Project description:We describe initial development of RNA profiling-based assays for detecting pyrethroid pesticides in water. We conducted 48-hour flow-through exposures of Pimephales promelas larvae to lab water and eight nominal concentrations of each of four pyrethroid insecticides: bifenthrin, cypermethrin, esfenvalerate, and (trans) permethrin. Concentration-response curves suggest steep dose responses, and LC50s below most published values. Expression microarray analysis of whole larvae suggests transcriptomic changes can be used to detect these pyrethroids at concentrations well below P. promelas LC50s, and below LC50s of about 70% of aquatic metazoan species. Geneset analysis and intersections between lists of differentially expressed genes failed to identify substantial functional overlaps between these toxicants. Geneset analysis was sensitive to parameter choice, which may reflect the large number of genes analyzed relative to the number of samples, and substantial biological variability relative to effect sizes. Several identified GO terms can be rationalized based on known pyrethroid action, but implications of other highlighted GO terms are unclear.
Project description:We describe initial development of RNA profiling-based assays for detecting pyrethroid pesticides in water. We conducted 48-hour flow-through exposures of Pimephales promelas adults and larvae to lab water and eight nominal concentrations of each of four pyrethroid insecticides: bifenthrin, cypermethrin, esfenvalerate, and (trans) permethrin. Concentration-response curves suggest steep dose responses, and LC50s below most published values. Expression microarray analysis of dissected brains from adults and whole larvae exposed to cypermethrin and bifenthrin suggests both assay formats can be used to detect these pyrethroids at concentrations well below P. promelas LC50s, and below LC50s of about 70% of aquatic metazoan species. Geneset analysis and intersections between lists of differentially expressed genes failed to identify substantial functional overlaps between these toxicants. Geneset analysis was sensitive to parameter choice, which may reflect the large number of genes analyzed relative to the number of samples, and substantial biological variability relative to effect sizes. Several identified GO terms can be rationalized based on known pyrethroid action, but implications of other highlighted GO terms are unclear.
Project description:We describe initial development of microarray-based assays for detecting 4 pyrethroid pesticides (bifenthrin, cypermethrin, esfenvalerate, and permethrin) in water. To facilitate comparison of transcriptional responses with gross apical responses, we estimated concentration-mortality curves for these pyrethroids using flow-through exposures of newly hatched Daphnia magna, Pimephales promelas adults, and 24 h posthatch P. promelas. Median lethal concentration (LC50) estimates were below most reported values, perhaps attributable to the use of flow-through exposures or of measured rather than nominal concentrations. Microarray analysis of whole P. promelas larvae and brains from exposed P. promelas adults showed that assays using either tissue type can detect these pyrethroids at concentrations below LC50 values reported for between 72 and 96% of aquatic species, depending on the pesticide. These estimates are conservative because they correspond to the lowest concentrations tested. This suggests that the simpler and less expensive whole-larval assay provides adequate sensitivity for screening contexts where acute aquatic lethality is observed, but the responsible agent is not known. Gene set analysis (GSA) highlighted several Gene Ontology (GO) terms consistent with known pyrethroid action, but the implications of other GO terms are less clear. Exploration of the sensitivity of results to changes in data processing suggests robustness of the detection assay results, but GSA results were sensitive to methodological variations. Environ Toxicol Chem 2019;38:2436-2446. Published 2019 Wiley Periodicals, Inc. on behalf of SETAC. This article is a US government work, and as such, is in the public domain in the United States of America.
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.
Project description:Variant antigens that are encoded by large multigene families play an important role in the adaptation and immune evasion of a wide range of pathogens. However, the study of their biological function is significantly hampered by the difficulty in controlling their expression in its cellular setting. The genomes of Plasmodium spp. encode a number of different multigene families that are thought to play a critical role for their survival. However, with the exception of the P. falciparum var genes very little is known about the biological roles of any of the other multigene families. Here we report a highly efficient genetic system to study variant antigens in Plasmodium spp. using the Selection Linked Integration method; we are able to activate the expression of a single member of a multigene of our choice using its endogenous promoter.
Project description:The objective of this study is to provide a novel method to study multigene proteins in Plasmodium spp. The method is based on selection linked integration (SLI), which allows positive selection of genomic integration events. By targeting specific members of multigene families, parasites are being selected for not only genomic integration but also expression of the targeted gene under its endogenous promoter
Project description:Α reduction of pyrethroid efficacy has been recently recorded in the olive fruit fly Bactrocera oleae, the most destructive insect pest of olives worldwide. We analyzed the transcriptomic differences between two highly pyrethroid resistant populations versus a relatively susceptible field population and two laboratory strains to gain more insight into the molecular mechanism of resistance. A large number of genes was found to be significantly differentially transcribed across the pairwise comparisons between resistant and susceptible insect populations. Interestingly, gene set analysis revealed that genes of the ‘electron carrier activity’ GO group were enriched in one specific pairwise transcriptomic comparison. As P450 monooxygenase enzymes are typically associated with this Molecular Function GO-group, this might reflect a P450-mediated resistance mechanism. These results suggest that transcriptional induction of the CYP6 P450s might be an important mechanism of pyrethroid resistance in B. oleae and pave the way for the development of synergists and molecular diagnostics for insecticide resistance management.