Investigating Hepatocyte Nuclear Factor 4 Alpha as a Central Regulator of Kidney Graft Repair
Bibliographic record
Abstract
Background: Kidney transplantation is the best treatment for end-stage kidney disease, but ischemia-reperfusion injury (IRI) harms all grafts. Previously, we showed better kidney function after IRI is tied to preserved expression of mitochondrial proteins in normothermic ex vivo kidney perfusion. We identified a regulator of these mitochondrial proteins, hepatocyte nuclear factor 4a (HNF4A), which may be a novel target for kidney repair. Our goal is to test if an HNF4A agonist, N-trans caffeoyltyramine (NCT), protects against IRI. Methods: We will evaluate NCT treatment in male and female primary proximal tubular cells (PTECs), and in male and female mice subjected to bilateral IRI. First, we inhibited HNF4A in primary PTECs, and then assessed gene expression, cell death and mitochondrial function. Lastly, kidney function and stucture of mice subjected to bilateral IRI were assessed on post operative day 2 and 14. Results: HNF4A inhibition in vitro increased PTEC death and decreased mitochondrial function. We developed a sex-specific model of bilateral IRI and demonstrated that warm ischemia (21-min males and 28-min females) impairs kidney function on post-operative day 2, indicated by elevated serum creatinine. Mice also developed tubulointerstitial fibrosis on post-operative day 14. Gene expression of Hnf4a and its targets (Cpt2, Etfb) and interactors (Ppargc1a, Ppara) all decreased on post operative day 2 and remained decreased on post operative day 14 in males. Conclusion: Based on the preliminary results, HNF4A is important for the metabolic function and viability of PTECs. If NCT reduces IRI by preserving mitochondrial function, it could be administered prior to transplantation to improve graft outcomes.
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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".