Genetic Diversity and Population Structure of Plasmodium Falciparum in Nigeria: Insights From Microsatellites Loci Analysis


 BackgroundMalaria remains a public health burden especially in Nigeria. To develop new malaria control and elimination strategies or refine existing ones, understanding parasite population diversity and transmission patterns is crucial. MethodsIn this study, we characterized parasite diversity and structure of Plasmodium falciparum isolates from 633 dried blood spot samples in Nigeria, using 12 microsatellite loci of P. falciparum. These microsatellites were amplified via semi-nested polymerase chain reaction (PCR) and fragments were analyzed using GeneMapper and GENALEX 6.5.ResultsEstimates of parasite diversity such as Mean complexity of infection (range: 1.71-2.66) and Expected heterozygosity (range: 0.76-0.82) were high, while parasite population sub-structuring was low (Analysis of molecular variance= 0.039, Fixation index= 0.038 and Linkage disequilibrium= 0.0219). Conclusion We conclude that the high level of genetic diversity and low population structuring in this study suggests that parasite populations circulating in Nigeria are homogenous. This implies that a uniform control strategy will be effective across the six geographical zones of Nigeria. The results obtained can be used as a baseline for parasite diversity and structure, aiding in the formulation of appropriate therapeutic and control strategies in Nigeria.

License:   This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Abstract Background Malaria remains a public health burden especially in Nigeria. To develop new malaria control and elimination strategies or re ne existing ones, understanding parasite population diversity and transmission patterns is crucial.

Methods
In this study, we characterized parasite diversity and structure of Plasmodium falciparum isolates from 633 dried blood spot samples in Nigeria, using 12 microsatellite loci of P. falciparum. These microsatellites were ampli ed via semi-nested polymerase chain reaction (PCR) and fragments were analyzed using GeneMapper and GENALEX 6.5.

Results
Estimates of parasite diversity such as Mean complexity of infection (range: 1.71-2.66) and Expected heterozygosity (range: 0.76-0.82) were high, while parasite population sub-structuring was low (Analysis of molecular variance= 0.039, Fixation index= 0.038 and Linkage disequilibrium= 0.0219).

Conclusion
We conclude that the high level of genetic diversity and low population structuring in this study suggests that parasite populations circulating in Nigeria are homogenous. This implies that a uniform control strategy will be effective across the six geographical zones of Nigeria. The results obtained can be used as a baseline for parasite diversity and structure, aiding in the formulation of appropriate therapeutic and control strategies in Nigeria.

Background
Although the incidence of malaria infections and malaria-associated mortality has reduced in many African countries [1,2,3], transmission continues in endemic regions despite intensi ed efforts towards prevention, control and eradication [4,5]. This is due, in part, to the high genetic diversity of Plasmodium falciparum that contributes to increased transmission rate and spread of resistant parasites [6]. Therefore, understanding the extent of genetic diversity, transmission intensity, and parasite population structure in Nigeria -the most malaria burdened country, is essential if the goal of malaria control or elimination is to be achieved.
Molecular techniques play important roles in the analyses of genetic diversity, transmission dynamics, and population structure of P. falciparum eld isolates. Early molecular studies focused mostly on the use of polymorphic markers such as merozoite surface protein 1 (msp-1) and merozoite surface protein 2 (msp-2) and glutamate-rich protein (Glurp) to characterise falciparum genetic diversity and structure in Nigeria [7,8,9]. These markers were also useful in monitoring drug e cacy with regards to classi cation of recurrent falciparum parasitaemia as re-infection or recrudescent infection [6,10,11]. However, there have been contrasting reports of polymorphisms in msp-1 and msp-2 in earlier studies in Nigeria [6,12,13,14] which is associated with the fact that these antigenic markers are often under intense immune pressure [15,16,17]. The genotyping results provided by these markers can therefore potentially lead to a masked and distorted view of the population structure and transmission patterns which may account for observed variations across parasite populations circulating in a given environment [6].
Microsatellites have been suggested to be better alternatives to msp-1, msp-2 and GLURP due to their abundance, putative neutrality and higher levels of polymorphisms [18]. This molecular technique remains one of the most e cient and reliable methods for analyzing the genetic diversity data of falciparum populations for epidemiological and drug e cacy purposes within countries and across continents [19]. In past studies of using microsatellite analyses, it was observed that parasites from areas of low malaria transmission [19] (<1% infection) have less genetic diversity but more population structure and greater linkage disequilibrium (i.e. more non-random association among alleles across multiple loci) [4,19,20,21]. Contrary, in regions of high malaria transmission, individuals are more likely to be infected by more than one P. falciparum parasite thereby resulting in an increase in the rate of recombination and subsequently, high diverse population with low linkage disequilibrium [18,19,22]. Although, some studies report a deviation from the norm whereby high levels of heterozygosity (a measure of genetic diversity) is observed in several low transmission countries [18,23,24]. This suggests that a high level of heterozygosity may re ect past human demographic processes as opposed to recent epidemiological factors [25].
The objective of this study was to investigate the genetic diversity of circulating Plasmodium falciparum parasites and their population structures in Nigerian children 6-96months old with uncomplicated infections, treated with artemisinin-based combination therapies (ACTs).

Sample collection
Two to three drops of nger-pricked blood samples were blotted on 3mm Whatman lter paper (Whatman International Limited, Maidstone, United Kingdom) before treatment initiation (Day 0). The blood samples impregnated on to lter papers were allowed to air-dry properly at room temperature, and dry blood spots (DBS) were kept in airtight envelopes with silica gel until analysed.
DNA extraction DNA was extracted from DBS for parasite genetic diversity and population structure studies as previously described [26]. DNeasy Blood and Tissue extraction kit (Qiagen, Germany) was used to extract parasite DNA from DBS following the manufacturer's protocol.
Peakscanner (Applied Biosystems) and GeneMarker (Softgenetics) software were used for normalization across runs and automatic determination of allele length and peak heights in samples containing multiple alleles per locus. Minor alleles were scored when the minor peaks were ≥ 20% the height of the predominant allele in the isolate and with a relative uorescent unit of at least 100.

Data analysis
Measures of parasite genetic diversity

Complexity of infection (COI)
The COI was computed as the number of alleles per microsatellite loci divided by the number of ampli ed samples per microsatellite loci. The mean COI per State was calculated as the average of all COI values per microsatellite loci.

Parasite allelic frequency
The allele frequencies, per locus were calculated using GENALEX 6.5 [28]. This frequency was calculated for each State involved in the study.

Parasite allelic diversity
The expected heterozygosity (He), which represents the probability of being infected by two parasites with different alleles at a given locus, was calculated using the formula: Where n is the number of isolates analysed, and p represents the frequency of each different allele at a locus [29]. He values range from 0 to 1. Values closer to 0 indicate little or no allelic diversity while values closer to 1 indicate high allelic diversity.

Measures of parasite population differentiation
Analysis of molecular variance (AMOVA) Inter-and intra-population variance was determined with analysis of molecular variance (AMOVA, i.e., ΦPT). ΦPT value of zero (0) is considered indicative of no genetic differentiation among populations.

Fixation index (Fst)
The population divergence was measured by calculating the xation index (Fst) for all pairs of parasite population in each State. The software, GENALEX 6.5 was used to compute the Fst value.

Principal component analysis (PCA)
Principal component analysis (PCA) was performed with the online program, ClustVis [30] across all nine States and separately for each State. Linkage disequilibrium (LD) Linkage disequilibrium (LD) was calculated for all nine States and separately for each State using the standardized index of association, (I S A ), (LIAN version 3.5 web interface) [31] and the majority allele at each locus in each infection. This index was calculated as Where VE is the expected variance of the n th number of loci for which two individuals differ. VD is the observed variance. Randomization test, previously described [32], was done to determine whether the ratio of VD/VE was signi cantly higher than 1.

Fixation index (Fst)
The xation index between parasite populations (Fst) is 0.038; that is, the genetic diversity between the nine States constituted 3.8% of the total genetic variance (p<0.05) which essentially suggests that all nine States are not signi cantly genetically diverse from each other.

Principal component analysis (PCA)
Based on the PCA plots, low diversity existed among the States. However, plots showing each State independently revealed within-population diversity especially in Bayelsa, Imo and Kano States (Figure 2 -Panels B, E and F, respectively).
Results obtained from analyses showed no signi cant index of association in all parasite populations considered as the obtained LD value was 0.0179 (Table 4). Although the highest LD value of 0.0715 was obtained in Adamawa State, and the lowest LD value of 0.0037 was obtained in Kwara State, there was no signi cant difference (p>0.05). This is indicative of similar parasite structuring in all nine States.

Discussion
Nigeria remains the country with the highest global malaria burden. Hence, molecular studies on P. falciparum diversity and population structure become essential in monitoring the impact of different intervention strategies in the control of malaria transmission. This study employed the use of 12 microsatellites to evaluate P. falciparum genetic diversity and population structure in nine Nigerian States. Although microsatellites are better alternatives to polymorphic markers such as msp-1, msp-2, and Glurp, there are only a few reports of its use in studies conducted in Nigeria.
Our analysis of the microsatellite data generated in this study revealed high parasite diversity across all states. For instance, the mCOI (measure of parasite diversity) in all nine States was high (ranging from 1.71-2.66). Although, higher mCOI values (4. 38-5.4) have been reported in earlier studies conducted prior to the introduction of artemisinin combination therapies (ACTs) [10,11,33], mCOI values obtained in this study suggest a steady decline in parasite diversity 13 years post-adoption of ACTs in Nigerian children. This may largely be attributed to the adoption and deployment of ACTs in Nigeria. In addition, other concurrent interventions such as broader distribution of long-lasting insecticide treated net (LLIN), may be a contributing factor [26]. Another measure of parasite diversity is the number of effective alleles (Ne) detected per microsatellite locus. It is expected that the number of Ne detected per locus is likely to be high in areas with high malaria endemicity and vice versa [5,19]. The observed mean Ne in parasites obtained from all States (5.4 -8.2) were comparable to those reported in other high-endemic regions of Sub-Saharan Africa [4,5,34]. The distribution of observed mean Ne in the Northern and Southern States were similar (p>0.05). This is equally expected as malaria endemicity continues to be high throughout Although parasite diversity was high, further analysis of microsatellite data generated revealed low parasite population differentiation. Analysis of molecular variance (AMOVA) and genetic differentiation index (F ST ) values obtained were 0.039 and 0.038 respectively, which is low [36,38]. This implies that about 96% of genetic variations observed among parasites were within populations. The principal component analysis (PCA) of all nine States further con rmed genetic similarities amongst parasite populations as similar clustering patterns consistent with low levels of genetic differentiation were observed. Linkage disequilibrium (LD) values for each parasite population ranged from 0.0037 in Kwara to 0.0715 in Adamawa. The overall association index was 0.0219, which is weaker than those typically reported in regions with low transmission [21,23]. Studies have associated low LD values such as those reported in this study, to high levels of malaria transmission; which leads to increased cross-breeding and meiotic recombination that results in LD breakdown [5,6,19,39]. Although the LD values obtained in this study remain low, there is a need to continually monitor parasite populations within Nigeria to detect new variants that may inform adaptation against interventions currently employed.
The perceived lack of genetic differentiation or sub-structuring between States as evidenced by results obtained from AMOVA, Fst, PCA and LD analysis, is probably as a result of immense human migration between these populations as part of the usual socioeconomic activities and indiscriminate vector migration within the country [6,40,41,42]. Figure 1 Geographic representation of sites (highlighted) in Nigeria where samples for in this study were collected. Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.