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. 2012 Aug;11(8):370-80.
doi: 10.1074/mcp.M111.016006. Epub 2012 Apr 13.

Urine metabolomics analysis for biomarker discovery and detection of jaundice syndrome in patients with liver disease

Xijun Wang  1 , Aihua Zhang, Ying Han, Ping Wang, Hui Sun, Gaochen Song, Tianwei Dong, Ye Yuan, Xiaoxia Yuan, Miao Zhang, Ning Xie, He Zhang, Hui Dong, Wei Dong
Affiliations

Urine metabolomics analysis for biomarker discovery and detection of jaundice syndrome in patients with liver disease

Xijun Wang et al. Mol Cell Proteomics. 2012 Aug.

Abstract

Metabolomics is a powerful new technology that allows for the assessment of global metabolic profiles in easily accessible biofluids and biomarker discovery in order to distinguish between diseased and nondiseased status information. Deciphering the molecular networks that distinguish diseases may lead to the identification of critical biomarkers for disease aggressiveness. However, current diagnostic methods cannot predict typical Jaundice syndrome (JS) in patients with liver disease and little is known about the global metabolomic alterations that characterize JS progression. Emerging metabolomics provides a powerful platform for discovering novel biomarkers and biochemical pathways to improve diagnostic, prognostication, and therapy. Therefore, the aim of this study is to find the potential biomarkers from JS disease by using a nontarget metabolomics method, and test their usefulness in human JS diagnosis. Multivariate data analysis methods were utilized to identify the potential biomarkers. Interestingly, 44 marker metabolites contributing to the complete separation of JS from matched healthy controls were identified. Metabolic pathways (Impact-value≥0.10) including alanine, aspartate, and glutamate metabolism and synthesis and degradation of ketone bodies were found to be disturbed in JS patients. This study demonstrates the possibilities of metabolomics as a diagnostic tool in diseases and provides new insight into pathophysiologic mechanisms.

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Figures

Fig. 1.
Fig. 1.
Metabolomic profiling of JS. A, OPLS-DA model results for JS group in positive mode. B, 3-D of OPLS-DA model for JS group. C, Loading plot of OPLS-DA of JS in positive mode. D, shows the combination of S- and VIP-score plots constructed from the supervised OPLS analysis of urine (ESI+ mode). Ions with the highest abundance and correlation in the JS group with respect to the controls are present on the upper far right hand quadrant, whereas ions with the lowest abundance and correlation in the JS group with respect to the control group are residing in the lower far left hand quadrant.
Fig. 2.
Fig. 2.
Analysis of the control and JS samples utilizing MetaboAnalyst's data annotation tools revealed differences between the two groups. A, Significance changes of the metabolite markers illustrated in the Biplot from PLS-plot. It can be readily observed that the concentrations of differential metabolites. B, Top 15 significant features of the metabolite markers based the VIP projection. C, Heat map visualization for the urine of JS. The heatmaps were constructed based on the potential candidates of importance, which were extracted with OPLS-DA analysis. Variable differences are revealed between the control and JS groups, with verified and known ions marked on the bottom corresponding to supplemental Table S2. Rows: samples; Columns: metabolites; Color key indicates metabolite expression value, blue: Lowest, red: highest.
Fig. 3.
Fig. 3.
Metabolomic profiling of YAH. A, OPLS-DA model results for YAH group in positive mode. B, 3-D of OPLS-DA model for YAH group. C, Loading plot of OPLS-DA of YAH in positive mode. Combination of S- and VIP-score plots constructed from the supervised OPLS analysis of urine (ESI+ mode).
Fig. 4.
Fig. 4.
Metabolomic profiling of YIH. A, OPLS-DA model results for YIH group in positive mode. B, 3-D of OPLS-DA model for YIH group. C, Loading plot of OPLS-DA of YIH in positive mode. D, shows the combination of S- and VIP-score plots constructed from the supervised OPLS analysis of urine (ESI+ mode).
Fig. 5.
Fig. 5.
Systems analysis of Metabolomic alterations of the YAH, YIH and control samples with MetaboAnalyst's data annotation tools. A, Top 15 significant features of the metabolite markers based the VIP scores of OPLS-DA. B, Heat map visualization constructed based on the differential metabolites of importance for the urine of YAH. Heatmap represents unsupervised hierarchical clustering of groups (rows). Variable differences marked on the bottom corresponding to supplemental Table S3 are revealed between the control and YAH groups. Rows: samples; Columns: differential metabolites; Color key indicates metabolite expression value, red: Lowest, yellow: highest. C, Heat map visualization constructed based on the differential metabolites of importance for the urine of YIH. Variable differences marked on the bottom corresponding to supplemental Table S4 are revealed between the control and YIH groups. Rows: samples; Columns: differential metabolites; Color key indicates metabolite expression value, green: Lowest, red: highest. D, Top 15 significant features of the metabolite markers based the VIP scores.
Fig. 6.
Fig. 6.
Hierarchical clustering and diagnostic potential of JS metabolite composition. A, Unsupervised hierarchical clustering of JS metabolite profiles. B, Profiles of the signatures that distinguish YAH from control samples. C, Dendrogam obtained from unsupervised hierarchical clustering of metabolite profiles for YIH and control samples.
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