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Currently, Duchenne muscular dystrophy (DMD) and the related condition Becker muscular dystrophy (BMD) can be usually diagnosed using physical examination and genetic testing. While BMD features partially functional dystrophin protein due to in-frame mutations, DMD largely features no dystrophin production because of out-of-frame mutations. However, BMD can feature a range of phenotypes from mild to borderline DMD, indicating a complex genotype–phenotype relationship. Despite two mutational hot spots in dystrophin, mutations can arise across the gene. The use of multiplex ligation amplification (MLPA) can easily assess the copy number of all exons, while next-generation sequencing (NGS) can uncover novel or confirm hard-to-detect mutations. Exon-skipping therapy, which targets specific regions of the dystrophin gene based on a patient’s mutation, is an especially prominent example of personalized medicine for DMD. To maximize the benefit of exon-skipping therapies, accurate genetic diagnosis and characterization including genotype–phenotype correlation studies are becoming increasingly important. In this article, we present the recent progress in the collection of mutational data and optimization of exon-skipping therapy for DMD/BMD.

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Exon skipping using antisense oligonucleotides (ASOs) has recently proven to be a powerful tool for mRNA splicing modulation. Several exon-skipping ASOs have been approved to treat genetic diseases worldwide. However, a significant challenge is the difficulty in selecting an optimal sequence for exon skipping. The efficacy of ASOs is often unpredictable, because of the numerous factors involved in exon skipping. To address this gap, we have developed a computational method using machine-learning algorithms that factors in many parameters as well as experimental data to design highly effective ASOs for exon skipping. eSkip-Finder (https://eskip-finder.org) is the first web-based resource for helping researchers identify effective exon skipping ASOs. eSkip-Finder features two sections: (i) a predictor of the exon skipping efficacy of novel ASOs and (ii) a database of exon skipping ASOs. The predictor facilitates rapid analysis of a given set of exon/intron sequences and ASO lengths to identify effective ASOs for exon skipping based on a machine learning model trained by experimental data. We confirmed that predictions correlated well with in vitro skipping efficacy of sequences that were not included in the training data. The database enables users to search for ASOs using queries such as gene name, species, and exon number.

The debilitating neuromuscular disorders Duchenne muscular dystrophy (DMD) and spinal muscular atrophy (SMA), which harm 1 in 5000 newborn males and 1 in 11,000 newborns, respectively, are marked by progressive muscle wasting among other complications. While DMD causes generalized muscle weakness due to the absence of the dystrophin protein, SMA patients generally face motor neuron degeneration because of the lack of the survival motor neuron (SMN) protein. Many of the most promising therapies for both conditions restore the absent proteins dystrophin and SMN. Antisense oligonucleotidemediated exon skipping and inclusion therapies are advancing clinically with the approved DMD therapies casimersen, eteplirsen, golodirsen, and viltolarsen, and the SMA therapy nusinersen. Existing antisense therapies focus on skeletal muscle for DMD and motor neurons for SMA, respectively. Through innovative techniques, such as peptide conjugation and multi-exon skipping, these therapies could be optimized for efficacy and applicability. By contrast, gene replacement therapy is administered only once to patients during treatment. Currently, only onasemnogene abeparvovec for SMA has been approved. Safety shortcomings remain a major challenge for gene therapy. Nevertheless, gene therapy for DMD has strong potential to restore dystrophin expression in patients. In light of promising functional improvements, antisense and gene therapies stand poised to elevate the lives of patients with DMD and SMA.