Abstract 78: Extracellular RNA Induces Venous Thrombosis Through TLR3
Bibliographic record
Abstract
Venous thromboembolism (VTE) afflicts 117 people per 100,000 each year and is an important cause of morbidity and mortality. Vascular and circulating cells may sense ‘pathologic ligands’ accumulating in the circulation via toll-like receptor (TLR) and translate signals into a pro-thrombotic state. TLR3 recognizes self-molecules generated during tissue damage and inflammation such as extracellular RNA (eRNA). We hypothesize that eRNA and TLR3 are involved in the development of venous thrombosis after vessel injury. Injection of RNase1 decreased the size of thrombi after FeCl3-induced inferior vena cava injury (IVC) compared to mice treated with vehicle. Using a specific fluorescent probe for RNA, we found that FeCl3 induced RNA release and thus increased RNA content in the thrombus. WT or TLR3 deficient (-/-) mice received an injection of vehicle, eRNA or poly(I:C) prior to thrombosis induction. In WT mice, thrombus size was increased by eRNA or poly(I:C). Interestingly, no change in thrombus size was observed in TLR3-/- mice regardless of the treatment. Poly(I:C) treatment was associated with higher amount of neutrophils within the thrombus of WT mice compared with vehicle. In TLR3-/- mice, poly(I:C) failed to increase the recruitment of neutrophils. Staining for citrulinated H3, a marker of neutrophil extracellular traps (NETs) formation, was increased by poly(I:C) in WT mice. Interestingly, in TLR3-/- mice, no increase of NET formation was found after poly(I:C) injection as compared to vehicle control. Poly(I:C) also increased the recruitment of monocytes within thrombi in WT but not in TLR3-/- mice. Preliminary results suggest that injection of eRNA may also promote the recruitment of monocytes, neutrophils and the formation of NETs in WT but not in TLR3-/- mice. Human neutrophils were then treated with conditioned media from HUVEC treated with poly(I:C) in presence of control siRNA or TLR3 siRNA. Media from HUVEC transfected with control siRNA and treated with poly(I:C) induced the migration of neutrophils, whereas media from TLR3 siRNA and poly(I:C) treated cells did not. These results suggest that eRNA and TLR3 may participate to thrombus formation by inducing a pro-inflammatory response leading to the recruitment of monocytes and neutrophils.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".