Targeted proteomic analysis of cognitive dysfunction in remitted major depressive disorder: Opportunities of multi-omics approaches towards predictive, preventive, and personalized psychiatry

Publication date: Available online 21 February 2018
Source:Journal of Proteomics
Author(s): K. Oliver Schubert, David Stacey, Georgia Arentz, Scott R. Clark, Tracy Air, Peter Hoffmann, Bernhard T. Baune
In order to accelerate the understanding of pathophysiological mechanisms and clinical biomarker discovery and in psychiatry, approaches that integrate multiple –omics platforms are needed. We introduce a workflow that investigates a narrowly defined psychiatric phenotype, makes use of the potent and cost-effective discovery technology of gene expression microarrays, applies Weighted Gene Co-Expression Network Analysis (WGCNA) to better capture complex and polygenic traits, and finally explores gene expression findings on the proteomic level using targeted mass-spectrometry (MS) technologies. To illustrate the effectiveness of the workflow, we present a proteomic analysis of peripheral blood plasma from patient's remitted major depressive disorder (MDD) who experience ongoing cognitive deficits. We show that co-expression patterns previous detected on the transcript level could be replicated for plasma proteins, as could the module eigengene correlation with cognitive performance. Further, we demonstrate that functional analysis of multi-omics data has the potential to point to cellular mechanisms and candidate biomarkers for cognitive dysfunction in MDD, implicating cell cycle regulation by cyclin D3 (CCND3), regulation of protein processing in the endoplasmatic reticulum by Thioredoxin domain-containing protein 5 (TXND5), and modulation of inflammatory cytokines by Tripartite Motif Containing 26 (TRI26).SignificanceThis paper discusses how data from multiple –omics platforms can be integrated to accelerate biomarker discovery in psychiatry. Using the phenotype of cognitive impairment in remitted major depressive disorder (MDD) as an example, we show that the application of a systems biology approach - weighted gene co-expression network analysis (WGCNA) - in the discovery phase, and targeted proteomic follow-up of results, provides a structured avenue towards uncovering novel candidate markers and pathways for personalized clinical psychiatry.



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