<scp>SIRT</scp>1 reduction causes renal and retinal injury in diabetes through endothelin 1 and transforming growth factor β1
Bibliographic record
Abstract
In diabetes, hyperglycaemia causes up-regulation of endothelin 1 (ET-1) and transforming growth factor beta 1 (TGF-β1). Previously we showed glucose reduces sirtuin1 (SIRT1), a class III histone deacetylase. Here, we investigated the regulatory role of SIRT1 on ET-1 and TGF-β1 expression. Human microvascular endothelial cells were examined following incubation with 25 mmol/l glucose (HG) and 5 mmol/l glucose (NG) with or without SIRT1 or histone acetylase p300 overexpression or knockdown. mRNA expressions of ET-1, TGF-β1, SIRT1, p300 and collagen 1α(I) were examined. SIRT1 enzyme activity, ET-1 and TGF-β1 protein levels were measured. Histone acetylation and endothelial permeability were further investigated. Similar analyses were performed in the kidneys and retinas of SIRT1 overexpressing transgenic mice with or without streptozotocin induced diabetes. Renal functions were evaluated. In the endothelial cells (ECs), HG caused increased permeability and escalated production of ET-1, TGF-β1, collagen Iα(I). These cells also showed increased p300 expression, histone acetylation and reduced SIRT1 levels. These changes were rectified in the ECs following p300 silencing or by SIRT1 overexpression, whereas SIRT1 knockdown or p300 overexpression in NG mimicked the effects of HG. High ET-1 and TGF-β1 levels were seen in the kidneys and retinas of diabetic mice along with micro-albuminuria and increased fibronectin protein (marker of glucose-induced cell injury) levels. Interestingly, these detrimental changes were blunted in SIRT1 overexpressing transgenic mice with diabetes. This study showed a novel SIRT1 mediated protection against renal and retinal injury in diabetes, regulated through p300, ET-1 and TGF-β1.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".