ADPKD-Causing Missense Variants in Polycystin-1 Disrupt Cell Surface Localization or Polycystin Channel Function
Ha, K.; Loeb, G. B.; Park, M.; Pinedo, A.; Park, C. H.; Brandes, N.; Ritu, F.; Ye, C. J.; Reiter, J. F.; Delling, M.
10.1101/2023.12.04.570035 bioRxivShow abstract
Autosomal dominant polycystic kidney disease (ADPKD) is the leading monogenic cause of kidney failure and affects millions of people worldwide. Despite the prevalence of this monogenic disorder, our limited mechanistic understanding of ADPKD has hindered therapeutic development. Here, we successfully developed bioassays that functionally classify missense variants in polycystin-1 (PC1). Strikingly, ADPKD pathogenic missense variants cluster into two major categories: 1) those that disrupt polycystin cell surface localization or 2) those that attenuate polycystin ion channel activity. We found that polycystin channels with defective surface localization could be rescued with a small molecule. We propose that small-molecule-based strategies to improve polycystin cell surface localization and channel function will be effective therapies for ADPKD patients.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Determinants of trafficking, conduction, and disease within a K+ channel revealed through multiparametric deep mutational scanning 92%
- Inactivation of Nphp2 in renal epithelial cells drives infantile nephronophthisis like phenotypes in mouse 91%
- Gain of channel function and modified gating properties in TRPM3 mutants causing intellectual disability and epilepsy 91%
Similar papers in this journal
Similar papers in this journal
- Enhancer and super-enhancer landscape in polycystic kidney disease 93%
- Intraflagellar transport-A deficiency attenuates ADPKD in a renal tubular- and maturation-dependent manner 91%
- Dynamic Single Cell Transcriptomics Defines Kidney FGF23/KL Bioactivity and Novel Segment-Specific Inflammatory Targets 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.