Atomic Force Microscopy Imaging Reveals the Domain Structure of Polycystin-1

Mutation of polycystin-1 (PC1) is the major cause of autosomal dominant polycystic kidney disease. PC1 has a predicted molecular mass of ∼460 kDa comprising a long multidomain extracellular N-terminal region, 11 transmembrane regions, and a short C-terminal region. Because of its size, PC1 has proven difficult to handle biochemically, and structural information is consequently sparse. Here we have isolated wild-type PC1, and several mutants, from transfected cells by immunoaffinity chromatography and visualized individual molecules using atomic force microscopy (AFM) imaging. Full-length PC1 appeared as two unequally sized blobs connected by a 35 nm string. The relative sizes of the two blobs suggested that the smaller one represents the N-terminus, including the leucine-rich repeats, the first polycystic kidney disease (PKD) domain, and the C-type lectin motif, while the larger one is the C-terminus, including the receptor for egg jelly (REJ) domain, all transmembrane domains, and the cytoplasmic tail. The intervening string would then consist of a series of tandem PKD domains. The structures of the various PC1 mutants were all consistent with this model. Our results represent the first direct visualization of the structure of PC1, and reveal the architecture of the protein, with intriguing implications for its function

Additional file 1: of A model to predict disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD): the ADPKD Outcomes Model

Variance covariance matrices for TKV and eGFR progression equation coefficients. Table S1. Variance covariance matrix for the TEMPO 3:4 TKV equation coefficients. Table S2. Variance covariance matrix for the TEMPO 3:4 eGFR equation coefficients. Example of using the TKV and eGFR progression equations to predict annual ADPKD progression. Applying the ADPKD-OM to alternative patient populations. Using CKD-Epi measurements to model eGFR progression. Table S3. Comparison of eGFR progression equation coefficient estimates. Table S4. Variance covariance matrix for the TEMPO 3:4 eGFR equation coefficients. Validation against CRISP I-derived progression equations. CRISP I-derived equations for TKV (Equation S1) and eGFR (Equation S2) progression. Table S5. TKV progression equation coefficient estimates, as derived from CRISP I. Table S6. eGFR progression equation coefficient estimates, as derived from CRISP I. Validation against HALT-PKD trial data. Validation against THIN data. Validation against Thong and Ong [40]. Equation S3. eGFR progression equation, derived by Thong and Ong [40]. Table S7. eGFR progression equation coefficient estimates, as derived from Thong and Ong [40]. (DOCX 51 kb

Effects of rare kidney diseases on kidney failure: a longitudinal analysis of the UK National Registry of Rare Kidney Diseases (RaDaR) cohort

Individuals with rare kidney diseases account for 5-10% of people with chronic kidney disease, but constitute more than 25% of patients receiving kidney replacement therapy. The National Registry of Rare Kidney Diseases (RaDaR) gathers longitudinal data from patients with these conditions, which we used to study disease progression and outcomes of death and kidney failure.People aged 0-96 years living with 28 types of rare kidney diseases were recruited from 108 UK renal care facilities. The primary outcomes were cumulative incidence of mortality and kidney failure in individuals with rare kidney diseases, which were calculated and compared with that of unselected patients with chronic kidney disease. Cumulative incidence and Kaplan-Meier survival estimates were calculated for the following outcomes: median age at kidney failure; median age at death; time from start of dialysis to death; and time from diagnosis to estimated glomerular filtration rate (eGFR) thresholds, allowing calculation of time from last eGFR of 75 mL/min per 1·73 m2 or more to first eGFR of less than 30 mL/min per 1·73 m2 (the therapeutic trial window).Between Jan 18, 2010, and July 25, 2022, 27 285 participants were recruited to RaDaR. Median follow-up time from diagnosis was 9·6 years (IQR 5·9-16·7). RaDaR participants had significantly higher 5-year cumulative incidence of kidney failure than 2·81 million UK patients with all-cause chronic kidney disease (28% vs 1%; p Background Methods Findings Interpretation Funding</p