A deep learning approach to identify gene targets of a therapeutic for human splicing disorders
Dadi Gao, Elisabetta Morini, Monica Salani, Aram J. Krauson, Anil Chekuri, Neeraj Sharma, Ashok Ragavendran, Serkan Erdin, Emily M. Logan, Wencheng Li, Amal Dakka, Jana Narasimhan, Xin Zhao, Nikolai A. Naryshkin, Christopher R. Trotta, Kerstin A. Effenberger, Matthew G. Woll, Vijayalakshmi Gabbeta, Gary M. Karp, Yong Yu, Graham D. Johnson, William D. Paquette, Garry R. Cutting, Michael E. Talkowski, Susan A. Slaugenhaupt
Abstract
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.