Xiaoya Gao1, Zifeng Huang2, Cailing Feng2, Chaohao Guan2, Ruidong Li3, Haiting Xie4, Jian Chen4, Mingchun Li4, Rongfang Que2, Bin Deng4, Peihua Cao5, Mengyan Li6, Jianjun Lu7, Yihong Huang8, Minzi Li9, Weihong Yang2, Xiaohua Yang2, Chunyan Wen2, Xiaomei Liang2, Qin Yang4, Yin-Xia Chao10, Ling-Ling Chan10, Midori A Yenari11, Kunlin Jin12, K Ray Chaudhuri13, Jing Zhang14, Eng-King Tan15, Qing Wang16. 1. Department of Neurology, Zhujiang Hospital, Southern Medical University, China. 2. Department of Neurology, Zhujiang Hospital of Southern Medical University, China. 3. Genetics, Genomics, and Bioinformatics Program, Department of Botany and Plant Sciences of University of California, USA. 4. Department of Neurology of Zhujiang Hospital of Southern Medical University, China. 5. Clinical Research Centre of Zhujiang Hospital of Southern Medical University, China. 6. Department of Neurology of First Municipal Hospital of Guangzhou, China. 7. Laboratory for Neuromodulation of Guangdong Second Provincial General Hospital, China. 8. Department of Neurology of Fifth Affiliated Hospital of Southern Medical University, China. 9. Department of Neurology, Zhujiang Hospital of Southern Medical University, Guanghzou, China. 10. Department of Neurology of National Neuroscience Institute, Singapore General Hospital, Duke-NUS Medical School. 11. University of California, San Francisco, USA. 12. Department of Pharmacology and Neuroscience, University of North Texas Health Science Center, Fort Worth, TX 76107, USA. 13. International Parkinson Foundation Centre of Excellence at Kings College Hospital and research director at Kings College Hospital, and Kings College, Denmark Hill, London, SE5 9RS UK. 14. Department of Pathology, University of Washington School of Medicine (USA). 15. Department of Neurology, National Neuroscience Institute, Singapore General Hospital, and a professor in the Duke-NUS Medical School, Singapore. 16. Head of Department of Neurology, Zhujiang Hospital, Southern Medical University, China.
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.
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.
Authors: Rachel A Kline; Lena Lößlein; Dominic Kurian; Judit Aguilar Martí; Samantha L Eaton; Felipe A Court; Thomas H Gillingwater; Thomas M Wishart Journal: Cells Date: 2022-08-26 Impact factor: 7.666