MicroRNA-200b Regulates Vascular Endothelial Growth Factor–Mediated Alterations in Diabetic Retinopathy
Bibliographic record
Abstract
OBJECTIVE: Diabetic retinopathy (DR) is a leading cause of blindness. Increased vascular endothelial growth factor (VEGF), promoting angiogenesis and increased permeability, is a key mechanistic abnormality in DR. We investigated microRNA (miRNA) alterations in DR with specific focus on miR-200b, and its downstream target, VEGF. RESEARCH DESIGN AND METHODS: miRNA expression profiling microarray was used to examine the retinas of streptozotocin-induced diabetic rats. Expressions of specific miRNAs were verified with PCR in the rat retina and in glucose-exposed endothelial cells. A target search, based on sequence complementarities, identified specific targets. We analyzed mRNA levels and protein expression in endothelial cells from large vessels and retinal capillaries and in the rat retina, with or without injection of miR-200b mimic or antagomir. Localization of miR-200b and its functional analysis in the rat and human retinas were performed. RESULTS: Alteration of several miRNAs, including downregulation of miR-200b, were observed in the retina in diabetes. Such downregulation was validated in the retina of diabetic rats and in endothelial cells incubated in glucose. In parallel, VEGF (target of miR-200b) mRNA and protein were elevated. In the retina, miR-200b was localized in neuronal, glial, and vascular elements. Transfection of endothelial cells and intravitreal injection of miR-200b mimic prevented diabetes-induced increased VEGF mRNA and protein. Also prevented were glucose-induced increased permeability and angiogenesis. Furthermore, transfection of miR-200b antagonists (antagomir) led to increased VEGF production. Similar alterations were seen in the human retina. CONCLUSIONS: These studies show a novel mechanism involving miR-200b in DR. Identification of such mechanisms may lead to the development of novel miRNA-based therapy.
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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 | 0.001 |
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".