Network Inference Algorithms Elucidate Nrf2 Regulation of Mouse Lung Oxidative Stress

• Published in: PLoS computational biology Vol. 4; no. 8; p. e1000166
• Main Authors: Taylor, Ronald C., Acquaah-Mensah, George, Singhal, Mudita, Malhotra, Deepti, Biswal, Shyam
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 29.08.2008; Public Library of Science (PLoS)
• Subjects: Algorithms; Amyotrophic lateral sclerosis; Animals; Artificial Intelligence; Computational Biology; Computational Biology/Signaling Networks; Computational Biology/Systems Biology; Computational Biology/Transcriptional Regulation; Diabetes; Disease; Experiments; Gene expression; Gene Expression Profiling - methods; Gene Regulatory Networks - drug effects; Gene Regulatory Networks - genetics; Guanine Nucleotide Exchange Factors - drug effects; Guanine Nucleotide Exchange Factors - genetics; Health sciences; Lung - metabolism; Mice; Mice, Knockout; NF-E2-Related Factor 2 - drug effects; NF-E2-Related Factor 2 - metabolism; Oligonucleotide Array Sequence Analysis - methods; Oxidative stress; Oxidative Stress - drug effects; Oxidative Stress - genetics; Oxidoreductases Acting on Sulfur Group Donors - drug effects; Oxidoreductases Acting on Sulfur Group Donors - genetics; Promoter Regions, Genetic; Proteins; Rheumatoid arthritis; Signal Transduction - genetics; Smoking - adverse effects; Smoking - genetics; Software; Transcription, Genetic - drug effects; Transcription, Genetic - genetics; Promoter Regions, Genetic; Oligonucleotide Array Sequence Analysis; Oxidative Stress; Signal Transduction; Artificial Intelligence; Lung; Gene Expression Profiling; Gene Regulatory Networks; Guanine Nucleotide Exchange Factors; Mice, Knockout; Algorithms; Animals; Oxidoreductases Acting on Sulfur Group Donors; NF-E2-Related Factor 2; Transcription, Genetic; Mice; Software; Smoking
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1000166

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.