Metabolic network-based predictions of toxicant-induced metabolite changes in the laboratory rat.

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