Proangiogenic Effects of Protease-Activated Receptor 2 Are Tumor Necrosis Factor-α and Consecutively Tie2 Dependent
Bibliographic record
Abstract
OBJECTIVE: Angiogenesis is essential physiologically in growth and pathologically in tumor development, chronic inflammatory disorders, and proliferative retinopathies. Activation of protease-activated receptor 2 (PAR2) leads to a proangiogenic response, but its mechanisms have yet to be specifically described. Here, we investigated the mode of action of PAR2 in retinal angiogenesis. METHODS AND RESULTS: PAR2-activating peptide, SLIGRL, increased retinal angiogenesis associated with an induction of vascular endothelial growth factor and angiopoetin-2 and most notably tie2 in the retina in vivo as well as in cultured neuroretinal endothelial cells. SLIGRL also induced release of the proinflammatory and angiogenic mediator tumor necrosis factor-alpha (TNF-alpha) via the MEK/extracellular signal-regulated kinase (ERK) (MEK/ERK) pathway in these endothelial cells. TNF-alpha, in turn, elicited tie2 expression by activating the MEK/ERK pathway. PAR2-evoked tie2 expression, endothelium proliferation (in vitro), and retinal neovascularization (in vivo) were abrogated by selective TNF-alpha blockers (neutralizing antibody infliximab and soluble TNF-alpha receptor-Fc fusion protein etanercept) as well as the MEK inhibitor PD98059. CONCLUSIONS: The proangiogenic properties of PAR2 are intertwined with its proinflammatory effects, such that in retinal vasculature, they depend on TNF-alpha and subsequent induction of tie2 via the MEK/ERK pathway.
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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".