Lef1 Haploinsufficient Mice Display a Low Turnover and Low Bone Mass Phenotype in a Gender- and Age-Specific Manner
Bibliographic record
Abstract
We investigated the role of Lef1, one of the four transcription factors that transmit Wnt signaling to the genome, in the regulation of bone mass. Microcomputed tomographic analysis of 13- and 17-week-old mice revealed significantly reduced trabecular bone mass in Lef1(+/-) females compared to littermate wild-type females. This was attributable to decreased osteoblast activity and bone formation as indicated by histomorphometric analysis of bone remodeling. In contrast to females, bone mass was unaffected by Lef1 haploinsufficiency in males. Similarly, females were substantially more responsive than males to haploinsufficiency in Gsk3beta, a negative regulator of the Wnt pathway, displaying in this case a high bone mass phenotype. Lef1 haploinsufficiency also led to low bone mass in males lacking functional androgen receptor (AR) (tfm mutants). The protective skeletal effect of AR against Wnt-related low bone mass is not necessarily a result of direct interaction between the AR and Wnt signaling pathways, because Lef1(+/-) female mice had normal bone mass at the age of 34 weeks. Thus, our results indicate an age- and gender-dependent role for Lef1 in regulating bone formation and bone mass in vivo. The resistance to Lef1 haploinsufficiency in males with active AR and in old females could be due to the reduced bone turnover in these mice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".