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Population genetic structure of the malaria vector Anopheles nili in sub-Saharan Africa

BACKGROUND: Anopheles nili is a widespread efficient vector of human malaria parasites in the humid savannas and forested areas of sub-Saharan Africa. Understanding An. nili population structure and gene flow patterns could be useful for the development of locally-adapted vector control measures. ME...

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Autores principales: Ndo, Cyrille, Antonio-Nkondjio, Christophe, Cohuet, Anna, Ayala, Diego, Kengne, Pierre, Morlais, Isabelle, Awono-Ambene, Parfait H, Couret, Daniel, Ngassam, Pierre, Fontenille, Didier, Simard, Frédéric
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2898787/
https://www.ncbi.nlm.nih.gov/pubmed/20540796
http://dx.doi.org/10.1186/1475-2875-9-161
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author Ndo, Cyrille
Antonio-Nkondjio, Christophe
Cohuet, Anna
Ayala, Diego
Kengne, Pierre
Morlais, Isabelle
Awono-Ambene, Parfait H
Couret, Daniel
Ngassam, Pierre
Fontenille, Didier
Simard, Frédéric
author_facet Ndo, Cyrille
Antonio-Nkondjio, Christophe
Cohuet, Anna
Ayala, Diego
Kengne, Pierre
Morlais, Isabelle
Awono-Ambene, Parfait H
Couret, Daniel
Ngassam, Pierre
Fontenille, Didier
Simard, Frédéric
author_sort Ndo, Cyrille
collection PubMed
description BACKGROUND: Anopheles nili is a widespread efficient vector of human malaria parasites in the humid savannas and forested areas of sub-Saharan Africa. Understanding An. nili population structure and gene flow patterns could be useful for the development of locally-adapted vector control measures. METHODS: Polymorphism at eleven recently developed microsatelitte markers, and sequence variation in four genes within the 28s rDNA subunit (ITS2 and D3) and mtDNA (COII and ND4) were assessed to explore the level of genetic variability and differentiation among nine populations of An. nili from Senegal, Ivory Coast, Burkina Faso, Nigeria, Cameroon and the Democratic Republic of Congo (DRC). RESULTS: All microsatellite loci successfully amplified in all populations, showing high and very similar levels of genetic diversity in populations from West Africa and Cameroon (mean Rs = 8.10-8.88, mean He = 0.805-0.849) and much lower diversity in the Kenge population from DRC (mean Rs = 5.43, mean He = 0.594). Bayesian clustering analysis of microsatellite allelic frequencies revealed two main genetic clusters in the dataset. The first one included only the Kenge population and the second grouped together all other populations. High Fst estimates based on microsatellites (Fst > 0.118, P < 0.001) were observed in all comparisons between Kenge and all other populations. By contrast, low Fst estimates (Fst < 0.022, P < 0.05) were observed between populations within the second cluster. The correlation between genetic and geographic distances was weak and possibly obscured by demographic instability. Sequence variation in mtDNA genes matched these results, whereas low polymorphism in rDNA genes prevented detection of any population substructure at this geographical scale. CONCLUSION: Overall, high genetic homogeneity of the An. nili gene pool was found across its distribution range in West and Central Africa, although demographic events probably resulted in a higher level of genetic isolation in the marginal population of Kenge (DRC). The role of the equatorial forest block as a barrier to gene flow and the implication of such findings for vector control are discussed.
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spelling pubmed-28987872010-07-08 Population genetic structure of the malaria vector Anopheles nili in sub-Saharan Africa Ndo, Cyrille Antonio-Nkondjio, Christophe Cohuet, Anna Ayala, Diego Kengne, Pierre Morlais, Isabelle Awono-Ambene, Parfait H Couret, Daniel Ngassam, Pierre Fontenille, Didier Simard, Frédéric Malar J Research BACKGROUND: Anopheles nili is a widespread efficient vector of human malaria parasites in the humid savannas and forested areas of sub-Saharan Africa. Understanding An. nili population structure and gene flow patterns could be useful for the development of locally-adapted vector control measures. METHODS: Polymorphism at eleven recently developed microsatelitte markers, and sequence variation in four genes within the 28s rDNA subunit (ITS2 and D3) and mtDNA (COII and ND4) were assessed to explore the level of genetic variability and differentiation among nine populations of An. nili from Senegal, Ivory Coast, Burkina Faso, Nigeria, Cameroon and the Democratic Republic of Congo (DRC). RESULTS: All microsatellite loci successfully amplified in all populations, showing high and very similar levels of genetic diversity in populations from West Africa and Cameroon (mean Rs = 8.10-8.88, mean He = 0.805-0.849) and much lower diversity in the Kenge population from DRC (mean Rs = 5.43, mean He = 0.594). Bayesian clustering analysis of microsatellite allelic frequencies revealed two main genetic clusters in the dataset. The first one included only the Kenge population and the second grouped together all other populations. High Fst estimates based on microsatellites (Fst > 0.118, P < 0.001) were observed in all comparisons between Kenge and all other populations. By contrast, low Fst estimates (Fst < 0.022, P < 0.05) were observed between populations within the second cluster. The correlation between genetic and geographic distances was weak and possibly obscured by demographic instability. Sequence variation in mtDNA genes matched these results, whereas low polymorphism in rDNA genes prevented detection of any population substructure at this geographical scale. CONCLUSION: Overall, high genetic homogeneity of the An. nili gene pool was found across its distribution range in West and Central Africa, although demographic events probably resulted in a higher level of genetic isolation in the marginal population of Kenge (DRC). The role of the equatorial forest block as a barrier to gene flow and the implication of such findings for vector control are discussed. BioMed Central 2010-06-12 /pmc/articles/PMC2898787/ /pubmed/20540796 http://dx.doi.org/10.1186/1475-2875-9-161 Text en Copyright ©2010 Ndo et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Ndo, Cyrille
Antonio-Nkondjio, Christophe
Cohuet, Anna
Ayala, Diego
Kengne, Pierre
Morlais, Isabelle
Awono-Ambene, Parfait H
Couret, Daniel
Ngassam, Pierre
Fontenille, Didier
Simard, Frédéric
Population genetic structure of the malaria vector Anopheles nili in sub-Saharan Africa
title Population genetic structure of the malaria vector Anopheles nili in sub-Saharan Africa
title_full Population genetic structure of the malaria vector Anopheles nili in sub-Saharan Africa
title_fullStr Population genetic structure of the malaria vector Anopheles nili in sub-Saharan Africa
title_full_unstemmed Population genetic structure of the malaria vector Anopheles nili in sub-Saharan Africa
title_short Population genetic structure of the malaria vector Anopheles nili in sub-Saharan Africa
title_sort population genetic structure of the malaria vector anopheles nili in sub-saharan africa
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2898787/
https://www.ncbi.nlm.nih.gov/pubmed/20540796
http://dx.doi.org/10.1186/1475-2875-9-161
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