Next Article in Journal
Parasitoid Wasp Acerophagus papayae: A Promising Solution for the Control of Papaya Mealybug Paracoccus marginatus in Cassava Fields in Vietnam
Next Article in Special Issue
Using MALDI-TOF MS to Identify Mosquitoes from Senegal and the Origin of Their Blood Meals
Previous Article in Journal
Behavioural Repeatability and Behavioural Syndrome in the Dung Beetle Copris umbilicatus (Coleoptera, Scarabaeidae)
Previous Article in Special Issue
Genetic Diversity and Population Structure of Anopheles funestus in Western Kenya Based on Mitochondrial DNA Marker COII
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Insects
Volume 14
Issue 6
10.3390/insects14060530
insects-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genetic Differentiation of Aedes aegypti (Diptera: Culicidae) in Areas with High Rates of Infestation in Mid-North Region of Brazil

by
Luzianny Farias Rodrigues
1,
Andrelina Alves de Sousa
2,
Walter Pinheiro Mendes Júnior
1,
Amanda Caroline Cardoso e Silva
1,
Maria Histelle Sousa do Nascimento
3,
Maria Claudene Barros
1,
Iracilda Sampaio
2 and
Elmary da Costa Fraga
1,*
1
Graduate Program in Biodiversity, Environment and Health, Laboratory of Genetics and Molecular Biology, Universidade Estadual do Maranhão—UEMA, Caxias 65604-380, MA, Brazil
2
Graduate Program in Genetics and Molecular Biology, Laboratory of Genetics and Molecular Biology, Universidade Federal do Pará—UFPA, Belém 66075-110, PA, Brazil
3
Graduate Program Network Biodiversity and Biotechnology of Legal Amazon, Biological Scienses Institute, Belém 66075-110, PA, Brazil
*
Author to whom correspondence should be addressed.
Insects 2023, 14(6), 530; https://doi.org/10.3390/insects14060530
Submission received: 4 April 2023 / Revised: 30 May 2023 / Accepted: 1 June 2023 / Published: 6 June 2023
(This article belongs to the Special Issue Biology, Genetics and Evolution of Mosquitoes)
Browse Figures
Review Reports Versions Notes

Abstract

:

Simple Summary

The mosquito Aedes (Stegomyia) aegypti represents a major challenge for public health due to its proliferation in many regions of the world and its capacity to disperse arboviruses. The verification of the genetic characteristics of this mosquito and the differentiation of these characteristics both within and between its populations in the Mid-North region of Brazil will be fundamentally important for the development of effective measures for the control of this disease vector. Previous studies of mitochondrial markers in the Mid-North region revealed two genetic lineages of Ae. aegypti, and high levels of genetic differentiation, which represent a potential risk for the occurrence of epidemics and a major problem for local public health authorities. Using microsatellite markers, the present study identified distinct genetic lineages of Ae. aegypti in areas with high rates of infestation. This analysis revealed the coexistence of two different genetic lineages, which were highly differentiated in genetic terms. These findings on the genetic status of the populations will be extremely important for the development of innovative strategies for the control of the populations of this disease vector, and effective measures for the protection of local human populations.

Abstract

Aedes aegypti is the principal vector of the arboviruses—yellow fever, dengue virus, chikungunya, and zika virus. Given the epidemiological importance of this mosquito, its capacity to adapt to different habitats, and its resistance to many types of control measures, systematic research into the genetic variability of the populations of this mosquito is one of the most important steps toward a better understanding of its population structure and vector competence. In this context, the present study verified the presence of distinct genetic lineages of Ae. aegypti in areas with high infestation rates, based on the analysis of microsatellite markers. The samples were collected in nine municipalities with high building infestation rates in the Mid-North region of Brazil. Six microsatellite loci were genotyped in the 138 samples, producing a total of 32 alleles, varying from one to nine alleles per locus in each of the different populations. The AMOVA revealed greater within-population genetic differentiation with high fixation rates. The general analysis of population structure, based on a Bayesian approach, revealed K = 2, with two Ae. aegypti lineages that were highly differentiated genetically. These data on the connectivity of the populations and the genetic isolation of the lineages provide important insights for the development of innovative strategies for the control of the populations of this important disease vector.

1. Introduction

Aedes (Stegomyia) aegypti (Linnaeus, 1762) is an invasive mosquito of the family Culicidae, which is native to Africa but is now widely distributed in the tropical and subtropical regions of the world [1]. This mosquito is a major concern for public health, given that it is the principal vector of the yellow fever arbovirus, all four dengue fever serotypes (DENV-1, DENV-2, DENV-3, and DENV-4), and the zika and chikungunya viruses [2,3,4,5].
The control of this vector is considered to be a major challenge for public health authorities around the world, given the capacity of the mosquito to adapt to different types of habitat, the hardiness of its eggs, passive dispersal, and its resistance to control measures [6,7,8]. These factors have contributed to the high rates of occurrence and the dispersal of arboviruses around the world [2,5].
In Brazil, Ae. aegypti was eradicated in the 1950s, according to the Pan American Health Organization (PAHO) but was subsequently reintroduced in the 1970s [9]. Since this time, Ae. aegypti has been considered to be one of the vectors that have had the greatest impact on public health in the country. Up until June 2022, for example, 1.172.882 probable cases of dengue fever were recorded in Brazil, in addition to 122.075 cases of chikungunya, and 5.699 of zika [10]. This scenario is further aggravated by the discovery of the occurrence of a new lineage of the zika virus in the country, which may mean a more virulent reemergence of this arbovirus in the near future [11].
These findings reinforce the need for the genetic monitoring of Ae. aegypti populations. The epidemiological importance of the species demands reliable data on its population structure. The SSRs are good molecular markers for the study of population genetics, given their high rates of mutation and polymorphism [12,13]. In particular, SSRs are low-cost, neutral markers that, in recent years, have provided valuable insights into genetic diversity, gene flow patterns, and intra- and inter-specific genetic differentiation in a range of studies, both in Brazil and around the world [14,15,16,17].
Studies of the genetic variability of Ae. aegypti populations are extremely important for the Mid-North region of Brazil, where a large proportion of the municipalities are at risk of infestation by this vector and are thus faced with the potential spread of the dengue, chikungunya, and zika arboviruses [10,18,19,20,21,22]. In two previous studies in Maranhão, one of the Brazilian states that make up the Mid-North region, Fraga et al. [23] and Sousa et al. [24] found high levels of intra-population genetic differentiation in Ae. aegypti, with evidence of the coexistence of two distinct genetic lineages in the state. It is important to note that populations of vectors with high levels of genetic differentiation within a given (micro or macro) region may present a greater competence for the transmission of diseases.
Given this, it will be essential to compile reliable data on the patterns of genetic structure and gene flow and the within- and between-population differentiation of Ae. aegypti in order to support the development of effective measures for the control of these populations within a given area. In this context, the present study builds on the results of three previous population studies of Ae. aegypti, two of which [23,24] focused on mitochondrial markers in the state of Maranhão, while the third [16] analyzed microsatellite markers in the Amazon Region. Here, microsatellite markers were genotyped in order to determine the possible presence of distinct genetic lineages in municipalities with high Ae. aegypti infestation rates in the Mid-North Region of Brazil.

2. Materials and Methods

2.1. Study Area and Sample Collection

The Mid-North (Meio-Norte) is a region in the western extreme of Northeastern Brazil, which encompasses the ecological transition between the Amazon and Cerrado biomes, including the state of Maranhão and the western half of Piauí [25]. The samples were collected in nine municipalities of the Mid-North region—six in Maranhão and three in Piauí (Figure 1)—that are known to have high Building Infestation Rates (BIRs), based on the results of the Rapid Index Ae. aegypti Survey (LIRAa) provided by the Brazilian Ministry of Health [18,19,26]. The details of the study areas and the collection of samples are shown in Table 1.
We obtained the samples using 20 ovitraps [27], which were distributed in different neighborhoods of each study municipality. To avoid collecting related individuals, the traps were set at distances of between 20 m and 5 km from each other [9]. Each trap was set in a shaded, poorly lit area adjacent to residential buildings and was retrieved after six days. The pallets containing eggs were taken to the Laboratory of Genetics and Molecular Biology at the Caxias campus of Maranhão State University (UEMA), where they were placed in individuals containers with water to allow for the hatching of eggs and the development of larvae to the L4 stage, following the protocol of Santos et al. [28].
All the biological specimens were identified as species using the taxonomic key of Consoli and Lourenço-de-Oliveira [1]. Once identified, the specimens were placed in 1.5 mL Eppendorf microtubes containing 70% ethanol, then frozen in a freezer at −80 °C. To avoid sampling siblings, only one larva from each container, and consequently, from different pallets, was selected for the molecular procedures.

2.2. Extraction and Amplification of the DNA, and Genotyping

The total DNA was extracted from the specimens using the Wizard Genomic DNA Purification kit (Promega, Madison, WI, USA), following the manufacturer’s instructions. The six polymorphic loci were amplified by Polymerase Chain Reaction (PCR) using the specific primers (Table 2) described by Huber et al. [29] and Chambers et al. [30]. The loci were amplified in a final volume of 7 μL containing 0.7 μL of the DNA (50 ng/μL), 3.5 of Master Mix-PCR Multiplex (QIAGEN, Germantown, MD, USA), 0.35 μL of the forward primer (100 μM/μL), 0.7 μL of the reverse primer (100 μM/μL), 0.35 μL of fluorescent dye (100 μM/μL), and 1.4 μL of ultrapure water. The amplification protocol of the microsatellite loci consisted of initial denaturation for nine minutes at 9 °C, followed by a second stage of 35 cycles of 94 °C for 30 s (denaturation), 57.5 °C for 90 s (annealing), and 72 °C for 90 s (extension), then a third stage of 15 cycles of 94 °C for 30 s (denaturation), 53 °C for 45 s (annealing), and 72 °C for 45 s, with a final extension of 5 min at 72 °C.
The PCR products were diluted by 10× in ultrapure water, with 0.3 μL of 600 LIZ Size Standard and 8.7 μL of formamide being added to 1 μL of this product. The plates containing 10 μL of each sample were placed in a thermocycler and heated to 95 °C for 5 min, after which the samples were sequenced in an ABI PRISM 3500 (Life Technologies, Carlsbad, CA, USA) automatic DNA sequencer, following the manufacturer’s recommendations.

2.3. Data Analysis

The results of the genotyping were analyzed in the GENEMARKER program [31] to verify potential patterns in the distribution of the alleles and the genotypes, which were inserted into an Excel spreadsheet to establish the database. The input files for the subsequent analyses were converted and created in CREATE 1.33 [32]. The MICRO-CHECKER 2.2.3 program [33] was used to analyze the presence of null alleles in the loci. Genetic variability was verified in GENALEX 6.5 [34], which was used to determine the frequency and distribution of the alleles per locus and per population, and the effective number of alleles Ne(allele), and the observed (Ho) and expected heterozygosity (He). The GENEPOP v.4.0.10 program [35] was used to test for Hardy–Weinberg Equilibrium (HWE) and Linkage Disequilibrium (LD), with 10,000 dememorizations, 1000 runs, and 10,000 comparisons per run. The Bonferroni correction was applied to the resulting HWE and LD matrices. The allelic richness (Ar) and the inbreeding coefficient (FIS) were calculated in FSTAT 2.9.3 [36]. The frequencies of the private alleles were estimated in GDA 1.1 [37]. Please note here that the preliminary analysis of the data from the populations of Governador Eugênio Barros, Timon, Floriano, Parnaíba, and Teresina, indicated that they formed a single cluster (denominated Piauí here), in which they are considered to be a single panmictic population by the STRUCTURE analysis. These samples were considered a single panmictic population, this criterion was used for AMOVA and Linkage Disequilibrium (LD) analyses.
The genetic differentiation of the populations was evaluated based on an Analysis of Molecular Variance (AMOVA), the FST index (genetic differentiation), and Nm (gene flow), all of which were calculated in ARLEQUIN 3.5 [38] with 10,000 permutations. Population structure was analyzed in STRUCTURE 2.3.4 [39], which is based on a Bayesian approach through a model that identifies the groups from their Hardy–Weinberg and linkage equilibria. The samples were assigned to a specific number of groups (K), with intervals of K = 1–12 and 20 repetitions, and a burn-in of 250,000 cycles followed by 750,000 Markov Chain Monte Carlo (MCMC) samples, assuming random sorting and correlated allele frequencies. The most probable K value was determined using the Delta K method of Evanno et al. [40] in STRUCTURE HARVESTER [41]. The CLUMPP v1.1.2 program [42] was used to summarize the results of the 20 permutations of the selected K value and provide the Q matrices based on the study populations. The results were plotted in STRUCTURE SELECTOR [43].
The effective size Ne(pop) of each population was estimated in NEESTIMATOR 1.3 [44], based on the Linkage Disequilibrium (LD) model. The BOTTLENECK 1.2.02 program [45] was used to find evidence of bottleneck events in the different populations. This program applied three different mutational models to the analysis of the data—the Infinite Alleles Model (IAM), the Stepwise Mutation Model (SMM), and the Twin-Phase Mutation Model (TPM). The heterozygosity rates under mutation-drift equilibrium for the TPM model assumed 95% of single-step mutations and 5% of multiple-step mutations, as recommended for microsatellite loci by Excoffier and Lischer [38]. The significance of the results was determined using the Wilcoxon test, as recommended in the manual, for less than 20 markers.

3. Results

Six microsatellite loci were genotyped in the 138 Ae. aegypti specimens collected from the nine populations surveyed in the Mid-North region of Brazil, producing a total of 828 genotypes and 32 alleles. Between one and nine alleles were recorded per locus per population, with the largest number (9) being recorded for locus B07 from Piauí, which was also the study area with the highest allelic richness (5.368). The smallest number of alleles (1) was identified for locus 3472 from Governador Eugênio Barros and locus H08 from Senador Alexandre Costa, with both these populations also having the lowest allelic richness (Table 3).
In terms of the estimates of the effective number of alleles Ne(allele), the highest frequency was recorded in the population of Barra do Corda (B07), while the lowest frequencies were registered in Governador Eugênio Barros (3472) and Senador Alexandre Costa, H08 (Table 3). A total of nine private alleles were found in populations—(Barra do Corda, São Domingos, Senador Alexandre Costa, Vargem Grande e Piaui (Table 3). While the samples from Piaui had the highest number of private alleles, Governador Eugênio Barros presented no private alleles whatsoever. The loci with the most populations with null alleles were B19 (found in five populations) and B07, for which null alleles were found in all populations (Table 3).
The Hardy–Weinberg Equilibrium (HWE) analysis was based on 36 tests, of which 15 (41.66%) indicated a significant deviation after applying the Bonferroni correction (p < 0.05), most (13) indicating heterozygote deficiency. The populations of Barra do Corda (3), and Piauí (4) had the highest numbers of loci in disequilibrium, whereas Senador Alexandre Costa presented the highest number of loci in equilibrium (Table 4).
The Analysis of Molecular Variance (AMOVA) revealed highly significant genetic variability when all the Mid-North populations were arranged in a single group, with 14.34% of the variation being found among populations and 85.66% within populations, with FST = 0.14340 (p = 0.00000 ± 0.00000). A second scenario was generated with the samples arranged in two groups, as indicated by the STRUCTURE analysis (Table 5), with one group formed by samples from Maranhão (Barra do Corda, São Domingos, Senador Alexandre Costa, and Vargem Grande) and the second group by two populations from Maranhão (Governador Eugênio Barros and Timon) and the three populations from Piauí (Floriano, Paranaíba, and Teresina) (Table 5).
The analysis found no significant genetic structuring between the groups, with FCT = 0.04357 (p = 0.00759 ± 0.00304), although significant genetic differentiation was found between samples in the same group, with FSC = 0.12144 (p = 0.00000 ± 0.00000). In both scenarios analyzed, the percentage of within-population genetic variation (85.66%; 84.03%) was much higher than that found between populations (14.34%) or among populations within groups (13.47%) (Table 5).
The estimates of genetic differentiation based on the pairwise FST and gene flow (Nm) values indicated highly significant genetic differentiation (p = 0.00000 ± 0.0000) in all the populations, with the highest fixation index (FST = 0.378) being found between the samples from Governador Eugênio Barros (GEB) and Vargem Grande which, in turn, had the lowest gene flow (Nm = 0.819). By contrast, the lowest genetic differentiation (FST = 0.013) was recorded between Timon and Teresina and, as expected, the highest gene flow (Nm = 35.328) (Table 6).
The general analysis of population structure based on the Bayesian approach revealed that the most probable number of genetic groups in the populations sampled was K = 2, indicating a mixed ancestry from two lineages (Figure 2). The first lineage, represented by the predominance of the red gene set, joined together four populations from Maranhão (Barra do Corda [BC], São Domingos [SD], Senador Alexandre Costa [SAC], and Vargem Grande [VG]). The second lineage, dominated by the green gene set, grouped two populations from Maranhão (Governador Eugênio Barros [GEB] and Timon [TI]), and three populations from Piauí, i.e., Floriano (FL), Parnaíba (PAR), and Teresina (TE).
Effective population size Ne(pop) was estimated based on the Linkage Disequilibrium (LD) model, with the lowest value being recorded for the populations from Maranhão (9.6) and the highest value (45.5) for the populations from Piauí. The heterozygosity tests were based on the three mutation models (IAM, TPM, and SMM), and the populations from both Maranhão and Piauí presented a significant excess of heterozygosity for all three of the models tested, which indicates that these populations passed through a recent bottleneck (Table 7).

4. Discussion

The loci analyzed in the present study presented levels of polymorphism similar to those reported for Ae. aegypti from other locations around the world. A total of 10 alleles were recorded here for the B07 locus, for example, in comparison with 13 alleles in the populations from São Paulo [46] and only six in those from Manaus [47]. Similarly, the six alleles found at locus 3472 in the present study are comparable with the eight alleles recorded by Paupy et al. [48] in northern Cameroon. Microevolution has significant implications in epidemiology. Rapid changes in allele frequencies may involve non-neutral characters, which may eventually influence the susceptibility of a given mosquito population to insecticides and chemical repellents [46].
The genetic composition of the Ae. aegypti populations of the Brazilian Mid-North revealed the existence of two lineages that are present in higher or lower proportions at the different localities, with mixed ancestry and significant genetic differentiation (Fst = 0.14340; p = 0.000) in the region.
The Bayesian analysis revealed clear evidence of genetic sorting in the samples, with the geographically closest populations grouping in the same cluster, given their genotypic similarities. However, some geographically distant populations were also genetically similar and were grouped in the same lineage, as in the case of the Governador Eugênio Barros (GEB) and Piauí populations. Given the reduced dispersal capacity of Ae. aegypti, which may range from only 50 m to 800 m [49,50], the genetic similarity found between different localities may be due to the passive dispersal of the mosquito, mediated by human transportation and highway traffic, which may enhance the connectivity of the different populations [51,52,53].
The clustering pattern of the first lineage indicated a low genetic connectivity between the populations (Nm = 0.819–5.505). This is evident in the high indices of genetic differentiation (FST = 0.083–0.378), which indicate limited levels of gene flow between populations. Other similar studies involving mitochondrial markers have revealed high levels of genetic differentiation in Ae. aegypti populations in Maranhão [24], with FST values ranging from −0.012 to 0.813. The findings of the present study reconfirm the existence of the two genetic lineages found by Fraga et al. [23] and Sousa et al. [24] using mitochondrial markers.
Maranhão state straddles the transition zone between the Amazon and Cerrado biomes, an ecotone with a high diversity of ecosystems, species, and climatic conditions [54]. The state is also an important tourist destination and shipping hub, subject to intense movements of both passengers and cargo, which may favor the introduction of new genetic groups of Ae. aegypti.
The second lineage also presented a high level of genetic connectivity, reflecting a high level of gene flow among its populations (Nm = 6.050–35.328), which is also reflected in its low FST values (0.013–0.076). This high level of gene flow may reflect the passive dispersal of Ae. aegypti throughout the region through the interconnection of long-distance transportation routes [55]. The genetic similarity between Timon and the populations from Piauí (FST = 0.013–0.039) in lineage 2 is probably related to the geographic proximity of these localities, which are connected by highways and waterways. It thus seems likely that the Timon population was founded by two or more waves of colonization originating in Piauí (PI).
The effective population Ne(pop) sizes recorded in the present study were relatively small but are consistent with the values recorded by Maitra et al. [16] in 15 Brazilian Ae. aegypti populations, based on 12 microsatellite loci Mendonça et al. [47] recorded a similar scenario in four populations from Manaus, in central Brazilian Amazonia, while Olanratmanee et al. [56] also found this pattern in five villages in Thailand. The low Ne values are likely the result of demographic processes, such as biased sex ratios, fluctuations in population size, and/or the differential breeding success of certain individuals [57].
This pattern is likely the result of successive genetic bottlenecks and founder events occurring either during the colonization process or as a consequence of measures taken to control the local mosquito populations. These processes cause a reduction in effective population size Ne(pop), which may reinforce the differentiation of populations by genetic drift [16]. This was especially clear in the samples from Maranhão, which had the lowest Ne(pop) values and, in turn, the highest levels of genetic divergence among its populations.
The analysis of genetic bottlenecks indicated that the study populations are not undergoing a process of expansion. However, there is clear evidence of the effects of genetic bottlenecks in both the Maranhão and Piauí populations, with all three mutation models tested here indicating that these populations have undergone a drastic reduction in size in the past, which is consistent with the low Ne values recorded for the populations. Invasive species often go through population bottlenecks, which tend to reduce their genetic diversity drastically, and in particular their allelic richness [58].
The populations from Maranhão and Piauí presented distinct demographic patterns. While evidence of genetic bottlenecks was found in both cases, effective population sizes were very small in Maranhão in comparison with the much higher Ne values recorded in Piauí. The demographic dynamics of Ae. aegypti populations may be influenced by environmental, climatic, and social factors (including human population density), and the intensive application of insecticides, which may generate distinct patterns of genetic structure in the Ae. aegypti populations [47,59,60]. These factors may combine to differentiate the genetic makeup of the different localities through the successive expansion and reduction or extinction and recolonization of these populations [9].
This intra-population variability may be derived from differences in the vectorial capacity of the mosquito, its predisposition to the virus, or even its resistance to insecticides or other ecological adaptations, all of which may limit the effectiveness of control measures [23].
The understanding of the genetic structure of Ae. aegypti populations and their relatedness is fundamentally important for the development of effective programs of vector control, given that populations with a reduced effective size may be more subject to the effects of genetic drift, which may lead to an intense loss of genetic diversity over only a few generations. For programs that target the genetic diversity of the mosquito populations, the effective size of the target population must be taken into consideration to ensure the success of control measures [16]. The commitment of local human populations is also extremely important to ensure the success of any initiative to combat this potentially dangerous disease vector.

5. Conclusions

The findings of the present study reveal the coexistence of two distinct sets of genes in the Ae. aegypti populations of the Mid-North region of Brazil, based on the analysis of microsatellite markers. The results of the analysis indicate high levels of genetic differentiation in the Maranhão populations and marked genetic connectivity among the Piauí populations, which present low levels of genetic differentiation that indicate frequent gene flow, presumably mediated by passive dispersal. The results of this study also indicate the possible introduction of a new set of genes from Piauí into Maranhão. These data on the connectivity and genetic isolation of the different Ae. aegypti populations represent valuable input for the establishment of guidelines for the development of innovative strategies for the effective control of the populations of this disease vector.

Author Contributions

Formal analysis: M.H.S.d.N.; investigation: L.F.R., A.A.d.S., W.P.M.J., A.C.C.e.S., M.H.S.d.N. and M.C.B.; resources: L.F.R., A.A.d.S., W.P.M.J., A.C.C.e.S. and M.H.S.d.N.; writing—review and editing: L.F.R., A.A.d.S., A.C.C.e.S., I.S. and E.d.C.F.; supervision: M.C.B., I.S. and E.d.C.F.; project administration: M.C.B., I.S. and E.d.C.F.; funding acquisition: E.d.C.F. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported financially by the Maranhão State Foundation for Research and Scientific and Technological Development (FAPEMA) process number 00772/15.

Data Availability Statement

The database used for this study is available https://www.doi.org/10.6084/m9.figshare.23295272.

Acknowledgments

We thank the Laboratory of Genetics and Molecular Biology (GENBIMOL) of the Caxias campus of the State University of Maranhão (UEMA) for all the support during the different stages of the study and to the National Council for Scientific and Technological Development-CNPQ for funding scientific initiation scholarships.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Consoli, R.A.G.B.; Oliveira, R.L. Principais Mosquitos de Importância Sanitária No Brasil; Editora Fiocruz: Rio de Janeiro, Brazil, 1994; ISBN 8585676035. [Google Scholar]
  2. Bryant, J.E.; Holmes, E.C.; Barrett, A.D.T. Out of Africa: A Molecular Perspective on the Introduction of Yellow Fever Virus into the Americas. PLoS Pathog. 2007, 3, 0668–0673. [Google Scholar] [CrossRef] [Green Version]
  3. De Oliveira, N.C.; de Faria Souza, D.T.; Martins, M.; de Souza, E.A.; Medeiros, M.O. Dinâmica Populacional de Aedes (Stegomyia) aegypti (Linnaeus, 1762) (DIPTERA: CULICIDAE); Campus Universitário de Rondonópolis: Rondonópolis, Brazil, 2017; Volume 16. [Google Scholar]
  4. Forattini, O.P. Culicidologia Médica: Identificaçäo, Biologia e Epidemiologia: V.2; Universidade de São Paulo: São Paulo, Brazil, 2002. [Google Scholar]
  5. Lima-Camara, T.N. Emerging Arboviruses and Public Health Challenges in Brazil. Rev. Saude Publica 2016, 50, 36. [Google Scholar] [CrossRef] [Green Version]
  6. Soares-da-Silva, J.; Ibiapina, S.S.; Bezerra, J.M.T.; Tadei, W.P.; Pinheiro, V.C.S. Variation in Aedes Aegypti (Linnaeus) (Diptera, Culicidae) Infestation in Artificial Containers in Caxias, State of Maranhão, Brazil. Rev. Soc. Bras. Med. Trop. 2012, 45, 174–179. [Google Scholar] [CrossRef] [Green Version]
  7. Bezerra, J.; Silva Santana, I.; Miranda, J.; Tadei, W.; Soares Pinheiro, V. Breeding Sites of Aedes Aegypti (Linnaeus) (Diptera, Culicidae): Study About the Containers in Dry and Rainy Seasons in Dengue-Endemic City. Rev. Pesq. Saude 2017, 18, 102–107. [Google Scholar]
  8. Vargas, L.D.L.; Ferreira, S.M.B.; Souza, M.D.; da Silva, C.A.L.; Shimoya-Bittencourt, W. Resistência Das Populações de Aedes (Stegomyia) aegypti (Linnaeus, 1762) (Insecta, Diptera, Culicidae) Aos Inseticidas Utilizados Para o Controle. Rev. Ciências Médicas E Biológicas 2022, 21, 98–116. [Google Scholar] [CrossRef]
  9. Scarpassa, V.M.; Cardoza, T.B.; Cardoso, R.P. Population Genetics and Phylogeography of Aedes Aegypti (Diptera: Culicidae) from Brazil. Am. J. Trop. Med. Hyg. 2008, 78, 895–903. [Google Scholar] [CrossRef] [Green Version]
  10. Brasil Boletim Epidemiológico Vol. 53 N° 24—Português (Brasil). Available online: https://www.gov.br/saude/pt-br/centrais-de-conteudo/publicacoes/boletins/boletins-epidemiologicos/edicoes/2022/boletim-epidemiologico-vol-53-no24/view (accessed on 20 July 2022).
  11. Kasprzykowski, J.I.; Fukutani, K.F.; Fabio, H.; Fukutani, E.R.; Costa, L.C.; Andrade, B.B.; Queiroz, A.T.L. A Recursive Sub-Typing Screening Surveillance System Detects the Appearance of the ZIKV African Lineage in Brazil: Is There a Risk of a New Epidemic? Int. J. Infect. Dis. 2020, 96, 579–581. [Google Scholar] [CrossRef]
  12. Li, Y.C.; Korol, A.B.; Fahima, T.; Nevo, E. Microsatellites within Genes: Structure, Function, and Evolution. Mol. Biol. Evol. 2004, 21, 991–1007. [Google Scholar] [CrossRef]
  13. Paupy, C.; Brengues, C.; Ndiath, O.; Toty, C.; Hervé, J.P.; Simard, F. Morphological and Genetic Variability within Aedes aegypti in Niakhar, Senegal. Infect. Genet. Evol. 2010, 10, 473–480. [Google Scholar] [CrossRef]
  14. Paupy, C.; le Goff, G.; Brengues, C.; Guerra, M.; Revollo, J.; Barja Simon, Z.; Hervé, J.P.; Fontenille, D. Genetic Structure and Phylogeography of Aedes aegypti, the Dengue and Yellow-Fever Mosquito Vector in Bolivia. Infect. Genet. Evol. 2012, 12, 1260–1269. [Google Scholar] [CrossRef]
  15. Fonzi, E.; Higa, Y.; Bertuso, A.G.; Futami, K.; Minakawa, N. Human-mediated marine dispersal influences the population structure of Aedes aegypti in the Philippine archipelago. PLoS Negl. Trop. 2015, 9, e0003829. [Google Scholar] [CrossRef] [Green Version]
  16. Maitra, A.; Cunha-Machado, A.S.; de Souza Leandro, A.; da Costa, F.M.; Scarpassa, V.M. Exploring Deeper Genetic Structures: Aedes aegypti in Brazil. Acta Trop. 2019, 195, 68–77. [Google Scholar] [CrossRef]
  17. Kumar, M.S.; Kalimuthu, M.; Selvam, A.; Mathivanan, A.; Paramasivan, R.; Kumar, A.; Gupta, B. Genetic structure and connectivity among Aedes aegypti populations within Madurai city in Southern India. Infect. Genet. Evol. 2021, 95, 105031. [Google Scholar] [CrossRef]
  18. Brasil Boletim Epidemiológico Vol. 51 N° 5—Português (Brasil). Available online: https://www.gov.br/saude/pt-br/centrais-de-conteudo/publicacoes/boletins/boletins-epidemiologicos/edicoes/2020/boletim_epidemiologico_svs_51.pdf/view (accessed on 21 July 2022).
  19. Brasil Boletim Epidemiológico Vol. 52-N° 48—Português (Brasil). Available online: https://www.gov.br/saude/pt-br/centrais-de-conteudo/publicacoes/boletins/boletins-epidemiologicos/edicoes/2021/boletim-epidemiologico-vol-52-no-48.pdf/view (accessed on 21 July 2022).
  20. Aragão, C.F.; Cruz, A.C.R.; Neto, J.P.N.; de Oliveira Monteiro, H.A.; da Silva, E.V.P.; da Silva, S.P.; dos Santos Andrade, A.T.; Tadei, W.P.; Pinheiro, V.C.S. Circulation of Chikungunya Virus in Aedes aegypti in Maranhão, Northeast Brazil. Acta Trop. 2018, 186, 1–4. [Google Scholar] [CrossRef]
  21. Aragão, C.F.; Pinheiro, V.C.S.; Neto, J.P.N.; da Silva, E.V.P.; Pereira, G.J.G.; do Nascimento, B.L.S.; Castro, K.D.S.; Maia, A.M.; Catete, C.P.; Martins, L.C.; et al. Natural Infection of Aedes aegypti by Chikungunya and Dengue Type 2 Virus in a Transition Area of North-Northeast Brazil. Viruses 2019, 11, 1126. [Google Scholar] [CrossRef] [Green Version]
  22. De Carvalho, A.C.P.; Couto Portela, F.; Azevedo Feitosa Ferro, T.; Quaresma Bomfim, M.R. Epidemiologia do vírus do dengue em São Luís, Maranhão, no período de 2002 a 2012. Rev. Patol. Trop. 2016, 45, 243. [Google Scholar] [CrossRef] [Green Version]
  23. Fraga, E.C.; Oliveira, D.R.S.; Aragão, D.G.; Sampaio, H.S.; Barros, M.C. Genetic Variability and Evidence of Two Distinct Lineages of Aedes aegypti (Diptera, Culicidae) on São Luís Island in Maranhão, Brazil. Open Trop. Med. J. 2013, 6, 11–18. [Google Scholar] [CrossRef]
  24. De Sousa, A.A.; Fraga, E.; Sampaio, I.; Schneider, H.; Barros, M.C. Genetic Differentiation in Populations of Aedes aegypti (Diptera, Culicidae) Dengue Vector from the Brazilian State of Maranhão. Rev. Bras. Entomol. 2017, 61, 51–59. [Google Scholar] [CrossRef] [Green Version]
  25. Rebouças, A.d.C. Água Na Região Nordeste: Desperdício e Escassez. Estud. Avançados 1997, 11, 127–154. [Google Scholar] [CrossRef]
  26. Brasil Liraa-1-2019.Pdf—Português (Brasil). Available online: https://www.gov.br/saude/pt-br/centrais-de-conteudo/arquivos/liraa-1-2019-pdf/view (accessed on 21 July 2022).
  27. Fay, R.W.; Perry, A.S. Laboratory Studies of Oviposilional Preferences of Aedes aegypti. Mosq. News 1965, 25, 276–281. [Google Scholar]
  28. Dos Santos, J.M.M.; Contel, E.P.B.; Kerr, W.E. Biologia de anofelinos amazônicos. 1—Ciclo biológico, postura e estádios larvais de Anopheles darlingi Root 1926 (Diptera: Culicidae) da Rodovia Manaus—Boa Vista. Acta Amaz. 1981, 11, 789–797. [Google Scholar] [CrossRef] [Green Version]
  29. Huber, K.; Mousson, L.; Rodhain, F.; Failloux, A.-B. Isolation and Variability of Polymorphic Microsatellite Loci in Aedes aegypti, the Vector of Dengue Viruses. Mol. Ecol. Notes 2001, 1, 219–222. [Google Scholar] [CrossRef]
  30. Chambers, E.W.; Meece, J.K.; McGowan, J.A.; Lovin, D.D.; Hemme, R.R.; Chadee, D.D.; McAbee, K.; Brown, S.E.; Knudson, D.L.; Severson, D.W. Microsatellite Isolation and Linkage Group Identification in the Yellow Fever Mosquito Aedes aegypti. J. Hered. 2007, 98, 202–210. [Google Scholar] [CrossRef] [Green Version]
  31. Hulce, D.; Li, X.; Snyder-Leiby, T.; Liu, C.J. GeneMarker® genotyping software: Tools to increase the statistical power of DNA fragment analysis. J. Biomol. Tech. JBT 2011, 22, S35. [Google Scholar]
  32. Coombs, J.A.; Letcher, B.H.; Nislow, K.H. Create: A Software to Create Input Files from Diploid Genotypic Data for 52 Genetic Software Programs. Mol. Ecol. Resour. 2008, 8, 578–580. [Google Scholar] [CrossRef] [PubMed]
  33. van Oosterhout, C.; Hutchinson, W.F.; Wills, D.P.M.; Shipley, P. MICRO-CHECKER: Software for Identifying and Correcting Genotyping Errors in Microsatellite Data. Mol. Ecol. Notes 2004, 4, 535–538. [Google Scholar] [CrossRef]
  34. Peakall, R.O.D.; Smouse, P.E. GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 2006, 6, 288–295. [Google Scholar] [CrossRef]
  35. Rousset, F. GENEPOP’007: A Complete Re-Implementation of the GENEPOP Software for Windows and Linux. Mol. Ecol. Resour. 2008, 8, 103–106. [Google Scholar] [CrossRef]
  36. GOUDET, J. FSTAT (Version 1.2): A Computer Program to Calculate F-Statistics. J. Hered. 1995, 86, 485–486. [Google Scholar] [CrossRef]
  37. Weir, B.S. Genetic Data Analysis II. Biometrics 1997, 53, 392. [Google Scholar] [CrossRef]
  38. Excoffier, L.; Lischer, H.E.L. Arlequin Suite Ver 3.5: A New Series of Programs to Perform Population Genetics Analyses under Linux and Windows. Mol. Ecol. Resour. 2010, 10, 564–567. [Google Scholar] [CrossRef] [PubMed]
  39. Novembre, J. Pritchard, Stephens, and Donnelly on Population Structure. Genetics 2016, 204, 391–393. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Evanno, G.; Regnaut, S.; Goudet, J. Detecting the Number of Clusters of Individuals Using the Software STRUCTURE: A Simulation Study. Mol. Ecol. 2005, 14, 2611–2620. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  41. Earl, D.A.; vonHoldt, B.M. STRUCTURE HARVESTER: A Website and Program for Visualizing STRUCTURE Output and Implementing the Evanno Method. Conserv. Genet. Resour. 2012, 4, 359–361. [Google Scholar] [CrossRef]
  42. Jakobsson, M.; Rosenberg, N.A. CLUMPP: A cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 2007, 23, 1801–1806. [Google Scholar] [CrossRef] [Green Version]
  43. Li, Y.L.; Liu, J.X. StructureSelector: A Web-Based Software to Select and Visualize the Optimal Number of Clusters Using Multiple Methods. Mol. Ecol. Resour. 2018, 18, 176–177. [Google Scholar] [CrossRef]
  44. Do, C.; Waples, R.S.; Peel, D.; Macbeth, G.M.; Tillett, B.J.; Ovenden, J.R. NeEstimator v2: Re-Implementation of Software for the Estimation of Contemporary Effective Population Size (Ne) from Genetic Data. Mol. Ecol. Resour. 2014, 14, 209–214. [Google Scholar] [CrossRef] [PubMed]
  45. Piry, S.; Luikart, G.; Cornuet, J.M. Computer note. BOTTLENECK: A computer program for detecting recent reductions in the effective size using allele frequency data. J. Hered. 1999, 90, 502–503. [Google Scholar] [CrossRef]
  46. Louise, C.; Vidal, P.O.; Suesdek, L. Microevolution of Aedes aegypti. PLoS ONE 2015, 10, e0137851. [Google Scholar] [CrossRef] [Green Version]
  47. Mendonça, B.A.A.; de Sousa, A.C.B.; de Souza, A.P.; Scarpassa, V.M. Temporal Genetic Structure of Major Dengue Vector Aedes aegypti from Manaus, Amazonas, Brazil. Acta Trop. 2014, 134, 80–88. [Google Scholar] [CrossRef]
  48. Paupy, C.; Brengues, C.; Kamgang, B.; Hervé, J.P.; Fontenille, D.; Simard, F. Gene flow between domestic and sylvan populations of Aedes aegypti (Diptera: Culicidae) in North Cameroon. J. Med. Entomol. 2014, 45, 391–400. [Google Scholar] [CrossRef]
  49. Honório, N.A.; Silva, W.C.; Leite, P.J.; Gonçalves, J.M.; Lounibos, L.P.; Lourenço-De-Oliveira, R. Dispersal of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) in an urban endemic dengue area in the State of Rio de Janeiro, Brazil. Mem. Inst. Oswaldo Cruz 2003, 98, 191–198. [Google Scholar] [CrossRef] [PubMed]
  50. Harrington, L.C.; Edman, J.D.; Kittayapong, P.; Coleman, R.C.; Clark, G.G.; Sithiprasasna, R.; Kitthawee, S.; Lerdthusnee, K.; Scott, T.W.; Costero, A.; et al. Dispersal of the dengue vector aedes aegypti within and BETWEEN rural communities. Am. J. Trop. Med. Hyg. 2005, 72, 209–220. [Google Scholar] [CrossRef]
  51. Bosio, C.F.; Harrington, L.C.; Norris, D.; Scott, T.W.; Jones, J.W.; Sithiprasasna, R. Genetic structure of aedes aegypti populations in thailand using mitochondrial dna. Am. J. Trop. Med. Hyg. 2005, 72, 434–442. [Google Scholar] [CrossRef]
  52. Da Silva, A.G.; Cunha, I.C.L.; Santos, W.S.; Luz, S.L.B.; Ribolla, P.E.M.; Abad-Franch, F. Gene flow networks among American Aedes aegypti populations. Evol. Appl. 2012, 5, 664–676. [Google Scholar] [CrossRef]
  53. Kotsakiozi, P.; Gloria-Soria, A.; Caccone, A.; Evans, B.; Schama, R.; Martins, A.J.; Powell, J.R. Tracking the return of Aedes aegypti to Brazil, the major vector of the dengue, chikungunya and Zika viruses. PLOS Negl. Trop. Dis. 2017, 11, e0005653. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Silva, F.B.; Rafael, J.; Santos, N.; Emerson, F.; Silva, C.; Dirceu, I.; Silva, C.; Lucyanne, M.; de Araújo, S.; Guterres, C.E.; et al. Evidências de Mudanças Climáticas Na Região de Transição Amazônia-Cerrado No Estado Do Maranhão Evidence of Climate Change in the Amazon-Savanna Transition Region in Maranhão State. Rev. Bras. Meteorol. 2016, 31, 330–336. [Google Scholar] [CrossRef] [Green Version]
  55. Carvajal, T.M.; Ogishi, K.; Yaegeshi, S.; Hernandez, L.F.T.; Viacrusis, K.M.; Ho, H.T.; Amalin, D.M.; Watanabe, K. Fine-scale population genetic structure of dengue mosquito vector, Aedes aegypti, in Metropolitan Manila, Philippines. PLOS Negl. Trop. Dis. 2020, 14, e0008279. [Google Scholar] [CrossRef]
  56. Olanratmanee, P.; Kittayapong, P.; Chansang, C.; Hoffmann, A.A.; Weeks, A.R.; Endersby, N.M. Population Genetic Structure of Aedes (Stegomyia) aegypti (L.) at a Micro-Spatial Scale in Thailand: Implications for a Dengue Suppression Strategy. PLOS Neglected Trop. Dis. 2013, 7, e1913. [Google Scholar] [CrossRef] [Green Version]
  57. Saarman, N.P.; Gloria-Soria, A.; Anderson, E.C.; Evans, B.R.; Pless, E.; Cosme, L.V.; Gonzalez-Acosta, C.; Kamgang, B.; Wesson, D.M.; Powell, J.R. Effective Population Sizes of a Major Vector of Human Diseases, Aedes aegypti. Evol. Appl. 2017, 10, 1031–1039. [Google Scholar] [CrossRef] [Green Version]
  58. Pless, E.; Gloria-Soria, A.; Evans, B.R.; Kramer, V.; Bolling, B.G.; Tabachnick, W.J.; Powell, J.R. Multiple introductions of the dengue vector, Aedes aegypti, into California. PLOS Neglected Trop. Dis. 2017, 11, e0005718. [Google Scholar] [CrossRef] [Green Version]
  59. Zanluca, C.; de Melo, V.C.A.; Mosimann, A.L.P.; Santos, G.I.V.; dos Santos, C.N.D.; dos Luz, K. First Report of Autochthonous Transmission of Zika Virus in Brazil. Mem. Inst. Oswaldo Cruz 2015, 110, 569–572. [Google Scholar] [CrossRef]
  60. Wilke, A.B.B.; Wilk-Da-Silva, R.; Marrelli, M.T. Microgeographic population structuring of Aedes aegypti (Diptera: Culicidae). PLoS ONE 2017, 12, e0185150. [Google Scholar] [CrossRef] [Green Version]
Insects 14 00530 g001 550
Figure 1. The municipalities with high Building Infestation Rates (BIRs) of Ae. aegypti that were surveyed in the Mid-North region (Maranhão and Piauí states) of northeastern Brazil. The color of each municipality indicates its predominant Ae. aegypti lineage, i.e., lineage 1 (red) or 2 (green).
Figure 1. The municipalities with high Building Infestation Rates (BIRs) of Ae. aegypti that were surveyed in the Mid-North region (Maranhão and Piauí states) of northeastern Brazil. The color of each municipality indicates its predominant Ae. aegypti lineage, i.e., lineage 1 (red) or 2 (green).
Insects 14 00530 g001
Insects 14 00530 g002 550
Figure 2. Genetic groups (K = 2) in the Ae. aegypti populations from the Mid-North region of Brazil sampled in the present study. Red areas = (BC: Barra do Corda; SD: São Domingos; SAC: Senador Alexandre Costa; VG: Vargem Grande); Green Areas = (GEB: Governador Eugênio Barros; TI: Timon; FL: Floriano; PAR: Parnaíba; TE: Teresina).
Figure 2. Genetic groups (K = 2) in the Ae. aegypti populations from the Mid-North region of Brazil sampled in the present study. Red areas = (BC: Barra do Corda; SD: São Domingos; SAC: Senador Alexandre Costa; VG: Vargem Grande); Green Areas = (GEB: Governador Eugênio Barros; TI: Timon; FL: Floriano; PAR: Parnaíba; TE: Teresina).
Insects 14 00530 g002
Table
Table 1. Geographic coordinates and sample sizes of the areas surveyed in the Mid-North region of northeastern Brazil.
Table 1. Geographic coordinates and sample sizes of the areas surveyed in the Mid-North region of northeastern Brazil.
StatePopulationAbbreviationCoordinates
(Lat./Long.)		Sample Size
MaranhãoBarra do CordaBC5°30′21″ S20
45°14′6″ W
Governador
Eugênio Barros		GEB *5°18′39″ S16
44°14′51″ W
São DomingosSD5°34′46″ S17
44°22′59″ W
Senador Alexandre CostaSAC5°15′21″ S16
44°03′21″ W
Vargem GrandeVG3°32′34″ S13
43°54′57″ W
TimonTI *5°05′38″ S14
42°50′13″ W
PiauíFlorianoFL6°46′2″ S12
43°1′33″ W
ParnaíbaPAR2°54′14″ S15
41°46′35″ W
TeresinaTE5°05′20″ S15
42°48′07″ W
* Ae. aegypti populations that were collected in the Maranhão state but that grouped in the cluster Piauí.
Table
Table 2. Description of the six microsatellite loci analyzed in Ae. aegypti in the present study.
Table 2. Description of the six microsatellite loci analyzed in Ae. aegypti in the present study.
LocusType of RepeatFpbr (Median)References
3472GAAA(GA)6 CAGACAGGAAAFAM82–117−0.0071[26]
B19CAT7VIC101–2050.0970[27]
B07GA15NED95–1950.1040[27]
M201ATA36PET92–140−0.3894[27]
H08TCG7VIC101–218−0.0097[27]
T3A7(TTTA)7(T)14PET107–2630.0045[26]
F = fluorophore; pb = base pairs; r = null allele frequency.
Table
Table 3. Genetic diversity of the samples of Ae. aegypti collected from nine municipalities in the Mid-North region of Brazil.
Table 3. Genetic diversity of the samples of Ae. aegypti collected from nine municipalities in the Mid-North region of Brazil.
Locus BCGEBSDSACVGPI
3472N201617161356
Na413426
Ne(allele)2.6941.0002.1813.4131.1662.899
Ar3.9521.0003.0004.0002.0004.669
Ap-----2
r0.244---0.238-
B19Na435323
Ne(allele)3.6062.5222.2492.6671.9882.154
Ar4.0003.0004.6653.0002.0002.858
Ap--1---
r0.3000.392-0.1950.0350.017
B07Na535359
Ne(allele)3.9802.0242.8331.6793.4493.670
Ar4.9523.0004.7052.9705.0005.368
Ap----12
r0.1930.0390.1890,0750.1760.126
M201Na222322
Ne(allele)1.4712.0001.9401.9621.2571.849
Ar2.0002.0002.0002.8132.0002.000
Ap---1--
r0.029-----
H08Na323123
Ne(allele)2.9861.8002.2611.0001.2572.974
Ar3.0002.0002.9861.0002.0003.000
Ap------
r--0,281--0.122
T3A7Na422324
Ne(allele)1.8671.8821.8412.9771.6492.544
Ar3.6512.0002.0003.0002.0003.411
Ap1----1
r0.0640.402--0.0110.003
MedianNa3.6662.1663.3332.8332.5004.500
Ne(allele)2.7471.8712.2182.3341.7942.682
Ar3.5932.1673.2262.7972.5003.551
BC: Barra do Corda; GEB: Governador Eugênio Barros; SD: São Domingos; SAC: Senador Alexandre Costa; VG: Vargem Grande; PI: (Timon; Floriano; Parnaíba; Teresina); N: number of analyzed mosquitoes per population; Na: number of alleles; Ne(allele): number of effective alleles; r: null allele frequency; Ar: allele richness; Ap: frequency of private alleles and (-): absence.
Table
Table 4. Measures of heterozygosity, the results of the tests of Hardy–Weinberg Equilibrium, and the inbreeding coefficients recorded for the populations of Ae. aegypti sampled in the present study.
Table 4. Measures of heterozygosity, the results of the tests of Hardy–Weinberg Equilibrium, and the inbreeding coefficients recorded for the populations of Ae. aegypti sampled in the present study.
Locus BCGEBSDSACVGPI
3472Ho0.3000.0000.6470.7500.0000.673
He0.6440.0000.5420.7070.1420.655
P-HWE0.001-0.7440.7200.0400.022
FIS0.541NA−0.166−0.0291.000−0.018
B19Ho0.2500.0630.5290.3570.4620.509
He0.7130.6040.5550.6300.4970.536
P-HWE0.0000.0000.0000.0060.5600.085
FIS0.6680.9030.0770.4630.1110.059
B07Ho0.4500.4380.4120.4380.4620.537
He0.7490.5060.6470.4860.7100.728
P-HWE0.0000.0270.0790.3700.0210.008
FIS0.4200.1670.3900.1320.3850.271
M201Ho0.3201.0000.8240.7500.2310.607
He0.3280.5000.4840.4900.2040.459
P-HWE0.5781.0001.0001.0001.0000.997
FIS0.088−1.000−0.684−0.506−0.091−0.314
H08Ho1.0000.6670.2000.0000.2310.500
He0.6650.4440.5580.0000.2040.664
P-HWE1.0001.0000.001-1.0000.007
FIS−0.482−0.474−0.684NA−0.0910.256
T3A7Ho0.3890.0000.7060.7500.3850.500
He0.4650.4690.4570.6640.3930.607
P-HWE0.1830.0001.0000.5910.6670.001
FIS0.1901.000−0.524−0.0980.0630.185
MedianHo0.4480.3610.5530.5070.2950.519
He0.5900.4200.5400.4960.3580.566
FIS0.2370.099−0.041−0.0060.0000.073
BC: Barra do Corda; GEB: Governador Eugênio Barros; SD: São Domingos; SAC: Senador Alexandre Costa; VG: Vargem Grande; PI: (Timon; Floriano; Parnaíba; Teresina); Ho: observed heterozygosity; See per Locus; He: expected heterozygosity; NA: Not Analyzed; FIS: inbreeding coefficient; P-HWE: the values in bold type indicate disequilibrium (Hardy–Weinberg) at the respective localities, following the Bonferroni sequential correction (p < 0.0042).
Table
Table 5. Results of the AMOVA for the Ae. aegypti populations sampled from the Mid-North region of Brazil.
Table 5. Results of the AMOVA for the Ae. aegypti populations sampled from the Mid-North region of Brazil.
Groups of SamplesSource of VariationVariation (%)Fixation Index
All:
BC, GEB, SD, SAC, VG, TI, FL, PAR, and TE		Among population14.34FST: 0.14340 ***
Within populations85.66
Two Groups:
Maranhão: BC, SD, SAC and VG
Piauí: GEB, TI, FL, PAR, and TE		Among groups4.36FST: 0.15971 ***
Among populations within groups13.47FSC: 0.12144 ***
Within populations85.01FCT: 0.04357
BC: Barra do Corda; GEB: Governador Eugênio Barros; SD: São Domingos; SAC: Senador Alexandre Costa; VG: Vargem Grande; TI: Timon; FL: Floriano; PAR: Parnaíba; TE: Teresina. Significance was tested by 10,000 permutations. The fixation index (FST) within the samples. The fixation index (FCT) between regions. The fixation index (FSC) among samples between the regions. *** p = 0.00000 ± 0.00000.
Table
Table 6. Genetic differentiation, gene flow, and the approximate geographic distances between the Ae. aegypti populations sampled in the present study.
Table 6. Genetic differentiation, gene flow, and the approximate geographic distances between the Ae. aegypti populations sampled in the present study.
POPBCGEBSDSACVGTIFLPARTE
BC 2.4065.5052.2154.0205.9095.5914.99012.239
GEB0.172 (130.6) 4.3311.1630.8194.0792.3825.4193.168
SD0.083 (132.9)0.103 (68.5) 2.3691.4634.3724.3393.7377.277
SAC0.184 (163)0.300 (38.8)0.174 (100.9) 1.3531.7051.8422.4402.362
VG0.110 (322)0.378 (241.6)0.254 (265)0.269 (280.3) 1.3571.6551.3662.277
TI0.078 (331.9)0.109 (207.8)0.102 (245.4)0.226 (169.7)0.269 (305.7) 12.31715.05435.328
FL0.0820 (388.9)0.173 (324.5)0.103 (256.6)0.213 (330.3)0.231 (555.2)0.039 (249.7) 6.05017.486
PAR0.091 (641.4)0.084 (517.3)0.117 (554.9)0.170 (512.3)0.267 (318.2)0.032 (343.5)0.076 (572.6) 8.141
TE0.039 (342.8)0.136 (212.3)0.064 (256.3)0.174 (180.6)0.180 (316.6)0.013 (13.8)0.027 (255.5)0.057 (335.4)
POP: Populations; BC: Barra do Corda; GEB: Governador Eugênio Barros; SD: São Domingos; SAC: Senador Alexandre Costa; VG: Vargem Grande; TI: Timon; FL: Floriano; PAR: Parnaíba; TE: Teresina. (FST—below the diagonal), (Nm—above the diagonal), (km—within parentheses). All the FST and Nm values are highly significant (p = 0.0000 ± 0.0000).
Table
Table 7. Effective population sizes Ne(pop) and the results of the bottleneck tests for the Ae. aegypti populations sampled in the presented study in the Mid-North region of Brazil.
Table 7. Effective population sizes Ne(pop) and the results of the bottleneck tests for the Ae. aegypti populations sampled in the presented study in the Mid-North region of Brazil.
Model MaranhãoPiauí
LD 9.645.5
95% CI 4.1–16.319.6–193.4
IAMHe < Heq00
He > Heq66
P (He > Heq)0.00780.0078
TPMHe < Heq01
He > Heq65
P (He > Heq)0.00780.0234
SMMHe < Heq22
He > Heq44
P (He > Heq)0.28120.5781
Maranhão: (Barra do Corda; São Domingos; Senador Alexandre Costa and Vargem Grande); Piauí: (Governador Eugênio Barros, Timon; Floriano; Parnaíba and Teresina); LD: Linkage Disequilibrium; CI: Confidence Interval; IAM: Infinite Alleles Model; SMM: Stepwise Mutation Model; TPM: Twin-Phase Mutation Model; He: expected heterozygosity; Heq: heterozygosity expected under mutation and drift equilibrium; He > Heq: number of loci with an excess of heterozygotes; P (He > Heq): Probability of an excess of heterozygotes at the locus based on the results of the Wilcoxon test. The values highlighted in bold script indicate a significant bottleneck (p < 0.05).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Rodrigues, L.F.; Sousa, A.A.d.; Mendes Júnior, W.P.; Cardoso e Silva, A.C.; Nascimento, M.H.S.d.; Barros, M.C.; Sampaio, I.; Fraga, E.d.C. Genetic Differentiation of Aedes aegypti (Diptera: Culicidae) in Areas with High Rates of Infestation in Mid-North Region of Brazil. Insects 2023, 14, 530. https://doi.org/10.3390/insects14060530

AMA Style

Rodrigues LF, Sousa AAd, Mendes Júnior WP, Cardoso e Silva AC, Nascimento MHSd, Barros MC, Sampaio I, Fraga EdC. Genetic Differentiation of Aedes aegypti (Diptera: Culicidae) in Areas with High Rates of Infestation in Mid-North Region of Brazil. Insects. 2023; 14(6):530. https://doi.org/10.3390/insects14060530

Chicago/Turabian Style

Rodrigues, Luzianny Farias, Andrelina Alves de Sousa, Walter Pinheiro Mendes Júnior, Amanda Caroline Cardoso e Silva, Maria Histelle Sousa do Nascimento, Maria Claudene Barros, Iracilda Sampaio, and Elmary da Costa Fraga. 2023. "Genetic Differentiation of Aedes aegypti (Diptera: Culicidae) in Areas with High Rates of Infestation in Mid-North Region of Brazil" Insects 14, no. 6: 530. https://doi.org/10.3390/insects14060530

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop