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Identification of complex Plasmodium falciparum genetic backgrounds circulating in Africa: a multicountry genomic epidemiology analysis

  • Olivo Miotto
  • , Alfred Amambua-Ngwa
  • , Lucas N. Amenga-Etego
  • , Muzamil M. Abdel Hamid
  • , Ishag Adam
  • , Enoch Aninagyei
  • , Tobias Apinjoh
  • , Gordon A. Awandare
  • , Philip Bejon
  • , Gwladys I. Bertin
  • , Marielle Bouyou-Akotet
  • , Antoine Claessens
  • , David J. Conway
  • , Umberto D'Alessandro
  • , Mahamadou Diakite
  • , Abdoulaye Djimdé
  • , Arjen M. Dondorp
  • , Patrick Duffy
  • , Rick M. Fairhurst
  • , Caterina I. Fanello
  • Anita Ghansah, Deus S. Ishengoma, Mara Lawniczak, Oumou Maïga-Ascofaré, Sarah Auburn, Anna Rosanas-Urgell, Varanya Wasakul, Nina F.D. White, Alexandria Harrott, Jacob Almagro-Garcia, Richard D. Pearson, Sonia Goncalves, Cristina Ariani, Zbynek Bozdech, William L. Hamilton, Victoria Simpson, Dominic P. Kwiatkowski
  • Mahidol-Oxford Tropical Medicine Research Unit
  • University of Oxford
  • London School of Hygiene & Tropical Medicine
  • Institute of Endemic Diseases Sudan
  • Qassim University
  • University of Health and Allied Sciences
  • University of Buea
  • Wellcome Trust Research Laboratories Nairobi
  • Université Paris Descartes
  • Université des sciences de la santé
  • University of Montpellier
  • University of Science
  • US Department of Health and Human Services
  • University of Ghana
  • National Institute for Medical Research Tanzania
  • Kampala International University
  • Wellcome Sanger Institute
  • Bernhard Nocht Insitute for Tropical Medicine
  • Charles Darwin University
  • Institute of Tropical Medicine Antwerp
  • Nanyang Technological University

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Background: The population structure of the malaria parasite Plasmodium falciparum can reveal underlying adaptive evolutionary processes. Selective pressures to maintain complex genetic backgrounds can encourage inbreeding, producing distinct parasite clusters identifiable by population structure analyses. Methods: We analysed population structure in 3783 P falciparum genomes from 21 countries across Africa, provided by the MalariaGEN Pf7 dataset. We used Principal Coordinate Analysis to cluster parasites, identity by descent (IBD) methods to identify genomic regions shared by cluster members, and linkage analyses to establish their co-inheritance patterns. Structural variants were reconstructed by de novo assembly and verified by long-read sequencing. Findings: We identified a strongly differentiated cluster of parasites, named AF1, comprising 47 (1·2%) of 3783 samples analysed, distributed over 13 countries across Africa, at locations over 7000 km apart. Members of this cluster share a complex genetic background, consisting of up to 23 loci harbouring many highly differentiated variants, rarely observed outside the cluster. IBD analyses revealed common ancestry at these loci, irrespective of sampling location. Outside the shared loci, however, AF1 members appear to outbreed with sympatric parasites. The AF1 differentiated variants comprise structural variations, including a gene conversion involving the dblmsp and dblmsp2 genes, and numerous single nucleotide polymorphisms. Several of the genes harbouring these mutations are functionally related, often involved in interactions with red blood cells including invasion, egress, and erythrocyte antigen export. Interpretation: We propose that AF1 parasites have adapted to some unidentified evolutionary niche, probably involving interactions with host erythrocytes. This adaptation involves a complex compendium of interacting variants that are rarely observed in Africa, which remains mostly intact despite recombination events. The term cryptotype was used to describe a common background interspersed with genomic regions of local origin. Funding: Bill & Melinda Gates Foundation.

Original languageEnglish
Article number100941
JournalThe Lancet Microbe
Volume5
Issue number12
DOIs
Publication statusPublished - Dec 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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