RNA meets toxicology: efficacy indicators from the experimental design of RNAi studies for insect pest management

Abstract

RNA interference (RNAi) selectively targets genes and silences their expression in vivo, causing developmental defects, mortality and altered behavior. Consequently, RNAi has emerged as a promising research area for insect pest management. However, it is not yet a viable alternative over conventional pesticides despite several theoretical advantages in safety and specificity. As a first step toward a more standardized approach, a machine learning algorithm was used to identify factors that predict trial efficacy. Current research on RNAi for pest management is highly variable and relatively unstandardized. The applied random forest model was able to reliably predict mortality ranges based on bioassay parameters with 72.6% accuracy. Response time and target gene were the most important variables in the model, followed by applied dose, double-stranded RNA (dsRNA) construct size and target species, further supported by generalized linear mixed effect modeling. Our results identified informative trends, supporting the idea that basic principles of toxicology apply to RNAi bioassays and provide initial guidelines standardizing future research similar to studies of traditional insecticides. We advocate for training that integrates genetic, organismal, and toxicological approaches to accelerate the development of RNAi as an effective tool for pest management. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.