SSRS banana

FUNPEC-RP www.funpecrp.com.brGenetics and Molecular Research 11 (2): 1093-1098 (2012)

Research Note

New microsatellite markers for bananas (Musa spp)

E.P. Amorim1, P.H. Silva2, C.F. Ferreira1, V.B.O. Amorim1, V.J. Santos3, A.D. Vilarinhos1, C.M.R. Santos4, M.T. Souza Jnior5 and R.N.G. Miller6

1Embrapa Mandioca e Fruticultura, Cruz das Almas, BA, Brasil2Universidade Federal de Lavras, Lavras, MG, Brasil3Universidade Federal do Recncavo da Bahia, Cruz das Almas, BA, Brasil4Embrapa Recursos Genticos e Biotecnologia, Braslia, DF, Brasil5Embrapa Agroenergia, Braslia, DF, Brasil6Departamento de Biologia Celular, Instituto de Cincias Biolgicas, Universidade de Braslia, Braslia, DF, Brasil

Corresponding author: E.P. AmorimE-mail: [email protected]

Genet. Mol. Res. 11 (2): 1093-1098 (2012)Received September 12, 2011Accepted January 16, 2012Published April 27, 2012DOI http://dx.doi.org/10.4238/2012.April.27.8

ABSTRACT. Thirty-four microsatellite markers (SSRs) were identified in EST and BAC clones from Musa acuminata burmannicoides var. Calcutta 4 and validated in 22 Musa genotypes from the Banana Germplasm Bank of Embrapa-CNPMF, which includes wild and improved diploids. The number of alleles per locus ranged from 2 to 14. The markers were considered highly informative based on their polymorphism information content values; more than 50% were above 0.5. These SSRs will be useful for banana breeding programs, for studies of genetic diversity, germplasm characterization and selection, development of saturated genetic linkage maps, and marker assisted selection.

Key words: Genetic breeding; Microsatellites; Primer validation;Musa spp

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INTRODUCTION

Bananas (Musa spp) are grown in more than 120 tropical and subtropical countries, mainly by small farmers. In 2010, the total harvested area and fruit production were approxi-mately 4.7 million hectares and 102 million tons, respectively (FAO, 2012). Such numbers indicate the importance of bananas as a strong commodity, playing key economic and social roles in many countries producing bananas worldwide.

Banana breeding programs are currently focused on the introgression of diverse traits that range from disease resistance/tolerance to yield and fruit quality (Heslop-Harrison and Shawarzacher, 2007). Molecular genetic studies are of fundamental importance for increas-ing our knowledge base and resources for accelerated genetic improvement of the crop, by allowing the analysis of genetic diversity, construction of genetic linkage maps, and quan-titative trait locus mapping of markers for alleles linked to resistance to biotic and abiotic stresses of particular importance. The availability of reliable and robust molecular markers such as simple sequence repeat (SSRs) might contribute to the acceleration of Musa breeding programs.

Although many Musa SSRs have been developed thus far by using a number of differ-ent strategies (Jarret et al., 1994; Crouch et al., 1997; Lagoda et al., 1998; Creste et al., 2003, 2006; Wang et al., 2008; Miller et al., 2010), the number of SSR loci available for genetic analysis is still limited, with many more validated and polymorphic SSRs required (Creste et al., 2004; Wang et al., 2009).

MATERIAL AND METHODS

A total of 52 SSR primer pairs were developed in this study from Musa acumina-ta expressed sequence tag and bacterial artificial chromosome clone consensus sequence data available in the DATA Musa database (http://bioinformatics.cenargen.embrapa.br/musagene/tiki-index.php?page=DATAMUSA) (Togawa et al., 2010). For this, sequences were analyzed using an in-house bioinformatics pipeline that detects microsatellite loci on the basis of a PERL script that scans for imperfect repeat SSRs using the scan-for-matches program (Dsouza et al., 1997). A maximum of 5 primer sets per SSR locus were designed using Primer3 (Rozen and Skaletsky, 2000). Optimization of polymerase chain reaction (PCR) amplification conditions was performed using the Fast PCR professional 5.2 software (http://www.biocenter.helsinki.fi/bi/programs/fastpcr.htm).

Microsatellites were then optimized and validated using M. acuminata accessions ranging from wild to improved diploids. For this, plantlets of 11 wild banana diploids (Bur-manica, Calcutta-4, Microcarpa, Berlin, Lidi, Khai Nai On, Nyarma Yik, Jari Buaya, Raja Uter, Sowmuk, and Tjau Lagada) and 11 improved diploids (017041-01, SH32-63, 042079-06, 013018-01, 003023-03, 013004-06, 001016-01, 091079-03, F2P2, 003115-Plant-1, and 15-Plant-2) from the Musa germplasm bank maintained at Embrapa Cassava and Fruits Research Centre (www.cnpmf.embrapa.br) were selected. These 22 diploids were chosen on the basis of agronomical characteristics and disease resistance.

Genomic DNA was extracted from young leaves of the wild and improved banana diploids by using the cetyltrimethylammonium bromide (CTAB) method proposed by Doyle

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Musa spp microsatellite primer validation

and Doyle (1990). PCRs contained 30 ng DNA, 1.5 mM MgCl2, 100 M each dNTPs, 0.2 mM each primer, and 0.75 U Taq polymerase in 10X buffer (Biosystems) in a final reaction volume of 15 L. Amplifications were carried out using a BIORAD MyCycler-Thermal Cycler with the following amplification program: 94C for 3 min, followed by 30 cycles of 94C for 40 s, 55C for 40 s (annealing temperature modified according to the primer used), and 72C for 1 min. A final polymerase extension step at 72C for 4 min was also included, followed by a final incubation at 10C. PCR optimization in-cluded identification of the optimal MgCl2 concentrations and annealing temperatures by using 3 different protocols. Amplification products were electrophoresed on 6% de-naturing polyacrylamide gels and silver stained according to the protocol described by Creste et al. (2001). Allele size was estimated using a 50-bp ladder molecular size stan-dard (Invitrogen).

RESULTS AND DISCUSSION

SSR markers combine several features of an ideal genetic marker, owing to their abundance and widespread dispersal in genomes, hypervariability, co-dominant nature, ac-cessibility, reliability, and ease of interpretation (Gaudeul et al., 2004). Therefore, assess-ment of Musa genetic variability solely by using SSR markers continues to be widely used (e.g., Amorim et al., 2008; Jesus et al., 2009).

Of the 52 primer pairs tested in this study, 1 was monomorphic (data not shown), 3 did not yield any PCR product, and 14 required adjustment in PCR conditions. Thirty-four SSR primer combinations produced polymorphic DNA amplification products and were validated as being useful markers for genetic studies in Musa spp (Table 1).

The number of alleles per primer varied from 2 to 14, with an average number of 5.73. Markers CNPMF-1, -9, -15, -21, -24, -25, and -56 presented the lowest number of alleles (2) and marker CNMPF-45 yielded the highest number of alleles (14). This CNPMF-series of SSR markers was classified according to their repeat motifs. The mi-crosatellites were compared with the NCBI public database (http://www.ncbi.nih.gov/BLAST/) using the BLASTx-Swissprot option. A cut-off E-value of

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E.P. Amorim et al.

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1097


Musa spp microsatellite primer validation

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7979

0.

7525

0.

4890

0

.013

0*

Unk

now

n -

-9

-A44

_300

0

R: T

TTG

CA

CTT

TGTT

CA

GG

CA

G C

NPM

F-56

M

ICR

O_G

CT_

M

abC

NPM

FBac

-56

F: A

AC

ATG

CA

GA

GG

GA

GTG

GTC

(G

CT)

6 2

309

56

0.60

94

0.78

95

0.20

24

0.00

30

1.00

0 Pr

otei

n B

revi

s rad

ix-li

ke 2

; 6e

-40

00

01-6

--12

--10

-

R: A

TTG

CTG

ATG

GA

GAT

GG

AG

G

O

sBR

XL2

A

40_6

0C

NPM

F-57

M

ICR

O_A

G_

Mab

CN

PMFB

ac-5

7 F:

TAT

CA

AG

CC

TAAT

CG

GC

CA

C

(AG

) 7 8

312

52

0.80

79

0.54

45

0.78

57

1.00

00

0.0

000*

So

lubl

e st

arch

synt

hase

3,

2e-2

1

0032

.SEQ

R: T

GC

ATC

AA

AA

ATTT

CC

AG

CTC

chlo

ropl

astic

/am

ylop

last

icC

NPM

F-60

M

ICR

O_A

G

Mab

CN

PMFB

ac-6

0 F:

TG

AA

ATC

TGA

AC

CC

TGG

TGG

(G

A) 18

8

243

56

0.78

95

0.89

47

0.76

15

0.83

90

0.72

00

NA

C1; t

rans

crip

tion

fact

or

2e-4

2

0044

.SEQ

0

R: A

CG

CA

CA

CA

CA

CA

CA

CA

ATG

CN

PMF-

61

MIC

RO

_AG

_ M

abC

NPM

FBac

-61

F: T

GAT

GC

TCTT

AA

CC

CTT

GC

C

(GA

) 18

8 26

1 56

0.

8306

0.

8182

0.

8080

0.

8820

0.

2120

N

AC1

; tra

nscr

iptio

n fa

ctor

2e

-42

00

44.S

EQ01

R: C

GG

TCC

GAT

TCA

ATAT

CG

TC

Tabl

e 1.

Con

tinue

d.

Nam

e, G

enba

nk a

cces

sion

No.

, loc

us n

ame,

prim

er s

eque

nce

(5'-3

'), re

peat

mot

if, N

A =

num

ber o

f alle

les;

bp

= ba

se p

air o

rigin

al c

lone

d al

lele

; Ta

= op

timum

an

neal

ing

tem

pera

ture

; HE =

exp

ecte

d he

tero

zygo

sity

; HO =

obs

erve

d he

tero

zygo

sity

; PIC

= p

olym

orph

ism

info

rmat

ion

cont

ent;

LD =

link

age

dise

quili

briu

m;

HW

E =

Har

dy-W

einb

erg

equi

libriu

m, B

LAST

x-Sw

issp

rot a

nd E

-val

ue f

or 3

4 M

usa

spp

SSR

loci

val

idat

ed in

22

bana

na a

cces

sion

s ra

ngin

g fr

om w

ild to

im

prov

ed d

iplo

ids.

*Sig

nifi

cant

dep

artu

re f

rom

HW

E.

1098


E.P. Amorim et al.

ACKNOWLEDGMENTS

Research partially supported by the CNPq - the National Council for Scientific and Technological Development (Project #506165/2004-3) and Embrapa (Macroprograma 2 - Ba-nana Genetic Breeding).

REFERENCES

Amorim EP, Reis RV, Santos-Serejao JA, Amorim VBO, et al. (2008). Variabilidade gentica estimada entre diplides de banana por meio de marcadores microssatlites. Pesq. Agropec. Bras. 43: 1045-1052.

Belkhir K, Boesa P, Goudet J, Chikhi L, et al. (1999). Genetix Lociciel Sous Windows TM pour la Genetique ds Populations Laboratoire Genome. CNRS UPR, Universit de Montpellier II, Montpellier.

Creste S, Tulmann-Neto A and Figueira A (2001). Detection of single sequence repeat polymorphism in denaturing polyacrylamide sequencing gels by silver staining. Plant Mol. Biol. Rep. 19: 299-306.

Creste S, Tulmann-Neto A, Silva SO and Figueira A (2003). Genetic characterization of banana cultivars (Musa spp.) from Brazil using microsatellite markers. Euphytica 132: 259-268.

Creste S, Tulmann-Neto A, Vencovsky R, Silva SO, et al. (2004). Genetic diversity of Musa diploid and triploid accessions from the Brazililan banana breeding program estimated by microsatellite markers. Genet. Resour. Crop Evol. 51: 723-733.

Creste S, Benatti TR, Orsi MR and Risterucci A-M (2006). Isolation and characterization of microsatellite loci from a commercial cultivar of Musa acuminata. Mol. Ecol. Notes 6: 303-306.

Crouch JH, Crouch HK, Ortiz R and Jarret RL (1997). Microsatellite markers for molecular breeding of Musa. InfoMusa 6: 5-6.

Doyle JJ and Doyle JL (1990). Isolation of plant DNA from fresh tissue. Focus 12: 13-15.Dsouza M, Larsen N and Overbeek R (1997). Searching for patterns in genomic data. Trends Genet. 13: 497-498.FAO (2012). FAOSTAT. Available at [http://faostat.fao.org]. Accessed April 12, 2012.Gaudeul M, Till-Bottraud I, Barjon F and Manel S (2004). Genetic diversity and differentiation in Eryngium alpinum L.

(Apiaceae): comparison of AFLP and microsatellite markers. Heredity 92: 508-518.Heslop-Harrison JS and Shawarzacher T (2007). Domestication, genomics and the future for banana. Ann. Bot. 100:

1073-1084.Jarret RL, Bhat KV, Cregan P, Ortiz P, et al. (1994). Isolation of microsatellite DNA markers in Musa. InfoMusa 3: 3-4.Jesus ON, Ferreira CF, Silva SO, Cmara TR, et al. (2009). Characterization of recommended banana cultivars using

morphological and molecular descriptors. Crop Breed. Appl. Biotechnol. 9: 164-173.Lagoda PJ, Noyer JL, Dambier D, Baurens FC, et al. (1998). Sequence tagged microsatellite site (STMS) markers in the

Musaceae. Mol. Ecol. 7: 659-663.Liu K and Muse SV (2005). PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics

21: 2128-2129.Miller RN, Passos MA, Menezes NN, Souza MT Jr, et al. (2010). Characterization of novel microsatellite markers in Musa

acuminata subsp. burmannicoides, var. Calcutta 4. BMC Res. Notes 3: 148.Rozen S and Skaletsky H (2000). Primer3 on the www for general users and for biologist programmers. Methods Mol.

Biol. 132: 365-386.Vilarinhos AD, Piffanelli P, Lagoda P, Thibivilliers S, et al. (2003). Construction and characterization of a bacterial

artificial chromosome library of banana (Musa acuminata Colla). Theor. Appl. Genet. 106: 1102-1106.Wang JY, Chen YY, Liu WL and Wu YT (2008). Development and application of EST-derived SSR markers for bananas

(Musa nana Lour.). Yi Chuan 30: 933-940.Wang JY, Zheng LS, Huang BZ, Liu WL, et al. (2009). Development, characterization, and variability analysis of

microsatellites from a commercial cultivar of Musa acuminata. Genet. Resour. Crop Evol. 57: 553-563.

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