Open Access

Microsatellite loci for Urochloa decumbens (Stapf) R.D. Webster and cross-amplification in other Urochloa species

  • Rebecca C. U. Ferreira1,
  • Letícia J. Cançado2,
  • Cacilda B. do Valle3,
  • Lucimara Chiari3 and
  • Anete P. de Souza1, 4Email author
BMC Research Notes20169:152

https://doi.org/10.1186/s13104-016-1967-9

Received: 2 October 2015

Accepted: 1 March 2016

Published: 10 March 2016

Abstract

Background

Forage grasses of the African genus Urochloa (syn. Brachiaria) are the basis of Brazilian beef production, and there is a strong demand for high quality, productive and adapted forage plants. Among the approximately 100 species of the genus Urochloa, Urochloa decumbens is one of the most important tropical forage grasses used for pastures due to several of its agronomic attributes. However, the level of understanding of these attributes and the tools with which to control them at the genetic level are limited, mainly due to the apomixis and ploidy level of this species. In this context, the present study aimed to identify and characterize molecular microsatellite markers of U. decumbens and to evaluate their cross-amplification in other Urochloa species.

Findings

Microsatellite loci were isolated from a previously constructed enriched library from one U. decumbens genotype. Specific primers were designed for one hundred thirteen loci, and ninety-three primer pairs successfully amplified microsatellite regions, yielding an average of 4.93 alleles per locus. The polymorphism information content (PIC) values of these loci ranged from 0.26 to 0.85 (average 0.68), and the associated discriminating power (DP) values ranged from 0.22 to 0.97 (average 0.77). Cross-amplification studies demonstrated the potential transferability of these microsatellites to four other Urochloa species. Structure analysis revealed the existence of three distinct groups, providing evidence in the allelic pool that U. decumbens is closely related to Urochloa ruziziensis and Urochloa brizantha. The genetic distance values determined using Jaccard’s coefficient ranged from 0.06 to 0.76.

Conclusions

The microsatellite markers identified in this study are the first set of molecular markers for U. decumbens species. Their availability will facilitate understanding the genetics of this and other Urochloa species and breeding them, and will be useful for germplasm characterization, linkage mapping and marker-assisted selection.

Keywords

Enriched library Forage Signalgrass Simple sequence repeat Transferability

Background

It has been estimated that 167 million hectares of pasture land in Brazil is used to feed a herd of approximately 208 million head of cattle [1]. These pastures consist mainly of forage grasses of the genus Urochloa (syn. Brachiaria), which were introduced from Africa [2]. These forage grasses have greatly contributed to the development of the national cattle industry of Brazil, establishing Brazil as the second largest beef producer and the main beef exporter in the world. The competitive advantage of cattle production in Brazil is the exclusive use of pasture [3]. Moreover, Brazil is the largest producer and exporter of tropical forage seeds in the world [2].

One of the most widely cultivated species of Urochloa is Urochloa decumbens Stapf., particularly U. decumbens cv. ‘Basilisk’. This species exhibits exceptional adaptation to the poor and acidic soils that are typical of the tropics and lead to good animal performance [4]. However, the molecular genetic information regarding this species is limited, mainly due to its reproducing predominantly via apomixis and because its ploidy levels range from diploid to pentaploid [5].

The need for new more productive and efficient cultivars has inspired the search for new tools to facilitate the selection process [3]. Thus, genetic and genomic studies are essential to advancing breeding programs via a better understanding of the genetic structure of the species. These types of studies can be conducted by using molecular tools, such as molecular markers.

Among all molecular markers, one of the most effective for plant genetics studies is the microsatellite, also known as the SSR (Simple Sequence Repeat). These markers are highly informative due to their multi-allelic nature, co-dominant inheritance, high transferability and broad distribution in the genomes of the species [68].

Whereas some microsatellite markers for Urochloa species have been developed [913], specific microsatellite markers for U. decumbens have not been reported. Specific microsatellite molecular markers can be very useful in assessing the genetic diversity of germplasms, performing linkage mapping, identifying quantitative trait loci (QTL), performing genome-wide selection and marker-assisted selection, and facilitating molecular based breeding to improve the economically importance characteristics of a species [6, 7]. Moreover, microsatellite markers identified in species with little genome information may be used for cross-amplification between related species [14].

The aims of the present study were to identify and characterize the first set of microsatellite markers for U. decumbens and to test their transferability to four other Urochloa species (U. brizantha, U. dictyoneura, U. humidicola and U. ruziziensis).

Methods

Thirty-four Urochloa genotypes were obtained from the Embrapa Beef Cattle collection, in Campo Grande, MS, Brazil for marker validation. Twenty of these genotypes are represented by U. decumbens accessions, six genotypes are intra-specific hybrids of the same species and the other eight genotypes are represented by two different germplasm accessions each from U. brizantha, U. humidicola, U. dictyoneura and U. ruziziensis. These other Urochloa species were used for the cross-amplification tests. The annotation numbers, accession numbers (as recorded in the Embrapa- BRA-, in the Embrapa Beef Cattle- EBC- and in the Center for Tropical Agriculture- CIAT- databases), genotypes, species identified, their mode of reproduction and the origin of the genotypes are shown in Table 1.
Table 1

Genotypes of U. decumbens and four other species of the genus Urochloa that were used to characterize the microsatellite markers and analyze their levels of transferability

AN

CIAT

BRA

EBC

Origin

MR

Genotype

Species

1

16494

004448

D005

Kenya

SEX

Germplasm accession

U. decumbens

2

16495

004456

D006

Kenya

SEX

Germplasm accession

U. decumbens

3

16497

004472

D007

Kenya

APO

Germplasm accession

U. decumbens

4

16498

004481

D008

Kenya

APO

Germplasm accession

U. decumbens

5

16499

004499

D009

Kenya

APO

Germplasm accession

U. decumbens

6

16500

004502

D010

Kenya

APO

Germplasm accession

U. decumbens

7

16501

004511

D011

Kenya

APO

Germplasm accession

U. decumbens

8

16504

004545

D014

Kenya

APO

Germplasm accession

U. decumbens

9

26295

004651

D024

Rwanda

SEX

Germplasm accession

U. decumbens

10

26300

004707

D028

Rwanda

APO

Germplasm accession

U. decumbens

11

26304

004740

D032

Rwanda

APO

Germplasm accession

U. decumbens

12

26308

004782

D035

Rwanda

SEX

Germplasm accession

U. decumbens

13

16491

004421

D036

Kenya

APO

Germplasm accession

U. decumbens

14

26306

004766

D040

Rwanda

SEX

Germplasm accession

U. decumbens

15

6370

000116

D059

Unknown

APO

Germplasm accession

U. decumbens

16

16100

001961

D061

Unknown

APO

Germplasm accession

U. decumbens

17

NA

001996

D070

Unknown

APO

Germplasm accession

U. decumbens

18

6298

000060

D077

Unknown

APO

Germplasm accession

U. decumbens

19

D024/27

CNPGC

SEX

Tetraploidized accession

U. decumbens

20

606

001058

D062

Uganda

APO

Germplasm accession

U. decumbens

21

R10

CNPGC

NA

Hybrid

U. decumbens

22

R44

CNPGC

APO

Hybrid

U. decumbens

23

R125

CNPGC

NA

Hybrid

U. decumbens

24

R144

CNPGC

APO

Hybrid

U. decumbens

25

R146

CNPGC

NA

Hybrid

U. decumbens

26

R182

CNPGC

NA

Hybrid

U. decumbens

27

16186

007889

DT157

Ethiopia

APO

Germplasm accession

U. dictyoneura

28

16188

007901

DT159

Ethiopia

APO

Germplasm accession

U. dictyoneura

29

NA

NA

R044

Unknown

SEX

Germplasm accession

U. ruziziensis

30

26163

005568

R102

Burundi

SEX

Germplasm accession

U. ruziziensis

31

16125

002844

B112

Ethiopia

APO

Germplasm accession

U. brizantha

32

26110

004308

B178

Burundi

APO

Germplasm accession

U. brizantha

33

26149

005118

H016

Burundi

APO

Germplasm accession

U. humidicola

34

6369

000370

H126

Unknown

APO

Germplasm accession

U. humidicola

AN annotation number, CIAT Center for Tropical Agriculture, BRA codes from Embrapa, CNPGC National Center for Research on Beef Cattle, EBC codes from Embrapa Beef Cattle, MR mode of reproduction- apomictic or sexual, NA not available

Genomic DNA was isolated from fresh leaves using the CTAB method [15]. The purity and concentration of the isolated DNA were determined using a NanoDrop1000 (Thermo) spectrophotometer and by electrophoresis in a 0.8 % agarose gel that was subsequently stained with ethidium bromide (5 µg/mL−1).

In a previous study, a microsatellite-enriched library of one U. decumbens genotype was constructed using the method described by Billotte et al. [16]. The sequences were then treated as described previously [9]. The microsatellites were identified using MISA software [17], and only mononucleotides with 12 or more repeats, dinucleotides with six or more repeats, trinucleotides with four or more repeats, and tetra, penta, and hexanucleotides with three or more repeats were considered. The DNA sequences determined in this study were deposited in GenBank under the accession numbers shown in Table 2.
Table 2

Description of the 93 SSR markers developed for U. decumbens

SSR locus

GenBank accession number

Primer sequences (5′–3′)

Repeat motif

Ta (°C)a

Size (bp)

NAb

PICc

DPd

Dec01

KT587691

F_CAAACGACTGCTGATGATGG

(AC)16

65°

250–280

5

0.68

0.89

R_TGAGAGGCTAAGAG/CAACCTG

Dec03

KT587692

F_AACTGAACGCTGCTTGGTCT

(GT)6

65°

240–260

3

0.58

0.63

R_GGTCCGGAATAAAAAGCACA

Dec05

KT587693

F_GGGCTCCTCATCAGCAGTAG

(GAC)4

65°

132–140

4

0.61

0.54

R_GATGCCTCTCGGGACTATCA

Dec06

KT587694

F_GTTCATGGGGGCAATCAGT

(CTGG)3

65°

120–130

4

0.70

0.54

R_CGTGATGTCTGAACGGATGA

Dec07

KT587695

F_CGAACACATTCACATACAACA

(AC)7

65°

226–242

5

0.74

0.87

R_CTGTCGGATTTATTTGCATTA

Dec09

KT587696

F_GCCCAACTGGAATGTGCTA

(TC)9

65°

240–280

5

0.72

0.91

R_CGACGTCCTTGTTGTTTGTC

Dec10

KT587697

F_GACGTCGAGGACAAACAACA

(CAAG)3

65°

216–256

6

0.79

0.86

R_TCCTTACCCTTGCGATTCAC

Dec11

KT587698

F_GGGGGAAAATGAGACAGACA

(AG)16

65°

154–198

8

0.80

0.94

R_GCTAACCAGACAGCCACCAC

Dec12

KT587699

F_CTCACACCCTCCTTCTGCTG

(GT)9

65°

196–226

9

0.82

0.97

R_CGATCGCTCCCTACTAGTGC

Dec13

KT587700

F_CCCCCGTAAAACAGACAAAA

(TA)6

65°

166–178

5

0.72

0.89

R_ACCATGATACAACGCTGCAA

Dec14

KT587701

F_AAACGGAGAAAGGGGATCAT

(GAC)4

65°

290–310

3

0.62

0.22

R_GAGCATACATGCAGCAGTGG

Dec17

KT587702

F_CCTTCGTCCATTACCCTGAA

(TG)9

65°

224–248

6

0.63

0.72

R_ATCCACCAGTGCACGTATGA

Dec18

KT587703

F_ACGCACACACACGAACAAAT

(CGAT)3

65°

180–202

6

0.78

0.96

R_ATTTCGACATGCCTGCAACT

Dec19

KT587704

F_AGGTTCGATAATCGGCACAC

(GT)7

65°

220–236

6

0.79

0.95

R_CGCAAGTGGTCAAGCAATTA

Dec20

KT587705

F_ACCTTGAACTCCTGCTTTTGT

(AC)10

65°

150–168

6

0.75

0.92

R_AGCACTATCACCAATCAGCAA

Dec21

KT587706

F_GCCGACATCAACTTCCATTT

(GT)7

65°

176–190

5

0.76

0.85

R_CTCCTTGGTCCAATTCCTCA

Dec22

KT587707

F_GTGTGTACGTGATGCTATGTG

(CTT)4

65°

186–192

4

0.47

0.57

R_ATCGATCTCACTGACCATGT

Dec24

KT587708

F_TAAAGAAACATGGGCCGGTA

(GCC)5

65°

210–226

5

0.73

0.86

R_TTATTCCTGGGATTGGGTTG

Dec26

KT587709

F_TCGGAAAACGCAGGAGAG

(CA)6

65°

180–190

4

0.68

0.59

R_GTTCAGTGGGTCTGGCTTGT

Dec27

KT587710

F_TGTACATGAATGGCAGCACA

(AGAT)3

65°

248–262

6

0.73

0.76

R_AACAGCAGCAGAGATGACGA

Dec28

KT587711

F_GTTCCTCCCAAGAAACCACA

(AC)6

65°

146–180

8

0.78

0.84

R_CCCAACATTCACCTGGTTCT

Dec29

KT587712

F_TGTTATAATCATCACCATGCTC

(GTA)4

65°

170–184

6

0.70

0.67

R_ACAGCTATTGCCACTACTTGA

Dec30

KT587713

F_CATTACGAGCACGCAGTCC

(CA)7

65°

152–164

5

0.71

0.59

R_TACCACTGCTGGACACGAGA

Dec31

KT587714

F_CGTTGTCAGCACACACACAC

(TCTA)3

65°

136–146

5

0.70

0.79

R_TACTACCACTGCTGGACACGA

Dec33

KT587715

F_TGTCGTGTGCGTTTTGTTTT

(CTT)4

60°

274–336

8

0.78

0.94

R_CTAAGATCCCCACTCCCACA

Dec35

KT587716

F_TTCTTGGACACACAGCCTTG

(TG)4

65°

274–290

6

0.72

0.88

R_GGGCTGAAAACATCATCACC

Dec36

KT587717

F_GAAGGTGATGATCGGCAGTT

(GCAG)3

65°

280

1

0.00

0.00

R_GTGTGCGTTGCTGCCTACTA

Dec37

KT587718

F_CCTCTCTTCCGTTTGCTCTG

(GTG)5

65°

198–218

5

0.70

0.81

R_TGAACAGGCACGGATTGATA

Dec39

KT587719

F_TAGGTGTCCCATTGGTCGAT

(GT)7

55°

166–182

5

0.64

0.34

R_AGGAGAGCTGCGTGTCATTT

Dec42

KT587720

F_CACGTCATGTACTGCGATCC

(GT)6

65°

220–230

3

0.56

0.68

R_GCGTCACACATACACACACG

Dec43

KT587721

F_CAGTCATCAGCATTCAGGTAT

(TG)11(AG)6

65°

212–228

5

0.74

0.91

R_ATAACTTGCGTATGTGCTCTC

Dec44

KT587722

F_CATGCTTAATCCAGAAATCAG

(AC)12

65°

182–226

6

0.78

0.94

R_TGTAAACCGGAAAGTGTACTG

Dec45

KT587723

F_TGGAGATGGAGATGGGAGTC

(GGAT)3

65°

210

1

0.00

0.00

R_CCCAAGGAATGGGATAGGTT

Dec47

KT587724

F_AGAGAGCTGATGGTCGTGGT

(GA)9

65°

210

1

0.00

0.00

R_TGGAAACTTGGGAGGATCTG

Dec48

KT587725

F_CTAACGCTATTGCTTTGCTT

(CT)45

65°

144–190

10

0.85

0.94

R_TGCAGAGAGAGAGAAGAGAGA

Dec49

KT587726

F_CAATGCATGCTTGGAACTTG

(GT)6

65°

166–180

5

0.65

0.74

R_CATCGGAGGGTAGATTGGTC

Dec50

KT587727

F_GAAACAGGACCATCAGATAGCA

(CA)6

65°

164–180

5

0.76

0.84

R_GGAATCTGCAGGTTTGGAAG

Dec51

KT587728

F_GCTGATCCTCGGATTGTGTT

(TG)21

65°

248–262

5

0.69

0.92

R_TAACTTGGACGCGCTAAAGG

Dec52

KT587729

F_CACGAATGCACATGCAATAA

(GT)6

65°

289–292

2

0.00

0.00

R_AGTGAACCAAACTGCCAGAA

Dec54

KT587730

F_GCCCTCTTTAACTCTGCTTTA

(CA)8

65°

236–252

5

0.75

0.92

R_GTATCTTCTTTCGGATGACCT

Dec55

KT587731

F_AGCACCATCATCTTTAACAAA

(ACACC)3

65°

212–224

6

0.78

0.73

R_CAAGGAATTTGCACTAAAAGA

Dec56

KT587732

F_GAACTTAATGGCGGAGTAGAC

(AG)14

55°

220–230

2

0.00

0.00

R_CACAGATTGCTGAATTGTTTC

Dec58

KT587733

F_ATTAGGATTGCGCACTGGTC

(GT)6

65°

286–298

5

0.64

0.8

R_ATCCGCATTCACAACCTCTC

Dec59

KT587734

F_GGTTAAAATGGTTCGCTGGA

(GT)7

65°

184–220

5

0.73

0.92

R_ACCTAGGCTCGCATGACAAT

Dec60

KT587735

F_ATTTCAGTTGCACATTCCA

(GT)6

55°

220–230

2

0.00

0.00

R_TCCAAAACTTAGCTCAGAAAG

Dec62

KT587736

F_AGGAAGGGTACGGTGTAGGC

(CA)7

65°

216–238

4

0.41

0.59

R_TCTACATGCACATCCGGAAA

Dec63

KT587737

F_GGGATATTTTCCGGATGT

(CTT)4

65°

218–226

3

0.51

0.7

R_CAGAGCTCAGAAAGTCGTTAC

Dec65

KT587738

F_TCGGATTCTTGGACAACCTC

(GGCC)3

65°

180

1

0.00

0.00

R_CCTCTACGCGAAAGATGGTC

Dec69

KT587739

F_GATGGCTACCTGCATTGGAT

(CCAT)3

55°

168–180

6

0.79

0.96

R_ATAAGGGGAGCCCTCAAAAA

Dec70

KT587740

F_AGCTGCCTCCACTTGACAAT

(TG)7

65°

256–268

5

0.72

0.62

R_AGGCCCTGATAGTCCCCTAA

Dec71

KT587741

F_GAGCTTCCCTGTGTCTGATA

(TG)10

55°

234–254

4

0.62

0.84

R_ATGACAATGACTATGCTGACC

Dec75

KT587742

F_ACAGGAGCCTTTATGCATGG

(ATGC)3

65°

150–166

5

0.68

0.69

R_GTCCTGTGTTGGTCGTTCCT

Dec76

KT587743

F_GTCACGTGCCATCACAAATC

(TAGC)3

65°

270

1

0.00

0.00

R_GCACACATGCATGATGACAA

Dec77

KT587744

F_TCCAAATGTACCGTCAATAAA

(AG)12

55°

234–260

7

0.76

0.9

R_CGTGTCTGCATTCAAAGTG

Dec78

KT587745

F_GCTTACCACATCCGGTGATT

(AC)8

65°

246–260

5

0.66

0.71

R_GAGAATGCTTCCCGTTCTTG

Dec83

KT587746

F_GGCTTGCTCCAAGAGATGAG

(CA)20

65°

174–198

4

0.66

0.72

R_TAGCTTGGCCTTTGTGTGTG

Dec84

KT587747

F_GGCTTGCTCCAAGAGATGAG

(AC)9

65°

220–250

7

0.78

0.95

R_TTCGTCACGTCAAAACAAGC

Dec86

KT587748

F_CCACCTCCCAGGATAGATGA

(TG)7

55°

140–180

9

0.80

0.94

R_AGATTGGGGGAGGAAGAAGA

Dec89

KT587749

F_CTGTTGCATCCACCACTTTTT

(TC)8

65°

146–180

4

0.55

0.41

R_CGGCAGCCTAAAGTGATTGT

Dec90

KT587750

F_CGGTGCTCCATGATTAGGAT

(GT)8

65°

278–326

7

0.77

0.82

R_GCGTAGCATCATCGAGAACA

Dec91

KT587751

F_GCCTCATCTGTTCATTCATT

(TG)7

55°

290–330

3

0.26

0.22

R_TGGCACTCTAACTTGTAGGC

Dec92

KT587752

F_AGCAATCCAAGCTGAAAGGA

(AC)7

65°

264–290

7

0.79

0.92

R_TTCCGCATGAAACAAAACTG

Dec93

KT587753

F_TTCGGTCAAAATCGAAAAGG

(AC)6

65°

226–244

5

0.72

0.95

R_GCATTGTTTCAGAGGCTTCG

Dec95

KT587754

F_AGCAACCCAAAGGTCAGCTA

(CT)24

65°

178–208

6

0.71

0.89

R_AGGAGGGATTCAAGGGAGAA

Dec96

KT587755

F_CATTCTGGTATGGCACGTTG

(CA)6

65°

148–154

4

0.66

0.85

R_ATTTACCGACCAGGCTGAAG

Dec97

KT587756

F_GGGCAGGCACTAGATTGATT

(TCTT)3

65°

176–184

4

0.61

0.72

R_TTGCTTGCTTGAGTTTGTGG

Dec98

KT587757

F_TAGGTGACAAGGCACGATCA

(AG)10

65°

252–272

7

0.76

0.95

R_GGGCCAACATACCAAAGAGA

Dec99

KT587758

F_TAAGAGACGAGTGCTCTGAAA

(AGCAGG)3

65°

210–228

7

0.77

0.91

R_TTGTGAATCGGTACTTTTGTC

Dec101

KT587759

F_CTCTAACTTTCGGCGTGGTC

(GGCC)3

65°

224–230

3

0.53

0.71

R_GGACGGTCCGACTTGTCTAA

Dec103

KT587760

F_ATGACGAACTTGCTCCCTACA

(AC)8

55°

176–206

4

0.51

0.71

R_ATCGATTCAGAGCCGCTTC

Dec105

KT587761

F_CCTTCTGTTCATTGCAGTCC

(TG)8

65°

174–180

4

0.56

0.65

R_TGGTACCACAATGCCAAATC

Dec106

KT587762

F_TCACGAACAACGATCAGAGC

(TG)7

55°

180–230

7

0.74

0.93

R_TCTTTACCCGTGCTGTTTCC

Dec108

KT587763

F_CATCACCGCATTTATGCAAG

(AG)8

65°

184–200

6

0.68

0.85

R_ACACACGTCCTCGTCTTCCT

Dec109

KT587764

F_CAGCACACTGAATCCTCTGC

(GT)6

65°

216–220

3

0.39

0.59

R_CCGTTGTTCCATCAGAACCT

Dec110

KT587765

F_CTCCGAAGATCCGAGCTATG

(GT)7

65°

178–184

4

0.31

0.41

R_CCCCTGGAGGCTATAAAAGG

Dec111

KT587766

F_TGATTAGGTGCTGACTGCTG

(ATTT)3

65°

178–186

5

0.50

0.57

R_CTGGAAGATGTATTTGGTGTGA

Dec112

KT587767

F_CCTCAAGAAGCTCTGGGATTT

(TGTT)3

55°

238–244

4

0.57

0.72

R_TGTGCAAACGTCAGTAGAGCA

Dec113

KT587768

F_TGGACTAACTGCACTGCCTGT

(GT)9

65°

208–224

7

0.74

0.94

R_CATGAGGAGCACAGCGAATA

Dec114

KT587769

F_CAAAGGCCATGCCTTGTACT

(GT)11

65°

214–220

4

0.62

0.72

R_CACTGCTCAGCCAATCCTAAG

Dec115

KT587770

F_GGCATATGTCTGAGTAAGTGTG

(TCT)4

55°

160–174

6

0.76

0.6

R_CCTGTTTCCATTGATTCTTTT

Dec116

KT587771

F_TCACTTCATCCATTCGCTTG

(TG)17

65°

274

1

0.00

0.00

R_AACATGACCGACTCCTACGG

Dec118

KT587772

F_ACACACCCCAACTCACACAA

(AC)6

65°

208–226

6

0.75

0.83

R_TGGTCATGGCAAAAGATGAA

Dec121

KT587773

F_TGCACAATGATGAACACAGG

(GT)7

65°

226–264

6

0.74

0.74

R_AGTGAACCAAACTGCCAGAA

Dec122

KT587774

F_CCTGCGTCACTCGAGAAAA

(TCTG)3

65°

268–292

6

0.76

0.93

R_CAATGTCATCGCCATTTCTG

Dec123

KT587775

F_TGAGCAACACTGGAGAATGG

(TC)9

65°

248–280

9

0.80

0.94

R_CGTACATGACAGGAGGGTGTT

Dec124

KT587776

F_AGAAGCCCCAGATGTTCTGA

(GT)9

65°

270–306

4

0.52

0.69

R_GCTAGTCGCGTCTACCGTTC

Dec125

KT587777

F_TCTGGGGTGGAAATGTTGAT

(CT)11

65°

202–214

4

0.61

0.34

R_CCCTTCACCTTGAGAAAGCA

Dec126

KT587778

F_GGATGGATTGATGGATGCTT

(GGCC)3

65°

268–304

7

0.77

0.93

R_AACCCGAAAGGCCTAAGCTA

Dec127

KT587779

F_CGTTGATCACACGTCTCAGG

(TTGC)3

65°

250–280

4

0.65

0.75

R_GATTTCGCCACCAACATTCT

Dec131

KT587780

F_CTTGTTACCTTCTGCACAATAAA

(GAA)5

65°

160–170

3

0.00

0.00

R_ATTAGTCTTTCCGTCCTTGTC

Dec132

KT587781

F_GTATCGGGTAGCAAGGCAAG

(AAGC)3

65°

220–240

2

0.00

0.00

R_GGAAATTCCTTACCCCGAAG

Dec133

KT587782

F_GGATGGAAGAGCACAAAAGC

(CT)7

65°

218–228

5

0.68

0.81

R_GCGTGTGTGTGTGTGTTTGA

Dec134

KT587783

F_CAGGCTTCCCCTCTCTCTCT

(AC)7

65°

220–260

8

0.76

0.93

R_GCAACCGGAAGAATTCATGT

Total average

     

4.93

0.68

0.77

aAmplification temperature (°C)

bMaximum number of alleles observed

cPolymorphism information content

dDiscrimination power

After the primer pairs were designed using Primer3Plus software [18], we added a M13 tail (5′CACGACGTTGTAAAACGAC-3′) to each forward primer. Polymerase chain reaction (PCR) assays were conducted as described previously [9]. The amplified products were separated by electrophoresis through 3 % agarose gels prior to vertical electrophoresis through 6 % denaturing polyacrylamide gels. The gels were then silver stained [19], and the product sizes were determined by comparison to those of a 10 bp DNA ladder (Invitrogen, Carlsbad, CA, USA).

We considered only the strongest bands because the less intense bands might have been stutter bands and an SSR was considered transferable when a band of the expected size was amplified via PCR and an appropriate SSR pattern was observed. Each SSR allele was treated as dominant due to the high ploidy levels of the genotypes; thus, this analysis was based on the presence (1) or absence (0) of a band in the polyacrylamide gels.

The genetic distance among the genotypes was evaluated according to Jaccard’s coefficient [20] based on a binary matrix constructed using the molecular data. This analysis was conducted using the software package NTSYSpc 2.11X [21]. An unrooted tree was constructed using the weighted neighbor-joining method (NJ) using DARwin 6.0.010 software [22].

The set of molecular data was also analyzed using the admixture model of STRUCTURE software version 2.3.4 [23] to infer the population structure of the 34 genotypes. The admixture model was tested using a period of burn-in with 100,000 iterations and a run length of 200,000. The number of K (clusters) was set from 2 to 20. To infer the appropriate number of clusters in our data, we used the ΔK statistic, which represents the rate of change in the log probability of the data between successive K values rather than the log probability of the data [24]. We retained the K value corresponding to the highest value of ΔK obtained using the online tool Structure Harvester [25].

The polymorphism information content (PIC) values were calculated to evaluate the levels of marker informativeness and to help choose primers for future studies [26]. To compare the efficacies of the markers used for varietal identification, the discrimination power (DP) value was determined for each primer [27].

Results

We analyzed 281 contigs, of which 128 were found to contain SSR. One hundred fifty-five SSR motifs were found, with the perfect microsatellite being the most abundant. Dinucleotide repeats were the most abundant class of microsatellite detected (59.36 %), followed by tetranucleotide (18.71 %), trinucleotide (12.26 %), mononucleotide (3.87 %), hexanucleotide (3.22 %) and pentanucleotide (2.58 %) repeats. Furthermore, 22 % of the microsatellite motifs were classified as class I motifs (>20 bp), and 78 % were classified as class II motifs (from 12 to 20 bp).

A total of 113 specific primer pairs were designed, and 93 SSR markers amplified from U. decumbens, with 82 of these being polymorphic. A total of 459 bands were scored, and the number of bands per locus was found to range from 1 to 10, with an average of 4.93 bands per locus (Table 2).

The PIC values of the 82 polymorphic loci ranged from 0.26 to 0.85 (average of 0.68), and the discrimination   power (DP) values ranged from 0.22 to 0.97 (average of 0.77) (Table 2).

Two genotypes of four other species of the genus Urochloa (U. brizantha, U. humidicola, U. dictyoneura and U. ruziziensis) (Table 1) were used to evaluate the transferability of the 93 SSR markers. All of the loci were tested using the same PCR conditions used for analysis of U. decumbens. Fifty-six percent of the loci were amplified in at least one U. dictyoneura genotype, 38 % were amplified in U. humidicola, 99 % were amplified in U. ruziziensis, and 92 % were amplified in U. brizantha. Amplification of 33 % of the microsatellite markers was achieved for all of the evaluated species. The microsatellite markers Dec07, Dec31, Dec33, Dec77 and Dec108 were only transferable for U. ruziziensis species (see Additional file 1).

Based on the allelic frequencies determined using STRUCTURE software [23], 28 % of the alleles are rare (frequency < 0.05), 57 % of these alleles are of intermediate abundance (0.05 < frequency < 0.30), and 15 % are abundant alleles (frequency > 0.30). We observed 43 rare alleles that are specific for U. decumbens, eight rare alleles specific for U. humidicola, seven specific for U. dictyoneura, four alleles specific for U. brizantha and two rare alleles specific for U. ruziziensis.

The Bayesian analysis performed using STRUCTURE software [23] revealed that the 34 Urochloa genotypes could be distributed into three distinct clusters (Fig. 1), as determined from the ΔK values that were generated using Structure Harvester software [24, 25] (see Additional file 2). Using a K value of three, 15 genotypes were allocated into Cluster I (6 to 9), 13 genotypes were grouped into Cluster II (21 to 19) and six genotypes were allocated into Cluster III (27 to 32) (Fig. 1).
Fig. 1

Analysis performed using an admixture model in STRUCTURE 2.3.4 software with correlated allele frequencies. The clustering profile obtained at K = 3 is indicated by different colors. Each of the 34 genotypes is represented by a single column broken into colored segments with lengths proportional to each of the K inferred gene pools. The scale on the left indicates the membership coefficients (Q) used to allocate the genotypes into clusters. The genotypes were named according to the annotated numbers listed in Table 1. Cluster I (from 6 to 9), Cluster II (from 21 to 19) and Cluster III (from 27 to 32)

The genetic distance values that were determined using Jaccard’s coefficient ranged from 0.06 (D062 and R10) to 0.76 (H016 and D009) (see Additional file 3). The unrooted neighbor-joining tree successfully discriminated all of the tested genotypes (Fig. 2).
Fig. 2

Unrooted neighbor-joining tree based on Jaccard’s coefficient for the 34 genotypes of the Urochloa species. The genotypes were named according to the annotated numbers listed in Table 1. The colors of the branches represent the clusters identified in Fig. 1, as follows: red Cluster I; green Cluster II; blue Cluster III

Discussion

In this report, we have described the first set of microsatellite markers for U. decumbens, which is an important tropical forage grass for which there is limited genetic information. The availability of a robust set of informative molecular markers is essential to accelerating its breeding programs as well as for germplasm characterization, genetic map development and marker-assisted selection.

In the present study, dinucleotide repeats were the most abundant class of microsatellites detected, followed by tetra, tri, mono, hexa and pentanucleotide repeats. Dinucleotide motifs have been found to be the most abundant type of microsatellites in plant genomes [28, 29]. Notably, the high occurrence of dinucleotide motifs can be attributed to both of the evaluated libraries having been enriched using (CT)8 and (GT)8 probes.

In total, 93 SSR markers were characterized, 82 of which were found to be polymorphic (88 %). The loci that did not exhibit polymorphism in the genotypes that we evaluated may be useful in other studies.

The Polymorphism Information Content (PIC) is an index used to qualify a marker for genetic studies and reflects the level of polymorphism detected. Seventy-seven markers tested in U. decumbens genotypes were found to be highly informative (PIC > 0.5) and five markers were found to be moderately informative (0.25 < PIC < 0.5), based on a previously proposed classification system [30] (Table 2). The Dec48 marker had the highest PIC value, 0.85, and the Dec91 marker had the lowest value, 0.26. The average PIC values for all of the markers was 0.68 (Table 2), indicating a high level of polymorphism.

To determine whether these molecular markers could discriminate the genotypes of U. decumbens, the discrimination power (DP) of each SSR locus was computed. The PD values ranged from 0.22 (Dec14 and Dec91) to 0.97 (Dec12), with an average value of 0.77.

The most informative loci in this panel of SSRs were Dec12, Dec48, Dec86 and Dec97 because they had the highest PIC and DP values (Table 2). In contrast, the Dec91 locus had low PIC and DP values (0.26 and 0.22, respectively), as expected due to its low levels of polymorphism and cross- amplification in all of the other Urochloa species tested, which suggests that this locus is a conserved region [11].

Structure analysis showed that the genotypes were distributed in three clusters and that each cluster was characterized by a set of allele frequencies at each locus and was represented by different colors (red, green and blue) as shown in Fig. 1. The best K number of clusters was determined using the ΔK method [24] and implemented in the online tool Structure Harvester [25] (see Additional file 2).

Cluster I included fifteen U. decumbens genotypes plus the U. ruziziensis genotypes, Cluster II contained only U. decumbens genotypes, and Cluster III contained the others Urochloa species, including U. dictyoneura, U. humidicola and U. brizantha (Fig. 1). The clustering of some of the U. decumbens genotypes with U. ruziziensis genotypes may be explained by the genetic proximity of these species [11, 13, 31, 32]. This fact is reflected in the allelic pools that are identified with different colors in Fig. 1.

Cluster II included genotypes 19 and 20, and six hybrids derived from crosses between these two genotypes that were grouped together (Fig. 1). These hybrids are members of an F1 population that will be mapped using the polymorphic SSRs described in this study. In Cluster III, which included three different Urochloa species, the predominant allelic pool is represented in blue, and only the U. brizantha genotypes showed some percentage of the red allelic pools, demonstrating their genetic proximity to U. decumbens (Fig. 1).

The tree constructed based on Jaccard’s coefficient successfully discriminated all of the tested genotypes (Fig. 2) and showed a distribution of these genotypes similar to that obtained using STRUCTURE software [23] (Fig. 1), although the two types of analysis used different statistical approaches. Moreover, this tree and the allelic pools that were determined indicated that U. decumbens and U. ruziziensis are more closely related to one another than to the other species (Figs. 1 and 2).

Based on the genetic values obtained using Jaccard’s coefficient, the lowest genetic distance was observed between the D062 and R10 genotypes (0.06). The R10 genotype should correspond to a hybrid that originated from a cross between D062 and D24/27, but the genetic distance observed shows that it is likely a false hybrid, which demonstrates the importance of using molecular markers to discriminate genotypes. The highest genetic distance (0.76) was observed between the D009 and H016 genotypes, representing U. decumbens and U. humidicola species, respectively, which are genetically distant species [11, 13, 31, 32] (see Additional file 3).

All of the microsatellite markers were transferable to at least one different species of the Urochloa genus, and 33 % of the markers were successfully amplified in all of the species, indicating their absolute transferability. The highest level of transferability was observed in U. ruziziensis, followed by U. brizantha, U. dictyoneura and U. humidicola (see Additional file 1). The higher proportion of successful PCR amplification for the U. ruziziensis and U. brizantha genotypes indicates the closer phylogenetic distance between these species and U. decumbens. Thus, U. brizantha, U. decumbens and U. ruziziensis form an agamic complex and produce fertile hybrids [33, 34], enhancing the Urochloa breeding program.

Silva et al. [12] developed 198 polymorphic microsatellite markers for U. ruziziensis and found that the percentages of markers potentially transferable to U. decumbens and U. humidicola were 92.9 % and 42.9 %, respectively, corroborating our results. Others studies showed that U. brizantha and U. ruziziensis are more closely related to U. decumbens than to U. humidicola and U. dictyoneura [11, 13, 31, 32]. Marker transferability is effective in reducing the time and cost of initial studies aimed at identifying microsatellite markers in related species; thus, these markers could be used in genetics studies, such as in those concerning intra-species molecular characterization, species differentiation, molecular identification, and characterization of interspecific hybrids [14].

The success of a breeding program can be accelerated by the effective use of molecular markers. Thus, the SSR markers developed in this study will be useful for U. decumbens breeding programs and possibly for those of other related Urochloa species.

Availability of supporting data

The datasets supporting the results of this article are included in the article.

Abbreviations

AN: 

annotation number

bp: 

base pairs

CAPES: 

Coordination of Improvement of Higher Education Personnel

CTAB: 

cetyltrimethyl ammonium bromide

DNA: 

deoxyribonucleic acid

DP: 

discrimination power

EBC: 

Embrapa Beef Cattle

EMBRAPA: 

Brazilian Agricultural Research Corporation

K: 

number of clusters

MCMC: 

Markov Chain Monte Carlo

NA: 

number of alleles

NJ: 

neighbor joining

PCR: 

polymerase chain reaction

PIC: 

polymorphism information content

Q: 

association coefficient determined using STRUCTURE analysis

QTL: 

quantitative trait loci

SSR: 

simple sequence repeat

Syn: 

synonym

Ta (°C): 

annealing temperature

Declarations

Authors’ contributions

LJC developed the microsatellite-enriched libraries. RCUF conducted the bioinformatics searches to identify the microsatellites, designed the flanking primers, validated the microsatellite markers, performed the statistical analysis and drafted the manuscript. CBV and LC participated in the design and implementation of the study. LC helped draft the manuscript. APS conceived and supervised the study and helped to draft the manuscript. All authors read and approved the final manuscript.

Acknowledgements

The authors thank the Fundação de Amparo à Pesquisa de SP (FAPESP 08/52197-4) and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES—Computational Biology Program) for grants; the Brazilian Agricultural Research Corporation (EMBRAPA Beef Cattle) for providing the Urochloa genotypes used. RCUF is a recipient of a graduate fellowship from CAPES-EMBRAPA Program.

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Centro de Biologia Molecular e Engenharia Genética (CBMEG), Universidade Estadual de Campinas (UNICAMP)
(2)
EMBRAPA Genetic Resources and Biotechnology, Brazilian Agricultural Research Corporation
(3)
EMBRAPA Beef Cattle, Brazilian Agricultural Research Corporation
(4)
Departamento de Biologia Vegetal, Instituto de Biologia, Universidade Estadual de Campinas (UNICAMP)

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© Ferreira et al. 2016