70 0.105430 1.25 0.489 0.95 0.299 0.62 0.083 0.95 0.771
1351 Protein disulfide-isomerase (P4HB) P07237 PDIA1 57 4.76 0.0121 1.37 0.000595 1.37 0.125 1.25 0.124 0.97 0.581 1.25 0.271
The p-values (p ≤0.01) of the one-way ANOVA indicate the variance of the respective proteoforms between the five groups of surviving and non-surviving COVID-19 patients on day 0 and day 4–5 and healthy controls. COVID-19-related
platelet protein changes are characterized by planned contrast analysis (p ≤0.05) between all patients with COVID-19 (n =13) from day 0 and healthy controls (n =12) and the average fold-change (FC). The evaluation of outcome-related
changes of these COVID-19-related proteins are calculated by planned contrast analysis from the survivors and non-survivors on day 0 and day 4–5. The planned contrast analysis (p ≤0.05) indicates proteins, which are significantly
changed dependent from the outcome on day 0 and day 4–5 as well as between day 0 and day 4–5. All these calculations are carried out with the values of the standardized protein abundance, quantified with the 2D-DIGE system.
2D-DIGE, two-dimensional differential in-gel electrophoresis; MW, molecular weight; pI, isoelectric point; FC, fold change.
Frontiers in Cardiovascular Medicine | www.frontiersin.org 11 November 2021 | Volume 8 | Article 779073
Ercan et al. Longitudinal Phenotyping of COVID-19 Platelets
FIGURE 4 | COVID-19-dependent course of the abundance of F13A1 in platelets. (A) Protein levels of F13A1 proteoform (spot 827). Scatter dot plot and time course
of COVID-19 patients of F13A1 standardized platelet protein spot abundance quantified by 2D-DIGE. (healthy controls: n=12; COVID-19 survivors: n=9; COVID-19
non-survivors: n=4). (B) Platelet proteins were separated according to their molecular weight (MW) and isoelectric point (pI). 2-D western blot (WB) image of platelet
F13A1 probed with monoclonal anti-F13A1 antibody (left). Cy2-labeled protein was applied to IEF on a 24cm pH 4–7 IPG-strip. Overlay of whole protein (black) and
F13A1 signal (white) (right). Overlay of 2D-DIGE gel vs. F13A1 WB-signal, obtained through the Online Image Editor (https://www.online-image-editor.com). (C)
Representative 1-D WB image of F13A1 in platelet proteins from COVID-19 survivors (n=4), COVID-19 non-survivors (n=1) and healthy controls (n=4). The
anti-F13A1 antibody detects two protein bands with molecular weight of 83 and 55 kDa. (D) Plasma F13A1 concentration at day 0 (COVID-19 survivors: n=45;
COVID-19 non-survivors: n=8). (E) Plasma levels of D-dimer at day 0 (COVID-19 survivors: n=45; COVID-19 non-survivors: n=8). Protein levels of F13A1 and
D-dimer were depicted as single values and mean. 2D-DIGE, two-dimensional differential in-gel electrophoresis; kDa, kilodalton.
of healthy controls (Figure 5C). TALDO1 is a part and
modulator of the pentose phosphate pathway which can
also supply ribonucleotides for virus replication. Therefore,
TALDO1 could be a potential drug target for antiviral
interventions. In line with these facts, the TALDO1 levels
of the platelets correlated with the nasopharyngeal virus load
of the COVID-19 patients (rS=0.481; p=0.043; n=18;
Figure 5D).
With a significant increase in EIF4A1 in platelets of COVID-
19 patients, we identified another protein that may be directly
related to viral RNA translation (50,51). The association of
mortality with EIF4A1 was similar to that of TALDO1, with a
Frontiers in Cardiovascular Medicine | www.frontiersin.org 12 November 2021 | Volume 8 | Article 779073
Ercan et al. Longitudinal Phenotyping of COVID-19 Platelets
FIGURE 5 | COVID-19-dependent course of the abundance of ANXA5, TALDO1 and EIF4A1 in platelets. Scatter dot plot and time course of ANXA5, TALDO1, and
EIF4A1 standardized platelet protein spot abundance in COVID-19 patients quantified by 2D-DIGE (healthy controls: n=12; COVID-19 survivors: n=9; COVID-19
non-survivors: n=4) and their correlation with nasopharyngeal virus load. (A) Protein levels of ANXA5 (spot 2288) and (B) scatter dot plot correlation analysis
(Pearson’s Rank correlation coefficient) of virus load and 2D-DIGE ANXA5 levels. (C) Protein levels of TALDO1 (spot 2160) and (D) scatter dot plot correlation analysis
(Spearman’s Rank correlation coefficient) of virus load and 2D-DIGE TALDO1 levels. (E) Protein levels of EIF4A1 (spot 1753) and (F) scatter dot plot correlation
analysis (Spearman’s Rank correlation coefficient) of virus load and 2D-DIGE EIF4A1 levels. 2D-DIGE, two-dimensional differential in-gel electrophoresis.
significant decrease (FC =0.74; p=0.009) in survivors between
days 0 and days 4–5. At the same time, levels in non-survivors
remained stable, resulting in significantly increased levels of
EIF4A1 in the non-surviving COVID-19 patients compared to
the survivors on days 4–5 (FC =1.41; p=0.019; Figure 5E).
Furthermore, we also found a significant correlation between the
amount of EIF4A1 in platelets and nasopharyngeal viral load of
COVID-19 patients (rS=0.598; p=0.009; n=18; Figure 5F).
Finally, two members of the protein disulfide isomerase
(PDI) family, PDIA6 and P4HB, which are critically responsible
for thrombus formation (52), were significantly increased in
COVID-19 patients compared to healthy controls (PDIA6
Frontiers in Cardiovascular Medicine | www.frontiersin.org 13 November 2021 | Volume 8 | Article 779073
Ercan et al. Longitudinal Phenotyping of COVID-19 Platelets
FIGURE 6 | COVID-19-dependent course of the abundance of PDIA6 and P4HB in platelets. (A) Protein levels of PDIA6 (spot 1602) and (B) of P4HB (spot 1351).
Scatter dot plot and time course of COVID-19 patients of PDIA6 and P4HB standardized platelet protein spot abundance quantified by 2D-DIGE (healthy controls: n=
12; COVID-19 survivors: n=9; COVID-19 non-survivors: n=4). 2D-DIGE, two-dimensional differential in-gel electrophoresis.
spot 1602: FC =1.40; p=0.002 and P4HB spot 1351:
FC =1.37; p=0.0006). Both thiol isomerases showed a
trend toward higher levels in the non-surviving COVID-
19 patients, although the results were not significant
(Figures 6A,B).
DISCUSSION
COVID-19-associated advanced severe inflammation in the
lungs is often seen associated with massive viral invasion and
widespread severe thrombotic microangiopathy. The SARS-
CoV-2 RNA is also detectable in platelets of COVID-19 patients
(31) and the virus directly causes a hyper-activation of the
platelets (26). Platelets of severely ill COVID-19 patients were
shown to be more activated compared to healthy and mild
courses (28). In this study, we monitored COVID-19 patients
over a period of 5 days and found that (1) basal integrin αIIbβ3
activation in platelets of non-surviving COVID-19 patients was
decreasing compared to survivors. In addition, using an unbiased
platelet proteome analysis, we found that (2) the total amount
of one part of this integrin complex, ITGA2B, was decreased
in all COVID-19 patients compared to healthy controls, and in
non-survivors the decrease was even stronger after 4–5 days.
COVID-19 dependent changes in the fibrin-crosslinking system
were demonstrated (3) by an increased consumption of intact
F13A1 in platelets, which was even more pronounced in the
plasma of non-surviving COVID-19 patients. The abundance of
(4) ANXA5 was significant higher in non-surviving COVID-
19 patients on day 0 compared to survivors with an even
higher increase on days 4–5. This phospholipid-binding has
already been previously characterized as an autoantigen of the
antiphospholipid syndrome (APS) of COVID-19. Finally, two
mortality-dependent changes in the platelet proteome were
identified in COVID-19 patients, which may be directly related
(5) to virus replication. On the one hand, we found an
increased level of the EIF4A1, which also enables viral RNA
translation, on the other hand we observed increased amounts
of the enzyme transaldolase, which supplies ribonucleotides for
virus replication.
Our initial finding was the decrease in basal platelet activation
in non-surviving COVID-19 patients within 4–5 day observation
period, which was detected by the activated integrin-αIIbβ3
complex. At the first glance, this reduction in platelet activation
status in non-surviving COVID-19 patients contradicts previous
study results that showed elevated platelet activation in patients
with severe vs. mild COVID-19 disease (28,29). However,
platelet activation status has not been monitored over time
during COVID-19. With a subsequent unbiased analysis of the
proteome, we took a closer look at the dynamic changes of the
platelet phenotype and the association with outcome of COVID-
19 and also included a cohort of healthy controls. Unexpectedly,
the proteome analysis showed the strongest change with a highly
significant reduction in the total amount of ITGA2B in platelets
from COVID-19 patients compared to healthy controls and—
similar to αIIbβ3 activation data—an even stronger decrease
in non-surviving patients compared to surviving COVID-19
patients. Of note, in diseases with a high risk of thrombosis,
such as lung cancer (36) and lupus anticoagulants (53), we have
previously observed a decrease in the total ITGA2B level in
the platelet proteome, but not to this extent, underlining the
magnitude of thrombotic dysregulation in COVID-19.
This depletion of ITGA2B in platelets during this
prothrombotic disease may be caused by the continuous
hyper-activation of platelets leading to persistent degranulation
and release of platelet extracellular vesicles. These membrane
shed vesicles contain fairly high levels of ITGA2B (54) and their
continuous release can lead to a decrease in the absolute amount
of ITGA2B in the whole platelets as well as on their surface.
In fact, elevated platelet extracellular vesicles concentrations
were detected in the plasma of COVID-19 patients (31). Thus,
Frontiers in Cardiovascular Medicine | www.frontiersin.org 14 November 2021 | Volume 8 | Article 779073
Ercan et al. Longitudinal Phenotyping of COVID-19 Platelets
the drop of the activated αIIbβ3 complex on platelets of non-
surviving COVID-19 patients, observed in the current study,
may be attributed to a generally declining amount of total
ITGA2B in their platelets due to hyper-activation. Strikingly,
it has already been shown that platelets from COVID-19
patients, activated via the GPVI receptor (30,32) show a reduced
activation of the integrin-αIIbβ3 complex in comparison to
controls. To establish a possible link to our results of diminished
ITGA2B in COVID-19, it should be noted that the activation
of the integrin αIIbβ3 complex via its altered conformation
is quantified by the binding of the antibody PAC-1. Thus, in
the case of reduced total ITGA2B amount in the platelets of
COVID-19 patients, less PAC-1 binding signal may be detected
compared to healthy controls, even during an increased activated
state of the integrin-αIIbβ3 complex, and thus hypo-reactivity of
the platelets may be concluded.
With the detection of a reduced abundance of an 83 kDa
spot of the coagulation factor F13A1 out of a total of three
2D-DIGE -measured proteoforms in the platelet proteome of
COVID-19 patients, we postulated an altered regulation of this
fibrin-stabilizing enzyme. This last zymogen in the coagulation
cascade can be activated by thrombin and also inactivated via
further enzymatic cleavage by thrombin (55) or plasmin (56),
with a resulting 55 kDa degradation product. The shift of F13A1
proteoforms from pI 6.05 to pI 5.85 is not related to the
cleavage by these enzymes but, can be caused by the activation
of the catalytic center via acetylation (57). However, we were
unable to find these acetylations using MS analysis. Nevertheless,
we could previously show that the COVID-19-related 83 kDa
proteoform with pI 5.85 has the strongest correlation with the
enzymatic activity of F13A1 in platelets, while the most alkaline
F13A1 proteoform with the pI 6.05 is the inactive one (36).
In addition, in our previous study we identified an accelerated
processing of F13A1 in the platelet proteome in patients with
lung cancer, which we recognized by an increased amount of
its 55 kDa inactivation product. This F13A1 breakdown product
could be detected immunologically only in COVID-19 patients
with a 1-D Western blot. With simultaneous detection of a
reduced level of an enzymatically active proteoform and an
increased level of the 55 kDa inactivation product in the platelets
of COVID-19 patients, it can be concluded that there is an
increased consumption of F13A1 with a slightly stronger trend
in non-survivors. Even more pronounced, a significantly lower
concentration of F13A1 in the plasma of non-surviving COVID-
19 patients compared to that of survivors additionally points also
here to an accelerated consumption of F13A1. Interestingly, it
has already been shown that the activity of F13A1 in the plasma
of COVID-19 patients is strongly reduced compared to healthy
controls and this decrease in F13A1 activity is more pronounced
in patients admitted to a high-care facility than in patients
admitted to general wards (58). The underlying mechanism
behind this acquired plasma F13A1 deficiency in COVID-19
patients and other conditions with thrombotic complications
(48,59–61) is uncertain, but a consumptive mechanism has
been suggested (62,63). Our results of the altered F13A1
processing thus show a functionally explanatory mechanism
for the increased consumption of F13A1 and an overarching
pathological change in the fibrin stabilization system of platelets
and in the plasma of COVID-19 patients.
Another change in platelet proteome that may be highly
relevant for the pathogenesis of COVID-19 is the increasing
amount of ANXA5 in the platelets of COVID-19 patients, which
was significantly higher in non-survivors. ANXA5 belongs to a
family of Ca2+-dependent phospholipid-binding proteins and
has strong anticoagulant and anti-apoptotic effects, which might
theoretically counteract the prothrombotic effects of a SARS-
CoV-2 infection. ANXA5 is pathologically associated with APS
via the occurrence of anti-ANXA5 autoantibodies (64). These
autoantibodies are described to neutralize the anticoagulant effect
of ANXA5 derived from endothelium and can thus increase
the risk of thrombosis in APS (65). In previous studies a
considerable proportion (50–75%) of hospitalized COVID-19
patients has been diagnosed with APS (66–68). Interestingly, in
the plasma of COVID-19 patients anti-ANXA5 autoantibodies
were found more frequently than the usual antibodies mediating
APS, (69). In a wider context, it is noteworthy that in systemic
lupus erythematosus increased concentration of anti-ANXA5
antibodies was accompanied by an increased concentration of
ANXA5 in the plasma. These increased plasma levels of ANXA5
correlated with corresponding platelet concentrations, which
suggests that the ANXA5 found in the plasma, originates from
the platelets (70). Overall these observations implicate, that
the COVID-19-related increased levels of ANXA5 in platelets
may also lead to increased concentrations of anti-ANXA5
antibodies and thus to COVID-19-related APS. In support of
this hypothesis, it was also found that the level of autoantibodies
against annexin A2, a protein important for fibrinolysis and
the protection of lung tissue, predicts mortality in hospitalized
COVID-19 patients (71). However, no association with mortality
for autoantibodies against ANXA5 was found in this study (71).
Notably, the presented results on elevated platelet ANXA5 levels
in fatal COVID-19 courses should also be relevant to an ongoing
study (NCT04748757), in which patients with severe COVID-
19 courses are infused with recombinant ANXA5 to counteract
inflammation and thrombosis.
SARS-CoV-2 can also directly enter into platelets, as platelets
express angiotensin-converting enzyme 2 (ACE2), a host cell
receptor for SARS-CoV-2, and transmembrane protease serine
subtype 2 (TMPRSS2), a serine protease for spike protein
priming. SARS-CoV-2-RNA has also been detected in platelets
from COVID-19 patients (31). SARS-CoV-2 and its spike protein
directly induce platelet activation (26) and can therefore also be
directly responsible for the prothrombotic state of COVID-19.
The correlations of the COVID-19-dependent platelet
proteins EIF4A1 and TALDO1 with the viral load of the patients
also suggest a direct replication of the virus in platelets. Both
platelet proteins are elevated in the COVID-19 patients on day
0 and then decrease after 4–5 days in the survivors, similar
to their virus load, while all of them remain elevated in the
non-survivors. In fact, platelets have previously been shown to
replicate single-stranded RNA and produce viral protein from
dengue virus and produce thereby infectious virus (72).
A SARS-CoV-2 protein interaction map recently identified
the host’s translational machinery as the primary target
Frontiers in Cardiovascular Medicine | www.frontiersin.org 15 November 2021 | Volume 8 | Article 779073
Ercan et al. Longitudinal Phenotyping of COVID-19 Platelets
for blocking SARS-CoV-2 replication by interfering with
one of the two main candidates, being EIF4A1 (73). The
virus regulates the host processes involved in protein
synthesis, such as the control of translation factors EIF4A.
The helicase EIF4A is part of the cellular EIF4F translation
initiation complex which is required for mRNA binding
to the ribosome. In line, inhibitors of this factor such
as Zotatifin and Rocaglate inhibit the EIF4A-dependent
mRNA translation initiation, which leads to greatly
reduced viral RNA translation in infected cells, including
SARS-CoV-2 (51).
Thus, our results thus provide first evidence that a
translational machinery in platelets are in fact accessible for
SARS-CoV-2 replication. Likewise, the increased amount of
the enzyme TALDO1 can be linked to increased activity of
the pentose phosphate pathway, which supplies the virus with
ribonucleotides, essential building blocks for its replication.
In addition, an unbiased proteome analysis has previously
demonstrated that the infection of Caco-2 cells with SARS-CoV-2
increases the expression of TALDO 1 (74).
In summary, similar to previously investigated prothrombotic
conditions such as lupus anticoagulants and lung cancer, we
found significantly increased levels of the two thrombosis-
promoting protein disulfide isomerases P4HB and PDIA6 and
a reduced total amount of ITGA2B in the platelets of COVID-
19 patients. The F13A1 degradation was modulated in a similar
way as in lung cancer. However, the significant changes regarding
increased levels of ANXA5, EIF4A1 and TALDO1 are so far
unique for the platelet proteome of COVID-19 patients.
Overall, our study has both limitations and strengths. From
a statistical point of view, our study is exploratory, including a
relatively small number of patients. Healthy controls used for
proteomics analysis were not exactly matched to patient age
and gender. Due to the limited sample size, it was not possible
to carry out further statistical subgroup analyses of mild and
severe COVID-19 courses in order to comprehensively assess
the influence of the various degrees of severity of COVID-19
on the platelet proteome. Moreover, patients with pneumonia
without SARS-CoV-2 infection and also SARS-CoV-2 infected
patients without pneumonia would also be very important
controls to find out which platelet proteome changes are specific
and lethal for COVID-19. One of the strengths of our study
is the repeated investigation of the platelet activation status as
well as their proteome over a period of 4–5 days and their
connection with mortality. The continuous mortality-dependent
change in the platelet activation status during 4–5 days as well
as the consistent course of many of the newly characterized
COVID-19-dependent platelet protein changes underline their
pathological relevance.
It is very important to note that the results of the current
proteome analysis certainly did not capture all changes in the
platelet proteome in COVID-19 patients. Due to the small
number of cases, a very strict quality selection was carried out
for the protein candidates included in the statistical proteome
analysis. Therefore, some COVID-19-dependent protein changes
in the platelets were probably not recorded. The selection of
the proteome analysis method using the 2D-DIGE technology
in the pH range 4–7 does not include the entire pH value 3–10
of the possible protein candidates. The 2-D analysis also has the
disadvantage that it cannot detect lower concentrated proteins
in biological samples and is therefore not as sensitive as LC-
MS-based proteome analyses. A major advantage of the 2-D
analysis, however, is that the biological samples do not have to
be digested into peptides as with the shotgun proteomics analysis
and thus intact proteins and their associated posttranslational
modifications (PTMs) are directly analyzed qualitatively and
quantitatively by 2-D (and 2D-DIGE). In fact, COVID-19
dependent reduction of only one (pI 5.85) out of three F13A1
proteoforms as well as changes of its 55 kDa inactivation product
would have been undetectable by shotgun proteome analysis.
In any case, the identified COVID-19-dependent changes
in platelets need to be validated in larger patient cohorts and
potential functional relationships must be investigated as well as
detailed in vitro studies on a possible direct replication of SARS-
CoV-2 in platelets. Nevertheless, the platelet protein alterations
detected in the current study, such as the increased concentration
of ANXA5, seem important for the pathology of COVID-19
and should therefore be made available to the public as soon
as possible.
Taken together, monitoring of the platelet phenotype of
COVID-19 patients over a period of 4–5 days showed that
the integrin αIIbβ3-based platelet activation status declined
in non-survivors compared to survivors. The subsequent
platelet proteome analysis provided the first evidence that
this detection of a reduction in the activated integrin-
αIIbβ3 complex was accompanied by a decrease in the
total amount of one integrin component, ITGA2B. The
current results suggest that in COVID-19 patients, continuous
“degranulation” and the release of platelet microvesicles lead
to features of “platelet exhaustion,” which is most likely
caused by persistent platelet hyper-activation. With an increased
consumption of F13A1 in platelets, which was even more
pronounced in the plasma of the non-survivors, a strong
change in the irreversible fibrin-crosslinking system occurred
in fatal COVID-19 courses. In addition, platelets from non-
survivors showed specific changes in proteins during this
observational period that are closely related to the autoimmune
response of APS and the replication of SARS-CoV-2. Thus,
the results of the current proteomics study suggest that
SARS-CoV-2 replication can also take place directly in the
platelets and can therefore directly and specifically activate
pathways of primary and secondary hemostasis. Accordingly,
the acquired data state a central role of platelets not only
in thromboinflammation during COVID-19 but also in the
viral replication of SARS-CoV-2, thereby covering several main
mechanisms in this disease. The implications of this study can
be critical to a deeper understanding of the pathology and
pathogenesis of COVID-19.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
Frontiers in Cardiovascular Medicine | www.frontiersin.org 16 November 2021 | Volume 8 | Article 779073
Ercan et al. Longitudinal Phenotyping of COVID-19 Platelets
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by Ethics Committee of the Medical University
of Vienna. The patients/participants provided their written
informed consent to participate in this study.
AUTHOR CONTRIBUTIONS
HE, WS, BJ, AA, and MZ contributed to conception and design
of the study. JS, EP, MT, CS, TS, MK, and AZ treated and
recruited the COVID-19 patients. WS, AS, AP, and DP made the
preparation of the samples and organized the database. HE and
WS made the analysis of the patient samples. J-WY made MS
analysis. MZ performed the statistical analysis. MZ and HE wrote
the first draft of the manuscript. WS, HE, AA, DP, and J-WY
wrote sections of the manuscript. All authors contributed to
manuscript revision, read, and approved the submitted version.
FUNDING
Austrian Federal Ministry of Education, Science and
Research, the Medical-Scientific Fund of the Mayor of Vienna
(COVID024) and the Austrian Science Fund (P-34783, P-32064;
SFB-54P04).
ACKNOWLEDGMENTS
A great thanks for all blood donors of this study.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fcvm.
2021.779073/full#supplementary-material
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