Integrated metabolic spatial‐temporal model for the prediction of ammonia detoxification during liver damage and regeneration

Abstract

The impairment of hepatic metabolism due to liver injury has high systemic relevance. However, it is difficult to calculate the impairment of metabolic capacity from a specific pattern of liver damage with conventional techniques. We established an integrated metabolic spatial-temporal model (IM) using hepatic ammonia detoxification as a paradigm. First, a metabolic model (MM) based on mass balancing and mouse liver perfusion data was established to describe ammonia detoxification and its zonation. Next, the MM was combined with a spatial-temporal model simulating liver tissue damage and regeneration after CCl 4 intoxication. The resulting IM simulated and visualized whether, where, and to what extent liver damage compromised ammonia detoxification. It allowed us to enter the extent and spatial patterns of liver damage and then calculate the outflow concentrations of ammonia, glutamine, and urea in the hepatic vein. The model was validated through comparisons with (1) published data for isolated, perfused livers with and without CCl 4 intoxication and (2) a set of in vivo experiments. Using the experimentally determined portal concentrations of ammonia, the model adequately predicted metabolite concentrations over time in the hepatic vein during toxin-induced liver damage and regeneration in rodents. Further simulations, especially in combination with a simplified model of blood circulation with three ammonia-detoxifying compartments, indicated a yet unidentified process of ammonia consumption during liver regeneration and revealed unexpected concomitant changes in amino acid metabolism in the liver and at extrahepatic sites. Conclusion: The IM of hepatic ammonia detoxification considerably improves our understanding of the metabolic impact of liver disease and highlights the importance of integrated modeling approaches on the way toward virtual organisms. (HEPATOLOGY 2014;60:2040-2051

Toxicogenomics directory of chemically exposed human hepatocytes

“…Downregulation of these genes may represent a state where the hepatocytes shift their balance from metabolism to regeneration. Examples are CPS1, the first enzyme of the urea cycle and key factor of ammonia detoxification (Simmer et al 1990;Schliess et al 2014); PCK1 as a main control enzyme of gluconeogenesis (Pilz et al 1992); SLC2A2, also known as the glucose carrier GLUT2 (Froguel et al 1991); CYP8B1, a key enzyme in bile acid metabolism (Gåfvels et al 1999); CYP4A11, the major fatty acid omegahydroxylase, which is involved in controlling the balance of lipids (Antoun et al 2006); ABCA8, one of the liver's ABC transporters (Tsuruoka et al 2002); and ADH4, an aldehyde dehydrogenase that metabolizes numerous substrates, including retinol, hydroxysteroids, and also ethanol (Kimura et al 2009). Such complex but stereotypical patterns of gene deregulation induced by a chemical can also be interpreted as a situation of disturbed hepatocyte physiology and could be the result of different insults to the liver.…”

Model Prediction and Validation of an Order Mechanism Controlling the Spatiotemporal Phenotype of Early Hepatocellular Carcinoma

“…The finally predicted mechanism known as HSA has been validated by subsequent experiments. The model was later extended by integrating tissue regeneration and metabolism (Schliess et al 2014;Drasdo et al 2014a, b;Ghallab et al 2016). In the study presented, the above described spatiotemporal model ) served as a starting point and was further developed and extended to simulate early tumor development.…”

Highlight report: physiologically-based modeling of diseased liver tissue

“…Moreover, techniques of spatio-temporal tissue modeling have been established that allow simulations of how specific biological processes contribute to liver function (Hoehme et al 2017;Friebel et al 2015;Drasdo et al 2014a, b;Schliess et al 2014;Vartak et al 2016). Such techniques allow the in silico analysis of which degree of loss of tissue causes loss of function (or metabolic capacity) under the assumption of a proportional relationship.…”