MicroRNA 9 Is a Regulator of Endothelial to Mesenchymal Transition in Diabetic Retinopathy
Bibliographic record
Abstract
Purpose: Diabetic retinopathy (DR) is a significant cause of blindness. Most research around DR focus on late-stage developments rather than early changes such as early endothelial dysfunction. Endothelial-to-mesenchymal transition (EndMT), an epigenetically regulated process whereby endothelial cells lose endothelial characteristics and adopt mesenchymal-like phenotypes, contributes to early endothelial changes in DR. The epigenetic regulator microRNA 9 (miR-9) is suppressed in the eyes during DR. MiR-9 plays a role in various diseases and regulates EndMT-related processes in other organs. We investigated the role miR-9 plays in glucose-induced EndMT in DR. Methods: We examined the effects of glucose on miR-9 and EndMT using human retinal endothelial cells (HRECs). We then used HRECs and an endothelial-specific miR-9 transgenic mouse line to investigate the effect of miR-9 on glucose-induced EndMT. Finally, we used HRECs to probe the mechanisms through which miR-9 may regulate EndMT. Results: We found that miR-9 inhibition was both necessary and sufficient for glucose-induced EndMT. Overexpression of miR-9 prevented glucose-induced EndMT, whereas suppressing miR-9 caused glucose-like EndMT changes. We also found that preventing EndMT with miR-9 overexpression improved retinal vascular leakage in DR. Finally, we showed that miR-9 regulates EndMT at an early stage by regulating EndMT-inducing signals such as proinflammatory and TGF-β pathways. Conclusions: We have shown that miR-9 is an important regulator of EndMT in DR, potentially making it a good target for RNA-based therapy in early DR.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".