Insights into the Regulation of GFR by the Keap1-Nrf2 Pathway
Bibliographic record
Abstract
Key Points Kelch-like erythroid cell-derived protein with CNC homology (ECH)-associated protein 1-NF (erythroid-derived 2)–like 2 pathway increases GFR without an appreciable increase in intraglomerular pressure. Kelch-like ECH-associated protein 1-NF (erythroid-derived 2)–like 2 pathway regulates GFR through changes in filtration area by modulating calcium dynamics and contractility in glomerular cells. Background Literature data suggest that the activation of the Kelch-like ECH-associated protein 1 (Keap1)-NF (erythroid-derived 2)–like 2 (Nrf2) pathway increases GFR in patients with type 2 diabetes and CKD. However, the mechanisms whereby the Keap1-Nrf2 pathway regulates GFR are unknown. Methods Various renal physiological parameters were assessed in C57BL/6 mice (wild-type), Nrf2 -deficient mice, and Nrf2 -activated Keap1- knockdown mice. In addition, these parameters were assessed after the administration of receptor targeting agent (RTA) dh404 (CDDO‐dhTFEA), an Nrf2 activator. Results Pharmacologic and genetic Keap1 - Nrf2 activation increased renal blood flow ( P < 0.05), glomerular volume ( P < 0.05), and GFR ( P < 0.05) but did not alter the afferent-to-efferent arteriolar diameter ratio or glomerular permeability. Calcium influx into the podocytes through transient receptor potential canonical (TRPC) channels in response to H 2 O 2 was suppressed by Keap1-Nrf2 activation and TRPCs inhibition. Treatment with a TRPC6 and TRPC5 inhibitors increased single-nephron GFR in wild-type mice. Conclusions In conclusion, the Keap1-Nrf2 pathway regulates GFR through changes in ultrafiltration by modulating redox-sensitive intracellular calcium signaling and cellular contractility, mediated through TRPC activity, in glomerular cells, particularly the podocytes.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".