Dystroglycanopathies: A Workflow to Improve Variant Interpretation, Disease Mechanism Understanding and Pathogenicity Predictions


Pre-Clinical Research

Poster Number: 256


Kaiyue Ma, PhD candidate, Yale, Shushu Huang, MD, Yale, Nicole Lake, PhD, Yale, Keryn Woodman, PhD, Yale, Angela Lek, PhD, Yale, Monkol Lek, PhD, yale university

Dystroglycanopathies are caused by mutations in enzymes involved in the glycosylation of alpha-dystroglycan (alpha-DG) and can result in a wide range of muscular disorders. The underlying pathogenic variants are typically ultra-rare with many unique to affected families, resulting in challenges in interpreting pathogenicity. We propose to work with “patients in flasks” to overcome this limitation and provide better variant interpretation needed to help undiagnosed patients.

We developed an adaptable workflow called SMuRF (Saturation Mutagenesis-Reinforced Functional assays) which utilizes gene knockout cell lines and pooled lentiviral variant rescue. We employed the IIH6C4 antibody, which binds to glycosylated alpha-DG to screen dystroglycanopathy genes using FKRP and LARGE1 as proof of concept. Using SMuRF, we generated functional scores for more than 99.5% of all possible single nucleotide variants (SNVs) for both FKRP and LARGE1.

Our result showed the expected trend for synonymous variants to have similar scores to wildtype and nonsense variants to be the most damaging. SMuRF scores correlate well with clinical reports, showing its potential to classify the variants that remained to be definitively classified. SMuRF revealed that the missense variants tend to be more disruptive in the catalytic domain than in the stem domain of FKRP. Similarly, SMuRF scores demonstrated that missense variants tend to be most disruptive in the XylT domain, which is critical for enzymatic activity in LARGE1. This suggests SMuRF can also help identify critical protein regions associated with disease mechanism. Lastly, SMuRF scores have a high correlation with EVE scores, which are prediction scores based on an evolutionary model, indicating its potential to validate and train computational methods.

In summary, SMuRF can be applied to characterize all possible small-size variants in genes associated with dystroglycanopathies and potentially be used to improve diagnosis rate, which is critical for clinical trial readiness.