The effect of estradiol during the early stages of osteoclast differentiation is associated with the accumulation of phosphorylated p53 in mitochondria and the inhibition of mitochondrial metabolism
Bibliographic record
Abstract
ABSTRACT Estrogen deficiency increases bone resorption and is a major contributor to osteoporosis. However, the molecular mechanisms mediating the effects of estrogen on osteoclasts remain unclear. This study aimed at elucidating the early metabolic effects of RANKL – the essential cytokine for osteoclastogenesis – and 17-beta-estradiol (E 2 ) on osteoclast progenitor cells, using RAW 264.7 macrophage cell line and bone marrow-derived macrophages as biological models. RANKL stimulated complex I activity, oxidative phosphorylation (OXPHOS), and mitochondria-derived ATP production, as early as 3 to 6 h. This up-regulation of mitochondrial bioenergetics was associated with an increased capacity to oxidize TCA cycle substrates, fatty acids, and amino-acids. E 2 inhibited all effects of RANKL on mitochondria metabolism. In the presence of RANKL, E 2 also decreased cell number and stimulated the mitochondrial-mediated apoptotic pathway, detected as early as 3h. Surprisingly, the pro-apoptotic effects of E 2 were associated with an accumulation of p392S-p53 in mitochondria. These findings elucidate early effects of RANKL on osteoclast progenitor metabolism and suggest novel p53-mediated mechanisms that contribute to postmenopausal osteoporosis.
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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".