Multiple oestradiol functions inhibit ferroptosis and acute kidney injury
Bibliographic record
Abstract
Acute tubular necrosis mediates acute kidney injury (AKI) and nephron loss1, the hallmark of end-stage renal disease2–4. For decades, it has been known that female kidneys are less sensitive to AKI5,6. Acute tubular necrosis involves dynamic cell death propagation by ferroptosis along the tubular compartment7,8. Here we demonstrate abrogated ferroptotic cell death propagation in female kidney tubules. 17β-oestradiol establishes an anti-ferroptotic state through non-genomic and genomic mechanisms. These include the potent direct inhibition of ferroptosis by hydroxyoestradiol derivatives, which function as radical trapping antioxidants, are present at high concentrations in kidney tubules and, when exogenously applied, protect male mice from AKI. In cells, the oxidized hydroxyoestradiols are recycled by FSP19,10, but FSP1-deficient female mice were not sensitive to AKI. At the genomic level, female ESR1-deficient kidney tubules partially lose their anti-ferroptotic capacity, similar to ovariectomized mice. While ESR1 promotes the anti-ferroptotic hydropersulfide system, male tubules express pro-ferroptotic proteins of the ether lipid pathway which are suppressed by ESR1 in female tissues until menopause. In summary, we identified non-genomic and genomic mechanisms that collectively explain ferroptosis resistance in female tubules and may function as therapeutic targets for male and postmenopausal female individuals. 17β-oestradiol establishes an anti-ferroptotic state in female kidney tubules.
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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".