Abstract

Glucose is an essential energy source for cells. In humans, its passive diffusion through the cell membrane is facilitated by members of the glucose transporter family (GLUT, SLC2 gene family). GLUT2 transports both glucose and fructose with low affinity and plays a critical role in glucose sensing mechanisms. Alterations in the function or expression of GLUT2 are involved in the Fanconi–Bickel syndrome, diabetes, and cancer. Distinguishing GLUT2 transport in tissues where other GLUTs coexist is challenging due to the low affinity of GLUT2 for glucose and fructose and the scarcity of GLUT-specific modulators. By combining in silico ligand screening of an inward-facing conformation model of GLUT2 and glucose uptake assays in a hexose transporter-deficient yeast strain, in which the GLUT1-5 can be expressed individually, we identified eleven new GLUT2 inhibitors (IC50 ranging from 0.61 to 19.3 µM). Among them, nine were GLUT2-selective, one inhibited GLUT1-4 (pan-Class I GLUT inhibitor), and another inhibited GLUT5 only. All these inhibitors dock to the substrate cavity periphery, close to the large cytosolic loop connecting the two transporter halves, outside the substrate-binding site. The GLUT2 inhibitors described here have various applications; GLUT2-specific inhibitors can serve as tools to examine the pathophysiological role of GLUT2 relative to other GLUTs, the pan-Class I GLUT inhibitor can block glucose entry in cancer cells, and the GLUT2/GLUT5 inhibitor can reduce the intestinal absorption of fructose to combat the harmful effects of a high-fructose diet.

Details

Title
Identification of new GLUT2-selective inhibitors through in silico ligand screening and validation in eukaryotic expression systems
Author
Schmidl Sina 1 ; Ursu Oleg 2 ; Iancu, Cristina V 3 ; Oreb Mislav 1 ; Oprea, Tudor I 4 ; Jun-yong, Choe 5 

 Goethe University Frankfurt, Institute of Molecular Biosciences, Faculty of Biological Sciences, Frankfurt am Main, Germany (GRID:grid.7839.5) (ISNI:0000 0004 1936 9721) 
 The University of New Mexico School of Medicine, Translational Informatics Division, Department of Internal Medicine, Albuquerque, USA (GRID:grid.266832.b) (ISNI:0000 0001 2188 8502); Merck & Co., Inc., Computational and Structural Chemistry, Kenilworth, USA (GRID:grid.417993.1) (ISNI:0000 0001 2260 0793) 
 East Carolina Diabetes and Obesity Institute, East Carolina University, Department of Chemistry, Greenville, USA (GRID:grid.255364.3) (ISNI:0000 0001 2191 0423) 
 The University of New Mexico School of Medicine, Translational Informatics Division, Department of Internal Medicine, Albuquerque, USA (GRID:grid.266832.b) (ISNI:0000 0001 2188 8502); UNM Comprehensive Cancer Center, The University of New Mexico, Albuquerque, USA (GRID:grid.266832.b) (ISNI:0000 0001 2188 8502) 
 East Carolina Diabetes and Obesity Institute, East Carolina University, Department of Chemistry, Greenville, USA (GRID:grid.255364.3) (ISNI:0000 0001 2191 0423); Rosalind Franklin University of Medicine and Science, Department of Biochemistry and Molecular Biology, The Chicago Medical School, North Chicago, USA (GRID:grid.262641.5) (ISNI:0000 0004 0388 7807) 
Publication year
2021
Publication date
2021
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2547771957
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.