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High-throughput Screening of Carbohydrate-degrading Enzymes Using Novel Insoluble Chromogenic Substrate Assay Kits
Published on: September 20, 2016
Measuring key human carbohydrate digestive enzyme activities using high-performance anion-exchange chromatography
Elizabeth Barber1, Michael J Houghton1, Rizliya Visvanathan1
1Department of Nutrition, Dietetics and Food, School of Clinical Sciences at Monash Health, Faculty of Medicine, Nursing and Health Sciences, Monash University, BASE Facility, Notting Hill, Victoria, Australia.
Abstract:
Carbohydrate digestion in the mammalian gastrointestinal tract is catalyzed by α-amylases and α-glucosidases to produce monosaccharides for absorption. Inhibition of these enzymes is the major activity of the drugs acarbose and miglitol, which are used to manage diabetes. Furthermore, delaying carbohydrate digestion via inhibition of α-amylases and α-glucosidases is an effective strategy to blunt blood glucose spikes, a major risk factor for developing metabolic diseases. Here, we present an in vitro protocol developed to accurately and specifically assess the activity of α-amylases and α-glucosidases, including sucrase, maltase and isomaltase. The assay is especially suitable for measuring inhibition by compounds, drugs and extracts, with minimal interference from impurities or endogenous components, because the substrates and digestive products in the enzyme activity assays are quantified directly by high-performance anion-exchange chromatography with pulsed amperometric detection (HPAE-PAD). Multiple enzyme sources can be used, but here we present the protocol using commercially available human α-amylase to assess starch hydrolysis with maltoheptaose as the substrate, and with brush border sucrase-isomaltase (with maltase, sucrase and isomaltase activities) derived from differentiated human intestinal Caco-2(/TC7) cells to assess hydrolysis of disaccharides. The wet-lab assay takes ~2-5 h depending on the number of samples, and the HPAE-PAD analysis takes 35 min per sample. A full dataset therefore takes 1-3 d and allows detection of subtle changes in enzyme activity with high sensitivity and reliability.
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