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. 2020 May 4;14(5):e0008279.
doi: 10.1371/journal.pntd.0008279. eCollection 2020 May.

Fine-scale population genetic structure of dengue mosquito vector, Aedes aegypti, in Metropolitan Manila, Philippines

Affiliations

Fine-scale population genetic structure of dengue mosquito vector, Aedes aegypti, in Metropolitan Manila, Philippines

Thaddeus M Carvajal et al. PLoS Negl Trop Dis. .

Abstract

Dengue is a highly endemic disease in Southeast Asia and is transmitted primarily by the mosquito, Aedes aegypti. The National Capital Region (NCR) of the Philippines, or Metropolitan Manila, is a highly urbanized area that is greatly affected by this arboviral disease. Urbanization has been shown to increase the dispersal of this mosquito vector. For this reason, we conducted a fine-scale population genetic study of Ae. aegypti in this region. We collected adult Ae. aegypti mosquitoes (n = 526 individuals) within the region (n = 21 study areas) and characterized the present population structure and the genetic relatedness among mosquito populations. We genotyped 11 microsatellite loci from all sampled mosquito individuals and analyzed their genetic diversity, differentiation and structure. The results revealed low genetic differentiation across mosquito populations which suggest high gene flow and/or weak genetic drift among mosquito populations. Bayesian analysis indicated multiple genetic structures (K = 3-6), with no clear genetically distinct population structures. This result implies the passive or long-distance dispersal capability nature Ae. aegypti possibly through human-mediated transportation. The constructed dendrogram in this study describes the potential passive dispersal patterns across Metropolitan Manila. Furthermore, spatial autocorrelation analysis showed the limited and active dispersal capability (<1km) of the mosquito vector. Our findings are consistent with previous studies that investigated the genetic structure and dual (active and passive) dispersal capability of Ae. aegypti in a fine-scale highly urbanized area.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Geographic midpoints of Ae. aegypti study areas (colored diamond) with their corresponding household sites (colored circles) in Metropolitan Manila.
Details of each study area can be seen in S1 Table. The map was prepared using ArcGIS version 10.2.2 from LandsatLook Viewer (http://landsatlook.usgs.gov/).
Fig 2
Fig 2. Dendrogram showing the genetic relatedness of each study area based on Cavalli Sforza and Edwards distance.
Colored lines indicate the genetic groups (n = 4). The map showing the study areas in respect to their genetic group assignment (colored circles). Blue lines in the map show the primary road network of the region. The map was prepared using ArcGIS version 10.2.2 from LandsatLook Viewer (http://landsatlook.usgs.gov/).
Fig 3
Fig 3
Individual barplots from the Bayesian analysis using Structure software indicated the distribution of genetic clusters across mosquito populations using (a) all (n = 526) sample individuals and (b) five standardized datasets (n = 10 per study area). Each individual is represented by a single horizontal line. Brackets are shown to separate study areas. The most likely number of genetic clusters in (a) is K = 4 while, in (b) the number ranges from K = 3 to K = 6.

References

    1. Weaver SC, Reisen WK. Present and future arboviral threats. Antiviral research. 2010. February 1;85(2):328–45. 10.1016/j.antiviral.200910008 - DOI - PMC - PubMed
    1. World Health Organization: Western Pacific Region (2012) Dengue: dengue in the Western Pacific region. Available: http://www.wpro.who.int/emerging_diseases/Dengue/en/index.html. Accessed June 2017
    1. Edillo FE, Halasa YA, Largo FM, Erasmo JN, Amoin NB, Alera MT, Yoon IK, Alcantara AC, Shepard DS. Economic cost and burden of dengue in the Philippines. The American journal of tropical medicine and hygiene. 2015. February 4;92(2):360–6. 10.4269/ajtmh.14-0139 - DOI - PMC - PubMed
    1. Carvajal TM, Viacrusis KM, Hernandez LF, Ho HT, Amalin DM, Watanabe K. Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines. BMC infectious diseases. 2018. December;18(1):183 10.1186/s12879-018-3066-0 - DOI - PMC - PubMed
    1. Bravo L, Roque VG, Brett J, Dizon R, L'Azou M. Epidemiology of dengue disease in the Philippines (2000–2011): a systematic literature review. PLoS neglected tropical diseases. 2014. November 6;8(11):e3027 10.1371/journal.pntd.0003027 - DOI - PMC - PubMed

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