Principal Coordinate Analysis.

<p>Microsatellite data for both pre- and post-ACTs were used to find out patterns and relationships within a multivariate dataset. This graph was plotted using genetic distance matrix; blue dots for pre- and red dots for post-ACTs populations. This data shows the separation of the parasite genetic profiles in the two ACTs eras.</p

Bayesian midpoint tree showing single cloned <i>P</i>. <i>falciparum</i> haplotypes cluster.

<p>The cluster is in relation to clearance rates (slope half-life in hours). This was constructed using 78 concatenated SNPs whose genetic variants at each point were used to construct parasite relatedness using MrBayes software. A 10,000,000 generations was used to run and construct the tree which gave standard deviation below 0.01 to generate higher posterior probability values.</p

Genetically Determined Response to Artemisinin Treatment in Western Kenyan <i>Plasmodium falciparum</i> Parasites

<div><p>Genetically determined artemisinin resistance in <i>Plasmodium falciparum</i> has been described in Southeast Asia. The relevance of recently described Kelch 13-propeller mutations for artemisinin resistance in Sub-Saharan Africa parasites is still unknown. Southeast Asia parasites have low genetic diversity compared to Sub-Saharan Africa, where parasites are highly genetically diverse. This study attempted to elucidate whether genetics provides a basis for discovering molecular markers in response to artemisinin drug treatment in <i>P</i>. <i>falciparum</i> in Kenya. The genetic diversity of parasites collected pre- and post- introduction of artemisinin combination therapy (ACT) in western Kenya was determined. A panel of 12 microsatellites and 91 single nucleotide polymorphisms (SNPs) distributed across the <i>P</i>. <i>falciparum</i> genome were genotyped. Parasite clearance rates were obtained for the post-ACT parasites. The 12 microsatellites were highly polymorphic with post-ACT parasites being significantly more diverse compared to pre-ACT (p < 0.0001). The median clearance half-life was 2.55 hours for the post-ACT parasites. Based on SNP analysis, 15 of 90 post-ACT parasites were single-clone infections. Analysis revealed 3 SNPs that might have some causal association with parasite clearance rates. Further, genetic analysis using Bayesian tree revealed parasites with similar clearance phenotypes were more closely genetically related. With further studies, SNPs described here and genetically determined response to artemisinin treatment might be useful in tracking artemisinin resistance in Kenya.</p></div

SNP-based genotypes and genetic variation seen in 15 single clones in Western Kenya in 2013–2014.

<p>Median-joining network diagram above shows genetic relationship of the western Kenya samples using 78 SNP haplotypes. Each circle in the network represents a unique haplotype profile with the size of the circle being proportional to the number of clones showing that particular haplotypes. The circle shown in red stands for samples with clearance rate < 2.55 slope half-life (hours) while those colored in blue represent those with clearance rate >2.55 hours. The black dots are hypothetical median vector generated by the software to connect existing haplotypes within the network with maximum parsimony.</p

F_ST values across the 12 MS loci for pre- and post-ACTs parasites.

<p>F_ST values across the 12 MS loci for pre- and post-ACTs parasites.</p

Post-test Analysis of the 3 SNPs using Bonferroni’s multiple comparison test.

<p>Post-test Analysis of the 3 SNPs using Bonferroni’s multiple comparison test.</p

Correlation between 78 genome-wide SNPs and parasite clearance half-life.

<p>The 3 SNPs out of 78 genome-wide SNPs genotyped which showed positive correlation with clearance half-life with statistical significance (p < 0.05; CI 95%). The SNPs are as follows: <b>*MAL12-1156125 (MAL12), MAL14-1199184 (MAL14A) and MAL14-3017684 (MAL14B)</b> showing their wild type (WT) and mutant (MT) states.</p