PARP activation and the alteration of vasoactive factors and extracellular matrix protein in retina and kidney in diabetes
Bibliographic record
Abstract
AIMS: The development of diabetic complications is associated with increased oxidative stress which may damage DNA leading to the activation of nuclear enzyme poly (ADP-ribose) polymerase (PARP). PARP overactivation may further exacerbate the oxidative state of the cell through its consumption of nicotinamide adenine dinucleotide. In diabetic retinopathy and nephropathy, early characteristic features include increased production of vasoactive factors such as endothelin 1 (ET-1) and increased synthesis of extracellular matrix (ECM) proteins such as fibronectin (FN) and its splice variant extra domain B containing (EDB(+)) FN. We investigated the role of PARP in the development of diabetic retinopathy and nephropathy. METHODS: Two models of diabetic complications were used. PARP-1 knockout mice and their respective wild type controls were fed a 30% galactose diet for 2 months. The rats were given injections of PARP inhibitor 3-aminobenzamide (30 mg/kg/day). RESULTS: Analysis of the retinal and kidney tissues showed hyperhexosemia-induced oxidative stress and increased expression of ET-1, FN and EDB(+) FN in association with increased transcriptional co-activator p300 along with p300-dependent transcription factors, myocyte enhancing factors 2A and 2C. Furthermore, we showed increased PARP expression in the kidneys and retina of the diabetic rats. PARP blockade in both animal models prevented these hyperhexosemia-induced effects. CONCLUSIONS: These findings suggests that hyperhexosemia and diabetes causes upregulation of ET-1, FN and EDB(+) FN at the transcriptional level in the retina and kidney via a signaling pathway mediated by PARP and an epigenetic mechanism involving p300 and MEF2 transcription factors. Understanding these mechanisms is important in identifying novel treatment targets.
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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".