Determination of wheat quality by mass spectrometry and multivariate data analysis

Foodomics: MS-based strategies in modern food science and nutrition

Modern research in food science and nutrition is moving from classical methodologies to advanced analytical strategies in which MS-based techniques play a crucial role. In this context, Foodomics has been recently defined as a new discipline that studies food and nutrition domains through the application of advanced omics technologies in which MS techniques are considered indispensable. Applications of Foodomics include the genomic, transcriptomic, proteomic, and/or metabolomic study of foods for compound profiling, authenticity, and/or biomarker-detection related to food quality or safety; the development of new transgenic foods, food contaminants, and whole toxicity studies; new investigations on food bioactivity, food effects on human health, etc. This review work does not intend to provide an exhaustive revision of the many works published so far on food analysis using MS techniques. The aim of the present work is to provide an overview of the different MS-based strategies that have been (or can be) applied in the new field of Foodomics, discussing their advantages and drawbacks. Besides, some ideas about the foreseen development and applications of MS-techniques in this new discipline are also provided.

Nutrição no pós-genoma: fundamentos e aplicações de ferramentas ômicas

Após seqüenciamento do genoma humano, os estudos genômicos têm se voltado à elucidação das funções de todos os genes, bem como à caracterização de suas interações com fatores ambientais. A nutrigenômica surgiu no contexto do pós-genoma humano e é considerada área-chave para a nutrição nesta década. Seu foco de estudo baseia-se na interação gene-nutriente. Esta ciência recente tem como objetivo principal o estabelecimento de dietas personalizadas, com base no genótipo, para a promoção da saúde e a redução do risco de doenças crônicas não transmissíveis como as cardiovasculares, o câncer, o diabetes, entre outras. Nesse contexto, é fundamental a aplicação na área de nutrição das ferramentas de genômica funcional para análise do transcritoma (transcritômica), do proteoma (proteômica) e do metaboloma (metabolômica). As aplicabilidades dessas metodologias em estudos nutricionais parecem ilimitadas, pois podem ser conduzidas em cultura de células, modelos de experimentação em animais, estudos pré-clinicos e clínicos. Tais técnicas apresentam potencial para identificar biomarcadores que respondem especificamente a um determinado nutriente ou composto bioativo dos alimentos e para estabelecer as melhores recomendações dietéticas individuais para redução do risco das doenças crônicas não transmissíveis e promoção da saúde.

Early prediction of wheat quality: analysis during grain development using mass spectrometry and multivariate data analysis

Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry and multivariate data analysis have been used for the determination of wheat quality at different stages of grain development. Wheat varieties with one of two different end-use qualities (i.e. suitable or not suitable for bread-making purposes) were investigated. The samples were collected from grains from 15 until 45 days post-anthesis (dpa). Gluten proteins from wheat grains were extracted and subsequently analysed by mass spectrometry. Discrimination partial least-squares regression and soft independent modelling of class analogy were used to determine the quality of new and unknown wheat samples. With these methods, we were able to predict correctly the end-use qualities at every stage investigated. This new fast technique, based on the rapidity of mass spectrometry combined with the objectivity of multivariate data analysis, offers a method that can replace the traditional rather time-consuming ones such as gel electrophoresis. This study focused on the determination of wheat quality at 15 dpa, when the grain is due for harvest 1 month later.

Technical aspects of functional proteomics in plants

Since the completion of genome sequences of several organisms, attention has been focused to determine the function and functional network of proteins by proteome analysis. This analysis is achieved by separation and identification of proteins, determination of their function and functional network, and construction of an appropriate database. Many improvements in separation and identification of proteins, such as two-dimensional electrophoresis, nano-liquid chromatography and mass spectrometry, have rapidly been achieved. Some new techniques which include top-down mass spectrometry and tandem affinity purification have emerged. These techniques have provided the possibility of high-throughput analysis of function and functional network of proteins in plants. However, to cope with the huge information emerging from proteome analyses, more sophisticated techniques and software are essential. The development and adaptation of such techniques will ease analyses of protein profiling, identification of post-translational modifications and protein-protein interaction, which are vital for elucidation of the protein functions.

Multivariate approaches in plant science

The objective of proteomics is to get an overview of the proteins expressed at a given point in time in a given tissue and to identify the connection to the biochemical status of that tissue. Therefore sample throughput and analysis time are important issues in proteomics. The concept of proteomics is to encircle the identity of proteins of interest. However, the overall relation between proteins must also be explained. Classical proteomics consist of separation and characterization, based on two-dimensional electrophoresis, trypsin digestion, mass spectrometry and database searching. Characterization includes labor intensive work in order to manage, handle and analyze data. The field of classical proteomics should therefore be extended to also include handling of large datasets in an objective way. The separation obtained by two-dimensional electrophoresis and mass spectrometry gives rise to huge amount of data. We present a multivariate approach to the handling of data in proteomics with the advantage that protein patterns can be spotted at an early stage and consequently the proteins selected for sequencing can be selected intelligently. These methods can also be applied to other data generating protein analysis methods like mass spectrometry and near infrared spectroscopy and examples of application to these techniques are also presented. Multivariate data analysis can unravel complicated data structures and may thereby relieve the characterization phase in classical proteomics. Traditionally statistical methods are not suitable for analysis of the huge amounts of data, where the number of variables exceed the number of objects. Multivariate data analysis, on the other hand, may uncover the hidden structures present in these data. This study takes its starting point in the field of classical proteomics and shows how multivariate data analysis can lead to faster ways of finding interesting proteins. Multivariate analysis has shown interesting results as a supplement to classical proteomics and added a new dimension to the field of proteomics.

Screening of Biomarkers in Rat Urine Using LC/Electrospray Ionization-MS and Two-Way Data Analysis

Biofluids, like urine, form very complex matrixes containing a large number of potential biomarkers, that is, changes of endogenous metabolites in response to xenobiotic exposure. This paper describes a fast and sensitive method of screening biomarkers in rat urine. Biomarkers for phospholipidosis, induced by an antidepressant drug, were studied. Urine samples from rats exposed to citalopram were analyzed using solid-phase extraction (SPE) and liquid chromatography mass spectrometry (LC/MS) analysis detecting negative ions. A fast iterative method, called Gentle, was used for the automatic curve resolution, and metabolic fingerprints were obtained. After peak alignment principal component analysis (PCA) was performed for pattern recognition, PCA loadings were studied as a means of discovering potential biomarkers. In this study a number of potential biomarkers of phospholipidosis in rats are discussed. They are reported by their retention time and base peak, as their identification is not within the scope of the study. In addition to the fact that it was possible to differentiate control samples from dosed samples, the data were very easy to interpret, and signals from xenobiotic-related substances were easily removed without affecting the endogenous compounds. The proposed method is a complement or an alternative to NMR for metabolomic applications.