Selection and dynamic metabolic response of rat biomarkers by metabonomics and multivariate statistical analysis combined with GC-MS.
Depression is a common complex psychiatric disorder but its pathophysiological mechanism is not yet fully understood. Metabonomics by GC-MS and multivariate statistical analysis were used to select potential biomarkers associated with CUMS (chronic unpredictable mild stress) depression. The dynamic metabolic changes in rat serum were investigated to find potential disease biomarkers and to investigate the pathology of depression induced by the CUMS depression model. The changes in behavior and serum metabolic profiles were investigated during a three-week CUMS exposure. Serum samples were collected on days 0, 6, 9, 12, 15 and 21, and the serum metabolic profiling was carried out using GC-MS, followed by multivariate analysis. The potential biomarkers were screened from metabolites by principal component analysis and correlation analysis. The peak area of potential biomarkers was used to identify changes in depression in rats and describe their dynamics. Exposure to CUMS for three weeks caused depression-like behavior in rats, as indicated by significant decreases in weight gain, sucrose consumption, ambulation number and rearing numbers. Six potential biomarkers in serum, including glycine (Gly), glutamic acid (Glu), fructose, citric acid, glucose and hexadecanoic acid, were subjected to screening by metabonomics and multivariate statistical analysis. It was found that fructose, glucose and Gly were increased in the model group, while hexadecanoic acid, Glu and citric acid were reduced in the model group. According to the results of principal component analysis and correlation analysis, the correlation coefficient between the behavior scores and potential biomarkers in serum were all more than 0.9. This result suggests that the progression of depression may be associated with perturbation of glycometabolism, amino acid metabolism and energy metabolism. Gly, Glu, fructose, citric acid, glucose and hexadecanoic acid appear to be suitable quantitative diagnostic biomarkers for depression. The representative and unique nature of these biomarkers needs to be verified by pharmacological experiments, including molecular pharmacology investigations of enzymes or genes.