title: In silico screening based on predictive algorithms as a design tool for exon skipping oligonucleotides in Duchenne muscular dystrophy.
abstract: The use of antisense 'splice-switching' oligonucleotides to induce exon skipping represents a potential therapeutic approach to various human genetic diseases. It has achieved greatest maturity in exon skipping of the dystrophin transcript in Duchenne muscular dystrophy (DMD), for which several clinical trials are completed or ongoing, and a large body of data exists describing tested oligonucleotides and their efficacy. The rational design of an exon skipping oligonucleotide involves the choice of an antisense sequence, usually between 15 and 32 nucleotides, targeting the exon that is to be skipped. Although parameters describing the target site can be computationally estimated and several have been identified to correlate with efficacy, methods to predict efficacy are limited. Here, an in silico pre-screening approach is proposed, based on predictive statistical modelling. Previous DMD data were compiled together and, for each oligonucleotide, some 60 descriptors were considered. Statistical modelling approaches were applied to derive algorithms that predict exon skipping for a given target site. We confirmed (1) the binding energetics of the oligonucleotide to the RNA, and (2) the distance in bases of the target site from the splice acceptor site, as the two most predictive parameters, and we included these and several other parameters (while discounting many) into an in silico screening process, based on their capacity to predict high or low efficacy in either phosphorodiamidate morpholino oligomers (89% correctly predicted) and/or 2'O Methyl RNA oligonucleotides (76% correctly predicted). Predictions correlated strongly with in vitro testing for sixteen de novo PMO sequences targeting various positions on DMD exons 44 (R² 0.89) and 53 (R² 0.89), one of which represents a potential novel candidate for clinical trials. We provide these algorithms together with a computational tool that facilitates screening to predict exon skipping efficacy at each position of a target exon.
authors: University of Alberta, Faculty of Medicine and Dentistry, Department of Medical Genetics, Edmonton, Alberta, Canada.
authors: Yusuke
authors: Y
authors: Echigoya
authors: UPMC-Sorbonne Universités-Univ. Paris 6, UPMC/INSERM UMRS974, CNRS FRE 3617, Center of Research in Myology, Paris, 75651 cedex 13, France.
authors: Vincent
authors: V
authors: Mouly
authors: UFR des Sciences de la Santé, Université de Versailles Saint-Quentin-en-Yvelines, 78180 Montigny-le-Bretonneux, France.
authors: Luis
authors: L
authors: Garcia
authors: University of Alberta, Faculty of Medicine and Dentistry, Department of Medical Genetics, Edmonton, Alberta, Canada; Muscular Dystrophy Canada Research Chair, University of Alberta, Edmonton, Alberta, Canada.
authors: Toshifumi
authors: T
authors: Yokota
authors: UPMC-Sorbonne Universités-Univ. Paris 6, UPMC/INSERM UMRS974, CNRS FRE 3617, Center of Research in Myology, Paris, 75651 cedex 13, France.
authors: William
authors: W
authors: Duddy
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016189:Dystrophin / Q000235:genetics; D005091:Exons / Q000235:genetics*; D057166:High-Throughput Screening Assays; D006801:Humans; D015233:Models, Statistical; D060172:Morpholinos / Q000235:genetics; D020388:Muscular Dystrophy, Duchenne / Q000235:genetics*; D009841:Oligonucleotides / Q000235:genetics*; D016376:Oligonucleotides, Antisense / Q000235:genetics; D012326:RNA Splicing / Q000235:genetics*
chemical_list: D016189:Dystrophin; D060172:Morpholinos; D009841:Oligonucleotides; D016376:Oligonucleotides, Antisense