The role of windows of selection and windows of dominance in the evolution of insecticide resistance in human disease vectors

Abstract

Persistent insecticides sprayed onto house walls, and incorporated into insecticide‐treated bednets, provide long‐acting, cost‐effective control of vector‐borne diseases such as malaria and leishmaniasis. The high concentrations that occur immediately postdeployment may kill both resistant and susceptible insects. However, insecticide concentration, and therefore killing ability, declines in the months after deployment. As concentrations decline, resistant insects start to survive, while susceptible insects are still killed. The period of time after deployment, within which the mortality of resistant individuals is lower than that of susceptible ones, has been termed the “window of selection” in other contexts. It is recognized as driving resistance in bacteria and malaria parasites, both of which are predominantly haploid. We argue that paying more attention to these mortality differences can help understand the evolution of insecticide resistance. Because insects are diploid, resistance encoded by single genes generates heterozygotes. This gives the potential for a narrower “window of dominance,” within the window of selection, where heterozygote mortality is lower than that of susceptible homozygotes. We explore the general properties of windows of selection and dominance in driving resistance. We quantify their likely effect using data from new laboratory experiments and published data from the laboratory and field. These windows can persist months or years after insecticide deployments. Differential mortalities of resistant, susceptible and heterozygous genotypes, after public health deployments, constitute a major challenge to controlling resistance. Greater attention to mortality differences by genotype would inform strategies to reduce the evolution of resistance to existing and new insecticides.

Modelling New Insecticide-Treated Bed-Nets for Malaria-Vector Control: How to Strategically Manage Resistance?

“…were they not to be possible for manufacturing reasons), mosaics tend to be favoured in their stead. Therefore, whilst our results support the general findings about high effectiveness in Levick et al (2017) that is focused on in subsequent analysis (South & Hastings, 2018;South et al, 2019), especially in contrast to the forerunning ideas from interpretation of the findings in Curtis (1985), the extensions that are implemented within the same parameter framework for two insecticides enable us to clarify that temporally-invariable strategies (mixtures/mosaics) tend to be favoured over temporally-variable strategies (rotations/sequences) in this model. In strategic terms, this suggests that the advantages of simultaneous selective pressures tend to outweigh the advantages of variable selective pressures.…”

“…The near-synchronous development of these new insecticides offers the current opportunity to build resistance management into mosquito control methods and maximize the chance of eradicating malaria. Here, building directly on existing modelling (Levick et al, 2017;South & Hastings, 2018;South et al, 2019), resistancemanagement strategies using multiple insecticides are compared to suggest how to deploy combinations of available and new insecticides on bed-nets for maximum impact. Although the strategy comparison considers what to do with any new insecticide, special attention to SC1, which is the only new insecticide that is known to the authors in having a strobilurin-like chemistry that is unusual for having a target-site that is encoded in the mitochondrial genome.…”

“…untested) insecticides in mosquitoes. Here we follow the precedent of an existing mathematical model that has been explored using simulations of parameter combinations (Levick et al, 2017), which has provided the parameter framework for other studies as well (South & Hastings, 2018;South et al, 2019). The model is 'deterministic' in that it only describes the evolutionary pressure from selection, which is suitable for making comparisons about the relative time to resistance under alternative resistance-management strategies because those strategies are primarily focussed on delaying the evolution of resistance by reducing the strength of selection for resistance.…”

“…Here, building on a parameter framework (Levick et al, 2017) that has been extensively analysed (South & Hastings, 2018;South et al, 2019), conceptual resistance-management strategies using multiple insecticides of rotations, mixtures and mosaics are compared to lower and upper benchmarks to assess what strategy (if any) should be taken to delay the evolution of resistance to the new insecticides. The model setup and analysis differs from Levick et al (2017) in identified ways, including: (i) the explicit assessment of complementary measures of strategy success such as population control, (ii) the comparison of a broader range of resistance-management strategies and (iii) the examination of a different range of scenarios that include new parameters.…”

Dicyclanil resistance in the Australian sheep blowfly, Lucilia cuprina, substantially reduces flystrike protection by dicyclanil and cyromazine based products

“…Cyromazine resistance was shown by bioassay to be incompletely dominant in the sheep blowfly ( Levot, 2012 ) as was the dicyclanil resistance reported here (Sales and Suann unpublished data). As a result, the window of selection is narrower than if either of the resistances were completely dominant ( South et al, 2019 ). However, as the insecticide degrades, the proportion of the population surviving within this selection window at any given time will be greater as it will also include heterozygotes.…”