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

Gao, Dadi; Morini, Elisabetta; Salani, Monica; Krauson, Aram J.; Ragavendran, Ashok; Erdin, Serkan; Logan, Emily; Chekuri, Anil; Li, Wencheng; Dakka, Amal; Naryshkin, Nikolai; Trotta, Chris; Effenberger, Kerstin A.; Woll, Matt; Gabbeta, Vijayalakshmi; Karp, Gary M.; Yu, Yanke; Johnson, Graham D.; Paquette, William D.; Talkowski, Michael E.; Slaugenhaupt, Susan A.

doi:10.1101/2020.02.03.932103

Cited by 4 publications

(6 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our work has been focused on the development of small molecule SMCs to correct ELP1 splicing defect in FD. We have shown that daily consumption of kinetin (6-furfuryl amino purine) rescues neuronal phenotype in our FD mouse model and, more recently, we have identified a novel SMC, BPN-15477, significantly more potent and efficacious than kinetin (40, 43). However, we have never tested the ability of our SMCs to correct ELP1 splicing in the retina.…”

Section: Resultsmentioning

confidence: 82%

“…As anticipated, we found that ELP1 exon 20 inclusion was significantly lower in this specific subpopulation of neurons when compared to the rest of the retina, demonstrating that the RGC loss in FD might result from ELP1 amounts falling below a cell-specific threshold. Finally, in order to test the ability of our newly identified SMC to correct ELP1 splicing in the retina, we administered BPN-15477 to the TgFD9 mice starting at birth through special formulated chow (43). BPN-15477 fully corrects ELP1 splicing defect in the retina, proving for the first time the therapeutic potential of an oral treatment to prevent retinal degeneration in FD.…”

Section: Discussionmentioning

confidence: 99%

“…Previously, our team has identified the small molecule kinetin (6-furfurylaminopurine) to be an orally active splicing modulator of ELP1 both in vitro and in vivo (41, 42). As part of the NIH Blueprint Neurotherapeutics Network we have created a new class of highly potent SMCs, using kinetin as a starting molecule, and identified BPN-15477, a more potent and efficacious ELP1 splicing modulator (43, 44). Despite this incredible progress, the field has not yet developed a therapy to prevent retinal degeneration in FD.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Selective retinal ganglion cell loss and optic neuropathy in a humanized mouse model of familial dysautonomia

Chekuri

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Familial dysautonomia (FD) is an autosomal recessive neurodegenerative disease caused by a splicing mutation in the gene encoding Elongator complex protein 1 (ELP1, also known as IKBKAP). This mutation results in tissue-specific skipping of exon 20 with a corresponding reduction of ELP1 protein, predominantly in the central and peripheral nervous system. Although FD patients have a complex neurological phenotype caused by continuous depletion of sensory and autonomic neurons, progressive visual decline leading to blindness is one of the most problematic aspect of the disease, as it severely affects their quality of life. To better understand the disease mechanism as well as to test the in vivo efficacy of targeted therapies for FD, we have recently generated a novel phenotypic mouse model, TgFD9; Elp1Δ20/flox. This mouse exhibits most of the clinical features of the disease and accurately recapitulates the tissue-specific splicing defect observed in FD patients. Driven by the dire need to develop therapies targeting retinal degeneration in FD, herein, we comprehensively characterized the progression of the retinal phenotype in this mouse, and we demonstrated that it is possible to correct ELP1 splicing defect in the retina using the splicing modulator compound (SMC) BPN-15477.

show abstract

Section: Resultsmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Selective retinal ganglion cell loss and optic neuropathy in a humanized mouse model of familial dysautonomia

Chekuri

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…To understand yeast gene expression using microarrays, Chen et al (2016) used autoencoders (Chen et al, 2016). Others have used Deep CNN model with success in understanding genomics (Zhou et al, 2018;Yuan & Joseph, 2019;Gao et al, 2020). A combination of one-dimensional CNN, RNN has been very popular in extracting meaningful information.…”

Section: Meta-analysis Of the Sars-cov2 Datasetmentioning

confidence: 99%

Applications of Deep Learning and Machine Learning in Computational Medicine

Adiga¹,

Biswas²,

Shyam³

2023

J Biochem Technol

View full text Add to dashboard Cite

Computational medicine has emerged due to the advances in medical technology in parallel with big data and artificial intelligence. A new way of treating complex diseases is evolving called 'Precision Medicine' fueled by big data extracting meaningful information from individual variability. At the forefront is biomedical research aiming to promote the area of precision medicine. Though traditional machine learning methods have built successful models for cancer diagnosis to sars-cov2 pulmonary infection, the advent of modern deep learning methods has had phenomenal growth in genomics, electronic health records, and drug development. The challenges in Deep learning applications in medicine include lack of data, privacy, heterogeneity of data, and interpretability. Analysis and discussion on these problems provide a reference to improve the application of deep learning in medical health.

show abstract

“…Source code to reproduce the results 88 in the paper is available on GitHub [https://github. com/talkowski-lab/SMC_CNN_Model].…”

Section: Data Availabilitymentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 4 publications

References 73 publications

Selective retinal ganglion cell loss and optic neuropathy in a humanized mouse model of familial dysautonomia

Selective retinal ganglion cell loss and optic neuropathy in a humanized mouse model of familial dysautonomia

Applications of Deep Learning and Machine Learning in Computational Medicine

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

Contact Info

Product

Resources

About