Issue 18, 2012

Exploratory urinary metabolic biomarkers and pathways using UPLC-Q-TOF-HDMS coupled with pattern recognition approach

Abstract

Metabolomics represents an emerging and powerful discipline concerned with the comprehensive analysis of small molecules and provides a powerful approach to discover biomarkers in biological systems. Recent development of biomarkers for diagnosis and therapeutic monitoring of liver-stagnation and spleen-deficiency syndrome (LSS)-type disease remains challenging. This study was undertaken to discover novel potential biomarkers for the non-invasive early diagnosis of human LSS. Urine samples which are potentially a rich source of metabolites were collected from patients with LSS, together with healthy control samples. Metabolite profiling was performed by ultra-performance liquid-chromatography/electrospray-ionization synapt high-definition mass spectrometry (UPLC-Q-TOF-HDMS) in conjunction with multivariate data analysis and ingenuity pathway analysis that were used to select the metabolites to be used for the non-invasive diagnosis of LSS. Twelve urinary differential metabolites contributing to the complete separation of LSS patients from matched healthy controls were identified involving several key metabolic pathways such as pentose and glucuronate interconversions, ascorbate, aldarate, cysteine, methionine, tyrosine, tryptophan, amino sugar and nucleotide sugar metabolism. More importantly, of the 12 differential metabolites, 4 metabolite markers, prolylhydroxyproline, L-homocystine, 2-octenoylcarnitine and α-N-phenylacetyl-L-glutamine, were effective for the diagnosis of human LSS, with an achieved sensitivity of 93.0%. These results demonstrate that robust metabolomics has the potential as a non-invasive strategy and promising screening tool to evaluate the potential of these metabolites in the early diagnosis of LSS patients and provides new insight into pathophysiological mechanisms.

Graphical abstract: Exploratory urinary metabolic biomarkers and pathways using UPLC-Q-TOF-HDMS coupled with pattern recognition approach

Supplementary files

Article information

Article type
Paper
Submitted
10 Jun 2012
Accepted
01 Jul 2012
First published
03 Jul 2012

Analyst, 2012,137, 4200-4208

Exploratory urinary metabolic biomarkers and pathways using UPLC-Q-TOF-HDMS coupled with pattern recognition approach

A. Zhang, H. Sun, Y. Han, Y. Yuan, P. Wang, G. Song, X. Yuan, M. Zhang, N. Xie and X. Wang, Analyst, 2012, 137, 4200 DOI: 10.1039/C2AN35780A

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