Heterogeneous Nuclear Ribonucleoprotein F Stimulates Sirtuin-1 Gene Expression and Attenuates Nephropathy Progression in Diabetic Mice
Bibliographic record
Abstract
We investigated the mechanism of heterogeneous nuclear ribonucleoprotein F (hnRNP F) renoprotective action in a type 2 diabetes (T2D) mouse model (db/db). Immortalized rat renal proximal tubular cells (IRPTCs) and kidneys from humans with T2D were also studied. The db/db mice developed hyperglycemia, oxidative stress, and nephropathy at age 20 weeks compared with their db/m littermates. These abnormalities, with the exception of hyperglycemia, were attenuated in db/db hnRNP F–transgenic (Tg) mice specifically overexpressing hnRNP F in their RPTCs. Sirtuin-1, Foxo3α, and catalase expression were significantly decreased in RPTCs from db/db mice and normalized in db/db hnRNP F–Tg mice. In vitro, hnRNP F overexpression stimulated Sirtuin-1 and Foxo3α with downregulation of acetylated p53 expression and prevented downregulation of Sirtuin-1 and Foxo3α expression in IRPTCs by high glucose plus palmitate. Transfection of Sirtuin-1 small interfering RNA prevented hnRNP F stimulation of Foxo3α and downregulation of acetylated p53 expression. hnRNP F stimulated Sirtuin-1 transcription via hnRNP F–responsive element in the Sirtuin-1 promoter. Human T2D kidneys exhibited more RPTC apoptosis and lower expression of hnRNP F, SIRTUIN-1, and FOXO3α than nondiabetic kidneys. Our results demonstrate that hnRNP F protects kidneys against oxidative stress and nephropathy via stimulation of Sirtuin-1 expression and signaling 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".