Analysis on dynamic variations of plasma metabolites of CUMS-induced depression rats by GC-MS metabolomics
10.16438/j.0513-4870.2018-0025
- VernacularTitle:基于GC-MS代谢组学研究抑郁大鼠血浆代谢物变化规律
- Author:
Cui WANG
1
;
Xue-yang JIA
1
;
Lu-wen HOU
1
;
Xue-mei QIN
1
;
Jian-guo LI
1
Author Information
1. Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan 030006, China
- Publication Type:ORIGINAL ARTICLES
- Keywords:
GC-MS;
metabolomics;
chronic unpredictable mild stress;
S-Plot;
analysis of variance-simultaneous component analysis;
depression
- From:
Acta Pharmaceutica Sinica
2018;53(6):980-986
- CountryChina
- Language:Chinese
-
Abstract:
To compare static and dynamic metabolomics data analysis of CUMS (chronic unpredictable mild stress)-induced depression, GC-MS spectrometry was conducted on the plasma metabolome. S-Plot and ANOVA (analysis of variance)-simultaneous component analysis (ASCA) were respectively applied to static and dynamic analysis of metabolomics data. Static metabolomics data analysis revealed three typical plasma metabolites including propionic acid, D-allose, and 9,12,15-octadecatrienoic acid, while dynamic me-tabolomics data analysis found seven typical metabolites including propionic acid, D-allose, My-inositol, me-thylamine, etc. The abundances of typical metabolites observed by dynamic metabolomics data analysis were consistent with the variation trends of body weight and sugar water preference rate of CUMS rats. In conclusion, dynamic metabolomics analysis revealed more typical plasma metabolites, which have the potential to explain variations of body weight and behavior parameter of CUMS-induced depression rats. Combination of static and dynamic metabolomics data analysis may provide a strong support to the pathological study of complex diseases.
