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Genomic analysis of Plasmodium vivax describes patterns of connectivity and putative drivers of adaptation in Ethiopia.

ABSTRACT: Ethiopia has the greatest burden of Plasmodium vivax in Africa, but little is known about the epidemiological landscape of parasites across the country. We analysed the genomic diversity of 137 P. vivax isolates collected nine Ethiopian districts from 2012 to 2016. Signatures of selection were detected by cross-country comparisons with isolates from Thailand (n = 104) and Indonesia (n = 111), representing regions with low and high chloroquine resistance respectively. 26% (35/137) of Ethiopian infections were polyclonal, and 48.5% (17/35) of these comprised highly related clones (within-host identity-by-descent > 25%), indicating frequent co-transmission and superinfection. Parasite gene flow between districts could not be explained entirely by geographic distance, with economic and cultural factors hypothesised to have an impact on connectivity. Amplification of the duffy binding protein gene (pvdbp1) was prevalent across all districts (16-75%). Cross-population haplotype homozygosity revealed positive selection in a region proximal to the putative chloroquine resistance transporter gene (pvcrt-o). An S25P variant in amino acid transporter 1 (pvaat1), whose homologue has recently been implicated in P. falciparum chloroquine resistance evolution, was prevalent in Ethiopia (96%) but not Thailand or Indonesia (35-53%). The genomic architecture in Ethiopia highlights circulating variants of potential public health concern in an endemic setting with evidence of stable transmission.

SUBMITTER: Kebede AM

PROVIDER: S-EPMC10682486 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Genomic analysis of Plasmodium vivax describes patterns of connectivity and putative drivers of adaptation in Ethiopia.

Kebede Alebachew Messele AM Sutanto Edwin E Trimarsanto Hidayat H Benavente Ernest Diez ED Barnes Mariana M Pearson Richard D RD Siegel Sasha V SV Erko Berhanu B Assefa Ashenafi A Getachew Sisay S Aseffa Abraham A Petros Beyene B Lo Eugenia E Mohammed Rezika R Yilma Daniel D Rumaseb Angela A Nosten Francois F Noviyanti Rintis R Rayner Julian C JC Kwiatkowski Dominic P DP Price Ric N RN Golassa Lemu L Auburn Sarah S

Scientific reports 20231127 1

Ethiopia has the greatest burden of Plasmodium vivax in Africa, but little is known about the epidemiological landscape of parasites across the country. We analysed the genomic diversity of 137 P. vivax isolates collected nine Ethiopian districts from 2012 to 2016. Signatures of selection were detected by cross-country comparisons with isolates from Thailand (n = 104) and Indonesia (n = 111), representing regions with low and high chloroquine resistance respectively. 26% (35/137) of Ethiopian in ...[more]

PMID: 38012191

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Project description:BackgroundThe global decline of malaria burden and goals for elimination has led to an increased interest in the fine-scale epidemiology of malaria. Micro-geographic heterogeneity of malaria infection could have implications for designing targeted small-area interventions.MethodsTwo-year longitudinal cohort study data were used to explore the spatial and spatio-temporal distribution of malaria episodes in 2040 children aged < 10 years in 16 villages near the Gilgel-Gibe hydropower dam in Southwest Ethiopia. All selected households (HHs) were geo-referenced, and children were followed up through weekly house-to-house visits for two consecutive years to identify febrile episodes of P. falciparum and P. vivax infections. After confirming the spatial dependence of malaria episodes with Ripley's K function, SatScanTM was used to identify purely spatial and space-time clusters (hotspots) of annual malaria incidence for 2 years follow-up: year 1 (July 2008-June 2009) and year 2 (July 2009-June 2010).ResultsIn total, 685 P. falciparum episodes (in 492 HHs) and 385 P. vivax episodes (in 290 HHs) were identified, representing respectively incidence rates of 14.6 (95% CI: 13.4-15.6) and 8.2 (95% CI: 7.3-9.1) per 1000 child-months at risk. In year 1, the most likely (128 HHs with 63 episodes, RR = 2.1) and secondary (15 HHs with 12 episodes, RR = 5.31) clusters of P. vivax incidence were found respectively in southern and north-western villages; while in year 2, the most likely cluster was located only in north-western villages (85 HHs with 16 episodes, RR = 4.4). Instead, most likely spatial clusters of P. falciparum incidence were consistently located in villages south of the dam in both years: year 1 (167 HHs with 81 episodes, RR = 1.8) and year 2 (133 HHs with 67 episodes, RR = 2.2). Space-time clusters in southern villages for P. vivax were found in August-November 2008 in year 1 and between November 2009 and February 2010 in year 2; while for P. falciparum, they were found in September-November 2008 in year 1 and October-November 2009 in year 2.ConclusionHotspots of P. falciparum incidence in children were more stable at the geographical level and over time compared to those of P. vivax incidence during the study period.

Project description:BackgroundEthiopia is one of the scarce African countries where Plasmodium vivax and P. falciparum co-exist. There has been no attempt to derive a robust prevalence estimate of P. vivax in the country although a clear understanding of the epidemiology of this parasite is essential for informed decisions. This systematic review and meta-analysis, therefore, is aimed to synthesize the available evidences on the distribution of P. vivax infection by different locations/regions, study years, eco-epidemiological zones, and study settings in Ethiopia.MethodsThis study was conducted in accordance with Preferred Reposting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines. Studies conducted and published over the last two decades (2000 to 2020) that reported an estimate of P. vivax prevalence in Ethiopia were included. The Cochrane Q (χ2) and the I2 tests were used to assess heterogeneity, and the funnel plot and Egger's test were used to examine publication bias. A p-value of the χ2 test <0.05 and an I2 value >75% were considered presence of considerable heterogeneity. Random effect models were used to obtain pooled estimate of P. vivax infection prevalence. This study is registered with PROSPERO (International Prospective Register of Systematic Reviews): ID CRD42020201761.ResultsWe screened 4,932 records and included 79 studies that enrolled 1,676,659 confirmed malaria cases, from which 548,214 (32.69%) were P. vivax infections and 1,116,581 (66.59%) were due to P. falciparum. The rest were due to mixed infections. The pooled estimate of P. vivax prevalence rate was 8.93% (95% CI: 7.98-9.88%) with significant heterogeneity (I2 = 100%, p<0.0001). Regional differences showed significant effects (p<0.0001, and I2 = 99.4%) on the pooled prevalence of P. vivax, while study years (before and after the scaling up of interventional activities) did not show significant differences (p = 0.9, I2 = 0%). Eco-epidemiological zones considered in the analysis did show a significant statistical effect (p<0.001, I2 = 78.5%) on the overall pooled estimate prevalence. Also, the study setting showed significant differences (p = 0.001, and I2 = 90.3%) on the overall prevalence, where significant reduction of P. vivax prevalence (4.67%, 95%CI: 1.41-7.93%, p<0.0001) was observed in studies conducted at the community level. The studies included in the review demonstrated lack of publication bias qualitatively (symmetrical funnel plot) and quantitatively [Egger's test (coefficient) = -2.97, 95% CI: -15.06-9.13, p = 0.62].ConclusionThe estimated prevalence of P. vivax malaria in Ethiopia was 8.93% with P. vivax prevailing in the central west region of Ethiopia, but steadily extending to the western part of the country. Its distribution across the nation varies according to geographical location, study setting and study years.

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Genomic analysis of Plasmodium vivax describes patterns of connectivity and putative drivers of adaptation in Ethiopia.

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Genomic analysis of Plasmodium vivax describes patterns of connectivity and putative drivers of adaptation in Ethiopia.

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