Proteasome Activators, PA28<i>α</i>and PA28<i>β</i>, Govern Development of Microvascular Injury in Diabetic Nephropathy and Retinopathy
Bibliographic record
Abstract
Diabetic nephropathy (DN) and diabetic retinopathy (DR) are major complications of type 1 and type 2 diabetes. DN and DR are mainly caused by injury to the perivascular supporting cells, the mesangial cells within the glomerulus, and the pericytes in the retina. The genes and molecular mechanisms predisposing retinal and glomerular pericytes to diabetic injury are poorly characterized. In this study, the genetic deletion of proteasome activator genes, PA28αand PA28βgenes, protected the diabetic mice in the experimental STZ-induced diabetes model against renal injury and retinal microvascular injury and prolonged their survival compared with wild type STZ diabetic mice. The improved wellbeing and reduced renal damage was associated with diminished expression of Osteopontin (OPN) and Monocyte Chemoattractant Protein-1 (MCP-1) in the glomeruli of STZ-injected PA28α/PA28βdouble knockout (Pa28αβDKO) mice and also in cultured mesangial cells and retinal pericytes isolated from Pa28αβDKO mice that were grown in high glucose. The mesangial PA28-mediated expression of OPN under high glucose conditions was suppressed by peptides capable of inhibiting the binding of PA28 to the 20S proteasome. Collectively, our findings demonstrate that diabetic hyperglycemia promotes PA28-mediated alteration of proteasome activity in vulnerable perivascular cells resulting in microvascular injury and development of DN and DR.
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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".