A key role of the PGC-1α/ERR-α pathway in regulation of angiogenic factors in proliferative diabetic retinopathy
Bibliographic record
Abstract
Background: PGC-1α is induced by hypoxia and interacts with the receptor ERR-α to stimulate angiogenic factors expression and promote angiogenesis. We investigated the possible role of the PGC-1α/ERR-α pathway in regulating angiogenic factors expression in proliferative diabetic retinopathy (PDR). Methods: We analysed vitreous fluid samples from PDR and non-diabetic patients and epiretinal fibrovascular membranes from PDR patients. Streptozotocin-treated rats were used as a model of diabetic retinopathy. Vitreous samples, epiretinal membranes, rat retinas, human retinal Müller glial cells and human retinal microvascular endothelial cells (HRMECs) were studied by Western blot analysis, ELISA and immunohistochemistry. Levels of reactive oxygen species (ROS) were determined with spectrofluorometric analysis. Results: Immunohistochemical analysis demonstrated co-expression of PGC-1α and ERR-α in endothelial cells and leukocytes in epiretinal membranes. Angiogenic activity, determined by the numbers of CD31-positive vessels, correlated significantly with PGC-1α and ERR-α expression levels. PGC-1α, ERR-α and the angiogenic biomarkers vascular endothelial growth (VEGF) and angiopoietin 2 were significantly increased in PDR vitreous samples. Diabetes induced upregulation of PGC-1α and ERR-α immunoreactive proteoforms in rat retinas. Cultured Müller cells and HRMECs constitutively expressed PGC-1α and ERR-α. In Müller cells, the PGC-1α inhibitor SR-18292 and the ERR-α selective inverse agonist XCT790 significantly attenuated VEGF, angiopoietin 2 and MCP-1/CCL2 upregulation induced by diabetic mimetic conditions. Treatment of Müller cells with the PGC-1α activator XLN005 induced significant upregulation of VEGF and attenuated ROS production induced by diabetic mimetic conditions. Conclusions: Our findings suggest that suppression of the PGC-1α/ERR-α pathway might impair the upregulation of angiogenic factors in 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 | 0.001 |
| 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".