A deep learning approach to identify gene targets of a therapeutic for human splicing disorders.
Dadi GaoElisabetta MoriniMonica SalaniAram J KrausonAnil ChekuriNeeraj SharmaAshok RagavendranSerkan ErdinEmily M LoganWencheng LiAmal DakkaJana NarasimhanXin ZhaoNikolai NaryshkinChristopher R TrottaKerstin A EffenbergerMatthew G WollVijayalakshmi GabbetaGary KarpYong YuGraham JohnsonWilliam D PaquetteGarry R CuttingMichael E TalkowskiSusan A SlaugenhauptPublished in: Nature communications (2021)
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.
Keyphrases
- endothelial cells
- gene expression
- machine learning
- deep learning
- genome wide
- induced pluripotent stem cells
- pluripotent stem cells
- dna methylation
- binding protein
- induced apoptosis
- cystic fibrosis
- oxidative stress
- stem cells
- artificial intelligence
- bone marrow
- transcription factor
- small molecule
- drug delivery
- smoking cessation
- human health
- amino acid