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Am. J. Trop. Med. Hyg., 78(3), 2008, pp. 479-491
Copyright © 2008 by The American Society of Tropical Medicine and Hygiene

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Genetic Relationships among Aedes aegypti Collections in Venezuela as Determined by Mitochondrial DNA Variation and Nuclear Single Nucleotide Polymorphisms

Ludmel Urdaneta-Marquez*, Christopher Bosio, Flor Herrera, Yasmin Rubio-Palis, Michael Salasek, AND William C. Black, IV
Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado; Centro de Investigaciones Biomedicas, Universidad de Carabobo-Nucleo Aragua, Maracay, Venezuela; Direccion de Control de Vectores, Ministerio de Salud, Maracay, Venezuela


ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
A population genetic analysis of gene flow was conducted among 619 Aedes aegypti from nine collections distributed among six geographic regions of Venezuela. Genetic markers included a 387-basepair region of the mitochondrial NADH dehydrogenase 4 (ND4) gene and single nucleotide polymorphisms (SNPs) at 11 nuclear loci. Genotypes at SNP loci were identified using melting curve analysis. Six different ND4 haplotypes were detected and patterns of variation suggested that collections were isolated by distance. The variance in SNP allele frequencies was much less than the variance in haplotype frequencies and a pattern of isolation by distance was not detected. Aedes aegypti from eight collections were orally challenged with dengue 2 virus. Disseminated infection rates ranged from 77% to 95%. The percentage of mosquitoes exhibiting a midgut infection barrier ranged from 2% to 15%, and those exhibiting a midgut escape barrier ranged from 2% to 18%. Venezuelan Ae. aegypti appear to be susceptible to dengue virus infection.


INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Aedes aegypti is the primary vector of yellow fever virus and dengue fever virus (DENV) worldwide.1 Dengue is a major public health challenge that has increased in prevalence as a result of re-expansion of the geographic range of Ae. aegypti, increasing and unplanned urbanization, increased movement of people, and insecticide resistance. An estimated 2.5 billion people living in 100 tropical and sub-tropical countries are at risk of epidemic dengue virus transmission.2 It is estimated that between 50 and 100 million cases of classic dengue fever and 500,000 cases of dengue hemorrhagic fever occur annually.3,4

Dengue viruses in Venezuela are maintained in a transmission cycle involving Ae. aegypti and humans. All four serotypes of dengue virus (DENV1–4) co-circulate in Venezuela,5,6 and there are several risk factors for DENV transmission that are specific to Venezuela. These include a large number of cities that generate an abundance of breeding containers caused by inadequate public trash disposal and poor urban planning in the placement of public water supplies that has forced many households to store water in cisterns and other open containers. Thirty towns located along the northern coast of Venezuela were surveyed for Ae. aegypti breeding sites and 55% of residences were found to harbor immature forms of Ae. aegypti.7 On average, there were 118 breeding sites per 100 residences and 24% of water receptacles contained immature forms.7 A recent study of Ae. aegypti productivity conducted in a cemetery in Trujillo, Venezuela estimated that approximately 47% of water holding containers supported Ae. aegypti immature forms and that there were on average 39 Aedes-infested containers/hectare.8 From this information, it was estimated that the daily output of adults for the entire cemetery was approximately 3,000 females. Habitat abundance is compounded by the appearance of organophosphate resistance in Apure, Táchira, Miranda, and Aragua states.9,10

Aedes aegypti is phenotypically polymorphic and populations vary in frequencies of biochemical and molecular genetic markers, and exhibit wide variation in vector competence for arboviruses.11–13 Knowledge of genetic variation and gene flow among Ae. aegypti populations is useful in following and predicting spatial and temporal dispersion of important genetic traits such as vector competence and insecticide resistance. Allozyme analysis was used in early population genetic studies to assess genetic relationships among worldwide Ae. aegypti collections.14–16 Mitochondrial DNA (mtDNA) has been widely used in population genetic studies of Ae. aegypti from different geographic and dengue endemic regions.17–20 Random amplified polymorphic DNA markers17,19 and microsatellites21,22 have been used to analyze local variation and patterns of gene flow.

Single nucleotide polymorphisms (SNPs) are the most abundant source of genetic variation among individual organisms. Previous studies have reported that the average SNP density in Ae. aegypti is 12 SNPs/kb, similar to the rate found in Drosophila melanogaster and Anopheles gambiae.23 However, estimates of individual genes among field-collected Ae. aegypti were much higher with 73 SNPs/kb in the abundant trypsin (tryp) gene24 and 104 SNPs/kb in the early tryp gene.25 Many of the techniques for detecting genotypes at individual SNP loci in individual insects are expensive and often require large platform arrays in which thousands of SNPs are detected at once.26 This is unaffordable for population genetics studies in which hundreds of insects are often analyzed. Melting curve SNP (mcSNP) genotyping is a useful alternative approach for diallelic genotyping of 10–20 loci in an organism.26–28 We have developed a single-tube mcSNP method to detect SNPs. We report on 10 SNPs and an insertion/deletion polymorphism in Ae. aegypti. We describe the allele-specific primers designed for selective amplification of each allele at these 11 loci and the conditions for reading the SNP genotype in individual mosquitoes.

The purpose of this study was to estimate genetic relationships among populations of Ae. aegypti from different geographic regions of Venezuela and to assess their vector competence for DENV-2. This is the first analysis of vector competence for any of the DENV serotypes in Venezuelan Ae. aegypti, and is the first study to use SNPs to study genetic variation and patterns of gene flow in this species and to directly compare this variation with the variation found using mitochondrial markers in this and prior studies.20


MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Aedes aegypti collections and extraction of DNA. From May through July 2004, nine collections of Ae. aegypti larvae were made in six geographic regions representing much of the geographic diversity of northern Venezuela. The geographic locations, regions, and sample sizes of all nine collections are shown in Figure 1Go.


Figure 1
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  		    FIGURE 1. Map of Venezuela showing the nine Aedes aegypti locations collections, the six regions, and the sample sizes. Shades of gray indicate altitudes in meters.

 
Northern Venezuela is extensively divided by mountain ranges. The Andes Mountains arise in the western part of the country and are the highest mountains in Venezuela (5 km, 16,600 feet.). Region 1 in our study contains the Zulia and Tachira collections along the western slopes of the Andes Mountains and region 2 contains the Lara and Portuguesa collections along the northeastern slopes of these mountains. The Andes range extends to the northeast to form the Venezuelan coastal range (Cordillera de la Costa), which runs along the central and eastern portions of the northern coast. Region 3 contains the Miranda collection from the large city of Los Teques located along the north slope of the Venezuelan coastal range at an elevation of 3,878 feet. Region 4 contains the Anzoategui collection from Barcelona east of the Venezuelan coastal range and approximately 70 km east along the coast, the Sucre collection from the city of Cumana. Region 5 is south of the Orinoco River and contains the Bolivar collection from the large city of Ciudad Bolivar. In contrast to the other collection sites, region 6 is located inland on a large plain and is represented by the Apure collection from the large city of San Fernando. Aedes aegypti are scarce in southern Venezuela because of low human densities.

Mosquito larvae were reared to adults in the laboratory, individually identified as Ae. aegypti, and then stored at –80°C until analyzed. DNA was obtained from individual mosquitoes by salt extraction,29 suspended in 100 µL of TE buffer (10 mM Tris-HCl, 1 mM EDTA, pH 8.0), and stored at –80°C.

Mitochondrial gene amplification and haplotype identification. We amplified a 387-basepair (bp) region of the NADH dehydrogenase 4 (ND4) gene using previously reported primers and polymerase chain reaction (PCR) conditions.19 The PCR was performed in 50-µL reactions using 1 µL of template DNA in a PTC-100 thermal cycler (MJ Research, Inc., Watertown, MA). Taq DNA polymerase was added directly in the mixture. Negative controls (all reagents except template DNA) were included in all PCR plates to detect contamination. The PCR products were analyzed by electrophoresis on a 1.6% agarose gel. The PCR product (4 µL) was mixed with 3 µL of loading buffer (10 mM NaOH, 95% formamide, 0.05% bromophenol blue, 0.05% xylene cyanol) and then fractionated on a single-strand conformation polymorphism (SSCP) gel.19

Discovery of SNPs. Five gene regions were amplified in the 94 Ae. aegypti listed in Table 1Go using the primers listed in Table 2Go. Figure 2Go shows primer locations. Amplified products were screened for polymorphisms with SSCP as described above. All novel SSCP genotypes were then sequenced to screen for SNPs. These sequences were then assembled into a single dataset and analyzed with DnaSP version 4.10.930 to determine the number of alternate nucleotides at each SNP site, whether the SNP involved a transversion or transition, estimate {pi} (number of nucleotide mismatches/number of pairwise comparisons),31 and to assess whether each SNP encoded a synonymous or replacement substitution. Once a SNP locus was selected, it was assigned the name of the gene followed by a numeric label indicating its distance in nucleotides from the adenine in the ATG start site.


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TABLE 1
Geographic origin, sex, and sample sizes of Aedes aegypti from Mexico, Thailand, and Senegal used to screen for single nucleotide-polymorphisms
 

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TABLE 2
Primers used to amplify nuclear genes containing single nucleotide polymorphisms
 

Figure 2
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  		    FIGURE 2. Amplified region of each of the four nuclear genes. Introns appear in lower case letters, primer sites are underlined, all single nucleotide polymorphism (SNP) sites are underlined, replacement substitutions are highlighted in gray, and the selected SNP is placed in a box.

 
Genotype identification of SNPs. Genotypes at SNP loci were detected using allele-specific PCR. Genotypes were determined in a single-tube PCR using two different allele-specific primers, each of which contained a 3' nucleotide corresponding to one of the two alleles and an opposite primer that amplified both alleles. Allele-specific primers were manufactured (Operon Inc., Huntsville, AL) with 5' tails32,33 designed to enable discrimination between SNP alleles on the basis of size or melting temperature. These were (5'-GCGGGCAGGGCGGCGGGG GCGGGGCC [allele-specific primer 1]-3' or 5'-GCGGGC [allele-specific primer 2]-3'). An intentional mismatch was introduced three bases in from the 3' end of allele-specific primers that differed by a transition.34 Table 3Go lists the oligonucleotides used for SNP detection and the predicted lengths of the PCR products.


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TABLE 3
Oligonucleotides used for allele-specific polymerase chain reaction*
 
The SNP PCR was performed in 25-µL reactions in 96-well Hard ShellTM plates with white wells (Bio-Rad Laboratories, Hercules, CA). Each reaction contained 12.5 µL of 2x IQTM SYBR® Green Supermix (Bio-Rad Laboratories) (final concentrations = 50 mM KCl, 20 mM Tris-HCl, pH 8.4, 0.2 mM of each dNTP, 0.625 units of iTaq® DNA polymerase, 3 mM MgCl2, 1x SYBR Green I, 10 nM fluorescein), 25 pm of each primer, approximately 100 ng of template DNA, and sterile filtered double-distilled water to give a final volume of 25 µL. The PCR wells were covered with Flat Cap Strips (Bio-Rad Laboratories) and placed in the Opticon 2 DNA Engine (MJ Research, Waltham, MA). Thermal cycling conditions were 1) 95°C for 12 minutes (initial denaturation), 2) 95°C for 20 seconds (denaturation), 3) 60°C for 1 minute (annealing), 4) 72°C for 30 sec (extension), 5) cycle to step 2, 39 times, 6) 72°C for 5 min (final extension), and 7) ramp from 65°C to 95°C at a rate of 0.2°C/sec (melting curve).

For detection by agarose gel electrophoresis, a 4.0% (w/v) agarose gel (GenePure HiRes agarose; ISCBioExpress, Kaysville, UT) was poured with 1x Tris-borate-EDTA (89 mM Tris-borate, 2 mM EDTA, pH 8.3). DNA fragments were fractionated by electrophoresis for 90 minutes at 80 V alongside a 25-bp DNA ladder (TrackIt; Invitrogen, Carlsbad, CA).

Statistical analysis of haplotype and allele frequencies. Variation in haplotype frequencies among and within regions was determined by analysis of molecular variance (AMOVA) using the computer program Arlequin version 3.01.35 This program also estimated pairwise FST values and Slatkin’s linearized FST [FST/(1–FST)]36 among collections and computed the significance of the variance components associated with each level of genetic structure by a nonparametric permutation test with 100,000 pseudoreplicates.35 Pairwise linearized FST values were used to construct a dendrogram among all collections by means of unweighted pair-group method with arithmetic averaging analysis37 in the NEIGHBOR procedure in PHYLIP3.5C.38 Linkage disequilibrium analysis was performed with LINKDIS.39

Vector competence. Mosquito collections were characterized for vector competence using an immunofluorescence assay (IFA) at 14 days post-oral challenge. The DENV-2 strain used was dengue 2 JAM1409, which was isolated in 1983 in Jamaica and belongs to the American Asian genotype.40,41 All procedures for growing virus in 14-day cell culture, quantifying the virus, and infecting mosquitoes with membrane feeders covered with sterile hog gut membranes have been reported.42 A highly DENV-2 susceptible Ae. aegypti colony called D2S343 served as a positive control to test for consistency in the quality and quantity of DENV-2 preparation and infection. Undiluted virus titers ranged from 7.5 to 8.5 log10 infectious virus/mL.

Fully engorged mosquitoes were removed from the feeding carton and held for 14 days at a constant temperature of 27°C and relative humidity of 80% in an insectary with a 12-hour photoperiod. Heads and abdomens were assayed for infection by IFA using a mouse-derived primary monoclonal antibody directed against a flavivirus E gene epitope.44,45 Mosquitoes were frozen at –70°C for DNA extractions and IFA. Heads were checked first for DENV-2 infections. If the head was uninfected, the abdomen was checked for DENV-2 infection.


RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Discovery of SNPs. Using the primers in Table 2Go, we amplified regions of the phosphoglucomutase (Pgm) (AAEL010037), {alpha}-amylase (Amy) (AAEL013421), glucose-6-phosphate isomerase (Gpi) (AAEL012994), and alkaline phosphatase (Aph) (AAEL000931) genes shown in Figure 2Go in the 94 mosquitoes listed in Table 1Go. These were then screened for sequence variation using SSCP. Table 4Go lists the number of insects that were sequenced but the final sequence DnaSP dataset for analysis of {pi} contained the sequences of all 94 mosquitoes. All primers and the associated analyses for the early tryp gene have been published,25 and the four kdr SNPs appear in the report by Saavedra-Rodriguez and others.46


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TABLE 4
Numbers of mosquitoes sequenced and size of the amplified region*
 
Figure 2Go shows the region amplified (primer sites underlined), with introns in lower case letters, all SNP sites underlined, replacement substitutions highlighted in gray, and the chosen SNP site in a box. Plots of {pi} across the amplified region and the locations of the SNP loci chosen are shown in Figure 3Go. Our selection of SNPs was biased towards those that 1) had a {pi} > 0.35, 2) contained transversions, and/or 3) encoded a replacement substitution. Amy-2 had the highest density of SNPs with approximately 124 SNPs/kb and {pi} = 0.0384, and Gpi had the lowest SNP density with approximately 30 SNPs/kb and {pi} = 0.0050. Amy2-447 and Amy2-450 involve a G-T transversion with {pi} values of 0.47 and 0.35, respectively, and Amy2-608 involves a T-C transition with a {pi} value of 0.49. All three encode synonymous substitutions. The five replacement substitutions at positions 40, 67, 125, 184, 214, and 305 had {pi} values of 0.05, 0.18, 0.01, 0.27, 0.04, and 0.46, respectively. The SNP at position 305 was an ideal SNP by our criteria but we were unable to design an assay for it because of its proximity to the reverse PCR primer. Aph 1,179 involves a G-T transversion with a {pi} value of 0.41 and all SNPs encoded synonymous substitutions. Gpi 1,500 involves a G-A transition with a {pi} value of 0.02 and all SNPs encoded synonymous substitutions. Only one SNP involved a transversion but had a lower {pi} value. The remainder of the SNPs in Gpi were not polymorphic in New World samples. Pgm 954 involves an A-C transversion with a {pi} value of 0.47 and all SNPs encoded synonymous substitutions. By definition, those SNPs in Figure 3Go that have a {pi} value > 0.50 involve more than two alternate nucleotides. These SNPs were not used because they required additional, more expensive SNP detection.


Figure 3
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  		    FIGURE 3. Plots of {pi} across the amplified nuclear genes. Arrows indicate the locations of the selected single nucleotide polymorphism loci.

 
Identification of SNPs. Figure 4Go shows the melting curves for the three genotypes at Amy2-447, Amy2-450, Amy2-608, TrypEarl, Aph 1,179, Gpi 1,500, and Pgm 954 SNP loci. All melting curve genotype profiles for the four kdr SNPs appear in the report by Saavedra-Rodriguez and others.46 Realizing that real-time PCR machines are not always available, we also fractionated PCR products by electrophoresis on 4.0% Tris-borate-EDTA agarose gels. Figure 4Go shows that the different tail lengths added to the allele specific primers for the purpose of producing different melting curves could also be detected by agarose gel electrophoresis for the Aph, TrypEarl, and Gpi loci. These results have been confirmed for the other four loci.


Figure 4
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  		    FIGURE 4. Melting curve patterns for the three genotypes at amylase (Amy)2-447, Amy2-450, Amy2-608, Aph 1,179, trypsin (Tryp)Earl, glucose-6-phosphate isomerase (Gpi) 1,500, and phosphoglucomutase (Pgm) 954 single nucleotide polymorphism loci. Electrophoresis in 4% GenePure HiRes agarose gel of allele-specific polymerase chain reaction products for Aph1,179, TrypEarl, and Gpi1,500 loci. T = T/T homozygotes; G = G/G homozygotes; K = G/T heterozygotes; D = deletion homozygotes; I = insertion homozygotes; H = indel heterozygotes; A = A/A homozygotes; G = G/G homozygotes; R = A/G heterozygotes. DNA size markers (25-basepair DNA ladder) are in lanes labeled m.

 
Mitochondrial DNA haplotype frequencies among collections. A 387-bp region of the ND4 gene was amplified and surveyed for variation by SSCP. Six different ND4 haplotypes were detected by SSCP. The ND4 fragment was sequenced in 30 mosquitoes, representing all 6 haplotypes. Mosquitoes with the same SSCP profiles had the same nucleotide sequences, confirming specificity of SSCP, and mosquitoes with the different SSCP profiles also had different nucleotide sequences, confirming SSCP sensitivity.

Haplotype frequencies are shown in Table 5Go. Three of the six haplotypes detected, (HVen1-3) were unique to Venezuela; the other three (HMex3, HMex5, and HMex6) were previously18 sampled in Mexico and HMex5 was also found in Thailand.47 Four haplotypes (HVen1, HMex6, HMex3, and HVen2) occurred at high frequencies throughout Venezuela. HVen1 and HMex6 were present in all collections.


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TABLE 5
NADH dehydrogenase 4 (ND4) haplotypes frequencies among the nine Venezuela collections
 
Variation in haplotype frequencies were compared among the six regions among collections within regions and among individual mosquitoes within collections by AMOVA (Table 6Go). Most (approximately 70%) of the variation in haplotype frequencies was caused by differences among mosquitoes in individual collections, whereas approximately 26% of the variation was caused by differences among regions and only approximately 3% was attributed to differences among collections within regions.


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TABLE 6
Analysis of molecular variance in NADH dehydrogenase 4 (ND4) haplotype and single nucleotide polymorphism (SNP) allele frequencies among collections*
 
Cluster analysis of pairwise FST/(1–FST) among the Venezuelan collections indicates four groups (Figure 5Go). The AMOVA among these groups estimated an FST of 0.227. With the exception of Zulia and Tachira, none of the collections cluster with respect to geographic region. A Mantel analysis of pairwise FST/(1–FST) against geographic distances indicated a highly significant correlation between genetic and geographic distances among collections (Figure 6Go). Although a significant correlation is usually interpreted as evidence of isolation by distance, few proximate collections were made in this study, making it difficult to assess the amount of genetic differentiation at a local level.


Figure 5
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  		    FIGURE 5. Unweighted pair group method with arithmetic mean cluster analysis of pairwise FST/(1–FST) for NADH dehydrogenase 4 and single nucleotide polymorphism markers among the nine collections.

 

Figure 6
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  		    FIGURE 6. Regression analysis of A, pairwise F ST/(1–FST) for the NADH dehydrogenase 4 (ND4) marker against ln(geographic distances (km)), B, pairwise FST/(1–FST) for the single nucleotide polymorphism (SNP) markers against ln(geographic distances (km)), C, pairwise FST/(1–FST) for ND4 marker vs. SNP markers.

 
SNP allele and genotype frequencies in collections. The kdrV1,016G allele was absent in all collections. The allele frequencies at each of the remaining 10 SNP loci are listed in Table 7Go. Wright’s FIS was estimated for each locus in each collection by the method of Weir and Cockerham48 to test for Hardy-Weinberg proportions among genotypes where FIS = 1-(heterozygotes observed/heterozygotes expected). If an excess of heterozygotes are observed, then FIS < 0 and if an excess of homozygotes are observed, then FIS > 0. A chi-square goodness of fit test was used to test the null hypothesis that FIS = 0.


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TABLE 7
Small nucleotide polymorphism allele frequencies at each locus*
 
A frequency histogram of the average FIS across loci is shown in Figure 7Go. At the Pgm 954 locus, there was a consistent deficiency of heterozygotes in seven of the nine collections. However, Hardy-Weinberg proportions were observed in the Miranda collection and there was an excess of heterozygotes in the Anzoátegui collection. At the Amy2-608 locus, there was a consistent excess of heterozygotes in all collections. The remainder of FIS estimates approximate zero.


Figure 7
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  		    FIGURE 7. Frequency histograms of the FIS averaged across collection for the 10 single nucleotide polymorphism (SNP) loci (top) and the Weir and Cockerham’s estimate of Wright’s F ST for NADH dehydrogenase 4 and the 10 SNP loci.

 
SNP allele frequencies among collections. The Weir and Cockerham estimate of Wright’s FST48 is reported for each locus (Table 4Go and Figure 7Go). The FST was ≤ 0.010 at 9 of the 11 loci but was large at the Pgm 954 locus and at the mitochondrial ND4 gene. As with the ND4, variation in SNP allele frequencies were compared among and within the six geographic regions by AMOVA.35 In contrast to the patterns detected with mtDNA, nearly all of the variation in SNP allele frequencies was attributable to mosquitoes in collections, whereas only approximately 2.2% of the variation was attributable to geographic regions and approximately 2.9% was caused by variation among collections within regions (Table 6Go).

Cluster analysis of pairwise FST/(1–FST) among collections based upon SNP loci is shown in Figure 5Go. The variance in SNP allele frequencies among the nine collections is much less than the variance in mtDNA haplotype frequencies. With the exception of Lara and Portuguesa, none of the clusters correspond with those obtained with the ND4. Furthermore, Mantel analysis of pairwise FST/(1–FST) against geographic distances indicated no correlation between genetic and geographic distances (Figure 6Go). No correlation was found between FST/(1–FST) among collections for SNP alleles versus ND4 haplotypes, and Figure 6Go shows again that the variance in SNP frequencies is much less than the variance in haplotype frequencies.

Linkage disequilibrium. An analysis of linkage disequilibrium was performed to determine whether alleles at the various SNP loci segregate independently of one another. DIT estimates the total disequilibrium in all collections. DIS estimates the disequilibrium caused by genetic drift, and DST estimates disequilibrium caused by linkage or a higher epistatic interaction among alleles. Of the 45 pairwise comparisons among alleles at the 10 SNP loci, 34 were not significant. Of the remaining 11, DST/DIT was small with the exception of SNP alleles from Amy2. For the most part alleles at SNP loci in Ae. aegypti in Venezuela were in linkage equilibrium and genetic drift accounted for most of the significant disequilibrium except among alleles from the same gene.

Vector competence. The D2S3 strain of Ae. aegypti demonstrated the high disseminated infection rate (DIR) with DEN-2 JAM1409 (Figure 8Go) that we have observed in earlier studies.11,49 All of the Venezuela collections also had a high disseminated infection rate ranging from 77% in Lara to 95% in Sucre. These differences were significant ({chi}2 = 15.76, degrees of freedom = 8, P = 0.046). Mosquitoes from Zulia and Sucre had a slight but significantly greater DIR than Lara, Portuguesa, Anzoategui, and Bolivar. Lara and Portuguesa mosquitoes had a lower DIR because of a significantly greater rate of mosquitoes with midgut infection barriers (MIBs), and Zulia and Sucre had a greater DIR because of a significantly lower rate of mosquitoes with midgut escape barriers (MEBs).


Figure 8
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  		    FIGURE 8. Percentages of mosquitoes from eight Venezuelan strains and the D2S3 strain that developed a disseminated infection, a midgut infection barrier, or a midgut escape barrier. Pairwise Fisher’s exact tests were performed on all collections. Strains with equivalent rates have the same a or b labels and these were significantly different from each other. Strains labeled ab were not different from either a or b groups.

 

DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
We detected six different ND4 haplotypes and our analyses suggested that collections were isolated by distance. In particular, mosquitoes from Zulia and Tachira west of the Andes Mountains were genetically distinct from the other collections as were the mosquitoes from Apure in the large plain. However, the variance in nuclear SNP allele frequencies was much less than the variance in mitochondrial haplotype frequencies and a pattern of isolation by distance was not detected. Aedes aegypti from the eight collections were orally challenged with DENV-2. Disseminated infection rates ranged from 77% to 95%. The percentage of mosquitoes exhibiting an MIB ranged from 2% to 15%, and those exhibiting an MEB ranged from 2% to 18%.

This is a second population genetic analysis of gene flow using the mitochondrial ND4 gene among Ae. aegypti collections in Venezuela. Herrera and others20 sampled 1,144 Ae. aegypti from 24 locations in Venezuela. They sampled from eight geographic regions including West Coast, Maracaibo Lake, West, Inland, Central, North, East, and South. Tachira and Zulia in the present study were located in the West region of Herrera and others,20 Anzoategui and Sucre were located the East region, and Bolivar was located in the South region. Lara and Portuguesa are closest to their Inland region. Miranda is closest to their North Region and Bolivar to their South Region. No site close to Apure was sampled in their study.

The overall pattern of variation among haplotypes is similar in the current and previous studies. Herrera and others20 found by AMOVA that 11.6% of the overall variation in haplotype frequencies arose among collections in regions, 10.8% among regions, and 77.6% within collections. These results correspond to F statistics of 0.108, 0.130, and 0.224, respectively. In our AMOVA, 3% of the variation arose among collections in regions, 26% among regions, and 70.0% within collections, which corresponded to F statistics of 0.264, 0.045, and 0.298, respectively. The difference between the two studies in the amounts of variation among collections in regions occurs because Herrera and others20 obtained 2–5 collections in each of their eight regions but only two of the six regions had more than one collection in our study. Under-sampling within regions may have caused us to underestimate the variation among local collections. This bias can be removed by comparing F-statistics across collections irrespective of region. Adding the F-statistics within and among regions with the data of Herrera and others20 yields a value of 0.238 (22%), and a combined F-value of 0.309 (29%) was obtained in the present study. This suggests that we made too few collections within regions to accurately estimate this component.

In addition, the phylogenetic groups in the analyses of the two studies are highly similar. Clade I in the study of Herrera and others20 contained collections from the Maracaibo Lake and West regions, and collections from their Inland, North, East, and South regions arose in a separate clade II. Similarly, Figure 5Go shows that Zulia (from their West region) and Tachira (West) arise in a clade separate from the clade containing Sucre (East), Lara (Inland), Portuguesa (Inland), Miranda (North), Bolivar (South), and Anzoátegui (East). Neither Apure nor any regions south of the Apure River were sampled in their study. Additionally, both studies detected a similar pattern of isolation by distance.

This is the first study to use SNPs to study genetic variation and patterns of gene flow in Ae. aegypti. Genotypes at 8 of the 10 SNP loci conformed to Hardy-Weinberg proportions but at the Pgm 954 locus there was a deficiency of heterozygotes in 7 of the 9 collections. In contrast, there was an excess of heterozygotes in all collections at the Amy2-608 locus. There are several possible explanations for heterozygote deficiencies. First, it is possible that the mcSNP assay was not equally sensitive to both alleles. For the Pgm 954 locus, this would mean that the mcSNP assay did not detect all heterozygotes. The C primer might anneal weakly; thus, the C allele was only amplified in C/C homozygotes and A/C heterozygotes were manifest as A homozygotes. However, this hypothesis is inconsistent with three observations. First, genotypes were in Hardy-Weinberg proportions in the Miranda collection (21 A/A, 24 A/C, 5 C/C). Second, there was an excess of heterozygotes in the Anzoátegui collection where there was 25 A/A, 27 A/C, and 1 C/C. Third, in a different study in Tapachula Mexico (Black WC, unpublished data), a consistent excess of heterozygotes was detected at Pgm 954.

Another possible explanation for heterozygote deficiency is that the allele specific primer annealing sites varied among collections. Thus, the allele-specific primers were preferentially sensitive to one allele in one collection but not in another. We consider this unlikely because in our initial search for SNPs in the 94 mosquitoes, the 22 nucleotides upstream of the Pgm 954 locus and the 20 nucleotides downstream of this locus were invariant (Figure 2Go). A third possibility is that this locus is subject to some type of diversifying selection such that the A versus C alleles are favored in different environments. However, it is hard to envision how selection would act on alternate synonymous substitutions. A fourth possibility is that the SNPs are in disequilibrium with alleles with replacement substitutions that are subject to diversifying selection. We do not have sufficient data on other SNP sites in Pgm to test this possibility.

There are also several possible explanations for the opposite trend of an excess of heterozygotes as seen in the Amy2-608 locus. The mcSNP assay may not be specific for either allele. For Amy2-608, the mcSNP assay may not have discriminated between C and T alleles. The C/C homozygotes would act as template for the T-specific alleles and conversely T/T homozygotes would act as template for the C-specific alleles. Thus, homozygotes would be incorrectly read as heterozygotes. With this scenario, allele frequencies should be approximately equal in all collections, and the mean FST among collections for Amy2-608 was 0.009, the smallest of all 10 loci. In addition, in a Tapachula Mexico study (Black WC, unpublished data), a consistent excess of heterozygotes (average FIS = –0.899) were also observed. For these reasons, lack of specificity of the mcSNP assay seems the most likely explanation for excess heterozygotes at the Amy2-608 locus. Amy2-608 in our analyses had little effect on the cluster analyses or AMOVA because the FST was small.

This is the first study to directly compare variation in SNP loci with the variation found using mitochondrial markers. From this and previous studies, it is clear that SNP densities are high in natural populations of Ae. aegypti. In contrast to the 12 SNPs/kb previously reported,23 30–124 SNPs/kb were detected in the 4 genes examined in the current study (Table 4Go). Genetic distances based on mitochondrial haplotype frequencies are 5–6 times larger than distances based on SNP markers (Figures 5Go–7GoGo). When comparing the frequencies in Table 5Go with the phylogenetic patterns in Figure 5Go, Zulia and Tachira share a high frequency (approximately 0.5) of the HMex3 haplotype compared with the remainder where HMex3 varies from 0.0 to 0.18. Similarly, Lara, Portuguesa, and Sucre all share a high frequency (approximately 0.38) of the HMex6 haplotype compared with 0.01–0.13 in the remainder. Apure is unique in having 58% of mosquitoes with haplotype HVen2; the next highest proportion was approximately 20% in Miranda and in the remainder of collections this haplotype was rare. However when comparing the SNP frequencies in Table 7Go with the phylogenetic patterns in Figure 5Go, no clear patterns are evident except that Lara and Portuguesa share a relatively low frequency (0.34–0.38) of the Pgm 954 A allele compared with the remainder where that allele varies from 0.66 to 0.89.

There are several genetic explanations for the differences produced by the two marker types. One possibility involves the differences in effective population size (Ne = number of reproducing mosquitoes) associated with biparental inheritance of nuclear markers compared with the maternal inheritance of mitochondrial markers. Applying the assumptions of Wright’s island model50


Formula 1

where Nem is the effective migration rate or the number of reproductive migrant mosquitoes exchanged among populations each generation. These equations imply that for low gene flow when Ne m < 1, FST(nuc) ~= FST(mito) but when Ne m > 1, FST(nuc) ~= 0.5FST(mito). Thus equation (1) predicts that FST(nuc) = rFST(mito) where 0.5 ≤ r ≤ 1. However, we found r = 0.18. Thus, reduced Ne alone does not explain the magnitude of the observed differences in variation between the two classes of markers.

Another possibility is that homoplasy exists among the SNP markers such that populations become homogeneous in their frequencies through approximately equal rates of forward and reverse mutations rather than through migration. One of the reasons for attempting to use SNPs with transversions was that we assumed forward and reverse mutation rates among transversions would be half of that among transitions. Within and among Drosophila species, the transition/transversion bias ({kappa}) is 2.09 (range = 0.90–5.14).52 In the four nuclear genes analyzed in this study, {kappa} varied from 1.32 (Aph) to 35.81 (Gpi) (Table 4Go). If forward and reverse mutation rates are approximately equal, then we would expect homoplasy among SNP alleles. Accordingly, we would expect a lower FST among transition SNPs than among transversion SNPs. The average FST among the six transition SNPs was 0.036, and the average among the three transversion SNPs was 0.076 and 0.048 for the TrypEarl indel. Even with this bias, we cannot account for the fact that FST among mtDNA was 5–6 times greater than among SNPs. A recent meta-analysis of variation in the mtDNA53 in approximately 3,000 animal species showed that mtDNA diversity is in general higher than in nuclear loci but that the magnitude of the difference varies greatly among animal phyla.

The biologic implications of these different patterns of variation between nuclear and mitochondrial markers are not trivial. The mitochondrial patterns established in this and the previous study20 among Venezuelan collections of Ae. aegypti are consistent with some degree of genetic isolation between western collections and those collections made throughout the remainder of northern Venezuela. In contrast, nuclear markers suggest homogeneity among all collections irrespective of geographic origin. At this time, we cannot reconcile these differences.

We performed a similar combined study of gene flow and vector competence among Ae. aegypti collections in Mexico.11 There the disseminated infection rates were more dynamic, ranging from 24% in Veracruz to 83% along the Pacific Coast and in the state of Quintana Roo. The percentage of mosquitoes exhibiting an MIB was also dynamic, ranging from 14% to 59%, and the MEB rate ranged from 4% to 43%. In contrast, disseminated infection rates among Ae. aegypti collections in Venezuela only varied by 18% (77–95%) with only 2–15% of mosquitoes exhibiting an MIB and 2–18% exhibiting an MEB. Venezuelan Ae. aegypti appear to be uniformly highly susceptible to DENV infection. There have been no other regional studies of vector competence for DENV with which to compare these results. Earlier studies with yellow fever virus54–56 in Ae. aegypti documented a large difference in vector competence among countries but did not focus among collections within countries.

The consistently low variation in disseminated infection rates, MIB, and MEB rates among collections are consistent with the SNP results. If nuclear SNPs are homogenous in frequency perhaps those nuclear genes that condition vector competence are also similar among collections.

Received July 26, 2007. Accepted for publication November 28, 2007.

Acknowledgments: We thank the people of the Venezuelan communities for cooperation in sample collections; Hernan Guzman, Jose Parra, and Victor Sanchez for valuable help in mosquito collections; Jesus Gonzalez for help in establishing the parental Ae. aegypti colonies in Venezuela; and Saul Lozano-Fuentes for help in constructing the map of the Venezuelan collections sites.

Financial support: This work was supported in part by the Innovative Vector Control Consortium.

* Address correspondence to Ludmel Urdaneta-Marquez, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO 80523-1682. E-mail: ludmel{at}colostate.edu Back

Authors’ addresses: Ludmel Urdaneta-Marquez, Michael Salasek, and William C. Black IV, Department of Microbiology, Immunology and Pathology, 1682 Campus Delivery, Colorado State University, Fort Collins, CO 80523, Telephone: 970-491-8530, Fax: 970-491-1815, E-mails: ludmel{at}colostate.edu, mike.salasek{at}colostate.edu, and wcb4{at}lamar.colostate.edu. Christopher Bosio, Laboratory of Zoonotic Pathogens, Rocky Mountain Laboratories, National Institute of Allergy and Infectious Diseases/National Institutes of Health, 903 South Fourth Street, Hamilton, MT 59840, E-mail: bosioch{at}niaid.nih.gov. Flor Herrera and Yasmin Rubio-Palis, Centro de Investigaciones Biomedicas, Universidad de Carabobo-Nucleo Aragua, Maracay, Venezuela, E-mail: flormhq{at}cantv.net.


REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Monath TP, 1994. Dengue: the risk to developed and developing countries. Proc Natl Acad Sci USA 91: 2395–2400.[Abstract/Free Full Text]
  2. World Health Organization, 2000. Strengthening the Implementation of the Global Strategy for Dengue Fever/Dengue Hemorrhagic Fever Prevention and Control. Geneva: World Health Organization.
  3. Gubler DJ, 2002. The global emergence/resurgence of arboviral diseases as public health problems. Arch Med Res 33: 330–342.[Web of Science][Medline]
  4. Gubler DJ, 2002. Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century. Trends Microbiol 10: 100–103.[Medline]
  5. Ministerio de Salud y Desarrollo Social, 2000. Alerta, Reporte Epidemiologico Semanal 36. Caracas: Venezuela.
  6. Ministerio de Salud y Desarrollo Social, 2002. Alerta, Reporte Epidemiologico Semanal 52. Caracas: Venezuela.
  7. Barrera R, Navarro J, Mora J, Domínguez D, González J, 1995. Public service deficiencies and Aedes aegypti breeding sites in Venezuela. Bull Pan Am Health Organ 29: 193–205.[Medline]
  8. Abe M, McCall PJ, Lenhart A, Villegas E, Kroeger A, 2005. The Buen Pastor cemetery in Trujillo, Venezuela: measuring dengue vector output from a public area. Trop Med Int Health 10: 597–603.[Web of Science][Medline]
  9. Bisset JA, Rodriguez MM, Molina D, Diaz C, Soca LA, 2001. High esterases as mechanism of resistance to organophosphate insecticides in Aedes aegypti strains. Rev Cubana Med Trop 53: 37–43.[Medline]
  10. Rodriguez MM, Bisset J, De Fernandez DM, Lauzan L, Soca A, 2001. Detection of insecticide resistance in Aedes aegypti (Diptera: Culicidae) from Cuba and Venezuela. J Med Entomol 38: 623–628.[Web of Science][Medline]
  11. Bennett KE, Olson KE, Munoz Mde L, Fernandez-Salas I, Farfan-Ale JA, Higgs S, Black WC, Beaty BJ, 2002. Variation in vector competence for dengue 2 virus among 24 collections of Aedes aegypti from Mexico and the United States. Am J Trop Med Hyg 67: 85–92.[Abstract]
  12. Gubler DJ, Nalim S, Tan R, Saipan H, Sulianti Saroso J, 1979. Variation in susceptibility to oral infection with dengue viruses among geographic strains of Aedes aegypti. Am J Trop Med Hyg 28: 1045–1052.[Abstract/Free Full Text]
  13. Tabachnick WJ, Wallis GP, Aitken TH, Miller BR, Amato GD, Lorenz L, Powell JR, Beaty BJ, 1985. Oral infection of Aedes aegypti with yellow fever virus: geographic variation and genetic considerations. Am J Trop Med Hyg 34: 1219–1224.[Abstract/Free Full Text]
  14. Tabachnick WJ, 1991. The yellow fever mosquito: evolutionary genetics and arthropod-borne disease. Am Entomologist 37: 14–24.
  15. Tabachnick WJ, Powell JR, 1979. World-wide survey of genetic variation in the yellow fever mosquito, Aedes aegypti. Genet Res 34: 215–229.[Web of Science][Medline]
  16. Wallis GP, Tabachnick WJ, Powell JR, 1983. Macrogeographic genetic variation in a human commensal: Aedes aegypti, the yellow fever mosquito. Genet Res 41: 241–258.[Web of Science][Medline]
  17. Apostol BL, Black WC, Reiter P, Miller BR, 1996. Population genetics with RAPD-PCR markers: the breeding structure of Aedes aegypti in Puerto Rico. Heredity 76: 325–334.[Web of Science][Medline]
  18. Gorrochotegui-Escalante N, Gomez-Machorro C, Lozano-Fuentes S, Fernandez-Salas I, de Lourdes Munoz M, Farfan-Ale JA, Garcia-Rejon J, Beaty BJ, Black WC IV, 2002. Breeding structure of Aedes aegypti populations in Mexico varies by region. Am J Trop Med Hyg 66: 213–222.[Abstract]
  19. Gorrochotegui-Escalante N, de Lourdes Munoz M, Fernandez-Salas I, Beaty BJ, Black WC IV, 2000. Genetic isolation by distance among Aedes aegypti populations along the northeastern coast of Mexico. Am J Trop Med Hyg 62: 200–209.[Abstract]
  20. Herrera F, Urdaneta L, Rivero J, Zoghbi N, Ruiz J, Carrasquel G, Martinez JA, Pernalete M, Villegas P, Montoya A, Rubio-Palis Y, Rojas E, 2006. Population genetic structure of the dengue mosquito Aedes aegypti in Venezuela. Mem Inst Oswaldo Cruz 101: 625–633.[Web of Science][Medline]
  21. da Costa-da-Silva AL, Capurro ML, Bracco JE, 2005. Genetic lineages in the yellow fever mosquito Aedes (Stegomyia) aegypti (Diptera: Culicidae) from Peri. Mem Inst Oswaldo Cruz 100: 639–644.[Web of Science]
  22. Huber K, Le Loan L, Hoang TH, Ravel S, Rodhain F, Failloux AB, 2002. Genetic differentiation of the dengue vector, Aedes aegypti (Ho Chi Minh City, Vietnam) using microsatellite markers. Mol Ecol 11: 1629–1635.[Medline]
  23. Morlais I, Severson DW, 2003. Intraspecific DNA variation in nuclear genes of the mosquito Aedes aegypti. Insect Mol Biol 12: 631–639.[Web of Science][Medline]
  24. Black WC, Gorrochetegui-Escalante N, Randle NP, Donnelly MJ, 2008. The yin and yang of linkage disequilibrium: mapping of genes and nucleotides conferring insecticide resistance in insect disease vectors. Aksoy S, ed. Insect Transgenesis. Austin, TX: Landes Biosciences.
  25. Gorrochotegui-Escalante N, Lozano-Fuentes S, Bennett KE, Molina-Cruz A, Beaty BJ, Black WC, 2005. Association mapping of segregating sites in the early trypsin gene and susceptibility to dengue-2 virus in the mosquito Aedes aegypti. Insect Biochem Mol Bio 35: 771–788.
  26. Black WC, Vontas JG, 2007. Affordable assays for genotyping single nucleotide polymorphisms in insects. Insect Mol Biol 16: 377–387.[Web of Science][Medline]
  27. Papp AC, Pinsonneault JK, Cooke G, Sadee W, 2003. Single nucleotide polymorphism genotyping using allele-specific PCR and fluorescence melting curves. Biotechniques 34: 1068–1072.[Web of Science][Medline]
  28. Ye J, Parra EJ, Sosnoski DM, Hiester K, Underhill PA, Shriver MD, 2002. Melting curve SNP (McSNP) genotyping: a useful approach for diallelic genotyping in forensic science. J Forensic Sci 47: 593–600.[Web of Science][Medline]
  29. Black WC, DuTeau NM, 1997. RAPD-PCR and SSCP analysis for insect population genetic studies. Crampton J, Beard CB, Louis C, eds. The Molecular Biology of Insect Disease Vectors: A Methods Manual, 514–531.
  30. Rozas J, Sanchez-DelBarrio JC, Messeguer X, Rozas R, 2003. DnaSP, DNA polymorphism analyses by the coalescent and other methods. Bioinformatics 19: 2496–2497.[Abstract/Free Full Text]
  31. Nei M, Miller JC, 1990. A simple method for estimating average number of nucleotide substitutions within and between populations from restriction data. Genetics 125: 873–879.[Abstract]
  32. Germer S, Higuchi R, 1999. Single-tube genotyping without oligonucleotide probes. Genome Res 9: 72–78.[Abstract/Free Full Text]
  33. Wang J, Chuang K, Ahluwalia M, Patel S, Umblas N, Mirel D, Higuchi R, Germer S, 2005. High-throughput SNP genotyping by single-tube PCR with Tm-shift primers. Biotechniques 39: 885–893.[Web of Science][Medline]
  34. Okimoto R, Dodgson JB, 1996. Improved PCR amplification of multiple specific alleles (PAMSA) using internally mismatched primers. Biotechniques 21: 20.[Web of Science][Medline]
  35. Excoffier L, Smouse PE, Quattro JM, 1992. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131: 479–491.[Abstract]
  36. Slatkin M, 1993. Isolation by distance in equilibrium and non-equilibrium populations. Evolution Int J Org Evolution 47: 264–279.[Web of Science]
  37. Sneath PH, Sokal RR, 1962. Numerical taxonomy. Nature 193: 855–860.[Medline]
  38. Lim A, Zhang LX, 1999. WebPHYLIP: a web interface to PHYLIP. Bioinformatics 15: 1068–1069.[Abstract/Free Full Text]
  39. Black WC, Krafsur ES, 1985. A FORTRAN program for the calculation and analysis of 2-locus linkage disequilibrium coefficients. Theor Appl Genet 70: 491–496.[Web of Science]
  40. Deubel V, Kinney RM, Trent DW, 1986. Nucleotide sequence and deduced amino acid sequence of the structural proteins of dengue type 2 virus, Jamaica genotype. Virology 155: 365–377.[Web of Science][Medline]
  41. Diaz FJ, Black WC, Farfan-Ale JA, Lorono-Pino MA, Olson KE, Beaty BJ, 2006. Dengue virus circulation and evolution in Mexico: a phylogenetic perspective. Arch Med Res 37: 760–773.[Web of Science][Medline]
  42. Bennett KE, Olson KE, Munoz Mde L, Fernandez-Salas I, Farfan-Ale JA, Higgs S, Black WC, Beaty BJ, 2002. Variation in vector competence for dengue 2 virus among 24 collections of Aedes aegypti from Mexico and the United States. Am J Trop Med Hyg 67: 85–92.[Abstract]
  43. Bennett KE, Beaty BJ, Black WC, 2005. Selection of D2S3, an Aedes aegypti (Diptera: Culicidae) strain with high oral susceptibility to dengue 2 virus and D2 MEB, a strain with a midgut barrier to dengue 2 escape. J Med Entomol 42: 110–119.[Web of Science][Medline]
  44. Gould EA, Buckley A, Cammack N, 1985. Use of the biotin-streptavidin interaction to improve flavivirus detection by immunofluorescence and ELISA tests. J Virol Methods 11: 41–48.[Web of Science][Medline]
  45. Gould EA, Buckley A, Cammack N, Barrett AD, Clegg JC, Ishak R, Varma MG, 1985. Examination of the immunological relationships between flaviviruses using yellow fever virus monoclonal antibodies. J Gen Virol 66: 1369–1382.[Abstract/Free Full Text]
  46. Saavedra-Rodriguez K, Urdaneta-Marquez L, Rajatileka S, Moulton M, Flores AE, Fernandez-Salas I, Bisset J, Rodriguez M, Mccall PJ, Donnelly MJ, Ranson H, Hemingway J, Black WC, 2007. A mutation in the voltage gated sodium channel gene associated with pyrethroid resistance in Latin American Aedes aegypti. Insect Mol Biol 16: 785–798.[Web of Science][Medline]
  47. Bosio CF, Harrington LC, Jones JW, Sithiprasasna R, Norris DE, Scott TW, 2005. Genetic structure of Aedes aegypti populations in Thailand using mitochondrial DNA. Am J Trop Med Hyg 72: 434–442.[Abstract/Free Full Text]
  48. Weir BS, Cockerham CC, 1984. Estimating F-statistics for the analysis of population structure. Evolution Int J Org Evolution 38: 1358–1370.[Web of Science]
  49. Bennett KE, Beaty BJ, Black WC, 2005. Selection of D2S3, an Aedes aegypti (Diptera: Culicidae) strain with high oral susceptibility to Dengue 2 virus and D2MEB, a strain with a midgut barrier to Dengue 2 escape. J Med Entomol 42: 110–119.[Web of Science][Medline]
  50. Wright S, 1965. The interpretation of population structure by F-statistics with special regard to systems of mating. Evolution Int J Org Evolution 19: 395–420.[Web of Science]
  51. Slatkin M, 1991. Inbreeding coefficients and coalescence times. Genet Res 58: 167–175.[Web of Science][Medline]
  52. Dunn KA, Bielawski JP, Yang ZH, 2001. Substitution rates in Drosophila nuclear genes: implications for translational selection. Genetics 157: 295–305.[Abstract/Free Full Text]
  53. Bazin E, Glemin S, Galtier N, 2006. Population size does not influence mitochondrial genetic diversity in animals. Science 312: 570–572.[Abstract/Free Full Text]
  54. Lorenz L, Beaty BJ, Aitken TH, Wallis GP, Tabachnick WJ, 1984. The effect of colonization upon Aedes aegypti: susceptibility to oral infection with yellow fever cirus. Am J Trop Med Hyg 33: 690–694.[Abstract/Free Full Text]
  55. Tabachnick WJ, Wallis GP, Aitken TH, Miller BR, Amato GD, Lorenz L, Powell JR, Beaty BJ, 1985. Oral infection of Aedes aegypti with yellow fever virus: geographic variation and genetic considerations. Am J Trop Med Hyg 34: 1219–1224.[Abstract/Free Full Text]
  56. Wallis GP, Aitken TH, Beaty BJ, Lorenz L, Amato GD, Tabachnick WJ, 1985. Selection for susceptibility and refractoriness of Aedes aegypti to oral infection with yellow fever Virus. Am J Trop Med Hyg 34: 1225–1231.[Abstract/Free Full Text]



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