Initial development of a multigene ⿿omics-based exposure biomarker for pyrethroid pesticides

Abstract

Omics technologies have long since promised to address a number of long standing issues related to environmental regulation. Despite considerable resource investment, there are few examples where these tools have been adopted by the regulatory community, which is in part due to a focus of most studies on discovery rather than assay development. The current work describes the initial development of an omics based assay using 48h Pimephales promelas (FHM) larvae for identifying aquatic exposures to pyrethroid pesticides. Larval FHM were exposed to seven concentrations of each of four pyrethroids (permethrin, cypermethrin, esfenvalerate and bifenthrin) in order to establish dose response curves. Then, in three separate identical experiments, FHM were exposed to a single equitoxic concentration of each pyrethroid, corresponding to 33% of the calculated LC50. All exposures were separated by weeks and all materials were either cleaned or replaced between runs in an attempt to maintain independence among exposure experiments. Gene expression classifiers were developed using the random forest algorithm for each exposure and evaluated first by cross-validation using hold out organisms from the same exposure experiment and then against test sets of each pyrethroid from separate exposure experiments. Bifenthrin exposed organisms generated the highest quality classifier, demonstrating an empirical Area Under the Curve (eAUC) of 0.97 when tested against bifenthrin exposed organisms from other exposure experiments and 0.91 against organisms exposed to any of the pyrethroids. An eAUC of 1.0 represents perfect classification with no false positives or negatives. Additionally, the bifenthrin classifier was able to successfully classify organisms from all other pyrethroid exposures at multiple concentrations, suggesting a potential utility for detecting cumulative exposures. Considerable run-to-run variability was observed both in exposure concentrations and molecular responses of exposed fish across exposure experiments. The application of a calibration step in analysis successfully corrected this, resulting in a significantly improved classifier. Classifier evaluation suggested the importance of considering a number of aspects of experimental design when developing an expression based tool for general use in ecological monitoring and risk assessment, such as the inclusion of multiple experimental runs and high replicate numbers.