MYC-dependent oxidative metabolism regulates osteoclastogenesis via nuclear receptor ERRα
Bibliographic record
Abstract
Osteoporosis is a metabolic bone disorder associated with compromised bone strength and an increased risk of fracture. Inhibition of the differentiation of bone-resorbing osteoclasts is an effective strategy for the treatment of osteoporosis. Prior work by our laboratory and others has shown that MYC promotes osteoclastogenesis in vitro, but the underlying mechanisms are not well understood. In addition, the in vivo importance of osteoclast-expressed MYC in physiological and pathological bone loss is not known. Here, we have demonstrated that deletion of Myc in osteoclasts increases bone mass and protects mice from ovariectomy-induced (OVX-induced) osteoporosis. Transcriptomic analysis revealed that MYC drives metabolic reprogramming during osteoclast differentiation and functions as a metabolic switch to an oxidative state. We identified a role for MYC action in the transcriptional induction of estrogen receptor-related receptor α (ERRα), a nuclear receptor that cooperates with the transcription factor nuclear factor of activated T cells, c1 (NFATc1) to drive osteoclastogenesis. Accordingly, pharmacological inhibition of ERRα attenuated OVX-induced bone loss in mice. Our findings highlight a MYC/ERRα pathway that contributes to physiological and pathological bone loss by integrating the MYC/ERRα axis to drive metabolic reprogramming during osteoclast differentiation.
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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) | 0.001 | 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".