Proteomic Profiles in Patients with Thrombosis Due to COVID-19 Are Distinct from Non-COVID-19 Thrombosis
Notice bibliographique
Résumé
Abstract BACKGROUND. COVID-19 is a prothrombotic disease, characterized by endotheliopathy, hypercoagulability, and thromboembolic complications. We hypothesized that the pathogenesis of thromboembolism associated with COVID-19 might differ from thromboembolism in patients without COVID-19. In this study, we sought to evaluate the proteomic signatures of plasma from patients with venous thromboembolism with and without COVID-19. METHODS. Between December 17, 2020 and February 25, 2021 blood was collected from 48 hospitalized patients. Of these 24 had a confirmed diagnosis of COVID-19 infection (COVID+) and radiologic confirmation of arterial or venous thromboembolism (TE+); 17 had COVID-19 infection with absence of arterial thrombosis clinically and absence of venous thromboembolism on lower extremity Doppler ultrasound or chest CT angiography (COVID+/TE-), while 7 were arterial or venous thromboembolism in the absence of COVID-19 (COVID-/TE+). Blood was collected in sodium citrate tubes and centrifuged at 4000 rpm for 20 minutes, with resulting plasma supernatant used for protein profiling performed at Eve Technologies (Calgary, Alberta, Canada). Institutional Review Board approval was obtained for this study. Statistical analysis was performed using GraphPad Prism (v9.1, GraphPad Software, San Diego, CA) and R (v4, R Core Team). P values <0.05 were considered statistically significant. A heatmap was generated using Heatmapper (heatmapper.ca) to represent the concentrations of proteins. RESULTS. The median age was 63 years; overall 25 (52%) were men (13 [54%] among COVID+/TE+, 11 [65%] among COVID+/TE-, and 1 [14%] among COVID-/TE+). In COVID-19 patients who developed thromboembolic events, several proteins associated with inflammation, complement activation, and hemostasis were present at higher levels than in non-COVID-19 patients who developed thromboembolic events (Fig. 1). These included complement factors C2 and C5a, pentraxin-3 (PTX-3), lipocalin-2 (LCN2), resistin (RETN), platelet endothelial cell adhesion molecule-1 (Pecam1), serum amyloid A (SAA), and tissue factor (TF). The heatmap indicates relative protein levels detected in each subject (columns) for proteins (rows) that had statistically significant differences between groups (Fig. 2). Heatmap revealed relatively lower levels of all proteins in patients with thromboembolism without COVID-19 and relatively higher levels of proteins in patients with COVID-19, and especially in ICU patients with COVID-19 and thromboembolism. CONCLUSIONS. Thromboembolic complications in patients with COVID-19 are associated with increased levels of various proteins involved in complement activation and immunothrombotic cascades, compared to thrombotic events in the absence of COVID-19. Activation of the classical complement pathway as evidenced by a relative increase in complement factor C2 may lead to increased TF activation, reflecting more substantial endothelial damage in COVID-19 patients. Higher levels of Pecam1, SAA, LCN2, and RETN all point to increased endotheliopathy, inflammation, and tissue damage in COVID-19 compared to non-COVID-19 thrombosis. These findings may offer insights into novel therapeutic strategies to treat immunothrombotic complications of COVID-19. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».