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.Conclusion: 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.

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The ionotropic glutamate delta receptor GluD1, encoded by the GRID1 gene, is involved in synapse formation, function, and plasticity. GluD1 does not bind glutamate, but instead cerebellin and D-serine, which allow the formation of trans-synaptic bridges, and trigger transmembrane signaling. Despite wide expression in the nervous system, pathogenic GRID1 variants have not been characterized in humans so far. We report homozygous missense GRID1 variants in five individuals from two unrelated consanguineous families presenting with intellectual disability and spastic paraplegia, without (p.Thr752Met) or with (p.Arg161His) diagnosis of glaucoma, a threefold phenotypic association whose genetic bases had not been elucidated previously. Molecular modeling indicated that Arg161His and Thr752Met mutations alter the hinge between GluD1 cerebellin and D-serine binding domains and the stiffness of this latter domain, respectively. Expression, trafficking, physical interaction with metabotropic glutamate receptor mGlu1, and cerebellin binding of GluD1 mutants were not conspicuously altered. Conversely, we found that both GluD1 mutants hampered signaling of metabotropic glutamate receptor mGlu1/5 via the ERK pathway in neurons of primary cortical culture. Moreover, both mutants impaired dendrite morphology and excitatory synapse density in neurons of primary hippocampal culture. These results show that the clinical phenotypes are distinct entities segregating in the families as an autosomal recessive trait, and caused by pathophysiological effects of GluD1 mutants involving metabotropic glutamate receptor signaling and neuronal connectivity. Our findings unravel the importance of the GluD1 receptor signaling in sensory, cognitive and motor functions of the human nervous system.