Oxygen-sensing PHDs regulate bone homeostasis through the modulation of osteoprotegerin
Bibliographic record
Abstract
The bone microenvironment is composed of niches that house cells across variable oxygen tensions. However, the contribution of oxygen gradients in regulating bone and blood homeostasis remains unknown. Here, we generated mice with either single or combined genetic inactivation of the critical oxygen-sensing prolyl hydroxylase (PHD) enzymes (PHD1-3) in osteoprogenitors. Hypoxia-inducible factor (HIF) activation associated with Phd2 and Phd3 inactivation drove bone accumulation by modulating osteoblastic/osteoclastic cross-talk through the direct regulation of osteoprotegerin (OPG). In contrast, combined inactivation of Phd1, Phd2, and Phd3 resulted in extreme HIF signaling, leading to polycythemia and excessive bone accumulation by overstimulating angiogenic-osteogenic coupling. We also demonstrate that genetic ablation of Phd2 and Phd3 was sufficient to protect ovariectomized mice against bone loss without disrupting hematopoietic homeostasis. Importantly, we identify OPG as a HIF target gene capable of directing osteoblast-mediated osteoclastogenesis to regulate bone homeostasis. Here, we show that coordinated activation of specific PHD isoforms fine-tunes the osteoblastic response to hypoxia, thereby directing two important aspects of bone physiology: cross-talk between osteoblasts and osteoclasts and angiogenic-osteogenic coupling.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".