Identifying quantitative operation principles in metabolic pathways: a systematic method for searching feasible enzyme activity patterns leading to cellular adaptive responses

Background: Optimization methods allow designing changes in a system so that specific goals are attained. These techniques are fundamental for metabolic engineering. However, they are not directly applicable for investigating the evolution of metabolic adaptation to environmental changes. Although biological systems have evolved by natural selection and result in well-adapted systems, we can hardly expect that actual metabolic processes are at the theoretical optimum that could result from an optimization analysis. More likely, natural systems are to be found in a feasible region compatible with global physiological requirements. Results: We first present a new method for globally optimizing nonlinear models of metabolic pathways that are based on the Generalized Mass Action (GMA) representation. The optimization task is posed as a nonconvex nonlinear programming (NLP) problem that is solved by an outer- approximation algorithm. This method relies on solving iteratively reduced NLP slave subproblems and mixed-integer linear programming (MILP) master problems that provide valid upper and lower bounds, respectively, on the global solution to the original NLP. The capabilities of this method are illustrated through its application to the anaerobic fermentation pathway in Saccharomyces cerevisiae. We next introduce a method to identify the feasibility parametric regions that allow a system to meet a set of physiological constraints that can be represented in mathematical terms through algebraic equations. This technique is based on applying the outer-approximation based algorithm iteratively over a reduced search space in order to identify regions that contain feasible solutions to the problem and discard others in which no feasible solution exists. As an example, we characterize the feasible enzyme activity changes that are compatible with an appropriate adaptive response of yeast Saccharomyces cerevisiae to heat shock Conclusion: Our results show the utility of the suggested approach for investigating the evolution of adaptive responses to environmental changes. The proposed method can be used in other important applications such as the evaluation of parameter changes that are compatible with health and disease states.

Reduction of oxidative cellular damage by overexpression of the thioredoxin TRX2 gene improves yield and quality of wine yeast dry active biomass

Background: Wine Saccharomyces cerevisiae strains, adapted to anaerobic must fermentations, suffer oxidative stress when they are grown under aerobic conditions for biomass propagation in the industrial process of active dry yeast production. Oxidative metabolism of sugars favors high biomass yields but also causes increased oxidation damage of cell components. The overexpression of the TRX2 gene, coding for a thioredoxin, enhances oxidative stress resistance in a wine yeast strain model. The thioredoxin and also the glutathione/glutaredoxin system constitute the most important defense against oxidation. Trx2p is also involved in the regulation of Yap1p-driven transcriptional response against some reactive oxygen species. Results: Laboratory scale simulations of the industrial active dry biomass production process demonstrate that TRX2 overexpression increases the wine yeast final biomass yield and also its fermentative capacity both after the batch and fed-batch phases. Microvinifications carried out with the modified strain show a fast start phenotype derived from its enhanced fermentative capacity and also increased content of beneficial aroma compounds. The modified strain displays an increased transcriptional response of Yap1p regulated genes and other oxidative stress related genes. Activities of antioxidant enzymes like Sod1p, Sod2p and catalase are also enhanced. Consequently, diminished oxidation of lipids and proteins is observed in the modified strain, which can explain the improved performance of the thioredoxin overexpressing strain. Conclusions: We report several beneficial effects of overexpressing the thioredoxin gene TRX2 in a wine yeast strain. We show that this strain presents an enhanced redox defense. Increased yield of biomass production process in TRX2 overexpressing strain can be of special interest for several industrial applications.

Evolution and cellular function of monothiol glutaredoxins: involvement in iron-sulphur cluster assembly

A number of bacterial species, mostly proteobacteria, possess monothiol glutaredoxins homologous to the Saccharomyces cerevisiae mitochondrial protein Grx5, which is involved in iron–sulphur cluster synthesis. Phylogenetic profiling is used to predict that bacterial monothiol glutaredoxins also participate in the iron–sulphur cluster (ISC) assembly machinery, because their phylogenetic profiles are similar to the profiles of the bacterial homologues of yeast ISC proteins. High evolutionary cooccurrence is observed between the Grx5 homologues and the homologues of the Yah1 ferredoxin, the scaffold proteins Isa1 and Isa2, the frataxin protein Yfh1 and the Nfu1 protein. This suggests that a specific functional interaction exists between these ISC machinery proteins. Physical interaction analyses using low-definition protein docking predict the formation of strong and specific complexes between Grx5 and several components of the yeast ISC machinery. Two-hybrid analysis has confirmed the in vivo interaction between Grx5 and Isa1. Sequence comparison techniques and cladistics indicate that the other two monothiol glutaredoxins of S. cerevisiae, Grx3 and Grx4, have evolved from the fusion of a thioredoxin gene with a monothiol glutaredoxin gene early in the eukaryotic lineage, leading to differential functional specialization. While bacteria do not contain these chimaeric glutaredoxins, in many eukaryotic species Grx5 and Grx3/4-type monothiol glutaredoxins coexist in the cell.

A Cellular Perspective on Brain Energy Metabolism and Functional Imaging.

The energy demands of the brain are high: they account for at least 20% of the body's energy consumption. Evolutionary studies indicate that the emergence of higher cognitive functions in humans is associated with an increased glucose utilization and expression of energy metabolism genes. Functional brain imaging techniques such as fMRI and PET, which are widely used in human neuroscience studies, detect signals that monitor energy delivery and use in register with neuronal activity. Recent technological advances in metabolic studies with cellular resolution have afforded decisive insights into the understanding of the cellular and molecular bases of the coupling between neuronal activity and energy metabolism and point at a key role of neuron-astrocyte metabolic interactions. This article reviews some of the most salient features emerging from recent studies and aims at providing an integration of brain energy metabolism across resolution scales.