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ABSTRACT |
 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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In a previous study,8 we examined the distribution of mitochondrial DNA haplotypes among An. albimanus collections in Guatemala, to test for gene flow barriers using a 390-basepair region of the mitochondrial NADH dehydrogenase subunit 5 (ND5) gene. Phylogenetic analysis among the 15 most common haplotypes did not detect clades associated with geographic regions. Collections from different regions of Guatemala were genetically similar, as were collections from the same locations across three seasons. These results suggested that an earlier study of the An. albimanus ribosomal DNA intergenic spacer (IGS)9 had overestimated genetic differences between Atlantic and Pacific populations, possibly due to concerted evolution.8 Evidence from independent nuclear markers was therefore required to support the results obtained with the ND5 marker. The earlier ND5 study8 also suggested barriers to gene flow in Costa Rica and Panama with respect to western Central America, and that ND5 haplotype frequencies in South America differed significantly with respect to Central America.
The present study expands on our previous findings on the population structure and phylogenetic relationships among An. albimanus populations of Central and South America. In this study, four microsatellite (MS) markers have been used, in addition to the ND5 mitochondrial marker, to characterize larger An. albimanus collections from throughout Central and South America and the Caribbean.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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, and the geographic locations of all sampling sites are shown in Figure 1. Some of the collections from Guatemala and one from Costa Rica were used in a previous study.8 Mosquitoes were collected in cattle corrals and kept alive for 24 hours in cardboard cartons with sugared water. Afterwards, they were frozen and placed in 70% ethanol awaiting extraction of DNA.8 The DNA samples of An. bellator and An. cruzi individuals collected on the island of Trinidad and in Sao Paulo State, Brazil, respectively, were kindly provided by Dr. Richard Wilkerson (Museum Support Center, Smithsonian Institution, Suitland, MD).
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Microsatellite locus 1-90 was amplified in single mosquitoes using primers 1-90+ (5'-GCA TAA ATA ATA GCC AA CA-3') and 1-90- (5'-GTC ACA CTT CCG ACT ACA AA-3'). Microsatellite locus 2-14 was amplified with primers 2-14+ (5'-GCC CTT GCC AAG ATA AAA TGG AAA-3') and 2-14- (5'-TCA AAT AAT CCT AAA ACA CCG TCC-3'). Microsatellite locus 2-25 was amplified using primers 2-25+ (5'-GGT TTC CAG CCT CCA TTC TC-3') and 2-25- (5'-CCT TAC TGT GCT GGA ACA CG-3'). Microsatellite locus 6-41 was amplified using primers 6-41+ (5'-CGG CAT CCA TCC TTT CTC TG-3') and 6-41- (5'-GAC CTC GCG CCT TGT CAT AA-3').
Amplified MS alleles were size fractionated by electrophoresis on denaturing DNA sequencing gels and visualized by silver staining.10 On each gel, the reciprocal of the length of markers in a DNA ladder were regressed on the reciprocal of their mobility.11 The mobility of An. albimanus MS alleles was then entered into the regression equation to estimate size. In addition, the identity of the MS alleles was confirmed by sequencing, in triplicate, two of the most frequent alleles for each MS locus. Sequenced alleles served as references to estimate the number of repeats in other alleles.
Mitochondrial gene amplification and haplotype identification. The ND5 gene was amplified in individual mosquitoes using primers ND5P1 (5'-TWG CSC CTA ATC CKG CTA TA-3') and ND5M2 (5'-YTW GGA TGA GAT GGS TTA GG-3'), where Y = pyrimidine, R = purine, S = C or G, K = G or T, and W = A or T. The amplified regions correspond with nucleotides 7,282–7,671 in An. quadrimaculatus (GenBank #L04272) and nucleotides 7,169–7,558 in An. gambiae (GenBank # L20934). Amplification was done in an MJ Research (Watertown, MA) thermocycler with the following conditions: 95°C for five minutes, min, 80°C on hold while one unit of Taq polymerase was added to each tube, then 10 cycles of 92°C for one minute, 48°C for one minute, and 72° for 1.5 minutes; this was followed by 32 cycles of 92°C for one minute, 54°C for 35 seconds, and 72°C for 1.5 minutes; a final extension was done at 72°C for seven minutes. The PCR products were analyzed by single-strand conformational polymorphism (SSCP) analysis.10 The ND5 PCR products of the 26 most common ND5 haplotypes were sequenced along both strands and 390 basepairs, primers excluded, were used in the analysis.
Statistical analysis of haplotype and allele frequencies. Variation in mtDNA haplotype and MS allele frequencies was examined using the analysis of molecular variance (AMOVA) procedure on Arlequin version 1.1.12 The AMOVA was initially performed on collections from Central American countries and then among South American countries. The AMOVA next partitioned variation between Central and South America, and lastly between Cuba and continental populations. The significance of the variance components associated with each level of partitioning was tested using non-parametric permutation tests.12 Arlequin version 1.1 was also used to compute FST and RST, standardized measures of variation in haplotype frequencies,13 among all collections and pairwise between all possible pairs of collection. Effective migration rates (Nm) were estimated from FST or RST.14
Pairwise FST values were transformed to FST/(1 – FST) and regressed on pairwise geographic distances among collections to determine if geographic distance serves as a barrier to gene flow.14 Geographic distances were obtained by the GIS system using program ATLAS-GIS 3.0 (Environmental System Research Institute, Inc., Redlands, CA). This regression was repeated using a natural logarithm of geographic distance.15 Transformations, regression analyses, and Mantel’s test16 were performed with Arlequin version 1.1.12 The reciprocal of the estimated slope provides an estimate of the average effective population size (Ne).15 Pairwise transformed FST values were entered into a distance matrix and used to construct a dendrogram among all collections by cluster analysis using unweighted pair group method using averages (UPGMA) analysis17 in the NEIGHBOR procedure in PHYLIP3.5C.18
Phylogenetic and nucleotide diversity analysis of ND5 haplotype sequences. Haplotypes of the ND5 gene were manually aligned without gaps according to codon. Phylogenetic relationships among haplotypes were estimated with PAUP4.0b10 using maximum parsimony, maximum likelihood,19 and distance/neighbor joining.20,21
For each collection, the nucleotide sequences and the frequency of each haplotype were entered into Arlequin version 1.1. This analysis could not be completed for all individuals because we sequenced only the 26 most common haplotypes. For each collection, we estimated nucleotide diversity (
), the average number of nucleotide differences per site between two sequences (equation 10.5),22 and the number of differences between two randomly chosen alleles, theta (
) (equation 1.4a).23
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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). The number of individuals in which amplification failed (presumably due to mutations in the primer annealing sites) was small (17, 20, 12, and 2 of a total of 1,411 mosquitoes for MS 1-90, 2-14, 2-25, and 6-41, respectively). Of a total of 112 tests of goodness-of-fit to Hardy-Weinberg proportions (4 genotypes x 28 collections), only 20 were not in equilibrium, with most of them due to heterozygote deficiency (Table 2).
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). Allele frequency profiles at the four MS loci are shown in Figure 2.
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). Larger differences in allele frequencies were observed between Central and South America (Figure 2B, C, and D). The Cuban population differed to the greatest extent from the other populations (Figure 2A, B, and C).
The MS allele frequencies were compared using AMOVA (Table 3). Similar results were obtained for the four MS loci with the infinite allele mutation model and the stepwise mutation model. In all analyses, variation among individuals in collections accounted for most (84–97%) of the total variance. Among Central American countries, AMOVA estimated only ~3% of the variance among collections from Chiapas (Mexico) to Panama (Table 3). More variation was detected among South American countries (~6–8%) and among collections within countries (~1–4%) (Table 3). Between Central and South America, AMOVA estimated large variance (~8–11%) and ~3% among collections within regions (Table 3). These results suggest large genetic differences between Central and South American collections. The MS allele frequencies were lastly compared among Cuba and continental populations (Table 3). Significant variation occurred among regions (~6–11%) and similar variation was detected among collections (~5%). This suggests significant genetic differentiation between Cuban and continental collections.
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). All the Central American collections west of Costa Rica clustered together but were separate from the cluster containing Costa Rican and Panamanian collections. The Venezuelan collections clustered together and were the most distant relative to the Central American collections, followed by Cuba and Colombia.
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), as did regression analysis against the natural logarithm of the geographic distance (Figure 4B). The FST did not increase until geographic distances approached ~665 km (~e6.5). The average effective population size (Ne) was 96 individuals among collections (Figure 4B).
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). Sequencing was done on 26 of the most common haplotypes.
The level of nucleotide diversity in the ND5 sequence, including silent and non-silent sites, was moderate in most Central American populations (0.0013–0.0058), increased in Panama and South America (0.0047–0.0080), and was greatest in Cuba (0.0159) (Table 2). Of a total of 31 segregating sites, six resulted in amino acid replacements.
Haplotype frequency profiles of all 50 ND5 haplotypes are shown by country in Figure 5. Haplotype frequencies were similar for most countries in Central America (including Chiapas, Mexico) but differed substantially with those from Cuba, the Pacific region of Costa Rica, Panama, Colombia, and Venezuela (Figure 5). Haplotype 1 was the most frequent in both Cuba and Central America (Figure 5).
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0.1; the other five were shared with Central America and one was shared with Central and South America. There were 40 haplotypes present in Central America, of which 15 were unique but in low frequencies in Guatemala. Two unique haplotypes were present at low frequency in Panama, one in Mexico, and one in El Salvador. Central America shared seven haplotypes with South America. There were 14 haplotypes in South America, three were unique to Colombia and two were unique to Venezuela.
Haplotype frequencies were compared among Central American countries using AMOVA (Table 3). As with the MS alleles, variation among individuals in collections accounted for most (77–92%) of the total variance. Approximately 5% occurred among countries and ~2% among collections within countries. The variation detected among countries decreased to 0.9% when only those collections west of Panama were considered. This pattern suggests a panmictic population from Chiapas (Mexico) to Costa Rica. Among countries in South America, 25% of the variation arose among countries (Table 3). A large amount (16%) of variance occurred between Central and South American collections (Table 3). The AMOVA indicated that ~4% of the variation arose between Cuba and continental populations (Table 3).
Pairwise mtDNA FST/(1 – FST) were subject to cluster analysis (Figure 3B). As with the MS loci, all of the Central American collections clustered together, except for the Costa Rican collection of Puntarenas and the Panamanian collections. These collections separated from the Central American clusters as did the Cuban and South American collections, suggesting major differences between western Central America and Panama and South America.
The FST/(1 – FST) estimates for Central American collections were regressed against geographic distances to test for isolation by distance. No correlation was found among collections west of Panama (R2 = 0.01, Mantel probability = 0.207), but the inclusion of Panamanian collections in the analysis resulted in increase in slope and an apparent correlation (R2 = 0.52, Mantel probability = 0.001) (Figure 4C). The mtDNA variation therefore indicates that the Central American collections west of Panama seem to be panmictic.
Pairwise MS FST/(1 – FST) and ND5 FST/(1 – FST) for all collections were regressed and a significant correlation was detected (R2 = 0.36), suggesting that MS and mtDNA markers are giving similar patterns of variation.
Phylogenetic relationships among ND5 haplotypes.
The ND5 sequences of An. albimanus were manually aligned with the homologous regions of An. gambiae, An. quadrimaculatus, An. bellator, and An. cruzi as outgroups. Phylogenetic analysis identified a well-supported clade containing the three unique Cuban haplotypes that was basal to a clade with mainly South American haplotypes, and this in turn separated with slight bootstrap support from a clade formed by the rest of haplotypes (Figure 6).
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DISCUSSION |
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Barriers to gene flow were evident between Central and South American An. albimanus populations. However, in the present study the mtDNA marker suggests that one barrier probably occurs within Central America, west into Panama and Costa Rica. This was not detected in our prior study because of sparse geographic sampling in Panama and South America. The variation between Atlantic and Pacific Costa Rican populations increases toward Panama. This genetic difference may have been caused in part by a population contraction in Panama (e.g., due to more intense insecticide control). There were a smaller number of ND5 haplotypes and MS alleles in Panama compared with other Central American populations (Table 2). This barrier might be the mountain range that crosses Costa Rica and Western Panama, separating the Atlantic from the Pacific regions (Figure 1). The mountains reach close to both coasts in some areas. Further sampling of An. albimanus in that area would be required to confirm this as a barrier.
As in our previous study, An. albimanus populations in South America were genetically heterogeneous. This genetic differentiation is higher than the one previously detected using 25 allozymes in 11 populations of An. albimanus in Colombia.25 In that case, northern populations of An. albimanus clustered separately from the southern populations but with Nei distances24 less than 0.05. The high level of population structure found in South American An. albimanus also requires further examination. Our study documents for the first time small to moderate genetic differences between Caribbean and continental An. albimanus populations, suggesting that the Caribbean ocean represents a partial barrier to gene flow. It is interesting to note that between Cuban and the continental collections, MS markers exhibited larger variance than the mtDNA marker, but MS markers detected less variance than the mtDNA marker within Central America and between Central and South America collections. The MS markers may exhibit less variance at larger geographic distances due to size homoplasy and size constraints.26–29 Nucleotide diversity was greatest in the Cuban collections. Furthermore, phylogenetic analysis of ND5 sequences indicated that three of the unique Cuban haplotypes were basal in An. albimanus. Both observations are consistent with a hypothesis that continental An. albimanus populations originated in the Caribbean islands. In addition, the basal Cuban clade was more similar to the South American clade than to the larger clade containing predominantly haplotypes from Central America. This pattern is difficult to understand given the present distribution of An. albimanus. The species is not present in the Lesser Antilles or in eastern Venezuela (Figure 7), regions that would be the closest link between the Greater Antilles and South America.
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) compared with An. gambiae (~103).33 The small effective population size of An. albimanus in Central America suggests a bottleneck in its natural history. This does not seem to be caused by seasonal fluctuations since no significant difference in genetic composition was found between the rainy and the dry season.8 Although insecticide control in the region could have contributed to a bottleneck in the population, a founder effect of mosquitoes migrating from the Caribbean to Central America seems more consistent with the data.
Our results and the present distribution of An. albimanus are consistent with the gene flow pattern shown in Figure 7. The current An. albimanus populations originated in the Greater Antilles and moved across the Caribbean ocean to Central and South America by different routes. The Caribbean ocean represents a partial barrier to gene flow; An. albimanus populations in the continent may have lower nucleotide diversity due to genetic drift. Putative barriers to gene flow located in Costa Rica and Panama decrease gene flow among Central and South American populations.
The gene flow pattern deduced for An. albimanus has several implications for vector control. The population west of Costa Rica in Central America appears to be panmictic. Genetically modified mosquitoes released in these areas would not experience barriers to gene flow. The inferred barriers to gene flow in Costa Rica and Panama might predict larger differences in vector capacity between An. albimanus populations from Central, South America, and the Greater Antilles than among populations within each region.
Received September 5, 2003. Accepted for publication January 7, 2004.
Acknowledgments: We thank all of our collaborators in Latin American who provided mosquito collections. Dr. Mark Benedict (Centers for Disease Control and Prevention, Atlanta, GA) kindly provided us with primer sequences for MS 2-25. Dr. Richard Wilkerson kindly provided us with DNA samples of An. bellator and An. cruzi.
Financial support: This project was supported by the UNDP/World Bank/World Health Organization Special Program for Research and Training in Tropical Diseases (TDR), grant no. 971171 to Ana María P. de Mérida, and training award no. M8/181/4/M.422 to Alvaro Molina-Cruz.
Authors’ addresses: Alvaro Molina-Cruz, Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Twinbrook III, 12735 Twinbrook Parkway, Rockville, MD 20852, E-mails: amolina-cruz{at}niaid.nih.gov and amolinac{at}uvg.edu.gt. Ana María P. de Mérida, Katherine Mills, Fernando Rodríguez, Carolina Schoua, María Marta Yurrita, Eduviges Molina, and Margarita Palmieri, Medical Entomology Research and Training Unit, Universidad del Valle de Guatemala, 15 Avenida 11-95, Zona 15, VH III, Apartado Postal No. 82, 01901, Guatemala City, Guatemala, Telephone: 502-364-0336, Fax: 502-364-0052. William C. Black IV, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO 80523, Telephone: 970-4916136, Fax: 970-4911815, E-mail: wcb4{at}cvmbs.colostate.edu.
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) and between collections from Central American countries other from Panama (•) are indicated. Regression analysis of ND5 pairwise estimates excluding Panamanian collections (C-1) and including Panamanian collections (C-2) are indicated. Prob. = probability.
