Elevated Gα<sub>11</sub>expression in osteoblast lineage cells promotes osteoclastogenesis and leads to enhanced trabecular bone accrual in response to pamidronate
Bibliographic record
Abstract
Osteoblastic cells indirectly induce osteoclastogenesis in the bone microenvironment by expressing paracrine factors such as RANKL and M-CSF, leading to increased bone resorption. These cytokines can be regulated by a variety of intracellular pathways, which include G protein-coupled receptor signaling. To explore how enhanced signaling of the Gαq/11 pathway in osteoblast lineage cells may mediate osteoclast formation, we cocultured wild-type (WT) preosteoclasts with BMSCs derived from either WT or transgenic mice with osteoblast-specific overexpression of Gα11 (G11-Tg). G11-Tg cocultures had elevated osteoclast numbers with greater resorptive capacity and increased expression of Rankl, Rankl:Opg (osteoprotegerin), and M-csf compared with cocultures with WT BMSCs. As well, cocultures with G11-Tg BMSCs required a higher concentration of OPG to inhibit osteoclast formation and less angiotensin II to increase osteoclast size. These indicate that G11-Tg osteoblasts drive the increased osteoclast formation and osteopenia seen in G11-Tg mice. Pamidronate treatment of G11-Tg mice restored the trabecular bone loss phenotype, as bone mineral density, bone volume, trabecular number, separation, and expressions of osteoblastic and osteoclastic genes were comparable with WT parameters. These changes were characterized by enhanced accumulation of calcified cartilage in trabecular bone, demonstrating that resorption of the cartilaginous intermediate by osteoclasts is more affected by bisphosphonate treatment in G11-Tg mice. In conclusion, overexpression of Gα11 in osteoblastic cells promotes osteoclastogenesis by upregulation of Rankl and M-csf and bone loss by increased osteoclast resorption of the trabecular bone and cartilaginous matrix.
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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) | 0.002 | 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".