A deep sequencing tool for partitioning clearance rates following antimalarial treatment in polyclonal infections
Bailey, Jeffrey A.
Hathaway, Nicholas J
Saunders, David L.
Meshnick, Steven R.
Read, Andrew F.
Juliano, Jonathan J.
UMass Chan AffiliationsDepartment of Medicine, Division of Transfusion Medicine
Program in Bioinformatics and Integrative Biology
Document TypeJournal Article
Immunology and Infectious Disease
International Public Health
MetadataShow full item record
AbstractBACKGROUND 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.
SourceEvol Med Public Health. 2016 Jan 27;2016(1):21-36. doi: 10.1093/emph/eov036. Link to article on publisher's site
Permanent Link to this Itemhttp://hdl.handle.net/20.500.14038/25947
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Rights© The Author(s) 2016. Published by Oxford University Press on behalf of the Foundation for Evolution, Medicine, and Public Health. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as © The Author(s) 2016. Published by Oxford University Press on behalf of the Foundation for Evolution, Medicine, and Public Health. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.