Platelet factor 4 upregulates matrix metalloproteinase‐1 production in gingival fibroblasts
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Periodontitis is a highly prevalent chronic inflammatory disease that causes tooth loss, morbidity and confers an increased risk for systemic disease. Tissue destruction during periodontitis is due in large part to collagen-degrading matrix metalloproteinases (MMPs) released by resident cells of the periodontium in response to proinflammatory cytokines. Platelets are immune-competent blood cells with a newly recognized role in chronic inflammation; however, their role in the pathogenesis of periodontitis is undefined. Consequently, the objective of this study was to assess the effect of platelet factor 4 (PF4), a major platelet-derived cytokine, on MMP-1 (collagenase) expression in human gingival fibroblasts (HGFs). MATERIAL AND METHODS: HGFs were cultured in the presence or absence of recombinant PF4. Pro-MMP-1 secretion was quantified by enzyme-linked immunosorbent assay analysis of the cell culture supernatants. MMP-1 transcription was quantified by real-time polymerase chain reaction. Regulation of MMP-1 production by the p44/42 MAP kinase (MAPK) signaling pathway was examined in the presence or absence of PF4. RESULTS: Exposure to PF4 caused a ~ 2-3-fold increase in MMP-1 transcription and secretion from cultured HGFs. PF4 treatment also enhanced phosphorylation of p44/42 MAPK, which has been previously shown to induce MMP-1 expression in fibroblasts. Blockade of p44/42 MAPK signaling with the cell-permeant inhibitors PD98059 and PD184352 abrogated PF4-induced pro-MMP-1 transcription upregulation and release from cultured HGFs. CONCLUSION: We conclude that PF4 upregulates MMP-1 expression in HGFs in a p44/42 MAPK-dependent manner. These findings point to a previously unidentified role for platelets in the pathogenesis of periodontal diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".