Clonal Outbreak of Plasmodium falciparum Infection in Eastern Panama

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© The Author 2014. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: [email protected].

Clonal outbreak of Plasmodium falciparum in eastern Panama

Nicanor Obaldia III1*

, Nicholas K. Baro1*

, Jose E. Calzada2, Ana M. Santamaria

2, Rachel Daniels

3,

Wesley Wong1, Hsiao-Han Chang

4, Elizabeth J. Hamilton

1, Myriam Arevalo-Herrera

5, Socrates

Herrera5, Dyann F. Wirth

1,6, Daniel L. Hartl

3, Matthias Marti

1, Sarah K. Volkman

1,6,7,§

1Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, MA, United

States 02115

2Instituto Conmemorativo Gorgas de Estudios de la Salud, Panamá City, República of Panamá

3Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, United States

02138

4Department of Epidemiology, Harvard School of Public Health, Boston, MA, United States 02115

5Centro de Investigación Científica Caucaseco/Centro Latino Americano de Investigación en Malaria (CLAIM)

Cali, Colombia

6Broad Institute, Cambridge, MA, United States 02142

7Simmons College, Boston, MA, United States 02115

*These authors contributed equally to this work.

§Corresponding author: [email protected]; Tel: 617-432-1621; Fax: 617-432-4766

Journal of Infectious Diseases Advance Access published October 21, 2014 by guest on June 19, 2016

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Abstract

Identifying the source of resurgent parasites is paramount to strategic and successful intervention for malaria

elimination. Although malaria incidence in Panama is low, a recent outbreak resulted in a six-fold increase in

reported cases. We hypothesized parasites sampled from this epidemic might be related and exhibit clonal

population structure. We tested the genetic relatedness using informative single nucleotide polymorphisms and

drug resistance loci. We found the parasites to be clustered into three clonal subpopulations and shared

relatedness with parasites from Colombia. Two clusters of Panamanian parasites shared identical drug

resistance haplotypes, and all clusters shared a chloroquine-resistance genotype matching the pfcrt haplotype of

Colombian origin. Our findings suggest these resurgent parasite populations are highly clonal and likely

resulted from epidemic expansion of imported or vestigial cases. Outbreak investigation using genetic tools can

illuminate potential sources of epidemic malaria and guide strategies to prevent further resurgence in areas of

malaria elimination.

by guest on June 19, 2016http://jid.oxfordjournals.org/

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3 Malaria transmission has been greatly reduced in Panama over the past several decades, suggesting only

a limited number of parasite types may remain among infected individuals. After three decades of successful

malaria control, Panama experienced an outbreak of epidemic proportions between 2001 and 2005, accounting

for greater than 60 percent of all cases reported over the past 35 years[1,2]. Study of P. falciparum isolates

collected during the outbreak provides an opportunity to study the population structure of Panamanian parasites

and understand genetic signatures associated with reemerging parasites during an elimination campaign.

Genetic diversity in parasite populations has been linked to transmission intensity[3,4], with limited

genetic diversity and extensive linkage disequilibrium (LD)[4–6] observed in low-transmission settings. South

America shows evidence of decreased outcrossing and genetic drift primarily resulting from inbreeding and

asexual replication[7–9]. Population bottlenecks, low transmission, inbreeding, and epidemic expansions can

all give rise to clonal lineages[3,9–15]. We hypothesize that, with fewer and fewer parasite types, limited

opportunities for outcrossing during the mosquito phase of the lifecycle results in transmission of highly related

or even clonal parasites. Genetic traits, such as drug resistance, are inherited from one generation to the next,

and hence are predicted to be homogeneous among highly related parasites. Furthermore, the epidemic rise of

malaria transmission during 2001–2005 in Panama similarly predicts that these parasites would be highly

related or even genetically identical.

A molecular barcode has recently been deployed in Senegal, where barcoded parasites revealed

increases in and persistence of clonality coincident with increased deployment of malaria control

interventions[5]. Uncommon malaria cases or outbreaks[16] in settings of low malaria endemicity provide an

ideal opportunity to ascertain whether simple genetic fingerprinting tools like the molecular barcode can reveal

clonal parasite population structure. Using the molecular barcode and a panel of assays for drug resistance loci,

we detected highly related clonal and parasite lineages in Panama consistent with this hypothesis. Deployments

of genotyping tools that reveal parasite population structure in low transmission settings are critical for

successful malaria elimination campaigns.


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4 Results

Genotyping reveals three clonal parasite populations among P. falciparum from eastern Panama

Using a molecular barcode that assesses 24 neutral, unlinked single nucleotide polymorphisms (SNPs)

with a high minor allele frequency, we identified three clonal parasite populations from among 37 P. falciparum

samples collected in two provinces (Panamá and Darien) and mostly within three Amerindian reservations

(Kuna Yala, Madugandi and Embera-Wounan) in eastern Panama between 2003 and 2008 (Fig. 1). These SNP

data were then used to group the parasites into three clusters of essentially genetically identical parasites (Fig.

2). These three clonal clusters include isolates mainly from Kuna Yala (n = 15) (Group I); Darien (n = 5) and

Panamá (n = 15) Provinces, along with two from Kuna Yala (PE01 and PF02) (Group II); and two coastal

samples (PF002 and KY44) (Group III).

Twelve of the 24 alleles were polymorphic among the Panamanian isolates to delineate these three

groups, yielding 4,096 theoretically possible genotypes. Analysis of molecular variance (AMOVA) using the

molecular barcode evaluated the percentage of molecular variance within and between groups; 68% of the

variation was explained by comparing collection sites (p = 0.001) compared to 96% (p = 0.001) explained by

clonal groups, which we define as clusters of essentially genetically identical parasites regardless of geographic

site of isolation. These data imply that clone-correction was more consistent with these data; consequently the

clonal groups were used for additional genetic analysis.

Clonal subpopulations of eastern Panama are related to more genetically diverse parasites from neighboring

Colombia.

Given the very low prevalence of P. falciparum infection in Panama before and after this epidemic in

2001–2005, we sought to test whether these parasites were related to parasites from neighboring Colombia,

which exhibits higher malaria transmission. Twenty P. falciparum samples collected from Buenaventura and

Tumaco (Pacific coast) and Tierra Alta (northwestern region) in Colombia during 2011–2012 (Fig. 1), were

similarly genotyped by barcode and compared with those sampled from Panama. Structure analysis revealed


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5 two clusters with significant likelihood, K = 2 (ΔK = 108, Fig. 3a) and K = 5 (ΔK = 61, Fig. 3b). Structure

analysis recovered the highly clonal populations in Panama and found evidence of two additional populations in

Colombia (Fig. 3b, Group IV, yellow; and Group V, orange).

We further explored parasite population structure using a hierarchical clustering approach and principal

components analysis (PCA). To test the robustness of the clustering, we created an “outgroup, ” a population

outside the clade of interest using additional genotypes from Senegal. In the absence of the outgroup, Group II

isolates clustered together in one branch of the phylogenetic tree (Fig. 4) and were separated from the majority

of Colombian samples and Group I isolates. The two Colombian isolates (C0048 and C0027) that shared

fractional membership with Group 1, shared the same branch of the phylogenetic tree. Remaining Colombian

samples formed their own separate branch, distinct from the Panamanian samples. The majority of this

structure between Panamanian and Colombian groups was retained with the addition of continentally distinct

geographic outgroup (Supplemental Fig. 1) and greater genetic variation between Senegalese isolates was

evidenced by higher tree heights among the genotypes from this population.

Results of PCA supported this clustering analysis and revealed separate structure for Groups I, II, and III

as well as the Colombian samples, which were separated by the first two principal components (Fig. 5a, PC1).

Additional variation attributable to differences among Colombian samples (Fig. 5b, PC2, identified by orange,

red, and blue) was observed, and inclusion of a continental outgroup from Senegal shows differentiation

between these two continental populations (Fig. 5a, grey), while still distinguishing Group II from the American

parasite population (Fig. 5b, purple and green).

Further examination of the two parasite populations clustered by structure analysis (K = 2) reveals one

group contains only Panamanian samples (Group II, Darien and Panamà) and the other contains both

Panamanian (Group I and Group III) and Colombian isolates. The separation of Group II parasites is supported

by both PCA and phylogenetic tree analysis, which showed a distinct parasite population. The mixed

Panamanian and Colombian cluster contains several highly related parasites. For example, two Colombian

samples from Tierra Alta (C0027 and C0048) share 50% of their membership fraction with Group I; and, five

Colombian samples from Tumaco and Buenaventura (C90021, C9009, C9030, C3148 and C3004) share 50% or


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6 more of their membership fraction with Group III. The two coastal samples (KY44 and PF002, Group III) were

more closely related to Colombian parasites than to other Panamanian parasites (see Fig. 3), based upon

population structure analysis. Parallel analysis on a subset of monogenomic samples (containing less than two

N‟s among the 24 SNPs assayed) yielded similar results (Supplemental Figs. 2-5).

Clonal Panamanian parasite population supported by nearly identical drug resistance haplotypes

Drug pressure historically applied to P. falciparum selects drug resistant parasites. To test whether

parasites with identical barcodes shared drug resistant loci profiles we assayed 32 SNPs across six genes

associated with drug resistance (Supplementary Table 2). All Panamanian isolates harbored resistance

mutations in pfcrt, pfmdr1, pfdhps and pfdhfr, and excluding mixed calls for pfmdr1 in two samples (KY44 and

PF002), all had identical drug resistance haplotypes irrespective of their clonal group assignment. These two

samples (KY44 and PF002) that contained evidence of mixed calls for pfmdr1 were collected off the

Panamanian coast (Group III, Coastal) and lacked two additional changes including D1246 in PfMDR1 and

A437 in PfDHPS. The only other genetic variation among these drug resistance loci noted were mixed allele

calls from two Darien isolates suggesting the presence of both the mutant and wild-type alleles at amino acid

positions 86, 1042, and 1246 (DA29); or residue 1246 (DA04) of PfMDR1. Overall, there was significantly

greater diversity among drug resistance loci from Colombian parasites compared to Panamanian parasites

(Supplementary Table 2).

Drug resistance profiles are consistent with the relatedness between Colombian and eastern Panamanian

samples detected by the putatively neutral markers of the molecular barcode. All Panamanian samples have

amino acid changes at positions 75, 76, 97 and 220 in PfCRT, resulting in the CVMET-Q-S-N-I haplotype

previously reported to originate in Colombia and subsequently identified in Venezuela. Here, Colombian

samples from Tierra Alta also shared this PfCRT haplotype, indicating genetic similarity between these

Colombian samples and those found in Panama. For PfMDR1, over 80% of the Panamanian shared the triple

mutant haplotype N-F-R-S-D-Y (with amino acid changes: Y184F, N1042D, and D1246Y), while remaining

variation came from Group III parasites that shared the N-F-R-S-D-D haplotype, or from mixed alleles in some


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7 Darien isolates. Previous reports identify the N-F-R-S-D-D haplotype as having originated in Colombia, and it

is detected among coastal Panamanian parasites (Group III). Mixed allele calls in some Darien parasites that

shared nearly identical molecular barcodes were suggestive of gene amplification of pfmdr1, which was

confirmed by HRM analysis that detected the N86Y change among additional copies (Supplemental Table 2,

see DA29 for example). All isolates shared the double mutant I-C-N-I haplotype for PfDHFR, and Groups I

and II shared the SG-K-A-A PfDHPS haplotype. Group III again differed from other Panamanian parasites by

carrying the wild-type allele at position 437. A summary of identified drug-resistance associated haplotypes is

shown in Supplemental Fig. 6. Collectively, analysis of drug resistance alleles is consistent with members of

Group III being distinct from other Panamanian isolates and sharing some drug resistance haplotypic

information previously reported from Colombia.

Discussion

Application of molecular barcode genotyping to resurgent P. falciparum infections in Panama reveals

clonal parasite populations that are distinct from genetically diverse P. falciparum from nearby Colombia.

These clonal Panamanian parasites harbor nearly identical drug resistance loci, while the largely genetically

diverse Colombian parasites exhibited correspondingly heterogeneous drug resistance loci. Collectively these

data demonstrate the utility of simple molecular genotyping tools and approaches to reveal the similar or

dissimilar nature of P. falciparum parasites among clinical cases for the purposes of outbreak investigation, and

can identify genetically similar or identical parasites from among these cases. The 24 SNP barcode is useful to

differentiate parasite populations in low endemic settings, as most of the previous studies were done in high

endemic African settings. Although numerous reports from South and Central American malaria endemic

countries[1,12,14,17–21] have evaluated P. falciparum populations, these findings demonstrate the combination

of a comprehensive panel of both molecular barcode SNPs and drug resistance-associated SNPs can fingerprint

and identify the relatedness of transmitted parasites from clinical cases during resurgent malaria.

Given the highly related nature of these parasites from Panama, only 12 variant neutral SNPs were

sufficient to divide them into three clonal subpopulations that were confirmed by AMOVA and maintained this


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8 structure after subsequent clustering and PCA interrogation. Investigation of molecular variance revealed that

these clonal groups were more similar than groups formed by geographic information alone, consistent with the

almost identical barcodes shared by these populations. Thus, molecular genotyping techniques provide granular

information about parasite identity and relatedness that may aid in identifying the origin of new infections or

tracking the spread of infections during an outbreak setting to better identify and target reservoirs of infection.

These data support the hypothesis that eastern Panamanian isolates have limited genetic diversity and clonal

population structure. The persistence of clonal lineages throughout and after the epidemic is consistent with the

hypothesis that as parasite population structure becomes more pronounced, inbreeding occurs more frequently,

and opportunities for outcrossing are limited by low frequency of complex infections[4]. Furthermore, that

epidemic malaria can seed the expansion of specific clonal types that can spread from the original site of the

epidemic.

Parasite population cluster into groups irrespective of study site thus additional factors may contribute to

the clonal lineages observed. For example, it has been shown that the San Blas Range, which stretches along

the northern coast of eastern Panama adjacent to the San Blas Islands, may be an important physical and

sociodemographic barrier,[22] which has been shown to serve as a barrier to some malaria vector species[23].

One could thus speculate that these islands may serve as residual transmission reservoirs for geographically

isolated parasite populations, consistent with the lack of additional drug resistance mutations and lack of shared

alleles of barcode alleles as compared to mainland parasites.

Use of outgroups, first from neighboring Colombia and then from continentally distinct Senegal,

strengthen the conclusion that these Panamanian parasites were highly related and clonal. Although the time

difference between sample collection limit or comparison of these two populations, the addition of these

outgroups in the analysis maintained the underlying population structure among Panamanian parasites and

revealed the relatedness of parasites collected along the Caribbean coast to parasites from Colombia. The

addition of Senegalese samples maintains the population structure of the Central and South American

populations and reveals the limited diversity of the American populations by comparison. Molecular barcode


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9 data was used to identify clonal parasites in Colombia and Panama, distinguish relatedness between non-clonal

parasites and to infer trends in changing parasite populations in low transmission settings.

Drug resistance mutations were consistent with the observations that Colombian parasites were more

diverse than those from Panama; and, that the Group III parasites from the coastal region were more similar to

Colombian parasites than the other groups of Panamanian parasites among those sampled. This was detected by

the sharing of both pfcrt and pfmdr1 haplotypes among these parasites. Evidence of pfmdr1 copy number

variation was detected, consistent with the introduction of mefloquine as a first-line therapy in 2004[2] in

Panama, and previously documented cases demonstrating that increases in pfmdr1 copy number modulate

resistance to mefloquine[24–27]. Bacon et al. proposed two pfmdr1 founder lineages in the Amazonian Basin

Region, and that the double mutant N-F-R-S-D-D may have given rise to two independent drug-resistant

lineages [28]. Given the majority of triple mutant N-F-R-S-D-Y and two seemingly vestigial N-F-R-S-D-D

haplotypes from islands off the Caribbean coast (Group III), these findings are consistent with northward

dispersion of double mutant PfMDR1 haplotypes that likely have acquired additional mutations from selective

drug pressure. These findings are consistent with PfMDR1 haplotypes found in Amazonian Basin[28], Peru[9],

Ecuador[9], Venezuela [13], Bolivar[11], and Colombia [29]. The distinct nature of Group III parasites was

also detected when the pfdhfr and pfdhps loci were genotyped, with Group I and II parasites sharing the SG-K-

A-A PfDHPS haplotype. Parasites from the outbreak most likely expanded from residual/vestigial populations

from Panama, as parasites north of Panama harbor chloroquine sensitive pfcrt alleles and the CMET pfcrt

haplotype uniquely identifies parasites from the Panama/Colombia region. The ability to identify the source of

an outbreak demonstrates the utility of molecular tools for monitoring outbreaks in elimination settings.

Using molecular genotyping strategies, highly clonal P. falciparum parasites were detected in Panama

during an outbreak investigation in 2003–2005 that persisted after the epidemic subsided through 2008.

Interrogations of these clonal parasite populations to Colombian parasites are consistent with a northward

migration of drug resistant parasites from Colombia to Panama along the Atlantic coast and subsequent

expansion of clonal lineages. The highly related genetic signatures of these Panamanian parasites are consistent


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10 with clonal expansion during the outbreak, and their persistence is possibly related to the likely lack of

insufficient transmission that would normally allow for outcrossing between genetically distinct parasites or

geographic isolation of distinct parasite populations. These findings are consistent with data from other low-

transmission settings using whole genome sequence analysis[30,31]. Use of granular molecular fingerprinting

of parasites such as the molecular barcode tool allow identification of highly related parasites during outbreaks

and tracking of the potential spread of these infections critical for their interruption.


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11 Methods

Ethics Statement

Parasite samples were obtained from studies previously approved by the Gorgas Memorial Institutional

Review Board (ICGES-IRB), as part of a nested study conducted by the Ministry of Health (MINSA) and the

Central Public Health Laboratory (CPHL-ICGES) malaria surveillance and vector control program at Gorgas

Memorial Institute of Health Studies (ICGES), or by the Institutional Review Board (IRB) of the Malaria

Vaccine and Drug Development Center (MVDC) as part of a study conducted by the Centro Latino Americano

de Investigación en Malaria (CLAIM)[18] in Colombia. DNA extracted from field isolates was collected

anonymously and not linked to the identity of the donor.

Study Sites and P. falciparum Isolates

We examined 37 P. falciparum DNA field isolates collected during 2003–2008 from malaria endemic

provinces in eastern Panama (Panamá Province, n = 17; Kuna Yala, n = 15; Darien, n = 5) and 20 P. falciparum

DNA isolates collected during 2011–2012 from health care facilities of three malaria-endemic sites in Colombia

(Buenaventura, n = 5; Tumaco, n = 5, Tierra Alta, n = 10). Samples were collected at the corregimiento or

department level (smallest political division) together with geographic, demographic, and epidemiological

malaria data.

Inclusion criteria for Panamanian patient samples were malaria microscopic, PCR, OptiMAL® dipstick

test (DiaMed, Cressier, Switzerland) positive P. falciparum monoinfections (i.e. only the species P. falciparum

was present) obtained either by active or passive surveillance during the MINSA malaria surveillance program

or from regional hospital clinical cases reported to the CPHL-ICGES. Inclusion criteria for the Colombian

samples were malaria microscopic and PCR positive P. falciparum monoinfections obtained by the MVDC.

Samples containing P. vivax or mixed infections were excluded from the study.

Sample extraction and preparation

Genomic DNA was extracted from blood directly, or spotted onto Whatman FTA filter paper, using the

QIAmp DNA Blood Mini Kit (QIAGEN, Valencia, CA) according to manufacturer specifications. DNA


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12 samples were stored at ICGES in Panama City at -20°C or at MVDC in Colombia until genotyping analysis at

the Harvard School of Public Health, Boston, MA.

Genotyping

TaqMan Genotyping

Extracted DNA was pre-amplified using a pooled assay master mix containing forward and reverse

primers[32] with TaqMan® PreAmp Master Mix according to manufacturer‟s instructions (Life Technologies,

Grand Island, NY; Part. No. 4384256). Pre-amplified samples were genotyped as described [33]. Samples

were genotyped with both HRM and Taqman technologies

High Resolution Melting (HRM) Genotyping

Extracted DNA was pre-amplified using pooled forward and reverse primers for HRM drug resistance

assays, as described above[32], and assayed across 23 drug resistance loci[34]. In addition, we developed and

optimized HRM assays for the molecular barcode loci based on previous design criteria [34]. Samples were

PCR-amplified using a gradient thermocycler (Bio-Rad C1000), followed by HRM analysis on a LightScanner-

384 (BioFire Defense, Inc., Salt Lake City, UT) to optimize amplification conditions. All PCR amplifications

were performed using 2.0 μL Lightscanner Master Mix (BioFire Defense, Inc., Salt Lake City, UT), 2.5 μL of a

1:100 dilution of pre-amplified template, 0.5 μL primers and probes, and overlaid with 10 μL mineral oil.

Genomic DNA from cultured P. falciparum strains, with known genotype profiles representative of major and

minor alleles for the panel of SNPS in the molecular barcode, were used for assay validation and as genotyping

controls for all reaction plates. Optimal annealing temperatures were determined using gradient thermal cycling

for asymmetric PCR (10) in ratios of 5:1. Molecular barcode assays 10, 11, 13, 21, and 24 performed optimally

under asymmetric forward to reverse primer ratios of 5:1; all other assays required 1:5 primer asymmetry. Final

primer concentrations for a 5 μL total reaction volume were 0.5 μM excess primer, 0.1 μM limiting primer, and

0.4 μM of the 3‟-blocked probe. Amplification conditions were 95°C denaturation for 2 minutes, 50 cycles of

94°C for 5 seconds and 66°C for 30 seconds, and pre-melt cycle of 5 seconds each at 95°C and 37°C. The

change in fluorescence was recorded as samples were melted from 40 to 90°C on the LightScanner-384 and

analyzed using the Call-It module within the LightScanner software. Assay sequences are shown in


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13 Supplemental Table 1.

Statistical analysis

Samples were genotyped for 24 SNP markers in the molecular barcode using both TaqMan and HRM

assays, and SNP variant calls based on overall concordance between three to five replicates per sample. Assay

calls were imported into GenAlEx 6.5 for statistical and population genetic analysis[35]. Samples showing

strong fluorescence signals for both alleles in TaqMan assays or shared peaks in HRM assays were marked as

an “N” and treated as missing data in downstream genetic analysis. Two or more N‟s among the 24 SNPs

assayed were taken to indicate more than one P. falciparum genome present; Summary statistics were

calculated in GenAlEx 6.5 and genetic distance matrix was used to generate PCA plots using haploid genetic

distance between polymorphic alleles. When indicated, 40 random barcoded samples from Senegal were used

with Colombian and Panama populations to test for population structure in the presence of a group outside the

clade of interest.

Structure(v2.3.3)[36] analyses were run with 100,000 burn-in steps and followed by 10,000 iterations

and repeated 20 times. StructureHarvester[37] was used to identify the most likely number of subpopulations

(K) based on delta-K criterion,[38] and CLUMPP[39], and Python 2.7.3 were used for data visualization.

Phylogenetic trees were constructed using Bayesian Markov chain Monte Carlo (MCMC)[40]. TreeAnnotator

v1.7.5 and TreeStat v1.7.5 were used to generate a maximum clade credibility tree and the tree was visualized

with FIGTREE v1.3.1[41].

Acknowledgements.

We wish to acknowledge at Gorgas Memorial Institute of Health Studies in Panama: Nestor Sosa, Director, for

his logistical support and William Otero at Tropical Medicine Research for shipment of samples to HSPH.

Ruben Berrocal, Minister of SENACYT, Panama. Jorge Motta at SNI Panama, Ceferino Sanchez and Gines

Sanchez at TMR, Panama, Alan McGill at WRAIR, Silver Spring, MA, Steven Hoffman at SANARIA, Wilbur

Milhous at the USF for their encouragement and support, and Dan Neafsey for critical reading of the


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14 manuscript. Authors thank the participants of the community form malaria endemic regions of Panama and

Colombia.

Author contributions.

N.O.III provided samples, proposed the study and helped plan and design experiments, and analyzed and

interpreted the data and wrote the manuscript; N.K.B, planned and designed experiments, carried out

genotyping assays, analyzed and interpreted the data and wrote the manuscript. J.E.C collected samples and

participated in the planning and design of the study. A.M.S isolated and prepared P. falciparum field DNA

samples for shipping to HSPH and collected samples summary data. R.D converted the molecular barcode

assays to the HRM format, carried out quantification assays, analyzed data and reviewed the manuscript. W.W

and H-H.C. analyzed data and reviewed manuscript. E.J.H. optimized and carried out barcode assays. M.A.H.

and S.H. provided samples from Colombia and reviewed the manuscript. D.L.H, M.M., and D.F.W oversaw the

study planning and design; D.L.H. supervised the analysis and interpretation of the data. S.K.V advised N.O.III,

helped plan and design the experiments, carried out quantification assays, analyzed and interpreted data and

helped write the manuscript.

Footnotes

Funding.

This study was supported in part by a Doctoral fellowship to N.O.III from SENACYT-IFHARU, Panama and a

grant from the Ministerio de Economia y Finanzas de Panama-Gorgas Memorial Institute of Health Studies in

Panama. Colombian work was supported by NIAID/ICEMR program [grant number 5U19AI089702].

Genotyping data and analysis was supported by a grant from the Bill and Melinda Gates Foundation [grant

number OPP1053604].

Competing financial interests.

The authors declare no competing financial interests and do not have a commercial or other association that

might pose a conflict of interest.


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15 Previous meetings.

American Society of Tropical Medicine and Hygiene 62nd Annual Meeting; November 13-17, 2013; Marriott

Wardman Park Hotel, Washington, DC, USA.

ParaINsites: From Protozoa to worms; April 12, 2014; Harvard School of Public Health; Boston, MA, USA.

2014 Keystone Symposium: The Science of Malaria Eradication; February 2 – 7, 2014; Merida, Mexico.

Genomic Epidemiology of Malaria 2014; June 8 – 11, 2014; Wellcome Trust Genome Campus, Hinxton,

Cambridge, UK.

Corresponding author.

Sarah K. Volkman; 665 Huntington Avenue, Boston, MA 02115; [email protected]; Tel: 617-432-

1621; Fax: 617-432-4766


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23 Figure Legends

Figure 1. Map of Panama with regional locations by group of Plasmodium falciparum isolates collected

during 2003-2008. A map of eastern Panama showing study sites color coded by clonal groups identified by

the molecular barcode (Group I = blue, Group II = green, Group III = red, and Colombian = orange) is shown

with Province and Country boundaries (gray, dashed lines). The diameter of the circle corresponds to the

number of samples collected at each site. The base map for this figure was generated using Python 2.7.3 using

„shapefiles‟ freely available at (http://www.diva-gis.org/gdata).

Figure 2. Epidemiologic and Genotyping data for Panamanian and Colombian isolates. Isolate sample

names are color-coded by collection site (left: Darien = purple; Panamá = Green; Kuna Yala = Blue) and

epidemiologic data is shown with genotypes for each sample. Groups [I (blue), II (green), III (red), and

Colombia (orange)] are indicated on the right.

Figure 3. Membership fraction plot of eastern Panamanian and Colombian sub-populations.

Structure analysis showing the K=2 (A) and K=5 (B) clustering. The genotype of each sample is represented by

a single vertical line partitioned into segments in proportion of the estimated membership in the different

subpopulations. The upper most level of structure among Panamanian isolates, or the most likely number of

clusters (K = 2, A), and second most likely number of clusters (K=5, B) are shown. The sample name is color-

coded by region (Darien = purple; Panamá = Green; Kuna Yala = Blue; Coastal = Red; Colombia = Orange).

Figure 4. Phylogenetic tree of Plasmodium falciparum isolates from eastern Panama and Colombia.

Maximum clade credibility tree of eastern Panamanian and Colombian isolates is shown. Isolates are color

coded at terminal branch arms by study site and color coded (cartoon triangle) to indicate Group (Groups [I

(blue), II (green), III (red), and Colombia (orange)]. Branch nodes are labeled by the median-height at each

node for re-calculated genetic distances from 10,000 bootstrap replicates.

Figure 5. Principal component analysis of Plasmodium falciparum Panamanian isolates. Two-

dimensional plots of PCA are shown using 24 alleles of the molecular barcode for the Panamanian and


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24 Colombian isolates alone (A) with PC1 explaining 39% of variance and PC2 explaining 18% of the variance, or

with Senegalese out-group (B) with PC1 explaining 26% of variance and PC2 explaining 14% of the variance.

Samples are color coded by group (Group I = blue, Group II = green, Group III = red, Colombian = orange,

Senegalese = gray).


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Clonal Outbreak of Plasmodium falciparum Infection in Eastern Panama

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