Overexpression of heterogeneous nuclear ribonucleoprotein F stimulates renal Ace-2 gene expression and prevents TGF-β1-induced kidney injury in a mouse model of diabetes
Bibliographic record
Abstract
AIMS/HYPOTHESIS: We investigated whether heterogeneous nuclear ribonucleoprotein F (hnRNP F) stimulates renal ACE-2 expression and prevents TGF-β1 signalling, TGF-β1 inhibition of Ace-2 gene expression and induction of tubulo-fibrosis in an Akita mouse model of type 1 diabetes. METHODS: Adult male Akita transgenic (Tg) mice overexpressing specifically hnRNP F in their renal proximal tubular cells (RPTCs) were studied. Non-Akita littermates and Akita mice served as controls. Immortalised rat RPTCs stably transfected with plasmid containing either rat Hnrnpf cDNA or rat Ace-2 gene promoter were also studied. RESULTS: Overexpression of hnRNP F attenuated systemic hypertension, glomerular filtration rate, albumin/creatinine ratio, urinary angiotensinogen (AGT) and angiotensin (Ang) II levels, renal fibrosis and profibrotic gene (Agt, Tgf-β1, TGF-β receptor II [Tgf-βrII]) expression, stimulated anti-profibrotic gene (Ace-2 and Ang 1-7 receptor [MasR]) expression, and normalised urinary Ang 1-7 level in Akita Hnrnpf-Tg mice as compared with Akita mice. In vitro, hnRNP F overexpression stimulated Ace-2 gene promoter activity, mRNA and protein expression, and attenuated Agt, Tgf-β1 and Tgf-βrII gene expression. Furthermore, hnRNP F overexpression prevented TGF-β1 signalling and TGF-β1 inhibition of Ace-2 gene expression. CONCLUSIONS/INTERPRETATION: These data demonstrate that hnRNP F stimulates Ace-2 gene transcription, prevents TGF-β1 inhibition of Ace-2 gene transcription and induction of kidney injury in diabetes. HnRNP F may be a potential target for treating hypertension and renal fibrosis 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 | 0.001 |
| 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".