Fitness costs in the presence and absence of insecticide use explains abundance of two common Aedes aegypti kdr resistance alleles found in the Americas

Juan J. Silva; Cera R. Fisher; Anastacia E. Dressel; Jeffrey G. Scott

doi:10.1371/journal.pntd.0011741

Abstract

Aedes aegypti is the vector of viruses such as chikungunya, dengue, yellow fever and Zika that have a critical impact on human health. Control of adult mosquitoes is widely done using pyrethroids, but resistance has reduced the effectiveness of this class of insecticides. Resistance to pyrethroids in mosquitoes is commonly due to mutations in the voltage-gated sodium channel (Vgsc) gene (these mutations are known as knockdown resistance, kdr). In the Americas and the Caribbean, the most common kdr alleles are 410L+1016I+1534C and 1534C. In this study, we conducted a population cage experiment to evaluate changes in the allele and genotype frequencies of the 410L+1016I+1534C allele by crossing two congenic strains; one carrying the 410L+1016I+1534C and another with the 1534C allele. Changes in allele frequencies were measured over 10 generations in the absence of insecticide exposure. We also applied one cycle of selection with deltamethrin at F₉ to evaluate the changes in allele and genotype frequencies. Our findings indicate that fitness costs were higher with the 410L+1016I+1534C allele, relative to the 1534C allele, in the absence of deltamethrin exposure, but that the 410L+1016I+1534C allele provides a stronger advantage when exposed to deltamethrin relative to the 1534C allele. Changes in genotype frequencies were not in Hardy-Weinberg equilibrium and could not be explained by drift. Our results suggest the diametrically opposed fitness costs in the presence and absence of insecticides is a reason for the variations in frequencies between the 410L+1016I+1534C and 1534C alleles in field populations.

Author summary

Insecticide resistance limits our ability to control vectors of human diseases. Resistance to widely used pyrethroid insecticides can occur by mutations in the voltage gated sodium channel (Vgsc) and alleles with these mutations are collectively known as knockdown resistance (kdr). The frequency of resistance alleles is driven by selection with pyrethroid insecticides, but kdr alleles decrease in frequency in the absence of insecticide. The relative fitness of different kdr alleles to each other is largely unknown. We show that the 1534C allele is favored in the absence of pyrethroid, but that the 410L+1016I+1534C allele is favored when deltamethrin selection occurs. These results help to explain the relative frequency of these alleles that have been detected in field collections.

Citation: Silva JJ, Fisher CR, Dressel AE, Scott JG (2023) Fitness costs in the presence and absence of insecticide use explains abundance of two common Aedes aegypti kdr resistance alleles found in the Americas. PLoS Negl Trop Dis 17(11): e0011741. https://doi.org/10.1371/journal.pntd.0011741

Editor: Paul O. Mireji, Kenya Agricultural and Livestock Research Organization, KENYA

Received: July 11, 2023; Accepted: October 19, 2023; Published: November 1, 2023

Copyright: © 2023 Silva et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This research was supported by the National Institutes of Allergy and Infectious Diseases (5R21AI146513) to JGS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. All authors received some salary from the funding agency.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Aedes aegypti is the vector of viruses that have devastating impacts on public health and insecticide control of adult mosquitoes is most commonly carried out using pyrethroids. A. aegypti is the vector of viruses such as chikungunya, dengue, yellow fever and Zika [1–5]. To reduce the burden caused by these diseases, control of adult mosquitoes is mainly done using a class of insecticides known as pyrethroids, with deltamethrin being a common pyrethroid applied for control of A. aegypti in the field. Pyrethroid toxicity occurs due to the binding and disruption of the voltage-gated sodium channel (VGSC).

Resistance to pyrethroids in mosquitoes is commonly due to mutations in the Vgsc gene (mutations that cause pyrethroid resistance are known as knockdown resistance or kdr). There are 10 kdr alleles in A. aegypti based on the sequencing of either full-length cDNA or PCR products of domains I, II or III of the Vgsc gene (S1 Table) [6–15]. The most common kdr alleles found in the Americas are 410L+1016I+1534C, and 1534C [6,8,15,16] (amino acid numbering based on Musca domestica VGSC, GenBank: CAA65448.1). The 1534C allele confers 7- to 16-fold resistance to pyrethroids [17]. The 410L+1016I+1534C allele confers similar levels of resistance (compared to 1534C), except that the 410L+1016I+1534C allele gives higher levels of resistance to deltamethrin and flumethrin [18].

Relative to insecticide susceptible alleles, resistance alleles commonly have a fitness cost in the absence of insecticide exposure [19]. However, field populations often include multiple resistance alleles. Thus, the fitness of the different resistance alleles to each other (in both the presence and absence of insecticide) is important to understanding the evolution of resistance. For example, in Musca domestica the relative fitness in the presence of pyrethroids of the different kdr alleles is super-kdr (M918T+L1014F) > kdr (L1014F) > kdr-his (L1014H), but in the absence of insecticide the relative fitness is susceptible > kdr-his > kdr > super-kdr [20], suggesting that alleles with multiple mutations might have a greater fitness cost than those with a single mutation. In A. aegypti, the relative fitness costs of different kdr alleles (to other kdr alleles) is much less studied. This is problematic because many resistance alleles coexist in field populations, and some potentially impose more detrimental disadvantages relative to other resistance alleles in the absence of insecticide exposure. Therefore, it is valuable to understand not only the relative fitness of kdr alleles relative to the susceptible alleles, but also to understand the relative fitness of the different kdr alleles to each other. Such information is needed to better understand the patterns of alleles found in field populations.

In this study, we investigated the relative fitness of two A. aegypti kdr alleles (410L+1016I+1534C and 1534C) relative to each other, in the absence and presence of insecticide exposure. We wanted to assess whether a resistance allele with multiple mutations imposed greater fitness costs relative to the allele with only one mutation (in accordance with what was previously found in M. domestica). Overall, the 410L+1016I+1534C allele imposes both an astonishing fitness cost in the absence of insecticide, and a strong advantage relative to 1534C in the presence of deltamethrin exposure.

Materials and methods

Mosquito strains

Two congenic strains of A. aegypti were used in this study. Both strains share the same pyrethroid-susceptible background from the Rockefeller strain (ROCK), but carry different kdr alleles. The LMRKDR:ROCK (LKR) is a pyrethroid-resistant strain which is congenic to ROCK, but contains the kdr allele 410L+1016I+1534C [18]. The 1534C:ROCK is a pyrethroid-resistant strain which is congenic to ROCK, but contains the F1534C allele [17]. In both congenic strains, the kdr alleles are the only mechanism of resistance to pyrethroids [17,18].

Allele competition experiments

To conduct the allele competition experiments (also known as population cage experiments), the two congenic strains were crossed: Cross A (LKR females x 1534C:ROCK males) and reciprocal cross B (1534C:ROCK females x LKR males)(Fig 1). For each reciprocal cross, 400 unmated females and 200 males were released into a cage and were allowed to mate en masse for seven days. The offspring resulting from crosses A and B were split into three cages (A1, A2, A3, B1, B2 and B3) as shown in Fig 1. Mosquitoes were reared at 25.0–28.5°C (average = 25.6), 26.8–54.5% (average = 47.9%) relative humidity, and a 14:10-h (light/dark) photoperiod. All replicates were run at the same time. Mosquitoes were reared as previously described [21]. Approximately 800 pupae from the larval containers were placed into cages for each following generation. Additional emerging males and females were stored separately in 2 ml Eppendorf tubes at -80°C until they were genotyped.

Download:

Fig 1. Diagram of the experimental design used.

https://doi.org/10.1371/journal.pntd.0011741.g001

The genotyping of the mosquitoes was done by allele specific polymerase chain reaction (ASPCR) using primers that were allele specific at codon 1016 of the Vgsc (one allele has 410L+1016I+1534C and allele 1534C has V410+V1016+1534C). The genomic DNA from mosquito heads was extracted as previously described [22] and ASPCR was done using the following thermocycler conditions: For the 1534C allele, 94°C for 3 min, 36 cycles of 94°C for 30 sec, 62°C for 30 sec and 72°C for 1 min, and 72°C (7 min). For the 410L+1016I+1534C allele, 94°C for 3 min, 34 x (94°C for 30 sec, 65°C for 30 sec, 72°C for 1 min) and 72°C (7 min). We used 1 μL of extracted DNA as templates for ASPCR. The primers used for each PCR reaction are shown in S2 Table. Each PCR reaction was evaluated on a 1% agarose gel and was scored as homozygous V1016 (PCR band only with V1016 primers), homozygous for 1016I (ASPCR band only with 1016I primers) or heterozygous (PCR band with both 1016V and 1016I primers). For each biological replicate, 45 males and 45 females were genotyped. Every PCR plate included control DNA from 2 samples of LKR, 1534C:ROCK and F₁ (control DNA was previously sent for sequencing to validate the expected codon V1016I for each corresponding strain or F₁). In addition to the controls, the codon V1016I was verified for 16 samples from A2F5 and A1F10, eight samples each). The sequencing results for the controls and samples agreed with the results from the gels. Each sample was genotyped using two technical replicates by running PCR reactions and gels twice.

The genotyping results were scored independently by three judges (JJS, JGS and CRF). Each judge assigned a genotype for each mosquito in both technical replicates. If the technical replicates agreed, a final genotype was assigned. A consensus genotype was determined if there was agreement between two out of three judges. If three judges disagreed on the final genotype, then those samples were considered for a third technical replicate. Third replicates were done if there were more than 10 samples per biological replicate for which there was no consensus genotype; otherwise, these samples were dropped from analysis.

Selection of F₉ using deltamethrin

We selected 3-7-day old males and females with deltamethrin. The doses used for males and females (0.039 ng/mosquito and 0.078 ng/mosquito, respectively) were chosen to give approximately 80% kill. To conduct the selections, mosquitoes were knocked down with CO₂ and placed in a paper cup on a bucket filled with ice to keep them anesthetized.

Deltamethrin in acetone (AF10-1Sel, AF10-2Sel, AF10-3Sel, BF10-1Sel, BF10-2Sel and BF10-3Sel populations) or acetone only (AF10-1, AF10-2, AF10-3, BF10-1, BF10-2 and BF10-3 populations) was applied topically to each mosquito with a PB600-1 repeating dispenser and a 10 μL Hamilton syringe (Hamilton Company, Reno NV). Treated mosquitoes (40) were placed in a paper cup covered with nylon tulle and a cotton ball saturated with distilled water and held at 25°C for 24 hours. Both male and unmated female survivors were released in disposable cages (dimensions: A-15.7 x 15.7 x 24 inches, Restcloud Chengdu YiShouWeiSheng Technology Co., Ltd, China) and allowed to mate en masse. These mosquitoes were reared as described above and their offspring harvested for Vgsc genotyping.

Allele competition data analysis

Differences between allele and genotype frequencies across generations were tested using linear mixed models and checked for significance from F-values generated from ANOVA in R as previously described [21].

Deviation from Hardy-Weinberg equilibrium (HWE) within generations was assessed using a chi-square test (χ2) to compare the observed genotype counts relative to the expected genotype counts calculated using the allele frequencies of the same generation [21]. The level of significance for statistical analyses was p < 0.05. Simulations were used to evaluate the likelihood of the observed allele frequency changes due to genetic drift. Simulations were done with R software [23] and scripts applied as in a previous study [24]. The model assumed a diploid and panmictic population with a fixed size of 800 individuals and the initial 410L+1016I+1534C allele frequency for each generation was defined as the observed frequency in the previous interval. Simulations were repeated 50,000 times per generation and p-values were estimated as the number of simulations in which ending allele frequency was equal or more extreme than the initial value of the interval divided by the total number of simulations. The null hypothesis assumed all fluctuations in allele frequencies could be explained by genetic drift. The p-values obtained for the HWE and genetic drift were adjusted for multiple comparisons using Holm’s test [25].

Results

There were no significant differences in the allele frequencies over generations between reciprocal crosses A and B (Fig 1) for the unselected (ANOVA, p-value = 0. 951) or deltamethrin-selected generations (ANOVA, p-value = 0.174). There were no significant differences in the allele frequencies over generations between males and females in the absence (ANOVA, p-value = 0.888) nor presence of deltamethrin selection (ANOVA, p-value = 0.774. For this reason, allele frequencies from crosses A and B (and from males and females) were pooled for further analysis.

There was a remarkable fitness disadvantage to the 410L+1016I+1534C allele, relative to the 1534C allele, in the absence of insecticide. The fitness cost was most dramatic from the F₁ to F₃ and F₅ where the 410L+1016I+1534C allele decreased from an average frequency of 50.0% to 21.3% and 11.5%, respectively (S1 Table). The frequency of the 410L+1016I+1534C allele remained relatively unchanged after the F₅ (Fig 2 and S1 Table). The frequency of the 410L+1016I+1534C allele decreased in a trend that is best fitted by a power function model (F_1,34 = 276.3, r² = 0.89, ANOVA = 2.20x10^-16). Changes in genotype frequencies over time are shown in Fig 3. The genotype frequencies were not in HWE (within generation) at the F₃, were largely in HWE in the F₅ and F₇, and were variable between replicates for the F₇ and F₉ (S1 Table). Our simulations indicated that the changes in allele frequencies from the F₁ could not be accounted for by drift (all p-values = 0). However, as the changes in allele frequency between generations became more subtle (F₇-F₁₀), drift was found to possibly explain 8 out of 18 results (comparisons made to previous generation) (S1 Table).

Download:

Fig 2. Frequency changes of the 410L+1016I+1534C allele over 10 generations in the absence and presence of deltamethrin selection.

The solid black line represents the changes in allele frequencies in the absence of deltamethrin exposure are represented by a power function model (F_1,34 = 276.3, r² = 0.89). The dashed orange line represents the changes in allele frequencies from F₉ to F₁₀ in the presence of deltamethrin selection (linear model F_1,10 = 186.1, r² = 0.95). Different letters represent significant differences based on Tukey’s test.

https://doi.org/10.1371/journal.pntd.0011741.g002

Download:

Fig 3. Changes in genotype frequencies over generations in the absence and presence of deltamethrin selection.

Different letters represent significant differences based on Tukey’s test.

https://doi.org/10.1371/journal.pntd.0011741.g003

The 410L+1016I+1534C allele exhibited an extraordinarily strong fitness advantage after exposure to deltamethrin with the allele frequency rising from 16% to 62.1% (Fig 2). As expected for a population under selection, the genotype frequencies do not follow the assumptions of HWE (p-values were < 0.0001) (S1 Table). These results are consistent with the observation that the 410L+1016I+1534C allele gave higher levels of deltamethrin resistance than the 1534C allele [18].

Discussion

The frequencies of resistance alleles in populations are a function of the level of protection they provide during periods of insecticide exposure, and their relative fitness in the absence of insecticide. Relative to the 1534C allele, the 410L+1016I+1534C allele gives similar levels of resistance to common insecticides such as cyhalothrin, cypermethrin and etofenprox (resistance ratios of range from 7.6–14 for these alleles), but gives higher levels of resistance to deltamethrin and flumethrin (resistance ratios of 42 and 57 for the 410L+1016I+1534C allele, compared to 15 and 13 for the 1534C allele) [18]. A survey of A. aegypti populations in the Americas in 2016–2017 found that the 410L+1016I+1534C allele was more common than the 1534C allele [15]. Data collected from 25 field populations from Brazil (2017–2018) found the 1534C allele was the most common (40.0%) followed by the 410L+1016I+1534C allele (38.2%) [16]. In Colombia, (2013–2014) both the 1534C (43.9%) and 410L+1016I+1534C alleles (22.2%) were found [6]. In Mexico (2016) the 416L+1016I+1534C (68.4%) and 1534C alleles (25.9%) were common [8].

The frequencies of the 410L+1016I+1534C and 1534C alleles are strongly influenced by the fitness disadvantage of the 410L+1016I+1534C allele relative to the 1534C allele in the absence of insecticides. In contrast in the presence of deltamethrin, the 410L+1016I+1534C allele has an enormous fitness advantage. During periods of use of pyrethroids such as cyhalothrin (to which the 410L+1016I+1534C and 1534C alleles confer similar levels of resistance [17,18]), both alleles would be favored, relative to susceptible alleles. These results bear some similarity to kdr alleles in house fly where the super-kdr allele (having two mutations) confers higher levels of resistance, but also has a greater fitness cost in the absence of insecticide, compared to kdr-type alleles with a single mutation [20]. Given the importance of the VGSC to neurological function, it would be interesting to explore what neurophysiological changes might underline the fitness costs of kdr alleles.

The fitness cost of the 410L+1016I+1534C allele (relative to the 1534C allele) was remarkably higher than has been found for comparisons of kdr versus susceptible alleles. For example, using a similar population cage experiment, the 989P+1016G [26] and the F1534C [21] alleles were shown to have a fitness cost relative to a susceptible allele, but the change in allele frequencies across generations was far less pronounced (frequency of the kdr allele decreased in a linear manner and was >0.3 by the F₇) than we observed in this study. Another study used a strain of A. aegypti congenic to the pyrethroid-susceptible ROCK strain, but carrying an unknown kdr allele (although it was known to carry the V1016I mutation) and measured fitness costs. The frequency of the allele containing the 1016I mutation decreased through 15 generations in the absence of pyrethroid exposure (cross A: r² = 0.527, p = 0.0006 and cross B: r² = 0.569, p = 0.0003, respectively) [27] at a rate similar to what was seen for the 1534C and 989P+1016G alleles. It would be helpful to know what the allele was in those experiments, as well as what role (if any) the enhanced detoxification enzymes played in the changes in allele frequency.

Our findings show a strong fitness cost associated with the 410L+1016I+1534C allele in the absence of insecticide exposure, but this allele provides a fitness advantage when exposed to deltamethrin, relative to the 1534C allele. Given the dramatic fitness cost of the 410L+1016I+1534C allele relative to the 1534C allele in the absence of insecticide, it is possible that the functionality of the VGSC is more drastically impaired for the former allele compared to the latter and investigating the neurophysiological differences between these alleles would be insightful.

Supporting information

S1 Table. Genotype, allele frequencies for each replicate of the LKR x 1534C:ROCK experiment and their associated Hardy-Weinberg equilibrium (HWE) and genetic drift p-values.

https://doi.org/10.1371/journal.pntd.0011741.s001

(DOCX)

S2 Table. List of primers used for genotyping the V1016I mutation from LMRKDR:RK and 1534C:ROCK.

https://doi.org/10.1371/journal.pntd.0011741.s002

(DOCX)

Acknowledgments

We thank N. Buchon, P. Wang, R. Mertz, R. Norris, and L. Pfannenstiel for their valuable comments.

References

1. Yactayo S, Staples JE, Millot V, Cibrelus L, Ramon-Pardo P. Epidemiology of chikungunya in the Americas. J Infect Dis 2016; 214: S441–S445. pmid:27920170
- View Article
- PubMed/NCBI
- Google Scholar
2. Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The global distribution and burden of dengue. Nature 2013; 496: 504. pmid:23563266
- View Article
- PubMed/NCBI
- Google Scholar
3. WHO Dengue and severe dengue. http://www.who.int/mediacentre/factsheets/fs117/en/
4. ECDC (2015) Zika virus epidemic in the Americas: potential association with microcephaly and Guillain-Barré syndrome. Stockholm, Sweden: European Centre for Disease Prevention and Control.
5. CDC (2016) Zika virus spreads to new areas—Region of the Americas, May 2015–January 2016. Atlanta, Ga: Centers for Disease Control and Prevention. 55–58 p.
6. Granada Y, Mejía-Jaramillo A, Strode C, Triana-Chavez O. A point mutation V419L in the sodium channel gene from natural populations of Aedes aegypti is involved in resistance to λ-cyhalothrin in Colombia. Insects 2018; 9: 23.
- View Article
- Google Scholar
7. Haddi K, Tomé HVV, Du Y, Valbon WR, Nomura Y, Martins GF, et al. Detection of a new pyrethroid resistance mutation (V410L) in the sodium channel of Aedes aegypti: a potential challenge for mosquito control. Sci Rep 2017; 7: 46549. pmid:28422157
- View Article
- PubMed/NCBI
- Google Scholar
8. Saavedra-Rodriguez K, Maloof FV, Campbell CL, Garcia-Rejon J, Lenhart A, Penilla P, et al. Parallel evolution of vgsc mutations at domains IS6, IIS6 and IIIS6 in pyrethroid resistant Aedes aegypti from Mexico. Sci Rep 2018; 8: 6747. pmid:29712956
- View Article
- PubMed/NCBI
- Google Scholar
9. Srisawat R, Komalamisra N, Apiwathnasorn C, Paeporn P, Sittiruk R, Rongsriyam Y, et al. Field-collected permethrin-resistant Aedes aegypti from central Thailand contain point mutations in the domain IIS6 of the sodium channel gene (kdr). SE Asian J Trop Med 2012; 43: 1380–1386. pmid:23413701
- View Article
- PubMed/NCBI
- Google Scholar
10. Kawada H, Oo SZM, Thaung S E.K, Maung YNM, Thu HM, et al. Co-occurrence of point mutations in the voltage-gated sodium channel of pyrethroid-resistant Aedes aegypti populations in Myanmar. PLoS Negl Trop Dis 2014; 8: e3032. pmid:25077956
- View Article
- PubMed/NCBI
- Google Scholar
11. Murcia O, Henríquez B, Castro A, Koo S, Young J, Márquez R, et al. Presence of the point mutations Val1016Gly in the voltage-gated sodium channel detected in a single mosquito from Panama. Parasite Vector 2019; 12: 62.
- View Article
- Google Scholar
12. Harris AF, Rajatileka S, Ranson H. Pyrethroid resistance in Aedes aegypti from Grand Cayman. Am J Trop Med Hyg 2010; 83: 277–284.
- View Article
- Google Scholar
13. Chang C, Shen WK, Wang TT, Lin YH, Hsu EL, Dai SM. A novel amino acid substitution in a voltage-gated sodium channel is associated with knockdown resistance to permethrin in Aedes aegypti. Insect Biochem Mol Bio 2009; 39: 272–278.
- View Article
- Google Scholar
14. Kushwah RBS, Dykes CL, Kapoor N, Adak T, Singh OP. Pyrethroid-resistance and presence of two knockdown resistance (kdr) mutations, F1534C and a novel mutation T1520I, in Indian Aedes aegypti. PLoS Negl Trop Dis 2015; 9: e3332. pmid:25569164
- View Article
- PubMed/NCBI
- Google Scholar
15. Fan Y O’Grady P, Yoshimizu M, Ponlawat A, Kaufman PE, Scott JG. Evidence for both recombination and mutations in the evolution of kdr alleles in Aedes aegypti. PLoS Negl Trop Dis 2020; 14: e0008154.
- View Article
- Google Scholar
16. Costa MM, Campos KB, Brito LP, Roux E, Rodovalho CM, Bellinato DF, et al. Kdr genotyping in Aedes aegypti from Brazil on a nation-wide scale from 2017 to 2018. Sci Rep 2020; 10: 13267. pmid:32764661
- View Article
- PubMed/NCBI
- Google Scholar
17. Fan Y, Scott JG. The F1534C voltage sensitive sodium channel mutation confers 7- to 16-fold resistance to pyrethroid insecticides in Aedes aegypti. Pest Man Sci 2020; 76: 2251–2259. pmid:31981401
- View Article
- PubMed/NCBI
- Google Scholar
18. Silva JJ, Couam CN, Scott JG. Levels of cross-resistance to pyrethroids conferred by the Vssc knockdown resistance allele 410L+1016I+1534C in Aedes aegypti. PLoS Negl Trop Dis 2021; 15: e0009549. pmid:34252110
- View Article
- PubMed/NCBI
- Google Scholar
19. Freeman JC, Smith LB, Silva JJ, Fan Y, Sun H, Scott JG. Fitness studies of insecticide resistant strains: lessons learned and future directions. Pest Man Sci 2021; 77: 3847–3856. pmid:33506993
- View Article
- PubMed/NCBI
- Google Scholar
20. Rinkevich FD, Leichter CA, Lazo TA, Hardstone MC, Scott JG. Variable fitness costs for pyrethroid resistance alleles in the house fly, Musca domestica, in the absence of insecticide pressure. Pestic Biochem Physiol 2013; 105: 161–168.
- View Article
- Google Scholar
21. Fisher CR, Dressel AE, Silva JJ, Scott JG. A globally distributed insecticide resistance allele confers a fitness cost in the absence of insecticide in Aedes aegypti, the yellow fever mosquito. J Med Ent 2022; 60: 494–499.
- View Article
- Google Scholar
22. Smith LB, Kasai S, Scott JG. Voltage-sensitive sodium channel mutations S989P+V1016G in Aedes aegypti confer variable resistance to pyrethroids, DDT and oxadiazines. Pest Manag Sci 2017; 74: 737–745.
- View Article
- Google Scholar
23. R Development Core Team (2022) R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
24. Hardstone MC, Lazzaro BP, Scott JG. The effect of three environmental conditions on the fitness of cytochrome P450 monooxygenase-mediated permethrin resistance in Culex pipiens quinquefasciatus. BMC Evol Biol 2009; 9: 42.
- View Article
- Google Scholar
25. Holm S. A simple sequentially rejective multiple test procedure. Scand J Statist 1979; 6: 65–70.
- View Article
- Google Scholar
26. Smith LB, Silva JJ, Chen C, Harrington L, Scott JG. Fitness costs of individual and combined pyrethroid resistance mechanisms, kdr and CYP-mediated detoxification, in Aedes aegypti. PLoS Negl Trop Dis 2021; 15: e009271. pmid:33760828
- View Article
- PubMed/NCBI
- Google Scholar
27. Brito LP, Linss JG, Lima-Camara TN, Belinato TA, Peixoto AA, Lima JB, et al. Assessing the effects of Aedes aegypti kdr mutations on pyrethroid resistance and its fitness cost. PLoS One 2013; 8: e60878. pmid:23593337
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Yactayo S, Staples JE, Millot V, Cibrelus L, Ramon-Pardo P. Epidemiology of chikungunya in the Americas. J Infect Dis 2016; 214: S441–S445. pmid:27920170
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The global distribution and burden of dengue. Nature 2013; 496: 504. pmid:23563266
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. WHO Dengue and severe dengue. http://www.who.int/mediacentre/factsheets/fs117/en/

[ref4] 4. ECDC (2015) Zika virus epidemic in the Americas: potential association with microcephaly and Guillain-Barré syndrome. Stockholm, Sweden: European Centre for Disease Prevention and Control.

[ref5] 5. CDC (2016) Zika virus spreads to new areas—Region of the Americas, May 2015–January 2016. Atlanta, Ga: Centers for Disease Control and Prevention. 55–58 p.

[ref6] 6. Granada Y, Mejía-Jaramillo A, Strode C, Triana-Chavez O. A point mutation V419L in the sodium channel gene from natural populations of Aedes aegypti is involved in resistance to λ-cyhalothrin in Colombia. Insects 2018; 9: 23.
View Article
Google Scholar

[13] View Article

[14] Google Scholar

[ref7] 7. Haddi K, Tomé HVV, Du Y, Valbon WR, Nomura Y, Martins GF, et al. Detection of a new pyrethroid resistance mutation (V410L) in the sodium channel of Aedes aegypti: a potential challenge for mosquito control. Sci Rep 2017; 7: 46549. pmid:28422157
View Article
PubMed/NCBI
Google Scholar

[16] View Article

[17] PubMed/NCBI

[18] Google Scholar

[ref8] 8. Saavedra-Rodriguez K, Maloof FV, Campbell CL, Garcia-Rejon J, Lenhart A, Penilla P, et al. Parallel evolution of vgsc mutations at domains IS6, IIS6 and IIIS6 in pyrethroid resistant Aedes aegypti from Mexico. Sci Rep 2018; 8: 6747. pmid:29712956
View Article
PubMed/NCBI
Google Scholar

[20] View Article

[21] PubMed/NCBI

[22] Google Scholar

[ref9] 9. Srisawat R, Komalamisra N, Apiwathnasorn C, Paeporn P, Sittiruk R, Rongsriyam Y, et al. Field-collected permethrin-resistant Aedes aegypti from central Thailand contain point mutations in the domain IIS6 of the sodium channel gene (kdr). SE Asian J Trop Med 2012; 43: 1380–1386. pmid:23413701
View Article
PubMed/NCBI
Google Scholar

[24] View Article

[25] PubMed/NCBI

[26] Google Scholar

[ref10] 10. Kawada H, Oo SZM, Thaung S E.K, Maung YNM, Thu HM, et al. Co-occurrence of point mutations in the voltage-gated sodium channel of pyrethroid-resistant Aedes aegypti populations in Myanmar. PLoS Negl Trop Dis 2014; 8: e3032. pmid:25077956
View Article
PubMed/NCBI
Google Scholar

[28] View Article

[29] PubMed/NCBI

[30] Google Scholar

[ref11] 11. Murcia O, Henríquez B, Castro A, Koo S, Young J, Márquez R, et al. Presence of the point mutations Val1016Gly in the voltage-gated sodium channel detected in a single mosquito from Panama. Parasite Vector 2019; 12: 62.
View Article
Google Scholar

[32] View Article

[33] Google Scholar

[ref12] 12. Harris AF, Rajatileka S, Ranson H. Pyrethroid resistance in Aedes aegypti from Grand Cayman. Am J Trop Med Hyg 2010; 83: 277–284.
View Article
Google Scholar

[35] View Article

[36] Google Scholar

[ref13] 13. Chang C, Shen WK, Wang TT, Lin YH, Hsu EL, Dai SM. A novel amino acid substitution in a voltage-gated sodium channel is associated with knockdown resistance to permethrin in Aedes aegypti. Insect Biochem Mol Bio 2009; 39: 272–278.
View Article
Google Scholar

[38] View Article

[39] Google Scholar

[ref14] 14. Kushwah RBS, Dykes CL, Kapoor N, Adak T, Singh OP. Pyrethroid-resistance and presence of two knockdown resistance (kdr) mutations, F1534C and a novel mutation T1520I, in Indian Aedes aegypti. PLoS Negl Trop Dis 2015; 9: e3332. pmid:25569164
View Article
PubMed/NCBI
Google Scholar

[41] View Article

[42] PubMed/NCBI

[43] Google Scholar

[ref15] 15. Fan Y O’Grady P, Yoshimizu M, Ponlawat A, Kaufman PE, Scott JG. Evidence for both recombination and mutations in the evolution of kdr alleles in Aedes aegypti. PLoS Negl Trop Dis 2020; 14: e0008154.
View Article
Google Scholar

[45] View Article

[46] Google Scholar

[ref16] 16. Costa MM, Campos KB, Brito LP, Roux E, Rodovalho CM, Bellinato DF, et al. Kdr genotyping in Aedes aegypti from Brazil on a nation-wide scale from 2017 to 2018. Sci Rep 2020; 10: 13267. pmid:32764661
View Article
PubMed/NCBI
Google Scholar

[48] View Article

[49] PubMed/NCBI

[50] Google Scholar

[ref17] 17. Fan Y, Scott JG. The F1534C voltage sensitive sodium channel mutation confers 7- to 16-fold resistance to pyrethroid insecticides in Aedes aegypti. Pest Man Sci 2020; 76: 2251–2259. pmid:31981401
View Article
PubMed/NCBI
Google Scholar

[52] View Article

[53] PubMed/NCBI

[54] Google Scholar

[ref18] 18. Silva JJ, Couam CN, Scott JG. Levels of cross-resistance to pyrethroids conferred by the Vssc knockdown resistance allele 410L+1016I+1534C in Aedes aegypti. PLoS Negl Trop Dis 2021; 15: e0009549. pmid:34252110
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref19] 19. Freeman JC, Smith LB, Silva JJ, Fan Y, Sun H, Scott JG. Fitness studies of insecticide resistant strains: lessons learned and future directions. Pest Man Sci 2021; 77: 3847–3856. pmid:33506993
View Article
PubMed/NCBI
Google Scholar

[60] View Article

[61] PubMed/NCBI

[62] Google Scholar

[ref20] 20. Rinkevich FD, Leichter CA, Lazo TA, Hardstone MC, Scott JG. Variable fitness costs for pyrethroid resistance alleles in the house fly, Musca domestica, in the absence of insecticide pressure. Pestic Biochem Physiol 2013; 105: 161–168.
View Article
Google Scholar

[64] View Article

[65] Google Scholar

[ref21] 21. Fisher CR, Dressel AE, Silva JJ, Scott JG. A globally distributed insecticide resistance allele confers a fitness cost in the absence of insecticide in Aedes aegypti, the yellow fever mosquito. J Med Ent 2022; 60: 494–499.
View Article
Google Scholar

[67] View Article

[68] Google Scholar

[ref22] 22. Smith LB, Kasai S, Scott JG. Voltage-sensitive sodium channel mutations S989P+V1016G in Aedes aegypti confer variable resistance to pyrethroids, DDT and oxadiazines. Pest Manag Sci 2017; 74: 737–745.
View Article
Google Scholar

[70] View Article

[71] Google Scholar

[ref23] 23. R Development Core Team (2022) R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.

[ref24] 24. Hardstone MC, Lazzaro BP, Scott JG. The effect of three environmental conditions on the fitness of cytochrome P450 monooxygenase-mediated permethrin resistance in Culex pipiens quinquefasciatus. BMC Evol Biol 2009; 9: 42.
View Article
Google Scholar

[74] View Article

[75] Google Scholar

[ref25] 25. Holm S. A simple sequentially rejective multiple test procedure. Scand J Statist 1979; 6: 65–70.
View Article
Google Scholar

[77] View Article

[78] Google Scholar

[ref26] 26. Smith LB, Silva JJ, Chen C, Harrington L, Scott JG. Fitness costs of individual and combined pyrethroid resistance mechanisms, kdr and CYP-mediated detoxification, in Aedes aegypti. PLoS Negl Trop Dis 2021; 15: e009271. pmid:33760828
View Article
PubMed/NCBI
Google Scholar

[80] View Article

[81] PubMed/NCBI

[82] Google Scholar

[ref27] 27. Brito LP, Linss JG, Lima-Camara TN, Belinato TA, Peixoto AA, Lima JB, et al. Assessing the effects of Aedes aegypti kdr mutations on pyrethroid resistance and its fitness cost. PLoS One 2013; 8: e60878. pmid:23593337
View Article
PubMed/NCBI
Google Scholar

[84] View Article

[85] PubMed/NCBI

[86] Google Scholar

Figures

Abstract

Author summary

Introduction

Materials and methods

Mosquito strains

Allele competition experiments

Selection of F9 using deltamethrin

Allele competition data analysis

Results

Discussion

Supporting information

S1 Table. Genotype, allele frequencies for each replicate of the LKR x 1534C:ROCK experiment and their associated Hardy-Weinberg equilibrium (HWE) and genetic drift p-values.

S2 Table. List of primers used for genotyping the V1016I mutation from LMRKDR:RK and 1534C:ROCK.

Acknowledgments

References

Selection of F₉ using deltamethrin