Modelling and analysis of metabolic pathways

Combined in silico and 19F NMR analysis of 5-fluorouracil metabolism in yeast at low ATP conditions

The cytotoxic effect of 5-fluorouracil (5-FU) on yeast cells is thought to be mainly via a misincorporation of fluoropyrimidines into both RNA and DNA, not only DNA damage via inhibition of thymidylate synthase (TYMS) by fluorodeoxyuridine monophosphate (FdUMP). However, some studies on Saccharomyces cerevisiae show a drastic decrease in ATP concentration under oxidative stress, together with a decrease in concentration of other tri- and diphosphates. This raises a question if hydrolysis of 5-fluoro-2-deoxyuridine diphosphate (FdUDP) under oxidative stress could not lead to the presence of FdUMP and the activation of so-called 'thymine-less death' route. We attempted to answer this question with in silico modeling of 5-FU metabolic pathways, based on new experimental results, where the stages of intracellular metabolism of 5-FU in Saccharomyces cerevisiae were tracked by a combination of 19F and 31P NMR spectroscopic study. We have identified 5-FU, its nucleosides and nucleotides, and subsequent di- and/or triphosphates. Additionally, another wide 19F signal, assigned to fluorinated unstructured short RNA, has been also identified in the spectra. The concentration of individual metabolites was found to vary substantially within hours, however, the initial steady-state was preserved only for an hour, until the ATP concentration dropped by a half, which was monitored independently via 31P NMR spectra. After that, the catabolic process leading from triphosphates through monophosphates and nucleosides back to 5-FU was observed. These results imply careful design and interpretation of studies in 5-FU metabolism in yeast.

Changes in substrate availability in Escherichia coli lead to rapid metabolite, flux and growth rate responses

The interactions between the intracellular metabolome, fluxome and growth rate of Escherichia coli after sudden glycolytic/gluconeogenic substrate shifts are studied based on pulses of different substrates to an aerobic glucose-limited steady-state (dilution rate=0.1h(-1)). After each added glycolytic (glucose) and gluconeogenic (pyruvate and succinate) substrate pulse, no by-products were secreted and a pseudo steady state in flux and metabolites was achieved in about 30-40s. In the pulse experiments a large oxygen uptake capacity of the cells was observed. The in vivo dynamic responses showed massive reorganization and flexibility (1/100-14-fold change) of extra/intracellular metabolic fluxes, matching with large changes in the concentrations of intracellular metabolites, including reversal of reaction rate for pseudo/near equilibrium reactions. The coupling of metabolome and fluxome could be described by Q-linear kinetics. Remarkably, the three different substrate pulses resulted in a very similar increase in growth rate (0.13-0.3h(-1)). Data analysis showed that there must exist as yet unknown mechanisms which couple the protein synthesis rate to changes in central metabolites.

In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data

A significant goal in the post-genome era is to relate the annotated genome sequence to the physiological functions of a cell. Working from the annotated genome sequence, as well as biochemical and physiological information, it is possible to reconstruct complete metabolic networks. Furthermore, computational methods have been developed to interpret and predict the optimal performance of a metabolic network under a range of growth conditions. We have tested the hypothesis that Escherichia coli uses its metabolism to grow at a maximal rate using the E. coli MG1655 metabolic reconstruction. Based on this hypothesis, we formulated experiments that describe the quantitative relationship between a primary carbon source (acetate or succinate) uptake rate, oxygen uptake rate, and maximal cellular growth rate. We found that the experimental data were consistent with the stated hypothesis, namely that the E. coli metabolic network is optimized to maximize growth under the experimental conditions considered. This study thus demonstrates how the combination of in silico and experimental biology can be used to obtain a quantitative genotype-phenotype relationship for metabolism in bacterial cells.