Delaying quantitative resistance to pesticides and antibiotics

A tiny sample rapid visual detection technology for imidacloprid resistance in Aphis gossypii by CRISPR/Cas12a

Insecticide resistance monitoring is essential for guiding chemical pest control and resistance management policies. Currently, rapid and effective technology for monitoring the resistance of tiny insects in the field is absent. Aphis gossypii Glover is a typical tiny insect, and one of the most frequently reported insecticide-resistant pests. In this study, we established a novel CRISPR/Cas12a-based rapid visual detection approach for detecting the V62I and R81T mutations in the β1 subunit of the nAChR in A. gossypii, to reflect target-site resistance to imidacloprid. Based on the nAChR β1 subunit gene in A. gossypii, the V62I/R81T-specific RPA primers and crRNAs were designed, and the ratio of 10 μM/2 μM/10 μM for ssDNA/Cas12a/crRNA was selected as the optimal dosage for the CRISPR reaction, ensuring that Cas12a only accurately recognizes imidacloprid-resistance templates. Our data show that the field populations of resistant insects possessing V62I and R81T mutations to imidacloprid can be accurately identified within one hour using the RPA-CRISPR/Cas12a detection approach under visible blue light at 440-460 nm. The protocol for RPA-CRISPR detection necessitates a single less than 2 mm specimen of A. gossypii tissues to perform RPA-CRISPR detection, and the process only requires a container at 37 °C and a portable blue light at 440-460 nm. Our research represents the first application of RPA-CRISPR technology in insecticide resistance detection, offers a new method for the resistance monitoring of A. gossypii or other tiny insects, helps delay the development of resistance to imidacloprid, improves the sustainability of chemical control, and provides theoretical guidance for managing pest resistance.

Preventing multi-resistance: New insights for managing fungal adaptation

Sustainable crop protection is vital for food security, yet it is under threat due to the adaptation of a diverse and evolving pathogen population. Resistance can be managed by maximising the diversity of selection pressure through dose variation and the spatial and temporal combination of active ingredients. This study explores the interplay between operational drivers for maximising the sustainability of management strategies in relation to the resistance status of fungal populations. We applied an experimental evolution approach to three artificial populations of Zymoseptoria tritici, an economically significant wheat pathogen, each differing in initial resistance status. Our findings reveal that diversified selection pressure curtails the selection of resistance in naïve populations and those with low frequencies of single resistance. Increasing the number of modes of action most effectively delays resistance development, surpassing the increase in the number of fungicides, fungicide choice based on resistance risk, and temporal variation in fungicide exposure. However, this approach favours generalism in the evolved populations. The prior presence of multiple resistant isolates and their subsequent selection in populations override the effects of diversity in management strategies, thereby invalidating any universal ranking. Therefore, the initial resistance composition must be specifically considered in sustainable resistance management to address real-world field situations.