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Record W4415397492 · doi:10.1172/jci.insight.191311

Kcnn4/KCa3.1 inhibition blunts polycystic kidney disease progression in mouse models

2025· article· en· W4415397492 on OpenAlexafffund
Guanhan Yao, Almira Kurbegovic, Camila Parrot, William A. Foley, William Roman, Seth L. Alper, Marie Trudel

Bibliographic record

VenueJCI Insight · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Kidney Cyst Diseases
Canadian institutionsUniversité de MontréalMontreal Clinical Research Institute
FundersCanadian Institutes of Health Research
KeywordsPKD1Autosomal dominant polycystic kidney diseasePolycystic kidney diseaseCystKidneyAgonistCiliumDownregulation and upregulation

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.257
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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