School of Pharmacy, China Pharmaceutical University, Tongjiaxiang #24, Nanjing, China.
Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Tongjiaxiang #24, Nanjing, China.
J Chromatogr A. 2019 Dec 20;1608:460417. doi: 10.1016/j.chroma.2019.460417. Epub 2019 Aug 5.
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.
对吸收不良的天然药物代谢组进行特征描述具有挑战性。以前的鉴定策略通常依赖于非靶向扫描生物样品，这些生物样品来自于用 LC-MS/MS 以数据依赖方式给予天然药物的动物。因此，给药后显示出显著增加的物质被分配为潜在的代谢物。前体的准确 m/z 和相应的 MS/MS 碎片离子用于与草药成分匹配，并推断可能的代谢反应。然而，这些代谢物在复杂的生物基质中的低浓度常常阻碍了检测。在此，我们以银杏叶提取物 (GBE) 为例，开发了一种称为肠黏膜代谢组指导检测 (IMMD) 的策略来解决这一挑战。其原理是，吸收不良的天然产物通常在进入血液并分布到其他器官之前，被肠细胞浓缩和广泛代谢。因此，我们首先鉴定了 GBE 处理大鼠肠黏膜中的代谢物，然后将鉴定出的肠黏膜 GBE 代谢组用作 MRM 分析的靶向存储库。随后在大鼠血浆、肝脏和大脑中检查这些代谢物的存在。与基于 DDA 的策略相比，所得的 GBE 代谢组在血浆、肝脏和大脑中分别鉴定出 39、45 和 6 种代谢物，明显提高了覆盖率，而在相应区域中分别鉴定出 22、16 和 0 种代谢物。此外，我们整合了以前报道的非靶向诊断离子网络分析，以促进 GBE 成分的特征描述，并采用化学组-代谢组匹配方法 (CMMA) 协助 IMMD 鉴定 GBE 代谢组的身份。综合来看，我们建立了一个多方面的平台，以简化低生物利用度草药代谢组特征描述的工作流程。代谢组信息有望阐明天然产物的代谢途径，并揭示其生物功效的潜在机制。
