• View in gallery

    Location of the health centers in the Peruvian North Coast and Ecuadorian Amazon Basin (Piura, Tumbes and Pastaza). Black lines reflect international borders. This figure appears in color at www.ajtmh.org.

  • View in gallery

    Estimate of the number of Plasmodium vivax populations by Delta K values using STRUCTURE V2.1 software. The highest peak of ΔK = 145.4 for K = 3. This figure appears in color at www.ajtmh.org.

  • View in gallery

    Population structure of Plasmodium vivax populations in the Peruvian North Coast (Tumbes and Piura) and Ecuadorian Amazon Basin (Pastaza) using bar plot for K = 3. Each sample is represented by a single vertical bar divided into K colors, where K is the number of populations assumed. Each population is represented by a color, and the length of the colored bar depicts the estimated proportion of membership of the sample in each population.

  • 1.

    Mendis K, Sina BJ, Marchesini P, Carter R, 2001. The neglected burden of Plasmodium vivax malaria. Am J Trop Med Hyg 64: 97106.

  • 2.

    Guerra CA, Snow RW, Hay SI, 2006. Mapping the global extent of malaria in 2005. Trends Parasitol 22: 353358.

  • 3.

    Rosas-Aguirre A et al. 2016. Epidemiology of Plasmodium vivax malaria in Peru. Am J Trop Med Hyg 95: 133144.

  • 4.

    Saenz FE et al. 2017. Malaria epidemiology in low-endemicity areas of the northern coast of Ecuador: high prevalence of asymptomatic infections. Malar J 16: 300.

    • Search Google Scholar
    • Export Citation
  • 5.

    Krisher LK et al. 2016. Successful malaria elimination in the Ecuador-Peru border region: epidemiology and lessons learned. Malar J 15: 573.

  • 6.

    Carlton J, 2003. The Plasmodium vivax genome sequencing project. Trends Parasitol 19: 227231.

  • 7.

    Carlton JM et al. 2008. Comparative genomics of the neglected human malaria parasite Plasmodium vivax. Nature 455: 757763.

  • 8.

    Russell B, Suwanarusk R, Lek-Uthai U, 2006. Plasmodium vivax genetic diversity: microsatellite length matters. Trends Parasitol 22: 399401.

  • 9.

    Chenet SM, Schneider KA, Villegas L, Escalante AA, 2012. Local population structure of Plasmodium: impact on malaria control and elimination. Malar J 11: 412.

    • Search Google Scholar
    • Export Citation
  • 10.

    Van den Eede P et al. 2010. Multilocus genotyping reveals high heterogeneity and strong local population structure of the Plasmodium vivax population in the Peruvian Amazon. Malar J 9: 151.

    • Search Google Scholar
    • Export Citation
  • 11.

    Ferreira MU, Karunaweera ND, da Silva-Nunes M, da Silva NS, Wirth DF, Hartl DL, 2007. Population structure and transmission dynamics of Plasmodium vivax in rural Amazonia. J Infect Dis 195: 12181226.

    • Search Google Scholar
    • Export Citation
  • 12.

    Rezende AM, Tarazona-Santos E, Couto AD, Fontes CJ, De Souza JM, Carvalho LH, Brito CF, 2009. Analysis of genetic variability of Plasmodium vivax isolates from different Brazilian Amazon areas using tandem repeats. Am J Trop Med Hyg 80: 729733.

    • Search Google Scholar
    • Export Citation
  • 13.

    Imwong M et al. 2007. Contrasting genetic structure in Plasmodium vivax populations from Asia and South America. Int J Parasitol 37: 10131022.

  • 14.

    Schousboe ML et al. 2014. Global and local genetic diversity at two microsatellite loci in Plasmodium vivax parasites from Asia, Africa and South America. Malar J 13: 392.

    • Search Google Scholar
    • Export Citation
  • 15.

    Menegon M et al. 2016. Microsatellite genotyping of Plasmodium vivax isolates from pregnant women in four malaria endemic countries. PLoS One 11: e0152447.

    • Search Google Scholar
    • Export Citation
  • 16.

    Delgado-Ratto C et al. 2014. Population structure and spatio-temporal transmission dynamics of Plasmodium vivax after radical cure treatment in a rural village of the Peruvian Amazon. Malar J 13: 8.

    • Search Google Scholar
    • Export Citation
  • 17.

    Delgado-Ratto C et al. 2016. Population genetics of Plasmodium vivax in the Peruvian Amazon. PLoS Negl Trop Dis 10: e0004376.

  • 18.

    Griffing SM et al. 2011. South American Plasmodium falciparum after the malaria eradication era: clonal population expansion and survival of the fittest hybrids. PLoS One 6: e23486.

    • Search Google Scholar
    • Export Citation
  • 19.

    Manock SR et al. 2009. Etiology of acute undifferentiated febrile illness in the Amazon basin of Ecuador. Am J Trop Med Hyg 81: 146151.

  • 20.

    Snounou G, Viriyakosol S, Zhu XP, Jarra W, Pinheiro L, do Rosario VE, Thaithong S, Brown KN, 1993. High sensitivity of detection of human malaria parasites by the use of nested polymerase chain reaction. Mol Biochem Parasitol 61: 315320.

    • Search Google Scholar
    • Export Citation
  • 21.

    Imwong M, Sudimack D, Pukrittayakamee S, Osorio L, Carlton JM, Day NP, White NJ, Anderson TJ, 2006. Microsatellite variation, repeat array length, and population history of Plasmodium vivax. Mol Biol Evol 23: 10161018.

    • Search Google Scholar
    • Export Citation
  • 22.

    Pacheco MA, Lopez-Perez M, Vallejo AF, Herrera S, Arevalo-Herrera M, Escalante AA, 2016. Multiplicity of infection and disease severity in Plasmodium vivax. PLoS Negl Trop Dis 10: e0004355.

    • Search Google Scholar
    • Export Citation
  • 23.

    Excoffier L, Laval G, Schneider S, 2005. Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evol Bioinform Online 1: 4750.

    • Search Google Scholar
    • Export Citation
  • 24.

    Pritchard JK, Stephens M, Donnelly P, 2000. Inference of population structure using multilocus genotype data. Genetics 155: 945959.

  • 25.

    Evanno G, Regnaut S, Goudet J, 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14: 26112620.

    • Search Google Scholar
    • Export Citation
  • 26.

    Beerli P, 2006. Comparison of Bayesian and maximum-likelihood inference of population genetic parameters. Bioinformatics 22: 341345.

  • 27.

    Beerli P, Palczewski M, 2010. Unified framework to evaluate panmixia and migration direction among multiple sampling locations. Genetics 185: 313326.

    • Search Google Scholar
    • Export Citation
  • 28.

    Rezende AM, Tarazona-Santos E, Fontes CJ, Souza JM, Couto AD, Carvalho LH, Brito CF, 2010. Microsatellite loci: determining the genetic variability of Plasmodium vivax. Trop Med Int Health 15: 718726.

    • Search Google Scholar
    • Export Citation
  • 29.

    Aramburu Guarda J, Ramal Asayag C, Witzig R, 1999. Malaria reemergence in the Peruvian Amazon region. Emerg Infect Dis 5: 209215.

  • 30.

    Need JT, Rogers EJ, Phillips IA, Falcon R, Fernandez R, Carbajal F, Quintana J, 1993. Mosquitoes (Diptera: Culicidae) captured in the Iquitos area of Peru. J Med Entomol 30: 634638.

    • Search Google Scholar
    • Export Citation
  • 31.

    Reinbold-Wasson DD, Sardelis MR, Jones JW, Watts DM, Fernandez R, Carbajal F, Pecor JE, Calampa C, Klein TA, Turell MJ, 2012. Determinants of Anopheles seasonal distribution patterns across a forest to periurban gradient near Iquitos, Peru. Am J Trop Med Hyg 86: 459463.

    • Search Google Scholar
    • Export Citation
  • 32.

    Li J, Collins WE, Wirtz RA, Rathore D, Lal A, McCutchan TF, 2001. Geographic subdivision of the range of the malaria parasite Plasmodium vivax. Emerg Infect Dis 7: 3542.

    • Search Google Scholar
    • Export Citation
  • 33.

    Joy DA, Gonzalez-Ceron L, Carlton JM, Gueye A, Fay M, McCutchan TF, Su XZ, 2008. Local adaptation and vector-mediated population structure in Plasmodium vivax malaria. Mol Biol Evol 25: 12451252.

    • Search Google Scholar
    • Export Citation
  • 34.

    Abdullah NR et al. 2013. Plasmodium vivax population structure and transmission dynamics in Sabah Malaysia. PLoS One 8: e82553.

  • 35.

    Barry AE, Waltmann A, Koepfli C, Barnadas C, Mueller I, 2015. Uncovering the transmission dynamics of Plasmodium vivax using population genetics. Pathog Glob Health 109: 142152.

    • Search Google Scholar
    • Export Citation
  • 36.

    Koepfli C et al. 2015. Plasmodium vivax diversity and population structure across four continents. PLoS Negl Trop Dis 9: e0003872.

  • 37.

    Sutton PL, 2013. A call to arms: on refining Plasmodium vivax microsatellite marker panels for comparing global diversity. Malar J 12: 447.

    • Search Google Scholar
    • Export Citation
  • 38.

    Alexandre MA, Ferreira CO, Siqueira AM, Magalhaes BL, Mourao MP, Lacerda MV, Alecrim M, 2010. Severe Plasmodium vivax malaria, Brazilian Amazon. Emerg Infect Dis 16: 16111614.

    • Search Google Scholar
    • Export Citation
  • 39.

    Joshi H, Prajapati SK, Verma A, Kang’a S, Carlton JM, 2008. Plasmodium vivax in India. Trends Parasitol 24: 228235.

  • 40.

    Reid H, Vallely A, Taleo G, Tatem AJ, Kelly G, Riley I, Harris I, Henri I, Iamaher S, Clements AC, 2010. Baseline spatial distribution of malaria prior to an elimination programme in Vanuatu. Malar J 9: 150.

    • Search Google Scholar
    • Export Citation
  • 41.

    Van den Eede P, Erhart A, Van der Auwera G, Van Overmeir C, Thang ND, Hung le X, Anne J, D’Alessandro U, 2010. High complexity of Plasmodium vivax infections in symptomatic patients from a rural community in central Vietnam detected by microsatellite genotyping. Am J Trop Med Hyg 82: 223227.

    • Search Google Scholar
    • Export Citation
  • 42.

    Gray KA, Dowd S, Bain L, Bobogare A, Wini L, Shanks GD, Cheng Q, 2013. Population genetics of Plasmodium falciparum and Plasmodium vivax and asymptomatic malaria in Temotu Province, Solomon Islands. Malar J 12: 429.

    • Search Google Scholar
    • Export Citation
  • 43.

    Liu Y et al. 2014. Genetic diversity and population structure of Plasmodium vivax in central China. Malar J 13: 262.

  • 44.

    Hong NV et al. 2016. Population genetics of Plasmodium vivax in four rural communities in central Vietnam. PLoS Negl Trop Dis 10: e0004434.

 
 
 

 

 
 
 

 

 

 

 

 

 

Genetic Variability of Plasmodium vivax in the North Coast of Peru and the Ecuadorian Amazon Basin

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  • 1 U.S. Naval Medical Research Unit 6 (NAMRU-6), Lima, Peru;
  • | 2 Sanaria, Inc., Rockville, Maryland;
  • | 3 Emerge, Emerging Diseases and Climate Change Research Unit, School of Public Health and Administration Universidad Peruana Cayetano Heredia, Lima, Peru;
  • | 4 Naval Health Research Center, San Diego, California

In the Peruvian North Coast (PNC), the number of Plasmodium vivax malaria cases increased steadily from 2007 to 2010 despite a significant decline in the overall number of cases in Peru during the same period. To better understand the transmission dynamics of P. vivax populations in the PNC and the neighboring Ecuadorian Amazon Basin (EAB), we studied the genetic variability and population structure of P. vivax in these areas. One hundred and twenty P. vivax isolates (58 from Piura and 37 from Tumbes in the PNC collected from 2008 to 2010 and 25 from the EAB collected in Pastaza from 2001 to 2004) were assessed by five polymorphic microsatellite markers. Genetic variability was determined by expected heterozygosity (He) and population structure by Bayesian inference cluster analysis. We found very low genetic diversity in the PNC (He = 0–0.32) but high genetic diversity in the EAB (He = 0.43–0.70). Population structure analysis revealed three distinct populations in the three locations. Six of 37 (16%) isolates from Tumbes had an identical haplotype to that found in Piura, suggesting unidirectional flow from Piura to Tumbes. In addition, one haplotype from Tumbes showed similarity to a haplotype found in Pastaza, suggesting that this could be an imported case from EAB. These findings strongly suggest a minimal population flow and different levels of genetic variability between these two areas divided by the Andes Mountains. This work presents molecular markers that could be used to increase our understanding of regional malaria transmission dynamics, which has implications for the development of strategies for P. vivax control.

INTRODUCTION

Plasmodium vivax is the most widely disseminated cause of human malaria outside of the African continent. It is estimated to result in 80 million cases annually and is associated with high morbidity. Consequently, P. vivax infections are a significant public health burden and have a negative impact on economic development. Approximately 80% of clinical malaria cases in South America are due to P. vivax.1,2

Peru is a hypoendemic malaria area with low levels of transmission located in South America. In the Peruvian North Coast (PNC), the number of P. vivax malaria cases increased significantly between 2007 and 2010; from 1,733 (4%) to 3,922 (15%) despite a significant decline in the total number of cases from 43,467 to 26,878 in all of Peru.3 On the other hand, neighboring countries such as Ecuador reported a sustainable decrease in P. vivax cases since 2001 (106,641) through 2012 (558). This reduction in P. vivax cases was supported by economic growth and malaria control efforts by international organizations such as the Malaria Control in Border Zones of the Andean Region Program (PAMAFRO), Amazon Malaria Initiative of the United States Agency for International Development, and the Roll Back Malaria of the World Health Organization/Pan American Health Organization. However, this improvement of the economy in Ecuador created the need of new routes for exportation of agricultural products from the Amazon region to neighboring borders creating a risk of transmission of P. vivax from Ecuador to others countries such as Peru.4,5

Population migration of humans, animal reservoirs, and vectors between the PNC and the neighboring regions such as the Ecuadorian Amazon Basin (EAB) could affect the genetic variability, population structure, and number of cases of P. vivax in these regions during these periods.

Implementation of parasite control strategies for P. vivax requires an understanding of genetic variability as well as population structure, local epidemiology, and the dynamics of transmission caused by vectors and humans infected with this parasite.69 Genetic variability and population structure of P. vivax populations can be analyzed by microsatellite markers, simple sequence tandem repeats typically used in studies of population genetics in eukaryotic organisms. Studies of P. vivax using microsatellite markers in populations from endemic areas of South America such as the Amazon jungle of Colombia, Brazil, and Peru revealed high levels of heterogeneity (He = 0.44–0.80) and strong differentiation in P. vivax populations.1015

In Peru, the analysis of the genetic variability and population structure of P. vivax populations using microsatellite markers has been described for the Peruvian Amazon Basin only.10,16,17 Van den Eede et al.,10 found high levels of genetic variability, highly structured populations, and strong genetic differentiation. In addition, genetic studies of Plasmodium falciparum populations found a clonal substructure with the presence of five different clonets in the Peruvian Amazon Basin. In contrast, a single clonet in the PNC was found. The Andes Mountains appear to act as a barrier to the flow of P. falciparum populations between these areas.18

To better understand the transmission dynamics and population structure of P. vivax populations between the PNC and the neighboring EAB, we studied P. vivax isolates from these areas using five polymorphic microsatellite markers.

MATERIALS AND METHODS

Study areas and sample collection.

One hundred and twenty-seven P. vivax specimens were obtained from whole blood, filter papers impregnated with whole blood, or serum samples of patients with malaria. These samples were analyzed by microscopy at the time of collection to confirm P. vivax mono-infection. Ninety-five whole blood or filter papers impregnated with whole blood samples were from the PNC; 58 from the Department of Piura collected from 2008 to 2010 at the Bellavista Health Center and 37 from the Department of Tumbes collected from 2008 to 2009 at the Zarumilla Health Center and the Pampa Grande Health Center (Figure 1).

Figure 1.
Figure 1.

Location of the health centers in the Peruvian North Coast and Ecuadorian Amazon Basin (Piura, Tumbes and Pastaza). Black lines reflect international borders. This figure appears in color at www.ajtmh.org.

Citation: The American Journal of Tropical Medicine and Hygiene 99, 1; 10.4269/ajtmh.17-0498

Thirty-two serum samples were analyzed from the Province of Pastaza in the EAB, collected between 2001 and 2004 at Hospital de la IV División del Ejercito “Amazonas,” an Ecuadorian military hospital in the provincial capital of Puyo and at Hospital Vozandes del Oriente, a mission hospital in the nearby town of Shell (Figure 1).19 Both hospitals receive patients from throughout the region, including remote jungle villages and military outposts.

Samples from Ecuador were collected under the protocol “Etiology of Acute Undifferentiated Febrile Illness in the Amazon Basin of Ecuador.” This study was approved by the research ethics committee of Hospital Vozandes del Oriente, the Pastaza province directorate of the Ecuadorean Ministry of Public Health, and the U.S. Naval Medical Research Center Institutional Review Board (Protocol NMRCD.2001.0002) in compliance with all applicable federal regulations governing the protection of human subjects.19

This study was approved by the Institutional Review Board of the U.S. Naval Medical Research Unit 6 (Protocol NMRCD: PJT.NMRCD.075) in compliance with all applicable federal regulations governing the protection of human subjects.

Laboratory methods.

DNA was extracted from whole blood, filter papers impregnated with whole blood samples, or serum samples using the QIAamp DNA Blood Kit (QIAGEN, Valencia, CA) according to the manufacturer’s instructions. The samples were analyzed by species-specific Plasmodium polymerase chain reaction (PCR) to reconfirm P. vivax mono-infection.20

Five polymorphic dinucleotide microsatellite markers 14.185, 4.2771, 6.34, 12.335, and 7.67 were amplified using the primers and PCR conditions described by Imwong et al.21 The PCR product sizes were analyzed on an ABI 3130xl Genetic Analyzer (Applied Biosystems, Foster City, CA) using GeneScan 350 ROX Size Standard (Applied Biosystems) as an internal lane size standard. Fragment sizes were determined with GeneMapper® software v4.0 (Applied Biosystems).

To determinate alleles length and quantify peaks height, we used default microsatellite settings. Only the peaks greater than 100 relative fluorescence units (RFUs) were considered alleles, bands smaller than 100 RFUs were defined as background.

Population genetics were measured using the single or predominant allele (highest electrophoretic peak greater than 100 RFUs) observed at each locus to calculate allele frequencies. The predominant alleles were used to define haplotypes. Infections were defined as polyclonal if an isolate had more than one allele per locus. Only monoclonal infection samples were used for calculating genetic variability and structure population.10

Data analysis.

Population genetic variability and structure population analysis were calculated using only P. vivax monoclonal infection to avoid the presence of artificial haplotypes by multiple alleles per locus in the same sample.

Multiclonal infections were defined as identification of more than one allele per locus per sample. Multiplicity of infection (MOI) refers to the average number of distinct parasite genotypes concurrently infecting a patient and was calculated using the number of different alleles at each locus; single infections were reported by only one allele per locus at all of the genotype loci.10,14,22

We measured intra- and interpopulation diversity using the statistic expected heterozygosity (He) of each locus, which may be defined as the average probability that two alleles randomly obtained for each locus are different. We calculated the expected heterozygosity using He = (n/[n−1])(1−∑pi2), where n is the number of isolates analyzed and pi is the frequency of the ith allele in the population; values for He range are between 0 (null diversity) and 1. He, haplotypes, and allele number per locus were calculated using Arlequin software v3.1.23

We evaluated population structure using STRUCTURE software v2.1. This uses an alternative Bayesian approach (using no a priori data) to infer the most likely number of populations (K) represented in the total sample and to measure the probability that an individual is derived from each of these K populations based on the allele frequencies of each locus.24 The software was run with 20 replicates, with K varying from 1 to 10 with a burn-in of 100,000 and 500,000 iterations. For this analysis, we assumed the admixture model, which considers that each individual isolate may have ancestries from more than one of the K parental populations. The most probable number of populations was defined by calculating the ΔK value as described by Evanno et al.25

To explore gene flow between the PNC and the EAB, we inferred two migration models using a Bayesian approach implemented in MIGRATE-N. The first model assumed no restriction gene flow between PNC and EAB (panmictic) and the second model assumed gene flow restrictions between PNC and EAB (in-island). MIGRATE-N estimates the posterior probability distribution of the migration parameters, such as mutation-scaled migration rates (M) and population sizes (Θ), and calculates marginal log-likelihoods (log mL) for each model using a thermodynamic integration.26 The models were created considering the genetic structure and the knowledge of the people’s current mobility patterns in the PNC and the EAB. Each model was run on a 1 × 105 long chain with sampling every 10 generations and a 10% burn-in under a static heating scheme with four temperature chains of 1, 1.5, 3, and 1,000,000. The log mL was used to estimate the log Bayes factors and probabilities that were subsequently used for selection of the best model. The number of immigrants per generation (Nm) was calculated as the product of M by Θ.17,27

RESULTS

Multiplicity of infection and genetic variability in P. vivax isolates.

Multiple-clone infections accounted for 21% of all EAB samples (seven out of 32) and the average MOI for this population was 1.05 (range: 1.0–1.13; SD: ±0.06). On the other hand, the PNC population was composed exclusively of monoclonal infections (MOI = 1). Only monoclonal infections from the EAB and the PNC were used for genetic diversity and population structure analysis.

The number of alleles per locus ranged from one to six (Table 1). All microsatellites from isolates from the Department of Piura in the PNC were monomorphic. Two microsatellites (6.34 and 7.67) of isolates from the Department of Tumbes in the PNC were polymorphic with three alleles per locus; the remaining microsatellites were monomorphic. In contrast, all microsatellites from the EAB were polymorphic, with three to six alleles per locus.

Table 1

Description of microsatellite markers per locus

MarkerMotifChromosome locationSize range (bp)Dye
14.185AT14262–290VIC
4.2771AT482–1006FAM
6.34AC6136–166PET
12.335AT12155–197VIC
7.67AT7100–134VIC

AC = adenine-cytosine; AT = adenine-thymine; bp = base pairs. Dinucleotide microsatellites described by Imwong et al.21

Eighteen haplotypes were found, one in the Department of Piura, three in the Department of Tumbes, and 15 in the Province of Pastaza (Table 2). Six of the 37 (16%) isolates from the Department of Tumbes had an identical haplotype to that found in Piura (collected 2008–2009). In addition, one haplotype collected in 2008 in Tumbes showed high similarity (80% by structure analysis) to a haplotype found in 2003 in Pastaza.

Table 2

Haplotypes and genetic diversity (He) observed in the PNC and EAB

SitesAlleles per locusHe range per locusHaplotypeAlleles per microsatellite markers
Id14.1854.27716.3412.3357.67
H127285144160101
H227087144162105
H327085140160101
H427085144164115
H5272103149160101
H627585134160101
H727585144160101
EAB-Pastaza3–60.43–0.70H8272103149162109
H927585149160101
H1027585134166101
H1127085144160101
H12*2709515416095
H1327085144164109
H1427085149160101
H1527285152164101
H16*2708515416298
PNC-Tumbes1–30–0.32H1727085157162101
H1827085149162105
PNC-Piura10H1827085149162105

EAB = Ecuadorian Amazon Basin; PNC = Peruvian North Coast.

Haplotype shared between Pastaza and Tumbes, only 40% of microsatellites.

Haplotype shared between Tumbes and Piura, 100% of microsatellites.

Plasmodium vivax isolates from the PNC showed very low genetic diversity; null expected heterozygosity He = 0 per locus was found in Piura and He = 0–0.32 per locus in Tumbes. In contrast, the EAB showed high values of genetic diversity with He = 0.43–0.70 per locus (Table 2).

Population structure.

The population structure analysis identified the highest peak of ΔK = 145.4 for K = 3 (Figure 2). Three populations were identified; two populations in the PNC and one in the EAB. One subgroup was identified in the PNC with six out of 37 samples (16%) in the Department of Tumbes. As stated previously, this subgroup had an identical genotype to that found in Piura (Figure 3). One isolate from Tumbes had high similarity to a genotype found in Pastaza; this result is the same as that found in the haplotype analysis (Figure 3, Table 2).

Figure 2.
Figure 2.

Estimate of the number of Plasmodium vivax populations by Delta K values using STRUCTURE V2.1 software. The highest peak of ΔK = 145.4 for K = 3. This figure appears in color at www.ajtmh.org.

Citation: The American Journal of Tropical Medicine and Hygiene 99, 1; 10.4269/ajtmh.17-0498

Figure 3.
Figure 3.

Population structure of Plasmodium vivax populations in the Peruvian North Coast (Tumbes and Piura) and Ecuadorian Amazon Basin (Pastaza) using bar plot for K = 3. Each sample is represented by a single vertical bar divided into K colors, where K is the number of populations assumed. Each population is represented by a color, and the length of the colored bar depicts the estimated proportion of membership of the sample in each population.

Citation: The American Journal of Tropical Medicine and Hygiene 99, 1; 10.4269/ajtmh.17-0498

Genetic flow.

The gene flow was assessed using the information of the genetic structure of the parasite population from the structure analysis. From the two models tested (Panmictic and in-island), the second model was selected as the best model (log mL = −2,467.05, probability > 0.99). This model indicated that there are three distinct subpopulations with some migration flow occurring between them. This model is concordant with the results shown by structure analysis and the clear differentiation between study sites. According to this model, there is population flow between all sites, with a major contribution of immigrants from the Department of Piura to the Department of Tumbes in the PNC (Nm value of 3.434) compared with other sites. In contrast, we found lower migration flow from Pastaza in the EAB to Tumbes and Piura in the PNC (Nm values of 2.550 and 0.535, respectively). On the other hand, we found low migration flow from the Departments of Tumbes and Piura (Nm 1.377 and 2.242, respectively) to Pastaza (Table 3).

Table 3

Number of migrants per generation

Direction of migrationΘ valuesMNm
Tumbes → Pastaza1.181.1671.3770
Piura → Pastaza1.181.9002.2420
Pastaza → Tumbes0.347.5002.5500
Piura → Tumbes0.3410.1003.4340
Pastaza → Piura0.222.4330.5350
Tumbes → Piura0.228.5001.8700

Θ = population sizes; M = mutation-scaled migration rates; Nm = number of immigrants per generation.

DISCUSSION

In this study, we observed a low level of genetic variability and monomorphic P. vivax populations in the PNC. In contrast, the EAB revealed a highly heterogenous population with polymorphic alleles, a high number of haplotypes, a high level of genetic variability, and multiple-clone infection and MOI values similar to what others have observed in P. vivax populations from the Amazon Basin in Peru, Colombia, and Brazil.1017,28 Multiple-clone infection and MOI values in the EAB are within the range of those from other studies conducted in South America such as Brazil, Colombia, and Peru (0–70% and 1.1–3.0, respectively). Multiplicity of infection values has different genetic implications. Pacheco et al. reported a significant association between MOI values and disease severity by P. vivax infection in Colombia, with average values of 1.40 and 1.68 for uncomplicated and complicated cases, respectively. Low MOI value of 1.05 reported in the EAB is representative of hypoendemic malaria with low level of P. vivax population transmission. However, results could be biased because of small sample size (EAB N = 32), decreasing the number of P. vivax cases by effective malaria control and effects of DNA quality.

The PNC is a hypoendemic malaria zone separated by the Andes Mountains from other endemic malaria sites, including the Peruvian Amazon Basin and the EAB. Evidence of this geographic isolation is found in the population structure analysis, which revealed three distinct populations corresponding to each location; two populations in the PNC and one population in the EAB. We did not find shared P. vivax populations between the PNC and the EAB, indicating minimal population flow between these two areas. Similarly, Griffing et al.,18 2011 identified that P. falciparum population present in the PNC was monomorphic and shared no genetic similarity with populations on the other side of the Andes Mountains in the Peruvian Amazon Basin.

We observed one subpopulation (six of the 37) in the Department of Tumbes that was also identified in the Department of Piura. This suggests possible population flow between the two neighboring departments (150 km apart), between which there are no significant geographical barriers that may obstruct the movements of P. vivax populations by infected humans or vectors. This intrapopulation flow in the PNC is supported by MIGRATE-N analysis; we observed a major number of P. vivax immigrants from Piura to Tumbes, this interaction can be explained by human population migration from South to North. Tumbes shares a border with Ecuador; this strategic location promotes high levels of commerce and agriculture, thus creating an impetus for human population movement from surrounding areas, including Piura.

We did not find shared P. vivax populations between the PNC and the EAB, indicating minimal population flow between these two areas. Apparently, the risk of transmission of P. vivax populations by new routes of communications for exportation of agricultural products from the EAB to the PNC is very low. This was supported by STRUCTURE and MIGRATE-N analysis. However, one isolate from Tumbes in the PNC showed high similarity (80%) to a haplotype found in 2003 in Pastaza, which could be an imported case to Peru from the EAB. Nevertheless, these findings strongly indicate minimal population flow and different levels of genetic variability between these two areas divided by the Andes Mountains.

Another important aspect of the dynamics of transmission in P. vivax population between these regions is the role of the malaria vector. The PNC and EAB have different principal malaria vectors. In the PNC and EAB the principal malaria vectors are Anopheles albimanus and Anopheles darling, respectively.2932 A successful adaptation process between the parasite and the vector requires particular ecological and evolutionary factors; this adaptation could potentially make it more difficult to transmit P. vivax populations by different vector species.32,33

In addition, selective pressure from treatment policies implemented in the PNC during the malaria peak caused by the “El Niño” phenomenon (1997–2000) may have produced small P. vivax population with little or no genetic diversity. Also, isolation by Andes Mountains may have contributed to the development of a P. vivax population refuge, decreasing the probability of interactions with Amazonian P. vivax populations.18

The low genetic diversity of the P. vivax population we found in the PNC contrasts with the middle to high genetic diversity seen in previous studies. This information has important implications for future epidemiologic studies of malaria in this zone.9,16,3436

This study presented some limitations. First, different types of samples were collected at each study site. In the PNC, 90% of all samples were whole blood and 10% were from filter paper impregnated with whole blood. In both cases the yield of parasite DNA was optimal. However, the EAB samples isolated from serum resulted in lower yield and quality of parasite DNA, which led to shorter microsatellite peaks than in the PNC samples. This difference could impact the identification of polyclonal infections and underestimate the levels of polyclonal infections, MOI, and genetic diversity.

Second, sampling strategies and time collection were different between the PNC and EAB. Therefore, our results in terms of migration and comparison of genetic diversity might be biased because of the timing differences. In this regard, there is a need of more studies comparing the current diversity of P. vivax between these two regions after the decrease in cases in Ecuador.4,5 More studies are necessary to know the actual genetic diversity status and if this decreasing of P. vivax cases could affect the genetic diversity today.

Third, according to Imwong et al.,13 Russell et al.,8 Rezende et al.,28 and Sutton37 the repeat array length of microsatellites is directly associated with the number of alleles found in a P. vivax population. Therefore, genetic variability in the PNC and the EAB areas could be underestimated using these dinucleotide markers and additional markers with major repeat array length should be tested. In this study, only the marker 6.34 appears in the list of P. vivax genetic diversity markers recommended by Sutton. Interestingly, this marker reported the highest level of polymorphisms in the PNC and EAB.37 This evidence suggests that the actual genetic diversity could be higher and additional markers should be used for an accurate estimation and comparison of genetic data from other P. vivax–endemic regions. However, markers used in this study could give us a preliminary description of the genetic diversity status in these regions poorly studied.

In summary, the results obtained in this study strongly suggest a minimal population flow and different levels of genetic variability between these two areas divided by the Andes Mountains. The PNC showed an unusual P. vivax population isolated with very low genetic diversity compared with others P. vivax–endemic regions. In addition, these results could be used to increase our understanding of regional malaria transmission dynamics, new routes of transmission, imported cases, and P. vivax populations that could have unique clinical presentations, which are important for defining regional strategies for control and treatment of P. vivax.3844

Acknowledgments:

We would like to thank, Danett Bishop, Dionisia Gamboa, and Hugo Valdivia for their critical review of this manuscript.

REFERENCES

  • 1.

    Mendis K, Sina BJ, Marchesini P, Carter R, 2001. The neglected burden of Plasmodium vivax malaria. Am J Trop Med Hyg 64: 97106.

  • 2.

    Guerra CA, Snow RW, Hay SI, 2006. Mapping the global extent of malaria in 2005. Trends Parasitol 22: 353358.

  • 3.

    Rosas-Aguirre A et al. 2016. Epidemiology of Plasmodium vivax malaria in Peru. Am J Trop Med Hyg 95: 133144.

  • 4.

    Saenz FE et al. 2017. Malaria epidemiology in low-endemicity areas of the northern coast of Ecuador: high prevalence of asymptomatic infections. Malar J 16: 300.

    • Search Google Scholar
    • Export Citation
  • 5.

    Krisher LK et al. 2016. Successful malaria elimination in the Ecuador-Peru border region: epidemiology and lessons learned. Malar J 15: 573.

  • 6.

    Carlton J, 2003. The Plasmodium vivax genome sequencing project. Trends Parasitol 19: 227231.

  • 7.

    Carlton JM et al. 2008. Comparative genomics of the neglected human malaria parasite Plasmodium vivax. Nature 455: 757763.

  • 8.

    Russell B, Suwanarusk R, Lek-Uthai U, 2006. Plasmodium vivax genetic diversity: microsatellite length matters. Trends Parasitol 22: 399401.

  • 9.

    Chenet SM, Schneider KA, Villegas L, Escalante AA, 2012. Local population structure of Plasmodium: impact on malaria control and elimination. Malar J 11: 412.

    • Search Google Scholar
    • Export Citation
  • 10.

    Van den Eede P et al. 2010. Multilocus genotyping reveals high heterogeneity and strong local population structure of the Plasmodium vivax population in the Peruvian Amazon. Malar J 9: 151.

    • Search Google Scholar
    • Export Citation
  • 11.

    Ferreira MU, Karunaweera ND, da Silva-Nunes M, da Silva NS, Wirth DF, Hartl DL, 2007. Population structure and transmission dynamics of Plasmodium vivax in rural Amazonia. J Infect Dis 195: 12181226.

    • Search Google Scholar
    • Export Citation
  • 12.

    Rezende AM, Tarazona-Santos E, Couto AD, Fontes CJ, De Souza JM, Carvalho LH, Brito CF, 2009. Analysis of genetic variability of Plasmodium vivax isolates from different Brazilian Amazon areas using tandem repeats. Am J Trop Med Hyg 80: 729733.

    • Search Google Scholar
    • Export Citation
  • 13.

    Imwong M et al. 2007. Contrasting genetic structure in Plasmodium vivax populations from Asia and South America. Int J Parasitol 37: 10131022.

  • 14.

    Schousboe ML et al. 2014. Global and local genetic diversity at two microsatellite loci in Plasmodium vivax parasites from Asia, Africa and South America. Malar J 13: 392.

    • Search Google Scholar
    • Export Citation
  • 15.

    Menegon M et al. 2016. Microsatellite genotyping of Plasmodium vivax isolates from pregnant women in four malaria endemic countries. PLoS One 11: e0152447.

    • Search Google Scholar
    • Export Citation
  • 16.

    Delgado-Ratto C et al. 2014. Population structure and spatio-temporal transmission dynamics of Plasmodium vivax after radical cure treatment in a rural village of the Peruvian Amazon. Malar J 13: 8.

    • Search Google Scholar
    • Export Citation
  • 17.

    Delgado-Ratto C et al. 2016. Population genetics of Plasmodium vivax in the Peruvian Amazon. PLoS Negl Trop Dis 10: e0004376.

  • 18.

    Griffing SM et al. 2011. South American Plasmodium falciparum after the malaria eradication era: clonal population expansion and survival of the fittest hybrids. PLoS One 6: e23486.

    • Search Google Scholar
    • Export Citation
  • 19.

    Manock SR et al. 2009. Etiology of acute undifferentiated febrile illness in the Amazon basin of Ecuador. Am J Trop Med Hyg 81: 146151.

  • 20.

    Snounou G, Viriyakosol S, Zhu XP, Jarra W, Pinheiro L, do Rosario VE, Thaithong S, Brown KN, 1993. High sensitivity of detection of human malaria parasites by the use of nested polymerase chain reaction. Mol Biochem Parasitol 61: 315320.

    • Search Google Scholar
    • Export Citation
  • 21.

    Imwong M, Sudimack D, Pukrittayakamee S, Osorio L, Carlton JM, Day NP, White NJ, Anderson TJ, 2006. Microsatellite variation, repeat array length, and population history of Plasmodium vivax. Mol Biol Evol 23: 10161018.

    • Search Google Scholar
    • Export Citation
  • 22.

    Pacheco MA, Lopez-Perez M, Vallejo AF, Herrera S, Arevalo-Herrera M, Escalante AA, 2016. Multiplicity of infection and disease severity in Plasmodium vivax. PLoS Negl Trop Dis 10: e0004355.

    • Search Google Scholar
    • Export Citation
  • 23.

    Excoffier L, Laval G, Schneider S, 2005. Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evol Bioinform Online 1: 4750.

    • Search Google Scholar
    • Export Citation
  • 24.

    Pritchard JK, Stephens M, Donnelly P, 2000. Inference of population structure using multilocus genotype data. Genetics 155: 945959.

  • 25.

    Evanno G, Regnaut S, Goudet J, 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14: 26112620.

    • Search Google Scholar
    • Export Citation
  • 26.

    Beerli P, 2006. Comparison of Bayesian and maximum-likelihood inference of population genetic parameters. Bioinformatics 22: 341345.

  • 27.

    Beerli P, Palczewski M, 2010. Unified framework to evaluate panmixia and migration direction among multiple sampling locations. Genetics 185: 313326.

    • Search Google Scholar
    • Export Citation
  • 28.

    Rezende AM, Tarazona-Santos E, Fontes CJ, Souza JM, Couto AD, Carvalho LH, Brito CF, 2010. Microsatellite loci: determining the genetic variability of Plasmodium vivax. Trop Med Int Health 15: 718726.

    • Search Google Scholar
    • Export Citation
  • 29.

    Aramburu Guarda J, Ramal Asayag C, Witzig R, 1999. Malaria reemergence in the Peruvian Amazon region. Emerg Infect Dis 5: 209215.

  • 30.

    Need JT, Rogers EJ, Phillips IA, Falcon R, Fernandez R, Carbajal F, Quintana J, 1993. Mosquitoes (Diptera: Culicidae) captured in the Iquitos area of Peru. J Med Entomol 30: 634638.

    • Search Google Scholar
    • Export Citation
  • 31.

    Reinbold-Wasson DD, Sardelis MR, Jones JW, Watts DM, Fernandez R, Carbajal F, Pecor JE, Calampa C, Klein TA, Turell MJ, 2012. Determinants of Anopheles seasonal distribution patterns across a forest to periurban gradient near Iquitos, Peru. Am J Trop Med Hyg 86: 459463.

    • Search Google Scholar
    • Export Citation
  • 32.

    Li J, Collins WE, Wirtz RA, Rathore D, Lal A, McCutchan TF, 2001. Geographic subdivision of the range of the malaria parasite Plasmodium vivax. Emerg Infect Dis 7: 3542.

    • Search Google Scholar
    • Export Citation
  • 33.

    Joy DA, Gonzalez-Ceron L, Carlton JM, Gueye A, Fay M, McCutchan TF, Su XZ, 2008. Local adaptation and vector-mediated population structure in Plasmodium vivax malaria. Mol Biol Evol 25: 12451252.

    • Search Google Scholar
    • Export Citation
  • 34.

    Abdullah NR et al. 2013. Plasmodium vivax population structure and transmission dynamics in Sabah Malaysia. PLoS One 8: e82553.

  • 35.

    Barry AE, Waltmann A, Koepfli C, Barnadas C, Mueller I, 2015. Uncovering the transmission dynamics of Plasmodium vivax using population genetics. Pathog Glob Health 109: 142152.

    • Search Google Scholar
    • Export Citation
  • 36.

    Koepfli C et al. 2015. Plasmodium vivax diversity and population structure across four continents. PLoS Negl Trop Dis 9: e0003872.

  • 37.

    Sutton PL, 2013. A call to arms: on refining Plasmodium vivax microsatellite marker panels for comparing global diversity. Malar J 12: 447.

    • Search Google Scholar
    • Export Citation
  • 38.

    Alexandre MA, Ferreira CO, Siqueira AM, Magalhaes BL, Mourao MP, Lacerda MV, Alecrim M, 2010. Severe Plasmodium vivax malaria, Brazilian Amazon. Emerg Infect Dis 16: 16111614.

    • Search Google Scholar
    • Export Citation
  • 39.

    Joshi H, Prajapati SK, Verma A, Kang’a S, Carlton JM, 2008. Plasmodium vivax in India. Trends Parasitol 24: 228235.

  • 40.

    Reid H, Vallely A, Taleo G, Tatem AJ, Kelly G, Riley I, Harris I, Henri I, Iamaher S, Clements AC, 2010. Baseline spatial distribution of malaria prior to an elimination programme in Vanuatu. Malar J 9: 150.

    • Search Google Scholar
    • Export Citation
  • 41.

    Van den Eede P, Erhart A, Van der Auwera G, Van Overmeir C, Thang ND, Hung le X, Anne J, D’Alessandro U, 2010. High complexity of Plasmodium vivax infections in symptomatic patients from a rural community in central Vietnam detected by microsatellite genotyping. Am J Trop Med Hyg 82: 223227.

    • Search Google Scholar
    • Export Citation
  • 42.

    Gray KA, Dowd S, Bain L, Bobogare A, Wini L, Shanks GD, Cheng Q, 2013. Population genetics of Plasmodium falciparum and Plasmodium vivax and asymptomatic malaria in Temotu Province, Solomon Islands. Malar J 12: 429.

    • Search Google Scholar
    • Export Citation
  • 43.

    Liu Y et al. 2014. Genetic diversity and population structure of Plasmodium vivax in central China. Malar J 13: 262.

  • 44.

    Hong NV et al. 2016. Population genetics of Plasmodium vivax in four rural communities in central Vietnam. PLoS Negl Trop Dis 10: e0004434.

Author Notes

Address correspondence to Julio A. Ventocilla, Division of Immunology and Vaccine Development, Parasitology Department, U.S. Naval Medical Research Unit 6 (NAMRU-6), Lima, Peru. E-mail: julio.a.ventocilla.fn@mail.mil

Financial support: This work was supported by the Armed Forces Health Surveillance Branch (AFHSB) and its Global Emerging Infections Surveillance and Response System (GEIS), Work Unit Number: 6000.RAD1.F.B0601. During the period 2011–2012, this work was also supported by the National Institutes of Health Training Grant 2D43TW007393 “Peru Infectious Diseases Epidemiology Research Training Consortium,” awarded to the U.S. Naval Medical Research Unit 6 (NAMRU-6).

Authors’ addresses: Julio A. Ventocilla, Jorge Nuñez, Laura Lorena Tapia, Carmen M. Lucas, Andrés G. Lescano, and Kimberly A. Edgel, Department of Parasitology, U.S. Naval Medical Research Unit 6 (NAMRU-6), Lima, Peru, E-mails: julio.a.ventocilla.fn@mail.mil, jnunezco@gmail.com, laura.l.tapia2.ln@mail.mil, carmen.m.lucas2.ln@mail.mil, wlescano@hotmail.com, and kimberly.a.edgel.mil@mail.mil. Stephen R. Manock, Sanaria, Inc., Rockville, MD, E-mail: stevemanock@yahoo.com. Paul C. F. Graf, Naval Health Research Center, San Diego, CA, E-mail: paul.c.graf2.mil@mail.mil.

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