Flowering is one of the most important developmental programs that plants use to ensure survival and reproductive success. The timing of flowering is under the control of several interdependent pathways. The molecular and genetic background of the interaction between environmental factors and the floral transition in these cultivars are still not reported. TaVRT2 expression is up-regulated in the winter genotypes during the vegetative phase and in photoperiod-sensitive genotypes during short days, and is repressed by vernalization to a level that allows the transition to the reproductive phase. Protein-protein interaction studies revealed that TaVRT-2 interacts with proteins encoded by two important vernalization genes (TaVRT-1/VRN-1 and VRN-2) in wheat. These results support the hypothesis that TaVRT-2 is a putative repressor of the floral transition in wheat. This gene is located on the short arm of homologous group 7 chromosomes in hexapolid wheat. The TaVRT2 acts as a repressor of the floral transition in wheat. We found that the flowering suppressor of flowering suppressor gene might be located on the short arm of chromosome 7D using several chromosomal substitution or aneuploid lines. The genetic map has been constructed from segregation of 370 SSR loci using 210 recombinant inbred lines (RILs), which was established at F7 eneration by the single-seed descent method from F2 family derived from Mironnovavskaya 808 and Chinese Spring. To perform mapping on the TaVRT2 on the homologous group 7 chromosomes, three homogous genes, TaVRT-1, TaVRT-2, TaVRT-3, are isolated from two common wheat cultivars; Chinese Spring (CS), Mironnovavskaya 808 (M808) and nucleotide polymorphisms between two cultivars are detected.

The wild relative’s diploid species, which are reproductively isolated from one another, compromise populations with marked morphological variation, wide climatic tolerance, and adaptation to diverse habitats, and also vary genetically in biotic, abiotic stresses, and in seed protein content and quality. Large-scale proteomic analysis of three wild relatives of wheat grain (AA, BB, and DD genome) using matrix assisted laser desorption/ionization- time of flight (MALDI-TOF-MS), multi-dimensional protein identification technology (MudPIT), allowed the detection and classification of 213, 255 unique proteins (peptide match ≥ 2), which represents the most wide-ranging proteome exploitation to date. Development of standard proteomes exhibiting all of the proteins involved in normal physiology will facilitate the delineation of disease/defense (no. of unique protein (n) =33, 51), metabolism (n=15, 32), energy metabolism (n= 21, 27), protein synthesis (n=16, 22), folding/stability (n=17, 18), transcription (n=6, 18), cell growth/division (n=17, 17), signal transduction (n=16, 15), cellular organization (n=11, 12), development (n=9, 9), storage protein (n= 30, 7), transport facilitation (n=8, 6), and unclear classification (n= 14, 21), which is identification by using MALDI-TOF and LCQ DECA mass spectrometry couple to mascot database search, respectively. For instance, ABA inducible protein PHVA1 (HVA1), which can be induced by drought, cold, heat and salinity condition, and also basic endochitinase (RSCC, RSCA) showed defense against chitin containing fungal pathogens. Gluten (glutenin and gliadin), which is very important determinant for making high quality bread, noodles, and also associated with visco-elasticity. By using MALDI-TOF, we identified abundant disease related protein such as NBS-LRR involves in response to the presence of a foreign body or the occurrence of an injury, which result in restriction of damage to the organism attacked or prevention/recovery from the infection caused by the attack, puroindoline (a & b) and grain softness protein represents the molecular-genetic basis of grain texture. In addition, the PIN A and PIN B gene products have anti microbial properties with potential role in plant defense. Recent advances in mass spectrometry and bioinformatics have provided the means to characterize complex protein landscapes from a wide variety of organisms. Hierarchical clustering could be applied to protein information from different samples using Gene Pattern and NCSS software. Here we report also genome specific protein interaction network using Cytoscape software, which provides further insight into the molecular mechanism of biochemical pathways. By integrating shotgun proteomics with statistical and computation alanalyses, we developed promising understand about expressed protein and protein functions. Our approach should be applicable for marker assisted breeding or genetransfer for quality and stress research of cultivated wheat.

Seed color is an important trait affecting flour yield and quality in wheat. Seed color also is either tightly linked to or pleiotropically controls seed dormancy in wheat, because most of the red-seeded wheats are tolerant to pre-harvest sprouting in comparison to white-seeded wheats. Recently, metabolomics approaches have recently been used to assess the natural variance in metabolite content between individual plants, an approach with great potential for the improvement of the compositional quality of crops. Basically, in the study here, the simultaneous proteomic and metablomic approaches are being investigated to identify the expressed proteins of genes and specific metabolism responsible for the expression of red and white colors of seed. Red seed “Jinpum” and white seed “Kumkang” cultivars were used in this study to identify the storage proteins use of 2-DE, MALDI-TOF/MS. Here we optimize tissue extraction methods compatible with high-throughput, reproducible nuclear magnetic resonance (NMR) spectroscopy based metabolomics. It appears that the proteins expressed were different each other according to two different cultivars from the seeds of hexaploid wheat. Some selected protein spots were identified as follows: B3-hordein, Gamma-hordein-3,bifunctionalalpha amylase/subtilisin Inhibitor. To monitor metabolic profile, wheat grain was ground in liquid nitrogen, ensuring a homogeneous mix of the tissue, solution samples extracted from seed grains of two wheat cultivars were conducted to measurement of metabolite using 1H-1D NMR method. Representative 1H-1D NMR spectra showing the metabolic fingerprints of wheat grain extracted and presented in Fig. The different peaks, observed at 3.4 and 4.3 ppm, were detected and difference in each two cultivars. The metabolic fingerprint of each two wheat cultivars by 1H-1D NMR were analysed using partial least squares (PLS) in mutivariate analysis to confirm metabolic profiling between different cultivars and to screen chemical shift spectrum corresponding to metabolite specifically abundant in each cultivars. Profiling using 1H-1D NMR was applied to measure of abundance of major metabolite. In total metabolites were compared between “Jinpum” and “Kumkang” cultivars. Therefore, NMR based on the metabolic-phenotyping should be mostly applicable to systematic exploration of plant genetic resources as well as to metabolite based on the breeding program involved in crops productivity.