Hydropersulfides Inhibit Lipid Peroxidation and Protect Cells from Ferroptosis
Bibliographic record
Abstract
Hydropersulfides (RSSH) are believed to serve important roles in vivo, including as scavengers of damaging oxidants and electrophiles. The α-effect makes RSSH not only much better nucleophiles than thiols (RSH), but also much more potent H-atom transfer agents. Since HAT is the mechanism of action of the most potent small-molecule inhibitors of phospholipid peroxidation and associated ferroptotic cell death, we have investigated their reactivity in this context. Using the fluorescence-enabled inhibited autoxidation (FENIX) approach, we have found RSSH to be highly reactive toward phospholipid-derived peroxyl radicals ( k inh = 2 × 10 5 M –1 s –1 ), equaling the most potent ferroptosis inhibitors identified to date. Related (poly)sulfide products resulting from the rapid self-reaction of RSSH under physiological conditions (e.g., disulfide, trisulfide, H 2 S) are essentially unreactive, but combinations from which RSSH can be produced in situ (i.e., polysulfides with H 2 S or thiols with H 2 S 2 ) are effective. In situ generation of RSSH from designed precursors which release RSSH via intramolecular substitution or hydrolysis improve the radical-trapping efficiency of RSSH by minimizing deleterious self-reactions. A brief survey of structure–reactivity relationships enabled the design of new precursors that are more efficient. The reactivity of RSSH and their precursors translates from (phospho)lipid bilayers to cell culture (mouse embryonic fibroblasts), where they were found to inhibit ferroptosis induced by inactivation of glutathione peroxidase-4 (GPX4) or deletion of the gene encoding it. These results suggest that RSSH and the pathways responsible for their biosynthesis may act as a ferroptosis suppression system alongside the recently discovered FSP1/ubiquinone and GCH1/BH 4 /DHFR systems.
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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".