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Aedes aegypti (L.) is the primary vector of dengue virus in the Philippines, where dengue is endemic. We examined the genetic changes of Ae. aegypti collected from three selected sites in Cebu city, Philippines, during the relatively wet (2011–2012) and dry seasons (2012 and 2013). A total of 493 Ae. aegypti adults, reared in the laboratory from field-collected larvae, were analyzed using 11 microsatellite loci. Seasonal variation was observed in allele frequencies and allelic richness. Average genetic differentiation (DEST=0.018; FST=0.029) in both dry seasons was higher, due to reduced Ne, than in the wet season (DEST=0.006; FST=0.009). Thus, average gene flow was higher in the wet season than in the dry seasons. However, the overall FST estimate (0.02) inclusive of the two seasons showed little genetic differentiation as supported by Bayesian clustering analysis. Results suggest that during the dry season the intense selection that causes a dramatic reduction of population size favors heterozygotes, leading to small pockets of mosquitoes (refuges) that exhibit random genetic differentiation. During the wet season, the genetic composition of the population is reconstituted by the expansion of the refuges that survived the preceding dry season. Source reduction of mosquitoes during the nonepidemic dry season is thus recommended to prevent dengue re-emergence in the subsequent wet season.
Dengue is the fastest emerging arboviral infection in the world (World Health Organization–Western Pacific Region Office [WHO-WPRO] 2014a). Its maximum burden is found in the Asia Pacific region where 75% or about 1.8 billion out of 2.5 billion people at risk reside (WHO-WPRO 2014a). Philippines has ranked fourth in the number of dengue cases among the 10 Association of Southeast Asian Nations (ASEAN; WHO-WPRO 2013), with direct medical costs of $ 345 million (in 2012 US dollars; Edillo et al. 2015). Aedes aegypti (L.) and Aedes albopictus (Skuse) (Diptera: Culicidae) are the primary and secondary vectors of dengue viruses (DENVs; Flavivirus) in the country, respectively. National antidengue programs such as massive dengue information campaigns, distribution and training for the use of ovicidal and larvicidal traps in schools as well as a multi-sectoral search and destroy of mosquito larval habitats using insecticides have been done in the country (Department of Science and Technology [DOST] 2013, Edillo and Madarieta 2012). Despite the control programs in place, the Department of Health–National Epidemiology Center (DOH-NEC) reported 59,943 dengue cases from January 1 to September 6, 2014. However, these were 59.57% lower (148,279) than the same time period in 2013 (WHO-WPRO 2014b). Moreover, Cebu city ranked first in dengue cases in Central Visayas from 1997 to 2008 (Edillo and Madarieta 2012). Reasons include environmental risk factors, urbanization, human activities and movement, climate change, inadequate public health infrastructure, poor solid waste management, and lack of effective mosquito surveillance system (Beatty et al. 2011, Edillo and Madarieta 2012).
Understanding the genetic structure and gene flow among dengue mosquitoes is important for targeting management and control strategies (Endersby et al. 2009, Mendonca et al. 2014), particularly because the novel tetravalent dengue vaccine is only 56.5% efficacious among children in endemic Asian areas (Capeding et al. 2014), although development of tetravalent vaccines from various pharmaceutical companies is undergoing various stages of clinical tests (Center for Disease Control [CDC] 2013). Control of mosquito-borne diseases through introduction of antipathogen genes in mosquitoes depends highly on the genetic structure of populations. Monitoring temporal genetic changes is necessary in order to assess the influences of ecological and geographical variations in natural mosquito populations. Furthermore, population genetics can estimate the rate at which genes may spread within and between populations at various scales and can identify biological and physical features of the environment that may interfere with their movement (Lanzaro and Tripet 2003).
Distribution and relative abundance of insects are governed mostly by spatial scale climate (Andrewartha and Birch 1954, Sutherst and Maywald 1995). Ae. aegypti populations vary seasonally in Vietnam due to climatic, social factors, and insecticide use (Huber et al. 2002a,b), which may influence vector competence for DENV transmission (Failloux et al. 2002). In Manaus, Brazil, Ae. aegypti populations showed genetic homogeneity and extensive gene flow in the rainy season but were significantly structured in the dry season (Mendonca et al. 2014). Thus, seasonal genetic analyses are relevant in providing insights into the timing of vector control interventions particularly during interepidemic and epidemic periods of dengue transmission (Mendonca et al. 2014).
In the Philippines, there have been no studies on the population structure of Ae. aegypti. This study aimed to examine the seasonal genetic changes of Ae. aegypti subpopulations collected from selected sites in Cebu city, Philippines, in the wet season of 2011–2012 and dry seasons of 2012 and 2013.
The study sites included three barangays (smallest government administrative unit) in Cebu city, Philippines, namely, Babag (BBG), Basak San Nicolas (BSN), and Poblacion Pardo (PRD; Fig. 1). These sites were among the top 10 barangays with highest case fatality rate (CFR) of dengue illnesses in Cebu city in 2010 (Cebu City Health Department [CCHD] 2010). The sites were chosen based on: 1) CFR of dengue cases, 2) elevation, and 3) type of settings (rural and urban). The rural mountainous site, BBG, is located 11 km north from the urban sites (PRD and BSN). The coastal BSN is 2km away from PRD; both are 5km and 7km away from Cebu city’s center, respectively. Third- and fourth-instar larvae and pupae of Ae. aegypti were collected monthly with equal sampling effort for each site in the following periods: November 2011–February 2012 (wet season), March–May 2012 (dry season), June–July 2012 (wet season), and in April–May 2013 (dry season; Table 1). The country has a tropical climate characterized by two main seasons: relatively wet (June–February) and dry seasons (March–May). The country’s average temperature ranges from 25 to 32°C, with relative humidity around 77%.
Ae. aegypti females deposit eggs in several oviposition sites (Colton et al. 2003). To avoid sampling of siblings laid by a single female in the same oviposition containers, mosquito larvae and pupae were collected from multiple larval habitats (e.g., used rubber tires, plastic and metal drums, cemented water reservoirs, flower pots, and plastic water containers) in the field and around house premises. These larvae and pupae were placed in small rearing jars filled with distilled water (~ 150ml) and were covered with fine mesh cloth. These were fed with fish food (0.02g; Fwusow Industry Co., Ltd., Sha Lu Taichung, Taiwan) until they metamorphosed into adult mosquitoes inside the laboratory at 23°C. Adult mosquitoes were fed on 4% sucrose solution for three days before storage. Adult Ae. aegypti samples were identified according to Belkin (1962). They were placed in 15-ml Falcon tubes with 70–100% alcohol (5–10ml) and were stored at 7°C. Samples from field and house premises that were mostly collected from artificial containers were used for genotyping in the Powell Laboratory at Yale University, New Haven, CT.
Extraction of genomic DNA of Ae. aegypti was done using the DNeasy blood and tissue culture kit (Cat no. 69506; Qiagen, Hilden, Germany) following the manufacturer’s protocol and by using RNase A (Cat no. 19101; Qiagen) after sample digestion for RNA removal.
Microsatellite fragments were amplified using 11 polymorphic loci that have been previously used by Brown et al. (2011) and Slotman et al. (2007)(Supp Table 1 [online only]) for population genetic studies. Each pair of nonoverlapping loci was amplified in a multiplex PCR reaction with a fluorescent M13 primer (Boutin-Ganache et al. 2001). PCR reactions (10µl) contained 1x Type-it microsatellite PCR master mix (Cat no. 206243; Qiagen), 25nM of each forward primer, 250nM of each reverse primer, and 500nM of fluorescently labeled M13 primer. The reaction mix was placed in a thermocycler (GeneAmp PCR System 9700, Applied Biosystems, CA) with the following conditions: preincubation at 94°C for 10min following 35 cycles (94°C×30”, 54°C×30”, 72°C×30”) and a final incubation at 72°C for 5min (Slotman et al. 2007, Brown et al. 2011). PCR products were sequenced using Applied Biosystems 3730xl DNA Genetic Analyzer with a GS 500 Rox allele size standard (Cat no. 4340060A; Applied Biosystems, CA). Genotypes were scored using GENEMAPPER software (Applied Biosystems, CA).
Microsatellite allele frequency, Fisher’s exact test to detect linkage disequilibrium (LD) for pairwise loci, estimates of observed (HO) and expected heterozygosities (HE), goodness-of-fit probability tests for Hardy–Weinberg equilibrium (HWE), and Weir and Cockerham’s (1984) measure of inbreeding coefficient (FIS) were performed using GENEPOP v4.2 (Raymond and Rousset 1995). Gene flow (Nm) by seasons were calculated using Arlequin v. 3.5 (Excoffier et al. 2006). Effective population size (Ne) was calculated based on molecular coancestry method (Nomura 2008) in NeEstimator v. 2.01 (Do et al. 2014). Mean allelic richness (AR) and private allelic richness (PAR) for unique alleles for each locus in each subpopulation were determined using HP-RARE (Kalinowski 2005). Estimate of genetic differentiation using Jost’s DEST (Jost 2008) was assessed with the web-based application SMOGD (Crawford 2010). Pairwise estimates of FST between subpopulations and their significance were determined using GENDIST software (Kalinowski 2005). In order to assign individual mosquitoes to genetic groups based on their genotypic similarity across 11 microsatellite loci, the Bayesian clustering method in STRUCTURE v2.3.4 (Pritchard et al. 2000) was used considering an admixture model and assuming that allele frequencies were independent of one another. This program does not require any a priori information regarding sampling locations and determines the underlying genetic population among a set of individuals genotyped at multiple loci assuming all samples came from different unknown populations. Genotypes were run with a burn-in value of 100,000 iterations followed by 500,000 replications. The number of clusters (K) was determined by conducting seven independent runs for three subpopulations, all samples in each season (2011–2012 wet season, 2012 and 2013 dry seasons), and the entire dataset. The optimal number of K clusters was determined using the Delta K method (Evanno et al. 2005) using the HARVESTER software (Earl and vonHoldt 2012); data were processed in CLUMPP (Jakobsson and Rosenberg 2007), and then visualized using DISTRUCT (Rosenberg 2004). Two-way ANOVA, multivariate analyses, and T-tests were performed in the R software v. 3.0.1. (R Core Team 2004) using the HSAUR package (Everitt and Hothorn 2006).
We observed a total of 274 alleles across the 11 polymorphic microsatellite loci (Slotman et al. 2007, Brown et al. 2011) across all three sites in Cebu city and season collections (Table 1). Average allele frequencies (Supp Table 1 [online only]) at each locus ranged from 0.03 to 0.20 in the wet season and 0.03–1.0 in the dry season, when certain alleles might have been fixed due to random genetic drift as a consequence of the reduction in Ne observed during the dry season (Table 2). Mean AR and PAR for all subpopulations in the wet season were higher than in the dry season (Table 3). Student’s T-test showed that this difference in mean AR was significant between seasons (t=3.97, df=7, P=0.005) but mean PAR was not significantly different (t=1.28, df=7, P=0.28). Allelic frequencies were also significantly different between dry and wet seasons (P<0.05; Table 4 and Fig. 2). Although HO values were slightly lower than HE in most sites for the two seasons, except in BSN and PRD during the dry season of 2013 (Table 3), multivariate analysis showed that they were not statistically different (sites: F=0.854, df=8, P=0.62; seasons: F=2.759, df=1, P=0.07). No seasonal difference in HE was detected either (P>0.05).
Twelve deviations from HWE were detected in five (A1, AC2, AC5, AG2, AG5) of the 11 loci within the subpopulations after Bonferroni’s multiple correction (P<0.0009; Supp Table 2 [online only]). Eight of these 12 deviations from HWE occurred within the subpopulations of the dry season and four deviations were detected in the wet season. Separate LD analyses by seasons showed that only one out of 165 (0.6%) comparisons had significant difference between AC2 and AG2 loci in PRD in the wet season (2011–2012), whereas three out of 166 (1.8%) comparisons, namely, between AC2 and B2 loci and between AC2 and AG1 loci in PRD; and between AC2 and B2 loci in BBG in the dry season of 2013. The loci did not display LD across all subpopulations and seasons, thus, implying that they were not physically linked (Mendonca et al. 2014) in concordance with previous marker validation by Brown et al. (2011). Deviations from HWE are generally attributed to differential selection, LD, and non-random mating that may lead to heterozygous deficiency or inbreeding. The overall FIS per locus for each site in different seasons ranged from −0.018 (A1 locus) in 2013 dry season to 0.551 (AG2 locus) in 2012 dry season (Supp Table 3 [online only]), suggesting some degree of selection in the population. However, the seasonal average FIS estimates detected only incipient inbreeding in the 2011–2012 wet season and 2012 dry season and none in the 2013 dry season.
Overall genetic differentiation (DEST=0.014 and FST=0.02) in the current study was low (Table 5). Gene flow based on Nm values (Table 6) in the 2011–2012 wet season ranged from 61.0 to 16.4 (average=45.0) effective migrants per generation, suggesting higher dispersal rate of Ae. aegypti among subpopulations than those in the dry seasons of 2012 (range=29.8–13.8; average=19.5) and 2013 (range=8.7–5.5; average=6.8). Nevertheless, the subpopulations were not completely isolated from each other at any time, as the number of migrants was still significant in the dry seasons. This finding was also supported by the absence in genetic structure after Bayesian clustering analysis (Fig. 3). Clustering with a K=6 implies genetic homogeneity for all subpopulations in different seasons (Fig. 3C). With a K=2 (Fig. 3A) indicates that all Ae. aegypti samples could be grouped by two seasons, or by K=3 (Fig. 3B) would imply that all samples could be geographically differentiated regardless of seasonal difference but did not detect any significant pattern.
Results of the current study are consistent with previous studies (Whitlock and Barton 1997, Huber et al. 2002a, Mendonca et al. 2014) that observed seasonal genetic variation in Ae. aegypti populations. In Ho Chi Minh city, Vietnam, genetic differentiation of Ae. aegypti decreases in the wet season when larval habitats are abundant and it increases in the dry season when larval habitats are dramatically reduced, thus, restricting gene flow (Huber et al. 2002b). The same seasonal trend was found in Manaus, Brazil, where samples collected from the dry season are significantly structured and not in the rainy season due to their reduced population size (Mendonca et al. 2014). In contrast, Costa-Ribeiro et al. (2006) reported higher genetic structure of Ae. aegypti in the rainy season in Rio de Janeiro, Brazil. The differences could be due to the different sensitivity or usefulness of the markers (Lanzaro et al. 1995) or different ecological settings of the populations. Moreover, the Bayesian STRUCTURE method used in this study did not find evidence of genetic structure among our subpopulations between seasons (Fig. 3). However, we were able to detect a significant increase in allelic richness during the wet season relative to the dry season. It is likely that the decrease in allelic richness observed during the dry season is a consequence of the genetic drift experienced when the population size is considerably reduced (Table 2). The proliferation of larval habitats suitable for development of larvae and pupae during the wet season may subsequently lead to the increased number of alleles as the population expands and the ability of Ae. aegypti eggs to resist desiccation in the dry season (Trpis 1972). This may quickly reestablish the genetic diversity despite undergoing a period of harsh conditions. Our data are consistent with this hypothesis. The absence of genetic structure, expected during the dry season, can be explained by the persistence of considerable gene flow among subpopulations. Although migration among sites decreased in the dry season, the number of detected migrants was still significant.
We have shown that the Ae. aegypti population in Cebu city, Philippines, experienced temporal genetic changes due to genetic drift. The Ne showed greater variation between seasons (Table 2). The wet season (2011–2012) had the highest total Ne of 281.9 Ae. aegypti individuals. Total Ne was reduced in the 2012 dry season with 205 individuals and the least occurred in 2013 dry season with 59.2 individuals only. Campos et al. (2012) suggested that females tend to lay their eggs in fewer larval habitats and possibly closer to resting sites during the dry season. Although dengue cases in Cebu city in the wet season consistently increased from year-to-year (Edillo and Madarieta 2012), the reduction of genetic diversity during drift in mosquito population in the dry season did not seem to significantly affect recolonization in the next subsequent wet season. This might be attributed to reports that Ae. aegypti has developed evolutionary adaptability during extreme seasonal fluctuations (Cheng et al. 2012). Ae. aegypti eggs have been shown to survive desiccation by entering diapause or quiescence. Survival rates after a 120-d desiccation period range from 7 to 43% (Trpis 1972). In Tanzania, eggs deposited in rubber tires survive the entire dry season and hatch once larval habitats become available in the wet season (Trpis 1972). Resistance to desiccation may be the crucial factor for stabilizing population genetic diversity (Mendonca et al. 2014) during recolonization in the wet season. In Cebu city, artificial containers (drums and plastic containers) serve as the key repositories of pupal productivity that maintain Ae. aegypti population (Edillo et al. 2012). In temperate regions, Ae. aegypti populations survive the cold season at least partially in the egg stage. Cold-season mortality of Ae. aegypti eggs is lower (30.6%) in Buenos Aires, Argentina, than those from tropical regions during the dry season (Fischer et al. 2011).
The little genetic differentiation of Ae. aegypti subpopulations between rural (BBG) and urban sites (BSN and PRD) in Cebu city might be explained by human-related activities that facilitate the dispersal of the vector and the relative spatial proximity of the subpopulations. Subpopulation in the rural mountainous site did not appear to impede gene flow with the urban subpopulations in BSN and PRD in any of the seasons. We did not find evidence of a founder effect in any of subpopulations, as there was little genetic differentiation among sites in the dry season. Similarly, Brown et al. (2013) did not find spatial genetic variation of Ae. aegypti in the Florida Keys, USA.
A massive mosquito larvicide campaign has been continuously conducted in the Philippines (DOH 2011, DOST 2013). The yearly increase and decrease of dengue cases in the wet and dry seasons in Cebu (Edillo and Madarieta 2012), respectively, might be linked to temporal genetic changes in Ae. aegypti subpopulations but also could have been a consequence of antidengue campaigns such as insecticide use, or of temporal changes that are innate to virus ability to infect the hosts (Zhang et al 2013). Paupy et al. (2005) and Yébakima et al. (2004) showed that intensive insecticide use and social factors (i.e., human density) have caused distinct patterns in the genetic structure of dengue mosquitoes.
In conclusion, Ae. aegypti subpopulations in Cebu city, Philippines, are under intense selection in the dry season when larval habitats are scarce. During the wet season, genetic composition of the population is reconstituted by hatching of eggs that have survived the dry season, acting as reservoir for genetic diversity. Overall, Ae. aegypti population in Cebu city had little genetic differentiation although gene flow was higher in the wet season than in both years’ dry seasons. We recommend intensified vector control during the nonepidemic dry season to prevent population re-emergence and increased dengue transmission in the following wet season. Conducting temporal genetic studies in a geographically broader scale in the country should also be helpful in future dengue control efforts.
Supplementary data are available at Journal of Medical Entomology online.
We thank K. M. Bentoy, D. Saladores, and G. Mergal for their help in mosquito collections. S.L.S. was supported by Fr. Alingasa Research Funds of University of San Carlos and by NIH ROI AI10111 (J.R.P.).