Abstract: This study was conducted to develop DNA fingerprint patterns of released sorghum [Sorghum bicolor (L.) Moench] varieties in Ethiopia and to assess their genetic relationships. Twelve sorghum lines were genotyped using 39 SSR markers. The SSR analysis showed that 11 of the released lines could be identified by 28 positive and 4 negative unique alleles. E36-1 was identified by seven positive markers, i.e., mSbCIR238, mSbCIR240, Xcup53, Xtxp012, Xtxp145, Xtxp273 and Xtxp320 each having unique allele. B35 was differentiated by four positive; gpsb067, mSbCIR240, Xtxp012, Xtxp015 and two negative markers; Xtxp040 and SbAGB020. Baji could be identified by five positive unique markers; mSbCIR276, Xgap206, Xtxp021, Xtxp141 and Xtxp265 and one negative; Xtxp278 marker. Birmash was identified by four positive markers; Xcup14, Xtxp141, Xtxp145 and Xtxp320. Hormat and Teshale were differentiated with two positive markers each, i.e., Xtxp265, Xtxp320 and gpsb067, Xtxp021, respectively. The other four lines: Abshir, Birhan, Gambella-1107 and Gobye were uniquely identified by one positive marker each; Xtxp265, Xtxp320, mSbCIR238 and Xtxp057, respectively. However, Meko-1 was not uniquely identified by any of the markers used. Genetic dissimilarity among the lines ranged from 0.326-0.839 with an average of 0.672 and the genotypes were grouped into five clusters. The DNA data base generated could be used for proper identification of lines, control of infringement and determine seed mixtures. The information on genetic relationship can be used to select parental lines for crossing programme for development of hybrid sorghum varieties.
INTRODUCTION
Sorghum [Sorghum bicolor (L.) Moench] is an important crop in many parts of the world and used as food, feed and industrial purposes. It is a major crop in many parts of Africa and some Asian countries. Sorghum ranked fifth among the cereals produced worldwide after rice, wheat, maize and barley and third in Ethiopia after maize and teff (FAOSTAT., 2011). It is estimated that more than 300 million people especially from developing countries rely on sorghum as source of energy (Godwin and Gray, 2000). Some of the industrial uses of sorghum include preparation of beer, adhesives, dye, resins, ethanol and fuel (House, 1985; NAS., 1996).
In most cases registration and protection of crop varieties rely on morphological traits. The registration involves the documentation and recording of a series of relevant characters (UPOV., 2005) on both candidate and control varieties over at least a two year period. This system, coupled with the fact that discrimination among closely related types is often possible only at later stages of the plant cycle. This makes characterization on the basis of morphological characters difficult, time consuming and ambiguous which results in much interest in the use of molecular markers to complement, assist and/or validate the Distinctiveness Uniformity and Stability (DUS) analysis (De Riek et al., 2001; Tommasini et al., 2003; Galovic et al., 2006).
DNA-based molecular markers provide a solution by providing unique DNA profiles for the protection of new developed varieties, seed purity test and to characterize the varieties. These markers offer high resolving power, high degree of non-tissue specific polymorphism and free of environmental influence (Perry, 2004). The DNA fingerprinting helps to identify and differentiate crop varieties that might be difficult to characterize due to similar morphological characteristics or indistinct traits and to identify plants containing genes of interest such as the confirmation of transformation events (Galovic et al., 2006).
Now-a-days, breeders and seed industries use DNA based markers to develop "fingerprint" patterns of their varieties so as to identify the varieties thereby to protect their breeder’s rights and to avoid disputes arising from variety ownership claims. However, fingerprinting and genetic relationships among sorghum varieties and lines released for production in Ethiopian has not been done to date. Therefore, this study was initiated to develop a DNA fingerprint pattern of released lines in and to assess their genetic relationship thereby the information generated could be used by breeders and seed companies to distinguish the released varieties and choose potential parental lines for crossing to develop hybrid varieties.
MATERIALS AND METHODS
Plant materials: The study consists of 12 sorghum lines provided by Melkassa and Sirinka Agricultural Research Centers. The list of released lines is presented in Table 1.
SSR markers: Thirty nine Simple Sequence Repeats (SSRs) were used in the study, including 22 di, 9 tri and 4 tetra nucleotide or longer motifs and 4 compound repeats. These SSR markers were selected based on their uniform distribution in the sorghum genome. These are the same set of markers that were selected and used by the Generation Challenge Programme for genetic diversity assessment of global sorghum germplasm. List of the SSR markers, including primer sequences, information on repeat motif and length are given in Table 2.
| Table 1: | List of sorghum lines used in the DNA fingerprinting study |
| *NA: Information not available | |
| Table 2: | List of SSR markers, primer sequences, repeat motif and annealing temperatures |
DNA extraction: Seedlings were grown in the greenhouse during November, 2010. Fresh leaves of 10 individual plants were harvested in bulk from 14 days old seedlings and dried with silica gel in zip locked plastic bags and used for DNA extraction. The DNA was extracted following a modified CTAB (Cetyl Trimethyl Ammonium Bromide) extraction protocol (Mace et al., 2003) at BecA Laboratory, Nairobi, Kenya. A labeled 96 well tube box that contained one stainless steel grinding ball in each tube were put in ice bucket containing liquid nitrogen to chill the tubes.
Approximately 1.2 cm2 sorghum leaves were placed into 96 well strip tubes and slid with forceps to about 5 mm above the steel ball strip, caps were put tightly on the tubes, a third-folded paper towel on top of them and covered with a lid and stored at -20°C until ready to grind. Caps were removed carefully and about 450 μL of preheated (65°C) extraction buffer (100 mM Tris-HCl [pH 8], 1.4 M NaCl, 20 mM EDTA, 3% CTAB and 0.17% β-mercaptoethanol) was added to each sample and closed with caps. Samples were then macerated using a Geno/grinder (Geno 2000, Sigma) at 500 strokes/min for 2 min. The macerated samples were incubated for 40 min at 65°C in water bath with occasional mixing. Solvent extraction was done by adding 450 μL chloroform: isoamylalcohol (24:1) to each tube and inverted twice to mix. The tubes were centrifuged at 3500 rpm for 15 min and the entire upper aqueous layer was transferred to fresh strip tubes. About 500 μL of pre-cooled isopropanol (stored at -20°C) was added and inverted three times to mix and kept in -20°C freezer for 2 h. Then the tubes were centrifuged at 3500 rpm for 30-35 min. The supernatant was then decanted, the pellet was air dried for 30 min and dissolved in 200 μL low salt Tris-EDTA (TE) buffer (10 mM Tris, 0.1 mM EDTA, pH 8). To remove RNA from the DNA solution, 3 μL RNase A (10 mg mL–1) was added to each sample and incubated for 30 min at 37°C. A second phase solvent extraction was done by adding 200 μL chloroform: isoamyl alcohol (24:1) to each sample and inverted twice to mix and centrifuged at 3500 rpm for 15 min.
The upper layer was transferred to new strip tubes and 500 μL ethanol: sodium acetate solution was added to each sample, then inverted twice to mix and placed in -20°C for 2 h and then centrifuged at 3,500 rpm for 30 min. The supernatant was decanted and the pellets washed with 200 μL 70% precooled ethanol. The tubes were centrifuged at 3,500 rpm for 5 min, the supernatant decanted and the pellet air-dried for about an hour. Finally, pellets from each sample were dissolved in 100 μL low-salt TE buffer and stored at -20°C.
Determination of DNA quality and concentration: The quality and quantity of the isolated DNA was determined by comparing the fluorescence of aliquots of DNA samples with a known concentration of l-DNA after running them on 0.8% agarose gel (8 g agarose dissolved in100 mL 1×TBE) buffer that contained 0.3 mg mL–1 ethidium bromide solution. For this purpose, samples were prepared by mixing 3 mL of the DNA solution, 3 mL loading dye and 4 mL distilled water and loaded on the gel and run at 120 volts for 30 min in 1×TBE buffer. At the end of electrophoresis, the gel was visualized using UV light and photographed using a video capture (Flowgen IS 1000). All samples were normalized to the same concentration level and used for PCR.
Polymerase chain reaction conditions and amplifications: The PCR was performed using Gene-Amp PCR System 9600 (PE-Applied Biosystems) in 96 well plates in a total reaction volume of 10 μL that consisted of 1 μL DNA, 1 μL PCR buffer, 2 μL MgCl2, 1.0 μL reverse primer, 1.0 μL forward primer directly labelled with 6-FAM (VIC, NED, PET fluorescent dyes), 0.5 μL of each dNTP, 0.04 μL Taq DNA polymerase and 3.46 H2O. The amplification profile consisted of initial denaturation of the template DNA at 95°C for 3 min, followed by 35 cycles, each for 30 sec at 95°C (denaturation), 1 min at 56°C (annealing) and 1 min at 72°C (extension) and a final extension at 72°C for 3 min.
Capillary electrophoresis: After the PCR, a small amount of samples from each primer pair product were randomly selected and checked for proper amplification and product intensity on 2% agarose gel and an ABI plate was prepared with a total volume of 10 μL (9.0 μL from a mix of an injection solution (1 mL formamide and 12 μL GS500 LIZ (Perkin Elmer-Applied Biosystems) for 96 well platesand 1.0 μL of PCR products from each of the 6-FAM, VIC, NED and PET-labelled PCR products were pooled together).
The DNA fragments were denatured at 95oC for 3 mins, chilled quickly and size-fractioned using ABI 3730 capillary DNA Sequencer (Perkin Elmer-Applied Biosystems). In this system, the labeled PCR products were detected using a laser and capillary electrophoresis based on their fluorescent dye and fragment size. The peaks were sized and the alleles mapped using Gene Mapper software version 3.7 (Perkin Elmer-Applied Biosystems) and presented as alleles scored as estimated fragment sizes in base pairs compared to the internal size standard GS500LIZ-3730.
Data analysis: The pair-wise Genetic Similarity (GS) matrix was calculated based on Jaccard’s similarity coefficient (Jaccard, 1908): MSij = Nij/(Nii+Nij+Njj), where MSij is the DNA marker similarity index between the ith and jth genotype, Nij is the number of bands present in both genotypes Nii is the number of bands present in the ith genotype but lacking in the jth genotype and Njj is the number of bands lacking in the ith genotype but present in the jth genotype.
Clustering was done by an agglomerative hierarchical classification (Rohlf, 1992), employing unweighed pair group method using arithmetic averages (UPGMA). To test the goodness of fit of clustering to the similarity matrix, co-phenetic correlation (r) was calculated using the equation:
r = (Σ Xi Yi-Σ Xi ΣYi/n)/SXi SYi
where, Xi and Yi are the similarity or distance values of the original and cophenetic matrix, respectively. The SXi and SYi are the standard deviations for each variable. Data analysis was doneusing Darwin5 statistical package version 5.0.158.
RESULTS AND DISCUSSION
Identification of varieties using unique markers: Unique markers are defined as bands that specifically identify varieties from the others by their presence or absence. As previously mentioned by El-Awady et al. (2008), alleles that are present in one variety but not found in the others are termed Positive Unique Markers (PUM), whereas a Negative Unique Markers (NUM) is the opposite of positive markers. In the present study, it was possible to differentiate the 11 released lines using SSR markers. Twenty one SSRs out of the 39 revealed 32 unique alleles (28 positive and 4 negative) (Table 3).
| Table 3: | Unique positive and negative SSR alleles with their sizes for Ethiopian released sorghum lines |
| -: Absent | |
The E36-1 was identified by seven markers, i.e., mSbCIR238, mSbCIR240, Xcup53, Xtxp012, Xtxp145, Xtxp273 and Xtxp320 each having unique allele. B35 was differentiated by four PUM, i.e., gpsb067, mSbCIR240, Xtxp012, Xtxp015 and two NUM, i.e., Xtxp040 and SbAGB020. Baji could be identified by five positive unique markers (mSbCIR276, Xgap206, Xtxp021, Xtxp141 and Xtxp265) and one negative (Xtxp278). Birmash was identified by four positive markers (Xcup14, Xtxp141, Xtxp145 and Xtxp320). Hormat and Teshale were differentiated with two positive markers each, i.e., Xtxp265, Xtxp320 and gpsb067, Xtxp021, respectively. The other four lines-Abshir, Birhan, Gambella-1107 and Gobye were uniquely identified by one positive marker each Xtxp265, Xtxp320, mSbCIR238 and Xtxp057, respectively. However, Meko-1 was not uniquely identified by any of the markers used.
As indicated in Table 3 more unique alleles were produced by Xtxp320 which alone uniquely distinguishes four of the 12 released lines, namely E36-1, Birhan, Hormat and Birmash. In Xgap206, nine alleles were scored, however, the number of unique allele is only one which uniquely identified only Baji.
El-Awady et al. (2008) reported identification of four out of nine Sorghum bicolor genotypes with nine SSR markers. According to Bandelj et al. (2002), a minimum of three microsatellite markers were found sufficient for rapid and unambiguous discrimination of olive varieties. In another study by Olufowote et al. (1997), as few as six, well-chosen SSLPs were sufficient to discriminate between 71 related lines of rice. A study conducted in Bangladesh discriminated 26 rice cultivars out of 34 using three SSR markers (Rahman et al., 2009). Sarao et al. (2009) differentiated genotypes of basmati rice from the non-basmati rice using four markers. Similarly, Chakravarthi and Naravaneni (2006) uniquely identified nine rice genotypes out of 15. They stated that fingerprinting makes identification and characterization of genotypes easy and it further helps in background selections during back-cross breeding programs. Kwon et al. (2005) studied fingerprinting of pepper and distinguished 60 of 66 varieties using 27 polymorphic SSR markers.
The SSR markers and their respective unique alleles could have a number of potential applications including the determination of cultivar purity, varietal identification, varietal ownership dispute resolution and other similar applications. The present study attempted to find out a set of microsatellite markers to differentiate lines released in Ethiopia, providing meaningful data that can be used by sorghum breeders and seed companies in distinguishing the lines.
Genetic relationships among lines: To determine the genetic relationships among the 12 lines based on the SSR data generated, Jaccard’s similarity coefficient was employed. Genetic dissimilarity among the lines ranged from 0.326-0.839 with an average of 0.672. The highest dissimilarity was observed between B35 and Teshale (0.839) followed by B35 and Gobye (0.833). The lowest dissimilarity was observed between Meko-1 and Teshale (0.326) followed by Meko-1 and Gambella-1107 (0.348).
The dissimilarity coefficients (Table 4) were used to produce an agglomerative hierarchical classification by employing unweighted pair group method using arithmetic averages (UPGMA). The dendrogram consisted of five clusters (Fig. 1). The first cluster contains Baji and Birmash. The second contains Birhan, Gobye and Abshir. The third cluster contains three sub groups. The first subgroup contains Hormat and Gambella-1107, the second Meko-1 and Teshale and the third 76T1-23. B35, the known stay-green source genotype obtained from the Zera Zera line of Ethiopia was put in a separate cluster which indicates that it has a wide genetic distance from the other varieties. Similarly, E36-1, the other stay green source originated in Ethiopia was assigned in a separate cluster.
| Fig. 1: | Genetic relationship among 12 sorghum released lines |
| Table 4: | Pairwise genetic dissimilarity among 12 sorghum varieties based on Jaccard similarity coefficient using 39 SSR markers |
Although, pedigree data was not available for each line used in this study, the grouping of the lines could be based on their pedigree relationship or similarity in their source breeding materials. Meko-1 and Teshale, the most similar released lines were introduced from ICRISAT which might have similar source material. Birhan, Gobye and Abshir grouped in the first cluster were introduced from Purdue University which might have also the same source material.
The high cophenetic correlation (r = 0.97) observed in the present study between the dissimilarity matrix and the dendrogram is an indicative of a good representation of the plot to the dissimilarities. Similar studies by Chakauya et al. (2006) reported less values for cophenetic correlation (r = 0.71). The results of the present study indicated that there is a wide genetic dissimilarity among the studied lines. Hallauer and Miranda (1988) reported that the genetic distance information is useful to breeders for planning crosses, in assigning lines to specific heterotic groups and for precise identification with respect to plant varietal protection. Studies of genetic diversity and genetic relatedness assisted by molecular markers can improve the use of the different genotypes in breeding programs and the design of new crosses.