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Multimodal analysis of gene expression from postmortem brains and blood identifies synaptic vesicle trafficking genes to be associated with Parkinson's disease.
- Source :
-
Briefings in Bioinformatics . Jul2021, Vol. 22 Issue 4, p1-15. 15p. - Publication Year :
- 2021
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Abstract
- Objective We aimed to identify key susceptibility gene targets in multiple datasets generated from postmortem brains and blood of Parkinson's disease (PD) patients and healthy controls (HC). Methods We performed a multitiered analysis to integrate the gene expression data using multiple-gene chips from 244 human postmortem tissues. We identified hub node genes in the highly PD-related consensus module by constructing protein–protein interaction (PPI) networks. Next, we validated the top four interacting genes in 238 subjects (90 sporadic PD, 125 HC and 23 Parkinson's Plus Syndrome (PPS)). Utilizing multinomial logistic regression analysis (MLRA) and receiver operating characteristic (ROC), we analyzed the risk factors and diagnostic power for discriminating PD from HC and PPS. Results We identified 1333 genes that were significantly different between PD and HCs based on seven microarray datasets. The identified MEturquoise module is related to synaptic vesicle trafficking (SVT) dysfunction in PD (P < 0.05), and PPI analysis revealed that SVT genes PPP2CA, SYNJ1, NSF and PPP3CB were the top four hub node genes in MEturquoise (P < 0.001). The levels of these four genes in PD postmortem brains were lower than those in HC brains. We found lower blood levels of PPP2CA, SYNJ1 and NSF in PD compared with HC, and lower SYNJ1 in PD compared with PPS (P < 0.05). SYNJ1, negatively correlated to PD severity, displayed an excellent power to discriminating PD from HC and PPS. Conclusions This study highlights that SVT genes, especially SYNJ1, may be promising markers in discriminating PD from HCs and PPS. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14675463
- Volume :
- 22
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Briefings in Bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 152575441
- Full Text :
- https://doi.org/10.1093/bib/bbaa244