The Estrogen Receptor-α in Osteoclasts Mediates the Protective Effects of Estrogens on Cancellous But Not Cortical Bone
Bibliographic record
Abstract
Estrogens attenuate osteoclastogenesis and stimulate osteoclast apoptosis, but the molecular mechanism and contribution of these effects to the overall antiosteoporotic efficacy of estrogens remain controversial. We selectively deleted the estrogen receptor (ER)alpha from the monocyte/macrophage cell lineage in mice (ERalpha(LysM)(-/-)) and found a 2-fold increase in osteoclast progenitors in the marrow and the number of osteoclasts in cancellous bone, along with a decrease in cancellous bone mass. After loss of estrogens these mice failed to exhibit the expected increase in osteoclast progenitors, the number of osteoclasts in bone, and further loss of cancellous bone. However, they lost cortical bone indistinguishably from their littermate controls. Mature osteoclasts from ERalpha(LysM)(-/-) were resistant to the proapoptotic effect of 17beta-estradiol. Nonetheless, the effects of estrogens on osteoclasts were unhindered in mice bearing an ERalpha knock-in mutation that prevented binding to DNA. Moreover, a polymeric form of estrogen that is not capable of stimulating the nuclear-initiated actions of ERalpha was as effective as 17beta-estradiol in inducing osteoclast apoptosis in cells with the wild-type ERalpha. We conclude that estrogens attenuate osteoclast generation and life span via cell autonomous effects mediated by DNA-binding-independent actions of ERalpha. Elimination of these effects is sufficient for loss of bone in the cancellous compartment in which complete perforation of trabeculae by osteoclastic resorption precludes subsequent refilling of the cavities by the bone-forming osteoblasts. However, additional effects of estrogens on osteoblasts, osteocytes, and perhaps other cell types are required for their protective effects on the cortical compartment, which constitutes 80% of the skeleton.
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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 | 0.001 |
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".