Characterizing the genomic variation and population dynamics of Plasmodium falciparum malaria parasites in and around Lake Victoria, Kenya

Abstract

Characterising the genomic variation and population dynamics of Plasmodium falciparum parasites in high transmission regions of Sub-Saharan Africa is crucial to the long-term efficacy of regional malaria elimination campaigns and eradication. Whole-genome sequencing (WGS) technologies can contribute towards understanding the epidemiology and structural variation landscape of P. falciparum populations, including those within the Lake Victoria basin, a region of intense transmission. Here we provide a baseline assessment of the genomic diversity of P. falciparum isolates in the Lake region of Kenya, which has sparse genetic data. Lake region isolates are placed within the context of African-wide populations using Illumina WGS data and population genomic analyses. Our analysis revealed that P. falciparum isolates from Lake Victoria form a cluster within the East African parasite population. These isolates also appear to have distinct ancestral origins, containing genome-wide signatures from both Central and East African lineages. Known drug resistance biomarkers were observed at similar frequencies to those of East African parasite populations, including the S160N/T mutation in the pfap2mu gene, which has been associated with delayed clearance by artemisinin-based combination therapy. Overall, our work provides a first assessment of P. falciparum genetic diversity within the Lake Victoria basin, a region targeting malaria elimination.

Selective whole-genome amplification reveals population genetics of Leishmania braziliensis directly from patient skin biopsies

“…SWGA is based on the use of organism-specific, short oligonucleotide primers and a high-fidelity, highly processive polymerase to preferentially amplify large segments of the target genome. Effective SWGA protocols have resulted in sequencing-ready samples that are enriched for specific target microbial genomes and which have been used to address biologically important questions in several microorganisms, including Mycobacterium tuberculosis, Wolbachia spp., Plasmodium spp., Neisseria meningitidis, Coxiella burnetii, Wuchereria bancrofti, and Treponema pallidum [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. The ability to carry out SWGA without specialized equipment or reagents makes it feasible to implement in low-and middle-income countries (LMICs) where laboratory resources may be limited [16,21].…”

How has mass drug administration with dihydroartemisinin-piperaquine impacted molecular markers of drug resistance? A systematic review

“…As part of this review, whole genome sequence data was used to analyse the prevalence of the above-mentioned SNPs in P. falciparum samples from studies worldwide [ 44 – 46 ] (n = 4001) (Fig. 4 ).…”

“…4 A diagram showing the global frequencies of mutations in the pfmdr1, pfcrt and pfexo genes, which are potential markers of DHA-PPQ resistance. These frequencies were calculated using whole genome sequence data from recent studies [ 44 – 46 ]. n is the number of samples containing a mutant allele, and N is the total number of successfully sequenced samples.…”

A fast machine-learning-guided primer design pipeline for selective whole genome amplification

“…SWGA is a powerful and cost-effective tool for researchers looking to generate genomic data for microbial systems. Effective SWGA protocols have resulted in next-generation sequencing (NGS)-ready samples that are enriched for specific target microbial genomes and have been used to address biologically important questions in several microorganisms, including Mycobacterium tuberculosis, Wolbachia spp, Plasmodium spp, Neisseria meningitidis, Coxiella burnetii , and Wuchereria bancrofti (Clarke et al, 2017; Sundararaman et al, 2016; Guggisberg et al, 2016; Oyola et al, 2016; Cowell et al, 2017; Loy et al, 2018; Small et al, 2018; Leichty and Brisson, 2014; Morgan et al, 2020; Cocking et al, 2020; Itsko et al, 2020; Osborne et al, 2021; Ibrahim et al, 2020; Benavente et al, 2019).…”