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Comprehensive functional characterization of SGCB coding variants predicts pathogenicity in limb-girdle muscular dystrophy type R4/2E

Genetic testing is essential for patients with a suspected hereditary myopathy. More than 50% of patients clinically diagnosed with a myopathy carry a variant of unknown significance in a myopathy gene, often leaving them without a genetic diagnosis. Limb-girdle muscular dystrophy (LGMD) type R4/2E...

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Detalles Bibliográficos
Autores principales: Li, Chengcheng, Wilborn, Jackson, Pittman, Sara, Daw, Jil, Alonso-Pérez, Jorge, Díaz-Manera, Jordi, Weihl, Conrad C., Haller, Gabe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Clinical Investigation 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10266784/
https://www.ncbi.nlm.nih.gov/pubmed/37317968
http://dx.doi.org/10.1172/JCI168156
Descripción
Sumario:Genetic testing is essential for patients with a suspected hereditary myopathy. More than 50% of patients clinically diagnosed with a myopathy carry a variant of unknown significance in a myopathy gene, often leaving them without a genetic diagnosis. Limb-girdle muscular dystrophy (LGMD) type R4/2E is caused by mutations in β-sarcoglycan (SGCB). Together, β-, α-, γ-, and δ-sarcoglycan form a 4-protein transmembrane complex (SGC) that localizes to the sarcolemma. Biallelic loss-of-function mutations in any subunit can lead to LGMD. To provide functional evidence for the pathogenicity of missense variants, we performed deep mutational scanning of SGCB and assessed SGC cell surface localization for all 6,340 possible amino acid changes. Variant functional scores were bimodally distributed and perfectly predicted pathogenicity of known variants. Variants with less severe functional scores more often appeared in patients with slower disease progression, implying a relationship between variant function and disease severity. Amino acid positions intolerant to variation mapped to points of predicted SGC interactions, validated in silico structural models, and enabled accurate prediction of pathogenic variants in other SGC genes. These results will be useful for clinical interpretation of SGCB variants and improving diagnosis of LGMD; we hope they enable wider use of potentially life-saving gene therapy.