Metabolomics Coupled with Multivariate Data and Pathway Analysis on Potential Biomarkers in Gastric Ulcer and Intervention Effects of Corydalis yanhusuo Alkaloid

Abstract

Metabolomics, the systematic analysis of potential metabolites in a biological specimen, has been increasingly applied to discovering biomarkers, identifying perturbed pathways, measuring therapeutic targets, and discovering new drugs. By analyzing and verifying the significant difference in metabolic profiles and changes of metabolite biomarkers, metabolomics enables us to better understand substance metabolic pathways which can clarify the mechanism of Traditional Chinese Medicines (TCM). Corydalis yanhusuo alkaloid (CA) is a major component of Qizhiweitong (QZWT) prescription which has been used for treating gastric ulcer for centuries and its mechanism remains unclear completely. Metabolite profiling was performed by high-performance liquid chromatography combined with time-of-flight mass spectrometry (HPLC/ESI-TOF-MS) and in conjunction with multivariate data analysis and pathway analysis. The statistic software Mass Profiller Prossional (MPP) and statistic method including ANOVA and principal component analysis (PCA) were used for discovering novel potential biomarkers to clarify mechanism of CA in treating acid injected rats with gastric ulcer. The changes in metabolic profiling were restored to their base-line values after CA treatment according to the PCA score plots. Ten different potential biomarkers and seven key metabolic pathways contributing to the treatment of gastric ulcer were discovered and identified. Among the pathways, sphingophospholipid metabolism and fatty acid metabolism related network were acutely perturbed. Quantitative real time polymerase chain reaction (RT-PCR) analysis were performed to evaluate the expression of genes related to the two pathways for verifying the above results. The results show that changed biomarkers and pathways may provide evidence to insight into drug action mechanisms and enable us to increase research productivity toward metabolomics drug discovery.

Comprehensive metabonomic analysis of heart tissue from isoproterenol-induced myocardial infarction rat based on reversed-phase and hydrophilic interaction chromatography coupled to mass spectrometry

“…LC–MS data were analyzed by the Agilent Mass Hunter (version‐2.0) and Mass profiler professional (MPP) software. The MFE (molecular feature extraction) algorithm incorporated in the Agilent Mass Hunter software looks for mass signals (ions) that are covariant in time, considers possible relationships (isotopes, adducts, dimers, multiple charge states), and generates an extracted compound chromatogram and compound mass spectrum for each molecular feature . The LC–MS data were firstly used to create mass features that correspond to molecules detected across the different LC–MS runs.…”

“…Then the resulting feature files for each sample were processed by t test and PCA analysis utilizing the MPP software. The metabolites that met a criterion of p < 0.05 and a fold change > 1.5 were selected to generate formulas and search through the database incorporated in the software for further study .…”

Metabolomics and partial least square discriminant analysis to predict history of myocardial infarction of self-claimed healthy subjects: validity and feasibility for clinical practice

“…Therefore, metabolites offer a direct molecular signature of status of cells that reflect changes in the phenotype and molecular physiology and are useful for evaluation of the efficacy of medical treatments [ 6 , 7 ]. Recent innovations in instrumentation, bioinformatics tools and softwares have enable comprehensive analysis of cellular metabolites and therefore enhance the potential of metabolomics to be adopted for clinical diagnostics and industries [ 8 , 9 ]. Advanced techniques, such as NMR, GCMS/QTOF and UPLCMS/QTOF have allowed researchers to determine biomarkers for detection and monitoring of the progress of diseases using urine and serum samples [ 10 - 12 ].…”

Drug target identification using network analysis: Taking active components in Sini decoction as an example

“…These two methods have been proved successful in target identification of both many compounds and one drug 6 7 8 9 . Whereas metabolomics has been mainly developed to identify drug(s)-affected pathways 10 11 , the “readout”, such as proteins in the pathway, is often far downstream from the drug targets. Therefore using metabolomics for target identification run into the bottleneck.…”