BIHAREAN BIOLOGIST 9 (1): 47-54 ©Biharean Biologist, Oradea, Romania, 2015 Article No.: 141131 http://biozoojournals.ro/bihbiol/index.html

Genetic diversity and phylogenetic relationship of Iranian indigenous cucurbits investigated by Inter Simple Sequence Repeat (ISSR) markers

Ehsan ESMAILNIA, Mehdi AREFRAD*, Samira SHABANI, Mohammadreza KARIMI,

Fatemeh VAFADAR and Ali DEHESTANI

Genetics and Agricultural Biotechnology Institute of Tabarestan (GABIT) Sari, Mazandaran, Iran. P.O.Box: 578, Tel: +98 151 382575

*Corresponding author, M. Arefrad, E-mail: mehdiarefrad@yahoo.com

Received: 27. April 2014 / Accepted: 22. September 2014 / Available online: 11. April 2015 / Printed: June 2015

Abstract. Inter Simple Sequence Repeat (ISSR) markers were employed to study the genetic diversity among 30 indigenous species of Iranian cucurbit from five different genera of Cucurbitaceae. Eleven out of seventeen studied primers amplified a total of 283 bands, out of which 263 (92.93%) were polymorphic. The mean Polymorphism Information Content (PIC) was estimated at 0.327. Among the primers, ISSR15 exhibited the highest polymorphic bands and PIC value and was recognized as the most appropriate and discriminating primer to investigate genetic diversity. The results of clustering analysis showed that the least distance was observed between two species of Cucumis L. genus from Mashhad and Sabzevar with a genetic similarity value of 68.34%, while the highest genetic distance was observed between two species of Cucumis L. and Cucurbita L. genera from Dastgerd and Hamadan with a genetic similarity value of 20.71%. Although Cucurbita maxima and Cucurbita maschata were genetically similar, they weren't classified in the same cluster. These results indicated an extensive genetic variation within Iranian Cucumis L. and Cucurbita L. germplasm. The high genetic variability among studied species would be beneficial for selection of a core collection to facilitate germplasm management to be used in cucurbit breeding and conservation programs. The results of the present study confirmed that fingerprinting of cucurbits genotypes for identification purposes could be achieved by the ISSR technique.

Key words: Cucurbitaceae, intra and inter genus, genetic diversity, similarity matrix, ISSR markers.

Introduction Cucurbits (Cucurbitaceae) are among the most important plant families supplying humans with edible products and useful fibers (Bisognin 2002). The main diversity center of cucurbits was traditionally believed to be in Africa, however recent molecular systematic studies, suggested that they may be primarily originated from central and Southeast Asia, West Africa, Madagascar and Mexico (Schaefer & Renner 2011, Raghami et al. 2014). In addition, Sebastian et al. (2010) proposed that melon and cucumber were both originated from Asia and had numerous relatives in Austra-lia and around the Indian Ocean which were overlooked previously.

Several species of Cucurbitaceae are economically more important than others including: melon (Cucumis melo L.); cucumber (Cucumis sativus L.); watermelon (Citrullus lana-tus); summer squash (Cucurbita pepo); winter squash (Cucur-bita maschata); pumpkin (Cucurbita maxima); Bottlegourd (La-genaria siceraria) and Loofah (Luffa acutangula) (Bisognin 2002). Although inter-specific hybridization of them have been employed in breeding programs more than in any other family, there is still a high potential for increasing its application for germplasm and cultivar development (Bi-sognin 2002). Iran is one of the major cucurbit producers in the world, as accounted for more than half of the total vege-table production and more than 150,000 ha area of agricul-tural land is devoted to cucurbit cultivation (second cucum-ber and gherkins producer) (FAO 2012). While there is no improved cultivar of cucurbit family for growing commer-cially in Iran, and farmers have maintained local population and exchanged seeds with surrounding areas (Barzegar et al. 2013). For an effective breeding program, information con-cerning the nature of genetic diversity within and among species is a prerequisite for any crop improvement program. Genetic diversity is commonly measured by genetic distance or genetic similarity (Weir 1990). On the other hand, land-

races are important source of genetic diversity for improve-ment of cultivated species and they could be applied in breeding programs.

Many studies have been employed to assess the genetic diversity among the cucurbit family all over the world (Be-hera et al. 2008, Hadia et al. 2008, Dje et al. 2010, Ji et al. 2012, Manohar et al. 2012, Zhang et al. 2012). In comparison to the world, few studies have been conducted to illustrate the ge-netic variability and genetic phylogeny of the Iranian cucur-bits. On the other hand, existing information is far from drawing a precise picture of the genetic relationships in this family. Some recent investigations through molecular meth-ods have distinguished some unknown genotypes. In this way Feyzian et al. (2007) assessed 38 Iranian melon acces-sions using RAPD markers, however these markers were not able to discriminate horticultural groups of melon. Soltani et al. (2010) showed a large variability in the Iranian melon germplasm using RAPD, but they focused mainly on Cucu-mis melo var. flexuosus. Raghami et al. (2014) studied 24 Ira-nian melon accessions along with 28 other melon genotypes from other countries using SSR markers and reported a low genetic variability for Iranian melon germplasm.

Many methods have been employed to assess the genetic diversity among the cucurbit family, ranging from morpho-logical traits to molecular markers. Recently, most of the studies have been designed for assessment of genetic varia-tion using molecular markers because they show genetic dif-ferences on a more detailed level without interferences from environmental factors and they can be highly polymorphic (e.g. Levi et al. 2004, Behera et al. 2008, Yi et al. 2009, Kong et al. 2011). Various types of DNA markers such as RFLPs (Garcia-Mas et al. 2000), AFLPs (Ferriol et al. 2003), SSRs (Barzegar et al. 2013, Raghami et al. 2014), RAPD (Oshing-boye et al. 2013) and ISSR (Dje et al. 2010) have been used to determine genetic diversity in different species of Cucurbita-ceae. Inter-simple sequence repeats (ISSRs) had been devel-oped based on the microsatellite loci and the similar princi-

E. Esmailnia et al.

48

ple of RAPD. This molecular marker was reported to detect a higher portion of genomic variation than RFLP and was con-sidered to achieve a higher reproducibility than RAPD markers (Zietkiewicz et al. 1994, Dalamu et al. 2012). Also ISSR marker technology is repeatable, effective, simple and quick, which combines together the advantages of RAPD, SSR and AFLP resulting in lower cost as well as required DNA amounts (Reddy et al. 2002, Hadia et al. 2008, Wang et al. 2009, Dje et al. 2010). In addition, ISSR markers have a greater robustness in repeatability and show a high variabil-ity (Bornet & Branchard 2001). It seems that ISSR molecular markers are more useful because they show high genetic polymorphism, providing valuable site information and re-vealing the various microsatellite variations between indi-viduals (Reddy et al. 2002).

A correct understanding of the genetic relationship and genetic variability of the plant populations is an essential prerequisite for any successful breeding program. In order to complete the information of Iranian cucurbit germplasm and develop the production of improved cucurbits cultivars this investigation was designed. The objective of this study was to identify inter-genus and intra-genus phylogenetic rela-tionships, genetic diversity and distance among and within some cultivated, commercial and wild species of Cucurbita-ceae from different regions of Iran using ISSR molecular marker.

Materials and methods Plant material Thirty different species of Cucurbitaceae were investigated; twenty six local species from five distinct genera (Cucumis L., Cucurbita L., Lagenaria L., Citrullus L. and Luffa L.), three commercial cultivars and one wild species. Seeds of these species were collected from several provinces of Iran including Tehran, Mazandaran, Golestan, Isfahan, Gilan, Hamadan, Khuzestan, Azarbaijan, Khorasan-Razavi and Semnan (Fig. 1). Among thirty species, twenty one species were be-longed to Cucumis L. genus, six species related to Cucurbita L. genus and one species from each of Luffa L., Lagenaria L. and Citrullus L. genus (Table 1). The seeds of thirty species were planted in Genetics and Agricultural Biotechnology Institute of Tabarestan (GABIT) re-search farm and the young, newly developed leaves were collected for subsequent DNA extraction.

DNA extraction Approximately 100 mg of fresh young leaves of each accession was grinded into powder with liquid nitrogen. Then the genomic DNA was extracted through Dellaporta method (Dellaporta et al. 1983) with some modifications. DNA pellet was dissolved in 50 μL of ddH2O. Extracted DNA was quantified on 0.8% agarose gel, with a run time of 60 min at 90 V in 1X TAE buffer, and the concentration was evaluated by spectrophotometer. Finally, the DNA samples were stored at - 20 °C for further studies.

Figure 1. Geographical distribution of the sampling area from which the investigated genotypes were collected.

Polymerase Chain Reaction (PCR) and gel electrophoresis A preliminary screening was carried out; using seventeen inter sim-ple sequence repeat (ISSR) primers in order to select those with po-lymorphic, reproducible bands. Standardized PCR reaction mixture consisted 12 μL of a solution containing 8.19 μL of water, 70 ng ge-nomic DNA, 0.2 μL (1 unit per reaction) of Taq DNA polymerase, 0.32 μL dNTP mix, 0.94 μL primer in 1X PCR reaction buffer. Ampli-fication condition were performed in a thermocycler (Applied Bio-systems, Budapest, Hungary) which were programmed as follows: an initial denaturation step at 94 °C for 5 min followed by 35 ampli-fication cycles with three steps for each: 1 min denaturation at 94 °C, 1 min annealing at 53-56 °C, and 1 min elongation at 72 °C. The reac-tions were followed by a 7 min extension at 72 °C and were eventu-

Table 1. Names and origin of the 30 cucurbit genotypes studied using ISSR markers.

Code Name Sampling region Code Name Sampling region 1 Cucumis melo var. reticulatus Gorgab 16 Cucumis melo Dezful 2 Cucurbita maxima Sanandaj 17 Cucumis melo Garmsar 3 Cucumis melo Dezful 18 Citrullus lanatus Commercial 4 Cucumis melo Gorgan 19 Cucurbita maschata Babol 5 Cucumis melo Sabzevar 20 Cucumis melo var. cantalupensis Varamin 6 Luffa acutangula ---------- 21 Cucumis sativus Dastgerd 7 Lagenaria siceraria Babolsar 22 Cucumis melo Commercial 8 Cucumis melo Sabzevar 23 Cucurbita maschata Shahrod 9 Cucumis melo var. cantalupensis Varamin 24 Cucumis melo Sari 10 Cucumis sativus Babol 25 Cucurbita maschata Babol 11 Cucumis melo Gorgan 26 Cucumis melo Mashhad 12 Cucumis melo Commercial 27 Cucumis melo var. dudaim Rasht 13 Cucumis melo Mashhad 28 Cucurbita maxima Urmia 14 Cucumis melo Sabzevar 29 Cucumis melo Dezful 15 Cucumis melo var. agrestis Gorgan 30 Cucurbita maxima Hamadan

Genetic diversity of Iranian cucurbits

49

ally stored at 4 °C. The results of amplification reactions were ana-lyzed on 1.8% agarose gel in 1X TAE buffer and run at 85 V for 2 hours. Thereafter, amplified fragments were stained with ethidium bromide solution and visualized under UV radiation (Sambrook et al. 2001). Data analysis After visualization and documentation of gels, data analyses were performed. In the genetic relationship study, each amplified frag-ment was treated as a unit character regardless of its intensity and scored in terms of a binary code (presence-1, absence-0) in each stud-ied genotype, thus creating a binary data matrix. Since ISSR is a dominant marker, the presence of a band was interpreted as either a heterozygote or dominant homozygote and the absence of a band as a recessive homozygote. The data matrix structure was assembled by binomial (0/1) data and used as input data for further analysis using NTSYS version 2.02 software program (Rohlf 1998). To test whether clusters in the dendrogram agreed with information from the simi-larity matrix, cophenetic correlation matrices were created from the dendrogram and compared with the similarity matrix. Similarity for ISSR data was computed using the Jaccard’s similarity index and cluster diagrams were generated with the Unweighted Pair Group Method using Arithmetic averages (UPGMA) algorithm. The result-ing clusters were expressed as dendrogram. Allelic polymorphism Information Content (PIC) was calculated for each locus by the for-mula: PIC = 2Pi (1 - Pi) (Bhat 2002). Principal Component Analysis (PCA), a multivariate approach which is more informative regarding distances among major groups (Hauser & Crovello 1982), was per-formed. This can complement the cluster analysis and identify pat-terns of association among different accessions. Results Evaluation of ISSR PCR results Characterization of the genetic relationship between culti-vated, commercial, and wild species of some important gen-era of Cucurbitaceae was conducted by 17 ISSR primers, out of which 11 primers exhibited clear and reproducible frag-ments with multiband patterns in each genotype that were selected for further analysis and considered as informative and polymorphic primers (Fig. 2). Amplification with the se-lected primers allowed the visualization of 283 bands with sizes between 200 to 2500 bp, of which 263 bands were po-lymorphic (92.27%). The highest number of bands was pro-duced by ISSR9 (total of 36 bands) and the lowest number of bands was obtained by ISSR17 (18 bands), with an average number of 25.72 bands per primer. The percentage of poly-morphism ranged from 72% for ISSR20 to 100% for ISSR9, ISSR11, ISSR15 and ISSR18, with an average of 92.27% polymorphism per primer. The ISSR18 exhibited the highest size variation of amplified fragments (200-2500 bp) and the lowest size variation was observed for ISSR20 (200-800 bp). The PIC value varied from 0.221 (ISSR20) to 0.391 (ISSR15), with an average of 0.327. The highest and lowest PIC values were obtained by ISSR15 and ISSR20, respectively. Charac-teristics of all primers have been shown in Table 2.

Genetic relationship and clustering analysis Genetic similarity was varied from 20.71% to 68.34%, with an average of 41.7%. The highest similarity was observed among two species of Cucumis L. genus (C. melo L. from Mashhad and Sabzevar) with a genetic similarity value of 68.34% (Table 3). Also it was revealed that the least similar-ity was observed between two species of Cucumis L. and Cu-

curbita L. genera (C. sativus L. from Dastgerd and C. maxima from Hamadan, respectively) with a genetic similarity value of 20.71%.

To construct a dendrogram, the cophenetic correlation coefficient, a measure of the correlation between the similar-ity represented on the dendrogram and the actual degree of similarity was calculated for each genotype. Among differ-ent methods, the highest value was observed for UPGMA based on Jaccard’s coefficient (r = 0.876) corresponding to a good fit. The dendrogram divided the investigated species into seven major clusters (Fig. 3). The first main cluster with a genetic similarity value of 40% divided into two sub-clusters in which all species of Cucumis melo L. were located. The first sub-clusters with a genetic similarity value of 47% comprises of six species of which four species related to C. melo (G3, G4, G5 and G8 ) and two others related to two va-rieties of C. melo including var. reticulatus (G1) and var. can-talupensis (G9). The second sub-cluster with a genetic simi-larity value of 45.5% consists of thirteen species of C. melo (G11, G12, G13, G14, G15, G20, G22, G29, G26, G24, G27, G16 and G17). The second main cluster formed by species of Cu-cumis sativus (G10 and G21) which has a genetic similarity value of 42.9%. The third main cluster consisted of two sub-clusters with a genetic similarity value of 40.5%. In the first sub-cluster the only species of Luffa L. genus (G6) located and in the second one two species of Cucurbita maschata (G19 and G25) and one species of Cucurbita maxima (G28) placed. Three species of Citrullus lanatus (G18), Lagenaria siceraria (G7) and one species of Cucurbita maschata (G23) separately made the fourth, fifth and sixth clusters respectively. Two species of C. maxima (G2 and G30) made the seventh cluster with a genetic similarity value of 30% (Fig. 3).

Principle Component Analysis (PCA) was performed based on the similarity matrix to describe the variability and relationship among accessions in a two-dimensional mode. There was a high accordance between the results of PCA and clustering dendrogram but accessions weren't separated precisely by PCA when compared with UPGMA dendro-gram (Fig. 4).

Discussion The information on polymorphism is useful in the assess-ment of genetic diversity, genetic relationships and in the breeding programs (Hadia et al. 2008). High level of poly-morphism between investigated species of Cucurbitaceae was separately obtained by Stepansky et al. (1999) in Cucu-mis melo L.; Levi et al. (2004) in Citrullus lanatus; Dey et al. (2006) in bitter gourd; Hadia et al. (2008) in three species of Cucurbita L. genus (C. pepo, C. maxima and C. moschata); Dje et al. (2010) in C. lanatus and Manohar et al. (2012) in Cucu-mis sativus. In addition some other reports demonstrated that RAPD markers showed lower polymorphism with (6.9 bands/alleles and 70% of polymorphic bands) than ISSR with (9 bands/alleles and 90% of polymorphic bands) among C. melo (Stepansky et al. 1999). Moreover, Paris et al. (2003) and Sensoy et al. (2007) illustrated in Cucurbita pepo and C. melo respectively that ISSR markers frequently de-tected a higher level of polymorphism than that detected with other dominant markers (RAPD or AFLP). These re-

E. Esmailnia et al.

50

Figure 2. DNA fingerprinting patterns for 30 genotypes of Cucurbitaceae. ISSR profiles obtained with primers A, ISSR12 and B, ISSR7. M, Molecular DNA marker.

Table 2. List of 11 ISSR primers, annealing temperatures, number of polymorphic band, percentage of poly-

morphic bands and Polymorphic Information Content (PIC = 2 pi (1 - pi)) generated by per primer used for assessment of genetic diversity.

PIC Polymorphism (%)

No. of polymorphic fragment

Total no. of band

Annealing temperatures (°C)

Sequence (5' to 3')

Primer

0.337 90 26 29 54 C(GA)8 ISSR 7 0.352 100 36 36 55 (AG)8C ISSR 9 0.353 94 30 32 55 (AG)8G ISSR 10 0.363 100 26 26 55 (GA)8C ISSR 11 0.304 89 23 26 54 (GA)8A ISSR 12 0.350 95 18 19 54 (TC)8C ISSR 13 0.391 100 20 20 55 (AC)8G ISSR 15 0.246 83 15 18 54 (AC)8C ISSR 17 0.373 100 30 30 55 (ATC)6T ISSR 18 0.309 92 24 26 56 (ATC)6C ISSR 19 0.221 72 15 21 56 (ATG)6G ISSR 20

263 283 Total 0.327 92.27 23.91 25.72 Mean

sults confirmed that not only ISSR markers are extensive po-lymorphic within the studied species, but also they are espe-cially efficient to distinguish the differences between geno-types and usefulness of them for genetic relationship analy-sis which can be useful in the breeding programs of cucurbit family.

Polymorphic Information Content (PIC) is a parameter that refers to the value of a marker for detecting the degree of polymorphism within a population. To determine PIC values of each ISSR primer we analyzed the mean of PIC values for all loci of each ISSR primer. It is assumed that a PIC > 0.5 accounts for a highly informative marker, 0.5 > PIC > 0.25 for an informative marker, and PIC < 0.25 for a slightly informative marker (Botstein et al. 1980). In the pre-sent study the polymorphic bands have PIC value between 0.25 and 0.5 which indicating that the ISSR markers used in the present study were informative and they could be effec-tively used in genetic diversity studies and breeding pro-grams. Behera et al. (2008) when defined the genetic diver-sity of bitter gourd, evaluated the PIC value of 0.17 and 0.40 for RAPD and ISSR respectively. Furthermore Inan et al. (2012) illustrated the greater PIC values of 0.73 in some Cu-curbita species by ISSR markers. The greater discriminatory power of ISSR markers may be due to comparatively higher

values of average polymorphic information content as well as the diverse nature of the genotypes.

The relative efficiency of molecular markers can be measured by the amount of polymorphism and PIC value of accessions under investigation (Grativol et al. 2011). Also, Wang et al. (2009) stated that the relationship between geno-types depended on number and frequency of the amplified fragments per primers. As a result, ISSR15 primer with the highest polymorphic bands and PIC value was recognized as the most appropriate and discriminating primer to estimate genetic similarity among species of Cucurbitaceae.

Genetic similarity obtained in the present study, imply-ing a high level of genetic variation between investigated species (Table 3). These results is comparable with the re-sults of Mohammed et al. (2012) who reported that the ge-netic similarity coefficients ranged between 0.15 and 0.62 in 10 cucurbits species using thirteen RAPD primers. Whereas, Sidkar et al. (2010) reported lower genetic similarities ranged from 9% to 30%, with a mean of 19% in eight genera of Cu-curbitaceae by ISSR markers. The wide range of genetic variation by ISSR markers in the present study revealed high level of diversity among the Iranian germplasm of cucurbits species.

All over the worlds (especially in Iranian) few studies

Ta

ble

3. G

enet

ic si

mila

rity

val

ues a

mon

g th

e 30

mem

bers

of I

rani

an C

ucur

bita

ceae

gen

otyp

es a

s det

erm

ined

by

11 IS

SR m

arke

rs u

sing

Jacc

ard

sim

ilari

ty c

oeffi

cien

t.

1

2 3

4 5

6 7

8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

2 0.

30

1.00

3 0.

50

0.32

1.

00

4 0.

47

0.29

0.

54

1.00

5 0.

47

0.31

0.

52

0.67

1.

00

6 0.

33

0.31

0.

36

0.37

0.

45

1.00

7 0.

30

0.30

0.

29

0.35

0.

34

0.33

1.

00

8 0.

40

0.27

0.

45

0.52

0.

58

0.32

0.

32

1.00

9 0.

51

0.26

0.

44

0.49

0.

52

0.36

0.

30

0.50

1.

00

10

0.35

0.

29

0.31

0.

38

0.39

0.

35

0.37

0.

37

0.36

1.

00

11

0.

35

0.27

0.

39

0.51

0.

46

0.33

0.

36

0.51

0.

49

0.36

1.

00

12

0.36

0.

30

0.40

0.

46

0.48

0.

35

0.35

0.

49

0.45

0.

39

0.59

1.

00

13

0.

38

0.28

0.

35

0.43

0.

46

0.35

0.

32

0.48

0.

53

0.36

0.

61

0.66

1.

00

14

0.36

0.

29

0.40

0.

49

0.48

0.

36

0.36

0.

48

0.48

0.

37

0.56

0.

64

0.68

1.

00

15

0.

30

0.29

0.

31

0.40

0.

39

0.31

0.

35

0.38

0.

40

0.38

0.

44

0.48

0.

48

0.48

1.

00

16

0.32

0.

26

0.29

0.

34

0.33

0.

33

0.27

0.

36

0.39

0.

36

0.40

0.

38

0.45

0.

42

0.35

1.

00

17

0.

36

0.24

0.

38

0.39

0.

38

0.37

0.

27

0.42

0.

40

0.30

0.

44

0.41

0.

46

0.49

0.

39

0.58

1.

00

18

0.31

0.

28

0.33

0.

32

0.37

0.

34

0.29

0.

35

0.34

0.

34

0.38

0.

32

0.32

0.

35

0.31

0.

36

0.39

1.

00

19

0.

34

0.32

0.

34

0.38

0.

37

0.44

0.

32

0.33

0.

31

0.32

0.

32

0.32

0.

32

0.36

0.

33

0.25

0.

31

0.30

1.

00

20

0.36

0.

28

0.32

0.

40

0.44

0.

34

0.36

0.

41

0.42

0.

37

0.42

0.

49

0.51

0.

52

0.46

0.

40

0.49

0.

28

0.30

1.

00

21

0.

30

0.27

0.

32

0.37

0.

33

0.26

0.

32

0.36

0.

31

0.43

0.

35

0.34

0.

32

0.34

0.

35

0.31

0.

37

0.34

0.

25

0.38

1.

00

22

0.33

0.

30

0.38

0.

49

0.45

0.

34

0.35

0.

48

0.41

0.

42

0.50

0.

48

0.46

0.

55

0.46

0.

40

0.46

0.

33

0.36

0.

63

0.45

1.

00

23

0.

28

0.30

0.

28

0.30

0.

30

0.34

0.

30

0.29

0.

28

0.26

0.

34

0.30

0.

34

0.35

0.

29

0.23

0.

28

0.27

0.

36

0.33

0.

29

0.34

1.

00

24

0.36

0.

32

0.34

0.

41

0.44

0.

35

0.36

0.

41

0.40

0.

42

0.41

0.

44

0.44

0.

47

0.47

0.

33

0.40

0.

34

0.35

0.

53

0.35

0.

54

0.33

1.

00

25

0.

31

0.31

0.

32

0.37

0.

35

0.40

0.

30

0.31

0.

30

0.32

0.

35

0.29

0.

35

0.34

0.

36

0.26

0.

29

0.30

0.

57

0.32

0.

32

0.38

0.

34

0.36

1.

00

26

0.34

0.

30

0.31

0.

35

0.42

0.

33

0.26

0.

37

0.42

0.

33

0.39

0.

42

0.47

0.

42

0.38

0.

35

0.42

0.

32

0.34

0.

49

0.35

0.

50

0.33

0.

46

0.33

1.

00

27

0.

35

0.31

0.

36

0.42

0.

44

0.34

0.

30

0.40

0.

37

0.40

0.

47

0.43

0.

41

0.45

0.

43

0.38

0.

44

0.31

0.

31

0.46

0.

35

0.52

0.

29

0.48

0.

35

0.49

1.

00

28

0.35

0.

26

0.33

0.

38

0.38

0.

36

0.32

0.

38

0.38

0.

31

0.36

0.

37

0.33

0.

38

0.33

0.

27

0.35

0.

26

0.46

0.

38

0.32

0.

39

0.30

0.

38

0.52

0.

38

0.37

1.

00

29

0.

34

0.29

0.

39

0.44

0.

44

0.36

0.

29

0.45

0.

49

0.41

0.

47

0.46

0.

47

0.47

0.

51

0.37

0.

45

0.34

0.

35

0.54

0.

39

0.64

0.

33

0.54

0.

37

0.63

0.

59

0.42

1.

00

30

0.25

0.

40

0.25

0.

30

0.33

0.

34

0.31

0.

34

0.28

0.

26

0.34

0.

33

0.35

0.

35

0.34

0.

31

0.26

0.

32

0.39

0.

30

0.21

0.

32

0.29

0.

38

0.32

0.

32

0.34

0.

29

0.32

1-

Cucu

mis

melo

var

. ret

icul

atus

, 2- C

ucur

bita

max

ima,

3- C

ucum

is m

elo, 4

- Cuc

umis

mel

o, 5

- Cuc

umis

melo

, 6- L

uffa

acu

tang

ula,

7- L

agen

aria

sic

erar

ia, 8

- Cuc

umis

melo

, 9- C

ucum

is m

elo v

ar. c

anta

lupe

nsis

, 10-

Cuc

umis

sa

tivus

, 11-

Cuc

umis

melo

, 12-

Cuc

umis

melo

, 13-

Cuc

umis

melo

, 14-

Cuc

umis

melo

, 15-

Cuc

umis

melo

var

. agr

estis

, 16-

Cuc

umis

melo

, 17-

Cuc

umis

melo

, 18-

Citr

ullu

s lan

atus

, 19-

Cuc

urbi

ta m

asch

ata,

20-

Cuc

umis

melo

var

. ca

ntal

upen

sis,

21-

Cuc

umis

sativ

us, 2

2- C

ucum

is m

elo, 2

3- C

ucur

bita

mas

chat

a, 2

4- C

ucum

is m

elo, 2

5- C

ucur

bita

mas

chat

a, 2

6- C

ucum

is m

elo, 2

7- C

ucum

is m

elo v

ar. d

udai

m, 2

8- C

ucur

bita

max

ima,

29-

Cuc

umis

melo

, 30

- Cuc

urbi

ta m

axim

a.

E. Esmailnia et al.

52

Figure 3. Dendrogram based on Jaccard’s similarity coefficient and UPGMA algorithm showing the genetic relationship among 30 genotypes of Cucurbitaceae family analyzed by ISSR markers.

Figure 4. 2D plot derived from Principal Component Analysis (PCA) of 30 cucurbits accessions based on ISSR molecular markers.

have been conducted about genetic diversity at inter- and in-tra- genus level of Cucurbitaceae. Based on the genetic simi-larity value, UPGMA dendrogram and PCA diagram, the highest similarity was observed among two species of Cu-cumis genus (C. melo from Mashhad and Sabzevar) with a genetic similarity value of 68.34% (Table 3, Fig. 3 and 4). A similar result was reported in Turkish melons (Sensoy et al. 2007) and cucumber (Hu et al. 2010). By contrast, after ana-lyzing combined chloroplast sequences for 123 of the 130 genera of Cucurbitaceae, Renner et al. (2007) concluded that genetic distance between Cucumis sativus and C. melo was over estimated compared to their genetic distance based on the morphological characteristics. Zhang et al. (2012) re-ported a high genetic similarity between C. sativus and C. melo based on using molecular data and morphological traits. Using SSR markers to study the Cucurbitaceae, Weng (2010) demonstrated a high genetic similarity between melon

and cucumber (0.933). Interestingly, Kocyan et al. (2007) ap-praised phylogenetic relationships within Cucurbitaceae based on chloroplast DNA sequences from two genes (Intron and spacers) and indicated a high similarity between C. melo and C. sativus. These results altogether strongly suggested that different species of Cucumis genus are genetically simi-lar, whereas different species of Cucumis and Cucurbita gen-era are genetically distant from each other.

Awareness the phylogenetic relationships in the genus Cucumis are important, because the germplasm and natural composition can provide valuable information to improve melon and cucumber breeding (Zhang et al. 2012). Accord-ing to the results all the Cucumis melo L. species sit in the first cluster. Among them, two species from Mashhad and Sab-zevar were the most closely related species with a similarity value of 68.34% (Table 3). It was also observed that although two species of Cucumis melo (G3 and G16) were collected

Genetic diversity of Iranian cucurbits

53

from same geographical origin (Dezful), they illustrated the low similarity value of 29% (Table 3). These results are to some extent in agree with the Mohammed et al. (2012) who reported that the most similarity was observed between two species of C. melo with a genetic similarity of 62%. On the other hand, In Japan Nakata et al. (2005) evaluated 67 melon cultivars by RAPD and SSR markers and then clustered them into three horticultural groups. It can be comprehend that C. melo species due to their genetic similarity, located in separate cluster and because of the inevitable out-crossing, this genetic variation generated among the C. melo popula-tion (Fig. 3).

Two of the nineteen investigated species of Cucumis ge-nus (G12 and G22) were commercial cultivars and belonged to inodorous varieties. G12 showed low genetic distance with inodorous variety from Mashhad (G13) and Sabzevar (G14) with a similarity value of 66% and 64% respectively. Also the other commercial cultivar (G22) indicated low ge-netic distance with inodorous variety from Dezfol (G29) with a similarity value of 64%. These results could somehow indi-cate that these native genotypes were ancestors of the recent commercial cultivars. So it can be realized that because of the observed distance among Iranian melon germplasm, their Intra-specific hybrids could play an important role in development of modern melon cultivars.

Cucurbita genus is a member of the economically impor-tant Cucurbit family and all the species of this genus origi-nated from America (Hadia et al. 2008). Iran is not in the primary center of diversification of the Cucurbita genus; however two species of them i.e. C. maxima and C. moschata are planted extensively in Iran. Based on the results, these two species were not distinguished completely and they were irregularly located in various clusters. The G28 (C. maxima from Urmia) located distance from G2 and G30 (C. maxima from Sanandaj and Hamedan respectively), on the other hand the G23 (C. maschata from Shahrod) placed dis-tance from G25 and G19 (C. maschata from Babol). In this way Inan et al. (2012) and Hadia et al. (2008) reported the same result that, C. maxima and C. maschata were located in two separate sub-clusters of one main cluster. Furthermore, Mohammed et al. (2012) and Kocyan et al. (2007) illustrated that although both C. maxima and C. maschata belong to Cu-curbita genus, they were not classified in the same cluster. It can be understood that there is an extensive genetic diversity within various species of Cucurbita genus.

The findings of the present study confirmed that finger-printing of important species of Iranian Cucurbitaceae germ-plasm for identification purposes and breeding programs could be easily achieved by the ISSR technique. It was re-vealed that not only ISSR markers (especially for ISSR15) are extensively polymorphic within the cucurbits species, but also they are especially efficient for distinguishing the differ-ences between species. Our results also demonstrated a great genetic variability among different species of Iranian Cucu-mis and Cucurbita genera, which would be useful for cucur-bit breeding and conservation programs to enhance the effi-ciency of germplasm management.

Acknowledgements. This research was conducted with the financial support of the Genetics and Agricultural Biotechnology Institute of Tabarestan (GABIT), Sari, Iran. References Barzegar, R., Peyvast, G.h., Ahadi, A.M., Rabiei, B., Ebadi, A.A., Babagolzadeh,

A. (2013): Population structure and genetic variability studies among Iranian Cucurbita (Cucurbita pepo L.) accessions, using genomic SSRs and implications for their breeding potential. Biochemical Systematics and Ecology 50: 187–198.

Behera, T.K., Singh, A.K., Staub, J.E. (2008): Comparative analysis of genetic diversity in Indian bitter gourd (Momordica charantia L.) using RAPD and ISSR markers for developing crop improvement strategies. Scientia Horticulturae 115: 209–217.

Bhat, K.V. (2002): Proceedings of the short-term training course on molecular marker application in plant breeding. Molecular data analysis, New Delhi: ICAR, 5, 26-Oct.

Bisognin, D.A. (2002): Origin and evolution of cultivated cucurbits. Cicncia Rural 32: 715-723.

Bornet, B., Branchard, M. (2001): Nonanchored Inter Simple Sequence Repeat (ISSR) markers: Reproducible and specific tools for genome fingerprinting. Plant Molecular Biology Reporter 19: 209-215.

Botstein, D., White, R.L., Skolnick, M., Davis, R.W. (1980): Construction of genetic linkage map in man using restriction fragment length polymorphisms. American Journal of Human Genetics 32: 314-331.

Dalamu., Behera, T.K., Gaikwad, A.B., Saxena, S., Bharadwaj, C., Munshi, A.D. (2012): Morphological and molecular analyses define the genetic diversity of Asian bitter gourd (Momordica charantia L.). Australian Journal of Crop Science 6: 261-267.

Dellaporta, S.L., Wood, J., Hicks, J.B. (1983): A plant DNA minipreparation. Plant Molecular Biology Reporter 1: 19-21.

Dje, Y., Tahi, C.G., Bi, A.L.Z., Baudoin, J.P., Bertin, P. (2010): Use of ISSR markers to assess genetic diversity of African edible seeded Citrullus lanatus landraces. Scientia Horticulturae 124: 159-164.

Dey, S.S., Singh, A.K., Chandel, D., Behera, T.K. (2006): Genetic diversity of bitter gourd (Momordica charantia L) genotypes revealed by RAPD markers and agronomic traits. Scientia Horticulturae 109: 21–28.

FAO (2012): FAOSTAT agricultural database. <http://apps.fao.org>. Ferriol, M., Pico, M.B., Nuez, F. (2003): Genetic diversity of some accessions of

Cucurbita maxima from Spain using RAPD and SBAP markers. Genetic Resources and Crop Evolution 50: 227-238.

Feyzian, E., Javaran, M.J., Dehghani, H., Zamyad, H. (2007): Analysis of the genetic diversity among some of Iranian melon (Cucumis melo L.) landraces using morphological and RAPD molecular markers. Journal of Agricultural Science and Technology 11: 151–162.

Garcia-Mas, J., Oliver, M., Gomez-Paniagua, H., De vicente, M.C. (2000): Comparing AFLP, RAPD and RFLP markers for measuring genetic diversity in melon. Theoretical and Applied Genetics 101: 860–864.

Grativol, C., da Fonseca Lira-Medeiros, C., Hemerly, A.S., Ferreira, P.C.G. (2011): High efficiency and reliability of inter-simple sequence repeats (ISSR) markers for evaluation of genetic diversity in Brazilian cultivated Jatropha curcas L. accessions. Molecular Biology Reports 38: 4245–4256

Hadia, H.A., Abdel-Razzak, H.S., Hafez, E.E. (2008): Assessment of genetic relationships among and within Cucurbita species using RAPD and ISSR markers. Journal of Applied Sciences Research 4: 515-525.

Hauser, L.A., Crovello, T.J. (1982): Numerical analysis of genetic relationships in Thelypodieae (Brassicaceae). Systematic Botany 7: 249-268.

Hu, J., Li, J., Liang, F., Liu, L., Si, S. (2010): Genetic relationship of a cucumber germplasm collection revealed by newly developed EST-SSR markers. Journal of Genetics 89: 28–32.

Inan, N., Yildiz, M., Sensoy, S., Kafkas, S., Abak, K. (2012): Efficacy of ISSR and SRAP techniques for molecular characterization some Cucurbita genotypes including naked (hull-less) seed pumpkin. The Journal of Animal & Plant Sciences 22: 126-136.

Ji, Y., Luo, Y., Hou, B., Wanga, W., Zhao, J., Yang, L., Xueb, Q., Dingb, X. (2012): Development of polymorphic microsatellite loci in Momordica charantia (Cucurbitaceae) and their transferability to other cucurbit species. Scientia Horticulturae 140: 115–118.

Kocyan, A., Zhang, L.B., Schaefer, H., Renner, S.S. (2007): A multi-locus chloroplast phylogeny for the Cucurbitaceae and its implications for character evolution and classification. Molecular Phylogenetics and Evolution 44: 553–577.

Kong, Q., Xiang, C., Yang, J., Yu, Z. (2011): Genetic variations of Chinese melon landraces investigated with EST-SSR markers. Journal of Horticultural Science & Biotechnology 52: 163-169.

E. Esmailnia et al.

54

Levi, A., Thomas, C.E., Newman, M., Reddy, O.U.K., Zhang, X., Xu, Y., (2004): ISSR and AFLP markers differ among watermelon cultivars with limited genetic diversity. Journal of the American Society for Horticultural Science 129: 553–558.

Manohar, S.H., Murthy, H.N. (2012): Estimation of phenotypic divergence in a collection of Cucumis melo including shelf-life of fruit. Scientia Horticulturae 148: 74-82.

Mohammed, I.A., Gumaa, A.G.N., Kamal, N.M., Alnor, Y.S., Ali, A.M. (2012): Genetic Diversity among Some Cucurbits Species Determined by Random Amplified Polymorphic DNA (RAPD) Marker. International Journal of Plant Research 2: 131-137.

Nakata, E., Staub, J.E., Lopez-Sese, A.I., Nurit, K. (2005): Genetic diversity of Japanese melon cultivars (Cucumis melo L.) as assessed by random amplified polymorphic DNA and simple sequence repeat markers. Genetic Resources and Crop Evolution 52: 405–419.

Oshingboye, A.D., Adeyemi, T.O., Ogundipe, O.T. (2013): Phylogenetic and genomic relationships among melon populations based on RAPD. International Journal of Botany 9(2): 91-95.

Paris, H.S., Yonah, N., Portnoy, V., Mozes-Daube, N., Tzuri, G., Katzir, N. (2003): Assessment of genetic relationships in Cucurbita pepo (Cucurbitaceae) using DNA markers. Theoretical and Applied Genetics 106: 971-978.

Raghami, M., Lopez-Sese, A.I., Hasandokht, M.R., Zamani, Z., Fattahi Moghadam, M.R., Kashi, A. (2014): Genetic diversity among melon accessions from Iran and their relationships with melon germplasm of diverse origins using microsatellite markers. Plant Systematics and Evolution 300: 139-151.

Reddy, M.P., Sarla, N., Siddiq, E.A. (2002): Inter simple sequence repeat (ISSR) polymorphism and its application in plant breeding. Euphytica 128: 9–17.

Renner, S.S., Schaefer, H., Kocyan, A. (2007): Phylogenetics of Cucumis (Cucurbitaceae): Cucumber (C. sativus) belongs in an Asian/Australian clade far from melon (C. melo). BMC Evolutionary Biology 7: 58–69.

Rohlf, F.J. (1998): NTSYS-pc numerical taxonomy and multivariate analysis system Version 2.02. Exeter Publications Setauket, New York.

Sambrook, J., Maccallum, P., Russel, D. (2001): Molecular cloning: A laboratory manual. 3rd ed. Cold Springs Harbour Press, New York.

Schaefer, H., Renner, S.S. (2011): Cucurbitaceae. pp. 112–174. In: Kubitzki, K. (ed), Families and genera of flowering plants, Springer.

Sebastian, P., Schaefer, H., Telford, I.R.H., Renner, S.S. (2010): Cucumber (Cucumis sativus) and melon (C. melo) have numerous wild relatives in Asia

and Australia, and the sister species of melon is from Australia. Proceedings of the National Academy of Sciences of the USA 107: 14269–14273.

Sensoy, S., Buyukalaca, S., Abak, K. (2007): Evaluation of genetic diversity in Turkish melons (Cucumis melo L.) based on phenotypic characters and RAPD markers. Genetic Resources and Crop Evolution 54: 1351-1365.

Sidkar, B., Bhattacharya, M., Mukherjee, A., Banerjee, A., Ghosh, E., Ghosh, B., Roy, S.C. (2010): Genetic diversity in important members of Cucurbitaceae using isozyme, RAPD and ISSR markers. Biologia Plantarum 54: 135-140.

Soltani, F., Akashi, Y., Kashi, A., Zamani, Z., Mostofi, Y., Kato, K. (2010): Characterization of Iranian melon landraces of Cucumis melo L. Groups Flexuosus and Dudaim by analysis of morphological characters and random amplified polymorphic DNA. Breeding Science 60: 34–45.

Stepansky, A., Kovalski, I., Perl-Treves, R. (1999): Intra-specific classification of melons (Cucumis melo L.) in view of their phenotypic and molecular variation. Plant Systematics and Evolution 217: 313-333.

Wang, A., Yu, Z., Ding, Y. (2009): Genetic diversity analysis of wild close relatives of barley from Tibet and the Middle East by ISSR and SSR markers. Comptes Rendus Biologies 332: 393–403.

Weir, B.S. (1990): Genetic data analysis methods for discrete genetic data. Sinauer Associates Incorporated, Sunderland, Mass., U.S.A.

Weng, Y. (2010): Genetic diversity among Cucumis metuliferus populations revealed by cucumber microsatellites. Hortscience 45: 214–219.

Yi, S.S., Akashi, Y., Tanaka, K., Cho, T.T., Khaing, M.T., Yoshino, H., Nishida, H., Yamamoto, T., Kyaw, W., Kato, K. (2009): Molecular analysis of genetic diversity in melon landraces (Cucumis melo L.) from Myanmar and their relationship with melon germplasm from East and South Asia. Genetic Resources and Crop Evolution 56: 1149–1161.

Zhang, W.W., Pan, J.S., He, H.L., Zhang, C., Li, Z., Zhao, J.L., Yuan, X.J., Zhu, L.H., Huang, S.W., Cai, R. (2012): Construction of a high density integrated genetic map for cucumber (Cucumis sativus L.). Theoretical and Applied Genetics 124: 249–259.

Zietkiewicz, E., Rafalski, A., Labuda, D. (1994): Genome fingerprinting by simple sequence repeat (SSR)-anchored polymerase chain reaction amplification. Genomics 20: 176-183.