Molecular epidemiology at the community level has an important guiding role in zoonotic disease control programmes where genetic markers are suitably variable to unravel the dynamics of local transmission. We evaluated the molecular diversity of Trypanosoma cruzi, the etiological agent of Chagas disease, in southern Ecuador (Loja Province). This kinetoplastid parasite has traditionally been a paradigm for clonal population structure in pathogenic organisms. However, the presence of naturally occurring hybrids, mitochondrial introgression, and evidence of genetic exchange in the laboratory question this dogma.
Eighty-one parasite isolates from domiciliary, peridomiciliary, and sylvatic triatomines and mammals were genotyped across 10 variable microsatellite loci. Two discrete parasite populations were defined: one predominantly composed of isolates from domestic and peridomestic foci, and another predominantly composed of isolates from sylvatic foci. Spatial genetic variation was absent from the former, suggesting rapid parasite dispersal across our study area. Furthermore, linkage equilibrium between loci, Hardy-Weinberg allele frequencies at individual loci, and a lack of repeated genotypes are indicative of frequent genetic exchange among individuals in the domestic/peridomestic population.
These data represent novel population-level evidence of an extant capacity for sex among natural cycles of T. cruzi transmission. As such they have dramatic implications for our understanding of the fundamental genetics of this parasite. Our data also elucidate local disease transmission, whereby passive anthropogenic domestic mammal and triatomine dispersal across our study area is likely to account for the rapid domestic/peridomestic spread of the parasite. Finally we discuss how this, and the observed subdivision between sympatric sylvatic and domestic/peridomestic foci, can inform efforts at Chagas disease control in Ecuador.
Trypanosoma cruzi is transmitted by blood sucking insects known as triatomines. This protozoan parasite commonly infects wild and domestic mammals in South and Central America. However, triatomines also transmit the parasite to people, and human infection with T. cruzi is known as Chagas disease, a major public health concern in Latin America. Understanding the complex dynamics of parasite spread between wild and domestic environments is essential to design effective control measures to prevent the spread of Chagas disease. Here we describe T. cruzi genetic diversity and population dynamics in southern Ecuador. Our findings indicate that the parasite circulates in two largely independent cycles: one corresponding to the sylvatic environment and one related to the domestic/peridomestic environment. Furthermore, our data indicate that human activity might promote parasite dispersal among communties. This information is the key for the design of control programmes in Southern Ecuador. Finally, we have encountered evidence of a sexual reproductive mode in the domestic T. cruzi population, which constitutes a new and intriguing finding with regards to the biology of this parasite.
Citation: Ocaña-Mayorga S, Llewellyn MS, Costales JA, Miles MA, Grijalva MJ (2010) Sex, Subdivision, and Domestic Dispersal of Trypanosoma cruzi Lineage I in Southern Ecuador. PLoS Negl Trop Dis 4(12): e915. https://doi.org/10.1371/journal.pntd.0000915
Editor: Philippe Büscher, Institute of Tropical Medicine, Belgium
Received: September 13, 2010; Accepted: November 15, 2010; Published: December 14, 2010
Copyright: © 2010 Ocaña-Mayorga et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The project described was supported by award number 1 R15 AI077896-01 from the Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health (DMID/NIAID/NIH), USA; the European Union Seventh Framework Programme, contract number 223034 (ChagasEpiNet); UNICEF/UNPD/World Bank/WHO Special Programme for Research and Training in Tropical Diseases (TDR/WHO) award IDs A20785 and A60655; PLAN Internacional Ecuador; Children's HeartLink, USA; Ecuadorian Ministry of Health and National Secretary for Science and Technology of Ecuador (SENACYT). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
Competing interests: The authors have declared that no competing interests exist.
Chagas disease, caused by the protozoan Trypanosoma cruzi, is the most important parasitic infection in Latin America . An estimated 10 million people carry the infection, while another 90 million live at risk . This vector-borne zoonosis causes severely debilitating and potentially deadly disease in more than a third of infected people . Mucosal or abrasion contact with the infected faeces of hematophagous triatomine bugs constitutes the major mode of transmission .
Chagas disease is endemic to several regions in Ecuador, including the warm inter-Andean valleys of the southern province of Loja, where the main vectors are Rhodnius ecuadoriensis, Triatoma carrioni, Panstrongylus chinai, and Panstrongylus rufotuberculatus , . Loja Province is currently targeted by the Ecuadorian Chagas Disease Control Program. Complementing disease prevention efforts, recent progress has been made in understanding local vector dynamics –. However, parasite molecular epidemiology could also play a role in guiding effective intervention measures.
Molecular diversity was first recognised in T. cruzi in the early 1970s . Six major genetic subdivisions, known as discrete typing units (DTUs), are currently recognized (TcI–TcVI ), with distributions loosely defined by geography, transmission cycle, and ecology . TcI predominates in northern South America, causes significant human disease ,  and occurs in both domestic and sylvatic cycles of parasite transmission. Of major interest to those planning sustainable control strategies in this region is the extent to which these cycles are connected –. The provision of such data relies on the evaluation of molecular diversity ‘hidden’ at the sub-DTU level –.
Hypervariable molecular markers, like microsatellites, have given new and unprecedented insight into the population genetics of other important parasitic zoonoses –. For the first time, specific hypotheses regarding parasite dispersal and reproduction can be addressed. However, the validity of molecular epidemiological data depends heavily on study design. Numerous confounders, including biased sampling (e.g., sampling only one host in a heteroxenous transmission system ), population subdivision in both space and time (leading to Wahlund effects ), and low sample size all influence the estimation of key population genetic parameters. Historically, such biases have acted as an impediment to obtaining useful epidemiological information from parasite molecular data, and, particularly in T. cruzi, to resolving the frequency of sex in natural populations.
Here we present microsatellite data for 10 variable loci amplified from a large number of TcI isolates collected from domestic, peridomestic, and sylvatic hosts and vectors in and around several adjacent communities in Loja Province, Ecuador. We evaluate evidence for genetic subdivision between transmission cycles, anthropogenic dispersal of parasites between communities, and panmixia among a subset of strains.
Study area and sampling
Sixteen communities in Loja Province, southern Ecuador, were sampled (Figure 1). These communities were located at altitudes less than 2,200 m and were representative of the ecological diversity of the province. Trypanosomes were isolated from triatomines and small mammals (rodents and opossums) captured at domestic (within dwellings), peridomestic (near dwellings and/or associated with human activities, e.g., crop stores, chicken roosts, wood and rock piles), and sylvatic (more than 20 meters from dwellings) foci (Table S1). Written informed consents from the head of the houses for domiciliary bug searches and capture of mammals near houses were obtained. These documents have been approved by the institutional review board from National Institute of Health (NIH), Ohio University (OU) and Pontifical Catholic University of Ecuador (PUCE). Vertebrates were euthanized to obtain samples; all procedures were carried out in strict accordance with the protocol approved by the Ohio University Institutional Animal Care and Use Committee (IACUC). The Ohio University IACUC adheres to the guidelines in the United States Government Code of Federal Regulations (CFR), Title 9, Chapter 1, Subchapter A- Animal Welfare Parts 1–3 and the United States Health Research Extension Act of 1985, Public Law 99–158 “Animals in Research”.
Black dots indicate locations from which isolates were obtained. Colored circles indicate proportion of LOJASylv (green) and LOJADom/Peri (red) genotypes (see Figure 2) among T. cruzi isolates from foci designated as sylvatic (S) or domestic/peridomestic (D/P). Crossed circles indicate absence of isolates from a particular location.The area of each pie chart represents the number of samples. LOJADom/Peri is more common among D/P foci, largely irrespective of community. However, some cross-propagation between transmission cycles is evident. Insert shows in gray the location of Loja province within Ecuador and of Ecuador within South America.
Molecular identification of T. cruzi and lineage genotyping
Trypanosome species was determined by PCR amplification of the kinetoplast minicircle region as in Vallejo et al. . Discrete Typing Units (DTU) genotyping was achieved by assaying a combination of three nuclear loci as described by Lewis et al. .
Ten previously identified polymorphic microsatellite loci were studied (Table S2) . These loci are distributed across seven T. cruzi chromosomes and include two groups of physically linked markers . Allelic products were amplified using previously described reaction conditions . Allele sizes were determined using an automated capillary sequencer (AB3730, Applied Biosystems, UK) in conjunction with a fluorescently tagged size standard and were manually checked for errors. All isolates were typed “blind” to control for user bias. By reference to a representative panel of strains, no cross reactivity was identified between T. rangeli and the microsatellite primers used in this study. Population-level genetic diversity was assessed using sample size corrected allelic richness (Ar) in FSTAT 188.8.131.52  and number of private (population specific) alleles per locus (PA). FIS, a measure of the distribution of heterozygosity within and between individuals, was estimated per locus per population in FSTAT 184.108.40.206. FIS can vary between −1 (all loci heterozygous for the same alleles) and +1 (all loci homozygous for different alleles). FIS = 0 indicates Hardy-Weinberg allele proportions. The extent of population subdivision between isolates from different transmission cycles was estimated using (FST) in ARLEQUIN v3.1 and statistical significance assessed via 10,000 random permutations of alleles between populations . Similarly, within-population subdivision was examined in ARLEQUIN v3.1, in this case using a hierarchal Analysis of Molecular Variance (AMOVA). Population-level heterozygosity indices were also calculated in ARLEQUIN v3.1 and associated significance levels for p values derived after sequential Bonferroni correction to minimise the likelihood of Type 1 errors . Individual-level pair-wise distances were estimated using DAS (1-proportion of shared alleles at all loci / n)  under an IAM and δμ2  under an SMM in MICROSAT . DAS values form the basis of the dendrogram in Figure 2. To accommodate multi-allelic loci, a script was written in Microsoft Visual Basic to make multiple random diploid re-samplings of each multilocus profile (software available on request). Individual-level genetic distances were calculated as the mean across multiple re-sampled datasets. A Mantel's test for the effect of isolation by distance within populations (pair-wise genetic vs. geographic distance) was implemented in Genelax 6 using 10,000 random permutations . Linkage disequilibrium indices, pair-wise (RGGD) and multilocus (IA), were calculated in LINKDOS  and MULTILOCUS1.3b , respectively. Multiple diploid re-samplings were also made to evaluate the influence of multi-allelic loci on IA, the results of which are shown in Table 1. Assignment of individuals to populations was made by reference to the topology of the DAS derived tree. Secondarily, this model-free population assignment was corroborated using STRUCTURE (Figure S1) . Sample affiliations are listed in Table S1.
Continental-scale affiliations of Loja TcI strains are shown in the insert figure and the local subdivision between transmission cycles is shown in the main figure. T. cruzi DAS values correspond to the mean of 1,000 random diploid re-samplings of the dataset. Solid grey triangles indicate samples from other regions of South America while colored triangles indicate Ecuadorian populations (LOJADom/Peri and LOJASylv). Branch color codes indicate capture environment. Blue: domicile; Red: peridomicile; Green: sylvatic. Sample codes were defined as follows: M: mammal; Dm: Didelphis marsupialis; Rr: Rattus rattus; Ss: Sciureus stramineus; V: vector; Pc: Panstrongylus chinai; Tc: Triatoma carrioni; Re: Rhodnius ecuadoriensis; A: adult; N: nymph. Values in italics correspond to bootstraps (%) over 10,000 trees drawn from 100 randomly sampled diploid datasets. Values in bold correspond to topological stability (percentage congruent trees) over 1,000 randomly sampled diploid datasets (see Methods).
Eighty-one isolates of T. cruzi were obtained from triatomines and mammals. All were genotyped as TcI. Kinetoplast analysis detected the presence of mixed infection with T. rangeli in nine isolates from the sylvatic environment (Table S1). In total, a dataset of 1,637 alleles was derived across all loci, excluding missing data (Table S3). Multiple (≥3) alleles were observed at 3.08% of loci.
Isolate clustering and population subdivision by transmission cycle
We evaluated patterns of clustering and subdivision among parasite strains in the Loja samples based upon their microsatellite profiles. To identify genetically distinct groups we relied on three lines of evidence: neighbor-joining analysis based on pair-wise genetic distance; model-based population assignment (STRUCTURE); and the statistical significance of the fixation index FST.The deepest and most robust (56.5%) internal branching within the neighbor-joining tree constructed from pair-wise genetic distance values (DAS) supported the delineation of two populations (Figure 2 and Table 1). No pattern or diversification by host or vector was observed within these populations. The observed bipartite subdivision was unaffected by the presence of multi-allelic loci (100% congruence, Figure 2) and was used as a means to define the populations examined in later analyses (See Table 1). Sample allocation between these two populations was exactly corroborated by the optimal number of clusters (k) derived using STRUCTURE software as defined by Evanno et al.  by Δk (Figure S1). One population, henceforth called LOJADom/Peri, was predominantly composed of isolates from domestic and peridomestic foci, the other, henceforth LOJASylv, of isolates from the sylvatic environment. Estimates of genetic subdivision (FST) between a priori populations (transmission cycle defined) corroborated this pattern of dispersal. No evidence for subdivision existed between domestic and peridomestic isolates (FST = 0.027, p = 0.354), whereas subdivision between these populations (grouped) and sylvatic samples was pronounced (FST = 0.212, p<0.0001). Naturally, reassignment of outliers to their “correct” genetic groups according to neighbor-joining and STRUCTURE analyses further inflated the latter estimate (FST LOJADom/Peri−LOJASylv = 0.397, p<0.0001). These outliers are evidence for some, albeit limited, parasite dispersal between domestic/peridomestic transmission cycles and sylvatic transmission cycles as evident in Figure 1 and 2.
Following the identification of two genetically distinct groups of parasite strains circulating in this endemic area, the genetic diversity of each was evaluated and compared. Estimates of allelic richness (Ar) did not demonstrate dramatic difference between LOJADom/Peri and LOJASylv (Table 1); both populations showed considerable genetic diversity. More private alleles per locus (PA) were found in the larger and marginally more diverse sylvatic population (PA = 2.0; Table 1). In conjunction with its apparent genetic distance from other South American TcI populations (Figure 2), the lack of private alleles within LOJADom/Peri (PA = 0.8) suggests diversification of this population from a local source.
Geographic dispersal within populations
In light of the role played by transmission cycles in structuring the local parasite population, we compared the rate of parasite dispersal within LOJADom/Peri with that within LOJASilv. This rate is inversely proportional to the amount of spatial structure (or isolation by distance (IBD)) in the population. Interestingly, tests for IBD among individuals from LOJADom/Peri and LOJASylv showed statistically significant and epidemiologically important differences between these two populations. Infinite allele models (IAMs) of microsatellite mutation intrinsically overestimate genetic distances between closely related isolates as compared to stepwise mutational models (SMMs). To circumvent possible bias we chose to test for IBD under both. Strong evidence for spatial structure in LOJASylv existed regardless (DAS−RXY = 0.265, P<0.0001; δμ2−RXY = 0.177, p = 0.001). Among isolates from LOJADom/Peri, no spatial structure was evident from either measure (DAS−RXY = 0.100, p = 0.164; δμ2−RXY = −0.05, p = 0.384). Results are summarised in Figure 3 and strongly suggest more rapid parasite dispersal among domestic and peridomestic foci than that occurring between sylvatic locales at the same spatial scale.
A spatial structure was evident within LOJASylv isolates, while no spatial genetic structure was found among LOJADom/Peri isolates. Genetic (DAS) and geographic (km) distance were compared. Closed circles and line correspond to samples from LOJASylv (RXY = 0.265, p = 0.000; Slope = 0.0015±0.0001 (SE)); open circles and dashed line correspond to samples from LOJADom/Peri (RXY = 0.100, p = 0.164; Slope = 0.0004±0.0004 (SE)). Equivalent statistics under δμ2 are LOJASylv RXY = 0.177, p = 0.001; LOJADom/Peri RXY = −0.052, p = 0.384.
Compelling evidence for sex in LOJADom/Peri
Several approaches were employed to estimate the rate of genetic recombination within the parasite populations identified in Loja. Multiple indicators suggested frequent sex among trypanosomes of LOJADom/Peri. Pair-wise inter-locus linkage (RGGD) was infrequent (5.5%; Table 1) even among physically linked loci (3/4 physically linked locus pairs, those on the same chromosome, were not statistically linked) and despite abundant allelic diversity available within this population for inter-correlation (the statistical power of RGGD drops dramatically with decreasing population-level genetic diversity). Infrequent pair-wise linkage is consistent with the lack of significance attributable to the index of association (IA) (median p = 0.13, P≥0.05 in 89% of 1000 resampled diploid datasets; Table 1), and with the null hypothesis of random mating that must be assumed. Additionally, tests for deficit or excess of heterozygosity in this population showed no significant deviation from Hardy-Weinberg expectations, reflected by mean values for the inbreeding coefficient (FIS) across loci that approximate zero (Table 1). Finally, repeated multilocus genotypes, indicative of clonal reproduction, were absent from this population while present in LOJASylv. Other aspects of LOJASylv diversity pointed to predominant clonality, especially strong pair-wise (38.5% of locus pairs) and multilocus linkage (IA P<0.001) in all diploid resampled datasets (Table 1), but also strong deviation from Hardy-Weinberg levels of heterozygosity under all three measures employed (Table 1). Consistent with spatial structure identified in this population, however, an AMOVA conducted across isolates from San Jacinto and Bramaderos, which make up the majority of LOJASylv strains (Figure 1 and Table S1), did demonstrate significant but weak FST (FST = 0.173, P<0.0001, 16,000 permutations), evidence that a Wahlund effect could be depressing heterozygosity. Correspondingly, estimates of linkage disequilibrium might also be somewhat inflated by subdivision in this population , and it is difficult to reject the possibility that recombination may occur in the sylvatic populations at a micro-geographic scale.
This study constitutes a first attempt to understand the population dynamics of T. cruzi at a local scale using high-resolution molecular markers. The sample includes isolates from different transmission cycles, vectors, hosts, and adjacent communities. This arrangement aims to minimise sample bias and maximise the resulting molecular epidemiological inference. However, all field studies are affected by the natural abundance of hosts and vectors in different transmission cycles, and we cannot claim a perfect dataset. Nonetheless, we can report strong evidence for parasite diversification by transmission cycle, human involvement in parasite dispersal, and the possibility of sex in one parasite population.
The presence of the T cruzi lineage I in southern Ecuador is consistent with reports of this DTU throughout northern South America , , . In our study, as in other studies, sub-DTU level diversity of the parasite occurred independently of vector and host , , . Instead, we found evidence that transmission cycle (domestic, peridomestic, or sylvatic) is likely to be the major driver behind parasite differentiation, apparently a phenomenon common to T. cruzi populations across much of northern South America ,  but never before studied on a local scale. On the basis of our data, we suggest that widespread, internationally distributed TcI subgroups associated with specific transmission cycles may not exist. A lack of connectivity between LOJADom/Peri and domestic TcI from Venezuela, VENDom, (Figure 2) exemplifies this. Furthermore, clear cross-propagation of parasites between transmission cycles (Figure 2) and few private alleles in LOJADom/Peri (Table 1) suggests that these domestic groups are likely to emerge and diversify from local sylvatic sources.
T. cruzi is the only stercorarian trypanosome of medical importance . Natural transmission efficiency by this route (contamination with vector feces) is very low. The rate of transmission from infected Triatoma infestans to humans in Argentina, for example, is estimated at approximately one in 650 bites . As with R. prolixus in Venezuela , R. ecuadoriensis, a major disease vector in Loja, occurs at high frequency in both domestic and sylvatic locales . Our data suggest that even if vector invasion from sylvatic foci is common, as in Venezuela , associated transmission of parasites to domestic foci is too infrequent to break up population subdivision. Where cross-propagation does occur, circumstantial evidence incriminates synanthropic mammals as the link between transmission cycles. Didelphis marsupialis infected with parasites from the LOJADom/Peri group were found at both peridomestic (Isolate Numbers (IN) 9 and 13, Figure 2) and sylvatic locales (IN 6 and 17, Figure 2). Furthermore, a R. rattus individual captured at a peridomestic site was found infected with a LOJASylv strain (IN 31, Figure 2). Finally P. chinai and T. carrioni adults and nymphs, so far thought to be exclusively domestic and peridomestic triatomine species in Loja (IN 27,28,58,68 and 81, Figure 2) , were found infected with a LOJASylv strain, likely as a result of contact with invasive sylvatic mammals. This blurring of the lines between transmission cycles is likely to mirror local environmental change, where human activity is driving land-use transformation.
Parasite sampling in Loja was undertaken across an area only 50 km in radius (Figure 1). However, this area encompassed several ecological zones punctuated by high mountains (>2,500 m in elevation) and deep interconnecting valleys. Spatial genetic diversification among sylvatic isolates is an expected outcome given such barriers to host and vector migration (Figure 1 and 3). Conversely, parasites belonging to the LOJADom/Peri group lack this signature, a finding possibly linked to rapid anthropogenic dispersal in the form of infected individuals, livestock, or passively transported vectors and/or small peridomestic mammals.
T. cruzi has, until recently, been a paradigm for clonal population structure in pathogenic organisms , . However, the presence of naturally occurring hybrids , mitochondrial introgression , and a capacity for genetic exchange in the laboratory  has challenged this dogma. The frequent observation of linkage disequilibrium in T. cruzi may partially stem from cryptic population subdivision (temporal, spatial, and/or genetic) to which linkage statistics are intrinsically sensitive . Frustratingly, if assignment software with intrinsic Hardy-Weinberg assumptions (e.g., STRUCTURE  or BAPS ) is used to account for subdivision prior to linkage analysis, the resulting populations will be sorted to maximise adherence to Hardy-Weinberg allelic frequencies, with artifactual sexuality a possible result , . Fortunately, in our study, the status of LOJADom/Peri as a stable deme is corroborated by distance-based, model-free assignment, as well as STRUCTURE. In conjunction with Hardy-Weinberg allele frequencies at individual loci, we consider, therefore, that linkage equilibrium among isolates from LOJADom/Peri represents strong evidence for frequent genetic exchange among field isolates of T. cruzi. We believe that the relatively small sample size of LOJADom/Peri (n = 18) does not affect this conclusion, partly due to the ample genetic diversity present in this popualtion, but also because the lack of spatial subdivision in this group suggests frequent contact and opportunity for mixis. Thus it constitutes exactly the group of strains between which genetic exchange might be expected to occur. We cannot rule out the possibility that genetic exchange may also occur in the sylvatic cycle, if the role that substructure found in LOJASylv played in inflating linkage statistics IA and RGGD could be taken into account. However, more focused high-density sample collection from multiple individual localities would be required to address such a hypothesis. Furthermore, we cannot infer the cellular mechanism of genetic recombination events on the basis of these data. Hardy-Weinberg allelic allelic frequencies are consistent with classical meiosis. However, the lack of haploid life stages so far observed in T. cruzi are not consistent with classical meiosis, nor are the genetic exchange events so far observed in vitro .
Molecular epidemiology at this scale has an important guiding role to play in Chagas disease control programmes. Future efforts in Loja province must account for inter-domiciliary and inter-community parasite dispersal. This includes sustained surveillance and coordinated region-wide spraying campaigns to eliminate local vector re-invasion sources and community education to target passive triatomine dispersal routes. It is also clear that the role of synanthropic mammals cannot be overlooked as these represent an important potential link between sylvatic and domestic foci.
We have shown that microsatellite markers, adequate sample sizes, and associated population statistics give fundamental insight into the genetic exchange in T. cruzi. Our results, skewed toward samples from the vector, intuitively imply that the vector may be a site of genetic exchange, as is the case for T. brucei  and Leishmania major . The data also indicate, not surprisingly, that the majority of events probably occur within a T. cruzi lineage between epidemiologically linked strains, and these events have therefore historically been difficult to detect. The intriguing mechanisms of genetic exchange in T. cruzi warrant further investigation of their functional, adaptive, and epidemiological significance.
Clustering of populations based on STRUCTURE analysis. Results from structure analysis corresponded with the structure defined by DAS values. Both analyses identified two distinct populations among Ecuadorian samples. Ten replicates per value k were made assuming a no-admixture model, and with a burn in of 100,000 followed by 1,000,000 interactions of the algorithm. Delta k calculated according to Evanno et al. .
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Location, habitat, host, and lineage of Trypanosoma isolated in Loja Province, Ecuador.
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Trypanosoma cruzi primers employed in this study.
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Special thanks to Michael Lewis, Matthew Yeo and Michael Gaunt from the London School of Hygiene and Tropical Medicine, John Rivett-Carnac of Chorus Consulting, Danette Pratt from Ohio University, Alejandra Zurita, Josselyn García, Anita Villacís, Esteban Baus and César Yumiseva from Centro de Investigación en Enfermedades Infecciosas, Pontificia Universidad Católica del Ecuador, for technical assistance, and to the field personnel from the Programa Nacional de Control y Vigilancia de la Enfermedad de Chagas en Ecuador.
Conceived and designed the experiments: SOM MJG. Performed the experiments: SOM MSL MJG. Analyzed the data: SOM MSL JAC MJG. Wrote the paper: SOM MSL JAC MAM MJG.
- 1. Miles MA, Llewellyn MS, Lewis MD, Yeo M, Baleela R, et al. (2009) The molecular epidemiology and phylogeography of Trypanosoma cruzi and parallel research on Leishmania: looking back and to the future. Parasitology 136: 1509–1528.MA MilesMS LlewellynMD LewisM. YeoR. Baleela2009The molecular epidemiology and phylogeography of Trypanosoma cruzi and parallel research on Leishmania: looking back and to the future.Parasitology13615091528
- 2. Coura JR, Dias JC (2009) Epidemiology, control and surveillance of Chagas disease: 100 years after its discovery. Mem Inst Oswaldo Cruz 104: Suppl 131–40.JR CouraJC Dias2009Epidemiology, control and surveillance of Chagas disease: 100 years after its discovery.Mem Inst Oswaldo Cruz104Suppl 13140
- 3. Moncayo A, Silveira AC (2009) Current epidemiological trends for Chagas disease in Latin America and future challenges in epidemiology, surveillance and health policy. Mem Inst Oswaldo Cruz 104: Suppl 117–30.A. MoncayoAC Silveira2009Current epidemiological trends for Chagas disease in Latin America and future challenges in epidemiology, surveillance and health policy.Mem Inst Oswaldo Cruz104Suppl 11730
- 4. Aguilar H, Abad-Franch F, Racines V, Paucar A (1999) Epidemiology of Chagas Disese in Ecuador. A brief review. Mem Inst Oswaldo Cruz 94: 387–393.H. AguilarF. Abad-FranchV. RacinesA. Paucar1999Epidemiology of Chagas Disese in Ecuador. A brief review.Mem Inst Oswaldo Cruz94387393
- 5. Grijalva MJ, Palomeque-Rodríguez FS, Costales JA, Davila S, Arcos-Teran L (2005) High household infestation rates by synanthropic vectors of Chagas disease in southern Ecuador. J Med Entomol 42: 68–74.MJ GrijalvaFS Palomeque-RodríguezJA CostalesS. DavilaL. Arcos-Teran2005High household infestation rates by synanthropic vectors of Chagas disease in southern Ecuador.J Med Entomol426874
- 6. Pinto CM, Ocana-Mayorga S, Lascano MS, Grijalva MJ (2006) Infection by trypanosomes in marsupials and rodents associated with human dwellings in Ecuador. J Parasitol 92: 1251–1255.CM PintoS. Ocana-MayorgaMS LascanoMJ Grijalva2006Infection by trypanosomes in marsupials and rodents associated with human dwellings in Ecuador.J Parasitol9212511255
- 7. Grijalva MJ, Villacis AG (2009) Presence of Rhodnius ecuadoriensis in sylvatic habitats in the southern highlands (Loja Province) of Ecuador. J Med Entomol 46: 708–711.MJ GrijalvaAG Villacis2009Presence of Rhodnius ecuadoriensis in sylvatic habitats in the southern highlands (Loja Province) of Ecuador.J Med Entomol46708711
- 8. Miles M, Toye P, Oswald S, Godfrey D (1977) The identification by isoenzyme patterns of two distinct strain-groups of Trypanosoma cruzi, circulating independently in a rural area of Brazil. Transactions of the Royal Society for Tropical Medicine and Hygiene 71: 217–225.M. MilesP. ToyeS. OswaldD. Godfrey1977The identification by isoenzyme patterns of two distinct strain-groups of Trypanosoma cruzi, circulating independently in a rural area of Brazil.Transactions of the Royal Society for Tropical Medicine and Hygiene71217225
- 9. Zingales B, Andrade SG, Briones MR, Campbell DA, Chiari E, et al. (2009) A new consensus for Trypanosoma cruzi intraspecific nomenclature: second revision meeting recommends TcI to TcVI. Mem Inst Oswaldo Cruz 104: 1051–1054.B. ZingalesSG AndradeMR BrionesDA CampbellE. Chiari2009A new consensus for Trypanosoma cruzi intraspecific nomenclature: second revision meeting recommends TcI to TcVI.Mem Inst Oswaldo Cruz10410511054
- 10. Anez N, Crisante G, da Silva FM, Rojas A, Carrasco H, et al. (2004) Predominance of lineage I among Trypanosoma cruzi isolates from Venezuelan patients with different clinical profiles of acute Chagas' disease. Trop Med Int Health 9: 1319–1326.N. AnezG. CrisanteFM da SilvaA. RojasH. Carrasco2004Predominance of lineage I among Trypanosoma cruzi isolates from Venezuelan patients with different clinical profiles of acute Chagas' disease.Trop Med Int Health913191326
- 11. Zafra G, Mantilla JC, Valadares HM, Macedo AM, González CI (2008) Evidence of Trypanosoma cruzi II infection in Colombian chagasic patients. Parasitol Res 103: 731–734.G. ZafraJC MantillaHM ValadaresAM MacedoCI González2008Evidence of Trypanosoma cruzi II infection in Colombian chagasic patients.Parasitol Res103731734
- 12. Guhl F (2007) Chagas disease in Andean countries. Mem Inst Oswaldo Cruz 102: Suppl 129–38.F. Guhl2007Chagas disease in Andean countries.Mem Inst Oswaldo Cruz102Suppl 12938
- 13. Sanchez-Martin MJ, Feliciangeli MD, Campbell-Lendrum D, Davies CR (2006) Could the Chagas disease elimination programme in Venezuela be compromised by reinvasion of houses by sylvatic Rhodnius prolixus bug populations? Trop Med Int Health 11: 1585–1593.MJ Sanchez-MartinMD FeliciangeliD. Campbell-LendrumCR Davies2006Could the Chagas disease elimination programme in Venezuela be compromised by reinvasion of houses by sylvatic Rhodnius prolixus bug populations?Trop Med Int Health1115851593
- 14. Fitzpatrick S, Feliciangeli MD, Sanchez-Martin MJ, Monteiro FA, Miles MA (2008) Molecular Genetics Reveal That Silvatic Rhodnius prolixus Do Colonise Rural Houses. PLoS Negl Trop Dis 2: e210.S. FitzpatrickMD FeliciangeliMJ Sanchez-MartinFA MonteiroMA Miles2008Molecular Genetics Reveal That Silvatic Rhodnius prolixus Do Colonise Rural Houses.PLoS Negl Trop Dis2e210
- 15. Herrera C, Bargues MD, Fajardo A, Montilla M, Triana O, et al. (2007) Identifying four Trypanosoma cruzi I isolate haplotypes from different geographic regions in Colombia. Infection, Genetics and Evolution 7: 535–539.C. HerreraMD BarguesA. FajardoM. MontillaO. Triana2007Identifying four Trypanosoma cruzi I isolate haplotypes from different geographic regions in Colombia.Infection, Genetics and Evolution7535539
- 16. Llewellyn MS, Miles MA, Carrasco HJ, Lewis MD, Yeo M, et al. (2009) Genome-scale multilocus microsatellite typing of Trypanosoma cruzi discrete typing unit I reveals phylogeographic structure and specific genotypes linked to human infection. PLoS Pathog 5: e1000410.MS LlewellynMA MilesHJ CarrascoMD LewisM. Yeo2009Genome-scale multilocus microsatellite typing of Trypanosoma cruzi discrete typing unit I reveals phylogeographic structure and specific genotypes linked to human infection.PLoS Pathog5e1000410
- 17. Llewellyn MS, Lewis MD, Acosta N, Yeo M, Carrasco HJ, et al. (2009) Trypanosoma cruzi IIc: Phylogenetic and Phylogeographic Insights from Sequence and Microsatellite Analysis and Potential Impact on Emergent Chagas Disease. PLoS Negl Trop Dis 3: e510.MS LlewellynMD LewisN. AcostaM. YeoHJ Carrasco2009Trypanosoma cruzi IIc: Phylogenetic and Phylogeographic Insights from Sequence and Microsatellite Analysis and Potential Impact on Emergent Chagas Disease.PLoS Negl Trop Dis3e510
- 18. Koffi M, De Meeus T, Bucheton B, Solano P, Camara M, et al. (2009) Population genetics of Trypanosoma brucei gambiense, the agent of sleeping sickness in Western Africa. Proc Natl Acad Sci U S A 106: 209–214.M. KoffiT. De MeeusB. BuchetonP. SolanoM. Camara2009Population genetics of Trypanosoma brucei gambiense, the agent of sleeping sickness in Western Africa.Proc Natl Acad Sci U S A106209214
- 19. Kuhls K, Chicharro C, Cañavate C, Cortes S, Campino L, et al. (2008) Differentiation and Gene Flow among European Populations of Leishmania infantum MON-1. PLoS Negl Trop Dis 2: e261.K. KuhlsC. ChicharroC. CañavateS. CortesL. Campino2008Differentiation and Gene Flow among European Populations of Leishmania infantum MON-1.PLoS Negl Trop Dis2e261
- 20. Rougeron V, De Meeus T, Hide M, Waleckx E, Bermudez H, et al. (2009) Extreme inbreeding in Leishmania braziliensis. Proc Natl Acad Sci U S A 106: 10224–10229.V. RougeronT. De MeeusM. HideE. WaleckxH. Bermudez2009Extreme inbreeding in Leishmania braziliensis.Proc Natl Acad Sci U S A1061022410229
- 21. Morrison LJ, Tweedie A, Black A, Pinchbeck GL, Christley RM, et al. (2009) Discovery of mating in the major African livestock pathogen Trypanosoma congolense. PLoS One 4: e5564.LJ MorrisonA. TweedieA. BlackGL PinchbeckRM Christley2009Discovery of mating in the major African livestock pathogen Trypanosoma congolense.PLoS One4e5564
- 22. MacLeod A, Tweedie A, Welburn SC, Maudlin I, Turner CM, et al. (2000) Minisatellite marker analysis of Trypanosoma brucei: reconciliation of clonal, panmictic, and epidemic population genetic structures. Proc Natl Acad Sci U S A 97: 13442–13447.A. MacLeodA. TweedieSC WelburnI. MaudlinCM Turner2000Minisatellite marker analysis of Trypanosoma brucei: reconciliation of clonal, panmictic, and epidemic population genetic structures.Proc Natl Acad Sci U S A971344213447
- 23. Criscione CD, Poulin R, Blouin MS (2005) Molecular ecology of parasites: elucidating ecological and microevolutionary processes. Mol Ecol 14: 2247–2257.CD CriscioneR. PoulinMS Blouin2005Molecular ecology of parasites: elucidating ecological and microevolutionary processes.Mol Ecol1422472257
- 24. De Meeus T, Lehmann L, Balloux F (2006) Molecular epidemiology of clonal diploids: a quick overview and a short DIY (do it yourself) notice. Infect Genet Evol 6: 163–170.T. De MeeusL. LehmannF. Balloux2006Molecular epidemiology of clonal diploids: a quick overview and a short DIY (do it yourself) notice.Infect Genet Evol6163170
- 25. Vallejo GA, Guhl F, Chiari E, Macedo AM (1999) Species specific detection of Trypanosoma cruzi and Trypanosoma rangeli in vector and mammalian hosts by polymerase chain reaction amplification of kinetoplast minicircle DNA. Acta Trop 72: 203–212.GA VallejoF. GuhlE. ChiariAM Macedo1999Species specific detection of Trypanosoma cruzi and Trypanosoma rangeli in vector and mammalian hosts by polymerase chain reaction amplification of kinetoplast minicircle DNA.Acta Trop72203212
- 26. Lewis MD, Ma J, Yeo M, Carrasco HJ, Llewellyn MS, et al. (2009) Genotyping of Trypanosoma cruzi: systematic selection of assays allowing rapid and accurate discrimination of all known lineages. Am J Trop Med Hyg 81: 1041–1049.MD LewisJ. MaM. YeoHJ CarrascoMS Llewellyn2009Genotyping of Trypanosoma cruzi: systematic selection of assays allowing rapid and accurate discrimination of all known lineages.Am J Trop Med Hyg8110411049
- 27. Weatherly DB, Boehlke C, Tarleton RL (2009) Chromosome level assembly of the hybrid Trypanosoma cruzi genome. BMC Genomics 10: 255.DB WeatherlyC. BoehlkeRL Tarleton2009Chromosome level assembly of the hybrid Trypanosoma cruzi genome.BMC Genomics10255
- 28. Goudet J (1995) FSTAT Version 1.2: a computer program to calculate F-statistics. J Heredity 86: 485–486.J. Goudet1995FSTAT Version 1.2: a computer program to calculate F-statistics.J Heredity86485486
- 29. Excoffier L, Laval G, Schneider S (2005) Arlequin ver. 3.0: An integrated software package for population genetics data analysis. Evolutionary Bioinformatics Online 1: 47–50.L. ExcoffierG. LavalS. Schneider2005Arlequin ver. 3.0: An integrated software package for population genetics data analysis.Evolutionary Bioinformatics Online14750
- 30. Rice W (1989) Analysing tables of statistical tests. Evolution 43: 223–225.W. Rice1989Analysing tables of statistical tests.Evolution43223225
- 31. Stephens JC, Gilbert DA, Yuhki N, O'Brien SJ (1992) Estimation of heterozygosity for single-probe multilocus DNA fingerprints. Mol Biol Evol 9: 729–743.JC StephensDA GilbertN. YuhkiSJ O'Brien1992Estimation of heterozygosity for single-probe multilocus DNA fingerprints.Mol Biol Evol9729743
- 32. Goldstein DB, Ruiz Linares A, Cavalli-Sforza LL, Feldman MW (1995) Genetic absolute dating based on microsatellites and the origin of modern humans. Proc Natl Acad Sci U S A 92: 6723–6727.DB GoldsteinA. Ruiz LinaresLL Cavalli-SforzaMW Feldman1995Genetic absolute dating based on microsatellites and the origin of modern humans.Proc Natl Acad Sci U S A9267236727
- 33. Minch E, Ruiz-Linares A, Goldstein D, Feldman M, Cavalli-Sforza L (1995) E. MinchA. Ruiz-LinaresD. GoldsteinM. FeldmanL. Cavalli-Sforza1995MICROSAT- The Microsatellite Distance Program (Stanford University Press, Stanford). MICROSAT- The Microsatellite Distance Program (Stanford University Press, Stanford).
- 34. Peakall R, Smouse P (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 6: 288–295.R. PeakallP. Smouse2006GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research.Molecular Ecology Notes6288295
- 35. Garnier-Gere P, Dillmann C (1992) A computer program for testing pairwise linkage disequilibria in subdivided populations. J Hered 83: 239.P. Garnier-GereC. Dillmann1992A computer program for testing pairwise linkage disequilibria in subdivided populations.J Hered83239
- 36. Agapow PM, Burt A (2001) Indices of multilocus linkage disequilibrium. Molecular Ecology Notes 1: 101–102.PM AgapowA. Burt2001Indices of multilocus linkage disequilibrium.Molecular Ecology Notes1101102
- 37. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14: 2611–2620.G. EvannoS. RegnautJ. Goudet2005Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study.Mol Ecol1426112620
- 38. Maynard Smith J, Smith NH, O'Rourke M, Spratt BG (1993) How Clonal are Bacteria? Proceedings of the National Academy of Sciences 90: 4384–4388.J. Maynard SmithNH SmithM. O'RourkeBG Spratt1993How Clonal are Bacteria?Proceedings of the National Academy of Sciences9043844388
- 39. Mejia-Jaramillo AM, Pena VH, Triana-Chavez O (2009) Trypanosoma cruzi: Biological characterization of lineages I and II supports the predominance of lineage I in Colombia. Exp Parasitol 121: 83–91.AM Mejia-JaramilloVH PenaO. Triana-Chavez2009Trypanosoma cruzi: Biological characterization of lineages I and II supports the predominance of lineage I in Colombia.Exp Parasitol1218391
- 40. Carrasco HJ, Torrellas A, García C, Segovia M, Feliciangeli MD (2005) Risk of Trypanosoma cruzi I (Kinetoplastida: Trypanosomatidae) transmission by Panstrongylus geniculatus (Hemiptera: Reduviidae) in Caracas (Metropolitan District) and neighboring States, Venezuela. Int J Parasitol 35: 1379–1384.HJ CarrascoA. TorrellasC. GarcíaM. SegoviaMD Feliciangeli2005Risk of Trypanosoma cruzi I (Kinetoplastida: Trypanosomatidae) transmission by Panstrongylus geniculatus (Hemiptera: Reduviidae) in Caracas (Metropolitan District) and neighboring States, Venezuela.Int J Parasitol3513791384
- 41. Spotorno OA, Córdova L, Solari IA (2008) Differentiation of Trypanosoma cruzi I subgroups through characterization of cytochrome b gene sequences. Infection, Genetics and Evolution 8: 898–900.OA SpotornoL. CórdovaIA Solari2008Differentiation of Trypanosoma cruzi I subgroups through characterization of cytochrome b gene sequences.Infection, Genetics and Evolution8898900
- 42. Hoare CA (1972) CA Hoare1972The trypanosomes of mammals (Blackwell Scientific Publications). The trypanosomes of mammals (Blackwell Scientific Publications).
- 43. Rabinovich JE, Wisnivesky-Colli C, Solarz ND, Gurtler RE (1990) Probability of transmission of Chagas disease by Triatoma infestans (Hemiptera: Reduviidae) in an endemic area of Santiago del Estero, Argentina. Bull World Health Organ 68: 737–746.JE RabinovichC. Wisnivesky-ColliND SolarzRE Gurtler1990Probability of transmission of Chagas disease by Triatoma infestans (Hemiptera: Reduviidae) in an endemic area of Santiago del Estero, Argentina.Bull World Health Organ68737746
- 44. Tibayrenc M, Kjellberg F, Ayala F (1990) A Clonal Theory of Parasitic Protozoa: The Population Structures of Entamoeba, Giardia, Leishmania, Naegleria, Plasmodium, Trichomonas, and Trypanosoma and their Medical and Taxonomical Consequences. Proc Natl Acad Sci U S A 87: 2414–2418.M. TibayrencF. KjellbergF. Ayala1990A Clonal Theory of Parasitic Protozoa: The Population Structures of Entamoeba, Giardia, Leishmania, Naegleria, Plasmodium, Trichomonas, and Trypanosoma and their Medical and Taxonomical Consequences.Proc Natl Acad Sci U S A8724142418
- 45. Tibayrenc M, Ayala FJ (2002) The clonal theory of parasitic protozoa: 12 years on. Trends Parasitol 18: 405–410.M. TibayrencFJ Ayala2002The clonal theory of parasitic protozoa: 12 years on.Trends Parasitol18405410
- 46. Machado CA, Ayala FJ (2001) Nucleotide sequences provide evidence of genetic exchange among distantly related lineages of Trypanosoma cruzi. Proc Natl Acad Sci U S A 98: 7396–7401.CA MachadoFJ Ayala2001Nucleotide sequences provide evidence of genetic exchange among distantly related lineages of Trypanosoma cruzi.Proc Natl Acad Sci U S A9873967401
- 47. Gaunt MW, Yeo M, Frame IA, Stothard JE, Carrasco HJ, et al. (2003) Mechanism of genetic exchange in American trypanosomes. Nature 421: 936–939.MW GauntM. YeoIA FrameJE StothardHJ Carrasco2003Mechanism of genetic exchange in American trypanosomes.Nature421936939
- 48. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155: 945–959.JK PritchardM. StephensP. Donnelly2000Inference of population structure using multilocus genotype data.Genetics155945959
- 49. Corander J, Waldmann P, Sillanpaa MJ (2003) Bayesian analysis of genetic differentiation between populations. Genetics 163: 367–374.J. CoranderP. WaldmannMJ Sillanpaa2003Bayesian analysis of genetic differentiation between populations.Genetics163367374
- 50. Holzmuller P, Herder S, Cuny G, De Meeus T (2009) From clonal to sexual: a step in T. congolense evolution? Trends Parasitol 26: 56–60.P. HolzmullerS. HerderG. CunyT. De Meeus2009From clonal to sexual: a step in T. congolense evolution?Trends Parasitol265660
- 51. Gibson W, Stevens J (1999) Genetic exchange in the trypanosomatidae. Adv Parasitol 43: 1–46.W. GibsonJ. Stevens1999Genetic exchange in the trypanosomatidae.Adv Parasitol43146
- 52. Akopyants NS, Kimblin N, Secundino N, Patrick R, Peters N, et al. (2009) Demonstration of genetic exchange during cyclical development of Leishmania in the sand fly vector. Science 324: 265–268.NS AkopyantsN. KimblinN. SecundinoR. PatrickN. Peters2009Demonstration of genetic exchange during cyclical development of Leishmania in the sand fly vector.Science324265268