Epoxyeicosatrienoic acids protect cardiac cells during starvation by modulating an autophagic response
Bibliographic record
Abstract
Epoxyeicosatrienoic acids (EETs) are cytochrome P450 epoxygenase metabolites of arachidonic acid involved in regulating pathways promoting cellular protection. We have previously shown that EETs trigger a protective response limiting mitochondrial dysfunction and reducing cellular death. Considering it is unknown how EETs regulate cell death processes, the major focus of the current study was to investigate their role in the autophagic response of HL-1 cells and neonatal cardiomyocytes (NCMs) during starvation. We employed a dual-acting synthetic analog UA-8 (13-(3-propylureido)tridec-8-enoic acid), possessing both EET-mimetic and soluble epoxide hydrolase (sEH) inhibitory properties, or 14,15-EET as model EET molecules. We demonstrated that EETs significantly improved viability and recovery of starved cardiac cells, whereas they lowered cellular stress responses such as caspase-3 and proteasome activities. Furthermore, treatment with EETs resulted in preservation of mitochondrial functional activity in starved cells. The protective effects of EETs were abolished by autophagy-related gene 7 (Atg7) short hairpin RNA (shRNA) or pharmacological inhibition of autophagy. Mechanistic evidence demonstrated that sarcolemmal ATP-sensitive potassium channels (pmKATP) and enhanced activation of AMP-activated protein kinase (AMPK) played a crucial role in the EET-mediated effect. Our data suggest that the protective effects of EETs involve regulating the autophagic response, which results in a healthier pool of mitochondria in the starved cardiac cells, thereby representing a novel mechanism of promoting survival of cardiac cells. Thus, we provide new evidence highlighting a central role of the autophagic response in linking EETs with promoting cell survival during deep metabolic stress such as starvation.
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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 | 0.000 |
| 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".