Unraveling toxicological mechanisms and predicting toxicity classes with gene dysregulation networks

ABSTRACT The use of genes for distinguishing classes of toxicity has become well established. In this paper we combine the reconstruction of a gene dysregulation network (GDN) with a classifier to assign unseen compounds to their appropriate class. Gene pairs in the GDN are dysregulated in the sense that they are linked by a common expression pattern in one class and differ in this pattern in another class. The classifier gives a quantitative measure on this difference by its prediction accuracy. As an in‐depth example, gene pairs were selected that were dysregulated between skin cells treated with either sensitizers or irritants. Pairs with known and novel markers were found such as HMOX1 and ZFAND2A, ATF3 and PPP1R15A, OXSR1 and HSPA1B, ZFP36 and MAFF. The resulting GDN proved biologically valid as it was well‐connected and enriched in known interactions, processes and common regulatory motifs for pairs. Classification accuracy was improved when compared with conventional classifiers. As the dysregulated patterns for heat shock responding genes proved to be distinct from those of other stress genes, we were able to formulate the hypothesis that heat shock genes play a specific role in sensitization, apart from other stress genes. In conclusion, our combined approach creates added value for classification‐based toxicogenomics by obtaining novel, well‐distinguishing and biologically interesting measures, suitable for the formulation of hypotheses on functional relationships between genes and their relevance for toxicity class differences. Copyright © 2012 John Wiley & Sons, Ltd. In this paper we combine network reconstruction with classification of unseen compounds to their appropriate toxicity class. We select gene pairs that are linked by a common expression pattern in one class and differ in this expression pattern in another class. The resulting gene dysregulation network provides novel and well‐distinguishing pairs of markers. Moreover, paired expressions by their specific pattern generate new hypotheses on regulatory origin and/or common functionality of genes in the pairs, and how these differ between classes.