Protein kinase D and Gβγ mediate sustained nociceptive signaling by biased agonists of protease-activated receptor-2
Bibliographic record
Abstract
Proteases sustain hyperexcitability and pain by cleaving protease-activated receptor-2 (PAR 2 ) on nociceptors through distinct mechanisms. Whereas trypsin induces PAR 2 coupling to Gα q , Gα s , and β-arrestins, cathepsin-S (CS) and neutrophil elastase (NE) cleave PAR 2 at distinct sites and activate it by biased mechanisms that induce coupling to Gα s , but not to Gα q or β-arrestins. Because proteases activate PAR 2 by irreversible cleavage, and activated PAR 2 is degraded in lysosomes, sustained extracellular protease-mediated signaling requires mobilization of intact PAR 2 from the Golgi apparatus or de novo synthesis of new receptors by incompletely understood mechanisms. We found here that trypsin, CS, and NE stimulate PAR 2 -dependent activation of protein kinase D (PKD) in the Golgi of HEK293 cells, in which PKD regulates protein trafficking. The proteases stimulated translocation of the PKD activator Gβγ to the Golgi, coinciding with PAR 2 mobilization from the Golgi. Proteases also induced translocation of a photoconverted PAR 2 -Kaede fusion protein from the Golgi to the plasma membrane of KNRK cells. After incubation of HEK293 cells and dorsal root ganglia neurons with CS, NE, or trypsin, PAR 2 responsiveness initially declined, consistent with PAR 2 cleavage and desensitization, and then gradually recovered. Inhibitors of PKD, Gβγ, and protein translation inhibited recovery of PAR 2 responsiveness. PKD and Gβγ inhibitors also attenuated protease-evoked mechanical allodynia in mice. We conclude that proteases that activate PAR 2 by canonical and biased mechanisms stimulate PKD in the Golgi; PAR 2 mobilization and de novo synthesis repopulate the cell surface with intact receptors and sustain nociceptive signaling by extracellular proteases.
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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".