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

Isoenzyme expression changes in response to high temperature determine the metabolic regulation of increased glycolytic flux in yeast

Qualitative phenotypic changes are the integrated result of quantitative changes at multiple regulatory levels. To explain the temperature-induced increase of glycolytic flux in fermenting cultures of Saccharomyces cerevisiae, we quantified the contributions of changes in activity at many regulatory levels. We previously showed that a similar temperature increase in glucose-limited cultivations lead to a qualitative change from respiratory to fermentative metabolism, and this change was mainly regulated at the metabolic level. In contrast, in fermenting cells, a combination of different modes of regulation was observed. Regulation by changes in expression and the effect of temperature on enzyme activities contributed much to the increase in flux. Mass spectrometric quantification of glycolytic enzymes revealed that increased enzyme activity did not correlate with increased protein abundance, suggesting a large contribution of post-translational regulation to activity. Interestingly, the differences in the direct effect of temperature on enzyme kinetics can be explained by changes in the expression of the isoenzymes. Therefore, both the interaction of enzyme with its metabolic environment and the temperature dependence of activity are in turn regulated at the hierarchical level.

Testing biochemistry revisited: how in vivo metabolism can be understood from in vitro enzyme kinetics

A decade ago, a team of biochemists including two of us, modeled yeast glycolysis and showed that one of the most studied biochemical pathways could not be quite understood in terms of the kinetic properties of the constituent enzymes as measured in cell extract. Moreover, when the same model was later applied to different experimental steady-state conditions, it often exhibited unrestrained metabolite accumulation.

Here we resolve this issue by showing that the results of such ab initio modeling are improved substantially by (i) including appropriate allosteric regulation and (ii) measuring the enzyme kinetic parameters under conditions that resemble the intracellular environment. The following modifications proved crucial: (i) implementation of allosteric regulation of hexokinase and pyruvate kinase, (ii) implementation of V_max values measured under conditions that resembled the yeast cytosol, and (iii) redetermination of the kinetic parameters of glyceraldehyde-3-phosphate dehydrogenase under physiological conditions.

Model predictions and experiments were compared under five different conditions of yeast growth and starvation. When either the original model was used (which lacked important allosteric regulation), or the enzyme parameters were measured under conditions that were, as usual, optimal for high enzyme activity, fructose 1,6-bisphosphate and some other glycolytic intermediates tended to accumulate to unrealistically high concentrations. Combining all adjustments yielded an accurate correspondence between model and experiments for all five steady-state and dynamic conditions. This enhances our understanding of in vivo metabolism in terms of in vitro biochemistry.

Baker's yeast is widely applied in modern biotechnology, for instance for production of heterologous protein or biofuel. For such applications a thorough understanding of the central energy metabolism of the bug is crucial. Nevertheless, even for this well-known organism, attempts to build models ab initio, based on independently measured characteristics of the catalysts (the enzymes), seldom gives reliable results. A key problem in this field is that enzyme characteristics are often studied under non-physiological conditions that do not resemble the environment inside the cell. In this study we measured the enzyme characteristics under physiological conditions and assembled the results into a computational model of yeast energy metabolism. We show that this simple trick greatly improves the predictive value of the computational model. This allowed us to predict correctly how yeast cells adapt to nitrogen starvation, an industrially relevant situation, in which remodeling of the proteome strongly affects cellular energy metabolism.

Real-time determination of intracellular oxygen in bacteria using a genetically encoded FRET-based biosensor

Background

Molecular oxygen (O₂) is one of the key metabolites of all obligate and facultative aerobic pro- and eukaryotes. It plays a fundamental role in energy homeostasis whereas oxygen deprivation, in turn, broadly affects various physiological and pathophysiological processes. Therefore, real-time monitoring of cellular oxygen levels is basically a prerequisite for the analysis of hypoxia-induced processes in living cells and tissues.

Results

We developed a genetically encoded Förster resonance energy transfer (FRET)-based biosensor allowing the observation of changing molecular oxygen concentrations inside living cells. This biosensor named FluBO (fluorescent protein-based biosensor for oxygen) consists of the yellow fluorescent protein (YFP) that is sensitive towards oxygen depletion and the hypoxia-tolerant flavin-binding fluorescent protein (FbFP). Since O₂ is essential for the formation of the YFP chromophore, efficient FRET from the FbFP donor domain to the YFP acceptor domain only occurs in the presence but not in the absence of oxygen. The oxygen biosensor was used for continuous real-time monitoring of temporal changes of O₂ levels in the cytoplasm of Escherichia coli cells during batch cultivation.

Conclusions

FluBO represents a unique FRET-based oxygen biosensor which allows the non-invasive ratiometric readout of cellular oxygen. Thus, FluBO can serve as a novel and powerful probe for investigating the occurrence of hypoxia and its effects on a variety of (patho)physiological processes in living cells.

Metabolic engineering of Saccharomyces cerevisiae for bioconversion of D-xylose to D-xylonate

An NAD(+)-dependent D-xylose dehydrogenase, XylB, from Caulobacter crescentus was expressed in Saccharomyces cerevisiae, resulting in production of 17 ± 2 g D-xylonate l(-1) at 0.23 gl(-1)h(-1) from 23 g D-xylose l(-1) (with glucose and ethanol as co-substrates). D-Xylonate titre and production rate were increased and xylitol production decreased, compared to strains expressing genes encoding T. reesei or pig liver NADP(+)-dependent D-xylose dehydrogenases. D-Xylonate accumulated intracellularly to ∼70 mgg(-1); xylitol to ∼18 mgg(-1). The aldose reductase encoding gene GRE3 was deleted to reduce xylitol production. Cells expressing D-xylonolactone lactonase xylC from C. crescentus with xylB initially produced more extracellular D-xylonate than cells lacking xylC at both pH 5.5 and pH 3, and sustained higher production at pH 3. Cell vitality and viability decreased during D-xylonate production at pH 3.0. An industrial S. cerevisiae strain expressing xylB efficiently produced 43 g D-xylonate l(-1) from 49 g D-xylose l(-1).

Kinetic modelling of plant metabolic pathways

This paper provides a review of kinetic modelling of plant metabolic pathways as a tool for analysing their control and regulation. An overview of different modelling strategies is presented, starting with those approaches that only require a knowledge of the network stoichiometry; these are referred to as structural. Flux-balance analysis, metabolic flux analysis using isotope labelling, and elementary mode analysis are briefly mentioned as three representative examples. The main focus of this paper, however, is a discussion of kinetic modelling, which requires, in addition to the stoichiometry, a knowledge of the kinetic properties of the constituent pathway enzymes. The different types of kinetic modelling analysis, namely time-course simulation, steady-state analysis, and metabolic control analysis, are explained in some detail. An overview is presented of strategies for obtaining model parameters, as well as software tools available for simulation of such models. The kinetic modelling approach is exemplified with discussion of three models from the general plant physiology literature. With the aid of kinetic modelling it is possible to perform a control analysis of a plant metabolic system, to identify potential targets for biotechnological manipulation, as well as to ascertain the regulatory importance of different enzymes (including isoforms of the same enzyme) in a pathway. Finally, a framework is presented for extending metabolic models to the whole-plant scale by linking biochemical reactions with diffusion and advective flow through the phloem. Future challenges include explicit modelling of subcellular compartments, as well as the integration of kinetic models on the different levels of the cellular and organizational hierarchy.

Canonical modeling of the multi-scale regulation of the heat stress response in yeast

Heat is one of the most fundamental and ancient environmental stresses, and response mechanisms are found in prokaryotes and shared among most eukaryotes. In the budding yeast Saccharomyces cerevisiae, the heat stress response involves coordinated changes at all biological levels, from gene expression to protein and metabolite abundances, and to temporary adjustments in physiology. Due to its integrative multi-level-multi-scale nature, heat adaptation constitutes a complex dynamic process, which has forced most experimental and modeling analyses in the past to focus on just one or a few of its aspects. Here we review the basic components of the heat stress response in yeast and outline what has been done, and what needs to be done, to merge the available information into computational structures that permit comprehensive diagnostics, interrogation, and interpretation. We illustrate the process in particular with the coordination of two metabolic responses, namely the dramatic accumulation of the protective disaccharide trehalose and the substantial change in the profile of sphingolipids, which in turn affect gene expression. The proposed methods primarily use differential equations in the canonical modeling framework of Biochemical Systems Theory (BST), which permits the relatively easy construction of coarse, initial models even in systems that are incompletely characterized.

Propeptide of aminopeptidase 1 protein mediates aggregation and vesicle formation in cytoplasm-to-vacuole targeting pathway

Misfolded protein aggregation causes disease and aging; autophagy counteracts this by eliminating damaged components, enabling cells to survive starvation. The cytoplasm-to-vacuole targeting pathway in yeast encompasses the aggregation of the premature form of aminopeptidase 1 (prApe1) in cytosol and its sequestration by autophagic proteins into a vesicle for vacuolar transport. We show that the propeptide of Ape1 is important for aggregation and vesicle formation and that it is sufficient for binding to prApe1 and Atg19. Defective aggregation disrupts vacuolar transport, suggesting that aggregate shape is important in vesicle formation, whereas Atg19 binding is not sufficient for vacuolar transport. Aggregation involves hydrophobicity, whereas Atg19 binding requires additional electrostatic interactions. Ape1 dodecamerization may cluster propeptides into trimeric structures, with sufficient affinity to form propeptide hexamers by binding to other dodecamers, causing aggregation. We show that Ape1 aggregates bind Atg19 and Atg8 in vitro; this could be used as a scaffold for an in vitro assay of autophagosome formation to elucidate the mechanisms of autophagy.

Directionality of individual kinesin-5 Cin8 motors is modulated by loop 8, ionic strength and microtubule geometry

Kinesin-5 motors fulfil essential roles in mitotic spindle morphogenesis and dynamics as slow, processive microtubule (MT) plus-end directed motors. The Saccharomyces cerevisiae kinesin-5 Cin8 was found, surprisingly, to switch directionality. Here, we have examined directionality using single-molecule fluorescence motility assays and live-cell microscopy. On spindles, Cin8 motors mostly moved slowly (∼25 nm/s) towards the midzone, but occasionally also faster (∼55 nm/s) towards the spindle poles. In vitro, individual Cin8 motors could be switched by ionic conditions from rapid (380 nm/s) and processive minus-end to slow plus-end motion on single MTs. At high ionic strength, Cin8 motors rapidly alternated directionalities between antiparallel MTs, while driving steady plus-end relative sliding. Between parallel MTs, plus-end motion was only occasionally observed. Deletion of the uniquely large insert in loop 8 of Cin8 induced bias towards minus-end motility and affected the ionic strength-dependent directional switching of Cin8 in vitro. The deletion mutant cells exhibited reduced midzone-directed motility and efficiency to support spindle elongation, indicating the importance of directionality control for the anaphase function of Cin8.

Standardized assay medium to measure Lactococcus lactis enzyme activities while mimicking intracellular conditions

Knowledge of how the activity of enzymes is affected under in vivo conditions is essential for analyzing their regulation and constructing models that yield an integrated understanding of cell behavior. Current kinetic parameters for Lactococcus lactis are scattered through different studies and performed under different assay conditions. Furthermore, assay conditions often diverge from conditions prevailing in the intracellular environment. To establish uniform assay conditions that resemble intracellular conditions, we analyzed the intracellular composition of anaerobic glucose-limited chemostat cultures of L. lactis subsp. cremoris MG 1363. Based on this, we designed a new assay medium for enzyme activity measurements of growing cells of L. lactis, mimicking as closely as practically possible its intracellular environment. Procedures were optimized to be carried out in 96-well plates, and the reproducibility and dynamic range were checked for all enzyme activity measurements. The effects of freezing and the carryover of ammonium sulfate from the addition of coupling enzymes were also established. Activities of all 10 glycolytic and 4 fermentative enzymes were measured. Remarkably, most in vivo-like activities were lower than previously published data. Yet, the ratios of V(max) over measured in vivo fluxes were above 1. With this work, we have developed and extensively validated standard protocols for enzyme activity measurements for L. lactis.

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

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

A novel role for a glycosylphosphatidylinositol-anchored aspartyl protease, CgYps1, in the regulation of pH homeostasis in Candida glabrata

Proteases, key virulence factors of many bacterial and fungal pathogens, are pivotally important for nutrient acquisition, invasion and adherence to host cells and evasion/escape from host immune cells. In this study, we report a novel role for CgYps1, member of a family of 11 GPI-linked aspartyl proteases, in a human opportunistic fungal pathogen, Candida glabrata, in the regulation of pH homeostasis under acidic environmental conditions. We show that CgYps1 is required to survive low-external-pH environment and the inability of Cgyps1Δ mutant to maintain pH homeostasis results in intracellular acidification and increased reactive oxygen species (ROS) production. We also provide evidence that the reduced intracellular pH in Cgyps1Δ mutant under acidic conditions is, partly, owing to the diminished activity of a plasma membrane proton pump, CgPma1, an orthologue of a key component of pH homeostasis machinery in Saccharomyces cerevisiae, Pma1. In addition, we have examined C. glabrata's response to low environmental pH via genome-wide expression analysis and several genes required for protein folding/modification and stress response pathways including seven of the CgYPS genes were found to be upregulated. Lastly, we show that C. glabrata responds to acidic environment by reducing total β-glucan levels in the cell wall in a CgYps1-dependent manner.

A new view of the bacterial cytosol environment

The cytosol is the major environment in all bacterial cells. The true physical and dynamical nature of the cytosol solution is not fully understood and here a modeling approach is applied. Using recent and detailed data on metabolite concentrations, we have created a molecular mechanical model of the prokaryotic cytosol environment of Escherichia coli, containing proteins, metabolites and monatomic ions. We use 200 ns molecular dynamics simulations to compute diffusion rates, the extent of contact between molecules and dielectric constants. Large metabolites spend ∼80% of their time in contact with other molecules while small metabolites vary with some only spending 20% of time in contact. Large non-covalently interacting metabolite structures mediated by hydrogen-bonds, ionic and π stacking interactions are common and often associate with proteins. Mg²⁺ ions were prominent in NIMS and almost absent free in solution. Κ⁺ is generally not involved in NIMSs and populates the solvent fairly uniformly, hence its important role as an osmolyte. In simulations containing ubiquitin, to represent a protein component, metabolite diffusion was reduced owing to long lasting protein-metabolite interactions. Hence, it is likely that with larger proteins metabolites would diffuse even more slowly. The dielectric constant of these simulations was found to differ from that of pure water only through a large contribution from ubiquitin as metabolite and monatomic ion effects cancel. These findings suggest regions of influence specific to particular proteins affecting metabolite diffusion and electrostatics. Also some proteins may have a higher propensity for associations with metabolites owing to their larger electrostatic fields. We hope that future studies may be able to accurately predict how binding interactions differ in the cytosol relative to dilute aqueous solution.

The cytosol is the major cellular environment housing the majority of cellular activity. Although the cytosol is an aqueous environment, it contains high concentrations of ions, metabolites, and proteins, making it very different from dilute aqueous solution, which is frequently used for in vitro biochemistry. Recent advances in metabolomics have provided detailed concentration data for metabolites in E.coli. We used this information to construct accurate atomistic models of the cytosol solution. We find that, unlike the situation in dilute solutions, most metabolites spend the majority of their time in contact with other metabolites, or in contact with proteins. Furthermore, we find large non-covalently interacting metabolite structures are common and often associated with proteins. The presence of proteins reduced metabolite diffusion owing to long lasting correlations of motion. The dielectric constant of these simulations was found to differ from that of pure water only through a large contribution from proteins as metabolite and monatomic ion effects largely cancel. These findings suggest specific protein spheres of influence affecting metabolite diffusion and the electrostatic environment.

Intracellular pH is a tightly controlled signal in yeast

BACKGROUND

Nearly all processes in living cells are pH dependent, which is why intracellular pH (pH(i)) is a tightly regulated physiological parameter in all cellular systems. However, in microbes such as yeast, pH(i) responds to extracellular conditions such as the availability of nutrients. This raises the question of how pH(i) dynamics affect cellular function.

SCOPE OF REVIEW

We discuss the control of pH(i,) and the regulation of processes by pH(i), focusing on the model organism Saccharomyces cerevisiae. We aim to dissect the effects of pH(i) on various aspects of cell physiology, which are often intertwined. Our goal is to provide a broad overview of how pH(i) is controlled in yeast, and how pH(i) in turn controls physiology, in the context of both general cellular functioning as well as of cellular decision making upon changes in the cell's environment.

MAJOR CONCLUSIONS

Besides a better understanding of the regulation of pH(i), evidence for a signaling role of pH(i) is accumulating. We conclude that pH(i) responds to nutritional cues and relays this information to alter cellular make-up and physiology. The physicochemical properties of pH allow the signal to be fast, and affect multiple regulatory levels simultaneously.

GENERAL SIGNIFICANCE

The mechanisms for regulation of processes by pH(i) are tightly linked to the molecules that are part of all living cells, and the biophysical properties of the signal are universal amongst all living organisms, and similar types of regulation are suggested in mammals. Therefore, dynamic control of cellular decision making by pH(i) is therefore likely a general trait. This article is part of a Special Issue entitled: Systems Biology of Microorganisms.

Optimization of a blueprint for in vitro glycolysis by metabolic real-time analysis

Recruiting complex metabolic reaction networks for chemical synthesis has attracted considerable attention but frequently requires optimization of network composition and dynamics to reach sufficient productivity. As a design framework to predict optimal levels for all enzymes in the network is currently not available, state-of-the-art pathway optimization relies on high-throughput phenotype screening. We present here the development and application of a new in vitro real-time analysis method for the comprehensive investigation and rational programming of enzyme networks for synthetic tasks. We used this first to rationally and rapidly derive an optimal blueprint for the production of the fine chemical building block dihydroxyacetone phosphate (DHAP) via Escherichia coli's highly evolved glycolysis. Second, the method guided the three-step genetic implementation of the blueprint, yielding a synthetic operon with the predicted 2.5-fold-increased glycolytic flux toward DHAP. The new analytical setup drastically accelerates rational optimization of synthetic multienzyme networks.

Systems biology from micro-organisms to human metabolic diseases: the role of detailed kinetic models

Human metabolic diseases are typically network diseases. This holds not only for multifactorial diseases, such as metabolic syndrome or Type 2 diabetes, but even when a single gene defect is the primary cause, where the adaptive response of the entire network determines the severity of disease. The latter may differ between individuals carrying the same mutation. Understanding the adaptive responses of human metabolism naturally requires a systems biology approach. Modelling of metabolic pathways in micro-organisms and some mammalian tissues has yielded many insights, qualitative as well as quantitative, into their control and regulation. Yet, even for a well-known pathway such as glycolysis, precise predictions of metabolite dynamics from experimentally determined enzyme kinetics have been only moderately successful. In the present review, we compare kinetic models of glycolysis in three cell types (African trypanosomes, yeast and skeletal muscle), evaluate their predictive power and identify limitations in our understanding. Although each of these models has its own merits and shortcomings, they also share common features. For example, in each case independently measured enzyme kinetic parameters were used as input. Based on these 'lessons from glycolysis', we will discuss how to make best use of kinetic computer models to advance our understanding of human metabolic diseases.

Quantitative analysis of flux regulation through hierarchical regulation analysis

Regulation analysis is a methodology that quantifies to what extent a change in the flux through a metabolic pathway is regulated by either gene expression or metabolism. Two extensions to regulation analysis were developed over the past years: (i) the regulation of V(max) can be dissected into the various levels of the gene-expression cascade, such as transcription, translation, protein degradation, etc. and (ii) a time-dependent version allows following flux regulation when cells adapt to changes in their environment. The methodology of the original form of regulation analysis as well as of the two extensions will be described in detail. In addition, we will show what is needed to apply regulation analysis in practice. Studies in which the different versions of regulation analysis were applied revealed that flux regulation was distributed over various processes and depended on time, enzyme, and condition of interest. In the case of the regulation of glycolysis in baker's yeast, it appeared, however, that cells that remain under respirofermentative conditions during a physiological challenge tend to invoke more gene-expression regulation, while a shift between respirofermentative and respiratory conditions invokes an important contribution of metabolic regulation. The complexity of the regulation observed in these studies raises the question what is the advantage of this highly distributed and condition-dependent flux regulation.

Simulation of cellular biochemical system kinetics

The goal of realistically and reliably simulating the biochemical processes underlying cellular function is achievable through a systematic approach that makes use of the broadest possible amount of in vitro and in vivo data, and is consistent with all applicable physical chemical theories. Progress will be facilitated by establishing: (1) a concrete self-consistent theoretical foundation for systems simulation; (2) extensive and accurate databases of thermodynamic properties of biochemical reactions; (3) parameterized and validated models of enzyme and transporter catalytic mechanisms that are consistent with physical chemical theoretical foundation; and (4) software tools for integrating all these concepts, data, and models into a cohesive representation of cellular biochemical systems. Ongoing initiatives are laying the groundwork for the broad-based community cooperation that will be necessary to pursue these elements of a strategic infrastructure for systems simulation on a large scale.

Model-based inference of biochemical parameters and dynamic properties of microbial signal transduction networks

Because of the inherent uncertainty about quantitative aspects of signalling networks it is of substantial interest to use computational methods that allow inferring non-measurable quantities such as rate constants, from measurable quantities such as changes in protein abundances. We argue that true biochemical parameters like rate constants can generally not be inferred using models due to their non-identifiability. Recent advances, however, facilitate the analysis of parameter identifiability of a given model and automated discrimination of candidate models, both being important techniques to still extract quantitative biological information from experimental data.

The renaissance of continuous culture in the post-genomics age

The development of continuous culture techniques 60 years ago and the subsequent formulation of theory and the diversification of experimental systems revolutionised microbiology and heralded a unique period of innovative research. Then, progressively, molecular biology and thence genomics and related high-information-density omics technologies took centre stage and microbial growth physiology in general faded from educational programmes and research funding priorities alike. However, there has been a gathering appreciation over the past decade that if the claims of systems biology are going to be realised, they will have to be based on rigorously controlled and reproducible microbial and cell growth platforms. This revival of continuous culture will be long lasting because its recognition as the growth system of choice is firmly established. The purpose of this review, therefore, is to remind microbiologists, particularly those new to continuous culture approaches, of the legacy of what I call the first age of continuous culture, and to explore a selection of researches that are using these techniques in this post-genomics age. The review looks at the impact of continuous culture across a comprehensive range of microbiological research and development. The ability to establish (quasi-) steady state conditions is a frequently stated advantage of continuous cultures thereby allowing environmental parameters to be manipulated without causing concomitant changes in the specific growth rate. However, the use of continuous cultures also enables the critical study of specified transition states and chemical, physical or biological perturbations. Such dynamic analyses enhance our understanding of microbial ecology and microbial pathology for example, and offer a wider scope for innovative drug discovery; they also can inform the optimization of batch and fed-batch operations that are characterized by sequential transitions states.

Enzyme kinetics informatics: from instrument to browser

A limited number of publicly available resources provide access to enzyme kinetic parameters. These have been compiled through manual data mining of published papers, not from the original, raw experimental data from which the parameters were calculated. This is largely due to the lack of software or standards to support the capture, analysis, storage and dissemination of such experimental data. Introduced here is an integrative system to manage experimental enzyme kinetics data from instrument to browser. The approach is based on two interrelated databases: the existing SABIO-RK database, containing kinetic data and corresponding metadata, and the newly introduced experimental raw data repository, MeMo-RK. Both systems are publicly available by web browser and web service interfaces and are configurable to ensure privacy of unpublished data. Users of this system are provided with the ability to view both kinetic parameters and the experimental raw data from which they are calculated, providing increased confidence in the data. A data analysis and submission tool, the kineticswizard, has been developed to allow the experimentalist to perform data collection, analysis and submission to both data resources. The system is designed to be extensible, allowing integration with other manufacturer instruments covering a range of analytical techniques.