Abstract
BackgroundDuchenne muscular dystrophy, a fatal muscle-wasting disease, is characterized by dystrophin deficiency caused by mutations in the dystrophin gene. Skipping of a target dystrophin exon during splicing with antisense oligonucleotides is attracting much attention as the most plausible way to express dystrophin in DMD. Antisense oligonucleotides have been designed against splicing regulatory sequences such as splicing enhancer sequences of target exons. Recently, we reported that a chemical kinase inhibitor specifically enhances the skipping of mutated dystrophin exon 31, indicating the existence of exon-specific splicing regulatory systems. However, the basis for such individual regulatory systems is largely unknown. Here, we categorized the dystrophin exons in terms of their splicing regulatory factors.ResultsUsing a computer-based machine learning system, we first constructed a decision tree separating 77 authentic from 14 known cryptic exons using 25 indexes of splicing regulatory factors as decision markers. We evaluated the classification accuracy of a novel cryptic exon (exon 11a) identified in this study. However, the tree mislabeled exon 11a as a true exon. Therefore, we re-constructed the decision tree to separate all 15 cryptic exons. The revised decision tree categorized the 77 authentic exons into five groups. Furthermore, all nine disease-associated novel exons were successfully categorized as exons, validating the decision tree. One group, consisting of 30 exons, was characterized by a high density of exonic splicing enhancer sequences. This suggests that AOs targeting splicing enhancer sequences would efficiently induce skipping of exons belonging to this group.ConclusionsThe decision tree categorized the 77 authentic exons into five groups. Our classification may help to establish the strategy for exon skipping therapy for Duchenne muscular dystrophy.
Highlights
Duchenne muscular dystrophy, a fatal muscle-wasting disease, is characterized by dystrophin deficiency caused by mutations in the dystrophin gene
To examine the splicing regulatory factors that characterize particular exons, we constructed decision trees classifying authentic from cryptic exons using indexes of splicing regulatory factors as decision markers
Analyzing the exon recognition parameters of these exons compared to the authentic dystrophin exons can give insight into which splicing regulatory elements play a critical role in the splicing of dystrophin exons
Summary
To examine the splicing regulatory factors that characterize particular exons, we constructed decision trees classifying authentic from cryptic exons using indexes of splicing regulatory factors as decision markers. The decision tree revealed that the strength of the 3’ss calculated by maximum entropy (ME3’ss) was the first splitting variable, with a cut-off point of 1.39 At this node, four cryptic exons were classified into one group. ME3’ss was the first splitting variable, with a cut-off point of 1.39 At this node, four cryptic exons were classified into one group (group a; Figure 3). On the “no” branch at the fourth node, SIZE with a cut-off point of 144 was used, categorizing the final data into one exon group consisting of exons 2 and 3 (group D) and one group containing four cryptic exons (group d). Group E (exons 70 and 78) was categorized through six nodes; the final one discriminating it from group c (cryptic exons 2b and 77a) For this categorization, the strength of the 3’ss was tested twice using different indexes (ME3’ss and SH3’ss). We did not identify any particular categorization characteristics for these exons
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