Therapeutic Effect and Metabolic Mechanism of A Selenium-Polysaccharide from Ziyang Green Tea on Chronic Fatigue Syndrome

Abstract

Ziyang green tea was considered a medicine food homology plant to improve chronic fatigue Ssyndrome (CFS) in China. The aim of this research was to study the therapeutic effect of selenium-polysaccharides (Se-TP) from Ziyang green tea on CFS and explore its metabolic mechanism. A CFS-rats model was established in the present research and Se-TP was administrated to evaluate the therapeutic effect on CFS. Some serum metabolites including blood urea nitrogen (BUN), blood lactate acid (BLA), corticosterone (CORT), and aldosterone (ALD) were checked. Urine metabolites were analyzed via gas chromatography-mass spectrometry (GC-MS). Multivariate statistical analysis was also used to check the data. The results selected biomarkers that were entered into the MetPA database to analyze their corresponding metabolic pathways. The results demonstrated that Se-TP markedly improved the level of BUN and CORT in CFS rats. A total of eight differential metabolites were detected in GC-MS analysis, which were benzoic acid, itaconic acid, glutaric acid, 4-acetamidobutyric acid, creatine, 2-hydroxy-3-isopropylbutanedioic acid, l-dopa, and 21-hydroxypregnenolone. These differential metabolites were entered into the MetPA database to search for the corresponding metabolic pathways and three related metabolic pathways were screened out. The first pathway was steroid hormone biosynthesis. The second was tyrosine metabolism, and the third was arginine-proline metabolism. The 21-hydroxypregnenolone level of rats in the CFS group markedly increased after the Se-TP administration. In conclusion, Se-TP treatments on CFS rats improved their condition. Its metabolic mechanism was closely related to that which regulates the steroid hormone biosynthesis.

Keywords: chronic fatigue syndrome; metabonomics; polysaccharide.

Figure 1

Schedule of experimental design.

Figure 2

Separation and purification of Se-TP: (a) separation of the crude polysaccharides using a DEAE-52 cellulose column, and (b) purification of sample using a Sephadex G-150 column.

Figure 3

Detection results of samples using an HPLC chromatograms test: (a) standard monosaccharides (1—mannose, 2—ribose, 3—rhamnose, 4—glucuronic acid, 5—galacturonic acid, 6—glucose, 7—xylose, 8—galactose, 9—arabinose, 10—fucose); (b) monosaccharide composition of Se-TP.

Figure 4

Molecular morphology images of Se-TP: (A) at magnifications of 2000×, and (B) at magnifications of 10,000×. The diameter of the spherical particles was approximately 5 μm and the surface was smooth.

Figure 5

Evaluation of GC-MS detection model through the PCA scores plot of QC samples. Gathered points of QC sample showed that the PCA model was reliable.

Figure 6

Results of multivariate statistical analysis: (A) PCA scores plot (CFS group vs. NC group), (B) PCA scores plot (Se-TP group vs. CFS group), (A₁) OPLS-DA scores plot (CFS group vs. NC group), (B₁) OPLS-DA scores plot (Se-TP group vs. CFS group).

Figure 7

Validation of the OPLS-DA model using a permutation test. The permutations were performed, and the resulting R² and Q² values were plotted. Green circle: R²; blue square: Q². The green line represents the regression line for R² and the blue line for Q².

Figure 8

Detection of the metabolic pathway topology analysis. (A) CFS group vs. NC group. (B) Se-TP group vs. CFS group. (a: citrate cycle; b: alanine–aspartate–glutamate metabolism; c: sphingolipid metabolism; d: steroid hormone biosynthesis; e: tyrosine metabolism; f: arginine-proline metabolism).