Effect of Ultra-High Pressure Homogenization (UHPH) and Conventional Thermal Pasteurization on the Volatile Composition of Tiger Nut Beverage.

Tiger nut beverages are non-alcoholic products that are characterized by their pale color and soft flavor. Conventional heat treatments are widely used in the food industry, although heated products are often damaging to their overall quality. Ultra-high pressure homogenization UHPH) is an emerging technology that extends the shelf-life of foods while maintaining most of their fresh characteristics. The present work deals with the comparison of the effect of conventional thermal homogenization-pasteurization (H-P, 18 + 4 MPa at 65 °C, 80 °C for 15 s.) and UHPH (at 200 and 300 MPa, and inlet temperature of 40 °C), on the volatile composition of tiger nut beverage. Headspace-solid phase microextraction (HS-SPME) was used for detecting volatile compounds of beverages, which were then identified by gas chromatography-mass spectrometry (GC-MS). A total of 37 different volatile substances were identified in tiger nut beverages, which were primarily grouped into the aromatic hydrocarbons, alcohols, aldehydes and terpenes chemical families. Stabilizing treatments increased the total amount of volatile compounds (H-P > UHPH > R-P). H-P was the treatment that produced the most changes in the volatile composition of RP, while treatment at 200 MPa had a minor impact. At the end of their storage, these products were also characterized by the same chemical families. This study evidenced the UHPH technology as an alternative processing of tiger nut beverages production that minimally modifies their volatile composition.

Sorption of 4-ethylphenol and 4-ethylguaiacol by suberin from cork.

Cork shows an active role in the sorption of volatile phenols from wine. The sorption properties of 4-ethylphenol and 4-ethylguaiacol phenols in hydro-alcoholic medium placed in contact with suberin extracted from cork were especially investigated. To that purpose, suberin was immersed in model wine solutions containing several concentrations of each phenol and the amount of the compound remaining in the liquid phase was determined by SPME-GC-MS. Sorption isotherms of 4-ethylguaiacol and 4-ethylphenol by suberin followed the Henry's model. The solid/liquid partition coefficients (KSL) between the suberin and the model wine were also determined for several other volatile phenols. Suberin displayed rather high sorption capacity, which was positively correlated to the hydrophobicity of the volatile. Finally, the capacity of suberin to decrease the concentration of 4-ethylphenol and 4-ethylguaiacol was also tested in real wines affected by a Brettanomyces character. It also lead to a significant reduction of their concentration in wine.

Characterisation of volatile profile in soymilk treated by ultra high pressure homogenisation.

The effect of ultra high pressure homogenisation (UHPH) on the volatile profile of soymilk was studied and compared with conventional treatments. Soymilk was treated at 200 MPa combined with two inlet temperatures (55 or 75 °C) and treated at 300 MPa at 80 °C inlet temperature. UHPH-treated soymilks were compared with base product (untreated sample), pasteurised soymilk (90 °C, 30s) and ultra high temperature (UHT; 142 °C, 6s) treated samples. Volatile compounds were extracted by solid-phase microextraction and were identified by gas chromatography coupled with mass spectrometry. Pasteurisation and UHPH treatments at 200 MPa produced few changes in the volatile composition, reaching similar values to untreated soymilk. UHT treatment produced the most important effects on volatile profile compared to UHPH at 300MPa and 80 °C. Hexanal was the most abundant compound detected in all treatments. The effect of UHPH technology on volatile profile induced modifications depending on the combinations of processing parameters.

Effect of genetic regions on the correlation between single point mutation variability and morbidity.

Cross-species sequence comparisons have suggested that cross-species sequence variability is correlated with functionality. The goal of this study was to extend this observation at different genetic regions, focusing on the morbidity of Single Nucleotide Polymorphisms (SNPs). A set of deleterious SNPs was compared to a set of neutral SNPs. Both samples were stratified according to the location of the SNPs at different genetic regions. Deleterious SNPs were observed to be less variable across species than neutral SNPs, these differences being significant for missense mutations (p=1.1 × 10(-9)), SNPs in introns (p=6.4 × 10(-3)), and SNPs in unknown regions (p=8 × 10(-4)).

Predictability of gene ontology slim-terms from primary structure information in Embryophyta plant proteins.

BACKGROUND: Proteins are the key elements on the path from genetic information to the development of life. The roles played by the different proteins are difficult to uncover experimentally as this process involves complex procedures such as genetic modifications, injection of fluorescent proteins, gene knock-out methods and others. The knowledge learned from each protein is usually annotated in databases through different methods such as the proposed by The Gene Ontology (GO) consortium. Different methods have been proposed in order to predict GO terms from primary structure information, but very few are available for large-scale functional annotation of plants, and reported success rates are much less than the reported by other non-plant predictors. This paper explores the predictability of GO annotations on proteins belonging to the Embryophyta group from a set of features extracted solely from their primary amino acid sequence. RESULTS: High predictability of several GO terms was found for Molecular Function and Cellular Component. As expected, a lower degree of predictability was found on Biological Process ontology annotations, although a few biological processes were easily predicted. Proteins related to transport and transcription were particularly well predicted from primary structure information. The most discriminant features for prediction were those related to electric charges of the amino-acid sequence and hydropathicity derived features. CONCLUSIONS: An analysis of GO-slim terms predictability in plants was carried out, in order to determine single categories or groups of functions that are most related with primary structure information. For each highly predictable GO term, the responsible features of such successfulness were identified and discussed. In addition to most published studies, focused on few categories or single ontologies, results in this paper comprise a complete landscape of GO predictability from primary structure encompassing 75 GO terms at molecular, cellular and phenotypical level. Thus, it provides a valuable guide for researchers interested on further advances in protein function prediction on Embryophyta plants.

Yeast-yeast interactions revealed by aromatic profile analysis of Sauvignon Blanc wine fermented by single or co-culture of non-Saccharomyces and Saccharomyces yeasts.

There has been increasing interest in the use of selected non-Saccharomyces yeasts in co-culture with Saccharomyces cerevisiae. The main reason is that the multistarter fermentation process is thought to simulate indigenous fermentation, thus increasing wine aroma complexity while avoiding the risks linked to natural fermentation. However, multistarter fermentation is characterised by complex and largely unknown interactions between yeasts. Consequently the resulting wine quality is rather unpredictable. In order to better understand the interactions that take place between non-Saccharomyces and Saccharomyces yeasts during alcoholic fermentation, we analysed the volatile profiles of several mono-culture and co-cultures. Candida zemplinina, Torulaspora delbrueckii and Metschnikowia pulcherrima were used to conduct fermentations either in mono-culture or in co-culture with S. cerevisiae. Up to 48 volatile compounds belonging to different chemical families were quantified. For the first time, we show that C. zemplinina is a strong producer of terpenes and lactones. We demonstrate by means of multivariate analysis that different interactions exist between the co-cultures studied. We observed a synergistic effect on aromatic compound production when M. pulcherrima was in co-culture with S. cerevisiae. However a negative interaction was observed between C. zemplinina and S. cerevisiae, which resulted in a decrease in terpene and lactone content. These interactions are independent of biomass production. The aromatic profiles of T. delbrueckii and S. cerevisiae in mono-culture and in co-culture are very close, and are biomass-dependent, reflecting a neutral interaction. This study reveals that a whole family of compounds could be altered by such interactions. These results suggest that the entire metabolic pathway is affected by these interactions.

An information theory-based tool for characterizing the interaction environment of a protein.

In recent years large amounts of information have been accumulated in proteomic, genetic and metabolic databases. Much effort has been dedicated to developing methods that successfully exploit, organize and structure this information. However, there is no application, that we know of, that semantically characterizes the interaction environment in which a protein exists. A high-throughput software package has been developed to retrieve information from publicly available databases, such as the Gene Ontology Annotation (GOA) database and the Human Proteome Resource Database (HPRD) and structure their information. This information is presented to the user as groups of semantically described dense interaction subnetworks that interact with a target protein.

MISS: a non-linear methodology based on mutual information for genetic association studies in both population and sib-pairs analysis.

MOTIVATION: Finding association between genetic variants and phenotypes related to disease has become an important vehicle for the study of complex disorders. In this context, multi-loci genetic association might unravel additional information when compared with single loci search. The main goal of this work is to propose a non-linear methodology based on information theory for finding combinatorial association between multi-SNPs and a given phenotype. RESULTS: The proposed methodology, called MISS (mutual information statistical significance), has been integrated jointly with a feature selection algorithm and has been tested on a synthetic dataset with a controlled phenotype and in the particular case of the F7 gene. The MISS methodology has been contrasted with a multiple linear regression (MLR) method used for genetic association in both, a population-based study and a sib-pairs analysis and with the maximum entropy conditional probability modelling (MECPM) method, which searches for predictive multi-locus interactions. Several sets of SNPs within the F7 gene region have been found to show a significant correlation with the FVII levels in blood. The proposed multi-site approach unveils combinations of SNPs that explain more significant information of the phenotype than their individual polymorphisms. MISS is able to find more correlations between SNPs and the phenotype than MLR and MECPM. Most of the marked SNPs appear in the literature as functional variants with real effect on the protein FVII levels in blood. AVAILABILITY: The code is available at http://sisbio.recerca.upc.edu/R/MISS_0.2.tar.gz

Search of phenotype related candidate genes using gene ontology-based semantic similarity and protein interaction information: application to Brugada syndrome.

This work presents a methodology for finding phenotype candidate genes starting from a set of known related genes. This is accomplished by automatically mining and organizing the available scientific literature using Gene Ontology-based semantic similarity. As a case study, Brugada syndrome related genes have been used as input in order to obtain a list of other possible candidate genes related with this disease. Brugada anomaly produces a typical alteration in the Electrocardiogram and carriers of the disease show an increased probability of sudden death. Results show a set of semantically coherent proteins that are shown to be related with synaptic transmission and muscle contraction physiological processes.

Transcription factor binding site detection through position cross-mutual information variability analysis.

Regulatory sequence detection is a fundamental challenge in computational biology. One key process in protein synthesis starts with the binding of the transcription factor to its binding site. Different sites can show binding to the same factor. This variability found in binding sequences increases the difficulty of their detection using computational algorithms. In this manuscript, a method for the detection of binding sites is proposed, based on the correlation between binding sequence positions through information theoretical measures. Efficiency values of the method are reported in the form of Receiver Operating Characteristic curves on the detection of different transcription factors of the Saccharomyces cerevisiae organism. We compare our results with other known motif detection Motif Discovery scan (MDscan).