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01.12.2019 | Research | Ausgabe 1/2019 Open Access

Malaria Journal 1/2019

High Plasmodium falciparum genetic diversity and temporal stability despite control efforts in high transmission settings along the international border between Zambia and the Democratic Republic of the Congo

Zeitschrift:
Malaria Journal > Ausgabe 1/2019
Autoren:
Julia C. Pringle, Amy Wesolowski, Sophie Berube, Tamaki Kobayashi, Mary E. Gebhardt, Modest Mulenga, Mike Chaponda, Thierry Bobanga, Jonathan J. Juliano, Steven Meshnick, William J. Moss, Giovanna Carpi, Douglas E. Norris
Wichtige Hinweise

Supplementary information

Supplementary information accompanies this paper at https://​doi.​org/​10.​1186/​s12936-019-3023-4.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

Background

While the utility of parasite genotyping for malaria elimination has been extensively documented in low to moderate transmission settings, it has been less well-characterized in holoendemic regions. High malaria burden settings have received renewed attention acknowledging their critical role in malaria elimination. Defining the role for parasite genomics in driving these high burden settings towards elimination will enhance future control programme planning.

Methods

Amplicon deep sequencing was used to characterize parasite population genetic diversity at polymorphic Plasmodium falciparum loci, Pfama1 and Pfcsp, at two timepoints in June–July 2016 and January–March 2017 in a high transmission region along the international border between Luapula Province, Zambia and Haut-Katanga Province, the Democratic Republic of the Congo (DRC).

Results

High genetic diversity was observed across both seasons and in both countries. No evidence of population structure was observed between parasite populations on either side of the border, suggesting that this region may be one contiguous transmission zone. Despite a decline in parasite prevalence at the sampling locations in Haut-Katanga Province, no genetic signatures of a population bottleneck were detected, suggesting that larger declines in transmission may be required to reduce parasite genetic diversity. Analysing rare variants may be a suitable alternative approach for detecting epidemiologically important genetic signatures in highly diverse populations; however, the challenge is distinguishing true signals from potential artifacts introduced by small sample sizes.

Conclusions

Continuing to explore and document the utility of various parasite genotyping approaches for understanding malaria transmission in holoendemic settings will be valuable to future control and elimination programmes, empowering evidence-based selection of tools and methods to address pertinent questions, thus enabling more efficient resource allocation.
Zusatzmaterial
Additional file 1: Fig. S1. Top: Pfama1; bottom: Pfcsp. Distributions across 1000 replicates of collector’s curve analysis show the number of unique haplotypes (Y-axis) found among a randomly selected group of samples of increasing size (X-axis). Curves on the left were generated using the raw (unrarefied) dataset, while curves on the right were generated from the dataset rarefied to a depth of 200 reads per sample. Fig. S2. For each amplicon (top: Pfama1, bottom: Pfcsp), we performed 1000 re-sampling replicates of rarefaction. For each replicate we estimated MOI in each individual using the rarefied data. The distribution of MOI estimates across all rarefaction re-sampling replicates are plttoed along the Y-axis. The X-axis shows the MOI estimate from the raw data. The red dashed line is the Y = X line, or what would be expected if there was no difference between the estimate using rarefied data and the true estimate. Fig. S3. Pairwise genetic relatedness (the proportion of loci which are identical between two parasites) is plotted for all pairs of parasites from different countries or from the same country for each amplicon, Pfama1 (left) and Pfcsp (right). Comparisons between two parasites from individuals both under 5 years old are shown in pink. Comparisons between two parasites from individuals both 5 years or older are shown in blue. Comparisons between parasites from an individual 5 years or older and an individual under 5 years are shown in yellow. Fig. S4. Haplotype frequency distributions are plotted for the haplotypes present in each population (left: DRC right: Zambia; light grey bars: 2016 samples; dark grey bars: 2017 samples). Fig. S5. Left: plots the proportion of haplotypes within the four country-year “populations” considered to be rare. Here rare haplotypes are those represented at 2% or less in the “population.” Right: plots the proportion of haplotypes within the four country-year “populations” that were observed only once (singletons). Fig. S6. Dicriminatory analysis of principal components (DAPC) was performed using R package, adgenet. DAPC performs linear discriminat analysis on principal components in order to maximize separation of a priori groups. A, B: Pfama1; C, D: Pfcsp. A, C: DAPC performed using all sequences regardless of population frequency shows no linear function that can classify the principal components of the parasite seqeunces reliably by country. B, D: DAPC using only rare haplotypes (singletons) results in more refined population discrimination for Pfcsp. Fig. S7. Dicriminatory analysis of principal components (DAPC) was performed using R package, adgenet. DAPC performs linear discriminat analysis on principal components in order to maximize separation of a priori groups. A, B: Pfama1; C, D: Pfcsp. A, C: DAPC performed using all sequences regardless of population frequency shows no linear function that can classify the principal components of the parasite seqeunces reliably by country. B, D: DAPC using only rare haplotypes (2% or less frequency) results in more refined population discrimination for Pfcsp.
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