Curcumin Modulates DNA Methyltransferase Functions in a Cellular Model of Diabetic Retinopathy
Bibliographic record
Abstract
Hyperglycaemia‐induced oxidative stress appears to be involved in the aetiology of diabetic retinopathy (DR), a major public health issue, via altering DNA methylation process. We investigated the effect of hyperglycaemia on retinal DNA methyltransferase (DNMT) expression in diabetic mice, using Gene Expression Omnibus datasets. We also evaluated the effect of curcumin both on high glucose‐induced reactive oxygen species (ROS) production and altered DNMT functions, in a cellular model of DR. We observed that three months of hyperglycaemia, in insulin‐deficient Ins2Akita mice, decrease DNMT1 and DNMT3a expression levels. In retinal pigment epithelium (RPE) cells, we also demonstrated that high glucose‐induced ROS production precedes upregulation of DNMT expression and activity, suggesting that changes in DNMT function could be mediated by oxidative stress via a potential dual effect. The early effect results in decreased DNMT activity, accompanied by the highest ROS production, while long‐term oxidative stress increases DNMT activity and DNMT1 expression. Interestingly, treatment with 25 μM curcumin for 6 hours restores ROS production, as well as DNMT functions, altered by the exposure of RPE to acute and chronic high glucose concentration. Our study suggests that curcumin may represent an effective antioxidant compound against DR, via restoring oxidative stress and DNMT functions, though further studies are recommended.
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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) | 0.001 | 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".