Genetic diversity of seed storage protein in the Ethiopian garden cress (Lepidium sativum L.)

The Ethiopian garden cress (Lepidium sativum L.) is an important crop extensively used as food and medicine. In this study, total seed storage proteins of 112 garden cress genotypes collected from diverse growing regions in Ethiopia were investigated to assess patterns of genetic diversity and relationships. Using sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), a total of 1774 stable protein bands were identified through discontinuous electrophoresis. Of these, 1597 bands were polymorphic. A maximum of 20 protein sub-units in the range of 15 to 75 kDa were observed per genotype. The similarity coefficient among these genotypes ranged from 0.25 to 1.00 with an average genetic dissimilarity of 0.2754. On the basis of Ward Euclidian distance, the genotypes were grouped into five major clusters, the largest one comprised of 62 genotypes (Cluster III) followed by 26 genotypes in Cluster I. Cluster IV and V contained a total of 14 genotypes that were the most distantly related to other groups, and thus can be potentially used as parents for exploitation of heterotic effects in hybrid breeding programs. Our findings using SDS-PAGE profiles revealed no obvious association between geographic region of origin and germplasm clustering. However, the polymorphism and cluster analysis indicated that garden cress genotypes differed greatly in the composition of seed proteins. This shows that protein profiling could be used as a rapid and reliable method for genetic diversity studies. In order to fully explore the protein based genetic diversity in garden cress germplasm, techniques such as 2-D gel electrophoresis are recommended in future studies. 
 
   
 
 Key words: Cluster analysis, dissimilarity index, garden cress, Lepidium sativum, protein polymorphism, protein profiling, sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), seed storage protein.


INTRODUCTION
Garden cress (Lepidium sativum L.) belongs to the family Brassicaceae and the genus Lepidium which contains about 150 species that are distributed throughout almost all temperate and subtropical regions of the world (Bermejo and Leon, 1994;Wadhwa et al., 2012;Rava, 2016). Garden cress is widely cultivated in Africa, Europe, Russia and North America. It is a fast growing, edible herb and an important medicinal plant having a wide range of desirable effects on human health since the Vedic era about 3000 years ago (Manohar et al., source: https://doi.org/10.7892/boris.120357 | downloaded: 12.2.2020 2012; Doke and Guha, 2014). Various beneficial effects have been observed with the consumption of garden cress. Different parts of the plant including roots, seeds and leaves have been used as a source of functional food and/or medicine (Rava, 2016).
The seeds and leaves of garden cress contain volatile oils and have been consumed as salad and as spice (Amare, 1976;Wadhwa et al., 2012;Rava, 2016). Its seeds are rich source of proteins, carbohydrate, fat (for example omega-3 fatty acids), dietary fiber, vitamins (tocopherol, β--carotene and ascorbic acids), minerals (K, Mg, P, Ca, Fe), and other essential nutrients and phytochemicals (Gokavi et al., 2004;Doke and Guha, 2014). This indicates that the seeds of garden cress play vital role as a promising multipurpose medicinal and nutritional plant. Nowadays, garden cress is becoming popular not only because of its superior medicinal and nutritional values but also due to its contribution in the biofortification of nutritionally inferior crops (Manohar et al., 2012;Singh et al., 2015) in order to ensure the nutritional security of the global population.
Diversity existing in germplasms of crops collected from diverse growing regions need to be properly characterized and evaluated to improve strategies for conservation and utilization towards cultivar development (Parashar et al., 2015;Sharma and Krishna, 2017). Several strategies have been adopted for germplasm characterization including the use of morphological markers (Kancherla and Bhalla, 2003), seed storage protein markers and DNA molecular markers (Rahman and Hirata, 2004). Using quantitative, biochemical and molecular markers suitable germplasm for future plant breeding programs can be identified. Studies in 49 Ethiopian garden cress landraces using morphological and yield-related traits revealed huge variability at diverse agro-ecological zones (Temesgen et al., 2013a, b). Similarly, large diversity was recorded among 85 Ethiopian garden cress genotypes using inter simple sequence repeat (ISSR) (Said and Kassahun, 2015).
Storage or structural seed proteins, encoded by families of polymorphic genes (Mandal and Mandal, 2000) have been extensively used as a genetic marker as they are largely independent of environmental fluctuations (Hameed et al., 2009). Unlike morphological markers (Siddiqui and Naz, 2009), the banding patterns of protein markers are stable (Iqbal et al., 2005;Nasar et al., 2006;Iqbal et al., 2014). Due to these benefits, protein markers have been widely applied in the analysis of genetic diversity within and between accessions, in studying plant domestication in relation to genetic resource conservation and breeding, and in establishing genome relationships (Kakaei and Kahrizi, 2011;Hameed et al., 2012;Sharma and Krishna, 2017). Seed storage protein profiling has been used in investigating diversity among selected varieties of Brassica napus (Nasar et al., 2006;Choudhary et al., 2015). Similarly, seed protein profiles were instrumental in the identification of intraspecific genetic divergence in rape seed .
The use of sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) in studying plant proteins was found to be simple and inexpensive; hence, it has applications in the improvement of the crop of interest through breeding (Zada et al., 2013;Sharma and Krishna, 2017). However, so far, only few studies were made regarding the diversity of storage proteins in the garden cress germplasm. Gianazza et al. (2007) examined the influence of different concentrations of cadmium on the garden cress plants using seed protein storage marker. However, genetic diversity in the seed protein of garden cress was so far not made using the SDS-PAGE, at least in the Ethiopian germplasm collections. Hence, the current study was conducted to investigate using SDS-PAGE diversity in the seed storage protein of the Ethiopian garden cress accessions collected from diverse agro-ecological regions in the country.

Plant materials and protein samples
Lists of plant materials used in the study and their origin are presented in Table 1. For extraction of proteins, mortar and pestle were used to crush and grind seeds of each genotype. Defatting of about 0.1 g of flour was carried out with chloroform, methanol and acetone in the ratio of 2:1:1 as described by Geetha and Balamurugan (2011) for mustard genotypes. Next day, about 0.05 g of flour was suspended in 1 ml extraction buffer (0.125 M Tris-Cl, 4% SDS, 20% v/v glycerol, 1% 0.2 M DTT, 25 mM EDTA, pH 6.8). Homogenized mixture was incubated at room temperature for 3 h. Protein extraction buffer was properly mixed by vortexing for 5 to 10 min intermittently. The solubilized samples were centrifuged at 14000 rpm for 10 min at 4°C, and the clear supernatant saved at 4°C until they were run gel electrophoresis following the methods of Roy and Kumar (2014) and Buckseth and Singh (2016) with some modification. The gel electrophoresis was run by mixing of 30 µl of the protein extract with 30 µl of sample loading buffer (0.1% of bromophenol blue, 2% SDS, 6% v/v glycerol, 2.5% 0.5 M Tris-HCL PH 6.8 and 1% 0.5 M DTT) followed by vortexing and heating at 65°C for 10 min to ensure complete denaturation, and then with brief vortexing and spinning at room temperature just before loading on gel (Bollag et al., 2002;Nisar et al., 2016).
The SDS-PAGE was carried out in various combinations and optimized to 12.5% acrylamide gel concentration and by loading 30 µl of samples to obtain the best resolution. The procedure  developed by Laemmli (1970) was followed for gel preparation and running. 30 µl of samples digested was loaded into the wells of 4% acrylamide stacking gel (1.5 mm thick) for protein separation. Electrophoresis was carried out at a constant 250 V for the medium slab vertical gel apparatus until bromophenol blue marker crossed bottom of the gel. Pre-stained protein marker, ranging from 10 to 250 kDa (precision plus protein dual color standard supplied by BIO-RAD) was run for reference to molecular weight of respective protein bands in KDa. After complete run, the gels were fixed and stained with 0.5% coomassie brilliant blue (CBB) R-250 in acetic acid: methanol: water (10:40:50 volume ratio) for 3 h and destained in the same acetic acid-methanol-water solution except CBB for overnight (Bollag et al., 2002;Sadia et al., 2009) with constant and gentle shaking.

Data analysis
Gel evaluation for data scoring was done on a light box and rechecked by using photograph that was taken by high resolution camera supported by white light illuminator. The experiment was repeated twice to check the reproducibility of the protein bands. A band presence was coded (1), while the absence of bands scored as (0). Only reproducible bands occurring in high frequency were scored by identifying each protein band carried out according to standard proteins. The intensity of bands was not taken into consideration but only the presence of the bands was taken as indicative. Presence and absence of the bands were entered in a binary data matrix. Based on results of electrophoretic band spectra, similarity index was calculated for all possible pairs of protein type's electrophore-grams. Similarities among genotypes were estimated using Jaccard coefficient of similarity (Jaccard, 1908). Depending on the electrophoretic band spectra, similarity index (S) was designed for all pairs of protein band pattern by the subsequent formula: S = a/ (a + b) where S = similarity index, a = Number of bands common to a and b protein types, b = Number of bands in protein type 'b'. The similarity matrix was generated and converted to a dissimilarity matrix. The different bands in the range of 15-75 kDa were used for calculation of similarity indices. Polymorphism % was calculated using the formula: Polymorphism (%) = [Number of polymorphic bands/Total number of bands] ×100.
The generated data matrix was then used for descriptive statistics and for constructing dendrogram by the ward method using Darwin version 6 Software.

Genetic diversity in seed storage protein
A typical electrophoretic banding patterns and their distribution is presented in Figure 1. A maximum of 20 protein sub-units were observed per genotype within the range of protein molecular weight of 15 to 75 kDa. A total of 1774 polypeptide bands with an average of 15.8 bands per genotype were obtained ( Table 2). Out of the total of 1774 bands, 1597 were polymorphic while 177 were monomorphic (data not shown). The banding patterns revealed large variations among genotypes in the low molecular weight protein profiles. This revealed considerable variations in five regions (A to E) (Figure 1). Region A contains relatively high molecular weight proteins ranging from 50 to 75 kDa while in Region B, three protein sub-units ranging in size from 37 to 50 kDa are observed. Five protein bands were found for Region C which ranged from 25 to 37 kDa while Region D comprised of four protein sub units ranging from 20 to 25 kDa. The last part, Region E, contains small size proteins with molecular weight ranging from 15 to 20 kDa. All these five regions showed both light and dark stained bands and were polymorphic except for the last two bands of Region E which were monomorphic (Table 1). This indicates that the proportion of polymorphic bands over the total bands detected were 90%. Ten genotypes (namely, 241777, 229203, 235892, 229205, 214243, 208030, 229199, 237991, 216816 and CG2) showed the highest number of protein bands (each 20 bands) followed by 12 genotypes (207542, 229202, 229799, 212628, 229204, CG14, 238273, 90021, 208667, 219961, 242609, and CG6) each with 19 bands. Three genotypes (90002, 229799 and 90022) had minimum number of protein bands ranging from 7 to 9. Protein sub units located on band number 19 and 20 were the most frequent (Table 2). Similar banding patterns were reported for Brassica species (Rabbani et al., 2001;Nasar et al., 2006;Turi et al., 2010). SDS-PAGE has of paramount importance in separating and characterizing the proteins and estimating the extent of genetic diversity in the present set of garden cress germplasm. The banding pattern in the total seed protein showed close relationships among these studied genotypes. Similarity coefficient among these genotypes ranged from 25 to 100%. This is in agreement with earlier study on Brassica carinata where a similarity coefficient of 50 to 100% was reported (Zada et al., 2013). Proteins have been used as markers for the assessment of genetic diversity in many crops (Iqbal et al., 2005;Nisar et al., 2016;Singh et al., 2017). The seed protein fragments exhibited appreciable polymorphism among the Ethiopian genotypes of garden cress, being used for the study of variability (Table 2). Consequently, electrophoretic analysis of the seed proteins had direct relationship to the genetic background of the proteins, and hence it is a potential marker for the study of genetic diversity and varietal identification. Similar studies have been carried out using protein marker for the study of genetic diversity and/or varietal identification in many crops, as mentioned above (Iqbal et al., 2005;Netra and Prasad, 2007;Nisar et al., 2016;Singh et al., 2017). In general, DNA markers are more robust to detect variability among different genotypes. The only diversity study on garden cress using DNA markers was the one using less efficient inter simple sequence repeat (ISSR) (Said and Kassahun, 2015).

Cluster analysis of seed storage proteins
The cluster analysis showed that the genotypes were divided into three main groups (SG1-SG3) consisting of five major clusters and several sub-clusters ( Figure 2). According to the magnitude of this genetic distance, genotypes from Cluster IV and V (SG3) were most divergent from the other two groups (SG1 and SG2). Such divergent genotypes should be used for designing effective breeding programs for evolving genetically vigor and variable breeding lines. Similarly, Cluster I and II represent relatively diverse group as compared to Cluster III. Thus, crossing between genotypes from Cluster I, II, III and genotypes from Cluster IV and V gene pools could create more genetic variability than crosses within genotypes of each main group (Figure 2). They are used Cluster III was the largest among all five clusters and consisted of 62 most similar genotypes (55% of the total), revealing low genetic diversity at genomic level (Table 3). However, the results might indicate the limitation on the number of markers used in the current study. In this connection, Opond-Konadu et al. (2005) reported the absence of large genetic difference among cowpea genotypes which hindered the use of protein electrophoresis to investigate diversity. Hence, genotypes in cluster III need to be further investigated in combination with 2D electrophoresis to minimize the lower variability detection efficiency of SDS-PAGE (Javaid et al., 2004;Jan et al., 2016). In this case, the integration of the usual electrophoresis separation with isoelectric focusing point electrophoresis to maximize the resolving power of seed storage protein markers due to amphoteric nature of amino acids is useful.
In general, the seed storage protein profiling generates wide array of polymorphism, hence could serve as a valuable tool in determining the extent of genetic diversity. Thus, SDS-PAGE marker data provided more sub-groupings and revealed considerable amount of genetic diversity.

Conclusions
Based on similarity indices, the dendrogram divided the genotypes into three groups and five clusters, indicating the genetic relationships among genotypes. The grouping of genotypes into clusters did not associate with their geographic distribution. Seed storage protein profile could be economically useful marker to assess genetic diversity in garden cress germplasm. Predominately polymorphic proteins were noted in SDS-PAGE analysis used for selection of desirable genotypes in the garden cress improvement programs. However, the study revealed that nearly half of the genotypes were grouped into similar clusters requiring further analysis with a combination of 2D electrophoresis. The hybridization among the genotypes from distantly related groups is suggested in order to enhance future breeding programs towards the development of desirable varieties.

CONFLICT OF INTERESTS
The authors have not declared any conflict of interests.