Skip to main content
Download PDF

Plasmodium falciparum population structure inferred by msp1 amplicon sequencing of parasites collected from febrile patients in Kenya

Malaria Journal volume 22, Article number: 263 (2023) Cite this article

Abstract

Background

Multiplicity of infection (MOI) is an important measure of Plasmodium falciparum diversity, usually derived from the highly polymorphic genes, such as msp1, msp2 and glurp as well as microsatellites. Conventional methods of deriving MOI lack fine resolution needed to discriminate minor clones. This study used amplicon sequencing (AmpliSeq) of P. falciparum msp1 (Pfmsp1) to measure spatial and temporal genetic diversity of P. falciparum.

Methods

264 P. falciparum positive blood samples collected from areas of differing malaria endemicities between 2010 and 2019 were used. Pfmsp1 gene was amplified and amplicon libraries sequenced on Illumina MiSeq. Sequences were aligned against a reference sequence (NC_004330.2) and clustered to detect fragment length polymorphism and amino acid variations.

Results

Children < 5 years had higher parasitaemia (median = 23.5 ± 5 SD, p = 0.03) than the > 5–14 (= 25.3 ± 5 SD), and those > 15 (= 25.1 ± 6 SD). Of the alleles detected, 553 (54.5%) were K1, 250 (24.7%) MAD20 and 211 (20.8%) RO33 that grouped into 19 K1 allelic families (108–270 bp), 14 MAD20 (108–216 bp) and one RO33 (153 bp). AmpliSeq revealed nucleotide polymorphisms in alleles that had similar sizes, thus increasing the K1 to 104, 58 for MAD20 and 14 for RO33. By AmpliSeq, the mean MOI was 4.8 (± 0.78, 95% CI) for the malaria endemic Lake Victoria region, 4.4 (± 1.03, 95% CI) for the epidemic prone Kisii Highland and 3.4 (± 0.62, 95% CI) for the seasonal malaria Semi-Arid region. MOI decreased with age: 4.5 (± 0.76, 95% CI) for children < 5 years, compared to 3.9 (± 0.70, 95% CI) for ages 5 to 14 and 2.7 (± 0.90, 95% CI) for those > 15. Females’ MOI (4.2 ± 0.66, 95% CI) was not different from males 4.0 (± 0.61, 95% CI). In all regions, the number of alleles were high in the 2014–2015 period, more so in the Lake Victoria and the seasonal transmission arid regions.

Conclusion

These findings highlight the added advantages of AmpliSeq in haplotype discrimination and the associated improvement in unravelling complexity of P. falciparum population structure.

Background

Malaria is a life-threatening infectious disease caused by parasites of the genus Plasmodium transmitted through bites of infected female Anopheles mosquitoes. From 2000 to 2016, the World Health Organization (WHO) recorded significant progress in combating malaria in endemic areas. However, data from the 2021 WHO World Malaria Report showed that the progress in reducing global malaria cases is stalling. Malaria cases numbered 241 million, up from 227 million in 2020, 627 000 people died of malaria of which 80% were children younger than 5 years and Africa had 95% of global malaria cases [1]. To combat the disease burden, intensive intervention efforts were put in place, including treatment with anti-malarial combination therapies, use of insecticide-treated bed nets (ITNs) and indoor residual spraying (IRS). In Kenya, malaria is a leading cause of morbidity and mortality with over 80–90% of malaria infections due to P. falciparum [2]. The Ministry of Health estimates that 70% of the population in Kenya live in areas where malaria transmission occurs 8–12 months of the year [2].

Early molecular studies revealed that the parasites exist as a pool of genetic “clones” within a single host, and such multi-clones contributes to the ability of P. falciparum to evade the host immune response and develop resistance to anti-malarial drugs [3,4,5]. It has been suggested that multiclonal malaria infections can influence clinical outcomes in a manner that is dependent on transmission intensity [6], and may negatively impact an individual’s response to anti-malarial drug treatment [7]. RTS,S/AS01 remains the most advanced malaria vaccine and is now recommended by WHO as additional armament to help control malaria in children living in regions with moderate to high transmission [1].

MSP1 is the most abundant surface antigen in the blood stage of P. falciparum and plays a crucial role in the initial low affinity attachment of parasite to red blood cell membrane during erythrocyte invasion [8]. MSP1 contains 17 blocks of which block 2 shows extensive allelic polymorphism [9, 10], represented by three allelic families namely K1, MAD20 and RO33.

For malaria research, the term MOI (multiplicity of infection) is defined as the number of distinct clones per individual infection. In a given population, the calculated average MOI from the individuals values, has been proposed as a valuable metric for studying infection dynamics, including of transmission intensity [11], and therefore could be used for monitoring success of malaria control programs. Conversely, other studies have demonstrated a lack of correlation between malaria transmission intensity and MOI [12, 13]. One potential confounder in these association studies is the use of different genotyping methods, some of them lacking fine resolution needed to discriminate minor clones. One way of addressing this gap is by use of next generation sequencing of target microsatellites [14].

In this study, P. falciparum msp1 (Pfmsp1) was used to illustrate the utility of amplicon deep sequencing (AmpliSeq) in determining the malaria parasite clonal diversity beyond what can be provided by conventional approaches that use allele sizes. SeekDeep, a bioinformatics pipeline designed for analysis of haplotype frequency from amplicon deep sequencing data has been used successfully in several studies investigating malaria population genetics [8, 15, 16].

Methods

Sample collection, assay validation and quality control

A laboratory strain of P. falciparum 3D7 was used to initiate and maintain a malaria culture as described by [17], with minor modifications. Briefly, growth of the 3D7 parasites was initiated in washed group O+ human RBC diluted to 5% haematocrit in complete RPMI 1640 media supplemented with 0.2% bicarbonate, 25 mM HEPES, 50 µg/mL gentamicin and 10% heat inactivated human serum. Culture was maintained in 25 cm2 corning flasks (Corning incorporated, Corning NY, USA) with daily replacement of growth medium. To enrich for early ring stages parasites, culture was synchronized with 5% D-sorbitol in distilled water, which lyses RBCs containing late ring stages and other mature parasites [18]. This treatment was repeated every 48 h until > 98% of the parasites were in the early ring stage as confirmed by microscopy. Parasites were allowed to grow to a parasitemia of 3.6% (equivalent to 18,000 parasites/µL). Serial dilutions of the ring stage parasites were made to 0.55 parasites/µL and amplified using a real time qPCR to determine the limit of detection. The lowest parasite density that yielded usable Pfmsp1 sequence was used as a cutoff for selecting the field samples with adequate parasite density that would good sequence data.

DNA preparation from P. falciparum 3D7 culture and blood samples

QIAamp DNA Blood Mini Kit (Qiagen) was used extract DNA from 200 μl of cultured parasites and study samples as recommended by the manufacturer. A region of Pfmsp1 (NC_004330.2) from nucleotides 1201627 to 1201710 (includes the K1, MAD20 and RO33 alleles) was amplified in a primary PCR using 5′-CTAGAAGCTTTAGAAGATGCAGTATTG-3′ as forward primer and 5'-CTTAAATAGTATTCTAATTCAAGTGGATCA-3' as reverse primer [19]. A subsequent nested PCR was performed using a combination of degenerate primers (Table 1) to accommodate strain differences in the Pfmsp1 gene. The primers also included the Illumina adapter overhang. Briefly, in the primary PCR, 3 μl of DNA template, 0.625 µM of each primer and 1X NEB Next HIFI master mix were used in a 25 μl reaction that included initial denaturation at 95 °C for 5 min, followed by 25 cycles of denaturation at 94 °C for 1 min, annealing at 58 °C for 2 min and extension at 72 °C for 2 min, then a single annealing step at 58 °C for 2 min and final extension at 72 °C for 5 min. In the secondary PCR, 2.5 μl of DNA template, 0.2 µM of each primer, 1X NEB Next HIFI master mix were used in a 25 μl reaction. Cycling conditions included initial denaturation at 95 °C for 5 min, followed by 25 cycles of denaturation at 94 °C for 30 s, annealing at 55 °C for 30 s and extension at 72 °C for 30 s, then a final extension at 72 °C for 5 min. Amplicons were visualized on 1% agarose gel stained with gel red (Invitrogen, Carlsbad, CA).

Table 1 List of primer mix used for secondary PCR and deep sequencing of msp1 gene (NC_004330.2)
Full size table

Amplicon library preparation and sequencing

Amplicons were cleaned using AmpureXP beads (Beckman Coulter, IN, USA) followed by a dual indexing PCR to allow multiplexing of samples. For this, a 50 μL reaction consisting of 5 μL of purified amplicons, 5 μL of each Nextera XT i7 and i5 Index Primer (Illumina, USA), 25 μL of NEBNext High-Fidelity 2X PCR Master Mix (New England Bio-labs, MA, US) and 10 μL of PCR grade water (Thermo Fisher Scientific, CA, USA) was made and amplified by PCR at 95 °C for 3 min, followed by 12 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s, and a final extension at 72 °C for 5 min. The indexed amplicon libraries were purified with AMPure XP beads according to the manufacturer’s instructions (Beckman Coulter Genomics, CA, USA), and then quantified on Qubit Fluorometer 2.0 using Qubit dsDNA HS assay kit according to the manufacturer’s protocol (ThermoFisher Scientific, CA, USA). Libraries were normalized to a concentration of 4 nM and then pooled. The pooled samples were denatured and diluted to a final concentration of 12 pM, then spiked with 5% PhiX (Illumina, USA) as a sequencing control and then sequenced on MiSeq platform (Illumina, USA) using MiSeq 600 cycle reagent kit V3 (Illumina, USA).

Haplotype calling and determination of multiplicity of infections

Haplotypes of Pfmsp1 were determined using SeekDeep v2.6.0 [20]. Briefly, raw sequencing reads were filtered and trimmed based on the read length and quality scores using the extractor module in SeekDeep with the paired-end feature. After quality filtering, the reads were merged, chimeras removed and the sequences clustered at the sample level by qluster, and finally assembled based on the msp1 reference gene (NC_004330.2) to generate msp1 haplotypes. The assembled haplotypes were analyzed by processCluster algorithm which compared sample haplotypes and generated individual and population-level haplotypes and statistics. A final mapping of all sequence reads to selected reference sequences was performed with the CLC Genomics workbench (CLC Inc, Aarhus, Denmark) and queried against the nucleotide database (GenBank) using the Nucleotide Basic Local Alignment Search Tool (BLASTn) [21]. A haplotype was defined as a group of sequences within a cluster that represented the same allele of Pfmsp1. The MOI, defined as the number of distinct msp1 haplotypes in an individual infection (varying by length and at the nucleotide level) was determined for the different alleles (K1, MAD20 and RO33). The number of distinct genotypes for the K1, MAD20 and RO33 in each sample were added and the sum regarded as the MOI for that individual. The calculated group average was regarded as mean MOI. The expected heterozygosity was calculated from the frequencies of the different alleles within the population according to the formula: He = 1–Σ(pi^2), where He is the expected heterozygosity, pi is the frequency of the i-th allele in the population.

Statistical analysis

GraphPad prism and R software were used for visualization and statistical analyses. Box plots comparing the identity between groups were created with GraphPad Prism 9 software [22]. Paired sample t-test was used to compare parasite densities (Ct-values) in the different age groups. Sequence tables generated by SeekDeep were analysed using R with the packages Phyloseq v.1.22 [23] along with packages ggpubr v0.2.5 [24] and vegan v.2.5 [25]. Figures were generated using the following R packages: ggplot2 v.3.2.1 [26], ggthemes v.4.2.0 [27], cowplot v.1.0.0 [28] and viridis v.0.5.1 [29]. A p-value of < 0.05 was considered statistically significant.

Results

Limit of detection and quality control

18S rRNA qPCR of cultured parasites consistently detected P. falciparum to as low as 0.6 parasite/µl. By Amplicon sequencing of Pfmsp1, lowest parasite density that yielded usable Pfmsp1 sequence was 9 parasites/µL.

Demography and malaria parasitaemia in the study population

A total of 247 samples with P. falciparum parasitaemia ≥ 9 parasites/µL were selected for inclusion in the study, 122 (49%) were from females, and the median age was 7 years (interquartile range (IQR): 1–66). 83 (30%) were younger than 5 years, 99 (36%) between 5 and 14 years and 92 (34%) > 15 years. A total of 91 (33%) samples were from the malaria endemic lake region, 48 (18%) from the epidemic prone highlands region and 108 (39%) from arid regions that have seasonal malaria. qPCR Ct values were used as surrogate for malaria parasite density (Fig. 1): children < 5 years had higher parasitaemia (mean Ct = 23.37, SD =  ± 5) compared to 5 and 14 years (mean = 25.52, SD =  ± 5) and > 15 years (mean = 23.72 SD =  + 6) years). These differences were only significant for under 5 years and 5 to 14 year age groups (p = 0.0245).

Fig. 1
figure 1

Scatter plots showing qPCR Ct values in different age groups. Children < 5 years had higher parasitaemia than older age groups, but the difference was only significant when compared to the 5–14 years

Full size image

Pfmsp1 genetic diversity

Genetic diversity by fragment size polymorphisms

After quality filtering, the Pfmsp1 sequences from 274 samples, 247 (84%) passed the Q30 scores. The mean number of reads per sample was 30,998 (range 487–49,983). Based on size, 1,014 alleles (size range: 108–270 bp) were obtained of which, 553 (54.5%) were K1, 250 (24.7%) were MAD20 and 211 (20.8%) were RO33 that grouped into 19 K1 allelic families (108–270 bp), 14 MAD20 (108–216 bp) and one RO33 (153 bp) (Fig. 2).

Fig. 2
figure 2

Distribution and prevalence of Pfmsp1 allelic families in the study samples, based on fragment sizes showing 19 K1 (108–270 bp), 14 MAD20 (108–216 bp) and one RO33 (153 bp)

Full size image

Genetic diversity by nucleotide and amino acids polymorphisms

Sequence analysis of the K1, MAD20 and RO33 alleles revealed nucleotide polymorphisms in alleles that had similar sizes, thus increasing the number of allelic families from 19 to 104 for K1, 14 to 58 for MAD20 and 1 to 14 for RO33 (Additional file 1: Table S1). Most K1 diversity was due to duplications and deletions of the repeat amino acid motifs SGT and SGP and all the 104 sequences of K1 were nonsynonymous (Fig. 3, Panel A). The distribution and frequency of these substitutions were not random and were highest in the first half of block 2. MAD20 sequences were represented essentially by different combinations and deletions of the amino acid motifs SGG, SVA, SVT, and SKG. Synonymous nucleotide replacements were found in the repeat motifs SGG and SVA in 18 out of 58 of MAD20 sequences (Panel B). Unlike K1, the substitutions were concentrated in the middle part. All the 14 substitutions for RO33 were nonsynonymous (Panel C). The 14 RO33 sequences were nearly identical, however six non-synonymous amino acid substitutions were frequently found in codons A63T, A79V, K89N, G90D, G96D and D103N. For RO33, the substitutions were concentrated in the last 1/3 of block 2.

Fig. 3
figure 3

Frequencies of amino acid substitutions across the Pfmsp1 block 2, showing nonsynonymous amino acid substitutions for K1 and RO33 (Panels A and C) and synonymous and nonsynonymous substitutions for MAD20 (Panel B). The rows represents individual sequences, columns represent the amino acid substitutions. Lollipop plot show the distribution and frequency of the substitutions. For K1, the substitutions were concentrated in the first half of block 2, middle part for MAD20 and last 1/3 for RO33

Full size image

Multiplicity of infections

As shown in Table 2, the average number of alleles (shown as MOIs) were relatively stable in all the age groups, but slightly higher in the younger age groups (< 5 years mean = 4.5 ± 0.76, 95% CI) than the 5–14 years (3.9 ± 0.70, 95% CI) and those older > 15 years (2.7 ± 0.90, 95% CI). Females had similar allele frequency to males (mean = 4.2 ± 0.66, 95% CI) compared to males (4.0 ± 0.61, 95% CI). The average number of alleles in the malaria endemic lake region (4.8 ± 0.78, 95% CI) and the epidemic prone highland region (mean = 4.4 ± 1.03, 95% CI) were higher than in the seasonal malaria arid regions (mean = 3.4 ± 0.62, 95% CI). The expected heterozygosity (He), a measure of the probability of being infected by two parasites with different alleles at a given locus in all the regions was high (> 0.98).

Table 2 Plasmodium falciparum clonal diversity by age, gender and malaria endemicity
Full size table

In general, the temporal distribution of alleles was least stable in the malaria epidemic prone highland region of Kisii compared to the endemic Lake Victoria region or the seasonal transmission arid region. Overall, alleles frequency were low in the 2010–2014 period, and increased thereafter, more so in the Lake Victoria and the seasonal transmission arid regions (Fig. 4).

Fig. 4
figure 4

Temporal variation in allelic families in regions of different malaria endemicity. Over time, alleles’ distribution were least stable in the epidemic prone highland region of Kisii, compared to the malaria endemic Lake Victoria region or the seasonal malaria transmission arid region. In general, alleles frequency were low in the 2010–2014 period, and increased thereafter, more so in the Lake Victoria and the seasonal transmission arid regions

Full size image

Discussion

In this study, AmpliSeq of the highly polymorphic Pfmsp1 gene was used to characterize the spatial and temporal allelic structure of P. falciparum in three regions of differing malaria endemicities. The choice of Pfmsp1 was based on several factors. First, it is highly polymorphic, contains several SNPs, likely maintained via balancing selection by immune pressure in the human host and furthermore previous studies in malaria endemic regions have identified over 60 polymorphic sites within Pfmsp1 [8, 15]. To demonstrate the applicability of AmpliSeq, we first evaluated the lowest parasitaemia density that would give reliable sequence data. 3D7 cultured ring stage malaria parasites produced usable Pfmsp1 AmpliSeq at a parasitaemia of about 10 parasites/μL. Using this parasitaemia cut-off, 274 samples with P. falciparum parasitaemia of ≥ 9 parasites/µL were evaluated. As would be expected, children under 5 years had statistically significant higher malaria parasitaemia compared to those older than 5 years (Fig. 1).

AmpliSeq that combines the size and internal sequence polymorphism improved the power to detect multi clonal infections. As shown in Fig. 2 and Additional file 1: Table S1, Pfmsp1 AmpliSeq generated 1,014 size alleles that mapped to K1 (54.5%), MAD20 (24.7%) and RO33 (20.8%) and grouped to 34 allelic families (19 K1, 14 MAD20 and one RO33). By including sequence polymorphisms internal to the sequences, the overall increase in the number of allelic families was by 5x (34 to 176): 5.5x for K1 (19 to 104), 4.1x for MAD20 (from 14 to 58) and 14x for RO33 (from 1 to 14). Clearly, the use of size to deduce clonal multiplicity underestimates the number of clones in an infection. These findings corroborates previous studies that used AmpliSeq for estimating MOI [30,31,32,33]. As has been observed in other studies, K1 was found to be the dominant allelic family [34,35,36]. This is unlike the RO33 that was reported as the dominant allele in parasites collected from Malaysia [37], Brazil [38], and Gabon [39] and unlike MAD20 allele that was the most prevalent in Myanmar [40, 41], Thailand [41], Iran [42], Pakistan [43], and Colombia [44], Senegal [45]. Pfmsp1 haplotyping for the population was high, with a an expected heterozygosity value of 0.98.

Both synonymous and nonsynonymous amino acids substitutions were identified across the Pfmsp1 block 2 (Fig. 3). For K1 and RO33, only nonsynonymous substitutions were identified (Fig. 3, Panels A and C), while for MAD20, both synonymous and nonsynonymous substitutions were identified (Fig. 3, Panel B). The substitutions were not random: For K1, the substitutions were concentrated in the first half of block 2, middle part for MAD20 and last 1/3 for RO33.

Previous studies have shown that most alleles fluctuate significantly over the years and can differ across endemic areas [46, 47]. The present data suggest unequal allelic structure in the three areas of malaria endemicities (Fig. 4). In general, the period before 2010 and up to end of 2013 was marked by lowest allele frequencies, and indirectly malaria prevalence. This period coincided with the introduction, adoption and widespread use of artemisinin-based combination therapy (ACT) following withdrawal of sulfadoxine/sulfalene-pyrimethamine [48]. The epidemic prone Kisii highland region had little or no malaria prior to 2014. This is not surprising considering that the malaria cases seen in Kisii are largely introduced from the neighboring malaria endemic Lake Victoria region and, therefore, if there is low incidences of malaria in the latter region, there will be even fewer malaria cases in Kisii. Thereafter, there was a big burst in multi clonal infections in 2014–2015 in all the sites. At least for Kisii, this bust coincided with an outbreak of what was referred to as highland malaria [49]. For unknown reasons, infections in Kisii declined to near zero by 2019. This is unlike in the malaria endemic Lake Victoria region where the alleles’ distribution were more stable. In the arid region where malaria has seasonal distribution, the allelic structure has been on increase, and by 2018–2019, the distribution resembled the malaria endemic region. Further studies are needed to determine what is behind the stabilization of malaria cases in the arid region and how much of the change is attributable to climate change.

MOI has been found to be high in children compared to adults and increases with transmission intensity [6, 33, 50]. The current study confirm these observations (Table 2): MOI in children < 5 was higher in younger age group (< 5 years mean = 4.5 ± 0.76, 95% CI) than the 5–14 years (3.9 ± 0.70, 95% CI) and those older > 15 years (2.7 ± 0.90, 95% CI). MOI was also influenced by malaria intensity, was higher in samples from malaria endemic Lake Victoria basin and the semi-arid region compared to the epidemic prone highland region of Kisii. The average number of alleles in the malaria endemic lake region (mean = 4.8 ± 0.78, 95% CI) and the epidemic prone highland region (mean = 4.4 ± 1.03, 95% CI) were higher than in the seasonal malaria arid regions (mean = 3.4 ± 0.62, 95% CI). The expected heterozygote (He) was very high (> 0.98) and was independent of transmission pattern. Similar findings have been reported before [51].

Conclusion

There are over 500 different Pfmsp1 sequences that are available in public databases. Our study adds 176 distinct allelic sequences to this database. The Pfmsp1 antigen has been highly studied as a malaria vaccine candidate and to date, data has demonstrated that MSP1 based vaccine protection against clinical malaria is strain-specific and, therefore, a clear understanding of MSP1 diversity is critical to developing an effective malaria vaccine [52]. Haplotype frequency was influenced by age, gender and transmission settings, highlighting the complexity of determinants of P. falciparum population structure. One of the limitation of the study is that the sampling was only possible in patients with parasitaemia cutoff of 10 parasites/μL, thus under representing haplotypes from patients with low parasite density, thereby failing to capture the full diversity of haplotypes present in the population. There is probably no way to solve this shortcoming since technology used is not sensitive enough to sequence very low parasite load. Nevertheless, the analytical depth of AmpliSeq give high confidence that the data obtained is robust and provide a credible overview of the P. falciparum population structure in the study populations and regions.

Availability of data and materials

Raw sequence data generated in this study are available from the National Centre for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under BioProject ID: PRJNA983533.

References

  1. WHO. World malaria report 2021. Geneva: World Health Organization; 2021.

    Google Scholar 

  2. Kenya Demographic and Health Survey. 2021 Kenya Malaria Indicator Survey. Nairobi, Kenya, 2021.

  3. Anderson TJC, Haubold B, Williams JT, Estrada-franco JG, Richardson L, Mollinedo R, et al. Microsatellite markers reveal a spectrum of population structures in the malaria parasite Plasmodium falciparum. Mol Biol Evol. 2018;17:1467–82.

    Google Scholar 

  4. Dzikowski R, Deitsch KW. Genetics of antigenic variation in Plasmodium falciparum. Curr Genet. 2009;55:103–10.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Ferreira MU, Nunes S, Wunderlich G. Antigenic diversity and immune evasion by malaria parasites. Clin Diagn Lab Immunol. 2004;11:987–95.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Mahdi M, Hamid A, Elamin AF, Albsheer MMA, Abdalla AAA, Mahgoub NS, et al. Multiplicity of infection and genetic diversity of Plasmodium falciparum isolates from patients with uncomplicated and severe malaria in Gezira State. Sudan Parasit Vectors. 2016;9:362.

    Google Scholar 

  7. Mavoko HM, Kalabuanga M, Delgado-ratto C, Maketa V, Mukele R, Fungula B, et al. Uncomplicated clinical malaria features, the efficacy of artesunate-amodiaquine and their relation with multiplicity of infection in the Democratic Republic of Congo. PLoS ONE. 2016;11:e0157074.

    Google Scholar 

  8. Lin JT, Hathaway NJ, Saunders DL, Lon C, Balasubramanian S, Kharabora O, et al. Using amplicon deep sequencing to detect genetic signatures of Plasmodium vivax relapse. J Infect Dis. 2015;212:999–1008.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Miller RH, Hathaway NJ, Kharabora O, Mwandagalirwa K, Tshefu A, Meshnick SR, et al. A deep sequencing approach to estimate Plasmodium falciparum complexity of infection (COI) and explore apical membrane antigen 1 diversity. Malar J. 2017;16:490.

    PubMed  PubMed Central  Google Scholar 

  10. Tanabe K, Mackay M, Goman M, Scaife JG. Allelic dimorphism in a surface antigen gene of the malaria parasite Plasmodium falciparum. J Biol Mol. 1987. https://doi.org/10.1016/0022-2836(87)90649-8.

    Article  Google Scholar 

  11. Tusting LS, Bousema T, Smith DL, Drakeley C. Measuring changes in Plasmodium falciparum transmission: precision, accuracy and costs of metrics. Adv Parasitol. 2014;84:151–208.

    PubMed  PubMed Central  Google Scholar 

  12. Alam MT, De Souza DK, Vinayak S, Griffing SM, Poe AC, Duah NO, et al. Selective sweeps and genetic lineages of Plasmodium falciparum drug-resistant alleles in Ghana. J Infect Dis. 2011;203:220–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Duah NO, Matrevi SA, Quashie NB, Abuaku B, Koram KA. Genetic diversity of Plasmodium falciparum isolates from uncomplicated malaria cases in Ghana over a decade. Parasit Vectors. 2016;9:416.

    PubMed  PubMed Central  Google Scholar 

  14. Touray AO, Mobegi VA, Wamunyokoli F, Herren JK. Diversity and multiplicity of P. falciparum infections among asymptomatic school children in Mbita, Western Kenya. Sci Rep. 2020;10:5924.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Boyce RM, Hathaway N, Fulton T, Reyes R, Matte M, Ntaro M, et al. Reuse of malaria rapid diagnostic tests for amplicon deep sequencing to estimate Plasmodium falciparum transmission intensity in western Uganda. Sci Rep. 2018;8:10159.

    PubMed  PubMed Central  Google Scholar 

  16. Parobek CM, Lin JT, Saunders DL, Barnett EJ, Lon C, Lanteri CA, et al. Selective sweep suggests transcriptional regulation may underlie Plasmodium vivax resilience to malaria control measures in Cambodia. Proc Natl Acad Sci USA. 2016;113:e8096–105.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Trager W, Jensen J. Human malaria parasites in continuous culture. Science. 1976;193:673–5.

    CAS  PubMed  Google Scholar 

  18. Lambros C, Vanderberg JP. Synchronization of Plasmodium falciparum erythrocytic stages in culture. J Parasitol. 1979;65:418–20.

    CAS  PubMed  Google Scholar 

  19. Liljander A, Wiklund L, Falk N, Kweku M, Mrtensson A, Felger I, et al. Optimization and validation of multi-coloured capillary electrophoresis for genotyping of Plasmodium falciparum merozoite surface proteins (msp1 and 2). Malar J. 2009;8:78.

    PubMed  PubMed Central  Google Scholar 

  20. Hathaway NJ, Parobek CM, Juliano JJ, Bailey A. SeekDeep: single-base resolution de novo clustering for amplicon deep sequencing. Nucleaic Acids Res. 2018;46:e21.

    CAS  Google Scholar 

  21. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.

    CAS  PubMed  Google Scholar 

  22. Alex S, You L, Daniel L, Feng J, Arriaga EA, Piskounova E, et al. GraphPad prism user guide. Neoplasia. 2014;16:2591–8.

    Google Scholar 

  23. McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Kassambara A. ggpubr:“ggplot2” based publication ready plots. cran. 2020. https://cran.r-project.org/web/packages/ggpubr/readme/README.html

  25. Oksanen AJ, Blanchet FG, Friendly M, Kindt R, Legendre P, Mcglinn D, et al. Vegan: Community Ecology Package. http://CRAN.Rproject.org/package=vegan

  26. Wickham H, Chang W. Package ‘ggplot2. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=af53fd2f5b9e81b6edec0c13e1b3babd34bda399

  27. Arnold JB. ggthemes: Extra Themes, Scales and Geoms for “ggplot2.” R Packag version 424. 2021; https://cran.r-project.org/package=ggthemes

  28. Wilke CO. cowplot: Streamlined Plot Theme and Plot Annotations for “ggplot2”. R package version 2020. https://cran.r-project.org/web/packages/cowplot/index.html

  29. Garnier S, Ross N, Rudis R, Camargo PA, Sciaini M, Scherer C. Package ‘viridis’, 2023. https://cran.r-project.org/web/packages/viridis/index.html

  30. Juliano JJ, Porter K, Mwapasa V, Sem R, Rogers WO, Ariey F, et al. Exposing malaria in-host diversity and estimating population diversity by capture-recapture using massively parallel pyrosequencing. Proc Natl Acad Sci USA. 2010;107:20138–43.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Koepfli C, Mueller I. Malaria epidemiology at the clone level. Trends Parasitol. 2017;33:974–85.

    PubMed  PubMed Central  Google Scholar 

  32. Lerch A, Koepfli C, Hofmann NE, Messerli C, Wilcox S, Kattenberg JH, et al. Development of amplicon deep sequencing markers and data analysis pipeline for genotyping multi-clonal malaria infections. BMC Genomics. 2017;18:864.

    PubMed  PubMed Central  Google Scholar 

  33. Zhong D, Lo E, Wang X, Yewhalaw D, Zhou G, Atieli HE, et al. Multiplicity and molecular epidemiology of Plasmodium vivax and Plasmodium falciparum infections in East Africa. Malar J. 2018;17:185.

    PubMed  PubMed Central  Google Scholar 

  34. Ariey F, Witkowski B, Amaratunga C, Beghain J, Langlois AC, Khim N, et al. A molecular marker of artemisinin-resistant Plasmodium falciparum malaria. Nature. 2014;505:50–5.

    PubMed  Google Scholar 

  35. Chenet SM, Branch OLH, Escalante AA, Lucas CM, Bacon DJ. Genetic diversity of vaccine candidate antigens in Plasmodium falciparum isolates from the Amazon basin of Peru. Malar J. 2008;7:93.

    PubMed  PubMed Central  Google Scholar 

  36. Takala S, Branch OL, Escalante AA, Kariuki S, Wootton J, Lal AA. Evidence for intragenic recombination in Plasmodium falciparum: identification of a novel allele family in block 2 of merozoite surface protein-1: Asembo bay area cohort project XIV. Mol Biochem Parasitol. 2002;125:163–71.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Atroosh WM, Al-Mekhlafi HM, Mahdy MAK, Surin J. The detection of pfcrt and pfmdr1 point mutations as molecular markers of chloroquine drug resistance, Pahang, Malaysia. Malar J. 2012;11:251.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Kimura E, Mattei D, di Santi SM, Scherf A. Genetic diversity in the major merozoite surface antigen of Plasmodium falciparum: high prevalence of a third polymorphic form detected in strains derived from malaria patients. Gene. 1990;91:57–62.

    CAS  PubMed  Google Scholar 

  39. Kun JF, Schmidt-Ott RJ, Lehman LG, Lell B, Luckner D, Greve B, et al. Merozoite surface antigen 1 and 2 genotypes and rosetting of Plasmodium falciparum in severe and mild malaria in Lambaréné, Gabon. Trans R Soc Trop Med Hyg. 1998;92:110–4.

    CAS  PubMed  Google Scholar 

  40. Kang JM, Moon SU, Kim JY, Cho SH, Sohn WM, Kim TS, et al. Genetic polymorphism of merozoite surface protein-1 and merozoite surface protein-2 in Plasmodium falciparum field isolates from Myanmar. Malar J. 2010;9:131.

    PubMed  PubMed Central  Google Scholar 

  41. 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.

    CAS  PubMed  Google Scholar 

  42. Zakeri S, Bereczky S, Naimi P, Gil JP, Djadid ND, Färnert A, et al. Multiple genotypes of the merozoite surface proteins 1 and 2 in Plasmodium falciparum infections in a hypoendemic area in Iran. Trop Med Int Health. 2005;10:1060–4.

    CAS  PubMed  Google Scholar 

  43. Ghanchi NK, Mårtensson A, Ursing J, Jafri S, Bereczky S, Hussain R, et al. Genetic diversity among Plasmodium falciparum field isolates in Pakistan measured with PCR genotyping of the merozoite surface protein 1 and 2. Malar J. 2010;9:1.

    PubMed  PubMed Central  Google Scholar 

  44. Gómez D, Chaparro J, Rubiano C, Rojas OM, Wasserman M. Genetic diversity of Plasmodium falciparum field samples from an isolated Colombian village. Am J Trop Med Hyg. 2002;67:611–6.

    PubMed  Google Scholar 

  45. Niang M, Thiam LG, Loucoubar C, Sow A, Sadio BD, Diallo M, et al. Spatio-temporal analysis of the genetic diversity and complexity of Plasmodium falciparum infections in Kedougou, southeastern Senegal. Parasit Vectors. 2017;10:33.

    PubMed  PubMed Central  Google Scholar 

  46. Kiwuwa MS, Ribacke U, Moll K, Byarugaba J, Lundblom K, Färnert A, et al. Genetic diversity of Plasmodium falciparum infections in mild and severe malaria of children from Kampala. Uganda Parasitol Res. 2013;112:1691–700.

    PubMed  Google Scholar 

  47. Yuan L, Zhao H, Wu L, Li X, Parker D, Xu S, et al. Plasmodium falciparum populations from northeastern Myanmar display high levels of genetic diversity at multiple antigenic loci. Acta Trop. 2013;125:53–9.

    CAS  PubMed  Google Scholar 

  48. Amin AA, Zurovac D, Kangwana BB, Greenfield J, Otieno DN, Akhwale WS, et al. The challenges of changing national malaria drug policy to artemisinin-based combinations in Kenya. Malar J. 2007;6:72.

    PubMed  PubMed Central  Google Scholar 

  49. Shanks GD, Biomndo K, Guyatt HL, Snow RW. Travel as a risk factor for uncomplicated Plasmodium falciparum malaria in the highlands of western Kenya. Trans R Soc Trop Med Hyg. 2005;99:71–4.

    CAS  PubMed  Google Scholar 

  50. Yavo W, Konaté A, Mawili-Mboumba DP, Kassi FK, Tshibola Mbuyi ML, Angora EK, et al. Genetic polymorphism of msp 1 and msp 2 in Plasmodium falciparum isolates from Côte d’Ivoire versus Gabon. J Parasitol Res. 2016;2016:3074803.

    PubMed  PubMed Central  Google Scholar 

  51. Mwingira F, Nkwengulila G, Schoepflin S, Sumari D, Beck HP, Snounou G, et al. Plasmodium falciparum msp1, msp2 and glurp allele frequency and diversity in sub-Saharan Africa. Malar J. 2011;10:79.

    PubMed  PubMed Central  Google Scholar 

  52. Otsyula N, Angov E, Bergmann-Leitner E, Koech M, Khan F, Bennett J, et al. Results from tandem Phase 1 studies evaluating the safety, reactogenicity and immunogenicity of the vaccine candidate antigen Plasmodium falciparum FVO merozoite surface protein-1 (MSP142) administered intramuscularly with adjuvant system AS01. Malar J. 2013;12:29.

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We are grateful for the research subjects who participated and provided study samples. 

Disclaimer

Material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its publication. The opinions or assertions contained herein are the private views of the author, and they are not to be construed as official, or as reflecting true views of the Department of the Army or the Department of Defense. The investigators have adhered to the policies for protection of human subjects as prescribed in AR 70–25.

Funding

Armed Forces Health Surveillance Division (AFHSD) and its Global Emerging Infections Surveillance and Research Branch (ProMIS P0095_22_KY and P0091_23_KY). The funders had no role in data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Basic Science Laboratory, United States Army Medical Research Directorate, Kisumu, Kenya

    Brian Andika, Kimita Gathii, Josphat Nyataya, George Awinda, Beth Mutai & John Waitumbi

  2. Department of Biochemistry, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

    Brian Andika & Naomi Maina

  3. Department of Biochemistry, University of Nairobi, Nairobi, Kenya

    Victor Mobegi

Authors
  1. Brian Andika
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Victor Mobegi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kimita Gathii
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Josphat Nyataya
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Naomi Maina
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. George Awinda
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Beth Mutai
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. John Waitumbi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

BA performed all the assays, performed genome sequencing and associated bioinformatics analysis and interpretation, wrote the first draft, reviewed and edited the manuscript. VM assisted BA in data analysis and interpretation, reviewed and edited the manuscript. JN assisted BA in qPCR assay execution, and interpretation of MOI data. KG supervised the bioinformatics analysis and interpretation. NM assisted BA in data analysis and interpretation, reviewed and edited the manuscript. GA was in charge of maintaining sample inventory, performed the malaria culture, including parasite synchronization and harvesting of ring stages. BM supervised all aspects of the assays, and revised and edited the manuscript. JW conceived the study, worked with BA on the multiple versions of the manuscript, provided the resources, and obtained funding.

Corresponding author

Correspondence to John Waitumbi.

Ethics declarations

Ethics approval and consent to participate

Eligible subjects were recruited under a study protocol that was approved by the Ethical Review Committee of the Kenya Medical Research Institute (IRB protocol KEMRI SERU # 1282) and the Walter Reed Army Institute of Research Human Subjects Protection Board in the United States of America (WRAIR HSPB # 1402).

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

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

Supplementary Information

Additional file 1.

Pfmsp1 block 2 nucleotide sequences showing the KI (sheet 1), MAD20 (sheet 2) and RO33 (sheet 3) allelic families, including the repeat motifs found in K1 and MAD20 (shown in bolded blue fonts) and the polymorphic nucleotides (shown in bold red fonts).

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

Andika, B., Mobegi, V., Gathii, K. et al. Plasmodium falciparum population structure inferred by msp1 amplicon sequencing of parasites collected from febrile patients in Kenya. Malar J 22, 263 (2023). https://doi.org/10.1186/s12936-023-04700-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12936-023-04700-5

Keywords

Malaria Journal

ISSN: 1475-2875

Contact us