10.5061/DRYAD.THT76HF0R
Argyropoulos, Dionne
0000-0002-8068-0215
University of Melbourne
Ruybal‐Pesántez, Shazia
University of Melbourne
University of Melbourne
Deed, Samantha L.
University of Melbourne
University of Melbourne
Oduro, Abraham R.
0000-0002-4191-7419
Navrongo Health Research Centre
Navrongo Health Research Centre
Dadzie, Samuel K.
0000-0002-4105-1010
Noguchi Memorial Institute for Medical Research
Noguchi Memorial Institute for Medical Research
Appawu, Maxwell A.
Noguchi Memorial Institute for Medical Research
Asoala, Victor
Navrongo Health Research Centre
Pascual, Mercedes
0000-0003-3575-7233
University of Chicago
Koram, Kwadwo A.
0000-0003-4274-6516
Noguchi Memorial Institute for Medical Research
Day, Karen P.
0000-0002-6115-6135
University of Melbourne
Appawu, Maxwell A.
Noguchi Memorial Institute for Medical Research
Tiedje, Kathryn E.
0000-0003-3305-0533
University of Melbourne
Asoala, Victor
Navrongo Health Research Centre
Pascual, Mercedes
0000-0003-3575-7233
University of Chicago
Koram, Kwadwo A.
0000-0003-4274-6516
Noguchi Memorial Institute for Medical Research
Day, Karen P.
0000-0002-6115-6135
University of Melbourne
Tiedje, Kathryn E.
0000-0003-3305-0533
University of Melbourne
The impact of indoor residual spraying on Plasmodium falciparum
microsatellite variation in an area of high seasonal malaria transmission
in Ghana, West Africa
Dryad
dataset
2021
Fogarty International Center
R01‐TW009670
National Institute of Allergy and Infectious Diseases
R01‐AI149779
2021-11-29T00:00:00Z
2021-11-29T00:00:00Z
en
https://doi.org/10.1111/mec.16029
25542 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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 (He : 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.
For all participants with microscopically
confirmed P. falciparum infections (i.e., isolates), two 5 x 5 mm sections
were cut from each dried blood spot and placed in a 1.5‐ml centrifuge
tube, with genomic DNA (gDNA) being extracted using the QIAmp DNA mini kit
(Qiagen) as previously described (Tiedje et al., 2017). A subset of 200
microscopic P. falciparum isolates from both the pre‐IRS (T1) and post‐IRS
(T2) surveys were selected for microsatellite genotyping based on their
multiplicity of infection (MOI) (i.e., number of genetically
distinct P. falciparum genomes) as determined using var genotyping
(see Supporting Information Methods, Figure S1). Briefly, using this
approach we estimated the MOI based on the number of var DBLα types
identified per isolate, using a cutoff value of 60 var DBLα types
per P. falciparum genome. Isolates with ≤60 var DBLα types were classified
as single‐clone infections (MOI = 1), while isolates with
>60 var DBLα types were classified as multiple‐clone infections
(MOI > 1). To facilitate a more accurate assignment of the
fluorescent peaks during the analysis (described below), only those
isolates with a MOI = 1 or 2, were selected for the microsatellite
genotyping (Anderson et al., 1999). The P. falciparum isolates (N = 400)
selected from the pre‐ and post‐IRS surveys were genotyped using a
verified panel of 12 putatively neutral microsatellite markers located
across the 14 chromosomes as described by Anderson et al. (1999): TA1,
2490, TA81, TA87, TA109, TA60, POLYA, TA42, ARA2, PfG377, PfPK2, and TA40,
with modified cycling conditions as specified in Ruybal‐Pesántez et al.
(2017). Fluorescently‐labelled PCR products were sent to a commercial
sequencing facility (Macrogen Inc., South Korea) for capillary
electrophoresis and fragment analysis on an Applied Biosystems 3730xl DNA
analyser (ThermoFisher Scientific). Raw data files were imported using
GeneMarker (SoftGenetics LLC), normalised based on the size standard
LIZ500, and scored using customised panels as previously described
(Anderson et al., 1999; Ruybal‐Pesántez et al., 2017). All major peaks
that were within the expected marker base pair (bp) range and were spaced
at intervals corresponding to trinucleotide (3 bp) repeats were considered
to be true alleles. Any peak less than 33% of the primary peak (i.e.,
local max) for a locus was considered a minor allele and not interpreted
as a true allele. Background noise was defined as any peak <200
fluorescent units (Anderson et al., 1999). These data were cleaned using R
package base v. 3.5.0 (R Core Team, 2018) and then processed using TANDEM
v. 1.09 (Matschiner & Salzburger, 2009), which is optimal to
assign an allele to each trinucleotide microsatellite locus for each
isolate. We combined data from the pre‐ and post‐IRS surveys prior to
binning alleles with TANDEM to ensure each survey could be compared
accurately to each other. For the 200 isolates investigated in the pre‐
and post‐IRS surveys, the median genotyping success was 89.2% in the
pre‐IRS survey and 92.8% in the post‐IRS survey for the 12 microsatellite
markers (Table S2) as expected for low‐density
asymptomatic P. falciparum infections. Since isolate genotyping success
for TA1 and TA42 was <75% pre‐IRS and/or post‐IRS (Table S2), these
loci were subsequently removed, with 10 microsatellite loci included for
the downstream multilocus microsatellite analyses (Note: All
12 microsatellite loci, including TA1 and TA42, were successfully
amplified and genotyped for the 3D7 positive controls, thus the
possibility of null alleles could not be excluded). Finally, only those
isolates with genotyping data at ≥3 microsatellite loci were included,
resulting in 192 (96.0%) and 200 (100%) isolates from the pre‐ and
post‐IRS surveys, respectively (Table (Table1,1, Figure S1).