Therapy of hyperammonemia
Agata Widera11Leibniz Research Centre for Working Environment and Human Factors at TU Dortmund (IfADo), Ardeystrasse 67, 44139 Dortmund, Germany
EXCLI J 2015;14:Doc1270
Recently, Ghallab and colleagues have identified a novel strategy to reduce hyperammonemia in mice (Ghallab et al., 2015). The authors reduced blood ammonia concentrations by infusing a cocktail of glutamate dehydrogenase and its cofactors alpha-ketoglutarate and NADPH. This approach may be clinically relevant, because therapy of hyperammonemia is challenging (Levesque et al., 1999; Enns et al., 2007; Poh and Chang, 2012). Currently hemodialysis is the treatment of choice for reducing strongly elevated blood ammonia concentrations (Ghallab et al., 2015; Clay and Hainline, 2007; Rajpoot and Gargus, 2004). Therefore, infusion of glutamate dehydrogenase may represent a less invasive alternative.
At first glance, therapy of hyperammonemia with glutamate dehydrogenase seems counterintuitive. It is known that glutamate dehydrogenase generates ammonia in the periportal comportment of the liver lobule, which is then further metabolized by urea cycle enzymes (Ghallab et al., 2015). Therefore, one may expect that glutamate dehydrogenase leads to an increase of ammonia instead of reducing its concentration. The hypothesis that glutamate dehydrogenase may detoxify ammonia came from a systems biology approach (Drasdo et al., 2014). Recently, techniques of spatio-temporal modeling have been established (Drasdo, 2014; Hoehme et al., 2010). These techniques are based on reconstructions of tissue, where the position of each cell is known in a three dimensional space (Hammad et al., 2014; Friebel et al., 2015; Vartak et al., 2015; Bartl et al., 2015). Next, metabolic models can be integrated into the spatio-temporal model (Schliess et al., 2014; Godoy et al., 2013). Such models can be used to simulate, for example, the concentration of ammonia and associated metabolites in the liver vein (representing the liver 'outflow') for a given concentration in the portal vein (representing the 'inflow' of blood). Moreover, it can be simulated to which degree induction of liver damage compromises ammonia detoxification by the liver (Schliess et al., 2014). Using such integrated spatio/temporal-metabolic models, Ghallab and colleagues have shown that the currently known metabolic pathways of ammonia metabolism by urea cycle and glutamine synthetase are not sufficient to explain the experimentally obtained data. Finally, modeling led to the prediction of an adaptive mechanism that occurs under conditions of toxic liver damage: glutamate dehydrogenase that normally supplies the urea cycle with ammonia switches its catalytic orientation to consume ammonia (Ghallab et al., 2015).
Currently, hepatotoxicity represents an intensively studied topic (Campos et al., 2014; Vitins et al., 2014; Liu et al., 2014; Messner et al., 2013; Shimada et al., 2012; Sumi et al., 2011; Abdel-Bakhy et al., 2011) and in vitro systems are frequently used in these studies (Grinberg et al., 2014; Valente et al., 2015; Ghallab et al., 2014; Reif, 2014; Ilkavets, 2013). The study of Ghallab et al. shows that some adaptive mechanisms in response to toxicity may depend on complex features of tissue architecture and may be difficult to detect in vivo. For example, metabolic enzymes may adapt their flow rates or even switch their orientation. To nevertheless understand such complex situations, the novel techniques of mathematical modeling as introduced in the study of Ghallab et al. (2015) represent a valuable tool.
1. Abdel-Bakky MS, Hammad MA, Walker LA, Ashfaq MK. Silencing of tissue factor by antisense deoxyoligonucleotide prevents monocrotaline/LPS renal injury in mice. Arch Toxicol. 2011;85:1245-56.
2. Bartl M, Pfaff M, Ghallab A, Driesch D, Henkel SG, Hengstler JG. Optimality in the zonation of ammonia detoxification in rodent liver. Arch Toxicol. 2015;89:2069-78.
3. Campos G, Schmidt-Heck W, Ghallab A, Rochlitz K, Pütter L, Medinas DB, et al. The transcription factor CHOP, a central component of the transcriptional regulatory network induced upon CCl4 intoxication in mouse liver, is not a critical mediator of hepatotoxicity. Arch Toxicol. 2014;88:1267-80.
4. Clay AS, Hainline BE. Hyperammonemia in the ICU. Chest. 2007;132:1368–78.
5. Drasdo D, Bode J, Dahmen U, Dirsch O, Dooley S, Gebhardt R, et al. The virtual liver: state of the art and future perspectives. Arch Toxicol. 2014;88:2071-5.
6. Drasdo D, Hoehme S, Hengstler JG. How predictive quantitative modelling of tissue organisation can inform liver disease pathogenesis. J Hepatol. 2014;61:951-6.
7. Enns GM, Berry SA, Berry GT, Rhead WJ, Brusilow SW, Hamosh A. Survival after treatment with phenylacetate and benzoate for urea-cycle disorders. N Engl J Med. 2007;356:2282–92.
8. Friebel A, Neitsch J, Johann T, Hammad S, Hengstler JG, Drasdo D, et al. TiQuant: software for tissue analysis, quantification and surface reconstruction. Bioinformatics. 2015;31:3234-6.
9. Ghallab A. Human non-parenchymal liver cells for co-cultivation systems. EXCLI J. 2014;13:1295-6.
10. Ghallab A. The rediscovery of HepG2 cells for prediction of drug induced liver injury (DILI). EXCLI J. 2014;13:1286-8.
11. Ghallab A, Cellière G, Henkel SG, Driesch D, Hoehme S, Hofmann U, et al. Model guided identification and therapeutic implications of an ammonia sink mechanism. J Hepatol. 2015 Nov 27. pii:S0168-8278(15)00776-X [epub ahead of print].
12. Godoy P, Hewitt NJ, Albrecht U, Andersen ME, Ansari N, Bhattacharya S, et al. Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME. Arch Toxicol. 2013;87:1315-530.
13. Grinberg M, Stöber RM, Edlund K, Rempel E, Godoy P, Reif R, et al. Toxicogenomics directory of chemically exposed human hepatocytes. Arch Toxicol. 2014;88:2261-87.
14. Hammad S, Hoehme S, Friebel A, von Recklinghausen I, Othman A, Begher-Tibbe B, et al. Protocols for staining of bile canalicular and sinusoidal networks of human, mouse and pig livers, three-dimensional reconstruction and quantification of tissue microarchitecture by image processing and analysis. Arch Toxicol. 2014;88:1161-83.
15. Hoehme S, Brulport M, Bauer A, Bedawy E, Schormann W, Hermes M, et al. Prediction and validation of cell alignment along microvessels as order principle to restore tissue architecture in liver regeneration. Proc Natl Acad Sci U S A. 2010;107:10371-6.
16. Ilkavets I. A special issue about hepatotoxicity and hepatocyte in vitro systems. Arch Toxicol. 2013;87:1313-4.
17. Levesque R, Leblanc M, Cardinal J, Teitlebaum J, Skrobik Y, Lebrun M. Haemodialysis for severe hyperammonaemic coma complicating urinary diversions. Nephrol Dialysis Transplant. 1999;14:458–61.
18. Liu A, Krausz KW, Fang ZZ, Brocker C, Qu A, Gonzalez FJ. Gemfibrozil disrupts lysophosphatidylcholine and bile acid homeostasis via PPARα and its relevance to hepatotoxicity. Arch Toxicol. 2014;88:983-96.
19. Messner S, Agarkova I, Moritz W, Kelm JM. Multi-cell type human liver microtissues for hepatotoxicity testing. Arch Toxicol. 2013;87:209-13.
20. Poh Z, Chang PE. A current review of the diagnostic and treatment strategies of hepatic encephalopathy. Int J Hepatol. 2012;2012:480309.
21. Rajpoot DK, Gargus JJ. Acute hemodialysis for hyperammonemia in small neonates. Pediatr Nephrol. 2004;19:390–5.
22. Reif R. Concepts of predictive toxicology. EXCLI J. 2014;13:1292-4.
23. Schliess F, Hoehme S, Henkel SG, Ghallab A, Driesch D, Böttger J, et al. Integrated metabolic spatial-temporal model for the prediction of ammonia detoxification during liver damage and regeneration. Hepatology. 2014;60:2040-51.
24. Shimada H, Hashiguchi T, Yasutake A, Waalkes MP, Imamura Y. Sexual dimorphism of cadmium-induced toxicity in rats: involvement of sex hormones. Arch Toxicol. 2012;86:1475-80.
25. Sumi D, Sasaki T, Miyataka H, Himeno S. Rat H9c2 cardiac myocytes are sensitive to arsenite due to a modest activation of transcription factor Nrf2. Arch Toxicol. 2011;85:1509-16.
26. Valente MJ, Araújo AM, Silva R, Bastos ML, Carvalho F, Guedes de Pinho P, et al. 3,4-Methylenedioxypyrovalerone (MDPV): in vitro mechanisms of hepatotoxicity under normothermic and hyperthermic conditions. Arch Toxicol. 2015 Dec 16. [Epub ahead of print].
27. Vartak N, Damle-Vartak A, Richter B, Dirsch O, Dahmen U, Hammad S, et al. Cholestasis-induced adaptive remodeling of interlobular bile ducts. Hepatology. 2015 Nov 26. doi: 10.1002/hep.28373. [Epub ahead of print].
28. Vitins AP, Kienhuis AS, Speksnijder EN, Roodbergen M, Luijten M, van der Ven LT. Mechanisms of amiodarone and valproic acid induced liver steatosis in mouse in vivo act as a template for other hepatotoxicity models. Arch Toxicol. 2014;88:1573-88.