Intermittent preventive treatment with dihydroartemisinin-piperaquine in Ugandan schoolchildren selects for Plasmodium falciparum transporter polymorphisms that modify drug sensitivity.

Dihydroartemisinin-piperaquine (DP) offers prolonged protection against malaria, but its impact on Plasmodium falciparum drug sensitivity is uncertain. In a trial of intermittent preventive treatment in schoolchildren in Tororo, Uganda, in 2011 to 2012, monthly DP for 1 year decreased the incidence of malaria by 96% compared to placebo; DP once per school term offered protection primarily during the first month after therapy. To assess the impact of DP on selection of drug resistance, we compared the prevalence of key polymorphisms in isolates that emerged at different intervals after treatment with DP. Blood obtained monthly and at each episode of fever was assessed for P. falciparum parasitemia by microscopy. Samples from 160 symptomatic and 650 asymptomatic episodes of parasitemia were assessed at 4 loci (N86Y, Y184F, and D1246Y in pfmdr1 and K76T in pfcrt) that modulate sensitivity to aminoquinoline antimalarials, utilizing a ligase detection reaction-fluorescent microsphere assay. For pfmdr1 N86Y and pfcrt K76T, but not the other studied polymorphisms, the prevalences of mutant genotypes were significantly greater in children who had received DP within the past 30 days than in those not treated within 60 days (86Y, 18.0% versus 8.3% [P = 0.03]; 76T, 96.0% versus 86.1% [P = 0.05]), suggesting selective pressure of DP. Full sequencing of pfcrt in a subset of samples did not identify additional polymorphisms selected by DP. In summary, parasites that emerged soon after treatment with DP were more likely than parasites not under drug pressure to harbor pfmdr1 and pfcrt polymorphisms associated with decreased sensitivity to aminoquinoline antimalarials. (This study has been registered at ClinicalTrials.gov under no. NCT01231880.)

Drug-resistant genotypes and multi-clonality in Plasmodium falciparum analysed by direct genome sequencing from peripheral blood of malaria patients.

Naturally acquired blood-stage infections of the malaria parasite Plasmodium falciparum typically harbour multiple haploid clones. The apparent number of clones observed in any single infection depends on the diversity of the polymorphic markers used for the analysis, and the relative abundance of rare clones, which frequently fail to be detected among PCR products derived from numerically dominant clones. However, minority clones are of clinical interest as they may harbour genes conferring drug resistance, leading to enhanced survival after treatment and the possibility of subsequent therapeutic failure. We deployed new generation sequencing to derive genome data for five non-propagated parasite isolates taken directly from 4 different patients treated for clinical malaria in a UK hospital. Analysis of depth of coverage and length of sequence intervals between paired reads identified both previously described and novel gene deletions and amplifications. Full-length sequence data was extracted for 6 loci considered to be under selection by antimalarial drugs, and both known and previously unknown amino acid substitutions were identified. Full mitochondrial genomes were extracted from the sequencing data for each isolate, and these are compared against a panel of polymorphic sites derived from published or unpublished but publicly available data. Finally, genome-wide analysis of clone multiplicity was performed, and the number of infecting parasite clones estimated for each isolate. Each patient harboured at least 3 clones of P. falciparum by this analysis, consistent with results obtained with conventional PCR analysis of polymorphic merozoite antigen loci. We conclude that genome sequencing of peripheral blood P. falciparum taken directly from malaria patients provides high quality data useful for drug resistance studies, genomic structural analyses and population genetics, and also robustly represents clonal multiplicity

Scale-up of Malaria Rapid Diagnostic Tests and Artemisinin-Based Combination Therapy: Challenges and Perspectives in Sub-Saharan Africa.

Guido Bastiaens and colleagues describe barriers to achieving scale-up and appropriate use of rapid diagnostic tests and artemisinin-based combination therapy for malaria in sub-Saharan Africa. Please see later in the article for the Editors' Summary

Estimation of malaria haplotype and genotype frequencies: a statistical approach to overcome the challenge associated with multiclonal infections

BACKGROUND: Reliable measures of anti-malarial resistance are crucial for malaria control. Resistance is typically a complex trait: multiple mutations in a single parasite (a haplotype or genotype) are necessary for elaboration of the resistant phenotype. The frequency of a genetic motif (proportion of parasite clones in the parasite population that carry a given allele, haplotype or genotype) is a useful measure of resistance. In areas of high endemicity, malaria patients generally harbour multiple parasite clones; they have multiplicities of infection (MOIs) greater than one. However, most standard experimental procedures only allow measurement of marker prevalence (proportion of patient blood samples that test positive for a given mutation or combination of mutations), not frequency. It is misleading to compare marker prevalence between sites that have different mean MOIs; frequencies are required instead. METHODS: A Bayesian statistical model was developed to estimate Plasmodium falciparum genetic motif frequencies from prevalence data collected in the field. To assess model performance and computational speed, a detailed simulation study was implemented. Application of the model was tested using datasets from five sites in Uganda. The datasets included prevalence data on markers of resistance to sulphadoxine-pyrimethamine and an average MOI estimate for each study site. RESULTS: The simulation study revealed that the genetic motif frequencies that were estimated using the model were more accurate and precise than conventional estimates based on direct counting. Importantly, the model did not require measurements of the MOI in each patient; it used the average MOI in the patient population. Furthermore, if a dataset included partially genotyped patient blood samples, the model imputed the data that were missing. Using the model and the Ugandan data, genotype frequencies were estimated and four biologically relevant genotypes were identified. CONCLUSIONS: The model allows fast, accurate, reliable estimation of the frequency of genetic motifs associated with resistance to anti-malarials using prevalence data collected from malaria patients. The model does not require per-patient MOI measurements and can easily analyse data from five markers. The model will be a valuable tool for monitoring markers of anti-malarial drug resistance, including markers of resistance to artemisinin derivatives and partner drugs