The variability of SMCHD1 gene in FSHD patients: evidence of new mutations. (10th October 2019) - Strafella, Claudia; Caputo, Valerio; Galota, Rosaria Maria; Campoli, Giulia; Bax, Cristina; Colantoni, Luca; Minozzi, Giulietta; Orsini, Chiara; Politano, Luisa; Tasca, Giorgio; Novelli, Giuseppe; Ricci, Enzo; et al.
Abstract: In this study, we investigated the sequence of ( Structural Maintenance of Chromosomes flexible Hinge Domain containing 1) SMCHD1 gene in a cohort of clinically defined FSHD (facioscapulohumeral muscular dystrophy) patients in order to assess the distribution of SMCHD1 variants, considering the D4Z4 fragment size in terms of repeated units (RUs; short fragment: 1–7 RU, borderline: 8-10RU and normal fragment: >11RU). The analysis of SMCHD1 revealed the presence of 82 variants scattered throughout the introns, exons and 3'untranslated region (3′UTR) of the gene. Among them, 64 were classified as benign polymorphisms and 6 as VUS (variants of uncertain significance). Interestingly, seven pathogenic/likely pathogenic variants were identified in patients carrying a borderline or normal D4Z4 fragment size, namely c.182_183dupGT (p.Q62Vfs * 48), c.2129dupC (p.A711Cfs * 11), c.3469G>T (p.G1157 * ), c.5150_5151delAA (p.K1717Rfs * 16) and c.1131+2_1131+5delTAAG, c.3010A>T (p.K1004 * ), c.853G>C (p.G285R). All of them were predicted to disrupt the structure and conformation of SMCHD1, resulting in the loss of GHKL-ATPase and SMC hinge essential domains. These results are consistent with the FSHD symptomatology and the Clinical Severity Score (CSS) of patients. In addition, five variants (c. * 1376A>C, rs7238459; c. * 1579G>A, rs559994; c. * 1397A>G, rs150573037; c. * 1631C>T, rs193227855; c. * 1889G>C, rs149259359) were identified in the 3′UTR region of SMCHD1, suggesting a possible miRNA-dependent regulatory effect on FSHD-related pathways. The present study highlights the clinical utility of next-generation sequencing (NGS) platforms for the molecular diagnosis of FSHD and the importance of integrating molecular findings and clinical data in order to improve the accuracy of genotype–phenotype correlations.