W.A. van Winden

When transcriptome meets metabolome: fast cellular responses of yeast to sudden relief of glucose limitation

Within the first 5 min after a sudden relief from glucose limitation, Saccharomyces cerevisiae exhibited fast changes of intracellular metabolite levels and a major transcriptional reprogramming. Integration of transcriptome and metabolome data revealed tight relationships between the changes at these two levels. Transcriptome as well as metabolite changes reflected a major investment in two processes: adaptation from fully respiratory to respiro‐fermentative metabolism and preparation for growth acceleration. At the metabolite level, a severe drop of the AXP pools directly after glucose addition was not accompanied by any of the other three NXP. To counterbalance this loss, purine biosynthesis and salvage pathways were transcriptionally upregulated in a concerted manner, reflecting a sudden increase of the purine demand. The short‐term dynamics of the transcriptome revealed a remarkably fast decrease in the average half‐life of downregulated genes. This acceleration of mRNA decay can be interpreted both as an additional nucleotide salvage pathway and an additional level of glucose‐induced regulation of gene expression.

Metabolite and reaction inference based on enzyme specificities

Motivation: Many enzymes are not absolutely specific, or even promiscuous: they can catalyze transformations of more compounds than the traditional ones as listed in, e.g. KEGG. This information is currently only available in databases, such as the BRENDA enzyme activity database. In this article, we propose to model enzyme aspecificity by predicting whether an input compound is likely to be transformed by a certain enzyme. Such a predictor has many applications, for example, to complete reconstructed metabolic networks, to aid in metabolic engineering or to help identify unknown peaks in mass spectra. Results: We have developed a system for metabolite and reaction inference based on enzyme specificities (MaRIboES). It employs structural and stereochemistry similarity measures and molecular fingerprints to generalize enzymatic reactions based on data available in BRENDA. Leave-one-out cross-validation shows that 80% of known reactions are predicted well. Application to the yeast glycolytic and pentose phosphate pathways predicts a large number of known and new reactions, often leading to the formation of novel compounds, as well as a number of interesting bypasses and cross-links. Availability: Matlab and C++ code is freely available at https://gforge.nbic.nl/projects/mariboes/ Contact: d.deridder@tudelft.nl Supplementary information: Supplementary data are available at Bioinformatics online.

Energetic and metabolic transient response of Saccharomyces cerevisiae to benzoic acid

Saccharomyces cerevisiae is known to be able to adapt to the presence of the commonly used food preservative benzoic acid with a large energy expenditure. Some mechanisms for the adaptation process have been suggested, but its quantitative energetic and metabolic aspects have rarely been discussed. This study discusses use of the stimulus response approach to quantitatively study the energetic and metabolic aspects of the transient adaptation of S. cerevisiae to a shift in benzoic acid concentration, from 0 to 0.8 mm. The information obtained also serves as the basis for further utilization of benzoic acid as a tool for targeted perturbation of the energy system, which is important in studying the kinetics and regulation of central carbon metabolism in S. cerevisiae. Using this experimental set‐up, we found significant fast‐transient (< 3000 s) increases in O2 consumption and CO2 production rates, of ∼ 50%, which reflect a high energy requirement for the adaptation process. We also found that with a longer exposure time to benzoic acid, S. cerevisiae decreases the cell membrane permeability for this weak acid by a factor of 10 and decreases the cell size to ∼ 80% of the initial value. The intracellular metabolite profile in the new steady‐state indicates increases in the glycolytic and tricarboxylic acid cycle fluxes, which are in agreement with the observed increases in specific glucose and O2 uptake rates.

Efficient modeling of 13C-labeling distributions in microorganisms

Metabolic networks can be analysed using 2D [13C,1H] COSY (NMR) measurements of 13C-labeled metabolites. A framework is presented whereby the steady state reaction rates are deduced from conventional isotopomer balances. This model is reduced by removing redundant nodes and lumping equilibrium pools. Conversion of the balances to the recently introduced bondomer notation further reduces the complexity. When the reduction approaches are applied to the glycolysis and pentose phosphate pathway, the number of equations is reduced by a factor of three without loss of information.

Modeling the metabolism of Penicillin-G formation

Micro-organisms have been used since many decades for the production of valuable chemicals for food, pharmaceutical and bulk industries. Examples are amino acids, vitamins, antibiotics or alcohols and organic acids (Table VII.1). Improvement of the production properties has been achieved using random classical mutation techniques. The development of recombinant-DNA techniques, the unraveling of complete genomes and genome wide information measurement (DNA chips) have recently opened the possibility of precise modifications in microbial metabolism. The goals of such “rational metabolic engineering” are “de novo” or improved production of desirable chemical compounds. Rational metabolic engineering opens the possibility to use micro-organisms for the production of a wider scope of bulk and fine chemicals. This is called the “cell factory concept”. Table VII.1 Microbial production processes Product Market volume (tons/yr.) Lysine 100.000 Glutamic acid 1000.000 Functional proteins 10.000 s-lactam antibiotics 45.000 Ethanol 25.000.000 Lactic acid 50.000