YTHDF1 promotes p53 translation and induces ferroptosis during acute cerebral ischemia/reperfusion through m6A-dependent binding
Bibliographic record
Abstract
Abstract The rapid escalation of oxidative and nitrosative stress during ischemia/reperfusion (I/R) triggers neuronal damage, leading to severe neurological deficits and long-term disability. N6-methyladenosine (m6A), a highly abundant RNA modification in the brain, undergoes dynamic changes following acute I/R injury, and regulates stroke pathogenesis and neurological outcomes. However, the molecular mechanisms by which m6A influences acute I/R injury responses remain elusive. Our study reveals that the expression of key I/R pathogenesis pathways positively correlates with the expression of m6A reader proteins. Modulating expression of YTHDF1, a neuron-enriched reader protein of m6A, results in bidirectional changes in oxidative stress response and neuronal viability under I/R conditions. We have identified p53 mRNA as a critical target of m6A methylation and YTHDF1, driving the translation of p53 protein in a context- and m6A-dependent manner, which exacerbates oxidative stress and ferroptosis. This novel mechanism suggests the potential of targeting the m6A reader protein as a strategic avenue for developing neuroprotective therapies to mitigate I/R injury. Graphical abstract m6A-dependent YTHDF1 binding to p53 mRNA promotes its translation and ferroptosis during acute cerebral ischemia/reperfusion (I/R) Critical points: • I/R upregulates YTHDF1 expression and its binding to m6A-modfied p53 mRNA; • Binding by YTHDF1 promotes translation of p53 mRNA and induces ferroptosis; • AAV-mediated knockdown of YTHDF1 alleviates I/R-induced neuronal damage in acute phase.
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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) | 0.005 | 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".