Neuropeptide Release from Dental Pulp Cells by RgpB via Proteinase-Activated Receptor-2 Signaling
Bibliographic record
Abstract
Dental pulp inflammation often results from dissemination of periodontitis caused mostly by Porphyromonas gingivalis infection. Calcitonin gene-related peptide and substance P are proinflammatory neuropeptides that increase in inflamed pulp tissue. To study an involvement of the periodontitis pathogen and neuropeptides in pulp inflammation, we investigated human dental pulp cell neuropeptide release by arginine-specific cysteine protease (RgpB), a cysteine proteinase of P. gingivalis, and participating signaling pathways. RgpB induced neuropeptide release from cultured human pulp cells (HPCs) in a proteolytic activity-dependent manner at a range of 12.5-200 nM. HPCs expressed both mRNA and the products of calcitonin gene-related peptide, substance P, and proteinase-activated receptor-2 (PAR-2) that were also found in dental pulp fibroblast-like cells. The PAR-2 agonists, SLIGKV and trypsin, also induced neuropeptide release from HPCs, and HPC PAR-2 gene knockout by transfection of PAR-2 antisense oligonucleotides inhibited significantly the RgpB-elicited neuropeptide release. These results indicated that RgpB-induced neuropeptide release was dependent on PAR-2 activation. The kinase inhibitor profile on the RgpB-neuropeptide release from HPC revealed a new PAR-2 signaling pathway that was mediated by p38 MAPK and activated transcription factor-2 activation, in addition to the PAR-2-p44/42 p38MAPK and -AP-1 pathway. This new RgpB activity suggests a possible link between periodontitis and pulp inflammation, which may be modulated by neuropeptides released in the lesion.
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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) | 0.001 | 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".