Radiation-induced bone loss in mice is ameliorated by inhibition of HIF-2α in skeletal progenitor cells
Bibliographic record
Abstract
Radiotherapy remains a common treatment modality for cancer despite skeletal complications. However, there are currently no effective treatments for radiation-induced bone loss, and the consequences of radiotherapy on skeletal progenitor cell (SPC) survival and function remain unclear. After radiation, leptin receptor-expressing cells, which include a population of SPCs, become localized to hypoxic regions of the bone and stabilize the transcription factor hypoxia-inducible factor-2α (HIF-2α), thus suggesting a role for HIF-2α in the skeletal response to radiation. Here, we conditionally knocked out HIF-2α in leptin receptor-expressing cells and their descendants in mice. Radiation therapy in littermate control mice reduced bone mass; however, HIF-2α conditional knockout mice maintained bone mass comparable to nonirradiated control animals. HIF-2α negatively regulated the number of SPCs, bone formation, and bone mineralization. To test whether blocking HIF-2α pharmacologically could reduce bone loss during radiation, we administered a selective HIF-2α inhibitor called PT2399 (a structural analog of which was recently FDA-approved) to wild-type mice before radiation exposure. Pharmacological inhibition of HIF-2α was sufficient to prevent radiation-induced bone loss in a single-limb irradiation mouse model. Given that ~90% of patients who receive a HIF-2α inhibitor develop anemia because of off-target effects, we developed a bone-targeting nanocarrier formulation to deliver the HIF-2α inhibitor to mouse bone, to increase on-target efficacy and reduce off-target toxicities. Nanocarrier-loaded PT2399 prevented radiation-induced bone loss in mice while reducing drug accumulation in the kidney. Targeted inhibition of HIF-2α may represent a therapeutic approach for protecting bone during radiation therapy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| 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".