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[Characterization of 19 novel gene mutation sites associated with autosome-dominant polycystic kidney disease].
- Source :
-
Zhonghua nei ke za zhi [Zhonghua Nei Ke Za Zhi] 2024 Sep 01; Vol. 63 (9), pp. 866-873. - Publication Year :
- 2024
-
Abstract
- By analyzing the of genetic testing data of patients with renal polycystic kidney disease and their relatives, this study aims to identify unreported novel gene mutation sites associated with autosomal dominant polycystic kidney disease (ADPKD). Structural prediction software was employed to investigate protein structural changes before and after mutations, explore genotype-phenotype correlations, and enrich the ADPKD gene database. In this single-center retrospective study, patients with multiple renal cysts diagnosed from January 2019 to February 2023 at the Zhong Da Hospital Southeast University were included. Genetic and clinical data of patients and their families were collected. Unreported novel gene mutation sites associated with ADPKD were identified. The AlphaFold v2.3.1 software was used to predict protein structures. Changes in protein structure before and after mutations were compared to explore genotype-phenotype correlations and enrich the ADPKD gene database. Twelve mutated genes associated with renal cysts were detected in 52 families. Nineteen novel gene mutation sites associated with ADPKD were identified, including 17 mutations in the PKD1 gene (one splicing mutation, seven frameshift mutations, four nonsense mutations, one whole-codon insertion, and four missense mutations); one ALG9 missense mutation; and one chromosomal structural variation. Truncating mutations in the PKD1 gene were correlated with a more severe clinical phenotype, while non-truncating mutations were associated with greater clinical heterogeneity. Numerous novel gene mutation sites associated with ADPKD remain unreported. Therefore, it is essential to analyze the pathogenicity of these novel mutation sites, establish genotype-phenotype correlations, and enrich the ADPKD gene database.
Details
- Language :
- Chinese
- ISSN :
- 0578-1426
- Volume :
- 63
- Issue :
- 9
- Database :
- MEDLINE
- Journal :
- Zhonghua nei ke za zhi
- Publication Type :
- Academic Journal
- Accession number :
- 39183159
- Full Text :
- https://doi.org/10.3760/cma.j.cn112138-20231228-00412