A deep learning approach to identify gene targets of a therapeutic for human splicing disorders
| Autor: | Nikolai Naryshkin, Anil Kumar Chekuri, Amal Dakka, Monica Salani, Yong Yu, Graham Johnson, Serkan Erdin, Garry R. Cutting, Elisabetta Morini, Kerstin Effenberger, William D. Paquette, Susan A. Slaugenhaupt, Xin Zhao, Ashok Ragavendran, Vijayalakshmi Gabbeta, Wencheng Li, Neeraj Sharma, Jana Narasimhan, Emily M. Logan, Michael E. Talkowski, Gary Mitchell Karp, Aram J. Krauson, Christopher R. Trotta, Dadi Gao, Woll Matthew G |
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| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
RNA Splicing Science Transgene Cystic Fibrosis Transmembrane Conductance Regulator General Physics and Astronomy Mice Transgenic tau Proteins Computational biology Biology medicine.disease_cause Article General Biochemistry Genetics and Molecular Biology Transcriptome Mice 03 medical and health sciences Exon Deep Learning 0302 clinical medicine Target identification Phenethylamines Gene expression medicine Animals Humans Gene Mutation Multidisciplinary HEK 293 cells Computational Biology Exons General Chemistry Sterol Esterase Computational biology and bioinformatics Pyridazines HEK293 Cells 030104 developmental biology Gene Targeting RNA splicing MutL Protein Homolog 1 030217 neurology & neurosurgery |
| Zdroj: | Nature Communications, Vol 12, Iss 1, Pp 1-15 (2021) Nature Communications |
| ISSN: | 2041-1723 |
| DOI: | 10.1038/s41467-021-23663-2 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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