Variants of uncertain significance (VUS) model show

MAZZANTI, CHIARA; CARCIONE, MICAELA;; FLORENCIA GILIBERTO; LEONELA LUCE

Congreso; REUNIÓN ANUAL DE SOCIEDAD DE BIOCIENCIAS SAIC-SAI-SAFIS 2020 LXV REUNIÓN ANUAL DE LA SOCIEDAD ARGENTINA DE INVESTIGACIÓN CLÍNICA; 2020

92. (405) VARIANTS OF UNCERTAIN SIGNIFICANCE (VUS) MODEL SHOWMicaela Carcione, Chiara Mazzanti, Leonela Luce, Florencia Giliberto.Universidad de Buenos Aires, Facultad de Farmacia y Bioquímica, Cátedra de Genética, Buenos Aires, Argentina; CONICET - Universidad de Buenos Aires, Instituto de Inmunología, Genética y Metabolismo (INIGEM), Buenos Aires, Argentina.Muscular Dystrophies (MD) are a group of rare inherited diseases that cause weakness and progressive degeneration of muscle tissue. The clinical symptoms of these pathologies overlap, hindering differential diagnosis, which is of paramount importance to establish the standard of care. Among them, Dystrophinopathies are the most prevalent type of MD and are caused by mutations in the DMD gene. Genetic or molecular studies are the gold standard for reaching a MD differential diagnosis, for which molecular alterations in MD associated genes can be detected by Whole Exome Sequencing (WES). One of the major challenges of the Next Generation Sequencing (NGS) data interpretation is the occurrence of Variants of Uncertain Significance (VUS). The present work aims to provide a thorough strategy to analyze the effect of VUS, applying different predictive software, conservation/evolutionary and protein modeling tools. A cohort of 141 patients with presumptive clinical diagnosis of dystrophinopathy and negative MLPA result was analyzed by WES. We deepened the screening to all the MD associated genes included in the Gene Table of Neuromuscular Disorders. In a subset of 6 individuals, we detected VUS in the following genes: DMD (2/6), FKRP (2/6) and POMT2 (2/6). We implemented several predictive software to analyze the effect of VUS, and UCSF ChimeraX for protein modeling. Also, in one case, we could do a segregation analysis of the variants. The implemented strategy provided new insights to predict more accurately the effect of the identified sequence variants and even reclassified them. Finally, this work provides alternative approaches for the analysis of sequence variants, especially when functional studies are not possible to be carried out, to determine the effect of VUS.