It is possible to predict onset of Alzheimer’s Disease using deep learning-based Splice-AI

Scientists have found a method to predict the onset of Alzheimer’s Disease using deep learning-based Splice-AI. This research outcome was published in PNAS, a world-renowned academic journal.

Alternative splicing variant regulates gene expression and influences diverse phenotypes. Especially, genetic variants arising due to RNA splicing are frequently found in individuals having neurodevelopmental disorders. The research team revealed splicing hidden within the transcriptome to AD models via deep learning-based Splice-AI.

The novel 14 alternative splicing sites in the PLCg1 gene body, the key element of the signal transduction mechanism, were identified through deep learning.

Especially, Splice-AI analysis predicted a total of 14 splicing sites in the human PLCg1 gene, accurate delta scores, and positions were analyzed and a novel splicing site in exon 26 of human PLCg1 was identified. (exon 26 of the PLCg1 gene is 100% identical with exon 27 of the same gene in mice in terms of amino acid sequence).

* (Splicing) A form of RNA processing that regulates gene expression through the medium of genetic information

* (PLCg1, phospholipase c gamma-1) An essential protein involved in cell signal transduction and human cell growth and death

* (Exon) A part of a gene that contains protein synthesis information

Abnormal RNA processing was identified with an SNV in exon 27 of the PLCg1 gene within the brain of AD mouse models.

The research team revealed for the first time that SNV lead to changes in amino acids of proteins at exon 27. This region is very important for homeostasis because, mutated sequence is evolutionary conserved sequence through various species such as human, ape, mouse, chicken, zebrafish and so on. Moreover, AD specific nucleotide alteration sites were distributed in the histone modification region in PLCg1 gene body during the brain development.

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