Methodology article

A hierarchical approach employing metabolic and gene expression profiles to identify the pathways that confer cytotoxicity in HepG2 cells

Zheng Li^1* , Shireesh Srivastava^1* , Xuerui Yang¹ , Sheenu Mittal^1,5 , Paul Norton⁴ , James Resau⁴ , Brian Haab⁴ and Christina Chan^1,2,3

¹  Department of Chemical Engineering and Material Science, Michigan State University, East Lansing, MI 48824, USA

²  Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA

³  Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA

⁴  Van Andel Institute, Grand Rapids, MI 49503, USA

⁵  Department of Biochemistry and Molecular Biology, Michigan State University, USA

author email corresponding author email* Contributed equally

BMC Systems Biology 2007, 1:21doi:10.1186/1752-0509-1-21

Published:	11 May 2007

Abstract

Background

Free fatty acids (FFA) and tumor necrosis factor alpha (TNF-α) have been implicated in the pathogenesis of many obesity-related metabolic disorders. When human hepatoblastoma cells (HepG2) were exposed to different types of FFA and TNF-α, saturated fatty acid was found to be cytotoxic and its toxicity was exacerbated by TNF-α. In order to identify the processes associated with the toxicity of saturated FFA and TNF-α, the metabolic and gene expression profiles were measured to characterize the cellular states. A computational model was developed to integrate these disparate data to reveal the underlying pathways and mechanisms involved in saturated fatty acid toxicity.

Results

A hierarchical framework consisting of three stages was developed to identify the processes and genes that regulate the toxicity. First, discriminant analysis identified that fatty acid oxidation and intracellular triglyceride accumulation were the most relevant in differentiating the cytotoxic phenotype. Second, gene set enrichment analysis (GSEA) was applied to the cDNA microarray data to identify the transcriptionally altered pathways and processes. Finally, the genes and gene sets that regulate the metabolic responses identified in step 1 were identified by integrating the expression of the enriched gene sets and the metabolic profiles with a multi-block partial least squares (MBPLS) regression model.

Conclusion

The hierarchical approach suggested potential mechanisms involved in mediating the cytotoxic and cytoprotective pathways, as well as identified novel targets, such as NADH dehydrogenases, aldehyde dehydrogenases 1A1 (ALDH1A1) and endothelial membrane protein 3 (EMP3) as modulator of the toxic phenotypes. These predictions, as well as, some specific targets that were suggested by the analysis were experimentally validated.