PPARγ2 expression in growth plate chondrocytes is regulated by p38 and GSK‐3
Bibliographic record
Abstract
Although peroxisome proliferator activated receptor (PPAR)gamma remains a critical regulator of preadipocyte differentiation, new roles have been discovered in inflammation, bone morphogenesis, endothelial function, cancer, longevity and atherosclerosis. Despite the demonstration of PPARgamma expression in chondrocytes, its role and the pathways affecting its expression and activity in chondrocytes remain largely unknown. We investigated the effects of PPARgamma activation on chondrocyte differentiation and its participation in chondrocyte lipid metabolism. PPARgamma2 expression is highly regulated during chondrocyte differentiation in vivo and in vitro PPARgamma activation with troglitazone resulted in increased Indian hedgehog expression and reduced collagen X expression, confirming previously described roles in the inhibition of differentiation. However, the major effect of PPARgamma2 in chondrocytes appears to be on lipid metabolism. During differentiation chondrocytes increase expression of the lipid-associated metabolizing protein, Lpl, which is accompanied by increased gene expression of PPARgamma. PPARgamma expression is suppressed by p38 activity, but requires GSK-3 activity. Furthermore, Lpl expression is regulated by p38 and GSK-3 signalling. This is the first study demonstrating a relationship between PPARgamma2 expression and chondrocyte lipid metabolism and its regulation by p38 and GSK-3 signalling.
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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) | 0.002 | 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".