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Lookup NU author(s): Dr Elisa MolinariORCiD, Professor John SayerORCiD
© 2020 by the American Society of Nephrology.The class of human genetic kidney diseases is extremely broad and heterogeneous. Accordingly, the range of associated disease phenotypes is highly variable. Many children and adults affected by inherited kidney disease will progress to ESKD at some point in life. Extensive research has been performed on various different disease models to investigate the underlying causes of genetic kidney disease and to identify disease mechanisms that are amenable to therapy. We review some of the research highlights that, by modeling inherited kidney disease, contributed to a better understanding of the underlying pathomechanisms, leading to the identification of novel genetic causes, new therapeutic targets, and to the development of new treatments. We also discuss how the implementation of more efficient genome-editing techniques and tissue-culture methods for kidney research is providing us with personalized models for a precision-medicine approach that takes into account the specificities of the patient and the underlying disease. We focus on the most common model systems used in kidney research and discuss how, according to their specific features, they can differentially contribute to biomedical research. Unfortunately, no definitive treatment exists for most inherited kidney disorders, warranting further exploitation of the existing disease models, as well as the implementation of novel, complex, human patient–specific models to deliver research breakthroughs.
Author(s): Molinari E, Sayer JA
Publication type: Article
Publication status: Published
Journal: Clinical Journal of the American Society of Nephrology
Year: 2020
Volume: 15
Issue: 6
Pages: 855-872
Online publication date: 08/06/2020
Acceptance date: 02/04/2016
Date deposited: 24/06/2020
ISSN (print): 1555-9041
ISSN (electronic): 1555-905X
Publisher: American Society of Nephrology
URL: https://doi.org/10.2215/CJN.08890719
DOI: 10.2215/CJN.08890719
PubMed id: 32139361
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