Proteomic signatures of Post-Vaccination/Post-Infection Syndrome (PV/PIS): Insights into immune dysregulation and coagulopathy

Proteomic signatures of Post-Vaccination/Post-Infection Syndrome (PV/PIS): Insights into immune dysregulation and coagulopathy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Proteomic signatures of Post-Vaccination/Post-Infection Syndrome (PV/PIS): Insights into immune dysregulation and coagulopathy Maxine Waters, Mare Vlok, Elouise E. Kroon, Maritha J. Kotze, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6521005/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract During the global rollout of COVID-19 vaccines a subset of individuals reported persistent symptoms following vaccination, with clinical presentations overlapping those of Long COVID requiring individualised treatment decisions. Distinguishing between vaccine-related adverse events and post-infectious sequelae remains challenging, particularly given the possibility of asymptomatic or mild SARS-CoV-2 infection prior to or after vaccination. To avoid this complexity, we define this patient group as presenting with Post-Vaccination/Post-Infection Syndrome (PV/PIS). In this study, we performed a proteomic analysis of plasma from 30 individuals with PV/PIS compared to healthy controls. Using mass spectrometry, we identified significant alterations in coagulation factors, acute phase proteins, and immune response modulators in the PV/PIS group. Notably, elevated levels of serum amyloid A1 and A2, attractin, and coagulation factors X and XI were observed, alongside downregulation of immune-regulatory proteins. These findings suggest that PV/PIS is characterized by persistent immune dysregulation and coagulopathy, with proteomic signatures only partially overlapping those previously reported in prior proteomics analysis on Long COVID samples collected prior to vaccination availability. Our results highlight the complex interplay between immune activation, endothelial dysfunction, and coagulation pathologies in PV/PIS, while also highlighting distinct differences between these systems in Long COVID and PV/PIS, paving the way for more targeted protein research in these conditions. Infectious Diseases Cell Communication and Signaling proteomics Long COVID PV/PIS heterogenous amyloid deposits (microclots) inflammatory molecules Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Post-acute sequelae of COVID-19 (PASC), commonly known as Long COVID, is estimated to have affected over 400 million individuals worldwide, contributing to an annual economic burden of $1 trillion – representing approximately 1% of the global economy 1 . Initially recognised by affected individuals themselves and reported via social media and various community forums 2 , Long COVID has since been acknowledged as a potentially disabling condition by the Centres for Disease Control and Prevention (CDC) 3 . Ewing et al. (2025) published expert consensus recommendations for physicians, emphasising the need for individualised treatment in cases where vaccination exacerbated Long COVID symptoms or resulted in adverse vaccine side effects 4 . The World Health Organization (WHO) defines Long COVID as the persistence or emergence of new symptoms beyond two months after an acute SARS-CoV-2 infection, with no alternative explanation 5 . This condition affects multiple physiological systems and presents with a broad spectrum of symptoms, including neurological (brain fog, extreme fatigue, sleep disturbances), cardiovascular (orthostatic intolerance, chest pain, shortness of breath, tachycardia), musculoskeletal (joint pain, muscle weakness), and psychosocial manifestations (depression, anxiety, social isolation) 6-10 . Notably, more than 200 symptoms have been linked to Long COVID globally, with affected individuals experiencing varying combinations of symptoms or cyclic patterns of symptom recurrence 3,11 . Symptom severity and duration in Long COVID are not always dependent on a patient’s hospitalisation for acute COVID. Severe cases have been observed in both patients who were hospitalised for acute COVID 12,13 and in those who were not 14 . Long COVID is a complex condition resulting from various influences, including host-specific factors (e.g. co-morbidities), viral dynamics, environment exposures, and genetic predisposition 15 . It affects individuals across all demographics, irrespective of age, sex, race, or baseline health 1 . Additionally, vaccination status has been suggested as a potential factor influencing disease trajectory 16,17 . To date, more than 13 billion COVID-19 vaccine doses have been administered globally, to mitigate the impact of the pandemic 18 . However, some vaccinated individuals have reported persistent symptoms resembling both acute COVID and Long COVID, leading to the term “vaccine injury” 15,19 . The underlying mechanisms remain poorly understood, with several factors – including vaccine type, host genetics, prior SARS-CoV-2 infection, and timing of the vaccination – potentially influencing any adverse outcomes 19,20 . COVID-19 vaccines fall into four primary categories: adenovirus vector-based vaccines (e.g., AstraZeneca’s Vaxzevria and Covishield ChAdOx1 and Johnson-Janssen’s Ad26.COV2.S), mRNA-based vaccines (e.g., mRNA, Moderna’s Spikevax mRNA-1273 and Pfizer-BioNTech’s Comirnaty BNT162b2), adjuvanted protein vaccines (e.g., Norvavax’s Nuvaxovid and Covovax NVX CoV 2373), and inactivated virus vaccines (e.g., Sinopharm’s Covilo, Sinovac’s CoronaVac and Bharat Biotech’s Covaxin) 20 . The complex interplay between SARS-CoV-2 infection, immune response, and vaccination presents significant challenges in distinguishing Long COVID from vaccine injury. Misdiagnosis of Long COVID and/or vaccine injury can easily occur due to the overlapping symptoms and the challenge of establishing a clear timeline. This is especially difficult when distinguishing between Long COVID caused solely by infection and cases where vaccination may also involve vaccine injury ( Fig. 1 ). In many cases, timeline of disease onset is undetermined 19 , making it nearly impossible to delineate causality with confidence. Current research on Long COVID suggests multiple pathophysiological mechanisms, including autoimmunity, immune dysregulation, viral persistence, gut dysbiosis, and microvascular dysfunction 1,21,22 . One of the most noteworthy findings in recent studies is the presence of hypercoagulability across COVID-19-related conditions, including Long COVID and vaccine injury 21,23 . The initial SARS-CoV-2 infection induces pronounced inflammatory response, disrupting the balance between pro- and anti-coagulant pathways, ultimately leading to endothelial dysfunction 24,25 . In addition, the spike protein itself is capable of inducing anomalous clotting that is resistant to fibrinolysis 25 , the extent reflecting the virulence of the SARS-CoV-2 variant in a manner that implies that this is on the aetiological pathway of the disease 26 . Considering the complex interplay of multiple factors influencing the development and progression of Long COVID, alongside the potentially distinct mechanisms underlying adverse vaccine side effects, understanding the relationship between Long COVID and vaccine-related injuries is crucial. The development of both Long COVID and vaccine-related symptoms can follow multiple, overlapping timelines and pathways, as illustrated in Fig. 1 . However, we wish to recognise that millions of individuals developed Long COVID prior to the availability of vaccines. Nonetheless, grasping these timelines provides valuable understanding, facilitating the subsequent application of a systems approach by both clinicians and researchers 15,19,20 . Given the significant overlap between symptoms attributed to vaccine-related adverse effects and those seen in Long COVID, alongside the difficulty in definitively excluding prior asymptomatic or mild SARS-CoV-2 infection, we have chosen to refer to the patient group recruited for this current paper, as individuals with Post-Vaccination/Post-Infection Syndrome (PV/PIS). This term acknowledges the complexity of distinguishing between vaccine-associated immune dysregulation and post-infectious sequelae, particularly in populations where acute infection timelines may be unclear (asymptomatic or underreported cases). We recognize that both vaccination and SARS-CoV-2 infection (symptomatic or asymptomatic) may contribute to ongoing immune, inflammatory and coagulation abnormalities. By adopting the PV/PIS terminology in this current paper, we aim to avoid premature attribution of causality and instead focus on describing the proteomic and pathophysiological features present in this patient group. Materials and methods Ethical clearance This research forms a part of a larger study funded by the South African Medical Research Council (SAMRC, grant number 96847), where ethics approval was obtained from the Health Research Ethics Council (HREC) at Stellenbosch University, South Africa (project reference number N22/11/133, ID: #26785). The study protocol ensured that all participants were fully briefed on the experimental objectives, potential risks, and all pertinent study details. Additionally, their informed consent was obtained for both sample collection and storage for multi-disciplinary research. Throughout the study and across all research activities, rigorous adherence to ethical standards was meticulously upheld, following the principles outlined in the Declaration of Helsinki, the South African Guidelines for Good Clinical Practice, and the Medical Research Council Ethical Guidelines for Research. Sample recruitment and patient inclusion criteria A questionnaire was used to collect information on age, gender, pre-existing co-morbidities, other clinical characteristics, vaccination status, as well as details regarding the diagnosis of acute and/or Long COVID and/or vaccine event(s) that might have resulted in persistent symptom onset. Additionally, data on the severity of the condition(s) and self-reported symptoms associated with these conditions were gathered. Our study cohort included 16 ostensibly healthy participants to serve as the control group (12 females; 4 males; mean age 43.9 ± 15.2). Control participants were individuals who had been vaccinated at least once and/or had experienced acute COVID but did not have Long COVID or experienced prolonged symptoms typical of PV/PIS after vaccination. Notably, participant selection for this study was not based on vaccination type. Among the 16 ostensibly healthy participants, 9 had a history of acute COVID infection, and had recovered without prolonged health issues. Two of the healthy controls developed pericarditis after vaccination but recovered fully after early treatment (see Table S1 ). The remaining healthy participants did not report a prior infection; however, the possibility of an asymptomatic acute infection cannot be rule out. We recruited 14 individuals that adhered to our criteria of PV/PIS (9 females; 5 males; mean age 59.9 ± 15.8). The inclusion criteria for our PV/PIS cohort required participants to have received at least one dose of any COVID-19 vaccine between 2020 and 2024. Notably, all our PV/PIS participants received only Pfizer-BioNTech vaccines between mid-2021 to mid-2022. Eligible participants experienced symptoms for at least 12 weeks post-vaccination. If participants had a documented acute COVID infection (either before or after vaccination), confirmation based on polymerase-chain reaction (PCR) or antigen testing was required. Exclusion criteria included individuals who experience acute COVID infection at the time of vaccination or were actively receiving treatment for acute or Long COVID. Most of our PV/PIS cohort were vaccinated during the third wave of COVID-19 between May and Sep 2021 (Delta variant peak), shortly before the emergence of the Omicron variant in Dec 2021 (see Fig. S1 ). Sample collection Blood samples were collected via venepuncture by a licensed medical practitioner, or certified phlebotomist. These samples included the following: three 3 mL sodium citrate (3.2%) tubes (BD Vacutainer®, 369714), and one 5 mL serum (silicone/polymer gel) blood tube (BD Vacutainer®, 367986) for blood pathology studies. The sodium citrate tubes were then transported to the blood laboratory at Stellenbosch University. Only one whole blood (WB) sample was centrifuged at 3000 ×g for 15 min at room temperature. The supernatant platelet poor plasma (PPP) was carefully removed and aliquoted into a 1.5 mL Eppendorf tube and stored at -80 ℃. All other samples collected were used as part of research outside the scope of this study. Fluorescence microscopy Sample preparation for platelet poor plasma (PPP) to study heterogenous amyloid deposits (microclots) At room temperature, within 24h of sample collection, 49 μL of platelet poor plasma (PPP) was aliquoted into a 1.5 mL Eppendorf tube. The samples were stained with 1 μL of Thioflavin T (ThT, Sigma-Aldrich, St. Louis, MO, USA) with a final concentration of 0.005 mM, while protected from light. The stained samples were incubated in a light-protected container at room temperature for 30 min. After incubation, 3 μL of the stained PPP sample was used to prepare PPP smears for viewing and analysis. This ThT method was previously established to visualise abnormal clotting in various inflammatory conditions 27-29 . Viewing of platelet-poor plasma (PPP)to study heterogenous amyloid deposits (microclots) The PPP smears were examined using a Zeiss AxioObserver 7 fluorescent microscope with a Plan-Apochromat 63×/1.4 Oil DIC M27 objective (Carl Zeiss Microscopy, Munich, Germany). For ThT, the excitation wavelength range was set between 450 nm to 488 nm, with an emission range from 499 nm to 529 nm 25,30,31 . Representative micrograph images were captured at random, with a minimum of four images acquired per sample. Proteomics: digestion of heterogenous amyloid deposits (microclots) in platelet poor plasma (PPP) Sample preparation and trypsin digestion For proteomics analysis, 30 platelet-poor plasma (PPP) samples, previously stored at -80 ℃, were selected, including controls (n = 16), and PV/PIS (n = 14), and 100 μL of each sample was aliquoted out. These pre-aliquoted samples were then stored at -20 ℃ until use. A 20× dilution of the stored PPP was prepared by combining 5 μL of the naïve sample with 45 μL of 10 mM ammonium bicarbonate (NH4HCO3). The protein concentration of each sample solution was determined using a nanodrop spectrophotometer (ThermoFisher), measuring absorbance at 280 nm. This sample set had an increased viscosity of the PPP compared to previous studies by Pretorius et al. (2021), and Kruger et al. (2022), necessitating adjustments to the heterogenous amyloid deposits (microclots) analysis protocol outlined by Pretorius et al. (2021). Each sample was standardised using a set volume of 25 μL naïve PPP sample, combined with 100 μL of phosphate-buffered saline (PBS, McKesson) to better mimic physiological conditions. The sample solution was placed in a Falcon tube and rotated on a tube rotator, for 50 – 60 min, until homogenised. The samples were centrifuged at 12 000 ×g for 10 min at room temperature to separate the heterogenous amyloid deposits (microclots; insoluble fraction of PPP) from the soluble portion. The supernatant was removed, leaving 8 μL of sample solution behind. The remaining solution was reconstituted with 72 μL PBS, to achieve a 10× dilution factor and centrifuged again at 12 000 ×g, for 10 min at room temperature 16,32 . Carefully, to avoid disturbing the heterogenous microclot pellet, 72 μL of soluble fraction of the sample solution was removed. The pellet was further dissolved by the adding 10 μL of a solution containing 5 mM tris (2-carboxyethyl)phosphine (TCEP, Sigma-Aldrich) and 250 mM triethylammonium bicarbonate (TEAB, ThermoFisher) to the remaining 8 μL of sample solution. The samples were vortexed briefly (5 s), then incubated in a heating block for 30 min at 60 ℃, followed by 30 min at 44 ℃. After incubation, the samples were briefly centrifuged at room temperature using a mini benchtop centrifuge to re-incorporate any evaporated sample that may have settled in the lid of the Eppendorf tube and then allowed to cool to room temperature. To further denature the insoluble heterogenous deposits, 20 μL of 50% methanol was added to each sample. To block the cysteine residues, the samples were alkylated with 3 μL of iodoacetamide, protected from light, and incubated at room temperature for 30 min. To each sample, 4 μL of a solution containing dithiothreitol (DTT, Sigma-Aldrich) and TEAB (ThermoFisher) was added. The pellet was further digested by adding 10 μL of a trypsin solution, containing 0.5 μg/μL trypsin (Pierce), and incubated at 37 ℃, for 18h. Following incubation, 5 μL of 5% trifluoroacetic acid (TFA, Sigma-Aldrich), with a final concentration of 1% v/v, was added to each sample to acidify and terminate the reaction. The samples were then centrifuged at 12 000 ×g for 10 min at room temperature to pellet out the trypsin precipitate. The supernatant was transferred to conical inserts (ALWSCI technologies). The samples were dried under vacuum for 30 min, using a rotary evaporator. Dried samples were resuspended in 30 μL of analytical grade water, and peptide concentration was measured using a nanodrop spectrophotometer (ThermoFisher), absorbance measured at 220 nm. After measuring the peptide concentration, the samples were dried again under vacuum using the rotary evaporator. Dried samples were resuspended in 20 μL of a loading buffer containing 2% acetonitrile (Burdick and Jackson) in analytical grade water with 0.1% formic acid (Sigma-Aldrich). The digested heterogenous microclot pellet was loaded directly in the autosampler set to 7 ℃. Liquid chromatography of digested pellet Liquid chromatography was conducted as previously described by Pretorius et al. (2021), Kruger et al. (2022), and Nunes et al. (2024), using Thermo Scientific Ultimate 3000 RSLC (ThermoFisher, 2019), equipped with a 20 mm × 100 μm C18 trap column (Thermo Scientific), and a CSH 25 cm × 75 μm with a 1.7 μm particle size C18 column (Waters) analytical column 16,32,33 . The solvent system for loading consisted of 2% acetonitrile (Burdick and Jackson) with 0.1% formic acid (Sigma-Aldrich) in analytical grade water. Solvent A consisted of analytical grade water, with 0.1% formic acid (Sigma-Aldrich), while solvent B consisted of 100% acetonitrile (Burdick and Jackson) with 0.1% formic acid (Sigma-Aldrich). Samples were loaded onto the trap column at a flow rate of 2 μL/min, from a temperature-controlled autosampler set 7 ℃, using loading solvent. Loading was performed for 4 mins before elution onto the analytical column. The flow rate was set to 300 nL/min, and the gradient was generated as follows: 5% - 30% solvent B over 65 min and 30-45% solvent B from 65 – 80 min. Chromatography was performed at 45 ℃, with the outflow directed to the mass spectrometer using a stainless-steel nano-bore emitter. Mass spectrometry of the digested pellet Data independent acquisition (DIA) mass spectrometry analysis was performed using ThermoScientific Fusion mass spectrometer, equipped with a Nanospray Flex ionisation source. The prepared samples were introduced through a stainless-steel nano-bore emitter. Data was collected in a positive mode with spray volage set 2.0 kV and ion transfer capillary at 290 ℃. Polysiloxane ions, at 445.12003 m/z, were used for internal calibration of the spectra. For MS1 scans, the Orbitrap detector was set to a resolution of 60, 000 over a scan range of 375 – 1500 m/z, with an automatic gain control (AGC) target set to standard. Data acquisition was conducted in profile mode. In higher-energy C-trap dissociation (HCD) mode, precursor ions were selected for fragmentation using the quadrupole mass analyser with HCD energy set to 30%. The precursor mass range was set to 500 – 900 m/z, with an isolation window of 20 m/z. Fragment ions were detected using the Orbitrap mass analyser set to a resolution of 30, 000. The AGC target was set to custom. Data acquisition was performed in centroid mode. Mass spectrometry data analysis The raw data files were processed using FragPipe Analysis Pipeline (version 22.0), with the included DIA SpecLib Quant workflow. The Uniprot human database concatenated with the SARS-CoV-2 database, from UniProt (https://www.uniprot.org/taxonomy/9606 ; https://www.uniprot.org/uniprotkb?query=694009), was used as reference. MSFragger search parameters included fragment mass tolerances set to 20 parts per million (PPM) for mass calibration and optimisation; and enzyme was set to select trypsin, allowing for 2 mis-cleavages. Fixed modifications were set to carbamidomethyl-C, and variable modifications were set to methionine (M) oxidation, protein end terminal acetylation, and the deamidation of asparagine and glutamine residues (NQ). Peptide spectral match (PSM) validation was done with percolator and rescored using MSBooster in FragPipe. Protein inference was performed using ProteinProphet. Quantification was done using DIA-NN with a false discovery rate (FDR) set to 0.01. Protein group (pg_matrix.tsv) and experiment annotations (.tsv) files, generated by using the FragPipe Pipeline, were uploaded into FragPipe-Analyst (http://fragpipe-analyst.nesvilab.org/) for statistical analysis and validation. The minimum percentage of non-missing values globally and in at least one condition, was set to 0. The adjusted p-value cutoff was 0.05, and the Log2 fold cutoff was 1. Median-centred normalisation was used, with Perseus imputation type and Benjamini-Hochberg method for FDR correction. Protein-protein interaction networks were viewed using the STRING database (version 12.0, https://string-db.org/). Statistically significant protein changes (determined in the above FragPipe analysis) were uploaded to the multiple protein search tool. Under the clusters tab in STRING, Markov Cluster (MCL) algorithm was selected, and the inflation parameter was set to 3, to perform clustering analysis. Functional enrichment analysis was applied to identify over-represented pathways, molecular functions, and biological processes within each cluster. Amyloidogenic potential of significant proteins was predicted using AmyloGram (http://biongram.biotech.uni.wroc.pl/AmyloGram/, accessed on 19 March 2025). A cut-off value (probability threshold) of 0.5 was applied. A cut-off value of 0.5 will automatically yield the following values for the following parameters: a sensitivity value set to 0.8658, specificity value set to 0.7852 and a Matthews correlation coefficient (MCC) of 0.6268. These values are automatically adjusted with any changes made to the cut-off value. The amyloid probability and fraction of amyloid residues were recorded for each significant protein change. Statistical Analysis All data from demographics, including the subsequent statistical evaluations, were processed using GraphPad Prism (version 10.2.3). The Shapiro-Wilk test was used for normality testing. For parametric data, statistical significance was determined using unpaired t-test. A 95% confidence level was applied, with statistical significance was considered at p < 0.05. Significance levels were indicated by asterisks (*= p < 0.05; ** = p < 0.01; *** = p < 0.001; and **** = p < 0.0001). Parametric data is presented as mean ± standard deviation (SD). Statistical analysis for proteomics data is detailed above. Comparative Analysis Comparative analysis was performed using the Spotfire® program (http://spotfire.com/, version 12.0, accessed on 14 April 2025) to visualise and interpret proteomic differences between the current study and previously published Long COVID data by Kruger et al. (2022). Proteins identified as having significant changes in the current study were compared to those reported in the Long COVID dataset 32 . To standardise the scale across datasets, fold changes values for downregulated proteins were converted to their reciprocals, such that values 1 reflected upregulation. A scatter plot was generated in Spotfire®, with fold change plotted on the x-axis, and p-value on the y-axis. A trend line was included to aid in the visual interpretation of data distribution. Proteins were assessed for overlap and directional consistency between the two studies. Results Study cohort and demographics Sample demographics are shown in Table 1 and Figure 2 represents the various ways PV/PIS participants developed their condition. The development and progression of PV/PIS is not exclusive to a single developmental pathway but can manifest through various pathways. The majority of the healthy individuals who reported that they suffered from an acute infection, experienced acute symptoms such as cough (55.6%), sore throat (33.3%), fever (55.6%) and headache (33.3%) ( Table S1 ). Similarly, among the 14 PV/PIS participants, 8 had a prior acute COVID infection, with most reporting cough (62.5%), sore throat (62.5%), fever (87.5%), myalgia (62.5%), malaise (50.0%) and dysgeusia (37.5%) ( Table S1 ). These symptoms resolved soon after the infection cleared, with no persistent symptoms reported. Regarding vaccination status, 81.3% of the healthy individuals were vaccinated, receiving either Pfizer, J&J, or a combination of both. In contrast, all PV/PIS participants had exclusively received Pfizer ( Table 1 ). Notably, participant selection for this study was not based on vaccination type. Table 1 . Sample cohort and demographics from healthy participants, and participants with Post Vaccination/Post-Infection Syndrome (PV/PIS). Sample demographics Platelet poor plasma stored previously Mean age [with ± SD] Controls (n=16) 43.9 (±15.2) PV/PIS (n=14) 59.9 (±15.8) Gender distribution across all groups Controls (n=16) 12 Females; 4 Males PV/PIS (n=14) 9 Females; 5 Males Clinical characteristics of participants Variables % In 16 controls % In 14 PV/PIS Chronic medication 12.5 57.1 Previous smoker 0 21.4 Current smoker 0 35.7 Overweight/obese 18.8 21.4 Cardiovascular disease 6.3 14.3 Hypertension 6.3 35.7 Arthritis 0 21.4 Cancer 6.3 7.1 Type 2 diabetes 0 21.4 Anaemia 6.3 7.1 Rosacea 6.3 0 Irritable bowel syndrome 6.3 21.4 Hypothyroidism 0 14.3 Dyslipidaemia 25.0 21.4 Anxiety/depression 18.8 50.0 Neurodevelopmental disorders 0 7.1 Percentage (%) of participants vaccinated Controls (n=16) 81.3 PV/PIS (n=14) 100 Type of vaccine administered Vaccine type and/or combination % In 16 controls % In 14 PV/PIS Pfizer only 56.3 100 J&J only 31.3 0 Pfizer and J&J 6.3 0 All demographics data was subjected to a Shapiro-Wilks normality test. An unpaired T-test was performed on parametric data, with data are represented as mean ± standard deviation (SD). Heterogenous amyloid deposits (microclots) in platelet-poor plasma (PPP) as viewed with fluorescence microscopy Previous studies 16,31 have shown that naïve platelet-poor plasma (PPP) from healthy individuals and type 2 diabetes mellitus (T2DM) participants exhibit significantly fewer heterogenous amyloid deposits (microclots) upon exposure to thioflavin T (ThT) compared to acute COVID and Long COVID participants. In this study, we demonstrate that PV/PIS participants also exhibit substantial formation of heterogenous amyloid deposits (microclots), comparable to that seen in Long COVID. Figure 3 provides examples of naïve PPP samples exposed to ThT from both control and PV/PIS group Liquid chromatography and mass spectrometry (LC-MS) of digested pellet deposits in platelet-poor plasma (PPP) Proteomics data analysis of control (n = 16) and PV/PIS (n = 14) samples was performed using FragPipe Analysis Pipeline (version 22.0) and FragPipe-Analyst (http://fragpipe-analyst.nesvilab.org/). Significant protein results are shown below in Table 2 . This study identified 26 proteins that were significantly downregulated and 26 proteins that were significantly upregulated in PV/PIS group compared to controls. Changes in protein levels are represented as fold changes, with downregulated proteins denoted as reciprocal values. Figure 4 shows a volcano plot for an overview of protein distribution for the pairwise comparison (controls vs. PV/PIS). Figure 5 shows an overview of functional protein clusters for proteins that are up- or downregulated by more than 1-fold with a p-value < 0.05. Functional protein cluster changes in PV/PIS can give systematic indications of pathways up or downregulated in PV/PIS After homogenisation and trypsinisation, this mass spectrometry-based proteomics analysis revealed an increase in coagulation proteins factors X and XI, as well as an increase in acute inflammatory response proteins, including serum amyloid A1 (SAA1) and serum amyloid A2 (SAA2). In contrast, proteins associated with immune response, such as Protein S100A9 and other immunoglobulin complex components, were downregulated in PV/PIS participants compared to controls. Table 2 . Significant protein changes in pairwise analysis of heterogenous amyloid deposits (microclots) from PV/PIS individuals and controls. Significant protein changes in pairwise analysis of PV/PIS heterogenous amyloid deposits (microclots) compared to controls Downregulated proteins Protein Name Protein ID Gene ID Fold Change p value Proteins appear in both PV/PIS (n=14) and control (n=16) groups. Alpha-1-acid glycoprotein 2 P19652 ORM2 0.198 <0.001 CD5 antigen-like O43866 CD5L 0.283 <0.001 Zinc-alpha-2-glycoprotein P25311 AZGP1 0.309 <0.001 Keratin, type II cytoskeletal 6B P04259 KRT6B 0.347 <0.001 Keratin, type II cytoskeletal 3 P12035 KRT3 0.368 <0.001 Apolipoprotein D P05090 APOD 0.382 <0.001 Transmembrane protein 256 Q8N2U0 TMEM256 0.407 <0.001 Collagen alpha-3 chain P12111 COL6A3 0.415 <0.001 Protein S100-A9 P06702 S100A9 0.422 0.001 Platelet factor 4 P02776 PF4 0.433 <0.001 Albumin P02768 ALB 0.435 <0.001 Immunoglobulin lambda-like polypeptide 5 B9A064 IGLL5 0.524 <0.001 Transthyretin P02766 TTR 0.581 <0.001 Keratinocyte differentiation-associated protein P60985 KRTDAP 0.617 0.003 Immunoglobulin kappa variable 2-28 A0A075B6P5 IGKV2-28 0.633 <0.001 Keratin, type I cytoskeletal 10 P13645 KRT10 0.671 <0.001 Keratin, type II cytoskeletal 2 epidermal P35908 KRT2 0.680 0.002 Junction plakoglobin P14923 JUP 0.746 0.004 Alpha-1-acid glycoprotein 1 P02763 ORM1 0.752 <0.001 Immunoglobulin kappa light chain P0DOX7 P0DOX7 0.775 <0.001 Keratin, type I cytoskeletal 14 P02533 KRT14 0.787 0.003 Keratin, type I cytoskeletal 9 P35527 KRT9 0.806 0.004 Keratin, type II cytoskeletal 1 P04264 KRT1 0.813 0.002 Keratin, type I cytoskeletal 15 P19012 KRT15 0.840 0.008 Vitamin D-binding protein P02774 GC 0.847 <0.001 Serotransferrin P02787 TF 0.971 <0.001 Upregulated proteins Protein Name Protein ID Gene ID Fold Change p value Proteins appear in both PV/PIS (n=14) and control (n=16) groups. Inter-alpha-trypsin inhibitor heavy chain H4 Q14624 ITIH4 1.07 <0.001 Immunoglobulin heavy variable 5-51 A0A0C4DH38 IGHV5-51 1.08 <0.001 Plasma protease C1 inhibitor P05155 SERPING1 1.09 <0.001 Alpha-1-antichymotrypsin P01011 SERPINA3 1.13 <0.001 Serine/threonine-protein phosphatase 2A regulatory subunit B Q06190 PPP2R3A 1.13 <0.001 Apolipoprotein M O95445 APOM 1.16 <0.001 Carboxypeptidase B2 Q96IY4 CPB2 1.18 <0.001 Inter-alpha-trypsin inhibitor heavy chain H3 Q06033 ITIH3 1.28 <0.001 Cholinesterase P06276 BCHE 1.31 0.004 Ficolin-3 O75636 FCN3 1.33 <0.001 Apolipoprotein C-IV P55056 APOC4 1.33 0.001 Thyroxine-binding globulin P05543 SERPINA7 1.37 <0.001 BPI fold-containing family B member 1 Q8TDL5 BPIFB1 1.37 0.016 Corticosteroid-binding globulin P08185 SERPINA6 1.38 <0.001 Filamin-A P21333 FLNA 1.46 <0.001 Prenylcysteine oxidase 1 Q9UHG3 PCYOX1 1.52 <0.001 Coagulation factor XI P03951 F11 1.63 <0.001 Serum amyloid A-2 protein P0DJI9 SAA2 1.66 0.022 Complement factor I P05156 CFI 1.67 <0.001 Serum amyloid A-1 protein P0DJI8 SAA1 1.72 0.014 Plasma serine protease inhibitor P05154 SERPINA5 1.73 0.012 Immunoglobulin heavy variable 2-26 A0A0B4J1V2 IGHV2-26 1.78 0.015 Coagulation factor X P00742 F10 2.06 <0.001 Tetranectin P05452 CLEC3B 2.31 <0.001 Haptoglobin-related protein P00739 HPR 2.44 <0.001 Attractin O75882 ATRN 2.57 <0.001 In this pairwise comparison, fold changes of proteins (or fragments thereof) that were found to be decreased, are denoted as a reciprocal values. Significance was determined at p<0.05. Comparative overview of PV/PIS cohort vs. a previously described Long COVID cohort Using the Spotfire® program (http://spotfire.com/, version 12.0, accessed on 14 April 2025), plasma proteomic changes in the PV/PIS cohort were visualised in a scatter plot alongside the proteins reported in a previously published Long COVID proteomic study by Kruger et al. (2022). In Figure 6A , a broader distribution of protein changes is observed in the Long COVID group (blue dots), while PV/PIS cohort displays a tighter pattern of protein changes (red dots). Figures 6B and 6C , highlight each population separately allowing for visual identification of the specific with significant expression changes in each cohort. Discussion Since the onset of the COVID-19 pandemic, several SARS-CoV-2 vaccines have been developed to reduce transmission rates and enhance immunity. Among these, mRNA-based vaccines, such as Pfizer-BioNTech (BNT162b2) and Moderna (mRNA-1273), were widely deployed due to their rapid availability and their efficacy across different populations. These vaccines utilise lipid nanoparticles to encapsulate and deliver mRNA, which then leads to the production of neutralising antibodies and activation of T-cell immune response, which may contribute to reduced viral spread and severity of infection 34 , 35 . However, recent evidence highlights limitations in long-term efficacy of mRNA vaccine technology, particularly against emerging SARS-CoV-2 variants. While these vaccines were highly effective against the original strain, inducing strong IgM and IgG responses 35 , the spike (S) protein – the primary target of vaccine-induced immunity – undergoes significant selective pressure. This evolutionary pressure drives the accumulation of mutations in the S gene, facilitating the emergence of new variants with altered immune evasion properties. 36 , 37 , as well as an altered propensity to drive heterogenous amyloid deposit (microclot) formation 25 , 26 . A recent comparative study by Christie et al. (2022) analysed spike protein mutations in the Alpha, Beta and Delta variants, demonstrating a progressive ability of the virus to enter cells independently of spike-ACE2 interactions. This adaption correlates with increased transmissibility and reduced vaccine effectiveness 38 . More recent variants, such as Omicron, exhibit enhanced immune evasion, leading to a measurable decline in mRNA vaccine efficacy 39 – 41 . Notably, most of our PV/PIS cohort were administered with vaccines shortly before the first detection of the Omicron variant in 2021 (see Fig. S1 ). This timing suggests that a significant portion of our PV/PIS population had been vaccinated with mRNA-based vaccines while the virus continued to evolve, plausibly contributing to the reduced effectiveness of the vaccine against this variant. Additionally, concerns have emerged regarding the potential role of mRNA vaccines in inducing a “spike effect”, where endogenous spike protein interacts with the ACE2 receptor, mimicking aspects of COVID-19 pathology 35 . The administration of mRNA vaccines elicits a robust T-cell response, with CD4 + and CD8 + T-cells specific to spike protein persisting in circulation for up to 6 months post-vaccination 42 – 44 . Recent studies indicate that an acute COVID infection can induce a strong Th17 immune response, which plays a key role in cytokine cascade activation. Th17 immune response promotes inflammation via the release of IFN-γ, IL-6 and IL-17, contributing to immune dysregulation and persistent inflammation 45 . An imbalance in Th1/Th2 immune responses, driven by excessive Th17 activation, has been implicated in SARS-CoV-2 pathogenesis 46 . Notably, post-mRNA vaccination, elevated IL-17 levels have been reported, triggering an immediate and intense inflammatory response 47 (Fig. 7 ). A dysregulated Th17 response is also observed in pregnancy disorder pre-eclampsia 48 , which bears many similarities to responses associated with Long COVID infection 49 . The upregulation of serum amyloid A (SAA), attractin (ATRN) and complement factor I (Table 2 ), in the PV/PIS participants may modulate cytokine production, promote T-cell activation and recruitment – including Th17 cell migration – and disrupt immune homeostasis in affected individuals (Fig. 7 ). Inflammation, immune dysregulation and amyloidogenic proteins in PV/PIS We identified several acute phase response proteins, also known as acute phase reactants (APR) that had significant changes in protein concentrations. These APRs are non-specific inflammatory markers that demonstrate a significant change during an inflammatory state (whether acute or chronic). These APRs can contribute to several symptoms, such as that of fever, fatigue, and amyloidosis. Primary mediators of APRs gene expression include interleukins (IL-6 and IL-1β), TNF- \(\:\alpha\:\) , and growth factors, amongst others. Positive APRs, such as serum amyloid A (SAA), are typically increased during inflammation and negative APRs, such as albumin, are decreased 50 , 51 . Our findings show the upregulation of positive APRs, such as SAA-1 (1.72-fold increase) and SAA-2 (1.66-fold increase), and the downregulation of negative APRs, such as albumin (2.3-fold decrease) in PV/PIS relative to controls (Table 2 ). The significant upregulation of SAA-1 and SAA-2 in the PV/PIS cohort can be connected to the activation of monocytes and dendritic cells in PV/PIS participants (Fig. 7 ). The activation of these cells can trigger the release of damage-associated molecular patterns (DAMPs) and pattern-associated molecular patterns (PAMPs). These molecules in turn activate the innate immune system via pattern recognition receptors (PRRs), leading to exacerbated inflammatory and immune responses 52 . This cascade of events is typically observed following vaccination and/or during acute SARS-CoV-2 infection. However, in PV/PIS participants, including those in our cohort, the interplay between post-vaccination and the residual impact of SARS-CoV-2 infection – whether present or absent – may contribute to the cyclic disease pattern observed in these individuals. The brief mechanisms outlined above culminate in the activation of inflammatory and immune responses. However, persistent stimulation of these pathways, coupled with the potential circulating presence of SARS-CoV-2, can lead to dysregulation not only of the immune and inflammatory systems but also of the coagulation system. This coagulopathy, typically characterised by dysfunctional haemostasis and endothelial dysfunction, can further amplify inflammation and immune activation, driving a hyperinflammatory, hypercoagulable state and favouring Th17-skewed immune response; thus, mirroring the pathophysiological mechanisms observed in acute COVID infection. In contrast, the downregulation of the negative APR, albumin, in PV/PIS relative to controls may be attributed to a reduction in its synthesis, likely to conserve amino acids for the production of positive APRs 51 . This redistribution of amino acids facilitates the enhanced synthesis of positive APRs, as well as other pro-inflammatory molecules, thereby promoting the expression of inflammatory genes and the translation of their respective proteins. While not a direct driver of the cyclical disease pattern, this shift in protein synthesis indirectly contributes to the heightened inflammatory state observed in the PV/PIS cohort. Chronic elevation of positive APRs (SAA-1 and SAA-2) in PV/PIS contributes not only to sustained inflammation and immune dysregulation but also poses a risk for secondary amyloidosis 53 , 54 . In PV/PIS, SAA proteins may misfold and aggregate into amyloid fibrils. Using the AmyloGram predictive amyloidogenicity computational model ( http://biongram.biotech.uni.wroc.pl/AmyloGram/ , accessed on 19 March 2025) 55 , we assessed the amyloidogenic potential of SAA-1 and SAA-2. SAA-1 received an amyloid score of 0.793, with 0.230 of its residues predicted to be amyloidogenic, while SAA-2 had a slightly higher score of 0.845, with 0.221 of its residues predicted to be amyloidogenic (see Table S2 ). Although both proteins show a relatively low fraction of amyloidogenic residues, the high amyloid scores suggest these regions in SAA-1 and SAA-2 are particularly prone to misfolding. Furthermore, the limited fraction of amyloid-prone regions, within these proteins, can drive pathogenic amyloid formation when chronically elevated, as is characteristic of PV/PIS 55 . Our study identified a 2.57-fold upregulation of attractin (ATRN) in PV/PIS participants relative to controls (Table 2 ). Like many multifunctional proteins, ATRN plays a critical role in immune modulation. During inflammation, soluble attractin (sATRN) is released, facilitating immune cell recruitment, while membrane-bound attractin (mATRN) is expressed on activated monocytes, dendritic cells, and endothelial cells, promoting cell adhesion and migration 56 , 57 . The expression of mATRN on endothelial cells is upregulated in response to elevated pro-inflammatory cytokines. Once immune cells are recruited, mATRN undergoes proteolytic shedding generating sATRN and reducing its surface expression on both endothelial and immune cells 56 , 57 . The observed high fold-change in ATRN may indicate prolonged immune cell recruitment and sustained shedding, which could contribute to increased vascular permeability, endothelial dysfunction, and immune dysregulation, promoting Th17 immune response in PV/PIS participants. Additionally, ATRN demonstrated one of the highest predicted amyloid scores (0.918) using AmyloGram, with 0.276 of its residues predicted to be amyloidogenic ( http://biongram.biotech.uni.wroc.pl/AmyloGram/ , accessed on 19 March 2025; see Table S2 ) 55 . As with the SAA proteins, the limited fractions of amyloidogenic residues in ATRN are highly susceptible to misfolding, especially within pathophysiological environments, such as that of PV/PIS. The abnormal increase in ATRN expression and shedding may promote protein misfolding further exacerbating endothelial dysfunction in PV/PIS participants. Given its amyloidogenic nature, ATRN is highly likely to cross-seed with fibrinogen 58 , 59 , reinforcing the hyperinflammation and hypercoagulable state observed in PV/PIS participants. We identified the upregulation of inter-alpha inhibitor heavy chain H3 (ITIH3; 1.28-fold increase) and heavy chain H4 (ITIH4; 1.07-fold increase) in PV/PIS participants relative to controls (Table 2 ). These proteins belong to the inter-alpha-trypsin inhibitor (ITI) family, a group of plasma protease inhibitors that play crucial roles in maintaining extracellular matrix (ECM) and modulating inflammation 60 , 61 . While the precise mechanisms underlying their functions remain poorly understood, ITIH4 has been proposed to act as an APR in response to infection, with its serum levels increasing during states of heightened inflammation 62 . The elevated presence of ITIH3 and ITIH4 in PV/PIS participants may serve as an additional driver of immune activation. However, their prolonged upregulation could contribute to immune dysregulation. Notably, using AmyloGram ( http://biongram.biotech.uni.wroc.pl/AmyloGram/ , accessed on 19 March 2025) 55 , computational modelling predicted high amyloid scores for both proteins, with ITIH3 and ITIH4 scoring 0.913 and 0.902, respectively (see Table S2 ). The potential misfolding of these proteins and their subsequent amyloid aggregation could facilitate cross-seeding with fibrinogen amyloid fibrils 58 , 59 , exacerbating the hyperinflammation and hypercoagulable state observed in PV/PIS. Persistent inflammation and coagulation abnormalities in PV/PIS A persistent inflammatory environment perpetuates immune activation, leading to the initiation of coagulation cascades 63 and increased endothelial permeability via pro-inflammatory cytokine signalling 64 , 65 . We identified a significant upregulation in complement and coagulation functional protein groups, as seen in Fig. 5 , with particular emphasis on the upregulation of intrinsic coagulation factors X (2.06-fold increase) and XI (1.63-fold increase) (Table 2 ). Platelet factor 4 (PF4) was found to be decreased by 2.31-fold in PV/PIS participants (Table 2 ). A reduction or absence of PF4 has been linked to increased IL-17 production and enhanced Th17 cell-mediated inflammation 66 , 67 . Additionally, IL-17A has been shown to influence platelet release of pro-angiogenic factors, which has the can indirectly affect PF4 expression and function 68 . In the context of chronic inflammatory conditions, such as PV/PIS, IL-17 may modulate PF4 expression through various indirect signalling pathways. However, the precise mechanisms and outcomes of this regulation process are not fully understood and warrant further research to elucidate the underlying interactions. Linking amyloidogenicity and coagulopathies in PV/PIS Secondary amyloidosis in proteins such as those mentioned above (SAA, ATRN, and ITI family) can have a downstream effect on the coagulation system, promoting the misfolding of proteins involved in coagulation, such as that of fibrinogen. Our findings indicate a non-significant upregulation of fibrinogen alpha (0.038-fold increase) and beta chains (0.670-fold increase) in the PV/PIS cohort compared to controls. Fibrinogen has been shown to adopt an amyloid-like structure when exposed to inflammatory and oxidative environments 16 , 32 , 69 . Additionally, other amyloidogenic proteins, such as the APRs mentioned above, have been shown to cross-seed amyloidogenic fibrin production 58 , 59 . The binding of these proteins may induce conformational changes in fibrinogen, affecting its detectability and potentially masking significant differences between PV/PIS and control groups in our proteomic analysis. Ultimately, the misfolding of protein structures with a high propensity of forming amyloid-type fibrils, combined with dysregulated haemostasis and immune responses, helps drive the formation of heterogenous amyloid deposits (microclots) in PV/PIS, as observed in Fig. 3 . Comparing PV/PIS with Long COVID A proteomics study done by Kruger et al. (2022) employed a double trypsin digestion protocol to analyse the plasma proteome, including proteins trapped inside heterogenous amyloid deposits (microclots), of Long COVID participants prior to vaccination rollout in South Africa 32 . We conducted a comparative analysis between the protein dataset from Kruger et al. and our PV/PIS cohort. Interesting, the proteins significantly downregulated in each cohort were mutually exclusive, with no overlap observed. Conversely, only one protein was found to be significantly upregulated in both Long COVID and PV/PIS groups (see Fig. S3 ). This divergence is further illustrated by the scatter plot distribution shown in Fig. 6 A. In PV/PIS (Fig. 6 C), most proteins exhibit a narrow range of expression changes, clustering around a slight up or downregulation. In contrast, the Long COVID (Fig. 6 B) cohort demonstrates a broader distribution, with some proteins exhibiting modest expression changes and others showing pronounced up or downregulation. These observed differences in protein expression profiles between PV/PIS and Long COVID suggest distinct mechanistic pathways that ultimately converge on similar pathophysiological outcomes. For instance, Kruger et al. (2022) reported the upregulation of ITIH1 and ITIH2 in individuals with Long COVID, implicating persistent dysregulation of ITI family proteins in post-viral inflammatory states. In contrast, our study findings indicate that ITIH3 and ITIH4 may play a more prominent role in PV/PIS. This distinction exemplifies the concept of “symptomatic overlap with mechanistic heterogeneity”, emphasising the need to differentiate the molecular pathways underlying these chronic conditions. Notably, the serum proteome signature developed by Völlmy et al. (2021) to predict mortality in severe COVID-19 patients, showed opposite trends in protein abundance for ITIH1/ITIH2 and ITIH3/ITIH4 during disease progression. The observation that ITIH3 and ITIH4 was more abundant in non-survivors may point to a more severe phenotype in the PV/PIS group compared to Long COVID cases. Evaluation of the predication power of mortality risk panel against other COVID-19 serum proteomics using independent cohorts in different countries, has validated plasma proteomics as reproducible and meaningful biomarker panels 70 Conclusion This study highlights the complex immunological and coagulopathic mechanisms associated with PV/PIS inflammation and coagulation, drawing some parallels with Long COVID pathology resulting exclusively after an acute infection event (where no vaccine was involved). While our findings suggest that PV/PIS may phenotypically mirror the persistent inflammation, coagulopathies, and endothelial dysfunction observed in Long COVID, the underlying protein expression patterns and mechanistic drivers appear fundamentally distinct. Notably, most differentially expressed proteins in our PV/PIS cohort were not shared with those reported by Kruger et al. (2022) in individuals with Long COVID. This stark contrast in proteomic signatures emphasises the likelihood that PV/PIS and Long COVID, while symptomatically overlapping, arise from divergent molecular and immunopathological pathways that may differ in disease severity and treatment requirements. The challenge of distinguishing between the individuals who developed Long COVID prior to vaccination, those who experienced Long COVID and subsequently developed vaccine-related complications, and those in whom the vaccine triggered further adverse reactions leading to PV/PIS, remains a major obstacle in both clinical and research settings. Many individuals with PV/PIS may have had one or more prior SARS-CoV-2 infections and/or Long COVID (Fig. 1 ), further complicating the clinical differentiation of these pathologies (see Table S1 ). The overlap between acute infection, vaccination and pathogenesis of Long COVID and/or PV/PIS presents a major hurdle in constructing clearly defined “pure PV/PIS” cohorts. Genetic, environmental and immunological factors all interact to influence the severity and persistent of PV/PIS. Moreover, the timing of vaccination relative to viral exposure, especially in the context of successive variant waves, may modulate immune response and influence downstream pathogenicity of these conditions. Despite these limitations, proteomics provides a promising approach to addressing these diagnostic challenges. By analysing protein profiles, it becomes possible to identify distinctive biomarkers that can be developed into targeted assays, enabling more accurate differentiation between PV/PIS and Long COVID. Declarations Acknowledgements: We extend our gratitude to the participants and their families who participated in this study. Our gratitude goes to the medical practitioners who referred individuals for participation in this study. We wish to express our thanks to Janine Cronje for her administrative and research assistance. Author Contributions: MV, EP and MW contributed to the conceptualisation, methodology and data analysis. MV designed the original experimental protocol. EEK, CS and CV managed participant recruitment and sample collection. MW and EP led the manuscript writing process. EP, MJK and KR oversaw funding acquisition and project administration. All authors provided critical input during the discussion of results and manuscript curation. Funding All authors express their gratitude to the South African Medical Research Council (SAMRC) for funds granted by the Department of Science and Innovation (grant number 96847) that supported this proteomics research. J.M.N. and E.P. thank Kanro Research Foundation for funding. D.B.K. thanks the Balvi Foundation (grant 18) and the Novo Nordisk Foundation for funding (grant NNF20CC0035580). The funders were not involved in study design, data collection and analysis, decision to publish or preparation of the manuscript. Consent for publication Written informed consent was obtained from all the participants prior to the inclusion in the study. 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ITIH4 acts as a protease inhibitor by a novel inhibitory mechanism. Sci Adv 7 . 10.1126/sciadv.aba7381. Bonaventura, A., Vecchié, A., Dagna, L., Martinod, K., Dixon, D.L., Van Tassell, B.W., Dentali, F., Montecucco, F., Massberg, S., Levi, M., and Abbate, A. (2021). Endothelial dysfunction and immunothrombosis as key pathogenic mechanisms in COVID-19. Nat Rev Immunol 21 , 319-329. 10.1038/s41577-021-00536-9. Schwameis, M., Schörgenhofer, C., Assinger, A., Steiner, M.M., and Jilma, B. (2015). VWF excess and ADAMTS13 deficiency: a unifying pathomechanism linking inflammation to thrombosis in DIC, malaria, and TTP. Thromb Haemost 113 , 708-718. 10.1160/th14-09-0731. Martinelli, N., Montagnana, M., Pizzolo, F., Friso, S., Salvagno, G.L., Forni, G.L., Gianesin, B., Morandi, M., Lunardi, C., Lippi, G., et al. (2020). A relative ADAMTS13 deficiency supports the presence of a secondary microangiopathy in COVID 19. Thromb Res 193 , 170-172. 10.1016/j.thromres.2020.07.034. Shi, G., Field, D.J., Ko, K.A., Ture, S., Srivastava, K., Levy, S., Kowalska, M.A., Poncz, M., Fowell, D.J., and Morrell, C.N. (2014). Platelet factor 4 limits Th17 differentiation and cardiac allograft rejection. J Clin Invest 124 , 543-552. 10.1172/jci71858. Chakraverty, R. (2014). An unexpected role for platelets in blocking Th17 differentiation. J Clin Invest 124 , 480-482. 10.1172/jci74231. Gatsiou, A., Sopova, K., Tselepis, A., and Stellos, K. (2021). Interleukin-17A Triggers the Release of Platelet-Derived Factors Driving Vascular Endothelial Cells toward a Pro-Angiogenic State. Cells 10 . 10.3390/cells10081855. Kell, D.B., Laubscher, G.J., and Pretorius, E. (2022). A central role for amyloid fibrin microclots in long COVID/PASC: origins and therapeutic implications. Biochemical Journal 479 , 537-559. 10.1042/bcj20220016. Völlmy, F., van den Toorn, H., Zenezini Chiozzi, R., Zucchetti, O., Papi, A., Volta, C.A., Marracino, L., Vieceli Dalla Sega, F., Fortini, F., Demichev, V., et al. (2021). A serum proteome signature to predict mortality in severe COVID-19 patients. Life Sci Alliance 4 . 10.26508/lsa.202101099. Additional Declarations The authors declare potential competing interests as follows: Kotze MJ. is a non-executive director and shareholder of Gknowmix (Pty) Ltd. Pretorius E. is an author of a patent DIAGNOSTIC METHOD FOR LONG COVID PCT application number GB2105644.5 and is the managing director of BioCODE Technologies. All other authors have no competing interests to declare. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6521005","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447497063,"identity":"65a2f4ae-1ede-4b52-aa2e-484504928078","order_by":0,"name":"Maxine Waters","email":"","orcid":"","institution":"Stellenbosch University","correspondingAuthor":false,"prefix":"","firstName":"Maxine","middleName":"","lastName":"Waters","suffix":""},{"id":447497064,"identity":"028a4fac-0a50-4aae-9613-06d6e8216e42","order_by":1,"name":"Mare Vlok","email":"","orcid":"","institution":"Tracelabs","correspondingAuthor":false,"prefix":"","firstName":"Mare","middleName":"","lastName":"Vlok","suffix":""},{"id":447497065,"identity":"a3fde24c-c48b-4531-83b5-28977d8bb43b","order_by":2,"name":"Elouise E. Kroon","email":"","orcid":"","institution":"Stellenbosch University","correspondingAuthor":false,"prefix":"","firstName":"Elouise","middleName":"E.","lastName":"Kroon","suffix":""},{"id":447497066,"identity":"f76925b2-ca41-4753-9aba-d374ee4396eb","order_by":3,"name":"Maritha J. Kotze","email":"","orcid":"","institution":"Stellenbosch University","correspondingAuthor":false,"prefix":"","firstName":"Maritha","middleName":"J.","lastName":"Kotze","suffix":""},{"id":447497067,"identity":"0ae78d66-25a9-4ca9-94fe-440d6d4ca400","order_by":4,"name":"Kelebogile E. Moremi","email":"","orcid":"","institution":"Stellenbosch University","correspondingAuthor":false,"prefix":"","firstName":"Kelebogile","middleName":"E.","lastName":"Moremi","suffix":""},{"id":447497068,"identity":"7d85f9ff-255e-4274-9704-ae5867aae616","order_by":5,"name":"Sunday O. Oladejo","email":"","orcid":"","institution":"Stellenbosch University","correspondingAuthor":false,"prefix":"","firstName":"Sunday","middleName":"O.","lastName":"Oladejo","suffix":""},{"id":447497069,"identity":"9e1a193d-4aa5-4721-b8b2-82ee8a6bea36","order_by":6,"name":"Kanshukan Rajartnam","email":"","orcid":"","institution":"Stellenbosch University","correspondingAuthor":false,"prefix":"","firstName":"Kanshukan","middleName":"","lastName":"Rajartnam","suffix":""},{"id":447497070,"identity":"326fd9d0-62c4-4a2a-b4c9-48fdd285f84d","order_by":7,"name":"Jean M. Nunes","email":"","orcid":"","institution":"Stellenbosch University","correspondingAuthor":false,"prefix":"","firstName":"Jean","middleName":"M.","lastName":"Nunes","suffix":""},{"id":447497071,"identity":"df4082d1-e074-47e4-96d6-00c2062d3125","order_by":8,"name":"Chantelle Venter","email":"","orcid":"","institution":"Stellenbosch University","correspondingAuthor":false,"prefix":"","firstName":"Chantelle","middleName":"","lastName":"Venter","suffix":""},{"id":447497072,"identity":"610e7326-63d4-4d2b-8d71-7b5d157c2ca0","order_by":9,"name":"Chantelle J Scott","email":"","orcid":"","institution":"Stellenbosch University","correspondingAuthor":false,"prefix":"","firstName":"Chantelle","middleName":"J","lastName":"Scott","suffix":""},{"id":447497073,"identity":"41eefbb1-badd-43c7-91b1-8e56ba4045f1","order_by":10,"name":"Douglas B Kell","email":"","orcid":"","institution":"University of Liverpool","correspondingAuthor":false,"prefix":"","firstName":"Douglas","middleName":"B","lastName":"Kell","suffix":""},{"id":447497074,"identity":"d5bb99fe-fcc1-4e6c-bbe0-9e2db19499ce","order_by":11,"name":"Etheresia Pretorius","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYJCCAwwMNqRrSSPdosMkqDVvb3944OeO8/YGtxsYP/xgqJMnqEXmzBmDg71nbiduuHOAWbKHgc2wgZAWCYkchgO8bbcTDG4kMEgzMPAwEtYi//zBwb9t5+yBWph/A/n2RNjCYHCYt+0A44YbCWxAWwwSCWvhyTE4LHsmOXHmjcQ2yx6DhGTCWtiPP/74doedPd+N5MM3flTU2RLUAgYQL4NIA6LUw7WMglEwCkbBKMABAAr3PLZbI/btAAAAAElFTkSuQmCC","orcid":"","institution":"Stellenbosch University","correspondingAuthor":true,"prefix":"","firstName":"Etheresia","middleName":"","lastName":"Pretorius","suffix":""}],"badges":[],"createdAt":"2025-04-24 13:06:36","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6521005/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6521005/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81962598,"identity":"8e839fd2-f7f0-4d65-8b1d-511b0c13aaa1","added_by":"auto","created_at":"2025-05-05 11:12:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":500351,"visible":true,"origin":"","legend":"\u003cp\u003eEstablishing the connection between Long COVID and vaccine-related injuries. The development of Long COVID and vaccine injury can follow various pathways. \u003cstrong\u003e1)\u003c/strong\u003e a healthy unvaccinated individual may contract acute COVID, subsequently recovering to a healthy state; or experience worsening symptoms, which can lead to the development of “pure Long COVID”. \u003cstrong\u003e2)\u003c/strong\u003e a healthy individual who receives a vaccination may either establish immunity and remain ostensibly healthy or may encounter vaccine-related injuries without experiencing acute COVID infection. \u003cstrong\u003e3)\u003c/strong\u003e a previously healthy individual may initially contract acute COVID that progresses into Long COVID. If this Long COVID patient becomes vaccinated, it could potentially exacerbate their condition, resulting in a dual diagnosis of both Long COVID and vaccine injury. \u003cstrong\u003e4)\u0026nbsp;\u003c/strong\u003ea previously healthy individual gets vaccinated and may develop vaccine-related injuries. Subsequently, the patient contracts acute COVID, which progresses into Long COVID, leading to a dual diagnosis of vaccine injury followed by Long COVID. This highlights the intricate interplay and complexity inherent to these conditions.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6521005/v1/2d1b36a3bb955c681fcd5319.png"},{"id":81962597,"identity":"9ad6d390-88ac-4091-846f-ace161c2968c","added_by":"auto","created_at":"2025-05-05 11:12:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":218489,"visible":true,"origin":"","legend":"\u003cp\u003eDevelopmental pathways leading to Post-Vaccination/Post-Infection (PV/PIS). \u003cstrong\u003e1)\u003c/strong\u003e shows 50% of PV/PIS cases were directly caused by the vaccination itself, representing pure post-vaccination complications. \u003cstrong\u003e2)\u003c/strong\u003e shows that 7% of PV/PIS cases developed acute COVID following vaccination, exacerbating the vaccine’s effects. \u003cstrong\u003e3)\u003c/strong\u003e shows 7% of PV/PIS cases had prior acute COVID infection before vaccination. \u003cstrong\u003e4)\u0026nbsp;\u003c/strong\u003eshows that 7% of PV/PIS cases were vaccinated before contracting acute COVID, which later progressed to Long COVID, intensifying the vaccine’s impact, resulting in post-vaccination complications and Long COVID. \u003cstrong\u003e5)\u0026nbsp;\u003c/strong\u003eshows that 29% of PV/PIS cases were vaccinated between acute COVID infections, further exacerbating the vaccine’s effects. It is important to note that possible asymptomatic exposure was not accounted for in this figure. Created in https://Biorender.com.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6521005/v1/cb2ae17d82ca6644e9378b82.png"},{"id":81963592,"identity":"c727e87d-7b37-498a-8c21-6590acdcb056","added_by":"auto","created_at":"2025-05-05 11:20:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":466610,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative static fluorescent micrographs of platelet poor plasma (PPP) exposed to thioflavin T (ThT). \u003cstrong\u003eA\u003c/strong\u003e shows representative heterogenous amyloid deposits (microclots) in healthy (control) individuals. \u003cstrong\u003eB\u003c/strong\u003e shows extensive formation of heterogenous amyloid deposits (microclots) PV/PIS participants. Created in https://Biorender.com.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6521005/v1/f5e97e87d0c1723f78dfaa42.png"},{"id":81962601,"identity":"0614cdb5-0d31-4d6d-965d-1e27664f055d","added_by":"auto","created_at":"2025-05-05 11:12:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":213225,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot illustrating the distribution of proteins for pairwise comparison (controls vs. PV/PIS). The plot highlights significant proteins, represented by black dots, giving a preliminary overview of protein changes between sample groups. The x-axis denotes fold change to indicate the degree of up or downregulation in PV/PIS relative to controls, with negative values indicating downregulation and positive values indicating upregulation. The y-axis represents the -log10 of p values, reflecting the statistical significance of the observed changes, with significance taken as p\u0026lt;0.05. Statistical analysis was done using FragPipe Analyst (http://fragpipe-analyst.nesvilab.org/).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6521005/v1/4cd6bc0920b1cb2c2f95789b.png"},{"id":81965265,"identity":"05cf0824-4d79-41cf-b545-d4fb1dda95f1","added_by":"auto","created_at":"2025-05-05 11:28:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1340380,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional protein-protein interaction networks and clustering analysis for the pairwise comparison of PV/PIS vs. controls. \u003cstrong\u003eA\u0026nbsp;\u003c/strong\u003efunctional protein interaction network of all significant differentially expressed proteins. \u003cstrong\u003eB\u003c/strong\u003e network representation of proteins significantly upregulated in PV/PIS samples compared to controls. \u003cstrong\u003eC\u003c/strong\u003e network representation of proteins significantly downregulated in PV/PIS samples compared to controls. Nodes represent proteins, and edges (solid and dotted lines) indicate predicted functional associations. Solid lines indicate strong-high confidence interactions and/or direct physical interactions; however, dotted lines indicate weaker or indirect interactions, functional associations or computationally predicted relationships. Nodes of the same colour representing functionally related groups of proteins, while white nodes are additional interactors suggested by STRING. Clustering and visualisation were performed using the STRING database (version 12.0, https://string-db.org/).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6521005/v1/1b5a70616ea0976516f97181.png"},{"id":81963589,"identity":"0e84c3b2-1a09-4ec5-9639-b7882b1b9547","added_by":"auto","created_at":"2025-05-05 11:20:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1279197,"visible":true,"origin":"","legend":"\u003cp\u003eComparative visualisation of proteomic profiles in PV/PIS participants and a previously published Long COVID cohort (Kruger et al., 2022). \u003cstrong\u003eA\u003c/strong\u003e all proteins found to be significantly expressed in both the current PV/PIS cohort and the Long COVID cohort (Kruger et al., 2022). \u003cstrong\u003eB\u003c/strong\u003e proteins significantly expressed in the Long COVID (Kruger et al., 2022). \u003cstrong\u003eC\u003c/strong\u003e proteins significantly expressed in the PV/PIS cohort as identified in this study. Comparative analysis was performed using Spotfire® program (http://spotfire.com/, version 12.0, accessed on 14 April 2025).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6521005/v1/ac1b2252a6e2d52c3abfab51.png"},{"id":81962607,"identity":"4ca9eaae-dd89-42e4-af88-7da7e7764f43","added_by":"auto","created_at":"2025-05-05 11:12:58","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":426980,"visible":true,"origin":"","legend":"\u003cp\u003eThe role of T-Helper immune response in PV/PIS. SARS-CoV-2 and endogenous spike protein from mRNA vaccines can activate dendritic cells, triggering a Th17 immune response and promoting inflammation. This increase in inflammatory state can contribute to heterogenous amyloid deposits (microclots) and thrombus formation, as well as increase endothelial permeability, leading to the release of other pro-coagulant factors and adhesion molecules. Created in https://Biorender.com.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6521005/v1/0168851fffe6998fca8a895b.png"},{"id":81967880,"identity":"47067c98-2200-42cc-87ec-a649b526df37","added_by":"auto","created_at":"2025-05-05 11:45:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5495623,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6521005/v1/085fbdaa-0e46-48d8-87fe-15858a1bdf46.pdf"},{"id":81962600,"identity":"a84418ed-3e3b-46af-afc3-191868269982","added_by":"auto","created_at":"2025-05-05 11:12:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1033226,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6521005/v1/96927a7aa800aaa8695d9290.pdf"},{"id":81963588,"identity":"bb76dc48-ce02-47bf-85ff-2c3f764a48b6","added_by":"auto","created_at":"2025-05-05 11:20:58","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":428400,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.docx","url":"https://assets-eu.researchsquare.com/files/rs-6521005/v1/4029dc9cc822c3e3ea6d2c6e.docx"}],"financialInterests":"The authors declare potential competing interests as follows: Kotze MJ. is a non-executive director and shareholder of Gknowmix (Pty) Ltd.\nPretorius E. is an author of a patent DIAGNOSTIC METHOD FOR LONG COVID PCT application number GB2105644.5 and is the managing director of BioCODE Technologies.\nAll other authors have no competing interests to declare.","formattedTitle":"\u003cp\u003eProteomic signatures of Post-Vaccination/Post-Infection Syndrome (PV/PIS): Insights into immune dysregulation and coagulopathy\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePost-acute sequelae of COVID-19 (PASC), commonly known as Long COVID, is estimated to have affected over 400 million individuals worldwide, contributing to an annual economic burden of $1 trillion \u0026ndash; representing approximately 1% of the global economy \u003csup\u003e1\u003c/sup\u003e. Initially recognised by affected individuals themselves and reported via social media and various community forums \u003csup\u003e2\u003c/sup\u003e, Long COVID has since been acknowledged as a potentially disabling condition by the Centres for Disease Control and Prevention (CDC) \u003csup\u003e3\u003c/sup\u003e. Ewing et al. (2025) published expert consensus recommendations for physicians, emphasising the need for individualised treatment in cases where vaccination exacerbated Long COVID symptoms or resulted in adverse vaccine side effects \u003csup\u003e4\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe World Health Organization (WHO) defines Long COVID as the persistence or emergence of new symptoms beyond two months after an acute SARS-CoV-2 infection, with no alternative explanation \u003csup\u003e5\u003c/sup\u003e. This condition affects multiple physiological systems and presents with a broad spectrum of symptoms, including neurological (brain fog, extreme fatigue, sleep disturbances), cardiovascular (orthostatic intolerance, chest pain, shortness of breath, tachycardia), musculoskeletal (joint pain, muscle weakness), and psychosocial manifestations (depression, anxiety, social isolation) \u003csup\u003e6-10\u003c/sup\u003e. Notably, more than 200 symptoms have been linked to Long COVID globally, with affected individuals experiencing varying combinations of symptoms or cyclic patterns of symptom recurrence \u003csup\u003e3,11\u003c/sup\u003e. Symptom severity and duration in Long COVID are not always dependent on a patient\u0026rsquo;s hospitalisation for acute COVID. Severe cases have been observed in both patients who were hospitalised for acute COVID \u003csup\u003e12,13\u003c/sup\u003e and in those who were not \u003csup\u003e14\u003c/sup\u003e. \u0026nbsp;Long COVID is a complex condition resulting from various influences, including host-specific factors (e.g. co-morbidities), viral dynamics, environment exposures, and genetic predisposition \u003csup\u003e15\u003c/sup\u003e. It affects individuals across all demographics, irrespective of age, sex, race, or baseline health \u003csup\u003e1\u003c/sup\u003e. Additionally, vaccination status has been suggested as a potential factor influencing disease trajectory \u003csup\u003e16,17\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo date, more than 13 billion COVID-19 vaccine doses have been administered globally, to mitigate the impact of the pandemic \u003csup\u003e18\u003c/sup\u003e. However, some vaccinated individuals have reported persistent symptoms resembling both acute COVID and Long COVID, leading to the term \u0026ldquo;vaccine injury\u0026rdquo; \u003csup\u003e15,19\u003c/sup\u003e. The underlying mechanisms remain poorly understood, with several factors \u0026ndash; including vaccine type, host genetics, prior SARS-CoV-2 infection, and timing of the vaccination \u0026ndash; potentially influencing any adverse outcomes \u003csup\u003e19,20\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eCOVID-19 vaccines fall into four primary categories: adenovirus vector-based vaccines (e.g., AstraZeneca\u0026rsquo;s Vaxzevria and Covishield ChAdOx1 and Johnson-Janssen\u0026rsquo;s Ad26.COV2.S), mRNA-based vaccines (e.g., mRNA, Moderna\u0026rsquo;s Spikevax mRNA-1273 and Pfizer-BioNTech\u0026rsquo;s Comirnaty BNT162b2), adjuvanted protein vaccines (e.g., Norvavax\u0026rsquo;s Nuvaxovid and Covovax NVX CoV 2373), and inactivated virus vaccines (e.g., Sinopharm\u0026rsquo;s Covilo, Sinovac\u0026rsquo;s CoronaVac and Bharat Biotech\u0026rsquo;s Covaxin) \u003csup\u003e20\u003c/sup\u003e. The complex interplay between SARS-CoV-2 infection, immune response, and vaccination presents significant challenges in distinguishing Long COVID from vaccine injury. Misdiagnosis of Long COVID and/or vaccine injury can easily occur due to the overlapping symptoms and the challenge of establishing a clear timeline. This is especially difficult when distinguishing between Long COVID caused solely by infection and cases where vaccination may also involve vaccine injury (\u003cstrong\u003eFig. 1\u003c/strong\u003e). In many cases, timeline of disease onset is undetermined \u003csup\u003e19\u003c/sup\u003e, making it nearly impossible to delineate causality with confidence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCurrent research on Long COVID suggests multiple pathophysiological mechanisms, including autoimmunity, immune dysregulation, viral persistence, gut dysbiosis, and microvascular dysfunction \u003csup\u003e1,21,22\u003c/sup\u003e. One of the most noteworthy findings in recent studies is the presence of hypercoagulability across COVID-19-related conditions, including Long COVID and vaccine injury \u003csup\u003e21,23\u003c/sup\u003e. The initial SARS-CoV-2 infection induces pronounced inflammatory response, disrupting the balance between pro- and anti-coagulant pathways, ultimately leading to endothelial dysfunction \u003csup\u003e24,25\u003c/sup\u003e. In addition, the spike protein itself is capable of inducing anomalous clotting that is resistant to fibrinolysis \u003csup\u003e25\u003c/sup\u003e, the extent reflecting the virulence of the SARS-CoV-2 variant in a manner that implies that this is on the aetiological pathway of the disease \u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eConsidering the complex interplay of multiple factors influencing the development and progression of Long COVID, alongside the potentially distinct mechanisms underlying adverse vaccine side effects, understanding the relationship between Long COVID and vaccine-related injuries is crucial. The development of both Long COVID and vaccine-related symptoms can follow multiple, overlapping timelines and pathways, as illustrated in \u003cstrong\u003eFig. 1\u003c/strong\u003e. However, we wish to recognise that millions of individuals developed Long COVID prior to the availability of vaccines. Nonetheless, grasping these timelines provides valuable understanding, facilitating the subsequent application of a systems approach by both clinicians and researchers \u003csup\u003e15,19,20\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGiven the significant overlap between symptoms attributed to vaccine-related adverse effects and those seen in Long COVID, alongside the difficulty in definitively excluding prior asymptomatic or mild SARS-CoV-2 infection, we have chosen to refer to the patient group recruited for this current paper, as individuals with Post-Vaccination/Post-Infection Syndrome (PV/PIS). This term acknowledges the complexity of distinguishing between vaccine-associated immune dysregulation and post-infectious sequelae, particularly in populations where acute infection timelines may be unclear (asymptomatic or underreported cases). We recognize that both vaccination and SARS-CoV-2 infection (symptomatic or asymptomatic) may contribute to ongoing immune, inflammatory and coagulation abnormalities. By adopting the PV/PIS terminology in this current paper, we aim to avoid premature attribution of causality and instead focus on describing the proteomic and pathophysiological features present in this patient group.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eEthical clearance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research forms a part of a larger study funded by the South African Medical Research Council (SAMRC, grant number 96847), where ethics approval was obtained from the Health Research Ethics Council (HREC) at Stellenbosch University, South Africa (project reference number N22/11/133, ID: #26785). The study protocol ensured that all participants were fully briefed on the experimental objectives, potential risks, and all pertinent study details. Additionally, their informed consent was obtained for both sample collection and storage for multi-disciplinary research. Throughout the study and across all research activities, rigorous adherence to ethical standards was meticulously upheld, following the principles outlined in the Declaration of Helsinki, the South African Guidelines for Good Clinical Practice, and the Medical Research Council Ethical Guidelines for Research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample recruitment and patient inclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA questionnaire was used to collect information on age, gender, pre-existing co-morbidities, other clinical characteristics, vaccination status, as well as details regarding the diagnosis of acute and/or Long COVID and/or vaccine event(s) that might have resulted in persistent symptom onset. Additionally, data on the severity of the condition(s) and self-reported symptoms associated with these conditions were gathered.\u003c/p\u003e\n\u003cp\u003eOur study cohort included 16 ostensibly healthy participants to serve as the control group (12 females; 4 males; mean age 43.9 \u0026plusmn; 15.2). Control participants were individuals who had been vaccinated at least once and/or had experienced acute COVID but did not have Long COVID or experienced prolonged symptoms typical of PV/PIS after vaccination. Notably, participant selection for this study was not based on vaccination type. Among the 16 ostensibly healthy participants, 9 had a history of acute COVID infection, and had recovered without prolonged health issues. Two of the healthy controls developed pericarditis after vaccination but recovered fully after early treatment (see \u003cstrong\u003eTable S1\u003c/strong\u003e). The remaining healthy participants did not report a prior infection; however, the possibility of an asymptomatic acute infection cannot be rule out.\u003c/p\u003e\n\u003cp\u003eWe recruited 14 individuals that adhered to our criteria of PV/PIS (9 females; 5 males; mean age 59.9 \u0026plusmn; 15.8). The inclusion criteria for our PV/PIS cohort required participants to have received at least one dose of any COVID-19 vaccine between 2020 and 2024. Notably, all our PV/PIS participants received only Pfizer-BioNTech vaccines between mid-2021 to mid-2022. Eligible participants experienced symptoms for at least 12 weeks post-vaccination. If participants had a documented acute COVID infection (either before or after vaccination), confirmation based on polymerase-chain reaction (PCR) or antigen testing was required. Exclusion criteria included individuals who experience acute COVID infection at the time of vaccination or were actively receiving treatment for acute or Long COVID.\u003c/p\u003e\n\u003cp\u003eMost of our PV/PIS cohort were vaccinated during the third wave of COVID-19 between May and Sep 2021 (Delta variant peak), shortly before the emergence of the Omicron variant in Dec 2021 (see \u003cstrong\u003eFig. S1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample collection \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBlood samples were collected via venepuncture by a licensed medical practitioner, or certified phlebotomist. These samples included the following: three 3 mL sodium citrate (3.2%) tubes (BD Vacutainer\u0026reg;, 369714), and one 5 mL serum (silicone/polymer gel) blood tube (BD Vacutainer\u0026reg;, 367986) for blood pathology studies. The sodium citrate tubes were then transported to the blood laboratory at Stellenbosch University. Only one whole blood (WB) sample was centrifuged at 3000 \u0026times;g for 15 min at room temperature. The supernatant platelet poor plasma (PPP) was carefully removed and aliquoted into a 1.5 mL Eppendorf tube and stored at -80 ℃. All other samples collected were used as part of research outside the scope of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFluorescence microscopy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSample preparation for platelet poor plasma (PPP) to study heterogenous amyloid deposits (microclots)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt room temperature, within 24h of sample collection, 49 \u0026mu;L of platelet poor plasma (PPP) was aliquoted into a 1.5 mL Eppendorf tube. The samples were stained with 1 \u0026mu;L of Thioflavin T (ThT, Sigma-Aldrich, St. Louis, MO, USA) with a final concentration of 0.005 mM, while protected from light. The stained samples were incubated in a light-protected container at room temperature for 30 min. After incubation, 3 \u0026mu;L of the stained PPP sample was used to prepare PPP smears for viewing and analysis. This ThT method was previously established to visualise abnormal clotting in various inflammatory conditions \u003csup\u003e27-29\u003c/sup\u003e. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eViewing of platelet-poor plasma (PPP)to study heterogenous amyloid deposits (microclots)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PPP smears were examined using a Zeiss AxioObserver 7 fluorescent microscope with a Plan-Apochromat 63\u0026times;/1.4 Oil DIC M27 objective (Carl Zeiss Microscopy, Munich, Germany). For ThT, the excitation wavelength range was set between 450 nm to 488 nm, with an emission range from 499 nm to 529 nm \u003csup\u003e25,30,31\u003c/sup\u003e. Representative micrograph images were captured at random, with a minimum of four images acquired per sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProteomics: digestion of heterogenous amyloid deposits (microclots) in platelet poor plasma (PPP)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSample preparation and trypsin digestion\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor proteomics analysis, 30 platelet-poor plasma (PPP) samples, previously stored at -80 ℃, were selected, including controls (n = 16), and PV/PIS (n = 14), and 100 \u0026mu;L of each sample was aliquoted out. These pre-aliquoted samples were then stored at -20 ℃ until use.\u003c/p\u003e\n\u003cp\u003eA 20\u0026times; dilution of the stored PPP was prepared by combining 5 \u0026mu;L of the na\u0026iuml;ve sample with 45 \u0026mu;L of 10 mM ammonium bicarbonate (NH4HCO3). The protein concentration of each sample solution was determined using a nanodrop spectrophotometer (ThermoFisher), measuring absorbance at 280 nm. \u003c/p\u003e\n\u003cp\u003eThis sample set had an increased viscosity of the PPP compared to previous studies by Pretorius et al. (2021), and Kruger et al. (2022), necessitating adjustments to the heterogenous amyloid deposits (microclots) analysis protocol outlined by Pretorius et al. (2021). Each sample was standardised using a set volume of 25 \u0026mu;L na\u0026iuml;ve PPP sample, combined with 100 \u0026mu;L of phosphate-buffered saline (PBS, McKesson) to better mimic physiological conditions. The sample solution was placed in a Falcon tube and rotated on a tube rotator, for 50 \u0026ndash; 60 min, until homogenised. The samples were centrifuged at 12 000 \u0026times;g for 10 min at room temperature to separate the heterogenous amyloid deposits (microclots; insoluble fraction of PPP) from the soluble portion. The supernatant was removed, leaving 8 \u0026mu;L of sample solution behind. The remaining solution was reconstituted with 72 \u0026mu;L PBS, to achieve a 10\u0026times; dilution factor and centrifuged again at 12 000 \u0026times;g, for 10 min at room temperature \u003csup\u003e16,32\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eCarefully, to avoid disturbing the heterogenous microclot pellet, 72 \u0026mu;L of soluble fraction of the sample solution was removed. The pellet was further dissolved by the adding 10 \u0026mu;L of a solution containing 5 mM tris (2-carboxyethyl)phosphine (TCEP, Sigma-Aldrich) and 250 mM triethylammonium bicarbonate (TEAB, ThermoFisher) to the remaining 8 \u0026mu;L of sample solution. The samples were vortexed briefly (5 s), then incubated in a heating block for 30 min at 60 ℃, followed by 30 min at 44 ℃. After incubation, the samples were briefly centrifuged at room temperature using a mini benchtop centrifuge to re-incorporate any evaporated sample that may have settled in the lid of the Eppendorf tube and then allowed to cool to room temperature. \u003c/p\u003e\n\u003cp\u003eTo further denature the insoluble heterogenous deposits, 20 \u0026mu;L of 50% methanol was added to each sample. To block the cysteine residues, the samples were alkylated with 3 \u0026mu;L of iodoacetamide, protected from light, and incubated at room temperature for 30 min. To each sample, 4 \u0026mu;L of a solution containing dithiothreitol (DTT, Sigma-Aldrich) and TEAB (ThermoFisher) was added. The pellet was further digested by adding 10 \u0026mu;L of a trypsin solution, containing 0.5 \u0026mu;g/\u0026mu;L trypsin (Pierce), and incubated at 37 ℃, for 18h.\u003c/p\u003e\n\u003cp\u003eFollowing incubation, 5 \u0026mu;L of 5% trifluoroacetic acid (TFA, Sigma-Aldrich), with a final concentration of 1% v/v, was added to each sample to acidify and terminate the reaction. The samples were then centrifuged at 12 000 \u0026times;g for 10 min at room temperature to pellet out the trypsin precipitate. The supernatant was transferred to conical inserts (ALWSCI technologies). The samples were dried under vacuum for 30 min, using a rotary evaporator. Dried samples were resuspended in 30 \u0026mu;L of analytical grade water, and peptide concentration was measured using a nanodrop spectrophotometer (ThermoFisher), absorbance measured at 220 nm. After measuring the peptide concentration, the samples were dried again under vacuum using the rotary evaporator. Dried samples were resuspended in 20 \u0026mu;L of a loading buffer containing 2% acetonitrile (Burdick and Jackson) in analytical grade water with 0.1% formic acid (Sigma-Aldrich). The digested heterogenous microclot pellet was loaded directly in the autosampler set to 7 ℃.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLiquid chromatography of digested pellet\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLiquid chromatography was conducted as previously described by Pretorius et al. (2021), Kruger et al. (2022), and Nunes et al. (2024), using Thermo Scientific Ultimate 3000 RSLC (ThermoFisher, 2019), equipped with a 20 mm \u0026times; 100 \u0026mu;m C18 trap column (Thermo Scientific), and a CSH 25 cm \u0026times; 75 \u0026mu;m with a 1.7 \u0026mu;m particle size C18 column (Waters) analytical column \u003csup\u003e16,32,33\u003c/sup\u003e. \u003c/p\u003e\n\u003cp\u003eThe solvent system for loading consisted of 2% acetonitrile (Burdick and Jackson) with 0.1% formic acid (Sigma-Aldrich) in analytical grade water. Solvent A consisted of analytical grade water, with 0.1% formic acid (Sigma-Aldrich), while solvent B consisted of 100% acetonitrile (Burdick and Jackson) with 0.1% formic acid (Sigma-Aldrich).\u003c/p\u003e\n\u003cp\u003eSamples were loaded onto the trap column at a flow rate of 2 \u0026mu;L/min, from a temperature-controlled autosampler set 7 ℃, using loading solvent. Loading was performed for 4 mins before elution onto the analytical column. The flow rate was set to 300 nL/min, and the gradient was generated as follows: 5% - 30% solvent B over 65 min and 30-45% solvent B from 65 \u0026ndash; 80 min. Chromatography was performed at 45 ℃, with the outflow directed to the mass spectrometer using a stainless-steel nano-bore emitter. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMass spectrometry of the digested pellet \u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData independent acquisition (DIA) mass spectrometry analysis was performed using ThermoScientific Fusion mass spectrometer, equipped with a Nanospray Flex ionisation source. The prepared samples were introduced through a stainless-steel nano-bore emitter. Data was collected in a positive mode with spray volage set 2.0 kV and ion transfer capillary at 290 ℃. Polysiloxane ions, at 445.12003 m/z, were used for internal calibration of the spectra. \u003c/p\u003e\n\u003cp\u003eFor MS1 scans, the Orbitrap detector was set to a resolution of 60, 000 over a scan range of 375 \u0026ndash; 1500 m/z, with an automatic gain control (AGC) target set to standard. Data acquisition was conducted in profile mode. \u003c/p\u003e\n\u003cp\u003eIn higher-energy C-trap dissociation (HCD) mode, precursor ions were selected for fragmentation using the quadrupole mass analyser with HCD energy set to 30%. The precursor mass range was set to 500 \u0026ndash; 900 m/z, with an isolation window of 20 m/z. Fragment ions were detected using the Orbitrap mass analyser set to a resolution of 30, 000. The AGC target was set to custom. Data acquisition was performed in centroid mode.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMass spectrometry data analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data files were processed using FragPipe Analysis Pipeline (version 22.0), with the included DIA SpecLib Quant workflow. The Uniprot human database concatenated with the SARS-CoV-2 database, from UniProt (https://www.uniprot.org/taxonomy/9606 ; https://www.uniprot.org/uniprotkb?query=694009), was used as reference. MSFragger search parameters included fragment mass tolerances set to 20 parts per million (PPM) for mass calibration and optimisation; and enzyme was set to select trypsin, allowing for 2 mis-cleavages. Fixed modifications were set to carbamidomethyl-C, and variable modifications were set to methionine (M) oxidation, protein end terminal acetylation, and the deamidation of asparagine and glutamine residues (NQ). Peptide spectral match (PSM) validation was done with percolator and rescored using MSBooster in FragPipe. Protein inference was performed using ProteinProphet. Quantification was done using DIA-NN with a false discovery rate (FDR) set to 0.01. \u003c/p\u003e\n\u003cp\u003eProtein group (pg_matrix.tsv) and experiment annotations (.tsv) files, generated by using the FragPipe Pipeline, were uploaded into FragPipe-Analyst (http://fragpipe-analyst.nesvilab.org/) for statistical analysis and validation. The minimum percentage of non-missing values globally and in at least one condition, was set to 0. The adjusted p-value cutoff was 0.05, and the Log2 fold cutoff was 1. Median-centred normalisation was used, with Perseus imputation type and Benjamini-Hochberg method for FDR correction.\u003c/p\u003e\n\u003cp\u003eProtein-protein interaction networks were viewed using the STRING database (version 12.0, https://string-db.org/). Statistically significant protein changes (determined in the above FragPipe analysis) were uploaded to the multiple protein search tool. Under the clusters tab in STRING, Markov Cluster (MCL) algorithm was selected, and the inflation parameter was set to 3, to perform clustering analysis. Functional enrichment analysis was applied to identify over-represented pathways, molecular functions, and biological processes within each cluster. \u003c/p\u003e\n\u003cp\u003eAmyloidogenic potential of significant proteins was predicted using AmyloGram (http://biongram.biotech.uni.wroc.pl/AmyloGram/, accessed on 19 March 2025). A cut-off value (probability threshold) of 0.5 was applied. A cut-off value of 0.5 will automatically yield the following values for the following parameters: a sensitivity value set to 0.8658, specificity value set to 0.7852 and a Matthews correlation coefficient (MCC) of 0.6268. These values are automatically adjusted with any changes made to the cut-off value. The amyloid probability and fraction of amyloid residues were recorded for each significant protein change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data from demographics, including the subsequent statistical evaluations, were processed using GraphPad Prism (version 10.2.3). The Shapiro-Wilk test was used for normality testing. For parametric data, statistical significance was determined using unpaired t-test. A 95% confidence level was applied, with statistical significance was considered at p \u0026lt; 0.05. Significance levels were indicated by asterisks (*= p \u0026lt; 0.05; ** = p \u0026lt; 0.01; *** = p \u0026lt; 0.001; and **** = p \u0026lt; 0.0001). Parametric data is presented as mean \u0026plusmn; standard deviation (SD). Statistical analysis for proteomics data is detailed above.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparative Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparative analysis was performed using the Spotfire\u0026reg; program (http://spotfire.com/, version 12.0, accessed on 14 April 2025) to visualise and interpret proteomic differences between the current study and previously published Long COVID data by Kruger et al. (2022). Proteins identified as having significant changes in the current study were compared to those reported in the Long COVID dataset \u003csup\u003e32\u003c/sup\u003e. To standardise the scale across datasets, fold changes values for downregulated proteins were converted to their reciprocals, such that values \u0026lt;1 reflected downregulation and values \u0026gt;1 reflected upregulation. A scatter plot was generated in Spotfire\u0026reg;, with fold change plotted on the x-axis, and p-value on the y-axis. A trend line was included to aid in the visual interpretation of data distribution. Proteins were assessed for overlap and directional consistency between the two studies.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eStudy cohort and demographics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSample demographics are shown in \u003cstrong\u003eTable 1\u003c/strong\u003e and \u003cstrong\u003eFigure 2\u003c/strong\u003e represents the various ways PV/PIS participants developed their condition. The development and progression of PV/PIS is not exclusive to a single developmental pathway but can manifest through various pathways.\u003c/p\u003e\n\u003cp\u003eThe majority of the healthy individuals who reported that they suffered from an acute infection, experienced acute symptoms such as cough (55.6%), sore throat (33.3%), fever (55.6%) and headache (33.3%) (\u003cstrong\u003eTable S1\u003c/strong\u003e). Similarly, among the 14 PV/PIS participants, 8 had a prior acute COVID infection, with most reporting cough (62.5%), sore throat (62.5%), fever (87.5%), myalgia (62.5%), malaise (50.0%) and dysgeusia (37.5%) (\u003cstrong\u003eTable S1\u003c/strong\u003e). These symptoms resolved soon after the infection cleared, with no persistent symptoms reported.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding vaccination status, 81.3% of the healthy individuals were vaccinated, receiving either Pfizer, J\u0026amp;J, or a combination of both. In contrast, all PV/PIS participants had exclusively received Pfizer (\u003cstrong\u003eTable 1\u003c/strong\u003e). Notably, participant selection for this study was not based on vaccination type.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eSample cohort and demographics from healthy participants, and participants with Post Vaccination/Post-Infection Syndrome (PV/PIS).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 601px;\"\u003e\n \u003cp\u003eSample demographics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 601px;\"\u003e\n \u003cp\u003ePlatelet poor plasma stored previously\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean age [with \u0026plusmn; SD]\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eControls (n=16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 301px;\"\u003e\n \u003cp\u003e43.9 (\u0026plusmn;15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003ePV/PIS (n=14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 301px;\"\u003e\n \u003cp\u003e59.9 (\u0026plusmn;15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender distribution across all groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eControls (n=16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 301px;\"\u003e\n \u003cp\u003e12 Females; 4 Males\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003ePV/PIS (n=14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 301px;\"\u003e\n \u003cp\u003e9 Females; 5 Males\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical characteristics of participants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% In 16 controls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% In 14 PV/PIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eChronic medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e57.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003ePrevious smoker\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e35.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eOverweight/obese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e35.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eArthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eCancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eType 2 diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eAnaemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eRosacea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eIrritable bowel syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eHypothyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eDyslipidaemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eAnxiety/depression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eNeurodevelopmental disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage (%) of participants vaccinated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eControls (n=16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 301px;\"\u003e\n \u003cp\u003e81.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003ePV/PIS (n=14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 301px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of vaccine administered\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVaccine type and/or combination\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% In 16 controls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% In 14 PV/PIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003ePfizer only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e56.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eJ\u0026amp;J only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e31.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003ePfizer and J\u0026amp;J\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 200px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 601px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll demographics data was subjected to a Shapiro-Wilks normality test. An unpaired T-test was performed on parametric data, with data are represented as mean \u0026plusmn; standard deviation (SD).\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogenous amyloid deposits (microclots) in platelet-poor plasma (PPP) as viewed with fluorescence microscopy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrevious studies \u003csup\u003e16,31\u003c/sup\u003e have shown that na\u0026iuml;ve platelet-poor plasma (PPP) from healthy individuals and type 2 diabetes mellitus (T2DM) participants exhibit significantly fewer heterogenous amyloid deposits (microclots) upon exposure to thioflavin T (ThT) compared to acute COVID and Long COVID participants. In this study, we demonstrate that PV/PIS participants also exhibit substantial formation of heterogenous amyloid deposits (microclots), comparable to that seen in Long COVID. \u003cstrong\u003eFigure 3\u003c/strong\u003e provides examples of na\u0026iuml;ve PPP samples exposed to ThT from both control and PV/PIS group\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLiquid chromatography and mass spectrometry (LC-MS) of digested pellet deposits in platelet-poor plasma (PPP)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProteomics data analysis of control (n = 16) and PV/PIS (n = 14) samples was performed using FragPipe Analysis Pipeline (version 22.0) and FragPipe-Analyst (http://fragpipe-analyst.nesvilab.org/). Significant protein results are shown below in \u003cstrong\u003eTable 2\u003c/strong\u003e. This study identified 26 proteins that were significantly downregulated and 26 proteins that were significantly upregulated in PV/PIS group compared to controls. Changes in protein levels are represented as fold changes, with downregulated proteins denoted as reciprocal values. \u003cstrong\u003eFigure 4\u003c/strong\u003e shows a volcano plot for an overview of protein distribution for the pairwise comparison (controls vs. PV/PIS). \u003cstrong\u003eFigure 5\u003c/strong\u003e shows an overview of functional protein clusters for proteins that are up- or downregulated by more than 1-fold with a p-value \u0026lt; 0.05. Functional protein cluster changes in PV/PIS can give systematic indications of pathways up or downregulated in PV/PIS\u003c/p\u003e\n\u003cp\u003eAfter homogenisation and trypsinisation, this mass spectrometry-based proteomics analysis revealed an increase in coagulation proteins factors X and XI, as well as an increase in acute inflammatory response proteins, including serum amyloid A1 (SAA1) and serum amyloid A2 (SAA2). In contrast, proteins associated with immune response, such as Protein S100A9 and other immunoglobulin complex components, were downregulated in PV/PIS participants compared to controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eSignificant protein changes in pairwise analysis of heterogenous amyloid deposits (microclots) from PV/PIS individuals and controls.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"664\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 664px;\"\u003e\n \u003cp\u003eSignificant protein changes in pairwise analysis of PV/PIS heterogenous amyloid deposits (microclots) compared to controls\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 664px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDownregulated proteins\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFold Change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 664px;\"\u003e\n \u003cp\u003eProteins appear in both PV/PIS (n=14) and control (n=16) groups.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eAlpha-1-acid glycoprotein 2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP19652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eORM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eCD5 antigen-like\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eO43866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eCD5L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eZinc-alpha-2-glycoprotein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP25311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eAZGP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eKeratin, type II cytoskeletal 6B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP04259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eKRT6B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eKeratin, type II cytoskeletal 3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP12035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eKRT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eApolipoprotein D\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP05090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eAPOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eTransmembrane protein 256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eQ8N2U0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eTMEM256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eCollagen alpha-3 chain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP12111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eCOL6A3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eProtein S100-A9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP06702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eS100A9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003ePlatelet factor 4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP02776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003ePF4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP02768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eImmunoglobulin lambda-like polypeptide 5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eB9A064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eIGLL5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eTransthyretin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP02766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eTTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eKeratinocyte differentiation-associated protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP60985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eKRTDAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eImmunoglobulin kappa variable 2-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eA0A075B6P5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eIGKV2-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eKeratin, type I cytoskeletal 10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP13645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eKRT10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eKeratin, type II cytoskeletal 2 epidermal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP35908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eKRT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eJunction plakoglobin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP14923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eJUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eAlpha-1-acid glycoprotein 1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP02763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eORM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eImmunoglobulin kappa light chain\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP0DOX7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eP0DOX7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eKeratin, type I cytoskeletal 14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP02533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eKRT14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eKeratin, type I cytoskeletal 9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP35527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eKRT9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eKeratin, type II cytoskeletal 1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP04264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eKRT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eKeratin, type I cytoskeletal 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP19012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eKRT15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eVitamin D-binding protein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP02774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eSerotransferrin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP02787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 664px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpregulated proteins\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 245px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFold Change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 664px;\"\u003e\n \u003cp\u003eProteins appear in both PV/PIS (n=14) and control (n=16) groups.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eInter-alpha-trypsin inhibitor heavy chain H4 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eQ14624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eITIH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eImmunoglobulin heavy variable 5-51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eA0A0C4DH38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eIGHV5-51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003ePlasma protease C1 inhibitor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP05155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eSERPING1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eAlpha-1-antichymotrypsin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP01011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eSERPINA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eSerine/threonine-protein phosphatase 2A regulatory subunit B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eQ06190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003ePPP2R3A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eApolipoprotein M\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eO95445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eAPOM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eCarboxypeptidase B2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eQ96IY4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eCPB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eInter-alpha-trypsin inhibitor heavy chain H3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eQ06033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eITIH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eCholinesterase\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP06276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eBCHE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eFicolin-3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eO75636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eFCN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eApolipoprotein C-IV\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP55056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eAPOC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eThyroxine-binding globulin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP05543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eSERPINA7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eBPI fold-containing family B member 1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eQ8TDL5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eBPIFB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eCorticosteroid-binding globulin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP08185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eSERPINA6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eFilamin-A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP21333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eFLNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003ePrenylcysteine oxidase 1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eQ9UHG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003ePCYOX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eCoagulation factor XI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP03951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eF11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eSerum amyloid A-2 protein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP0DJI9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eSAA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eComplement factor I\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP05156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eSerum amyloid A-1 protein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP0DJI8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eSAA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003ePlasma serine protease inhibitor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP05154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eSERPINA5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eImmunoglobulin heavy variable 2-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eA0A0B4J1V2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eIGHV2-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eCoagulation factor X\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP00742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eF10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eTetranectin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP05452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eCLEC3B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eHaptoglobin-related protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eP00739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eHPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eAttractin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eO75882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eATRN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 664px;\"\u003e\n \u003cp\u003eIn this pairwise comparison, fold changes of proteins (or fragments thereof) that were found to be decreased, are denoted as a reciprocal values. Significance was determined at p\u0026lt;0.05.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eComparative overview of PV/PIS cohort vs. a previously described Long COVID cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the Spotfire\u0026reg; program (http://spotfire.com/, version 12.0, accessed on 14 April 2025), plasma proteomic changes in the PV/PIS cohort were visualised in a scatter plot alongside the proteins reported in a previously published Long COVID proteomic study by Kruger et al. (2022). In \u003cstrong\u003eFigure 6A\u003c/strong\u003e, a broader distribution of protein changes is observed in the Long COVID group (blue dots), while PV/PIS cohort displays a tighter pattern of protein changes (red dots). \u003cstrong\u003eFigures 6B and 6C\u003c/strong\u003e, highlight each population separately allowing for visual identification of the specific with significant expression changes in each cohort.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSince the onset of the COVID-19 pandemic, several SARS-CoV-2 vaccines have been developed to reduce transmission rates and enhance immunity. Among these, mRNA-based vaccines, such as Pfizer-BioNTech (BNT162b2) and Moderna (mRNA-1273), were widely deployed due to their rapid availability and their efficacy across different populations. These vaccines utilise lipid nanoparticles to encapsulate and deliver mRNA, which then leads to the production of neutralising antibodies and activation of T-cell immune response, which may contribute to reduced viral spread and severity of infection \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, recent evidence highlights limitations in long-term efficacy of mRNA vaccine technology, particularly against emerging SARS-CoV-2 variants. While these vaccines were highly effective against the original strain, inducing strong IgM and IgG responses \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, the spike (S) protein \u0026ndash; the primary target of vaccine-induced immunity \u0026ndash; undergoes significant selective pressure. This evolutionary pressure drives the accumulation of mutations in the S gene, facilitating the emergence of new variants with altered immune evasion properties. \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, as well as an altered propensity to drive heterogenous amyloid deposit (microclot) formation \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA recent comparative study by Christie et al. (2022) analysed spike protein mutations in the Alpha, Beta and Delta variants, demonstrating a progressive ability of the virus to enter cells independently of spike-ACE2 interactions. This adaption correlates with increased transmissibility and reduced vaccine effectiveness \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. More recent variants, such as Omicron, exhibit enhanced immune evasion, leading to a measurable decline in mRNA vaccine efficacy \u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, most of our PV/PIS cohort were administered with vaccines shortly before the first detection of the Omicron variant in 2021 (see \u003cb\u003eFig. S1\u003c/b\u003e). This timing suggests that a significant portion of our PV/PIS population had been vaccinated with mRNA-based vaccines while the virus continued to evolve, plausibly contributing to the reduced effectiveness of the vaccine against this variant. Additionally, concerns have emerged regarding the potential role of mRNA vaccines in inducing a \u0026ldquo;spike effect\u0026rdquo;, where endogenous spike protein interacts with the ACE2 receptor, mimicking aspects of COVID-19 pathology \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The administration of mRNA vaccines elicits a robust T-cell response, with CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T-cells specific to spike protein persisting in circulation for up to 6 months post-vaccination \u003csup\u003e\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent studies indicate that an acute COVID infection can induce a strong Th17 immune response, which plays a key role in cytokine cascade activation. Th17 immune response promotes inflammation via the release of IFN-γ, IL-6 and IL-17, contributing to immune dysregulation and persistent inflammation \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. An imbalance in Th1/Th2 immune responses, driven by excessive Th17 activation, has been implicated in SARS-CoV-2 pathogenesis \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Notably, post-mRNA vaccination, elevated IL-17 levels have been reported, triggering an immediate and intense inflammatory response \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). A dysregulated Th17 response is also observed in pregnancy disorder pre-eclampsia \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, which bears many similarities to responses associated with Long COVID infection \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. The upregulation of serum amyloid A (SAA), attractin (ATRN) and complement factor I (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), in the PV/PIS participants may modulate cytokine production, promote T-cell activation and recruitment \u0026ndash; including Th17 cell migration \u0026ndash; and disrupt immune homeostasis in affected individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eInflammation, immune dysregulation and amyloidogenic proteins in PV/PIS\u003c/h2\u003e \u003cp\u003eWe identified several acute phase response proteins, also known as acute phase reactants (APR) that had significant changes in protein concentrations. These APRs are non-specific inflammatory markers that demonstrate a significant change during an inflammatory state (whether acute or chronic). These APRs can contribute to several symptoms, such as that of fever, fatigue, and amyloidosis. Primary mediators of APRs gene expression include interleukins (IL-6 and IL-1β), TNF-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:\\)\u003c/span\u003e\u003c/span\u003e, and growth factors, amongst others. Positive APRs, such as serum amyloid A (SAA), are typically increased during inflammation and negative APRs, such as albumin, are decreased \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Our findings show the upregulation of positive APRs, such as SAA-1 (1.72-fold increase) and SAA-2 (1.66-fold increase), and the downregulation of negative APRs, such as albumin (2.3-fold decrease) in PV/PIS relative to controls (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe significant upregulation of SAA-1 and SAA-2 in the PV/PIS cohort can be connected to the activation of monocytes and dendritic cells in PV/PIS participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The activation of these cells can trigger the release of damage-associated molecular patterns (DAMPs) and pattern-associated molecular patterns (PAMPs). These molecules in turn activate the innate immune system via pattern recognition receptors (PRRs), leading to exacerbated inflammatory and immune responses \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis cascade of events is typically observed following vaccination and/or during acute SARS-CoV-2 infection. However, in PV/PIS participants, including those in our cohort, the interplay between post-vaccination and the residual impact of SARS-CoV-2 infection \u0026ndash; whether present or absent \u0026ndash; may contribute to the cyclic disease pattern observed in these individuals. The brief mechanisms outlined above culminate in the activation of inflammatory and immune responses. However, persistent stimulation of these pathways, coupled with the potential circulating presence of SARS-CoV-2, can lead to dysregulation not only of the immune and inflammatory systems but also of the coagulation system. This coagulopathy, typically characterised by dysfunctional haemostasis and endothelial dysfunction, can further amplify inflammation and immune activation, driving a hyperinflammatory, hypercoagulable state and favouring Th17-skewed immune response; thus, mirroring the pathophysiological mechanisms observed in acute COVID infection.\u003c/p\u003e \u003cp\u003eIn contrast, the downregulation of the negative APR, albumin, in PV/PIS relative to controls may be attributed to a reduction in its synthesis, likely to conserve amino acids for the production of positive APRs \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. This redistribution of amino acids facilitates the enhanced synthesis of positive APRs, as well as other pro-inflammatory molecules, thereby promoting the expression of inflammatory genes and the translation of their respective proteins. While not a direct driver of the cyclical disease pattern, this shift in protein synthesis indirectly contributes to the heightened inflammatory state observed in the PV/PIS cohort.\u003c/p\u003e \u003cp\u003eChronic elevation of positive APRs (SAA-1 and SAA-2) in PV/PIS contributes not only to sustained inflammation and immune dysregulation but also poses a risk for secondary amyloidosis \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. In PV/PIS, SAA proteins may misfold and aggregate into amyloid fibrils. Using the AmyloGram predictive amyloidogenicity computational model (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biongram.biotech.uni.wroc.pl/AmyloGram/\u003c/span\u003e\u003cspan address=\"http://biongram.biotech.uni.wroc.pl/AmyloGram/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 19 March 2025) \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, we assessed the amyloidogenic potential of SAA-1 and SAA-2. SAA-1 received an amyloid score of 0.793, with 0.230 of its residues predicted to be amyloidogenic, while SAA-2 had a slightly higher score of 0.845, with 0.221 of its residues predicted to be amyloidogenic (see \u003cb\u003eTable S2\u003c/b\u003e). Although both proteins show a relatively low fraction of amyloidogenic residues, the high amyloid scores suggest these regions in SAA-1 and SAA-2 are particularly prone to misfolding. Furthermore, the limited fraction of amyloid-prone regions, within these proteins, can drive pathogenic amyloid formation when chronically elevated, as is characteristic of PV/PIS \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study identified a 2.57-fold upregulation of attractin (ATRN) in PV/PIS participants relative to controls (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Like many multifunctional proteins, ATRN plays a critical role in immune modulation. During inflammation, soluble attractin (sATRN) is released, facilitating immune cell recruitment, while membrane-bound attractin (mATRN) is expressed on activated monocytes, dendritic cells, and endothelial cells, promoting cell adhesion and migration \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe expression of mATRN on endothelial cells is upregulated in response to elevated pro-inflammatory cytokines. Once immune cells are recruited, mATRN undergoes proteolytic shedding generating sATRN and reducing its surface expression on both endothelial and immune cells \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. The observed high fold-change in ATRN may indicate prolonged immune cell recruitment and sustained shedding, which could contribute to increased vascular permeability, endothelial dysfunction, and immune dysregulation, promoting Th17 immune response in PV/PIS participants.\u003c/p\u003e \u003cp\u003eAdditionally, ATRN demonstrated one of the highest predicted amyloid scores (0.918) using AmyloGram, with 0.276 of its residues predicted to be amyloidogenic (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biongram.biotech.uni.wroc.pl/AmyloGram/\u003c/span\u003e\u003cspan address=\"http://biongram.biotech.uni.wroc.pl/AmyloGram/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 19 March 2025; see \u003cb\u003eTable S2\u003c/b\u003e) \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. As with the SAA proteins, the limited fractions of amyloidogenic residues in ATRN are highly susceptible to misfolding, especially within pathophysiological environments, such as that of PV/PIS. The abnormal increase in ATRN expression and shedding may promote protein misfolding further exacerbating endothelial dysfunction in PV/PIS participants. Given its amyloidogenic nature, ATRN is highly likely to cross-seed with fibrinogen \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, reinforcing the hyperinflammation and hypercoagulable state observed in PV/PIS participants.\u003c/p\u003e \u003cp\u003eWe identified the upregulation of inter-alpha inhibitor heavy chain H3 (ITIH3; 1.28-fold increase) and heavy chain H4 (ITIH4; 1.07-fold increase) in PV/PIS participants relative to controls (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These proteins belong to the inter-alpha-trypsin inhibitor (ITI) family, a group of plasma protease inhibitors that play crucial roles in maintaining extracellular matrix (ECM) and modulating inflammation \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. While the precise mechanisms underlying their functions remain poorly understood, ITIH4 has been proposed to act as an APR in response to infection, with its serum levels increasing during states of heightened inflammation \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe elevated presence of ITIH3 and ITIH4 in PV/PIS participants may serve as an additional driver of immune activation. However, their prolonged upregulation could contribute to immune dysregulation. Notably, using AmyloGram (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biongram.biotech.uni.wroc.pl/AmyloGram/\u003c/span\u003e\u003cspan address=\"http://biongram.biotech.uni.wroc.pl/AmyloGram/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 19 March 2025) \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, computational modelling predicted high amyloid scores for both proteins, with ITIH3 and ITIH4 scoring 0.913 and 0.902, respectively (see \u003cb\u003eTable S2\u003c/b\u003e). The potential misfolding of these proteins and their subsequent amyloid aggregation could facilitate cross-seeding with fibrinogen amyloid fibrils \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, exacerbating the hyperinflammation and hypercoagulable state observed in PV/PIS.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003ePersistent inflammation and coagulation abnormalities in PV/PIS\u003c/h2\u003e \u003cp\u003eA persistent inflammatory environment perpetuates immune activation, leading to the initiation of coagulation cascades \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e and increased endothelial permeability via pro-inflammatory cytokine signalling \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. We identified a significant upregulation in complement and coagulation functional protein groups, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, with particular emphasis on the upregulation of intrinsic coagulation factors X (2.06-fold increase) and XI (1.63-fold increase) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePlatelet factor 4 (PF4) was found to be decreased by 2.31-fold in PV/PIS participants (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A reduction or absence of PF4 has been linked to increased IL-17 production and enhanced Th17 cell-mediated inflammation \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Additionally, IL-17A has been shown to influence platelet release of pro-angiogenic factors, which has the can indirectly affect PF4 expression and function \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. In the context of chronic inflammatory conditions, such as PV/PIS, IL-17 may modulate PF4 expression through various indirect signalling pathways. However, the precise mechanisms and outcomes of this regulation process are not fully understood and warrant further research to elucidate the underlying interactions.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eLinking amyloidogenicity and coagulopathies in PV/PIS\u003c/h2\u003e \u003cp\u003eSecondary amyloidosis in proteins such as those mentioned above (SAA, ATRN, and ITI family) can have a downstream effect on the coagulation system, promoting the misfolding of proteins involved in coagulation, such as that of fibrinogen.\u003c/p\u003e \u003cp\u003eOur findings indicate a non-significant upregulation of fibrinogen alpha (0.038-fold increase) and beta chains (0.670-fold increase) in the PV/PIS cohort compared to controls. Fibrinogen has been shown to adopt an amyloid-like structure when exposed to inflammatory and oxidative environments \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Additionally, other amyloidogenic proteins, such as the APRs mentioned above, have been shown to cross-seed amyloidogenic fibrin production \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. The binding of these proteins may induce conformational changes in fibrinogen, affecting its detectability and potentially masking significant differences between PV/PIS and control groups in our proteomic analysis.\u003c/p\u003e \u003cp\u003eUltimately, the misfolding of protein structures with a high propensity of forming amyloid-type fibrils, combined with dysregulated haemostasis and immune responses, helps drive the formation of heterogenous amyloid deposits (microclots) in PV/PIS, as observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eComparing PV/PIS with Long COVID\u003c/h2\u003e \u003cp\u003eA proteomics study done by Kruger et al. (2022) employed a double trypsin digestion protocol to analyse the plasma proteome, including proteins trapped inside heterogenous amyloid deposits (microclots), of Long COVID participants prior to vaccination rollout in South Africa \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. We conducted a comparative analysis between the protein dataset from Kruger et al. and our PV/PIS cohort. Interesting, the proteins significantly downregulated in each cohort were mutually exclusive, with no overlap observed. Conversely, only one protein was found to be significantly upregulated in both Long COVID and PV/PIS groups (see \u003cb\u003eFig. S3\u003c/b\u003e). This divergence is further illustrated by the scatter plot distribution shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA. In PV/PIS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), most proteins exhibit a narrow range of expression changes, clustering around a slight up or downregulation. In contrast, the Long COVID (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB) cohort demonstrates a broader distribution, with some proteins exhibiting modest expression changes and others showing pronounced up or downregulation.\u003c/p\u003e \u003cp\u003eThese observed differences in protein expression profiles between PV/PIS and Long COVID suggest distinct mechanistic pathways that ultimately converge on similar pathophysiological outcomes. For instance, Kruger et al. (2022) reported the upregulation of ITIH1 and ITIH2 in individuals with Long COVID, implicating persistent dysregulation of ITI family proteins in post-viral inflammatory states. In contrast, our study findings indicate that ITIH3 and ITIH4 may play a more prominent role in PV/PIS. This distinction exemplifies the concept of \u0026ldquo;symptomatic overlap with mechanistic heterogeneity\u0026rdquo;, emphasising the need to differentiate the molecular pathways underlying these chronic conditions. Notably, the serum proteome signature developed by V\u0026ouml;llmy et al. (2021) to predict mortality in severe COVID-19 patients, showed opposite trends in protein abundance for ITIH1/ITIH2 and ITIH3/ITIH4 during disease progression. The observation that ITIH3 and ITIH4 was more abundant in non-survivors may point to a more severe phenotype in the PV/PIS group compared to Long COVID cases. Evaluation of the predication power of mortality risk panel against other COVID-19 serum proteomics using independent cohorts in different countries, has validated plasma proteomics as reproducible and meaningful biomarker panels \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the complex immunological and coagulopathic mechanisms associated with PV/PIS inflammation and coagulation, drawing some parallels with Long COVID pathology resulting exclusively after an acute infection event (where no vaccine was involved). While our findings suggest that PV/PIS may phenotypically mirror the persistent inflammation, coagulopathies, and endothelial dysfunction observed in Long COVID, the underlying protein expression patterns and mechanistic drivers appear fundamentally distinct. Notably, most differentially expressed proteins in our PV/PIS cohort were not shared with those reported by Kruger et al. (2022) in individuals with Long COVID. This stark contrast in proteomic signatures emphasises the likelihood that PV/PIS and Long COVID, while symptomatically overlapping, arise from divergent molecular and immunopathological pathways that may differ in disease severity and treatment requirements.\u003c/p\u003e \u003cp\u003eThe challenge of distinguishing between the individuals who developed Long COVID prior to vaccination, those who experienced Long COVID and subsequently developed vaccine-related complications, and those in whom the vaccine triggered further adverse reactions leading to PV/PIS, remains a major obstacle in both clinical and research settings. Many individuals with PV/PIS may have had one or more prior SARS-CoV-2 infections and/or Long COVID (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), further complicating the clinical differentiation of these pathologies (see \u003cb\u003eTable S1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThe overlap between acute infection, vaccination and pathogenesis of Long COVID and/or PV/PIS presents a major hurdle in constructing clearly defined \u0026ldquo;pure PV/PIS\u0026rdquo; cohorts. Genetic, environmental and immunological factors all interact to influence the severity and persistent of PV/PIS. Moreover, the timing of vaccination relative to viral exposure, especially in the context of successive variant waves, may modulate immune response and influence downstream pathogenicity of these conditions. Despite these limitations, proteomics provides a promising approach to addressing these diagnostic challenges. By analysing protein profiles, it becomes possible to identify distinctive biomarkers that can be developed into targeted assays, enabling more accurate differentiation between PV/PIS and Long COVID.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our gratitude to the participants and their families who participated in this study. Our gratitude goes to the medical practitioners who referred individuals for participation in this study. We wish to express our thanks to Janine Cronje for her administrative and research assistance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMV, EP and MW contributed to the conceptualisation, methodology and data analysis. MV designed the original experimental protocol. EEK, CS and CV managed participant recruitment and sample collection. MW and EP led the manuscript writing process. EP, MJK and KR oversaw funding acquisition and project administration. All authors provided critical input during the discussion of results and manuscript curation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors express their gratitude to the South African Medical Research Council (SAMRC) for funds granted by the Department of Science and Innovation (grant number 96847) that supported this proteomics research. J.M.N. and E.P. thank Kanro Research Foundation for funding. \u0026nbsp;D.B.K. thanks the Balvi Foundation (grant 18) and the Novo Nordisk Foundation for funding (grant NNF20CC0035580). The funders were not involved in study design, data collection and analysis, decision to publish or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all the participants prior to the inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKotze MJ. is a non-executive director and shareholder of Gknowmix (Pty) Ltd.\u003c/p\u003e\n\u003cp\u003ePretorius E. is an author of a patent\u0026nbsp;DIAGNOSTIC METHOD FOR LONG COVID PCT application number GB2105644.5 and is the managing director of BioCODE Technologies.\u003c/p\u003e\n\u003cp\u003eAll other authors have no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl-Aly, Z., Davis, H., McCorkell, L., Soares, L., Wulf-Hanson, S., Iwasaki, A., and Topol, E.J. 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Life Sci Alliance \u003cem\u003e4\u003c/em\u003e. 10.26508/lsa.202101099.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"613c209c-5dc7-4b89-a4ac-44fdb2a15559","identifier":"10.13039/501100001322","name":"South African Medical Research Council","awardNumber":"96847","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Stellenbosch University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"proteomics, Long COVID, PV/PIS, heterogenous amyloid deposits (microclots), inflammatory molecules","lastPublishedDoi":"10.21203/rs.3.rs-6521005/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6521005/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDuring the global rollout of COVID-19 vaccines a subset of individuals reported persistent symptoms following vaccination, with clinical presentations overlapping those of Long COVID requiring individualised treatment decisions. Distinguishing between vaccine-related adverse events and post-infectious sequelae remains challenging, particularly given the possibility of asymptomatic or mild SARS-CoV-2 infection prior to or after vaccination. To avoid this complexity, we define this patient group as presenting with Post-Vaccination/Post-Infection Syndrome (PV/PIS). In this study, we performed a proteomic analysis of plasma from 30 individuals with PV/PIS compared to healthy controls. Using mass spectrometry, we identified significant alterations in coagulation factors, acute phase proteins, and immune response modulators in the PV/PIS group. Notably, elevated levels of serum amyloid A1 and A2, attractin, and coagulation factors X and XI were observed, alongside downregulation of immune-regulatory proteins. These findings suggest that PV/PIS is characterized by persistent immune dysregulation and coagulopathy, with proteomic signatures only partially overlapping those previously reported in prior proteomics analysis on Long COVID samples collected prior to vaccination availability. Our results highlight the complex interplay between immune activation, endothelial dysfunction, and coagulation pathologies in PV/PIS, while also highlighting distinct differences between these systems in Long COVID and PV/PIS, paving the way for more targeted protein research in these conditions.\u003c/p\u003e","manuscriptTitle":"Proteomic signatures of Post-Vaccination/Post-Infection Syndrome (PV/PIS): Insights into immune dysregulation and coagulopathy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 11:12:53","doi":"10.21203/rs.3.rs-6521005/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"10be4a02-628a-4ff8-8cd6-269ae91a84cb","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47750151,"name":"Infectious Diseases"},{"id":47750152,"name":"Cell Communication and Signaling"}],"tags":[],"updatedAt":"2025-05-05T11:12:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-05 11:12:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6521005","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6521005","identity":"rs-6521005","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}