Article

Linking biological activity with herbal constituents by systems biology-based approaches: effects of Panax ginseng in type 2 diabetic Goto-Kakizaki rats.

CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China.
Molecular BioSystems (Impact Factor: 3.35). 09/2011; 7(11):3094-103. DOI: 10.1039/c1mb05254c
Source: PubMed

ABSTRACT Although a number of animal experiments and clinical trials have investigated the effects of ginseng roots on diabetes, the relationship between their therapeutic effects on diabetes and the quality and the growth age of this herb have not yet been reported. This study systematically investigated the effects of 3- to 6-year-old ginseng roots on glycemic and plasma lipid control in a rat model of type 2 diabetes. Six groups of male Goto-Kakizaki (GK) rats received either metformin, 3- to 6-year-old ginseng roots, or no treatment. The treatments were administered twice daily for 9 weeks. A combined approach was used that involved applying liquid chromatography-mass spectrometry-based lipidomics, measuring biochemical parameters and profiling the components of ginseng roots of different ages. Compared to the untreated controls, treatment with 4- and 6-year-old ginseng roots significantly improved glucose disposal, and 5-year-old ginseng treatment significantly increased high density lipoprotein cholesterol. Treatment with 6-year-old ginseng significantly decreased total plasma triacylglyceride (TG) and very-low-density lipoprotein cholesterol and improved plasma glycated hemoglobin (HbA1c). In addition, treatment with 4- to 6-year-old ginseng influenced plasma lipidomics in diabetic GK rats by reducing TG lipid species. Metformin significantly reduced fasting blood glucose by 41% and reduced HbA1c by 11%, but showed no effects on the plasma lipid parameters. The present study demonstrates that ginseng roots show growth age-dependent therapeutic effects on hyperlipidemia and hyperglycemia in diabetic GK rats. These age-dependent effects may be linked with the variation in both the ratios and concentrations of specific bioactive ginsenosides in ginseng roots of different growth ages. This study introduced novel systems biology-based approaches for linking biological activities with potential active components in herbal mixtures.

1 Bookmark
 · 
124 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Here, we isolated and characterized a new ginsenoside-transforming β-glucosidase (BglQM) from Mucilaginibacter sp. QM49 that shows biotransformation activity for various major ginsenosides. The gene responsible for this activity, bglQM, consists of 2,346 bp, and is predicted to encode 781 amino acid residues and this enzyme has a molecular mass of 85.6 kDa. Sequence analysis of BglQM revealed that it could be classified into glycoside hydrolase family 3. The enzyme was overexpressed in Escherichia coli BL21(DE3) using a MBP-fused pMAL-c2x (TEV) vector system. Overexpressed recombinant BglQM could efficiently transform the protopanaxatriol-type ginsenosides, Re and Rg1, into Rg2(S) and Rh1(S), respectively, by hydrolyzing one glucose moiety attached to the C20 position at pH 8.0 and 30°C. The Km values for p-nitrophenyl-β-d-glucopyranoside, Re and Rg1 were 37.0 ± 0.4 μM, 3.22 ± 0.15 and 1.48 ± 0.09 mM, and the Vmax values were 33.4 ± 0.6 μmol min(-1) mg(-1) of protein, 19.2 ± 0.2 and 28.8 ± 0.27 nmol min(-1) mg(-1) of protein, respectively. A crude protopanaxatriol-type ginsenoside mixture (PPTGM) was treated with BglQM followed by silica column purification to produce Rh1(S) and Rg2(S) at chromatographic purities of 98±0.5% and 97±1.2%, respectively. This is the first report of gram-scale production of Rh1(S) and Rg2(S) from PPTGM using a novel ginsenoside-transforming β-glucosidase of glycoside hydrolase family 3.
    Applied and Environmental Microbiology 06/2013; · 3.95 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Metabolites have played an essential role in our understanding of life, health, and disease for thousands of years. This domain became much more important after the concept of metabolism was discovered. In the 1950s, mass spectrometry was coupled to chromatography and made the technique more application-oriented and allowed the development of new profiling technologies. Since 1980, TNO has performed system-based metabolic profiling of body fluids, and combined with pattern recognition has led to many discoveries and contributed to the field known as metabolomics and systems biology. This review describes the development of related concepts and applications at TNO in the biomedical, pharmaceutical, nutritional, and microbiological fields, and provides an outlook for the future. © 2013 Wiley Periodicals, Inc. Mass Spec Rev.
    Mass Spectrometry Reviews 04/2013; · 7.74 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Liquid chromatography-mass spectrometry (LC-MS)-based lipidomics has undergone dramatic developments over the past decade. This review focuses on state of the art in LC-MS-based lipidomics, covering all the steps of global lipidomic profiling. By reviewing 185 original papers and application notes, we can conclude that current advanced LC-MS-based lipidomics methods involve: (1) lipid extraction schemes using chloroform/MeOH or methyl tert-butyl ether (MTBE)/MeOH, both with addition of internal standards covering each lipid class; (2) LC separation of lipids using short microbore C18 or C8 columns with sub-2-µm or 2.6–2.8-µm (fused-core) particle size with analysis time <30 min; (3) electrospray ionization in positive- and negative-ion modes with full spectra acquisition using high-resolution MS with capability to MS/MS. Phospholipids (phosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, phosphatidylserines, phosphatidylglycerols) followed by sphingomyelins, di- and tri-acylglycerols, and ceramides were the most frequently targeted lipid species.
    TrAC Trends in Analytical Chemistry 09/2014; 61:192–206. · 6.35 Impact Factor

Full-text

Download
174 Downloads
Available from
Jun 2, 2014