Dexamethasone sensitizes to ferroptosis by glucocorticoid receptor–induced dipeptidase-1 expression and glutathione depletion
Bibliographic record
Abstract
Dexamethasone is widely used as an immunosuppressive therapy and recently as COVID-19 treatment. Here, we demonstrate that dexamethasone sensitizes to ferroptosis, a form of iron-catalyzed necrosis, previously suggested to contribute to diseases such as acute kidney injury, myocardial infarction, and stroke, all of which are triggered by glutathione (GSH) depletion. GSH levels were significantly decreased by dexamethasone. Mechanistically, we identified that dexamethasone up-regulated the GSH metabolism regulating protein dipeptidase-1 (DPEP1) in a glucocorticoid receptor (GR)–dependent manner. DPEP1 knockdown reversed the phenotype of dexamethasone-induced ferroptosis sensitization. Ferroptosis inhibitors, the DPEP1 inhibitor cilastatin, or genetic DPEP1 inactivation reversed the dexamethasone-induced increase in tubular necrosis in freshly isolated renal tubules. Our data indicate that dexamethasone sensitizes to ferroptosis by a GR-mediated increase in DPEP1 expression and GSH depletion. Together, we identified a previously unknown mechanism of glucocorticoid-mediated sensitization to ferroptosis bearing clinical and therapeutic implications.
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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".