Genotoxic stress and activation of novel DNA repair enzymes in human endothelial cells and in the retinas and kidneys of streptozotocin diabetic rats
Bibliographic record
Abstract
BACKGROUND: Mammalian excision repair cross-complementing 1 (ERCC1) and ERCC4 (a.k.a xeroderma pigmentosum complementation group F) are nucleotide excision repair enzymes, which excise the 5' end of damaged DNA. ERCC1 and ERCC4 have an interactive relationship with poly (adenosine diphosphate ribose) polymerase (PARP). We studied the role of ERCC1 and ERCC4 in glucose-induced extracellular matrix protein production in human endothelial cells and in the retinas and kidneys of streptozotocin diabetic rats. METHODS: Human umbilical vein endothelial cells were grown with low (5 mM) and high glucose (25 mM). The cells were subjected to ERCC1 and ERCC4 small interfering RNA transfections, PARP blocker (3-aminobenzamide, ABA) and p300 blocker (curcumin). Retinas and kidneys from 1-month-old streptozotocin diabetic rats with or without treatment with curcumin and ABA were examined. Cells and tissues were studied for oxidative stress markers, fibronectin, ERCC1 and ERCC4, PARP and p300 mRNA. Western blot of nuclear proteins was performed. RESULTS: ERCC1 and ERCC4 messenger RNA and protein levels were higher in high glucose than in low glucose, along with increasing oxidative stress and augmented p300 and fibronectin production. ABA, curcumin, ERCC1 and ERCC4 silencing reduced such upregulations and oxidative stress. Similar changes were seen in the kidneys and retinas of diabetic rats. ABA and curcumin treatment significantly reduced such changes. CONCLUSIONS: These data indicate that glucose-induced ERCC1 and ERCC4 upregulation leads to increased fibronectin production via a p300-dependent pathway in umbilical endothelial cells, as well as in the retina and in the kidneys of streptozotocin diabetic rats. ERCC1 and ERCC4 may play important roles in the development of diabetic retinopathy and nephropathy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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) | 0.000 | 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 teacher head, 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".