A diverse and specific Phytophthora infestans sensu lato population associated with solanum betaceum in southern Solombia

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(1)1. A diverse and specific Phytophthora infestans sensu lato population associated. 2. with Solanum betaceum in Southern Colombia.. 3 4. Mideros, M.F., Cárdenas, M., Tabima, J., Castillo, Y.V, Obando, C., Lagos, L.E.,. 5. Bernal, A., Goss, E., Grunwald, N., Matute, D.R., and Restrepo, S.. 6 7. Laboratorio de Micología y Fitopatología, Universidad de los Andes, Bogotá,. 8. Colombia. Grupo de genética de patosistemas, Universidad de Nariño, Pasto,. 9. Colombia. Emerging Pathogens Institute and Department of Plant Pathology,. 10. University of Florida, PO Box 110680, Gainesville, FL, 32611. [email protected].. 11. Horticultural Crops Research Laboratory, USDA ARS, 3420 NW Orchard Ave.,. 12. Corvallis, OR 97330. [email protected]. Department of Ecology and. 13. Evolution, The University of Chicago 1101 East 57th StreetChicago, IL. 14. 60637, USA.. 15 16. Corresponding author: [email protected]. 17. 1.

(2) 1. Abstract. 2 3. New populations within the genus Phytophthora, associated with late blight have been. 4. reported in recent years. In South America, the presence of P. andina associated with. 5. native and exotic plants, among which is tree tomato (Solanum betaceum), have been. 6. described as the result of the complex process of evolution of these plant pathogens.. 7. In the present study nuclear, mitochondrial sequences and SSR markers were used to. 8. determine the genetic diversity and structure of Phytophthora samples collected from. 9. S. betaceum crops in the Nariño and Putumayo departments. In addition, detached-. 10. leaf assays were used to test aggressiveness of the strains on several cultivars of the. 11. host. Our results showed a P. infestans sensu lato population characterized by a high. 12. genetic diversity with individuals showing different levels of ploidy and high. 13. clonality, supported by the presence of only the A1 mating type. Isolates showed a. 14. high specificity to the host with a wide range of aggressiveness that could not be. 15. related to the genetic variation found. Ancestral polymorphism, as a result of cultural. 16. conditions of the host and an ongoing hybridization process in the region were. 17. suggested as hypothesis to explain the variation found. Results of this study contribute. 18. to the understanding of the evolutionary mechanisms associated with P. infestans. 19. sensu lato in native hosts.. 20 21. Keywords: P. infestans sensu lato, S. betaceum, clonality, haplotypic variation, SSR.. 22. 2.

(3) 1. INTRODUCTION. 2. The Andean region is considered to be the center of origin and diversity of. 3. different species of Solanaceous plants including both cultivated and wild species. 4. (Prohens and Nuez, 2000; Hijmans and Spooner, 2001; Bohs, 2007; Pratt et al.,. 5. 2008). These species act as potential reservoirs of plant pathogens, mainly of species. 6. of the genus Phytophthora that represent important constraints on crops of. 7. economical importance in the world (Erwin and Ribeiro, 1996; Patiño et al., 1997;. 8. Becktell et al., 2006).. 9. Different hypotheses suggest that populations of P. infestans are evolving on. 10. Solanum hosts in highland of South America (Flier et al., 2003; Adler et al., 2004a;. 11. Oliva et al., 2007; Gomez-Alpizar et al., 2008; Oliva et al., 2010b). An example of. 12. this evolution process has been reported in Ecuador where a new potential specie. 13. designated as Phytophthora andina has been found infecting native and wild host. 14. (Oliva et al., 2010b). This specie shows different genetic and pathogenic features as. 15. the presence of mixed mating types (A1 and A2), the production of viable oospores in. 16. interspecific crosses, a high genetic diversity, genetic subdivision among groups and. 17. host specificity, and presence of partial hybridization and sexual recombination events. 18. (Oliva et al., 2002; Gomez-Alpizar et al., 2007; Gomez-Alpizar et al., 2008; Oliva et. 19. al., 2010b).. 20. Although the origin and evolution of these populations remain unclear, some. 21. authors consider that it is possible that this specie could have originated from a. 22. hybridization process between P. infestans and P. mirabilis (Kroon et al., 2004; Blair,. 23. 2008; Gomez-Alpizar et al., 2008) and thus supporting the hypothesis of a South. 24. American origin of Phytophthora infestans (Oliva et al., 2002; Gomez-Alpizar et al.,. 25. 2007; Gomez-Alpizar et al., 2008; Oliva et al., 2010b). Although evidence of this 3.

(4) 1. process has been difficult to establish in natural conditions (Fry et al., 2009), it is. 2. clear that tropical regions offer an appropriate scenario for some species to evolve as a. 3. result of sympatric speciation and coexist as mixed populations. Such is the case of P.. 4. infestans and P. andina populations that coexist in solanaceous species (Goodwin et. 5. al., 1999; Flier et al., 2003; Grünwald and Flier, 2005; Mallet, 2007). Under the. 6. scenario of sexual reproduction, high variation among populations might be found. 7. (Mayton et al., 2000). In these populations strong evidence exist for the presence of. 8. host-adapted isolates (Chacon et al., 2007) and thus this host preference could. 9. generate a strong selection pressure on pathogen genotypes acting as a divergent force. 10. that produces local and regional adapted population both at a temporal and spatial. 11. level (Thrall et al., 2003; Fitt et al., 2006; Oliva et al., 2010b). In addition, the host. 12. adaptation could define the outcome of the evolutionary process in the host-pathogen. 13. interactions and is thought to be a crucial factor influencing the disease dynamics. 14. (Kirchner and Roy, 2001 ; Suassuna et al., 2004). However, the underlying force. 15. behind these processes and the epidemiological consequences of such variation,. 16. remain poorly understood (Thrall et al., 2003).. 17. However, while we believe that P. andina is a new species, our own analyses. 18. show that further work is needed to understand the evolutionary history of P. andina.. 19. AFLP analysis and host specialization indicate that P. andina is a new species but are. 20. clearly in conflict with the phylogenetic analysis of nuclear and mitochondrial. 21. sequence data. More work should include analysis of reproductive isolation, gene. 22. flow and population structure. Previous work has shown that the P. infestans isolates. 23. from the southern part of Colombia had a higher genetic variation when comparing to. 24. the isolates obtained in other Colombian regions and suggested the presence of P.. 25. infestans/P. andina co-occurrence (Cárdenas et al., 2011). Since the coexistence of P. 4.

(5) 1. infestans/P. andina in the southern Colombia would provide a good system model to. 2. investigate the effects of the pathogen’s genetic structure in plant disease epidemics,. 3. in this study we aimed to estimate the genetic variability and aggressiveness. 4. components of a set of P. infestans sensu lato isolates from S. betaceum in the Nariño. 5. and Putumayo departments in order to determine if in the southern region of. 6. Colombia a different Phytophthora species does exist, in addition to P. infestans, with. 7. a high specificity on S. betaceum as occurred in Ecuador.. 8 9. MATERIALS AND METHODS. 10. Isolates collection. Isolates were collected from Nariño and Putumayo states. 11. in Colombia. Commercial fields of tree tomato (Solanum betaceum) were visited and. 12. plants showing symptoms similar to late blight disease were sampled in different. 13. localities. Lesions were placed in tomato tree agar (TTA) at room temperature in. 14. darkness. Isolates were maintained by serial transfers in the same agar. All isolates. 15. were characterized by morphological (Erwin and Ribeiro, 1986) and molecular. 16. features (ITS sequencing). The morphological description of each isolate was. 17. performed from sporangia that were grown on TTA for 2 weeks in darkness. In total,. 18. three plugs (5mm of diameter) were taken from the periphery of the colony and. 19. placed in 1 ml of distilled water. Measurements were made at 400x with an optical. 20. microscope on 30 randomly selected sporangia using macBiophotonics Image J. 21. software (www.macbiophotonics.ca) (Erwin and Ribeiro, 1996; Oliva et al., 2010b).. 22. Additionally, mating type and mitochondrial haplotype were determined as previously. 23. described (Erwin and Ribeiro, 1996; Griffith and Shaw, 1998).. 24. 5.

(6) 1. DNA extraction and genotyping. Each isolate was inoculated in pea broth for. 2. 10 to 15 days at 20ªC before DNA extraction. Mycelia were washed in sterile water. 3. and macerated with liquid nitrogen. DNA was extracted following the protocol. 4. described by Griffith and Shaw (1998) and stored at -20ºC. Mitochondrial haplotypes. 5. were assessed as described by Griffith & Shaw (1998). Region P1 (1118 bp), P2. 6. (1070 bp), P3 (1308 bp) and P4 (964 bp) of the mitochondrial genome were amplified. 7. and digested with restriction enzymes CfoI, MspI and EcoRI (Griffith and Shaw,. 8. 1998). Band patterns were classified according to mtDNA haplotypes: Ia, Ib, Ic, IIa. 9. and IIb previously reported for P. infestans populations (Griffith and Shaw, 1998;. 10. Ordoñez et al., 2000; Adler et al., 2002; Gavino and Fry, 2002).. 11. To estimate the total genetic diversity present in the samples, six genetic. 12. regions: -tubulin, Ras, AvR3a, coxI (Cardenas et al., 2011), Additionally, SC16. 13. (Cespedes et al., in prep) and Avr2 (Oh et al., 2009) were amplified and sequenced as. 14. previously described.. 15. Eleven microsatellite (SSR) markers were amplified: Pi02, Pi16, Pi04, Pi33,. 16. Pi56, Pi63, Pi70, Pi89 (Lees et al., 2006 ) and 4B, D13, and G11 (Knapova and Gisi,. 17. 2002). Loci were divided in three different panels and three multiplex reactions were. 18. carried out: Panel 1 (Pi02, Pi89 and Pi4B), panel 2 (PiG11, Pi04, Pi70; Pi56 and Pi63). 19. and panel 3 (Pi16 and Pi33, Pi4G). Amplifications were performed following the. 20. EUCABLIGTH protocol for SSR analysis of P. infestans (SCRI, Scottish Crop. 21. Research Institute, UK). Samples were running in a 3100 Avant ABI Prism DNA. 22. sequencer at the Horticultural Crops Research Laboratory, United States Department. 23. of Agriculture – Agricultural Research Service (Corvallis, OR) according to the. 24. manufacturer’s instructions (Applied Biosystems) and using Liz-500 as a size. 25. standard (GeneScan-500; Applied Biosystems). DNA fragments were automatically 6.

(7) 1. sized with the GeneMapperTMV.3.5 software. Alleles were assigned according to the. 2. SCRI Protocols for SSR analysis of P. infestans (www.eucablight.org). Six samples. 3. from P. infestans (samples Pi20063228 and PiAv076) and P. andina (samples. 4. EC3210, EC3215, EC3836 and EC3655) with previously determined allele sizes were. 5. included as reference.. 6 7. Phylogenetic reconstructions. Using the complete Ras region and Cox 1. 8. gene, maximum likelihood and Bayesian approaches were used to determine the. 9. phylogenetic relationships of the samples. Sequences were aligned using MUSCLE. 10. algorithm (Edgar, 2004) with 8 iterations. Maximum likelihood approaches were. 11. made in PhyML v3.0 (Guindon et al., 2009) using the substitution model determined. 12. by ModelTest 3.7 (Posada and Crandall, 1998), and statistical support was assessed. 13. by means of a 1000 replicate bootstrap. Additionaly, Bayesian inference approach. 14. was made using Mr. Bayes v3.1.2 (Huelsenbeck and Ronquist, 2005) with 1000000. 15. MCMC generations, four chains and a 25% burnin to avoid non-convergent results.. 16 17. Genotypic variation and population structure. A multilocus microsatellite. 18. genotype (MLG) was assigned to each isolate with the GenoDive Software v2.0b20. 19. (Meirmans and Van Tienderen, 2004). Isolates with the same MLG were treated as. 20. clones. In order to assess clonality, the number of different MLG assigned and the. 21. effective number of genotypes were estimated. In addition, to determine the genotypic. 22. diversity of our sample, the Stoddart and Taylor’s genotypic diversity indices. 23. (Stoddart and Taylor, 1988), genotypic diversity with and without correction for. 24. sample size (Nei, 1987), evenness and the Shannon-Wiener index of diversity. 25. corrected for sample size (Chao and Shen, 2003) were estimated. Support for all 7.

(8) 1. indices was assessed with a bootstrap using 1000 replicates (Grünwald et al., 2003).. 2. Additionally, we estimated the clonal fraction of the sample as the individual. 3. proportion originated from asexual reproduction. All these analyses were performed. 4. with clone correction implemented in GenoDive version 2.0b20 (Meirmans and Van. 5. Tienderen, 2004). Pairwise genetic distances between multilocus genotypes (MLG). 6. were estimated using Bruvo´s distance (Bruvo et al., 2004). These genetic distances. 7. were then used to construct a minimum spanning genotype network using the. 8. software Minspnet (Excoffier and Smouse, 1994). Network was visualized with. 9. HapStar version 6.0 (Teacher and Griffiths, 2011) and editing in the free open-source. 10. SVG graphics editor Inkscape version 0.48.1 (www.inkscape.org). The resulting. 11. network was compared with the nuclear and mitochondrial haplotypes obtained.. 12. Additionally, each microsatellite locus was evaluated for deviation from the. 13. Hardy-Weinberg equilibrium (p<0.05) using an heterozygosity-based estimator (Gis). 14. implemented in GenoDive version 2.0b20 (Meirmans and Van Tienderen, 2004). This. 15. test was performed to see whether or not the observed genotypic frequencies deviate. 16. from the frequencies expected under random mating (Michalakis and Excoffier,. 17. 1996).. 18. To infer genetic population structure, several statistical analyses were. 19. employed. Analysis of variance based on F-statistics and AMOVA were used to test. 20. for genetic structuring defined by the geographical regions (Nariño and Putumayo),. 21. ploidy level (aneuploid and tetraploid) and cultivar host. We used SPAGeDi v1.3. 22. (Hardy and Vekemans, 2002) to calculate ANOVA-based global and pairwise F-. 23. statistics and R-statistics with significant p values obtained after 1000 random. 24. permutations of genes, individuals, and populations. AMOVA (Excoffier et al., 1992). 8.

(9) 1. was calculated with Genalex (Peakall and Smouse, 2006) following the method. 2. described by (Lo et al., 2009).. 3. Microsatellite-based genetic distances were calculated with Nei genetic. 4. distance (Ds) to infer population’s relationships using the Populations software. 5. v1.2.31 (Copyright© 1999, Olivier Langella). UPGMA trees were constructed from. 6. the resulting matrix using Fig Tree v1.3.1. Additionally, population structure was. 7. estimated with the clustering approach implemented in Instruct Software that does not. 8. assume Hardy-Weinberg within populations. Model 3 was implemented, inferring. 9. population structure with admixture and selfing rates at the individual level. Monte. 10. Carlo Markov chain (MCMC) simulations were run for K = 1–5, with 5 independent. 11. chains for each K. Each simulation was run with a burn-in length of 100 000 MCMC. 12. generations followed by 20000 MCMC iterations. This approach was used for. 13. Phytophthora populations in previous studies using Bayesian Markov Chain Monte. 14. Carlo programs as Structure (Pritchard et al., 2000).. 15. For the nucleotide data, the number of haplotypes, Nucleotide diversity (π),. 16. haplotypic diversity (Hd) and Tajima’s D were estimated for each genetic region with. 17. DnaSP v.4.90.1 (Rozas et al., 2003). The genealogical relationships among haplotypes. 18. were established by statistical parsimony with a 95% connection limit and gaps as. 19. missing states as is implemented in the TCS software v1.21 (Clement et al., 2000).. 20. Migrate-n (Beerli, 2006) was used to estimate theta and the direction and amount of. 21. gene flow between Nariño and Putumayo samples, using all 6 loci. Five hundred short. 22. chains with 5000 sampled genealogies and 20 short chains with 50000 genealogies. 23. were run. Heating was set to be active with 4 temperatures (1.0, 1.5, 2.5 and 3.0).. 24. 9.

(10) 1. Variation in aggressiveness and virulence among P. infestans sensu lato. 2. isolates. Forty isolates were chosen to evaluate different aggressiveness components.. 3. Four S. betaceum cultivars, displaying differences in levels and components of partial. 4. resistance were selected from a previous work (Lebreton et al., 1999; Carlisle et al.,. 5. 2002) and grown in glasshouse with natural conditions before evaluation.. 6. Plant inoculations were carried out according to what was described. 7. previously with some modifications. In general, sporangia were collected in 3 ml of. 8. sterile water from 10 -15 days TTA cultures. Suspension was standardized to a. 9. concentration of 3.5 × 105 sporangia ml-1 with a hemocytometer. For the. 10. aggressiveness test, sporangia suspension was inoculated on six leaves of each tree. 11. tomato cultivar. Leaves were placed in a humidity chamber and were maintained. 12. under controlled condition (15-18°C, 98% of relative humidity) during an evaluation. 13. period of 10 days. Aggressiveness components including sporulation density (SD),. 14. lesion size (LS), infection efficiency (IE), incubation period (IP), disease severity. 15. percentage (DSP), sporulation rate (SR), latency period (LP), growth rate (LGR) and. 16. Area Under the Lesion Expansion Curve (AULEC) were evaluated (Lebreton et al.,. 17. 1999; Suassuna et al., 2004). All statistical analyses were performed using the SPSS. 18. statistical package v.15.0 (Statistical package for social science, IBM®). Data were. 19. transformed to ensure normality. The effects of isolate, cultivar and genotype on each. 20. one of the aggressiveness components were tested through an analysis of variance,. 21. ANOVA. Isolates were clustered according to their aggressiveness using UPGMA. 22. method taking into account all the variables analyzed with SPSS statistical package. 23. (Statistical package for social science, IBM®).. 24 25. RESULTS 10.

(11) 1. P. infestans sensu lato isolates. A collection of 117 Phytophthora isolates. 2. was obtained from the 33 sampled sites. For all isolates, morphological characteristics. 3. were more similar to macroscopic and microscopic features previously reported for P.. 4. andina than to P. infestans. Particularly, sporangia were larger than those from P.. 5. infestans and showed size variation that ranged from 48.38 m to 60. 3m in length. 6. with an average of 55.99 m ± 10.83 and from 29.36 m to 45.19 m in width with. 7. an average of 36.23 m ± 8.46. The length:width ratio ranged from 1.2 m to 1.9m. 8. (data not shown). Nonetheless, tree topologies generated by phylogenetic analysis. 9. showed no difference between the strains sequenced in this study and several samples. 10. of previously identified strains of P. infestans and P. andina (see below). Thus from. 11. now on we will considered our samples P. infestans sensu lato. All isolates were. 12. classified as Ia mt DNA haplotype. The in vitro evaluation of mating type showed that. 13. the isolates corresponded only to the A1 mating type (data not shown). Isolates. 14. information can be found in the supplementary material (Table S1).. 15 16. Genetic diversity of P. infestans sensu lato population from SSR markers.. 17. Only nine microsatellite loci were found to be polymorphic and heterozygous in the. 18. sample (Pi02, Pi89, 4B, G11, Pi70, Pi56, Pi63, D13 and Pi16). Loci Pi04 and Pi33. 19. were monomorphic in all studied individuals and thus were not included in the. 20. analyses. Genetic characterization of 117 isolates from 33 different localities. 21. (supplementary information Table 1) revealed 33 alleles in total for all evaluated loci.. 22. All loci were not in Hardy-Weinberg equilibrium (p<0.007) except for PI70 (Table 1).. 23. MLGs were assigned assuming a stepwise mutation model (SMM), 27. 24. different MLGs were obtained using this method. Only seven MLGs were shared. 25. between Nariño and Putumayo, and some MLGs were specific for each state, five 11.

(12) 1. MLGs in Nariño and 15 in Putumayo. Clonal diversity was high, in average 81% of. 2. clones showed a distinct genotype, and clonal fraction ranged from 0,84 in Nariño to. 3. 0.7 in Putumayo indicating that samples are reproducing asexually. Not significant. 4. difference in clonal diversity was found between Nariño and Putumayo (p<0.001). All. 5. estimated indexes of genetic diversity were not significantly different between Nariño. 6. and Putumayo (p <0.001). Evenness values showed how the MLG are distributed. In. 7. Nariño the MLG were more evenly distributed than in Putumayo, nonetheless no. 8. significant differences could be found between the two regions (p>0.05) (Table 2).. 9. Relationships between the different MLGs found in the minimum spanning. 10. network analysis are displayed in Figure 1. The center of the network was represented. 11. by the commonest MLG while MLGs composed by just one isolate were represented. 12. in the boundaries. MLGs with one isolate are connected by just one single mutational. 13. step to the more abundant MLGs, and corresponded to new alleles reported in this. 14. study (Figure 2). The MLGs were apparently more related to P. andina than to P.. 15. infestans MLGs .. 16 17. Subdivision and genetic structure. FST and RST values (Table 3) did not showed. 18. significant differentiation between geographical localities and suggested lower levels. 19. of genetic structure according to localities sampled (FST = 0.0105; RST = 0.000, p <. 20. 0.001). Similar results were observed when tested for cultivar subdivision (FST =. 21. 0.0038; RST = 0.0089, p < 0.001). However moderate genetic differentiation was. 22. observed among aneuploid and tetraploid populations (FST = 0.1105; RST = 0.1477, p. 23. < 0.001). Similar results were obtained for the analysis of molecular variance. 24. (AMOVA), which showed that only 33.7% of the total genetic variation could be. 25. explained by differences between Nariño and Putumayo, while the remaining 66.3% 12.

(13) 1. was due to differences between individuals within populations (Table 4) and the same. 2. could be obtained when testing for differences in ploidy and cultivar (Table 4). These. 3. results evidenced a low genetic divergence and a high gene flow between. 4. geographical localities and cultivar host. Inbreeding coefficient (FIS) for all. 5. comparisons indicated a high number of heterozygous individuals, which confirmed. 6. AMOVA results (Table 4).. 7. Similar results were found in UPGMA dendrogram of Nei´s genetic distance. 8. (Ds). MLG patterns revealed a low level of genetic structure between Nariño and. 9. Putumayo. Individuals were divided into three major clusters (A, B and C) according. 10. to the ploidy level. Cluster A contained aneuploid individuals, cluster B grouped. 11. tetraploid samples and cluster C clustered reference samples included in this study. 12. (Figure 2).. 13. The bayesian inference of the genetic structure in P. infestans samples showed. 14. that isolates were not clustered geographically, which indicated a low population. 15. differentiation within Nariño and Putumayo states (Figure 3).. 16 17. Nuclear and mitochondrial sequences.. 18. Six loci (SC16, -tubulin, Avr2, Avr3, the exon region of Ras and Cox 1) were. 19. used to determine nucleotide diversity, number of haplotypes (Table 5) and. 20. population structure between Nariño and Putumayo samples. Intron Ras was excluded. 21. because no polymorphism could be observed between samples. Although the. 22. nucleotide diversity was low, the number of haplotypes was high for most of the loci,. 23. excepting for Avr2. The Tajima’s D test failed to reject the null hypothesis of neutral. 24. evolution; this indicated that none of the genetic regions amplified were submitted to. 25. a selection pressure. 13.

(14) 1. Nuclear and mitochondrial sequences revealed similar results to that obtained. 2. with SSR markers. The low levels of nucleotide diversity and genetic variability could. 3. be the result of a clonal reproducing population. In addition, a low structure between. 4. geographical regions and a high number of migrants (Table 6) agreed with the. 5. information found for microsatellite data. Network reconstructions showed the. 6. haplotypic variation found in the southern Colombia. As a general pattern, the most. 7. abundant haplotypes for each locus were shared between Nariño and Putumayo,. 8. except for SC16 and eras where no shared haplotypes were found between geographic. 9. regions (Figure 4).. 10 11. Aggressiveness components in Phytophthora infestans sensu lato isolates. 12. Variation in aggressiveness components among 40 strains was observed when. 13. these were inoculated in four S. betaceum cultivars with different levels of late blight. 14. resistance. Results showed that isolates differed significantly in all components except. 15. in lesion growth rate (LGR) and latency period (LP) (Table 7). Differences among. 16. isolates for each of the analyzed variables are shown in the supplementary material. 17. Figure S1. UPGMA clustering showed that the isolates were mainly distributed in. 18. three phenotypic groups (Figure 6). Group C corresponded to the more aggressive. 19. isolates that generally were able to infect all cultivars tested with highly values of SD. 20. (2.0x104±6506.87), LS (6.68±1.52), LGR (4.20±0.50), SR (881.25±401.30) and. 21. AULEC (1148,3±377.22). Incubation period values were smaller when isolates were. 22. more aggressive (5.55±0.56) and these isolates were able to infect each point of. 23. inoculation on the leaf with higher infection efficiency (0.42±0.50). These. 24. characteristics showed that aggressive isolates can infect the leaf rapidly causing. 25. tissue necrosis in a short time, invade the whole leaf and produce a large number of 14.

(15) 1. sporangia that ensure its dispersion at the end of the invasion. Less aggressive isolates. 2. were divided in two groups (A and B); Group A was unable to infect all cultivars, and. 3. the few cultivars infected showed low values of lesion size (3.84±1.0), sporulation. 4. density (1.2x10±5976.3) and AULEC (506.64±171.52). In contrast, group B showed. 5. low values of LS (1.68±0.92), DSP (8.11±4.86), AULEC (174.14 ±133.70) and. 6. infection efficiency (0.09±0.07), but showed high values of sporulation density. 7. (7.1x104±5655.2) and incubation period (7.83±046). Means of aggressiveness. 8. components for each cluster are shown in Table 7 and Figure 8. These groups are. 9. different than genetic groups found with SSR.. 10. Pearson’s coefficient values showed a high correlation among aggressiveness. 11. components. There was a strong positive correlation between LS and DSP (r = 0.834;. 12. p<0.001), IE and %I (r = 0.802; p<0.001) and AULEC and DSP (r = 0.888, p<0.001). 13. meaning that isolates with a high aggressiveness showed a high lesion size,. 14. sporulation intensity and DSP, resulting in a high AULEC value leading to more. 15. disease on the plants. IP component was the only one with a negative correlation for. 16. all components evaluated (material supplementary Table S2).. 17 18. DISCUSSION. 19. Our results evidenced the existence of a unique population of P. infestans. 20. sensu lato associated with S. betaceum in southwest Colombia. According to most of. 21. the molecular results, we could not be able to differentiate our isolates from the. 22. species P. andina and P. infestans. However, morphological features and some. 23. molecular markers revealed unique patterns among these isolates.. 24. The P. infestans sensu lato populations in southwest Colombia show a higher. 25. level of diversity when compared with P. infestans isolates from potato and tree 15.

(16) 1. tomato plants in the rest of Colombia (Cárdenas et al., 2011). Divergent haplotypes in. 2. P. infestans samples from S. betaceum in Southwest Colombia were found for Avr3a. 3. and -tubulin genes in a previous study (Cardenas et al., 2011) and were confirmed in. 4. this study. A wide diversity of haplotypes has been found in P. infestans associated. 5. with S. betaceum, which corresponded to the high number of haplotypes found in -. 6. tubulin, e Ras, SC16, Avr3a and Cox 1 found in this study. However, because a. 7. sampling effort like the one used in the present study had never been performed the. 8. variation obtained could correspond to an ancestral polymorphism that was not. 9. revealed before. Similar results have been found in Ecuador where P. infestans. 10. populations from hosts others than potato and tomato showed high levels of genetic. 11. diversity (Adler et al., 2002; Adler et al., 2004b; Oliva et al., 2007; Oliva et al.,. 12. 2010b).. 13. An alternative hypothesis, other than ancestral polymorphism, to explain the. 14. unique molecular characteristics of the isolates from Southwest Colombia is the. 15. existence of interspecies sexual crosses or other genetic interactions among. 16. Phytophthora species sharing habitats. Microsatellites results showed clearly defined. 17. aneuploid and tetraploid individuals. In Phytophthora species, aneuploid and. 18. tetraploid individuals on natural conditions have been observed regularly among. 19. sexual offsprings resulted from interspecific sexual crosses involving normal meiosis. 20. (Carter et al., 1999; Dobrowolski et al., 2002; Van der Lee et al., 2004). In asexual. 21. populations, aneuploid and poliploid conditions have been related with other. 22. interactions, including intra-specific crossing and selfing, mitotic crossing over, gene. 23. conversion and extra-chromosomal elements (Goodwin, 1997; Abu-El Samen et al.,. 24. 2003). In P. infestans, selfing process represents an alternative mechanism for. 25. increasing genetic variation (Drenth et al., 1995; Smart et al., 1999). We did not find 16.

(17) 1. the A2 mating type in our P. infestans isolates and thus it is probably that it was. 2. present in a very low frequency so we were not able to sample it or that the studied. 3. population is changing by a mitotic recombination process. The latter process has. 4. played an important role in the evolution of the worldwide P infestans populations. 5. (McDonald and Linde, 2002) and probably also in generating genotypic variability in. 6. the pathogen populations from S. betaceum. Nonetheless, it is likely that a potential. 7. hybrid origin of the isolates could be associated with the formation of individuals with. 8. different ploidy levels, as suggested for P. andina (Oliva et al., 2010). However the. 9. molecular markers employed in the present study were not able to allow us to. 10. distinguish between an ancestral polymorphism in the sampled geographic region and. 11. recently generation of hybrid individuals.. 12. It is important to highlight the fact that although the P. infestans sensu lato. 13. samples from southwest Colombia showed higher levels of diversity, the collected. 14. samples, as well as in the rest of the country corresponded to a clonal population with. 15. low genetic structure and asexual reproduction (Salgado et al., 2008; Gilchrist et al.,. 16. 2009; Raigosa et al., 2009; Cardenas et al., 2011). Low levels of genetic diversity. 17. were found when evaluating nuclear and mitochondrial DNA and similar results have. 18. been found in previous studies (Gomez-Alpizar et al., 2007; Cardenas et al., 2011).. 19. For the avirulence genes, Avr3a and SC16 showed a higher number of haplotypes. 20. compared to Avr2. Several studies have reported high sequence polymorphisms in. 21. Avr3a populations related with selection pressure from the host defense machinery. 22. (Oliva et al., 2010a; Huitema et al., 2004). However, our results showed that the. 23. samples have not been submitted to a selection pressure although Tajima’s D test. 24. values obtained showed a negative tendency.. 17.

(18) 1. The reduced number of Avr2 haplotypes might be and indicator that variation. 2. in this gene could not be relevant in the P. infestans sensu lato /S. betaceum. 3. interaction and could be related with a fitness cost in the absence of the corresponding. 4. R gene in the host (Montarry et al., 2010) since avirulence polymorphisms could be. 5. only maintained in the pathogen population (Tellier and Brown, 2007) when. 6. constantly exposed to the corresponding host Resistance (R) gene. Changes or. 7. mutations in Avr2 virulence gene impose a fitness cost in P. infestans sensu lato. 8. population and could explain the low number of haplotypes found in this study.. 9. Similar conclusions have been obtained in other pathosystem evaluated when strong. 10. diversification was associated with differences in R gene recognition by Avr genes.. 11. Future efforts are necessary to define the presence or absence of R genes in S.. 12. betaceum.. 13. The lack of genetic structure found in this study was in agreement with. 14. previous studies in plant pathogens populations where genetic variation is not. 15. geographically structured. Our initial hypothesis was that samples from Putumayo. 16. could be genetically divergent since tree tomatoes in this state are grown in the. 17. isolated Sibundoy valley surrounded by mountains and Paramo ecosystems. In our. 18. case, one possible explanation for a lack of geographical structure could be. 19. geographical distance between collected samples, since short distances allowed high. 20. flow between populations (McDermott and McDonald, 1993; Linde et al., 2002) in. 21. spite of the apparent geographical isolation.. 22. Apparently, the generation of new genotypes in the P. infestans sensu lato. 23. sample in southwest Colombia has been the result of mitotic recombination, and/or. 24. mutation or by the introduction of a few closely related genotypes, genetic drift. 25. (Mascheretti et al., 2008; Goss et al., 2009). Only 7 MLGs were shared between the 18.

(19) 1. two geographical regions, Nariño and Putumayo and corresponded to the more. 2. abundant MLGs. These genotypes were related among them by a single mutational. 3. step. The genetic mechanisms mentioned above have been proved to be significant in. 4. shaping the populations of P. infestans in potato (Goodwin et al., 1994) and other. 5. species of Phytophthora as P. cinnamomi where phenotypic variation identified. 6. within clonal lineages was explained by mitotic recombination (Dobrowolski et al.,. 7. 2003). In P. ramorum it is suggested that newly found populations in nurseries may. 8. be characterized by rapid mutation, genetic drift, and limited gene flow (Goss et al.,. 9. 2009). A similar scenario can be explained by the evenness results found. Under strict. 10. asexual reproduction, genetic variation among isolates is distributed among the. 11. genotypes found and these should diversify over time into discrete clonal lineages. 12. characterized by a mix of closely related genotypes, as is our case. In contrast, if. 13. exchange of genetic information among individuals is happening through sexual. 14. reproduction, genomic variation should be evenly distributed across isolates. Similar. 15. conclusion could be inferred from high values of expected heterozygosis across the. 16. loci, since it is possible that high values of heterozygozity are related with the nature. 17. of microsatellites markers because these regions showed higher mutation rates than. 18. other genotypic markers (Schlötterer, 2000; Estoup et al., 2002; Nybom, 2004). In. 19. high polyploids such mutations may accumulate faster over time and could result in. 20. an increased heterozygozity (Lo et al., 2009).. 21. The presence of unique alleles within the studied population suggested that. 22. this population could be the result of a strong founder effect and subsequent genetic. 23. bottleneck as reported in other Phytophthora species populations (Goodwin et al.,. 24. 1994; Goodwin, 1997; Huang et al., 2004; Ivors et al., 2004; Prospero et al., 2004;. 25. Prospero et al., 2007). In fact two situations could support this hypothesis: First, S. 19.

(20) 1. betaceum do not constitute a continuous crop. Instead, rotation of cultivars in the. 2. same crop has allowed frequent local extinction, stochastic recolonization and. 3. subsequent genetic drift in the plant pathogen population. These conditions allowed. 4. several genotypes to survive in the same field. It is probable that cultural practices and. 5. misuse of fungicides might be partially responsible for this selection. Second, P.. 6. infestans isolates in this geographic region, showed a high host specificity since they. 7. were not able to infect S. tuberosum in detached leaves assays performed previously. 8. (data not shown). This specificity limits the local dispersal mechanism of. 9. Phytophthora populations. In both situations the stochastic nature of recolonization. 10. processes are likely to be the key factor driving the divergence inside the population. 11. (Brown and Hovmoller, 2002).. 12. Our results suggested that the population of P. infestans in the sampled. 13. regions was evolving according to the clonal with limited recombination model. 14. proposed by Heirman (2006). This model provides strong evidence of the importance. 15. of the mitotic recombination process in the emergence and evolution of microbial. 16. pathogens as diverse as viruses, bacteria, fungi, and parasites (Heitman, 2006).. 17. To the best of our knowledge, this is the first study aimed to correlate the. 18. genetic diversity of P. infestans samples with aggressiveness in Colombia. No. 19. correlation was obtained between the evaluated aggressiveness components and. 20. genotypes; nonetheless, previous studies have demonstrated a negative relationship. 21. between them (Miller et al., 1997; Carlisle et al., 2002; Pariaud et al., 2009).. 22. However, our results showed that the P. infestans population in S. betaceum exhibited. 23. a broad range of cultivar infection with high values of the main aggressiveness. 24. component but exhibited a low LP. Previous studies had showed that low LP may be. 25. associated with highly pathogenic plant pathogens that have a low dispersal, and 20.

(21) 1. limited sexual reproduction (Clément et al., 2010) which is in agreement with our. 2. population genetics analyses results. These features warranty that the pathogen can be. 3. maintained within the host more time to ensure a constant nutrients supply, mainly in. 4. hemibiotrophic pathogens as P. infestans because its survival depends on its host.. 5. Only some aspects are known about evolution in plant pathogen interaction caused by. 6. P. infestans on S. betaceum since this disease was recently reported (Adler et al.,. 7. 2004a; Chacon et al., 2006; Oliva et al., 2010b). Despite this, the aggressiveness. 8. results obtained in this study showed a high adaptability of P. infestans sensu lato in. 9. S. betaceum. Adaptability of Phytophthora populations and generation of aggressive. 10. isolates are correlated with the occurrence of new genetic combinations in isolates. 11. with a considerably shorten latent periods (LP) that allows the completion of their life. 12. cycle rapidly and to overcome specific resistance genes in the host (Widmark et al.,. 13. 2007; Lehtinen et al., 2009). However, our results showed that there was no. 14. relationship between the presence of new allelic variants or new combinations of. 15. genotypes and the aggressiveness level. Both aneuploid and tetraploid isolates were. 16. able to infect different cultivars with high levels of infection (Group A) or low levels. 17. of infection (Group Band C).. 18. In conclusion, this study revealed that a diverse P. infestans sensu lato. 19. population that displayed a clonal structure with different levels of ploidy, affected S.. 20. betaceum in the southwestern part of Colombia. The genetic variability of this. 21. population is possibly the result of mitotic recombination, a founder effect or the. 22. recovery of a bottleneck according to the crop culture practices in this region.. 23. However, the possibility of sexual recombination intra and/or inter species cannot be. 24. discarded at this time, and the observed variation could be the result of an ongoing. 25. hybridization process. An adaptation of P. infestans sensu lato to S. betaceum was 21.

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(30) 1. Table 1. Allele frequencies for each locus and each group of isolates of Phytophthora infestans sensu lato. Each locus was tested for deviation. 2. from Hardy–Weinberg proportions (H0: random association of gametes) (Values are based in Gis per locality and locus). Nariño Pi02. Pi89. 4B. G11. Pi70. Alle Allele. Freq.. Allele. Freq.. Allele. Freq.. Pi56. Alle Freq.. le. Pi63. D13. Pi16. Alle Freq.. le. Freq.. Allele. Freq.. Allele. Freq.. Allele. Freq.. le. 144. 0.336. 122. 0.464. 213. 0.312. 130. 0.456. 189. 0.016. 174. 0.552. 148. 0.52. 108. 1. 177. 0.552. 158. 0. 181. 0.488. 253. 0.008. 156. 0.096. 192. 0.984. 176. 0.448. 151. 0.48. 112. 0. 178. 0.44. 161. 0.664. 183. 0.04. 257. 0. 158. 0. 182. 0. 186. 0.008. 270. 0.128. 160. 0.32. 188. 0.008. 273. 0.168. 162. 0.128. 278. 0.288. 166. 0. 281. 0.072. 283. 0.024. 0.001a. 0.002 a. 0.001 a. 0.001 a. 0.283 a. 0.006 a. 0.001 a. --- a. 0.001 a. 30.

(31) Putum ayo Pi89. Pi02. 4B. G11. Pi70. Pi56. Allele. Freq.. Allele. Freq.. Allele. Freq.. Allele. Freq.. Allele. Freq.. Allele. 144. 0.335. 122. 0.479. 213. 0.307. 130. 0.3852. 189. 0. 174. Pi63 Freq.. Allele. 0.509. Freq.. Pi16 Freq.. Freq.. 177. 0.4786. 178. 0.5097. 0.976 108. 1. 7. 0.0039. 181. 0.521. 253. 0. 156. 0.1634. 161. 0.6615. 183. 0. 257. 0.004. 158. 0.0739. 182. 0.0039. 186. 0. 270. 0.07. 160. 0.1362. 188. 0.0078. 273. 0.284. 162. 0.2335. 278. 0.261. 166. 0.0078. 281. 0.055. 283. 0.020. 0.001b. 0.001 b. 0.001b. 0.001 b. --- b. 176. 0.001 b. 151. 0.001 b. 0.541. Allele. 158. 192. 0.490. Allele. 0.459 148. 7 1. D13. 112. 0.001 b. 0.023. 0.001 b. Overal l. 31.

(32) Pi89. Pi02. G11. Pi70. Pi56. Pi63. D13. Pi16. Allele. Freq.. Allele. Freq.. Allele. Freq.. Allele. Freq.. Allele. Freq.. Allele. Freq.. Allele. Freq.. Allele. Freq.. Allele. Freq.. 144. 0.3351. 122. 0.474. 213. 0.309. 130. 0.408. 189. 0.005. 174. 0.524. 148. 0.479. 108. 0.984. 177. 0.5026. 158. 0.0026. 181. 0.511. 253. 0.003. 156. 0.1414. 192. 0.995. 176. 0.476. 151. 0.521. 112. 0.016. 178. 0.4869. 161. 0.6623. 183. 0.013. 257. 0.003. 158. 0.0497. 182. 0.0026. 186. 0.003. 270. 0.089. 160. 0.1963. 188. 0.0079. 273. 0.246. 162. 0.199. 278. 0.270. 166. 0.0052. 281. 0.060. 283. 0.021. 0.001c. 1. 4B. a,b,c. 0.001 c. 0.001 c. 0.001 c. 0.283 c. 0.001 c. 0.001 c. 0.001 c. 0.001 c. Hardy-Weinberg values for loci in Nariño, Putumayo and Overall sample. 32.

(33) 1. Table 2. Genetic diversity parameters for Phytophthora infestans sensu lato isolates from S. betaceum in southwest Colombia inferred from SSR. 2. markers. Localitya. Sample. Number of. Observed. size. MLGb. heterocigocity Ho. Clonal Fractionc. Genetic. Genetic Diversity. Diversity. Shannon g. Evennessh. (Nei)f. NA. 39. 12(6). 0.56. 0.84. 0.854. 1.00. 0.50. PU. 78. 22(6). 0.56. 0.7. 0.818. 1.12. 0.23. 3. a. Geographic localities analyzed in this study NA: Nariño; PU: Putumayo. 4. b. Number of MLG shared with the other population are showed in parenthesis.. 5. c. Clonal fraction calculated as 1- (Number of different genotypes/ total number of isolates ). 6. d. Nei´s genetic diversity corrected for sample size (Simpson´s diversity index), also known as "expected heterozygosity” (He). 7. e. Shannon index corrected for sample size. 8. f. evenness value : 1.0 indicates that all genotypes have equal frequencies.. 33.

(34) 1. Table 3. F-statistics and R-statistics for SSR (ANOVA-based) data in Phyophthora. 2. infestans sensu lato samples, estimated for geographical localities, ploidy level and. 3. cultivar host.. 4 Populations. 5. F-statistics. R-statistics. FIT. FIS. FST. RIT. RIS. RST. Ploidy. -0.2522. -0.2578. 0.1105. -0.3371. -0.3475. 0.1477. Locality. -0.248. -0.2612. 0.0105. -0.343. -0.3429. 0. Cultivar. -0.2597. -0.2649. 0.0038. -0.3641. -0.3473. 0.0089. Two side p values were all p <0.001. 6. 34.

(35) 1 2. Table 4. Analysis of molecular variance (AMOVA) indicating the proportion of. 3. variation within and among populations of Phytophthora infestans sensu lato .. Source of variation. df. SSD. MSD. Est. Var.. % variation. Ploidy Populations. Individual within populations. 1. 54.114. 18.038. 0.954. 31%. 115. 200.457. 2.133. 2.133. 69%. 1. 32.291. 16.145. 0.577. 16%. 115. 352.222. 3.117. 3.117. 84%. 1. 22.396. 22.396. 0.340. 9%. 115. 419.703. 3.527. 3.527. 91%. Cultivar Populations. Individual within populations State-locality Populations Individual within populations. 35.

(36) 1. Table 5. Nucleotide polymorphism information for all loci evaluated in P. infestans. 2. sensu lato samples.. 3 Number of. Nucleotide. Haplotype. Haplotypes. Diversity (π). Diversity (Hd). SC16. 17. 0.01038. 0.971. 0.16761. NS. -tubulin. 20. 0.00635. 0.905. -0.08916. NS. Avr3. 13. 0.00557. 0.779. -1.72024. NS. Avr2. 3. 0.0064. 0.151. -0.08916. NS. Cox. 9. 0.001. 0.53. -1.3277. NS. eRas. 8. 0.00567. 0.699. -0.08916. NS. iRas. 0. 0. 0. 0. -. Tajima's D. 4. 36.

(37) 1. Table 6. Population Structure measured by θ for each locus and localities.. 2 Putumayo. Nariño. SC16. 0.04777. 0.00764. -tubulin. 0.03242. 0.00676. Avr3. 0.01244. 0.00366. Avr2. 0.0134. 0.00104. eRas. 0.021. 0.00528. Cox. 0.016. 0.00115. All. 0.01422. 0.00307. 37.

(38) 1. Table 7. Aggressiveness components of Phytophthora infestans isolates from S. betaceum. Means of component aggressiveness in high. 2. aggressive (Group A) and low aggressive isolated (Group B and C). Cluster. lesion size. Disease. Infection. Growth rate. Sporulation rate. Latency. Sporulation. Area Under the. (LS). severity. efficiency. (LGR). (SR). period (LP). density (SD). Lesion Expansion. percentage. (IE). Curve (AULEC). (DSP) mean. SD. mean. SD. mean. SD. mean. SD. mean. SD. mean. SD. mean. SD. mean. SD. A. 3.84 1.00. 15.81 4.16. 0.26 0.09. 3.33 0.81. 417.02 230.98. 5.76 1.29. 1.2E+04 5973.72. 506.40 171.5. B. 1.68 0.92. 8.11 4.86. 0.09 0.07. 1.88 0.60. 189.42 105.91. 3.35 1.18. 7.1E+03 5655.72. 174.14 133.7. C. 6.68 1.52. 31.11 7.80. 0.42 0.09. 4.20 0.50. 881.25 401.30. 6.92 0.77. 2.0E+04 6506.87. 1148.30 377.2. 3 4 5. 38.

(39) 1. Figure 1. Minimum-Spaning network (Excoffier 1993) showing genetic relationships. 2. between multilocus genotypes (MLGs) found in Phytophthora infestans sensu lato. 3. collection. Circle sizes are proportional to the number of individuals with the. 4. corresponding genotype and colors represent its distribution in Nariño (red) and. 5. Putumayo (yellow). Numbers between segments connecting nodes represent Bruvo´s. 6. genetic distance (Bruvo et al. 2004). Dotted lines represent probably relationship. 7. produced in MNS software.. 8 9. Figure 2. UPGMA dendrogram showing the relationship among MLGs obtained in. 10. this study. Nei´s standard genetic distance was used as implemented in Populations v. 11. 1.2.32 software. Colors represent ploidy levels Red: triploids and Blue: tetraploid. 12. populations, respectively. Blank labels are samples of reference used in this study.. 13 14. Figure 3. Genetic subdivision among Phytophthora infestans sensu lato samples. 15. based on Bayesian cluster analysis. Bayesian cluster analysis using Graphical. 16. representation of the data set for Nariño and Putumayo states. B) Graphical. 17. representation of data with the most likely K = 2, where each color corresponds to a. 18. suggested cluster and each individual is represented by a vertical bar. The Y-axis. 19. represents the probability of assignment of an individual to each cluster.. 20 21. Figure 4. Haplotype network generated on the basis of the genes evaluated in P.. 22. infestans samples. Each line between points represents a single mutational step. An. 23. haplotype is represented by a circle whose size is proportional to the number of. 24. individuals showing that haplotype. Haplotypes are colored to match the respective. 25. geographical region: blue = Nariño, green = Putumayo. 39.

(40) 1. Figure 5. Phylogenetic reconstructions from Ras and Cox1 genes. Support values are. 2. above branches, they represent bootstrap values for ML and posterior probabilities for. 3. BI: ML/BI.. 4 5. Figure 6. UPGMA dendrogram showing similarity among aggressiveness. 6. components for 40 isolates of Phytophthora andina populations evaluated on S.. 7. betaceum cultivars. Group A corresponded to aggressive isolates and Group B to less. 8. aggressive isolates. Colors are showing ploidy level. Red: triploid and blue:. 9. tetraploid, Pictures show aggressiveness in evaluated cultivars.. 10 11. Figure 7. Box plot of means of aggressiveness components evaluated for each cluster. 12. obtained in UPGMA analyzed. X-axis indicated cluster (A, B, C) and y-axis indicate. 13. value of each component evaluated.. 14. 40.

(41) 1. Figure 1. 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21. 41.

(42) 1. Figure 2.. 2. 3 4 5 6 7 8 9 42.

(43) 1. Figure 3.. 2. 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 43.

(44) 1. Figure 4.. 2 3 4 5 6 7 8 9 10 11 12 13 14 44.

(45) 1. Figure 5.. 2 3 4 5 6 7 8 9 10 11. 45.

(46) 1 2 3 4 5 6 7 8 9 10 11 12. Figure 6.. 46.

(47) 1 2. Figure 7.. 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 47.

(48) 1. Table S1. Phytophthora infestans isolates obtained from S. betaceum in the south west of Colombia.. 2 Sample. ID. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26. N9001 N9002 N9003 N9004 N9005 N9006 N9007 N9008 N9009 N9010 N9011 N9012 N9013 N9014 N9015 N9016 N9019 N9021 N9022 N9023 N9024 N9025 N9027 N9028 N9029 N9030. Collection year 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009. State. Locality 1. Locality 2. Variety. mtDNAhaplotype. NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO. Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco Buesaco. Medina orejuela Medina orejuela Medina orejuela Medina orejuela Medina orejuela Medina orejuela Medina orejuela Medina orejuela Medina orejuela Medina orejuela Medina orejuela Medina orejuela Medina orejuela Medina orejuela Medina orejuela Medina orejuela Medina orejuela Llano largo Llano largo Llano largo Llano largo Llano largo Llano largo Llano largo Llano largo Llano largo. ND ND INJERTO INJERTO ND ND ND ND ND ND ND ND ND ND INJERTO INJERTO ND COMUN COMUN COMUN COMUN COMUN COMUN COMUN COMUN COMUN. Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia. Mating type A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1. MLG 1 1 2 4 3 7 4 27 3 5 4 6 1 5 4 3 1 1 7 7 7 1 3 3 3 7. 48.

(49) 27 28 29 30 31 32 33 34 35 36 37 38 39. N9031 N9033 N9035 N9036 N9039 N9041 N9042 N9046 N9056 N9057 N9065 N9069 N9070. 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009. NARIÑO NARIÑO NARIÑO NARIÑO NARIÑO. Buesaco Buesaco Buesaco Buesaco Buesaco. NARIÑO NARIÑO NARIÑO NARIÑO. Pasto Consaca Consaca Iles. 40. P8098. 2008. Putumayo. 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56. P8001 P8003 P8005 P8006 P8008 P8010 P8011 P8012 P8014 P8015 P8016 P8017 P8018 P8022 P8029 P8030. 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008. Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo. San francisco Santiago Santiago Santiago Santiago Santiago Colon Colon Colon Santiago Santiago Santiago Santiago Santiago Santiago Santiago Santiago. Llano largo Rosal del monte Rosal del monte Rosal del monte Rosal del monte. ND COMUN COMUN ND ND. Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia. A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1. Mocondino El tejar El tejar Villa nueva. COMUN COMUN COMUN MANZANO. Chinayaco Muchivioy Muchivioy Cascajo Balsayaco Balsayaco Las americas Barrio centro Avenida los termales La yé La yé La yé La yé La yé San andres Quinchuapamba Quinchuapamba. 3. HOLANDES. Ia. A1. 4. HIBRIDO HIBRIDO ND HIBRIDO COMUN HOLANDES HOLANDES INJERTO COMUN HOLANDES HOLANDES HOLANDES ND HIBRIDO HIBRIDO HIBRIDO. Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia. A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1. 4 1 1 7 1 1 3 3 7 1 3 29 1 1 3 16. 7 1 7 8 9 1 7 1 3 28 10 1. 49.

(50) 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87. P8031 P8049 P8050 P8051 P8054 P8055 P8056 P8057 P8058 P8059 P8060 P8061 P8063 P8064 P8066 P8069 P8070 P8071 P8072 P8073 P8074 P8075 P8076 P8077 P8079 P8080 P8081 P8082 P8083 P8084 P8085. 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008. Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo. Santiago Colon Colon Colon Santiago Santiago Santiago Santiago Santiago Santiago Santiago Santiago Santiago Santiago Santiago Colon Colon Colon Colon Colon Colon Colon Colon Colon Colon Colon Santiago Colon Colon Colon Santiago. Quinchuapamba Casco urbano Casco urbano Casco urbano Vichoy Vichoy Vichoy Vichoy Vichoy Vichoy Vichoy Vichoy Vichoy Vichoy Vichoy Las palmas Las palmas Las palmas Las palmas Las palmas San pedro San pedro San pedro San pedro San pedro San pedro Diviso La josefina San pedro San pedro Vichoy. COMUN COMUN HOLANDES HOLANDES HIBRIDO COMUN COMUN COMUN COMUN ND COMUN ND ND ND COMUN HOLANDES COMUN COMUN COMUN HIBRIDO ND INJERTO COMUN ND COMUN ND HOLANDES ND HIBRIDO HIBRIDO HIBRIDO. Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia. A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1. 1 24 1 1 3 12 1 3 11 5 12 3 1 3 1 12 3 13 3 14 7 15 9 1 15 16 17 18 1 3 19. 50.

(51) 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104. P8087 P8088 P8091 P8093 P8094 P8097 P8098 P8099 P9102 P9103 P9105 P9109 P9110 P9113 P9114 P9115 P9120. 2008 2008 2008 2008 2008 2008 2008 2008 2009 2009 2009 2009 2009 2009 2009 2009 2009. Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo. San francisco San francisco San francisco San francisco San francisco San francisco San francisco San francisco San francisco San francisco San francisco San francisco San francisco San francisco San francisco San francisco San francisco. 105. P9127. 2009. Putumayo. San francisco. 106 107 108 109 110 111 112 113 114 115 116 117. P9128 P9129 P9144 P9146 P9147 P9148 P9150 P9151 P9153 P9158 P9159 P9164. 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009. Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo Putumayo. Sibundoy Sibundoy Sibundoy Sibundoy Sibundoy Sibundoy Sibundoy Sibundoy Sibundoy Sibundoy Sibundoy Sibundoy. Casco urbano Casco urbano Casco urbano Chinayaco Chinayaco Chinayaco Chinayaco Chinayaco San silvestre La menta La menta La menta La menta San antonio San antonio Casco urbano San silvestre San jose del chunga El ejido Machindioy San felix Cabunayaco San felix San felix Villa fatima San felix Villa fatima San felix Villa flor San felix. ND ND SILVESTRE COMUN ND ND HOLANDES INJERTO ND COMUN ND COMUN ND INJERTO COMUN ND COMUN. Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia. A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1. 1 1 3 7 1 1 3 20 3 3 1 1 1 4 21 15 1. COMUN. Ia. A1. 7. COMUN ND HIBRIDO COMUN COMUN COMUN COMUN COMUN COMUN COMUN COMUN COMUN. Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia Ia. A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1. 22 23 1 3 1 2 1 2 1 24 1 1. 1 51.