Regulation of Osteoclast Growth and Fusion by mTOR/raptor and mTOR/rictor/Akt
Bibliographic record
Abstract
Osteoclasts are giant bone cells formed by fusion from monocytes and uniquely capable of a complete destruction of mineralized tissues. Previously, we have demonstrated that in energy-rich environment not only osteoclast fusion index (the number of nuclei each osteoclast contains), but also cytoplasm volume per single nucleus was increased. The goal of this study was to investigate the regulation of metabolic sensor mTOR during osteoclast differentiation in energy-rich environment simulated by addition of pyruvate. We have found that in the presence of pyruvate, the proportion of mTOR associated with raptor increased, while mTOR-rictor-mediated Akt phosphorylation decreased. Inhibition of mTOR with rapamycin (10 nM) significantly interfered with all aspects of osteoclastogenesis. However, rapamycin at 1 nM, which preferentially targets mTOR-raptor complex, was only effective in control cultures, while in the presence of pyruvate osteoclast fusion index was successfully increased. Inhibition of Akt drastically reduced osteoclast fusion, however in energy-rich environment, osteoclasts of comparable size were formed through increased cytoplasm growth. These data suggest that mTOR-rictor mediated Akt signaling regulates osteoclast fusion, while mTOR-raptor regulation of protein translation contributes to fusion-independent cytoplasm growth. We demonstrate that depending on the bioenergetics microenvironment osteoclastogenesis can adjust to occur through preferential multinucleation or through cell growth, implying that attaining large cell size is part of the osteoclast differentiation program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".