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

ABSTRACT: Pre-mRNA splicing is a key control point in human gene expression. Disturbances in splicing due to mutation or aberrant splicing regulatory networks lead to dysregulated protein expression and contribute to a substantial fraction of human disease. Several classes of active and selective splicing modulator compounds (SMCs) have been recently identified and establish that pre-mRNA splicing represents a viable target for therapy. This also raises the intriguing possibility that SMCs may have broad capabilities to ameliorate aberrant splicing across multiple human disorders. We describe herein the identification of BPN-15477, a novel SMC that restores correct splicing of exon 20 in the Elongator complex protein 1 (ELP1) gene carrying the major IVS20+6T>C mutation responsible for familial dysautonomia. Given that BPN-15477 corrects splicing and increases full-length ELP1 protein in vivo, we developed a machine learning approach to evaluate the therapeutic potential of BPN-15477 to correct splicing in other human genetic diseases. Using transcriptome sequencing from compound-treated fibroblast cells, we identified treatment responsive sequence signatures, the majority of which center at the 5' splice site of exons whose inclusion or exclusion is modulated by SMC treatment. We then leveraged this model to identify 155 human disease genes that harbor ClinVar mutations predicted to alter pre-mRNA splicing as potential targets for BPN-15477 treatment. Using in vitro splicing assays, we validated representative predictions by demonstrating successful correction of splicing defects caused by mutations in the genes responsible for cystic fibrosis (CFTR), cholesterol ester storage disease (LIPA), Lynch syndrome (MLH1) and familial frontotemporal dementia (MAPT). Importantly, we also validated these predictions in two disease relevant cellular models for LIPA and CFTR, confirming that treatment increases functional protein and confirming the clinical potential for our model predictions. Our study shows that deep learning techniques can identify a complex set of sequence signatures and predict response to pharmacological modulation, strongly supporting the use of in silico approaches to expand the therapeutic potential of drugs that modulate splicing.

ORGANISM(S): Homo sapiens

PROVIDER: GSE158947 | GEO | 2021/03/29

REPOSITORIES: GEO

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Project description:Tissue Factor Pathway Inhibitor-2 is Induced by Fluid Shear Stress in Vascular Smooth Muscle Cells and Affects Cell Proliferation and Survival Introduction: Vascular smooth muscle cells (SMCs) are exposed to fluid shear stress (FSS) after interventional procedures such as balloon-angioplasty. Whereas the effects of hemodynamic forces on endothelial cells are explored in detail, the influence of FSS on smooth muscle cell function is poorly characterized. Here, we investigated the effect of FSS on SMC gene expression and function. Methods: Laminar FSS of arterial level (14 dynes/cm2) was applied to SMC cultures for 24 h in a parallel-plate flow chamber. The effect of FSS on gene expression was first screened with microarray technology and the results further verified by real time (RT) PCR and immunoblotting. Protein expression was also studied in the rat carotid artery after balloon-injury and DNA synthesis and apoptosis was examined in SMCs in vitro. Results: Microarrays identified tissue-pathway inhibitor-2 (TFPI-2) as the most differentially expressed gene by FSS in cultured SMCs. The regulatory effect of FSS on the expression of TFPI-2 was confirmed by RT-PCR and immunobloting demonstrating a more than 400-fold increase in TFPI-2 expression in SMCs exposed to FSS compared to static controls and a consistent upregulation at the protein level. Functionally, SMC proliferation was decreased by FSS and recombinant TFPI-2 was found to inhibit SMC proliferation and induce SMC apoptosis as indicated by activation of caspase-3. In vivo, TFPI-2 expression was found to be up-regulated 5, 10 and 20 h after rat carotid balloon-injury and immunohistochemistry demonstrated TFPI-2 protein in luminal SMCs exposed to FSS in rat carotid intimal hyperplasia 10 days after balloon-injury. Conclusion: FSS strongly influence gene expression in cultured SMCs and induce TFPI-2 expression, which is also expressed after rat carotid balloon injury in luminal SMCs exposed to FSS. Functionally, TFPI-2 may play an important role in vessel wall repair by regulating SMC proliferation and survival. Further studies are needed to elucidate the mechanisms by which TFPI-2 control SMC function. 3 x 2 samples from a paired fluid shear stress experiment on smooth muscle cells.

Project description:Smooth muscle cells (SMCs) display dynamic plasticity by changing phenotype under various pathophysiological conditions. Uncovering how SMCs regulate their phenotype is a key to understanding the molecular mechanisms of a number of gastrointestinal diseases. MicroRNAs (miRNAs), generated in cells by Dicer, have been identified as regulators of the differentiation state of SMCs. The goal of this study was to investigate the role of miRNAs during the development of gastrointestinal SMCs in a transgenic animal model. We generated SMC-specific Dicer (smDicer) null animals that express the reporter, green fluorescence protein (eGFP), in a SMC-specific manner. eGFP labeled SMCs were used for morphological, cytometric and genetic studies of smDicer null mutant and wild type control mice. The structure of the bowel wall was examined by confocal microscopy, and function was evaluated by recording spontaneous and evoked contractions. SMCs were purified with fluorescence-activated cell sorting and SM specific gene expression studies were performed with genechip arrays, PCR and quantitative PCR. Bioinformatic analyses were used to characterize the interaction between miRNAs and target genes. SMC-specific knockout of Dicer prevented SMC miRNA biogenesis, causing dramatic changes in phenotype, function, and global gene expression in SMCs. Profiling and bioinformatic analyses showed SMC phenotype is regulated by a complex network of positive and negative feedback by SM miRNAs, serum response factor (SRF), and other transcriptional factors. SM miRNAs play an important role in growth, development and survival of SMCs. Phenotypic changes of SMCs may be regulated through multiple pathways in an interaction network of SM miRNAs, SRF, and additional transcriptional factors. We used microarrays to identify genetic changes during the development of gastric smooth muscle cells in the smDicer null mice. Mouse mRNA microarrays were performed on GeneChipÂ® Mouse Genome 430 2.0 Arrays (Affymetrix). Total RNAs isolated from the jejunal muscularis of the smDicer knockout (KO) and the wild type (WT) control mice at the ages of ~3 weeks were used for the arrays. The cDNA synthesis, hybridization, cDNA labeling, and scanning were performed. Gene expression between the smDicer KO and the WT controls were compared.

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

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