Ref‐1 redox activity regulates retinal neovascularization by modulating transcriptional activation of <scp>HIF</scp> ‐1α
Bibliographic record
Abstract
Retinal neovascularization impairs visual function and is a hallmark of several neovascular eye diseases, including retinopathy of prematurity (ROP) and proliferative diabetic retinopathy (PDR). Current treatments include intravitreal injections of anti-vascular endothelial growth factor (VEGF) biologics, but these therapeutics are often accompanied by high treatment burden and resistance to therapy. Prior studies indicate that APE1/Ref-1, a multifunctional protein with both endonuclease (APE1) and redox-mediated transcriptional regulatory activity (Ref-1), activates multiple pro-angiogenic and pro-inflammatory signaling pathways by chemically reducing key cysteine residues in transcription factors, thereby activating them. Here, we investigated the previously unexplored role of Ref-1 in retinal neovascularization. We demonstrate that Ref-1 is highly expressed in endothelial cells in human PDR and in the oxygen-induced retinopathy (OIR) mouse model of retinal neovascularization. Ref-1 is also highly expressed in microglia and astrocytes in OIR. A small molecule Ref-1 redox inhibitor, APX2009, decreased retinal neovascularization in OIR after systemic delivery. In vitro, hypoxic endothelial cells did not exhibit upregulation of Ref-1 but rather increased Ref-1 nuclear localization. APX2009 decreased hypoxic endothelial cell proliferation and HIF-1α transcriptional activation. Thus, Ref-1 redox activity may be a novel therapeutic target for the treatment of retinal neovascularization, making APX2009 a promising systemic therapeutic approach for the treatment of vascular retinopathies such as ROP and PDR.
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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".