Background
Plasmodium falciparum is the deadliest species of human malaria parasites (99.7%) and the most prevalent in Africa, responsible for 92% of malaria cases and 93% of death caused by malaria [
1]. A better understanding of malaria epidemiology could be helpful for disease control and prevention. The extent of genetic diversity and multiplicity of infection (MOI) are essential to understand malaria epidemiological patterns, providing insight into the dynamics of malaria transmission, human exposure to mosquito bites, acquisition of malaria immunity [
2‐
5] and assessing of malaria control interventions [
6‐
8].
Plasmodium falciparum merozoite surface protein 1 (PfMSP1) and merozoite surface protein 2 (PfMSP2) are polymorphic antigens which have been extensively studied in malaria molecular epidemiology [
9‐
11]. Polymerase chain reaction (PCR)-based genotyping of the polymorphic regions of the corresponding genes (block 2 of
Pfmsp1 and block 3 of
Pfmsp2) can be used to assess the allelic diversity and to determine MOI [
12]. Therefore, many previous studies have used these two markers in order to evaluate genetic diversity, population structure and MOI [
13‐
16]. However, these PCR-based methods may underestimate the extent of allelic diversity in co-infected hosts due to the use of size-polymorphisms to infer alleles; a particular problem when using short fragment amplification [
17‐
19]. Microsatellite typing is an alternative tool to assess genetic diversity but might fail to detect many minor clones as it requires a cut-off of 33% of the predominant peak for a minimal peak height [
20‐
22]. Similarly, high resolution melting (HRM) has been used for genotyping of single nucleotide polymorphisms (SNP), but shows limitations in the detection of indels [
23]. Advances in next generation sequencing (NGS) technologies and bioinformatic analysis has enabled the accurate detection of clones and even minor clones, overcoming the limitations of these other methods [
24]. These techniques provide a more accurate estimation of MOI compared to the standard PCR-based methods [
25,
26] and accurate assessment of genetic diversity in
P. falciparum populations [
27]. For example, amplicon deep sequencing of
Pfmsp1 from 222 samples collected in Ethiopia revealed 307 haplotypes out of which 99 were predominant haplotypes (the clone had the highest frequency within infection). In addition, a mean MOI of 2.68 was found by this study [
24].
In Senegal, most of the studies about
P. falciparum genetic diversity and malaria molecular epidemiology [
7,
8,
28‐
30] have been carried out in the Thies region, a low malaria endemicity area. Thus, limited information on malaria molecular epidemiology are available for other areas of the country and further detailed studies are needed to understand the genetic diversity and population structure country-wide. Here, multiplexed amplicon deep sequencing
of Pfmsp1 and
Pfmsp2 genes in
P. falciparum isolates sampled from a malaria hyper-endemic area in the South (Kedougou region) and a pre-elimination areas in the North (Podor and Matam) was performed. This data was used to understand the population structure, to estimate allelic diversities and MOI, and to understand the population dynamics, gene flow and evolutionary pattern of the malaria parasite
P. falciparum in Senegal.
Discussion
A better understanding of
P. falciparum population structure could help to improve the local monitoring of parasite transmission, particularly in areas where
P. falciparum genetic diversity has been poorly documented. Therefore, the aim of this study was to report the genetic diversity of
P. falciparum and parasite population structure by performing multiplexed amplicon deep sequencing of
Pfmsp1 and
Pfmsp2 from two understudied areas in Senegal with significantly different endemicity, the Southern (malaria hyper-endemic) and Northern (malaria pre-elimination) areas. A high degree of polymorphism of the genes
Pfmsp1 and
Pfmsp2 was found in Senegal. A total of one hundred thirty-five different alleles were identified; 56
Pfmsp1 alleles (26 K1-like, 14 MAD20-like and 16 RO33-like) and 79
Pfmsp2 alleles (54 IC3D7-like and 25 FC27-like). These results are consistent with a previous observation of high levels of polymorphism in both
Pfmsp1 (8K1-like, 14MAD20-like, 6 RO33-like) and
Pfmsp2 (41 IC3D7-like, 18 FC27-like) using an amplicon deep sequencing technique in Myanmar [
44]. Similar results have been also reported by Aspeling-Jones et al. who identified 225 different K1-like, 123 different MAD20-like and 9 distinct RO33-like in Africa and Asia [
45]. Most of these alleles were detected in the South, hyper-endemic area, 33 and 53 distinct alleles were observed for
Pfmsp1 (18 K1-like, 8 MAD20-like, and 7 RO33-like) and
Pfmsp2 (35 IC3D7-like and 18 FC27-like), respectively. Comparable results from elsewhere in sub-Saharan Africa (Gabon and Ivory-Cost) reported by Yavo et al
. underlined the same diversity with 27 K1-like, 22 MAD20-like and 18 RO33-like alleles for
Pfmsp1 and 28 IC3D7-like and 20 FC27-like for
Pfmsp2 [
16]. In areas with high malaria transmission intensity the probability of genetic recombination between circulating strains is high and could affected the variation, the numbers and arrangements of amino acid repeats units of
Pfmsp1 and
Pfmsp2, which are the main factors driving the increased allelic diversity [
44,
46‐
48]. It is known that a large number of mutations in parasite populations arms the organism to successfully battle out adverse environmental conditions through adaptation. Indeed, it has been shown that
P. falciparum genetic diversity is indicative of the ability of malaria parasites to adapt to their hosts by selection of advantageous traits, such as drug resistance and antigenic variability [
49].
For
Pfmsp1, despite a haplotype diversity of Hd = 0.76 in the North, the negative value of TD suggests there is clonal expansion of the parasite. A similar finding was also reported by the team of Daniels et al
. in 2013 in Thiès, using a 24 SNP molecular barcode approach [
30]. This situation suggests a high self-fertilization rate between genetically identical parasites during the sexual stages in the mosquito in these areas where malaria transmission is declining. However, in the South the
P. falciparum population exhibits a high Hd (Hd = 0.93) and a TD value significantly different from expected under the neutral model of molecular evolution (D = 2.0453). This variability in parasite population genetics observed across the two regions reflected the difference in malaria transmission intensity reported in these areas [
2]. These findings suggest that the genetic diversity of
P. falciparum is greater in high malaria transmission areas and decreases when transmission regresses [
2,
4]. The neighbour-joining tree of different allele types revealed two parasite clusters in the South as in the North and many clones sharing same patterns in amino acid level showing parental link between strains. Finally, the low Fst value of
Pfmsp1 between parasite population from the South and the North indicates the presence of gene flow between these populations, likely facilitated by extensive human migration events between regions causing the vector’s displacement.
For
Pfmsp2, three clusters with a high Hd and π were observed associated with a balancing selection (Positive TD value) in each area and an almost panmictic parasite population (low Fst and genetic relatedness) between the two studies sites. As widely known, malaria parasites use genetic diversity as an escape measure either from human immunity or treatment by anti-malarials [
50,
51]. Therefore the high genetic diversity of parasites found in these areas is alarming. Indeed, a larger number of different allele types in the parasite population may be able help parasites more easily adapt to the environmental conditions such as malaria control measures [
52,
53]. In addition, the low genetic differentiation between the parasite populations from the two study sites indicates a gene flow within the population probably deriving from the high mobility of infected human hosts. This situation has been observed to cause malaria outbreaks in malaria residual foci (unpublished data) and a rebound of
P. falciparum genetic diversity in the low malaria transmission region of Thies, as previously reported [
7]. Such events could be a major challenge for malaria control and elimination in Senegal. Multiclonal infections were higher in the South, suggesting that this is more common in areas of high malaria endemicity. This is consistent with previous observations for high transmission areas, such as in Ethiopia [
24]. Allelic polymorphism was also higher in the South, which could increase the likelihood of recombination (genetic crossing-over) in these areas [
46]. Recombination can generate novel parasite variants, giving rise to new genetic diversity that may exhibit phenotypic differences such as virulence, drug resistance or immune evasion [
54,
55]. MOI is an indicator of malaria transmission level because: it has been found to be higher in high malaria transmission areas and decreases when transmission declines [
2,
56‐
61]. MOI mean was significantly higher in the South than in the North (
p =
0.001), suggesting that malaria transmission remains still active in this area. Moreover, the low mean MOI found in the North (1.76), may suggest a decline in malaria transmission levels, underlining the effectiveness of the scale-up in malaria control measures since 2006 [
32].
This study was carried out from malaria symptomatic patients. As known, in endemic areas asymptomatic malaria or sub-microscopic infections is thought to represent the majority of the infections [
62]. Therefore, missing data about parasites genetic diversity from these asymptomatic patients may constitute a limitation of this study. Despite this limitation, the results of this study contribute insight into the genetic diversity of the
P. falciparum population in two malaria endemic areas in Senegal. In addition, this study argues that multiplexed amplicon deep sequencing represents an important advance for surveillance of parasites populations within the country. Although this approach is currently more expensive and longer per-sample that standard genotyping methods it offers several advantages, especially in the ability to scale to large numbers of samples as will be needed for large-scale disease surveillance. The costs of sequencing are also rapidly declining and methods are becoming faster and more portable. Finally, this method allows the sequencing of several genes of interest in one run and can be readily adapted to other genes as the sequencing of anti-malaria resistance markers.
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