Challenges in estimating insecticide selection pressures from mosquito field data.

Molecular and Biochemical Parasitology Group, Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
PLoS Neglected Tropical Diseases (Impact Factor: 4.49). 11/2011; 5(11):e1387. DOI: 10.1371/journal.pntd.0001387
Source: PubMed

ABSTRACT Insecticide resistance has the potential to compromise the enormous effort put into the control of dengue and malaria vector populations. It is therefore important to quantify the amount of selection acting on resistance alleles, their contributions to fitness in heterozygotes (dominance) and their initial frequencies, as a means to predict the rate of spread of resistance in natural populations. We investigate practical problems of obtaining such estimates, with particular emphasis on Mexican populations of the dengue vector Aedes aegypti. Selection and dominance coefficients can be estimated by fitting genetic models to field data using maximum likelihood (ML) methodology. This methodology, although widely used, makes many assumptions so we investigated how well such models perform when data are sparse or when spatial and temporal heterogeneity occur. As expected, ML methodologies reliably estimated selection and dominance coefficients under idealised conditions but it was difficult to recover the true values when datasets were sparse during the time that resistance alleles increased in frequency, or when spatial and temporal heterogeneity occurred. We analysed published data on pyrethroid resistance in Mexico that consists of the frequency of a Ile1,016 mutation. The estimates for selection coefficient and initial allele frequency on the field dataset were in the expected range, dominance coefficient points to incomplete dominance as observed in the laboratory, although these estimates are accompanied by strong caveats about possible impact of spatial and temporal heterogeneity in selection.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Seven F1 strains of Aedes aegypti (L.) were evaluated by bottle bioassay for resistance to the pyrethroids d-phenothrin, permethrin, deltamethrin, lambda-cyalothrin, bifenthrin, cypermethrin, alpha-cypermethrin, and z-cypermethrin. The New Orleans strain was used as a susceptible control. Mortality rates after a 1 h exposure and after a 24 h recovery period were determined. The resistance ratio between the 50% knockdown values (RR(KC50)) of the F1 and New Orleans strains indicated high levels of knockdown resistance. The RR(KC50) with alpha-cypermethrin varied from 10 to 100 among strains indicating high levels of knockdown resistance. Most of the strains had moderate resistance to d-phenothrin. Significant but much lower levels of resistance were detected for lambda-cyalothrin, permethrin, and cypermethrin. For zeta-cypermethrin and bifenthrin, only one strain exhibited resistance with RR(KC50) values of 10- and 21-fold, respectively. None of the strains showed RR(KC50) >10 with deltamethrin, and moderate resistance was seen in three strains, while the rest were susceptible. Mosquitoes from all strains exhibited some recovery from all pyrethroids except d-phenothrin. Regression analysis was used to analyze the relationship between RR(LC50) and RR(KC50). Both were highly correlated (R2 = 0.84-0.97) so that the slope could be used to determine how much additional pyrethroid was needed to ensure lethality. Slopes ranged from 0.875 for d-phenothrin (RR(LC50) approximately equal to RR(KC50)) to 8.67 for lambda-cyalothrin (-8.5-fold more insecticide needed to kill). Both RR(LC50) and RR(KC50) values were highly correlated for all pyrethroids except bifenthrin indicating strong cross-resistance. Bifenthrin appears to be an alternative pyrethroid without strong cross-resistance that could be used as an alternative to the current widespread use of permethrin in Mexico.
    Journal of Economic Entomology 04/2013; 106(2):959-69. · 1.61 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Pyrethroids are the most used insecticide class worldwide. They target the voltage gated sodium channel (NaV), inducing the knockdown effect. In Aedes aegypti, the main dengue vector, the AaNaV substitutions Val1016Ile and Phe1534Cys are the most important knockdown resistance (kdr) mutations. We evaluated the fitness cost of these kdr mutations related to distinct aspects of development and reproduction, in the absence of any other major resistance mechanism. To accomplish this, we initially set up 68 crosses with mosquitoes from a natural population. Allele-specific PCR revealed that one couple, the one originating the CIT-32 strain, had both parents homozygous for both kdr mutations. However, this pyrethroid resistant strain also presented high levels of detoxifying enzymes, which synergistically account for resistance, as revealed by biological and biochemical assays. Therefore, we carried out backcrosses between CIT-32 and Rockefeller (an insecticide susceptible strain) for eight generations in order to bring the kdr mutation into a susceptible genetic background. This new strain, named Rock-kdr, was highly resistant to pyrethroid and presented reduced alteration of detoxifying activity. Fitness of the Rock-kdr was then evaluated in comparison with Rockefeller. In this strain, larval development took longer, adults had an increased locomotor activity, fewer females laid eggs, and produced a lower number of eggs. Under an inter-strain competition scenario, the Rock-kdr larvae developed even slower. Moreover, when Rockefeller and Rock-kdr were reared together in population cage experiments during 15 generations in absence of insecticide, the mutant allele decreased in frequency. These results strongly suggest that the Ae. aegypti kdr mutations have a high fitness cost. Therefore, enhanced surveillance for resistance should be priority in localities where the kdr mutation is found before new adaptive alleles can be selected for diminishing the kdr deleterious effects.
    PLoS ONE 01/2013; 8(4):e60878. · 3.53 Impact Factor
  • Source
    Proceedings of the National Academy of Sciences 01/2015; · 9.81 Impact Factor

Full-text (3 Sources)

Available from
Jul 10, 2014