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

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.

Achievement of Small Target Detection for Sea Ship Based on CFAR-DBN

“…Training datasets are trained under the traditional CFAR [12], BPNN [15], Faster R-CNN [16], SSD512 [17], and CFAR-DBN algorithms. After the corresponding training model is obtained, the test dataset is tested under the above five algorithms.…”

“…As a hot technology with rapid development in the field of artificial intelligence in recent years, deep learning has been widely used in various industries. Deep learning comprehensively learns the abstract information from low level to high level through nonlinear elements [15][16][17], which reduces the complex preprocessing in the early stage of artificial intelligence and improves the efficiency of target detection and recognition. At present, the research on ship target detection in SAR images based on depth learning has become a hot topic in the application of related fields.…”

In silico Methods for Identification of Potential Therapeutic Targets

“…Several studies have reported DL for therapeutic target prediction in recent years [175][176][177]. For example, Wang et al [178] constructed a framework that combines a biased support vector machine and a stacked auto-encoder DL model to identify drug target proteins.…”

Dysregulated Spliceosome Gene Expression May Be a Common Process in Brains of Neurological and Psychiatric Disorders

“…Six associated pathways were over-represented in all 6 disorders, including the major and the minor mRNA splicing pathways and RNA metabolism. Therefore, we found that aberrations in the mRNA splicing process may be a common trajectory to many complex brain disorders involving the spliceosome complex.Recently, AS dysregulation was reported in AD, where several mis-splicing events in the brain have been associated with amyloid burden and neuro brillary [21], ASD, SCZ [28][29][30][31], and Huntington's disease [32,33], furthermore, emerging splicing therapeutics are promising therapeutic approaches in aberrant/deregulated AS [34][35][36][37][38], and clinical trials are currently underway for spinal muscular atrophy (https://clinicaltrials.gov/ct2/show/NCT04240314).We hypothesize that the excessive splicing changes observed in brain conditions may be related to changes in their regulators, spliceosome-related genes (SGs). Therefore, this study aims at identifying SG expression changes in multiple brain disorders using postmortem brain transcriptome data.…”

“…Recently, AS dysregulation was reported in AD, where several mis-splicing events in the brain have been associated with amyloid burden and neuro brillary [21], ASD, SCZ [28][29][30][31], and Huntington's disease [32,33], furthermore, emerging splicing therapeutics are promising therapeutic approaches in aberrant/deregulated AS [34][35][36][37][38], and clinical trials are currently underway for spinal muscular atrophy (https://clinicaltrials.gov/ct2/show/NCT04240314).…”