Ibrahim, S.S., Riveron, J.M., Bibby, J., Irving, H., Yunta, C., Paine, M.J., Wondji, C.S. (2015). Allelic Variation of Cytochrome P450s Drives Resistance to Bednet Insecticides in a Major Malaria Vector. PLoS Genet. 11(10): e1005618.
FlyBase ID
FBrf0230077
Publication Type
Research paper
Abstract
Scale up of Long Lasting Insecticide Nets (LLINs) has massively contributed to reduce malaria mortality across Africa. However, resistance to pyrethroid insecticides in malaria vectors threatens its continued effectiveness. Deciphering the detailed molecular basis of such resistance and designing diagnostic tools is critical to implement suitable resistance management strategies. Here, we demonstrated that allelic variation in two cytochrome P450 genes is the most important driver of pyrethroid resistance in the major African malaria vector Anopheles funestus and detected key mutations controlling this resistance. An Africa-wide polymorphism analysis of the duplicated genes CYP6P9a and CYP6P9b revealed that both genes are directionally selected with alleles segregating according to resistance phenotypes. Modelling and docking simulations predicted that resistant alleles were better metabolizers of pyrethroids than susceptible alleles. Metabolism assays performed with recombinant enzymes of various alleles confirmed that alleles from resistant mosquitoes had significantly higher activities toward pyrethroids. Additionally, transgenic expression in Drosophila showed that flies expressing resistant alleles of both genes were significantly more resistant to pyrethroids compared with those expressing the susceptible alleles, indicating that allelic variation is the key resistance mechanism. Furthermore, site-directed mutagenesis and functional analyses demonstrated that three amino acid changes (Val109Ile, Asp335Glu and Asn384Ser) from the resistant allele of CYP6P9b were key pyrethroid resistance mutations inducing high metabolic efficiency. The detection of these first DNA markers of metabolic resistance to pyrethroids allows the design of DNA-based diagnostic tools to detect and track resistance associated with bednets scale up, which will improve the design of evidence-based resistance management strategies.
