Combined Transcriptomics Analysis for Classification of Adverse Effects As a Potential End Point in Effect Based Screening

Environmental risk assessment relies on the use of bioassays to assess the environmental impact of chemicals. Gene expression is gaining acceptance as a valuable mechanistic end point in bioassays and effect-based screening. Data analysis and its results, however, are complex and often not directly applicable in risk assessment. Classifier analysis is a promising method to turn complex gene expression analysis results into answers suitable for risk assessment. We have assembled a large gene expression data set assembled from multiple studies and experiments in the springtail Folsomia candida, with the aim of selecting a set of genes that can be trained to classify general toxic stress. By performing differential expression analysis prior to classifier training, we were able to select a set of 135 genes which was enriched in stress related processes. Classifier models from this set were used to classify two test sets comprised of chemical spiked, polluted, and clean soils and compared to another, more traditional classifier feature selection. The gene set presented here outperformed the more traditionally selected gene set. This gene set has the potential to be used as a biomarker to test for adverse effects caused by chemicals in springtails to provide end points in environmental risk assessment.