Combining genetic crosses and pool targeted DNA-seq for untangling genomic variations associated with resistance to multiple insecticides in the dengue vectorAedes aegypti
Authors: Cattel Julien , Faucon Frédéric , Lepéron Bastien , Sherpa Stéphanie , Monchal Marie , Grillet Lucie , Gaude Thierry , Laporte Frederic , Dusfour Isabelle , Reynaud Stéphane , David Jean-Philippe 
Published: March 2019

In addition to combating vector-borne diseases, studying the adaptation of mosquitoes to insecticides provides a remarkable example of evolution-in-action driving the selection of complex phenotypes. Indeed, most resistant mosquito populations show multi-resistance phenotypes as a consequence of the variety of insecticides employed and of the complexity of selected resistance mechanisms. Such complexity makes challenging the identification of alleles conferring resistance to specific insecticides and prevents the development of molecular assays to track them in the field. Here we showed that combining simple genetic crosses with pool targeted DNA-seq can enhance the specificity of resistance allele’s detection while maintaining experimental work and sequencing effort at reasonable levels. A multi-resistant population of the mosquitoAedes aegyptiwas exposed to three distinct insecticides (deltamethrin, bendiocarb and fenitrothion) and survivors to each insecticide were crossed with a susceptible strain to generate 3 distinct lines. F2 individuals from each line were then segregated with 2 insecticide doses. Bioassays supported the improved segregation of resistance alleles between lines. Hundreds of genes covering all detoxifying enzymes and insecticide targets together with more than 7,000 intergenic regions equally spread over mosquito genome were sequenced from pools of F0 and F2 individuals unexposed or surviving insecticide. Differential coverage analysis identified 39 detoxification enzymes showing an increased gene copy number in association with resistance. Combining an allele frequency filtering approach with a Bayesian FST-based genome scan identified multiple genomic regions showing strong selection signatures together with 50 non-synonymous variations associated with resistance. This study provides a simple and cost-effective approach to improve the segregation of resistant alleles in multi-resistant populations while reducing false positives frequently arising when comparing populations showing divergent genetic backgrounds. The identification of these insecticide resistance markers paves the way for the design of novel DNA-based resistance tracking assays.

Author summary

In addition to combating vector-borne diseases, understanding how mosquitoes adapt to insecticides provides a remarkable example of evolution-in-action. However, the variety of insecticides used and the complexity of adaptive mechanisms make it difficult to identify the genetic changes conferring resistance to each insecticide. Here we combined simple controlled crosses with mass DNA sequencing for enhancing the specificity of resistance gene detection. A multi-resistant mosquito population was exposed to three distinct insecticides and survivors were crossed with a susceptible strain to generate 3 distinct mosquito lines. Individuals from the second generation of each line were then segregated based on their resistance to each insecticide. Bioassays supported the improved segregation of genetic resistance markers between lines. Hundreds of genes potentially involved in resistance together with thousands non-genic regions equally spread over mosquito genome were sequenced from individuals from each line. Genomic analyses identified detoxification enzymes showing an increased gene copy number in association with resistance and multiple genomic regions showing strong selection signatures and carrying point mutations associated with resistance. Such approach improves the specificity of resistance gene detection in field mosquito populations resisting to multiple insecticides and paves the way for the design of novel DNA-based resistance tracking tools.