Network Inference Algorithms Elucidate Nrf2 Regulation of Mouse Lung Oxidative Stress

Abstract

A variety of cardiovascular, neurological, and neoplastic conditions have been associated with oxidative stress, i.e., conditions under which levels of reactive oxygen species (ROS) are elevated over significant periods. Nuclear factor erythroid 2-related factor (Nrf2) regulates the transcription of several gene products involved in the protective response to oxidative stress. The transcriptional regulatory and signaling relationships linking gene products involved in the response to oxidative stress are, currently, only partially resolved. Microarray data constitute RNA abundance measures representing gene expression patterns. In some cases, these patterns can identify the molecular interactions of gene products. They can be, in effect, proxies for protein–protein and protein–DNA interactions. Traditional techniques used for clustering coregulated genes on high-throughput gene arrays are rarely capable of distinguishing between direct transcriptional regulatory interactions and indirect ones. In this study, newly developed information-theoretic algorithms that employ the concept of mutual information were used: the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE), and Context Likelihood of Relatedness (CLR). These algorithms captured dependencies in the gene expression profiles of the mouse lung, allowing the regulatory effect of Nrf2 in response to oxidative stress to be determined more precisely. In addition, a characterization of promoter sequences of Nrf2 regulatory targets was conducted using a Support Vector Machine classification algorithm to corroborate ARACNE and CLR predictions. Inferred networks were analyzed, compared, and integrated using the Collective Analysis of Biological Interaction Networks (CABIN) plug-in of Cytoscape. Using the two network inference algorithms and one machine learning algorithm, a number of both previously known and novel targets of Nrf2 transcriptional activation were identified. Genes predicted as novel Nrf2 targets include Atf1, Srxn1, Prnp, Sod2, Als2, Nfkbib, and Ppp1r15b. Furthermore, microarray and quantitative RT-PCR experiments following cigarette-smoke-induced oxidative stress in Nrf2^+/+ and Nrf2^−/− mouse lung affirmed many of the predictions made. Several new potential feed-forward regulatory loops involving Nrf2, Nqo1, Srxn1, Prdx1, Als2, Atf1, Sod1, and Park7 were predicted. This work shows the promise of network inference algorithms operating on high-throughput gene expression data in identifying transcriptional regulatory and other signaling relationships implicated in mammalian disease. A variety of conditions including certain cancers and heart diseases, diabetes mellitus, and rheumatoid arthritis have been associated with the generation of high levels of highly reactive molecular species under conditions known as “oxidative stress.” A number of protein molecules have been identified as participants in an elaborate response to oxidative stress. Sustained elevated generation of reactive species can overwhelm this response and lead to disease conditions. In these studies, we make use of data generated from over 250 studies (microarrays) in which messenger RNA levels of the gene precursors of mouse lung proteins have been examined collectively. We have made use of computational approaches to help identify the key regulatory relationships among the proteins that respond to oxidative stress. Nrf2, a protein known as a master regulator of oxidative stress response, was a principal focus of our studies. Among the novel regulatory targets of Nrf2 we identified is Als2, a protein involved in amyotrophic lateral sclerosis (Lou Gehrig's disease). We also identify important candidate three-party regulatory relationships, one of which involves the recently discovered Srxn1, an antioxidant protein that reverses S-glutathionylation, a common posttranslational modification associated with diseases such as Parkinson's disease, diabetes, hyperlipidemia, Friedreich's ataxia, renal cell carcinoma, and HIV/AIDS. These studies demonstrate the utility of network inference algorithms and affirm that Nrf2 has a direct regulatory role over the expression of other genes responding to oxidative stress.