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Modelling of energy-driven switch for glucagon and insulin secretion.

Authors :
Grubelnik V
Zmazek J
Markovič R
Gosak M
Marhl M
Source :
Journal of theoretical biology [J Theor Biol] 2020 May 21; Vol. 493, pp. 110213. Date of Electronic Publication: 2020 Feb 25.
Publication Year :
2020

Abstract

We present a mathematical model of the energy-driven metabolic switch for glucagon and insulin secretion from pancreatic alpha and beta cells, respectively. The energy status related to hormone secretion is studied for various glucose concentrations. Additionally, the physiological response is studied with regards to the presence of other metabolites, particularly the free-fatty acids. At low glucose, the ATP production in alpha cells is high due to free-fatty acids oxidation in mitochondria, which enables glucagon secretion. When the glucose concentration is elevated above the threshold value, the glucagon secretion is switched off due to the contribution of glycolytic ATP production, representing an "anaerobic switch". On the other hand, during hypoglycemia, the ATP production in beta cells is low, reflecting a "waiting state" for glucose as the main metabolite. When glucose is elevated above the threshold value, the oxidative fate of glucose in mitochondria is the main source of energy required for effective insulin secretion, i.e. the "aerobic switch". Our results show the importance of well-regulated and fine-tuned energetic processes in pancreatic alpha and beta cells required for efficient hormone secretion and hence effective blood glucose regulation. These energetic processes have to be appropriately switched on and off based on the sensing of different metabolites by alpha and beta cells. Our computational results indicate that disturbances in cell energetics (e.g. mitochondrial dysfunction), and dysfunctional metabolite sensing and distribution throughout the cell might be related to pathologies such as metabolic syndrome and diabetes.<br /> (Copyright © 2020. Published by Elsevier Ltd.)

Details

Language :
English
ISSN :
1095-8541
Volume :
493
Database :
MEDLINE
Journal :
Journal of theoretical biology
Publication Type :
Academic Journal
Accession number :
32109481
Full Text :
https://doi.org/10.1016/j.jtbi.2020.110213