Insight into redox-regulated gene networks in vascular cells

Abstract

To understand the complex nature of the atherogenic response initiated by oxidative stress in vascular smooth muscle cells (vSMCs), computational prediction methodology was employed to define putative gene-gene and gene-environment interactions in vSMCs subjected to oxidative chemical stress. Computational relationships were derived from the global gene expression profiles of murine cells challenged with a chemical pro-oxidant to cause oxidative stress or cells treated with anti-oxidant prior to oxidative injury. Target clones were chosen based on their biological relevance within the context of the atherogenic response and included lysyl oxidase, matrix metalloproteinase 2, insulin like growth factor binding protein 5, and lymphocyte antigen 6c. Established biological relationships were derived computationally confirming the usefulness of the algorithm in uncovering novel biological relationships worthy of future investigation. Thus, the predictive algorithm can be a useful tool to advance the frontiers of biological discovery.

Figure 1

Three gene combinations to predict the behavior of selected target genes. The values are the COD values for each predictor and the effect that addition of each predictor has on overall model prediction potential

Figure 2

Network inference based on overlapping edges in the predictor-target relationships resolved using CoD values