Abstract
BackgroundSteroid-resistant nephrotic syndrome (SRNS) is characterized by unresponsiveness of nephrotic range proteinuria to standard steroid therapy, and is the main cause of childhood renal failure. The identification of more than 53 monogenic causes of SRNS has led researchers to focus on the genetic mutations related to the molecular mechanisms of the disease. Mutations in the PLCE1 gene, which encodes phospholipase C epsilon 1 (PLCε1), have been described in patients with early-onset SRNS characterized by progressive renal failure. In this study we screened for PLCE1 mutations in Egyptian children with SRNS. This is a descriptive case series study aiming to screen for PLCE1 gene mutations by direct sequencing of five exons—9, 12, 15, 19, 27—in 20 Egyptian children with SRNS who entered the Nephrology Unit, Faculty of Medicine, Ain-Shams University from November 2015 to December 2017. The variants detected were submitted to in silico analysis.ResultsWe screened for mutations in five selected exons of PLCE1 gene. We identified seven variants in the five selected exons with homozygous and heterozygous inheritance pattern, two are intronic variants, two are silent variants, and three are missense variants. We identified four novel variants two are silent with no clinical significance and two are missense with uncertain clinical significance and pathogenic in-silico predictions; one p.Arg1230His in exon 12, the other is p.Glu1393Lys in exon 15.ConclusionsWe identified four novel mutations, findings which added to the registered SNP spectrum of the PLCE1 gene. These results widen the spectrum of PLCE1 gene mutations and support the importance of genetic testing in different populations of SRNS patients, therefore, to assess the vulnerability of Egyptian children to SRNS candidate genes, further studies needed on a larger number of cases which undoubtedly provide new insights into the pathogenic mechanisms of SRNS and might help in control of the patient. Additionally, the use of computational scoring and modeling tools may assist in the evaluation of the way in which the SNPs affect protein functionality.
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