Insect Science (2014) 00, 1–12, DOI 10.1111/1744-7917.12129

ORIGINAL ARTICLE

Genetic structure of the whitefly Bemisia tabaci populations in Colombia following a recent invasion Fernando D´ıaz1 , Nancy M. Endersby2 and Ary A. Hoffmann2 1 Department

of Biology, Universidad del Valle, Cali, Colombia; and

2 Department

of Genetics, Pest and Disease Vector Group, Bio 21

Institute, University of Melbourne, Parkville, Victoria 3010, Australia

Abstract The whitefly Bemisia tabaci (Gennadius) is one of the most important pests causing economic losses in a variety of cropping systems around the world. This species was recently found in a coastal region of Colombia and has now spread inland. To investigate this invasive process, the genetic structure of B. tabaci was examined in 8 sampling locations from 2 infested regions (coastal, inland) using 9 microsatellite markers and the mitochondrial COI gene. The mitochondrial analysis indicated that only the invasive species of the B. tabaci complex Middle East–Asia Minor 1 (MEAM 1 known previously as biotype B) was present. The microsatellite data pointed to genetic differences among the regions and no isolation by distance within regions. The coastal region in the Caribbean appears to have been the initial point of invasion, while the inland region in the Southwest showed genetic variation among populations most likely reflecting founder events and ongoing changes associated with climatic and topographical heterogeneity. These findings have implications for tracking and managing B. tabaci. Key words Bemisia tabaci; Colombia; microsatellites; Middle East–Asia Minor 1 (MEAM 1); mtCOI; population structure

Introduction Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is one of the most important pests affecting agriculture (Oliveira et al., 2001; Perring, 2001). Nymphs and adults cause damage to the host plants directly by feeding on phloem sap, and indirectly by excretion of honeydew on leaves and as a virus vector. The honeydew promotes growth of sooty mold fungi causing problems in production; however, the most important losses are due to the transmission of viruses in the genus Begomovirus (Geminiviridae) by this pest (Ca˜nas et al., 2004; Cu´ellar & Morales, 2006). B. tabaci is highly polyphagous and affects at least 600 host plant species in tropical and subtropical regions (Wan et al., 2009), including edible, or-

Correspondence: Fernando D´ıaz, Department of Biology, Universidad del Valle, Calle 13 No. 100-00, Cali, Valle del Cauca 76001000, Colombia. Tel: +57 3187838780; email: [email protected]

namental, and fiber crops (Morales & Anderson, 2001). In addition, this whitefly has a high reproductive rate, a high capacity for dispersion and resistance to several insecticides (Ca˜nas et al., 2004), which complicates control options. B. tabaci has expanded throughout the world, dispersing to all continents except the Antarctic. Populations from different locations vary in their response to local climates, host plants, virus transmission and insecticide resistance, leading to the concept of B. tabaci consisting of several biotypes (Oliveira et al., 2001; Perring, 2001). However, recent phylogenetic analyses have suggested that B. tabaci is a species complex rather than a complex of biotypes (De Barro et al., 2011). The species status has been confirmed by crossing experiments for several putative groups showing reproductive incompatibility (Wang et al., 2010; Xu et al., 2010; Sun et al., 2011). Because these species are morphologically indistinguishable, cryptic species of B. tabaci have been differentiated with genetic markers including allozymes, RAPD, AFLP, RFLP, and microsatellite markers (Brown et al., 2000; Cervera et al., 2000; De 1

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Barro, 2005). However, most B. tabaci groups have been defined by sequence variation in the mitochondrial COI gene and nuclear ITS1 gene (Boykin et al., 2007; Chu et al., 2007; Frohlich et al., 1999; De Barro et al., 2000; De Barro & Ahmed, 2011). In the Americas, 3 major clades of B. tabaci have been reported (Shatters et al., 2009): these fall within New World 1 and 2 (NW 1 and 2 known previously as biotype A) and invasive Middle East–Asia Minor 1 (MEAM 1 previously biotypes B and B2) and Mediterranean clades (Med or Biotype Q) (Boykin et al., 2007; De Barro et al., 2011). These invasive clades have displaced the native NW clades from some countries in America (De Barro et al., 2011), but not in all countries (Alemandri et al., 2012; Barbosa et al., 2014). In Colombia, populations of B. tabaci have been defined principally by esterase profiles or RAPD markers (Quintero et al., 1998; Quintero et al., 2001; Rodr´ıguez & Cardona, 2001). These studies detected MEAM 1 for the first time in Colombia in 1995 and point to this species colonizing crops initially in the coastal Caribbean region of Colombia (Quintero et al., 1998; Quintero et al., 2001). Then, between 1997 and 2003, this species also appears to have increased in importance in the more mountainous Southwest region of Colombia (Valle del Cauca), displacing both the native NW 1 (Biotype A) (De Barro et al., 2011) and the other agriculturally important whitefly, Trialeurodes vaporariorum (Westwood) (Hemiptera: Aleyrodidae) from some localities (Rodr´ıguez et al., 2005). So far, only NW 1 and MEAM 1 have been reported in Colombia (Quintero et al., 2001; Rodr´ıguez & Cardona, 2001; Rodr´ıguez et al., 2005); the invasive Med species has not so far been detected, despite being present in other parts of the Americas (McKenzie et al., 2009; De Barro et al., 2011). In this study, we assess the presence of different species in the B. tabaci complex in Colombia using the COI marker, and we determine population structure across populations within the MEAM 1 species with polymorphic microsatellite markers. We used microsatellites taken from different libraries (De Barro et al., 2003; Tsagkarakou & Roditakis, 2003; Delatte et al., 2006; Dalmon et al., 2008; Gauthier et al., 2008; Fontes et al., 2012) with the aim of evaluating the genetic structure of B. tabaci populations between and within the coastal Caribbean region and the mountainous Southwest region. Genetic structure among the MEAM 1 populations was used to assess possible movement routes following invasion and to determine whether there was local genetic structure across the main agricultural regions that might reflect barriers to gene flow.

Fig. 1 Collection locations of the whitefly B. tabaci analyzed in this study from Colombia. The codes for each sample are as in Table 1.

Materials and methods Whitefly sampling Samples of B. tabaci were collected from 8 locations in the Southwest region from the Andes mountains and the coastal Caribbean region (Table 1 and Fig. 1). These regions are separated by 480 km and differ in topography, altitude, temperature regimens, humidity, agricultural activities, host plants, and natural enemies of whitefly. The whitefly distribution in the Southwest region covers altitudes between 1 000 and 1 500 masl. The Caribbean region is close to the Atlantic coast, and represents a flat area at sea level. The Southwest region is therefore much more heterogeneous in terms of topography and local climate. Five locations covering different elevations were sampled in March 2011 for the Southwest region, and 3 locations were sampled in September 2011 for the Caribbean region. At each location, crops were checked for nymphs of whitefly in the leaves. Adult whiteflies were not sampled because of the difficulty of identifying species at the adult stage. Leaves with nymphs were brought to the lab for identification, and B. tabaci nymphs were then  C

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Table 1 Genetic variation in Colombian populations of B. tabaci (N, number of females sampled; Na, number of alleles; Ae, effective number of alleles; Ho, observed heterozygosity; He, expected heterozygosity; FIS , inbreeding coefficient). Region

Location

Southwest Rozo Pradera Cajamarca Pavas Regaderos Caribbean Retiro de los Indios Trementino Sampu´es

Code Latitude, Longitude (Elevation masl) ROZ PRA CAJ PAV REG RIN TRE SAM

3.61639, −76.3892 (972) 3.43333, −76.2333 (1108) 4.47722, −76.21389 (1493) 3.675, −76.5836 (1406) 3.75889, −76.2153 (1395) 8.8575, −75.81444 (13) 8.81667, −75.4667 (66) 9.18361, −75.3817 (159)

Host plant

N

Na

Ae

Ho

He

FIS

Soybean String bean Cucumber String bean Tomato Eggplant Eggplant Eggplant

45 46 45 45 20 45 45 45

3.56 3.22 3.56 3.00 2.33 4.22 3.44 4.11

1.66 1.70 1.76 1.62 1.59 2.23 1.95 2.17

0.25 0.28 0.27 0.24 0.32 0.32 0.29 0.31

0.33 0.26 0.32 0.15 0.33 0.15 0.32 0.25 0.32 −0.06 0.42 0.19 0.39 0.26 0.43 0.25

Note: Locus P7 was not used in estimating these values because of scoring issues and deviations from HW equilibrium (Table 2).

reared to the adult stage. Only diploid females were genotyped because these provided more information on breeding structure and allele frequencies than haploid males. In total, 336 females were collected and genotyped, 45– 46 for location except REG because of the low incidence of the whitefly in this location. Samples were stored in absolute ethanol at −80 °C. Extraction of DNA DNA was extracted by modifying the method described by De Barro and Driver (1997). Individual females were homogenized in 20 μL of lysis buffer (50 mmol/L KCl, 10 mmol/L Tris pH 8.4, 0.45% Tween 20, 0.45% Triton X-100, 0.5 mg/mL Proteinase K). Thereafter, each sample was incubated at 65 °C for 20 min and then at 90 °C for 10 min. Finally, 40 μL of ultra-pure water was added, completing a 60 μL final volume. The extracted samples were stored at −20 °C. The DNA concentration was estimated at 6–7 ng/μL per sample according to fluorescence of ethidium bromide in agarose gels (2.0%) compared with the standard Hyperladder II (Bioline Australia). PCR amplification of mtCOI Six individuals per location were randomly selected in order to identify the whitefly species. The mtDNA region for the Cytochrome Oxidase I (COI) gene was amplified using 2 different primer systems: CI-J-2195 and L2-N-3014 (Simon et al., 1994), producing an 860 bp fragment, and Btab–UniR and Btab–UniL (Shatters et al., 2009), producing a 745 bp fragment. The sequence data were clearer to read with primers Btab–UniR and Btab–UniL (Shatters et al., 2009) and only these primers were used for COI analysis. The PCR reaction was developed in a 30 μL volume, using 4.5 μL of DNA (30 ng),  C 2014

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0.2 μmol/L forward and reverse primers, 0.15 mmol/L dNTPs, 3 mmol/L MgCl2 , 1× buffer solution (50 mmol/L KCl, 100 mmol/L Tris-HCl, pH 8.5) and 1.5 U Taq polymerase. The PCR program involved 5 min at 94 °C followed by 35 cycles at 94 °C for 30 sec, 46 °C for 1 min, and 72 °C for 1 min, then the final extension at 72 °C for 10 min. DNA concentration was estimated according to fluorescence by ethidium bromide on agarose gels (2.0%). PCR products were sent for sequencing to Macrogen Inc. (Korea).

Analyses of mtCOI data Sequences were processed with Sequence scanner software version 1.0 (Applied Biosystems) and BioEdit sequence alignment editor software (Hall, 1999). Sequences with low quality and bases not firmly identified were excluded. Six whitefly females were sequenced per population, and then these sequences were compared with sequences for the different species reported for the B. tabaci complex in the world by phylogenetic analyses using Mega 5.0 software (Tamura et al., 2011). Sequences representing the 11 high-level and 24 low-level B. tabaci groups according to Boykin et al. (2007) and De Barro et al. (2011) were taken from the Global Bemisia dataset (https://data.csiro.au/dap/landingpage?pid=csiro:6002). Phylogenetic inference analyses were performed using the Neighbor-Joining (NJ) method (Saitou & Nei, 1987), with evolutionary distances computed by Kimura 2-parameter (Kimura, 1980) and Jukes–Cantor (Jukes & Cantor, 1969) methods. The confidence of the nodes was assessed by 10 000 bootstrap replicates (Felsenstein, 1985). Other methods including UPGMA method (Sneath & Sokal, 1973), maximum likelihood (ML) (Felsenstein, 1981), and maximum parsimony (MP) (Fitch, 1981) led to similar topologies.

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PCR amplification of microsatellites

2012). Regions were compared for diversity with FSTAT software version 2.9.3.2 (Goudet, 1995). Microchecker 2.2.3 (van Oosterhout et al., 2004) was used to test for the presence of null alleles and potential genotyping errors due to stuttering and allelic dropouts. After Bonferroni correction, there was evidence for null alleles at 2 loci (mb05 and mb02; Table 2). Population structure was examined by including and excluding these loci, but only the results for all 9 loci are presented because exclusion of the loci did not affect the conclusions. Genetic structure of populations was analyzed with a Bayesian method in STRUCTURE software version 2.3.4 (Pritchard et al., 2000) and through F statistics. For the Bayesian analyses, the number of clusters in the data, K, was tested between K = 1 and K = 9 with and without the option for admixture, and 20 runs for each prior. The method suggested by Evanno et al. (2005) was used to determine the optimal K. The genetic structure of B. tabaci populations was also estimated globally and pairwise by computing FST using θ (allele identity-based) according to Weir and Cockerham (1984) with GenAlEx version 6.5 (Peakall & Smouse, 2012) and Arlequin version 3.5.1.3 (Excoffier & Lischer, 2010). Because of controversy about the best statistic to estimate the genetic structure of populations (Jost, 2008; Ryman & Leimar, 2009), we also estimated pairwise population differentiation using the alternative measures F’ST (Hedrick, 2005) and Dest (Jost, 2008) in the package DiveRsity version 1.5.5 in R (Keenan et al., 2013). When mutation is important relative to migration, FST decreases with the population heterozygosity and the number of alleles (Ryman & Leimar, 2009; Whitlock, 2011) and alternative measures may provide a better estimate of differentiation. We compared pairwise FST values estimated following Weir and Cockerham (1984) with the pairwise estimates of F’ST and Dest by Mantel tests (Mantel, 1967) with FSTAT version 2.9.3.2 (Goudet, 1995), using a matrix of linearized estimates of pairwise population differentiation (e.g., FST /1 – FST ). To test for mutation rate effects, the population differentiation statistics were correlated with the number of alleles per locus with the package DiveRsity version 1.5.5 in R. We also estimated population structure by RST (allele size-based) following Slatkin (1995). Microsatellites follow a stepwise mutation model and RST was computed following this model, which performs better than FST comparisons when mutation is important relative to migration, because it is not affected by high mutation rates (Slatkin, 1995; Whitlock, 2011). However, RST can suffer high sampling variance and the mutation effect on genetic structure (permuting alleles) was therefore carried out using SpaGeDi software version 1.4 (Hardy &

Primers for each locus were end-labeled adding a “pigtail” (GTTTCTT) at the 5 end of the reverse primer and 1 of 4 different tails at the 5 end of the forward primer. The system of universal tailed primers was used to introduce a fluorescent dye during the PCR reaction according to Blacket et al. (2012). Four different kinds of tails were used for detection with 4 labeled primers: tail GCCTCCCTCGCGCCA (Roche, 2006) for FAM, GCCTTGCCAGCCCGC (Roche, 2006) for NED, CGGAGAGCCGAGAGGTG (Blacket et al., 2012) for PET, CAGGACCAGGCTACCGTG (Blacket et al., 2012) for VIC. PCR reactions were performed in a 10 μL final volume, including 5 μL of Qiagen multiplex PCR kit, 1.5 μL of DNA (10 ng), 0.15 μmol/L of forward primer, 0.5 μmol/L of reverse primer, and 0.2 μmol/L of the corresponding fluorescent primer (Blacket et al., 2012). The PCR program involved 15 min at 95 °C followed by 40 cycles at 94°C for 30 sec, the annealing temperature for each primer (Table 2) for 1.5 min and 72 °C for 1 min, then the final extension at 60 °C for 30 min. Five microliters of each amplification product with different dyes were mixed and sent for Genescan service to Macrogen Inc. with a capillary analyzer ABI3730XL. Allele sizes for R software genotyping were estimated with Genemarker version 2.2.0 (Softgenetics).

Analyses of microsatellite data In total, 36 microsatellites from different libraries reported for B. tabaci were evaluated (De Barro et al., 2003; Tsagkarakou & Roditakis, 2003; Delatte et al., 2006; Tsagkarakou et al., 2007; Dalmon et al., 2008; Gauthier et al., 2008; Fontes et al., 2012) for the samples from Colombia. Only 22 microsatellites resulted in positive amplification and 10 were polymorphic. These 10 microsatellite loci were evaluated for Hardy–Weinberg equilibrium and linkage disequilibrium in genotypic frequencies for each locus and location (Table 2). An exact test (Weir, 1996) was run in both analyses with Genepop software version 4.2.1 (Raymond & Rousset, 1995). These tests employed the Markov Chain method based on 100 batches with 5 000 permutations per batch. For these multiple comparisons, a Bonferroni correction was applied with a global significance level α = 0.05. Genetic diversity was quantified in B. tabaci locations by number of alleles per locus (Na), effective number of alleles per locus (Ae), observed heterozygosity (Ho), expected heterozygosity (He), and inbreeding coefficient (FIS ) computed with GenAlEx software version 6.5 (Peakall & Smouse,  C

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Table 2 Characteristics of 10 microsatellite loci studied in B. tabaci: repeat motif, thermal conditions for amplification, observed allele size range, Genbank accession number and genetic diversity estimators by locus (Na, number of alleles; Ae, effective number of alleles; Ho, observed heterozygosity; He, expected heterozygosity). HW reflects significance for deviation from Hardy–Weinberg equilibrium. Locus

Repeat

AT (°C) Allele range GenBank No. Reference

Na

Ae

Ho

He

HW

BEM18 BT-b69 P11 mb05 BEM25 145 P7 Btls1.6 mb02 mb07

(CT)30imp (AC)12 (GT)9 (AC)8 (CTT)10 (AC)9 (GT)8 (TG)8 (CT)16 (AC)8

60 62 55 62 47 59 50 54 58 62

3.00 3.00 2.00 6.00 9.00 4.00 8.00 10.00 9.00 5.00

1.19 1.15 1.44 1.32 2.88 1.41 3.23 2.64 3.63 1.46

0.11 0.14 0.29 0.13 0.52 0.28 0.41 0.46 0.35 0.27

0.13 0.14 0.30 0.21 0.61 0.30 0.66 0.57 0.66 0.30

NS NS NS NS NS NS

164–180 186–192 197–199 250–262 165–195 241–259 192–201 246–259 220–238 256–262

AY145457 AY183678 AJ866708 GF109950 AY145462 AM779550 AJ866710 BV726568 GF109947 GF109952

De Barro et al. (2003) Tsagkarakou and Roditakis (2003) Delatte et al. (2006) Fontes et al. (2012) De Barro et al. (2003) Dalmon et al. (2008) Delatte et al. (2006) Gauthier et al. (2008) Fontes et al. (2012) Not published

***

NS NS NS

Notes: Percentage success of PCR amplifications for each locus across all populations is given in Table S1. AT (°C) = Annealing temperature (°C). Na and Ae were calculated over all populations. Ho and He were calculated as a population mean. HW test was done for each locus and population. Results indicate significance after Bonferroni correction (α = 0.05). NS, not significant (P > 0.05). *** Significant (P < 0.001).

Vekemans, 2002). This test determines whether stepwise mutations contributed to genetic structure following Hardy et al. (2003). A Mantel test (Mantel, 1967) was also run to assess the correlation between FST and RST for pairwise population differentiation using FSTAT version 2.9.3.2 (Goudet, 1995). An Analysis of Molecular Variance (AMOVA) was run as an hierarchical model with populations nested in regions (Southwest and Caribbean region) following Excoffier et al. (1992) and RST (allele size-based) following Slatkin (1995). Mantel tests were also run to examine isolation by distance (IBD), comparing the correlation between FST /1 – FST and the logarithm of Euclidian geographical distance (Rousset, 1997). This analysis was carried out for all possible pairwise comparisons and for comparisons restricted by region. Finally, MIGRATE version 3.5.2 was run to test the migration model between regions by MCMC simulations. The population structure established through the STRUCTURE analysis and FST were used to define the demes and corresponded to the 2 regions (see results). Four migration models were tested: a full model with free migration between the 2 structured regions, a one-way model with migration between regions from Caribbean to Southwest, a one-way model with migration between regions in the reverse direction, and a panmictic model. Marginal likelihoods and Bayes Factors were estimated to calculate the probability for each model with the main hypothesis being tested by the second model involving migration from the Caribbean to the Southwest.  C 2014

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Results Species identification by mtCOI sequences Based on alignments of sequencing data in the COI gene, only one species of the B. tabaci complex was present in both Colombian regions; no difference was found among individuals sequenced. In the phylogenetic analyses using NJ, all Colombian samples clustered with the MEAM 1 clade sequence rather than other species of the complex in America (Fig. 2). Similar topologies were obtained with the other phylogenetic approaches (results not shown).

Microsatellite variation There were 2–10 alleles per locus for the 10 polymorphic microsatellites, with an average of 5.9 per locus and 59 alleles in total for the 8 B. tabaci samples (Table 2). Microsatellite P7 showed significant deviation from Hardy–Weinberg equilibrium and some ambiguities during scoring and was not used in the analyses (Table 2). No associations were detected between alleles in pairwise comparisons of the loci, suggesting that they belonged to independent linkage groups. Based on genetic diversity indicators (Na, He, and Ho) (Table 1), samples of B. tabaci populations in the Caribbean region were significantly more diverse compared with samples from the Colombian Southwest region, Na (P = 0.008), He (P = 0.017), and Ho (P = 0.035).

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Fig. 2 Rooted Neighbor-Joining (NJ) tree showing the evolutionary relationship among cytochrome oxidase I sequences of B. tabaci. Evolutionary distances were computed using the Kimura 2-parameter method. Sequences representing the 11 high-level and 24 low-level B. tabaci groups previously identified according to Boykin et al. (2007) and De Barro et al. (2011) are used as comparisons (group name, collecting place, and Genbank accession number are indicated). No difference was found among Colombian sequences, Colombia in the topology represents 48 sequences. One B. afer sequence was added to this comparison as an outgroup. Numbers associated with nodes are based on 10 000 bootstrap iterations and only nodes supported up to 50% are shown.

between FST and Dest matrices (Mantel test: r = 0.86, P = 0.018) (Fig. 3). Therefore, despite the overall differences in degree of population structure estimated by the indices (F’ST > FST > Dest ) (Fig. 3), differences between populations using these indices indicated a similar pattern of population structure. All statistics were positively correlated with the number of alleles (FST : r = 0.61, F’ST : r = 0.93, and Dest : r = 0.93), suggesting that FST was

With one exception (REG), all locations showed a deficiency of heterozygotes, with inbreeding coefficients ranging between 0.15 and 0.26 (Table 1). Genetic structure of populations A significant correlation was found between FST and F’ST matrices (Mantel test: r = 0.84, P = 0.033) and  C

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Table 3 Hierarchical AMOVA with B. tabaci populations nested in regions (Southwest vs. Caribbean). Effect

df

FST analysis Regions Pops (regions) Within pops Total RST analysis Regions Pops (regions) Within pops Total

SS

MS

Var. comp.

%

Stat.

P

1 6 664 671

31.40 17.51 847.21 896.11

31.40 2.92 1.28

0.088 0.020 1.276 1.384

6.4% 1.4% 92.2%

FRT = 0.063 FSR = 0.015 FST = 0.078

0.011

Genetic structure of the whitefly Bemisia tabaci populations in Colombia following a recent invasion.

The whitefly Bemisia tabaci (Gennadius) is one of the most important pests causing economic losses in a variety of cropping systems around the world. ...
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