• View in gallery

    Autocorrelogram plot of the genetic correlation coefficient (r) as a function of distance. The permuted 95% confidence intervals (dashed lines) and bootstrapped 95% confidence error bars are also showed.

  • View in gallery

    Results of STRUCTURE analyses for the sampled locations from San Martín (Capayán Department, Catamarca Province, Argentina). A model of K = 7 was most supported. Each column represents an individual where cluster membership assignment is on the y axis.

  • 1.

    Pérez de Rosas AR, Segura EL, García BA, 2007. Microsatellite analysis of genetic structure in natural Triatoma infestans (Hemiptera: Reduviidae) populations from Argentina: its implication in assessing the effectiveness of Chagas' disease vector control programmes. Mol Ecol 16: 14011412.

    • Search Google Scholar
    • Export Citation
  • 2.

    Pérez de Rosas AR, Segura EL, Fichera L, García BA, 2008. Macrogeographic and microgeographic genetic structure of the Chagas' disease vector Triatoma infestans (Hemiptera: Reduviidae) from Catamarca, Argentina. Genetica 133: 247260.

    • Search Google Scholar
    • Export Citation
  • 3.

    Pérez de Rosas AR, Segura EL, García BA, 2011. Molecular phylogeography of the Chagas' disease vector Triatoma infestans in Argentina. Heredity 107: 7179.

    • Search Google Scholar
    • Export Citation
  • 4.

    Pérez de Rosas AR, Segura EL, Bareiro Guiñazú AL, Fusco O, García BA, 2013. Fine-scale genetic structure in populations of Triatoma infestans (Hemiptera, Reduviidae). Genetica 141: 107117.

    • Search Google Scholar
    • Export Citation
  • 5.

    Marcet PL, Mora MS, Cutrera AP, Jones L, Gürtler RE, Kitron U, Dotson EM, 2008. Genetic structure of Triatoma infestans populations in rural communities of Santiago del Estero, northern Argentina. Infect Genet Evol 8: 835846.

    • Search Google Scholar
    • Export Citation
  • 6.

    Pizarro JC, Gilligan LM, Stevens L, 2008. Microsatellites reveal a high population structure in Triatoma infestans from Chuquisaca, Bolivia. PLoS Negl Trop Dis 2: e202.

    • Search Google Scholar
    • Export Citation
  • 7.

    Piccinali RV, Gürtler RE, 2015. Fine-scale genetic structure of Triatoma infestans in the Argentine Chaco. Infect Genet Evol 34: 143152.

  • 8.

    Zietkiewicz E, Rafalski A, Labuda D, 1994. Genome fingerprinting by simple sequence repeat (SSR)-anchored polymerase chain reaction amplification. Genomics 20: 176183.

    • Search Google Scholar
    • Export Citation
  • 9.

    Peakall R, Smouse PE, Huff DR, 1995. Evolutionary implications of allozyme and RAPD variation in diploid populations of dioecious buffalograss Buchloe dactyloides. Mol Ecol 4: 135147.

    • Search Google Scholar
    • Export Citation
  • 10.

    Meirmans PG, 2006. Using the AMOVA framework to estimate a standardized genetic differentiation measure. Evolution 60: 23992402.

  • 11.

    Wright S, 1951. The genetical structure of populations. Ann Hum Genet 15: 323354.

  • 12.

    Peakall R, Smouse PE, 2012. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28: 25372539.

    • Search Google Scholar
    • Export Citation
  • 13.

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

  • 14.

    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
  • 15.

    Peakall R, Ruibal M, Lindenmayer DB, 2003. Spatial autocorrelation analysis offers new insights into gene flow in the Australian bush rat, Rattus fuscipes. Evolution 57: 11821195.

    • Search Google Scholar
    • Export Citation
  • 16.

    Soliani C, Rondan-Dueñas J, Chiappero MB, Martínez M, Da Rosa EG, Gardenal CN, 2010. Genetic relationships among populations of Aedes aegypti from Uruguay and northeastern Argentina inferred from ISSR-PCR data. Med Vet Entomol 24: 316323.

    • Search Google Scholar
    • Export Citation
  • 17.

    Mendki MJ, Sharma AK, Veer V, Agrawal OP, Prakash S, Parashar BD, 2011. Population genetic structure of Culex quinquefasciatus in India by ISSR marker. Asian Pac J Trop Med 4: 357362.

    • Search Google Scholar
    • Export Citation
  • 18.

    Schofield CJ, Lehane MJ, McEwen P, Catala SS, Gorla DE, 1992. Dispersive flight by Triatoma infestans under natural climatic conditions in Argentina. Med Vet Entomol 6: 5156.

    • Search Google Scholar
    • Export Citation
  • 19.

    Lehane MJ, McEwen PK, Whitaker CJ, Schofield CJ, 1992. The role of temperature and nutritional status in flight initiation by Triatoma infestans. Acta Trop 52: 2738.

    • Search Google Scholar
    • Export Citation
  • 20.

    Gürtler RE, Canale DM, Spillmann C, Stariolo R, Salomón OD, Blanco S, Segura EL, 2004. Effectiveness of residual spraying of peridomestic ecotopes with deltamethrin and permethrin on Triatoma infestans in rural western Argentina: a district-wide randomized trial. Bull World Health Organ 82: 196205.

    • Search Google Scholar
    • Export Citation

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Application of Inter-Simple Sequence Repeat Markers in the Analysis of Populations of the Chagas Disease Vector Triatoma infestans (Hemiptera, Reduviidae)

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  • 1 Instituto de Investigaciones en Ciencias de la Salud (INICSA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) and Cátedra de Bioquímica y Biología Molecular, Facultad de Ciencias Médicas, Universidad Nacional de Córdoba, Córdoba, Argentina.

Here we apply inter-simple sequence repeat (ISSR) markers to explore the fine-scale genetic structure and dispersal in populations of Triatoma infestans. Five selected primers from 30 primers were used to amplify ISSRs by polymerase chain reaction. A total of 90 polymorphic bands were detected across 134 individuals captured from 11 peridomestic sites from the locality of San Martín (Capayán Department, Catamarca Province, Argentina). Significant levels of genetic differentiation suggest limited gene flow among sampling sites. Spatial autocorrelation analysis confirms that dispersal occurs on the scale of ∼469 m, suggesting that insecticide spraying should be extended at least within a radius of ∼500 m around the infested area. Moreover, Bayesian clustering algorithms indicated genetic exchange among different sites analyzed, supporting the hypothesis of an important role of peridomestic structures in the process of reinfestation.

Introduction

Triatoma infestans (Hemiptera, Reduvidae) is the main vector of Chagas disease in South America between the latitudes 10° and 46° S. The analyses of the spatial genetic structure of populations at fine scale can provide insight into the dynamic population and evolutionary process of T. infestans and a complementary approach to help improve vector control strategies.

Among the polymerase chain reaction (PCR)-based marker techniques, the microsatellites have been used to evaluate the genetic structure of natural populations of T. infestans. Analyses based on these markers strongly supported the existence of some type of stratification in T. infestans populations and support the hypothesis of vector population recovery from survivors of the insecticide-treated areas, highlighting the value of population genetic analyses in assessing the effectiveness of Chagas disease vector control programs.17 Microsatellites have been widely used as genetic markers although drawbacks for their use are the time and cost required to characterize them. Inter-simple sequence repeats (ISSRs) have also been widely used as genetic markers because they permit the detection of DNA variation without the need to isolate and sequence specific DNA fragments.8 Moreover, they are highly polymorphic, of easy application, and of low costs. However, there are no previous reports on the use of ISSRs for genetic characterization of populations of the species. In this study, we use ISSR markers to explore the spatial genetic structure at fine scale and dispersal patterns in populations of T. infestans.

Materials and Methods

A total of 134 T. infestans specimens were captured in 11 peridomestic sites of nine houses from the locality of San Martín (Capayán Department, Catamarca Province, Argentina) (Table 1), which are located at geographical distances ranging from 39 m to 16.42 km.

Table 1

Sampling site and sample size of Triatoma infestans from the locality of San Martín (Capayán Department, Catamarca Province, Argentina)

Capture siteHabitatSample size
BJChicken coop16
BR IGoat corral4
BR IIShed23
BR IIIChicken coop16
BLGoat corral16
FDChicken coop16
MARabbit hutch9
PFGoat corral12
PAShed3
REGoat corral4
RHShed15

Numbers indicate different habitats from one dwelling.

Thirty primers were initially used for amplification of ISSRs by PCRs. Five of these primers, which allowed obtain reproducible and variable banding patterns among individuals, were selected for the study (see Supplemental Table 1). Reaction products were visualized after electrophoresis on a 1.5% agarose gel (1× Tris-acetate ethylenediaminetetraacetic acid buffer) containing 0.1 μg/mL of ethidium bromide and photographed under ultraviolet light. The images were analyzed using the software Gel-Pro-Analyzer (Media Cybernetics, Rockville, MD). The different ISSR amplified fragments (bands) were considered as different loci and each band was considered as “present = 1” and “absent = 0” for each individual.

The percentage of polymorphic loci (P), unbiased haploid genetic diversity (uH), and informative index (I, equivalent to Shannon index) was calculated at each locus and sampling site. An analysis of molecular variance (AMOVA) was carried out to obtain the variance components, Φ, similar to the F statistics of Wright.911 Isolation by distance was examined by calculating the correlation coefficient between the pairwise ΦST values and geographical distances (as straight-line distance between all pairs of sampling sites) and was tested using the Mantel test after 1,000 permutations. All these analyses were performed using the program GENALEX version 6.502 (The Australian National University, Canberra, Australia).12

A Bayesian approach implemented in the program STRUCTURE version 2 (University of Chicago, Chicago, IL) was used to infer the number of populations (K) in the data set without prior information of the sampling locations.13 The admixture model and correlated allele frequencies were assumed. A series of five independent runs for each value of K between 1 and 11 were conducted. Each run had a burn-in period of 100,000 and a 1,000,000 run length. The most likely number of clusters (K) was determined using the ΔK method, as well as by examining the plateau of the estimated probability of the data.14

Spatial genetic structure was assessed using an approach to microspatial autocorrelation analysis implemented in the program GENALEX version 6.2.12 This software calculates the multilocus autocorrelation coefficient r among individual genotypes for a specified number of distance classes. The distance classes were selected to make the number of pairwise comparisons in each bin as even as possible to avoid noise in the confidence limits that can cause unequal sample size. To test whether the r correlation coefficient was significantly different from the null hypothesis of no spatial genetic structure, 1,000 random permutations were performed to determine upper and lower confidence intervals (CIs) for the null hypothesis.15

Results and Discussion

A total of 90 polymorphic bands were detected across the 134 individuals captured from 11 sampled locations (Table 1). The percentage of polymorphic loci (P) per sampling site ranged from 35.56% in MA to 65.56% in BR II with an average value of 50.14%. The average unbiased haploid genetic diversity (uH) was of 0.18 and ranged from 0.14 in the samples from PF and FD to 0.25 in BR III. The average information index (I, equivalent to Shannon index) was of 0.25, with the lowest value in the sample from PF (I = 0.19) and the highest value in the sample from BR III (I = 0.34) (see Supplemental Table 2). In Aedes aegypti and Culex quinquefasciatus were detected higher levels of genetic diversity using ISSR markers, with an average value of uH of 0.31 and an average value of the Shannon index of 0.36, respectively.16,17 However, although the levels of genetic diversity detected in this study were moderate, it is important to point out that the analysis of a greater number of primers will allow to explore a greater proportion of the genome, increasing the potential of ISSR markers for the analysis of T. infestans populations.

The overall ΦST value of 0.26 is significantly different from zero (P = 0.001), as are all pairwise ΦST values (P < 0.001), including the different sampled sites within the same house. The pairwise ΦST values ranged from 0.15 between the samples from BJ and PF to 0.39 between RH and PF (see Supplemental Table 3). There was not a significant association between geographical distance and genetic differentiation (ΦST) among sites (Mantel r = 0.09, P = 0.31). This pattern suggests restricted gene flow among sampling sites, where allele frequencies could drift independently without relation to the geographic distances separating them. These results are in agreement with previous works that suggested a high degree of subdivision in the population into breeding units with restricted possibilities of genic exchange.1,2,4 In this regard, T. infestans is primarily restricted to domestic and peridomestic environments (such as chicken coops and pig or goat corrals) particularly in rural areas and usually remains in the same house or in its immediate vicinity during its lifetime.

Spatial autocorrelation analysis for the 134 individuals indicated that significantly positive autocorrelations were found in the first (70 m, r = 0.142, 95% CI: 0.008, −0.006) and third distance classes (1,000 m, r = 0.013, 95% CI: 0.007, −0.008), with an x intercept at 469 m. Significant negative values were observed in the second distance class (600 m, r = −0.047, 95% CI: 0.008, −0.008) and at distances equal to and greater than 14 km (r = −0.07, 95% CI: 0.006, −0.008) (Figure 1). In populations with restricted gene flow by distance, a positive correlation (r) is expected at the shorter distance classes. This correlation will become zero at distance classes where drift predominates over gene flow, declining to negative correlation, sometimes followed by oscillation of positive and negative values. In this respect, in concordance with one previous study carried out with microsatellite markers,4 the scale of structuring detected in the spatial autocorrelation analyses suggests that dispersal typically occurs on the scale of approximately 469 m. The active dispersal inferred for T. infestans by autocorrelation is within the flight range indicated for this species,18,19 and suggests that insecticide spraying should be extended at least within a radius of ∼500 m around the infested area.

Figure 1.
Figure 1.

Autocorrelogram plot of the genetic correlation coefficient (r) as a function of distance. The permuted 95% confidence intervals (dashed lines) and bootstrapped 95% confidence error bars are also showed.

Citation: The American Society of Tropical Medicine and Hygiene 96, 3; 10.4269/ajtmh.16-0717

The high degree of isolation detected among different sampling sites was consistent with results obtained by the program STRUCTURE. Seven different genetic clusters were identified (Figure 2). Individuals from MA, BR II, BR III, BL, RH, FD, PF, and RE formed nearly homogeneous clusters. In contrast, samples from BJ, BR I, and PA were found to consist of a mixture of clusters, which share different percentage of ancestry (relative proportion of the genome of an individual originating from each inferred cluster) with the rest of the capture sites analyzed. The individuals from two goat corrals (PF and RE) were grouped into a cluster with more than 86% of ancestry shared among them, and the sample from BJ (chicken coop) shares with this cluster around 30% of the ancestry. The sites PF and BJ, which are geographically close, showed the lowest level of genetic differentiation (ΦST = 0.15), suggesting gene flow between the sites. Similarly, the individuals from BJ share 23% of ancestry with the individuals obtained in other geographically close goat corral (BL). Moreover, the bugs from another goat corral (BR I), share about 55% of ancestry with those from a chicken coop from the same house (BR III), and the insects captured in PA (shed) share 40% of ancestry with those from MA (rabbit hutch). It is known that insecticide spraying in animal corrals has limited effectiveness. Particularly, goat corrals have been known to support abundant population of T. infestans. These environments has higher prevalence of T. infestans before and after residual spraying with insecticides than other peridomestic environments and may increase the risk of reinfestation in the whole area.20 The shared ancestry among different sampling sites suggest genetic exchange among individuals from these places, supporting the hypothesis that peridomestic structures would be involved in the reinfestation process. These results highlight the importance of entomological surveillance of peridomestic environments in the insecticide-treated areas.

Figure 2.
Figure 2.

Results of STRUCTURE analyses for the sampled locations from San Martín (Capayán Department, Catamarca Province, Argentina). A model of K = 7 was most supported. Each column represents an individual where cluster membership assignment is on the y axis.

Citation: The American Society of Tropical Medicine and Hygiene 96, 3; 10.4269/ajtmh.16-0717

  • 1.

    Pérez de Rosas AR, Segura EL, García BA, 2007. Microsatellite analysis of genetic structure in natural Triatoma infestans (Hemiptera: Reduviidae) populations from Argentina: its implication in assessing the effectiveness of Chagas' disease vector control programmes. Mol Ecol 16: 14011412.

    • Search Google Scholar
    • Export Citation
  • 2.

    Pérez de Rosas AR, Segura EL, Fichera L, García BA, 2008. Macrogeographic and microgeographic genetic structure of the Chagas' disease vector Triatoma infestans (Hemiptera: Reduviidae) from Catamarca, Argentina. Genetica 133: 247260.

    • Search Google Scholar
    • Export Citation
  • 3.

    Pérez de Rosas AR, Segura EL, García BA, 2011. Molecular phylogeography of the Chagas' disease vector Triatoma infestans in Argentina. Heredity 107: 7179.

    • Search Google Scholar
    • Export Citation
  • 4.

    Pérez de Rosas AR, Segura EL, Bareiro Guiñazú AL, Fusco O, García BA, 2013. Fine-scale genetic structure in populations of Triatoma infestans (Hemiptera, Reduviidae). Genetica 141: 107117.

    • Search Google Scholar
    • Export Citation
  • 5.

    Marcet PL, Mora MS, Cutrera AP, Jones L, Gürtler RE, Kitron U, Dotson EM, 2008. Genetic structure of Triatoma infestans populations in rural communities of Santiago del Estero, northern Argentina. Infect Genet Evol 8: 835846.

    • Search Google Scholar
    • Export Citation
  • 6.

    Pizarro JC, Gilligan LM, Stevens L, 2008. Microsatellites reveal a high population structure in Triatoma infestans from Chuquisaca, Bolivia. PLoS Negl Trop Dis 2: e202.

    • Search Google Scholar
    • Export Citation
  • 7.

    Piccinali RV, Gürtler RE, 2015. Fine-scale genetic structure of Triatoma infestans in the Argentine Chaco. Infect Genet Evol 34: 143152.

  • 8.

    Zietkiewicz E, Rafalski A, Labuda D, 1994. Genome fingerprinting by simple sequence repeat (SSR)-anchored polymerase chain reaction amplification. Genomics 20: 176183.

    • Search Google Scholar
    • Export Citation
  • 9.

    Peakall R, Smouse PE, Huff DR, 1995. Evolutionary implications of allozyme and RAPD variation in diploid populations of dioecious buffalograss Buchloe dactyloides. Mol Ecol 4: 135147.

    • Search Google Scholar
    • Export Citation
  • 10.

    Meirmans PG, 2006. Using the AMOVA framework to estimate a standardized genetic differentiation measure. Evolution 60: 23992402.

  • 11.

    Wright S, 1951. The genetical structure of populations. Ann Hum Genet 15: 323354.

  • 12.

    Peakall R, Smouse PE, 2012. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28: 25372539.

    • Search Google Scholar
    • Export Citation
  • 13.

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

  • 14.

    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
  • 15.

    Peakall R, Ruibal M, Lindenmayer DB, 2003. Spatial autocorrelation analysis offers new insights into gene flow in the Australian bush rat, Rattus fuscipes. Evolution 57: 11821195.

    • Search Google Scholar
    • Export Citation
  • 16.

    Soliani C, Rondan-Dueñas J, Chiappero MB, Martínez M, Da Rosa EG, Gardenal CN, 2010. Genetic relationships among populations of Aedes aegypti from Uruguay and northeastern Argentina inferred from ISSR-PCR data. Med Vet Entomol 24: 316323.

    • Search Google Scholar
    • Export Citation
  • 17.

    Mendki MJ, Sharma AK, Veer V, Agrawal OP, Prakash S, Parashar BD, 2011. Population genetic structure of Culex quinquefasciatus in India by ISSR marker. Asian Pac J Trop Med 4: 357362.

    • Search Google Scholar
    • Export Citation
  • 18.

    Schofield CJ, Lehane MJ, McEwen P, Catala SS, Gorla DE, 1992. Dispersive flight by Triatoma infestans under natural climatic conditions in Argentina. Med Vet Entomol 6: 5156.

    • Search Google Scholar
    • Export Citation
  • 19.

    Lehane MJ, McEwen PK, Whitaker CJ, Schofield CJ, 1992. The role of temperature and nutritional status in flight initiation by Triatoma infestans. Acta Trop 52: 2738.

    • Search Google Scholar
    • Export Citation
  • 20.

    Gürtler RE, Canale DM, Spillmann C, Stariolo R, Salomón OD, Blanco S, Segura EL, 2004. Effectiveness of residual spraying of peridomestic ecotopes with deltamethrin and permethrin on Triatoma infestans in rural western Argentina: a district-wide randomized trial. Bull World Health Organ 82: 196205.

    • Search Google Scholar
    • Export Citation

Author Notes

* Address correspondence to Beatriz Alicia García, Facultad de Ciencias Médicas, Instituto de Investigaciones en Ciencias de la Salud (INICSA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Córdoba, Pabellón Argentina 2do Piso, Ciudad Universitaria, 5000 Córdoba, Argentina. E-mail: bgarcia@biomed.uncor.edu

Financial support: This research was supported by grants from Agencia Nacional de Promoción Científica y Tecnológica (FONCyT-Argentina), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET-Argentina), and Secretaría de Ciencia y Tecnología, Universidad Nacional de Córdoba (SECyT-UNC-Argentina).

Disclosure: Alicia R. Pérez de Rosas and Beatriz A. Garcia are Career Investigators of CONICET.

Authors' addresses: Alicia R. Pérez de Rosas, María F. Restelli, Cintia J. Fernández, María J. Blariza, and Beatriz A. García, Cátedra de Bioquímica y Biología Molecular, Facultad de Ciencias Médicas, Universidad Nacional de Córdoba, Córdoba, Argentina, E-mails: arperez@biomed.fcm.unc.edu.ar, restelli.florencia@gmail.com, cjudithfernandez@gmail.com, mariablariza@yahoo.com.ar, and bgarcia@biomed.uncor.edu.

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