Integrated Metabolomics and Proteomics Analysis of Hippocampus in a Rat Model of Depression

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.

Introduction

Major depressive disorder (MDD) is one of the most common mental disorders, with a lifetime prevalence of 16.2% (Kupfer et al., 2012). MDD can be long-lasting or recurrent, substantially impairing quality of life and functioning (Saarni et al., 2007). However, MDD is a multifactorial disorder with substantial molecular alterations and pathway dysregulations involved, which makes the pathophysiology of MDD complicated and largely unknown. Animal models are essential tools for molecular studies of depression. Among these, the chronic unpredictable mild stress (CUMS) rat model, which mimics the variable and unpredictable stressors encountered in human daily life, is probably the most valid and commonly used model of depression (Yan et al., 2010).

As a critical brain region for memory and mood regulation, the hippocampus plays important roles in the pathogenesis of MDD (Campbell and Macqueen, 2004). A reduction of hippocampal volume, indicated by magnetic resonance imaging, has been widely reported in MDD patients (Campbell et al., 2004). Neuropathological studies have shown decreased neuronal and glial size, decreased expression of synaptic markers, loss of dendrites, reduced neurogenesis and increased apoptosis in the hippocampus of animals in models of depression (Harrison, 2002, Pittenger and Duman, 2008, Lucassen et al., 2006). In addition, because of its regulatory effects on the hypothalamus–pituitary–adrenal (HPA) axis, the hippocampus plays an important role in stress responses, which makes it susceptible in stress-related psychiatric disorders (Liu et al., 2017). Thus, this region of brain is the ideal material to investigate the molecular mechanisms of MDD.

In the past decade, omics technologies have been widely applied as useful tools for molecular profiling, identification of biomarkers, characterization of complex biochemical systems, and for examination of pathophysiological processes in various diseases. Our team has previously completed a series of research studies on depression in MDD patients (Zheng et al., 2013a, Zheng et al., 2013b, Zheng et al., 2012, Xu et al., 2012) and animal models (Mu et al., 2007, Yang et al., 2013, Chen et al., 2015, Liu et al., 2016, Zhou et al., 2017) using omics technologies. With respect to the hippocampus, dysfunction of energy metabolism, neurogenesis, synaptic plasticity, and neurotransmission has been found in previous proteomic studies of postmortem MDD patients and in rodent models of depression (Martins-de-Souza et al., 2012a, Martins-de-Souza et al., 2012b, Mu et al., 2007, Henningsen et al., 2012, Han et al., 2015).

However, the biological interpretation of data from a single type of omics study can be a great challenge due to complex biochemical regulation at multiple levels. Thus, integrated analysis by combining multiple types of omics data is promising and may help to identify potential biological relationships and improve understanding of entire biological mechanisms (Cavill et al., 2016). Among these, metabolomics–proteomics is a powerful combination and frequently applied in pathophysiological research, including in psychiatric disorders such as anxiety (Filiou et al., 2011, Zhang et al., 2011) and schizophrenia (Wesseling et al., 2013, Wesseling et al., 2015). In research on depression, our previous study implemented integrated analysis of metabolomic and proteomic profiling, which found significantly perturbed energy metabolism at the level of both the metabolome and the proteome in the cerebellum of chronically stressed rats (Shao et al., 2015).

In this study, we aim to gain further insights into the molecular mechanisms of depression in rat hippocampus. A well-established rat depression model, CUMS, was applied. Isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomic and gas chromatography–mass spectrometry (GC–MS)-based metabolomic approaches were employed to obtain unbiased profiling data. Moreover, Ingenuity Pathway Analysis (IPA) and Integrated Molecular Pathway Level Analysis (IMPaLA) were used for integrated molecular pathway and network analyses. The results will help to enhance comprehensive understanding of the pathogenesis of MDD.