Link between food and health: From gene expression to nutritional recommendations

Joint research in food and health could have large potential benefits and could substantially reduce risks for metabolic diseases. Nutritionists now work transdisciplinarily using molecular and cellular biology as well as physiology to develop research strategies going from knowledge on gene expression to dietary recommendations in order to meet individual requirements. Changing food habits in order to follow nutritional recommendations is hard but possible. To reach this aim nutrition research needs to move from a focused approach towards a broader approach including human sciences and “art du bien manger”. Professionals in food and hospitality have a genuine and essential knowledge in culinary art, products presentation and gastronomy. They must certainly be key actors to help nutritionists and scientists to implement in a practical way the nutritional recommendations in the general population, with a central question: How to change «eating» towards healthy, tasty food by changing food offer?     

Intra- and inter-omic fusion of metabolic profiling data in a systems biology framework

The explosion of -omics technologies in the characterization and prediction of defined physiological or pathological states necessitates a parallel development in data integration and visualization tools in order to display and interpret vast amounts of data in an efficient manner. Here we summarize some of the key achievements in this area and compare the strengths and limitations of each method with respect to their use in representing biological processes in a systems biology environment.     

Unraveling the molecular repertoire of tears as a source of biomarkers: Beyond ocular diseases

Proteomics and metabolomics investigations of body fluids present several challenges for biomarker discovery of several diseases. The search for biomarkers is actually conducted in different body fluids, even if the ideal biomarker can be found in an easily accessible biological fluid, because, if validated, the biomarker could be sought in the healthy population. In this regard, tears could be considered an optimum material obtained by noninvasive procedures. In the past years, the scientific community has become more interested in the study of tears for the research of new biomarkers not only for ocular diseases. In this review, we provide a discussion on the current state of biomarkers research in tears and their relevance for clinical practice, and report the main results of clinical proteomics studies on systemic and eye diseases. We summarize the main methods for tear samples analyses and report recent advances in “omics” platforms for tears investigations. Moreover, we want to take stock of the emerging field of metabolomics and lipidomics as a new and integrated approach to study protein-metabolites interplay for biomarkers research, where tears represent a still unexplored and attractive field.     

CE-TOF MS-based metabolomic profiling revealed characteristic metabolic pathways in postmortem porcine fast and slow type muscles

To determine key compounds and metabolic pathways associated with meat quality, we profiled metabolites in postmortem porcine longissimus lumborum (LL) and vastus intermedius (VI) muscles with different aging times by global metabolomics using capillary electrophoresis-time of flight mass spectrometry. Loading analyses of the principal component analysis showed that hydrophilic amino acids and β-alanine-related compounds contributed to the muscle type positively and negatively, respectively, whereas glycolytic and ATP degradation products contributed to aging time. At 168 h postmortem, LL samples were characterized by abundance of combinations of amino acids, dipeptides, and glycolytic products, whereas the VI samples were characterized by abundance of both sulfur-containing compounds and amino acids. The AMP and inosine contents in the VI were approx. 10 times higher than those in the LL at 4 h postmortem, suggesting different rates of inosine 5′-monophosphate (IMP) accumulation by adenylate kinase 7 and 5′-nucleotidase, and subsequent different production levels of IMP and hypoxanthine between these two porcine muscles.     

When LC-HRMS metabolomics gets ISO17025 accredited and ready for official controls – application to the screening of forbidden compounds in livestock

ABSTRACT

Within the particular context of controlling chemical residues in food, an alternative to targeted approaches has emerged; it consists in the characterisation of physiological perturbations induced upon exposure of animals to a given chemical substance/class of substances to highlight suitable biomarkers addressing safety and/or regulatory issues. Metabolomics in particular has been investigated in the hope of identifying such biomarkers, and a range of studies have demonstrated the efficiency of the strategy. Until very recently, steps remained to be taken towards official or commercial implementation of corresponding tools. In particular, the lack of guidelines and criteria to validate such methods that do not target specific chemical species per se, constituted a bottleneck. In the present work, a metabolomics model dedicated to the detection of β-agonist administration in bovines has been developed and fully validated; criteria (selectivity, robustness, stability, suspicion threshold definition, false positive and false negative rates) have been proposed in agreement with EU expectations (Dec 2002/657), enabling demonstration that performances comply with screening requirements.

Although some of the biomarkers involved in the prediction model remain un-elucidated, the corresponding LC-HRMS method has recently been ISO17025 accredited, allowing for the very first official implementation of a metabolomics based strategy within French National Monitoring Plans.     

Identifying Metabolite and Protein Biomarkers in Unstable Angina In-patients by Feature Selection Based Data Mining Method

Unstable angina(UA)is the most dangerous type of Coronary Heart Disease(CHD)to cause more and more mortal and morbid world wide.Identification of biomarkers for UA at the level of proteomics and metaboiomics is a better avenue to understand the inner mechanism of it.Feature selection based data mining method is better suited to identify biomarkers of UA.In this study,we carried out clinical epidemiology to collect plasmas of UA in-patients and controls.Proteomics and metabolomics data were obtained via two-dimensional difference gel electrophoresis and gas chromatography techniques.We presented a novel computational strategy to select biomarkers as few as possible for UA in the two groups of data.Firstly,decision tree was used to select biomarkers for UA and 3-fold cross validation was used to evaluate computational performances for the three methods.Alternatively,we combined independent t test and classification based data mining method as well as backward elimination technique to select,as few as possible,protein and metabolite biomarkers with best classification performances.By the method,we selected 6 proteins and 5 metabolites for UA.The novel method presented here provides a better insight into the pathology of a disease.