Metabolomic analysis of the hippocampus in a rat model of chronic mild unpredictable stress-induced depression based on a pathway crosstalk and network module approach

Background: The molecular alterations underlying the pathogenesis of depression have not been systematically defined. Increasing evidence suggests that hippocampus metabolism is strongly involved in the pathogenesis of chronic mild unpredictable stress (CUMS)-induced depression. The principal objective of this study was to reveal important information concerning the pathogenesis of depression through a comprehensive analysis of metabolites in the hippocampus in a CUMS rat model. Methods: Metabolites related to metabolic changes in the hippocampus in the CUMS model were collected from a depression-specific database and published literature. Potential metabolite pathways were identified by the Omicsolution tool. Then, crosstalk analysis was carried out to investigate the relationship between different important pathways. In addition, MetaboAnalyst was used to analyze potential metabolites for drug-related metabolite enrichment analysis, which was used to study hippocampus metabolite-related drug pathways in a CUMS model. Then, a metabolite-protein interaction (MPI) network was constructed and analyzed to identify important metabolites and proteins. The functional modules were extracted using the CNM network decomposition algorithm. Finally, neurotransmitters in the hippocampus of rats with CUMS depression were detected to verify the important pathways. Results: In the current study, 53 significantly enriched pathways related to the 107 identified metabolites were selected, and the top ranked enriched pathways included arginine and proline metabolism, neuroactive ligand-receptor interaction, phenylalanine metabolism, bile secretion, and glutathione metabolism. Pathway crosstalk analysis showed that the significantly enriched pathways were divided into two interrelated modules, which were mainly involved in metabolism, signal transduction, neurotransmitters, and the endocrine system. Enrichment analysis of drug-related metabolic KEGG pathways identified the antibiotic pathways as the most important pathways. In the MPI network, the hub metabolites were phosphate, arachidonic acid, oxoglutaric acid, L-glutamic acid, and glutathione, and the hub proteins were Got1, Got2, Tat, Ccbl1, Ccbl2, Il4i1. A total of 16 functional modules were extracted from the MPI network by using the CNM algorithm. Finally, metabolites related to serotonergic synapses, dopaminergic synapses, and glutamatergic synapses were found to be involved in the pathology of depression. Conclusion: We found that neurotransmitter pathways (serotonergic synapses, dopaminergic synapses and glutamatergic synapses) in the hippocampus play a crucial role in the underlying molecular mechanism of depression, which provides useful clues for identifying the detailed depression-associated metabolic profiles. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This study comprehensively analyzed metabolites in the hippocampus in a CUMS rat model to reveal important information concerning the pathogenesis of depression, highlighting the crucial role of neurotransmitter pathways in the underlying molecular mechanism of depression.