Uwe Sauer

Robustness of cellular functions.

Robustness, the ability to maintain performance in the face of perturbations and uncertainty, is a long-recognized key property of living systems. Owing to intimate links to cellular complexity, however, its molecular and cellular basis has only recently begun to be understood. Theoretical approaches to complex engineered systems can provide guidelines for investigating cellular robustness because biology and engineering employ a common set of basic mechanisms in different combinations. Robustness may be a key to understanding cellular complexity, elucidating design principles, and fostering closer interactions between experimentation and theory.

Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli

To which extent can optimality principles describe the operation of metabolic networks? By explicitly considering experimental errors and in silico alternate optima in flux balance analysis, we systematically evaluate the capacity of 11 objective functions combined with eight adjustable constraints to predict 13C‐determined in vivo fluxes in Escherichia coli under six environmental conditions. While no single objective describes the flux states under all conditions, we identified two sets of objectives for biologically meaningful predictions without the need for further, potentially artificial constraints. Unlimited growth on glucose in oxygen or nitrate respiring batch cultures is best described by nonlinear maximization of the ATP yield per flux unit. Under nutrient scarcity in continuous cultures, in contrast, linear maximization of the overall ATP or biomass yields achieved the highest predictive accuracy. Since these particular objectives predict the system behavior without preconditioning of the network structure, the identified optimality principles reflect, to some extent, the evolutionary selection of metabolic network regulation that realizes the various flux states.

Metabolic networks in motion: 13C‐based flux analysis

Many properties of complex networks cannot be understood from monitoring the components—not even when comprehensively monitoring all protein or metabolite concentrations—unless such information is connected and integrated through mathematical models. The reason is that static component concentrations, albeit extremely informative, do not contain functional information per se. The functional behavior of a network emerges only through the nonlinear gene, protein, and metabolite interactions across multiple metabolic and regulatory layers. I argue here that intracellular reaction rates are the functional end points of these interactions in metabolic networks, hence are highly relevant for systems biology. Methods for experimental determination of metabolic fluxes differ fundamentally from component concentration measurements; that is, intracellular reaction rates cannot be detected directly, but must be estimated through computer model‐based interpretation of stable isotope patterns in products of metabolism.

13 C-based metabolic flux analysis

Stable isotope, and in particular 13C-based flux analysis, is the exclusive approach to experimentally quantify the integrated responses of metabolic networks. Here we describe a protocol that is based on growing microbes on 13C-labeled glucose and subsequent gas chromatography mass spectrometric detection of 13C-patterns in protein-bound amino acids. Relying on publicly available software packages, we then describe two complementary mathematical approaches to estimate either local ratios of converging fluxes or absolute fluxes through different pathways. As amino acids in cell protein are abundant and stable, this protocol requires a minimum of equipment and analytical expertise. Most other flux methods are variants of the principles presented here. A true alternative is the analytically more demanding dynamic flux analysis that relies on 13C-pattern in free intracellular metabolites. The presented protocols take 5–10 d, have been used extensively in the past decade and are exemplified here for the central metabolism of Escherichia coli.

From good old biochemical analyses to high-throughput omics measurements and back

Matthias Heinemann obtained a PhD in biochemical engineering from the RWTH Aachen University (Germany); did a postdoc with Sven Panke in the bioprocess lab of the ETH Zurich (Switzerland) followed by a group leader position at the Institute of Molecular Systems Biology at ETH Zurich (research unit of Uwe Sauer); since August 2009 he is professor for molecular systems biology at the University of Groningen (The Netherlands) leading a research program aiming at generating a system-level understanding of (microbial) metabolism.

Cross-platform comparison of methods for quantitative metabolomics of primary metabolism.

Quantitative metabolomics is under intense development, and no commonly accepted standard analytical technique has emerged, yet. The employed analytical methods were mostly chosen based on educated guesses. So far, there has been no systematic cross-platform comparison of different separation and detection methods for quantitative metabolomics. Generally, the chromatographic separation of metabolites followed by their selective detection in a mass spectrometer (MS) is the most promising approach in terms of sensitivity and separation power. Using a defined mixture of 91 metabolites (covering glycolysis, pentose phosphate pathway, the tricarboxylic acid (TCA) cycle, redox metabolism, amino acids, and nucleotides), we compared six separation methods designed for the analysis of these mostly very polar primary metabolites, two methods each for gas chromatography (GC), liquid chromatography (LC), and capillary electrophoresis (CE). For analyses on a single platform, LC provides the best combination of both versatility and robustness. If a second platform can be used, it is best complemented by GC. Only liquid-phase separation systems can handle large polar metabolites, such as those containing multiple phosphate groups. As assessed by supplementing the defined mixture with (13)C-labeled yeast extracts, matrix effects are a common phenomenon on all platforms. Therefore, suitable internal standards, such as (13)C-labeled biomass extracts, are mandatory for quantitative metabolomics with any methods.

Experimental Identification and Quantification of Glucose Metabolism in Seven Bacterial Species

The structurally conserved and ubiquitous pathways of central carbon metabolism provide building blocks and cofactors for the biosynthesis of cellular macromolecules. The relative uses of pathways and reactions, however, vary widely among species and depend upon conditions, and some are not used at all. Here we identify the network topology of glucose metabolism and its in vivo operation by quantification of intracellular carbon fluxes from 13C tracer experiments. Specifically, we investigated Agrobacterium tumefaciens, two pseudomonads, Sinorhizobium meliloti, Rhodobacter sphaeroides, Zymomonas mobilis, and Paracoccus versutus, which grow on glucose as the sole carbon source, represent fundamentally different metabolic lifestyles (aerobic, anaerobic, photoheterotrophic, and chemoheterotrophic), and are phylogenetically distinct (firmicutes, gamma-proteobacteria, and alpha-proteobacteria). Compared to those of the model bacteria Escherichia coli and Bacillus subtilis, metabolisms of the investigated species differed significantly in several respects: (i) the Entner-Doudoroff pathway was the almost exclusive catabolic route; (ii) the pentose phosphate pathway exhibited exclusively biosynthetic functions, in many cases also requiring flux through the nonoxidative branch; (iii) all aerobes exhibited fully respiratory metabolism without significant overflow metabolism; and (iv) all aerobes used the pyruvate bypass of the malate dehydrogenase reaction to a significant extent. Exclusively, Pseudomonas fluorescens converted most glucose extracellularly to gluconate and 2-ketogluconate. Overall, the results suggest that metabolic data from model species with extensive industrial and laboratory history are not representative of microbial metabolism, at least not quantitatively.

Metabolic flux responses to pyruvate kinase knockout in Escherichia coli.

The intracellular carbon flux distribution in wild-type and pyruvate kinase-deficient Escherichia coli was estimated using biosynthetically directed fractional 13C labeling experiments with [U-13C6]glucose in glucose- or ammonia-limited chemostats, two-dimensional nuclear magnetic resonance (NMR) spectroscopy of cellular amino acids, and a comprehensive isotopomer model. The general response to disruption of both pyruvate kinase isoenzymes in E. coli was a local flux rerouting via the combined reactions of phosphoenolpyruvate (PEP) carboxylase and malic enzyme. Responses in the pentose phosphate pathway and the tricarboxylic acid cycle were strongly dependent on the environmental conditions. In addition, high futile cycling activity via the gluconeogenic PEP carboxykinase was identified at a low dilution rate in glucose-limited chemostat culture of pyruvate kinase-deficient E. coli, with a turnover that is comparable to the specific glucose uptake rate. Furthermore, flux analysis in mutant cultures indicates that glucose uptake in E. coli is not catalyzed exclusively by the phosphotransferase system in glucose-limited cultures at a low dilution rate. Reliability of the flux estimates thus obtained was verified by statistical error analysis and by comparison to intracellular carbon flux ratios that were independently calculated from the same NMR data by metabolic flux ratio analysis.

Multidimensional optimality of microbial metabolism

Metabolic Networking Understanding complex biological networks, such as those underlying cellular metabolism, requires evaluation not only of the network connections but also the flux through the various biochemical pathways. Schuetz et al. (p. 601) explored the evolutionary constraints that appear to be most critical for the metabolic network in the bacteria Escherichia coli using a combination of experimental tests of reaction flux under various conditions along with mathematical modeling. As a pathway evolves, there are likely to be competing objectives that must be satisfied. Key objectives for the bacterium were strong performance under a given environmental condition, balanced by a requirement for adaptability—minimizing the adjustments required to respond to changed conditions. A key design principle of bacterial metabolic networks is optimal performance, but not at the expense of adaptability. Although the network topology of metabolism is well known, understanding the principles that govern the distribution of fluxes through metabolism lags behind. Experimentally, these fluxes can be measured by 13C-flux analysis, and there has been a long-standing interest in understanding this functional network operation from an evolutionary perspective. On the basis of 13C-determined fluxes from nine bacteria and multi-objective optimization theory, we show that metabolism operates close to the Pareto-optimal surface of a three-dimensional space defined by competing objectives. Consistent with flux data from evolved Escherichia coli, we propose that flux states evolve under the trade-off between two principles: optimality under one given condition and minimal adjustment between conditions. These principles form the forces by which evolution shapes metabolic fluxes in microorganisms’ environmental context.

Quantitative Phosphoproteomics Reveal mTORC1 Activates de Novo Pyrimidine Synthesis

Coordinating Metabolism Growth factors help to coordinate metabolism with growth in part by stimulating the activity of the protein kinase mTORC1 (mechanistic target of rapamycin complex 1). Ben-Sahra et al. (p. 1323, published online 21 February) and Robitaille et al. (p. 1320, published online 21 February) independently identified a key target of mTORC1—carbamolyl-phosphate synthase 2, or CAD, the rate-limiting enzyme for de novo synthesis of pyrimidines. Metabolomic profiling and phosphoproteomic analyses of normal cells and cells lacking signaling by mTORC1 converged on CAD as a key point at which growth-promoting signals also ramp up production of nucleic acids. In addition to its role in stimulating protein and lipid synthesis, the kinase mammalian target of rapamycin stimulates nucleotide biosynthesis. The Ser-Thr kinase mammalian target of rapamycin (mTOR) controls cell growth and metabolism by stimulating glycolysis and synthesis of proteins and lipids. To further understand the central role of mTOR in cell physiology, we used quantitative phosphoproteomics to identify substrates or downstream effectors of the two mTOR complexes. mTOR controlled the phosphorylation of 335 proteins, including CAD (carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase). CAD catalyzes the first three steps in de novo pyrimidine synthesis. mTORC1 indirectly phosphorylated CAD-S1859 through S6 kinase (S6K). CAD-S1859 phosphorylation promoted CAD oligomerization and thereby stimulated de novo synthesis of pyrimidines and progression through S phase of the cell cycle in mammalian cells. Thus, mTORC1 also stimulates the synthesis of nucleotides to control cell proliferation.