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. 2017 Apr 5;11(4):e0005526.
doi: 10.1371/journal.pntd.0005526. eCollection 2017 Apr.

In the hunt for genomic markers of metabolic resistance to pyrethroids in the mosquito Aedes aegypti: An integrated next-generation sequencing approach

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In the hunt for genomic markers of metabolic resistance to pyrethroids in the mosquito Aedes aegypti: An integrated next-generation sequencing approach

Frederic Faucon et al. PLoS Negl Trop Dis. .

Abstract

Background: The capacity of Aedes mosquitoes to resist chemical insecticides threatens the control of major arbovirus diseases worldwide. Until alternative control tools are widely deployed, monitoring insecticide resistance levels and identifying resistance mechanisms in field mosquito populations is crucial for implementing appropriate management strategies. Metabolic resistance to pyrethroids is common in Aedes aegypti but the monitoring of the dynamics of resistant alleles is impeded by the lack of robust genomic markers.

Methodology/principal findings: In an attempt to identify the genomic bases of metabolic resistance to deltamethrin, multiple resistant and susceptible populations originating from various continents were compared using both RNA-seq and a targeted DNA-seq approach focused on the upstream regions of detoxification genes. Multiple detoxification enzymes were over transcribed in resistant populations, frequently associated with an increase in their gene copy number. Targeted sequencing identified potential promoter variations associated with their over transcription. Non-synonymous variations affecting detoxification enzymes were also identified in resistant populations.

Conclusion /significance: This study not only confirmed the role of gene copy number variations as a frequent cause of the over expression of detoxification enzymes associated with insecticide resistance in Aedes aegypti but also identified novel genomic resistance markers potentially associated with their cis-regulation and modifications of their protein structure conformation. As for gene transcription data, polymorphism patterns were frequently conserved within regions but differed among continents confirming the selection of different resistance factors worldwide. Overall, this study paves the way of the identification of a comprehensive set of genomic markers for monitoring the spatio-temporal dynamics of the variety of insecticide resistance mechanisms in Aedes aegypti.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Gene families affected by DE genes in resistant populations.
Genes showing a fold change ≥ 3 in either directions, and q-value ≤ 0.001 in any resistant population as compared to all susceptible strains were considered differentially expressed. Biological categories mostly affected were identified by comparing their proportion between all genes detected by RNA-seq and those being differentially expressed using a one-sided Fisher’s exact test (*** p ≤ 0.001).
Fig 2
Fig 2. Transcription profiles of detoxification enzymes associated with deltamethrin resistance.
Color scale shows the mean Log2 Fold Change between resistant populations and all susceptible strains. Stars indicate a fold change q value ≤ 0.001. Black ‘+’ marks indicate a significant positively correlation between Log2 transcription ratios and Log2 CNVs (Pearson’s r ≥ 0.7 and p ≤ 0.05). Grey ‘+’ marks indicate genes belonging to genomic clusters displaying CNV associated with deltamethrin resistance as reported in [28].
Fig 3
Fig 3. Promoter elements potentially associated with the up regulation of detoxification genes in resistant populations.
Only variations correlated with transcription level and favouring the binding of potential enhancer elements are shown. Potential initiation sites (Inr) and TATA boxes are indicated. For each gene, variations detected in the upstream region are shown as empty and filled dots depending on the significance of the correlation between their allele frequency and gene transcription level.
Fig 4
Fig 4. Gene families affected by polymorphism variations in resistant populations.
The frequency of each gene family was compared between all detected polymorphic SNPs (top) and both differential SNPs (Diff SNPs) and non-synonymous differential SNPs (NS Diff SNPs) using a one-sided Fisher’s exact test (*p ≤ 0.05,*** p ≤ 0.001).
Fig 5
Fig 5. Non-synonymous differential SNPs affecting detoxification genes.
Color scale shows the mean allele frequency variation between each resistant strain and all susceptible strains. Yellow/blue colors indicate an increase/decrease frequency of the variant allele in the resistant population respectively. Chromosome, supercontig, base position, nucleotide variation (ref>var), strand, position in cDNA, position in protein, amino-acid change (ref>var), gene accession and gene name are indicated. Stars indicate genes previously affected by NS Diff SNPs as reported in [28]. Boxed amino acid changes indicate variations likely affecting substrate binding site.

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