HDL impairs osteoclastogenesis and induces osteoclast apoptosis via upregulation of ABCG1 expression
Bibliographic record
Abstract
Cholesterol is one of the major components of biological membranes and has an important function in osteoclast formation and survival. It has been reported that high-density lipoprotein (HDL) promotes cholesterol efflux from osteoclasts and induces their apoptosis, but the underlying mechanisms are unclear. In this study, we investigated how HDL promotes osteoclast cholesterol efflux and explored its effect on osteoclast formation and survival. Our results showed that the maximum diameter and fusion index of osteoclasts were decreased, while the ratios of osteoclasts with pyknotic nuclei were increased when cells were treated with HDL (600 ng/ml), as revealed by tartrate-resistant acid phosphatase-positive staining and microscopy assay. HDL enhanced cellular cholesterol efflux from osteoclasts in both concentration- and time-dependent manners. The ability of HDL3 to stimulate cholesterol efflux was stronger than preβ-HDL, HDL2, and ApoAI. Knockdown of ABCG1 expression reduced HDL-mediated cholesterol efflux and restored the HDL-induced reduction in osteoclast formation. Finally, HDL3 promoted sphingomyelin efflux from osteoclasts and reduced the expression of caveolin-1. Together, the findings demonstrate that HDL3 upregulates ABCG1 expression and promotes cholesterol efflux from osteoclast, impairs cholesterol homeostasis in osteoclasts, and consequently enhances osteoclast apoptosis.
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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 | 0.001 |
| 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".