The impact of indoor residual spraying on Plasmodium falciparum microsatellite variation in an area of high seasonal malaria transmission in Ghana, West Africa

Abstract

Here, we report the first population genetic study to examine the impact of indoor residual spraying (IRS) on Plasmodium falciparum in humans. This study was conducted in an area of high seasonal malaria transmission in Bongo District, Ghana. IRS was implemented during the dry season (November-May) in three consecutive years between 2013 and 2015 to reduce transmission and attempt to bottleneck the parasite population in humans towards lower diversity with greater linkage disequilibrium. The study was done against a background of widespread use of long-lasting insecticidal nets, typical for contemporary malaria control in West Africa. Microsatellite genotyping with 10 loci was used to construct 392 P. falciparum multilocus infection haplotypes collected from two age-stratified cross-sectional surveys at the end of the wet seasons pre- and post-IRS. Three-rounds of IRS, under operational conditions, led to a >90% reduction in transmission intensity and a 35.7% reduction in the P. falciparum prevalence (p < .001). Despite these declines, population genetic analysis of the infection haplotypes revealed no dramatic changes with only a slight, but significant increase in genetic diversity (H_e : pre-IRS = 0.79 vs. post-IRS = 0.81, p = .048). Reduced relatedness of the parasite population (p < .001) was observed post-IRS, probably due to decreased opportunities for outcrossing. Spatiotemporal genetic differentiation between the pre- and post-IRS surveys (D = 0.0329 [95% CI: 0.0209 - 0.0473], p = .034) was identified. These data provide a genetic explanation for the resilience of P. falciparum to short-term IRS programmes in high-transmission settings in sub-Saharan Africa.

Keywords: Plasmodium falciparum; genetic epidemiology; indoor residual spraying; malaria elimination; microsatellite genotyping; neutral genetic variation.

FIGURE 1

Study area, study design, and the rollout of indoor residual spraying (IRS) in northern Ghana. (a) The distribution of the compounds (i.e., households) included in this study from the two catchment areas in Bongo District: Vea/Gowrie, lower left (purple) and Soe, upper right (green). The compounds in Vea/Gowrie and Soe are approximately 20–40 km apart. The location of Bongo District in the Upper East Region of Ghana is shown in the insert map (upper left). Note: The human population in Bongo District resides in rural communities made up of small farm settlements scattered throughout the district. For the purposes of this study not all compounds in Bongo District were geolocated and therefore are not included on the map. (b) Study design showing the timing of the two age‐stratified cross‐sectional surveys (T1 and T2) in Bongo, Ghana. The first survey (T1) was conducted at the end of the wet season pre‐IRS in October 2012, while the second survey (T2) was conducted end of the wet season post‐IRS in October 2015. Three‐rounds of IRS with different organophosphates were implemented in the dry season across Bongo as indicated in grey: Round 1 (October 2013–January 2014, Vectoguard 40WP), Round 2 (May–July 2014, Actellic 50EC), and Round 3 (December 2014–February 2015, Actellic 300CS). Long‐lasting insecticidal nets (LLINs) were mass distributed in Bongo District by the NMCP/GHS between 2010–2012 as indicated. The mosquitos are used to denote the entomology surveys that were undertaken monthly between February 2013 and September 2015 in Bongo (see Supporting Information Methods). (c) Timing and distribution of IRS across the Upper East, Upper West, and Northern Regions of Ghana, West Africa between 2012 to 2015. Highlighted in dark red are the IRS programmes funded by the President's Malaria Initiative (PMI) and in light red are those funded by the Global Fund in partnership with the AngloGold Ashanti Malaria Control Programme (AGAMal). Bongo District is denoted with by the black hashed lines. The thick black lines are used to signify borders between Ghana and Burkina Faso (light grey) to the north, and Togo (light grey) to the west. Bongo District shares a northern border with the Nahouri Province in Burkina Faso where no IRS was implemented before and/or during the study period (PMI, 2017). Districts in Ghana where there is no shading (white) indicate that no IRS programmes were ongoing between 2012 and 2015

FIGURE 2

Distribution of the number of P. falciparum clones (i.e., multiplicity of infection (MOI)) in each P. falciparum isolate sampled pre‐IRS (T1, N = 192, dark blue) and post‐IRS (T2, N = 200, light blue) based on the microsatellite genotyping. There were no significant differences in the proportion of clones from pre‐ to post‐IRS (χ² = 10.833, p = .055). Note: The numbers reflect the percentage of participants pre‐ and post‐IRS (%, n/N) in each MOI category

FIGURE 3

Distribution of allele frequencies for the microsatellite loci genotyped pre‐IRS (T1, October 2012) and post‐IRS (T2, October 2015) using the “dominant infections” data set. The Jost's D and G_ST values have been provided for each locus, along with the number of isolates that were genotyped per locus. For the number of isolates (N) with data for each locus, please see Table S6 for more details

FIGURE 4

Pairwise linkage disequilibrium (${\overline{r}}_{\mathrm{d}}$) for the P. falciparum infections using the “dominant infections” with complete haplotypes (i.e., no missing data, see Section 2) pre‐IRS (T1, October 2012) and post‐IRS (T2, October 2015) (N = 165). The colour key provided corresponds to the p‐value for each pairwise comparison where grey indicates a nonsignificant p‐value (p ≥ .05) and red represents a significant p‐value (p < .05)

FIGURE 5

Distribution of the pairwise allele sharing (P _AS) scores overtime by comparing the "dominant infections" with complete haplotypes (i.e., no missing data, see Section 2) from the pre‐IRS survey (T1, October 2012) to those in the post‐IRS survey (T2, October 2015). The median P _AS was 0.2 and is indicated by the black dotted line. The P _AS scores between 0.7 to 1.0 are shown in the upper right insert. There were 6804 pairwise comparisons between the 81 haplotypes from the pre‐IRS survey (T1) and the 84 haplotypes from the post‐IRS survey (T2) compared (see Table S12)

FIGURE 6

The genetic relatedness networks visualized spatially in Bongo for (a) pre‐IRS (T1, October 2012), (b)post‐IRS (T2, October 2015), and (c) pre‐ versus post‐IRS. These networks were constructed using the "dominant infections" with complete haplotypes pre‐IRS (N = 81 isolates compared), post‐IRS (N = 84 isolates compared), and pre‐ versus post‐IRS (N = 165 isolates compared). Each node represents an isolate and its geographic location in Bongo (i.e., compound/household location in each catchment area): purple corresponds to isolates in Vea/Gowrie and green corresponds to isolates in Soe. The edges in the networks (black lines) denote the pairwise relatedness between isolates at the selected pairwise allele sharing (P _AS) ≥0.70 threshold (i.e., identical at ≥7 of the 10 microsatellite loci). This threshold was selected to visualise the genetic similarity between isolates that probably resulted from recent transmission and/or recombination events. Note in (b), in Vea/Gowrie there is one isolate pair within the same household denoted with a black circle

FIGURE 7

Spatiotemporal genetic differentiation between the P. falciparum populations in in Bongo as assessed using the “dominant infections” with complete haplotypes pre‐IRS (T1, October 2012) and post‐IRS (T2, October 2015). (a) Matrix of pairwise G_ST (upper diagonal) and Jost's D (lower diagonal) indices between the catchment areas (i.e., Vea/Gowrie and Soe). (b–d) Visualizations of the Jost's D pairwise comparisons. Each node represents an isolate and its geographic location in Bongo (i.e., catchment area): purple corresponds to isolates in Vea/Gowrie and green corresponds to isolates in Soe. (b) Vea/Gowrie versus Soe post‐IRS (T2) (Jost's D = 0.0680, p < .01), (c) Vea/Gowrie pre‐IRS (T1, right panel) to Vea/Gowrie post‐IRS (T2, left panel) (Jost's D = 0.0608, p ≥ .05, ns), (d) Soe pre‐IRS (T1, right panel) to Soe post‐IRS (T2, left panel) (Jost's D = 0.0698, p < .01). Note: In (a), grey indicates comparisons between pre‐ versus post‐IRS (T1 vs. T2); dark blue for Vea/Gowrie versus Soe pre‐IRS (T1); light blue for Vea/Gowrie versus Soe post‐IRS (T2). The colour intensity corresponds to the p‐value for each pairwise comparison where paler colouring indicates a nonsignificant p‐value (p ≥ .05) and intense colouring indicates significant p‐values (p < .05). *p‐value < .05; **p‐value < .01; ***p‐value < .001