Computational protocol: Mass Spectrometry-Based Metabolomic and Lipidomic Analyses of the Effects of Dietary Platycodon grandiflorum on Liver and Serum of Obese Mice under a High-Fat Diet

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Protocol publication

[…] GC-TOF-MS and UPLC-Q-TOF-MS data files were converted to CDF format using ChromaTOF v4.44 (Leco Co., St. Joseph, MI, USA) and MassLynx softwares (Waters Corp., Milford, MS, USA), respectively. After conversion, MS data were processed using the metAlign software package (http://www.metalign.nl) to obtain a data matrix containing retention times, accurate masses, and normalized peak intensities, using sample names and peak area information as variables. The resulting data matrix was processed using SIMCA-P+ (version 12.0, Umetrics, Umea, Sweden) for multivariate statistical analysis. For data processing of lipid profiles, nominal ion mass spectra data files from the ion trap mass spectrometer (“.raw” files) were directly loaded into the Genedata Expressionist MSX module (Genedata AG, Basel, Switzerland) []. Data on all detected peaks, including m/z and intensity values, were exported as Excel files. To normalize spectral data, the intensities of each sample were summed, and each value (-fold) was divided by the sum of the intensities. The resulting Excel data were exported to SIMCA P+ software (version 13.0, Umetrics, Umea, Sweden).Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) modeling were performed to obtain information on differences in metabolite profiles between experimental groups. The discriminated variables were selected based on variable importance in the projection (VIP) value (>1.0 or >0.7) and p-value (<0.05, one-way ANOVA followed by Duncan’s multiple range test) using SIMCA-P+ software and PASW Statistics 18 (SPSS Inc., Chicago, IL, USA). Following multivariate statistical analysis, the peaks corresponding to selected variables were confirmed in the original chromatograms and were positively or tentatively identified using either commercial standard compounds in comparison with the mass spectra and retention time or on the basis of the NIST mass spectral database (National Institute of Standards and Technology, FairCom, Gaithersburg, MD, USA), in-house library, and references for GC-TOF-MS. For UPLC-Q-TOF-MS, the assignment of metabolites contributing to the observed variance was performed by elemental composition analysis software with the calculated mass, mass tolerance (mDa and ppm), double bond equivalents (DBEs), and iFit algorithm implemented in MassLynx and by the Human Metabolome Database (HMDB, http://www.hmdb.com) and Lipid Maps Database (http://www.lipidmaps.org). In addition, serum and liver lipids were identified by comparison with the MS/MS fragmentation patterns of commercially available standards or by using the LIPID MAPS Lipidomics Gateway (http://www.lipidmaps.org), the Human Metabolome Database (HMDB; http://www.hmdb.com), and/or our in-house lipid library (LipidBlast) []. Significantly different metabolites were represented as a fold change that was calculated by dividing the mean of the peak intensity of each metabolite from each of the two groups. The statistical significance in each metabolite between each of the two groups was determined by Student’s t-test (p-value < 0.05). In addition, the statistical analysis for mice characteristics and biochemical parameters variations between each group was evaluated using a one-way analysis of variance (ANOVA) followed by Duncan’s multiple range test. […]

Pipeline specifications

Software tools ChromaTOF, MetAlign, Genedata Expressionist
Application MS-based untargeted metabolomics
Organisms Mus musculus
Chemicals Amino Acids, Cholesterol, Glycine, Lysophosphatidylcholines, Maltose, Methionine, Phosphatidylcholines, Phosphatidylethanolamines, Purines, Tyrosine, Uracil, Glutamic Acid, Hypoxanthine, Succinic Acid