Investigation of α-Glucosidase Inhibitory Metabolites from Tetracera scandens Leaves by GC-MS Metabolite Profiling and Docking Studies.

Ahmed Nokhala,Qamar Uddin Ahmed,Muhammad Safwan Ahamad Bustamam,Zainul Amiruddin Zakaria,Mohammad Jamshed Siddiqui

doi:10.3390/biom10020287

Abstract

Stone leaf (Tetracera scandens) is a Southeast Asian medicinal plant that has been traditionally used for the management of diabetes mellitus. The underlying mechanisms of the antidiabetic activity have not been fully explored yet. Hence, this study aimed to evaluate the α-glucosidase inhibitory potential of the hydromethanolic extracts of T. scandens leaves and to characterize the metabolites responsible for such activity through gas chromatography–mass spectrometry (GC–MS) metabolomics. Crude hydromethanolic extracts of different strengths were prepared and in vitro assayed for α-glucosidase inhibition. GC–MS analysis was further carried out and the mass spectral data were correlated to the corresponding α-glucosidase inhibitory IC50 values via an orthogonal partial least squares (OPLS) model. The 100%, 80%, 60% and 40% methanol extracts displayed potent α-glucosidase inhibitory potentials. Moreover, the established model identified 16 metabolites to be responsible for the α-glucosidase inhibitory activity of T. scandens. The putative α-glucosidase inhibitory metabolites showed moderate to high affinities (binding energies of −5.9 to −9.8 kcal/mol) upon docking into the active site of Saccharomyces cerevisiae isomaltase. To sum up, an OPLS model was developed as a rapid method to characterize the α-glucosidase inhibitory metabolites existing in the hydromethanolic extracts of T. scandens leaves based on GC–MS metabolite profiling.

Highlights

Omics technologies, including genomics, transcriptomics and proteomics, aim to give a holistic view about a biological system by studying different subcellular components (i.e., DNA, RNA and protein respectively) [1]
The developed model was considered valid since the values of R2Y and Q2Y were above 0.5 (0.973 and 0.921, respectively) and the values of root mean square error of estimation (RMSEE) and root mean square error of cross validation (RMSEcv) were low
The developed model was considered valid since the values of R2Y and Q2Y were above 0.5 (0.973 and 0.921, respectively) and the values of root mean square error of estimation (RMSEE) and root mean square error of cross validation (RMSEcv) were low (5.118 and 7.850, respectively) [4,28,29]

Summary

Introduction

Omics technologies, including genomics, transcriptomics and proteomics, aim to give a holistic view about a biological system by studying different subcellular components (i.e., DNA, RNA and protein respectively) [1]. Metabolomics has become a routine approach in studies aiming to evaluate the activities of medicinal plants or to characterize the metabolites responsible for such activities [4,5,6,7]. Classical methods for determination of the active plant metabolites rely on the activity-guided fractionation and purification techniques. This process usually takes a long time and may end up with the isolation of few known metabolites with already known activities [8]. The high-throughput nature makes the metabolomics approach more suitable to fulfill the high demands of the modern drug discovery policies, hoping to bring the natural products into the spotlight again as a potential source of new drug leads [2]

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Conclusion