Project description:Bifenthrin is a commonly detected pesticide in California surfacewaters; however the effects of bifenthrin on aquatic organisms are complex and poorly understood. This study presents the transcriptome-wide response of the inland silverside, Menidia beryllina, to chronic 14 d exposures to bifenthrin.
Project description:This work describes the initial development of an omics based assay using 48Hr Pimephales promelas (FHM) larvae for identifying aquatic exposures to four pyrethroids (permethrin, cypermethrin, esfenvalerate and bifenthrin). 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. 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.
