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Systems biology for identifying liver toxicity pathways.

Li Z, Chan C - BMC Proc (2009)

Bottom Line: Drug-induced liver toxicity is one of the leading causes of acute liver failure in the United States, exceeding all other causes combined.Systems biology approaches were developed to integrate multi-level data, i.e., gene expression, metabolite profile, toxicity measurements and a priori knowledge to identify gene targets for modulating liver toxicity.Targets that modulate liver toxicity, in vitro, were computationally predicted and some targets were experimentally validated.

View Article: PubMed Central - HTML - PubMed

Affiliation: Cellular and Molecular Biology Lab, Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI 48824, USA. lizheng1@gmail.com

ABSTRACT
Drug-induced liver toxicity is one of the leading causes of acute liver failure in the United States, exceeding all other causes combined. The objective of this paper is to describe systems biology methods for identifying pathways involved in liver toxicity induced by free fatty acids (FFA) and tumor necrosis factor (TNF)-alpha in human hepatoblastoma cells (HepG2/C3A). Systems biology approaches were developed to integrate multi-level data, i.e., gene expression, metabolite profile, toxicity measurements and a priori knowledge to identify gene targets for modulating liver toxicity. Targets that modulate liver toxicity, in vitro, were computationally predicted and some targets were experimentally validated.

No MeSH data available.


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Simplified toxicity network. LDH (in red) is the phenotype node and all other nodes are genes predicted to be relevant to toxicity using Bayesian network analysis.
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Figure 2: Simplified toxicity network. LDH (in red) is the phenotype node and all other nodes are genes predicted to be relevant to toxicity using Bayesian network analysis.

Mentions: To identify the gene targets that may be perturbed to reduce liver toxicity, we integrated the toxicity measurements with gene expression profile using the TIPSĀ© approach. A simplified toxicity relevant network was reconstructed as shown in Figure 2. A more detailed network is shown in reference [6]. The network was used to predict the effect of perturbing a gene on the liver toxicity. For example, we predicted the probability of a high level of toxicity upon palmiate treatment should be reduced significantly by up-regulating stearoyl-CoA desaturase (SCD). The prediction was experimentally confirmed with SCD activation using two chemical agents, clofibrate and ciprofibrate. Other predictions made by the model are discussed further in reference [6]. In addition, we applied a modified GA/PLS to identify genes relevant to multiple cellular responses, including liver toxicity and Triglyceride (TG) accumulation [11]. The analyses identified NADH dehydrogenase and mitogen activated protein kinases (MAPKs) were relevant to both cytotoxicity and lipid accumulation. Indeed, inhibiting NADH dehydrogenase and c-Jun N-terminal kinase (JNK) reduced cytotoxicity significantly and increased intracellular TG accumulation. In fact much greater reduction in the toxicity was observed upon inhibiting the NADH dehydrogenase or MAPK than for the stearoyl-CoA desaturase (SCD) activation [11], thus suggesting the incorporation of more information, i.e. more metabolites, is beneficial.


Systems biology for identifying liver toxicity pathways.

Li Z, Chan C - BMC Proc (2009)

Simplified toxicity network. LDH (in red) is the phenotype node and all other nodes are genes predicted to be relevant to toxicity using Bayesian network analysis.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2654485&req=5

Figure 2: Simplified toxicity network. LDH (in red) is the phenotype node and all other nodes are genes predicted to be relevant to toxicity using Bayesian network analysis.
Mentions: To identify the gene targets that may be perturbed to reduce liver toxicity, we integrated the toxicity measurements with gene expression profile using the TIPSĀ© approach. A simplified toxicity relevant network was reconstructed as shown in Figure 2. A more detailed network is shown in reference [6]. The network was used to predict the effect of perturbing a gene on the liver toxicity. For example, we predicted the probability of a high level of toxicity upon palmiate treatment should be reduced significantly by up-regulating stearoyl-CoA desaturase (SCD). The prediction was experimentally confirmed with SCD activation using two chemical agents, clofibrate and ciprofibrate. Other predictions made by the model are discussed further in reference [6]. In addition, we applied a modified GA/PLS to identify genes relevant to multiple cellular responses, including liver toxicity and Triglyceride (TG) accumulation [11]. The analyses identified NADH dehydrogenase and mitogen activated protein kinases (MAPKs) were relevant to both cytotoxicity and lipid accumulation. Indeed, inhibiting NADH dehydrogenase and c-Jun N-terminal kinase (JNK) reduced cytotoxicity significantly and increased intracellular TG accumulation. In fact much greater reduction in the toxicity was observed upon inhibiting the NADH dehydrogenase or MAPK than for the stearoyl-CoA desaturase (SCD) activation [11], thus suggesting the incorporation of more information, i.e. more metabolites, is beneficial.

Bottom Line: Drug-induced liver toxicity is one of the leading causes of acute liver failure in the United States, exceeding all other causes combined.Systems biology approaches were developed to integrate multi-level data, i.e., gene expression, metabolite profile, toxicity measurements and a priori knowledge to identify gene targets for modulating liver toxicity.Targets that modulate liver toxicity, in vitro, were computationally predicted and some targets were experimentally validated.

View Article: PubMed Central - HTML - PubMed

Affiliation: Cellular and Molecular Biology Lab, Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI 48824, USA. lizheng1@gmail.com

ABSTRACT
Drug-induced liver toxicity is one of the leading causes of acute liver failure in the United States, exceeding all other causes combined. The objective of this paper is to describe systems biology methods for identifying pathways involved in liver toxicity induced by free fatty acids (FFA) and tumor necrosis factor (TNF)-alpha in human hepatoblastoma cells (HepG2/C3A). Systems biology approaches were developed to integrate multi-level data, i.e., gene expression, metabolite profile, toxicity measurements and a priori knowledge to identify gene targets for modulating liver toxicity. Targets that modulate liver toxicity, in vitro, were computationally predicted and some targets were experimentally validated.

No MeSH data available.


Related in: MedlinePlus