Wnt/β-Catenin Signaling Is a Normal Physiological Response to Mechanical Loading in Bone
Bibliographic record
Abstract
A preliminary expression profiling analysis of osteoblasts derived from tibia explants of the high bone mass LRP5 G171V transgenic mice demonstrated increased expression of canonical Wnt pathway and Wnt/beta-catenin target genes compared with non-transgenic explant derived osteoblasts. Therefore, expression of Wnt/beta-catenin target genes were monitored after in vivo loading of the tibia of LRP5 G171V transgenic mice compared with non-transgenic mice. Loading resulted in the increased expression of Wnt pathway and Wnt/beta-catenin target genes including Wnt10B, SFRP1, cyclin D1, FzD2, WISP2, and connexin 43 in both genotypes; however, there was a further increased in transcriptional response with the LRP5 G171V transgenic mice. Similar increases in the expression of these genes (except cyclin D1) were observed when non-transgenic mice were pharmacologically treated with a canonical Wnt pathway activator, glycogen synthase kinase 3beta inhibitor and then subjected to load. These in vivo results were further corroborated by in vitro mechanical loading experiments in which MC3T3-E1 osteoblastic cells were subjected to 3400 microstrain alone for 5 h, which increased the expression of Wnt10B, SFRP1, cyclin D1, FzD2, WISP2, and connexin 43. Furthermore, when MC3T3-E1 cells were treated with either glycogen synthase kinase 3beta inhibitor or Wnt3A to activate Wnt signaling and then subjected to load, a synergistic up-regulation of these genes was observed compared with vehicle-treated cells. Collectively, the in vivo and in vitro mechanical loading results support that Wnt/beta-catenin signaling is a normal physiological response to load and that activation of the Wnt/beta-catenin pathway enhances the sensitivity of osteoblasts/osteocytes to mechanical loading.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".