Skip to main content
Download PDF

Genetic diversity of Plasmodium falciparum populations in three malaria transmission settings in Madagascar

Malaria Journal volume 20, Article number: 239 (2021) Cite this article

Abstract

Background

Assessment of the genetic diversity of Plasmodium falciparum parasites from various malaria transmission settings could help to define tailored local strategies for malaria control and elimination. Such assessments are currently scarce in Madagascar. The study presented here aimed to bridge this gap by investigating the genetic diversity of P. falciparum populations in three epidemiological strata (Equatorial, Tropical and Fringes) in Madagascar.

Methods

Two-hundred and sixty-six P. falciparum isolates were obtained from patients with uncomplicated malaria enrolled in clinical drug efficacy studies conducted at health centres in Tsaratanana (Equatorial stratum), Antanimbary (Tropical stratum) and Anjoma Ramartina (Fringes) in 2013 and 2016. Parasite DNA was extracted from blood samples collected before anti-malarial treatment. Plasmodium species were identified by nested PCR targeting the 18 S rRNA gene. The genetic profiles of P. falciparum parasites were defined by allele-specific nested PCR on the polymorphic regions of the msp-1 and msp-2 genes.

Results

Fifty-eight alleles were detected in the P. falciparum samples tested: 18 alleles for msp-1 and 40 for msp-2. K1 (62.9%, 139/221) and FC27 (69.5%, 114/164) were the principal msp-1 and msp-2 allele families detected, although the proportions of the msp-1 and msp-2 alleles varied significantly between sites. Polyclonal infections were more frequent at sites in the Equatorial stratum (69.8%) than at sites in the Tropical stratum (60.5%) or Fringes (58.1%). Population genetics analyses showed that genetic diversity was similar between sites and that parasite flow within sites was limited.

Conclusions

This study provides recent information about the genetic diversity of P. falciparum populations in three transmission strata in Madagascar, and valuable baseline data for further evaluation of the impact of the control measures implemented in Madagascar.

Background

Falciparum malaria remains a major infectious disease in humans, affecting millions of people in tropical areas, despite the progress of the last decade, achieved principally by scaling up key interventions (vector control measures and better management of malaria cases) [1]. In 2019, the World Health Organization (WHO) recorded 229 million malaria cases, leading to 409,000 deaths, mostly in pregnant women and children under the age of five years living in sub-Saharan Africa (94%) [1].

In Madagascar (total population: 26,969,306 in 2019), malaria is a major public health issue, the fourth leading cause of morbidity in health centres and of mortality in hospitals. In 2019, the incidence of malaria was estimated at 76.1/1,000 inhabitants, with a mean of 2,052,071 cases of malaria (range 1,535,000–2,642,000) and 5,073 deaths from the disease (range 180–9580) annually [1]. These worryingly high figures were due mostly to a significant increase in the number of cases in districts located in high-transmission areas, several malaria outbreaks in the south, and exceptional climatic conditions (cyclones and floods) in recent years [2].

One of the major challenges facing policy makers is the tremendous variability of malaria transmission across Madagascar, depending on regional variations in rainfall, temperature and elevation. The country is typically divided into five epidemiological strata: (i) the Equatorial stratum on the east coast, where malaria transmission is highest and perennial; (ii) the Tropical stratum on the west coast, with seasonal transmission spanning around 6 months (October–April); (iii) the Sub-desert stratum in the south, characterized by a dry and hot climate prone to episodic outbreaks; (iv) the Highland; and, (v) Fringes stratum in the centre of the country, where malaria transmission rates are low and unstable between January and April [3, 4].

The malaria control interventions recommended by the WHO are currently performed free of charge throughout the country by the Malagasy Malaria Control Programme (MMCP). These interventions are based on vector control measures (long-lasting insecticide-treated nets and the indoor spraying of insecticides) and the prompt and effective management of malaria cases detected in health facilities and in the community (i.e., use of rapid diagnostic tests (RDT) for malaria and artemisinin-based combined therapy (ACT) for treatment). No specific strategies for malaria control tailored to the epidemiological context have been designed and implemented locally or regionally. Furthermore, the impact of strategies has been assessed only on the basis of the estimated number of malaria cases recorded by hospital and health centre staff and community workers [4].

The genotyping of Plasmodium falciparum parasites has been shown to be a useful tool for exploring genetic diversity (i.e., the complexity and size of the parasite populations) and multiplicity of infection (MOI), i.e., the number of clones per sample, which is generally considered to be strongly correlated with transmission intensity [5,6,7,8]. Indeed, parasite genetic diversity and MOI are high in areas with high rates of malaria transmission, whereas they tend to be markedly lower in regions implementing effective malaria control strategies [7, 9]. One of the most widely used techniques for assessing the genetic diversity and MOI of P. falciparum is based on the detection, by PCR, of polymorphisms in genes encoding merozoite surface proteins, such as MSP-1 and MSP-2 [10,11,12,13]. For MSP-1 (encoded by the msp-1 gene located on chromosome 9), block 2 is the most polymorphic region, and three families of alleles with polymorphisms of this region have been described (K1, MAD20 and RO33). For MSP-2 (encoded by the msp-2 gene located on chromosome 2), block 3 is the most polymorphic region, and two families of alleles have been defined on the basis of polymorphisms of this region (FC27 and IC/3D7).

In Madagascar, where malaria transmission rates vary considerably, the routine monitoring of P. falciparum genotypes and of the genetic diversity of parasite populations would probably be very useful [14]. However, very little is currently known about the genetic diversity of P. falciparum in Madagascar. In this context, the study presented here aimed to provide new data concerning the genetic diversity of P. falciparum populations and MOI for malaria parasites isolated from patients with symptomatic malaria in three areas of the island in which the disease is endemic.

Methods

Study sites and blood sample collection

Blood samples containing P. falciparum were obtained from symptomatic patients presenting at local health centres and enrolled in therapeutic studies evaluating the efficacy of artesunate-amodiaquine treatment in 2013 and 2016 [15]. The patients included were at least six months old, presented uncomplicated falciparum malaria (defined as a positive smear for P. falciparum and fever, with body temperature ≥ 37.5 °C) and were resident in one of three zones of endemic malaria in Madagascar: Anjoma Ramartina, a city located in the Fringes, Tsaratanana on the eastern coast (Equatorial stratum) and Antanimbary on the western coast (Tropical stratum) (Fig. 1). Informed consent was obtained from the participants or their parents (for children). Finger-prick blood samples were collected on the day of enrolment, before treatment. The blood samples (100 µL) were used to generate blood films and were spotted onto 3-MM Whatman 903 filter paper (Merck KGaA, Darmstadt, Germany), which were then air-dried and placed individually in plastic bags with desiccant, for transportation to the Malaria Research Laboratory (laboratory of the NMCP) where they were stored at 4 °C until DNA extraction for a maximum of six months.

Fig. 1
figure 1

Location of the study sites, according to epidemiological stratum, in Madagascar

Full size image

Microscopy and parasite counts

Thick and thin blood film slides were stained by incubation with 10% Giemsa solution for 30 min. The stained slides were examined under a light microscope (× 100) for the detection and identification of Plasmodium species and for parasite counts by two experienced microscopists. Parasite densities were recorded as the average of the two counts, calculated per 500 white blood cells (WBC), and expressed as the number of parasites/µL of blood, assuming a mean WBC count of 8000/µL of blood. Blood smears with discordant results (differences between the two microscopists in species diagnosis, in parasite density of > 50% or in the presence of parasites) were re-examined by a third, independent microscopist, and parasite density was calculated by averaging the two closest counts [16].

DNA extraction

Genomic DNA was extracted from the dried blood spots using the QIAamp DNA Blood Mini Kit as per the manufacturer’s instructions (Qiagen, CA, USA) and stored at – 20 °C for further use.

Plasmodium species identification and msp-1/msp-2 genotyping

Genus- and species-specific nested PCRs targeting the 18 S rRNA gene were performed, as described by Snounou et al. [10, 17]. The polymorphic regions of the merozoite surface protein genes msp-1 (block 2), msp-2 (block 3) were amplified by nested PCR. In the first round of PCR, oligonucleotide primers were used to target conserved genomic regions within msp-1 (block 2) msp-2 (block 3). In the second round of PCR, the polymorphic families of msp-1 (K1, MAD20, and RO33) and msp-2 (FC27 and 3D7) alleles were amplified with specific primers. The primers and conditions used for first and second rounds of PCR were as described by Oyebola et al. [18]. The PCR products were separated by electrophoresis on a 2% agarose gel, with visualization of the fragments under a gel imager (Gel Doc XR, Biorad) after ethidium bromide staining. The sizes of the alleles (± 20 bp) were determined with molecular weight standards (100 bp DNA Ladder, Invitrogen). DNA from reference P. falciparum strains (3D7, Dd2 and 7G8) was included in each run as a control.

Multiplicity of infection

The MOI or number of genotypes per infection was calculated by dividing the total number of fragments detected for one antigenic marker by the number of samples positive for the marker concerned. The mean MOI was calculated by dividing the total number of fragments detected for both the msp-1 and msp-2 loci by the number of samples positive for both markers. Isolates carrying more than one family of alleles were considered to correspond to polyclonal infections, whereas the presence of a single allele family was considered to indicate a monoclonal infection [19].

Statistical analyses

Statistical analyses were performed with MedCalc version 12 (Mariakerke, Belgium). Mann-Whitney tests were used for non-parametric comparisons and Student’s t tests or one-way ANOVA were used for parametric comparisons. For proportions (expressed as percentages), χ2 or Fisher’s exact tests were used. p values below 0.05 were considered significant. Genetic diversity was assessed by calculating Nei’s unbiased expected heterozygosity (He) from haploid data as follows: He = [n/(n – 1)][1 – pi] (n = the number of isolates sampled; pi = the frequency of the itch allele [20]. Population genetic differentiation was assessed with Wright’s F statistic [21]. Population genetic parameters were computed with FSTAT software, v2. 9. 4 [22].

Results

Study populations

Two-hundred and sixty-six P. falciparum isolates were obtained from patients with uncomplicated malaria seeking anti-malarial treatment at health centres in Anjoma Ramartina (n = 85), Tsaratanana (n = 79) and Antanimbary (n = 102) (Fig. 1). The characteristics of the study populations are described in Table 1.

Table 1 Characteristics of the patients enrolled in Anjoma Ramartina, Tsaratanana and Antanimbary
Full size table

Frequency and genetic diversity of the msp-1 and msp-2 allele families

Two-hundred and sixty six isolates were genotyped: for 245 isolates (92.1%), amplification was successful for at least one of the two genes, whereas for 138 (56.3%), the targeted regions of both msp-1 and msp-2 were amplified. For msp-1, 18 alleles were observed (10 K1-types, 7 MAD20-types and one RO33-type) with band sizes of 130–270 bp. The number of alleles detected for msp-2 was higher. In total, 40 different msp-2 alleles were found, 23 belonging to the 3D7 family and 17 to the FC27 family (fragment sizes of 280–700 bp). The proportions of each allele, by allelic family, are presented in Fig. 2.

Fig. 2
figure 2

Distribution and proportions of msp-1 and msp-2 alleles

Full size image

msp-1 genotyping

The RO33 allelic family was found to be monomorphic (with an amplified fragment size of 160 bp), and accounted for 36.7% (81/221) of all msp-1 genotypes. The K1-type alleles predominated, at a frequency of 62.9% (139/221). The most frequent alleles were 200 bp (24%), 180 bp (19%), 220 bp (14%), and 160 bp (11%) long. Two K1-type alleles were present in 15.8% (22/139) of the isolates. MAD20-type alleles were less frequent (53.4%, 118/221). The 200-bp allele was the most frequent (64%), followed by the 180-bp allele (17%) (Fig. 2). The presence of two MAD20 alleles was also observed, but at a low frequency (2.5%, 3/118).

msp-2 genotyping

The 3D7 and FC27 alleles were detected in 49.4% (81/164) and 69.5% (114/164) of P. falciparum isolates, respectively. Most of the msp-2 alleles were observed at low frequency, but seven alleles were more frequent (3D7 allelic family: 400 bp, 15%; 350 bp, 13%; 500 bp, 10%; and, FC27 allelic family: 500 bp, 24%; 520 bp, 13%; 460 bp, 11%; 480 bp, 11%). Several alleles from the same allelic family were present in 13.6% of isolates for the 3D7 family (two alleles in 10/81 and three alleles in 1/81 samples) and in 11.4% for the FC37 family (two alleles). The distribution of the msp-1 and msp-2 allelic families is presented in Fig. 3. The proportions of the msp-1 and msp-2 alleles varied significantly between the three sites, as shown in Table 2.

Fig. 3
figure 3

Overall distribution of msp-1 and msp-2 genotypes

Full size image
Table 2 Distribution of msp-1 and msp-2 allelic families by study site
Full size table

Monoclonal versus polyclonal infections

The proportion of monoclonal infections, as defined by msp-1 genotyping, was estimated at 55.6% (123/221). The presence of a single allele was most frequent for the K1 allelic family (29.4%), followed by MAD20 (17.2%) and RO33 (9.0%). Combinations of alleles from the RO33, MAD20 and K1 allelic families were detected in 98/221 samples (44.4%). The most frequent combination was K1/MAD20 (16.7%). The proportion of polyclonal infections was significantly higher in Tsaratanana (Equatorial stratum) (60.8%. p = 0.02, Chi-squared test) than at the other two sites (40.3 and 42.5%). According to msp-2 genotyping results, most isolates carried a single allele (monoclonal infection; 81.1%. 133/164). The presence of a single allele was most frequent for the FC27 family (50.6%. 83/164). A combination of alleles from the 3D7 and FC27 allelic families was detected in 31/164 samples (18.9%) (Table 3).

Table 3 Proportion of multiclonal infections defined on the basis of msp-1 and msp-2 genotyping, by study site, age group and parasite density group
Full size table

Population genetic measures: multiplicity of infection, expected heterozygosity and genetic differentiation between sites

The estimated MOI at the three sites is summarized in Table 4. The number of msp-1 and msp-2 genotypes per isolate ranged from 1 to 4 and 1 to 3, respectively. The mean MOI per msp-1 or combined msp-1 and msp-2 genotype was significantly higher for isolates from Tsaratanana (Equatorial stratum) (1.92, p = 0.001 and 2.52, p = 0.04, respectively) than for isolates from the other two sites (1.50 and 1.51 and 2.02 and 2.25, for Antanimbary (Tropical stratum) and Anjoma Ramartina (Fringes), respectively). These trends were confirmed in the analysis by age group: the mean MOI (msp-1 and combined msp-1/msp-2) for isolates obtained from patients aged 5–15 years was higher in Tsaratanana (Equatorial stratum) than in Anjoma Ramartina (Fringes) (p = 0.004 and p = 0.007, Mann-Whitney test) and Antanimbary (Tropical stratum) (p = 0.02 for msp-1, Mann-Whitney test).

Table 4 Multiplicity of infection (MOI) per msp-1 or msp-2 and combined msp-1/msp-2 genotype, by study site, age group and parasite density group
Full size table

The expected heterozygosity (He) of isolates from the three sites is presented in Table 5. At all sites, He was higher for msp-2 genotypes (0.823–0.892) than for msp-1 genotypes (0.413–0.489). However, He was similar between study sites, age groups and parasite density groups.

Table 5 Expected heterozygosity (He) estimated by msp-1, msp-2 and combined msp-1/msp-2 genotyping, by study site
Full size table

The estimated fixation index (Fst), measuring the population differentiation due to genetic structure at each site, was not significant (Table 6). This finding is consistent with the proportion of genotypes common to the various study sites, as shown in the Table 7. The highest proportion of genotype sharing (6%) was observed between Anjoma Ramartina and Tsaratanana.

Table 6 Estimation of the fixation index (Fst) between study sites
Full size table
Table 7 Estimated proportions of msp-1/-2 genotypes common to different study sites
Full size table

Discussion

Little is currently known about the genetic diversity of P. falciparum populations in Madagascar. PCR genotyping analysis with the polymorphic markers msp-1 and msp-2 were performed to gain insight into the genetic diversity of the populations of this parasite species in three regions with different patterns of malaria transmission in Madagascar. Excluding genotyping data from clinical trials assessing drug efficacy (performed to distinguish between recrudescence and re-infections in enrolled patients presenting recurrences during follow-up), only two studies, performed in 2000 and 2008, have reported similar analyses [14, 23].

The total number of different msp-1 and msp-2 alleles at the three sites (18 and 40, respectively) confirms the high level of malaria transmission in Madagascar. These numbers are similar to those reported in African countries, such as Nigeria, the Republic of Congo, the Central African Republic, Equatorial Guinea and Senegal [24,25,26,27,28].

The predominant alleles were K1-type alleles for msp-1 and FC27-type alleles for msp-2. These findings are consistent with previous reports for Madagascar [14] and other settings in Africa (Nigeria [18, 26], Congo Brazzaville [29], Mauritania [30], Benin [31], Gabon [32, 33], Ivory Coast [34], Cameroon, [35], Ethiopia [36,37,38,39]), India [40], and Southeast Asia [41]. However, they contrast with recent reports from Myanmar [42], where MAD20 and 3D7 were the most prevalent alleles.

The distribution of the msp-1 and msp-2 allelic families differed significantly between sites (Table 2). Genotyping revealed that half the individuals had P. falciparum isolates with a single msp-1 allele. The proportion of isolates with more than one msp-1 allele was significantly higher in patients living in the Equatorial stratum, probably reflecting the higher rates of malaria transmission in this setting. This association was confirmed by the significantly higher proportion of polyclonal infections at Tsaratanana (Equatorial stratum, 60.8%) than at the other two sites (40.3 and 42.5%) (Table 3). msp-2 allelic diversity contrasted more strongly between sites: FC27-type alleles were much more frequent than 3D7-type alleles in the Tropical zone, whereas 3D7-type alleles predominated in the Equatorial and Fringes stratum.

The number of msp-1 and msp-2 genotypes per isolate ranged from 1 to 4 and 1–3, respectively. Again, the mean MOI values per msp-1 or combined msp-1 and msp-2 genotype were significantly higher for isolates from patients living in Tsaratanana (Equatorial stratum) (1.92 and 2.52) than for isolates from patients living at the other two sites (1.51 and 2.02, respectively, at Anjoma Ramartina; 1.50 and 2.25, respectively at Antanimbary). These values are similar to those reported in some African countries, such as Ghana [43], Congo Brazzaville [28], and Ethiopia [36,37,38,39], but are lower than those reported in Nigeria [26] and Gabon [44]. A similar association was found if the analysis was performed by age group. The mean MOI (msp-1 and combined msp-1/msp-2) of isolates obtained from patients aged 5–15 years was higher for Tsaratanana (Equatorial stratum) than for Anjoma Ramartina (Fringes) and Antanimbary (Tropical stratum). However, no association was found between mean MOI and parasite density, contrary to the findings of several other reports [45].

These data confirm that MOI assessments provide a good assessment of malaria transmission intensity and this metric may be considered a useful tool for evaluating the impact of the vector control measures (long-lasting insecticide-treated bed nets and indoor spraying with insecticides) currently implemented throughout Madagascar. Population genetics analyses revealed no significant difference between sites, age groups and parasite density groups. Furthermore, Fst estimates and the low proportion of genotypes common to different study sites indicated that the circulation of the parasite population between sites remained limited.

This study provides recent data for the genetic diversity of P. falciparum. It is, however, subject to several limitations. The principal limitation was the use of the msp-1 and msp-2 markers for genotyping, which, like other markers based on DNA fragment size, may decrease estimates of genetic diversity. Nevertheless, msp-1 and msp-2 are generally considered to be robust polymorphic markers for which genotyping is straightforward in poorly equipped laboratories, such as those available in Madagascar. A second limitation is that the sample collection sites were not selected specifically for this study, but for a clinical trial assessing the efficacy of artesunate-amodiaquine. This limits the opportunities for data extrapolation. Finally, as no direct measurements of malaria transmission, such as the entomological inoculation rate (EIR), were available for the selected sites, it was not possible to investigate the association between these metrics and genetic diversity.

Conclusions

Despite several limitations, this study provides recent genetic diversity data for P. falciparum isolates collected in three regions with different transmission patterns. The information obtained is valuable for guiding the decisions of policy-makers to improve anti-malaria strategies. The continuous evaluation of these metrics would facilitate evaluations of the control measures implemented in Madagascar. Furthermore, the two markers studied here can be used in investigations of malaria outbreaks, which have been frequent in recent years [46] in the southern areas of the country, which generally have low transmission rates. This approach could be used to determine whether outbreaks are due to the clonal expansion of local or imported P. falciparum clones and for the design of more appropriate strategies for preventing outbreaks.

Availability of data and materials

The data are available from the National Malaria Control Programme of Madagascar.

References

  1. WHO. World Malaria Report 2020. Geneva: World Health Organization; 2020.

    Google Scholar 

  2. Ihantamalala FA, Rakotoarimanana FMJ, Ramiadantsoa T, Rakotondramanga JM, Pennober G, Rakotomanana F, et al. Spatial and temporal dynamics of malaria in Madagascar. Malar J. 2018;17:58.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Howes RE, Mioramalala SA, Ramiranirina B, Franchard T, Rakotorahalahy AJ, Bisanzio D, et al. Contemporary epidemiological overview of malaria in Madagascar: operational utility of reported routine case data for malaria control planning. Malar J. 2016;15:502.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Arambepola R, Keddie SH, Collins EL, Twohig KA, Amratia P, Bertozzi-Villa A, et al. Spatiotemporal mapping of malaria prevalence in Madagascar using routine surveillance and health survey data. Sci Rep. 2020;10:18129.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Chenet SM, Schneider KA, Villegas L, Escalante AA. Local population structure of Plasmodium: impact on malaria control and elimination. Malar J. 2012;11:412.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Chenet SM, Taylor JE, Blair S, Zuluaga L, Escalante AA. Longitudinal analysis of Plasmodium falciparum genetic variation in Turbo, Colombia: implications for malaria control and elimination. Malar J. 2015;14:363.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Daniels R, Chang HH, Sene PD, Park DC, Neafsey DE, Schaffner SF, et al. Genetic surveillance detects both clonal and epidemic transmission of malaria following enhanced intervention in Senegal. PLoS One. 2013;8:e60780.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Nkhoma SC, Nair S, Al-Saai S, Ashley E, McGready R, Phyo AP, et al. Population genetic correlates of declining transmission in a human pathogen. Mol Ecol. 2013;22:273–85.

    Article  PubMed  Google Scholar 

  9. Hoffmann EH, da Silveira LA, Tonhosolo R, Pereira FJ, Ribeiro WL, Tonon AP, et al. Geographical patterns of allelic diversity in the Plasmodium falciparum malaria-vaccine candidate, merozoite surface protein-2. Ann Trop Med Parasitol. 2001;95:117–32.

    Article  CAS  PubMed  Google Scholar 

  10. Snounou G, Zhu X, Siripoon N, Jarra W, Thaithong S, Brown KN, et al. Biased distribution of msp1 and msp2 allelic variants in Plasmodium falciparum populations in Thailand. Trans R Soc Trop Med Hyg. 1999;93:369–74.

    Article  CAS  PubMed  Google Scholar 

  11. Kanunfre KA, Leoratti FM, Hoffmann EH, Durlacher RR, Ferreira AW, Moraes-Avila SL, et al. Differential recognition of Plasmodium falciparum merozoite surface protein 2 variants by antibodies from malaria patients in Brazil. Clin Diagn Lab Immunol. 2003;10:973–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hamid MM, Mohammed SB, El Hassan IM. Genetic diversity of Plasmodium falciparum field isolates in Central Sudan inferred by PCR genotyping of merozoite surface protein 1 and 2. N Am J Med Sci. 2013;5:95–101.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Tanabe K, Sakihama N, Kaneko O, Saito-Ito A, Kimura M. A PCR method for molecular epidemiology of Plasmodium falciparum msp-1. Tokai J Exp Clin Med. 1998;23:375–81.

    CAS  PubMed  Google Scholar 

  14. Sallenave-Sales S, Daubersies P, Mercereau-Puijalon O, Rahimalala L, Contamin H, Druilhe P, et al. Plasmodium falciparum: a comparative analysis of the genetic diversity in malaria-mesoendemic areas of Brazil and Madagascar. Parasitol Res. 2000;86:692–8.

    Article  CAS  PubMed  Google Scholar 

  15. Raobela O, Andriantsoanirina V, Rajaonera DG, Rakotomanga TA, Rabearimanana S, Ralinoro F, et al. Efficacy of artesunate-amodiaquine in the treatment of falciparum uncomplicated malaria in Madagascar. Malar J. 2018;17:284.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. WHO. Methods for surveillance of antimalarial drug efficacy. Geneva: World Health Organization; 2009.

    Google Scholar 

  17. Snounou G, Viriyakosol S, Zhu XP, Jarra W, Pinheiro L, do Rosario VE, et al. High sensitivity of detection of human malaria parasites by the use of nested polymerase chain reaction. Mol Biochem Parasitol. 1993;61:315–20.

    Article  CAS  PubMed  Google Scholar 

  18. Oyebola MK, Idowu ET, Olukosi YA, Iwalokun BA, Agomo CO, Ajibaye OO, et al. Genetic diversity and complexity of Plasmodium falciparum infections in Lagos, Nigeria. Asian Pac J Trop Biomed. 2014;4:87–91.

    Article  Google Scholar 

  19. Hastings IM, Nsanzabana C, Smith TA. A comparison of methods to detect and quantify the markers of antimalarial drug resistance. Am J Trop Med Hyg. 2010;83:489–95.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Nei M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics. 1978;89:583–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wright S. The interpretation of population structure by F-statistics with special regard to systems of mating. Evolution. 1965;19:395–420.

    Article  Google Scholar 

  22. Goudet J, Perrin N, Waser P. Tests for sex-biased dispersal using bi-parentally inherited genetic markers. Mol Ecol. 2002;11:1103–14.

    Article  CAS  PubMed  Google Scholar 

  23. Durand R, Ariey F, Cojean S, Fontanet A, Ranaivo L, Ranarivelo LA, et al. Analysis of circulating populations of Plasmodium falciparum in mild and severe malaria in two different epidemiological patterns in Madagascar. Trop Med Int Health. 2008;13:1392–9.

    Article  PubMed  Google Scholar 

  24. Chen JT, Li J, Zha GC, Huang G, Huang ZX, Xie DD, et al. Genetic diversity and allele frequencies of Plasmodium falciparum msp1 and msp2 in parasite isolates from Bioko Island, Equatorial Guinea. Malar J. 2018;17:458.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Dolmazon V, Matsika-Claquin MD, Manirakiza A, Yapou F, Nambot M, Menard D. Genetic diversity and genotype multiplicity of Plasmodium falciparum infections in symptomatic individuals living in Bangui (CAR). Acta Trop. 2008;107:37–42.

    Article  CAS  PubMed  Google Scholar 

  26. Olasehinde GI, Yah CS, Singh R, Ojuronbge OO, Ajayi AA, Valecha N, et al. Genetic diversity of Plasmodium falciparum field isolates from south western Nigeria. Afr Health Sci. 2012;12:355–61.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Ndiaye T, Sy M, Gaye A, Ndiaye D. Genetic polymorphism of Merozoite Surface Protein 1 (msp1) and 2 (msp2) genes and multiplicity of Plasmodium falciparum infection across various endemic areas in Senegal. Afr Health Sci. 2019;19:2446–56.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Gueye NSG, Ntoumi F, Vouvoungui C, Kobawila SC, NKombo M, Mouanga AM, Deibert J, et al. Plasmodium falciparum merozoite protein-1 genetic diversity and multiplicity of infection in isolates from Congolese children consulting in a pediatric hospital in Brazzaville. Acta Trop. 2018;183:78–83.

    Article  CAS  PubMed  Google Scholar 

  29. Mayengue PI, Kouhounina Batsimba D, Niama RF, Ibara Ottia R, Malonga-Massanga A, Fila-Fila GPU, et al. Variation of prevalence of malaria, parasite density and the multiplicity of Plasmodium falciparum infection throughout the year at three different health centers in Brazzaville, Republic of Congo. BMC Infect Dis. 2020;20:190.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Ahmedou Salem MS, Ndiaye M, OuldAbdallahi M, Lekweiry KM, Bogreau H, Konate L, et al. Polymorphism of the merozoite surface protein-1 block 2 region in Plasmodium falciparum isolates from Mauritania. Malar J. 2014;13:26.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Ogouyemi-Hounto A, Gazard DK, Ndam N, Topanou E, Garba O, Elegbe P, et al. Genetic polymorphism of merozoite surface protein-1 and merozoite surface protein-2 in Plasmodium falciparum isolates from children in South of Benin. Parasite. 2013;20:37.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Aubouy A, Migot-Nabias F, Deloron P. Polymorphism in two merozoite surface proteins of Plasmodium falciparum isolates from Gabon. Malar J. 2003;2:12.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Bouyou-Akotet MK, M’Bondoukwe NP, Mawili-Mboumba DP. Genetic polymorphism of merozoite surface protein-1 in Plasmodium falciparum isolates from patients with mild to severe malaria in Libreville, Gabon. Parasite. 2015;22:12.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Yavo W, Konate A, Mawili-Mboumba DP, Kassi FK, Tshibola Mbuyi ML, Angora EK, et al. Genetic polymorphism of msp1 and msp2 in Plasmodium falciparum isolates from Cote d’Ivoire versus Gabon. J Parasitol Res. 2016;2016:3074803.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Metoh TN, Chen JH, Fon-Gah P, Zhou X, Moyou-Somo R, Zhou XN. Genetic diversity of Plasmodium falciparum and genetic profile in children affected by uncomplicated malaria in Cameroon. Malar J. 2020;19:115.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Abamecha A, El-Abid H, Yilma D, Addisu W, Ibenthal A, Bayih AG, et al. Genetic diversity and genotype multiplicity of Plasmodium falciparum infection in patients with uncomplicated malaria in Chewaka district, Ethiopia. Malar J. 2020;19:203.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Mohammed H, Hassen K, Assefa A, Mekete K, Tadesse G, Taye G, et al. Genetic diversity of Plasmodium falciparum isolates from patients with uncomplicated and severe malaria based on msp-1 and msp-2 genes in Gublak, North West Ethiopia. Malar J. 2019;18:413.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Mohammed H, Kassa M, Mekete K, Assefa A, Taye G, Commons RJ. Genetic diversity of the msp-1, msp-2, and glurp genes of Plasmodium falciparum isolates in Northwest Ethiopia. Malar J. 2018;17:386.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Mohammed H, Mindaye T, Belayneh M, Kassa M, Assefa A, Tadesse M, et al. Genetic diversity of Plasmodium falciparum isolates based on MSP-1 and MSP-2 genes from Kolla-Shele area, Arbaminch Zuria District, southwest Ethiopia. Malar J. 2015;14:73.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Bharti PK, Shukla MM, Sharma YD, Singh N. Genetic diversity in the block 2 region of the merozoite surface protein-1 of Plasmodium falciparum in central India. Malar J. 2012;11:78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Khaminsou N, Kritpetcharat O, Daduang J, Charerntanyarak L, Kritpetcharat P. Genetic analysis of the merozoite surface protein-1 block 2 allelic types in Plasmodium falciparum clinical isolates from Lao PDR. Malar J. 2011;10:371.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Soe TN, Wu Y, Tun MW, Xu X, Hu Y, Ruan Y, et al. Genetic diversity of Plasmodium falciparum populations in southeast and western Myanmar. Parasit Vectors. 2017;10:322.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Adjah J, Fiadzoe B, Ayanful-Torgby R, Amoah LE. Seasonal variations in Plasmodium falciparum genetic diversity and multiplicity of infection in asymptomatic children living in southern Ghana. BMC Infect Dis. 2018;18:432.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Ndong Ngomo JM, M’Bondoukwe NP, Yavo W, Bongho Mavoungou LC, Bouyou-Akotet MK, Mawili-Mboumba DP. Spatial and temporal distribution of Pfmsp1 and Pfmsp2 alleles and genetic profile change of Plasmodium falciparum populations in Gabon. Acta Trop. 2018;178:27–33.

    Article  CAS  PubMed  Google Scholar 

  45. Peyerl-Hoffmann G, Jelinek T, Kilian A, Kabagambe G, Metzger WG, von Sonnenburg F. Genetic diversity of Plasmodium falciparum and its relationship to parasite density in an area with different malaria endemicities in West Uganda. Trop Med Int Health. 2001;6:607–13.

    Article  CAS  PubMed  Google Scholar 

  46. Kesteman T, Rafalimanantsoa SA, Razafimandimby H, Rasamimanana HH, Raharimanga V, Ramarosandratana B, et al. Multiple causes of an unexpected malaria outbreak in a high-transmission area in Madagascar. Malar J. 2016;15:57.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

We thank all study participants, the parents and guardians of children for agreeing to participation in these studies, the staff of the three healthcare facilities and the members of the study team: Stéphane Rabearimanana, Michel Abrahim Marolahy, Tiavina Rakotomanjaka, Brunette Razanadrazanina, Noeline Rasoarilalao, Marie Ange Rason, Jaona Rahajarimanitriniaina and Claude L.G. Raherinjafy Rogelin.

Funding

This work was supported by The Global Fund: NSA2 (MDG-M-PSI 635).

Author information

Author notes

  1. Fanomezantsoa Ralinoro and Arsene Ratsimbasoa contributed equally to this work

Authors and Affiliations

  1. National Malaria Control Programme of Madagascar, Androhibe, Antananarivo, Madagascar

    Fanomezantsoa Ralinoro, Tovonahary Angelo Rakotomanga & Arsene Ratsimbasoa

  2. Faculty of Sciences, University of Antananarivo, Antananarivo, Madagascar

    Fanomezantsoa Ralinoro, Tovonahary Angelo Rakotomanga, Danielle A. Doll Rakoto & Victor Jeannoda

  3. Centre National d’Application de Recherches Pharmaceutiques, Analamahitsy, Antananarivo, Madagascar

    Rianasoambolanoro Rakotosaona

  4. Malaria Genetics and Resistance Unit and INSERM U1201, Institut Pasteur Paris, Paris, France

    Didier Menard

  5. Faculty of Medicine, University of Fianarantsoa, Fianarantsoa, Madagascar

    Arsene Ratsimbasoa

Authors
  1. Fanomezantsoa Ralinoro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tovonahary Angelo Rakotomanga
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rianasoambolanoro Rakotosaona
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Danielle A. Doll Rakoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Didier Menard
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Victor Jeannoda
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Arsene Ratsimbasoa
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AR conceived, designed, coordinated the study, and supervised the enrolment and follow-up of the patients. FR, TR, AR and DM analysed the data and map. FR and TR performed the molecular assay. FR, JV, AR, and DM wrote the manuscript. All the authors read and approved the final manuscript.

Corresponding authors

Correspondence to Fanomezantsoa Ralinoro or Arsene Ratsimbasoa.

Ethics declarations

Ethics approval and consent to participate

The study protocol was reviewed and approved by the Ethics Committee of the Ministry of Health of Madagascar (No. 083/MSANP/CE/11-2012).

Consent for publication

All the authors have agreed to the submission of this manuscript for publication.

Competing interests

The authors have no competing interests to declare.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ralinoro, F., Rakotomanga, T.A., Rakotosaona, R. et al. Genetic diversity of Plasmodium falciparum populations in three malaria transmission settings in Madagascar. Malar J 20, 239 (2021). https://doi.org/10.1186/s12936-021-03776-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12936-021-03776-1

Keywords

Malaria Journal

ISSN: 1475-2875

Contact us