Trend of N86Y and Y184F Mutations in Pfmdr1 Gene in Children Under Seasonal Malaria Chemoprevention Coverage in Nanoro, Burkina Faso

Abstract

Background

Seasonal malaria chemoprevention (SMC) is an effective malaria preventive intervention in sub-Sahara Africa. However, as with any other drug-based intervention, the large-scale deployment of this strategy could lead to Amodiaquine plus Sulfadoxine-Pyrimethamine (AQSP) drug pressure on the circulating parasites population with selection for specific alleles that could compromise the impact of the intervention in the near future. This study aimed to assess the distribution of the Pfmdr1 mutation involved in resistance to AQ before and after the annual campaign of SMC in the health district of Nanoro.

Methods

Randomly selected dried blood spots collected prior (n = 100) and after (n = 100) the 2021 SMC campaign were used for the detection of mutation in codons 86 and 184 of the Pfmdr1 gene using a nested PCR with restriction fragment length polymorphism approach.

Results

No significant change in the prevalence of Pfmdr1 N86Y mutation was observed before and after the SMC campaign (p = 0.28). The mutant allele 86Y was observed at low prevalences, representing only 2.17% and 6.12%, respectively, before and after the SMC campaign. Patients harboring the mutant Pfmdr1 86Y allele exhibited higher parasite densities compared to patients with the wild-type Pfmdr1 N86 allele (p = 0.04). A significant increase in the prevalence of the mutant allele 184 F was observed in the period before and after the SMC campaign (p = 0.03).

Conclusion

This selective pressure needs to be closely monitored in order to preserve the efficacy of this intervention for a long-term period in Burkina Faso.

Background

Malaria remains a major public health concern in sub-Sahara Africa. In 2022, the number of reported malaria cases were estimated at 249 million with 608,000 malaria-related deaths reported worldwide [1]. The World Health Organization (WHO) Africa Region accounted for approximately 94% (233 million) of estimated malaria cases and 95% (580,000) of deaths. Around 76% of all malaria deaths in 2022 occurred among African children under five [1]. In Burkina Faso, 11,656,675 malaria cases were recorded, including 539,488 severe cases, and 4,243 deaths. Out of them, 2,925 were children under the age of 5 [2]. Malaria is highly seasonal in Burkina Faso, with transmission peak occurring during the rainy season [3]. To reduce the burden of malaria, the country has adopted several control measures, including the seasonal malaria chemoprevention (SMC) in children under five years old. SMC consists of the administration of a full treatment course of amodiaquine + sulfadoxine-pyrimethamine (AQSP) to children aged 3–59 months on a monthly basis during the high transmission season regardless of the children malaria infection status [4–6]. SMC was introduced in Burkina Faso in 2014 in only 7 health districts but was expanded later to all health districts in 2019 [7]. SMC is implemented from June /July to October in Burkina Faso. Since its implementation, SMC has been found to be effective in reducing malaria prevalence and anemia, as well as fever episodes [8–10]. However, one of the drawbacks with antimalarial therapy is the selection of resistant parasites [3]. In fact drug pressure eliminates sensitive parasites, while less sensitive parasites survive and spread [11, 12]. Many drug pressures have likely been involved in the selection of resistant parasites, but mass administration of antimalarial drugs was probably the most important [13]. Prophylactic or therapeutic failures resulting from this selection can lead to a re-emergence of malaria, with an increase in transmission, morbidity and mortality, particularly among children under 5 years [12]. Consequently, AQSP use during SMC, which is a kind of mass treatment targeting the specific age groups of children under 5 years of age, could induce the selection of specific sensitive reduced strains which could compromise the long-term effectiveness of the intervention [14, 15]. Resistance to antimalarial drugs results from spontaneous mutations altering both structure and molecular activity of the antiplasmodial drugs target or preventing the drug from reaching their target [16]. Single-nucleotide polymorphisms (SNPs) in the P. falciparum multidrug resistance gene 1 (Pfmdr1), which codes for the P-glycoprotein transporter (Pgh-1), have been implicated in antimalarial drug resistance [17]. Out of various Pfmdr1 SNPs reported so far, N86Y, Y184F, S1034C, N1042D and D1246Y are the most frequent [18]. These SNPs are associated with tolerance or sensitivity modulation of parasites to specific antimalarial drug [19–22]. Codons N86Y, Y184F and D1246Y are associated with changes in sensitivity to lumefantrine (LU) and amodiaquine in sub-Saharan Africa [23]. Previous studies have concluded that asparagine substitution for tyrosine in codon 86 (N86Y) (most common mutation in Asian and African parasites) and tyrosine substitution for phenylalanine in codon 184 (Y184F) are associated with AQ resistance [24–26]. Few studies conducted in the early stage of SMC implementation in Burkina Faso addressed the selective impact of this intervention which have reported no significant effect of this intervention for selecting specific alleles in P. falciparum resistance genes [3, 9, 27]. Given the fact that this intervention is widely applied at national level, further monitoring is crucial to early detect any emergence and spread of potentially resistant parasites. Therefore, this study aimed to assess the impact of SMC on the distribution of Pfmdr1 N86Y and Y184F alleles in the Nanoro area of Burkina Faso.

Materials and Methods

Study Site

The study carried out in the health district of Nanoro in Burkina Faso. Nanoro is a rural area in the central part of Burkina Faso located at about 85 km from Ouagadougou, the capital city. The climate is Sudano-Sahelian with two (02) seasons: a rainy season occurring from May to October, during which the water reservoirs are supplied and become the preferred place for the proliferation of malaria vectors, and a dry season from November to April, characterized by the harmattan, and during which there is an increase of respiratory infections. Therefore, malaria transmission is seasonal, making the area an ideal place for SMC intervention which was implemented since 2016 in the area [28, 29]. In the health district of Nanoro, SMC campaign is applied yearly from July to October on a monthly basis i.e. 4 rounds/per year. Plasmodium falciparum represents the major species (90%), followed by Plasmodium malariae (3–8%) and Plasmodium ovale (0.5-2%) [30].

Source of Samples

The dried blood spots (DBS) used for this study were collected in June and in November 2021. These samples were collected from children under SMC coverage and enrolled in the control (no intervention) arms of two independent studies: SMC-NUT trial (NCT04238845) for samples collected in June and the SMC-RST trial (NCT04816461) for samples collected in November. The SMC-NUT trial aim was to demonstrate that SMC + nutritional supplement arm was superior to the SMC alone arm in reducing the incidence of malaria. It ran for one year, from July 2020 to June 2021. The SMC-RST aim was to demonstrate that SMC for children between aged 3–59 months associated with the screening household members and treatment if proven positive is superior to SMC alone in reducing the incidence of clinical malaria after 1 year. It began in July 2021, one month after the SMC-NUT project, and ran for two years. During each of these trials, samples were taken from the children under SMC coverage for RDTs, smears and thick drops for microscopy and dried blood spots for pcr analyses. Samples for the SMC-NUT trial were taken during the first 6 months and the last month of the study which corresonponded to June. The June samples were considered as the samples before 2021 SMC campaign. For the SMC-RST trial, samples were taken during the first 6 months of the study in 2021. The samples collected in November were considered as samples after 2021 SMC campaign. The dried blood spots were first placed in pcr bags, either individually or in batches, taking care to add silica gel. They were then stored in boxes and kept at room temperature in the laboratory. In this study, we analysed systematically all positives samples in June and selected equal number in November. The usability of the samples was not verified. Description of the protocols of these two studies was previously detailed elsewhere [31, 32].

Haemoglobin and Body Temperature

Haemoglobin (Hb) level was measured using Hemocue^® 801+ (SOC-HE121916, Danayer group, Angelholm, Sweden). Blood obtained by pricking the fingertip was drawn into a microcuvette, which was inserted into the analyser and the Hb value in g/dL was immediately read. Contactless clinical thermomether (Microlife NC 200, Switzerland) was used for the measurement of body temperature. The measurement in degrees Celsius was taken automatically as soon as a correct distance of 1 to 5 cm from the forehead was recognized.

Determination of Parasite Density

Malaria slides were prepared from peripheral blood obtained from finger prick and were stained with 3% Giemsa for 30 min. Slides were double read using Olympus CX21 microscope (Olympus Corporation, Tokyo, Japan) for the detection of asexual forms against 200 white blood cells and a negative result were declared after examination of 100 microscopic fields. When counting was completed, the number of parasites density was calculated and expressed as ‘parasites per microliter of blood’ from the mathematical formula: Number of parasites counted x 8,000/ Number of white blood cells = parasites per microliter: (200). If > 10 parasites/100 fields were observed when counting 200 WBCs, the formula based on 200 WBCs was applied. If < 9 parasites/100 fields were observed when counting 200 WBCs, then the count should be continued until reaching 500 WBCs and apply the formula based on 500 WBCs. If more than 500 parasites were observed without counting 200 WBCs, the count was stopped when the reading of the last field was completed and the parasitemia was calculated according to the previous formula. Final parasite density represented arithmetic mean of the two readings. Then, the results of the two readings are subject to a quality control check. A third reader was appealed in case of discrepancy (difference between the results of the two readers) defined as follow: (i) difference in Plasmodium species identification; (ii) positive result for one of the readers and negative for the other; and, (iii) if the higher count divided by the lower count was ≥ 2 and then the two closest were considered among the three readings leaving the extremely highest or lowest reading was considered as wrong reading.

Molecular Analysis

Plasmodium DNA was extracted from DBS using the tween^®20-chelex^®100 resin method [33]. Briefly, a portion of approximately 3 mm of each DBS was cut and introduced into a 1.5 ml Eppendorf tube into which was added 1 ml of 0.5% Tween^®20 -PBS (phosphate-buffered saline)1X. This was followed by incubation at 4 °C for at least 12 h and the supernatant was thereafter discarded. After adding 1x PBS to each tube and incubation at 4 °C for 30 min, the supernatant was aspirated and discarded. Furthermore, 150µL of 10% Chelex^® 100 resin solution was added to each tube followed by 10 min incubation at 95 °C. During this incubation period, each tube was briefly vortexed (5 to 10 s) at least twice. Finally, the tubes were centrifuged at 13,200 rpm for 5 min and the clear supernatant containing the extracted DNA was collected and transferred to DNA storage tubes for immediate use or stored at -20 °C for later analyses.

Single nucleotide polymorphism in codons 86 and 184 genes were detected by nested PCR followed by restriction enzyme digestion as previously described [14, 34]. A primary and nested PCRs were amplified by adding a 2 µl DNA template to a 23 µl reaction mix (here the mix reagents: 14.5 µl of DNA water free, 6.5 µl of master mix ready to load (SOLIS BIODYNE, Estonia) and 1 µM of each primer). The first primer pair for primary PCR was MDR-A1 (5’-TGT-TGAAAGATGGGTAAAGAGCAGAAAGAG-3’) and MDR-A3 (5’-TAGTTTCT-TATTACATATGACACCACAAACA-3’). The second primer pair for secondary PCR was MDR-A2 (5’GTCAAACGTGCATTTTT-TATTAATGACCATTTA-3’) and MDR-A4 (5’AAAGATGGTAACCTCAGTAT-CAAAGAAGAG-3’). Thermal cycling conditions for the two amplifications were as follows: Primary denaturation was conducted for 3 min at 94 °C followed by 30 cycles of denaturation at 94 °C for 1 min, annealing at 56 °C for 1 min and extension at 72 °C for 45 s. A final extension steps was carried out at 72 °C for 5 min and the PCR products were hold at 4 °C. The nested PCR round used the product of the primary PCR as DNA template. Digestion for Pfmdr1 N86Y was done using AfIII restriction enzyme (New England Biolabs Inc., Ipswich, Massachusetts, USA) at 37 °C for 1h30mins. Wild type isolates yielded a single band of 560 bp while two bands of 227 Pb and 333 Pb were observed for the mutant type after revelation on 2.5% ethidium bromide stained agarose gel. Digestion for Pfmdr1 F184Y was done with the restriction enzyme DraI (New England Biolabs Inc, Ipswich, Massachusetts, USA) at 37 °C for 15 h. Two bands of 204 Pb and 242 Pb were observed in wild type parasite strains while three bands of 114 Pb, 173 Pb and 242 Pb were observed for the mutant type. P. falciparum clone 3D7 was used as the wild type control for both codons, while clone Dd2 and HB3 were used as the mutant control for codon 86 and 184 respectively. The electrophoresis was run at 100 volts for 01 h. Gel imaging was carried out using a trans ultraviolet illuminator (Biotec-Fischer GmbH Daimlerstraße 6 35447 Reiskirchen, Germany). The results were then classified as wild type, mutant or mixed (when both alleles were present). Cases of mixed infection were classified as mutant.

Statistical Analysis

A descriptive analysis using proportions, means (standard deviation) and median (Q1– Q3) for qualitative and quantitative variables, respectively was performed. Geometric means and the corresponding 95% confidence interval were used to describe parasite density. Chi-square test or Fisher’s exact test were used to compare the proportions of different alleles before and after SMC intervention. In addition, the effect of age, sex, time period (before and after SMC campaign) and parasite density (< 1,000/µl and ≥ 1,000/µl) on Pfmdr1 N86Y and Y184F mutations was assessed using binomial generalized linear models. To this end, a univariate logistic regression was performed using the aforementioned variables, and variables with a p-value of less than 0.2 were included in the multivariate analysis. Data were analyzed using Stata IC version 14 (Stat Corp, College Station, Texas, USA) software and a p-value < 0.05 was considered statistically significant.

Results

The baseline characteristics of the SMC participants are summarized in Table 1 (Table 1). Before the 2021 SMC campaign, the median age was 3 years (2.35–4.24) and after the SMC campaign, it was 2.71 years (1.70–3.69). Females represented 56% (56/100) of the participants before SMC and 49% (49/100) after the SMC campaign. The mean hemoglobin levels observed were 9.89 g/dl (± 1.59) and 9.16 g/dl (± 1.63) before and after SMC respectively. The geometric means of parasite density was 1098.7/µl (95%CI = 834.8-1145.9) and 1753.5/µl (95%CI = 1135–2708) before and after SMC respectively.

Table 1.

Baseline characteristics of participants and samples

Variable	Before SMC	After SMC
	n = 100	n = 100
Median age (years) (Q1-Q3)	3 (2.35–4.24)	2.71 (1.70–3.69)
Gender, n (%)
Female	56 (56.00)	49 (49.00)
Male	44 (44.00)	51 (51.00)
Hemoglobin mean(g/dL) (SD)	9.89 (1.59)	9.16 (1.63)
Temperature mean (°C) (SD)	36.8 (0.83)	36.7 (0.69)
Geometric mean of parasite density (95%CI)	1098.7 (834.8-1145.9)	1753.5 (1135–2708)

SMC, Seasonal malaria chemoprevention; n, sample size; SD, standard deviation; CI, confidence interval

Prevalence of Pfmdr1 86 and 184 Alleles Before and After the SMC Campaign

Genotyping of the Pfmdr1 gene was successful on 92% (92/100) and 98% (98/100) of samples, respectively before and after SMC campaign respectively. Table 2 shows the prevalences of the different alleles in point 86 and 184 of the Pfmdr1 gene (Table 2). No significant change of the prevalence of Pfmdr1 N86Y mutation was observed before and after the SMC campaign. Wild-type N86 allele was highly and comparably prevalent either before 97.83% (90/92) or after 93.88% (92/98) SMC campaign (p = 0.28). The mutant 86Y allele was observed at low prevalences representing only 2.17% (2/92) and 6.12% (6/98) before and after SMC campaign respectively. In contrast, significant reduction of the prevalence of the Y184 allele was observed following the SMC campaign. This prevalence was 52.17% (48/92) prior to the SMC campaign and went down to 36.73% (36/98) (p = 0.03) after SMC campaign (Fig. 1). For the 184 F allele, the prevalences before and after SMC campaign were 47.83% (44/92) and 63.27% (62/98) respectively. The N86/Y184 haplotype was present at prevalences of 52.17% (48/92) and 36.73% (62/98), before and after SMC campaign respectively and the difference was statistically significant (p = 0.03). Another haplotype, the N86/184F, had prevalences of 45.65% (42/92) and 57.14% (56/98). The double mutant haplotype 86Y/184F was also found at prevalences of 2.17% (2/92) and 6.12% (6/98). However, the 86Y/Y184 haplotype was not found.

Table 2.

Period-based comparison of Pfmdr1 alleles and haplotypes

	Before SMC		After SMC		p-value
	n/N	%	n/N	%
N86	90/92	97.83	92/98	93.88	0.28
86Y	2/92	2.17	6/98	6.12
Y184	48/92	52.17	36/98	36.73	0.03
184 F	44/92	47.83	62/98	63.27
N86/Y184	48/92	52.17	36/98	36.73	0.03
N86/184F	42/92	45.65	56/98	57.14	0.11
86Y/184F	2/92	2.17	6/98	6.12	0.28
86Y/Y184	0/92	0	0/98	0	-

n, number of mutant or wild-type alleles; N, number of samples successfully genotyped

p-values are based on the chi-square test or the Fischer Exact test

Fig. 1.

A Distribution of alleles in point 86 and 184 of Pfmdr1 gene before and after SMC campaign. A Prevalence of Pfmdr1 N86Y before and after SMC campaign, green color represents the wildtype allele N86 and orange color represents the mutant allele Y86. B Prevalence of Pfmdr1 Y184F before and after SMC campaign, pink color represents the wildtype allele Y184 and violet color the mutant allele F184

Relationship Between Parasite Density and Mutations in Points 86 and 184 of the Pfmdr1 Gene

Table 3 presents the results of the multivariate analysis of the relationship between the Pfmdr1 N86Y mutation and parasite density (Table 3). The occurrence of mutations in Pfmdr1 N86Y had an influence on parasite density. Mutant Pfmrdr1 86Y allele trends to exhibit higher parasite densities compare wild type Pfmdr1 N86 allele (p = 0.04).

Table 3.

Multivariate analysis of the relationship between parasite density and Pfmdr1 N86Y mutation

Variables	Crude OR (CI à 95%)	p-value	Adjusted OR (CI à 95%)	p-value
Age (years)	1.18 (0.66–2.09)	0.58
Gender
Female	1
Male	1.94 (0.45–8.38)	0.37
Period
Before SMC	1		1
After SMC	2.93 (0.58–14.93)	0.19	3.80 (0.73–19.83)	0.11
Parasite density
Low (< 1,000/µl)	1		1
Moderate to high (≥ 1,000/µl)	0.22 (0.04–1.11)	0.07	0.19 (0.04–0.98)	0.04

OR, odds ratio

Unlike point 86 mutation in Pfmdr1 gene, no effect of mutation Y184 F on parasite density was observed (Table 4).

Table 4.

Multivariate analysis of the relationship between parasite density and Pfmdr1 Y184F mutation

Variables	Crude OR (CI 95%)	p-value	Adjusted OR (CI à 95%)	p-value
Age	0.83 (0.66–1.04)	0.11	0.85 (0.67–1.08)	0.20
Gender
Female	1		1
Male	1.58 (0.89–2.83)	0.12	1.57 (0.87–2.83)	0.13
Period
Before SMC	1		1
After SMC	1.88 (1.05–3.35)	0.03	1.71 (0.94–3.10)	0.08
Parasite density
Low (< 1,000/µl)	1
Moderate to high (≥ 1,000/µl)	0.82 (0.45–1.48)	0.51

OR, odds ratio

Discussion

The results of this study show an increase in the prevalence of the Pfmdr1 86Y mutant allele in the period before and after the season of SMC, although this increase is not statistically significant. A similar trend was reported in studies conducted in Burkina Faso in 2014 by Somé et al. (29.1–37.5% ) and in 2023 by Compaoré et al. (1.4–2.2% ) [3, 27]. This suggests the absence of strong selection pressure of the 86Y mutant allele by Amodiaquine during SMC campaign. Besides AQSP used in SMC intervention, other antimalarials specially those used as first line treatment could influences the prevalence of these alleles. In Burkina Faso, the withdrawal of chloroquine since 2005 and the progressive removal of Artesunate -Amodiaquine since the implementation of SMC in 2014 from the list of first line treatments of uncomplicated malaria in children has undoubtedly led to the re-emergence of parasites sensitive to aminoquinolines [35–38]. AQ is a 4-aminoquinoline similar to chloroquine (CQ), and therefore the mode of action of the two drugs appears to be similar [39]. In addition, a positive correlation between the IC50s of AQ and CQ was reported in the ex-vivo study conducted by Tinto et al., suggesting the existence of cross-resistance between the two drugs [40]. Current first line treatment of uncomplicated malaria include AL (widely used) and recently Dihydroartemisinin-Piperaquine (DHAPPQ) and Pyronaridine-Artesunate (PA) [41]. The low prevalence of the mutant alleles reported in this study may simply reflect the relatively common use of AL (Artemether-lumefantrine) as first-line drug for the treatment of uncomplicated malaria in Burkina Faso. Indeed, some studies have shown that lumefantrine selects positively the wild N86 allele in contrast to Amodiaquine which selects positively the mutant allele 86Y [14, 42–45]. However, the prevalences of this mutation in this study were relatively lower than those obtained in previous study conducted in the Houet province of Burkina Faso by Somé et al. in 2014 (29.1–37.5% ) [3]. A decline in prevalence of the mutant allele 86Y over time was observed and this may be attributable to the change in treatment policy as suggested previously [14]. The difference in malaria epidemiological facies between the two studies could also explain why prevalence is higher in the Somé et al. compared to that of our study. Indeed, malaria transmission is hyper-endemic in Nanoro [31, 46] while Somé and al study was conducted in holoendemic malaria transmission area [47, 48]. Studies have shown that when transmission is high, multiple infections predominate which may lead to the spread of mutant parasites [49]. As transmission is intense in the holoendemic zone, with a longer seasonal peak than in the hyper-endemic zone [50, 51], the high prevalence of mutants in the Somé et al. study compared with those of our study could be justified. Studies in Uganda have reported a significant decrease in the Pfmdr1 86Y mutant allele in the period before and after SMC campaign, (4.84–0.79% ) for the first study, and ( 5–1.8%) for the second [52, 53]. However, in the area where the Uganda studies were carried out, SMC is applied yearly from May to October in 5 rounds compared to Nanoro where SMC is applied from July to October in only 4 rounds. This decreasing trend in the Pfmdr1 N86Y mutation in Uganda compared unlike this study could be related to the counter-selection exerted by lumefantrine on this mutation [44, 54]. Indeed, in Uganda, AL is the national treatment regimen and its widespread use has increased both the prevalence and frequency of wild Pfmdr1 N86 alleles and in parallel the increase in Pfmdr1 Y184F mutant alleles, thus being accompanied by a decrease in lumefantrine sensitivity and an increased sensitivity to aminoquinolines over time [55]. The small-scale use of AS/AQ (Artesunate/Amodiaquine) in Uganda as an alternative first-line drug for the treatment of uncomplicated malaria could also partly explain this decline in the prevalence of mutant alleles [56].

Of particular interest, patients harboring Pfmdr1 86Y allele exhibited higher parasite densities compare to patients with Pfmdr1 86 N allele. This may suggest that the spread of these strains could be perceived as a threat for malaria control as increasing parasitemia is a severity criterion of malaria infection. Opposite trend was reported in Saudi Arabia, Uganda and Tanzania [57–59]. Although causality has not yet been established, it would appear that the association between the Pfmdr1 86Y mutation and the level of parasitemia is linked to the multiplicity of infections and the virulence of the parasite. Indeed parasites with reduced virulence are thought to carry the Pfmdr1 86Y mutant allele [55, 57, 58, 60]. As this allele is associated with resistance to AQ, the spread of this mutation could affect the impact of SMC intervention and this would increase malaria related-morbidity and mortality, particularly in children under 5 years old.

The prevalence of the 184 F mutant allele increased significantly before and after SMC campaign suggesting a selective pressure of SMC intervention for this particular 184 F allele. These results are consistent with those of other studies conducted in Burkina Faso (55.3–58.3% ) and Uganda (3.23–85.61% ) for the first study and (41–53%) for the second [27, 52, 53]. However, the role of the 184 F mutation in resistance to AQ remains unclear [52]. Some studies have shown that this allele has a limited effect on its own, but its presence with the wild-type N86 allele is known to be associated with reduced sensitivity to certain antimalarial drugs [26, 61]. Other studies suggest that the use of CQ has selected different haplotypes of Pfmdr1 at different locations, depending on the predominant alleles of Plasmodium falciparum CQ resistance transporter (Pfcrt) and other genes present in these populations [62]. Analysis of the genetic diversity around the Pfmdr1 gene could provide evidence that a selective sweep has favoured the spread of alleles, as it has been shown for the Plasmodium falciparum dihydrofolate reductase (Pfdhfr ) and Plasmodium falciparum dihydropteroate synthetase (Pfdhps) alleles associated with resistance to SP [62, 63]. The involvement of this mutation therefore worths particular attention with focus on its relationship with SMC intervention [14, 64].

An increase in the prevalence of the N86/F184 haplotype was also observed though this was not statistically significant. This haplotype is known to be associated with reduced sensitivity to lumefantrine [65, 66]. An increase in the prevalence of 86Y/F184 double mutant haplotype before and after SMC campaign was observed, but this increase was not statistically significant. Interestingly, the 86Y/Y184 haplotype which is known to be associated with AQ resistance in Africa [62, 67] was absent and this supports the efficacy of SMC intervention in Nanoro area.

A limitation of this study is the number of samples genotyped which was limited to microscopy positive samples with the potential of missing rare haplotypes from sub-patent parasitaemia. Another limitation of this study is the short duration of the study, allowing only the assessment of the impact of SMC over a single yearly campaign while the selective pressure could be very apparent with the repetition of the number of SMC campaigns over time.

Conclusions

This study reported a lower prevalence of N86Y mutation with no significant change in the period before and after the 2021 SMC campaign. However, patients harboring Pfmdr1 86Y allele exhibited higher parasite densities compare to those with Pfmdr1 86 N allele. Importantly, the prevalence of the 184 F mutant allele increased significantly in the period before and after SMC campaign suggesting a selective pressure of SMC intervention for this particular 184 F allele. This selective pressure needs to be closely monitored in order to preserve the efficacy of this intervention for a long-term period in Burkina Faso.

Author Contributions

SP, KB, RT, TC, BS, and TH implemented the study and supervised fieldwork, MS, KB, SP, CE, KC, KE, DK, BI, and RT, contributed to data management, MS, KA, SP, RT, BI contributed in statistical analysis, MS, SP, KB, KA, BS, TH, contributed in drafting the manuscript and all authors read and approved the manuscript.

Funding

This study was supported by Expertise France L’Initiative.

Data Availability

No datasets were generated or analysed during the current study.

Declarations

Consent for Publication

Not applicable.

Competing Interests

The authors declare no competing interests.