Developments in toxicogenomics: understanding and predicting compound-induced toxicity from RNA-Seq data

The toxicogenomics field aims to understand and predict toxicity by using ‘omics’ data in order to study systems-level responses to compound treatments. In recent years there has been a rapid increase in publicly available toxicological and ‘omics’ data, particularly gene expression data, and a corresponding development of methods for its analysis.

Researchers from the University of Cambridge summarize recent progress relating to the analysis of RNA-Seq and microarray data, review relevant databases, and highlight recent applications of toxicogenomics data for understanding and predicting compound toxicity. These include the analysis of differentially expressed genes and their enrichment, signature matching, methods based on interaction networks, and the analysis of co-expression networks. In the future, these state-of-the-art methods will likely be combined with new technologies, such as whole human body models, to produce a comprehensive systems-level understanding of toxicity that reduces the necessity of in vivo toxicity assessment in animal models.

Methods and technologies utilized in the toxicogenomics field


The figure represents the use of qRT-PCR, microarray, and RNA-Seq methods to measure transcriptomic response, which in the context of this review may refer to the response to compound treatment, or the comparison of diseased/toxic and healthy states. Measured gene expression can then be analysed, using various computational methods, to understand and predict toxicity. These methods include differential gene expression analysis, gene expression signature matching, protein–protein interaction network (PPI network), and co-expression network analysis. 

Alexander-Dann B, Pruteanu LL, Oerton E, Sharma N, Berindan-Neagoe I, Módos D, Bender A. (2018) Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data. Mol Omics [Epub ahead of print]. [article]

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