Massively-parallel functional interrogation of genetic variation in LGMD-associated sarcoglycan genes

点击翻译按钮获取中文摘要

PROJECT SUMMARY Mutations in α-, β-, γ-, and δ-sarcoglycan cause sarcoglyanopathies, a subset of limb-girdle muscular dystrophy (LGMD) with devastating effects for patients including muscle wasting, progressive weakness, degeneration of skeletal muscle and often premature death. Accurately diagnosing patients with LGMD before symptom onset or early in the course of the disease has the potential to enable the use of preventative gene therapy or other therapeutics and in the majority of cases can only be done in presymptomatic cases through genetic testing. When a new DNA variant in one of these genes is observed in a patient, however, there is often insufficient evidence to classify it as pathogenic. Within this study, we will use a new approach to express and characterize every possible missense variant in the SGCA, SGCB, SGCG and SGCD genes to advance our understanding of sarcoglycan biology, improve the interpretation of genetic variation in the SGC genes, and advance LGMD care and treatments. We will employ deep mutational scanning, a method for measuring the effects of massive numbers of missense variants of a protein simultaneously. We will express a library of all possible SGC missense variants in cultured human cells and measure the effect of each by exploiting a simple but robust characteristic of pathogenic SGC gene variants, disruption of proper protein trafficking. Our two aims are: 1) Quantifying the effect of nearly every possible SGC missense variant on SGC protein trafficking and membrane localization, and 2) Predict and validate the pathogenicity of every possible SGC missense variant by integrating multiple functional assays from Aim 1 to create a pathogenicity score for each variant and by confirming variant predictions biochemically using tissue samples from LGMD patients with VUS. These aims will reveal how each possible missense variant in SGC genes impact expression, transport, function or interaction with other SGC proteins. The functional data we generate, the analyses we propose, and tools we build will transform the characterization of SGC variants. They will also serve as a resource to better understand sarcoglycan biology, improve the clinical translation of sarcoglycanopathies and LGMD using genetic information, and inform new treatments.