Academic journal article Environmental Health Perspectives

Unraveling Gene-Gene Interactions Regulated by Ligands of the Aryl Hydrocarbon Receptor *

Academic journal article Environmental Health Perspectives

Unraveling Gene-Gene Interactions Regulated by Ligands of the Aryl Hydrocarbon Receptor *

Article excerpt

The co-expression of genes coupled to additive probabilistic relationships was used to identify gene sets predictive of the complex biological interactions regulated by ligands of the aryl hydrocarbon receptor (Ahr). To maximize the number of possible gene-gene combinations, data sets from murine embryonic kidney, fetal heart, and vascular smooth muscle cells challenged in vitro with ligands of the Ahr were used to create predictor/training data sets. Biologically relevant gene predictor sets were calculated for Ahr, cytochrome P450 1B1, insulin-like growth factor-binding protein-5, lysyl oxidase, and osteopontin. Transcript levels were categorized into ternary expressions and target genes selected from the data set and tested for all possible combinations using three gene sets as predictors of transitional level. The goodness of prediction for each set was quantified using a multivariate nonlinear coefficient of determination. Evidence is presented that predictor gene combinations can be effectively used to resolve gene-gene interactions regulated by Ahr ligands, Key words: aryl hydrocarbon receptor, bioinformatics, gene networks, genomics. Environ Health Perspect 112:403-412 (2004). doi:10.1289/txg.6758 available via[Online 14 January 2004]


The assumption that deconstructive methodologies accurately describe the complexity of biological processes is inadequate at best. Until recently, functional genetic studies have been of limited scope and able to elucidate the role of only one or a few genes at a time. Several limitations render these techniques nonconducive to large-scale expression analysis; therefore, nucleotide hybridization technologies are now used to monitor the expression of thousands of genes at a given point in time. A fundamental goal of genomics research is to understand individual gene expression patterns within the symphonic context of the transcriptome and to unravel the genetic networks responsible for health and disease. However, the interactive gene networks responsible for expression of altered cellular phenotypes have not been fully defined.

To date, most microarray experiments have used correlation analysis to identify common genomic responses to a particular stimulus, or multivariate methodologies to examine more complex gene-gene interactions, Univariate methodologies can be used to identify, common genomic responses to a particular stimulus but do not account for multiple influences on gene expression. Logistic regression and stepwise regression, on the other hand, are multivariate approaches successfully used to examine more complex genomic interactions but require prior knowledge of the system and assume linearity in assigning biological relatedness.

To understand the complex nature of cellular transformation in cancer, computational prediction methodology has been used to examine global patterns of gene expression (Kim et al. 2000a, 2000b). This method identifies associations between the expression patterns of individual genes by determining whether knowledge of the transcriptional levels of a small gene set predicts the associated transcriptional state of another gene. Although mRNA is not the final product of a gene, transcription is a critical component in the regulatory cascade and therefore provides an ideal point of investigation. A key goal in networks analysis is the development of analytical tools to delineate how individual gene actions are integrated into complex biological systems at the organelle, cell, organ, and organism levels.

The goal of this study was to unravel biological networks regulated by ligands of the aryl hydrocarbon receptor (Ahr). Ahr is a ligand-activated transcription factor involved in the regulation of cellular growth, differentiation, and metabolism in all species examined (Carlson and Perdew 2002). Ahr is a member of the large basic helix-loop-helix-PAS (bHLH-PAS) homology domain family of transcription factors that includes proteins involved in myoblast differentiation, such as myogenic differentiation antigen 1; the cellular response to hypoxia, such as Ahr nuclear translocator (Arnt) and hypoxia-inducible factor-1; the Drosophila neurogenic protein Sim (single-minded), and the Drosophila circadian rhythm protein Per (period). …

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