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    Map of the study area. Dots indicate sampled villages, shaded areas indicate the geographic clusters predicted to harbor distinct parasite populations. Broad geographic regions are indicated: Momase (clusters 1–9), Highlands (clusters 10–13), Southern (clusters 14–21), and Islands (clusters 22–27). See Supplemental Table 1 for names of clusters and villages.

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    Complexity of infection in different geographic clusters and regions of Papua New Guinea. Proportion of polyclonal infections (solid line) and mean multiplicity of infection (MOI) (dashed line) relative to prevalence (all ages) in different geographic clusters and regions for (A) Plasmodium vivax and (B) Plasmodium falciparum.

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    Frequency distribution of multiplicity of infection in Papua New Guinea. Frequency of multiplicity of infection (MOI) values among all samples for (A) Plasmodium vivax and (B) Plasmodium falciparum. Numbers above the columns indicate proportions.

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    Genetic diversity of Plasmodium vivax and Plasmodium falciparum in different geographic clusters and regions of Papua New Guinea. Genetic diversity as measured by heterozygosity (solid line) and allelic richness (dashed line) relative to prevalence (all ages) in different geographic clusters and regions for (A) Plasmodium vivax (PvMS16) and (B) Plasmodium falciparum (Pfmsp2).

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    Association between parasite prevalence and infection complexity in different geographic clusters of Papua New Guinea. Association between age-corrected prevalence and mean multiplicity of infection (MOI) for (A) Plasmodium vivax and (B) Plasmodium falciparum. Association between age-corrected prevalence and polyclonality for (C) P. vivax and (D) P. falciparum. The dashed lines indicate the best fit for Spearman's rho and P values indicated on each plot.

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    Association between infection complexity and genetic diversity in different geographic clusters of Papua New Guinea. Association between mean multiplicity of infection (MOI) and heterozygosity (He) for (A) Plasmodium vivax (B) Plasmodium falciparum. Association between mean MOI and allelic Richness (Rs) for (C) P. vivax and (D) P. falciparum. The dashed lines indicate the best fit for mean MOI and genetic diversity with Spearman's rho and P values indicated on each plot.

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Higher Complexity of Infection and Genetic Diversity of Plasmodium vivax Than Plasmodium falciparum Across All Malaria Transmission Zones of Papua New Guinea

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  • 1 Population Health and Immunity Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Australia.
  • | 2 Department of Medical Biology, University of Melbourne, Parkville, Australia.
  • | 3 Department of Epidemiology and Preventative Medicine, Monash University, Clayton, Australia.
  • | 4 European Public Health Microbiology (EUPHEM) Training Programme, European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden.
  • | 5 Statens Serum Institut, Copenhagen, Denmark.
  • | 6 Swiss Tropical and Public Health Institute, Basel, Switzerland.
  • | 7 University of Basel, Basel, Switzerland.
  • | 8 Papua New Guinea Institute of Medical Research, Goroka, Papua New Guinea.
  • | 9 Institut Pasteur, Paris, France.

Plasmodium falciparum and Plasmodium vivax have varying transmission dynamics that are informed by molecular epidemiology. This study aimed to determine the complexity of infection and genetic diversity of P. vivax and P. falciparum throughout Papua New Guinea (PNG) to evaluate transmission dynamics across the country. In 2008–2009, a nationwide malaria indicator survey collected 8,936 samples from all 16 endemic provinces of PNG. Of these, 892 positive P. vivax samples were genotyped at PvMS16 and PvmspF3, and 758 positive P. falciparum samples were genotyped at Pfmsp2. The data were analyzed for multiplicity of infection (MOI) and genetic diversity. Overall, P. vivax had higher polyclonality (71%) and mean MOI (2.32) than P. falciparum (20%, 1.39). These measures were significantly associated with prevalence for P. falciparum but not for P. vivax. The genetic diversity of P. vivax (PvMS16: expected heterozygosity = 0.95, 0.85–0.98; PvMsp1F3: 0.78, 0.66–0.89) was higher and less variable than that of P. falciparum (Pfmsp2: 0.89, 0.65–0.97). Significant associations of MOI with allelic richness (rho = 0.69, P = 0.009) and expected heterozygosity (rho = 0.87, P < 0.001) were observed for P. falciparum. Conversely, genetic diversity was not correlated with polyclonality nor mean MOI for P. vivax. The results demonstrate higher complexity of infection and genetic diversity of P. vivax across the country. Although P. falciparum shows a strong association of these parameters with prevalence, a lack of association was observed for P. vivax and is consistent with higher potential for outcrossing of this species.

Introduction

Malaria is caused by protozoan parasites of the genus Plasmodium and transmitted by female anopheline mosquitoes.1 Globally, an estimated 3.2 billion people are at risk of developing malaria. Despite increased global efforts to control and eventually eliminate malaria, it is still one of the major health problems causing an estimated 214 million new cases and 438,000 deaths in 2015 alone.2 Plasmodium falciparum and Plasmodium vivax cause the majority of this malaria burden. Although P. falciparum is the more virulent of the two species, P. vivax has a wider geographic range and is more difficult to control.35 In areas where P. falciparum and P. vivax are co-endemic, control efforts have had less impact on P. vivax, resulting in it becoming the more dominant infection in the community in some areas.2 The main reason for this shift might lie in malaria control measures largely targeted at P. falciparum, and that P. vivax is more difficult to treat and diagnose because of its unique biology, in particular its ability to form dormant liver-stage infections known as hypnozoites.3 P. vivax is, therefore, recognized as a major obstacle to the control and elimination of malaria in co-endemic regions.69

Coinfection or superinfection of hosts with multiple Plasmodium strains (also known as multiple infection) is common in endemic areas, which may result from the bite of mosquitoes infected with more than one clone or from multiple bites, respectively.10,11 Multiple infection is a prerequisite for sexual recombination between different strains (outcrossing), which takes place within the mosquito midgut after ingestion of an infected blood meal. Interaction and competition of different strains for limited resources within a host during their life cycles may be important for survival.12 Although poorly understood, this competition to survive might lead to increased virulence,13 higher transmissibility,14,15 and emergence of drug resistance.1619 High proportions of multiple infections and high genetic diversity of P. vivax even in low prevalence areas indicate that P. vivax populations are more stable under pressure and that more intensive and sustained interventions will be needed to control and eventually eliminate this parasite.2022 Measuring the complexity of infection and genetic diversity of parasite populations across different endemicities may be used as an indicator to evaluate the efficacy of control and elimination strategies.23

The burden of malaria in Papua New Guinea (PNG) is among the highest in the Asia Pacific region.5,8,24 Four of the five malaria species known to infect humans, P. falciparum, P. vivax, Plasmodium malariae, and Plasmodium ovale s.l., are endemic in PNG. The intensity of malaria transmission is geographically variable even on small spatial scales with P. falciparum and P. vivax accounting for the majority of infections.2528 From 2004 to 2009, PNG conducted the first country-wide free distribution of long-lasting insecticide treated mosquito nets (LLINs) supported by the Global Fund to Fight AIDS, Tuberculosis and Malaria.29 After the initial roll out of LLIN, a countrywide malaria indicator survey was undertaken to assess malaria prevalence in 49 villages from 16 provinces (of a total of 20), representing all malaria endemic areas of PNG. Additionally, 19 villages were included from six sentinel sites, which had not yet been covered by LLINs. Malaria prevalence, based on light microscopy, was 12% overall but varied widely between areas (0–49.7%) and P. falciparum was more prevalent than P. vivax in most areas. Lower prevalence in some regions was associated with LLIN use, strongly supporting the application of this strategy to control malaria in PNG.30

New tools are needed to monitor the success of malaria control programs. Molecular epidemiology can provide such a tool, by providing deeper insight into parasite transmission intensity than traditional prevalence surveys by measuring the complexity of infection and population genetic diversity accumulating as a result of co-transmission of multiple genetically distinct clones. Such studies have demonstrated that P. vivax is more genetically diverse than P. falciparum, with more complex infections,3137 but it is not well understood how these parameters relate to endemicity for P. vivax. Previous studies in PNG have focused on the highest transmission region on the north coast of PNG, namely the East Sepik and Madang Province.3842 Therefore, we aimed to gather genotyping data from all areas covered in the national malaria indicator survey mentioned earlier. Using this data, we have compared the molecular epidemiology of P. vivax and P. falciparum populations from all malaria-endemic areas of PNG representing a range of transmission intensities.

Materials and Methods

Study sites and samples.

A total of 8,936 samples were collected during a household-based national malaria indicator survey between October 2008 and August 2009 (Table 1). This survey included 49 villages from 16 provinces, which had already covered by free LLIN and 19 villages from six sentinel sites pre-LLIN distribution.30 Further details of the survey methodology are published elsewhere.29,30 We assigned the villages to 27 “geographic clusters” predicted to harbor distinct parasite populations on the basis of topography and predicted human movement (Figure 1, Supplemental Table 1). Genomic DNA was extracted from whole-blood samples using the QiaAmp DNA Extraction Kit (Qiagen, Chadstone, Victoria, Australia) or the FavorprepTM genomic DNA extraction kit (Favorgen, Ping-Tung, Taiwan). Light microscopy (LM) and ligase detection reaction fluorescent microsphere assay (LDR-FMA) were performed to identify samples infected with different Plasmodium species.43

Table 1

Summary of parasite isolates and genotyped samples

Cluster no.Cluster nameNPv+PvMS16Pvmsp1F3Pf+Pfmsp2
1West Sepik: Yapsie area5343925316625
2West Sepik: Aitape area1673427304634
3East Sepik: Angoram2544134391610
4Madang-Bogia2643727336051
5Madang: Ramu Valley334150981246034
6Morobe: Markham Valley14347553960104
7Morobe: Bulolo76372026376
8Morobe: Mumeng area2035439487521
9Morobe: Huon Peninsula7971189811812747
 Overall Momase2,772557423488547332
10Eastern Highlands4507977707026
11EHP: Obura-Wonenara901919193nd
12WHP: Angalimp-South Waghi14320nd3030
13Western Highlands3243338404310
 Overall Highlands1,00715113412914666
14Gulf cluster no. 1274943518
15Gulf cluster no. 22430nd9nd
16Oro (northern)77368172521830
17Central Coastal613567595494
18Western: Balimo area244764657103
19Western: Wipim area368392737364
20Milne Bay2007nd4nd
21Milne Bay: Kiriwina10510nd137
 Overall Southern2,82026516921739056
22Manus3039764736034
23WNB: Kimbe Bay3523015248063
24WNB: South coast37067484910288
25ENB: Gazelle Peninsula4438049527533
26New Ireland4396273838179
27Bougainville430302228327
 Overall Island2,337366271309430304
 Total1,3399971,1431,5137588,936

EHP = Eastern Highlands Province; ENB = East New Britain; LDR-FMA = ligase detection reaction fluorescent microsphere assay; N = Number of samples; nd = not done; Pv+ = number of samples Plasmodium vivax positive by LDR-FMA; Pf+ = number of samples Plasmodium falciparum positive by LDR-FMA; WHP = Western Highlands Province; WNB = West New Britain. Dashes indicate that no samples were successfully genotyped. Bold values are to highlight them as aggregated populations.

Figure 1.
Figure 1.

Map of the study area. Dots indicate sampled villages, shaded areas indicate the geographic clusters predicted to harbor distinct parasite populations. Broad geographic regions are indicated: Momase (clusters 1–9), Highlands (clusters 10–13), Southern (clusters 14–21), and Islands (clusters 22–27). See Supplemental Table 1 for names of clusters and villages.

Citation: The American Society of Tropical Medicine and Hygiene 96, 3; 10.4269/ajtmh.16-0716

Polymerase chain reaction and genotyping.

PvMS16 and Pvmsp1F3 markers were used to genotype P. vivax positive samples (N = 1,339) since the use of these two highly diverse markers combined has previously demonstrated the ability to track clones, measure multiplicity of infection (MOI), and to provide insight into parasite genetic diversity within populations.39,44 Plasmodium vivax samples were genotyped by multiplex first-round and secondary nested polymerase chain reaction (PCR) as per published protocols.42,44 Plasmodium falciparum positive samples (N = 1,513) were genotyped using the highly polymorphic Pfmsp2 marker as previously described.45 A nested multiplex PCR approach was used to amplify 3D7 and/or FC27 family alleles of the gene encoding the highly polymorphic antigen, Pfmsp2, using family-specific fluorescent dye–labeled primers.

For all markers, PCR products were run on a 2% w/v agarose gel to check for bands within the correct size range and 10 μL of each positive PCR product was sent to a commercial sequencing facility for fragment analysis (GeneScan, Applied Biosystems, Foster City, CA). The resulting chromatograms were analyzed using GeneMapper® Software Version 4.0 (Applied Biosystems) calibrating against the internal size standard GSLIZ500. A cutoff of 1,000 relative fluorescence units (RFUs) were used to distinguish true peaks from background signal and to adjust run-to-run variation, the fluorescence intensity of some sample plates was reduced, and in this instance, the cutoff was lowered to 300 RFU. All major peaks (i.e., those within the size range with the highest RFUs) and any additional alleles with minimum 33% height of the predominant allele were scored. All traces were inspected manually to confirm call quality.

Data analysis.

We determined the prevalence of infection by dividing the total number of positive infections (Pv+ or Pf+) by the number of samples screened (N). Age-adjusted P. vivax and P. falciparum prevalence data for 2- to 10-year-olds (PvPR2–10 and PfPR2–10) were used to determine the association between parasite prevalence with other genetic diversity parameters.46 Capillary electrophoresis allowed high-resolution detection of the number of parasite clones, i.e., MOI within each isolate (sample) based on the number of alleles detected for the respective markers. As two markers were used for P. vivax, MOI was based on the maximum number of alleles detected for either marker. The number of clones (c) for each population was determined by summing the total number of clones per isolate. Mean MOI for each population was calculated by dividing the total number of clones detected by the number of samples positive for the relevant marker/s (n). Another measure of the complexity of infection, polyclonality, was determined by calculating the proportion of samples carrying multiple infections (MOI > 1) in each population. Isolates with single alleles for both markers were classified as single infections, whereas those isolates with multiple alleles for at least one marker were classified according to the maximum number of alleles counted for either marker.

Diversity analyses were performed on the genotyping data using R package “Hierfstat,” (http://www.unil.ch/popgen/softwares/hierfstat.htm) and FSTAT software version 2.9.3.47 The number of alleles (A), allele frequency, expected heterozygosity (He), and allelic richness (Rs) were calculated for each parasite population in different geographic areas in PNG. The expected heterozygosity, represents the probability of being infected by two parasites of the same species with different alleles at a given locus and was calculated using the following formula: He = [n/(n − 1)] [(1 − Σpi2)], where n is the number of isolates sampled and pi is the allele frequency (p) at the ith locus. Allelic richness considers the average number of alleles normalized for the smallest sample size among the sampled populations based on the rarefaction method of Hurlbert and others.48 Spearman's rho was used to measure associations between prevalence, complexity of infection, and diversity parameters. χ2 tests or one-way analysis of variance were used to measure differences among two groups or more than two groups, respectively. A P value of ≤ 0.05 was considered statistically significant. Statistical analyses were done using GraphPad Prism Software version 6.0.

Results

Prevalence and complexity of infection of P. vivax and P. falciparum throughout PNG.

A total of 1,339 P. vivax- and 1,513 P. falciparum-infected samples were identified by LDR-FMA (Table 1). Of these, 892 P. vivax samples were successfully genotyped with PvMS16 and Pvmsp1F3 markers, and a total of 3,059 clones were identified (Table 1, Supplemental Table 2). Overall, there was no significant difference in the number of clones counted, mean MOI, and polyclonality between PvMS16 and Pvmsp1F3 analyzed individually or in combination (Supplemental Figure 1, Supplemental Table 2). For P. falciparum, among 758 samples successfully genotyped with Pfmsp2, relatively low numbers of clones (c = 1,049) were identified (Table 1, Supplemental Table 3). Of these, 79.8% of clones had 3D7-type Pfmsp2 alleles and 21.2% had FC27-type Pfmsp2 alleles.

Despite significant variability of P. vivax prevalence among geographic areas,30 polyclonality (mean = 70.8%, range = 52.5–93.0%) and MOI (mean = 2.32, range = 1.8–2.7) based on the two markers combined were high in all geographic areas (Figure 2A, Supplemental Table 2). For P. falciparum, lower and more variable polyclonality (mean = 21.5%, range = 0–47.1%) and mean MOI (mean = 1.4, range = 1–1.6) were observed (Figure 2B, Supplemental Table 3). Aggregating the geographic clusters into the four major geographical regions of PNG (Momase, Highlands, Southern, and Islands), P. vivax had very high polyclonality in all regions (68.9–73.2%) (Figure 2A, Supplemental Table 2), whereas for P. falciparum, the Momase region, encompassing the entire north coast where prevalence is high, had the highest polyclonality (23.4%) and mean MOI (1.45) while the Highlands region, where prevalence was lower, had very low polyclonality (19.6%) and the lowest MOI (1.2) (Figure 2B, Supplemental Table 3).

Figure 2.
Figure 2.

Complexity of infection in different geographic clusters and regions of Papua New Guinea. Proportion of polyclonal infections (solid line) and mean multiplicity of infection (MOI) (dashed line) relative to prevalence (all ages) in different geographic clusters and regions for (A) Plasmodium vivax and (B) Plasmodium falciparum.

Citation: The American Society of Tropical Medicine and Hygiene 96, 3; 10.4269/ajtmh.16-0716

The frequency distribution of multiple infections was also different between the two species. For P. vivax, only 29.3% samples had single infections, most of the samples had two clones (36.5%) and a large proportion of infections had three clones (21.2%), four clones (7.4%), and greater than four clones (5.1%) (Figure 3A). Whereas, the majority of P. falciparum samples had single infections (80%), with most of the remaining samples having only two clones (14.6%) and only a small proportion had three clones (4%) or greater (1.4%) (Figure 3B). For P. vivax, a maximum of nine clones were identified in a sample, whereas for P. falciparum, there was a maximum of seven clones.

Figure 3.
Figure 3.

Frequency distribution of multiplicity of infection in Papua New Guinea. Frequency of multiplicity of infection (MOI) values among all samples for (A) Plasmodium vivax and (B) Plasmodium falciparum. Numbers above the columns indicate proportions.

Citation: The American Society of Tropical Medicine and Hygiene 96, 3; 10.4269/ajtmh.16-0716

Genetic diversity of P. vivax and P. falciparum populations throughout PNG.

For P. vivax, a total of 111 PvMS16 and 60 Pvmsp1F3 alleles were identified with varying alleles and allele frequencies observed in the parasite populations of different geographic clusters. The most frequent allele identified for Pvmsp1F3 marker was the 268 bp allele (34%) followed by 241 (25%), 256 (12%), and 262 bp (9%) alleles (Supplemental Figure 2A). In contrast, the PvMS16 marker showed a large number of low frequency (< 10%) alleles. The most common allele identified was allele with 269 bp (8%) followed by 377 (6%), 242 (5%), and 263 bp (3%) (Supplemental Figure 2B). For P. falciparum, a total of 105 Pfmsp2 alleles were identified with the 348 bp allele being the most predominant (11%) followed by 252 (9%), 175 (8%), and 486 bp (6%) alleles. A variable number of alleles unique to different populations were observed (Supplemental Figure 2C).

Plasmodium vivax had high genetic diversity (PvMS16: mean Rs = 22, range = 15–25, based on a minimum sample size of 15, mean He = 0.95, range = 0.85–0.98; msp1F3: Rs = 9, 5–13 based on a minimum sample size of 24, He = 0.78, 0.66–0.90; Figure 4A, Supplemental Table 4). The genetic diversity of Pfmsp2 in P. falciparum populations across all geographic areas was also high (Rs = 7, 4.6–8.8 based on a minimum sample size of 10, He = 0.89, 0.65–0.97), except for Morobe, Mumeng (cluster 8). This suggests that despite high prevalence, this population may be genetically isolated relative to the others (Figure 4B, Supplemental Table 5). Higher diversity of P. vivax was observed for both markers based on Rs, and for PvMS16 using He. As He is an allele frequency–based measure, this likely reflects the different allele frequency distributions of the two P. vivax markers.

Figure 4.
Figure 4.

Genetic diversity of Plasmodium vivax and Plasmodium falciparum in different geographic clusters and regions of Papua New Guinea. Genetic diversity as measured by heterozygosity (solid line) and allelic richness (dashed line) relative to prevalence (all ages) in different geographic clusters and regions for (A) Plasmodium vivax (PvMS16) and (B) Plasmodium falciparum (Pfmsp2).

Citation: The American Society of Tropical Medicine and Hygiene 96, 3; 10.4269/ajtmh.16-0716

Relationship between prevalence, complexity of infection, and parasite genetic diversity.

Plasmodium vivax had high polyclonality and mean MOI in all parasite populations despite different parasite prevalence, consistent with high complexity of P. vivax infections within the host regardless of endemicity and prevalence in different areas. Spearman's rank correlation analysis showed neither mean MOI nor polyclonality was significantly associated with age-corrected P. vivax prevalence (Figure 5A and C). However, for P. falciparum, polyclonality and mean MOI showed a statistically significant correlation with age-corrected prevalence (P < 0.02; Figure 5B and D).

Figure 5.
Figure 5.

Association between parasite prevalence and infection complexity in different geographic clusters of Papua New Guinea. Association between age-corrected prevalence and mean multiplicity of infection (MOI) for (A) Plasmodium vivax and (B) Plasmodium falciparum. Association between age-corrected prevalence and polyclonality for (C) P. vivax and (D) P. falciparum. The dashed lines indicate the best fit for Spearman's rho and P values indicated on each plot.

Citation: The American Society of Tropical Medicine and Hygiene 96, 3; 10.4269/ajtmh.16-0716

Plasmodium vivax genetic diversity measures (He and Rs) were not significantly associated with age-corrected prevalence (rho = 0.016, P = 0.94) nor mean MOI (Figure 6A and C). Plasmodium falciparum genetic diversity also did not show any association with age-corrected prevalence (rho = 0.21, P = 0.38); however, both allelic richness and expected heterozygosity showed a highly significant positive correlation with mean MOI (Figure 6B and D).

Figure 6.
Figure 6.

Association between infection complexity and genetic diversity in different geographic clusters of Papua New Guinea. Association between mean multiplicity of infection (MOI) and heterozygosity (He) for (A) Plasmodium vivax (B) Plasmodium falciparum. Association between mean MOI and allelic Richness (Rs) for (C) P. vivax and (D) P. falciparum. The dashed lines indicate the best fit for mean MOI and genetic diversity with Spearman's rho and P values indicated on each plot.

Citation: The American Society of Tropical Medicine and Hygiene 96, 3; 10.4269/ajtmh.16-0716

Discussion

The molecular epidemiology of the sympatric malaria parasite species P. falciparum and P. vivax throughout all endemic regions of PNG highlights their contrasting biology and the potential of complexity of infection and genetic diversity as markers of transmission intensity, at least for P. falciparum. The greater complexity of infection of P. vivax across a wide range of endemicities illustrates its higher potential for outcrossing and subsequent generation of genetic diversity, which was high in all populations and higher than P. falciparum as described in other studies conducted on the north coast of PNG39,42,49 and in Asia.33,5052 The complexity of infection was somewhat variable among different regions and geographic clusters for both species, and is comparable to that found in a wide range of higher transmission settings in other countries.27,28,33,38,5357 However, the vast majority of P. vivax infections were polyclonal and MOI was almost 2-fold higher than that of P. falciparum. Furthermore, although complexity of infection was associated with prevalence for P. falciparum, there was no association between these parameters for P. vivax. Genetic diversity was high throughout the country for both species and was associated with MOI for P. falciparum but not for P. vivax, which indicates that diversity at the P. vivax loci studied is close to saturation point across all malaria-endemic areas of PNG. The greater complexity of infection and higher diversity of P. vivax are likely the results of recurrent blood-stage infection due to relapse in this species, which account for 80% of all blood-stage infections in PNG.58

The complexity of infection has implications for testing drug efficacy, disease severity, and population diversity.59,60 Therefore, accurate detection of parasite clones circulating within the host is crucial and depends on the assay sensitivity, the type and number of the molecular markers analyzed, and the criteria used to identify predominant and minor alleles.55,61 The use of relaxed criteria to score minor peaks significantly increases artifacts that falsely increase multiclonal infections.62 Because the markers used are highly diverse, we used more stringent criteria to minimize the introduction of artifacts (see Methods). The prevalence of polyclonal infections and mean MOI in the separate analyses of the P. vivax markers PvMS16 and Pvmsp1F3 was similar to the two markers in combination, demonstrating that either marker could have been used to describe this aspect of P. vivax epidemiology. The similar total number and frequency distribution of PvMS16 and Pfmsp2 alleles warrants the use of these markers alone. However, Pvmsp1F3 was included because it, like Pfmsp2, is an antigen, with variation influenced by immune selection.63,64 When the diversity of Pfmsp2 and Pvmsp1F3 was compared, P. vivax diversity based on the allelic richness within each parasite population was higher, despite fewer alleles overall in the P. vivax marker. However, the expected heterozygosity of Pvmsp1F3 was lower than that of Pfmsp2. Other studies have shown that Pvmsp1 has limited diversity compared with msp1 in other Plasmodium species65 and the lower overall diversity compared with Pfmsp2 suggests its functional requirements may restrict diversity. Immune (balancing) selection66 or recent reductions in population size due to control activities64 may also influence the diversity measures differently. An additional reason for using two markers for P. vivax is that the detectability of clones may be lower than that of P. falciparum due to this species causing lower density infections.67 Based on the markers used, the average number of clones per infection for P. vivax was almost double that of P. falciparum, and would be even higher if clones were missed for this reason. Therefore, the use of the two chosen markers on different chromosomes for P. vivax is valid and increases the accuracy of the MOI and diversity measurements described here.

Based on the combination of PvMS16 and Pvmsp1F3 markers, P. vivax had a very high proportion of polyclonal infections with 70% of all genotyped samples having more than one clone and a high mean MOI of 2.3. For P. falciparum Pfmsp2, however, only 20% of all genotyped samples had polyclonal infections and there was a much lower mean MOI of 1.39, despite the higher prevalence of P. falciparum than P. vivax in many areas.30 The contrasting complexity of infection for the two species is comparable to previous reports from the PNG north coast39,68 and demonstrates that this pattern is also observed across areas with widely varying endemicity including areas with low parasite prevalence.30 This may be in part due to lower transmission areas having increasing proportions of imported infections, which reflects the MOI and diversity of the infection origin,22,69,70 and this effect would be enhanced for P. vivax by the fact that relapse can occur 1–3 years after the primary infection.71 The high complexity of infection for P. vivax regardless of parasite prevalence is also comparable to other studies where high infection complexity and diversity were found even with sustained low parasite prevalence such as in South America33,72,73 and Sri Lanka.74,75 Polyclonal infections are common in malaria-endemic areas of different countries for both species and can arise from a single mosquito bite carrying multiple clones or from inoculation by different mosquitoes carrying single clones. However, relapses of P. vivax infection due to the reactivation of heterologous hypnozoites from previous infections can directly contribute to increased MOI, which may sustain high diversity in low transmission areas for a longer period than P. falciparum after reduction in transmission.3,22,61 This unique biology thus may explain the high complexity of P. vivax infection at a range of parasite prevalences in PNG.

Some of the results suggest that malaria control initiatives in PNG prior to the survey including LLIN and indoor residual spraying in some areas29,30 have had a greater impact on P. falciparum. The highest mean MOI and polyclonality for P. falciparum was recorded in a parasite population from a sentinel site (Morobe: Bulolo, cluster 7), which had been not yet been covered by the free LLIN distribution at the time of the survey.29 However, there was no difference observed for P. vivax between sentinel sites and other areas that were covered by the free LLIN distribution. This finding is in line with more marked reductions in incidence and prevalence of P. falciparum than P. vivax following the first national distribution of LLIN.76 This may not fully explain the situation in PNG, since a reduction in both P. falciparum and P. vivax transmission was observed in some areas after LLIN distribution.51,76 A study from Indonesia showed a positive correlation between the rate of polyclonal infections and annual parasite incidence indicating that polyclonality might provide a complementary gauge of local transmission intensity.33 In the case of P. falciparum in PNG, this measure correlates well with prevalence, however in this and other studies,77,78 P. vivax infections had a high proportion of multiple clones even in areas with decreasing transmission, so polyclonality may be less effective for determining the transmission of the latter species.

The existence of multiple clone infections is believed to be one prerequisite for genetically diverse parasite populations, since it allows the simultaneous transmission of distinct clones that recombine in the mosquito, allowing outcrossing and generating novel genotypes.12 Genetic diversity was high for both P. vivax and P. falciparum throughout PNG. Diversity of the P. vivax markers PvMS16 and Pvmsp1F3 was similar to diversity reported from other endemic settings42,75,77,78 and similar to previous reports from the north coast of PNG.42,44,79 Despite high variability in complexity of infection, P. falciparum genetic diversity based on the Pfmsp2 marker was also similar among different geographic areas. However, in some clusters such as the Eastern Highlands (cluster 10) and Morobe: Mumeng area (cluster 8), the genetic diversity of P. falciparum was lower indicating much lower transmission or relatively isolated populations in those geographic areas. The levels of diversity on the north coast (Madang and East Sepik) in this study were similar to previous reports from the PNG north coast in 2005–2006 (mean He = 0.93)49 and African parasite populations from high endemicity areas (He = 0.96).80 Moreover, the finding that there was no significant association between parasite prevalence and genetic diversity for either species suggests that diversity is maintained at high levels despite the different levels of malaria endemicity in the different geographic areas. Therefore, substantial reductions in transmission by integrated control efforts will be required to impact parasite diversity and population structure in PNG. The mean MOI however was significantly correlated with parasite genetic diversity (Rs and He) for P. falciparum consistent with within host complexity as a critical factor for creation of new genotypes,81 whereas there was a lack of association for P. vivax because there was high within host complexity and high levels of diversity across the country. For both species it will therefore be important to investigate diversity using panels of ten or more less diverse genome wide markers such as microsatellite markers37,82,83 or single nucleotide polymorphisms.41,84 If chosen carefully, neutral genome wide markers will provide a more accurate estimate of diversity amongst individual parasites and populations, to further investigate the observed variability between regions.

The contrasting molecular epidemiology of P. vivax and P. falciparum described here indicates that differences in P. vivax biology have a major impact on its complexity of infection and diversity, compared with P. falciparum. This includes the ability to relapse,71 the earlier appearance of transmission stages, lower density infections, and a faster acquisition of immunity,3 the latter two of which may compromise diagnosis and subsequent treatment. Plasmodium vivax also has a wider geographical range due to its ability to develop within the mosquito vector at lower temperatures,4 and at least in part due to a wider vector range in some endemic regions (though in PNG all local malaria vectors transmit both species).85,86 Of particular note is relapse, which arises from triggering of the dormant liver stage to cause multiple sequential blood-stage infections in the absence of another mosquito bite.58 These relapses not only cause further illness to the individual, they also provide more opportunities for multiple clones to be co-transmitted, resulting in outcrossing during sexual recombination in the mosquito midgut. Relapse can thus contribute to greater complexity of infection and higher levels of parasite diversity and may also increase gene flow through human movement and help to maintain larger, more stable parasite populations than P. falciparum.23,87,88

This survey was conducted as a baseline to future surveys monitoring the success of the PNG national malaria control program with the first countrywide LLIN distribution completed in the year of the survey (2008). Since then, Plasmodium spp. prevalence in the general population at sentinel sites has decreased from 15.7% in 2009 to below 4.8% in 2011.76,89 Although the decrease was significant for P. falciparum (10.1% in 2009 down to 2.5% in 2011), P. vivax parasite rates had a less significant decline (5.9% to 2.4%) and there was a shift to P. vivax dominance in some areas.76 A significant decrease in both species was also noted in malaria cases in sentinel health facilities and the P. vivax shift was even less dramatic and up to half of all clinical cases in one sentinel site were due to P. vivax. This holds true in other P. vivax and P. falciparum co-endemic countries such as Ethiopia and Solomon Islands where the relative proportion of P. vivax cases has drastically increased.9093

The data also indicate a change in the proportion of Pfmsp2 alleles over time. The current study demonstrated that 80% of P. falciparum isolates had 3D7-type alleles. This family of alleles is highly diverse and dominant in all parasite populations of PNG. However, a 2005 survey conducted on the north coast showed only 68% of clones harbored the 3D7-type alleles49 and in 1992, it was reported that the msp2 FC27 allele was at almost equal frequencies with 3D7. A phase 1-2b vaccine (Combination B) trial conducted in the 1990s in the same region of PNG included only the 3D7 allele of Pfmsp2. Infections from vaccines had a lower prevalence of the 3D7 allele than the control group, and were more likely to experience clinical episodes with parasites carrying the alternate FC27 allele.95 A higher prevalence of 3D7 antibody responses after vaccination suggested that acquired immune responses are an important driver of these changing allele frequencies.96 These changing allele frequencies would need to be considered in future Pfmsp2-based vaccines.

In conclusion, although the prevalence of P. falciparum and P. vivax was highly variable throughout PNG, the infection complexity was associated with this variation only for P. falciparum. P. falciparum genetic diversity varies according to the infection complexity, whereas P. vivax infection complexity and diversity remain high throughout PNG. This suggests that P. vivax has a higher potential for outcrossing and generation of diversity at a range of transmission levels, and even when P. falciparum infections are more common. Even though the current control strategies had a great impact on parasite transmission, superinfection and genetically diverse P. vivax parasite populations will be a future challenge for the PNG national malaria control program requiring rigorous and sustained control efforts to reach the goal of elimination from 2025.5 As the markers used were highly diverse, further studies using less diverse, neutral markers and data from the years after the interventions were introduced are required for dissecting parasite population structure and understand changes in parasite diversity due to intensification of control efforts.

ACKNOWLEDGMENTS

We are very grateful to Papua New Guinean communities, particularly the volunteers and their families for providing blood samples during the survey. We would like to thank staff of the Papua New Guinea Institute of Medical Research for their generous ongoing support, especially the field researchers who conducted the countrywide survey, the microscopy team, and the laboratory team who performed DNA extraction and malaria molecular diagnosis on these samples. We also acknowledge the assistance from the National Department of Health, in particular Leo Makita. We also extend our thanks to Sofonias K. Tessema helping with data analysis, and the Victorian State Government Operational Infrastructure Support and Australian Government National Health and Medical Research Council Independent Research Institute Infrastructure Support Scheme.

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