Journal of Plant Pathology (2012), 94 (3), 601-608 Edizioni ETS Pisa, 2012 601 POPULATION STRUCTURE OF THE BARLEY PATHOGEN PYRENOPHORA TERES f. TERES IN LITHUANIA G. Statkeviciute1, K. Jonaviciene1, R. Semaskiene2, A. Leistrumaite3, Z. Dabkevicius2 and G. Brazauskas1 1Laboratory of Genetics and Physiology, Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry, Instituto a. 1, Akademija, 58344, Kedainiai reg., Lithuania 2Department of Plant Pathology and Protection, Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry, Instituto a. 1, Akademija, 58344, Kedainiai reg., Lithuania 3Department of Cereal Breeding, Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry, Instituto a. 1, Akademija,58344, Kedainiai reg., Lithuania SUMMARY The amplified fragment length polymorphism (AFLP) technique was used to study the genetic structure of Pyrenophora teres, the causal agent of barley net blotch disease. Samples were collected from five locations in Lithuania in 2008. All 145 isolates had unique AFLP patterns. Hierarchical analysis of molecular variance (AMOVA) detected most of the variation (86.9%) within a field population. No correlation was identified between the population pairwise fixation indices and the distance between the sampling locations. However, genetic differentiation (FST=0.131) among field populations was significant (P<0.001). The smallest percentage of variation (5.8%) was observed between isolates of different mating types within population, the genetic differentiation was low (Fsc=0.064, P<0.001). Multilocus association test showed that populations were in linkage disequilibrium, suggesting that reproduction occurs mainly asexually. Key words: net blotch, Hordeum vulgare, AFLP, genetic diversity. INTRODUCTION Pyrenophora teres (Sacc.) Shoem. is an ascomycete fungus causing the net blotch disease of barley, which has negative effect on grain yield and quality by reducing the green leaf area and grain size. Disease symptoms are seen during the growing season on barley leaves and leaf sheaths as net or spot type blotches. Two types of leaf symptoms are associated with two different special forms of the fungus: the net-form net blotch, caused by P. teres f. teres (PTT), and the spot-form net blotch, caused by P. teres f. maculata (PTM) (Smedegård-Petersen, 1971). Lesions of PTT are characterised by narrow, dark brown longitudinal streaks with transverse Corresponding author: G. Statkeviciute Fax: +370.34737179 E-mail: grazinastat@lzi.lt lines, giving the lesions a net-like appearance (Parry, 1990). Lesions may be surrounded by chlorotic areas and large areas of dead tissue can also be present. Lesions of PTM are dark brown and elliptical in shape and may be surrounded by a chlorotic halo (Parry, 1990). The fungus survives on stubble and seeds (Shipton, 1973). Its ascospores or conidia are transported by wind and water splashing. Direct infection of barley leaves can occur but it does not produce systemic infection. The occurrence of spot and net types is variable among European countries and elsewhere. Net type is more common in Scandinavia, Baltic sea region and Russia (Afanasenko et al., 2007; Serenius et al., 2005; Statkeviciute et al., 2010), whereas both types of P. teres are common in Italy (Rau et al., 2003). The spot form dominates in Australia (McLean et al., 2009) and recently was detected in Hungary for the first time (Fiscor et al., 2010). The resistance to spot and net types is inherited independently (Ho et al., 1996; Scott, 1992). Although it is possible that the differential occurrence of these types could be related to the popularity of certain barley varieties in different regions, yet the exact reasons are not clear. Pyrenophora teres has a mixed reproductive system, with one generation of sexual reproduction occurring on the stubble between crops and several generations of asexual reproduction occurring during the growing season of the crop (Peever and Milgroom, 1994). Sexual reproduction is possible only if two fungal strains of different mating types interact, because the fungus is selfsterile and heterothallic (McDonald, 1963). Both PTT and PTM are able to adapt to changes in the genetic makeup of host populations by generating genotypic diversity via sexual reproduction (Peever and Milgroom, 1994; Rau et al., 2003). However, asexual reproduction spores are more important for the spread of net blotch, since conidia are formed during all growing season, while ascospores are produced only once, at the beginning of the season. Due to this mixed reproductive system, P. teres constitutes a greater risk for overcoming resistance genes compared to strictly asexual or strictly sexual pathogens (McDonald and Linde, 2002). This is because the sexual cycle leads to the production of new 602 Pyrenophora teres population structure in Lithuania Journal of Plant Pathology (2012), 94 (3), 601-608 genotypes from which the frequency of individuals with best fitness can increase through an asexual reproductive phase. Infection by P. teres can lead to 15 to 40% yield losses in barley worldwide (Jordan et al., 1985; Steffenson et al., 1991). Yield reductions in susceptible cultivars up to 50% have been reported from Lithuania (Semaskiene et al., 2009). Pathogen infection reduces the thousandkernel weight as well as plumpness and test weight that affect negatively malting and feed quality (Robinson, 2000). Foliar fungicides can be used to maintain yield quantity and quality; however, producers incur additional costs as chemical control is not always effective, it may require multiple applications (Campbell and Crous, 2002) and is not an option for organic production systems. Both resistance genes and fungicides may select pathogen populations towards higher tolerance to these control methods, leading to the loss of effectiveness. Therefore, investigation of the genetic structure of P. teres populations is fundamental to understand the evolutionary potential of these pathogens and to achieve durable resistance. The genetic structure of plant pathogens population indicates the ability of pathogens to adapt to changes in the genetic makeup of host population and can be used to predict how long a control measure or resistance source is likely to be effective (Campbell et al., 2002; Serenius et al., 2007). Pathogens with a high evolutionary potential are more likely to overcome resistance (McDonald and Linde, 2002). The genetic diversity of P. teres populations across wide geographical areas has been investigated using restriction fragment length polymorphism (RFLP), random amplified polymorphic DNA (RAPD), microsatellite DNA and amplified fragment length polymorphism (AFLP) markers (Bogacki et al., 2010; Jonsson et al., 2000; Lehmensiek et al., 2010; Leisova et al., 2005; Serenius et al., 2007; Wu et al., 2003). High levels of genetic diversity were detected within net blotch field populations in these studies, showing geographical distance as a key factor in determining the level of genetic differentiation between populations. High genetic variability among PTT populations from different countries or continents has been observed whereas the population from closely located fields revealed low genetic variation (Serenius et al., 2007). Barley is an intensively cultivated cereal crop worldwide and the most widely grown spring cereal in Lithuania, but information on P. teres genetic diversity is limited. Only net type net blotch has been detected in Lithuania so far, nevertheless high genetic diversity was observed within a subsample of 24 isolates (Statkeviciute et al., 2010). Study on the virulence of net blotch populations in Lithuania detected nineteen pathotypes, with only two of them being present in all sampling locations, suggesting high genetic diversity in net blotch Fig. 1. Location of the collection sites in Lithuania. Journal of Plant Pathology (2012), 94 (3), 601-608 populations (Skurdeniene, 2000). Based on these studies high genotype diversity and sexual reproduction in P. teres field populations were expected. The objective of this study was to investigate the genetic structure of P. teres populations sampled from geographically distant fields located in the main barleygrowing areas of Lithuania. MATERIALS AND METHODS Field sampling. Spring barley leaves with net blotch symptoms were sampled from five geographic Lithuanian regions at the milk growth stage in the 2008 growing season. The PTT collection included 145 isolates sampled from the following districts: Birzai, Ignalina, Klaipeda, Kedainiai and Marijampole, representing the whole spring barley growing areas of the country (Fig. 1). The collection consisted of 30 or more leaves sampled within each field by conducting a circular sweep in which one infected leaf per plant was harvested at regular 2 m intervals. Leaves were kept in paper bags at room temperature until the fungus was cultured. Fungal isolates. Monoconidial isolates were obtained from dry leaf material. The leaves were cut into 8-10 mm segments, surface sterilized in 50% ethanol for 15 sec, then in 0.5% sodium hypochlorite solution for 30 sec, and finally rinsed with sterile deionised water twice. Leaf segments were blotted dry and aseptically transferred onto sterile moistened filter paper in Petri dishes and incubated at room temperature and ambient day⁄night light conditions. After 3-5 days, where sporulation occurred, a single conidium representing the collection was aseptically transferred into 1.5% V8 agar plates and incubated for 10 days under the same conditions as above. DNA isolation. Small sections of mycelium were added to 20 ml liquid V8 medium in a 50 ml srew-cap tube and incubated on a rotating shaker at 100 rpm, 20°C for 4-5 days. Medium was then poured off and mycelium was washed with deionised water. DNA was isolated according to a modified protocol of Lassner et al. (1989) with additional wash step with chlorophorm and RNA digestion with ribonuclease A. To quantify DNA, a 1:50 dilution of DNA was made and compared against a series of λ DNA dilutions (5, 10, 20, 30, 40, and 50 ng/µl). Samples were run on 1% agarose gels for 2 h at 115 V and stained with ethidium bromide. Net/spot form-specific PCR. Amplification was conducted according to Williams et al. (2001) in a 15 µl reaction mixture, containing 1X DreamTaq PCR buffer (Fermentas, Lithuania), 1.5 mM MgCl2, 1 µM of each primer (MAT, PTT-R, PTM-F and PTM-R; Metabion, Statkeviciute et al. 603 Germany), 0.2 mM dNTP, 0.8 U DreamTaq polymerase (Fermentas, Lithuania), 25 ng of DNA. PCR thermal profile was 94°C for 1 min, 10 cycles of 94°C for 30 sec, 65°C (-1°C/cycle) for 30 sec, 72°C for1 min, followed by 24 cycles of 94°C for 30 sec, 55°C for 30 sec, 72°C for 1 min. Reaction was ended with 5 min extension at 72°C. Mating type-specific PCR. PCR (Rau et al., 2005) was carried out in 20 µl reaction mixture, containing 1X DreamTaq PCR buffer (Fermentas, Lithuania) 2.5 mM MgCl2, 1 µM of each primer (MAT1-F, MAT1-R, MAT2-F, MAT2-R; Metabion, Germany), 0.2 mM dNTP, 1 U DreamTaq polymerase (Fermentas, Lithuania), 25 ng of DNA. PCR thermal profile was 94°C for 1 min, followed by 30 cycles of 94°C for 60 sec, 55°C for 60 sec, 72°C for 90 sec. Reaction was ended with 10 min extension at 72°C. AFLP analysis. 500 ng of genomic DNA were digested with 10 U Fast Digest (FD) TaqI at 65°C for 10 min, then immediately digested with 10 U of FD AseI at 35°C for 35 min. The two steps of digestion were conducted in a FD buffer (Fermentas, Lithuania) in a total volume of 50 µl. Fifty pM AseI and 5 pM TaqI adapters were ligated to digested genomic DNA using 1 U of T4 ligase supplemented with FD buffer (Fermentas, Lithuania). PCR reaction solution (50 µl) for pre-amplification contained 1 µl ligation mix, 2 µM of each primer with one selective nucleotide, 0.25 mM dNTP mix, 2 mM MgCl2, and 1.25 U DreamTaq polymerase (Fermentas, Lithuania). The PCR reaction was conducted with the Mastercycler gradient thermo cycler (Eppendorf, Germany) at 94°C for 1 min; 25 cycles of 94°C for 30 sec, 55°C for 30 sec and 72°C for 1 min, followed by 10 min at 72°C for final extension. For selective amplification, the PCR solution included 10 µl of 20X diluted pre-amplification product, 0.05 µM of 6-FAM-labelled primer FAM+AG and 0.03 µM of primer T+A or 6-FAM-labelled primer FAM+AC and 0.03 µM of primer T+A as the second amplification, 2 mM MgCl2, 0.2 mM dNTP mix, and 0.2 U DreamTaq polymerase in 20 µl total reaction volume. The PCR reaction was done using the following program: 12 cycles of 94°C for 30 sec; 65°C (-0.7°C/cycle) for 30 sec; 72°C for 1 min; and 24 cycles at 94°C for 30 sec; 56°C for 30 sec; 72°C for 1 min, followed by a final extension step of 10 min at 72°C. PCR products were separated via capillary electrophoresis on an ABI 3130 DNA analyser (Applied Biosystems, UK). Fragment sizes at each locus were determined by comparing the mobility of PCR products with that of a LIZ 500 internal size standard (Applied Biosystems, UK) using GeneMapper software v4.0 (Applied Biosystems, UK). The sequences of adapters and primers used in all stages of AFLP analysis are reported in Table 1. 604 Pyrenophora teres population structure in Lithuania Journal of Plant Pathology (2012), 94 (3), 601-608 Table 1. The sequences of adapters and primers used in AFLP analysis. Adaptors Pre-amplification primers Selective amplification primers Primer name OLIGO41/ A-Top OLIGO42 / A-bottom OLIGO61 / T-Top OLIGO62 / T-bottom OLIGO43 / A00 OLIGO63 / T00 Fam+AG Fam+AC T+A Data analysis. AFLP profiles were scored for presence (1) or absence (0) of bands in the range of 50-500 bp and entered into a binary matrix. Analysis of molecular variance was computed using the software ARLEQUIN version 3.0 (Excoffier et al., 2005) with 1023 permutations. Genotypic data were partitioned into five groups in order to test for genetic variation among and within sampling locations. Shannon’s information index was calculated with PopGene v. 1.32 (Yeh et al., 1997). Indices of multilocus analysis IA and rd (Brown et al., 1980; Maynard Smith et al., 1993) were calculated with MultiLocus v. 1.2.2 (Agapov and Burt, 2000). A similarity matrix was constructed using the Dice coefficient (Dice, 1945) with the NTSYS-pc version 2.2 of software package (Exeter Software, USA) (Rohlf, 2005). Cluster analysis of the matrix values was done by employing the unweighted pair-group method with arithmetic mean (UPGMA) (Sneath and Sokal, 1973) provided in the SAHN program of NTSYS-pc and a dendrogram was produced using Tree Plot. Primer sequence 5’- CTCGTAGACTGCGTACC 5’- TAGGTACGCAGTC 5’- GACGATGAGTCCTGAC 5’- CGGTCAGGACTCAT 5’- CTCGTAGACTGCGTACCTAAT 5’- GACGATGAGTCCTGACCGA 5’-GACTGCGTACCTAATAG 5’-GACTGCGTACCTAATAC 5’-GATGAGTCCTGACCGAA 233 km), while the highest FST=0.298 was observed between Ignalina and Kedainiai sampling sites (geographic distance 143 km). The largest distance between sampling sites in Klaipeda and Ignalina was 317 km, however the population pairwise fixation index was moderate RESULTS The two primer combinations used in the AFLP analyses amplified 207 fragments within the size range of 50-500 bp, 12 (5.8%) of which were common to all P. teres isolates and 12 (5.8%) were identified in single isolates only. One hundred and eighty-three (88.4%) fragments of the 207 identified were polymorphic (0.014 < frequency <1). All AFLP patterns were unique (Table 2). Shannon’s information index values were very similar among individual populations (h=0.66-0.68, Table 2), the average across all loci combined being 0.67. The isolates from the Marijampole population were the most variable based on pairwise differences between isolates and percentage of polymorphic loci, while Shannon’s information index was highest for isolates from the Ignalina population (Table 2). No correlation between pairwise differences and geographic distances between populations was detected (r=-0.05, P=0.88). The lowest pairwise difference (FST=0.047) was estimated between Klaipeda and Birzai sampling sites (geographic distance Fig. 2. Dendrogram of net blotch isolates constructed using UPGMA algorithm based on Dice similarity coefficient. Isolates from different sampling locations are indicated by initials: K, Kedainiai; I, Ignalina; M, Marijampole; B, Birzai; KL, Klaipeda. 0.66 0.67 0.68 0.67 0.66 0.007*** 0.015*** 0.022*** 0.017*** 0.012*** 0.81*** 2.05*** 2.55*** 2.24*** 1.50*** 60.4 71.5 60.4 69.1 63.8 Average of pairwise differences 25.6 39.1 36.1 34.6 33.0 Statkeviciute et al. 605 (FST=0.195). In general, population specific fixation indices between Ignalina and other populations tended to be higher. Multilocus association indices IA and rd differed from zero for all populations (Table 3). Hierarchical analysis of molecular variance (AMOVA) detected most of the variation between isolates within populations (Table 4). The isolates were separated into groups according to their mating types and AMOVA was performed to obtain variation not only between populations but also between mating type groups within populations in order to see if the mating type groups were genetically differentiated. The highest variation (84.2%) was observed within populations and the variation between mating type groups (5.8%) was lower than variation between locations (10.0%) (Table 4). In a dendrogram constructed on the basis of Dice similarity coefficient (Fig. 2) isolates clustered into two major groups, where the first (cluster A) was composed of all isolates from Ignalina and the second (cluster B) comprised all isolates from Kedainiai and 20 isolates from Birzai. Isolates from Klaipeda and Marijampole intermixed in both clusters. The similarity coefficient between the two clusters reached 0.74. Although some clustering according to the sampling location could be observed, and genetic variation between tlocations was significant (P<0.001), however most variation was detected within the field populations. Population-specific fixation indices were similar in all locations, 0.131 on average. Genetic variation between the mating types within field population was also significant (P<0.001), however fixation index was low (0.064). 125 148 125 143 132 DISCUSSION Kedainiai 29 29 Marijampole 30 30 Ignalina 30 30 Klaipeda 28 28 Birzai 28 28 ***P < 0.001 for IA and rd, number of permutations 1000 Polymorphic loci (%) Polymorphic loci (N°) Haplotypes Isolates Population name Table 2. Population specific molecular diversity estimates and multilocus association indices of Pyrenophora teres populations. IA rd Shanon’s index Journal of Plant Pathology (2012), 94 (3), 601-608 AFLP markers were used to determine the genetic structure among and within P. teres f. teres populations, sampled at five geographically distant fields, representing various spring barley growing areas of Lithuania. AFLP technology has some limitations due to dominant nature of markers, but it has some important advantages as well, i.e. markers are highly reproducible, no sequence information of the research object (genomic DNA) is needed, high numbers of the markers can be generated allowing discrimination among closely related individuals (Majer et al., 1996). This made AFLP a method of choice in many P. teres studies (Lehmensiek et al., 2010; Leisova et al., 2005; Rau et al., 2003; Serenius et al., 2005, 2007). AseI and TaqI restriction enzymes were previously employed in AFLP to study genetic diversity of the oilseed rape pathogen Leptosphaeria maculans, and proved to be reliable, providing good repeatability and high numbers of markers (Brazauskiene et al., 2011). Previous studies have investigated the genetic variation of P. teres isolates collected from different regions 606 Pyrenophora teres population structure in Lithuania Journal of Plant Pathology (2012), 94 (3), 601-608 Table 3. Population pairwise fixation indices and distances between sampling locations. Kedainiai Marijampole Ignalina Birzai Klaipeda Kedainiai – 110 km 143 km 106 km 178 km Marijampole 0.128*** – 209 km 212 km 202 km Ignalina 0.298*** 0.144*** – 118 km 317 km Birzai 0.079*** 0.051*** 0.153*** – 233 km Klaipeda 0.074*** 0.063*** 0.195*** 0.047*** – ***P ≤ 0.001, number of permutations 1023 all over the world (Bakonyi and Justesen, 2007; Campbell et al., 2002; Jonsson et al., 2000; Lehmensiek et al., 2010; Rau et al., 2003; Leisova et al., 2005; Serenius et al., 2007). Different numbers of isolates representing one field population have been used in different investigations, ranging from 2 to 52. Unequal sample sizes are common in population surveys, making comparison between the results from different studies more difficult. Similar size populations samples were used in this study (28-30 per field) to minimize the discrepancies. Unique AFLP patterns were obtained for all isolates. This is in line with the findings by Jonsson et al. (2000), where every P. teres isolate had a unique RAPD pattern, moreover, the number of unique isolates in the current study was much higher compared with P. teres isolates from Italy (Rau et al., 2003) and Finland (Serenius et al., 2005, 2007). Our results are therefore in concordance with those obtained by ISSR marker analysis in a study on net blotch population genetic structure (Statkeviciute et al., 2010), where a high percentage of variation (88.8%) was found within field population, and further support previous investigations on P. teres population structure (Jonsson et al., 2000; Lehmensiek et al., 2010; Rau et al., 2003; Serenius et al., 2005). Shannon’s information index revealed a higher gene diversity for five PTT populations from Lithuania (mean values ranging from 0.66 to 0.68), than that reported by Serenius et al. (2007) who obtained the high- est index (0.33) for a PTT population from Krasnodar (Russia). Shannon’s indices obtained for Sardinian net blotch populations by Rau et al. (2003) were also lower, ranging from 0.03 to 0.086. P. teres field populations comprised of both MAT1 and MAT2 mating types were present in all sampling locations, making sexual reproduction possible. The variation percentage between mating types within population was small but significant (5.76, P<0.0001), fixation index (0.064) was also highly significant (P<0.0001). Multilocus association indices IA and rd showed that PTT populations were in linkage disequilibrium. These findings suggest that sexual reproduction might take place but is unlikely to play a major role in P. teres populations. Various results have been reported in other studies, either supporting or rejecting the hypothesis of random mating in P. teres populations (Bogacki et al., 2010; Rau et al., 2003; Serenius et al., 2007). Even though high genetic diversity within field populations and equal distribution of mating types was established, multilocus association indices significantly differed from zero, rejecting the hypothesis of random mating in Finnish PTT populations (Serenius et al., 2007) as well as for P. teres populations from South Australia (Bogacki et al., 2010). However, random mating was observed in Sardinian PTT populations (Rau et al., 2003). The genetic structure of plant pathogen populations can be used to predict how rapidly a pathogen can Table 4. Analysis of molecular variance (AMOVA) between and within populations. Source of variation Between locationsb Within populations Degrees of freedom 4 140 Variance components 2.54 16.86 Sum of squares 362 2360 Percentage of variation 13.09 86.91 Between locationsc 4 1.95 362 10.04 Between mating types within 5 1.12 155 5.76 populations Within populations 135 16.34 2205 84.21 a Probability of a larger value obtained by chance, determined by 1023 permutations b Mating types were not considered c Isolates were separated into groups within a location according to their mating types. Fixation indices 0.131 <0.0001 0.100 0.002 0.064 <0.0001 0.158 <0.0001 Probabilitya Journal of Plant Pathology (2012), 94 (3), 601-608 evolve and overcome host resistance or adapt to chemical control (Lehmensiek et al., 2010; Serenius et al., 2007). Pathogens with high population genetic diversity and sexual reproduction system pose greater risk for overcoming both biological and chemical control measures (McDonald and Linde, 2002). No evidence of sexual reproduction in P. teres field populations was found in this study despite the fact that both mating types coexist in close proximity but high intra-population diversity based on gene diversity indices was estimated. Other factors, like high mutation rate, large population size or retrotransposons can create a large genetic diversity in asexually reproducing populations (McDonald and Linde, 2002). However, DNA markers are unlike to identify variation at the pathotype level (Lehmensiek et al., 2010). The information about P. teres population structure in countries neighboring Lithuania is scarce. High genetic diversity and no evidence of random mating were found in Finnish PTT populations (Serenius et al., 2007). Low genetic diversity based on previous virulence studies was expected in Finland (Serenius et al., 2005), in constrast to Lithuania, where a high pathotype diversity was identified (Skurdeniene, 2000). Higher population gene diversity indices and lower fixation indices between populations in Lithuanian P. teres populations in comparison to Finnish populations (Serenius et al., 2007) could indicate the possibility of local pathogen populations to evolve and adapt to disease control measures faster. Variation between sampling locations wavers, as it depends on many factors, including the distance between locations as well as the P. teres form. Higher genetic divergence has been detected for P. teres f. teres populations than for P. teres f. maculata (Rau et al., 2003; Serenius et al., 2007; Lehmensiek et al., 2010). Leisova et al. (2005) maintain that variability is more influenced by the year of sampling than the geographic origin of the isolate. No specifity for geographic location was detected using AFLP markers among isolates of Botrytis cinerea in Lithuania (Valiuskaite et al., 2010). Therefore, genetic differentiation might not correlate with distance between locations. Many factors may influence the genetic diversity within and between crop pathogen populations, including agronomical practices, environmental conditions and the source of initial inoculum. P. teres field population from Ignalina region differed from other populations based on population specific FST indices. The Ignalina region covers large part of the Aukstaitija National Park, therefore it is mostly used for recreation and nature conservation purposes, agricultural activities are limited and the area of arable land is significantly lower compared to other regions where net blotch samples were collected. Moreover, farm-saved seed is widely used by the farmers in the region. These factors might have determined genetic differentiation of the Ignalina PTT population. Similar results were obtained for oilseed Statkeviciute et al. 607 rape pathogen L. maculans in a study by Brazauskiene et al. (2011) who hypothesized that the absence of nearby inoculum and the use of farm-saved seed limited the spread of the pathogen in the similar Varena region. In summary, this is the first wide-scale study of P. teres population structure in Lithuania based on AFLP markers, revealing high genetic diversity within field populations of PTT isolates from different locations in the country. Small but significant percentages of variation between mating types within populations indicates that sexual reproduction might occur occasionally but asexual reproduction dominates. ACKNOWLEDGEMENTS The study has been supported by the Research Council of Lithuania (grant No. PMK-07). Authors thank dr. D. Baniulis (Institute of Horticulture, LRCAF, Lithuania) for assistance in AFLP analysis. REFERENCES Afanasenko O., Mironenko N., Filatova O., Kopahnke D., Krämer I., Ordon F., 2007. Genetics of host-pathogen interactions in the Pyrenophora teres f. teres (net form)-barley (Hordeum vulgare) pathosystem. 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