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. 2021 Mar 3:17:1176934321999640.
doi: 10.1177/1176934321999640. eCollection 2021.

Plasmodium falciparum Malaria Parasites in Ghana Show Signatures of Balancing Selection at Artemisinin Resistance Predisposing Background Genes

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Plasmodium falciparum Malaria Parasites in Ghana Show Signatures of Balancing Selection at Artemisinin Resistance Predisposing Background Genes

Kwesi Z Tandoh et al. Evol Bioinform Online. .

Abstract

Sub-Saharan Africa is courting the risk of artemisinin resistance (ARTr) emerging in Plasmodium falciparum malaria parasites. Current molecular surveillance efforts for ARTr have been built on the utility of P. falciparum kelch13 (pfk13) validated molecular markers. However, whether these molecular markers will serve the purpose of early detection of artemisinin-resistant parasites in Ghana is hinged on a pfk13 dependent evolution. Here, we tested the hypothesis that the background pfk13 genome may be present before the pfk13 ARTr-conferring variant(s) is selected and that signatures of balancing selection on these genomic loci may serve as an early warning signal of ARTr. We analyzed 12 198 single nucleotide polymorphisms (SNPs) in Ghanaian clinical isolates in the Pf3K MalariaGEN dataset that passed a stringent filtering regimen. We identified signatures of balancing selection in 2 genes (phosphatidylinositol 4-kinase and chloroquine resistance transporter) previously reported as background loci for ARTr. These genes showed statistically significant and high positive values for Tajima's D, Fu and Li's F, and Fu and Li's D. This indicates that the biodiversity required to establish a pfk13 background genome may have been primed in clinical isolates of P. falciparum from Ghana as of 2010. Despite the absence of ARTr in Ghana to date, our finding supports the current use of pfk13 for molecular surveillance of ARTr in Ghana and highlights the potential utility of monitoring malaria parasite populations for balancing selection in ARTr precursor background genes as early warning molecular signatures for the emergence of ARTr.

Keywords: Plasmodium falciparum; Population genomics; artemisinin resistance; malaria; molecular surveillance; signatures of balancing selection.

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Conflict of interest statement

Declaration Of Conflicting Interests:The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Distribution of multiplicity of infection in Ghana samples of P. falciparum analyzed (n = 617): (A) is a histogram showing the number of samples with within sample F statistic (Fws) on the vertical axis within the range specified on the horizontal axis and (B) is a scatter plot that shows the distribution of the samples with Fws. The red line is at Fws = 0.95, the cutoff point for MOI.
Figure 2.
Figure 2.
P. falciparum population structure analysis for Ghana samples.
Figure 3.
Figure 3.
Summary of SNPs characteristics: (A) Frequency distribution of the non-reference allele for each of the biallelic SNPs in the sample of P. falciparum clinical isolates from Ghana (N = 274) and (B) distribution of numbers of protein-coding genes (N = 2256) with each given number of SNPs in the Ghana population sample of P. falciparum clinical isolates.
Figure 4.
Figure 4.
Distribution of SNPs effect across all genomic positions analyzed.
Figure 5.
Figure 5.
Genome-wide distribution of Tajima’s D values summarizing the site frequency spectra for P. falciparum genes.

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