Catalase Prevents Maternal Diabetes–Induced Perinatal Programming via the Nrf2–HO-1 Defense System
Bibliographic record
Abstract
We investigated whether overexpression of catalase (CAT) in renal proximal tubular cells (RPTCs) could prevent the programming of hypertension and kidney disease in the offspring of dams with maternal diabetes. Male offspring of nondiabetic and diabetic dams from two transgenic (Tg) lines (Hoxb7-green fluorescent protein [GFP]-Tg [controls] and Hoxb7/CAT-GFP-Tg, which overexpress CAT in RPTCs) were studied from the prenatal period into adulthood. Nephrogenesis, systolic blood pressure, renal hyperfiltration, kidney injury, and reactive oxygen species (ROS) generation were assessed. Gene expression of transforming growth factor-β1 (TGF-β1), nuclear factor erythroid 2p45-related factor-2 (Nrf2), and heme oxygenase-1 (HO-1) was tested in both in vitro and in vivo studies. Renal dysmorphogenesis was observed in offspring of Hoxb7-GFP-Tg dams with severe maternal diabetes; the affected male offspring displayed higher renal ROS generation and developed hypertension and renal hyperfiltration as well as renal injury with heightened TGF-β1 expression in adulthood. These changes were ameliorated in male offspring of diabetic Hoxb7/CAT-GFP-Tg dams via the Nrf2-HO-1 defense system. CAT promoted Nrf2 nuclear translocation and HO-1 gene expression, seen in both in vitro and in vivo studies. In conclusion, CAT overexpression in the RPTCs ameliorated maternal diabetes-induced perinatal programming, mediated, at least in part, by triggering the Nrf2-HO-1 defense system.
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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".