The cultivated sesame, Sesamum indicum L., belongs to the family Pedaliaceae. Even though the origin of sesame is still in debate, Mehra (1967) and Mahajan (2007) stated that Ethiopian region is accepted as the origin of cultivated sesame. In addition, Bedigian (1981) argues that, owing to the wide genetic diversity in Africa, it is reasonable to assume that this subcontinent is the primary center of origin and India would then be thought of as a secondary center for sesame. It is perhaps one of the oldest crops cultivated by man, having been grown in the Near East and Africa for over 5,000 years for cooking and medicinal needs. Generally, 65% of world sesame production is used for edible oil extraction and 35% for confectionary purpose. The fatty acid composition is rather attractive, due to the high level of unsaturated fatty acids.

Sesame is grown in more than 60 countries in the world. According to FAO (2011) Ethiopia is ranked 6th in sesame production with 327,741 tons (10%) of production per year. Sesame seed is by far the leading crop in the countries oil seeds export where by more than 90% of the production is directed to export followed by Niger seed CSA (2010). The Ethiopian whitish Humera type is known for its taste (sweet) in the world market hence it is exported to the confectionary market where white seeded types are demanded by the consumers (Wijnands et al., 2009).

The main objective of sesame groups in National Agricultural Research Systems of Ethiopia is to develop cultivars concentrating on achieving a variety that combines disease resistance, white seeded and high yielding characters and works towards increased market values. The research in the country is geared towards evaluation of new collected accessions and exotic materials to develop a sesame variety with desirable characteristics with a combination of trait of interest including considerable oil content which are important for the export market (Tadele, 2005).

To strengthen the research program and broaden the genetic bases in sesame breeding, Ethiopian Institute of Agricultural Research has assembled 221 germplasm collections during 2002-2004 from different sesame growing regions in Ethiopia and preserved at Werer Agricultural Research Center. This makes the total sesame accessions preserved in Ethiopia to around 870 when it adds up with the IBC collection. However genetic variability studies, using any marker system, have not yet been conducted for the IBC collection. To meet the objectives of the national sesame improvement, information on genetic diversity and their relationship within and among collected accessions is inevitable.

Genetic markers are widely used by breeders and conservationists to study genetic diversity. A number of markers exist that can be used as tool to discern genetic variability within among populations. Each marker system has its own advantages and disadvantages in terms of ease of use and/or the degree of information provided. One of the genetic marker, molecular markers, are identifiable DNA sequence, found at specific locations of the genome, and transmitted from one generation to the next. The presence of various types of molecular markers, and differences in their principles, methodologies, and applications require careful consideration in choosing one or more of such methods. No molecular markers are said to be ideal and fulfill all requirements needed by researchers. Thus, one can choose among the variety of molecular techniques, according to the kind of study to be undertaken. To be called a suitable molecular marker it should be highly polymorphic, co-dominant inheritance, frequent occurrence and even distribution throughout the genome, selectively neutral behavior, easy access, easy and fast assay, low cost and high throughput, high reproducibility, and transferability between laboratories, populations and/or species (Weising et al., 2005).

Inter-simple sequence repeat (ISSR) is amplification of DNA segments present at an amplifiable distance in between two identical microsatellite repeat regions oriented in opposite direction (Zietkiewicz et al., 1994). ISSR marker, in addition to its suitability to genetic diversity study, is highly polymorphic, reproducible, cost effective, requires no prior information of the sequence (Bornet et al., 2002). These facts suggest that ISSR could be an unbiased tool to evaluate the changes of diversity in agronomically important crops (Brantestam et al., 2004).

ISSR markers are now being applied for cultivar identification, assessment of genetic diversity in various plant species and in determining genetic diversity and phylogenetic relationships within and among cultivated crops (Hou et al., 2005; Joshi et al., 2000). In agronomically important crops such as sesame this marker is used to study the patterns and level of diversity. Hence, the ISSR marker assay has been chosen to study the level of diversity and patterns of distribution of sesame genetic resources in Ethiopia.

Experiment material and DNA extraction

Sesame germplasm sample consisting of 120 accessions (82 Ethiopian and 38 exotic) were drawn from the over 636 accessions currently maintained at the gene bank at the institute of Biodiversity Conservation, Addis Ababa, Ethiopia (Table 1 and Figure 1). The sampling procedure was set in such a way that accessions from the major sesame growing regions were sufficiently represented in the sample. Germplasm passport data were used to make sure that the major regions are represented. Additional sesame samples from Africa and other parts of the world were included for comparative analysis. The samples were laid out in augmented randomized block design in Werer Agricultural Research Center, Ethiopia on station field plots. The accessions were grown in 2 row plots of 4 m length.

Young leaves (3-5 in number) were collected separately from 5 randomly selected individual plants per accession three to four weeks after planting and dried in silica gel. Approximately equal amounts of the dried leaf samples were bulked for each accession and ground with sterile pestle and mortar with addition of liquid nitrogen. Total genomic DNA were isolated from about 0.2 g of the pulverized leaf sample using modified triple Cetyl Trimethyl Ammonium Bromide (CTAB) extraction technique as describe by Borsch et al. (2003). The isolated DNA samples were visualized using 1% ethidium bromide stained agarose gel under UV light. Based on a test gel result of DNA quantity (band intensity) and quality (absence or presence of minimum smear) the second extraction of most and the first extraction of some samples were selected for further PCR amplification. The selected genomic DNA samples were diluted with sterile distilled water in 1:5 ratios.

ISSR analysis

The ISSR marker assay was conducted at Genetics Research Laboratory of the Microbial, Cellular and Molecular Biology Program Unit, Faculty of Life Sciences, Addis Ababa University, Addis Ababa. A set of primer kit was obtained from the University of British Colombia (primer kit UBC 900). Based on published work of Kim et al. (2002) and lab experience, a total of 12 primers were used for the initial testing of primers variability and reproducibility. A total of eight sesame DNA samples from geographically distant localities were selected to screen primers and optimize the reaction conditions. Finally, a total of six polymorphic and reproducible ISSR primers were selected for the final analysis (Table 2).

The polymerase chain reaction was conducted in Biometra 2000 T3 Thermo cycler. PCR amplification was carried out in a 25 µl reaction mixture containing 1 µl template DNA, 14.0 µl H₂0, 5.0 µl dNTP (1.25 mM), 2.5 µl Taq buffer (10xThermopol reaction buffer), 2.0 µl MgCl₂ (2 mM), 0.3 µl primer (20 pmol/ml) and 0.2 µl Taq Polymerase (5 u/ml). The amplification program was set as 4 min preheating and initial denaturation at 94°C, followed by 40 cycles of 15 sat 94°C, 1 min primer annealing at (45°C/48°C) based on primers used, 1 min and  30 sextension at 72°C and the final extension for 7 min at 72°C. The PCR products were stored at 4°C until loading on gel for electrophoresis. The lid temperature was held at 105°C. The PCR products were stored at 4°C until loaded on gel for electrophoresis. The amplification products were differentiated by electrophoresis using an agarose gel (1.67% agarose with 100 ml 1xTBE) and 8 µl amplification product of each sample with 2 µl loading dye (6 times concentrated) was loaded on gel. DNA marker 1 k bp (molecular ladder) was used to estimate molecular weight and size of the fragments. Electrophoreses was done for 2 h at constant voltage of 100 V. The DNA was stained for 30 min with (10 mg/ml) ethidum bromide (EtBr) which was mixed with 450 ml distilled water and distained for 30 minuwith 450 ml of distilled water.

Statistical analysis

Clearly resolved, unambiguous bands were scored visually for their presence or absence for each primer and sample. ISSR profiles/bands were scored manually for each individual accession from the gel photograph. The bands were recorded as discrete characters, presence ‘1’ or absence ‘0’ and ‘?’ for missing data. To analyse the scored ‘1’ ‘0’ fragment data assembled in data matrix. The total number of bands, distribution of bands across accessions, number of polymorphic bands in a set of accessions, and average number of bands per primer were calculated. Similarity matrix was generated based on the simple-matching coefficient (Jaccard’s), using the presence/absence data for individual ISSR fragment.

Based on recorded bands different software’s were used for analysis. POPGENE version1.32 software (Yeh et al., 1999) was used to calculate genetic diversity for each population as number of polymorphic loci and percent polymorphism, Shanon index, coefficient of gene differentiation (G_ST) (Nei, 1973), Nei’s standard genetic distance (GD) (Nei, 1972) and gene flow estimates (McDermott and McDonald, 1993). Analysis of molecular variance (AMOVA) was used to calculate variation among and within population using Areliquin version 3.01 (Excoffier et al., 2006). NTSYS- pc version 2.02 (Rohlf, 2004) and Free Tree 0.9.1.50 (Pavlicek et al., 1999) software’s were used to calculate Jaccard’s similarity coefficient which is calculated with the formula:

Where, 'a ' is the total number of bands shared between individuals i and j, 'b' is the total number of bands present in individual i but not in individual j and 'c' is the total number of bands present in individual j but not in individual i.

The unweight pair group method with arithmetic mean (UPGMA) (Sneath and Sokal, 1973) was used to analyze and compare the population and generates phenogram using NTSYS- pc version 2.02 (Rohlf, 2004). The Neighbor Joining (NJ) method (Saitou and Nei1987; Studier and Keppler, 1988) was used to compare individual genotypes and evaluate patterns of genotype clustering using Free Tree 0.9.1.50 Software (Pavlicek et al., 1999).

Banding patterns of the ISSR primers used

Analysis of molecular variance

Analysis of molecular variance was carried out on the overall ISSR data score of sesame accession without and with grouping (Tables 5 and 6). AMOVA without grouping revealed that higher percentage of variation (94.09%) is attributed to the within population variation while the remaining variation is due to the among population variation (5.90%). The grouping AMOVA analysis, considering the Ethiopian accessions as one group and the exotic ones as another, also showed 5.88% among populations within group variation, 94.09% within group variation and 0.026% among group variation (Table 6). The variations was found to be highly significant at P=0.00, 1000 permutations.

Genetic distance

Inter-population genetic distance (D) ranged from 0.031 to 0.165 for the total 19 population (Table 7). Administrative region based population classification of the Ethiopian sesame accessions revealed inter population genetic distance ranging from 0.041 to 0.16. Samples from Illubabore were distantly related to samples of Wello (0.150), Shewa (0.116) Harerge (0.144) and Gamo Goffa (0.134) (Table 8). Among the pairwise population comparisons made within Ethiopian population, samples from Welega and Harerge showed the highest genetic distance (0.160) and samples from Shewa and Harerge showed the least genetic distance (0.041). In general from the exotic accessions samples of South East Asia is distantly related to most of the Ethiopian accession. Genetic distance between the other pairwise combinations of populations was very low with the least genetic distance between samples of Mexico and Tanzania (0.031).

Clustering analysis

Cluster analysis of Ethiopian sesame populations revealed two major groups and three outliers (Cultivated, Welega and Illubabore). The first major cluster again forked into two sub groups the first containing Tigray, Harerge and Shewa populations, while the second contained Gamo Goffa, and Wello populations which are characterized as the lowest sesame producers of the country.

More over the migration of the welo people to the south may have its own impact on the clustering of Gamo Goffa and Welo (Baker, 2001). Seeds of some sesame germplasm might have been transported from Welo to Gamo Gofa along with human (Figure 2). The second major cluster comprise of Gojam, Benishangul Gumuz and Gonder populations which embodies the north western parts of the country. 

Genetic diversity and application of ISSR marker

In this study, the AMOVA analysis showed highly significant (P=0.00) genetic differences among populations and within populations. Of the total variation, 5% attributed to among population and 95% attributed to within populations. Even though based on AMOVA analysis, a high estimate of genetic differentiation between populations of inbred species is expected this may not be a general truth if there is high gene flow represented by seed movement through human involvement (Edossa et al., 2007).

Moreover, pollen movement facilitated by insect could also have a role for the observed pattern. The lower among group variance of this study can be explained in line with the above argument. Gene flow is the exchange or movement of gametes, individuals, and populations on a geographic scale (Joseph and Bruce 1993). Gene flow, in conjunction with other evolutionary forces, can result in the spread of single genes (or DNA sequences), genotypes, and even the establishment of whole populations in different regions. The movement of one individual per generation between populations is sufficient to prevent substantial differentiation between those populations (Joseph and Bruce, 1993).

The values obtained from Nm, the product of the effective size of individual populations (N) and the rate of migration among them (m); show the approximate number of individuals migrating from one population to the other, in a typical island model. Generally, if Nm < 1 local differentiation of populations will result, and if Nm > 1 there will be little differentiation among populations (Wright, 1951). The overall Nm (1.28) value of this study is considered to be higher according to Slatkin (1981, 1985), Caccone (1985) and Waples (1987) grouping. They grouped Nm values into three categories: high (Nm > 1.000), intermediate, (0.250 - 0.990), and low (0.000 - 0.249). The fact that Nm value of this study is higher indicates gene flow between populations is obvious which will agree with the AMOVA result showing there is low variation among population.

The G_ST value of this result also shows the lack of population differentiation which goes with Laurentin and Karlovsky (2006) work. They found a G_ST value of 0.20 (below this study 0.25) and suggested the lack of association between geographical origin and population differentiation. In their finding particularly Indian, African and Chinese-Japanese-Korean accessions are distributed throughout clusters in UPGMA analysis and the whole two-dimensional space in PCA. Toan et al. (2011) also found an intermediate estimate of gene flow (Nm) value of 0.38 on average in there study. This lack of association between geographical distribution and classification based on molecular markers in sesame was explained by the exchange of sesame seeds among widely separated locations which could be due to movement/migration of peoples from one place to another along with their own seed or the purchase of seeds from other places for cultivation. These results also became apparent from the cluster analysis.  

This study shows that bulk sampling strategy for DNA analysis and ISSR marker are important for genetic diversity study in sesame. The study also shows, among the sesame accessions held by IBC, in comparison, Ethiopian accessions are more diverse than the exotic ones. Separate analysis of the Ethiopian accession revealed there are places with higher genetic diversity which will be valuable for the future collection, conservation and improvement strategies. Regions that showed relatively high level of genetic diversity should be the main focus for future collection and population improvement based on selection. The present study also showed weak association between genetic variation of sesame accessions and their ecological regions of origin. The most likely important factor affecting the genetic structure of sesame in this region is possibly human activities. This show there could be a possibility of sampling plants with the same genetic constitution from different administrative regions. This problem becomes sever if an administrative region is represented by small sample as in the case of IBC.

The authors have not declared any conflict of interest.

Dagmawi T. Woldesenbet is thankful to Rural Capacity Building Project (RCBP) and the Private Public Partnership for sesame for funding the research; Institute of Biodiversity Conservation  (Addis Ababa, Ethiopia) for kindly providing seeds and passport data of the landrace accessions; Werer Agricultural Research Center (Werer, Ethiopia) for provision of physical resources and other facilities; the Faculty of Life Sciences of Addis Ababa University for giving me the chance for this study. I am thankful to all the authors for their valuable assistance.