Article

A metabonomic characterization of CCl4-induced acute liver failure using partial least square regression based on the GC/MS metabolic profiles of plasma in mice.

Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
Journal of Chromatography B (impact factor: 2.89). 07/2008; 870(2):178-85. DOI:10.1016/j.jchromb.2008.05.049 pp.178-85
Source: PubMed

ABSTRACT This work characterized the metabolism disorders of acute liver failure (ALF) induced by carbon tetrachloride (CCl(4)) in a mouse model with different dosage of intoxication (100, 500 and 1000 mg/kg). Metabolic profiles of mice plasma were detected by gas chromatography/mass spectrometry (GC/MS) after chemical derivatization. Here an effective information-extracting approach was implemented on the basis of partial least square regression analysis (PLS-RA). PLS modeling was achieved with two kinds of Y-vectors for the acquired metabonomics data and eight metabolites with different changing behaviors were selected. ALF of mice induced by CCl(4) was characterized by the elevation of glutamate, citrate, serine and threonine, as well as the decrease of alpha-glycerophosphate, docosahexaenoic acid, palmitic acid and oleic acid in plasma. The difference in the concentrations of serine, threonine, palmitic acid and oleic acid remained insignificant between the control and 100mg/kg groups, while significant distinction appeared when comparing the control and two higher dosed groups. The underlying regulation of CCl(4)-perturbed metabolic pathways was discussed according to the selected metabolites. The present study demonstrated a great potential of PLS-RA in exploiting a comprehensive metabolic effects of CCl(4) intoxication and its efficient capability to reveal the hepatotoxic mechanism of ALF induced by reactive oxygen species (ROS).

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  • Article: Radix Paeoniae Rubra and Radix Paeoniae Alba Attenuate CCl4-Induced Acute Liver Injury: An Ultra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS) Based Metabolomic Approach for the Pharmacodynamic Study of Traditional Chinese Medicines (TCMs).
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    ABSTRACT: Metabolomics has been frequently used in pharmacodynamic studies, especially those on traditional Chinese medicine (TCM). Radix Paeoniae Alba and Radix Paeoniae Rubra are popularly used in TCM, and both have hepatoprotective effects. In this study, a CCl(4)-induced acute liver injury rat model was established and confirmed by the observed serum aminotransferase activities. The metabolomics approach was applied to study the influence of Radix Paeoniae Alba and Radix Paeoniae Rubra on the metabolic changes in rats with acute liver injury. The partial least-squares-discriminant analysis (PLS-DA) of rat serum and their ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) fingerprints allowed discrimination of controlled, acute liver injury-model rats after administration of the two types of TCMs. The time-dependent PLS-DA plots showed that the changes in the metabolic patterns of the rats, which were administered with the TCMs, had stabilized within 2 h after they received the intraperitoneal CCl(4) injection. The results indicated the protective effect of TCMs against liver injury. Several potential biomarkers were detected and identified, which included creatine, deoxycholic acid, choline, 5-methylenetetrahydrofolate, folic acid, and glycocholic acid. The physiological significance of these metabolic changes was discussed.
    International Journal of Molecular Sciences 01/2012; 13(11):14634-47. · 2.60 Impact Factor

Keywords

acquired metabonomics data
 
acute liver failure
 
carbon tetrachloride
 
CCl(4)-perturbed metabolic pathways
 
comprehensive metabolic effects
 
different dosage
 
effective information-extracting approach
 
efficient capability
 
gas chromatography/mass spectrometry
 
glutamate
 
hepatotoxic mechanism
 
higher dosed groups
 
Metabolic profiles
 
metabolism disorders
 
mouse model
 
oleic acid
 
PLS-RA
 
reactive oxygen species
 
ROS
 
significant distinction
 

Xin Huang