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

一种深度学习方法,用于识别人类剪接障碍治疗的基因靶点

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作者: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 Naryshkin, Christopher R Trotta, Kerstin A Effenberger, Matthew G Woll, Vijayalakshmi Gabbeta, Ga

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.

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