Rapid discovery of novel compounds of a traditional herbal medicine is of vital significance for pharmaceutical industry and plant metabolic pathway analysis. However, discovery of unknown or trace natural products is an ongoing challenge. This study presents a universal targeted data-independent acquisition and mining strategy to globally profile and effectively match novel natural product analogues from an herbal extract. The famous medical plant Gastrodia elata was selected as an example. This strategy consists of three steps: (i) acquisition of accurate parent and adduct ions (PAIs) and the product ions data of all eluting compounds by untargeted full-scan MS(E) mode; (ii) rapid compound screening using diagnostic product ions (DPIs) network and in silico analogue database with SUMPRODUCT function to find novel candidates; and (iii) identification and isomerism discrimination of multiple types of compounds using ClogP and ions fragment behavior analyses. Using above data mining methods, a total of 152 compounds were characterized, and 70 were discovered for the first time, including series of phospholipids and novel gastroxyl derivatives. Furthermore, a number of gastronucleosides and phase II metabolites of gastrodin and parishins were discovered, including glutathionylated, cysteinylglycinated and cysteinated compounds, and phosphatidylserine analogues. This study extended the application of classical DPIs filter strategy and developed a structure-based screening approach with the potential for significant increase of efficiency for discovery and identification of trace novel natural products.
Keywords: Diagnostic product ions network; Gastrodia elata; In silico analogue database; Parishins; Phospholipids; SUMPRODUCT function.
Copyright © 2016 Elsevier B.V. All rights reserved.