RNA mis-splicing in children with myotonic dystrophy is associated with physical function
Julia M. HartmaniD, Kobe IkegamiiD, Marina ProvenzanoiD, Kameron BatesiD, Amanda ButleriD, Aileen S. JonesiD, Kiera N. BerggreniD, Jeanne M. DekdebruniD, Marnee J. McKayiD, Jennifer BaldwiniD, Kayla CornettiD, Joshua BurnsiD, Michael KieferiD, Nicholas Elwood JohnsoniD, Melissa A. HaleiD
Abstract
ObjectivesDysregulated RNA alternative splicing is the hallmark of myotonic dystrophy type 1 (DM1). However, the association between RNA mis-splicing and physical function in children with the most severe form of disease, congenital myotonic dystrophy (CDM), is unknown. Methods82 participants (42 DM1 adults & 40 CDM children) with muscle biopsies and measures of myotonia, motor function, and strength were combined from five observational studies. Data were normalized and correlated with an aggregate measure of alternative splicing dysregulation, [MBNL]inferred in skeletal muscle biopsies. Multiple linear regression analysis was performed to predict [MBNL]inferred using clinical outcome measures alone. Similar analyses were performed to predict 12-month physical function using baseline metrics. ResultsMyotonia (measured via vHOT) was significantly correlated with RNA mis-splicing in our cross-sectional population of all DM1 individuals; CDM participants alone displayed no myotonia despite a similar range of RNA mis-splicing. Measures of motor performance and muscle strength were significantly associated with [MBNL]inferred in our cohort of all DM1 individuals and when assessing CDM children independently. Multiple linear regression analyses yielded two models capable of predicting [MBNL]inferred from select clinical outcome assessments alone in all subjects (adjusted R2 = 0.6723) or exclusively in CDM children (adjusted R2 = 0.5875). InterpretationOur findings establish significant correlations between skeletal muscle performance and a composite measure of alternative splicing dysregulation, [MBNL]inferred, in DM1. The strength of these correlations and the development of the predictive models will assist in designing efficacious clinical trials for individuals with DM1, particularly CDM.
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