Extracellular RNA induce neutrophil recruitment via endothelial TLR3 during venous thrombosis after vascular injury
Bibliographic record
Abstract
Abstract Background Venous thromboembolism is associated with endothelial cell activation that contributes to the inflammation-dependent activation of the coagulation system. Cellular damages are associated with the release of different species of extracellular RNA (eRNA) involved in inflammation and coagulation. TLR3, which recognizes (viral) double-stranded RNA, single-stranded RNA, and also self-RNA fragments might be the receptor of these eRNA during venous thromboembolism. We investigate how eRNA regulate endothelial function through TLR3 and contribute to venous thromboembolism. Methods and Results Thrombus formation and size in WT and TLR3 deficient (-/-) mice were monitored by ultrasonography after venous thrombosis using the FeCl 3 and stasis models. Mice were treated with RNase1, poly(I:C) or RNA extracted from murine endothelial cells (eRNA). Gene expression and signaling pathway activation were analyzed in HEK293T cells overexpressing TLR3 in response to eRNA or in HUVECs transfected with a siRNA against TLR3. Plasma clot formation on treated HUVECs was analyzed. Thrombosis exacerbated RNA release in vivo and increased RNA content within the thrombus. RNase1 treatment reduced thrombus size compared to vehicle-treated mice. Poly(I:C) and eRNA treatments increased thrombus size in WT mice, but not in TLR3 -/- mice, by bolstering neutrophil recruitment. Mechanistically, TLR3 activation in endothelial cells promotes CXCL5 secretion and neutrophil recruitment in vitro. eRNA triggered plasma clot formation. eRNA mediate these effects through TLR3-dependent activation of NFκB. Conclusions We show that eRNA and TLR3 activation enhance venous thromboembolism through neutrophil recruitment and secretion of CXCL5.
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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 | 0.000 |
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".