Diagnosis of Alzheimer's disease specific phospholipase c gamma-1 SNV by deep-learning based approach for high throughput screening
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA635365 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR11855666.fastq.gz | Fastqsanger.gz | |||
| SRR11855667.fastq.gz | Fastqsanger.gz |
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Project description:Alzheimer’s disease (AD) is a multifactorial disease which is caused by inherited autosomal recessive as familial and late-onset as non-familial changes. However, the genetics of AD underlining variation or mutation are not well understood. Exon skipping through the occurrence of the unpredicted genetic events leads to variation of translation, but the event does not can be exploited for AD diagnosis. We identified a gene as phospholipase c gamma-1 (PLCγ1) with severe genetic modification in AD through deep-learning based high throughput screening which showed single nucleotide variants (SNVs). Genome-wide association study (GWAS) data from AD mouse model demonstrated that AD specific novel SNVs are preferentially occurred H3K27ac accumulated region at PLCγ1 gene body during the brain development. Moreover, exon skipping in mature mRNA was caused by single nucleotide insertion at 27th exon of PLCγ1 gene. In addition, we show mutation site was evolutionally conserved in various species included human which means important region for homeostasis. Collectively, our findings suggest the genetic variation and possibility of diagnosis potent of PLCγ1 for AD.
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Project description: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.
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* Is the pooled diagnostic accuracy of optical diagnosis for predicting deep sm invasion in large non-pedunculated polyps ≥ 20 mm assessed in routine colonoscopies increased in those gastroenterologists participating in the e-learning module?
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* the pooled sensitivity of optical diagnosis for predicting deep submucosal invasion in large non-pedunculated polyps > 20 mm assessed in routine colonoscopies is increased.
* the pooled diagnostic accuracy of optical diagnosis for predicting deep sm invasion in large non-pedunculated polyps > 20 mm is increased.
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* the pooled rate of histology with high-grade dysplasia, intramucosal cancer or submucosal invasion decreased in lesions treated with piecemeal endoscopic resection.
* the diagnostic accuracy for predicting deep submucosal invasion in a test with pictures after participating is increased.
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