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J Hepatol. 2016 Apr;64(4):860-71. doi: 10.1016/j.jhep.2015.11.018. Epub 2015 Nov 27.

Model-guided identification of a therapeutic strategy to reduce hyperammonemia in liver diseases.

Author information

1
Leibniz Research Centre for Working Environment and Human Factors at the Technical University Dortmund, Dortmund, Germany; Department of Forensic Medicine and Toxicology, Faculty of Veterinary Medicine, South Valley University, Qena, Egypt. Electronic address: ghallab@ifado.de.
2
Sorbonne Universités, Inria, UPMC Univ Paris 06, Lab. J.L. Lions UMR CNRS 7598, Paris, France.
3
BioControl Jena GmbH, Jena, Germany.
4
Institute of Computer Science and Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany.
5
Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart and University of Tuebingen, Germany.
6
Institute of Biochemistry, Faculty of Medicine, University of Leipzig, Leipzig, Germany.
7
Leibniz Research Centre for Working Environment and Human Factors at the Technical University Dortmund, Dortmund, Germany.
8
Institute of Neurophysiology and Center for Molecular Medicine Cologne (CMMC), University of Cologne, Robert-Koch-Str. 39, 50931 Cologne, Germany.
9
Computational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany.
10
Clinic for Gastroenterology, Hepatology and Infectious Diseases, Heinrich-Heine-University, Düsseldorf, Germany.
11
Sorbonne Universités, Inria, UPMC Univ Paris 06, Lab. J.L. Lions UMR CNRS 7598, Paris, France; Institute of Computer Science and Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany.
12
Leibniz Research Centre for Working Environment and Human Factors at the Technical University Dortmund, Dortmund, Germany. Electronic address: hengstler@ifado.de.

Abstract

BACKGROUND & AIMS:

Recently, spatial-temporal/metabolic mathematical models have been established that allow the simulation of metabolic processes in tissues. We applied these models to decipher ammonia detoxification mechanisms in the liver.

METHODS:

An integrated metabolic-spatial-temporal model was used to generate hypotheses of ammonia metabolism. Predicted mechanisms were validated using time-resolved analyses of nitrogen metabolism, activity analyses, immunostaining and gene expression after induction of liver damage in mice. Moreover, blood from the portal vein, liver vein and mixed venous blood was analyzed in a time dependent manner.

RESULTS:

Modeling revealed an underestimation of ammonia consumption after liver damage when only the currently established mechanisms of ammonia detoxification were simulated. By iterative cycles of modeling and experiments, the reductive amidation of alpha-ketoglutarate (α-KG) via glutamate dehydrogenase (GDH) was identified as the lacking component. GDH is released from damaged hepatocytes into the blood where it consumes ammonia to generate glutamate, thereby providing systemic protection against hyperammonemia. This mechanism was exploited therapeutically in a mouse model of hyperammonemia by injecting GDH together with optimized doses of cofactors. Intravenous injection of GDH (720 U/kg), α-KG (280 mg/kg) and NADPH (180 mg/kg) reduced the elevated blood ammonia concentrations (>200 μM) to levels close to normal within only 15 min.

CONCLUSION:

If successfully translated to patients the GDH-based therapy might provide a less aggressive therapeutic alternative for patients with severe hyperammonemia.

KEYWORDS:

Ammonia; Liver damage; Liver regeneration; Spatio-temporal model; Systems biology

PMID:
26639393
DOI:
10.1016/j.jhep.2015.11.018
[Indexed for MEDLINE]
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