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The purpose of this study was to examine the effects of two pasture feeding systems—perennial ryegrass (GRS) and perennial ryegrass and white clover (CLV)—and an indoor total mixed ration (TMR) system on the (a) rumen microbiome; (b) rumen fluid and milk metabolome; and (c) to assess the potential to distinguish milk from different feeding systems by their respective metabolomes. Rumen fluid was collected from nine rumen cannulated cows under the different feeding systems in early, mid and late lactation, and raw milk samples were collected from ten non-cannulated cows in mid-lactation from each of the feeding systems. The microbiota present in rumen liquid and solid portions were analysed using 16S rRNA gene sequencing, while 1H-NMR untargeted metabolomic analysis was performed on rumen fluid and raw milk samples. The rumen microbiota composition was not found to be significantly altered by any feeding system in this study, likely as a result of a shortened adaptation period (two weeks’ exposure time). In contrast, feeding system had a significant effect on both the rumen and milk metabolome. Increased concentrations of volatile fatty acids including acetic acid, an important source of energy for the cow, were detected in the rumen of TMR and CLV-fed cows. Pasture feeding resulted in significantly higher concentrations of isoacids in the rumen. The ruminal fluids of both CLV and GRS-fed cows were found to have increased concentrations of p-cresol, a product of microbiome metabolism. CLV feeding resulted in increased rumen concentrations of formate, a substrate compound for methanogenesis. The TMR feeding resulted in significantly higher rumen choline content, which contributes to animal health and milk production, and succinate, a product of carbohydrate metabolism. Milk and rumen-fluids were shown to have varying levels of dimethyl sulfone in each feeding system, which was found to be an important compound for distinguishing between the diets. CLV feeding resulted in increased concentrations of milk urea. Milk from pasture-based feeding systems was shown to have significantly higher concentrations of hippuric acid, a potential biomarker of pasture-derived milk. This study has demonstrated that 1H-NMR metabolomics coupled with multivariate analysis is capable of distinguishing both rumen-fluid and milk derived from cows on different feeding systems, specifically between indoor TMR and pasture-based diets used in this study.

The Human Metabolome Database or HMDB (www.hmdb.ca) is a web-enabled metabolomic database containing comprehensive information about human metabolites along with their biological roles, physiological concentrations, disease associations, chemical reactions, metabolic pathways, and reference spectra. First described in 2007, the HMDB is now considered the standard metabolomic resource for human metabolic studies. Over the past decade the HMDB has continued to grow and evolve in response to emerging needs for metabolomics researchers and continuing changes in web standards. This year's update, HMDB 4.0, represents the most significant upgrade to the database in its history. For instance, the number of fully annotated metabolites has increased by nearly threefold, the number of experimental spectra has grown by almost fourfold and the number of illustrated metabolic pathways has grown by a factor of almost 60. Significant improvements have also been made to the HMDB’s chemical taxonomy, chemical ontology, spectral viewing, and spectral/text searching tools. A great deal of brand new data has also been added to HMDB 4.0. This includes large quantities of predicted MS/MS and GC–MS reference spectral data as well as predicted (physiologically feasible) metabolite structures to facilitate novel metabolite identification. Additional information on metabolite-SNP interactions and the influence of drugs on metabolite levels (pharmacometabolomics) has also been added. Many other important improvements in the content, the interface, and the performance of the HMDB website have been made and these should greatly enhance its ease of use and its potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics science.

The quality of sparkling wines can deteriorate due to browning, which is mainly attributed to the oxidation of phenols. The aim of this study was to assess the browning (measured as the absorbance at 420 nm) of two real series of Cava sparkling wines (A and B) during aging sur lie (biological aging, with lees) and commercial storage (without lees) under real conditions, over almost two years, since lees may reduce the effect by adsorbing the precursors and the browning compounds. The role of phenolics and 5-HMF (5-hydroxymethylfurfural) was also assessed. A significant linear increment in browning was observed in all four Cava series in both kinds of aging. No differences in browning were observed between the two types of aging (biological vs. commercial storage). In contrast, the 5-HMF content increased linearly with time although its rate of formation was dependent on the type of aging (biological or storage). The phenolic compounds trans-coutaric and p-coumaric increased significantly with time and were significantly correlated with browning. The most aged sparkling wines (Gran Reserva) had highest A420 and 5-HMF values. 5-HMF was correlated with browning and can be used as a quality marker.

The aim of this study was to elucidate the structure of a potential ageing marker for Cava sparkling wine. In order to clarify the structure of this compound, NMR spectroscopy and hyphenated UHPLC-DAD-MS/MS techniques were used. We identified the hitherto unknown compound as 5-hydroxymethyl-2-furfuraldehyde (5-HMF). This is the first time that this compound has been reported in sparkling wines. A survey, based on the analysis of 80 commercial sparkling wines, showed that 5-HMF is present between 0.25 and 12.81 mg/L, and is compared with those reported for other types of wine. Hypothetical origin of 5-HMF in Cava sparkling wine is discussed.

In the present work it is reviewed the the statistical tools available for the biomarker discovery process. Resampling techniques, feature selection, classifiers and metric to evaluate the performance of the biomarkers is reviewed. All the above concepts are tested, applying them into an appropriate work-flow, in a real prostate cancer study. The aim is to find biomarkes to be able to classify men depending the aggressiveness of the cancer after an intervention, from the analysis of the miRNA expression obtained from qPCR. Three different comparisons are assessed, and in each one a total of 20 models are compared to find the best. The R/Bioconductor CMA package is used to perform the analysis. Potential biomarkers are found, although further studies are recommended before its commercialization or other use.