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Front Physiol. 2019 Mar 1;10:161. doi: 10.3389/fphys.2019.00161. eCollection 2019.

Network Modeling of Liver Metabolism to Predict Plasma Metabolite Changes During Short-Term Fasting in the Laboratory Rat.

Author information

1
Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, United States.
2
Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, United States Army Medical Research and Materiel Command, Fort Detrick, MD, United States.
3
Department of Chemical and Biomolecular Engineering, Vanderbilt University School of Engineering, Nashville, TN, United States.
4
Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN, United States.

Abstract

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:

central carbon flux; fasting; gluconeogenesis; liver; metabolic network; metabolomics; plasma; rat

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