Loss of PI3Kα Mediates Protection From Myocardial Ischemia–Reperfusion Injury Linked to Preserved Mitochondrial Function
Bibliographic record
Abstract
Background Identifying new therapeutic targets for preventing the myocardial ischemia–reperfusion injury would have profound implications in cardiovascular medicine. Myocardial ischemia–reperfusion injury remains a major clinical burden in patients with coronary artery disease. Methods and Results We studied several key mechanistic pathways known to mediate cardioprotection in myocardial ischemia–reperfusion in 2 independent genetic models with reduced cardiac phosphoinositide 3‐kinase‐α (PI3Kα) activity. P3Kα‐deficient genetic models (PI3KαDN and PI3Kα‐Mer‐Cre‐Mer) showed profound resistance to myocardial ischemia–reperfusion injury. In an ex vivo reperfusion protocol, PI3Kα‐deficient hearts had an 80% recovery of function compared with ≈10% recovery in the wild‐type. Using an in vivo reperfusion protocol, PI3Kα‐deficient hearts showed a 40% reduction in infarct size compared with wild‐type hearts. Lack of PI3Kα increased late Na + current, generating an influx of Na + , facilitating the lowering of mitochondrial Ca 2+ , thereby maintaining mitochondrial membrane potential and oxidative phosphorylation. Consistent with these functional differences, mitochondrial structure in PI3Kα‐deficient hearts was preserved following ischemia–reperfusion injury. Computer modeling predicted that PIP3, the product of PI3Kα action, can interact with the murine and human Na V 1.5 channels binding to the hydrophobic pocket below the selectivity filter and occluding the channel. Conclusions Loss of PI3Kα protects from global ischemic–reperfusion injury linked to improved mitochondrial structure and function associated with increased late Na + current. Our results strongly support enhancement of mitochondrial function as a therapeutic strategy to minimize ischemia–reperfusion injury.
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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) | 0.001 | 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".