Integrated metabolomics and lipidomics profiling of hippocampus reveal metabolite biomarkers in a rat model of chronic unpredictable mild stress-induced depression

Abstract

Background: Prolonged exposure to stress triggers depression, threatening human health. Thus, to thoroughly understand the underlying pathophysiologic mechanism of chronic unpredictable mild stress (CUMS)-induced depression is urgently needed. Ultra-high-performance liquid chromatography-mass spectroscopy (UPLC-MS)-based lipidomic and metabolomic approaches has been used for discovering metabolite biomarkers to develop new diagnostic and therapeutic means. Thus, our study aimed to conduct integrated metabolomics and lipidomics to identify metabolites and lipids biomarkers in the hippocampus in rat models of CUMS-induced depression.

Methods: Twelve eight-week-old male Sprague-Dawley rats (weight 210±30 g) were randomly distributed to one of the following two groups (n=6): control or CUMS. Established UPLC-MS-based lipidomic and metabolomic approaches were used to determine the metabolites and lipids in the hippocampus of rats. SICMA-P and GraphPad software were performed to discover potential metabolites and lipids biomarkers in the hippocampus of rats between the two groups.

Results: A total of 35 potential metabolites and 171 lipids were identified and found to be mainly related to amino acid and lipid metabolism. These metabolites were involved in different metabolic pathways and connected to each other, which might participate in the occurrence and development of depression.

Conclusions: Our findings underlined the metabolites, lipids and metabolic pathways that were changed in the hippocampus in CUMS compared to the controls, providing novel insights in the metabolism in the hippocampus of rats and revealing the new lipid-related targets.

Keywords: Chronic unpredictable mild stress (CUMS); depression; lipidomics; metabolomics; ultra-high-performance liquid chromatography-mass spectroscopy (UPLC-MS).

Figure 1

An overview of the experimental procedure of integrated lipidomic and metabolomic analyses. CUMS, chronic unpredictable mild stress; CUMS, chronic unpredictable mild stress; UPLC, ultra-high-performance liquid chromatography; MS, mass spectroscopy.

Figure 2

Depression-like behaviors were assessed by (A) the sucrose preference test, and (B) the forced swimming test. Data are the means ± SD (n=6). **, P<0.01 CUMS control when compared to the control group. CUMS, chronic unpredictable mild stress.

Figure 3

OPLS score plots of the CUMS group and the control group of metabolomics: (A) ESI+, R²X=0.468, R²Y=0.965, Q²=0.788, (B) ESI−, R²X=0.548, R²Y=0.974, Q²=0.835. The OPLS score plots of the CUMS group and the control group of lipidomics: (C) ESI+, R²X=0.755, R²Y=0.981, Q²=0.71, (D) R²X=0.773, R²Y=0.966, Q²=0.898. CUMS, chronic unpredictable mild stress; OPLS, orthogonal partial least-squares; ESI, electrospray ionization.

Figure 4

Pathway analysis using all the significant metabolites revealed significant differences in glycerophospholipid metabolism, glycine, serine, and threonine metabolism; phenylalanine metabolism; and alanine, aspartate, and glutamate metabolism between the control group and the CUMS group. In the scatter plot, the x-axis indicates the impact on the pathway and the y-axis indicates significant changes in a pathway by the detected metabolites (red). Cxxxxx numbers in the above pathways are identifiers for metabolites mapped in a KEGG pathway. C00350 (phosphatidylethanolamine), C00157 (phosphatidylcholine), C04230 [lysoPC (18:1(9Z))], C01996 (acetylcholine), C00416 [PA (16:0/16:0)], C02737 [PS (16:0/16:0)], and C00670 (glycerophosphocholine) were the detected metabolites in glycerophospholipid metabolism. C00576 (betaine aldehyde), C00188 (L-threonine), C02737 [PS (16:0/16:0)], and C00078 (L-tryptophan) were the detected metabolites in glycine, serine, and threonine metabolism. CO7086 (phenylacetic acid), C05598 (phenyl acetyl glycine), and C03519 (N-acetyl-L-phenylalanine) were the detected metabolites in phenylalanine metabolism. C00064 (L-glutamine) and C00334 (γ-aminobutyric acid) were the detected metabolites in alanine, aspartate, and glutamate metabolism. Blocks in red indicate the detected metabolites and blocks in blue indicate other metabolites present in a given pathway.