Tree Genetics & Genomes (2014) 10:703–710 DOI 10.1007/s11295-014-0715-3
ORIGINAL PAPER
Ex situ conservation of underutilised fruit tree species: establishment of a core collection for Ficus carica L. using microsatellite markers (SSRs) F. C. Balas & M. D. Osuna & G. Domínguez & F. Pérez-Gragera & M. López-Corrales
Received: 10 April 2013 / Revised: 1 October 2013 / Accepted: 20 February 2014 / Published online: 20 March 2014 # Springer-Verlag Berlin Heidelberg 2014
Abstract Ex situ germ plasm collections of woody crops are necessary to ensure the optimal use of plant genetic resources. The fig tree (Ficus carica L.) germ plasm bank, consisting of 229 accessions, is located in Centro de Investigación ‘La Orden’. Despite great progress in conservation, ex situ collections face size and organization problems. Core collections obtained from structured samples of bigger collections are a useful tool to improve germ plasm management. In this work, we used simple sequence repeat (SSR) markers to establish a core collection in this underutilised Mediterranean fruit tree species. Four approaches have been carried out (random sampling, maximization, simulated annealing and stepwise clustering) to determine the best method to develop a core collection in this woody plant. The genetic diversity obtained with each subset was compared with that of the complete collection. It was found that the most efficient way to achieve the maximum diversity was the maximization strategy, which, with 30 accessions, recovers all the SSR alleles and does not show significant differences in allele frequency distribution in any of the loci or in the variability parameters (HO, HE) between the whole and core collections. Thus, this core collection, a representative of most fig diversity conserved in the germ plasm bank, could be used as a basis for plant material exchange among researchers and breeders.
Keywords Ex situ conservation . Germ plasm bank . Fig tree . SSRs . Core collection . Plant genetic resources Communicated by A. Dandekar F. C. Balas : M. D. Osuna : G. Domínguez : F. Pérez-Gragera : M. López-Corrales (*) Centro de Investigación ‘La Orden’, Autovía A-5 Km 372, 06187, Guadajira Badajoz, Spain e-mail:
[email protected]
Introduction For the last 40 years, a great progress in collection and conservation of plant genetic resources has occurred; so much that, currently, some germ plasm banks face size and organization problems. Ex situ germ plasm collections have been developed in different crop species to preserve and promote the utilisation of plant genetic resources in agriculture (Diwan et al. 1995). However, a few collections have grown to the point that it is difficult to use and preserve the genetic diversity they contain, going against the objectives for which they were created (van Hintum et al. 2003). The management, evaluation and use of large germ plasm collections is expensive and inefficient as a result of redundancies and duplications and the impossibility of analysing in detail the accessions conserved (Grenier et al. 2000; van Hintum et al. 2003). As a consequence, the long-term conservation of collections can be en dangered. Nevertheless, collection management can be significantly improved if regeneration, characterization and evaluation steps are focused on a subset of individuals that represent the diversity conserved in the whole germ plasm collection (Escribano et al. 2008). For this reason, core collections began to be created, defined as a subset of a larger germ plasm collection that maximizes the possible genetic diversity of a crop species with minimum redundancy. Given that there is minimal similarity between entries, they are smaller and represent the genetic diversity of the whole collection. Core collections do not replace the whole collections from which these are obtained but allow to optimize their management (Frankel and Brownn 1984). A core collection is a structured sample of a bigger collection, with an easier-to-handle size that is a reference subset that automatically highlights priorities that require attention for decision-making (van Hintum et al. 2003).
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Since Frankel (1984) presented his proposal, an extensive bibliography on the theoretical and practical establishment of core collections has appeared (Brown et al. 1987; Erksine and Muehlbauer 1991; Boukema and van Hintum 1994; Anderson et al. 1996; Zhang et al. 2000; Potts et al. 2012; Belaj et al. 2012), and these have been accepted as an effective tool to improve the use and conservation of plant genetic resources collections. The global plan of action for the conservation and sustainable utilisation of plant genetic resources for food and agriculture recommends core collections as a necessary activity to progress the use of genetic resources (FAO 1996). This need is imperative in some underutilised woody species collections that inhabit wildly in most of the main producer areas, where synonyms and homonyms are especially common due to easy vegetative propagation. For these tree crops, as fig, the cost of maintaining unwanted duplicate genotypes in ex situ germ plasm collections can be a limiting factor (Giraldo et al. 2008a). This makes appropriate germ plasm characterization and diversity studies necessary to optimize fig genetic resources management (López-Corrales et al. 2012). There are some strategies for creating a core collection, and since this is the first core collection created for a fig germ plasm bank, the use of different approaches is needed to ensure the appropriate number of accessions that maintain representation of the genetic diversity of the entire collection. Ancient tradition in this crop cultivation in association with soil and climate adaptability and the enormous ability of vegetative reproduction of fig that promotes dispersion and crop use has resulted in a huge varietal diversity from a tight genetic pool, not only located in the Mediterranean Basin (Kislev et al. 2006) but also in other places as different as the USA, South Africa or China (FAOSTAT 2013). As an example, Condit (1955) described more than 600 cultivars of fig. With the purpose of conserving and studying this diversity, a fig germ plasm bank was established in Centro de Investigación ‘La Orden’ (Badajoz, Spain), where, since 2007, 416 fig accessions from different areas of the world have been characterized. Both morphological and molecular protocols have been used: International Union for the Protection of New Varieties of Plants (UPOV) descriptor for fig (data not published) and microsatellites (SSRs), respectively. Besides genotypic and phenotypic parameters, passport information was taken into account (López-Corrales et al. 2012). After these primary characterizations, many homonyms and synonyms were found, and almost 45 % of accessions were discarded. Finally, 229 varieties were identified as unique and were joined in the bank (data not published). Common fig Ficus carica L., 2n=26, belongs to Moraceae family, composed by more than 1,400 species and classified into about 40 genera (Watson and Dallwitz 2004). The genus Ficus L. includes more than 700 species, mainly spread in the
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tropics (Berg 2003). This fruit species seems to be evolved from F. carica var. rupestris, which spread throughout the Mediterranean Basin before being domesticated, with various simultaneous selection focuses in this area (Khadari and Kjellberg 2009). The use of this plant by humans is ancestral, and there are archaeobotanical evidences that indicate the use of parthenocarpic fruits and the possible culture of fig 14,000 years ago, showing that fig was the first domesticated crop of the Neolithic Revolution (Kislev et al. 2006). Common fig is a gynodioecious species with two different morphs: female trees that produce syconia with female flowers that will develop into edible seeded figs (syconia with multiple one-seed fruit or drupelets) and caprifigs that produce syconia with male and female flowers with a shorter style than the ones of female trees. Pollen is only produced by caprifigs, so the reproductive system is functionally dioecious (Kjellberg et al. 1987). Three types of female figs are grown commercially: the common type that develops fruit parthenocarpically, the Smyrna type that requires pollination with pollen from caprifigs (caprification) to develop fruit and the San Pedro type that produces a first crop (breba) parthenocarpically and a second crop (fig) only after pollination. Common-type figs can produce one (unifera types) or two crops (bifera types) (Flaishman et al. 2008). Mediterranean countries continue being the main producers (Turkey, Egypt, Algeria, Morocco, Spain), although this crop has extended throughout other mild weather regions of the world (USA, Brazil, Japan). Global production is >1 million tonnes, with an increasing international trade of US$800 million per year (FAOSTAT 2013). Therefore, the aim of this study is to establish a core collection from the fig germ plasm bank maintained in Centro de Investigación La Orden by using molecular markers (SSRs) and four different approaches to optimize the conservation and use of this ex situ collection.
Materials and methods Plant material Two hundred twenty-nine unique fig varieties maintained in Centro de Investigación La Orden fig germ plasm bank have been fully evaluated following the characterization protocol designed by this group (López-Corrales et al. 2012). These cultivars not only include Spanish accessions but also others from different regions abroad (Table 1). Genomic DNA extraction, PCR and electrophoresis Molecular characterization was performed from genomic DNA extracted from fresh fig leaves using a commercial DNA extraction kit (Biotools, Madrid, Spain), amplified by
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Table 1 Origin of fig accessions maintained in Centro de Investigación La Orden fig germ plasm bank
Table 2 Locus name and sequence of nine polymorphic microsatellites used to manage fig germ plasm
Country
Number of accessions
SSR
Sequence (5′–3′)
1 1 2 195
LMFC15
F: CGGAGAAAGATTTAGAATTTG R: ATTCCAGAGACGAAAGGTCT F: TTAAGAATACGTCCTTGGTAT R: GAGATTTCGTTGACTTCATT
Region
Argentina Cyprus Egypt Spain Andalusia (7) Aragon (5) Balearic Islands (68) Canary Islands (14) Castile and León (2) Castile-La Mancha (4) Catalonia (29) Extremadura (57) Valencian Community (9) Ethiopia France Greece Israel Italy Libya Portugal Turkey USA
LMFC17 LMFC19 LMFC23 LMFC24 LMFC30 LMFC32
1 12 4 2 1 1 4 2 3
MFC1 MFC2
F: CTTATGAAAACTCGGTAGAAG R: AATGAATGGAAATGATCTTG F: TTTCGTGTCTAACGATCAAAAA R: CTCCCATCTCCAACTCCATC F: ACTTCTTCATATTTGGTATAGG R: TTCATAAACTGGTCTAAAAGA F: TTGTCCGTTTCTTATACAAT R: TCTTTTTAGGCAGATGTTAG F: GAAAGAAAGTCGAATAATGTA R: TATAAAGAGGGTGGTCTTAGT F: ACTAGACTGAAAAAACATTGC R: TGAGATTGAAAGGAAACGAG F: GCTTCCGATGCTGCTCTTA
2005): number of alleles per locus (A), allele frequency (Fr), expected heterozygosity (HE) and observed heterozygosity (HO). Construction of the core collection
PCR (Bio-Rad T100 Thermal Cycler, Hercules, CA, USA) using the nine pairs of microsatellite primers SSR: LMFC15, LMFC17, LMFC19, LMFC23, LMFC24, LMFC30 and LMFC32 (Giraldo et al. 2005) and MFC1 and MFC2 (Khadari et al. 2001) (Table 2) following conditions described by Giraldo et al. (2008b). The suitability of these nine SSRs to discriminate between accessions was proven by Giraldo et al. (2005, 2008a), who developed a genomic library of 20 primers for this species, concluding that this set of nine primers is an optimal tool for fig research. Size estimation of amplified fragments was carried out using a DNA analyser (4300 DNA Analyser; Li-Cor, Lincoln, NE, USA). Germ plasm bank establishment Data obtained from characterization was analysed with NTSY Spc version 2.2 software (Exeter Software, Setauket, NY, USA) from which a dendrogram based on Unweighted Pair Group Method with Arithmetic Mean (UPGMA) method was constructed (Nei and Li 1979). Data analysis Genetic diversity parameters were analysed in the complete collection using Arlequin 3.5.1.2 software (Excoffier et al.
Four approaches were used to construct the core collection. Five or six subsets per approach, with a different number of entries, were determined to establish the optimal core collection size. 1. Three methods do not based on the UPGMA method dendrogram of the entire collection: Random sampling (R) The accessions forming the subsets were selected from the whole collection by random sampling without replacement. This strategy assumes no prior knowledge about the original collection, except for its total size. Subsets of 10, 20, 30, 40 and 50 accessions were selected randomly. M strategy (M) (Schoen and Brown 1995) M strategy was performed using MSTRAT software (Gouesnard et al. 2001). This strategy maximizes the number of alleles sampled at each locus, and the redundant accessions are eliminated by an iterative process. The programme uses an algorithm based on an iterative maximization procedure. First, a subset of accessions is selected randomly from total accessions of the entire collection. Next, each
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accession is deleted in turn, and the subset that maintains the highest level of allele richness is retained. In the second step, the accession that introduces the greatest increase in the diversity criterion in the subset is retained (Escribano et al. 2008). In this case, Nei’s diversity index (Nei 1987) is used as the second optimization criterion. Both steps are repeated until the richness of the subset is no longer improved. In this approach, subsets of 10, 20, 30, 40 and 50 accessions were chosen, plus the optimized subset given by the algorithm, composed by 15 accessions. Simulated annealing algorithm using the Core Set function in PowerMarker version 3.25 software (Liu and Muse 2005) under the criterion of maximizing allelic richness (SA) From the complete set of accessions, a subset of accessions is chosen at random. Each accession has a weighted base on the number of its specific alleles. Next, additional accessions are selected from the remaining ones, based on their weights and swapped with the original subset. The number of alleles (SA) is then evaluated, and the new set is accepted if SA is increased. Swapping is continued for a predefined number of itinerations. As in the M strategy, subsets of 10, 20, 30, 40 and 50 accessions and an optimized subset of 62 accessions were selected. 2. One method based on a dendrogram of 229 genotypes generated with similarity values obtained from analysis with SSR markers:
above for all the loci with the whole germ plasm bank using Arlequin version 3.5.1.2 software (Excoffier et al. 2005). Nonparametric statistical procedures were performed to determine if diversity among the different core sets represents the diversity of the complete collection. Thus, to determine the number of loci with significantly different allele frequencies, each locus was analysed independently, comparing the Fr of each allele at each locus between the entire collection and each core subset using the chi-squared test (Escribano et al. 2008). These comparisons were repeated with each subset size for the four approaches performed using SPSS software (IBM SPSS, Chicago, IL, USA). Validation of the core collection The selection of the best subset was carried out according to diversity parameters, and the representativity of the core collection was validated according to the following criteria (Escribano et al. 2008): 1. Recovery of all the SSR alleles, A, present in the whole collection. 2. Absence of significant differences in allele frequency, Fr, distribution in at least 95 % of the loci between the core and whole collections. 3. No significant differences in variability parameters, HE and HO, between the core and whole collections.
Results Stepwise clustering with random sampling (S) according to Hu et al. (2000) In this strategy, for each pair of accessions clustered in the dendrogram, one of them is chosen at random for the core subset; when the cluster is formed by a single accession, this one is also selected. Then, another dendrogram is performed with the selected accessions, and the process is repeated until the desired subset size is obtained. Because the number of individuals in each group varies according to the topology of the dendrogram generated, the sizes of the subgroups do not correspond with intervals of 10 individuals but are approximates to the 10 interval: 15, 22, 36, 49 and 70.
Characterization of the subsets and comparison to the entire collection Following the methods described to develop the different core subsets, these subsets obtained were compared with the complete collection based on diversity parameters as described
Microsatellite diversity Based on SSR data (44 alleles for the nine loci), the analysis of the 229 accessions revealed a total of 44 alleles, with an average of 4.89 fragments/SSR. The number of alleles ranged from 2 (LMFC23) to 9 (LMFC30). The HO ranged from 0.17 at the LMFC23 locus to 0.79 at the MFC1 locus, with a mean of 0.51. Expected heterozygosity ranged from 0.16 in LMFC23 to 0.86 in LMFC30. Sampling of core collections It was studied that the performance of three methods was not based in the UPGMA dendrogram (M, SA and R strategies), and one was based on the dendrogram (S strategy) (see “Materials and methods” for details) to built core collections from a total data set of 229 unique genotypes. The performance of these sampling strategies for assembling core collection was studied over a range of putative core collection (sample) size. For each sample size, the performance of each strategy was compared with the whole
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collection. The results of the variability parameters obtained from the different subsets compared with the whole collection are shown in Table 3. When comparing the several strategies to capture the maximum genetic diversity, with the M strategy, the total number of alleles of the whole collection is recovered with a minimum of 15 accessions, while the other strategies do not reach that value even with 50 accessions. This shows that the sampling efficiency of the M strategy was superior to the others. Under the optimal size of a core collection capturing all the 44 alleles, the subsets obtained with the different strategies were classified according to their observed and expected heterozygosity (analogous to Nei’s gene diversity index). With the subset of 15 accessions, three loci showed significantly different allele frequencies compared with the whole collection (Table 3). The smallest collection that satisfies all the validation parameters was the one of 30 accessions with the M strategy. In the total number of alleles recovered in the core with 30 accessions, the HE and HO were similar to those of the entire collection. Regarding the allele frequencies, the chi-square tests showed that the core subset of 30 had a similar Table 3 Variability parameters for the different core subsets compared with the whole collection Methods
Collection size
A
HE
HO
Fr
Whole collection Random
229 10 20 30 40 50 10 15 20 30 40 50 10 20
44 30 38 38 39 41 43 44 43 44 44 44 33 36
0.50 0.53 0.53 0.50 0.53 0.52 0.65 0.62 0.56 0.53 0.54 0.52 0.61 0.52
0.51 0.58 0.50 0.48 0.54 0.51 0.59 0.67 0.55 0.50 0.53 0.53 0.49 0.52
0 1 0 0 1 0 5 3 1 0 1 1 3 0
30 40 50 62 15 22 36 49 70
36 39 41 41 36 39 42 43 42
0.52 0.51 0.52 0.51 0.59 0.62 0.59 0.58 0.56
0.50 0.50 0.50 0.50 0.54 0.59 0.59 0.56 0.53
2 0 1 0 3 3 3 2 1
Maximization strategy
Simulated annealing
Stepwise clustering
A number of alleles, Fr number of loci with significantly different allele frequencies compared with the whole collection by chi-squared test (P
