Leveraging genomic insights from the neglected malaria parasites P. malariae and P. ovale using selective whole genome amplification (SWGA) approach - Fathia Ben-Rached; Amit Kumar Subudhi; Chang Li; Mariah Alawi; Rohit Satyam; Sui Xu; Guoding Zhu; Raeece Naeem; Sara Mfarrej; Di Liu; Zenaida Stead; Caroline Askonas; et al. (BMC Genomics)
Abstract Background Systematic genomics-guided population-based studies on the neglected malaria parasites, P. malariae, P. ovale curtisi, and P. ovale wallikeri species remain challenging due to their low parasitemia, underestimation, and lack of comprehensive genetic analysis. Techniques that cost-effectively allow the enriching of the genome of interest from complex genome backgrounds for sequencing studies help immensely to perform genomic analyses. One such technique is selective whole-genome amplification (SWGA). Results We applied SWGA using specifically designed primer sets targeting the pathogen genome to enrich parasite DNA from clinical samples. This enabled cost-effective and high-quality WGS for these neglected malaria species. WGS on SWGA-treated samples demonstrated improved genome coverage. Our method outperformed the published protocol for P.malariae with higher enrichment of the targeted genome. On average, P. malariae had 93% of the genome covered by ≥ 10 reads; parallel improvements in genome coverage were achieved for both P. ovale spp. with 81% on average of the genome covered by ≥ 10 reads. Consequently, the detection of thousands of additional SNPs not detectable in pre-SWGA samples was facilitated after SWGA, allowing more substantial downstream population genomics analysis, particularly for the polymorphic and antimalarial genes of great interest for all the species. Furthermore, leveraging the long DNA fragments generated by SWGA, we achieved high-quality genome assemblies for P. malariae and P. ovale using PacBio long reads sequencing technology. Conclusions SWGA approach implemented here provides a powerful tool for enhancing genomic analysis of these neglected parasites, revealing population diversity, drug resistance markers, and hypervariable regions. This methodology constitutes a transformative tool to surmount the challenges of genomic analysis for neglected malaria parasites and can improve malaria research and control.