Original Research ARTICLE
Network Modeling of Liver Metabolism to Predict Plasma Metabolite Changes During Short-Term Fasting in the Laboratory Rat
- 1Biotechnology HPC Software Applications Institute (BHSAI), United States
- 2Department of Chemical and Biomolecular Engineering, Vanderbilt University, United States
The liver—a central metabolic organ that integrates whole-body metabolism to maintain glucose and fatty-acid regulation, and detoxify ammonia—is susceptible to injuries induced by drugs and toxic substances. Although plasma metabolite profiles are increasingly investigated for their potential to detect liver injury earlier than current clinical markers, their utility may be compromised because such profiles are affected by the nutritional state and the physiological state of the animal, and by contributions from extrahepatic sources. To tease apart the contributions of liver and non-liver sources to alterations in plasma metabolite profiles, here we sought to computationally isolate the plasma metabolite changes originating in the liver during short-term fasting. We used a constraint-based metabolic modeling approach to integrate central carbon fluxes measured in our study, and physiological flux boundary conditions gathered from the literature, into a genome-scale model of rat liver metabolism. We then measured plasma metabolite profiles in rats fasted for 5–7 or 10–13 h to test our model predictions. Our computational model accounted for two-thirds of the observed directions of change (an increase or decrease) in plasma metabolites, indicating their origin in the liver. Specifically, our work suggests that changes in plasma lipid metabolites, which are reliably predicted by our liver metabolism model, are key features of short-term fasting. Our approach provides a mechanistic model for identifying plasma metabolite changes originating in the liver.
Keywords: metabolic network, rat, Liver, Plasma, Metabolomics, Fasting, Central carbon fluxes, Gluconeogenesis
Received: 06 Aug 2018;
Accepted: 11 Feb 2019.
Edited by:Kai Breuhahn, Universität Heidelberg, Germany
Reviewed by:Johann Rohwer, Stellenbosch University, South Africa
Edoardo Saccenti, Wageningen University & Research, Netherlands
Copyright: © 2019 Vinnakota, Pannala, Wall, Rahim, Estes, Trenary, O'Brien, Printz, Reifman, Shiota, Young and Wallqvist. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Dr. Kalyan C. Vinnakota, Biotechnology HPC Software Applications Institute (BHSAI), Frederick, MD 21702, Maryland, United States, email@example.com
Dr. Jamey D. Young, Vanderbilt University, Department of Chemical and Biomolecular Engineering, Nashville, 37240, Tennessee, United States, firstname.lastname@example.org
Dr. Anders Wallqvist, Biotechnology HPC Software Applications Institute (BHSAI), Frederick, MD 21702, Maryland, United States, email@example.com