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. 2021 Feb 16;15(2):e0008492.
doi: 10.1371/journal.pntd.0008492. eCollection 2021 Feb.

Insecticide resistance and genetic structure of Aedes aegypti populations from Rio de Janeiro State, Brazil

Affiliations

Insecticide resistance and genetic structure of Aedes aegypti populations from Rio de Janeiro State, Brazil

Rafi Ur Rahman et al. PLoS Negl Trop Dis. .

Abstract

Vector control largely relies on neurotoxic chemicals, and insecticide resistance (IR) directly threatens their effectiveness. In some cases, specific alleles cause IR, and knowledge of the genetic diversity and gene flow among mosquito populations is crucial to track their arrival, rise, and spread. Here we evaluated Aedes aegypti populations' susceptibility status, collected in 2016 from six different municipalities of Rio de Janeiro state (RJ), to temephos, pyriproxyfen, malathion, and deltamethrin. We collected eggs of Ae. aegypti in Campos dos Goytacazes (Cgy), Itaperuna (Ipn), Iguaba Grande (Igg), Itaboraí (Ibr), Mangaratiba (Mgr), and Vassouras (Vsr). We followed the World Health Organization (WHO) guidelines and investigated the degree of susceptibility/resistance of mosquitoes to these insecticides. We used the Rockefeller strain as a susceptible positive control. We genotyped the V1016I and F1534C knockdown resistance (kdr) alleles using qPCR TaqMan SNP genotyping assay. Besides, with the use of Ae. aegypti SNP-chip, we performed genomic population analyses by genotyping more than 15,000 biallelic SNPs in mosquitoes from each population. We added previous data from populations from other countries to evaluate the ancestry of RJ populations. All RJ Ae. aegypti populations were susceptible to pyriproxyfen and malathion and highly resistant to deltamethrin. The resistance ratios for temephos was below 3,0 in Cgy, Ibr, and Igg populations, representing the lowest rates since IR monitoring started in this Brazilian region. We found the kdr alleles in high frequencies in all populations, partially justifying the observed resistance to pyrethroid. Population genetics analysis showed that Ae. aegypti revealed potential higher migration among some RJ localities and low genetic structure for most of them. Future population genetic studies, together with IR data in Ae aegypti on a broader scale, can help us predict the gene flow within and among the Brazilian States, allowing us to track the dynamics of arrival and changes in the frequency of IR alleles, and providing critical information to improving vector control program.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Map of Rio de Janeiro State with the six municipalities where we collected Aedes spp. for our study.
(Maps were created with open-source scripts in R platform. The shapefiles used to insert the correct map boundaries were all retrieved from public domain sources: Geobr R package, available at https://cran.r-project.org/web/packages/geobr/vignettes/intro_to_geobr.html).
Fig 2
Fig 2. Resistance ratios (RR50) to temephos in Aedes aegypti populations from Rio de Janeiro State, Brazil.
We obtained the RR50 after calculating the lethal concentration (LC50) of each population and comparing it with the LC50 of the reference strain.
Fig 3
Fig 3. Proportional life-stage mortality of larvae of Aedes aegypti populations from Rio de Janeiro exposed to pyriproxyfen for 7–10 days.
Adults’ emergence was 100% inhibited in the reference lineage Rockefeller (Rock) and all populations. Values over the bars indicate the mortality percentage at the end of the test (when 100% of the control condition larvae emerged into adults).
Fig 4
Fig 4. Mortality of Aedes aegypti from RJ populations exposed to the adulticides: (A) organophosphate malathion (20μg/bottle) and (B) pyrethroid deltamethrin 0.05%.
We used Rockefeller (Rock) as the susceptible reference. Dots represent the mean mortality of each performed bioassay with total mean and SEM error bars.
Fig 5
Fig 5. Contemporary migration estimates of Aedes aegypti in Rio de Janeiro State and frequency of the kdr alleles.
The values do not indicate migration rates but are indicative of possible migration routes.
Fig 6
Fig 6. Genetic ancestry of Aedes aegypti populations from Rio de Janeiro.
A) with a reference panel of the global populations of Ae. aegypti, estimated using 21,601 SNP loci in LEA with K = 4. Each bar represents one individual. Different colors represent different genetic clusters, and the proportion of a color indicates the probability of an individual assigned to that genetic cluster. We obtained population from other countries described in Kotsakiozi et al. 2018 [90]. Population names and regions are on the x-axis. B) only populations from Rio de Janeiro State with K = 3. C and D. Admixture proportions of each population.
Fig 7
Fig 7. PCA plots showing the first two principal components (PCs).
The numbers in the parentheses on the axes indicate the percentage of total variation explained by the PCs. The eclipses represent an 80% confidence level. A. Clustering of populations from Rio de Janeiro State with other populations from different countries. B. Clustering the populations from Rio de Janeiro State only. C. Ideal number of principal components used with DAPC. D. Discriminant function 1 showing three distinct genetic clusters in our data with minimal overlap. E. DAPC for our populations. The graph represents the individuals as dots and the groups as inertia ellipses. A bar plot of eigenvalues for the discriminant analysis (DA eigenvalues) at the inset at the right. The inset bars represent the number of discriminant functions retained in the calculations; we used the first two to generate the plot. On the left, a graph shows how many principal components we used.
Fig 8
Fig 8. Phylogeny of the Rio de Janeiro mosquitoes generated in IQTree.
We developed the tree with 16,596 SNP loci. We used four Ae. mascarensis samples as the outgroup. The number on each node indicates the support level estimated by 1000 ultrafast bootstraps.
Fig 9
Fig 9. Global estimates of genetic differentiation (Fst) among populations of Aedes aegypti in Rio de Janeiro (lower left) and sliding window estimates across the genome (upper right).

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