OCY454 Osteocytes as an in Vitro Cell Model for Bone Remodeling Under Mechanical Loading
Bibliographic record
Abstract
Osteocytes' mechano-regulation of bone formation and resorption is key to maintaining appropriate bone health. Although extensive in vitro studies have explored osteocyte mechanobiology using the well-established MLO-Y4 cell model, the low amount of sclerostin secreted by this cell line renders it inadequate for studying cross-talk between osteocytes and osteoblasts under mechanical loading. Here, we investigated the potential of the sclerostin-expressing OCY454 osteocyte cell model in fulfilling this role. Fully differentiated OCY454 cells were tested for mechano-sensitivity by measuring changes in protein secretion, total adenosine triphosphate (ATP) content, and intracellular calcium in response to oscillatory fluid flow. Increases in sclerostin expression and total ATP content were observed. However, very low levels of receptor activator of the nuclear factor κ-B ligand were detected, and there was a great inconsistency in calcium response. Conditioned medium (CM) from OCY454 cells was then used to culture osteoblast and osteoclast precursors. Osteoblast activity was quantified with alkaline phosphatase (ALP) and Alizarin Red S stain, while osteoclast differentiation was quantified with tartrate-resistant acid phosphatase (TRAP) staining. We demonstrated that mechanically stimulated OCY454 cells released soluble factors that increased osteoblasts' ALP activity (p < 0.05) and calcium deposition (p < 0.05). There was also a significant decrease of large-sized TRAP-positive osteoclasts when osteoclast precursors were treated with CM from flow-stimulated OCY454 cells (p < 0.05). Results from this study suggest that OCY454 cells respond to mechanical loading with the release of key factors such as sclerostin to regulate downstream bone cells, thus demonstrating its potential as a novel cell model for in vitro osteocyte mechanobiology studies. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:1681-1689, 2019.
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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 | 0.002 | 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".