Biased signaling by proteinase‐activated receptor 1 (PAR1) via activation with neutrophil and cockroach serine proteinases: tracking of distinct receptor dynamics with dual fluorochrome tagged receptors
Bibliographic record
Abstract
PAR1 is activated by the unmasking of a ‘tethered’ receptor‐activating sequence by thrombin or by a PAR1‐activating peptide (TFLLR‐NH 2 ). Both agonists cause calcium & MAPKinase (MAPK) signalling. However, we found that neutrophil elastase (NE), proteinase‐3 (PR3) and cockroach allergen trypsin (CAT) disarm PAR1 for thrombin signaling, but selectively stimulate MAPKinase (but not Ca ++ ). We hypothesized that this biased PAR1 signalling by NE, PR3 & CAT would correlate with distinct receptor dynamics (clustering‐internalization). To analyze the dynamics of biased PAR1 activation, we expressed PAR1 with (1) an N‐terminal mCherry red tag that is removed by proteinase, but NOT peptide activation and (2) a C‐terminal EYFP green tag which is retained upon activation by either peptide or enzyme. Dually‐tagged mCherry/EYFP‐PAR1 was visualized as a yellow halo at the cell membrane. PAR1 activation by thrombin caused internalization of ‘green‐labeled’ PAR1, lacking its mCherry signal; whereas TFLLR‐NH 2 caused internalization of ‘yellow’ receptor that retains both fluorophores. In contrast, NE, PR3 and CAT activation removed the mCherry tag, leaving the ‘activated’ YFP‐tagged ‘green’ receptor retained at the cell surface. We conclude that differential biased PAR1 signaling correlates with its differential trafficking (internalization: calcium & MAPK vs. membrane retention: MAPK only). Support: CIHR Canada
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".