Introduction

Grape (Vitis vinifera L.), one of the earliest domesticated fruit, is a highly valuable crop, especially for the derived products wines and spirits. In Italy, grapevines are extensively cultivated, with about 800,000 ha of vineyards; however, their distribution in the country is unequal, with Sicily representing one of the most significant wine regions. Wine and vine have accompanied the history of Sicily since its beginning; it is believed that wild vines were present in Sicily far earlier than Greeks colonized the island (Costantini 1989). The long history of cultivation has led to a high number of autochthonous grapevine varieties, which represents an important source of genetic diversity. Accordingly, Sicilian varieties are characterized by high variability (Carimi et al. 2010), having been subjected to mass selection by growers for centuries.

‘Nero d’Avola’ is currently the most important and diffused red berry cultivar in Sicily, with 15% of the vineyard surface, and this ancient variety is utilized for the production of wines exported worldwide, among which several DOC wines such as Etna Rosso and Cerasuolo di Vittoria. The cultivar, also known as ‘Calabrese’ was clearly described by Cupani (1696) in the seventeenth century with the name ‘Calaurisi’; however, its presence in Sicily is known since ancient times. The history and origin of this variety have been mixed with that of ‘Nerello’ and ‘Calabrese’ groups. The name, as reported by Geremia (1839), suggests a Calabrian origin, but no real evidence supports this theory. An alternative hypothesis, shared by some authors (Di Rosa and Gringeri 1966), derives instead its dialect name ‘Calaulisi’ after Caia-Avola (grape of Avola). In any case, ‘Nero d’Avola’ was originally cultivated near Avola (a small town in south-eastern Sicily) several hundred years ago.

Grapevine varieties are asexually propagated in order to maintain their distinctive individual characters. Phenotypic variations among clones belonging to the same cultivar may be due to somatic mutations in the genomes, accumulated during several vegetative propagation cycles. Therefore, in many cases, a cultivar comprises several different populations of clones, sharing a panel of similar phenotypic traits. On the other hand, homogeneity within a variety is often crucial to nursery and wine industries, thus it is rather important to be able to estimate intra-varietal variability and select for different clones. Moreover, the characterization and identifications of clones is an important step for disease control and breeding programs.

The identification of grapevine varieties has traditionally been based on ampelography, the description of morphological plant traits during different stages of development. However, this technique is heavily affected by environmental constraints, and often leads to false attributions such as homonyms and synonymies, especially when used within one cultivar.

The advent of molecular markers in the ‘80s, especially those PCR-based such as microsatellites (also called Simple Sequence Repeat, SSR), has revolutionized cultivar identification and parentage analysis of grapevines. In addition, a large number of potential polymorphic sequences are currently available for grapevine molecular analysis. By using microsatellites it is possible to characterize and compare genotypes independently from phenotype. Microsatellite markers are highly polymorphic, which allows the creation of unique genetic profiles for all grapevine cultivars. Moreover, they are inherited in a co-dominant Mendelian fashion, thus allowing the analysis of parentage. In spite of the high number of studies discriminating among varieties, reports for clonal identification within a single cultivar are limited (Regner et al. 2000; González-Techera et al. 2004; Moncada et al. 2005, 2006; Regner et al. 2006; Moncada and Hinrichsen 2007).

In the present paper, 22 nuclear microsatellites were utilized to assess the intra-varietal genetic diversity in samples of ‘Nero d’Avola’ collected in different vineyards located in three provinces of Sicily. For the first time, genetic diversity within the ‘Nero d’Avola’ variety is reported.

Materials and methods

Plant material

Plant material was collected in vineyards located in three provinces of Sicily (Syracuse, Caltanissetta and Trapani). A total of 30 vineyards were sampled, with 14 from Syracuse, 8 from Caltanissetta and 8 from Trapani. The oldest vineyards (more than 40 years) were located in Syracuse (SR-1, SR-2, SR-3 and SR-4), while all the other vineyards were between 10 and 30 years old (Table 1). Ten different plants were sampled for old vineyard whilst, in the other vineyards, 3 plants were chosen. Plants collected in the same vineyard were identified by different capital letters. A total of 118 plants were sampled for DNA extraction and microsatellite analyses (Table 1).

Table 1 List of vineyards/plants analyzed at 22 SSR
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DNA extraction and microsatellite analysis

Total genomic DNA was extract from young leaves or inner wood from young cuts and stored at −80°C until used. The extraction was carried out by the Doyle and Doyle (1990) CTAB method and DNA was quantified in 1% agarose gels.

Microsatellite analysis was based on 22 markers, 13 of VVMD series (#5, 7, 17, 21, 24, 25, 26, 27, 28, 31, 32, 34 and 36), 8 of VrZAG series (#7, 12, 15, 21, 25, 62, 67 and 79) and VVS2 (Table 2) as proposed by the GENRES 081 Project (European Vitis Database, www.genres.de/vitis/vitis.htm). The forward primer of each marker was labeled with one of the three unique ABI PRISM fluorescent dyes: 6-FAM, JOE, TAMRA. PCR amplification was performed using the Qiagen multiplex PCR kit with the following conditions for all markers: 5 min at 95°C (HotStarTaq DNA Polymerase activation step) followed by 35 cycles consisting of 60 s at 94°C (denaturation), 60 s at 50–56°C (annealing temperatures, Table 2), 2 min at 72°C (extension). In the last cycle, extension time at 72°C was increased to 7 min. Each sample was amplified at least twice to correct for possible mistyping or amplification errors. PCR products were size-separated by capillary electrophoresis performed on a genetic analyzer (ABI Prism 3130, Applied Biosystems, Inc.).

Table 2 List of microsatellite loci analyzed
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Data analysis

For each microsatellite locus, several parameters were analyzed: the number of alleles per locus (n) and their frequency; the observed (Ho) and expected (He) heterozygosities; the screening ability, based on the probability of identity (PI) (Paetkau et al. 1995) and the polymorphic information content (PIC) (Weber 1990) calculated as reported in Carimi et al. (2010). The best microsatellites were those displaying high Ho and PIC and low PI values. Allele frequencies, He and Ho, estimated frequency of null alleles (r), and PI were calculated with the software IDENTITY (Wagner and Sefc 1999, version 1.0; Centre for Applied Genetics, University of Agricultural Sciences, Vienna). Finally, the discrimination power (Dj) of each microsatellite was calculated according to Tessier et al. (1999):

$$ {\text{D}}_{\text{j}} = 1 - {\text{C}}_{\text{j}} = 1 - \sum\limits_{\text{i = 1}}^{\text{I}} {{\text{c}}_{\text{i}} } = 1 - \sum\limits_{\text{i = 1}}^{\text{I}} {{\text{p}}_{\text{i}} \frac{{\left( {{\text{Np}}_{\text{i}} - 1} \right)}}{{{\text{N}} - 1}}} $$

where ci and Cj are the confusion probability for the ith genotype of the given jth microsatellite and the confusion probability for the jth microsatellite, respectively; pi is the ith genotype frequency; N is the number of individuals analyzed.

Based on microsatellite data, Simple Matching (SM) coefficients were estimated with the SIMQUAL program from NTSYS-pc ver. 2.02j package (Exeter Software, Setauket, NY). A dendrogram was generated by using SM coefficient and the unweighted pair group method with arithmetic mean (UPGMA) cluster analysis of the similarity using the SAHN-clustering and the TREE program of the NTSYS-pc ver. 2.02j package (Rohlf 1998). Total genetic diversity of Nei (Ht) (Siegfried 2000) and Shannon’s index (S) (Gryta et al. 2005) were used to summarize the data and their standard deviations. All calculations and analyses were conducted using POPGENE program version 1.31 (Yeh et al. 1999). The genetic relationship among ‘Nero d’Avola’ clones was further investigated by principal coordinate analysis (PCoA) by using the GenAlEx 6 program (Peakall and Smouse 2006).

Results and discussion

The molecular analysis on 118 plants of ‘Nero d’Avola’ (coming from 30 different vineyards) showed that 11 out of 22 loci (50%) were polymorphic and revealed 15 different genetic profiles. Polymorphic and common genetic profiles are reported in Table 3. Polymorphisms at different loci were observed among vineyards and in two cases (SR-2 and SR-3) genetic differences were detected within the same vineyard (Table 3). In particular, the vineyard SR-2 showed three different genetic profiles (VIII, IX, XV), with one (profile VIII) characterizing eight out of ten plants; in the same way, the genetic profile X was prevalent in vineyard SR-3 (nine out of ten plants). The most frequent genetic profile (I) was carried by samples coming from all provinces and seems to be the main genetic profile describing the ‘Nero d’Avola’ cultivar group.

Table 3 Genetic profiles of each vineyard (reported in bold)/plant (alphabetic list in bracket) at 11 polymorphic microsatellites analyzed
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The 11 polymorphic loci led to the detection of a total 27 polymorphic alleles (Table 4). The main genetic parameters such as expected (He) and observed (Ho) heterozygosity, estimated frequencies of null alleles (r), probability of identity (PI), polymorphic information content (PIC), Shannon’s index, Nei’s genetic diversity are reported (Table 4).

Table 4 Genetic parameters at the 11 polymorphic microsatellite loci
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The number of observed alleles per locus was not high, as expected for clones of the same variety. In particular, the highest information content was provided by locus VVMD26 with four different alleles, while only three were observed in VVMD5, VVMD28 and VVMD36; only two alleles were observed for seven microsatellites (VVMD17, VVMD31, VrZAG7 VrZAG21 VrZAG25, VrZAG62 and VrZAG67) (Table 4). The observed heterozygosity (Ho) ranged from 0.03 for VVMD31, VrZAG21 and VrZAG67 to 1 for VVMD36. The mean of Ho was 0.471, slightly higher than expected by random sorting of gametes (0.326). This result agrees with what reported by Sefc et al. (2000), where a certain level of heterozygosity is explained as a consequence of both natural and human selection against homozygosity in grapevine plants. Estimated frequency of null alleles (r) was negative for ten out of 11 loci. However, r parameter developed by Brookfield (1996) estimates null allele frequency in the case of panmixya rather than breeding selection (Costantini et al. 2005). The probability of identity (PI) ranged from 0.075 to 0.942; the highest value was provided by loci VVMD31, VrZAG21 and VrZAG67. However, the probability to find different plants with the same profile at whole loci was low (PI = 6.35 × 103).

The major discrimination power (Dj) was found for locus VVMD26 (71%) and the lowest (6%) for loci VVMD31, VVMD36, VrZAG21 and VrZAG67, with a mean of 30% for all loci, indicating a low efficient discrimination of the genotypes, as expected for a single cultivar. Finally, an additional measure of genetic variation was obtained using Shannon’s index (S) and Nei’s genetic diversity (Ht) with means of 0.53 and 0.32, respectively.

The dendrogram of genetic relationships among the clones, based on simple matching (SM) coefficient and UPGMA algorithm, showed 15 different genetic profiles clustered in three groups and one outgroup, as reported in Fig. 1. The most abundant and diffuse genetic profile (I) was present in 14 vineyards and suggests a genetic relationships among plants of ‘Nero d’Avola’ sampled in different areas of Sicily. A larger group including about 60% of plants sampled, coming from 21 vineyards, showed >85% of similarity (profiles I–IV). The genetic profile II differed from I only for one allele at locus VrZAG62 (183:183 instead of 183:199), while the genetic profile III differed from I for one allele at locus VVMD26 (245:251 instead of 245:247) and one at locus VVMD28 (239:251 instead of 231:239). Profiles I and IV differed at the three loci VVMD5, VVMD26 and VVMD28. Moreover, four vineyards of Caltanissetta (showing profile V, VI and VII) were sorted in a second small group rather close to the main one. This observation suggests a similar origin of the samples harbouring profiles I–VII with the presence of limited events of mutation.

Fig. 1
figure 1

Dendrogram of genetic relationships among 118 plants, sampled in 30 different vineyards, based on 11 genomic microsatellites. Genetic distances were estimated by simple matching (SM) coefficient and UPGMA cluster analyses were adopted using the SAHN-clustering and TREE program of the NTSYS-pc ver. 2.02j package (Rohlf 1998). The three main groups and the genetic profiles (from I to XV) are shown (plants with different genetic profiles but coming from the same vineyards are indicated with asterisks)

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Plants collected in the oldest vineyards (SR-2, SR-3 and SR-4, more than 40 years old) together with two vineyards of Trapani (TP-4 and TP-5) differed from the major group (profiles I, II, III and IV) described above, with a similarity level of 0.79 (Fig. 1). Only one plant (SR-2B), sampled in the oldest vineyard of Syracuse (SR-2) seemed to be an outgroup, clearly distinguished from the other clusters, displaying a heterozygous status at three loci (VVMD31, VrZAG21 and VrZAG67) in contrast with any other plants (Table 3) and a diallelic status at the locus VVMD36 (alleles 264:268), different from the genotype (244:268) observed in all other plants. This sample (SR-2B), collected in the most ancient vineyard (48 years old), might even not be a real ‘Nero d’Avola’ plant, taking into account that farmers in the past used to cultivate ‘Nero d’Avola’ mixed with plants belonging to the ‘Nerello’ group of red berry cultivars.

Genetic relationships among genotypes are well highlighted also by PCoA analysis (Fig. 2), where the main group including the genetic profiles I, II, III and IV (coming from 21 out of 30 vineyards analyzed) was maintained; in particular, the coordinate 1 (46%) was able to distinguish these profiles from the others.

Fig. 2
figure 2

Principal Coordinate Analysis (PCoA) based on genetic profiles from microsatellite analysis on 118 grapevine plants, coming from 30 vineyards. The first axis accounts 46% of the genetic variation while the second axis 25%. Genetic profiles are shown in brackets (plants with different genetic profiles coming from the same vineyards are indicated with asterisks)

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Conclusion

This is the first report on genetic characterization of ‘Nero d’Avola’, the most important red berry grape cultivar in Sicily. The molecular analysis of 118 plants, proceeding from 30 vineyards, revealed 15 different genetic profiles. Among them, profile I was prevalent (41.5%) and present in samples coming from all the studied provinces. Eleven out of 22 microsatellite markers utilized in the present study were highly informative among the analyzed clones of ‘Nero d’Avola’ cultivar group. According to these results, most of the studied plants/vineyards are characterized by close kinship; only one plant seemed to be genetically distant from the others, and this observation suggests either a different origin or its belonging to a different group of red berry. No correlation was found between genetic clustering and sampling areas. Ampelographic studies and phenotypic descriptions might be used to complement the genetic data of this famous cultivar.