MyMedR

Displaying all 6 publications

Abstract:

Sort:

Fulltext Genomic analysis of a pre-elimination Malaysian Plasmodium vivax population reveals selective pressures and changing transmission dynamics

Auburn S, Benavente ED, Miotto O, Pearson RD, Amato R, Grigg MJ, et al.

Nat Commun, 2018 Jul 03;9(1):2585.
PMID: 29968722 DOI: 10.1038/s41467-018-04965-4

The incidence of Plasmodium vivax infection has declined markedly in Malaysia over the past decade despite evidence of high-grade chloroquine resistance. Here we investigate the genetic changes in a P. vivax population approaching elimination in 51 isolates from Sabah, Malaysia and compare these with data from 104 isolates from Thailand and 104 isolates from Indonesia. Sabah displays extensive population structure, mirroring that previously seen with the emergence of artemisinin-resistant P. falciparum founder populations in Cambodia. Fifty-four percent of the Sabah isolates have identical genomes, consistent with a rapid clonal expansion. Across Sabah, there is a high prevalence of loci known to be associated with antimalarial drug resistance. Measures of differentiation between the three countries reveal several gene regions under putative selection in Sabah. Our findings highlight important factors pertinent to parasite resurgence and molecular cues that can be used to monitor low-endemic populations at the end stages of P. vivax elimination.
Fulltext Estimating Geographical Variation in the Risk of Zoonotic Plasmodium knowlesi Infection in Countries Eliminating Malaria

Shearer FM, Huang Z, Weiss DJ, Wiebe A, Gibson HS, Battle KE, et al.

PLoS Negl Trop Dis, 2016 Aug;10(8):e0004915.
PMID: 27494405 DOI: 10.1371/journal.pntd.0004915

BACKGROUND: Infection by the simian malaria parasite, Plasmodium knowlesi, can lead to severe and fatal disease in humans, and is the most common cause of malaria in parts of Malaysia. Despite being a serious public health concern, the geographical distribution of P. knowlesi malaria risk is poorly understood because the parasite is often misidentified as one of the human malarias. Human cases have been confirmed in at least nine Southeast Asian countries, many of which are making progress towards eliminating the human malarias. Understanding the geographical distribution of P. knowlesi is important for identifying areas where malaria transmission will continue after the human malarias have been eliminated.
METHODOLOGY/PRINCIPAL FINDINGS: A total of 439 records of P. knowlesi infections in humans, macaque reservoir and vector species were collated. To predict spatial variation in disease risk, a model was fitted using records from countries where the infection data coverage is high. Predictions were then made throughout Southeast Asia, including regions where infection data are sparse. The resulting map predicts areas of high risk for P. knowlesi infection in a number of countries that are forecast to be malaria-free by 2025 (Malaysia, Cambodia, Thailand and Vietnam) as well as countries projected to be eliminating malaria (Myanmar, Laos, Indonesia and the Philippines).
CONCLUSIONS/SIGNIFICANCE: We have produced the first map of P. knowlesi malaria risk, at a fine-scale resolution, to identify priority areas for surveillance based on regions with sparse data and high estimated risk. Our map provides an initial evidence base to better understand the spatial distribution of this disease and its potential wider contribution to malaria incidence. Considering malaria elimination goals, areas for prioritised surveillance are identified.
Fulltext Genomic Analysis of Plasmodium vivax in Southern Ethiopia Reveals Selective Pressures in Multiple Parasite Mechanisms

Auburn S, Getachew S, Pearson RD, Amato R, Miotto O, Trimarsanto H, et al.

J Infect Dis, 2019 Oct 22;220(11):1738-1749.
PMID: 30668735 DOI: 10.1093/infdis/jiz016

The Horn of Africa harbors the largest reservoir of Plasmodium vivax in the continent. Most of sub-Saharan Africa has remained relatively vivax-free due to a high prevalence of the human Duffy-negative trait, but the emergence of strains able to invade Duffy-negative reticulocytes poses a major public health threat. We undertook the first population genomic investigation of P. vivax from the region, comparing the genomes of 24 Ethiopian isolates against data from Southeast Asia to identify important local adaptions. The prevalence of the Duffy binding protein amplification in Ethiopia was 79%, potentially reflecting adaptation to Duffy negativity. There was also evidence of selection in a region upstream of the chloroquine resistance transporter, a putative chloroquine-resistance determinant. Strong signals of selection were observed in genes involved in immune evasion and regulation of gene expression, highlighting the need for a multifaceted intervention approach to combat P. vivax in the region.
Fulltext Genomic analysis of local variation and recent evolution in Plasmodium vivax

Pearson RD, Amato R, Auburn S, Miotto O, Almagro-Garcia J, Amaratunga C, et al.

Nat Genet, 2016 Aug;48(8):959-964.
PMID: 27348299 DOI: 10.1038/ng.3599

The widespread distribution and relapsing nature of Plasmodium vivax infection present major challenges for the elimination of malaria. To characterize the genetic diversity of this parasite in individual infections and across the population, we performed deep genome sequencing of >200 clinical samples collected across the Asia-Pacific region and analyzed data on >300,000 SNPs and nine regions of the genome with large copy number variations. Individual infections showed complex patterns of genetic structure, with variation not only in the number of dominant clones but also in their level of relatedness and inbreeding. At the population level, we observed strong signals of recent evolutionary selection both in known drug resistance genes and at new loci, and these varied markedly between geographical locations. These findings demonstrate a dynamic landscape of local evolutionary adaptation in the parasite population and provide a foundation for genomic surveillance to guide effective strategies for control and elimination of P. vivax.
Fulltext An open dataset of Plasmodium vivax genome variation in 1,895 worldwide samples

MalariaGEN, Adam I, Alam MS, Alemu S, Amaratunga C, Amato R, et al.

Wellcome Open Res, 2022;7:136.
PMID: 35651694 DOI: 10.12688/wellcomeopenres.17795.1

This report describes the MalariaGEN Pv4 dataset, a new release of curated genome variation data on 1,895 samples of Plasmodium vivax collected at 88 worldwide locations between 2001 and 2017. It includes 1,370 new samples contributed by MalariaGEN and VivaxGEN partner studies in addition to previously published samples from these and other sources. We provide genotype calls at over 4.5 million variable positions including over 3 million single nucleotide polymorphisms (SNPs), as well as short indels and tandem duplications. This enlarged dataset highlights major compartments of parasite population structure, with clear differentiation between Africa, Latin America, Oceania, Western Asia and different parts of Southeast Asia. Each sample has been classified for drug resistance to sulfadoxine, pyrimethamine and mefloquine based on known markers at the dhfr, dhps and mdr1 loci. The prevalence of all of these resistance markers was much higher in Southeast Asia and Oceania than elsewhere. This open resource of analysis-ready genome variation data from the MalariaGEN and VivaxGEN networks is driven by our collective goal to advance research into the complex biology of P. vivax and to accelerate genomic surveillance for malaria control and elimination.
A molecular barcode and web-based data analysis tool to identify imported Plasmodium vivax malaria

Trimarsanto H, Amato R, Pearson RD, Sutanto E, Noviyanti R, Trianty L, et al.

Commun Biol, 2022 Dec 23;5(1):1411.
PMID: 36564617 DOI: 10.1038/s42003-022-04352-2

Traditionally, patient travel history has been used to distinguish imported from autochthonous malaria cases, but the dormant liver stages of Plasmodium vivax confound this approach. Molecular tools offer an alternative method to identify, and map imported cases. Using machine learning approaches incorporating hierarchical fixation index and decision tree analyses applied to 799 P. vivax genomes from 21 countries, we identified 33-SNP, 50-SNP and 55-SNP barcodes (GEO33, GEO50 and GEO55), with high capacity to predict the infection's country of origin. The Matthews correlation coefficient (MCC) for an existing, commonly applied 38-SNP barcode (BR38) exceeded 0.80 in 62% countries. The GEO panels outperformed BR38, with median MCCs > 0.80 in 90% countries at GEO33, and 95% at GEO50 and GEO55. An online, open-access, likelihood-based classifier framework was established to support data analysis (vivaxGEN-geo). The SNP selection and classifier methods can be readily amended for other use cases to support malaria control programs.