The metabolism and hepatotoxicity of APAP are the subjects of considerable research activity, both in clinical studies and in identifying early markers of acute liver failure. Even though the major metabolic pathways of APAP metabolism (conjugation with activated sulfate and glucuronic acid and oxidation by cytochrome P450s) are known, further study is needed to elucidate the interactions among APAP metabolism, hepatic function and induced signaling, and how these factors coordinately contribute to hepatotoxicity. Many current approaches focus on general toxicological parameters and microarray analysis to identify differentially expressed genes associated with hepatotoxicity as potential toxicity markers. However, these studies do not fully assess hepatic metabolic function or transcriptional regulation. The aim of our present study is to explore the regulation of APAP metabolism and induced hepatotoxicity by employing metabolic, genomic and bioinformatics methods. Mechanistic models including elementary mode analysis and a kinetic model system were developed to uncover the relationships among APAP metabolism, fasting, ethanol/drug metabolism and hepatic metabolism, while a gene regulatory network was constructed to discern levels of transcriptional control induced by APAP.
We reconstructed a hepatic detoxification network by adding to previous descriptions of central metabolism reactions corresponding to acetaminophen and ethanol detoxification pathways. The combined network consists of 106 metabolites and 121 reactions, and pathway analysis was performed using the FluxAnalyzer software[3]. The reaction network included hepatic, APAP, and ethanol metabolism and the synthesis of conjugative species. Pathway analysis revealed the impact of APAP on the overall hepatocyte metabolic network including its effects on amino acid uptake, cysteine biotransformation, ATP synthesis and ethanol metabolism. We were able to show the importance of metabolite (cysteine) and ATP availability in preventing the formation of the oxidative toxic intermediate NAPQI by the synthesis of PAPS, UDP-glucuronic acid and glutathione. The metabolism of ethanol was also shown to be ATP dependent; this additional cellular demand contributes to the depletion of detoxifying species, thus leading to increased hepatotoxicity. Lastly, our results identified cysteine to be a critical amino acid in detoxification, which coincides with the current treatment of acetaminophen overdose, administration of N-acetylcysteine as a source of cysteine to aid in glutathione synthesis.
A kinetic model of APAP elimination was developed by compiling experimental data on molecular rate processes from animal[4], liver microsomes[5], and cellular studies[6,7,8,9]. Dynamic modeling of APAP metabolism illustrated the formation of toxic metabolites in a dose-dependent fashion and the requirement for conjugation species. The time scale for onset of hepatotoxic effects was elucidated from the dynamics of depletion of conjugation species. In addition, we were able to show how fasting increases an individual's susceptibility to acute liver failure when taking equivalent drug doses.
Analysis of gene expression profiles of liver cells from rats administered APAP revealed down regulation of genes involved in cellular energy pathways including: gluconeogenesis, fatty acid synthesis, and cholesterol synthesis as a consequence of APAP toxicity. In addition, genes associated with key hepatic functions, such as urea production, were differentially down regulated while genes associated with APAP detoxification and oxidative stress, such as glutathione S-transferase, were up regulated. Along with a gene expression analysis, transcriptional regulation was studied by combining DNA microarrays and promoter analysis with computational methods to elucidate the connection between APAP-induced signaling and hepatic function (cytochrome P450 activation). Gene regulatory networks were constructed from biological knowledge of regulatory elements located on gene promoters and by a computational search method to identify putative transcription factor binding sites. This was accomplished by searching promoters of co-expressed genes with position weight matrices of transcription factors determined to be over-represented in the genes of interest. Our approach utilized clustering and statistic tools to reduce false positives and to elucidate putative transcription factor binding sites important in co-regulation for our system. We then used the constructed gene regulatory network and differential gene expression data to determine levels of transcriptional control.
This comprehensive method can potentially aid in deducing drug-drug interactions, determining dietary requirements to reduce toxic susceptibility, identifying genomic hepatotoxicity markers, and deducing ways to reduce drug-induced toxic effects through regulatory control. Taken together, our results provide evidence that metabolic, genomic and bioinformatic methods allow us to advance our understanding of hepatic metabolic function, transcriptional regulatory control, and hepatotoxicity.
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