Platelet activation by SARS-CoV-2 implicates the release of active tissue factor by infected cells
Bibliographic record
Abstract
Platelets are hyperactivated in coronavirus disease 2019 (COVID-19). However, the mechanisms promoting platelet activation by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are not well understood. This may be due to inherent challenges in discriminating the contribution of viral vs host components produced by infected cells. This is particularly true for enveloped viruses and extracellular vesicles (EVs), as they are concomitantly released during infection and share biophysical properties. To study this, we evaluated whether SARS-CoV-2 itself or components derived from SARS-CoV-2-infected human lung epithelial cells could activate isolated platelets from healthy donors. Activation was measured by the surface expression of P-selectin and the activated conformation of integrin αIIbβ3, degranulation, aggregation under flow conditions, and the release of EVs. We find that neither SARS-CoV-2 nor purified spike activates platelets. In contrast, tissue factor (TF) produced by infected cells was highly potent at activating platelets. This required trace amounts of plasma containing the coagulation factors FX, FII, and FVII. Robust platelet activation involved thrombin and the activation of protease-activated receptor (PAR)-1 and -4 expressed by platelets. Virions and EVs were identified by electron microscopy. Through size-exclusion chromatography, TF activity was found to be associated with a virus or EVs, which were indistinguishable. Increased TF messenger RNA (mRNA) expression and activity were also found in lungs in a murine model of COVID-19 and plasma of severe COVID-19 patients, respectively. In summary, TF activity from SARS-CoV-2-infected cells activates thrombin, which signals to PARs on platelets. Blockade of molecules in this pathway may interfere with platelet activation and the coagulation characteristic of COVID-19.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".