Pattern recognition approaches and computational systems tools for ultra performance liquid chromatography-mass spectrometry-based comprehensive metabolomic profiling and pathways analysis of biological data sets.

Metabolomics represents an emerging and powerful discipline that provides an accurate and dynamic picture of the phenotype of biosystems through the study of potential metabolites that could be used for therapeutic targets and discovery of new drugs. Metabolomic network construction has led to the integration of metabolites associated with the caused perturbation of multiple pathways. Herein, we present a method for the construction of efficient networks with regard to that Jujuboside B (JuB) protects against insomnia as a case study. UPLC/ESI-SYNAPT-HDMS coupled with pattern recognition methods including PCA, PLS-DA, OPLS-DA, and computational systems analysis were integrated to obtain comprehensive metabolomic profiling and pathways of the large biological data sets. Among the regulated pathways, twelve biomarkers were identified and tryptophan metabolism, phenylalanine, tyrosine, tryptophan biosynthesis, arachidonic acid metabolism, and phenylalanine metabolism related network were acutely perturbed. Results not only supplied a systematic view of the development and progression of insomnia but also were used to analyze the therapeutic effects of JuB, a widely used anti-insomina medicine in clinics. The results showed that JuB administration could provide satisfactory effects on insomnia through partially regulating the perturbed pathway. We have constructed a metabolomic feature network of JuB to protect against insomnia. The most promising use in the near future would be to clarify pathways for the drugs and get biomarkers for these pathways, to help guide testable predictions, provide insights into drug action mechanisms, and enable us to increase research productivity toward metabolomic drug discovery.