Glutathione limits RUNX2 oxidation and degradation to regulate bone formation
Bibliographic record
Abstract
Reactive oxygen species (ROS) are natural products of mitochondrial oxidative metabolism and oxidative protein folding. ROS levels must be well controlled, since elevated ROS has been shown to have deleterious effects on osteoblasts. Moreover, excessive ROS is thought to underlie many of the skeletal phenotypes associated with aging and sex steroid deficiency in mice and humans. The mechanisms by which osteoblasts regulate ROS and how ROS inhibits osteoblasts are not well understood. Here, we demonstrate that de novo glutathione (GSH) biosynthesis is essential in neutralizing ROS and establish a proosteogenic reduction and oxidation reaction (REDOX) environment. Using a multifaceted approach, we demonstrate that reducing GSH biosynthesis led to acute degradation of RUNX2, impaired osteoblast differentiation, and reduced bone formation. Conversely, reducing ROS using catalase enhanced RUNX2 stability and promoted osteoblast differentiation and bone formation when GSH biosynthesis was limited. Highlighting the therapeutic implications of these findings, in utero antioxidant therapy stabilized RUNX2 and improved bone development in the Runx2+/- haplo-insufficient mouse model of human cleidocranial dysplasia. Thus, our data establish RUNX2 as a molecular sensor of the osteoblast REDOX environment and mechanistically clarify how ROS negatively impacts osteoblast differentiation and bone formation.
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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 | 0.001 | 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".