Regulation of Cholesterol Homeostasis by Hedgehog Signaling in Osteoarthritic Cartilage
Bibliographic record
Abstract
OBJECTIVE: With no effective therapies to attenuate cartilage degeneration in osteoarthritis (OA), the result is pain and disability. Activation of hedgehog (HH) signaling causes changes related to the progression of OA, with higher levels of Gli-mediated transcriptional activation associated with increased disease severity. To elucidate the mechanism through which this occurs, this study sought to identify genes regulated by HH signaling in human OA chondrocytes. METHODS: Using human OA cartilage samples, microarray analyses were performed to detect changes in gene expression when the HH pathway was modulated. Results were analyzed for differentially expressed genes, grouped into functional networks, and validated in independent samples. To investigate the effects of chondrocyte-specific sterol accumulation, we generated mice lacking Insig1 and Insig2, which are major negative regulators of cholesterol homeostasis, under Col2a1 regulatory elements. RESULTS: HH signaling was found to regulate genes that govern cholesterol homeostasis, and this led to alterations in cholesterol accumulation in chondrocytes. A higher level of Gli-mediated transcription resulted in accumulation of intracellular cholesterol. In genetically modified mice, chondrocyte-specific cholesterol accumulation was associated with an OA phenotype. Reducing cholesterol accumulation attenuated the severity of OA in mice in vivo and decreased the expression of proteases in human OA cartilage in vitro. CONCLUSION: HH signaling regulates cholesterol homeostasis in chondrocytes, and intracellular cholesterol accumulation contributes to the severity of OA. Our findings have therapeutic implications, since reduction of HH signaling reversed cholesterol accumulation and statin treatment attenuated cartilage degeneration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".