André B. Canelas

Quantitative evaluation of intracellular metabolite extraction techniques for yeast metabolomics.

Accurate determination of intracellular metabolite levels requires well-validated procedures for sampling and sample treatment. Several methods exist for metabolite extraction, but the literature is contradictory regarding the adequacy and performance of each technique. Using a strictly quantitative approach, we have re-evaluated five methods (hot water, HW; boiling ethanol, BE; chloroform-methanol, CM; freezing-thawing in methanol, FTM; acidic acetonitrile-methanol, AANM) for the extraction of 44 intracellular metabolites (phosphorylated intermediates, amino acids, organic acids, nucleotides) from S. cerevisiae cells. Two culture modes were investigated (batch and chemostat) to check for growth condition dependency, and three targeted platforms were employed (two LC-MS and one GC/MS) to exclude analytical bias. Additionally, for the determination of metabolite recoveries, we applied a novel approach based on addition of (13)C-labeled internal standards at different stages of sample processing. We found that the choice of extraction method can drastically affect measured metabolite levels, to an extent that for some metabolites even the direction of changes between growth conditions can be inverted. The best performances, in terms of efficacy and metabolite recoveries, were achieved with BE and CM, which yielded nearly identical levels for the metabolites analyzed. According to our results, AANM performs poorly in yeast and FTM cannot be considered adequate as an extraction method, as it does not ensure inactivation of enzymatic activity.

Measuring enzyme activities under standardized in vivo-like conditions for systems biology

Realistic quantitative models require data from many laboratories. Therefore, standardization of experimental systems and assay conditions is crucial. Moreover, standards should be representative of the in vivo conditions. However, most often, enzyme–kinetic parameters are measured under assay conditions that yield the maximum activity of each enzyme. In practice, this means that the kinetic parameters of different enzymes are measured in different buffers, at different pH values, with different ionic strengths, etc. In a joint effort of the Dutch Vertical Genomics Consortium, the European Yeast Systems Biology Network and the Standards for Reporting Enzymology Data Commission, we have developed a single assay medium for determining enzyme–kinetic parameters in yeast. The medium is as close as possible to the in vivo situation for the yeast Saccharomyces cerevisiae, and at the same time is experimentally feasible. The in vivo conditions were estimated for S. cerevisiae strain CEN.PK113‐7D grown in aerobic glucose‐limited chemostat cultures at an extracellular pH of 5.0 and a specific growth rate of 0.1 h−1. The cytosolic pH and concentrations of calcium, sodium, potassium, phosphorus, sulfur and magnesium were determined. On the basis of these data and literature data, we propose a defined in vivo‐like medium containing 300 mm potassium, 50 mm phosphate, 245 mm glutamate, 20 mm sodium, 2 mm free magnesium and 0.5 mm calcium, at a pH of 6.8. The Vmax values of the glycolytic and fermentative enzymes of S. cerevisiae were measured in the new medium. For some enzymes, the results deviated conspicuously from those of assays done under enzyme‐specific, optimal conditions.

Determination of the cytosolic free NAD/NADH ratio in Saccharomyces cerevisiae under steady‐state and highly dynamic conditions

The coenzyme NAD plays a major role in metabolism as a key redox carrier and signaling molecule but current measurement techniques cannot distinguish between different compartment pools, between free and protein‐bound forms and/or between NAD(H) and NADP(H). Local free NAD/NADH ratios can be determined from product/substrate ratios of suitable near‐equilibrium redox reactions but the application of this principle is often precluded by uncertainties regarding enzyme activity, localization and coenzyme specificity of dehydrogenases. In Saccharomyces cerevisiae, we circumvented these issues by expressing a bacterial mannitol‐1‐phosphate 5‐dehydrogenase and determining the cytosolic free NAD/NADH ratio from the measured [fructose‐6‐phosphate]/[mannitol‐1‐phosphate] ratio. Under aerobic glucose‐limited conditions we estimated a cytosolic free NAD/NADH ratio between 101(±14) and 320(±45), assuming the cytosolic pH is between 7.0 and 6.5, respectively. These values are more than 10‐fold higher than the measured whole‐cell total NAD/NADH ratio of 7.5(±2.5). Using a thermodynamic analysis of central glycolysis we demonstrate that the former are thermodynamically feasible, while the latter is not. Furthermore, we applied this novel system to study the short‐term metabolic responses to perturbations. We found that the cytosolic free NAD–NADH couple became more reduced rapidly (timescale of seconds) upon a pulse of glucose (electron‐donor) and that this could be reversed by the addition of acetaldehyde (electron‐acceptor). In addition, these dynamics occurred without significant changes in whole‐cell total NAD and NADH. This approach provides a new experimental tool for quantitative physiology and opens new possibilities in the study of energy and redox metabolism in S. cerevisiae. The same strategy should also be applicable to other microorganisms. Biotechnol. Bioeng. 2008;100: 734–743.

Integrated multilaboratory systems biology reveals differences in protein metabolism between two reference yeast strains

The field of systems biology is often held back by difficulties in obtaining comprehensive, high-quality, quantitative data sets. In this paper, we undertook an interlaboratory effort to generate such a data set for a very large number of cellular components in the yeast Saccharomyces cerevisiae, a widely used model organism that is also used in the production of fuels, chemicals, food ingredients and pharmaceuticals. With the current focus on biofuels and sustainability, there is much interest in harnessing this species as a general cell factory. In this study, we characterized two yeast strains, under two standard growth conditions. We ensured the high quality of the experimental data by evaluating a wide range of sampling and analytical techniques. Here we show significant differences in the maximum specific growth rate and biomass yield between the two strains. On the basis of the integrated analysis of the high-throughput data, we hypothesize that differences in phenotype are due to differences in protein metabolism.

A comprehensive method for the quantification of the non-oxidative pentose phosphate pathway intermediates in Saccharomyces cerevisiae by GC–IDMS

A gas chromatography isotope dilution mass spectrometry (GC-IDMS) method was developed for the quantification of the metabolites of the non-oxidative part of pentose phosphate pathway (PPP). A mid-polar GC column (Zebron ZB-AAA, 10m, film composition 50% phenyl 50% dimethyl polysiloxane) was used for the chromatographic separation of the intermediates. The optimized GC-MS procedure resulted in improved separation performances and higher sensitivities compared to previous methods. Furthermore, the use of (13)C-labeled cell extracts as internal standards improved the data quality and eliminated the need to perform a recovery check for each metabolite. The applicability of the new method was demonstrated by analyzing intracellular metabolite levels in samples derived from aerobic glucose-limited chemostat cultures of Saccharomyces cerevisiae at steady state as well as following a short-term glucose pulse. The major achievements of the proposed quantitative method are the independent quantification of the epimers ribulose-5-phosphate and xylulose-5-posphate and the measurement of compounds present at very low concentrations in biological samples such as erythrose-4-phosphate and glyceraldehyde-3-phosphate.

Quantitative Analysis of the High Temperature-induced Glycolytic Flux Increase in Saccharomyces cerevisiae Reveals Dominant Metabolic Regulation

A major challenge in systems biology lies in the integration of processes occurring at different levels, such as transcription, translation, and metabolism, to understand the functioning of a living cell in its environment. We studied the high temperature-induced glycolytic flux increase in Saccharomyces cerevisiae and investigated the regulatory mechanisms underlying this increase. We used glucose-limited chemostat cultures to separate regulatory effects of temperature from effects on growth rate. Growth at increased temperature (38 °C versus 30 °C) resulted in a strongly increased glycolytic flux, accompanied by a switch from respiration to a partially fermentative metabolism. We observed an increased flux through all enzymes, ranging from 5- to 10-fold. We quantified the contributions of direct temperature effects on enzyme activities, the gene expression cascade and shifts in the metabolic network, to the increased flux through each enzyme. To do this we adapted flux regulation analysis. We show that the direct effect of temperature on enzyme kinetics can be included as a separate term. Together with hierarchical regulation and metabolic regulation, this term explains the total flux change between two steady states. Surprisingly, the effect of the cultivation temperature on enzyme catalytic capacity, both directly through the Arrhenius effect and indirectly through adapted gene expression, is only a moderate contribution to the increased glycolytic flux for most enzymes. The changes in flux are therefore largely caused by changes in the interaction of the enzymes with substrates, products, and effectors.

Dynamics of Glycolytic Regulation during Adaptation of Saccharomyces cerevisiae to Fermentative Metabolism

ABSTRACT The ability of bakers yeast (Saccharomyces cerevisiae) to rapidly increase its glycolytic flux upon a switch from respiratory to fermentative sugar metabolism is an important characteristic for many of its multiple industrial applications. An increased glycolytic flux can be achieved by an increase in the glycolytic enzyme capacities (V max) and/or by changes in the concentrations of low-molecular-weight substrates, products, and effectors. The goal of the present study was to understand the time-dependent, multilevel regulation of glycolytic enzymes during a switch from fully respiratory conditions to fully fermentative conditions. The switch from glucose-limited aerobic chemostat growth to full anaerobiosis and glucose excess resulted in rapid acceleration of fermentative metabolism. Although the capacities (V max) of the glycolytic enzymes did not change until 45 min after the switch, the intracellular levels of several substrates, products, and effectors involved in the regulation of glycolysis did change substantially during the initial 45 min (e.g., there was a buildup of the phosphofructokinase activator fructose-2,6-bisphosphate). This study revealed two distinct phases in the upregulation of glycolysis upon a switch to fermentative conditions: (i) an initial phase, in which regulation occurs completely through changes in metabolite levels; and (ii) a second phase, in which regulation is achieved through a combination of changes in V max and metabolite concentrations. This multilevel regulation study qualitatively explains the increase in flux through the glycolytic enzymes upon a switch of S. cerevisiae to fermentative conditions and provides a better understanding of the roles of different regulatory mechanisms that influence the dynamics of yeast glycolysis.

Development of quantitative metabolomics for Pichia pastoris

Accurate, reliable and reproducible measurement of intracellular metabolite levels has become important for metabolic studies of microbial cell factories. A first critical step for metabolomic studies is the establishment of an adequate quenching and washing protocol, which ensures effective arrest of all metabolic activity and removal of extracellular metabolites, without causing leakage of metabolites from the cells. Five different procedures based on cold methanol quenching and cell separation by filtration were tested for metabolomics of Pichia pastoris regarding methanol content and temperature of the quenching solution as key parameters. Quantitative evaluation of these protocols was carried out through mass balance analysis, based on metabolite measurements in all sample fractions, those are whole broth, quenched and washed cells, culture filtrate and quenching and washing solution. Finally, the optimal method was used to study the time profiles of free amino acid and central carbon metabolism intermediates in glucose-limited chemostat cultures. Acceptable recoveries (>90%) were obtained for all quenching procedures tested. However, quenching at −27°C in 60% v/v methanol performed slightly better in terms of leakage minimization. We could demonstrate that five residence times under glucose limitation are enough to reach stable intracellular metabolite pools. Moreover, when comparing P. pastoris and S. cerevisiae metabolomes, under the same cultivation conditions, similar metabolite fingerprints were found in both yeasts, except for the lower glycolysis, where the levels of these metabolites in P. pastoris suggested an enzymatic capacity limitation in that part of the metabolism.

Fast sampling of the cellular metabolome.

Obtaining meaningful snapshots of the metabolome of microorganisms requires rapid sampling and immediate quenching of all metabolic activity, to prevent any changes in metabolite levels after sampling. Furthermore, a suitable extraction method is required ensuring complete extraction of metabolites from the cells and inactivation of enzymatic activity, with minimal degradation of labile compounds. Finally a sensitive, high-throughput analysis platform is needed to quantify a large number of metabolites in a small amount of sample. An issue which has often been overlooked in microbial metabolomics is the fact that many intracellular metabolites are also present in significant amounts outside the cells, and may interfere with the endometabolome measurements. Attempts to remove the extracellular metabolites with dedicated quenching methods often induce release of intracellular metabolites into the quenching solution. For eukaryotic microorganisms, leakage can be minimized by adaptation of the quenching method. For prokaryotic cells this had not yet been accomplished, so the application of a differential method whereby metabolites are measured in the culture supernatant as well as in total broth samples, to calculate the intracellular levels by subtraction, seems to be the most suitable approach. Here we present an overview of different sampling, quenching, and extraction methods developed for microbial metabolomics, described in the literature. Detailed protocols are provided for rapid sampling, quenching, and extraction for measurement of metabolites in total broth samples, washed cell samples and supernatant, to be applied for quantitative metabolomics of both eukaryotic and prokaryotic microorganisms.

Global test for metabolic pathway differences between conditions.

In many metabolomics applications there is a need to compare metabolite levels between different conditions, e.g., case versus control. There exist many statistical methods to perform such comparisons but only few of these explicitly take into account the fact that metabolites are connected in pathways or modules. Such a priori information on pathway structure can alleviate problems in, e.g., testing on individual metabolite level. In gene-expression analysis, Goemans global test is used to this extent to determine whether a group of genes has a different expression pattern under changed conditions. We examined if this test can be generalized to metabolomics data. The goal is to determine if the behavior of a group of metabolites, belonging to the same pathway, is significantly related to a particular outcome of interest, e.g., case/control or environmental conditions. The results show that the global test can indeed be used in such situations. This is illustrated with extensive intracellular metabolomics data from Escherichia coli and Saccharomyces cerevisiae under different environmental conditions.