ABSTRACT
A Se-targeted bottom-up proteomics approach was developed for the identification of Se-containing proteins in rice grown naturally on seleniferous soils. The proteins were separated by 2D gel electrophoresis. The position of Se-containing spots was tentatively identified by the correlation between the 1D isoelectrofocusing (IEF) and 1D SDS electropherograms of a sample aliquot and confirmed by (78)Se imaging in the 2D gel. The method was complemented by the ICP-MS assisted shotgun proteomics approach. The proteins were identified by capHPLC with the dual ICP MS and electrospray Orbitrap MS detection. The first ever comprehensive study of rice selenoproteome revealed the presence of selenium, as both selenomethionine (SeMet) and selenocysteine (SeCys) residues, in a dozen proteins including a 19 kDa globulin, granule-bound starch synthase, and the family of glutelin-type seed storage proteins.
Subject(s)
Oryza/chemistry , Plant Proteins/chemistry , Selenium/analysis , Selenocysteine/analysis , Selenomethionine/analysis , Amino Acid Sequence , Chromatography, High Pressure Liquid , Electrophoresis, Gel, Two-Dimensional , Molecular Sequence Data , Proteomics , Spectrometry, Mass, Electrospray Ionization , Tandem Mass SpectrometryABSTRACT
Rice is a staple food for nearly half the world's population. The discrimination of geographical origin of rice in order to its authenticity is essential to prevent mislabeling and adulteration problems. The multi-element fingerprinting has a great potential for the differentiation of rice grains. A study of the capability of the high resolution inductively coupled plasma mass spectrometry (HR-ICP-MS) methodology for multi-element fingerprinting of rice has been carried out. A total of 31 Thai jasmine rice and 5 foreign (France, India, Italy, Japan and Pakistan) rice samples were analysed by high resolution ICP-MS after acid digestion. Accuracy of the whole procedure was verified by the analysis of rice flour standard reference material (NIST SRM 1568a). The concentrations of 21 elements were evaluated and used as chemical indicator to discriminate the origin of rice samples. The classification of rice samples was carried out based on elemental composition by a radar plot and multivariate data analysis, including principal component analysis (PCA) and discriminant analysis (DA). Thai jasmine rice can be differentiated from foreign rice samples by radar plots and multivariate data analysis. Furthermore, the DA can differentiate Thai jasmine rice samples according to each region of origin (northern, northeastern or central regions of Thailand). Therefore, multi-element fingerprinting combined with the use of multivariate statistical techniques can be considered as a powerful tool for rice authentication.