Nuclear Factor Erythroid 2-Related Factor 2 Drives Podocyte-Specific Expression of Peroxisome Proliferator-Activated Receptor γ Essential for Resistance to Crescentic GN
Bibliographic record
Abstract
Necrotizing and crescentic rapidly progressive GN (RPGN) is a life-threatening syndrome characterized by a rapid loss of renal function. Evidence suggests that podocyte expression of the transcription factor peroxisome proliferator-activated receptor γ (PPARγ) may prevent podocyte injury, but the function of glomerular PPARγ in acute, severe inflammatory GN is unknown. Here, we observed marked loss of PPARγ abundance and transcriptional activity in glomerular podocytes in experimental RPGN. Blunted expression of PPARγ in podocyte nuclei was also found in kidneys from patients diagnosed with crescentic GN. Podocyte-specific Pparγ gene targeting accentuated glomerular damage, with increased urinary loss of albumin and severe kidney failure. Furthermore, a PPARγ gain-of-function approach achieved by systemic administration of thiazolidinedione (TZD) failed to prevent severe RPGN in mice with podocyte-specific Pparγ gene deficiency. In nuclear factor erythroid 2-related factor 2 (NRF2)-deficient mice, loss of podocyte PPARγ was observed at baseline. NRF2 deficiency markedly aggravated the course of RPGN, an effect that was partially prevented by TZD administration. Furthermore, delayed administration of TZD, initiated after the onset of RPGN, still alleviated the severity of experimental RPGN. These findings establish a requirement for the NRF2-PPARγ cascade in podocytes, and we suggest that these transcription factors have a role in augmenting the tolerance of glomeruli to severe immune-complex mediated injury. The NRF2-PPARγ pathway may be a therapeutic target for RPGN.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".