Metabolic network-based predictions of toxicant-induced metabolite changes in the laboratory rat
| Autor: | Martha L. Wall, Shanea K. Estes, Tracy P. O’Brien, Kalyan C. Vinnakota, Irina Trenary, Masakazu Shiota, Jamey D. Young, Anders Wallqvist, Richard L. Printz, Jaques Reifman, Venkat R. Pannala |
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| Rok vydání: | 2018 |
| Předmět: |
Male
0301 basic medicine Metabolite Glycogenolysis lcsh:Medicine Metabolic network Biology Bioinformatics Article Rats Sprague-Dawley 03 medical and health sciences chemistry.chemical_compound Animals Laboratory Metabolic flux analysis Gene expression medicine Animals Pyruvates lcsh:Science Acetaminophen Multidisciplinary lcsh:R Metabolism Metabolic Flux Analysis Laboratory rat 030104 developmental biology Gene Expression Regulation Liver chemistry Metabolome lcsh:Q Metabolic Networks and Pathways Toxicant medicine.drug |
| Zdroj: | Scientific Reports, Vol 8, Iss 1, Pp 1-18 (2018) Scientific Reports |
| ISSN: | 2045-2322 |
| Popis: | In order to provide timely treatment for organ damage initiated by therapeutic drugs or exposure to environmental toxicants, we first need to identify markers that provide an early diagnosis of potential adverse effects before permanent damage occurs. Specifically, the liver, as a primary organ prone to toxicants-induced injuries, lacks diagnostic markers that are specific and sensitive to the early onset of injury. Here, to identify plasma metabolites as markers of early toxicant-induced injury, we used a constraint-based modeling approach with a genome-scale network reconstruction of rat liver metabolism to incorporate perturbations of gene expression induced by acetaminophen, a known hepatotoxicant. A comparison of the model results against the global metabolic profiling data revealed that our approach satisfactorily predicted altered plasma metabolite levels as early as 5 h after exposure to 2 g/kg of acetaminophen, and that 10 h after treatment the predictions significantly improved when we integrated measured central carbon fluxes. Our approach is solely driven by gene expression and physiological boundary conditions, and does not rely on any toxicant-specific model component. As such, it provides a mechanistic model that serves as a first step in identifying a list of putative plasma metabolites that could change due to toxicant-induced perturbations. |
| Databáze: | OpenAIRE |
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