Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Environmental toxicology and chemistry ; h5-index 0.0

Lack of consistent findings in different experimental settings remains to be a major challenge in toxicogenomics. The present study investigated, whether consistency between findings of different microarray experiments can be improved when the analysis is based on a common reference frame ("toxicogenomic universe"), which can be generated using the machine learning-algorithm of the self-organizing map (SOM). This algorithm arranges and clusters genes on a two-dimensional grid according to their similarity in expression across all considered data. In the present study, nineteen data sets, comprising of 54 different adult fathead minnow liver exposure experiments, were retrieved from Gene Expression Omnibus and used to train a SOM. The resulting toxicogenomic universe aggregates 58,872 probes to 2,500 nodes and was used to project, visualize and compare the fingerprints of these 54 different experiments. For example, we could identify a common pattern, with 14% of significantly regulated nodes in common, in the data sets of an interlaboratory study of ethinylestradiol exposures, previously published by Feswick et al. (2017). Consistency could be improved compared to the 5% total overlap in regulated genes reported before. Furthermore, we could determine a specific and consistent estrogen-related pattern of differentially expressed nodes and clusters in the toxicogenomic universe applying additional clustering steps and comparing all obtained fingerprints. This study shows that the SOM-based approach is useful for generating comparable toxicogenomic fingerprints and improving consistency between results of different experiments. This article is protected by copyright. All rights reserved.

Krämer Stefan, Busch Wibke, Schüttler Andreas


consistency, estrogens, gene expression, self-organizing map, toxicogenomics