Systematic impacts of chronic unpredictable mild stress on metabolomics in rats

Abstract

Depression is the most common disabling psychiatric disease, with a high prevalence and mortality. Chronic unpredictable mild stress (CUMS) is a well-accepted method used to mimic clinical depression. Recent evidence has consistently suggested that the cumulative effects of CUMS could lead to allostatic overload in the body, thereby inducing systemic disorders; however, there are no previous systematic metabonomics studies on the main stress-targeted tissues associated with depression. A non-targeted gas chromatography-mass spectrometry (GC-MS) approach was used to identify metabolic biomarkers in the main stress-targeted tissues (serum, heart, liver, brain, and kidney) in a CUMS model of depression. Male Sprague-Dawley rats were randomly allocated to the CUMS group (n = 8) or a control group (n = 8). Multivariate analysis was performed to identify the metabolites that were differentially expressed between the two groups. There were 10, 10, 9, 4, and 7 differentially expressed metabolites in the serum, heart, liver, brain and kidney tissues, respectively, between the control and CUMS groups. These were linked to nine different pathways related to the metabolism of amino acids, lipids, and energy. In summary, we provided a comprehensive understanding of metabolic alterations in the main stress-targeted tissues, helping to understand the potential mechanisms underlying depression.

Figure 1

Depression-like behaviors were evaluated by (a) the sucrose preference test, and (b) the forced swimming test. Data are the means ± SD (n = 8). **p < 0.01 CUMS control when compared to the control group.

Figure 2

Representative GC–MS total ion current (TIC) chromatograms of the serum (a), heart tissue (b), liver tissue (c), brain tissue (d), and kidney tissue (e) samples from a mixture of the CUMS and control rats.

Figure 3

OPLS scores and permutation tests of the OPLS-DA models: serum (a,b), heart tissue (c,d), liver tissue (e,f), brain tissue (g,h), and kidney tissue (i,j) samples.

Figure 4

Heatmap of differentially expressed metabolites in the serum (a), heart tissue (b), liver tissue (c), brain tissue (d), and kidney tissue (e) samples in CUMS rats compared to the controls. The color of each section is proportional to the significance of the change in metabolites (red, up-regulated; blue, down-regulated). Rows correspond to the samples, and columns correspond to the metabolites.

Figure 5

Summary of pathway analysis using MetaboAnalyst 4.0. Serum (a): (a) alanine, aspartate and glutamate metabolism, (b) phenylalanine, tyrosine, and tryptophan biosynthesis, (c) D-glutamine and D-glutamate metabolism, (d) arginine and proline metabolism, and (e) linoleic acid metabolism. Heart tissue (b): (f) glycine, serine, and threonine metabolism. Liver tissue (c): (e) linoleic acid metabolism. Brain tissue (d): (g) pyruvate metabolism. Kidney tissue (e): (h) aminoacyl-tRNA biosynthesis and (i) methane metabolism.

Figure 6

Schematic diagram of the proposed metabolic pathways in main stress-targeted tissues (serum, heart, liver, brain and kidney) of CUMS rats compared to the controls (as shown in different colors). Metabolites marked in red represent the significant biomarkers found in stress-targeted tissues.