Abstract

Success in eliminating malaria will depend on whether parasite evolution outpaces control efforts. Here, we show that Plasmodium falciparum parasites (the deadliest of the species causing human malaria) found in low-transmission-intensity areas have evolved to invest more in transmission to new hosts (reproduction) and less in within-host replication (growth) than parasites found in high-transmission areas. At the cellular level, this adaptation manifests as increased production of reproductive forms (gametocytes) early in the infection at the expense of processes associated with multiplication inside red blood cells, especially membrane transport and protein trafficking. At the molecular level, this manifests as changes in the expression levels of genes encoding epigenetic and translational machinery. Specifically, expression levels of the gene encoding AP2-G—the transcription factor that initiates reproduction—increase as transmission intensity decreases. This is accompanied by downregulation and upregulation of genes encoding HDAC1 and HDA1—two histone deacetylases that epigenetically regulate the parasite’s replicative and reproductive life-stage programmes, respectively. Parasites in reproductive mode show increased reliance on the prokaryotic translation machinery found inside the plastid-derived organelles. Thus, our dissection of the parasite’s adaptive regulatory architecture has identified new potential molecular targets for malaria control.

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Acknowledgements

This paper is published with the permission of the director of the Kenya Medical Research Institute (KEMRI). The authors are grateful to the study participants and the parasite culture laboratory at the KEMRI–Wellcome Trust Research Programme, Kilifi, Kenya. We also thank G. McFadden, M. Greischar and A. Read for helpful comments and H. Ginsburg for assistance with the gene sets for the enrichment tests. This work was supported by the Wellcome Trust (grant numbers 088634 to M.J.M. and 092741 and 077176 to K.M.).

Author information

Author notes

  1. Margaret J. Mackinnon is unaffiliated.

Affiliations

  1. Kenya Medical Research Institute–Wellcome Trust Research Programme, Kilifi, Kenya

    • Martin K. Rono
    • , Joyce M. Ngoi
    • , Moses M. Kortok
    •  & Kevin Marsh
  2. Pwani University Bioscience Research Centre, Pwani University, Kilifi, Kenya

    • Martin K. Rono
  3. Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK

    • Martin K. Rono
    •  & Kevin Marsh
  4. Department of Microbiology and Molecular Medicine, Medical Faculty, University of Geneva, Geneva, Switzerland

    • Mary A. Nyonda
  5. Columbia University Medical Center, New York, NY, USA

    • Sachel Mok
  6. Rochester Regional Health–Unity Hospital, Rochester, NY, USA

    • Abdullah S. Abdullah
  7. Department of Microbiology and Parasitology, Faculty of Medicine, Jazan University, Gizan, Jazan, Saudi Arabia

    • Mohammed M. Elfaki
  8. Walter Reed Army Institute of Research/Kenya Medical Research Institute, Kisumu, Kenya

    • John N. Waitumbi
  9. Faculty of Public Health and Tropical Medicine, Jazan University, Gizan, Jazan, Saudi Arabia

    • Ibrahim M. El-Hassan
  10. School of Biological Sciences, Nanyang Technological University, Singapore

    • Zbynek Bozdech
    • Margaret J. Mackinnon

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Contributions

M.K.R., M.A.N., J.J.S., J.M.N., M.M.E., A.S.A., M.M.K. and M.J.M. collected the data. Z.B. and S.M. provided the microarray materials. I.M.E., J.N.W., K.M. and M.J.M. organized the field work. M.J.M. and M.K.R. prepared the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Margaret J. Mackinnon.

Supplementary information

  1. Supplementary Information

    Supplementary Figures 1–9; Supplementary Tables 1, 3 and 5; Legends for Supplementary Figures 10–11 and Supplementary Tables 2, 4, 6, 7 and 8; Supplementary references.

  2. Life Sciences Reporting Summary

  3. Supplementary Table 2

    Genes showing significant differences in expression level between high (H) and low (L) transmission populations.

  4. Supplementary Table 4

    Summary of methods for adjusting for host, parasite and gene properties in the analyses.

  5. Supplementary Table 6

    Summary of network module properties.

  6. Supplementary Table 7

    Gene sets used for functional.

  7. Supplementary Table 8

    Abbreviated names for epigenetic and translational machinery genes used in the analyses.

  8. Supplementary Figure 10

    H–L differentiation among transport genes.

  9. Supplementary Figure 11

    H–L differentiation among trafficking genes.

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DOI

https://doi.org/10.1038/s41559-017-0419-9

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