Exon identity influences splicing induced by exonic variants and in silico prediction efficacy.

Background: Minigenes and in silico prediction tools are commonly used to assess the impact on splicing of CFTR variants. Exon skipping is often neglected though it could impact the efficacy of targeted therapies. The aim of the study was to identify exon skipping associated with CFTR variants and to evaluate in silico predictions of seven freely available software.

Methods: CFTR basal exon skipping was evaluated on endogenous mRNA extracted from non-CF nasal cells and on two CFTR minigene banks. In silico tools and minigene systems were used to evaluate the impact of CFTR exonic variants on exon skipping.

Results: Data showed that out of 65 CFTR variants tested, 26 enhanced exon skipping and that in silico prediction efficacy was of 50%-66%. Some in silico tools presented predictions with a bias towards the occurrence of splicing events while others presented a bias towards the absence of splicing events (non-detection including true negatives and false negatives). Classification of exons depending on their basal exon skipping level increased prediction rates up to 80%.

Conclusion: This study indicates that taking basal exon skipping into account could orientate the choice of the in silico tools to improve prediction rates. It also highlights the need to validate effects using in vitro assays or mRNA studies in patients. Eventually, it shows that variant-guided therapy should also target exon skipping associated with variants.