Mechanical induction of PGE2 in osteocytes blocks glucocorticoid-induced apoptosis through both the β-catenin and PKA pathways
Bibliographic record
Abstract
Glucocorticoids are known to induce osteocyte apoptosis, whereas mechanical loading has been shown to sustain osteocyte viability. Here we show that mechanical loading in the form of fluid-flow shear stress blocks dexamethasone-induced apoptosis of osteocyte-like cells (MLO-Y4). Prostaglandin E(2) (PGE(2) ), a rapidly induced signaling molecule produced by osteocytes, was shown to be protective against dexamethasone-induced apoptosis, whereas indomethacin reversed the antiapoptotic effects of shear stress. This protective effect of shear stress was mediated through EP2 and EP4 receptors, leading to activation of the cAMP/protein kinase A signaling pathway. Activation of phosphatidylinositol 3-kinase, an inhibitor of glycogen synthesis kinase 3, also occurred, leading to the nuclear translocation of β-catenin, an important signal transducer of the Wnt signaling pathway. Both shear stress and prostaglandin increased the phosphorylation of glycogen synthesis kinase 3 α/β. Lithium chloride, an activator of the Wnt pathway, also was protective against glucocorticoid-induced apoptosis. Whereas it is known that mechanical loading increases cyclooxygenase-2 and EP2 receptor expression and prostaglandin production, dexamethasone was shown to inhibit expression of these components of the prostaglandin pathway and to reduce β-catenin protein expression. β-catenin siRNA knockdown experiments abrogated the protective effects of PGE(2), confirming the central role of β-catenin in mediating the protection against dexamethasone-induced cell death. Our data support a central role for PGE(2) acting through the cAMP/PKA and β-catenin signaling pathways in the protection of osteocyte apoptosis by fluid-flow shear stress.
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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 | 0.000 |
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".