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. 2015 Sep;25(9):1347-59.
doi: 10.1101/gr.189225.115. Epub 2015 Jul 23.

Identifying genomic changes associated with insecticide resistance in the dengue mosquito Aedes aegypti by deep targeted sequencing

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Identifying genomic changes associated with insecticide resistance in the dengue mosquito Aedes aegypti by deep targeted sequencing

Frederic Faucon et al. Genome Res. 2015 Sep.

Abstract

The capacity of mosquitoes to resist insecticides threatens the control of diseases such as dengue and malaria. Until alternative control tools are implemented, characterizing resistance mechanisms is crucial for managing resistance in natural populations. Insecticide biodegradation by detoxification enzymes is a common resistance mechanism; however, the genomic changes underlying this mechanism have rarely been identified, precluding individual resistance genotyping. In particular, the role of copy number variations (CNVs) and polymorphisms of detoxification enzymes have never been investigated at the genome level, although they can represent robust markers of metabolic resistance. In this context, we combined target enrichment with high-throughput sequencing for conducting the first comprehensive screening of gene amplifications and polymorphisms associated with insecticide resistance in mosquitoes. More than 760 candidate genes were captured and deep sequenced in several populations of the dengue mosquito Ae. aegypti displaying distinct genetic backgrounds and contrasted resistance levels to the insecticide deltamethrin. CNV analysis identified 41 gene amplifications associated with resistance, most affecting cytochrome P450s overtranscribed in resistant populations. Polymorphism analysis detected more than 30,000 variants and strong selection footprints in specific genomic regions. Combining Bayesian and allele frequency filtering approaches identified 55 nonsynonymous variants strongly associated with resistance. Both CNVs and polymorphisms were conserved within regions but differed across continents, confirming that genomic changes underlying metabolic resistance to insecticides are not universal. By identifying novel DNA markers of insecticide resistance, this study opens the way for tracking down metabolic changes developed by mosquitoes to resist insecticides within and among populations.

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Figures

Figure 1.
Figure 1.
Gene families affected by gene amplifications associated with deltamethrin resistance. (Left) Frequency of each gene family among the 763 captured genes. (Right) Frequency of each gene family among the 41 genes affected by genic amplifications associated with deltamethrin resistance. Proportions of each gene family between all captured genes and genes affected by genic amplifications were compared using a one-sided Fisher's exact test: (***) P < 0.001.
Figure 2.
Figure 2.
Gene amplifications associated with deltamethrin resistance. (Left) CNV profiles of genes affected by gene amplifications associated with deltamethrin resistance. Color scale shows (R+)/(meanS) CNV for each resistant population, and overimposed "+" marks show (R+)/(R−) CNV. (Right) Location of gene amplifications on genomic supercontigs. Amplified genes are shown in red. Nonamplified genes are shown in maroon. Genes not included in the capture design are shown in gray. Chromosomal locations are shown as described in Timoshevskiy et al. (2014) and Juneja et al. (2014).
Figure 3.
Figure 3.
Supercontigs showing selection footprint. For each region, loci displaying a significant BayeScan3 Fsc Q-value <0.05 and both f(R+)-f(S) and f(R+)-f(R−) allele frequency variation occurring in the same direction were considered as outliers. For each supercontig, outlier loci densities were obtained by dividing the number of outlier loci by the total length of all captured genes (outlier loci per kb of captured region). Outlier loci densities are shown as a blue color scale. Gray stands for an absence of outlier loci. For each supercontig, horizontal bars show the number of captured genes from each gene family (upper bar) compared to those affected by outlier loci (lower bar). Supercontigs are clustered according to their putative chromosomal location as described in Timoshevskiy et al. (2014) and Juneja et al. (2014).
Figure 4.
Figure 4.
Best nonsynonymous polymorphisms associated with deltamethrin resistance. Only the 55 best differential nonsynonymous variants identified from frequency-based filtering and the Bayesian approach are shown (see Methods). For each region, allele frequency variation between each resistant population (R+ phenotypes) and their susceptible counterpart (S) are shown as a blue-yellow color scale. Blue indicates an enrichment in the reference allele, whereas yellow indicates an enrichment in the variant allele. Variants identified by the Bayesian approach are indicated by "B" marks. For variants passing frequency-based filtering or BayeScan3 filtering, allele frequency variation between R+ and R− phenotypes are shown as overimposed "+" marks. Variants are grouped by gene families and are described by the following annotations: chromosomal location (according to Juneja et al. 2014; Timoshevskiy et al. 2014), supercontig position, nucleotide change, amino acid position, amino acid change, gene accession number, and gene description. (*) Genes also found affected by CNVs linked to deltamethrin resistance. (**) The sodium channel S729P and F1249C variants correspond to the S989P and F1534C kdr mutations described in the literature due to changes in AAEL006019-RD transcript annotation.

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