ArticleNature communications2021
A deep learning approach to identify gene targets of a therapeutic for human splicing disorders.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 47 citations in OpenAlex.
- Review
- Specificity and exon target space of splicing modifying compounds.Nature communications · 2026Article
- Depleting prion protein using splice-switching small molecules.bioRxiv : the preprint server for biology · 2026Article
- Exploratory Analysis of ELP1 Expression in Whole Blood From Patients With Familial Dysautonomia.Annals of clinical and translational neurology · 2026Article
- AAV2-mediated intravitreal delivery of exon-specific U1 snRNA rescues optic neuropathy in familial dysautonomia.Molecular therapy. Nucleic acids · 2026Article
- Emerging therapeutic strategies in muscular dystrophy: an updated review on pathogenesis and treatment advances.Molecular biology reports · 2026Review
- Cell modeling and rescue of a novel noncoding genetic cause of glycogen storage disease IX.Genetics in medicine open · 2026Article
- The Establishment of Artificial RNA Cascade Circuits for Gene Regulation Based on Doxycycline-Induced Pre-mRNA Alternative Splicing.International journal of molecular sciences · 2025Article
- Big data and deep learning for RNA biology.Experimental & molecular medicine · 2024Review
- Splice modulators target PMS1 to reduce somatic expansion of the Huntington's disease-associated CAG repeat.Nature communications · 2024Article
- Specificity, synergy, and mechanisms of splice-modifying drugs.Nature communications · 2024Article
- A drug-free cardiovascular stent functionalized with tailored collagen supports in-situ healing of vascular tissues.Nature communications · 2024Article
- Age-dependent regulation of ELP1 exon 20 splicing in Familial Dysautonomia by RNA Polymerase II kinetics and chromatin structure.PloS one · 2024Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- The role of alternative pre-mRNA splicing in cancer progression.Cancer cell international · 2023Review
- Harnessing deep learning into hidden mutations of neurological disorders for therapeutic challenges.Archives of pharmacal research · 2023Review
- SRSF5 Regulates the Expression of BQ323636.1 to Modulate Tamoxifen Resistance in ER-Positive Breast Cancer.Cancers · 2023Article
- Imaging-based intelligent spectrometer on a plasmonic rainbow chip.Nature communications · 2023Article
- Development of an oral treatment that rescues gait ataxia and retinal degeneration in a phenotypic mouse model of familial dysautonomia.American journal of human genetics · 2023Article
- Investigation of early molecular alterations in tauopathy with generative adversarial networks.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
25 authors at 6 institutions in 1 country.
Funding
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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