Molecular Analysis of SARS-CoV-2 Spike Protein-Induced Endothelial Cell Permeability and vWF Secretion
Bibliographic record
Abstract
Coronavirus disease COVID-19, which is caused by severe acute respiratory syndrome coronavirus SARS-CoV-2, has become a worldwide pandemic in recent years. In addition to being a respiratory disease, COVID-19 is a 'vascular disease' since it causes a leaky vascular barrier and increases blood clotting by elevating von Willebrand factor (vWF) levels in the blood. In this study, we analyzed in vitro how the SARS-CoV-2 spike protein S1 induces endothelial cell (EC) permeability and its vWF secretion, and the underlying molecular mechanism for it. We showed that the SARS-CoV-2 spike protein S1 receptor-binding domain (RBD) is sufficient to induce endothelial permeability and vWF-secretion through the angiotensin-converting enzyme (ACE)2 in an ADP-ribosylation factor (ARF)6 activation-dependent manner. However, the mutants, including those in South African and South Californian variants of SARS-CoV-2, in the spike protein did not affect its induced EC permeability and vWF secretion. In addition, we have identified a signaling cascade downstream of ACE2 for the SARS-CoV-2 spike protein-induced EC permeability and its vWF secretion by using pharmacological inhibitors. The knowledge gained from this study could be useful in developing novel drugs or repurposing existing drugs for treating infections of SARS-CoV-2, particularly those strains that respond poorly to the existing vaccines.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".