Phenotypic diversity, disease progression, and pathogenicity of MVK missense variants in mevalonic aciduria

Abstract

Mevalonic aciduria (MVA) and Hyperimmunoglobulinemia D syndrome (HIDS) are disorders of cholesterol biosynthesis caused by variants in the MVK gene and characterized by increased urinary excretion of mevalonic acid. So far, 30 MVA patients have been reported, suffering from recurrent febrile crises and neurologic impairment. Here, we present an in-depth analysis of the phenotypic spectrum of MVA and provide an in-silico pathogenicity model analysis of MVK missense variants. The phenotypic spectrum of eleven MVA patients (age range 0-51 years) registered in the Unified European Registry for Inherited Metabolic Disorders database was systematically analyzed using terms of the Human Phenotype Ontology. Biochemical, radiological as well as genetic characteristics were investigated. Six of eleven patients have reached adulthood and four have reached adolescence. One of the adolescent patients died at the age of 16 years and one patient died shortly after birth. Symptoms started within the first year of life, including episodic fever, developmental delay, ataxia, and ocular involvement. We also describe a case with absence of symptoms despite massive excretion of mevalonic acid. Pathogenic variants causing MVA cluster within highly conserved regions, which are involved in mevalonate and ATP binding. The phenotype of adult and adolescent MVA patients is more heterogeneous than previously assumed. Outcome varies from an asymptomatic course to early death. MVK variants cluster in functionally important and highly conserved protein domains and show high concordance regarding their expected pathogenicity. This article is protected by copyright. All rights reserved.

Publication
In Journal of Inherited Metabolic Disease
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Heiko Brennenstuhl, MD, MBA
Medical Professional & Postdoctoral Research Fellow

I am interested in inherited metabolic disorders and neurosmuscular diseases with a movement pehnotype. I specialize in stem cell research and challenges and opportunities of high-throughput data analysis.