Metabolomics-based profiling of 4 avocado varieties using HPLC–MS/MS and GC/MS and evaluation of their antidiabetic activity
Inas Y. Younis, Amira R. Khattab, Nabil M. Selim, Mansour Sobeh, Seham S. El‐Hawary, Mahitab H. El Bishbishy
Abstract
Seven avocado "Persea americana" seeds belonging to 4 varieties, collected from different localities across the world, were profiled using HPLC-MS/MS and GC/MS to explore the metabolic makeup variabilities and antidiabetic potential. For the first time, 51 metabolites were tentatively-identified via HPLC-MS/MS, belonging to different classes including flavonoids, biflavonoids, naphthodianthrones, dihydrochalcones, phloroglucinols and phenolic acids while 68 un-saponified and 26 saponified compounds were identified by GC/MS analysis. The primary metabolic variabilities existing among the different varieties were revealed via GC/MS-based metabolomics assisted by unsupervised pattern recognition methods. Fatty acid accumulations were proved as competent, and varietal-discriminatory metabolites. The antidiabetic potential of the different samples was explored using in-vitro amylase and glucosidase inhibition assays, which pointed out to Gwen (KG) as the most potent antidiabetic sample. This could be attributed to its enriched content of poly-unsaturated fatty acids and polyphenolics. Molecular docking was then performed to predict the most promising phytoligands in KG variety to be posed as antidiabetic drug leads. The highest in-silico α-amylase inhibition was observed with chrysoeriol-4'-O-pentoside-7-O-rutinoside, apigenin-7-glucuronide and neoeriocitrin which might serve as potential drug leads for the discovery of new antidiabetic remedies.