Intramolecular Hydrogen Atom Transfer Enables Hydroperoxyl Radical Formation during the Peroxidation of Unsaturated Lipids
Bibliographic record
Abstract
Lipid peroxidation (autoxidation) is among the most well-studied free radical chain reactions and drives a cell death modality (ferroptosis) implicated in neurodegeneration and the damage resulting from reperfusion of ischemic tissue following stroke or organ transplant. In a recent study of structure-reactivity-potency relationships in phenoxazines─the most potent ferroptosis inhibitors yet identified─we found that electron-poor derivatives were able to trap more lipidperoxyl radicals than electron-neutral or electron-rich derivatives despite having lower inherent reactivity toward them. Herein, we report the results of our investigations to understand the basis for these surprising observations. Mechanistic and computational studies reveal a hitherto uncharacterized reaction in the context of lipid peroxidation: polyunsaturated fatty acid (PUFA)-derived peroxyl radicals can undergo an intramolecular H-atom transfer (HAT)/elimination sequence to form hydroperoxyl radicals. The hydroperoxyl radicals can then regenerate phenoxazines from their corresponding aminyl radicals, enabling them to trap additional lipidperoxyl radicals. Moreover, we show that lipid alcohols, such as farnesol and detoxification products of PUFA- and cholesterol-derived hydroperoxides, undergo similar chemistry, thereby converting chain-carrying lipidperoxyl radicals into hydroperoxyl radicals and retarding the propagation of lipid peroxidation. Ferroptosis inhibitors that can engage hydroperoxyl as a stoichiometric reductant are shown to have higher-than-expected potency, suggesting that this could be harnessed in the design of ferroptosis-targeting therapeutic candidates.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".