Massively-parallel functional interrogation of genetic variation in CMD-associated alpha-dystroglycan glycosylating enzymes

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PROJECT SUMMARY Mutations in genes that glycosylate alpha-dystroglycan (α-DG) are frequent causes of a spectrum of muscle disease ranging from congenital muscular dystrophy (CMD) to childhood and adult onset limb-girdle muscular dystrophy (LGMD). These devasting myopathies are deemed dystroglycanopathies and cause muscle wasting, progressive weakness, and degeneration of skeletal muscle leading to loss of ambulation, difficulties in breathing and premature death. The α-DG glycosyltransferase genes include, among others, FKTN, FKRP, POMT1, POMT2, and POMGNT1 and together account for >50% of genetically diagnosed CMD/LGMD. Accurately diagnosing patients with CMD or 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 most cases can only be done through genetic testing in pre-symptomatic individuals or prenatally. 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 assessed genes to advance our understanding of dystroglycan biology, improve the interpretation of genetic variation in dystroglycanopathy genes, and advance CMD/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. Further, as only a subset of CMD and LGMD patients have potentially pathogenic variants in known muscular dystrophy genes, we will perform CRISPR screens in different cellular contexts to identify genes contributing to abnormal alpha-dystroglycan function. Our two aims are: 1) Quantifying the effect of nearly every possible missense variant in FKTN, FKRP, POMT1, POMT2, and POMGNT1 on protein stability, alpha-dystroglycan glycosylation and cellular adhesion and 2) Perform in-depth analysis of dystroglycanopathy patient variants integrating multiple in vitro assays, clinical information and patient specimen biochemical analysis to validate our DMS approach and disseminate pathogenicity predictions. The functional data we generate, the analyses we propose, and tools we build will transform the characterization of dystroglycanopathy gene variants. They will also serve as a resource to better understand muscle biology, improve the clinical translation of dystroglycanopathies and CMD/LGMD using genetic information, and inform new treatments.