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Biology subjects

Thriemer, K.

Publications and source records attributed to Thriemer, K..

2 recordsLinked to original sources

Decoding Ethiopia's unique and structurally divergent Plasmodium falciparum populations using genomics

Previous genome-wide studies suggest that Ethiopian Plasmodium falciparum parasites are unique and structurally divergent from sub-Saharan African populations but were limited by small sample sizes. The emergence of molecular markers associated with reduced artemisinin susceptibility in northern Ethiopia, and the drug pressures created by sympatric P. falciparum and P. vivax, may have fostered a distinct and permissive genetic background for the emergence and spread of multidrug resistant parasites. To characterise parasite population structure and evolutionary dynamics in this setting, we generated whole-genome sequencing data for 163 P. falciparum isolates from southern Ethiopia (2017-2021) and integrated these with 854 isolates from across sub-Saharan Africa. Ethiopian parasites exhibited markedly reduced diversity, highly conserved genomes, and distinct admixture ancestry. Near-fixation of chloroquine and antifolate associated resistance markers, potentially driven by P. vivax co-circulation, suggests persistent drug-related selection pressures. We identified a previously undescribed deletion in the pfmdr1 5' UTR that is common in Ethiopia and potentially widespread across African populations. Although clinically relevant pfk13 variants have emerged in northern Ethiopia, these were not detected in southern Ethiopia. However, strong genetic connectivity between regions indicates the potential for rapid spread of resistance and is therefore important for public health policy and practice.

genomics↗

A molecular barcode and online tool to identify and map imported infection with Plasmodium vivax

Imported cases present a considerable challenge to the elimination of malaria. Traditionally, patient travel history has been used to identify imported cases, but the long-latency liver stages confound this approach in Plasmodium vivax. Molecular tools to identify and map imported cases offer a more robust approach, that can be combined with drug resistance and other surveillance markers in high-throughput, population-based genotyping frameworks. Using a machine learning approach incorporating hierarchical FST (HFST) and decision tree (DT) analysis applied to 831 P. vivax genomes from 20 countries, we identified a 28-Single Nucleotide Polymorphism (SNP) barcode with high capacity to predict the country of origin. The Matthews correlation coefficient (MCC), which provides a measure of the quality of the classifications, ranging from -1 (total disagreement) to 1 (perfect prediction), exceeded 0.9 in 15 countries in cross-validation evaluations. When combined with an existing 37-SNP P. vivax barcode, the 65-SNP panel exhibits MCC scores exceeding 0.9 in 17 countries with up to 30% missing data. As a secondary objective, several genes were identified with moderate MCC scores (median MCC range from 0.54-0.68), amenable as markers for rapid testing using low-throughput genotyping approaches. A likelihood-based classifier framework was established, that supports analysis of missing data and polyclonal infections. To facilitate investigator-lead analyses, the likelihood framework is provided as a web-based, open-access platform (vivaxGEN-geo) to support the analysis and interpretation of data produced either at the 28-SNP core or full 65-SNP barcode. These tools can be used by malaria control programs to identify the main reservoirs of infection so that resources can be focused to where they are needed most.

molecular biology↗