- Open Access
Multiplicity and molecular epidemiology of Plasmodium vivax and Plasmodium falciparum infections in East Africa
© The Author(s) 2018
- Received: 13 December 2017
- Accepted: 26 April 2018
- Published: 2 May 2018
Parasite genetic diversity and multiplicity of infection (MOI) affect clinical outcomes, response to drug treatment and naturally-acquired or vaccine-induced immunity. Traditional methods often underestimate the frequency and diversity of multiclonal infections due to technical sensitivity and specificity. Next-generation sequencing techniques provide a novel opportunity to study complexity of parasite populations and molecular epidemiology.
Symptomatic and asymptomatic Plasmodium vivax samples were collected from health centres/hospitals and schools, respectively, from 2011 to 2015 in Ethiopia. Similarly, both symptomatic and asymptomatic Plasmodium falciparum samples were collected, respectively, from hospitals and schools in 2005 and 2015 in Kenya. Finger-pricked blood samples were collected and dried on filter paper. Long amplicon (> 400 bp) deep sequencing of merozoite surface protein 1 (msp1) gene was conducted to determine multiplicity and molecular epidemiology of P. vivax and P. falciparum infections. The results were compared with those based on short amplicon (117 bp) deep sequencing.
A total of 139 P. vivax and 222 P. falciparum samples were pyro-sequenced for pvmsp1 and pfmsp1, yielding a total of 21 P. vivax and 99 P. falciparum predominant haplotypes. The average MOI for P. vivax and P. falciparum were 2.16 and 2.68, respectively, which were significantly higher than that of microsatellite markers and short amplicon (117 bp) deep sequencing. Multiclonal infections were detected in 62.2% of the samples for P. vivax and 74.8% of the samples for P. falciparum. Four out of the five subjects with recurrent P. vivax malaria were found to be a relapse 44–65 days after clearance of parasites. No difference was observed in MOI among P. vivax patients of different symptoms, ages and genders. Similar patterns were also observed in P. falciparum except for one study site in Kenyan lowland areas with significantly higher MOI.
The study used a novel method to evaluate Plasmodium MOI and molecular epidemiological patterns by long amplicon ultra-deep sequencing. The complexity of infections were similar among age groups, symptoms, genders, transmission settings (spatial heterogeneity), as well as over years (pre- vs. post-scale-up interventions). This study demonstrated that long amplicon deep sequencing is a useful tool to investigate multiplicity and molecular epidemiology of Plasmodium parasite infections.
- Multiplicity of infection
- Merozoite surface protein 1
- Amplicon deep sequencing
- Molecular epidemiology
- Within-host diversity
Malaria is one of the most common infectious diseases and an important public health problem worldwide. In 2016, there were an estimated 216 million cases and 445,000 deaths of malaria occurred worldwide; and nearly half of the world’s population lived in 91 countries and territories are at risk of malaria transmission . The majority of malaria cases and deaths (~ 90%) occur in sub-Saharan Africa. Plasmodium falciparum is the most prevalent malaria parasite in sub-Saharan Africa, while Plasmodium vivax is the most widespread human malaria with approximately 2.5 billion people at risk of infection worldwide . Plasmodium vivax is a major cause of anaemia in an area where P. falciparum and P. vivax co-exist . Relapses play an important role in the transmission of P. vivax in malaria endemic areas .With the scaling up of interventions since 2006, primarily mass distribution of insecticide-treated nets (ITNs), indoor residual spraying (IRS), and artemisinin-based combination therapy (ACT), malaria transmission has declined tremendously in the past decade .
The extent of genetic diversity and multiplicity of infection (MOI) is essential in understanding malaria epidemiological patterns, transmission intensity, host immune system, and parasite virulence for the development of anti-malarial vaccine as well as evaluating the impact of malaria control interventions. For example, MOI has been used for inferring disease epidemiology such as detecting parasite clearance rates subsequent to anti-malarial treatment  and examining the level of anti-malarial drug resistance [7, 8], the impact of transmission intensity on infection complexity , parasite virulence related to anti-malarial vaccine development [10, 11], and in-host ecology of malaria infections . Traditional PCR-based methods, such as microsatellite [13, 14] and merozoite surface protein (msp) genotyping [14–17], for assessing MOI estimation can lack both sensitivity and specificity, resulting in the apparent problem of underestimating disease complexity [18–20]. Compared to genotyping methods, amplicon deep sequencing provides a rapid, robust, high-throughput approach to detect sequence variants and estimate allele frequency by sequencing a genomic region multiple times, sometimes hundreds or even thousands of times . For example, ultra-deep sequencing of amplicons from the ribosomal, mitochondrion, and apicoplast encoded genes revealed a large complexity of coinfections with an unexpectedly high MOI in Plasmodium ovale and Plasmodium malariae infections in the endemic areas of Gabon . Use of length polymorphic genes such as msp2 in amplicon deep sequencing has been shown to display greater sensitivity in detecting minority clones .
Using pvmsp1 short amplicon deep sequencing, Lin et al.  identified 67 unique haplotypes from 78 Cambodian P. vivax samples with an average MOI of 3.6 within each individual. Over half of the recurrent infections were detected as relapse. Compared to the standard PCR based method, next-generation sequencing revealed up to sixfold higher MOI in Plasmodium infections [12, 21]. This technology has unquestionably advanced our understanding of the genetics and evolution of multiclonal infection. However, in previous studies, most of amplicon deep sequencing was performed on two platforms, 454/Roche or Ion Torrent with high error rate and short reads due to technological limitation. By contrast, the Illumina MiSeq/HiSeq platform can generate reads of up to 600 bp length with lower sequencing error rate.
Plasmodium merozoite surface protein 1 (msp1) is a highly abundant and the most polymorphic antigen, which has been extensively studied in the parasite population [24–26]. Plasmodium falciparum has seven variable blocks that are separated either by conserved or semi-conserved regions. The variable block 2 of pfmsp1 is the most polymorphic region of the antigen . Plasmodium vivax has nine variable regions that are separated by 10 interspecies conserved or intraspecies conserved blocks . The variable block 18, located in 42 kDa region of pvmsp1, has been identified to be the most polymorphic part of the antigen . These polymorphic regions could be good candidate markers for multiclonal detection of Plasmodium infection.
The present study was designed to address the following questions: (1) how useful is amplicon ultra-deep sequencing for determining multiplicity of Plasmodium infection and identifying P. vivax relapse? (2) is there any difference in multiplicity of Plasmodium infection between patients of different symptoms, ages, genders, time, and transmission settings? (3) does intensified intervention since 2006 affect MOI? To address the first question, different lengths of P. vivax amplicons and microsatellites for MOI and relapse estimation were compared. For the second and third questions, different groups of P. falciparum and P. vivax infected patients were compared.
Study site and sample collection
Sample collection of Plasmodium vivax in Ethiopia and Plasmodium falciparum in Kenya
(> 1 k)
PCR amplification and deep sequencing of pvmsp1 and pfmsp1
PCR amplification of each sample was conducted in a 20 μl reaction mixture containing 2 μl of genomic DNA, 4 μl of 5 × PCR buffer, 1 unit of high fidelity PrimeSTAR® GXL DNA Polymerase (Takara Bio USA, Inc., Mountain View, CA), and 10 pmol of each primer. The laboratory strains P. vivax Pakchong (MRA-342G) and P. falciparum 3D7 (MRA-102G) were also included as control. Amplification reactions were performed with an initial denaturation at 94 °C for 3 min, followed by 35 cycles at 94 °C for 30 s, 55 °C for 30 s and 72 °C for 60 s, with a final 6-min extension at 72 °C. Ten samples from each species were amplified in duplicate with unique barcode for confirmation of amplicon. Amplicons were cleaned and normalized to 1–2 ng/μl concentration using the SequalPrep Normalization Plate Kit (Life Technologies, Carlsbad, California). HiSeq Rapid SBS Kit v2 (with reads up to 2 × 250 bp) was used for library preparation. Multiple samples were pooled and sequenced on the Illumina Hiseq 2500 (384-well plate with dual indexing, UCI Genomics High-Throughput Facility).
Haplotype determination from deep sequencing
Haplotypes of pvmsp1 and pfmsp1 variants were determined by SeekDeep software developed by Bailey lab at University of Massachusetts Amherst (http://baileylab.umassmed.edu/seekdeep). This software uses a clustering method to construct the most likely haplotypes within a patient while removing false haplotypes due to PCR or sequencing error . Before running data on SeekDeep software, all paired-end reads were merged using Fastq-join software with the parameters: Number of percent maximum difference = 8, Number of minimum overlap = 30. Joined reads of each sample were grouped into different clusters after trimming of barcodes, tags, and primers. For each sample, haplotype clusters were determined by within-host reads cutoff frequency at 2.0%. EstimateS (v 9.1.0) program  was used to infer estimates of allelic richness. Sample-based rarefaction (haplotype accumulation) curves were plotted with 95% confidence intervals. The input matrix used msp1 haplotype abundance or incidence data for a set of related samples. Relapse or reinfection of P. vivax was classified based on previously published method .
Comparison of amplicon deep sequences of different length and microsatellite genotyping
To compare longer and short sequencing fragment in MOI determination, 117 bp fragment of pvmsp1 was extracted, which had the same size as the ones previously used for amplicon deep sequencing . MOI was evaluated using the same procedure as describe above. Our previous microsatellite genotyping data of P. vivax  were also included for comparison in the study. For P. falciparum, the shorter amplicon was not able to be extracted for comparison, due to large length difference and extensively polymorphism in the amplicon region. Analysis of variance (ANOVA) and mean comparisons were performed using the JMP statistical software package (JMP 12.2.0; SAS Institute, Cary, NC). Mean MOI was compared using the Tukey–Kramer HSD test (alpha = 0.05) or Student’s t test.
Sequence variation analysis and haplotype relationship within multiple infections
MAFFT v7 online version (https://mafft.cbrc.jp/alignment/server/) was used to align DNA sequences . Bioedit v7 was used to calculate sequences identity . Analysis of haplotype and nucleotide diversity was performed by using DnaSP v5 . The Nei’s unbiased expected heterozygosity (He) was calculated as a measure of overall genetic diversity for each genotype method . Analysis of Molecular Variance (AMOVA) was conducted by GenAlEx 6.5 to estimate sequences variation within- and between infections . The MEGA v7 was used to create a UPGMA phylogenetic tree . The tree was annotated using the online tool iTOL (interactive Tree of Life) v3 program (http://itol.embl.de/index.shtml) . The PopART v1.7 software was used to construct a minimum spanning haplotype network between haplotypes .
Sequence reads and haplotype determination
A total of 384 PCR reactions (362 samples and 2 controls as well as 20 replicate PCR reactions) were successfully amplified and sequenced, resulting in 166 M total reads, of which 120 M reads (74%) passed filter, including 100 M with Qscores > 30, with an exception of three P. vivax samples and two P. falciparum samples with less than 1000 reads that were excluded from the analyses. The haplotype clustering threshold was determined by a subset of samples with replicate PCR reactions. These samples were analysed separately by SeekDeep and the results were compared among replicates as well as with a single clone lab strain (Pakchong or 3D7) as a positive control sample. A threshold cutoff frequency of 2.0% was determined to provide identical results for minor clonal calling between replicates, instead of 0.5% default threshold cut-off .
For the 135 P. vivax samples, a total of 11,576,219 joined reads were obtained by the fast-join program, of which 4,657,238 (36.1%) were successfully clustered by SeekDeep with an average of 34,498 reads per sample at within-host cluster frequency > 2.0%. The pvmsp1 amplicon generated identical 422 bp fragment with 88 haplotypes. Among them, 21 P. vivax predominant haplotypes (the clone had the highest frequency within infection) were identified (GenBank acc. MG657437–MG657457, Additional file 2). NCBI nucleotide BLAST search identified that 10 out of the 21 unique haplotypes had a perfect match with sequences from GenBank and > 99% sequence similarity for the others against distinct sequences from GenBank (see Additional file 3).
For the 222 P. falciparum samples, a total of 42,832,457 merged reads were generated, of which 23,187,282 (52.5%) joined reads were clustered with an average of 104,447 reads per sample at within-host cluster frequency > 2.0%. The length of pfmsp1 amplicons varied from 239 to 410 bp with 307 unique haplotypes. Among them, 99 P. falciparum predominant haplotypes were identified (GenBank acc. MG675458-MG675556, Additional file 4). NCBI nucleotide BLAST search identified that 25 out of the 99 unique haplotypes had a perfect match with sequences from GenBank and a range of 84.4–99.7% sequence similarity for the others against distinct sequences from GenBank (see Additional file 5).
Haplotype diversity and population frequency distribution
For the 307 unique pfmsp1 haplotypes, a total of 20 amplicons with various fragment length were identified, of which the three amplicon sizes 266, 311, and 338 bp appeared in at least 10% of haplotypes (see Additional file 4). All 307 haplotypes could be successfully translated into completed amino acid sequences, resulting in 262 distinct amino acid haplotypes. Eight common nucleotide haplotypes each appeared in at least 10 samples (Fig. 2b, Additional file 4), while nearly 80% (243/307) of haplotypes appeared in only 1 individual sample with within-host frequency ranging from 3.4 to 100%. Approximately half (161/307) of the identified haplotypes were detected only as minority variants. Some of these minority variants were detected from multiple samples and multiple locations. NCBI nucleotide BLAST search identified that 9 out of the 161 minority haplotypes had a perfect match with sequences from GenBank and a range of 91.5–99.7% sequence similarity for the others against distinct sequences from GenBank (Additional file 5). Polyclonal infections were detected in 74.8% (166/222) samples, ranged from 2 to 9 clones per sample (Fig. 4b). The Nei’s unbiased expected heterozygosity at this locus was He = 0.95, indicating an average 95% probability to get 2 parasites clones with different pvmsp1 haplotypes from the population. Similar to P. vivax samples, there was no clear asymptote in accumulation curves from the estimates of allelic richness in all the 222 P. falciparum samples (Fig. 5b), suggesting more haplotypes might be found from increased samples.
Comparison of three methods for MOI and relapse determination in P. vivax infection
Comparison of three methods for determination of multiplicity of infection (MOI) of P. vivax: long fragment (422 bp) of pvmsp1 amplicon deep sequencing, short fragment (117 bp) of pvmsp1 amplicon deep sequencing, and microsatellite marker genotyping
Amplicon deep sequencing
Long fragment (422 bp)
Amplicon deep sequencing
Short fragment (117 bp)
Number of subject
Multiplicity of P. vivax infections in different patient groups
Multiplicity of P. falciparum infections in different patient groups
Multiplicity of infection (MOI), also termed complexity of infection (COI) is defined as the number of different parasite strains co-infecting a single host. MOI can be a useful indicator of immune status and transmission level. Traditionally, MOI was assessed by PCR genotyping of antigen protein genes (msp1, msp2, and glurp) and microsatellite markers, which were regarded as the gold standard because of their high polymorphism . However, these methods were unable to distinguish sequence variation among parasite strains and detect minority clones within a host. By using next-generation amplicon deep sequencing, the minority clone could be detected as low as 0.5% within-host infection frequency [6, 15]. In the study, the Illumina HiSeq platform combined with Rapid SBS Kit v2 generated reads up to 500 bp with high coverage depth (~ 35 k × for P. vivax and ~ 100 k × for P. falciparum). Compared to a previous study by Lin et al. that employed a 117 bp-fragment of pvmsp1 short amplicon deep sequencing , longer amplicon sequencing, by capturing a greater number of polymorphisms, revealed a higher MOI and improved power to detect multiclonal infections. Interestingly, using microsatellite markers with the same parasite population, multiclonal infections were detected only in 5.2% of the samples with an average MOI of 1.07 , significantly lower than that estimated by longer amplicon sequencing (a mean of 2.16 MOI). One possible reason might be the missed genotyping of polyclonal infections in some of the tested samples with microsatellite analysis. Such contrast suggested that transmission intensity may not be low. Together with high relapse as identified in the present study, there could be a much larger P. vivax reservoir that sustains continual transmission and makes elimination challenging.
The complexity of infection has been suggested to be associated with ages and symptoms in Plasmodium infections [55–58]. However, in this study, no significant difference was found in P. vivax MOI between the symptomatic and asymptomatic infections, adults and children, as well as between male and female groups. Similar patterns were also reported in other studies [59, 60]. In western Kenya, no notable difference was detected in the multiplicity of P. falciparum among asymptomatic school children in low transmission areas (highland) and in high transmission areas (lowland) over 10 years. However, in the high transmission areas (lowland), significantly difference in MOI was detected between locations (Kombewa vs Kendu Bay). The temporal changes in complexity of P. falciparum infections could be varied by transmission intensity and our findings indicated that multiclonal parasite genotypes could have remained steady over time in high transmission areas. Several researchers have reported correlations between clinical symptoms and higher MOI [60–68] while others did not find any associations [69–71]. Some studies reported that a reduced risk of clinical malaria was associated with multiclonal infections [72–74], while other studies reported that mono-infections and very common genotypes are more likely to develop severe malaria than multiclonal infections [70, 75]. A positive association between the proportion of polyclonal infections and parasite prevalence has been observed in parasite populations from Indonesia  and Papua New Guinea , while in other studies, no association or negative correlation was found between the rate of polyclonal infections and parasite prevalence [77, 78]. In Ethiopia, reported malaria cases were respectively 2.6 million and 2.2 million in 2011 and 2015, however, proportion of P. falciparum increased by 5% from 2011 to 2015 (Zhou unpublished data), indicating a relative weak reduction in transmission. In our study areas in Kenya, malaria parasite prevalence in school children in the lowland increased from 40 to 45% from 2011 to 2015 while it decreased from 16% in 2011 to 13% in 2015 in the highlands, results also indicated insignificant changes in transmission in the areas .
Analysis of molecular variance (AMOVA) of P. vivax infections using pvmsp1 deep sequencing
Source of variation
In the study, using long amplicon deep sequencing of high polymorphic makers, pvmsp1 and pfmsp1, minority clones were able to be detected in multiclonal infections. However, there are also a few limitations in the study: (1) only a small polymorphic genomic region is amplified, not covered whole genome variants; (2) the threshold for haplotype cluster calls needs to be determined by empirical methods in each study, due to various sequencing error rates in different sequencing platforms and computational strategies; (3) the PCR slippage might be present in early PCR cycle at the microsatellite repeat unit of length polymorphic pfmsp1 marker, which resulted in increased frequency of minority clones; (4) there was only a subset of samples with replicate PCR. In order to exclude PCR or sequencing errors, it is better to perform experiments in duplicate of all the samples and use appropriate controls in each study to help determine that no false calls are being made; (5) the low percentage of reads was clustered in our clinical samples compared to laboratory strain (> 99%), suggesting DNA template quality is important. This can be improved by removal of host DNA using an enzyme-based DNA degradation method that selectively digests and depletes human DNA contamination from malaria clinical samples . Another limitation is the lack of a mixed infection positive control, especially for pfmsp1 with the different product fragment lengths.
Long-amplicon deep sequencing is a powerful, high-throughput, sensitive method in measuring Plasmodium MOI and within-host diversity. Multiclonal infections were common among age groups, symptoms, genders, transmission settings, as well as over years (pre- vs. post-scale-up interventions) in P. vivax and P. falciparum infections. The study demonstrated that long amplicon deep sequencing is a useful tool to investigate multiplicity and molecular epidemiology of Plasmodium parasite infections.
DZ and GY conceived and designed the study. DZ, EL, and EH performed the experiments: DY, EL, YA, HA, and AG conducted sample collection and field supervision. DZ, EL, GZ, ML and XW analysed the data. DZ, EL, GZ, YA, DY, XW, EH and GY contributed to writing and refining the manuscript. All authors read and approved the final manuscript.
We thank participants of the study in Ethiopia and Kenya. We thank the staffs from Genomics High-Throughput Facility (GHTF) of University of California Irvine for assisting with Illumina sequencing. We thank Nick Hathaway and Edward Xia for help with the installation and use of the SeekDeep software.
The authors declare that they have no competing interests.
Availability of data and materials
The data supporting the findings of this article are included within the article.
Consent for publication
There are no details or image of any individual reported within the manuscript that requires consent for publication.
Ethics approval and consent to participate
Ethics approval was obtained from the Institutional Review Board (IRB) of Jimma University, Ethiopia, the Kenya Medical Research Institute, Kenya and University of California, Irvine, USA. Written informed consent was obtained from all consenting heads of households, patients or their guardians, and each individual who was willing to participate in the study.
The work was supported by grants from the National Institutes of Health (R01 AI050243, U19 AI129326 and D43 TW001505).
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