Genetic disorders pose a substantial health burden on the U.S. population. While traditional therapies for these disorders have focused on ameliorating the downstream phenotypic consequences, a revolution in genome editing is beginning to treat these diseases at the genetic level. One promising form of genetic therapy is called exon skipping. In exon skipping, the cell is forced to bypass the gene region that contains the mutation, producing a smaller version of the protein that restores function. The first clinical success in exon skipping disease treatment has recently received FDA approval for treatment of Duchenne muscular dystrophy (DMD), and several exon skipping strategies have shown pre-clinical and clinical promise. We propose a systematic pilot study to measure the potential of this exon skipping technique to correct disease genes throughout the genome. First, we establish a computational approach to identify exons known to harbor disease-causing mutations where it is unlikely to impact gene function if that exon is excluded. Next, we computationally predict whether these exons are likely to be skipped using our highly specific experimental technique. Then, we will apply a novel, high-throughput CRISPR/Cas9 genome editing assay to eliminate the splice site (to ultimately skip the adjacent exon) that quantifies the impact on splicing at thousands of these intron-exon boundaries. After determining a set of candidate exons that can be skipped efficiently, we will measure the impact of CRISPR/Cas9-mediated exon skipping on transcript structure and gene function for dozens of human disease exons, using appropriate cellular and biochemical assays for each gene. This exhaustive approach promises to chart a systematic path toward classifying disease genes that would be most amenable for future pre-clinical evaluation of permanent therapeutic exon skipping.
A new type of gene therapy has emerged in which the broken region of a defective gene is bypassed, which may help reduce or eliminate disease by providing cells with healthier versions of that gene. This idea of genetic bypassing has been shown to work for a few diseases that have been carefully studied, but we believe there is much wider promise of this strategy. Through a combination of computational prediction and large-scale experiments examining thousands of gene regions, we will develop a rational way to identify diseases that could be prime targets for genetic bypassing.
