Promotion of Zn(2+) uptake by Al (3+) in a Saccharomyces Cerevisiae mutant that lacks the ZRT1 gene encoding a high-affinity Zn transporter

Aluminum, a Friend or Foe of Higher Plants in Acid Soils

Aluminum (Al) is the most abundant metal in the earth's crust, but its availability depends on soil pH. Despite this abundance, Al is not considered an essential element and so far no experimental evidence has been put forward for a biological role. In plants and other organisms, Al can have a beneficial or toxic effect, depending on factors such as, metal concentration, the chemical form of Al, growth conditions and plant species. Here we review recent advances in the study of Al in plants at physiological, biochemical and molecular levels, focusing mainly on the beneficial effect of Al in plants (stimulation of root growth, increased nutrient uptake, the increase in enzyme activity, and others). In addition, we discuss the possible mechanisms involved in improving the growth of plants cultivated in soils with acid pH, as well as mechanisms of tolerance to the toxic effect of Al.

Identification of genes related to nitrogen uptake in wine strains of Saccharomyces cerevisiae

The yeast Saccharomyces cerevisiae is the main microorganism responsible for wine fermentation and its development influences the quality of wine. A problem affecting these types of fermentations, generating important losses in this industry, are the slow or stuck fermentations which may result from low nitrogen availability in the must. Therefore, several studies have been directed towards identifying genes involved in nitrogen metabolism using high throughput strategies which include subjecting the yeast to changes in the type or concentration of the available nitrogen source. However, this type of approach can also generate responses in the yeast that do not necessarily alter the expression of genes related to nitrogen metabolism. In this work, by using intraspecific hybridisation of wild wine yeast strains we obtained genetically and oenologically similar strains with differences in the consumption of nitrogen sources. Using the same must, the global expression patterns of these yeasts were compared by microarrays, the analysis of which identified 276 genes that varied in their expression between the strains analysed. The functional analysis of the genes with a known function indicates that some participate in nitrogen metabolism, alcoholic fermentation, ion transport and transcriptional regulation. Furthermore, differences were observed in the expression of genes which have been partially associated to nitrogen, as in the case of ZRT1 and ATO2. Interestingly, many of the genes identified have no known function or have not been previously associated to this phenotype.

A Boolean probabilistic model of metabolic adaptation to oxygen in relation to iron homeostasis and oxidative stress

Background

In aerobically grown cells, iron homeostasis and oxidative stress are tightly linked processes implicated in a growing number of diseases. The deregulation of iron homeostasis due to gene defects or environmental stresses leads to a wide range of diseases with consequences for cellular metabolism that remain poorly understood. The modelling of iron homeostasis in relation to the main features of metabolism, energy production and oxidative stress may provide new clues to the ways in which changes in biological processes in a normal cell lead to disease.

Results

Using a methodology based on probabilistic Boolean modelling, we constructed the first model of yeast iron homeostasis including oxygen-related reactions in the frame of central metabolism. The resulting model of 642 elements and 1007 reactions was validated by comparing simulations with a large body of experimental results (147 phenotypes and 11 metabolic flux experiments). We removed every gene, thus generating in silico mutants. The simulations of the different mutants gave rise to a remarkably accurate qualitative description of most of the experimental phenotype (overall consistency > 91.5%). A second validation involved analysing the anaerobiosis to aerobiosis transition. Therefore, we compared the simulations of our model with different levels of oxygen to experimental metabolic flux data. The simulations reproducted accurately ten out of the eleven metabolic fluxes. We show here that our probabilistic Boolean modelling strategy provides a useful description of the dynamics of a complex biological system. A clustering analysis of the simulations of all in silico mutations led to the identification of clear phenotypic profiles, thus providing new insights into some metabolic response to stress conditions. Finally, the model was also used to explore several new hypothesis in order to better understand some unexpected phenotypes in given mutants.

Conclusions

All these results show that this model, and the underlying modelling strategy, are powerful tools for improving our understanding of complex biological problems.