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BackgroundYeast is considered to be a workhorse of the biotechnology industry for the production of many value-added chemicals, alcoholic beverages and biofuels. Optimization of the fermentation is a challenging task that greatly benefits from dynamic models able to accurately describe and predict the fermentation profile and resulting products under different genetic and environmental conditions. In this article, we developed and validated a genome-scale dynamic flux balance model, using experimentally determined kinetic constraints.ResultsAppropriate equations for maintenance, biomass composition, anaerobic metabolism and nutrient uptake are key to improve model performance, especially for predicting glycerol and ethanol synthesis. Prediction profiles of synthesis and consumption of the main metabolites involved in alcoholic fermentation closely agreed with experimental data obtained from numerous lab and industrial fermentations under different environmental conditions. Finally, fermentation simulations of genetically engineered yeasts closely reproduced previously reported experimental results regarding final concentrations of the main fermentation products such as ethanol and glycerol.ConclusionA useful tool to describe, understand and predict metabolite production in batch yeast cultures was developed. The resulting model, if used wisely, could help to search for new metabolic engineering strategies to manage ethanol content in batch fermentations.

The yeast Saccharomyces cerevisiae is an important industrial microorganism. Nowadays, it is being used as a cell factory for the production of pharmaceuticals such as insulin, although this yeast has long been utilized in the bakery to raise dough, and in the production of alcoholic beverages, fermenting the sugars derived from rice, wheat, barley, corn and grape juice. S. cerevisiae has also been extensively used as a model eukaryotic system. In the last decade, genomic techniques have revealed important features of its molecular biology. For example, DNA array technologies are routinely used for determining gene expression levels in cells under different physiological conditions or environmental stimuli. Laboratory strains of S. cerevisiae are different from wine strains. For instance, laboratory yeasts are unable to completely transform all the sugar in the grape must into ethanol under winemaking conditions. In fact, standard culture conditions are usually very different from winemaking conditions, where multiple stresses occur simultaneously and sequentially throughout the fermentation. The response of wine yeasts to these stimuli differs in some aspects from laboratory strains, as suggested by the increasing number of studies in functional genomics being conducted on wine strains. In this paper we review the most recent applications of post-genomic techniques to understand yeast physiology in the wine industry. We also report recent advances in wine yeast strain improvement and propose a reference framework for integration of genomic information, bioinformatic tools and molecular biology techniques for cellular and metabolic engineering. Finally, we discuss the current state and future perspectives for using 'modern' biotechnology in the wine industry.

Phosphorus deficiency is one of the major nutrient stresses affecting plant growth. Plants respond to phosphate (Pi) deficiency through multiple strategies, including the synthesis of high-affinity Pi transporters. In this study, the expression pattern of one putative wheat high-affinity phosphate transporter, TaPT2, was examined in roots and leaves under Pi-deficient conditions. TaPT2 transcript levels increased in roots of Pi-starved plants. A 579 bp fragment of the TaPT2 promoter is sufficient to drive the expression of the GUS reporter gene specifically in roots of Pi-deprived wheat. This TaPT2 promoter fragment was also able to drive expression of the GUS reporter gene in transgenic Arabidopsis thaliana, under similar growth conditions. Conserved regions and candidate regulatory motifs were detected by comparing this promoter with Pi transporter promoters from barley, rice, and Arabidopsis. Altogether, these results indicate that there are conserved cis-acting elements and trans-acting factors that enable the TaPT2 promoter to be regulated in a tissue-specific and Pi-dependent fashion in both monocots and dicots.

ObjectiveTo compare the functional capacity of younger elderly individuals (60 to 79 years old) with that of older elderly individuals (≥ 80 years old) during the first 6 months after discharge from the intensive care unit.MethodsA multicenter prospective cohort study was conducted, in which data on intensive care unit admission and outcomes after hospital discharge (immediate post-discharge, after 3 months and after 6 months) were collected. Muscle strength was evaluated through the protocol of the Medical Research Council and dynamometry (handgrip); the ability to perform activities of daily life and functional independence were assessed by the Barthel index and the usual level of physical activity (International Physical Activity Questionnaire); and quality of life was assessed by the 12-Item Short-Form Health Survey Version 2.ResultsAmong the 253 patients included, 167 were younger elderly (between 61 and 79 years old), and 86 were older elderly (≥ 80 years old). During the sixth month of evaluation, the older elderlies presented a higher need for a caregiver (69.0% versus 49, 5%, p = 0.002). Functional capacity prior to intensive care unit admission and in the third month after discharge was lower in older elderlies than in younger ones (Barthel prior to the intensive care unit: 73.0 ± 30.0 versus 86.5 ± 22.6; p <0.001, Barthel in the third month: 63.5 ± 34.0 versus 71.5 ± 35.5, p = 0.03), as was the usual level of physical activity (International Physical Activity Questionnaire in the third month: active/very active 3.4% versus 18.3%, no physical activity 64.4% versus 39.7%, p < 0.001, and International Physical Activity Questionnaire in the sixth month: active/very active 5.8% versus 20.8%, no physical activity 69.2% versus 43.4%, p = 0.005). Older elderlies had lower muscle strength when assessed according to handgrip in both the dominant (14.5 ± 7.7 versus 19.9 ± 9.6, p = 0.008) and non-dominant limb (13.1 ± 6.7 versus 17.5 ± 9.1, p = 0.02). There were no differences in functional capacity loss or reported quality of life between the age groups.ConclusionAlthough there were great functional capacity losses after discharge from the intensive care unit in both age groups, there was no difference in the magnitude of functional capacity loss between younger (60 to 79 years) and older elderly individuals (≥ 80 years old) during the first 6 months after discharge from the intensive care unit.