Methodology article

A Three Stage Integrative Pathway Search (TIPS^©) framework to identify toxicity relevant genes and pathways

Zheng Li^1,4 , Shireesh Srivastava^1,6 , Sheenu Mittal^1,5 , Xuerui Yang¹ , Lufang Sheng¹ and Christina Chan^1,2,3

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

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

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

⁴MetagenX LLC. Okemos, MI, 48864, USA

⁵Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA

⁶National Institute on Alcohol Abuse and Alcoholism (NIAAA/NIH), 5625 Fishers Lane, Rockville, MD 20851, USA

author email corresponding author email

BMC Bioinformatics 2007, 8:202doi:10.1186/1471-2105-8-202

Published:	14 June 2007

Abstract

Background

The ability to obtain profiles of gene expressions, proteins and metabolites with the advent of high throughput technologies has advanced the study of pathway and network reconstruction. Genome-wide network reconstruction requires either interaction measurements or large amount of perturbation data, often not available for mammalian cell systems. To overcome these shortcomings, we developed a Three Stage Integrative Pathway Search (TIPS^©) approach to reconstruct context-specific active pathways involved in conferring a specific phenotype, from limited amount of perturbation data. The approach was tested on human liver cells to identify pathways that confer cytotoxicity.

Results

This paper presents a systems approach that integrates gene expression and cytotoxicity profiles to identify a network of pathways involved in free fatty acid (FFA) and tumor necrosis factor-α (TNF-α) induced cytotoxicity in human hepatoblastoma cells (HepG2/C3A). Cytotoxicity relevant genes were first identified and then used to reconstruct a network using Bayesian network (BN) analysis. BN inference was used subsequently to predict the effects of perturbing a gene on the other genes in the network and on the cytotoxicity. These predictions were subsequently confirmed through the published literature and further experiments.

Conclusion

The TIPS^© approach is able to reconstruct active pathways that confer a particular phenotype by integrating gene expression and phenotypic profiles. A web-based version of TIPS^© that performs the analysis described herein can be accessed at http://www.egr.msu.edu/tips webcite.