Identification of barley and rye varieties using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry with neural networks

MALDI-TOF mass spectrometry of hordeins: rapid approach for identification of malting barley varieties

A procedure for identification of malting barley varieties using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) of ethanol-soluble barley proteins (hordeins) is described. The hordeins were first extracted from milled barley grains by several extraction protocols (using different extraction agents and conditions). Hordein extracts were then analyzed directly via MALDI-TOF MS without any preliminary purification or separation step, and the protein profiles of analyzed hordein extracts were compared in order to find out the most suitable extraction procedure for mass spectrometric analysis. The optimized procedure was successfully applied to identification of 13 malting barley varieties. Our results revealed that the proposed mass spectrometry-based approach provides characteristic mass patterns of extracted hordeins, which can be advantageously used for barley variety identification.

Mass spectrometry analysis of gliadins in celiac disease

In recent years, scientific research on wheat gluten proteins has followed three main directions aimed at (1) finding relationships between individual genetic alleles coding for gliadins, high or low molecular weight glutenin subunits, and the viscoelastic dough properties of flour-derived products such as pasta and bread; (2) identifying prolamins and derived peptides involved in celiac disease, a pathological condition in which the small intestine of genetically predisposed individuals is reversibly damaged; and (3) developing and validating sensitive and specific methods for detecting trace amounts of gluten proteins in gluten-free foods for celiac disease patients. In this review, the main aspects of current and perspective applications of mass spectrometry and proteomic technologies to the structural characterization of gliadins are presented, with focus on issues related to detection, identification, and quantification of intact gliadins, as well as gliadin-derived peptides relevant to the biochemical, immunological, and toxicological aspects of celiac disease.

Bioinformatics approaches in clinical proteomics

Protein expression profiling is increasingly being used to discover, validate and characterize biomarkers that can potentially be used for diagnostic purposes and to aid in pharmaceutical development. Correct analysis of data obtained from these experiments requires an understanding of the underlying analytic procedures used to obtain the data, statistical principles underlying high-dimensional data and clinical statistical tools used to determine the utility of the interpreted data. This review summarizes each of these steps, with the goal of providing the nonstatistician proteomics researcher with a working understanding of the various approaches that may be used by statisticians. Emphasis is placed on the process of mining high-dimensional data to identify a specific set of biomarkers that may be used in a diagnostic or other assay setting.

Rice proteomics: a cornerstone for cereal food crop proteomes

Proteomics-a systematic study of proteins present in a cell, tissue, organ, or organism at a particular moment during the life cycle-that began with classical two-dimensional electrophoresis and its advancement during the 1990s, has been revolutionized by a series of tremendous technological developments in mass spectrometry (MS), a core technology. Proteomics is exerting its influence on biological function of genes and genomes in the era (21st century) of functional genomics, and for this reason yeast, bacterial, and mammalian systems are the best examples. Although plant proteomics is still in its infancy, evolving proteomic technologies and the availability of the genome sequences of Arabidopsis thaliana (L.) Heyhn, and rice (Oryza sativa L.), model dicotyledoneous and monocotyledoneous (monocot) species, respectively, are propelling it towards new heights, as evidenced by the rapid spurt in worldwide plant proteome research. Rice, with an immense socio-economic impact on human civilization, is a representative model of cereal food crops, and we consider it as a cornerstone for functional genomics of cereal plants. In this review, we look at the history and the current state of monocot proteomes, including barley, maize, and wheat, with a central focus on rice, which has the most extensive proteomic coverage to date. On one side, we highlight advances in technologies that have generated enormous amount of interest in plant proteomics, and the other side summarizes the achievements made towards establishing proteomes during plant growth & development and challenge to environmental factors, including disease, and for studying genetic relationships. In light of what we have learned from the proteomic journey in rice and other monocots, we finally reveal and assess their impact in our continuous strive towards completion of their full proteomes.

Matrix-assisted laser desorption ionization time-of-flight mass spectrometry analysis of zeins in mature maize kernels

A high-throughput method has been developed to allow rapid analysis of maize seed storage proteins by matrix-assisted laser desorption time-of-flight mass spectrometry. The extraction solution containing an organic solvent, a reducing agent, and a volatile base has been optimized to enable extraction of all classes of zein proteins (alpha-, beta-, gamma-, and delta-). A near-saturating concentration of matrix, 2-(4-hydroxyphenylazo)benzoic acid, was necessary to obtain strong peaks for the most lipophilic zeins, the alpha-zeins. Zein proteins with small mass differences, difficult to separate by sodium dodecyl sulfate polyacrylamide gel electrophoresis, were resolved through this analysis. Mass signals corresponding to the 10-kDa delta-, 15-kDa beta-, 16-kDa gamma-, 27-kDa gamma-, and several 19 and 22-kDa alpha-zeins were detected. The zein identities were further confirmed by the association of the number of cysteine residues in each zein MS peak, as determined by iodoacetamide derivatization, with the number predicted from its coding sequence. The relative zein abundance in the zein MS peaks was also correlated with the relative zein EST abundance among endosperm EST libraries. This method was utilized to examine the zein composition of a number of corn inbred lines and opaque mutants.

Determination of wheat quality by mass spectrometry and multivariate data analysis

Multivariate analysis has been applied as support to proteome analysis in order to implement an easier and faster way of data handling based on separation by matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry. The characterisation phase in proteome analysis by means of simple visual inspection is a demanding process and also insecure because subjectivity is the controlling element. Multivariate analysis offers, to a considerable extent, objectivity and must therefore be regarded as a neutral way to evaluate results obtained by proteome analysis. Proteome analysis of storage proteins from the wheat gluten complex based on two-dimensional electrophoresis and analysis of the N-terminal sequence has revealed a protein homologous to gamma-gliadins, tentatively associated with quality and within the molecular weight range 27-35 kDa. Further examinations of gliadin data based on mass spectrometry revealed that quality among wheat varieties could be determined by means of principal component analysis. Further examinations by interval partial least squares made it possible to encircle an overall optimal molecular weight interval from 31.5 to 33.7 kDa. The use of multivariate analysis on data from mass spectrometry has thus shown to be a promising technique to minimize the number of two-dimensional gels within the field of proteome analysis.

Variety identification of wheat using mass spectrometry with neural networks and the influence of mass spectra processing prior to neural network analysis

The performance of matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry with neural networks in wheat variety classification is further evaluated.1 Two principal issues were studied: (a) the number of varieties that could be classified correctly; and (b) various means of pre-processing mass spectrometric data. The number of wheat varieties tested was increased from 10 to 30. The main pre-processing method investigated was based on Gaussian smoothing of the spectra, but other methods based on normalisation procedures and multiplicative scatter correction of data were also used. With the final method, it was possible to classify 30 wheat varieties with 87% correctly classified mass spectra and a correlation coefficient of 0.90.