African trypanosomes cause lethal and neglected tropical diseases, known as sleeping sickness in humans and nagana in animals. Current therapies are limited, but fortunately, promising therapies are in advanced clinical and veterinary development, including acoziborole (AN5568 or SCYX-7158) and AN11736, respectively. These benzoxaboroles will likely be key to the World Health Organization's target of disease control by 2030. Their mode of action was previously unknown. We have developed a high-coverage overexpression library and use it here to explore drug mode of action in Initially, an inhibitor with a known target was used to select for drug resistance and to test massive parallel library screening and genome-wide mapping; this effectively identified the known target and validated the approach. Subsequently, the overexpression screening approach was used to identify the target of the benzoxaboroles, Cleavage and Polyadenylation Specificity Factor 3 (CPSF3, Tb927.4.1340). We validated the CPSF3 endonuclease as the target, using independent overexpression strains. Knockdown provided genetic validation of CPSF3 as essential, and GFP tagging confirmed the expected nuclear localization. Molecular docking and CRISPR-Cas9-based editing demonstrated how acoziborole can specifically block the active site and mRNA processing by parasite, but not host CPSF3. Thus, our findings provide both genetic and chemical validation for CPSF3 as an important drug target in trypanosomes and reveal inhibition of mRNA maturation as the mode of action of the trypanocidal benzoxaboroles. Understanding the mechanism of action of benzoxaborole-based therapies can assist development of improved therapies, as well as the prediction and monitoring of resistance, if or when it arises.

Insecticide resistance is a major threat to gains in malaria control, which have been stalling and potentially reversing since 2015. Studies into the causal mechanisms of insecticide resistance are painting an increasingly complicated picture, underlining the need to design and implement targeted studies on this phenotype. In this study, we compare three populations of the major malaria vector An. coluzzii: a susceptible and two resistant colonies with the same genetic background. The original colonised resistant population rapidly lost resistance over a 6-month period, a subset of this population was reselected with pyrethroids, and a third population of this colony that did not lose resistance was also available. The original resistant, susceptible and re-selected colonies were subject to RNAseq and whole genome sequencing, which identified a number of changes across the transcriptome and genome linked with resistance. Firstly, an increase in the expression of genes within the oxidative phosphorylation pathway were seen in both resistant populations compared to the susceptible control; this translated phenotypically through an increased respiratory rate, indicating that elevated metabolism is linked directly with resistance. Genome sequencing highlighted several blocks clearly associated with resistance, including the 2Rb inversion. Finally, changes in the microbiome profile were seen, indicating that the microbial composition may play a role in the resistance phenotype. Taken together, this study reveals a highly complicated phenotype in which multiple transcriptomic, genomic and microbiome changes combine to result in insecticide resistance.

The increasing levels of pesticide resistance in agricultural pests and disease vectors represents a threat to both food security and global health. As insecticide resistance intensity strengthens and spreads, the likelihood of a pest encountering a sub-lethal dose of pesticide dramatically increases. Here, we apply dynamic Bayesian networks to a transcriptome time-course generated using sub-lethal pyrethroid exposure on a highly resistant Anopheles coluzzii population. The model accounts for circadian rhythm and ageing effects allowing high confidence identification of transcription factors with key roles in pesticide response. The associations generated by this model show high concordance with lab-based validation and identifies 44 transcription factors putatively regulating insecticide-responsive transcripts. We identify six key regulators, with each displaying differing enrichment terms, demonstrating the complexity of pesticide response. The considerable overlap of resistance mechanisms in agricultural pests and disease vectors strongly suggests that these findings are relevant in a wide variety of pest species.