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Genetic diversity of Plasmodium vivax reticulocyte binding protein 2b in global parasite populations

Parasites & Vectors volume 15, Article number: 205 (2022) Cite this article

Abstract

Background

Plasmodium vivax reticulocyte binding protein 2b (PvRBP2b) plays a critical role in parasite invasion of reticulocytes by binding the transferrin receptor 1. PvRBP2b is a vaccine candidate based on the negative correlation between antibody titers against PvRBP2b recombinant proteins and parasitemia and risk of vivax malaria. The aim of this study was to analyze the genetic diversity of the PvRBP2b gene in the global P. vivax populations.

Methods

Near full-length PvRBP2b nucleotide sequences (190–8349 bp) were obtained from 88 P. vivax isolates collected from the China–Myanmar border (n = 44) and Thailand (n = 44). An additional 224 PvRBP2b sequences were retrieved from genome sequences from parasite populations worldwide. The genetic diversity, neutral selection, haplotype distribution and genetic differentiation of PvRBP2b were examined.

Results

The genetic diversity of PvRBP2b was distributed unevenly, with peak diversity found in the reticulocyte binding region in the N-terminus. Neutrality analysis suggested that this region is subjected to balancing selection or population bottlenecks. Several amino acid variants were found in all or nearly all P. vivax endemic regions. However, the critical residues responsible for reticulocyte binding were highly conserved. There was substantial population differentiation according to the geographical separation. The distribution of haplotypes in the reticulocyte binding region varied among regions; even the two major haplotypes Hap_6 and Hap_8 were found in only five populations.

Conclusions

Our data show considerable genetic variations of PvRBPb in global parasite populations. The geographic divergence may pose a challenge to PvRBP2b-based vaccine development.

Graphical Abstract

Background

Malaria remains a major threat to global health despite intensified control efforts in recent years. As the most widespread Plasmodium species, infection with Plasmodium vivax caused an estimated 7.9 million malaria cases worldwide in 2018, with 53% of the vivax burden being in the Southeast Asia region [1]. The elimination of P. vivax is challenging due to its dormant liver stage, which gives rise to the relapses of malaria [2,3,4]. Integrated interventions, including novel tools such as vaccines, are urgently needed for malaria elimination.

The invasion of red blood cells (RBCs) by the merozoites is an essential step in the asexual erythrocytic cycle of malaria parasites [5, 6]. Since merozoites are exposed to the host immune system, vaccine candidates are usually designed to target the merozoite surface proteins, with the aim to block erythrocyte invasion [7, 8]. Compared to P. falciparum, much fewer vaccine candidates have been identified for P. vivax, partially owing to the absence of a long-term in vitro culture system for this parasite [9, 10]. Plasmodium vivax requires the Duffy antigen receptor for chemokines (DARC) on the surface of the RBC for invasion. However, solid evidence of P. vivax infection in DARC-negative individuals in Africa suggests that this Plasmodium species may have evolved to explore alternative pathways for invasion [11, 12]. Plasmodium vivax shows a restricted tropism for reticulocytes with high levels of transferrin receptor 1 (TfR1 or CD71) [13]. Recently, TfR1 has been identified as the reticulocyte-specific receptor for P. vivax reticulocyte binding protein 2b (PvRBP2b), a ternary complex expressed in the schizont stage [14, 15]. PvRBP2b belongs to the PvRBP family, which is composed of at least 11 members with different binding preferences for normocytes or reticulocytes [5, 16,17,18,19]. Studies on the crystal structure of the N-terminal domain of PvRBP2b have revealed a similar structural scaffold to that of P. falciparum reticulocyte binding protein homolog 5 (PfRh5), a well-characterized vaccine candidate for P. falciparum [15, 20]. In one study, monoclonal antibodies against PvRBP2b or TfR1 mutants that impede the binding of PvRBP2b to the reticulocytes successfully blocked the entry of P. vivax into the reticulocytes, suggesting that PvRBP2b is a promising vaccine candidate targeting blood-stage infections [15].

The N-terminal domain of PvRBP2b is responsible for reticulocyte binding [14, 15], whereas the function of the C-terminal domain is not clear. Studies with the recombinant PvRBP2b N-terminal domain have detected antibodies against PvRBP2b in plasma samples collected from P. vivax-infected patients, supporting the notion that PvRBP2b contains immune recognition epitopes in the N-terminal domain [15, 18, 21]. In addition, immunoglobulin G (IgG) levels against PvRBP2b have been found to be negatively correlated with parasitemia and the risk of vivax infections [18, 21, 22]. These studies have highlighted the potential of PvRBP2b as a promising target for P. vivax vaccine development [14, 15].

A major challenge for the efficacy of blood-stage malaria vaccines is the extensive genetic diversity of the target antigens. Therefore, understanding the genetic diversity of vaccine candidates is necessary for designing effective vaccines and predicting vaccine efficacy. In this study, we analyzed the genetic diversity, phylogenetic relationship and population differentiation of PvRBP2b from 312 global P. vivax isolates, with the aim to provide the necessary information for PvRBP2b-based vaccine development.

Methods

Sample collection and ethics statements

This study used 88 dried blood spots on filter papers collected from P. vivax-infected patients attending the Laiza and Nabang hospitals in the China–Myanmar border area in 2014 (n = 44) and the Tha Song Yang hospital in western Thailand in 2011–2012 (n = 44). Malaria was diagnosed by microscopic examination, and finger-prick blood samples were collected.

PvRBP2b gene amplification and sequencing

Genomic DNA was extracted from the dried blood spots on filter papers using the QIAamp DNA Mini kit (Qiagen, Hilden, Germany). The studied parasites were part of the clinical samples genotyped by PCR–restriction fragment length polymorphism analysis of the polymorphic Msp3α and Msp3β genes in a previous study [23]. Only monoclonal infections were used for PvRBP2b sequencing. An 8160-bp fragment of the full-length protein-coding sequence of the PvRBP2b gene (8421 bp), i.e. PvRBP2b190–8349, corresponding to amino acids 64–2783 of the PvRBP2b protein was amplified from all the samples using KOD-Plus-Neo polymerase (Toyobo, Osaka, Japan). Given the large size of the gene, seven overlapping 1.5-kb fragments were amplified from each sample using seven pairs of primers (Additional file 1: Table S1). PCR amplification was carried out in a total reaction volume of 30 μl containing 1× KOD-Plus-Neo buffer, 200 µM dNTPs, 1 mM MgSO4, 250 nM primer, 0.4 units KOD Plus polymerase and 2 µl genomic DNA. The following cycling parameters were used: an initial denaturation at 94 °C for 5 min, followed by 35 cycles of denaturation at 94 °C for 15 s, annealing at a determined temperature (Additional file 1: Table S1) for 15 s, and extension at 68 °C for 90 s, with a final final extension at 68 °C for 5 min. The PCR products were separated in 1% agarose gels and then subjected to DNA sequencing using the ABI BigDye™ Terminator Reaction Ready kit (Applied Biosystems, Thermo Fisher Scientific, Waltham, MA, USA). The nucleotide sequences were deposited in the GenBank database under the accession numbers OM715042–OM715085 and OM715086-OM715129.

Sequence assembly and retrieval

The 88 PvRBP2b sequences were assembled using DNASTAR Lasergene software (DNASTAR, Madison, WI, USA ). In addition, 224 PvRBP2b sequences from 11 global locations were obtained from previous whole-genome sequencing projects [24,25,26,27,28,29]. Fastq files were downloaded from the Sequence Read Archive (SRA) of the National Center for Biotechnology Information. We used two parameters to exclude low-quality variants: (i) quality ≤ 40 and (ii) minor allele frequency < 0.01. Only single nucleotide polymorphism (SNP) variants from monoclonal infections were included. After filtering out unqualified sequences, the global sample set of sequences included those from Brazil (n = 36), Colombia (n = 28), Cambodia (n = 35), China–Myanmar border (n = 19), Ethiopia (n = 18), Indonesia (n = 3), Laos (n = 2), Malaysia (n = 4), Papua New Guinea (PNG) (n = 20), Thailand (n = 48) and Vietnam (n = 11). Isolate codes and SRA accession numbers of samples used in this analysis are given in Additional file 1: Table S2.

Analysis of genetic diversity and tests for detecting selections

A total of 312 PvRBP2b sequences were aligned with the reference sequence from the Salvador I (Sal I) strain (PVX_094255) using the Clustal W program in MEGA7 software. For the evaluation of PvRBP2b genetic diversity, the nucleotide diversity (π), the number of haplotypes (H) and haplotype diversity (Hd) were computed using DnaSP v5.10 software [30]. To test the departure from neutrality, Tajima’s D test [31], Fu and Li’s F* test [32] and Fu and Li’s D* test [32] were computed using DnaSP v5.10 software. Natural selection was determined by calculating the ratio of nonsynonymous (dN) to synonymous (dS) substitutions per nucleotide site (dN-dS), using the Nei-Gojobori method [33] with Jukes-Cantor correction for multiple substitutions. Statistical significance of the difference was estimated using the codon-based Z-test of selection implemented in MEGA 7 [33].

Finally, to determine the existence of specific codons targeted by selection in the global population [34], the FUBAR [35] and SLAC [36] methods, implemented in the Datamonkey webserver (http://www.datamonkey.org), were performed. Before analysis, putative recombination joints and parental sequences were determined using the RDP4 suite [37]. Recombination breakpoints were detected using seven methods (RDP, GENECONV, BOOTSCAN, MAXCHI, CHIMAERA, SISCAN and 3SEQ) in the RDP4 package. The probability of a putative recombination event was corrected by a Bonferroni procedure with a cutoff of P < 0.01. Only events supported by at least four of the seven methods were considered to be recombinants. Recombinants were then removed from the phylogenetic tree constructed by the maximum likelihood method implemented in MEGA7 using the nucleotide substitution Hasegawa-Kishino-Yano (HKY)+G+I model. As a last step, the prepared dataset was uploaded to the Datamonkey webserver for analysis.

Population differentiation, structure and phylogenetic relationship

To investigate population subdivision, Wright’s fixation index (FST) representing inter-population variance in allele frequencies was calculated using DnaSP v5.10 [30, 38, 39]. The genetic structure of all the vivax parasite populations was then elucidated using STRUCTURE v2.3.2 software based on the Bayesian analysis and admixture model [40, 41]. All samples were run at K = 2–7 (10 iterations each) with a burn-in period of 20,000 iterations followed by 1,200,000 Markov Chain Monte Carlo iterations. Then the optimal number of grouping was determined by ΔK using STRUCTURE HARVESTER v0.6.94 software [42, 43]. The partition of the clusters was presented using CLUMPP v1.1.2 [44] and the DISTRUCT 1.1 tools [45]. To determine the relationship among the parasites, phylogenetic analyses were performed using the neighbor-joining (NJ) method implemented in MEGA7 [33], and the phylogenetic tree was then optimized with the online tool ITOL [46]. A haplotype network based on the polymorphic sites in the reticulocyte binding region of PvRBP2b was constructed using the PHYLOVIZ 2.0 software with the NJ method [47].

Prediction of linear B-cell epitopes

The potential linear B-cell epitopes were predicted using the BCPreds prediction tool (http://ailab-projects1.ist.psu.edu:8080/bcpred/predict.html) [48]. Antigenicity was predicted using the VaxiJen v2.0 online tool (http://www.ddgpharmfac.net/vaxijen/VaxiJen/VaxiJen.html) [49]. BCPreds predicts a peptide length of 12 consecutive amino acids with a threshold of 0.8, whereas VaxiJen sets a threshold at 0.5. The overlapped regions of predicted linear B-cell epitopes by both methods were selected. The predicted three-dimensional (3D) structures of the reticulocyte binding region of PvRBP2b were constructed with the PHYRE2 algorithm [50] and further visualized and modeled using the molecular modeling tool PyMOL V2.3 [51]. The PvRBP2b amino acid sequence in the reference Sal I strain was used for prediction.

Results

Mutations revealed from global PvRBP2b sequences

Sequencing of the 8160-bp PvRBP2b fragment (190–8349 bp) was successful for the 88 P. vivax field isolates collected from the China–Myanmar and Thailand–Myanmar border areas. To gain a global perspective, we retrieved 224 PvRBP2b sequences from the whole-genome sequences of P. vivax isolates collected in multiple P. vivax endemic areas across the world. A preliminary analysis showed that the nucleotide diversity of sequences obtained by Sanger sequencing from the China-Myanmar border (0.00167) and Thailand (0.00217) was comparable to that obtained from the whole-genome sequencing projects (0.00116 and 0.00148, respectively). Therefore, the combined data were used for analysis. Alignment of all 312 PvRBP2b sequences with the Sal I reference identified 116 SNPs, including 103 nonsynonymous and 20 synonymous mutations. Of note, the 103 nonsynonymous mutations resulted in 96 amino acid changes. Of all nonsynonymous mutations, 75 SNPs showed allele frequencies of > 1% (Additional file 1: Table S3). The distributions of the 75 mutations in different areas are shown in Fig. 1 and Additional file 1: Table S3. The E136K, N349K, K363E, D366V/H, V395A/T, K412N, Q564R, D917E, N1529K, K1606E, E2265K and E2746G amino acid mutations were found in all or nearly all endemic sites, reflecting the high prevalence of PvRBP2b polymorphisms in the world. Among these, D917E approached fixation (95.5%).

Fig. 1
figure 1

Prevalence of amino acid substitutions in PvRBP2b among worldwide P. vivax populations. Positions and frequencies of amino acid changes in PvRBP2b among different populations are shown. The reticulocyte binding region of PvRBP2b (168–633 amino acids) is shaded. Abbreviations: PvRBP2b, Plasmodium vivax reticulocyte binding protein 2b

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The reticulocyte binding region on the N-terminus of PvRBP2b is critical for receptor binding and RBC invasion. It corresponds to the amino acids 168–633 since its flanking regions failed to be visualized in the PvRBP2b structure [14]. As shown in Additional file 1: Table S3, from a total of 75 nonsynonymous nucleotide substitutions with allele frequencies > 1%, almost 50% (35/75) were in the reticulocyte binding region, generating 28 amino acid mutations. In addition to the 11 nonsynonymous mutations (R217H, R242T/S, K288P, K309Q, K363E, D366V/H, G382R/E, E497K, D558E, Q564R and N591K) previously reported for the reticulocyte binding region [14], 17 additional nonsynonymous mutations (D220Y, T224R/K, S228P, E232K, L293V, N300K/D, D315Y, N349K, V395A/T, K412N, Q413E, K437E, H455Q, K575E, D578H, S586R and E631K) were reported in the present study (Fig. 2a). Among these, K363E and S586R are the residues known to interact with the receptor TfR1 [14]. K363E was prevalent in all endemic areas, with an allele frequency of 40.4%, whereas S586R was found only in Brazil (22.2%), Ethiopia (11.1%) and Thailand (1.1%) (Additional file 1: Table S3).

Fig. 2
figure 2

Three-dimensional structure of the PvRBP2b reticulocyte binding region. a A modified schematic diagram from [14] shows the important residues in the reticulocyte binding region composed of the N-terminal and C-terminal domains. The polymorphic residues previously reported are labeled in magenta, whereas the polymorphic residues newly identified in the present study are labeled in red. Residues interacting with TfR1 and Tf are shown in green and cyan, respectively. b Two orthogonal views of the three-dimensional model structure of the PvRBP2b reticulocyte binding region shows the 14 residues positively selected by both codon-based tests in the Datamonkey webserver and S586 positively selected by FUBAR. The hydrophobic binding region is shown in yellow. Abbreviations: Tf, transferrin; TfR1 transferrin receptor 1

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Genetic diversity of PvRBP2b

The population genetic indices were analyzed to assess the nucleotide diversity (π) of PvRBP2b (Table 1). The overall nucleotide diversity from 312 sequences was 0.00196, with the highest found in the population from Malaysia (0.00203), followed by Thailand (0.00198) and Vietnam (0.00193). The overall haplotype diversity (Hd) was high (0.997) (Table 1). The sliding window plots of nucleotide diversity revealed an uneven distribution, with the peaks located at nucleotides 1015–1134 within the reticulocyte binding region (Fig. 3a). Similarly, 46.7% of amino acid substitutions were clustered in the reticulocyte binding region (Fig. 3e). This result reflects the relatively high polymorphism in the reticulocyte binding region of PvRBP2b in the worldwide P. vivax populations.

Table 1 Genetic diversity of near-full length Plasmodium vivax reticulocyte binding protein 2b gene in global populations
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Fig. 3
figure 3

Nucleotide diversity, neutrality tests and amino acid polymorphisms. Sliding window plots of nucleotide diversity (π) (a), Tajima’s D* (b), Fu and Li’s F* (c) and Fu and Li’s D* (d) for PvRBP2b sequences are shown with a window size of 90 bp and a step size of 3 bp. Amino acid polymorphisms (e) are also visualized to the corresponding locations. The asterisk (*) depicts sites of statistical significance under balancing or directional selections (P < 0.05)

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Evidence of potential selections

Neutrality tests were conducted to evaluate whether the PvRBP2b gene followed the neutral equilibrium model of molecular evolution. Although the overall PvRBP2b sequence did not significantly deviate from neutrality, a sliding window analysis identified significant positive values for the 1015- to 1034-bp region by Tajima’s D* and Fu and Li’s F* tests, which paralleled the peak π value, reflecting balancing selection within the reticulocyte binding region or population bottlenecks in the global populations (Fig. 3b–d). In addition, some significant negative values for several C-terminal domains suggested population expansion or excess of singletons (Fig. 3b–d). The dN-dS statistic was positive for the reticulocyte-binding region among the global populations (Table 2), suggesting that polymorphisms found for this region of PvRBP2b were maintained by diversifying selection.

Table 2 Tests for selection in the PvRBP2b gene from global samples
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The likelihood-based algorithms were used to determine specific codons targeted by selection. Only one recombinant identified by the RDP, GENECONV, MAXCHI, CHIMAERA, SISCAN and 3SEQ methods implemented in the RDP suite was removed from the phylogenetic and selection analyses (Additional file 1: Table S4). Thirty-two positively selected and 11 negatively selected codons were identified by both the SLAC and FUBAR methods (Table 3). Fourteen of the positively selected mutations are located in the reticulocyte-binding region, presumptively associated with parasite invasion and/or immune recognition (Table 3). K363 and S586 have been shown to be the reticulocyte-binding sites [14]. In contrast to the positive selection at K363 confirmed by all three methods, S586 was supposed to be positively selected only by the FUBAR method (Table 3). Meanwhile, sites under purifying selection were scattered along the PvRBP2b (Table 3).

Table 3 Codon-based tests for selection in PvRBP2b gene
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Mutations within the 3D structure and predicted B-epitopes in the reticulocyte binding region

The 3D model structure with positively selected amino acid mutations mapped in the reticulocyte binding region of PvRBP2b showed that most mutated residues were on the surface of α helices, with the exceptions of V395, which was hidden inside the protein, and K412, which was located in the flexible loop structure (Fig. 2b). Most mutations were close to the hydrophobic binding region. Three peptides (270–281, 519–530, and 566–577 amino acids) in the reticulocyte binding region of PvRBP2b were predicted to be linear B-epitopes with the prediction scores of 0.839, 0.987, and 0.959, respectively, by the BCPreds and VaxiJen software (Table 4). However, only one polymorphic residue, K575, was presented in the predicted B-epitope of 566–577 amino acids. Seven additional polymorphic residues were found in the predicted linear B-epitopes in other regions of PvRBP2b (Table 4).

Table 4 Predicted PvRBP2b B-cell epitopes in the reticulocyte binding region
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Haplotype network analysis

The 17 SNPs with minor allele frequencies of > 5% in the PvRBP2b reticulocyte binding region were chosen for haplotype analysis, which yielded a total of 114 haplotypes among the global dataset of 312 sequences (Additional file 1: Table S5; Figs. 4, 5). Of the 114 haplotypes, 58 (50.9%) were region-specific, with a frequency of between 0.32 and 1.60%. There were many rare haplotypes; 92.1% (105/114) of the haplotypes were shared by no more than five parasite isolates, among which 70/114 were represented by single parasite isolates. Even the two major haplotypes, i.e. Hap_6 and Hap_8, with a frequency of 14.74 and 12.82%, respectively, were only found in five regional populations. Hap_8, the same as the Sal I haplotype, was most abundant in the China–Myanmar border (39.7%) and Colombia (42.9%). Hap_6, the predominant haplotype identified in Thailand, was also shared among the parasite populations from the China–Myanmar border, Cambodia, Brazil and Colombia. The haplotypes in Asia were distributed in the parasite populations included in this study, and Thailand harbored the highest haplotype diversity (52/114) (Additional file 1: Table S5; Figs. 4, 5). In contrast, the distribution pattern shown in the haplotype network was different from that of parasite populations from South America (Brazil and Colombia), Africa (Ethiopia) and Oceania (Papua New Guinea [PNG]) (Fig. 5). Of the 20 parasite isolates in PNG, 16 were from a unique haplotype restricted to this specific region (Additional file 1: Table S5; Fig. 4).

Fig. 4
figure 4

Map showing the distribution of PvRBP2b haplotypes. The frequencies of 114 haplotypes based on the 17 nonsynonymous amino acid mutations (> 5%) in the PvRBP2b reticulocyte binding region are depicted as pie charts and mapped to their geographical origins. The red line represents the region along the Myanmar–China border. Shared haplotypes are shown in color, and unique haplotypes are shown in gray

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Fig. 5
figure 5

Phylogenetic relationship of the parasite isolates based on the PvRBP2b reticulocyte binding region. a An unrooted phylogenetic tree. The tree was constructed using the neighbor-joining method with bootstrap supports from 1000 replicates. The origins of the parasite isolates are represented by different colors. b A haplotype network. The size of the pies reflects the frequency of the specific haplotype. The lengths of the lines connecting the pies measured from their centers are in proportion to the number of base pair substitutions separating the haplotypes. Endemic regions are represented by same colors used in the phylogenetic tree

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Population structure and differentiation

Analysis of the PvRBP2b sequences showed that the global parasites were optimally grouped into three clusters (K = 3) (Fig. 6a, c). These clusters were unevenly distributed among different geographical regions. The parasites from PNG and Indonesia were mainly represented by the purple cluster, whereas the parasites from Ethiopia, Brazil and Colombia occupied the green cluster. The red cluster represents parasites predominantly from the China–Myanmar border and Thailand. Interestingly, the remaining parasites from the China–Myanmar border and Thailand and parasites from other countries of the Greater Mekong Subregion (GMS) (Cambodia, Laos and Vietnam) showed genetic mixing of the green and purple clusters. When structure analysis was conducted using the PvRBP2b reticulocyte binding region, six clusters were identified with overlapping, worldwide distribution (Fig. 6b, d). It is noteworthy that only the parasites from the PNG and Indonesia formed genetically distinct clusters from other geographical regions.

Fig. 6
figure 6

Structure analysis of PvRBP2b sequences from the global P. vivax populations. Plots represent the relationship between Delta K and K values for the near full-length sequences of PvRBP2b (a) and for the reticulocyte binding region (b), as well as the genetic structures of the near full-length sequences of PvRBP2b at K = 3 (c) and of the reticulocyte binding region at K = 6 (d) in parasite populations from different endemic regions

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The genetic differentiation between two parasite populations was also evaluated via Wright’s fixation index (FST) using the entire PvRBP2b sequence and reticulocyte binding region, respectively. The heatmap of the FST values from both analyses revealed population differentiation patterns consistent with the structure analysis (Fig. 7). Consistent with the principle of isolation by distance, parasite populations were genetically similar within each continent (e.g. Brazil vs Colombia, PNG vs. Indonesia, countries within the GMS). In contrast, considerable differentiation was detected between populations from different continents. Notably, the PNG and Indonesia P. vivax populations had high levels of genetic differentiation from the rest of the global parasite populations. In comparison, parasite populations from the GMS were moderately differentiated from the South American parasite populations. Interestingly, parasites from Africa (represented by Ethiopia) showed little genetic differentiation from the GMS and South American parasites.

Fig. 7
figure 7

FST analysis of global P. vivax populations. Heatmaps show pairwise comparison for the near full-length sequence (a) and the reticulocyte binding region (b) in PvRBP2b among worldwide P. vivax populations. The numbers in the cells are the FST values between two endemic areas. Abbreviations: CM, China–Myanmar border; FST, Wright’s fixation index

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Discussion

In the present study, we evaluated the genetic diversity of PvRBP2b as a P. vivax malaria blood-stage vaccine candidate. The overall nucleotide diversity of PvRBP2b from the global samples was modest (0.00196), much lower than that of the highly polymorphic surface antigens, such as PvAMA1 (0.0093, n = 372) and PvMSP1 (0.0023–0.0495 for the conserved blocks and 0.1193–0.2055 for the variable blocks, n = 40) [52, 53]. The genetic polymorphisms of PvRBP2b were geographically heterogeneous, with higher diversity found in GMS countries than in South America (Brazil and Colombia), Africa (Ethiopia) and Oceania (PNG). A similar genetic diversity distribution pattern was also observed in PvAMA1, and it seemed to correlate with the larger effective population size in Southeast Asia [52]. The authors of a previous study speculated that P. vivax in Southeast Asia was possibly the source population [54]. The high transmission intensity and the frequent migrations of infected people among Southeast Asian countries might result in the large effective population size in this region. Recent continuous malaria control and elimination strategies successfully reduced the parasite incidence, but these seemed to have little short-term effect on the P. vivax population size [55, 56]. The relatively high levels of asymptomatic vivax infections in endemic populations possibly contributed to the maintenance of the effective population size [57, 58].

PvRBP2b-mediated reticulocyte invasion depends on the PvRBP2b–TfR1–Tf ternary complex [14]. Consistent with previous reports [15], we found that the reticulocyte binding region was under balancing selection; it accumulated most of the amino acid substitutions and had the highest diversity. Interestingly, most of the residues on the reticulocyte binding region were conserved. At least three residues (Y542, K600 and Y604) on PvRBP2b were critical to the reticulocyte binding and complex formation: mutations at these sites reduced binding affinity by around 80% [14]. These amino acids were absolutely conserved among all 312 samples, reflecting their essential function in reticulocyte binding. Most of the identified residues making contact with TfR1/Tf based on structure analysis [14] were conserved, with the exception of S586R and K363E, which had low (3.5%) and moderate (40.4%) allele frequencies, respectively. S586R had a limited distribution in Brazil, Ethiopia and Thailand, whereas K363E occurred in all P. vivax endemic areas. Additional nonsynonymous SNPs of low-modest frequencies (1.6–51%) were detected surrounding the interaction sites. Several residues within the reticulocyte binding region were under positive selection, raising the possibility that they resulted from the host immune selection [59]. Moreover, most of the positively selected residues are located on the α helices, some being close to the hydrophobic binding region. The K412N mutation had a frequency of 51.3% and resided in the flexible loop structure. Only one mutation, K575E, was mapped to the predicted linear B-cell epitope. However, it is still unclear whether these specific polymorphic residues would change protein structure by altering protein polarity and hydrophilicity, therefore adapting to the different TfR1 mutants (e.g. L212V, N348A, and S412G) [14, 60, 61]. More functional investigations are required to answer these questions.

One of the obstacles hindering the development of a successful malaria vaccine is the extensive genetic diversity of blood-stage antigens in P. vivax [62]. Naturally acquired antibodies against the reticulocyte binding region of PvRBP2b showed a strong association with reduced parasitemia [18, 21, 22], highlighting the vaccine potential of PvRBP2b. Although the overall genetic diversity of PvRBP2b was not as high as that of leading blood-stage vaccine candidates, PvAMA1 and PvMSP1, the testing of which has advanced to clinical trials [63], the reticulocyte binding region of PvRBP2b deserves further attention. This region carried almost 50% of the mutations in the entire PvRBP2b, and analysis of the global parasite samples identified 114 haplotypes. Furthermore, most haplotypes were region-specific and represented by a single parasite isolate, and very few haplotypes were shared worldwide. Even the predominant haplotypes Hap_6 (14.74%) and Hap_8 (12.82%) were distributed in only five regions. Of note, haplotype patterns in PNG were distinct from those of other worldwide populations. This enormous haplotype diversity may present a challenge to developing a PvRBP2b-based vaccine. Since an effective vaccine should include most of the common alleles relevant to the induction of immune responses to ensure sufficient coverage of the genetic diversity [64, 65], it is essential to determine whether the major PvRBP2b alleles confer strain-transcending immunity.

This study provides several lines of evidence confirming the geographical separation of global P. vivax populations. Pairwise FST comparison identified the considerably high levels of differentiation between the Oceania (PNG and Indonesia) parasite populations and other P. vivax endemic regions, whereas parasite populations in the GMS were much less differentiated. Interestingly, populations from South America appeared to be closely related to those from Africa (Ethiopia). Population structure analysis further reinforced this finding on population relatedness. The population differentiation pattern identified in the present study is in agreement with that reported in other population studies using individual genes [66], microsatellites [54, 67] or whole-genome sequences [24, 25, 68]. The distinct parasite genetic structure in PNG may reflect the limited gene flow between PNG and the rest of the world, while the unique RBC polymorphisms in the human populations may have also contributed to this difference [52, 67, 69, 70]. In the GMS, however, the high transmission intensity in some border areas and frequent host migrations among the different countries are likely responsible for the panmixia of parasite populations. It should be noted that this study was biased, with most of the samples originating from Southeast Asia and, consequently, the conclusions may not represent global parasite populations. Population genetics can help assess the effects of malaria control strategies, track the source of imported infections, and inform the vaccine design.

Conclusion

In conclusion, this study revealed a remarkably high level of genetic polymorphisms in PvRBP2b among the P. vivax populations in different endemic areas, with mutations clustered in the reticulocyte binding region. The genetic differentiation of parasite populations among different continents was notable, suggesting a potential need to cover major protein variants in case of strain-transcending immunity. Future studies addressing the functions of antibodies against different PvRBP2b variants are warranted.

Availability of data and materials

The data supporting the conclusion of this article are included within the article. Representative sequences are submitted to the GenBank database under the Accession Numbers: OM715042-OM715085 and OM715086-OM715129.

Abbreviations

DARC:

Duffy antigen receptor for chemokines

F ST :

Wright’s fixation index

GMS:

Greater Mekong subregion

H:

Number of haplotypes

Hd:

Haplotype diversity

PfRh5:

Plasmodium falciparum reticulocyte binding protein homolog 5

PNG:

Papua New Guinea

PvAMA1:

Plasmodium vivax apical membrane antigen

PvMSP:

Plasmodium vivax merozoite surface protein 1

PvRBP2b:

Plasmodium vivax reticulocyte binding protein 2b

RBCs:

Red blood cells

SNPs:

Single nucleotide polymorphisms

SRA:

Sequence read archive

TfR1:

Transferrin receptor 1

Ï€ :

Nucleotide diversity

References

  1. WHO. World malaria report 2019. Geneva: World Health Organization; 2019. https://www.who.int/publications/i/item/9789241565721. Accessed 8 Oct 2021.

    Google Scholar 

  2. Baird JK. Resistance to therapies for infection by Plasmodium vivax. Clin Microbiol Rev. 2009;22:508–34. https://doi.org/10.1128/CMR.00008-09.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Imwong M, Snounou G, Pukrittayakamee S, Tanomsing N, Kim JR, Nandy A, et al. Relapses of Plasmodium vivax infection usually result from activation of heterologous hypnozoites. J Infect Dis. 2007;195:927–33. https://doi.org/10.1086/512241.

    Article  CAS  PubMed  Google Scholar 

  4. Price RN, von Seidlein L, Valecha N, Nosten F, Baird JK, White NJ. Global extent of chloroquine-resistant Plasmodium vivax: a systematic review and meta-analysis. Lancet Infect Dis. 2014;14:982–91. https://doi.org/10.1016/S1473-3099(14)70855-2.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Singh S, Chitnis CE. Molecular signaling involved in entry and exit of malaria parasites from host erythrocytes. Cold Spring Harb Perspect Med. 2017;7:a026815. https://doi.org/10.1101/cshperspect.a026815.

  6. Singh S, Alam MM, Pal-Bhowmick I, Brzostowski JA, Chitnis CE. Distinct external signals trigger sequential release of apical organelles during erythrocyte invasion by malaria parasites. PLoS Pathog. 2010;6:e1000746.

    Article  Google Scholar 

  7. Mueller I, Shakri AR, Chitnis CE. Development of vaccines for Plasmodium vivax malaria. Vaccine. 2015;33:7489–95. https://doi.org/10.1016/j.vaccine.2015.09.060.

    Article  CAS  PubMed  Google Scholar 

  8. Tham WH, Beeson JG, Rayner JC. Plasmodium vivax vaccine research—we’ve only just begun. Int J Parasitol. 2017;47:111–8. https://doi.org/10.1016/j.ijpara.2016.09.006.

    Article  PubMed  Google Scholar 

  9. Noulin F, Borlon C, Van Den Abbeele J, D’Alessandro U, Erhart A. 1912–2012: a century of research on Plasmodium vivax in vitro culture. Trends Parasitol. 2013;29:286–94. https://doi.org/10.1016/j.pt.2013.03.012.

    Article  PubMed  Google Scholar 

  10. Udomsangpetch R, Kaneko O, Chotivanich K, Sattabongkot J. Cultivation of Plasmodium vivax. Trends Parasitol. 2008;24:85–8. https://doi.org/10.1016/j.pt.2007.09.010.

    Article  PubMed  Google Scholar 

  11. Howes RE, Reiner RC Jr, Battle KE, Longbottom J, Mappin B, Ordanovich D, et al. Plasmodium vivax transmission in Africa. PLoS Negl Trop Dis. 2015;9:e0004222. https://doi.org/10.1371/journal.pntd.0004222.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Mercereau-Puijalon O, Menard D. Plasmodium vivax and the Duffy antigen: a paradigm revisited. Transfus Clin Biol. 2010;17:176–83. https://doi.org/10.1016/j.tracli.2010.06.005.

    Article  CAS  PubMed  Google Scholar 

  13. Malleret B, Li A, Zhang R, Tan KS, Suwanarusk R, Claser C, et al. Plasmodium vivax: restricted tropism and rapid remodeling of CD71-positive reticulocytes. Blood. 2015;125:1314–24. https://doi.org/10.1182/blood-2014-08-596015.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Gruszczyk J, Huang RK, Chan LJ, Menant S, Hong C, Murphy JM, et al. Cryo-EM structure of an essential Plasmodium vivax invasion complex. Nature. 2018;559:135–9. https://doi.org/10.1038/s41586-018-0249-1.

    Article  CAS  PubMed  Google Scholar 

  15. Gruszczyk J, Kanjee U, Chan LJ, Menant S, Malleret B, Lim NTY, et al. Transferrin receptor 1 is a reticulocyte-specific receptor for Plasmodium vivax. Science. 2018;359:48–55.

    Article  CAS  Google Scholar 

  16. Galinski MR, Medina CC, Ingravallo P, Barnwell JW. A reticulocyte-binding protein complex of Plasmodium vivax merozoites. Cell. 1992;69:1213–26.

    Article  CAS  Google Scholar 

  17. Gruszczyk J, Lim NT, Arnott A, He WQ, Nguitragool W, Roobsoong W, et al. Structurally conserved erythrocyte-binding domain in Plasmodium provides a versatile scaffold for alternate receptor engagement. Proc Natl Acad Sci USA. 2016;113:E191-200.

    Article  CAS  Google Scholar 

  18. He WQ, Karl S, White MT, Nguitragool W, Monteiro W, Kuehn A, et al. Antibodies to Plasmodium vivax reticulocyte binding protein 2b are associated with protection against P. vivax malaria in populations living in low malaria transmission regions of Brazil and Thailand. PLoS Negl Trop Dis. 2019;13:e0007596.

    Article  Google Scholar 

  19. Miller LH, Mason SJ, Clyde DF, McGinniss MH. The resistance factor to Plasmodium vivax in blacks. The Duffy-blood-group genotype, FyFy. N Engl J Med. 1976;295:302–4.

    Article  CAS  Google Scholar 

  20. Crosnier C, Bustamante LY, Bartholdson SJ, Bei AK, Theron M, Uchikawa M, et al. Basigin is a receptor essential for erythrocyte invasion by Plasmodium falciparum. Nature. 2011;480:534–7. https://doi.org/10.1038/nature10606.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Franca CT, He WQ, Gruszczyk J, Lim NT, Lin E, Kiniboro B, et al. Plasmodium vivax reticulocyte binding proteins are key targets of naturally acquired immunity in Young Papua New Guinean children. PLoS Negl Trop Dis. 2016;10:e0005014.

    Article  Google Scholar 

  22. Hietanen J, Chim-Ong A, Chiramanewong T, Gruszczyk J, Roobsoong W, Tham WH, et al. Gene models, expression repertoire, and immune response of Plasmodium vivax reticulocyte binding proteins. Infect Immun. 2015;84:677–85.

    Article  Google Scholar 

  23. Zhao Y, Wang L, Soe MT, Aung PL, Wei H, Liu Z, et al. Molecular surveillance for drug resistance markers in Plasmodium vivax isolates from symptomatic and asymptomatic infections at the China-Myanmar border. Malar J. 2020;19:281. https://doi.org/10.1186/s12936-020-03354-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Diez Benavente E, Campos M, Phelan J, Nolder D, Dombrowski JG, Marinho CRF, et al. A molecular barcode to inform the geographical origin and transmission dynamics of Plasmodium vivax malaria. PLoS Genet. 2020;16:e1008576. https://doi.org/10.1371/journal.pgen.1008576.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Brashear AM, Fan Q, Hu Y, Li Y, Zhao Y, Wang Z, et al. Population genomics identifies a distinct Plasmodium vivax population on the China-Myanmar border of Southeast Asia. PLoS Negl Trop Dis. 2020;14:e0008506. https://doi.org/10.1371/journal.pntd.0008506.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ford A, Kepple D, Abagero BR, Connors J, Pearson R, Auburn S, et al. Whole genome sequencing of Plasmodium vivax isolates reveals frequent sequence and structural polymorphisms in erythrocyte binding genes. PLoS Negl Trop Dis. 2020;14:e0008234. https://doi.org/10.1371/journal.pntd.0008234.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Auburn S, Getachew S, Pearson RD, Amato R, Miotto O, Trimarsanto H, et al. Genomic analysis of Plasmodium vivax in southern Ethiopia reveals selective pressures in multiple parasite mechanisms. J Infect Dis. 2019;220:1738–49. https://doi.org/10.1093/infdis/jiz016.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. de Oliveira TC, Rodrigues PT, Menezes MJ, Goncalves-Lopes RM, Bastos MS, Lima NF, et al. Genome-wide diversity and differentiation in New World populations of the human malaria parasite Plasmodium vivax. PLoS Negl Trop Dis. 2017;11:e0005824. https://doi.org/10.1371/journal.pntd.0005824.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Hupalo DN, Luo Z, Melnikov A, Sutton PL, Rogov P, Escalante A, et al. Population genomics studies identify signatures of global dispersal and drug resistance in Plasmodium vivax. Nat Genet. 2016;48:953–8. https://doi.org/10.1038/ng.3588.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Librado P, Rozas J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics. 2009;25:1451–2.

    Article  CAS  Google Scholar 

  31. Tajima F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics. 1989;123:585–95.

    Article  CAS  Google Scholar 

  32. Fu YX, Li WH. Statistical tests of neutrality of mutations. Genetics. 1993;133:693–709.

    Article  CAS  Google Scholar 

  33. Kumar S, Stecher G, Tamura K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol. 2016;33:1870–4.

    Article  CAS  Google Scholar 

  34. Weaver S, Shank SD, Spielman SJ, Li M, Muse SV, Kosakovsky Pond SL. Datamonkey 2.0: a modern web application for characterizing selective and other evolutionary processes. Mol Biol Evol. 2018;35:773–7. https://doi.org/10.1093/molbev/msx335.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ladner JT, Wiley MR, Mate S, Dudas G, Prieto K, Lovett S, et al. Evolution and spread of ebola virus in Liberia, 2014–2015. Cell Host Microbe. 2015;18:659–69. https://doi.org/10.1016/j.chom.2015.11.008.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Kosakovsky Pond SL, Frost SD. Not so different after all: a comparison of methods for detecting amino acid sites under selection. Mol Biol Evol. 2005;22:1208–22. https://doi.org/10.1093/molbev/msi105.

    Article  CAS  PubMed  Google Scholar 

  37. Martin DP, Murrell B, Golden M, Khoosal A, Muhire B. RDP4: detection and analysis of recombination patterns in virus genomes. Virus Evol. 2015;1:vev003. https://doi.org/10.1093/ve/vev003.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Cockerham CC. Analyses of gene frequencies. Genetics. 1973;74:679–700.

    Article  CAS  Google Scholar 

  39. Cockerham CC, Weir BS. Covariances of relatives stemming from a population undergoing mixed self and random mating. Biometrics. 1984;40:157–64.

    Article  CAS  Google Scholar 

  40. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155:945–59.

    Article  CAS  Google Scholar 

  41. Hubisz MJ, Falush D, Stephens M, Pritchard JK. Inferring weak population structure with the assistance of sample group information. Mol Ecol Resour. 2009;9:1322–32. https://doi.org/10.1111/j.1755-0998.2009.02591.x.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Earl DA, Vonholdt BM. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour. 2012;4:359–61. https://doi.org/10.1007/s12686-011-9548-7.

    Article  Google Scholar 

  43. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005;14:2611–20. https://doi.org/10.1111/j.1365-294X.2005.02553.x.

    Article  CAS  PubMed  Google Scholar 

  44. Jakobsson M, Rosenberg NA. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics. 2007;23:1801–6. https://doi.org/10.1093/bioinformatics/btm233.

    Article  CAS  PubMed  Google Scholar 

  45. Rosenberg NA. DISTRUCT: a program for the graphical display of population structure. Mol Ecol Notes. 2004;4:137–8. https://doi.org/10.1046/j.1471-8286.2003.00566.x.

    Article  Google Scholar 

  46. Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–6. https://doi.org/10.1093/nar/gkab301.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Bandelt HJ, Forster P, Rohl A. Median-joining networks for inferring intraspecific phylogenies. Mol Biol Evol. 1999;16:37–48.

    Article  CAS  Google Scholar 

  48. El-Manzalawy Y, Dobbs D, Honavar V. Predicting linear B-cell epitopes using string kernels. J Mol Recognit. 2008;21:243–55. https://doi.org/10.1002/jmr.893.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics. 2007;8:4. https://doi.org/10.1186/1471-2105-8-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015;10:845–58. https://doi.org/10.1038/nprot.2015.053.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Baker NA, Sept D, Joseph S, Holst MJ, McCammon JA. Electrostatics of nanosystems: application to microtubules and the ribosome. Proc Natl Acad Sci USA. 2001;98:10037–41. https://doi.org/10.1073/pnas.181342398.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Arnott A, Mueller I, Ramsland PA, Siba PM, Reeder JC, Barry AE. Global population structure of the genes encoding the malaria vaccine candidate, Plasmodium vivax apical membrane antigen 1 (PvAMA1). PLoS Negl Trop Dis. 2013;7:e2506. https://doi.org/10.1371/journal.pntd.0002506.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Putaporntip C, Jongwutiwes S, Sakihama N, Ferreira MU, Kho WG, Kaneko A, et al. Mosaic organization and heterogeneity in frequency of allelic recombination of the Plasmodium vivax merozoite surface protein-1 locus. Proc Natl Acad Sci USA. 2002;99:16348–53. https://doi.org/10.1073/pnas.252348999.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Rougeron V, Elguero E, Arnathau C, Acuna Hidalgo B, Durand P, Houze S, et al. Human Plasmodium vivax diversity, population structure and evolutionary origin. PLoS Negl Trop Dis. 2020;14:e0008072. https://doi.org/10.1371/journal.pntd.0008072.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Nkhoma SC, Nair S, Al-Saai S, Ashley E, McGready R, Phyo AP, et al. Population genetic correlates of declining transmission in a human pathogen. Mol Ecol. 2013;22:273–85. https://doi.org/10.1111/mec.12099.

    Article  PubMed  Google Scholar 

  56. Li Y, Hu Y, Zhao Y, Wang Q, Ngassa Mbenda HG, Kittichai V, et al. Dynamics of Plasmodium vivax populations in border areas of the Greater Mekong sub-region during malaria elimination. Malar J. 2020;19:145. https://doi.org/10.1186/s12936-020-03221-9.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Liu Z, Soe TN, Zhao Y, Than A, Cho C, Aung PL, et al. Geographical heterogeneity in prevalence of subclinical malaria infections at sentinel endemic sites of Myanmar. Parasit Vectors. 2019;12:83. https://doi.org/10.1186/s13071-019-3330-1.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Zhao Y, Zeng J, Zhao Y, Liu Q, He Y, Zhang J, et al. Risk factors for asymptomatic malaria infections from seasonal cross-sectional surveys along the China-Myanmar border. Malar J. 2018;17:247. https://doi.org/10.1186/s12936-018-2398-y.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Weedall GD, Conway DJ. Detecting signatures of balancing selection to identify targets of anti-parasite immunity. Trends Parasitol. 2010;26:363–9. https://doi.org/10.1016/j.pt.2010.04.002.

    Article  CAS  PubMed  Google Scholar 

  60. Demogines A, Abraham J, Choe H, Farzan M, Sawyer SL. Dual host-virus arms races shape an essential housekeeping protein. PLoS Biol. 2013;11:e1001571. https://doi.org/10.1371/journal.pbio.1001571.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Abraham J, Corbett KD, Farzan M, Choe H, Harrison SC. Structural basis for receptor recognition by New World hemorrhagic fever arenaviruses. Nat Struct Mol Biol. 2010;17:438–44. https://doi.org/10.1038/nsmb.1772.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Laurens MB. The promise of a malaria vaccine-are we closer? Annu Rev Microbiol. 2018;72:273–92. https://doi.org/10.1146/annurev-micro-090817-062427.

    Article  CAS  PubMed  Google Scholar 

  63. Ntege EH, Takashima E, Morita M, Nagaoka H, Ishino T, Tsuboi T. Blood-stage malaria vaccines: post-genome strategies for the identification of novel vaccine candidates. Expert Rev Vaccines. 2017;16:769–79. https://doi.org/10.1080/14760584.2017.1341317.

    Article  CAS  PubMed  Google Scholar 

  64. Remarque EJ, Faber BW, Kocken CH, Thomas AW. A diversity-covering approach to immunization with Plasmodium falciparum apical membrane antigen 1 induces broader allelic recognition and growth inhibition responses in rabbits. Infect Immun. 2008;76:2660–70. https://doi.org/10.1128/IAI.00170-08.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Osier FH, Weedall GD, Verra F, Murungi L, Tetteh KK, Bull P, et al. Allelic diversity and naturally acquired allele-specific antibody responses to Plasmodium falciparum apical membrane antigen 1 in Kenya. Infect Immun. 2010;78:4625–33. https://doi.org/10.1128/IAI.00576-10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Bittencourt NC, Silva A, Virgili NS, Schappo AP, Gervasio J, Pimenta TS, et al. Plasmodium vivax AMA1: implications of distinct haplotypes for immune response. PLoS Negl Trop Dis. 2020;14:e0008471. https://doi.org/10.1371/journal.pntd.0008471.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Koepfli C, Rodrigues PT, Antao T, Orjuela-Sanchez P, Van den Eede P, Gamboa D, et al. Plasmodium vivax diversity and population structure across four continents. PLoS Negl Trop Dis. 2015;9:e0003872. https://doi.org/10.1371/journal.pntd.0003872.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Pearson RD, Amato R, Auburn S, Miotto O, Almagro-Garcia J, Amaratunga C, et al. Genomic analysis of local variation and recent evolution in Plasmodium vivax. Nat Genet. 2016;48:959–64. https://doi.org/10.1038/ng.3599.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Rosanas-Urgell A, Lin E, Manning L, Rarau P, Laman M, Senn N, et al. Reduced risk of Plasmodium vivax malaria in Papua New Guinean children with Southeast Asian ovalocytosis in two cohorts and a case-control study. PLoS Med. 2012;9:e1001305. https://doi.org/10.1371/journal.pmed.1001305.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Muller I, Bockarie M, Alpers M, Smith T. The epidemiology of malaria in Papua New Guinea. Trends Parasitol. 2003;19:253–9. https://doi.org/10.1016/s1471-4922(03)00091-6.

    Article  PubMed  Google Scholar 

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Acknowledgements

We would like to thank the patients participating in this study and providing the blood samples.

Funding

This work was supported by Grants U19AI089672 and U01AI155361 from the National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD, USA.

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Authors and Affiliations

  1. Department of Immunology, College of Basic Medical Science, China Medical University, Shenyang, 110122, Liaoning, China

    Xuexing Zhang, Haichao Wei, Yangminghui Zhang, Yan Zhao, Lin Wang, Yubing Hu, Yaming Cao & Qinghui Wang

  2. Department of Blood Transfusion Medicine, General Hospital of Northern Theater Command, Shenyang, 110015, Liaoning, China

    Haichao Wei

  3. Department of Blood Transfusion, Yantaishan Hospital, Yantai, 264000, Shandong, China

    Lin Wang

  4. Central Laboratory, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, China

    Yubing Hu

  5. Mahidol Vivax Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

    Wang Nguitragool & Jetsumon Sattabongkot

  6. College of Public Health, Global Health Infectious Disease Research (GHIDR) Program, Tampa, FL, USA

    John Adams & Liwang Cui

  7. Department of Internal Medicine, Morsani College of Medicine, University of South Florida, 3720 Spectrum Boulevard, Suite 304, Tampa, FL, 33612, USA

    Liwang Cui

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Contributions

XZ performed the experiments and drafted the manuscript. HW and YMZ, participated in the data analysis. YZ, LW, YH and JA participated in sample collection and sorting. WN, JS, LC,YC and QW conceived the study and revised the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yaming Cao or Qinghui Wang.

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Ethics approval and consent to participate

Written informed consent was obtained from the adult patients and the guardians of minor participants. The study protocol was approved by the ethics review boards of relevant institutions, and the use of the anonymized samples was approved by the institutional review board of China Medical University.

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Not applicable.

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The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1: Table S1.

Primers used for PvRBP2b gene amplification and sequencing. Table S2. Accession number, country of origin and referenced study for the 224 P. vivax isolates analyzed in this study. Table S3. Genetic mutations of near-full length PvRBP2b in different areas. Table S4. Recombination events detected in the near full-length PvRBP2b gene using the RDP4 package. Table S5. Sequences and distribution of PvRBP2b reticulocyte binding region haplotypes.

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Zhang, X., Wei, H., Zhang, Y. et al. Genetic diversity of Plasmodium vivax reticulocyte binding protein 2b in global parasite populations. Parasites Vectors 15, 205 (2022). https://doi.org/10.1186/s13071-022-05296-6

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