Liquid chromatography-mass spectrometry-based parallel metabolic profiling of human and mouse model serum reveals putative biomarkers associated with the progression of nonalcoholic fatty liver disease

Abstract

Nonalcoholic fatty liver disease (NAFLD) is the most common form of chronic liver disease in most western countries. Current NAFLD diagnosis methods (e.g., liver biopsy analysis or imaging techniques) are poorly suited as tests for such a prevalent condition, from both a clinical and financial point of view. The present work aims to demonstrate the potential utility of serum metabolic profiling in defining phenotypic biomarkers that could be useful in NAFLD management. A parallel animal model/human NAFLD exploratory metabolomics approach was employed, using ultra performance liquid chromatography-mass spectrometry (UPLC-MS) to analyze 42 serum samples collected from nondiabetic, morbidly obese, biopsy-proven NAFLD patients, and 17 animals belonging to the glycine N-methyltransferase knockout (GNMT-KO) NAFLD mouse model. Multivariate statistical analysis of the data revealed a series of common biomarkers that were significantly altered in the NAFLD (GNMT-KO) subjects in comparison to their normal liver counterparts (WT). Many of the compounds observed could be associated with biochemical perturbations associated with liver dysfunction (e.g., reduced Creatine) and inflammation (e.g., eicosanoid signaling). This differential metabolic phenotyping approach may have a future role as a supplement for clinical decision making in NAFLD and in the adaption to more individualized treatment protocols.

Figure 1

PCA scores plots discriminating GNMT-KO mice from their WT littermates [Upper plot was obtained from negative ion UPLC™-MS data (t[1]: R²X = 0.28, Q² = 0.20; t[2]: R²X = 0.09, Q² = 0.03), lower plot from positive ion data (t[1]: R²X = 0.23, Q² = 0.12; t[2]: R²X = 0.10, Q² = 0.007) ]: 4 month old WT (n = 6), open squares; 6.5 month old WT (n = 4), open triangles; 4 month old GNMT-KO (n = 4), squares; 6.5 month old GNMT-KO (n = 3), triangles. Duplicate sample injection data are shown in the plots.

Figure 2

Mean percent changes of (a) free fatty acids, (b) sn-1 monoacylglycerophosphocholine, (c) phosphosphingolipids, (d) bile acids in human NAFLD (S0 vs. S1, S2, S3, S3+NASH - right) and GNMT mice (GNMT-WT vs. GNMT-KO - left) sera. Positive and negative percentages indicate higher levels of metabolites in NAFLD (GNMT-KO) and healthy (GNMT-WT) sera, respectively. Unpaired Student’s t-test p-values are indicated where appropriate: *p < 0.15, **p < 0.1, ***p < 0.05. ^†Metabolite identifications performed by comparison of mass spectra and chromatographic retention times with those obtained using commercially available standards. All other identifications were performed by accurate mass database searching with fragment ion analysis. Lipid nomenclature follows the LIPID MAPS convention (www.lipidmaps.org). Raw data mean values and standard deviations within the different subgroups are detailed in supplementary tables 1 and 2.

Figure 3

Mean percent changes of diacylglycerophosphocholine in human NAFLD (S0 vs. S1, S2, S3, S3+NASH -right) and GNMT mice (GNMT-WT vs. GNMT-KO - left) sera. Positive and negative percentages indicate higher levels of metabolites in NAFLD (GNMT-KO) and healthy (GNMT-WT) sera, respectively. Unpaired Student’s t-test p-values are indicated where appropriate: *p < 0.15, **p < 0.1, ***p < 0.05. ^†Metabolite identifications performed by comparison of mass spectra and chromatographic retention times with those obtained using commercially available standards. All other identifications were performed by accurate mass database searching with fragment ion analysis. Lipid nomenclature follows the LIPID MAPS convention (www.lipidmaps.org). Raw data mean values and standard deviations within the different subgroups are detailed in supplementary tables 1 and 2.