Abstract
Malaria, a major cause of child mortality in Africa, is engendered by Plasmodium parasites that are transmitted by anopheline mosquitoes. Fitness of Plasmodium parasites is closely linked to the ecology and evolution of its anopheline vector. However, whether the genetic structure of vector populations impacts malaria transmission remains unknown. Here, we describe a partitioning of the African malaria vectors into generalists and specialists that evolve along ecological boundaries. We next identify the contribution of mosquito species to Plasmodium abundance using Granger causality tests for time-series data collected over two rainy seasons in Mali. We find that mosquito microevolution, defined by changes in the genetic structure of a population over short ecological timescales, drives Plasmodium dynamics in nature, whereas vector abundance, infection prevalence, temperature and rain have low predictive values. Our study demonstrates the power of time-series approaches in vector biology and highlights the importance of focusing local vector control strategies on mosquito species that drive malaria dynamics.
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Data availability
The full-length TEP1 and TEP1–TED sequences are available at the NCBI GenBank under accession numbers MF098568 to MF098592 and MF035727 to MF035924, respectively. The data that support the findings of this study are available from the corresponding author upon request.
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Acknowledgements
We thank A. Telschow, F. Grziwotz and S. F. Traore for helpful suggestions; L. Spohr, S. Koppitz, D. Coulibaly, M. Coulibaly, C. Omogo, J. Shikaya, L. Abate, E. Onana, J. P. Agbor, J. Mwaura and A. Gildenhard for technical support; and M. Doumbia, B. Doumbia and C. Okech for their help with sample collections. This research was supported by funds from EC FP7 under grant agreements N°223601 (MALVECBLOK) and N°242095 (EVIMalar). E.K.R. was a DAAD fellow.
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F.C., D.M., M.D., I.M., D.S. and E.A.L. conceived and designed the study. D.S., M.D., A.Diabaté, R.K.D., P.A.-A., D.M. and I.M. designed and managed sample collections, genotyping and sequencing. E.K.R., A.B., I.M. and E.A.L. contributed molecular tools. J.T., J.P., P.A.-A., M.M., A.B., S.E.N., P.B.S., P.M., A.T., D.S., M.D., M.K.R., E.K.R., C.V.Y. and H.K. conducted sample collections, processing and genotyping. P.B., A.B., P.M., P.B.S. and E.K.R. performed sequencing. E.K.R., A.B., P.B., M.G., I.M., F.C. and E.A.L. analysed the data. M.G. and E.A.L. conceived and designed the time series. M.G., Y.R., M.D. and D.S. designed and managed time-series collections. D.C., R.M., H.K., M.G., D.S. and A.Diarra performed time-series collections and genotyping. E.A.L. and M.G. analysed time-series data. M.G. designed and carried out population genetic analyses and time-series modelling. P.C.-B. contributed to time-series analyses. M.G., E.K.R., P.C.B. and E.A.L. wrote the manuscript.
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Supplementary Information
Supplementary Tables 1–3, Supplementary Tables 6–9, Supplementary Figures 1–6 and Supplementary References.
Supplementary Table 4
Sampling sites across Africa.
Supplementary Table 5
Vector autoregression models.
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Gildenhard, M., Rono, E.K., Diarra, A. et al. Mosquito microevolution drives Plasmodium falciparum dynamics. Nat Microbiol 4, 941–947 (2019). https://doi.org/10.1038/s41564-019-0414-9
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DOI: https://doi.org/10.1038/s41564-019-0414-9
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