<i>In Vitro</i> Screening Identifies TRPV4 and PAR1 as Targets for Endothelial Barrier Stabilization in COVID‐19
Bibliographic record
Abstract
Study objective Endothelial dysfunction and increased microvascular permeability are hallmarks of severe COVID‐19. At present, the underlying mechanisms of endothelial barrier failure in COVID‑19 remain elusive. Here, we show that increased thrombin activity in plasma from severe COVID‐19 patients activates endothelial protease‐activated receptor (PAR1), which mediates barrier failure by triggering TRPV4‐mediated Ca 2+ influx in lung microvascular endothelial cells. Methods Citrate plasma was sampled as part of the Pa‐COVID‐19 cohort study (ethics approval EA2/066/20) from patients with severe COVID‐19 (high flow O 2 or mechanically ventilated; WHO severity score: 5‐7) COVID‑19. Plasma samples were diluted to 10% (v/v) in cell culture medium without FCS and tested for their ability to disrupt barrier integrity of primary human pulmonary microvascular endothelial cells (HPMEC) monolayers by electrical cell‐substrate impedance sensing (ECIS), immunofluorescence for endothelial VE‐cadherin and F‐actin, western blot analyses of PAR‐1 cleavage, and real‐time Ca 2+ imaging. Plasma from healthy donors served as control. Results COVID‐19 plasma had elevated thrombin activity while levels of antithrombin III, a key anti‐coagulant with thromboprotective function were decreased. COVID‐19 plasma caused endothelial barrier dysfunction as measured by ECIS and gap formation in HPMEC monolayers. Endothelial barrier disruption and endothelial Ca 2+ influx in response to COVID‐19 plasma could be blocked by selective antagonists targeting thrombin (Argatroban), its receptor PAR1 (SCH79797), or TRPV4 (HC‐067047). Conclusion Here, we identify a novel signaling axis involving thrombin, its receptor PAR1, and TRPV4 as mechanism for increased microvascular permeability in COVID‑19. Targeting this signaling axis in endothelial barrier failure may provide a promising adjunctive therapy in 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) | 0.001 | 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 | 0.000 |
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".