Diabetes-Induced Extracellular Matrix Protein Expression Is Mediated by Transcription Coactivator p300
Bibliographic record
Abstract
Increased fibronectin expression is a key feature of diabetic angiopathy. We have previously shown that nuclear factor-kappaB (NF-kappaB) mediates fibronectin expression in endothelial cells and in organs affected by diabetes complications. p300, a transcription coactivator, may regulate NF-kappaB activity via poly(ADP-ribose) polymerase (PARP) activation. Hence, we examined the role of p300 in fibronectin expression in diabetes. High glucose induced fibronectin expression in the endothelial cells, which was associated with increased p300, PARP activity, and NF-kappaB activation. This p300 alteration is mediated by mitogen-activated protein kinase and protein kinase C and B. We then used p300 small interfering RNA (siRNA) and showed decreased fibronectin and PARP expression, as well as NF-kappaB activation, in the endothelial cells. Examination of the heart tissues of streptozotocin-induced diabetic mice revealed increased fibronectin and p300 mRNA. Intravenous injection of p300 siRNA resulted in decreased p300 levels and normalized fibronectin expression in the heart. We further investigated retinal tissues from streptozotocin-induced diabetic rats treated with intravitreal p300 siRNA injection. Similar to the heart, p300 siRNA inhibited fibronectin expression in the retina of the diabetic animals. These results indicate that transcriptional coactivator p300 may regulate fibronectin expression via PARP and NF-kappaB activation in diabetes.
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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".