Global Population Structure of the Genes Encoding the Malaria Vaccine Candidate, Plasmodium vivax Apical Membrane Antigen 1 (PvAMA1)

PLoS Negl Trop Dis. 2013 Oct 31;7(10):e2506. doi: 10.1371/journal.pntd.0002506. eCollection 2013.

Abstract

Background: The Plasmodium vivax Apical Membrane Antigen 1 (PvAMA1) is a promising malaria vaccine candidate, however it remains unclear which regions are naturally targeted by host immunity and whether its high genetic diversity will preclude coverage by a monovalent vaccine. To assess its feasibility as a vaccine candidate, we investigated the global population structure of PvAMA1.

Methodology and principal findings: New sequences from Papua New Guinea (PNG, n = 102) were analysed together with published sequences from Thailand (n = 158), India (n = 8), Sri Lanka (n = 23), Venezuela (n = 74) and a collection of isolates from disparate geographic locations (n = 8). A total of 92 single nucleotide polymorphisms (SNPs) were identified including 22 synonymous SNPs and 70 non-synonymous (NS) SNPs. Polymorphisms and signatures of balancing (positive Tajima's D and low FST values) selection were predominantly clustered in domain I, suggesting it is a dominant target of protective immune responses. To estimate global antigenic diversity, haplotypes comprised of (i) non-singleton (n = 40) and (ii) common (≥10% minor allele frequency, n = 23) polymorphic amino acid sites were then analysed revealing a total of 219 and 210 distinct haplotypes, respectively. Although highly diverse, the 210 haplotypes comprised of only common polymorphisms were grouped into eleven clusters, however substantial geographic differentiation was observed, and this may have implications for the efficacy of PvAMA1 vaccines in different malaria-endemic areas. The PNG haplotypes form a distinct group of clusters not found in any other geographic region. Vaccine haplotypes were rare and geographically restricted, suggesting potentially poor efficacy of candidate PvAMA1 vaccines.

Conclusions: It may be possible to cover the existing global PvAMA1 diversity by selection of diverse alleles based on these analyses however it will be important to first define the relationships between the genetic and antigenic diversity of this molecule.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Antigens, Protozoan / genetics*
  • Child
  • Child, Preschool
  • Cluster Analysis
  • Cross-Sectional Studies
  • DNA, Protozoan / chemistry
  • DNA, Protozoan / genetics
  • Female
  • Global Health
  • Haplotypes
  • Humans
  • Infant
  • Infant, Newborn
  • Malaria, Vivax / parasitology*
  • Male
  • Membrane Proteins / genetics*
  • Middle Aged
  • Molecular Sequence Data
  • Phylogeography
  • Plasmodium vivax / genetics*
  • Plasmodium vivax / isolation & purification
  • Polymorphism, Single Nucleotide*
  • Protozoan Proteins / genetics*
  • Sequence Analysis, DNA
  • Young Adult

Substances

  • Antigens, Protozoan
  • DNA, Protozoan
  • Membrane Proteins
  • Protozoan Proteins
  • apical membrane antigen I, Plasmodium

Associated data

  • GENBANK/KC702402
  • GENBANK/KC702403
  • GENBANK/KC702404
  • GENBANK/KC702405
  • GENBANK/KC702406
  • GENBANK/KC702407
  • GENBANK/KC702408
  • GENBANK/KC702409
  • GENBANK/KC702410
  • GENBANK/KC702411
  • GENBANK/KC702412
  • GENBANK/KC702413
  • GENBANK/KC702414
  • GENBANK/KC702415
  • GENBANK/KC702416
  • GENBANK/KC702417
  • GENBANK/KC702418
  • GENBANK/KC702419
  • GENBANK/KC702420
  • GENBANK/KC702421
  • GENBANK/KC702422
  • GENBANK/KC702423
  • GENBANK/KC702424
  • GENBANK/KC702425
  • GENBANK/KC702426
  • GENBANK/KC702427
  • GENBANK/KC702428
  • GENBANK/KC702429
  • GENBANK/KC702430
  • GENBANK/KC702431
  • GENBANK/KC702432
  • GENBANK/KC702433
  • GENBANK/KC702434
  • GENBANK/KC702435
  • GENBANK/KC702436
  • GENBANK/KC702437
  • GENBANK/KC702438
  • GENBANK/KC702439
  • GENBANK/KC702440
  • GENBANK/KC702441
  • GENBANK/KC702442
  • GENBANK/KC702443
  • GENBANK/KC702444
  • GENBANK/KC702445
  • GENBANK/KC702446
  • GENBANK/KC702447
  • GENBANK/KC702448
  • GENBANK/KC702449
  • GENBANK/KC702450
  • GENBANK/KC702451
  • GENBANK/KC702452
  • GENBANK/KC702453
  • GENBANK/KC702454
  • GENBANK/KC702455
  • GENBANK/KC702456
  • GENBANK/KC702457
  • GENBANK/KC702458
  • GENBANK/KC702459
  • GENBANK/KC702460
  • GENBANK/KC702461
  • GENBANK/KC702462
  • GENBANK/KC702463
  • GENBANK/KC702464
  • GENBANK/KC702465
  • GENBANK/KC702466
  • GENBANK/KC702467
  • GENBANK/KC702468
  • GENBANK/KC702469
  • GENBANK/KC702470
  • GENBANK/KC702471
  • GENBANK/KC702472
  • GENBANK/KC702473
  • GENBANK/KC702474
  • GENBANK/KC702475
  • GENBANK/KC702476
  • GENBANK/KC702477
  • GENBANK/KC702478
  • GENBANK/KC702479
  • GENBANK/KC702480
  • GENBANK/KC702481
  • GENBANK/KC702482
  • GENBANK/KC702483
  • GENBANK/KC702484
  • GENBANK/KC702485
  • GENBANK/KC702486
  • GENBANK/KC702487
  • GENBANK/KC702488
  • GENBANK/KC702489
  • GENBANK/KC702490
  • GENBANK/KC702491
  • GENBANK/KC702492
  • GENBANK/KC702493
  • GENBANK/KC702494
  • GENBANK/KC702495
  • GENBANK/KC702496
  • GENBANK/KC702497
  • GENBANK/KC702498
  • GENBANK/KC702499
  • GENBANK/KC702500
  • GENBANK/KC702501
  • GENBANK/KC702502
  • GENBANK/KC702503

Grants and funding

This work was supported by Project Grant 1003825 from the National Health and Medical Research Council (NHMRC) of Australia (http://www.nhmrc.gov.au). IM and JCR were supported by NHMRC Principal Research Fellowships. This work was made possible through Victorian State Government Operational Infrastructure Support and Australian Government NHMRC IRIISS (http://grants.myregion.gov.au/grant/infrastructure-support-funding). The authors gratefully acknowledge the contribution to this work of the Victorian Operational Infrastructure Support Program received by the Burnet Institute. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.