Citation
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.
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Official URL or Download Paper: https://www.mdpi.com/2218-273X/10/2/287
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Additional Metadata
Item Type: | Article |
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Divisions: | Faculty of Medicine and Health Science Halal Products Research Institute Institute of Bioscience |
DOI Number: | https://doi.org/10.3390/biom10020287 |
Publisher: | MDPI |
Keywords: | Tetracera scandens; Metabolite profiling; α-glucosidase inhibition; Orthogonal partial least squares; GC–MS metabolomics; Molecular docking |
Depositing User: | Nabilah Mustapa |
Date Deposited: | 03 May 2020 22:59 |
Last Modified: | 03 May 2020 22:59 |
Altmetrics: | http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.3390/biom10020287 |
URI: | http://psasir.upm.edu.my/id/eprint/38156 |
Statistic Details: | View Download Statistic |
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