MOLECULAR CHARACTERIZATION AND IDENTIFICATION OF GENETIC MODIFIERS IN DYSTROPHINOPATHIES

Mutations in the DMD gene lead to dystrophinopathies, a spectrum of clinical manifestations characterized by progressive muscle weakness and wasting. This includes Duchenne muscular dystrophy (DMD), the most severe form, and Becker muscular dystrophy (BMD), the milder form. Disease severity variability makes identifying prognostic markers, such as genetic modifiers, crucial. Indeed, genetic modifiers significantly influence dystrophinopathy severity and progression. The identification of genetic modifiers in BMD via Genome-Wide Association Study (GWAS) is part of a multicenter project which aims to elucidate the natural history of the disease, to collect retrospective and longitudinal data on disease progression to identify appropriate outcome measures, and to obtain DNA samples. Retrospective data enabled to describe and analyse a cohort of BMD 943 patients, examining disease milestones within specific DMD mutational groups. The median age at the last assessment was 26.0 (16.6-41.9) years, with a median age at diagnosis of 7.5 (4.0-14.0) years. In 55% of patients, the diagnosis was prompted by the incidental finding of hyperCKemia. At the last assessment, 13.5% of patients had lost the ability to walk at a median age estimated by Kaplan-Meier analysis of 69 years. Additionally, 30% of patients exhibited left ventricular impairment and 2.7% respiratory involvement. Among the mutations, 10%were out-of-frame mutations, 4% were nonsense mutations, and 86% were in-frame deletions/duplications. The subset of in-frame deletions was further classified based on the specific mutations. Patients with del45-49 mutation experienced an earlier LoA compared to those with del45-47 (P=1×10−4). Conversely, those with del45-55 (P=.005), del48 (P=.02), and del48-49 (P=.02) correlated with a later LoA compared to del45-47. Furthermore, del45-55 (P=.002) and del48 (P=.003) were significantly associated with decreased odds of developing a pathological LVEF% compared to del45-47. Our findings enhance the understanding of the natural history of BMD and the knowledge of the specific DMD mutation to define a prognosis in a subset of BMD patients, serving as a model for the design of future therapies. In addition, we developed a REDCap project to collect and analyze motor, cardiac, and respiratory longitudinal data, and began collecting DNA samples for the GWAS. For this study, we will build on expertise from another multicenter project aimed at identifying genetic modifiers in DMD. In this context, we performed a GWAS on LoA in a cohort of 493 DMD patients. Genotyping was done using high-density chips, and the GWAS employed Cox regression, with glucocorticoid treatment and the first 12 principal components as covariates. The analysis revealed a candidate locus on chromosome 1, upstream of the C1orf21 gene. This gene was upregulated in muscle biopsies from DMD patients compared to healthy controls, but its function remains unclear. C1orf21 could be a genetic modifier in DMD, though further functional studies are needed. Identifying novel genetic modifiers could guide prognosis and treatment strategies in DMD. Aligned with this goal, I investigated pharmacodynamic correlations using a longitudinal proteomic dataset to identify potential biomarkers for monitoring patients’ responses to steroid treatments, comparing prednisone, a canonical DMD drug with severe side effects like bone alterations, to vamorolone, a recently approved drug. Using the Somalogic SomaScan® assay, approximately 7,000 serum proteins from longitudinal samples in the vamorolone VBP15-004 trial were quantified. To explore relationships with seven bone biomarkers that had previously shown prednisone-specific reductions, I applied Weighted Gene Co-expression Network Analysis (WGCNA), which clusters proteins based on expression profiles. These lists of correlated proteins offer promising insights but require further investigation as WGCNA is a preliminary and exploratory analysis.