Kcnn4/KCa3.1 inhibition blunts polycystic kidney disease progression in mouse models
Bibliographic record
Abstract
The mechanisms underlying cyst growth and progression in Autosomal Dominant Polycystic Kidney Disease (ADPKD) remain unresolved. Since cyst expansion requires epithelial salt and water secretion likely involving basolateral membrane K+ recycling, we investigated the role of KCNN4-encoded K+ channel KCa3.1, inhibited by the potent, pharmacospecific, well-tolerated antagonist, senicapoc. We hypothesized that genetic and/or pharmacological inactivation of KCNN4/KCa3.1 would slow PKD progression. KCNN4 was upregulated in kidneys of patients with ADPKD and of mechanistically distinct PKD mouse models. Cyst expansion in Pkd1-/- murine metanephroi was stimulated by KCa3.1 agonist and was prevented/reversed by senicapoc. In rapidly and/or slowly progressive mouse Pkd1 models, Kcnn4 inactivation slowed renal cyst growth; attenuated PKD-stimulated cAMP and ERK/Myc signaling pathways; reduced PKD-associated ciliary elongation, cell proliferation, and fibrosis; improved renal function; and prolonged survival. Importantly, senicapoc treatment of Pkd1 mouse models phenocopied most effects of Kcnn4 inactivation. This first study on the efficacy of KCa3.1 inhibition in PKD progression recommends senicapoc as a clinical trial candidate for ADPKD.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".