Intestinal mucosal metabolites-guided detection of trace-level ginkgo biloba extract metabolome

The characterization of metabolome for poorly absorptive natural medicines is challenging. Previous identification strategy often relies on nontargeted scanning biological samples from animals administered with natural medicines in a data-dependent acquisition (DDA) mode by LC-MS/MS. Substances that displayed significant increases following drug administration are thus assigned as potential metabolites. The accurate m/z of precursors and the corresponding MS/MS fragment ions are used to match with herbal ingredients and to infer possible metabolic reactions. Nevertheless, the low concentration of these metabolites within complex biological matrices has often hampered the detection. Herein we developed a strategy termed intestinal mucosal metabolome-guided detection (IMMD) to tackle this challenge using ginkgo biloba (GBE) as an example. The rationale is that poorly absorptive natural products are usually concentrated and extensively metabolized by enterocytes before they enter the blood stream and distribute to other organs. Therefore, we firstly identified the metabolites from intestinal mucosa of GBE-treated rats, and then used the identified intestinal mucosal GBE metabolome as targeted repository for MRM analysis. The presences of these metabolites were subsequently examined in rat plasma, liver and brain. The resultant GBE metabolome showed significantly improved coverage with 39, 45 and 6 metabolites identified in plasma, liver and brain compared to 22, 16 and 0 metabolites from the corresponding regions via the DDA-based strategy. In addition, we integrated the previously reported nontargeted diagnostic ion network analysis to facilitate the characterization of GBE components, and a chemicalome-metabolome matching approach (CMMA) to assist the identity assignment of GBE metabolome with IMMD. Combinatorially, we establish a multi-faceted platform to streamline the workflow of metabolome characterization for herbal medicines of low bioavailability. The metabolome information is expected to shed light on the elucidation of metabolic pathways for natural products, and the underlying mechanisms of their biological efficacies.