Issue 19, 2013

Trajectory analysis of metabolomics profiling in liver injured rats using ultra-performance liquid chromatography coupled with mass spectrometry

Abstract

Metabolomics is a powerful new technology that allows for the assessment of global low-molecular-weight metabolites in a biological system. It has shown great potential in biomarker discovery and, in particular, has renewed the interest in metabolism across biology and medicine. Recent development of metabolomics biomarkers and pathways for trajectory analysis remains challenging. Here, metabolomics techniques are the assessment of endogenous metabolites and may provide additional insights into carbon tetrachloride-induced liver injury. Ultra-performance liquid chromatography/electrospray ionization quadruple time-of-flight mass spectrometry (UPLC/MS) coupled with pattern recognition methods were carried out to obtain comprehensive metabolomics profiling and pathways of large biological data sets. Analysis of metabolic trajectory data indicated that primary bile acid biosynthesis, riboflavin metabolism, and tryptophan metabolism pathways were acutely perturbed, and 15 differential metabolites were identified. More importantly, the results indicate these ions in the positive mode as potential biomarkers of liver injury. Network construction has led to the integration of metabolites associated with the multiple perturbation pathways. Our findings suggest that the robust metabolomics method is promising to analyze and evaluate the trajectory of induced liver injury.

Graphical abstract: Trajectory analysis of metabolomics profiling in liver injured rats using ultra-performance liquid chromatography coupled with mass spectrometry

Supplementary files

Article information

Article type
Paper
Submitted
08 Apr 2013
Accepted
31 Jul 2013
First published
31 Jul 2013

Anal. Methods, 2013,5, 5294-5301

Trajectory analysis of metabolomics profiling in liver injured rats using ultra-performance liquid chromatography coupled with mass spectrometry

A. Zhang, H. Sun, W. Sun, G. Jiao and X. Wang, Anal. Methods, 2013, 5, 5294 DOI: 10.1039/C3AY40578H

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