Tethered ligand‐derived peptides of proteinase‐activated receptor 3 (PAR<sub>3</sub>) activate PAR<sub>1</sub> and PAR<sub>2</sub> in Jurkat T cells
Bibliographic record
Abstract
Proteinase-activated receptors (PARs) can activate a number of signalling events, including T-cell signal-transduction pathways. Recent data suggest that the activation of PARs 1, 2 and 3 in Jurkat T-leukaemic cells induces tyrosine phosphorylation of the haematopoietic signal transducer protein, VAV1. To activate the PARs, this study used the agonist peptides SFLLRNPNDK, SLIGKVDGTS and TFRGAPPNSF, which are based on the sequences of the tethered ligand sequences of human PARs 1, 2 and 3, respectively. Here, we show that peptides based on either the human or murine PAR(3)-derived tethered ligand sequences (TFRGAP-NH(2) or SFNGGP-NH(2)) do not activate PAR(3), but rather activate PARs 1 and 2, either in Jurkat or in other PAR-expressing cells. Furthermore, whilst thrombin activates only Jurkat PAR(1), trypsin activates both PARs 1 and 2 and also disarms Jurkat PAR(1) for thrombin activation. We conclude therefore that in Jurkat or related T cells, signalling via PARs that can affect VAV1 phosphorylation is mediated via PAR 1 or 2, or both, and that distinct serine proteinases may potentially differentially affect T-cell function in the settings of inflammation.
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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) | 0.003 | 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".