Cathepsin S Causes Inflammatory Pain via Biased Agonism of PAR2 and TRPV4
Bibliographic record
Abstract
Serine proteases such as trypsin and mast cell tryptase cleave protease-activated receptor-2 (PAR2) at R(36)↓S(37) and reveal a tethered ligand that excites nociceptors, causing neurogenic inflammation and pain. Whether proteases that cleave PAR2 at distinct sites are biased agonists that also induce inflammation and pain is unexplored. Cathepsin S (Cat-S) is a lysosomal cysteine protease of antigen-presenting cells that is secreted during inflammation and which retains activity at extracellular pH. We observed that Cat-S cleaved PAR2 at E(56)↓T(57), which removed the canonical tethered ligand and prevented trypsin activation. In HEK and KNRK cell lines and in nociceptive neurons of mouse dorsal root ganglia, Cat-S and a decapeptide mimicking the Cat-S-revealed tethered ligand-stimulated PAR2 coupling to Gαs and formation of cAMP. In contrast to trypsin, Cat-S did not mobilize intracellular Ca(2+), activate ERK1/2, recruit β-arrestins, or induce PAR2 endocytosis. Cat-S caused PAR2-dependent activation of transient receptor potential vanilloid 4 (TRPV4) in Xenopus laevis oocytes, HEK cells and nociceptive neurons, and stimulated neuronal hyperexcitability by adenylyl cyclase and protein kinase A-dependent mechanisms. Intraplantar injection of Cat-S caused inflammation and hyperalgesia in mice that was attenuated by PAR2 or TRPV4 deletion and adenylyl cyclase inhibition. Cat-S and PAR2 antagonists suppressed formalin-induced inflammation and pain, which implicates endogenous Cat-S and PAR2 in inflammatory pain. Our results identify Cat-S as a biased agonist of PAR2 that causes PAR2- and TRPV4-dependent inflammation and pain. They expand the role of PAR2 as a mediator of protease-driven inflammatory pain.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".