ANRIL: A Regulator of VEGF in Diabetic Retinopathy
Bibliographic record
Abstract
Purpose: Long noncoding RNAs (lncRNAs) previously thought to be "dark matter" of the genome, play key roles in various biological processes. The lncRNA ANRIL is located at a genetic susceptibility locus for coronary artery diseases and type 2 diabetes. We examined the role of ANRIL in diabetic retinopathy, through study of its regulation of VEGF both in vitro and in vivo. Methods: Human retinal endothelial cells (HRECs) were subjected to incubation in high glucose to mimic diabetes. ANRIL expression was measured with or without small interfering RNA (siRNA) knockdown in HRECs. ANRIL knockout mice, with or without streptozotocin-induced diabetes, were also investigated. Cell and tissues were measured for VEGF mRNA and protein expression. Functional alterations in VEGF were determined through tube formation, cell proliferation, and retinal vascular permeability assays. Vascular endothelial growth factor regulation through ANRIL's interactions with polycomb repressive complex 2 (PRC2) components and p300 were studied thorough PRC2 blocker, siRNA, and RNA immunoprecipitation (RNA-IP) assays. Results: High glucose and diabetes caused ANRIL upregulation in HRECs and in the retina. Glucose-mediated elevation of ANRIL, on silencing, prevented VEGF expression. Such regulation involved ANRIL-mediated control of the PRC2 components p300 and miR200b. Direct binding of ANRIL to p300 and enhancer of zeste homolog 2 (EZH2; a PRC2 component) were elevated following exposure to high glucose levels. Conclusions: Our results demonstrate for the first time that ANRIL regulates VEGF expression and function in diabetic retinopathy. This regulation is mediated by p300, miR200b, and EZH2 of the PRC2 complex.
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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".