Network Inference Algorithms Elucidate Nrf2 Regulation of Mouse Lung Oxidative Stress

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

Effects of Ethanol and NAP on Cerebellar Expression of the Neural Cell Adhesion Molecule L1

The neural cell adhesion molecule L1 is critical for brain development and plays a role in learning and memory in the adult. Ethanol inhibits L1-mediated cell adhesion and neurite outgrowth in cerebellar granule neurons (CGNs), and these actions might underlie the cerebellar dysmorphology of fetal alcohol spectrum disorders. The peptide NAP potently blocks ethanol inhibition of L1 adhesion and prevents ethanol teratogenesis. We used quantitative RT-PCR and Western blotting of extracts of cerebellar slices, CGNs, and astrocytes from postnatal day 7 (PD7) rats to investigate whether ethanol and NAP act in part by regulating the expression of L1. Treatment of cerebellar slices with 20 mM ethanol, 10−12 M NAP, or both for 4 hours, 24 hours, and 10 days did not significantly affect L1 mRNA and protein levels. Similar treatment for 4 or 24 hours did not regulate L1 expression in primary cultures of CGNs and astrocytes, the predominant cerebellar cell types. Because ethanol also damages the adult cerebellum, we studied the effects of chronic ethanol exposure in adult rats. One year of binge drinking did not alter L1 gene and protein expression in extracts from whole cerebellum. Thus, ethanol does not alter L1 expression in the developing or adult cerebellum; more likely, ethanol disrupts L1 function by modifying its conformation and signaling. Likewise, NAP antagonizes the actions of ethanol without altering L1 expression

Unsupervised gene network inference with decision trees and Random forests

In this chapter, we introduce the reader to a popular family of machine learning algorithms, called decision trees. We then review several approaches based on decision trees that have been developed for the inference of gene regulatory networks (GRNs). Decision trees have indeed several nice properties that make them well-suited for tackling this problem: they are able to detect multivariate interacting effects between variables, are non-parametric, have good scalability, and have very few parameters. In particular, we describe in detail the GENIE3 algorithm, a state-of-the-art method for GRN inference