Spatiotemporal differentiation of Plasmodium vivax populations in the western Greater Mekong Subregion using a 22-SNP barcode

BACKGROUND: A high-resolution molecular tool for tracking and differentiating closely-related Plasmodium vivax populations is critically needed. This study aimed to develop and validate a novel single nucleotide polymorphism (SNP) barcode to monitor the progress of malaria elimination in the Great Mekong Subregion (GMS). METHODOLOGY/PRINCIPAL FINDINGS: A total of 210 P. vivax clinical samples were collected across four time points in three international border areas: China-Myanmar border, Thailand-Myanmar border, and Bangladesh-Myanmar border. Parasites were genotyped at 36 SNPs using MassARRAY technology (Sequenom), with Sanger sequencing validation for low-efficiency loci. The complexity of infection (COI) was estimated via a maximum likelihood approach implemented in COIL, while genetic diversity metrics were computed in GenAIEx version 6.5. Population differentiation was assessed through molecular variance analysis, Mantel rank test, and pairwise FST estimation. Genetic structure was resolved using principal component analysis, phylogenetic analysis, and ADMIXTURE. 198 samples were successfully genotyped at 22 validated SNPs, revealing 37.9% polyclonal infections. The proportion of polyclonal infections differed significantly among the five P. vivax populations (P = 0.0001, Pearson Chi-square test, χ2 = 23.15), with 2020 CMB samples having the highest proportion (56.1%). The average COI was highest in BMB parasites (1.109 ± 0.007). The TMB 2018 samples exhibited the maximal nucleotide diversity (π = 0.342 ± 0.033) and expected heterozygosity (He = 0.325 ± 0.04). The P. vivax populations from the western GMS showed significantly reduced genetic diversity in recent years compared to earlier timepoints (0.372 ± 0.009 vs. 0.426 ± 0.009; P < 0.0001, Student's t-test). Pairwise FST values indicated moderate to high genetic differentiation (0.165 - 0.417) across nine population pairs, except for the temporally proximal CMB populations, which showed low differentiation. Structure analysis consistently resolved three discrete genetic clusters corresponding to CMB, TMB, and BMB parasite populations. CONCLUSIONS/SIGNIFICANCE: This 22-SNP barcode provides a high-resolution genotyping tool capable of differentiating P. vivax parasite infections from the western GMS. Our data demonstrate that sustained malaria control interventions drive the fragmentation of P. vivax populations into genetically distinct transmission foci, creating opportunities for elimination strategies in border hotspots.

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Publication Details

Journal
PLoS neglected tropical diseases
Published
2026-06-29
DOI
https://doi.org/10.1371/journal.pntd.0014472
Primary Topic
Malaria Research and Control
Type
article
Field-Weighted Citation Impact
0.00

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article

Spatiotemporal differentiation of Plasmodium vivax populations in the western Greater Mekong Subregion using a 22-SNP barcode

Pyae Linn Aung, Zifang Wu, Myat Thu Soe, Yan Zhao et al.
PLoS neglected tropical diseases
Malaria Research and Control
article

Spatiotemporal differentiation of Plasmodium vivax populations in the western Greater Mekong Subregion using a 22-SNP barcode

Pyae Linn Aung, Zifang Wu, Myat Thu Soe, Yan Zhao, Myat Phone Kyaw, Zhaoqing Yang, Yaming Wu, Lin Wang, Weilin Zeng, Li Zheng, Awtum M. Brashear, Yaming Cao, Liwang Cui, Jetsumon Sattabongkot
article en

Abstract

BACKGROUND: A high-resolution molecular tool for tracking and differentiating closely-related Plasmodium vivax populations is critically needed. This study aimed to develop and validate a novel single nucleotide polymorphism (SNP) barcode to monitor the progress of malaria elimination in the Great Mekong Subregion (GMS). METHODOLOGY/PRINCIPAL FINDINGS: A total of 210 P. vivax clinical samples were collected across four time points in three international border areas: China-Myanmar border, Thailand-Myanmar border, and Bangladesh-Myanmar border. Parasites were genotyped at 36 SNPs using MassARRAY technology (Sequenom), with Sanger sequencing validation for low-efficiency loci. The complexity of infection (COI) was estimated via a maximum likelihood approach implemented in COIL, while genetic diversity metrics were computed in GenAIEx version 6.5. Population differentiation was assessed through molecular variance analysis, Mantel rank test, and pairwise FST estimation. Genetic structure was resolved using principal component analysis, phylogenetic analysis, and ADMIXTURE. 198 samples were successfully genotyped at 22 validated SNPs, revealing 37.9% polyclonal infections. The proportion of polyclonal infections differed significantly among the five P. vivax populations (P = 0.0001, Pearson Chi-square test, χ2 = 23.15), with 2020 CMB samples having the highest proportion (56.1%). The average COI was highest in BMB parasites (1.109 ± 0.007). The TMB 2018 samples exhibited the maximal nucleotide diversity (π = 0.342 ± 0.033) and expected heterozygosity (He = 0.325 ± 0.04). The P. vivax populations from the western GMS showed significantly reduced genetic diversity in recent years compared to earlier timepoints (0.372 ± 0.009 vs. 0.426 ± 0.009; P < 0.0001, Student's t-test). Pairwise FST values indicated moderate to high genetic differentiation (0.165 - 0.417) across nine population pairs, except for the temporally proximal CMB populations, which showed low differentiation. Structure analysis consistently resolved three discrete genetic clusters corresponding to CMB, TMB, and BMB parasite populations. CONCLUSIONS/SIGNIFICANCE: This 22-SNP barcode provides a high-resolution genotyping tool capable of differentiating P. vivax parasite infections from the western GMS. Our data demonstrate that sustained malaria control interventions drive the fragmentation of P. vivax populations into genetically distinct transmission foci, creating opportunities for elimination strategies in border hotspots.

PLoS neglected tropical diseasesVol. 20(6)
Mahidol University (TH), Kunming Medical University (CN), University of South Florida (US), Myanmar Oxford Clinical Research Unit (MM), China Medical University (CN)
National Institutes of Health, National Institute of Allergy and Infectious Diseases
Openalex Percentile: Top 7%
Malaria Research and Control
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