A Self-Organizing Map of the Fathead Minnow Liver Transcriptome to Identify Consistent Toxicogenomic Patterns across Chemical Fingerprints.

Abstract

Lack of consistent findings in different experimental settings remains 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 2-dimensional grid according to their similarity in expression across all considered data. In the present study, 19 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 2500 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. Consistency could be improved compared with 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 by applying additional clustering steps and comparing all obtained fingerprints. Our study shows that the SOM-based approach is useful for generating comparable toxicogenomic fingerprints and improving consistency between results of different experiments. Environ Toxicol Chem 2020;39:526-537. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.