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The field of metabolic modelling at the genomescale continues to grow with more models being created and curated. This comes with an increasing demand for adopting common principles regarding transparency and versioning, in addition to standardisation efforts regarding file formats, annotation and testing. Here, we present a standardised template for git-based and GitHub-hosted genome-scale metabolic models (GEMs) supporting both new models and curated ones, following FAIR principles (findability, accessibility, interoperability, and reusability), and incorporating bestpractices.standard-GEMfacilitates the reuse of GEMs across web services and platforms in the metabolic modelling field and enables automatic validation of GEMs. The use of this template for new models, and its adoption for existing ones, paves the way for increasing model quality, openness, and accessibility with minimal effort.Availabilitystandard-GEMis available fromgithub.com/MetabolicAtlas/standard-GEMunder the conditions of the CC BY 4.0 licence along with additional supporting material.

Microbial growth requires energy for maintaining the existing cells and producing components for the new ones. Microbes therefore invest a considerable amount of their resources into proteins needed for energy harvesting. Growth in different environments is associated with different energy demands for growth of yeastSaccharomyces cerevisiae, although the cross-condition differences remain poorly characterized. Furthermore, a direct comparison of the energy costs for the biosynthesis of the new biomass across conditions is not feasible experimentally; computational models, on the contrary, allow comparing the optimal metabolic strategies and quantify the respective costs of energy and nutrients. Thus in this study, we used a resource allocation model ofS. cerevisiaeto compare the optimal metabolic strategies between different conditions. We found thatS. cerevisiaewith respiratory-impaired mitochondria required additional energetic investments for growth, while growth on amino acid-rich media was not affected. Amino acid supplementation in anaerobic conditions also was predicted to rescue the growth reduction in mitochondrial respiratory shuttle-deficient mutants ofS. cerevisiae. Collectively, these results point to elevated costs of resolving the redox imbalance caused byde novobiosynthesis of amino acids in mitochondria. To sum up, our study provides an example of how resource allocation modeling can be used to address and suggest explanations to open questions in microbial physiology.

Budding yeast uses the well-conserved TORC1-Sch9 and cAMP-PKA signalling pathways to regulate adaptations to changing nutrient environments. Dynamic and single-cell measurements of the activity of these two cascades will improve our understanding of cellular adaptation of yeast. Here, we employed the AKAR3-EV biosensor developed for mammalian cells to measure the cellular phosphorylation status determined by Sch9 and PKA activity in budding yeast. Using various mutant strains and inhibitors, we show that AKAR3-EV robustly measures the Sch9- and PKA-dependent phosphorylation status in intact yeast cells. At the single-cell level, we found that the phosphorylation responses are homogenous for glucose, sucrose and fructose, but highly heterogeneous for mannose. The Sch9 and PKA pathways have a relatively high affinity for glucose (K0.5of 0.24 mM) under glucose derepressed conditions. Lastly, steady-state FRET levels of AKAR3-EV seem to be independent of growth rates, suggesting that the Sch9- and PKA-dependent phosphorylation activity are transient responses to nutrient transitions. We believe that the AKAR3-EV sensor is an excellent addition to the biosensor arsenal for illuminating cellular adaptation in single yeast cells.

The fission yeast Schizosaccharomyces pombe is a popular eukaryal model organism for cell division and cell cycle studies. With this extensive knowledge of its cell and molecular biology, S. pombe also holds promise for use in metabolism research and industrial applications. However, unlike the baker’s yeast Saccharomyces cerevisiae, a major workhorse in these areas, cell physiology and metabolism of S. pombe remain less explored. One way to advance understanding of organism-specific metabolism is construction of computational models and their use for hypothesis testing. To this end, we leverage existing knowledge of S. cerevisiae to generate a manually-curated high-quality reconstruction of S. pombe’s metabolic network, including a proteome-constrained version of the model. Using these models, we gain insights into the energy demands for growth, as well as ribosome kinetics in S. pombe. Furthermore, we predict proteome composition and identify growth-limiting constraints that determine optimal metabolic strategies under different glucose availability regimes, and reproduce experimentally determined metabolic profiles. Notably, we find similarities in metabolic and proteome predictions of S. pombe with S. cerevisiae, which indicate that similar cellular resource constraints operate to dictate metabolic organization. With these use cases, we show, on the one hand, how these models provide an efficient means to transfer metabolic knowledge from a well-studied to a lesser-studied organism, and on the other, how they can successfully be used to explore the metabolic behaviour and the role of resource allocation in driving different strategies in fission yeast.

In Nature, microbes live in very nutrient-dynamic environments. Rapid scavenging and consumption of newly introduced nutrients therefore offer a way to outcompete competitors. This may explain the observation that many microorganisms, including the budding yeast Saccharomyces cerevisiae, appear to keep “excess” glycolytic proteins at low growth rates, i.e. the maximal capacity of glycolytic enzymes (largely) exceeds the actual flux through the enzymes. However, such a strategy requires investment into preparatory protein expression that may come at the cost of current fitness. Moreover, at low nutrient levels, enzymes cannot operate at high saturation, and overcapacity is poorly defined without taking enzyme kinetics into account.Here we use computational modeling to suggest that in yeast the overcapacity of the glycolytic enzymes at low specific growth rates is a genuine excess, rather than the optimal enzyme demand dictated by enzyme kinetics. We found that the observed expression of the glycolytic enzymes did match the predicted optimal expression when S. cerevisiae exhibits mixed respiro-fermentative growth, while the expression of tricarboxylic acid cycle enzymes always follows the demand. Moreover, we compared the predicted metabolite concentrations with the experimental measurements and found the best agreement in glucose-excess conditions. We argue that the excess capacity of glycolytic proteins in glucose-scarce conditions is an adaptation of S. cerevisiae to fluctuations of nutrient availability in the environment.

Kinetic metabolic models of central metabolism have been proposed to understand how Saccharomyces cerevisiae navigates through nutrient perturbations. Yet, these models lacked important variables that constrain metabolism under relevant physiological conditions and thus have limited operational use such as in optimization of industrial fermentations. In this work, we developed a physiologically informed kinetic model of yeast glycolysis connected to central carbon metabolism by including the effect of anabolic reactions precursors, mitochondria and the trehalose cycle. A parameter estimation pipeline was developed, consisting of a divide and conquer approach, supplemented with regularization and global optimization. We show how this first mechanistic description of a growing yeast cell captures experimental dynamics at different growth rates and under a strong glucose perturbation, is robust to parametric uncertainty and explains the contribution of the different pathways in the network. Our work suggests that by combining multiple types of data and computational methods, complex but physiologically representative and robust models can be achieved.

Wine fermentation is an ancient biotechnological process mediated by different microorganisms such as yeast and bacteria. Understanding of the metabolic and physiological phenomena taking place during this process can be now attained at a genome scale with the help of metabolic models. In this chapter, we present a detailed protocol for modeling wine fermentation using genome-scale metabolic models. In particular, we illustrate how metabolic fluxes can be computed, optimized and interpreted, for both yeast and bacteria under winemaking conditions. We also show how nutritional requirements can be determined and simulated using these models in relevant test cases. This chapter introduces fundamental concepts and practical steps for applying flux balance analysis in wine fermentation, and as such, it is intended for a broad microbiology audience as well as for practitioners in the metabolic modeling field.

Central carbon metabolism comprises the metabolic pathways in the cell that process nutrients into energy, building blocks and byproducts. To unravel the regulation of this network upon glucose perturbation, several metabolic models have been developed for the microorganism Saccharomyces cerevisiae. These dynamic representations have focused on glycolysis and answered multiple research questions, but no commonly applicable model has been presented. This review systematically evaluates the literature to describe the current advances, limitations, and opportunities. Different kinetic models have unraveled key kinetic glycolytic mechanisms. Nevertheless, some uncertainties regarding model topology and parameter values still limit the application to specific cases. Progressive improvements in experimental measurement technologies as well as advances in computational tools create new opportunities to further extend the model scale. Notably, models need to be made more complex to consider the multiple layers of glycolytic regulation and external physiological variables regulating the bioprocess, opening new possibilities for extrapolation and validation. Finally, the onset of new data representative of individual cells will cause these models to evolve from depicting an average cell in an industrial fermenter, to characterizing the heterogeneity of the population, opening new and unseen possibilities for industrial fermentation improvement.

Yeasts constitute over 1,500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications, and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts’ metabolic specificity in batch cultures. Here, we propose a multiphase multiobjective dynamic genome-scale model of yeast batch cultures that describes the uptake of carbon and nitrogen sources and the production of primary and secondary metabolites. The model integrates a specific metabolic reconstruction, based on the consensus Yeast8, and a kinetic model describing the time-varying culture environment. In addition, we proposed a multiphase multiobjective flux balance analysis to compute the dynamics of intracellular fluxes. We then compared the metabolism of S. cerevisiae and Saccharomyces uvarum strains in a rich medium fermentation. The model successfully explained the experimental data and brought novel insights into how cryotolerant strains achieve redox balance. The proposed model (along with the corresponding code) provides a comprehensive picture of the main steps occurring inside the cell during batch cultures and offers a systematic approach to prospect or metabolically engineering novel yeast cell factories. IMPORTANCE Nonconventional yeast species hold the promise to provide novel metabolic routes to produce industrially relevant compounds and tolerate specific stressors, such as cold temperatures. This work validated the first multiphase multiobjective genome-scale dynamic model to describe carbon and nitrogen metabolism throughout batch fermentation. To test and illustrate its performance, we considered the comparative metabolism of three yeast strains of the Saccharomyces genus in rich medium fermentation. The study revealed that cryotolerant Saccharomyces species might use the γ-aminobutyric acid (GABA) shunt and the production of reducing equivalents as alternative routes to achieve redox balance, a novel biological insight worth being explored further. The proposed model (along with the provided code) can be applied to a wide range of batch processes started with different yeast species and media, offering a systematic and rational approach to prospect nonconventional yeast species metabolism and engineering novel cell factories.

Overflow metabolism is ubiquitous in nature, and it is often considered inefficient because it leads to a relatively low biomass yield per consumed carbon. This metabolic strategy has been described as advantageous because it supports high growth rates during nutrient competition.Here we experimentally evolved bacteria without nutrient competition by repeatedly growing and mixing millions of parallel batch cultures of E. coli. Each culture originated from a water-in-oil emulsion droplet seeded with a single cell. Unexpectedly we found that overflow metabolism (acetate production) did not change. Instead the numerical cell yield during the consumption of the accumulated acetate increased as a consequence of a reduction in cell size. Our experiments and a mathematical model show that fast growth and overflow metabolism followed by the consumption of the overflow metabolite, leads to a higher numerical cell yield and therefore a higher fitness compared to full respiration of the substrate. This provides an evolutionary scenario where overflow metabolism can be favourable even in the absence of nutrient competition.

Yeasts constitute over 1500 species with great potential for biotechnology. Still, the yeastSaccharomyces cerevisiaedominates industrial applications and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts’ metabolic specificity in batch cultures. Here we propose a multi-phase multi-objective dynamic genome-scale model of yeast batch cultures that describes the uptake of carbon and nitrogen sources and the production of primary and secondary metabolites. The model integrates a specific metabolic reconstruction, based on the consensus Yeast8, and a kinetic model describing the time-varying culture environment. Besides, we proposed a multi-phase multi-objective flux balance analysis to compute the dynamics of intracellular fluxes. We then compared the metabolism ofS. cerevisiaeandS. uvarumstrains in wine fermentation. The model successfully explained the experimental data and brought novel insights into how cryotolerant strains achieve redox balance. The proposed modeling captures the dynamics of metabolism throughout the batch and offers a systematic approach to prospect or engineer novel yeast cell factories.

The turbidity of milk prohibits the use of optical density measurements for strain characterizations. This often limits research to laboratory media. Here, we cleared milk through centrifugation to remove insoluble milk solids. This resulted in a clear liquid phase, termed milk serum, in which optical density measurements can be used to track microbial growth until a pH of 5.2 is reached. At pH 5.2 coagulation of the soluble protein occurs, making the medium opaque again. We found that behavior in milk serum was predictive of that in milk for 39 Lactococcus lactis (R2 = 0.81) and to a lesser extent for 42 Lactiplantibacillus plantarum (formerly Lactobacillus plantarum; R2 = 0.49) strains. Hence, milk serum can be used as an optically clear alternative to milk for comparison of microbial growth and metabolic characteristics. Characterization of the growth rate, specific acidification rate for optical density at a wavelength of 600 nm, and the amount of acid produced per unit of biomass for all these strains in milk serum, showed that almost all strains could grow in milk, with higher specific acidification and growth rates of Lc. lactis strains compared with Lb. plantarum strains. Nondairy Lc. lactis isolates had a lower growth and specific acidification rate than dairy isolates. The amount of acid produced per unit biomass was relatively high and similar for Lc. lactis dairy and nondairy isolates, as opposed to Lb. plantarum isolates. Lactococcus lactis ssp. lactis showed slightly lower growth and acidification rates when compared with ssp. cremoris. For Lc. lactis strains a doubling of the specific acidification rate occurred with a doubling of the maximum growth rate. This relation was not found for Lb. plantarum strains, where the acidification rate remained relatively constant across 39 strains with growth rates ranging from 0.2 h−1 to 0.3 h−1. We conclude that milk serum is a valuable alternative to milk for high-throughput strain characterization during milk fermentation.

Lactococcus lactis serves as a paradigm organism for the lactic acid bacteria (LAB). Extensive research into the molecular biology, metabolism and physiology of several model strains of this species has been fundamental for our understanding of the LAB. Genomic studies have provided new insights into the species L. lactis, including the resolution of the genetic basis of its subspecies division, as well as the control mechanisms involved in the fine-tuning of growth rate and energy metabolism. In addition, it has enabled novel approaches to study lactococcal lifestyle adaptations to the dairy application environment, including its adjustment to near-zero growth rates that are particularly relevant in the context of cheese ripening. This review highlights various insights in these areas and exemplifies the strength of combining experimental evolution with functional genomics and bacterial physiology research to expand our fundamental understanding of the L. lactis lifestyle under different environmental conditions.

In-depth understanding of microbial growth is crucial for the development of new advances in biotechnology and for combating microbial pathogens. Condition-specific proteome expression is central to microbial physiology and growth. A multitude of processes are dependent on the protein expression, thus, whole-cell analysis of microbial metabolism using genome-scale metabolic models is an attractive toolset to investigate the behaviour of microorganisms and their communities. However, genome-scale models that incorporate macromolecular expression are still inhibitory complex: the conceptual and computational complexity of these models severely limits their potential applications. In the need for alternatives, here we revisit some of the previous attempts to create genome-scale models of metabolism and macromolecular expression to develop a novel framework for integrating protein abundance and turnover costs to conventional genome-scale models. We show that such a model of Escherichia coli successfully reproduces experimentally determined adaptations of metabolism in a growth condition-dependent manner. Moreover, the model can be used as means of investigating underutilization of the protein machinery among different growth settings. Notably, we obtained strongly improved predictions of flux distributions, considering the costs of protein translation explicitly. This finding in turn suggests protein translation being the main regulation hub for cellular growth.

Metabolic interactions between cells affect microbial community compositions and hence their function in ecosystems. It is well-known that under competition for the exchanged metabolite, concentration gradients constrain the distances over which interactions can occur. However, interaction distances are typically quantified in two-dimensional systems or without accounting for competition or other metabolite-removal, conditions which may not very often match natural ecosystems. We here analyze the impact of cell-to-cell distance on unidirectional cross-feeding in a three-dimensional system with competition for the exchanged metabolite. Effective interaction distances were computed with a reaction-diffusion model and experimentally verified by growing a synthetic consortium of 1 µm-sized metabolite producer, receiver and competitor cells in different spatial structures. We show that receivers cannot interact with producers ∼15 µm away from them, as product concentration gradients flatten close to producer cells. We developed an aggregation protocol and created variants of the receiver cells’ import system, to show that within producer-receiver aggregates even low affinity receiver cells could interact with producers. These results show that competition or other metabolite-removal of a public good in a three-dimensional system reduces the interaction distance to the low micrometer-range, highlighting the importance of concentration gradients as physical constraint for cellular interactions., GREENERProjectH2020

Amino acids are attractive metabolites for the pharmaceutical and food industry field. On one hand, the construction of microbial cell factories for large-scale production aims to satisfy the demand for amino acids as bulk biochemical. On the other hand, amino acids enhance flavor formation in fermented foods. Concerning the latter, flavor formation in dairy products, such as cheese is associated with the presence of lactic acid bacteria (LAB). In particular, Lactococcus lactis, one of the most important LAB, is used as a starter culture in fermented foods. The proteolytic activity of some L. lactis strains results in peptides and amino acids, which are flavor compounds or flavor precursors. However, it is still a challenge to isolate bacterial cells with enhanced amino acid production and secretion activity. In this work, we developed a growth-based sensor strain to detect the essential amino acids isoleucine, leucine, valine, histidine and methionine. Amino acids are metabolites that can be secreted by some bacteria. Therefore, our biosensor allowed us to identify wild-type L. lactis strains that naturally secrete amino acids, by using co-cultures of the biosensor strain with potential amino acid producing strains. Subsequently, we used this biosensor in combination with a droplet-based screening approach, and isolated three mutated L. lactis IPLA838 strains with 5–10 fold increased amino acid-secretion compared to the wild type. Genome re-sequencing revealed mutations in genes encoding proteins that participate in peptide uptake and peptide degradation. We argue that an unbalance in the regulation of amino acid levels as a result of these gene mutations may drive the accumulation and secretion of these amino acids. This biosensing system tackles the problem of selection for overproduction of secreted molecules, which requires the coupling of the product to the producing cell in the droplets., Graphical abstract The development of a fluorescent sensor cell, able to grow only in the presence of essential amino acids, allows selection of strains with an improved capacity of amino acid production and secretion, by a droplet-based screening platform.Image 1, Highlights • An amino acid-responsive biosensor was developed for fluorescent-based screening. • Production of amino acids in wild-type Lactococcus lactis strains was improved after selection of EMS-mutants using high-throughput droplet-based screening technology. • Mutations in genes encoding proteins of the proteolytic system of L. lactis improve amino acid secretion. For instance glutamate secretion was enhanced 10-fold and leucine up to 30-fold. • The identification of target genes is important to improve amino acid production in other lactic acid bacteria (LAB).

The metabolic capabilities of cells determine their biotechnological potential, niches in multicellular host-organisms, fitness in ecosystems, and threat levels as a pathogen. Their comprehensive experimental characterisation is generally not feasible, in particular for unculturable and extremophile microorganisms. In principle, the full range of metabolic capabilities can be computed from an organism's annotated genome sequence using a metabolic network reconstruction. However, current computational methods cannot deal with genome-scale metabolic networks. Part of the problem is that these methods generally aim to enumerate all feasible metabolic pathways, while computation of all possible (elementally balanced) conversions between nutrients and products (elementary conversion modes, ECMs) would suffice. All the ECMs of a metabolic network capture its full metabolic capability. We have implemented ECM enumeration in ecmtool and illustrate its usage in several examples. This work contributes to the elucidation of the full metabolic footprint of any cell and provides an exhaustive list of its metabolic capabilities.

Central carbon metabolism comprises the metabolic pathways in the cell that process nutrients into energy, building blocks and byproducts. To unravel the regulation of this network upon glucose perturbation, several metabolic models have been developed for the microorganism Saccharomyces cerevisiae. These dynamic representations have focused on glycolysis and answered multiple research questions, but no commonly applicable model has been presented. This review systematically evaluates the literature to describe the current advances, limitations, and opportunities. Different kinetic models have unraveled key kinetic glycolytic mechanisms. Nevertheless, some uncertainties regarding model topology and parameter values still limit the application to specific cases. Progressive improvements in experimental measurement technologies as well as advances in computational tools create new opportunities to further extend the model scale. Notably, models need to be made more complex to consider the multiple layers of glycolytic regulation and external physiological variables regulating the bioprocess, opening new possibilities for extrapolation and validation. Finally, the onset of new data representative of individual cells will cause these models to evolve from depicting an average cell in an industrial fermenter, to characterizing the heterogeneity of the population, opening new and unseen possibilities for industrial fermentation improvement.

Adenosine 5-triphosphate (ATP) is the main free energy carrier in metabolism. In budding yeast, shifts to glucose-rich conditions cause dynamic changes in ATP levels, but it is unclear how heterogeneous these dynamics are at the single-cell level. Furthermore, pH also changes and affects readout of fluorescence-based biosensors for single-cell measurements. To measure ATP changes reliably in single yeast cells, we developed yAT1.03, an adapted version of the AT1.03 ATP biosensor, that is pH-insensitive. We show that pregrowth conditions largely affect ATP dynamics during transitions. Moreover, single-cell analyses showed a large variety in ATP responses, which implies large differences of glycolytic startup between individual cells. We found three clusters of dynamic responses, and show that a small subpopulation of wild type cells reached an imbalanced state during glycolytic startup, characterised by low ATP levels. These results confirm the need for new tools to study dynamic responses of individual cells in dynamic environments.

In single cells, all molecules fluctuate in concentration because synthesis and degradation events occur asynchronously in a probabilistic manner. Since molecules generally influence multiple reactions, concentration fluctuations can propagate through the entire molecular circuit of a cell. This causes single isogenic cells to vary in their phenotypic properties, which affects the fitness of the associated genotype. Currently, we have a good understanding of how fluctuations arise and propagate in small molecular circuits. Convincing experimental observations exist of fluctuations in the growth and fitness characteristics of single cells. We have however a poor understanding of which molecule concentration fluctuations cause emergent fluctuations in the phenotype of a single cell, how extensive fluctuation propagation is, and what its effects on fitness are. In this opinion paper, we discuss those aspects of current single-cell systems biology.

The cAMP-PKA signalling cascade in budding yeast regulates adaptation to changing environments. We developed yEPAC, a FRET-based biosensor for cAMP measurements in yeast. We used this sensor with flow cytometry for high-throughput single cell-level quantification during dynamic changes in response to sudden nutrient transitions. We found that the characteristic cAMP peak differentiates between different carbon source transitions, and is rather homogenous among single-cells, especially for transitions to glucose. The peaks are mediated by a combination of extracellular sensing and intracellular metabolism. Moreover, the cAMP peak follows Weber’s law; its height scales with the relative, and not the absolute, change in glucose. Lastly, our results suggest that the cAMP peak height conveys information about prospective growth rates. In conclusion, our yEPAC-sensor makes possible new avenues for understanding yeast physiology, signalling and metabolic adaptation.

Living cells can express different metabolic pathways that support growth. The criteria that determine which pathways are selected in which environment remain unclear. One recurrent selection is overflow metabolism: the simultaneous usage of an ATP-efficient and -inefficient pathway, shown for example in Escherichia coli, Saccharomyces cerevisiae and cancer cells. Many models, based on different assumptions, can reproduce this observation. Therefore, they provide no conclusive evidence which mechanism is causing overflow metabolism. We compare the mathematical structure of these models. Although ranging from flux balance analyses to self-fabricating metabolism and expression models, we can rewrite all models into one standard form. We conclude that all models predict overflow metabolism when two, model-specific, growth-limiting constraints are hit. This is consistent with recent theory. Thus, identifying these two constraints is essential for understanding overflow metabolism. We list all imposed constraints by these models, so that they can hopefully be tested in future experiments. Electronic supplementary material The online version of this article (10.1007/s00018-019-03380-2) contains supplementary material, which is available to authorized users.

Microorganisms produce extracellular compounds that affect the final product quality in fermentation processes. Selection of overproducing mutants requires coupling of the extracellular product to the producer genotype, which can be achieved by single-cell compartmentalization. Emulsions contain up to billions of microdroplets/mL which significantly increases the screening throughput compared to microtiter-plate-based selections. Factors affecting the success of screening in microdroplets include the nature of the producing organism (robust, no invasive growth), the product (not soluble in oil) and the product assay (preferably fluorescence based). Together these factors determine the required microdroplet production technique and sorting set-up. Because microdroplets allow relatively inexpensive ultrahigh-throughput screening, they are likely to become a standard tool in the strain selection toolbox of the fermentation industry.

Acetate is the most important precursor for methane in the degradation of organic matter. Only two genera of methanogenic archaea, Methanosarcina and Methanothrix (former Methanosaeta), are able to grow with acetate as sole energy and carbon source. Phylogenetically, Methanosarcina and Methanothrix both belong to the Methanosarcinales. These two genera show besides morphological differences, interesting differences in physiology. Methanosarcina is a generalist that can grow on a variety of substrates, while Methanothrix specialized in growth on acetate. The acetate metabolism shows differences in acetate activation and energy conservation. At conditions that are less favorable for acetoclastic methanogens, syntrophic acetate oxidation may occur. This, however, is not further addressed here.

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed.Here, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the model’s performance parameters, which supports informed model development and facilitates error detection.Memote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

Many organisms have several similar transporters with different affinities for the same substrate. Typically, high-affinity transporters are expressed when substrate is scarce and low-affinity ones when it is abundant. The benefit of using low instead of high-affinity transporters remains unclear, especially when additional nutrient sensors are present. Here, we investigate two hypotheses. It was previously hypothesized that there is a trade-off between the affinity and the catalytic efficiency of transporters, and we find some but no definitive support for it. Additionally, we propose that for uptake by facilitated diffusion, at saturating substrate concentrations, lowering the affinity enhances the net uptake rate by reducing substrate efflux. As a consequence, there exists an optimal, external-substrate-concentration dependent transporter affinity. A computational model of Saccharomyces cerevisiae glycolysis shows that using the low affinity HXT3 transporter instead of the high affinity HXT6 enhances the steady-state flux by 36%. We tried to test this hypothesis with yeast strains expressing a single glucose transporter modified to have either a high or a low affinity. However, due to the intimate link between glucose perception and metabolism, direct experimental proof for this hypothesis remained inconclusive. Still, our theoretical results provide a novel reason for the presence of low-affinity transport systems.

Protein investment costs are considered a major driver for the choice of alternative metabolic strategies. We tested this premise in Lactococcus lactis, a bacterium that exhibits a distinct, anaerobic version of the bacterial Crabtree/Warburg effect; with increasing growth rates it shifts from a high yield metabolic mode [mixed-acid fermentation; 3 adenosine triphosphate (ATP) per glucose] to a low yield metabolic mode (homolactic fermentation; 2 ATP per glucose). We studied growth rate-dependent relative transcription and protein ratios, enzyme activities, and fluxes of L. lactis in glucose-limited chemostats, providing a high-quality and comprehensive data set. A three- to fourfold higher growth rate rerouted metabolism from acetate to lactate as the main fermentation product. However, we observed hardly any changes in transcription, protein levels and enzyme activities. Even levels of ribosomal proteins, constituting a major investment in cellular machinery, changed only slightly. Thus, contrary to the original hypothesis, central metabolism in this organism appears to be hardly regulated at the level of gene expression, but rather at the metabolic level. We conclude that L. lactis is either poorly adapted to growth at low and constant glucose concentrations, or that protein costs play a less important role in fitness than hitherto assumed.

Microorganisms rely on binding-protein assisted, active transport systems to scavenge for scarce nutrients. Several advantages of using binding proteins in such uptake systems have been proposed. However, a systematic, rigorous and quantitative analysis of the function of binding proteins is lacking. By combining knowledge of selection pressure and physiochemical constraints, we derive kinetic, thermodynamic, and stoichiometric properties of binding-protein dependent transport systems that enable a maximal import activity per amount of transporter. Under the hypothesis that this maximal specific activity of the transport complex is the selection objective, binding protein concentrations should exceed the concentration of both the scarce nutrient and the transporter. This increases the encounter rate of transporter with loaded binding protein at low substrate concentrations, thereby enhancing the affinity and specific uptake rate. These predictions are experimentally testable, and a number of observations confirm them. Microorganisms rely on binding-protein assisted, active transport systems to scavenge for scarce nutrients. We combine knowledge of selection pressure and physiochemical constraints with mathematical modeling to elucidate the benefit these binding proteins confer. We show that, when the binding protein concentration exceeds both the nutrient and the transporter concentrations, they increase the substrate-transporter encounter rate and thereby the nutrient uptake rate.

Understanding cellular adaptation to environmental changes is one of the major challenges in systems biology. To understand how cellular systems react towards perturbations of their steady state, the metabolic dynamics have to be described. Dynamic properties can be studied with kinetic models but development of such models is hampered by limited in vivo information, especially kinetic parameters. Therefore, there is a need for mathematical frameworks that use a minimal amount of kinetic information. One of these frameworks is dynamic flux balance analysis (DFBA), a method based on the assumption that cellular metabolism has evolved towards optimal changes to perturbations. However, DFBA has some limitations. It is less suitable for larger systems because of the high number of parameters to estimate and the computational complexity. In this paper, we propose MetDFBA, a modification of DFBA, that incorporates measured time series of both intracellular and extracellular metabolite concentrations, in order to reduce both the number of parameters to estimate and the computational complexity. MetDFBA can be used to estimate dynamic flux profiles and, in addition, test hypotheses about metabolic regulation. In a first case study, we demonstrate the validity of our method by comparing our results to flux estimations based on dynamic 13C MFA measurements, which we considered as experimental reference. For these estimations time-resolved metabolomics data from a feast-famine experiment with Penicillium chrysogenum was used. In a second case study, we used time-resolved metabolomics data from glucose pulse experiments during aerobic growth of Saccharomyces cerevisiae to test various metabolic objectives.

A central theme in biology is to understand the molecular basis of fitness: which strategies succeed under which conditions; how are they mechanistically implemented; and which constraints shape trade-offs between alternative strategies. We approached these questions with parallel bacterial evolution experiments in chemostats. Chemostats provide a constant environment with a defined resource limitation (glucose), in which the growth rate can be controlled. UsingLactococcus lactis, we found a single mutation in a global regulator of carbon metabolism, CcpA, to confer predictable fitness improvements across multiple growth rates.In silicoprotein structural analysis complemented with biochemical and phenotypic assays, show that the mutation reprograms the CcpA regulon, specifically targeting transporters. This supports that membrane occupancy, rather than biosynthetic capacity, is the dominant constraint for the observed fitness enhancement. It also demonstrates that cells can modulate a pleiotropic regulator to work around limiting constraints.

After more than a century of research on glycolysis, we have detailed descriptions of its molecular organization, but despite this wealth of knowledge, linking the enzyme properties to metabolic pathway behavior remains challenging. These challenges arise from multi-layered regulation and the context and time dependence of component functions. However, when viewed as a system that functions according to the principles of supply and demand, a simplifying theoretical framework can be applied to study its regulation logic and to assess the coherence of experimental interpretations. These principles are universally applicable, as they emphasize the common metabolic tasks of glycolysis: the provision of free-energy carriers, and precursors for biosynthesis and stress-related compounds. Here we will review the regulation of multi-tasking by glycolysis and consider how an understanding of this central metabolic pathway can be pursued using general principles, rather than focusing on the biochemical details of constituent components.

Next-generation sequencing experiments have shown that microRNAs (miRNAs) are expressed in many different isoforms (isomiRs), whose biological relevance is often unclear. We found that mature miR-21, the most widely researched miRNA because of its importance in human disease, is produced in two prevalent isomiR forms that differ by 1 nt at their 3' end, and moreover that the 3' end of miR-21 is posttranscriptionally adenylated by the noncanonical poly(A) polymerase PAPD5. PAPD5 knockdown caused an increase in the miR-21 expression level, suggesting that PAPD5-mediated adenylation of miR-21 leads to its degradation. Exoribonuclease knockdown experiments followed by small-RNA sequencing suggested that PARN degrades miR-21 in the 3'-to-5' direction. In accordance with this model, microarray expression profiling demonstrated that PAPD5 knockdown results in a down-regulation of miR-21 target mRNAs. We found that disruption of the miR-21 adenylation and degradation pathway is a general feature in tumors across a wide range of tissues, as evidenced by data from The Cancer Genome Atlas, as well as in the noncancerous proliferative disease psoriasis. We conclude that PAPD5 and PARN mediate degradation of oncogenic miRNA miR-21 through a tailing and trimming process, and that this pathway is disrupted in cancer and other proliferative diseases.

Genome-scale metabolic models comprise stoichiometric relations between metabolites, as well as associations between genes and metabolic reactions and facilitate the analysis of metabolism. We computationally reconstructed the metabolic network of the lactic acid bacterium Streptococcus pyogenes M49. Initially, we based the reconstruction on genome annotations and already existing and curated metabolic networks of Bacillus subtilis, Escherichia coli, Lactobacillus plantarum and Lactococcus lactis. This initial draft was manually curated with the final reconstruction accounting for 480 genes associated with 576 reactions and 558 metabolites. In order to constrain the model further, we performed growth experiments of wild type and arcA deletion strains of S. pyogenes M49 in a chemically defined medium and calculated nutrient uptake and production fluxes. We additionally performed amino acid auxotrophy experiments to test the consistency of the model. The established genome-scale model can be used to understand the growth requirements of the human pathogen S. pyogenes and define optimal and suboptimal conditions, but also to describe differences and similarities between S. pyogenes and related lactic acid bacteria such as L. lactis in order to find strategies to reduce the growth of the pathogen and propose drug targets.

Protein expression is shaped by evolutionary processes that tune microbial fitness. The limited biosynthetic capacity of a cell constrains protein expression and forces the cell to carefully manage its protein economy. In a chemostat, the physiology of the cell feeds back on the growth conditions, hindering intuitive understanding of how changes in protein concentration affect fitness. Here, we aim to provide a theoretical framework that addresses the selective pressures and optimal evolutionary-strategies in the chemostat. We show that the optimal enzyme levels are the result of a trade-off between the cost of their production and the benefit of their catalytic function. We also show that deviations from optimal enzyme levels are directly related to selection coefficients. The maximal fitness strategy for an organism in the chemostat is to express a well-defined metabolic subsystem known as an elementary flux mode. Using a coarse-grained, kinetic model of Saccharomyces cerevisiae’s metabolism and growth, we illustrate that the dynamics and outcome of evolution in a chemostat can be very counter-intuitive: Strictly-respiring and strictly-fermenting strains can evolve from a common ancestor. This work provides a theoretical framework that relates a kinetic, mechanistic view on metabolism with cellular physiology and evolutionary dynamics in the chemostat.

Microbial communities are ubiquitously found in Nature and have direct implications for the environment, human health and biotechnology. The species composition and overall function of microbial communities are largely shaped by metabolic interactions such as competition for resources and cross-feeding. Although considerable scientific progress has been made towards mapping and modelling species-level metabolism, elucidating the metabolic exchanges between microorganisms and steering the community dynamics remain an enormous scientific challenge. In view of the complexity, computational models of microbial communities are essential to obtain systems-level understanding of ecosystem functioning. This review discusses the applications and limitations of constraint-based stoichiometric modelling tools, and in particular flux balance analysis (FBA). We explain this approach from first principles and identify the challenges one faces when extending it to communities, and discuss the approaches used in the field in view of these challenges. We distinguish between steady-state and dynamic FBA approaches extended to communities. We conclude that much progress has been made, but many of the challenges are still open.

One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular "task" of the network-its function-should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide.

Lactic acid-producing bacteria survive in distinct environments, but show common metabolic characteristics. Here we studied the dynamic interactions of the central metabolism in Lactococcus lactis, extensively used as a starter culture in the dairy industry, and Streptococcus pyogenes, a human pathogen. Glucose-pulse experiments and enzymatic measurements were performed to parameterize kinetic models of glycolysis. Significant improvements were made to existing kinetic models for L. lactis, which subsequently accelerated the development of the first kinetic model of S. pyogenes glycolysis. The models revealed an important role for extracellular phosphate in the regulation of central metabolism and the efficient use of glucose. Thus, phosphate, which is rarely taken into account as an independent species in models of central metabolism, should be considered more thoroughly in the analysis of metabolic systems in the future. Insufficient phosphate supply can lead to a strong inhibition of glycolysis at high glucose concentrations in both species, but this was more severe in S. pyogenes. S. pyogenes is more efficient at converting glucose to ATP, showing a higher tendency towards heterofermentative energy metabolism than L. lactis. Our comparative systems biology approach revealed that the glycolysis of L. lactis and S. pyogenes have similar characteristics, but are adapted to their individual natural habitats with respect to phosphate regulation. Database The mathematical models described here have been submitted to the Online Cellular Systems Modelling Database and can be accessed at http://jjj.biochem.sun.ac.za/database/Levering/index.html free of charge.

Comparative analyses, as carried out by comparative genomics and bioinformatics, have proven extremely powerful to obtain insight into the identity of specific genes that underlie differences and similarities across species. The central concept developed in this chapter is that important aspects of the functional differences between organisms derive not only from the differences in genetic components (which underlies comparative genomics) but also from dynamic, molecular (physical) interactions. Approaches that aim at identifying such networkbased rather than component-based homologies between species we shall call Comparative Systems Biology. It will be illustrated by a number of examples from metabolic networks from prokaryotes, via yeast, to man. The potential for species comparisons, at the genome-scale using classical approaches and at the more detailed level of dynamic molecular networks will be illustrated. In our opinion, comparative systems biology, as a marriage between bioinformatics and systems biology, will offer new insights into the nature of organisms for the benefit of medicine, biotechnology, and drug design. As dynamic modeling is becoming more mainstream in cell biology, the potential of comparative systems biology will become more evident. © 2010 John Wiley & Sons, Inc.

Aim: A medium with minimal requirements for the growth of Lactobacillus plantarum WCFS was developed. The composition of the minimal medium was compared to a genome-scale metabolic model of L. plantarum. Methods and Results: By repetitive single omission experiments, two minimal media were developed: PMM5 (true minimal medium) and PMM7 [a pseudominimal medium, supporting proper biomass formation of 350 mg l−1 dry weight (DW)]. The specific growth rate of L. plantarum on PMM7 was found to be 50% and 63% lower when compared to growth on established growth media (chemically defined medium and MRS, respectively). Using a genome-scale metabolic model of L. plantarum, it was predicted that PMM5 and PMM7 would not support the growth of L. plantarum. This is because the biosynthesis of para-aminobenzoic acid (pABA) was predicted to be essential for growth. The discrepancy in simulated growth and experimental growth on PMM7 was further investigated for pABA; a molecule which plays an important role in folate production. The growth performance and folate production were determined on PMM7 in the presence and absence of pABA. It was found that a 12 000-fold reduction in folate pools exerted no influence on formation of biomass or growth rate of L. plantarum cultures when grown in the absence of pABA. Conclusion: Largely reduced folate production pools do not have an effect on the growth of L. plantarum, showing that L. plantarum makes folate in a large excess. Significance and Impact of the study: These experiments illustrate the importance of combining genome-scale metabolic models with growth experiments on minimal media.

With the availability of genome sequences of many organisms and information about gene-protein-reaction (GPR) associations with respect to these organisms genome-scale metabolic networks can be reconstructed. In cellular systems biology these networks are used to model the behavior of metabolism in context of cell growth in terms of fluxes (reaction rates) through reactions in the network. Because the flux through each reaction can generally vary within a range, many flux distributions of the entire network are possible. However, since reactions are connected by common metabolites, reactions that are functionally coherent, are expected to highly correlate in terms of their flux value over different flux distributions. In this paper the genome-scale network of a lactic acid bacterium, named Lactococcus lactis MG1363, is used to generate flux distributions for multiple in silico environmental conditions, mimicking laboratory growth conditions. The flux distributions per condition are used to calculate a correlation matrix for each condition. Subsequently the correlations between the reactions are analyzed in a multivariate approach across the in silico environmental conditions in order to identify correlations that are invariant (i.e. independent of the environment) and correlations that are variant across conditions (i.e. dependent of the environment). The applied multivariate methods are Parallel Factor Analysis (PARAFAC) and Principal Component Analysis (PCA). The discussion of the results of both methods leads to the question whether latent variable models are suitable analyzing this type of data. Copyright © 2009 John Wiley & Sons, Ltd.

There has been considerable recent interest in deciphering the adaptive properties underlying the structure and function of metabolic networks. Various features of metabolic networks such as the global topology, distribution of fluxes, and mutational robustness, have been proposed to have adaptive significance and hence reflect design principles. However, whether evolutionary processes alternative to direct selection on the trait under investigation also play a role is often ignored and the selection pressures maintaining a given metabolic trait often remain speculative. Some systems-level traits might simply arise as by-products of selection on other traits or even through random genetic drift. Here, we ask which systems-level aspects of metabolism are likely to have adaptive utility and which could be better explained as by-products of other evolutionary forces. We conclude that the global topological characteristics of metabolic networks and their mutational robustness are unlikely to be directly shaped by natural selection. Conversely, models of optimal design revealed that various aspects of individual pathways and the behavior of the whole network show signs of adaptations, even though the exact selective forces often remain elusive. Comparative and experimental approaches, which so far have been relatively rarely employed, could help to distinguish between alternative adaptive scenarios. © HFSP Publishing.

Most fermented dairy products are manufactured using mixed starter cultures. These cultures are usually a well-balanced mixture of several different lactic acid bacteria. For the development of novel or improved dairy products, flavour characteristics are generally a primary target. In recent years, a number of high-throughput screening tools have emerged allowing the targeted selection of flavour-forming cultures and these will be illustrated with relevant examples. This includes methods directly targeting flavour production such as GC–MS analysis of flavour profiles on the head space of fermentation fluids. High-throughput tools and DNA microarrays are valuable tools for elucidating the regulatory responses to different substrates and processing conditions thus allowing rational intervention in fermentations to improve flavour production. Finally, genome-scale metabolic models are used to predict the production of relevant (flavour) components and to expand our knowledge about flavour-forming pathways. The integration of these approaches is anticipated to strengthen culture development programs enabling the production of well-tasting, fermented dairy products.

Obese humans are often insulin- and leptin resistant. Since leptin can affect glucose metabolism, it is conceivable that a lack of leptin signal transduction contributes to insulin resistance. It remains unclear whether leptin affects glucose metabolism via peripheral and/or central mechanistic routes. In the present study, we aimed: (i) to determine the relative contributions of lack of leptin signal transduction and adiposity to insulin resistance and (ii) to establish the impact of central leptin action on glucose metabolism. To address the first point, ob/ob mice were subjected to severe calorie restriction, so that their body weight became similar to that of wild-type mice. Insulin sensitivity was measured in obese ob/ob, lean (food restricted) ob/ob and lean, weight-matched wild-type mice. To address the second point, leptin (or vehicle) was i.c.v. infused to the lateral cerebral ventricle of ob/ob mice and insulin sensitivity was determined. Hyperinsulinaemic euglyceamic clamps were used to quantify insulin sensitivity. Food restriction barely affected body composition, although it profoundly curtailed body weight. Insulin suppressed hepatic glucose production (HGP) to a greater extent in lean ob/ob than in obese ob/ob mice, but its impact remained considerably less than in wild-type mice (% suppression: 11.8 +/- 8.9 versus 1.3 +/- 1.1 versus 56.6 +/- 13.0%/nmol, for lean, obese ob/ob and wild-type mice, respectively; P < 0.05). The insulin-mediated glucose disposal (GD) of lean ob/ob mice was also in between that of obese ob/ob and wild-type mice (37.5 +/- 21.4 versus 25.1 +/- 14.6 versus 59.6 +/- 17.3 mumol/min/kg/nmol of insulin, respectively; P < 0.05 wild-type versus obese ob/ob mice). Leptin infusion acutely enhanced both hepatic insulin sensitivity (insulin-induced inhibition of HGP) and insulin-mediated GD (9.1 +/- 2.4 versus 5.0 +/- 2.7%/nmol of insulin, and 25.6 +/- 5.6 versus 13.6 +/- 4.8 mumol/min/kg/nmol of insulin, respectively; P < 0.05 for both comparisons) in ob/ob mice. Both a lack of leptin signals and adiposity may contribute to insulin resistance in obese individuals. Diminution of central leptin signalling can critically affect glucose metabolism in these individuals.

Protein investment costs are considered a major driver for the choice of alternative metabolic strategies. We tested this premise in Lactococcus lactis, a bacterium that exhibits a distinct, anaerobic version of the bacterial Crabtree/Warburg effect; with increasing growth rates it shifts from a high yield metabolic mode [mixed-acid fermentation; 3 adenosine triphosphate (ATP) per glucose] to a low yield metabolic mode (homolactic fermentation; 2 ATP per glucose). We studied growth rate-dependent relative transcription and protein ratios, enzyme activities, and fluxes of L. lactis in glucose-limited chemostats, providing a high-quality and comprehensive data set. A three- to fourfold higher growth rate rerouted metabolism from acetate to lactate as the main fermentation product. However, we observed hardly any changes in transcription, protein levels and enzyme activities. Even levels of ribosomal proteins, constituting a major investment in cellular machinery, changed only slightly. Thus, contrary to the original hypothesis, central metabolism in this organism appears to be hardly regulated at the level of gene expression, but rather at the metabolic level. We conclude that L. lactis is either poorly adapted to growth at low and constant glucose concentrations, or that protein costs play a less important role in fitness than hitherto assumed.

Contains fulltext : 35606teusink.pdf (Publisher’s version ) (Closed access) Lactic acid bacteria (LAB) have a long tradition of use in the food industry, and the number and diversity of their applications has increased considerably over the years. Traditionally, process optimization for these applications involved both strain selection and trial and error. More recently, metabolic engineering has emerged as a discipline that focuses on the rational improvement of industrially useful strains. In the post-genomic era, metabolic engineering increasingly benefits from systems biology, an approach that combines mathematical modelling techniques with functional-genomics data to build models for biological interpretation and--ultimately--prediction. In this review, the industrial applications of LAB are mapped onto available global, genome-scale metabolic modelling techniques to evaluate the extent to which functional genomics and systems biology can live up to their industrial promise.

Contains fulltext : 36088teusink.pdf (Publisher’s version ) (Open Access) A genome-scale metabolic model of the lactic acid bacterium Lactobacillus plantarum WCFS1 was constructed based on genomic content and experimental data. The complete model includes 721 genes, 643 reactions, and 531 metabolites. Different stoichiometric modeling techniques were used for interpretation of complex fermentation data, as L. plantarum is adapted to nutrient-rich environments and only grows in media supplemented with vitamins and amino acids. (i) Based on experimental input and output fluxes, maximal ATP production was estimated and related to growth rate. (ii) Optimization of ATP production further identified amino acid catabolic pathways that were not previously associated with free-energy metabolism. (iii) Genome-scale elementary flux mode analysis identified 28 potential futile cycles. (iv) Flux variability analysis supplemented the elementary mode analysis in identifying parallel pathways, e.g. pathways with identical end products but different co-factor usage. Strongly increased flexibility in the metabolic network was observed when strict coupling between catabolic ATP production and anabolic consumption was relaxed. These results illustrate how a genome-scale metabolic model and associated constraint-based modeling techniques can be used to analyze the physiology of growth on a complex medium rather than a minimal salts medium. However, optimization of biomass formation using the Flux Balance Analysis approach, reported to successfully predict growth rate and by product formation in Escherichia coli and Saccharomyces cerevisiae, predicted too high biomass yields that were incompatible with the observed lactate production. The reason is that this approach assumes optimal efficiency of substrate to biomass conversion, and can therefore not predict the metabolically inefficient lactate formation.

Hepatic VLDL and glucose production is enhanced in type 2 diabetes and associated with hepatic steatosis. Whether the derangements in hepatic metabolism are attributable to steatosis or to the increased availability of FA metabolites is not known. We used methyl palmoxirate (MP), an inhibitor of carnitine palmitoyl transferase I, to acutely inhibit hepatic FA oxidation and investigated whether the FAs were rerouted into VLDL secretion and whether this would affect hepatic glucose production. After an overnight fast, male APOE3*Leiden transgenic mice received an oral dose of 10 mg/kg MP. Administration of MP led to an 83% reduction in plasma β-hydroxybutyrate (ketone body) levels compared with vehicle-treated mice (0.47 ± 0.07 vs. 2.81 ± 0.16 mmol/l, respectively; P < 0.01), indicative of impaired ketogenesis. Plasma FFA levels were increased by 32% and cholesterol and insulin levels were decreased by 17% and 50%, respectively, in MP-treated mice compared with controls. MP treatment led to a 30% increase in liver triglyceride (TG) content. Surprisingly, no effect on hepatic VLDL-TG production was observed between the groups at 8 h after MP administration. In addition, the capacity of insulin to suppress endogenous glucose production was unaffected in MP-treated mice compared with controls. In conclusion, acute inhibition of FA oxidation increases hepatic lipid content but does not stimulate hepatic VLDL secretion or reduce insulin sensitivity. Copyright © 2005 by the American Society for Biochemistry and Molecular Biology, Inc. Chemicals / CAS: 3 hydroxybutyrate dehydrogenase, 9028-38-0; cholesterol, 57-88-5; insulin, 9004-10-8; palmoxiric acid, 68170-97-8, 81556-15-2; apolipoprotein E3 (Leidein); Apolipoprotein E3; Apolipoproteins E; Fatty Acids, Nonesterified; Lipoproteins, VLDL; RNA, Messenger

The prospect of understanding the relationship between the genome and the physiology of an organism is an important incentive to reconstruct metabolic networks. The first steps in the process can be automated and it does not take much effort to obtain an initial metabolic reconstruction from a genome sequence. However, such a reconstruction is certainly not flawless and correction of the many imperfections is laborious. It requires the combined analysis of the available information on protein sequence, phylogeny, gene-context and co-occurrence but is also aided by high-throughput experimental data. Simultaneously, the reconstructed network provides the opportunity to visualize the "omics" data within a relevant biological functional context and thus aids the interpretation of those data.