Diminished osteoprotegerin production by gingival fibroblasts from periodontitis patients is associated with their reduced ability to suppress osteoclastogenesis
Bibliographic record
Abstract
Osteoclast-mediated alveolar bone resorption is a key hallmark of periodontitis. Gingival fibroblasts (GFs) play multifaceted roles in the progression of chronic inflammation; however, their involvement in osteoclastogenesis remains controversial. Here, we analyzed the effects of mediators secreted by GFs from healthy donors and periodontitis patients on osteoclast formation. We observed strong suppression of receptor activator of nuclear factor kappa-B ligand (RANKL)-induced osteoclastogenesis by conditioned media from healthy donor GFs, which was independent of any prior in vitro stimulation or infection. These media contained high levels of osteoprotegerin (OPG), and GFs were identified as the main OPG source in gingival tissue using a 3D organotypic reconstructed human gingiva model and a single-cell RNA-seq dataset. Notably, GFs from periodontitis patients produced significantly less OPG compared to GFs from healthy individuals and were less effective at suppressing osteoclastogenesis. Finally, siRNA-mediated silencing of OPG expression in GFs from periodontitis patients further decreased their inhibitory effect on osteoclast formation, confirming the central role of OPG in osteoclastogenesis regulation by GFs. Collectively, these results demonstrate that in healthy gingival tissue, GFs exert bone-protective effects by inhibiting osteoclast formation. However, prolonged exposure to the microenvironment of the inflamed gingival tissue could impair this protective function through sustained reduction of OPG production, which may contribute to alveolar bone resorption in periodontitis.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".