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.

MicroRNA-424 Protects Against Focal Cerebral Ischemia and Reperfusion Injury in Mice by Suppressing Oxidative Stress

“…Moreover, increasing Nrf2 level leads to the upregulation of MnSOD and EcSOD. [30][31][32][33][34] In this study, Nrf2 expression was induced by miR-424, suggesting that it is a target of miR-424 regulation; in addition, the protective function of miR-424 on neuron was inhibited by Nrf2 depletion and SOD inhibition. These results demonstrate that miR-424 positively regulates the endogenous antioxidant system in neurons, providing protection against oxidative damage during transient cerebral I/R.…”

The Reconstruction of Transcriptional Networks Reveals Critical Genes with Implications for Clinical Outcome of Multiple Myeloma

“…Reconstructing regulatory network from expression data, in this fashion, is a crucial step to understand the mechanisms underlying biological systems. Among several algorithms, ARACNe has been shown to outperform other Bayesian-based methods in its precision for the reconstruction of transcriptional networks (7,9,27,28). To estimate gene-gene correlation, ARACNe uses the MI, which does not assume a linear relationship between the gene expression levels and does not require any assumption about the distribution of variables.…”

Aldo-keto reductase-7A protects liver cells and tissues from acetaminophen-induced oxidative stress and hepatotoxicity

“…Sod2/rSod2, G6pdh/rG6pdh, Idh1, rNoq1, and rGpx-2 are very likely the direct transcriptional targets of Nrf2/rNrf2, 44,46 whereas it is not clear whether Cat and Gpx-1 are Nrf2 regulated. In this regard, it is intriguing that AKR7A is capable of stimulating the expression of these ROS-detoxifying enzymes (e.g., Cat, Sod2/rSod2, and Gpx-1/rGpx-2) and genes for NADPH-replenishing proteins (G6PDH, and NADPdependent IDH1), 33,44 regardless of the presence or absence of APAP.…”