Integrated Metabolomics and Proteomics Analysis of Hippocampus in a Rat Model of Depression. (10th February 2018) - Zhang, Yuqing
Highlights: Differential analysis identified 30 metabolites and 170 proteins in the hippocampus of CUMS rats. Impairment in amino acid metabolism and protein synthesis/degradation were involved in hippocampal response to CUMS. Dysregulation of glutamate and glycine metabolism and their transport/catabolism related proteins were implicated. Disturbances in fatty acid and glycerophospholipid metabolism linked to the alterations in related enzymes were found. Differentially expressed synapse-associated proteins were identified. Abstract: Major depressive disorder (MDD) is a prevalent and serious mental disorder with high rates of suicide and disability. However, the underlying pathogenesis of MDD is complicated and remains largely unclear. An integrated analysis of multiple types of omics data may improve comprehensive understanding of the entire molecular mechanism of MDD. In this study, we applied an integrated analysis of gas chromatography/mass spectrometry (GC–MS)-based metabolomics and isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomics to investigate changes in the hippocampus in the chronic unpredictable mild stress (CUMS) rat model of depression. Only the stress-susceptible rats in the CUMS group were selected for profiling against controls. Differential analysis identified 30 metabolites and 170 proteins between the two groups. The integrated analyses revealed four major changes in the hippocampus of CUMS rats: (1) impairment in amino acid metabolism and protein synthesis/degradation; (2) dysregulation of glutamate and glycine metabolism and their transport/catabolism related proteins; (3) disturbances in fatty acid and glycerophospholipid metabolism accompanied by alterations in the corresponding metabolic enzymes; (4) abnormal expression of synapse-associated proteins. These results provide further important insights into the pathophysiology of depression and may help identify potential targets for antidepressant drugs.