Sugar Tech

, Volume 14, Issue 4, pp 357–363 | Cite as

New Polymorphic EST-SSR Markers in Sugarcane

  • Dennis Crystian Silva
  • Marislane Carvalo Paz de Souza
  • Luiz Sérgio Costa Duarte Filho
  • João Messias dos Santos
  • Geraldo Veríssimo de Souza Barbosa
  • Cícero Almeida
Research Article

Abstract

New polymorphic SSR markers in sugarcane has potential usefulness to studies genetic diversity, genetic mapping, DNA fingerprinting and determination offspring as true hybrids, selfing or contaminant in germplasm from breeding program. SSR markers can be obtained in several ways, and the search for SSR-EST sequences an alternative with significant results. The aim of this study was to develop highly polymorphic SSR markers for germplasm studies in sugarcane. Di- tri- and tetra-nucleotide sequences were mined in the TGI bank and 53 EST-SSR markers developed. These markers were analyzed in five genotypes and showed to be highly polymorphic, with the PIC ranging between 0.48 and 0.95 with a mean of 0.86. The discriminatory power ranged from 0.35 to 0.94 with a mean of 0.82 and the genetic similarity was high in the accessions evaluated.

Keywords

Microsatellites Plant breeding Saccharum 

Introduction

Sugarcane (Saccharum spp.) belongs to the “saccharum” complex, characterized by having aneuploid and polyploid species (Grivet and Arruda 2002), containing between 100 and 130 chromosomes, with 70–80 % from S. officinarum (x = 10, 2n = 8x = 80), 10–20 % from S. spontaneum (x = 8, 2n = 5–16x = 40–128) and 10 % recombinants between the two genomes (D’Hont et al. 1996). This cultivated plant has high economic importance in that it is the raw material that produces sugar, ethanol and bioelectricity.

Genetic studies on sugarcane have increased over the last decade, especially with regard to the analysis of genetic resources using molecular techniques, such as DNA molecular markers (Cordeiro et al. 2001; Pan 2006; Parida et al. 2009; Pinto et al. 2004; Oliveira et al. 2009). Among the markers, microsatellites or SSR (simple sequence repeats) have important advantages, such as high variability, multiallelic, codominance, highly reproducible, abundant in the genomes and specific location on chromosomes (Agarwal et al. 2008; Gonçalves-Vidigal and Rubiano 2011, Kalia et al. 2011; Xu and Crouch 2008).

Microsatellite markers can be obtained from genomic sequences or EST sequences deposited in the database (Cordeiro et al. 2001; 2003). In sugarcane, the SUCEST project sequenced ESTs (expressed sequence tag) originating from different physiological stages, apical, vegetative and floral meristems, leaves in various stages of development, various root types, seeds, stems, callus cells, in addition to tissue infected with symbiotic bacteria (Vettore et al. 2001). These EST sequences allowed several studies, including the development of molecular markers to analyze the variability in sugarcane germplasm banks. The use of SSR markers has been reported in sugarcane, for example, Cordeiro et al. (1999; 2000; 2001; 2003); (Duarte Filho et al. 2010); Oliveira et al. (2009); Pan et al. (2007); Pan (2010); Parida et al. (2009); Silva (2001).

The development of SSR molecular markers allows the estimation of genetic variability, organizing crosses and carrying out marker-assisted selection in the breeding program RIDESA (Rede Interuniversitária para o Desenvolvimento do Setor Sucroenergético), which develops RB varieties (Republic of Brazil). Currently, RIDESA’s collection consists of 78 commercial varieties that occupy 58 % of the crop area in Brazil. The objective of this study was to develop and characterize new SSR markers for use in the germplasm bank of the Breeding Program of RB varieties.

Materials and Methods

Plant Material and DNA Extraction

Five accessions of sugarcane were obtained from the sugarcane breeding program from Republic of Brazil varieties (RB), at the Federal University of Alagoas (TUC73-518, RB845286, RB991511, RB961552 and RB961552). Young leaves were collected from each genotype and the genomic DNA extracted using the CTAB method as described by Saghai-Marrof et al. (1984) and quantification was performed in 1 % agarose gel.

SSR Markers and DNA Amplification

Microsatellite sequences containing motifs di-, tri- and tetra-nucleotides were mined in more than 250,000 DNA sequences of the EST sugarcane database deposited in the Gene Index Project (http://compbio.dfci.harvard.edu/tgi/) and using TRF software (Benson 1999). Primers were designed using Primer 3 software (http://primer3.sourceforge.net) and called SCC. The main parameters for primers were defined according to the following characteristics: primer length between 18–22 nucleotides, PCR product size between 100–300 bp, optimum annealing temperature of 60 °C and 40–65 % GC content. EST-SSR sequences were analyzed in the NCBI non-redundant database for likely homology and probable function with proteins. For PCR amplification, a final volume of 50 μl was used containing: 60 ng genomic DNA, 10× enzyme buffer, 1.5 mM MgCl2, 0.2 mM dNTP, 1U Taq DNA polymerase, 30 pmol of each primer. Conditions for amplification of thermal cycles were carried out in a thermocycler (PTC-100, MJ Research, Inc.) with 35 cycles of 94 °C for 1 min, 60 °C for 1 min and 72 °C for 1 min and a final extension of 72 °C for 10 min. The PRC amplification product was separated on 6 % polyacrylamide gel, using a 100 bp marker (Fermentas Life Science) for molecular weight determination of the respective DNA fragments amplified.

Data Analysis

Despite being considered co-dominant SSR markers, in this study they were considered as dominant markers, because in highly polyploid genomes such as that of sugarcane, the SSR markers have difficulty distinguishing the alleles of homologous chromosomes, making it difficult to determine heterozygosity or homozygosity at any particular locus. From this assumption, all possible alleles detected in the varieties have been converted to a binary system. For each individual, clear and distinct peaks were classified as absent (0) or present (1) to form the matrix that was used to estimate the following variables:

1) Number of alleles.

2) Number of alleles with absent or present among genotypes.

3) Discriminatory power (Dj) of the jth primer (Tessier et al. 1999):\( {D_{j} = 1- C_{j} } \), where \( {C_{j} = \sum\nolimits_{i=1}^{I} {p_{i} \frac{{\left( {N_{pi} - 1} \right)}}{N - 1}} } \) and in which pi is the frequency of the standard ith; N: sample size; I: total number of patterns generated.

4) Polymorphism content (PIC) obtained by the expression: \( {PIC = 1- \sum {pi^{2} } } \), where pi2 is the frequency of allele i raised to the second power for the jth marker (Powell et al. 1996).

5) The genetic similarity (GS) was calculated using the Jaccard coefficient, obtained by the expression: GS = a/(a + b + c), where a is the number of positive coincidences (1) in both genotypes, b is the negative number (0) for genotype i and positive (1) for genotype j, c is the positive number (1) for genotype i and negative (0) for genotype j (Reif et al. 2005). Jaccard similarity values were used to generate a clustering using the UPGMA procedure.

Results and Discussion

Search for EST-SSR

Results showed a high number of microsatellite sequences in the ESTs analyzed, which resulted in 1,507 sequences containing microsatellites, of which 36 % were dinucleotide, 58 % trinucleotide and 6 % tetranucleotide. The most common type was the GCG trinucleotide, with a frequency of 9 % (16 % among trinucleotides) (Fig. 1). Ninety-eight sequences containing the motif di- tri and tetranucleotides for obtaining molecular markers were selected. An analysis of these sequences in the NCBI (www.ncbi.nlm.nih.gov) showed that 25 % had homology to proteins involved in cellular metabolism, 25 % to proteins with unknown function and 50 % to hypothetical proteins. A comparison of the results found in the Gene Index Project showed some differences by Pinto et al. (2004), who also analyzed the SUCEST library, which showed a higher frequency of the tetranucleotide SSR type, and Cordeiro et al. (2001) found higher trinucleotide SSR frequency in the ESTs (Table 1).
Fig. 1

Distributions among (A) and within of di-, tri- and tetra-nucleotide SSRs (BD)

Table 1

Comparison of the three sugarcane SSRs derived from EST libraries

Types of repeats

EST libraries Gene Index Project

EST librariesa (SUCEST)

EST librariesb

Dinucleotides

36 %

8.2 %

46.2 %

Trinucleotides

58 %

30.5 %

30.6 %

Tetranucleotides

6 %

61.3 %

aFrom Pinto et al. (2004)

bFrom Cordeiro et al. (2001)

Microsatellite molecular markers can be obtained in several ways, such as from libraries for microsatellites (Kalia et al. 2011), identification of RAPDs (Cifarelli et al. 1995; Ender et al. 1996; Huang et al. 2008; Lunt et al. 1999; Sanches and Galetti 2006) and AFLPs (Kalia et al. 2011) from genomes (Zane et al. 2002) and EST sequences (Pinto et al. 2004). Among these strategies, the use of genome and EST sequences presents a great advantage by not requiring DNA library construction and sequencing, and by allowing for low-cost marker development. Similar work carried out by Pinto et al. (2004) using the SUCEST library showed a higher frequency of tetranucleotide SSRs, however, only 47 motifs were surveyed (type of microsatellite) compared with the 112 surveyed in this study. On the other hand, Cordeiro et al. (2001) found a higher trinucleotide SSR frequency in ESTs while the tetranucleotide type was less frequent in genomic sequences. In rice, genomic SSR analysis showed that the most frequent types of microsatellites are trinucleotides (59 %), followed by dinucleotides (24 %) then tetranucleitídeos (17 %) (Goff et al. 2002), suggesting that the trinucleotide type is the most frequent in sugarcane.

SSR markers are highly polymorphic and have had several applications such as gene mapping, genetic diversity in breeding programs and transfers to other organisms for comparative mapping (Varshney et al. 2005). However, it is essential that the SSR markers are highly polymorphic. Some studies in sugarcane, show that the PIC values vary greatly among markers, for example, Oliveira et al. (2009) found a variation between 0.16 and 0.94 (average 0.76), while Pinto et al. (2006) did a study that showed a variation between 0.21 and 0.93 (average 0.73) and Parida et al. (2009) showed values ranging from 0.67 to 0.86. In this study these values were 0.48–0.95 (average 0.82), suggesting highly polymorphic SSRs that can be used both to study genetic diversity and determine genetic patterns, for protection of the varieties by having a high discriminatory power.

Polymorphism Analysis

From a total of 1507 EST-SSRs, 98 sequences containing di-, tri-and tetranucleotides were selected to design primer pairs. These primers were analyzed in five genotypes of sugarcane. From these primers, 53 pairs were polymorphic in the genotypes examined, revealing 576 alleles, with an average of 11 alleles, and varying from 3 to 20 alleles per primer. The average numbers of present or absent alleles were 9 and 1.85, respectively. The allele sizes ranged from 110 to 505 bp. The primers’ discriminatory power (DJ) ranged from 0.35 to 0.94 and averaged 0.82. Polymorphism content (PIC) ranged between 0.48 and 0.95 and averaged 0.86 (Table 2). Dj was used to indicate the primer’s ability to be used for protection of the varieties.
Table 2

A list of 53 new polymorphic EST-SSR markers in sugarcane

EST-SSR

Motif

Primer (5′→ 3′)

Dj

PIC

No. of allelica

Allelic range (bp)

SCC01

(CATG)7

F: TCTCAGCAACAAGCACAAGC

0.93

0.94

7(4)

110–208

R: ATCCATCCAGCACAAACACA

SCC02

(AAAG)7

F: TCTCAGCAACAAGCACAAGC

0.81

0.85

9(7)

117–270

R: ATCCATCCAGCACAAACACA

SCC04

(TGTA)8

F: CAACGCCTCACCAAACCTAT

0.73

0.79

9(9)

118–292

R: CGTGGGGATGAACTACTCGT

SCC07

(AAAG)8

F: ATCGTCATTCCGTTTCCATC

0.88

0.90

14(12)

115–304

R: AAGACCCTGACGAGGCTGTA

SCC11

(TGTT)7

F: CAACGCCTCACCAAACCTAT

0.84

0.87

11(10)

186–303

R: CGTGGGGATGAACTACTCGT

SCC12

(CACG)10

F: GGGTGTTGGTGGTGATGTCT

0.82

0.85

9(8)

153–243

R: GCCGGCTACTTCAATTTGTT

SCC13

(GATA)17

F: GACACGTACGCTGGTGACAG

0.90

0.92

16(13)

267–342

R: CTGGAGGATAAGAACGAACGA

SCC14

(AAAC)8

F: AAGCTCGTCGGCTACTTCAA

0.85

0.88

13(12)

168–356

R: GAGGTGAACTGCCATGTGTT

SCC15

(GAAG)7

F: CCGCCTTTCCTGCCTTTAG

0.87

0.90

14(13)

171–243

R: GGACCACCAATCAACTGTCA

SCC16

(TTCT)7

F: CAGCAGCCAGCAGTTTTGTA

0.92

0.94

19(12)

191–327

R: GCAATGGAGCATGTCATCAA

SCC17

(TCTT)10

F: CTACCATGGGGTGAGCTTGT

0.89

0.91

16(13)

233–305

R: GCTAGCTGATATAAATCAATCTTCA

SCC18

(AAAC)7

F: CGGGCAAAGGTACACTCACT

0.89

0.92

15(14)

314–399

R: CAATCGATGCCTGAGTTCAA

SCC20

(AAG)8

F: ATGCCAGGGTTCTTCAAGTG

0.91

0.93

17(13)

223–370

R: CTTCGTCATAGCCATCGTCA

SCC21

(AGC)9

F: GAGCTGGAAAAGCAGAGCAC

0.89

0.91

15(11)

245–356

R: TGCTCACCATCCTGTTGTTC

SCC22

(AAG)15

F: CCTTCCTTGGCCTCTTCTCT

0.90

0.92

16(12)

275–396

R: TGCTGGTCGCAGTACTTGAT

SCC23

(ACC)9

F: GGAGGAGGCTGTGATTAGCA

0.94

0.95

15(13)

269–352

R: CTGTGGGACTACTCGCCTTC

SCC24

(CAC)10

F: GACCCAAAGGCATCAGACAT

0.92

0.94

20(18)

222–332

R: CGCTTGTAGATCCGGTAAGC

SCC25

(GA)40

F: TGGTGTCAGCTTTGCTCTGT

0.92

0.94

16(14)

216–264

R: ACATGCTTCTGCCCGTACTT

SCC26

(GT)22

F: CAGCGCATCTTGCTTATTTG

0.90

0.92

16(14)

214–298

R: GATCCATGGCGCTAGCTACT

SCC27

(GTA)11

F: GCTAGCCCGTACATTGGGTA

0.80

0.84

6(4)

237–294

R: TGGAGCTCCGTCTTCTTGTT

SCC28

(TCA)10

F: CCTCCTCTTCTTTGTTTTCATCA

0.94

0.95

16(15)

227–322

R: GCCTGCGATCGAGTATTAG

SCC29

(CAG)65

F: CAGCTGAACCCTCAACAGC

0.35

0.48

6(5)

264–369

R: CGGAGATCCCATTTGTTGCT

SCC30

(CT)18

F: TGGCGACATGCATAGTTGAT

0.75

0.80

10(10)

191–265

R: CGAATTCCTGAAATGCCATC

SCC34

(GCA)9

F: GAGCGAGGTGTCATCTGTGA

0.90

0.92

15(14)

180–285

R: CCTCCTCCTCGTCCTCTTCT

SCC38

(AGC)12

F: CAGCGACGTACGATGATGTT

0.87

0.90

13(9)

176–230

R: AGCACTGCTGATGCTAATCG

SCC39

(AT)29

F: GTCACGCTTCAGCATCGTAG

0.80

0.84

11(9)

159–225

R: CGTTTTGGATTTGCCTTTGT

SCC41

(CGC)10

F: CCTCCTCCTCCTCGTCCTAC

0.89

0.91

15(13)

205–388

R: GAGTTCCCCCAACACATCAG

SCC42

(CGG)9

F: AAAAGGAGAAGGCACCACCT

0.84

0.88

11(9)

180–240

R: GCTCACGCTTCCTCATCTCT

SCC43

(CT)15

F: CAGTCCCACGAACCAAACTT

0.85

0.88

11(6)

381–477

R: TTGCGGGGCAAATACACTAT

SCC44

(GAC)25

F: GCCGGGGATGAAGGACTC

0.90

0.92

17(16)

158–208

R: CGCTGTGCTGACGACTGG

SCC47

(TAA)9

F: CTCTTGGTTCCCCTCACAAA

0.86

0.89

11(8)

175–313

R: TCCATCCATCCTCTCCACTC

SCC49

(GT)19

F: CTCCCTCCTCTCGTCCTCTT

0.83

0.87

7(7)

226–260

R: TTCTTAATTTCCCCGTGCAG

SCC52

(CT)22

F: CGTCCTCAGATCACATCTCG

0.88

0.91

13(12)

252–347

R: ATCACGTCGAGGAGACCATC

SCC70

(TGTC)10

F: CATCACCGAATTCATCAGGA

0.76

0.81

7(6)

203–302

R: GTGCTGGGGTGATGAGATTT

SCC71

(GA)27

F: GGAAGATTTTCACACACACACG

0.80

0.84

7(4)

135–179

R: CTCTCACTCCTCCGTGCTTC

SCC72

(TA)66

F: TGTGTTCCATTCCGTTTTCA

0.74

0.79

7(6)

146–215

R: TTTTAGAGGCGAAAGAGAAAAA

SCC74

(CAG)8

F: CACCCTGTGGACCTGGAG

0.86

0.89

12(10)

206–274

R: AAAGGAACAGGCAGCAACAG

SCC76

(GA)12

F: CACGCTTGACATGAGAGGAA

0.94

0.95

9(8)

195–233

R: GTTCAGAACACGTGCAGCAT

SCC78

(TGC)9

F: AGCCATGGAGAAGAGAAGCA

0.55

0.64

3(1)

200–218

R: GGGAACACCCAGTTGACG

SCC79

(CTC)11

F: CTATCACCCCGCCAGTCAT

0.83

0.87

8(8)

177–217

R: GGTAGTAGGACGGGTTGCAG

SCC81

(GAC)9

F: GCCAAGGCTACTTGTGAAGG

0.82

0.86

10(7)

229–353

R: TGATCATCACCATCACCACA

SCC82

(CTC)10

F: CTATCCCATCCCGGAAAAA

0.86

0.89

12(10)

231–505

R: CCGACTTGAACACCACCAG

SCC83

(CT)17

F: CCATCCACCCATCAACTCTC

0.79

0.83

6(4)

400–447

R: ACGAGCGTCTGGTTCAGGTA

SCC86

(CT)17

F: GATCCCCACCTCAGGTCAC

0.79

0.83

7(7)

205–253

R: ATCACGTCGAGGAGACCATC

SCC87

(CT)14

F: CTGCCGGGCCATGTTAAG

0.74

0.80

6(6)

161–183

R: GACGGATACGTGCATGGAG

SCC89

CCT(9)

F: AGTGTTGCGAGAAGCAGCAG

0.79

0.83

9(9)

209–276

R: CCCATGGATCACATGACAGA

SCC90

(AGC)9

F: CAATTGCCAAAGCCTTCTTC

0.84

0.88

10(7)

178–231

R: GAGACTGTGTCTCCGTGCTG

SCC92

(ATCT)15

F: CTCCGCATTAGCCATTTCC

0.76

0.81

8(5)

150–200

R: TGGTACTCGTCCATGTCGTC

SCC93

(GCA)9

F: AATCCCAGCCCCGATGAT

0.70

0.76

7(5)

216–291

R: AGCCACACCTTGACCTTGAC

SCC94

(CTG)11

F: AGGAAGTCCCTGGTCATGC

0.85

0.88

3(3)

207–237

R: GCGGGCTACACCCAGATG

SCC96

(TGT)14

F: GCTATGGATAGGAGCGCTTG

0.72

0.78

6(6)

237–311

R: CAAGGCTAGTGAACCGGAAA

SCC97

(ACA)10

F: TATTTATGGCGCCTGCCTAT

0.69

0.75

6(4)

255–475

R: ACAGGAGCGCTTGGAGATTA

SCC98

(AGG)10

F: GAGATTCAGATCCGCACACA

0.54

0.63

4(3)

216–251

R: CCAGTCCCACAGGAACATCT

Mean

0.82

0.86

11(9)

aIn parenthesis is number of allelic with absence and present among accessions

Genetic similarity ranged from 0.38 between RB991511 and TUC73-518 to 0.50 between RB961552 and RB991532, and averaging 0.46. Using the average Jaccard similarity, clustering (Fig. 2) showed variety TUC73-518 in a separate clade, while the others in another clade.
Fig. 2

Dendrogram showing the genetic relationship among five genotypes of sugarcane, based on Jaccard’s similarity coefficient using 53 SSR markers

Analysis of genetic variability using EST-SSR markers has shown effectiveness to detect polymorphism in several eudicot species (Kumpatla and Mukhopadhyay 2005) and in sugarcane, EST-SSR studies have shown a great potential to generate polymorphism (Pinto et al. 2004; 2006), and organize of genetic variability in germplasm banks.

Results of the 53 primers analyzed showed considerable genetic similarity in five accessions of sugarcane. Similar values have been observed in several studies using different methods for estimating genetic variability in sugarcane (Cordeiro et al. 2001; 2003; Glynn et al. 2009; Oliveira et al. 2009; Pan 2006; Pinto et al. 2004; 2006; Silva et al. 2005; Coto et al. 2002). Using EST-SSRs, Pinto et al. (2006) found an average genetic similarity of 0.62 in 13 accessions evaluated. Alwala et al. (2006) using TRAP and AFLP markers detected a genetic similarity of 0.75 and 0.76, respectively. Clustering results suggest there is a correlation between polymorphism of markers and genetic variability; one example is the accession TUC73-518 (from Argentina) that was in a separate clade from the other accessions (RB variety from Brazil).

The primers EST-SSR developed in this study are utilized for studies about genetic diversity, genetic mapping, DNA fingerprinting and determination offspring as true hybrids, selfing or contaminant in germplasm from breeding program for genetic improvement of sugarcane from RIDESA (Rede Interuniversitária para o Desenvolvimento do Setor Sucroenergético—Interuniversity network for the development of the sugar-energy sector). The RIDESA collection consists of 78 commercial varieties, of which 36 are protected by the National Crop Protection Agency of the Ministry of Agriculture, Livestock and Supply and occupy 58 % of cultivated areas in Brazil.

Notes

Acknowledgments

Banco do Nordeste and FINEP for financing the project. PMGA for support and supplying biological material. UFAL for granting the PIBIC scholarship to students Dennis Crystian Silva, Marislane Souza and Luiz Sérgio Costa Duarte Filho.

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Copyright information

© Society for Sugar Research & Promotion 2012

Authors and Affiliations

  • Dennis Crystian Silva
    • 1
  • Marislane Carvalo Paz de Souza
    • 1
  • Luiz Sérgio Costa Duarte Filho
    • 1
  • João Messias dos Santos
    • 2
  • Geraldo Veríssimo de Souza Barbosa
    • 2
  • Cícero Almeida
    • 1
  1. 1.Laboratory of Genetics ResourcesCampus Arapiraca, Federal University of AlagoasArapiraca-ALBrazil
  2. 2.PMGCA/RIDESA/UFAL, Center for Agricultural SciencesFederal University of AlagoasMaceió-ALBrazil

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