Describing the current status of Plasmodium falciparum population structure and drug resistance within mainland Tanzania using molecular inversion probes
JavaScript is disabled for your browser. Some features of this site may not work without it.
Describing the current status of Plasmodium falciparum population structure and drug resistance within mainland Tanzania using molecular inversion probes
Moser, K. A.; Madebe, R. A.; Aydemir, O; Chiduo, M. G.; Mandara, C. I.; Rumisha, S. F.; Chaky, F; Denton, M.; Marsh, P. W.; Verity, R.; Watson, O. J.; Ngasala, B.; Mkude, S.; Molteni, F.; Njau, R.; Warsame, M.; Mandike, R.; Kabanywanyi, A. M.; Mahende, M. K.; Kamugisha, E.; Ahmed, M.; Kavishe, R. A.; Greer, G; Kitojo, C. A.; Reaves, E. J.; Mlunde, L; Bishanga, D; Mohamed, A.; Juliano, J. J.; Ishengoma, D. S.; Bailey, J. A.
High-throughput Plasmodium genomic data is increasingly useful in assessing prevalence of
clinically important mutations and malaria transmission patterns. Understanding parasite diversity is
important for identification of specific human or parasite populations that can be targeted by control
programs, and to monitor the spread of mutations associated with drug resistance. An up-to-date
understanding of regional parasite population dynamics is also critical to monitor the impact of
control efforts. However, this data is largely absent from high-burden nations in Africa, and to date,
no such analysis has been conducted for malaria parasites in Tanzania country-wide. To this end,
over 1,000 P. falciparum clinical isolates were collected in 2017 from 13 sites in seven administrative
regions across Tanzania, and parasites were genotyped at 1,800 variable positions genome-wide
using molecular inversion probes. Population structure was detectable among Tanzanian P.
falciparum parasites, roughly separating parasites from the northern and southern districts and
identifying genetically admixed populations in the north. Isolates from nearby districts were more
likely to be genetically related compared to parasites sampled from more distant districts. Known
drug resistance mutations were seen at increased frequency in northern districts (including two
infections carrying pfk13-R561H), and additional variants with undetermined significance for
antimalarial resistance also varied by geography. Malaria Indicator Survey (2017) data corresponded
with genetic findings, including average region-level complexity-of-infection and malaria prevalence
estimates. The parasite populations identified here provide important information on extant spatial
patterns of genetic diversity of Tanzanian parasites, to which future surveys of genetic relatedness
can be compared.