Nicola Zamboni

Deficiency in glutamine but not glucose induces MYC-dependent apoptosis in human cells

The idea that conversion of glucose to ATP is an attractive target for cancer therapy has been supported in part by the observation that glucose deprivation induces apoptosis in rodent cells transduced with the proto-oncogene MYC, but not in the parental line. Here, we found that depletion of glucose killed normal human cells irrespective of induced MYC activity and by a mechanism different from apoptosis. However, depletion of glutamine, another major nutrient consumed by cancer cells, induced apoptosis depending on MYC activity. This apoptosis was preceded by depletion of the Krebs cycle intermediates, was prevented by two Krebs cycle substrates, but was unrelated to ATP synthesis or several other reported consequences of glutamine starvation. Our results suggest that the fate of normal human cells should be considered in evaluating nutrient deprivation as a strategy for cancer therapy, and that understanding how glutamine metabolism is linked to cell viability might provide new approaches for treatment of cancer.

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.

Identification and Functional Expression of the Mitochondrial Pyruvate Carrier

Letting Pyruvate In Transport of pyruvate is an important event in metabolism whereby the pyruvate formed in glycolysis is transported into mitochondria to feed into the tricarboxylic acid cycle (see the Perspective by Murphy and Divakaruni). Two groups have now identified proteins that are components of the mitochondrial pyruvate transporter. Bricker et al. (p. 96, published online 24 May) found that the proteins mitochondrial pyruvate carrier 1 and 2 (MPC1 and MPC2) are required for full pyruvate transport in yeast and Drosophila cells and that humans with mutations in MPC1 have metabolic defects consistent with loss of the transporter. Herzig et al. (p. 93, published online 24 May) identified the same proteins as components of the carrier in yeast. Furthermore, expression of the mouse proteins in bacteria conferred increased transport of pyruvate into bacterial cells. Two components of the mitochondrial pyruvate transporter confer transport activity when expressed in bacteria. The transport of pyruvate, the end product of glycolysis, into mitochondria is an essential process that provides the organelle with a major oxidative fuel. Although the existence of a specific mitochondrial pyruvate carrier (MPC) has been anticipated, its molecular identity remained unknown. We report that MPC is a heterocomplex formed by two members of a family of previously uncharacterized membrane proteins that are conserved from yeast to mammals. Members of the MPC family were found in the inner mitochondrial membrane, and yeast mutants lacking MPC proteins showed severe defects in mitochondrial pyruvate uptake. Coexpression of mouse MPC1 and MPC2 in Lactococcus lactis promoted transport of pyruvate across the membrane. These observations firmly establish these proteins as essential components of the MPC.

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.

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.

Ultrahigh performance liquid chromatography-tandem mass spectrometry method for fast and robust quantification of anionic and aromatic metabolites.

Quantification of metabolites is of pivotal relevance in biology, where it complements more established omics techniques such as transcriptomics and proteomics. Here, we present a 25 min ion-pairing ultrahigh performance liquid chromatography-tandem mass spectrometry method that was developed for comprehensive coverage of central metabolism (glycolysis, pentose phosphate pathway, and tricarboxylic acid cycle) and closely related biosynthetic reactions. We demonstrate quantification of 138 compounds, including carboxylic acids, amino acids, sugar phosphates, nucleotides, and functionalized aromatics. Biologically relevant isomers such as sugar phosphates are individually quantified by combining chromatographic separation and fragmentation. The obtained sensitivity and robustness enabled the detection of more than half all tested compounds in each of eight diverse biological samples of 0.5-50 mg dry cell weight. We recommend this method for routine and yet comprehensive quantification of primary metabolites in a wide variety of biological matrices.

FiatFlux--a software for metabolic flux analysis from 13C-glucose experiments.

BackgroundQuantitative knowledge of intracellular fluxes is important for a comprehensive characterization of metabolic networks and their functional operation. In contrast to direct assessment of metabolite concentrations, in vivo metabolite fluxes must be inferred indirectly from measurable quantities in 13C experiments. The required experience, the complicated network models, large and heterogeneous data sets, and the time-consuming set-up of highly controlled experimental conditions largely restricted metabolic flux analysis to few expert groups. A conceptual simplification of flux analysis is the analytical determination of metabolic flux ratios exclusively from MS data, which can then be used in a second step to estimate absolute in vivo fluxes.ResultsHere we describe the user-friendly software package FiatFlux that supports flux analysis for non-expert users. In the first module, ratios of converging fluxes are automatically calculated from GC-MS-detected 13C-pattern in protein-bound amino acids. Predefined fragmentation patterns are automatically identified and appropriate statistical data treatment is based on the comparison of redundant information in the MS spectra. In the second module, absolute intracellular fluxes may be calculated by a 13C-constrained flux balancing procedure that combines experimentally determined fluxes in and out of the cell and the above flux ratios. The software is preconfigured to derive flux ratios and absolute in vivo fluxes from [1-13C] and [U-13C]glucose experiments and GC-MS analysis of amino acids for a variety of microorganisms.ConclusionFiatFlux is an intuitive tool for quantitative investigations of intracellular metabolism by users that are not familiar with numerical methods or isotopic tracer experiments. The aim of this open source software is to enable non-specialists to adapt the software to their specific scientific interests, including other 13C-substrates, labeling mixtures, and organisms.

Metabolic control of adult neural stem cell activity by Fasn-dependent lipogenesis

Mechanisms controlling the proliferative activity of neural stem and progenitor cells (NSPCs) have a pivotal role to ensure life-long neurogenesis in the mammalian brain. How metabolic programs are coupled with NSPC activity remains unknown. Here we show that fatty acid synthase (Fasn), the key enzyme of de novo lipogenesis, is highly active in adult NSPCs and that conditional deletion of Fasn in mouse NSPCs impairs adult neurogenesis. The rate of de novo lipid synthesis and subsequent proliferation of NSPCs is regulated by Spot14, a gene previously implicated in lipid metabolism, that we found to be selectively expressed in low proliferating adult NSPCs. Spot14 reduces the availability of malonyl-CoA, which is an essential substrate for Fasn to fuel lipogenesis. Thus, we identify here a functional coupling between the regulation of lipid metabolism and adult NSPC proliferation.

A roadmap for interpreting (13)C metabolite labeling patterns from cells.

Measuring intracellular metabolism has increasingly led to important insights in biomedical research. (13)C tracer analysis, although less information-rich than quantitative (13)C flux analysis that requires computational data integration, has been established as a time-efficient method to unravel relative pathway activities, qualitative changes in pathway contributions, and nutrient contributions. Here, we review selected key issues in interpreting (13)C metabolite labeling patterns, with the goal of drawing accurate conclusions from steady state and dynamic stable isotopic tracer experiments.

High-throughput, accurate mass metabolome profiling of cellular extracts by flow injection-time-of-flight mass spectrometry.

Direct injection of samples on high-resolving mass spectrometers is an effective way to maximize analytical throughput and yet allow analyte discrimination in complex samples by mass-to-charge ratio. We present a platform of flow injection electrospray-time-of-flight mass spectrometry to profile small molecules in >1400 biological extracts per day at native mass resolution. We comprehensively benchmark the performance with more than 5000 injections of chemically defined standards and Escherichia coli cellular extracts obtained from miniscale cultivations. For at least 90% of tested compounds, we attain a linear response over 3 decades of concentration, interday coefficient of variation of <20%, and a mass accuracy of <0.001 amu. In polar Escherichia coli fractions, we reproducibly detected >1500 distinct ions in each mode. The accurate mass and correlation analysis enabled one to assign with good confidence 400-800 ions to electrospray derivatives of metabolites listed in the genome-wide reconstruction of Escherichia coli metabolism. Hence, we attain a coverage of about one-quarter of the total number of compounds listed in the reconstruction. Finally, we present an exemplary screen with Escherichia coli deletion mutants to show the exquisite capacity of the platform to identify lesions in primary metabolism at high throughputs.