Vici syndrome in Israel: Clinical and molecular insights - Chorin, Odelia; Hirsch, Yoel; Rock, Rachel; Salzer Sheelo, Liat; Goldberg, Yael; Mandel, Hanna; Hershkovitz, Tova; Fleischer, Nicole; Greenbaum, Lior; Katz, Uriel; Barel, Ortal; Hamed, Nasrin; et al. (Frontiers in Genetics, 2022)
Introduction: Vici Syndrome is a rare, severe, neurodevelopmental/neurodegenerative disorder with multi-systemic manifestations presenting in infancy. It is mainly characterized by global developmental delay, seizures, agenesis of the corpus callosum, hair and skin hypopigmentation, bilateral cataract, and varying degrees of immunodeficiency, among other features. Vici Syndrome is caused by biallelic pathogenic variants in EPG5, resulting in impaired autophagy. Thus far, the condition has been reported in less than a hundred individuals. Objective and Methods: We aimed to characterize the clinical and molecular findings in individuals harboring biallelic EPG5 variants, recruited from four medical centers in Israel. Furthermore, we aimed to utilize a machine learning-based tool to assess facial features of Vici syndrome. Results: Eleven cases of Vici Syndrome from five unrelated families, one of which was diagnosed prenatally with subsequent termination of pregnancy, were recruited. A total of five disease causing variants were detected in EPG5: two novel: c.2554-5A>G and c.1461delC; and 3 previously reported: c.3447G>A, c.5993C>G, and c.1007A>G, the latter previously identified in several patients of Ashkenazi-Jewish (AJ) descent. Amongst 140,491 individuals screened by the Dor Yeshorim Program, we show that the c.1007A>G variant has an overall carrier frequency of 0.45% (1 in 224) among AJ individuals. Finally, based on two-dimensional facial photographs of individuals with Vici syndrome (n=19), a composite facial mask was created using the DeepGestalt algorithm, illustrating facial features typical of this disorder. Conclusions: We report on ten children and one fetus from five unrelated families, affected with Vici syndrome, and describe prenatal and postnatal characteristics. Our findings contribute to the current knowledge regarding the molecular basis and phenotypic features of this rare syndrome. Additionally, the deep learning-based facial gestalt adds to the clinician’s diagnostic toolbox and may aid in facilitating identification of affected individuals.