A deep sequencing tool for partitioning clearance rates following antimalarial treatment in polyclonal infections

Nicole Mideo; Jeffrey A Bailey; Nicholas J Hathaway; Billy Ngasala; David L Saunders; Chanthap Lon; Oksana Kharabora; Andrew Jamnik; Sujata Balasubramanian; Anders Björkman; Andreas Mårtensson; Steven R Meshnick; Andrew F Read; Jonathan J Juliano

doi:10.1093/emph/eov036

A deep sequencing tool for partitioning clearance rates following antimalarial treatment in polyclonal infections

Evol Med Public Health. 2016 Jan 27;2016(1):21-36. doi: 10.1093/emph/eov036.

Authors

Affiliations

¹ Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada; [email protected].
² 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;
³ Program in Bioinformatics and Integrative Biology, University of Massachusetts, Worcester, MA, USA;
⁴ Department of Parasitology, Muhimbili University of Health and Allied Sciences, Dar Es Salaam, Tanzania;
⁵ Division of Immunology and Medicine, USAMC Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand;
⁶ US Army Medical Component, Armed Forces Research Institute of Medical Sciences, Phnom Penh, Cambodia;
⁷ Division of Infectious Diseases, University of North Carolina School of Medicine, Chapel Hill, NC, USA;
⁸ Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada;
⁹ Malaria Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden;
¹⁰ Malaria Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden; Centre for Clinical Research Sörmland, Uppsala University, Sweden; Department of Women's and Children's Health, International Maternal and Child Health (IMCH), Uppsala University, Sweden;
¹¹ Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA;
¹² Center for Infectious Disease Dynamics, Department of Biology and Entomology, the Pennsylvania State University, University Park, PA, USA and.
¹³ Division of Infectious Diseases, University of North Carolina School of Medicine, Chapel Hill, NC, USA; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA; Curriculum in Genetics and Molecular Biology, University of North Carolina, Chapel Hill, NC, USA.

Abstract

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.

Keywords: amplicon sequencing; artemisinin; drug resistance; ecology; malaria; within-host selection.

Abstract

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