A deep learning approach to identify gene targets of a therapeutic for human splicing disorders

Pre-mRNA splicing is a key controller of human gene expression. Disturbances in splicing due to mutation lead to dysregulated protein expression and contribute to a substantial fraction of human disease. Several classes of splicing modulator compounds (SMCs) have been recently identified and establish that pre-mRNA splicing represents a target for therapy. We describe herein the identification of BPN-15477, a SMC that restores correct splicing of ELP1 exon 20. Using transcriptome sequencing from treated fibroblast cells and a machine learning approach, we identify BPN-15477 responsive sequence signatures. We then leverage this model to discover 155 human disease genes harboring ClinVar mutations predicted to alter pre-mRNA splicing as targets for BPN-15477. Splicing assays confirm successful correction of splicing defects caused by mutations in CFTR, LIPA, MLH1 and MAPT. Subsequent validations in two disease-relevant cellular models demonstrate that BPN-15477 increases functional protein, confirming the clinical potential of our predictions. Drugs that modify RNA splicing are promising treatments for many genetic diseases. Here the authors show that deep learning strategies can predict drug targets, strongly supporting the use of in silico approaches to expand the therapeutic potential of drugs that modulate RNA splicing.

Automated summary

Pre-mRNA splicing is crucial for human gene expression control, and disturbances in splicing due to mutation can lead to dysregulated protein expression. The identified SMC, BPN-15477, corrects splicing defects caused by mutations in specific genes, increasing functional protein levels, demonstrating its clinical potential for treating genetic diseases. The use of deep learning strategies to predict drug targets for RNA splicing modulation shows promise for expanding therapeutic potential.