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A deep sequencing tool for partitioning clearance rates following antimalarial treatment in polyclonal infections
Department of Ecology and Evolutionary Biology, University of Toronto, ON, Canada.
Division of Transfusion Medicine, Department of Medicine, University of Massachusetts, Worcester, MA, USA.
Program in Bioinformatics and Integrative Biology, University of Massachusetts, Worcester, MA, USA.
Department of Parasitology, Muhimbili University of Health and Allied Sciences, Dar Es Salaam, Tanzania.
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2016 (English)In: Evolution, medicine, and public health, ISSN 2050-6201, Vol. 2016, no 1, 21-36 p.Article in journal (Refereed) Published
Abstract [en]

BACKGROUND AND OBJECTIVES

Current tools struggle to detect drug-resistant malaria parasites when infections contain multiple parasite clones, which is the norm in high transmission settings in Africa. Our aim was to develop and apply an approach for detecting resistance that overcomes the challenges of polyclonal infections without requiring a genetic marker for resistance.

METHODOLOGY

Clinical samples from patients treated with artemisinin combination therapy were collected from Tanzania and Cambodia. By deeply sequencing a hypervariable locus, we quantified the relative abundance of parasite subpopulations (defined by haplotypes of that locus) within infections and revealed evolutionary dynamics during treatment. Slow clearance is a phenotypic, clinical marker of artemisinin resistance; we analyzed variation in clearance rates within infections by fitting parasite clearance curves to subpopulation data.

RESULTS

In Tanzania, we found substantial variation in clearance rates within individual patients. Some parasite subpopulations cleared as slowly as resistant parasites observed in Cambodia. We evaluated possible explanations for these data, including resistance to drugs. Assuming slow clearance was a stable phenotype of subpopulations, simulations predicted that modest increases in their frequency could substantially increase time to cure.

CONCLUSIONS AND IMPLICATIONS

By characterizing parasite subpopulations within patients, our method can detect rare, slow clearing parasites in vivo whose phenotypic effects would otherwise be masked. Since our approach can be applied to polyclonal infections even when the genetics underlying resistance are unknown, it could aid in monitoring the emergence of artemisinin resistance. Our application to Tanzanian samples uncovers rare subpopulations with worrying phenotypes for closer examination.

Place, publisher, year, edition, pages
2016. Vol. 2016, no 1, 21-36 p.
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Health Sciences
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URN: urn:nbn:se:uu:diva-277255DOI: 10.1093/emph/eov036PubMedID: 26817485OAI: oai:DiVA.org:uu-277255DiVA: diva2:904148
Note

De två första författarna delar förstaförfattarskapet.

Available from: 2016-02-18 Created: 2016-02-18 Last updated: 2017-05-03Bibliographically approved

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Mårtensson, Andreas

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