Cytochrome P450 2C8 ω3-Long-Chain Polyunsaturated Fatty Acid Metabolites Increase Mouse Retinal Pathologic Neovascularization—Brief Report
Bibliographic record
Abstract
OBJECTIVE: Regulation of angiogenesis is critical for many diseases. Specifically, pathological retinal neovascularization, a major cause of blindness, is suppressed with dietary ω3-long-chain polyunsaturated fatty acids (ω3LCPUFAs) through antiangiogenic metabolites of cyclooxygenase and lipoxygenase. Cytochrome P450 epoxygenases (CYP2C8) also metabolize LCPUFAs, producing bioactive epoxides, which are inactivated by soluble epoxide hydrolase (sEH) to transdihydrodiols. The effect of these enzymes and their metabolites on neovascularization is unknown. APPROACH AND RESULTS: The mouse model of oxygen-induced retinopathy was used to investigate retinal neovascularization. We found that CYP2C (localized in wild-type monocytes/macrophages) is upregulated in oxygen-induced retinopathy, whereas sEH is suppressed, resulting in an increased retinal epoxide:diol ratio. With a ω3LCPUFA-enriched diet, retinal neovascularization increases in Tie2-driven human-CYP2C8-overexpressing mice (Tie2-CYP2C8-Tg), associated with increased plasma 19,20-epoxydocosapentaenoic acid and retinal epoxide:diol ratio. 19,20-Epoxydocosapentaenoic acids and the epoxide:diol ratio are decreased with overexpression of sEH (Tie2-sEH-Tg). Overexpression of CYP2C8 or sEH in mice does not change normal retinal vascular development compared with their wild-type littermate controls. The proangiogenic role in retina of CYP2C8 with both ω3LCPUFA and ω6LCPUFA and antiangiogenic role of sEH in ω3LCPUFA metabolism were corroborated in aortic ring assays. CONCLUSIONS: Our results suggest that CYP2C ω3LCPUFA metabolites promote retinal pathological angiogenesis. CYP2C8 is part of a novel lipid metabolic pathway influencing retinal neovascularization.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".