Skip to content

Advertisement

You're viewing the new version of our site. Please leave us feedback.

Learn more

BMC Genomics

Open Access

Whole-genome sequencing of a Plasmodium vivax clinical isolate exhibits geographical characteristics and high genetic variation in China-Myanmar border area

Contributed equally
BMC Genomics201718:131

https://doi.org/10.1186/s12864-017-3523-y

Received: 6 July 2016

Accepted: 27 January 2017

Published: 6 February 2017

Abstract

Background

Currently in China, the trend of Plasmodium vivax cases imported from Southeast Asia was increased especially in the China-Myanmar border area. Driven by the increase in P. vivax cases and stronger need for vaccine and drug development, several P. vivax isolates genome sequencing projects are underway. However, little is known about the genetic variability in this area until now.

Results

The sequencing of the first P. vivax isolate from China-Myanmar border area (CMB-1) generated 120 million paired-end reads. A percentage of 10.6 of the quality-evaluated reads were aligned onto 99.9% of the reference strain Sal I genome in 62-fold coverage with an average of 4.8 SNPs per kb. We present a 539-SNP marker data set for P. vivax that can identify different parasites from different geographic origins with high sensitivity. We also identified exceptionally high levels of genetic variability in members of multigene families such as RBP, SERA, vir, MSP3 and AP2. The de-novo assembly yielded a database composed of 8,409 contigs with N50 lengths of 6.6 kb and revealed 661 novel predicted genes including 78 vir genes, suggesting a greater functional variation in P. vivax from this area.

Conclusion

Our result contributes to a better understanding of P. vivax genetic variation, and provides a fundamental basis for the geographic differentiation of vivax malaria from China-Myanmar border area using a direct sequencing approach without leukocyte depletion. This novel sequencing method can be used as an essential tool for the genomic research of P. vivax in the near future.

Keywords

Plasmodium vivax Next generation sequencingComparative genomicsSingle nucleotide polymorphisms (SNPs) vir China-Myanmar border

Background

Plasmodium vivax is the most widely distributed human malaria species and causes more illness than P. falciparum in many regions [1]. Half of the world’s population is estimated to be at risk for malaria caused by P. vivax [2, 3]. In China, P. vivax was for relatively long time the major species source of malaria infection. Due to the increasing numbers of Chinese laborers working abroad, the proportion of imported P. vivax was up recent years. The imported P. vivax malaria may bring out the high risk to the malaria-free localities where Anopheles sinensis mosquitoes are prevalent [4].

Previous research in P. vivax showed that members of multigene families are genetically variable [5]. Some mutations may help the parasite evade drug and human immune response, or stabilize the protein's structure and function [6]. This pattern was observed by sequencing P. vivax field isolates as well [7]. Developing novel sequencing approach directly from field samples to study genetic diversity in P. vivax is for a significant importance, since it allows to monitor genes involved in drug susceptibility as well as for identifying potential vaccine candidates. In P. falciparum, genetic diversity studies have been useful for rapidly identifying genomic regions in linkage disequilibrium leading to natural selection processes in vaccine targets and drug-resistant genes [811].

Driven by the increase in P. vivax cases and stronger need for drug development, several P. vivax isolates genome sequencing projects are underway and more sequence data were revealed [12, 13]. In P. falciparum, the genomes of many hundreds of isolates have been sequenced or genotyped already [14, 15], but the number of published P. vivax isolates genome is still low. Most of these projects focused on the regional characteristics of P. vivax isolates [1619], and it is only recently that there have been revelations on global population genomics-based studies [20, 21]. One of the main reasons was that P. vivax is not amenable to continuous in vitro culture. In general, leukocyte depletion is required to minimize contamination from host. Alternatively, monkey-adapted P. vivax strains serve as a renewable source, but this still requires a higher cost and more steps in quality control procedures.

In this research, we sequenced and annotated the first P. vivax genome sequence of a clinical isolate obtained from the China-Myanmar border area (CMB-1). Genomic DNA for CMB-1 isolate was extracted from the whole blood of P. vivax microscopically positive patient and single P. vivax infection for this area was confirmed by PCR [22]. Our mapping and de-novo assembling show that this approach has similar results conformed to the method used by the past and meets the requirements of high-sensitivity mutation detection as well. It allows us to look for genetic quirks that are unique to few individuals with less expensively but greater effectiveness, particularly in the current infection circumstances.

Due to the increasing numbers of Chinese laborers working abroad, the proportion of imported P. vivax was up recent years. Yunnan province was still the highest transmission area in P.R. China, particularly in the southern border areas adjacent to Myanmar [2325]. Moreover, little is known about the P. vivax genetic variability in CMB area. Compared to other strains and isolates, the CMB-1 isolate illustrated the highest discrepancies with the reference in principal components analysis (PCA) and could be precisely clustered according to geographic origin. Our analyses also reveal 661 novel predicted genes, suggesting a capacity for greater functional variation in P. vivax from this area.

The results of this study provide a novel whole-genome sequencing approach and genomic information concerning the current epidemiological scenario of vivax malaria in China-Myanmar border area, and contribute to a better understanding of P. vivax evolution.

Methods

Ethics statement

This study was conducted according to the principles expressed in the Declaration of Helsinki. After the study protocol, potential risks and potential benefits were explained to the participant, blood collection was made with written informed consent of the participant and following institutional ethical guidelines that were reviewed and approved by the ethics committee at National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention.

Genomic data

For our analyses, we used genome data previously published from seven monkey adapted strains: the P. vivax Salvador I reference strain (Sal I) [12], Salvador I re-sequenced strain [17], Belem [17], Chesson [26], Brazil-I, Peru, India-VII [5], Mauritania-I [27], and North Korean [5]. We have also referenced six human clinical isolates: Cambodia (C08, C15, and C127) and Madagascar (M08, M15, M19) [18]. We obtained the raw sequences of these strains which were deposited in the GenBank database under the following SRR number: (Sal I re-sequenced strain: SRR575089, Madagascar: SRR570031, SRR828416, SRR572651, Cambodia: SRR572648, SRR572650, SRR572649, Brazil: SRR332573, SRR332569, IQ07: SRR064844, SRR073125, India VII: SRR332913, SRR332914, North Korea: SRR332565, SRR332562, Mauritania I: SRR332413, SRR332408, Belem: SRR575087 and Chesson: SRR828528). For the India-VII, Brazil-I, North Korean and Mauritania samples, we obtained their sequences of protein coding genes from GenBank under the Assembly IDs: GCA_000320625.1, GCA_000320645.1, GCA_000320665.1, and GCA_000320685.1. In addition, we used the whole genome and CDS sequences of the Sal I reference from PlasmoDB database [28].

Sample collection and sequencing

Genomic DNA for P. vivax CMB-1 sequencing was extracted from the whole blood of a P. vivax patient. The blood sample was collected from a symptomatic malaria-infected patient with microscopically positive returning to Tengchong county in Yunnan province in 2010. The patient is a merchant and he had been in business to Kachin state, an area of China-Myanmar border. The sample was confirmed P. vivax mono-species infection by Plasmodium species PCR-based diagnosis [23]. Genomic DNA was extracted using the QIAGEN DNeasy Blood & Tissue Kit, and sheared into 500 bp fragments using a Covaris S2 instrument. The fragmented DNA molecules were used to construct the Illumina sequencing libraries with insert sizes of 250 bp. In our previous work, we reported an initial sequencing result of this sample [22]. Our preliminary sequencing generated 31,471,932 paired-end reads and 5.86% of the quality-evaluated reads were aligned onto 96.43% of the reference strain Sal I genome in 7.84-fold coverage. Here we re-sequenced the library on Illumina HiSeq 2500 and generated 120,797,632 paired-end reads of 125 bp. All Illumina raw sequencing reads have been submitted to the NCBI Short Read Archive (SRR no. SRX1519064). We filtered all reads by removing the adapter sequences and low quality sequences using Trimmomatic-3.0 [29].

Identification of SNP and Indel

We mapped sequencing reads from all samples to the P. vivax Sal I genome using BWA [30] and SAMtools-1.3 [31]. In our study the single nucleotide polymorphism (SNP) was defined as nucleotide positions covered by at least 10 reads in at least half of analyzed samples. To identify insertions and deletions (Indels), we followed the procedure described by Chan et al. [17]. Briefly, we analyzed all read pairs that did not map in the expected configuration (head-to-head within 1 kb from each other) and might be indicative of deletions or inversions.

De-novo assembly, gene predictions and vir genes identification

We first removed host DNA sequences by aligning all clean paired-end reads to Homo sapiens genome [32] using Tophat-2.0 [33] and the non-aligned reads were considered to P. vivax reads. To determine the optimal k-mer, we tested different k values and compared the resulting assemblies. We then generated a de-novo assembly from the remaining corrected read pairs using SPAdes-3.5.0 [34] and a k-mer of 115 bp. Finally, contigs with no more than 500 bp length were discarded. All assembly results were submitted to the NCBI (BioSample no. SAMN03702587).

An ab intio gene prediction process was performed using the GlimmerHMM [35] and Augustus [36] softwares for all contigs. The original annotations of the P. vivax Sal I genome was downloaded from NCBI and used as the training set for the hidden Markov model. We began by comparing all partial putative genes to each other using Cd-hit [37] and discarded the shorter one when two partial genes were more than 90% identical. For the remainder partial genes, we then compared them to CDS sequences of the Sal I genome using Blast + [38]. Each partial gene with 100% identity or best reciprocal hit to reference was recognized as counterpart. The remaining genes were finally compared with the SwissProt [39] and NR (non-redundant sequence) database by Blast + (e-value cutoff of 1E-10). We discarded all partial genes without valid Blast + result and considered the rest as novel genes.

In addition, we downloaded the sequences of the whole vir gene family (P. vivax variant genes), and performed a comparison using Blast + and MEME to find all potentially vir genes. We then assigned each novel vir gene into subfamily using a phylogenetic approach in Clustal-Omega [40] to check the classification accuracy.

Results

Summary of the sequencing and mapping

We analyzed the genomic DNA from a malaria patient blood sample, without depletion of human leukocytes. The sequencing generated 120,797,632 paired-end reads with an average read length of 125 bp. Low-quality bases and adapter were trimmed out by using Trimmomatic-3.0. The reads were aligned to the P. vivax Salvador I reference strain (Sal I) genome by using BWA. An amount of 12,869,743 (10.65%) of the 120 million quality-evaluated reads were aligned onto 99.9% of the 14 chromosomes in Sal I genome. Average genome coverage was 62 times although it was variable in subtelomeric regions. Compared with other sequencing project, our result showed similar reads but with less processes (Table 1).
Table 1

Sequencing and mapping summary statistics for samples from field isolates and monkey-adapted strains

 

Field isolates

Monkey-adapted strains

 

C08a

C15a

M08 a

M19 a

CMB-1

Belemb

Sal-1c

Sequencing and Mapping

Read Pairs

231,291,984

79,414,201

215,643,747

85,703,544

120,797,632

81,446,663

215,743,944

Mapped on P. vivax

34,614,679

13,158,959

43,936,074

39,416,672

12,869,743

57,900,412

2,842,699

Mapped (%)

14.97

16.57

20.37

45.99

10.65

71.09

1.32

Mapped on Human

108,735,752

46,106,026

117,596,401

19,186,018

98,622,782

92,739

3,779,857

Mapped (%)

47.01

58.06

54.53

22.39

81.64

0.11

1.75

Coverage

Average coverage

102

70

218

117

62

418

20

Genome covered (%)

93.22

93.50

95.36

97.02

99.90

95.73

51.43

aAdapted data from human clinical isolates: C08, C15, M08 and M19 [23]

bAdapted data from monkey adapted strains: Belem [20]

cAdapted data from monkey adapted strains: Salvador I [14]

A previous research has found that in South America strains (Belem and Brazil-I), very few reads could map to a 130 kb region at the subtelomeric end of chromosome 7 [7]. The main reason is a sharp decline of GC content along this subtelomeric region and accompanying enrichment of repeated sequences. In the North Korean and Cambodian samples, some parasites carried deletion while some had entire subtelomeric sequence, causing a significant but not complete reduction in coverage. However this independent deletion event did not appear in the India, Africa and CMB-1 samples (Fig. 1). It suggested that CMB-1 isolate maintained genetically homogeneous, meanwhile the subtelomeric deletion occurred in North Korea and Cambodia isolates [7].
Fig. 1

Telomeric deletion in Plasmodium vivax strains and isolates. a Pileup alignment of Illumina sequencing reads across the P. vivax genome. b A 150 kb deletion indicated by the decrease in sequence coverage (y-axis) at the telomeric end of chromosome 7. The bottom track shows the variation in GC content along this region. The lower coverage in North Korea indicates that only some of the parasites carry the deletion

Sequencing shows high genetic diversity of P. vivax CMB-1

After the quality-evaluated reads were mapped to the P. vivax reference strain Sal I, 125,142 variable positions were identified. With the same method we analyzed all strain sequences onto the Sal I reference genome. Overall, we identified 108,846 nucleotide variants in at least half of the sequenced P. vivax strains or isolates with an average of 4.84 SNPs per kb distributed throughout the genome (Additional file 1: Table S1). Totally, 33,616 of the 125,142 SNPs were positioned in the coding region of 3,986 genes on 14 chromosomes. These SNPs caused 27,382 non-synonymous in 4,143 genes and only 6,234 synonymous mutations in 2,178 genes. We also found 4,454 of the 14,882 Indels in the coding region and 1,797 genes involved. We calculated the average SNPs per base (%) for each gene and checked the top list. Just like in a recent study [41], we found that most of these highly polymorphic genes are associated with red blood cell invasion and immune evasion such as MSP7 (PVX_082665), Pv-fam-e (PVX_089875), RBP2c, MSP1, SERA (PVX_003840), Pv-fam-b (PVX_002525), as well as VIR, such as Vir22 (PVX_097530) and Vir12 (PVX_083590). We listed in Additional file 1: Table S5, those genes with at least 5 SNPs. The SNPs from members of multigene families showed higher degree of polymorphism and caused more sequence variation, including the major merozoite invasion-realated protein family; e.g. reticulocyte binding proteins (RBPs), merozoite surface protein 3 family (MSP3s), serine-repeat antigens (SERAs), and merozoite surface protein 7 family (MSP7s) (Table 2).
Table 2

Classification of some important multigene families in CMB-1 isolate

Protein class description

No. of genes

SNPs per gene

Average SNPs per base (%)

reticulocyte binding protein (PvRBP)

6

70.50

0.99

Merozoite surface protein 3 (MSP3)

11

55.91

1.96

serine-repeat antigen (SERA)

13

45.46

1.53

variable surface protein (VIR)

115

30.61

1.92

transcription factor with AP2

27

20.93

0.34

protein kinase

20

18.15

0.36

Merozoite surface protein 7 (MSP7)

11

16.18

1.46

Pv-fam-d

12

12.50

0.64

DNA-directed RPB

15

10.07

0.36

PST-A

6

9.17

0.56

Phist protein (Pf-fam-b)

22

8.73

0.66

DnaJ

14

8.43

0.53

serine/threonine kinase

25

7.76

0.24

RAD protein (Pv-fam-e)

39

7.13

0.80

tryptophan-rich antigen (Pv-fam-a)

19

6.63

0.38

6-cysteine

13

6.15

0.25

all genes in whole genome

5626

5.97

0.15

Numbers are calculated for SNPs covered by at least 10 reads. “No. of genes” indicates the number of genes of a particular function class

Plasmodium vivax can be distinguished by geographic distribution

To assess whether the regional differences induced genomic changes, we first performed a principal component analysis (PCA) of all strains using all identified SNPs. The PCA type is Spearman's correlation matrix, which is more appropriate on variables with different distributions than the Pearson's correlation matrix, and explained a high ratio in the first component which separates the Americas from Asia. Moreover, the reference genomes used in our PCA contained sequences from different hosts (human and monkey). P. vivax strains grown in monkeys serve as a renewable source of parasites, but it is unclear if these strains retain the complexity commonly observed in field isolates. Our PCA result did not reveal any clustering of samples according to their host. But, it showed that P. vivax clustered generally according to their geographic origin and the host switch was not a major determinant of the genetic diversity. It is similar to those of other recent studies, including one involving PCA to explore the global population structure and divides the New World from Old World samples [20]. As one of the Asia isolates, the CMB-1 isolate illustrated the highest discrepancies with the Sal I genome (Fig. 2). Thus, we constructed neighbor-joining (Fig. 3) and maximum likelihood trees (Additional file 2: Figure S1) based on the SNPs between samples with all variable positions. The phylogenetic tree was clearly clustered into three groups, including; Asia, Africa, and South America clades. As in PCA, the CMB-1 isolate looks more similar to East-Asia clades though physically located in Southeast Asia. It is worth mentioning that in our phylogenetic tree, the India strain was tagged under the Africa category instead of Asia, and more close to the South America clades in PCA approach.
Fig. 2

Genomic relationships among P. vivax strains and isolates. Principal component analysis based on 108,846 SNPs in field isolates and monkey-adapted strains. Colors correspond to the geographic origin of the samples: blue for Central and South America, green for Asia, grey for Africa and black for India. The PCA result showed that P. vivax clustered generally according to their geographic origin

Fig. 3

Neighbor-joining phylograms of P. vivax constructed from the 108,846 SNPs occurring in at least half of the samples. Lineages are colored according to geographic origin. Branch lengths indicate considerable diversity in P. vivax strain. Numbers at nodes indicate percentages of bootstrap support. It is worth mentioning that the India strain was tagged under the Africa category instead of Asia

In order to provide a standardized genetic marker set that identifies a genomic signature, Baniecki et al. [42] have defined a barcode consisting of 42 SNPs and analyzed the performance on 87 P. vivax clinical samples in South America (Brazil, French Guiana), Africa (Ethiopia) and Asia (Sri Lanka). Our results confirmed the existence of the 42-SNP barcode as a marker that identifies genomic signatures. However, as shown in Additional file 1: Table S2, the SNPs marker was less effective when we expanded the scope of testing to other strains or isolates (CMB-1, Belem, Chesson and Mauritania). In our study, the SNPs were divided into 3 geographically group and 539 of the 125,142 variable positions showed the consistency of the geographic distribution and were independently informative. We validated our markers in a SNP dataset released recently [21], 480 of the 539 SNPs could be found. Within the 212 Asian samples, we found 2,750 loci in 130 African unique SNP markers (2,750/27,560) and 41,005 loci in 275 Asian markers (41,005/58,300). These SNP markers are very effective in distinguishing the African and Asian samples. However we also found 12,984 loci in 75 mixed region marker (12,984/15,900), suggesting that the existing data do not yet support the New World samples signature. As shown in Additional file 1: Table S3 and Fig. 4, these SNPs marker listed for each group were various and involved in different genes.
Fig. 4

Example with 15 SNPs provides baseline to distinguish parasite infections and their geographic origins. The positions of the SNPs are shown along with the chromosome on which the SNP resides, and the position (coordinate number from PlasmoDB) on that chromosome, the Gene ID where the indicated SNP is located, and the reference and alternate alleles

De novo genome assembly of P. vivax CMB-1

We removed DNA sequences originating from the host genome by filtering the sequences similarity to Homo sapiens genome. The remaining reads were de novo assembled. We yielded a database composed of 8,409 contigs, with an average GC content of 39% and N50 lengths of 6.6 kb (Additional file 1: Table S4). A total of 29,755,552 bp of the estimated genome length was assembled with 5,714 contigs larger than 1,000 bp. We mapped all de-novo assembled CMB-1 contigs to the P. vivax Sal I genome. There are 5,966 of 8,409 contigs mapped to 14 chromosomes of Sal I genome using Blast + accounting for 22.5 Mb (or 99.8% of the 14 chromosomes overall length) with identity up to 95%. We then applied a mixed gene prediction process to identify putative genes in all of the contigs and compared the predicted genes with CDS of Sal I genome. A total of 16,028 partial genes loci was predicted by the ab intio gene prediction process. A number of 9,985 partial genes were high sequence similarity recognized as whole or exon part of all the 5,614 genes of P. vivax Sal I. Among them, 5,382 partial genes were discarded as valid Blast + results on common database were lacked. Overall, we identified 661 novel P. vivax genes, among which 258 genes were similar to annotated Plasmodium hypothetical proteins and 32 genes were similar to well-characterized Plasmodium genes.

In a recent research by Cornejo et al., authors found that natural selection acts not only by shaping the patterns of variation within genes but it also affects genome organization [41]. The problem of polymorphism and number of paralogs affect gene families differently. In this study we checked the SERA and MSP3 families to see whether any different patterns exist. The Sal I reference genome contains 12 MSP3 and 13 SERA genes. A total of 35 partial genes mapped to them with high identity but only 20 could be recognized as their orthologous by best bilateral Blast + contrast. The lack of 4 MSP3 (PVX_097685, PVX_097700, PVX_097715 and PVX_097695) and 1 SERA (PVX_003840) genes in CMB-1 isolate suggested a higher polymorphism which came from recent duplication events within the P. vivax lineage [43, 44].

Identification of novel vir multigene family member

The vir superfamily is variably expressed and encodes proteins that are exported to the host cell surface for the purpose of evading the host adaptive immune response [45]. The revealed P. vivax Sal I genome divided 346 vir genes into 12 different subfamilies. Later, Francisco et al. [46] confirmed that subfamilies A, D and H cannot longer be classified as vir genes. From 165 vir-like novel genes we identified 78 vir genes and 26 pir genes (Fig. 5). We then assigned each novel vir gene into corresponding subfamily using Clustal-Omega and reconstructed a phylogenetic tree using MEGA6 [47]. For each subfamily, we identified the most conservative motifs on the published vir genes in each subfamilies using MEME [48], and confirmed that these motifs exist in our novel genes (Fig. 6). Our analyses revealed that the proportion of novel vir genes assigned to subfamilies were quite different and suggested the presence of different subfamilies in different samples due to rapid accumulation of mutations. Overall, our analysis reinforces the notion that vir genes are extremely diverse, and the current catalogue of vir genes is likely to be far from complete.
Fig. 5

Analysis of novel vir and pir genes family predicted from the CMB-1 isolate. a Phylogenetic tree showing the relationships between the protein coding sequences of vir genes from the Sal I genome (dots) and those predicted from the CMB-1 contigs (triangle). Annotated vir genes (dots) are colored according to their subfamilies. Nodes used to assign predicted CMB-1 vir genes into subfamilies are shown by the colored branches derived from them. b The relationships between pir genes from the Sal I genome (dots) and those predicted from the CMB-1 contigs (triangle)

Fig. 6

An example of the motifs and organization in novel vir gene (subfamily J). a Five most conservative motifs derived from the published vir genes (subfamily J). b List of our novel vir genes in subfamily J, horizontal lines illustrate the number of domains, their architecture and approximate location in the gene sequence. The different-colored squares show the corresponding motif in part A

Discussion

One of our main purposes of this study was to determine whether our simplified sequencing approach caused systematic errors in genomic level. Sequencing the P. vivax genome has provided us insights into parasite biology but also has arisen many challenging questions. The major bottleneck for P. vivax studies is the fact that the parasite is not amenable to continuous in vitro culture. In general, the P. vivax DNA was extracted directly from patient blood samples with leukocyte depletion to minimize contamination from host. However, it always raises the question that the parasitemia is typically less than 10,000 parasites per μl of blood [17]. Furthermore, the parasites obtained from blood samples of infected patients are contaminated by the large proportion of human genomic DNA. This leads to a large consumption of blood sample in sequencing.

Therefore as an alternative, monkey-adapted P. vivax strains are typically generated by direct injection of parasitized erythrocytes from patients, and serve as a renewable source of parasites for in vitro and ex vivo experimental studies. Even though some studies have shown that monkey-adapted strain genomes remain representative of the original parasite genomes [7], and this approach still need more steps in quality control procedures. For example in Belem monkey-adapted strain, it still retains 0.11% human DNA contamination (Table 1).

Previous studies [17] have shown that a variable proportion of reads (15–46%) from field isolates could be mapped to the P. vivax genome and provided enough coverage. Judging from coverage, the monkey-adapted strains effect even better (Table 1). The Belem monkey-adapted strain showed more coverage in less mapping reads due to its large volume of sample and easy to remove leukocytes. However, the average genome coverage does not accurately represent the quality of the sequencing data. By contrast, only 10% of our CMB-1 sample reads mapped to the P. vivax genome with minimum coverage but provided the highest genome covered ratio. Furthermore, a great deal (22–58%) of the reads generated from field isolates mapped to the human genome although the blood samples were already processed on CF11 columns. This means that host DNA cannot be completely erased (Table 1).

Overall, our analyses suggested that direct sequencing approach requires only high parasitemia for vivax samples without leukocytes filtration, and produces more available reads with less processes (Table 1). By reducing manual steps, it both reduces errors and time, and is also cost-effective. The direct sequencing approach will be an essential tool for the study of this important malaria parasite.

The increasing Chinese investment and numbers of laborers working abroad bring out high risks for malaria infection from endemic areas. Myanmar was still the main imported source of vivax malaria and the number of patients from this area was increased significantly. Previous studies uncovered high degree of genetic polymorphism in P. vivax, which was translated into functional variation [5]. Our sequencing analysis identified high levels of genetic variability in members of multigene families in CMB-1 isolate. The SNP rates averaged 8.11 per gene in mutational genes and 5.97 per gene in whole genome (Table 2). In contrast, some multigene families such as reticulocyte binding proteins (PvRBP), merozoite surface protein 3 family (MSP3), serine-repeat antigens (SERA), variable surface proteins (vir) and merozoite surface protein 7 family (MSP7) had SNPs rate of 70.50, 55.91, 45.46, 30.61 and 16.18 per gene, respectively. Some members of these protein families have been shown high immunoreactive previously [49, 50]. We also observed the highest enormous diversity in MSP3 and vir genes (1.96% and 1.92% SNPs per base, respectively), far greater than any other family. Our findings confirm previous researches indicating that members of multigene families are high genetically variable [19]. On the other hand, the high number of SNPs in gene family is one of the common manifestations of genetic complexity, which usually comes from high genetic polymorphism. The polymorphism and number of paralogs affect gene families differently in P. vivax [41], and easily impede short reads accurate mapping. These conserved regions of gene family paralogs are similar but not identical and always lead to a high score with low quality alignment, especially in P. vivax which exhibit extraordinary genetic diversities. More studies in CMB area are still needed to further refine our estimates.

Our mapping approach shows that 22,174,850 (18.36%) CMB-1 reads cannot map to host genome, but at the same time only 10.65% reads can be mapped to P. vivax reference. These remaining ~8% reads should also be considered as a part of CMB-1 genome and effectively increased nucleotide coverage in de novo assemble processes. The same situation also occurred in the field isolates where numerous proportions of reads (25–38%) mapped neither host nor P. vivax reference genome.

By de novo assembly, we also identified 661 novel predicted genes, including 78 vir genes and 26 pir genes. We observed that these novel vir genes predicted in the CMB-1 isolate shared the same motif with given vir genes, regardless of whether they are located in reference genome or in some novel contigs which do not match the reference genome. The conserved structure suggests that most of the novel vir genes might be functional. In another hand, we found enormous variations in vir genes sequence from the same subfamily and the proportion of genes assigned to subfamilies was quite different between Sal I and CMB-1. This reinforces the notion that vir genes are extremely diverse between P. vivax strains [26].

In this study, we also present a 539-SNP data set for P. vivax that spanned all 14 chromosomes of the genome and involved 267 genes. Studies on SNP barcode have an early start, but most of them pursued higher adaptability of the classification and lower sample cost. Here we have given more attention to increase the accuracy and detect genotypes with high sensitivity. As shown in Additional file 1: Table S3, our SNP markers provide an informative data set to identify different parasites from Asia and Africa. The whole genome SNPs distribution data will help us further to identify the P. vivax infections from China-Myanmar border area.

The findings showed in this paper provide the whole genomic information of a vivax malaria case in China-Myanmar border area, where little is known about the genetic variability. The results of this work contribute to the acquisition of some knowledge on P. vivax genetic variation, especially for multigene families, from China-Myanmar border area.

Conclusions

Here we report the first P. vivax isolate (CMB-1) genome sequence of a clinical isolate in this area using a direct sequencing approach without leukocyte depletion. We present a 539-SNP marker data set for P. vivax that can identify different parasites from different geographic origins, and identified exceptionally high levels of genetic variability in members of multigene families. We also found that the direct sequencing approach could produce more available reads for mutation detection, and be used as an essential tool in the near future.

Abbreviations

CMB: 

China-Myanmar border area

MSP3: 

merozoite surface protein 3

PCA: 

principal components analysis

PCR: 

polymerase chain reaction

RBP: 

reticulocyte binding protein

SERA: 

serine-repeat antigen

SNP: 

single nucleotide polymorphism

vir

variable surface protein

Declarations

Acknowledgements

We would like to thank the staff from the Yunnan Institute of Parasitic Diseases for the assistance of the blood sample collection from P. vivax-infected individual.

Funding

This work was supported by the National Research and Development Plan of China (Grant No. 2016YFC1200500), the Fourth Round of Three-Year Public Health Action Plan (2015–2017) in Shanghai (Grant No. GWTD2015S06 and GWIV-29), the National Natural Science Foundation of China (Grant No. 81101266) and the Shanghai Municipal Commission of Health and Family Planning (Grant No. 20134326).

Availability of data and materials

All data supporting these findings are contained within the manuscript and supplementary tables. All Illumina raw sequencing reads have been submitted to the NCBI Short Read Archive (SRR no. SRX1519064).

Authors’ contributions

HS JC conceived and designed the experiments. SC YW BX conducted the experiments. HS JC analyzed the data. SC YW KK BX contributed the reagents/materials/analysis tools. SC HS JC drafted the manuscript. All authors have read and approved the final version of this manuscript.

Competing interests

The authors declare that they have no competing interests

Consent for publication

Not Applicable.

Ethics approval and consent to participate

Blood sample was collected with written informed consent of the participant and following institutional ethical guidelines that were reviewed and approved by the ethics committee at National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Key Laboratory of Parasite and Vector Biology Ministry of Health
(2)
Institute of Parasitic Diseases, Zhejiang Academy of Medical Sciences

References

  1. Price RN, Tjitra E, Guerra CA, Yeung S, White NJ, Anstey NM. Vivax malaria: neglected and not benign. Am J Trop Med Hyg. 2007;77(6 Suppl):79–87.PubMedPubMed CentralGoogle Scholar
  2. Price RN, Douglas NM, Anstey NM. New developments in Plasmodium vivax malaria: severe disease and the rise of chloroquine resistance. Curr Opin Infect Dis. 2009;22(5):430–5.View ArticlePubMedGoogle Scholar
  3. Guerra CA, Howes RE, Patil AP, Gething PW, Van Boeckel TP, Temperley WH, Kabaria CW, Tatem AJ, Manh BH, Elyazar IR. The international limits and population at risk of Plasmodium vivax transmission in 2009. PLoS Negl Trop Dis. 2010;4(8), e774.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Feng J, Xiao H, Zhang L, Yan H, Feng X, Fang W, Xia Z. The Plasmodium vivax in China: decreased in local cases but increased imported cases from Southeast Asia and Africa. Sci Rep. 2015;5:8847.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Neafsey DE, Galinsky K, Jiang RH, Young L, Sykes SM, Saif S, Gujja S, Goldberg JM, Young S, Zeng Q. The malaria parasite Plasmodium vivax exhibits greater genetic diversity than Plasmodium falciparum. Nat Genet. 2012;44(9):1046–50.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Wang Y, Ma A, Chen SB, Yang YC, Chen JH, Yin MB. Genetic diversity and natural selection of three blood-stage 6-Cys proteins in Plasmodium vivax populations from the China-Myanmar endemic border. Infect Genet Evol. 2014;28:167–74.View ArticlePubMedGoogle Scholar
  7. Chan ER, Barnwell JW, Zimmerman PA, Serre D. Comparative Analysis of Field-Isolate and Monkey-Adapted Plasmodium vivax Genomes. PLoS Negl Trop Dis. 2015;9(3), e0003566.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Amambua-Ngwa A, Tetteh KK, Manske M, Gomez-Escobar N, Stewart LB, Deerhake ME, Cheeseman IH, Newbold CI, Holder AA, Knuepfer E, et al. Population genomic scan for candidate signatures of balancing selection to guide antigen characterization in malaria parasites. PLoS Genet. 2012;8(11), e1002992.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Mobegi VA, Duffy CW, Amambua-Ngwa A, Loua KM, Laman E, Nwakanma DC, MacInnis B, Aspeling-Jones H, Murray L, Clark TG, et al. Genome-wide analysis of selection on the malaria parasite Plasmodium falciparum in West African populations of differing infection endemicity. Mol Biol Evol. 2014;31(6):1490–9.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Conway DJ. Paths to a malaria vaccine illuminated by parasite genomics. Trends Genet. 2015;31(2):97–107.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Ariey F, Witkowski B, Amaratunga C, Beghain J, Langlois AC, Khim N, Kim S, Duru V, Bouchier C, Ma L, et al. A molecular marker of artemisinin-resistant Plasmodium falciparum malaria. Nature. 2014;505(7481):50–5.View ArticlePubMedGoogle Scholar
  12. Carlton JM, Adams JH, Silva JC, Bidwell SL, Lorenzi H, Caler E, Crabtree J, Angiuoli SV, Merino EF, Amedeo P, et al. Comparative genomics of the neglected human malaria parasite Plasmodium vivax. Nature. 2008;455(7214):757–63.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Winzeler EA. Malaria research in the post-genomic era. Nature. 2008;455(7214):751–6.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Manske M, Miotto O, Campino S, Auburn S, Almagro-Garcia J, Maslen G, O’Brien J, Djimde A, Doumbo O, Zongo I. Analysis of Plasmodium falciparum diversity in natural infections by deep sequencing. Nature. 2012;487(7407):375–9.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Miotto O, Amato R, Ashley EA, MacInnis B, Almagro-Garcia J, Amaratunga C, Lim P, Mead D, Oyola SO, Dhorda M, et al. Genetic architecture of artemisinin-resistant Plasmodium falciparum. Nat Genet. 2015;47(3):226–34.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Winter DJ, Pacheco MA, Vallejo AF, Schwartz RS, Arevalo-Herrera M, Herrera S, Cartwright RA, Escalante AA. Whole genome sequencing of field isolates reveals extensive genetic diversity in Plasmodium vivax from Colombia. PLoS Negl Trop Dis. 2015;9(12), e0004252.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Chan ER, Menard D, David PH, Ratsimbasoa A, Kim S, Chim P, Do C, Witkowski B, Mercereau-Puijalon O, Zimmerman PA, et al. Whole Genome Sequencing of Field Isolates Provides Robust Characterization of Genetic Diversity in Plasmodium vivax. PLoS Negl Trop Dis. 2012;6(9), e1811.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Menard D, Chan ER, Benedet C, Ratsimbasoa A, Kim S, Chim P, Do C, Witkowski B, Durand R, Thellier M. Whole genome sequencing of field isolates reveals a common duplication of the Duffy binding protein gene in Malagasy Plasmodium vivax strains. PLoS Negl Trop Dis. 2013;7(11), e2489.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Dharia NV, Bright AT, Westenberger SJ, Barnes SW, Batalov S, Kuhen K, Borboa R, Federe GC, McClean CM, Vinetz JM. Whole-genome sequencing and microarray analysis of ex vivo Plasmodium vivax reveal selective pressure on putative drug resistance genes. Proc Natl Acad Sci U S A. 2010;107(46):20045–50.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Hupalo DN, Luo Z, Melnikov A, Sutton PL, Rogov P, Escalante A, Vallejo AF, Herrera S, Arévalo-Herrera M, Fan Q. Population genomics studies identify signatures of global dispersal and drug resistance in Plasmodium vivax. Nat Genet. 2016.Google Scholar
  21. Pearson RD, Amato R, Auburn S, Miotto O, Almagro-Garcia J, Amaratunga C, Suon S, Mao S, Noviyanti R, Trimarsanto H. Genomic analysis of local variation and recent evolution in Plasmodium vivax. Nat Genet. 2016.Google Scholar
  22. Shen HM, Chen SB, Wang Y, Chen JH. Whole-genome sequencing of a Plasmodium vivax isolate from the China-Myanmar border area. Mem Inst Oswaldo Cruz. 2015;110(6):814–6.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Zhou X, Huang J-L, Njuabe MT, Li S-G, Chen J-H, Zhou X-N. A molecular survey of febrile cases in malaria-endemic areas along China-Myanmar border in Yunnan province, People’s Republic of China. Parasite. 2014;21.Google Scholar
  24. Gao Q, Beebe NW, Cooper RD. Molecular identification of the malaria vectors Anopheles anthropophagus and Anopheles sinensis (Diptera: Culicidae) in central China using polymerase chain reaction and appraisal of their position within the Hyrcanus group. J Med Entomol. 2004;41(1):5–11.View ArticlePubMedGoogle Scholar
  25. Chen SB, Ju C, Chen JH, Zheng B, Huang F, Xiao N, Zhou X, Ernest T, Zhou XN. Operational research needs toward malaria elimination in China. Adv Parasitol. 2014;86:109–33.View ArticlePubMedGoogle Scholar
  26. Hester J, Chan ER, Menard D, Mercereau-Puijalon O, Barnwell J, Zimmerman PA, Serre D. De Novo Assembly of a Field Isolate Genome Reveals Novel Plasmodium vivax Erythrocyte Invasion Genes. PLoS Negl Trop Dis. 2013;7(12), e2569.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Collins WE, Nguyen-Dinh P, Sullivan JS, Morris CL, Galland GG, Richardson BB, Nesby S. Adaptation of a strain of Plasmodium vivax from Mauritania to New World monkeys and anopheline mosquitoes. J Parasitol. 1998;619–621.Google Scholar
  28. Aurrecoechea C, Brestelli J, Brunk BP, Dommer J, Fischer S, Gajria B, Gao X, Gingle A, Grant G, Harb OS, et al. PlasmoDB: a functional genomic database for malaria parasites. Nucleic Acids Res. 2009;37(suppl 1):D539–D543.View ArticlePubMedGoogle Scholar
  29. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Li H, Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics. 2010;26(5):589–95.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Gnerre S, Maccallum I, Przybylski D, Ribeiro FJ, Burton JN, Walker BJ, Sharpe T, Hall G, Shea TP, Sykes S, et al. High-quality draft assemblies of mammalian genomes from massively parallel sequence data. Proc Natl Acad Sci U S A. 2011;108(4):1513–8.View ArticlePubMedGoogle Scholar
  33. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013;14(4):R36.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19(5):455–77.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Majoros WH, Pertea M, Salzberg SL. TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders. Bioinformatics. 2004;20(16):2878–9.View ArticlePubMedGoogle Scholar
  36. Stanke M, Tzvetkova A, Morgenstern B. AUGUSTUS at EGASP: using EST, protein and genomic alignments for improved gene prediction in the human genome. Genome Biol. 2006;7 Suppl 1:S11.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22(13):1658–9.View ArticlePubMedGoogle Scholar
  38. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL. BLAST+: architecture and applications. Bioinformatics. 2009;10:421.PubMedPubMed CentralGoogle Scholar
  39. Consortium U. UniProt: a hub for protein information. D - 0411011 2015, 43(Database issue):D204-12. doi: 10.1093/nar/gku989.(- 1362-4962 (Electronic)):- D204-212.
  40. Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, Lopez R, McWilliam H, Remmert M, Söding J. Fast, scalable generation of high‐quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol. 2011;7(1):539.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Cornejo OE, Fisher D, Escalante AA. Genome-wide patterns of genetic polymorphism and signatures of selection in Plasmodium vivax. Genome Biol Evol. 2015;7(1):106–19.View ArticleGoogle Scholar
  42. Baniecki ML, Faust AL, Schaffner SF, Park DJ, Galinsky K, Daniels RF, Hamilton E, Ferreira MU, Karunaweera ND, Serre D. Development of a single nucleotide polymorphism barcode to genotype Plasmodium vivax infections. PLoS Negl Trop Dis. 2015;9(3), e0003539.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Rice BL, Acosta MM, Pacheco MA, Carlton JM, Barnwell JW, Escalante AA. The origin and diversification of the merozoite surface protein 3 (msp3) multi-gene family in Plasmodium vivax and related parasites. Mol Phylogenet Evol. 2014;78:172–84.View ArticlePubMedPubMed CentralGoogle Scholar
  44. Schneider KA, Escalante AA. Fitness components and natural selection: why are there different patterns on the emergence of drug resistance in Plasmodium falciparum and Plasmodium vivax? Malar J. 2013;12(1):1.View ArticleGoogle Scholar
  45. Fernandez-Becerra C, Yamamoto MM, Vencio RZ, Lacerda M, Rosanas-Urgell A, del Portillo HA. Plasmodium vivax and the importance of the subtelomeric multigene vir superfamily. Trends Parasitol. 2009;25(1):44–51.View ArticlePubMedGoogle Scholar
  46. Lopez FJ, Bernabeu M, Fernandez-Becerra C, del Portillo HA. A new computational approach redefines the subtelomeric vir superfamily of Plasmodium vivax. BMC genomics. 2013;14:8.View ArticlePubMedPubMed CentralGoogle Scholar
  47. Tamura K, Dudley J, Nei M, Kumar S. MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Mol Biol Evol. 2007;24(8):1596–9.View ArticlePubMedGoogle Scholar
  48. Bailey TL, Elkan C. Fitting a mixture model by expectation maximization to discover motifs in bipolymers. Proc Int Conf Intell Syst Mol Biol. 1994;2:28–36.PubMedGoogle Scholar
  49. Chen JH, Jung JW, Wang Y, Ha KS, Lu F, Lim CS, Takeo S, Tsuboi T, Han ET. Immunoproteomics profiling of blood stage Plasmodium vivax infection by high-throughput screening assays. J Proteome Res. 2010;9(12):6479–89.View ArticlePubMedGoogle Scholar
  50. Chen JH, Chen SB, Wang Y, Ju C, Zhang T, Xu B, Shen HM, Mo XJ, Molina DM, Eng M, et al. An immunomics approach for the analysis of natural antibody responses to Plasmodium vivax infection. Mol Biosyst. 2015;11(8):2354–63.View ArticlePubMedGoogle Scholar

Copyright

© The Author(s). 2017

Advertisement