Engineering the spatial organization of metabolic enzymes: mimicking nature's synergy

A growing body of evidence indicates that many cellular reactions within metabolic pathways are catalyzed not by free-floating 'soluble' enzymes, but via one or more membrane-associated multienzyme complexes. This type of macromolecular organization has important implications for the overall efficiency, specificity, and regulation of metabolic pathways. An ever-increasing number of biochemical and genetic studies on primary and secondary metabolism have laid a solid foundation for this model, providing compelling evidence in favor of the so-called channeling of intermediates between enzyme active sites and colocalization of enzymes inside a cell. In this review, we discuss several of nature's most notable multifunctional enzyme systems including the AROM complex and tryptophan synthase, each of which provides new fundamental insights into the structural organization of metabolic machinery within living cells. We then focus on the growing body of literature related to engineering strategies using protein chimeras and post-translational assembly mechanisms. Common among these techniques is the desire to mimic natural enzyme organization for optimizing the production of valuable metabolites with industrial and medical importance.     

Metabolic design: how to engineer a living cell to desired metabolite concentrations and fluxes

A biotechnological aim of genetic engineering is to increase the intracellular concentration or secretion of valuable compounds, while making the other concentrations and fluxes optimal for viability and productivity. Efforts to accomplish this based on over-expression of the enzyme, catalyzing the so-called "rate-limiting step," have not been successful. Here we develop a method to determine the enzyme concentrations that are required to achieve such an aim. This method is called Metabolic Design Analysis and is based on the perturbation method and the modular ("top-down") approach-formalisms that were first developed for the analysis of biochemical regulation such as, Metabolic Control Analysis. Contrary to earlier methods, the desired alterations of cellular metabolism need not be small or confined to a single metabolite or flux. The limits to the alterations of fluxes and metabolite concentrations are identified. To employ Metabolic Design Analysis, only limited kinetic information concerning the pathway enzymes is needed.     

Computer simulation of purine metabolism

A computer model of purine metabolism, including catabolism, salvage pathways and interconversion among nucleotides, is given. Steady-state rate equations corresponding to metabolic enzymes are written based on information from the literature about their kinetic behaviour. Numerical integration of this set of equations is performed employing selected parameters taken from the literature. After stabilization of purine compound concentrations is reached, simulation of enzyme deficit and enzyme overproduction is carried out. The latter is calculated by varying specified maximum velocities in the numerical integration. A pattern of intermediate metabolite concentrations is found. These results form a basis for the comparison of normal patterns or patterns reflecting the effects of inborn errors of metabolism. The aim of this paper is to demonstrate the usefulness of this computer simulation method in complex metabolism pathways.     

Filament formation by metabolic enzymes—A new twist on regulation

Compartmentalization of metabolic enzymes through protein–protein interactions is an emerging mechanism for localizing and regulating metabolic activity. Self-assembly into linear filaments is a common strategy for cellular compartmentalization of enzymes. Polymerization is often driven by changes in the metabolic state of the cell, suggesting that it is a strategy for shifting metabolic flux in response to cellular demand. Although polymerization of metabolic enzymes is widespread, observed from bacteria to humans, we are just beginning to appreciate their role in regulating cellular metabolism. In most cases, one functional role of metabolic enzyme filaments is allosteric control of enzyme activity. Here, we highlight recent findings, providing insight into the structural and functional significance of filamentation of metabolic enzymes in cells.     

SMET: Systematic multiple enzyme targeting – a method to rationally design optimal strains for target chemical overproduction

Identifying multiple enzyme targets for metabolic engineering is very critical for redirecting cellular metabolism to achieve desirable phenotypes, e.g., overproduction of a target chemical. The challenge is to determine which enzymes and how much of these enzymes should be manipulated by adding, deleting, under-, and/or over-expressing associated genes. In this study, we report the development of a systematic multiple enzyme targeting method (SMET), to rationally design optimal strains for target chemical overproduction. The SMET method combines both elementary mode analysis and ensemble metabolic modeling to derive SMET metrics including l-values and c-values that can identify rate-limiting reaction steps and suggest which enzymes and how much of these enzymes to manipulate to enhance product yields, titers, and productivities. We illustrated, tested, and validated the SMET method by analyzing two networks, a simple network for concept demonstration and an Escherichia coli metabolic network for aromatic amino acid overproduction. The SMET method could systematically predict simultaneous multiple enzyme targets and their optimized expression levels, consistent with experimental data from the literature, without performing an iterative sequence of single-enzyme perturbation. The SMET method was much more efficient and effective than single-enzyme perturbation in terms of computation time and finding improved solutions.     

iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks

Accurate determination of physiological states of cellular metabolism requires detailed information about metabolic fluxes, metabolite concentrations and distribution of enzyme states. Integration of fluxomics and metabolomics data, and thermodynamics-based metabolic flux analysis contribute to improved understanding of steady-state properties of metabolism. However, knowledge about kinetics and enzyme activities though essential for quantitative understanding of metabolic dynamics remains scarce and involves uncertainty. Here, we present a computational methodology that allow us to determine and quantify the kinetic parameters that correspond to a certain physiology as it is described by a given metabolic flux profile and a given metabolite concentration vector. Though we initially determine kinetic parameters that involve a high degree of uncertainty, through the use of kinetic modeling and machine learning principles we are able to obtain more accurate ranges of kinetic parameters, and hence we are able to reduce the uncertainty in the model analysis. We computed the distribution of kinetic parameters for glucose-fed E. coli producing 1,4-butanediol and we discovered that the observed physiological state corresponds to a narrow range of kinetic parameters of only a few enzymes, whereas the kinetic parameters of other enzymes can vary widely. Furthermore, this analysis suggests which are the enzymes that should be manipulated in order to engineer the reference state of the cell in a desired way. The proposed approach also sets up the foundations of a novel type of approaches for efficient, non-asymptotic, uniform sampling of solution spaces.     

Job contenders: roles of the β‐barrel assembly machinery and the translocation and assembly module in autotransporter secretion

Billions of years of evolution have yielded today's complex metabolic networks driven by efficient and highly specialized enzymes. In contrast, the metabolism of the earliest cellular life forms was likely much simpler with only a few enzymes of comparatively low activity. It has been speculated that these early enzymes had low specificities and in turn were able to perform multiple functions. In this issue of Molecular Microbiology, Ferla et al. describe examples of enzymes that catalyze chemically distinct reactions while using the same active site. Most importantly, the authors demonstrated that the comparatively weak activities of these multifunctional enzymes are each physiologically relevant. These findings contrast with simply promiscuous enzyme activities, which have been described numerous times but are not physiologically relevant. Ferla et al. elegantly combined initial bioinformatics searches for enzyme candidates with sound kinetic measurements, evolutionary considerations and even structural discussions. The phenomenon of multifunctionality appears to be a mechanism for bacteria with reduced genomes to compensate for their lack of certain enzymes. In the broader context of evolution, these organisms could be considered living model systems to study features of long‐extinct early cellular life.     

The generalized flux-minimization method and its application to metabolic networks affected by enzyme deficiencies

The flux-minimization method [Holzhütter, H.G., 2004. The principle of flux-minimization and its application to calculate stationary fluxes in metabolic networks. Eur. J. Biochem. 271, 2905-2922] has been proposed as an alternative to kinetic modeling to calculate stationary fluxes in metabolic networks. Here a generalization of this method is proposed that takes into account possible limitations of internal fluxes, e.g. due to enzyme defects or partial inhibition of enzyme activities by drugs. The generalized method consists in the minimization of an objective function which expresses the compromise that has to be made between minimization of internal fluxes on one hand and maintenance of the metabolic output required for various cellular functions on the other. Fulfillment of the latter condition is measured through a fitness function, which evaluates the relative deviation of the output fluxes from demanded target values. The method is applied to assess the metabolic consequences caused by a deficiency of enzymes involved in the metabolism of erythrocytes. The obtained results are in good agreement with those obtained on the basis of a comprehensive kinetic model.     

Identification of the Entner-Doudoroff pathway in an antibiotic-producing actinomycete species

The metabolic network of the central carbon metabolism represents the backbone of cellular metabolism and provides the precursors and cofactors required for synthesis of secondary metabolites. It is therefore pivotal to map the operating metabolic network in the central carbon metabolism in order to design metabolic engineering strategies towards construction of more efficient producers of specific metabolites. In this context, methods that allow rapid and reliable mapping of the central carbon metabolism are valuable. In the present study, a (13)C labelling-based method was used to identify the primary metabolic pathways of the poorly characterized antibiotic-producing actinomycete Nonomuraea sp. ATCC 39727. Surprisingly, it was found that Nonomuraea sp. ATCC 39272 predominantly metabolizes glucose via the Entner-Doudoroff (ED) pathway. This represents the first time that the ED pathway has been recognized as the main catabolic pathway in an actinomycete. The Nonomuraea genes encoding the key enzymes of the ED pathway were subsequently identified, sequenced and functionally described.     

Why metabolic enzymes are essential or nonessential for growth of Escherichia coli K12 on glucose

The genes encoding metabolic enzymes involved in glucose metabolism, the TCA cycle, and biosynthesis of amino acids, purines, pyrimidines, and cofactors would be expected to be essential for growth of Escherichia coli on glucose because the cells must synthesize all of the building blocks for cellular macromolecules. Surprisingly, 80 of 227 of these genes are not essential. Analysis of why these genes are not essential provides insights into the metabolic sophistication of E. coli and into the evolutionary pressures that have shaped its physiology. Alternative routes enabled by interconnecting pathways can allow a defective step to be bypassed. Isozymes, alternative enzymes, broad-specificity enzymes, and multifunctional enzymes can often substitute for a missing enzyme. We expect that the apparent redundancy in these metabolic pathways has arisen due to the need for E. coli to survive in a variety of habitats and therefore to have a metabolism that allows optimal exploitation of varying environmental resources and synthesis of small molecules when they cannot be obtained from the environment.     

Optimizing Metabolite Production Using Periodic Oscillations

Methods for improving microbial strains for metabolite production remain the subject of constant research. Traditionally, metabolic tuning has been mostly limited to knockouts or overexpression of pathway genes and regulators. In this paper, we establish a new method to control metabolism by inducing optimally tuned time-oscillations in the levels of selected clusters of enzymes, as an alternative strategy to increase the production of a desired metabolite. Using an established kinetic model of the central carbon metabolism of Escherichia coli, we formulate this concept as a dynamic optimization problem over an extended, but finite time horizon. Total production of a metabolite of interest (in this case, phosphoenolpyruvate, PEP) is established as the objective function and time-varying concentrations of the cellular enzymes are used as decision variables. We observe that by varying, in an optimal fashion, levels of key enzymes in time, PEP production increases significantly compared to the unoptimized system. We demonstrate that oscillations can improve metabolic output in experimentally feasible synthetic circuits.     

Using enzyme cascades in biocatalysis: Highlight on transaminases and carboxylic acid reductases

Biocatalysis, the use of enzymes in chemical transformations, is an important green chemistry tool. Cascade reactions combine different enzyme activities in a sequential set of reactions. Cascades can occur within a living (usually bacterial) cell; in vitro in ‘one pot’ systems where the desired enzymes are mixed together to carry out the multi-enzyme reaction; or using microfluidic systems. Microfluidics offers particular advantages when the product of the reaction inhibits the enzyme(s). In vitro systems allow variation of different enzyme concentrations to optimise the metabolic ‘flux’, and the addition of enzyme cofactors as required. Cascades including cofactor recycling systems and modelling approaches are being developed to optimise cascades for wider industrial scale use. Two industrially important enzymes, transaminases and carboxylic acid reductases are used as examples regarding their applications in cascade reactions with other enzyme classes to obtain important synthons of pharmaceutical interest.     

On the origin of intracellular compartmentation and organized metabolic systems

The history of the development of the ideas and research of organized metabolic systems during last three decades is shortly reviewed. The cell cytoplasm is crowded with solutes, soluble macromolecules such as enzymes, nucleic acids, structural proteins and membranes. The high protein density within the large compartments of the cells predominantly determines the major characteristics of cellular environment such as viscosity, diffusion and inhomogeneity. The fact that the solvent viscosity of cytoplasm is not substantially different from the water is explained by intracellular structural heterogeneity: the intrinsic macromolecular density is relatively low within the interstitial voids in the cell because many soluble enzymes are apparently integral parts of the insoluble cytomatrix and are not distributed homogeneously. The molecular crowding and sieving restrict the mobility of very large solutes, binding severely restrict the mobility of smaller solutes. One of consequence of molecular crowding and hindered diffusion is the need to compartmentalize metabolic pathway to overcome diffusive barriers. Although the movement of small molecules is slowed down in the cytoplasm, the metabolism can successfully proceed and even be facilitated by metabolite channeling which directly transfers the intermediate from one enzyme to an adjacent enzyme without the need of free aqueous-phase diffusion. The enhanced probability for intermediates to be transferred from one active site to the other by sequential enzymes requires stable or transient interactions of the relevant enzymes, which associate physically in non-dissociable, static multienzyme complexes--metabolones, particles containing enzymes of a part or whole metabolic systems. Therefore, within the living cell the metabolism depends on the structural organization of enzymes forming microcompartments. Since cells contain many compartments and microenvironments, the measurement of the concentration of metabolites in whole cells or tissues gives an average cellular concentration and not that which is actually sensed by the active site of a specific enzyme. Thus, the microcompartmentation could provide a mechanism which can control metabolic pathways. Independently and in parallel to the developments described above, the ideas of compartmentation came into existence from the necessity to explain important physiological phenomena, in particular in heart research and in cardiac electrophysiology. These phenomena demonstrated the physiological importance of the biophysical and biochemical mechanisms described in this review.     

The elucidation of metabolic pathways and their improvements using stable optimization of large-scale kinetic models of cellular systems

Metabolic engineering of cellular systems to maximize reaction fluxes or metabolite concentrations still presents a significant challenge by encountering unpredictable instabilities that can be caused by simultaneous or consecutive enhancements of many reaction steps. It can therefore be important to select carefully small subsets of key enzymes for their subsequent stable modification compatible with cell physiology. To address this important problem, we introduce a general mixed integer non-linear problem (MINLP) formulation to compute automatically which enzyme levels should be modulated and which enzyme regulatory structures should be altered to achieve the given optimization goal using non-linear kinetic models of relevant cellular systems. The developed MINLP formulation directly employs a stability analysis constraint and also includes non-linear biophysical constraints to describe homeostasis conditions for metabolite concentrations and protein machinery without any preliminary model simplification (e.g. linlog kinetics approximation). The framework is demonstrated on a well-established large-scale kinetic model of the Escherichia coli central metabolism used for the optimization of the glucose uptake through the phosphotransferase transport system (PTS) and serine biosynthesis. Computational results show that substantial stable improvements can be predicted by manipulating only small subsets of enzyme levels and regulatory structures. This means that while more efforts can be required to elucidate larger stable optimal enzyme level/regulation choices, no further significant increase in the optimized fluxes can be obtained and, therefore, such choices may not be worth the effort due to the potential loss of stability properties. The source for instability through saddle-node and Hopf bifurcations is identified, and all results are contrasted with predictions from metabolic control analysis.     

Identification of the NAD(+)-binding fold of glyceraldehyde-3-phosphate dehydrogenase as a novel RNA-binding domain

There is growing evidence that metabolic enzymes may act as multifunctional proteins performing diverse roles in cellular metabolism. Among these functions are the RNA-binding activities of NAD(+)-dependent dehydrogenases. Previously, we have characterized the glycolytic enzyme glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as an RNA-binding protein with preference to adenine-uracil-rich sequences. In this study, we used GST-GAPDH fusion proteins generated by deletion mutagenesis to search for the RNA binding domain. We established that the N-terminal 43 amino acid residues of GAPDH, which correspond to the first mononucleotide-binding domain of the NAD(+)-binding fold is sufficient to confer RNA-binding. We also provide evidence that this single domain, although it retains most of the RNA-binding activity, loses sequence specificity. Our results suggest a molecular basis for RNA-recognition by NAD(+)-dependent dehydrogenases and (di)nucleotide-binding metabolic enzymes that had been reported to have RNA-binding activity with different specificity. To support this prediction we also identified other members of the family of NAD(+)-dependent dehydrogenases with no previous history of nucleic acid binding as RNA binding proteins in vitro. Based on our findings we propose the addition of the NAD(+)-binding domain to the list of RNA binding domains/motifs.     

L-苯丙氨酸生产的代谢工程研究

L-苯丙氨酸是一种重要的食品和医药中间体。工业上一般采用酶法和发酵法来生产L-苯丙氨酸。代谢工程的兴起,使得更加理性的改造菌株成为可能,这更加促进了发酵法的广泛应用。主要介绍了代谢工程在L-苯丙氨酸生产菌的改造中的应用情况,其中涉及苯丙氨酸生物合成途径中相关基因及其酶的调控、中央代谢途径的改造和芳香族氨基酸生物合成支路的修饰。并探讨了将来的发展前景。     

The rationalization of high enzyme concentration in metabolic pathways such as glycolysis

The cellular concentration of enzymes of some major metabolic pathways, such as glycolysis, can approach millimolarity. This concentration of enzyme can catalyze in vitro rates which are 100-fold higher than maximum pathway flux. In an attempt to understand the need for such high enzyme concentration, an artificial metabolic pathway of five enzymes (apropos the central enzymes of glycolysis) has been modeled. Numerical methods were then used to determine the effect of enzyme concentration on: (1) the change in total free metabolite concentration as the pathway changes from low flux to high flux, (2) the time lag (transient time) in the rate of final product formation upon the transition from low flux to high flux. Both the changes in metabolite pool size and the transient time decreased with increased enzyme concentrations. When all enzymatic reactions were assigned Keq of unity, a concentration for each enzyme of 25 microM is sufficient to provide a transient time of 1 sec. When Keq different from unity are introduced, more enzyme is required to provide comparably short transient times. Under the latter condition, a pathway of sufficiently low transient time would require all the enzyme available in mammalian muscle. It is shown that there is little scope for further increases in either enzyme concentration or of catalytic efficiency of independent enzymes. Therefore, an alternative method of increasing efficiency is considered in which enzyme-bound metabolites can serve directly as substrates for subsequent enzymes in a metabolic pathway.     

Control of the flux in the arginine pathway of Neurospora crassa. Modulations of enzyme activity and concentration.

The influence of particular enzyme activities on the flux of metabolites in a pathway can be estimated by 'modulating' enzymes (i.e. changing turnover or concentration) and measuring the response in various parts of the system. By controlling the nuclear ration of two genetically different nuclear types in heterokaryons, the enzyme concentrations at four different steps in the arginine pathway were decreased over a range. This range was extended by the use of bradytrophs, mutant strains specifying enzymes with greatly diminished enzyme activities. Strains altered simultaneously at more than one step were also constructed by genetic recombination. By measuring the outputs of the pathway and the steady-state concentrations of intermediate pools, the fluxes in different parts of the pathway were calculated. This allowed the construction of flux/enzyme relationships, the slope of which is a measure of the sensitivity of a flux to the change in enzyme activity at that step. All fluxes were found to be considerably buffered for quite substantial decreases in the activities of all enzymes. Mass action plays an important part in this phenomenon, as do inhibition and repression. Because of the existence of expansion fluxes in growing systems, we find quantitatively different fluxes in different parts of the single pathway. For the same reason some enzyme modulations given decreased fluxes in one part and increased fluxes in another. The understanding of control in the pathway thus involves consideration of many mechanisms operating simultaneously and the estimation of changes in the whole system. The concept of a 'rate-limiting step' is found to be inadequate and is replaced by a quantitative measure, the Sensitivity Coefficient, which takes account of all the interactions. It is shown that control of the flux is shared among all the enzymes of the pathway. The results are discussed in terms of the theory of flux control.     

Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters

Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal biomass yield, the prediction of actual growth rate is a long standing goal. This gap stems from strictly relying on data regarding reaction stoichiometry and directionality, without accounting for enzyme kinetic considerations. Here we present a novel metabolic network-based approach, MetabOlic Modeling with ENzyme kineTics (MOMENT), which predicts metabolic flux rate and growth rate by utilizing prior data on enzyme turnover rates and enzyme molecular weights, without requiring measurements of nutrient uptake rates. The method is based on an identified design principle of metabolism in which enzymes catalyzing high flux reactions across different media tend to be more efficient in terms of having higher turnover numbers. Extending upon previous attempts to utilize kinetic data in genome-scale metabolic modeling, our approach takes into account the requirement for specific enzyme concentrations for catalyzing predicted metabolic flux rates, considering isozymes, protein complexes, and multi-functional enzymes. MOMENT is shown to significantly improve the prediction accuracy of various metabolic phenotypes in E. coli, including intracellular flux rates and changes in gene expression levels under different growth rates. Most importantly, MOMENT is shown to predict growth rates of E. coli under a diverse set of media that are correlated with experimental measurements, markedly improving upon existing state-of-the art stoichiometric modeling approaches. These results support the view that a physiological bound on cellular enzyme concentrations is a key factor that determines microbial growth rate.