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Plasma disturbance of phospholipid metabolism in major depressive disorder by integration of proteomics and metabolomics

Authors Gui SW, Liu YY, Zhong XG, Liu XY, Zheng P, Pu JC, Zhou J, Chen JJ, Zhao LB, Liu LX, Xu GW, Xie P

Received 30 January 2018

Accepted for publication 6 April 2018

Published 6 June 2018 Volume 2018:14 Pages 1451—1461


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 3

Editor who approved publication: Professor Wai Kwong Tang

Si-Wen Gui,1,2,* Yi-Yun Liu,1–3,* Xiao-Gang Zhong,1,2,4,* Xinyu Liu,5,* Peng Zheng,1–3 Jun-Cai Pu,1–3 Jian Zhou,1,2 Jian-Jun Chen,1,2 Li-Bo Zhao,6 Lan-Xiang Liu,1–3 Guowang Xu,5 Peng Xie1–3

1Chongqing Key Laboratory of Neurobiology, Chongqing, China; 2Institute of Neuroscience and the Collaborative Innovation Center for Brain Science, Chongqing Medical University, Chongqing, China; 3Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; 4School of Public Health and Management, Chongqing Medical University, Chongqing, China; 5CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China; 6Department of Neurology, Yongchuan Hospital of Chongqing Medical University, Chongqing, China

These authors contributed equally to this work

Major depressive disorder (MDD) is a highly prevalent mental disorder affecting millions of people worldwide. However, a clear causative etiology of MDD remains unknown. In this study, we aimed to identify critical protein alterations in plasma from patients with MDD and integrate our proteomics and previous metabolomics data to reveal significantly perturbed pathways in MDD. An isobaric tag for relative and absolute quantification (iTRAQ)-based quantitative proteomics approach was conducted to compare plasma protein expression between patients with depression and healthy controls (CON).
Methods: For integrative analysis, Ingenuity Pathway Analysis software was used to analyze proteomics and metabolomics data and identify potential relationships among the differential proteins and metabolites.
Results: A total of 74 proteins were significantly changed in patients with depression compared with those in healthy CON. Bioinformatics analysis of differential proteins revealed significant alterations in lipid transport and metabolic function, including apolipoproteins (APOE, APOC4 and APOA5), and the serine protease inhibitor. According to canonical pathway analysis, the top five statistically significant pathways were related to lipid transport, inflammation and immunity.
Conclusion: Causal network analysis by integrating differential proteins and metabolites suggested that the disturbance of phospholipid metabolism might promote the inflammation in the central nervous system.

Keywords: major depressive disorder, plasma proteomics, iTRAQ, metabolomics, integrative analysis

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