Prediction of Alzheimer’s disease-specific phospholipase c gamma-1 SNV by deep learning-based approach for high-throughput screening

Sung-Hyun Kim; Sumin Yang; Key-Hwan Lim; Euiseng Ko; Hyun-Jun Jang; Mingon Kang; Pann-Ghill Suh; Jae-Yeol Joo

doi:10.1073/pnas.2011250118

Edited by Lucio Cocco, University of Bologna, Bologna, Italy, and accepted by Editorial Board Member Solomon H. Snyder December 5, 2020 (received for review June 3, 2020)

Significance

DNA mutation within gene bodies contributes to abnormal translation and can lead to neurodegenerative disorders. High-throughput analysis is suitable for initial detection of gene mutations with details. Deep learning-based RNA splicing analysis facilitates accurate and precise predictions of genetic variants in DNA bodies. Although deep learning-based prediction methods have improved the screening of genetic variations for target diseases, there have been no reports showing a direct comparison with genetic information of an AD model. This study identified the gene mutations and abnormal splicing of PLCγ1 gene in AD using both high-throughput screening data and a deep learning-based prediction. Our findings provide insight for improvement in prediction and diagnosis of AD pathology.

Abstract

Exon splicing triggered by unpredicted genetic mutation can cause translational variations in neurodegenerative disorders. In this study, we discover Alzheimer’s disease (AD)-specific single-nucleotide variants (SNVs) and abnormal exon splicing of phospholipase c gamma-1 (PLCγ1) gene, using genome-wide association study (GWAS) and a deep learning-based exon splicing prediction tool. GWAS revealed that the identified single-nucleotide variations were mainly distributed in the H3K27ac-enriched region of PLCγ1 gene body during brain development in an AD mouse model. A deep learning analysis, trained with human genome sequences, predicted 14 splicing sites in human PLCγ1 gene, and one of these completely matched with an SNV in exon 27 of PLCγ1 gene in an AD mouse model. In particular, the SNV in exon 27 of PLCγ1 gene is associated with abnormal splicing during messenger RNA maturation. Taken together, our findings suggest that this approach, which combines in silico and deep learning-based analyses, has potential for identifying the clinical utility of critical SNVs in AD prediction.

Footnotes

↵¹S.-H.K., S.Y., and K.-H.L. contributed equally to this work.
↵²To whom correspondence may be addressed. Email: joojy{at}kbri.re.kr.

Author contributions: P.-G.S. and J.-Y.J. designed research; S.-H.K., S.Y., K.-H.L., H.-J.J., and J.-Y.J. performed research; E.K., M.K., P.-G.S., and J.-Y.J. contributed new reagents/analytic tools; K.-H.L., M.K., and J.-Y.J. analyzed data; and S.-H.K., S.Y., K.-H.L., M.K., P.-G.S., and J.-Y.J. wrote the paper.
The authors declare no competing interest.
This article is a PNAS Direct Submission. L.C. is a guest editor invited by the Editorial Board.
This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2011250118/-/DCSupplemental.