The C‐terminus of murine S100A9 protein inhibits hyperalgesia induced by the agonist peptide of protease‐activated receptor 2 (PAR<sub>2</sub>)
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: S100A9 protein induces anti-nociception in rodents, in different experimental models of inflammatory pain. Herein, we investigated the effects of a fragment of the C-terminus of S100A9 (mS100A9p), on the hyperalgesia induced by serine proteases, through the activation of protease-activated receptor-2 (PAR2). EXPERIMENTAL APPROACH: Mechanical and thermal hyperalgesia induced by PAR2 agonists (SLIGRL-NH2 and trypsin) was measured in rats submitted to the paw pressure or plantar tests, and Egr-1 expression was determined by immunohistochemistry in rat spinal cord dorsal horn. Calcium flux in human embryonic kidney cells (HEK), which naturally express PAR2, in Kirsten virus-transformed kidney cells, transfected (KNRK-PAR2) or not (KNRK) with PAR2, and in mouse dorsal root ganglia neurons (DRG) was measured by fluorimetric methods. KEY RESULTS: mS100A9p inhibited mechanical hyperalgesia induced by trypsin, without modifying its enzymatic activity. Mechanical and thermal hyperalgesia induced by SLIGRL-NH2 were inhibited by mS100A9p. SLIGRL-NH2 enhanced Egr-1 expression, a marker of nociceptor activation, and this effect was inhibited by concomitant treatment with mS100A9p. mS100A9p inhibited calcium mobilization in DRG neurons in response to the PAR2 agonists trypsin and SLIGRL-NH2, but also in response to capsaicin and bradykinin, suggesting a direct effect of mS100A9 on sensory neurons. No effect on the calcium flux induced by trypsin or SLIGRL in HEK cells or KNRK-PAR2 cells was observed. CONCLUSIONS AND IMPLICATIONS: These data demonstrate that mS100A9p interferes with mechanisms involved in nociception and hyperalgesia and modulates, possibly directly on sensory neurons, the PAR2-induced nociceptive signal.
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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 | 0.000 |
| 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) | 0.001 | 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".