Proteinase‐activated receptor‐4: evaluation of tethered ligand‐derived peptides as probes for receptor function and as inflammatory agonists <i>in vivo</i>
Bibliographic record
Abstract
1. We evaluated the ability of a number of peptides based on the tethered ligand sequences of human, rat and murine proteinase-activated receptor-4 (PAR(4)), to serve as receptor-activating probes or antagonists for bioassays carried out in vitro and for in vivo models of inflammation. 2. In a rat PAR(4)-dependent platelet aggregation assay, the relative potencies of the active sequences (AYPGKF-NH(2)>GYPGKF-NH(2)>GYPGFK-NH(2)>GFPGKP-NH(2)) were consistent with an activation of PAR(4). 3. In the aggregation assay, the reverse or partial reverse-sequence peptides (VQGPYG-NH(2), YAPGKF-NH(2) and FKGPYA-NH(2)) were inactive, while trans-cinnamoyl (Tc)-YPGKF-NH(2), Tc-APGKF-NH(2) and N-palmitoyl-SGRRYGHALR-NH(2) (pepducin P4pal-10) were antagonists. 4. However, in an endothelium-dependent NO-mediated rat aorta (RA) relaxation assay and in a gastric longitudinal muscle (LM) contraction assay, these antagonist peptides were agonists as were most other peptides, with distinct orders of potencies that differed for both the RA and LM assays and from the platelet assay. 5. We conclude that PAR(4)-derived tethered ligand peptide agonists can act at receptors other than PAR(4) and that a judicious choice of ligands is required to probe for PAR(4) function in bioassay systems and in particular for in vivo models. 6. By selecting from these peptides the ones most reliably reflecting PAR(4) activation (AYPGKF-NH(2) as a standard agonist; YAPGKF-NH(2) as a PAR(4)-inactive standard), we were able to establish an inflammatory role for the PAR(4)-activating peptides acting via a non-neurogenic mechanism in a rat paw oedema model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".