VEGF‐A expression in osteoclasts is regulated by NF‐κB induction of HIF‐1α
Bibliographic record
Abstract
Large osteoclasts (10+ nuclei), predominant in rheumatoid arthritis and periodontal disease, have higher expression of proteases and activating receptors and also have increased resorptive activity when compared to small (2-5 nuclei) osteoclasts. We hypothesized that large and small osteoclasts activate different signaling pathways. A Signal Transduction Pathway Finder Array was used to compare gene expression of large and small osteoclasts in RAW 264.7-derived osteoclasts. Expression of vascular endothelial growth factor A (Vegfa) was higher in large osteoclasts and this result was confirmed by RT-PCR. RT-PCR further showed that RANKL treatment of RAW cells induced Vegfa expression in a time-dependent manner. Moreover, VEGF-A secretion in conditioned media was also increased in cultures with a higher proportion of large osteoclasts. To investigate the mechanism of Vegfa induction, specific inhibitors for the transcription factors NF-kappaB, AP-1, NFATc1, and HIF-1 were used. Dimethyl bisphenol A, the HIF-1alpha inhibitor, decreased Vegfa mRNA expression, whereas blocking NF-kappaB, AP-1, and NFATc1 had no effect. Furthermore, the NF-kappaB inhibitor gliotoxin inhibited Hif1alpha mRNA expression. In conclusion, VEGF-A gene and protein expression are elevated in large osteoclasts compared to small osteoclasts and this increase is regulated by HIF-1. In turn, Hif1alpha mRNA levels are induced by RANKL-mediated activation of NF-kappaB. These findings reveal further differences in signaling between large and small osteoclasts and thereby identify novel therapeutic targets for highly resorptive osteoclasts in inflammatory bone loss.
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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 | 0.000 |
| 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".