Potential drug targets on insomnia and intervention effects of Jujuboside A through metabolic pathway analysis as revealed by UPLC/ESI-SYNAPT-HDMS coupled with pattern recognition approach

Potential metabolites from the metabolic pathways could be therapeutic targets and useful for the discovery of broad spectrum drugs. UPLC/ESI-SYNAPT-HDMS coupled with pattern recognition methods including PCA, PLS-DA, OPLS-DA and Heatmap were integrated to examine the global metabolic signature of insomnia and intervention effects of Jujuboside A (JuA). Six unique pathways of the insomnia were identified using Ingenuity Pathway Analysis (IPA) software. The VIP-value threshold cutoff of the metabolites was set to 10, above this threshold, were filtered out as potential target biomarkers. Sixteen distinct metabolites were identified from these pathways, and 6 of them can be considered for rational drug design. It was further experimental validation that the changes in metabolic profiling were restored to their baseline values after JuA treatment according to the multivariate data analysis. Potential metabolite network of the insomnia was preliminarily predicted JuA-target interaction networks, and could be further explored for in silico docking studies with suitable drugs. Thus, our method is an efficient procedure for drug target identification through metabolic analysis. It can guide testable predictions, provide insights into drug action mechanisms and enable us to increase research productivity toward metabolomic drug discovery.

Graphical abstractHeatmap visualization of intervention effects of Jujuboside A through metabolic pathway analysis.Figure optionsDownload full-size imageDownload high-quality image (148 K)Download as PowerPoint slideHighlights► Six unique pathways of the insomnia were identified. ► Sixteen distinct metabolites were identified for the insomnia. ► The metabolites identified are involved in various stages of insomnia progress. ► JuA-target interaction networks were further predicted. ► This approach may be used to increase metabolomic research productivity.