Biochemical Characterization of Bread Wheat (Triticum aestivum L.) Genotypes Based on SDS-PAGE  

D. Sharma , V. Saharan , Arunabh Joshi , D. Jain
Department of Molecular Biology and Biotechnology, Rajasthan College of Agriculture, Maharana Pratap University of Agriculture and Technology, Udaipur- 313 001, India
Author    Correspondence author
Triticeae Genomics and Genetics, 2015, Vol. 6, No. 2   doi: 10.5376/tgg.2015.06.0002
Received: 23 Feb., 2015    Accepted: 15 Apr., 2015    Published: 20 Apr., 2015
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Sharma et al., 2015, Biochemical Characterization of Bread Wheat (Triticum aestivum L.) Genotypes Based on SDS-PAGE, Triticeae Genomics and Genetics, Vol.6, No.2 1-7 (doi: 10.5376/tgg.2015.06.0002)


Genetic diversity of twelve Indian wheat genotypes was assessed on the basis of seed storage protein profiling on 10 and 15% sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE). Out of twelve genotypes, six genotypes are resistant to salinity, whereas remaining are susceptible. Genetic diversity was calculated using Shannon index, Nei’s index and unweighted pair group method with arithmetic mean (UPGMA) cluster analysis by constructing an electrogram of fragments of proteins, which were used to calculate Jaccard’s similarity coefficients between these varieties. Seed storage protein analyzed by 15 subunits ranging from 14.3-97.4 KDa protein molecular weight marker (PMW-M). The similarity coefficient ranged from 29% to 100%. The detected variation could be used to provide useful information on individual enzymes or transporters, activity as well as modification of structural protein, protein-protein interactions and stress dependent protein movement and can be used to classify adapted cultivars to improve the efficiency of wheat breeding programmes.

Bread wheat; Salinity; Diversity analysis; Similarity coefficient; SDS-PAGE

Abbrevations: DNA: Deoxy Ribose Nucleic Acid; SDS-PAGE: Sodium Dodecyl Sulphate Polyacrylamide Gel Electrophoresis, HMW: High Molecular Weight, UPGMA: Unweighted Pair Group Method with Arithmetic Mean, NTSYS-PC: Numerical Taxonomy and Multivariate Analysis System Program

Wheat (Triticum aestivum) is a member of poaceae family and believed to be originated from the Middle East region of Asia. Wheat (Triticum spp.) is one of the cereals extensively grown throughout the world in more than 50 countries and wheat flour is used to produce biscuits, prepare bread, confectionary products, noodles and vital wheat gluten or seitan. Wheat is also used as animal feed, brewing of wheat beer, for ethanol production, wheat based raw material for cosmetics (Kumar et al., 2011). It seems necessary to conserve genetic diversity for the improvement of future varieties. Thereby significant research on genetic resource characterization has been done on major cereals including rice, wheat and maize using morphological characters. Fresh water is becoming an increasingly inadequate resource worldwide requiring enhancement of water productivity in agriculture (Pereira, 2006). Salinity affects about 1000 million hectares land globally posing a difficult task of taking up agriculture and enhancing productivity in these areas. About 100 million ha in South and South-East Asia are covered by problem soils where rice is the staple crop. In India, 6.73 million ha land is salt affected, out of which 3.77 and 2.96 million ha are covered sodic and saline soils, respectively. Harnessing the potential and discreet management of such salt afflicted soils can play a significant role in escalating and sustaining the national and global food security. Various morphological, molecular and biochemical methods are available to analyze genetic diversity in segregating lines, germplasms and breeding lines. Evaluation of genetic diversity in wheat has been on differences in morphological and agronomic traits or pedigree information (Bernard et al.,1998). These methods have relied on, pedigree, morphological, agronomic performance, biochemical and molecular (DNA-based) data (Mohammadi and Prasanna, 2003). Previously morphological traits are used to analyze genetic diversity but they were time consuming and have large trait and environment interaction. Use of molecular marker can increase breeding efficiency and genetic gains. Hence over the past 30 years, molecular marker and biochemical technologies have been developed and applied to plant breeding, enabling breeders to use the genetic composition or genotypes of plants as a criterion for selection and breeding process. Currently, the genetic diversity of plant has been assessed more efficiently after the introduction of methods that reveals polymorphism directly at biochemical and molecular level. So the study of polymorphism is best done at the level of DNA and protein, the primary source of all biological information. Numbers of efforts also have made to characterize genetic diversity in wheat with biochemical markers. Comparison of "protein profiles" obtained from tissues under different conditions can indicate quantitative changes at protein level and unique proteins are potentially useful in ultimately understanding the cellular events that are occurring at that time. Similarly, a comparison of protein profiles from unstressed and stressed organisms could lead to the identification of proteins associated directly or indirectly with the stress response. The protein profiling of germplasm and use of genetic markers have been widely and effectively used to determine the taxonomic and evolutionary aspects of several crops (Murphy et al., 1990; Das and Mukarjee, 1995; Ghafoor et al., 2002).

Two types of subunits are present in wheat protein; the low molecular weight (10,000~70,000 Da) and the high molecular weight glutenin subunits (80,000~130,000 Da) (Bietz and Wall, 1972). Studies on the genetic determinism of wheat glutenin have revealed that HMW-GS genes are located on the long arm of chromosomes 1A, 1B, and 1D at loci Glu-A1, Glu-B1, and Glu-D1, respectively. And each Glu-1 locus consists of two tightly linked genes, which code for x- and y-type subunits (Payne, 1987). In hexaploid wheat, the Glu-A1 locus codes for 1Ax or null (N) subunit, whereas the Glu-B1 locus usually codes for 1Bx and 1By. Sometimes, Glu-B1 codes for 1Bx or 1By subunits only; and the Glu-D1 locus codes for 1Dx and 1Dy subunits. Thus, for hexaploid wheat, each genotype usually produces three to five HMW-GS (Payne and others, 1984b; 1987). Electrophoretic studies have revealed appreciable polymorphism in the number and mobility of HMW-GS in both bread wheat (Lawrence and Shepherd, 1980; Payne et al., 1980) and pasta wheat (Branlard et al., 1989). The Low Molecular Weight-Glutenin Subunits (LMW-GS) represents about one-third of the total seed protein and 60% of total gluten protein (Bietz and Wall, 1973).
A large number of germplasm lines can be characterized for biochemical markers in a short period of time. In addition, the data reflects more truly the genetic variability as biochemical markers are direct product of genes expression (Perry and McIntosh, 1991; Masood et al., 2000). Among biochemical techniques, SDS-PAGE is widely used due to its simplicity and effectiveness for describing the genetic structure of crop germplasm (Murphy et al., 1990; Javid et al., 2004; Anwar et al., 2003.). The analysis of storage protein variation in wheat has proved to be a useful tool not only for diversity studies but also to optimize variation in germplasm collections (Ciaffi et al., 1993; Masood et al., 2000).
1 Materials and Method
1.1 Plant materials
A set of twelve prominent wheat genotypes included 6 salt tolerant (KRL-1-4, KRL-19, Kharchia-65, KRL-210 KRL-213 and Raj-3765) and 6 salt susceptible (MP-4010, HD-2932, Lok-1, HI-8713, C-306 and Raj-4037), had been provided by All India Coordinated Research Project (AICRP) from Department of Agronomy, Rajasthan College of Agriculture, MPUAT, Udaipur, Rajasthan (India). (Table 1).

Table 1 Pedigree and origin details of bread wheat genotypes

1.2 Extraction of water soluble protein, dialysis, lyophilization and SDS-PAGE
After procurement of seeds all the experiment is conducted at Department of Molecular Biology and Biotechnology, Rajasthan College of Agriculture during March, 2012. The seeds were sterilized with 0.1% mercuric chloride solution for 2 min and used for protein extraction. Seed were homogenized in glass pestle and mortar with phosphate buffer (0.2 M, and pH 7.4). The homogenate was centrifuged at 5000 rpm for 15 min. The supernatant obtained was dialyzed, lyophilized and used for protein profiling (Seth and Khandelwal, 2008). Qualitative test for presence of protein was performed at every step.
Dialysis was carried out in semi permeable dialyzing tubes (15 cm×2.5 cm). The dissolved precipitate was dialyzed against distilled water. The dialysis was carried out for 24 hr with four changes of water at interval of 6 hr. A small quantity of precipitate formed at this stage was removed by centrifugation and supernatant were freezed.
The freeze supernatants lyophilized in separate petri plates and dried samples immediately packed in airtight bottles. The known amount of protein dissolved in phosphate buffer (pH 7.4) and subjected to vertical gel electrophoresis (SDS-PAGE).
The variability of seed storage-proteins was analyzed by using SDS-PAGE (Damania et al., 1983).
1.3 Preparation of separating gel (15% and 10%) and stacking gels
The 15% gel was prepared by mixing 20 ml of stock acrylamide solution, 8.0 ml tris HCl (8.9) and 11.4 ml of water. Subsequently 0.2 ml of ammonium per sulphate solution, 0.4 ml 10% SDS and 20 µl Tetramethylethylenediamine (TEMED) was added. The 10% gel was prepared by mixing of 13.3 ml of stock acrylamide solution, 8 ml tris HCl (8.9) and 18.1 ml of water. Subsequently 0.2 ml of ammonium per sulphate solution, 0.4 ml 10% SDS and 20 µl TEMED was added. The 3% stacking gel was prepared by addition of 4.0 ml of stock acrylamide solution, 2.0 ml tris HCl (6.7), and 2.0 ml of riboflavin solution and 8.0 ml of water. And this solution was mixed with 0.1 ml 10% SDS and 20 µl TEMED.
1.4 Preparation of sample and casting of gel
Protein sample (48 µl ) was mixed with 12 µl of 4x sample buffer (0.25M Tris-HCl pH 6.8 added with 8% SDS, 40% glycerol, 20% b-mercaptoethanol, 0.5% bromophenol blue) and heated in water bath containing boiling water for 2-3 min to ensure complete interaction between protein and SDS. Sample was cooled to room temperature.
1.5 Data analysis
The photographs of SDS-PAGE gel was used to study the protein profile of the all genotypes. The bands were designated on the basis of their molecular weight. The presence of protein band was scored as “1” and its absence as “0” (Table1, S-1 and S-2). Only bright, clearly distinguishable bands were used in genetic analysis. In the present study, the population diversity based on SDS PAGE banding patterns was calculated using POPGENE 1.31 (Yeh et al., 1999) software. The data matrices were then entered into NTSYS-PC (Numerical Taxonomy and Multivariate Analysis System Program) developed by Rholf (2000). The data were analyzed using SIMUQUAL Jaccard’s Similarity Coefficients formula (Sneath and Sokal, 1973). The matrix of similarity coefficients generated from data of 12 wheat genotypes was subjected to un weighted pair group method for arithmetic average (UPGMA) using NTSYS-PC, version 2.02 (Exeter software, New York).
2 Results and Discussion
The variability of seed storage-proteins was analyzed by using SDS-PAGE (Damania et al., 1983). The study comprises characterization of high and low molecular weight glutenin subunits of different wheat varieties by SDS-PAGE and assessment of genetic diversity among the given varieties with a protein weight marker of 14.3 kDa to 97.4 kDa. Variability of total seed storage proteins was investigated by using SDS-PAGE (Laemmli, 1970).
The results revealed a total of 117 and 101 electrophoretic bands on 10% and 15% gel (Figure 1) respectively. The size of polypeptides resolved ranged from 14.5 to 81.0 kd on 10% gel and 14.3 to 75 kd on 15% gel. For the analysis of banding pattern on the gels they were recorded as present or absent. For analysis each band was assigned a value of “1” for presence and “0” for absence.
One band (49.0 kd) was monomorphic for all genotypes on 10% gel. On 15% gel also one band was monomorphic (29 kd). Fifteen scorable bands were resolved on 10% gel in which 14 were polymorphic (93.33%). Similarly 13 scorable bands were resolved on 15% gel in which 12 were polymorphic (92.30%) (Table 2).

Figure 1 Protein Profile of Soluble Protein on SDS-PAGE (15%)

Table 2 Polymorphism information of protein bands analyzed on SDS-PAGE

The diversity in seed storage proteins has also been reported by Khan et al. for wheat varieties (Khan et al., 2002). Moreover, identification of three wheat genotypes including ILC-195, CM-2000 and CM-98/99 has also been reported by protein markers (Zeb et al., 2006). The genetic diversity estimates based on seed storage protein were low since the genotypes studied for high quality seed proteins; however this helps to classify varieties in different groups (Fufa et al., 2005).
Electrophoresis of proteins is a powerful tool for identification of genetic diversity and the SDS-PAGE is predominantly considered as a consistent technology because seed storage proteins are highly independent of environmental fluctuations (Javid et al., 2004; Iqbal et al., 2005). Seed protein patterns can also be used as a promising tool for distinguishing cultivars of particular crop species (Jha and Ohri, 1996; Seferoglua et al., 2006). However, only a few studies indicated that cultivar identification was not possible with the SDS-PAGE method (De Vries, 1996). The SDSPAGE is considered to be a sensible and reliable technique for species identification (Gepts, 1989).
2.1 Genetic relationship among the genotypes and cluster analysis
Protein profile obtained on SDS-PAGE with 10% and 15% gel was used to find genetic relationship among the genotypes. Results are illustrated in Table 3. The size of polypeptides resolved ranged from 14.3 to 81 kd. There is no unique band found in both gel. The polypeptides having molecular weight 49 kd & 29 kd are appeared in all genotypes in 10% and 15% gel respectively. A band of 71 kd is appeared only in G7 and G12 and absent in other genotypes. Polypeptide and 64 kd also absent in all genotypes except in G6 and G7 in 10%.

Table 3 The values of each different pattern for 12 bread wheat genotypes by using SDS-PAGE marker system

In 15% gel, the size of polypeptides resolved ranged from 14.3 to 75 kd. The polypeptide having molecular weight 66 kd is only appeared in G11 and G12. A band of 20.1 kd was appeared in all genotypes except in G1 and G2.
Jaccard’s similarity coefficient based SDS-PAGE banding pattern was used for cluster analysis to study genetic relationship. The range of genetic similarity was found in between 0.29-1.00 (Supplementary Table S-3). The dendogram (Figure 2) clearly indicated two main clusters. The cluster 1 includes G1 (MP-4010), G2 (HI-8713) and G3 (C-306). In subcluster salt susceptible genotypes G1 (MP-4010) and G2 (HI-8713) are exactly same to each other lies at 1.00 Jaccard similarity coefficients. Cluster 2 includes G4 (Lok-1), G5 (Raj-4037), G6 (HD-2932), G7 (KH-65), G8 (KRL-19), G9 (KRL-213), G10 (KRL-210), G11 (KRL-1-4) and G12 (Raj-3765). It has two subcluster, in which subcluster one includes salt susceptible genotypes G4 (Lok-1) and G5 (Raj-4037) are exactly same with similarity coefficient 1.00. Subcluster two includes genotypes G6 (HD-2932, G7 (KH-65), G8 (KRL-19), are also lies at 1.00 similarity coefficient. Both clusters are related to each other at 0.56 similarity coefficient.

Figure 2 Dendogram generated for wheat genotypes using UPGMA cluster analysis based on Jaccard Similarity Coefficient

2.2 Diversity analysis
In the present study, the population diversity based on SDS PAGE electrogram patterns was calculated using POPGENE 1.31 software. The Shannon diversity index (I) is one common diversity index often used to evaluate allelelic diversity in a locus. Shannon’s index accounts for both abundance and evenness of the alleles present (Shannon and Weaver, 1949), and are useful for understanding allele structure at a locus and measures gene diversity. A close perusal of Table 3 reveals that Shannon’s information index, was found to be 0.5355 and Nei’s (1973) gene diversity or expected heterozygosity (He) another common diversity index in population genetics was 0.3662 and total genetic diversity during the present study was found to be 0.3662 and also equivalent to Nei’s gene diversity or expected heterozygosity (He). Based on the fact that the diversity within variety was not observed during study, shows that varieties are well maintained to avoid any genetic contamination through a chance of cross pollination. Total genetic diversity (HT) and genetic diversity within varieties (HS) were used for the determination of the inter-variety genetic diversity (DST = HT − HS). However, there was no within variety diversity observed as a result inter- variety genetic diversity yielding a value of 0.3663. The GST parameter (relative magnitude of differentia-tion among varieties) was reasonable 1.000, explaining the low value of estimate of gene flow (Nm) which was 0.000.
3 Conclusion
Protein profiling of selected genotypes have been found effective to differentiate the selected genotypes of wheat. We had identified certain protein bands of 17 kd (10%) and 66 kd (15%) exclusively in salt tolerant genotypes. However protein profiling was not as effective as molecular marker but it can be very useful for demonstrating quantitative changes at protein level under different conditions and unique proteins are potentially useful in ultimately understanding the cellular events that are occurring at that time. Similarly, a comparison of protein profiles from unstressed and stressed organisms could lead to the identification of proteins associated directly or indirectly with the stress response This could be easily explain that protein/biochemical markers are have environment interaction, therefore protein/enzymes exclusively found in certain genotypes could able to express in certain environmental conditions (Ei-Akkad et al., 2002; Shehata et al., 2004; Rani et al., 2007; Morojele et al., 2010; Metwali et al., 2011). The detected variation could be used to provide useful information on individual enzymes or transporters, activity as well as modification of structural protein, protein-protein interactions and stress dependent protein movement and can be used to classify adapted cultivars to improve the efficiency of wheat breeding programmes Nevertheless, it could be concluded that seed storage protein profiles could be useful markers in cultivar identification, pedigree analysis, registration of new varieties, and in the characterization of genetic diversity and classification of adapted cultivars, thereby improving the efficiency of wheat breeding programs in cultivar development.
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