ATP-free biosynthesis of a high-energy phosphate metabolite fructose 1,6-diphosphate by in vitro metabolic engineering

Fructose 1,6-diphosphate (FDP) is a widely used medicine and is also a precursor of two important three-carbon phosphates – glyceraldehyde 3-phosphate (GA3P) and dihydroxyacetone phosphate (DHAP) for the biosynthesis of numerous fine chemicals. An in vitro synthetic cofactor-free enzymatic pathway comprised of four hyperthermophilic enzymes was designed to produce FDP from starch and pyrophosphate. All of four hyperthermophilic enzymes (i.e., alpha-glucan phosphorylase from Thermotaga maritima, phosphoglucomutase from Thermococcus kodakarensis, glucose 6-phosphate isomerase from Thermus thermophilus, and pyrophosphate phosphofructokinase from T. maritima) were overexpressed in E. coli BL21(DE3) and purified by simple heat precipitation. The optimal pH and temperature of one-pot biosynthesis were 7.2 and 70 °C, respectively. The optimal enzyme ratios of αGP, PGM, PGI and PFK were 2:2:1:2 in terms of units. Via step-wise addition of new substrates, up to 125 ± 4.6 mM FDP was synthesized after 7-h reaction. This de novo ATP-free enzymatic pathway comprised of all hyperthermophilic enzymes could drastically decrease the manufacturing costs of FDP and its derivatives GA3P and DHAP, better than those catalyzed by ATP-regeneration cascade biocatalysis, the use of mesophilic enzymes, whole cell lysates, and microbial cell factories.     

Protein design in systems metabolic engineering for industrial strain development

Accelerating the process of industrial bacterial host strain development, aimed at increasing productivity, generating new bio-products or utilizing alternative feedstocks, requires the integration of complementary approaches to manipulate cellular metabolism and regulatory networks. Systems metabolic engineering extends the concept of classical metabolic engineering to the systems level by incorporating the techniques used in systems biology and synthetic biology, and offers a framework for the development of the next generation of industrial strains. As one of the most useful tools of systems metabolic engineering, protein design allows us to design and optimize cellular metabolism at a molecular level. Here, we review the current strategies of protein design for engineering cellular synthetic pathways, metabolic control systems and signaling pathways, and highlight the challenges of this subfield within the context of systems metabolic engineering.     

Dynamic control in metabolic engineering: Theories,tools, and applications

Metabolic engineering has allowed the production of a diverse number of valuable chemicals using microbial organisms. Many biological challenges for improving bio-production exist which limit performance and slow the commercialization of metabolically engineered systems. Dynamic metabolic engineering is a rapidly developing field that seeks to address these challenges through the design of genetically encoded metabolic control systems which allow cells to autonomously adjust their flux in response to their external and internal metabolic state. This review first discusses theoretical works which provide mechanistic insights and design choices for dynamic control systems including two-stage, continuous, and population behavior control strategies. Next, we summarize molecular mechanisms for various sensors and actuators which enable dynamic metabolic control in microbial systems. Finally, important applications of dynamic control to the production of several metabolite products are highlighted, including fatty acids, aromatics, and terpene compounds. Altogether, this review provides a comprehensive overview of the progress, advances, and prospects in the design of dynamic control systems for improved titer, rate, and yield metrics in metabolic engineering.     

Quorum sensing-mediated protein degradation for dynamic metabolic pathway control in Saccharomyces cerevisiae

Dynamic regulation has been widely applied to optimize metabolic flux distribution. However, compared with prokaryotes, quorum sensing-mediated pathway control is still very limited in Saccharomyces cerevisiae. In this study, we designed quorum sensing-regulated protein degradation circuits for dynamic metabolic pathway control in S. cerevisiae. The synthetic quorum sensing circuits were developed by integration of a plant hormone cytokinin system with the endogenous yeast Ypd1-Skn7 signal transduction pathway and the positive feedback circuits were optimized by promoter engineering. We then constructed an auxin-inducible protein degradation system and used quorum sensing circuits to regulate auxin synthesis to achieve dynamic control of protein degradation. As a demonstration, the circuits were applied to control Erg9 degradation to produce α-farnesene and the titer of α-farnesene increased by 80%. The population-regulated protein degradation system developed here extends dynamic regulation to the protein level in S. cerevisiae and is a promising approach for metabolic pathway control.     

A guide to metabolic flux analysis in metabolic engineering: Methods,tools and applications

The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, “Metabolic fluxes and metabolic engineering” (Metabolic Engineering, 1: 1–11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. ¹³C-metabolic flux analysis (¹³C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.     

DFBAlab: a fast and reliable MATLAB code for dynamic flux balance analysis

Background

Dynamic Flux Balance Analysis (DFBA) is a dynamic simulation framework for biochemical processes. DFBA can be performed using different approaches such as static optimization (SOA), dynamic optimization (DOA), and direct approaches (DA). Few existing simulators address the theoretical and practical challenges of nonunique exchange fluxes or infeasible linear programs (LPs). Both are common sources of failure and inefficiencies for these simulators.

Results

DFBAlab, a MATLAB-based simulator that uses the LP feasibility problem to obtain an extended system and lexicographic optimization to yield unique exchange fluxes, is presented. DFBAlab is able to simulate complex dynamic cultures with multiple species rapidly and reliably, including differential-algebraic equation (DAE) systems. In addition, DFBAlab’s running time scales linearly with the number of species models. Three examples are presented where the performance of COBRA, DyMMM and DFBAlab are compared.

Conclusions

Lexicographic optimization is used to determine unique exchange fluxes which are necessary for a well-defined dynamic system. DFBAlab does not fail during numerical integration due to infeasible LPs. The extended system obtained through the LP feasibility problem in DFBAlab provides a penalty function that can be used in optimization algorithms.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0409-8) contains supplementary material, which is available to authorized users.     

Experimental determination of control of glycolysis in Lactococcus lactis

The understanding of control of metabolic processes requires quantitative studies of the importance of the different enzymatic steps for the magnitude of metabolic fluxes and metabolite concentrations. An important element in such studies is the modulation of enzyme activities in small steps above and below the wild-type level. We review a genetic approach that is well suited for both Metabolic Optimization and Metabolic Control Analysis and studies on the importance of a number of glycolytic enzymes for metabolic fluxes in Lactococcus lactis. The glycolytic enzymes phosphofructokinase (PFK), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), pyruvate kinase (PYK) and lactate dehydrogenase (LDH) are shown to have no significant control on the glycolytic flux in exponentially growing cells of L. lactis MG1363. Introduction of an uncoupled ATPase activity results in uncoupling of glycolysis from biomass production. With MG1363 growing in defined medium supplemented with glucose, the ATP demanding processes do not have a significant control on the glycolytic flux; it appears that glycolysis is running at maximal rate. It is likely that the flux control is distributed over many enzymes in L. lactis, but it cannot yet be excluded that one of the remaining glycolytic steps is a rate-limiting step for the glycolytic flux.     

Application of dynamic metabolic flux analysis for process modeling: Robust flux estimation with regularization,confidence bounds,and selection of elementary modes

In macroscopic dynamic models of fermentation processes, elementary modes (EM) derived from metabolic networks are often used to describe the reaction stoichiometry in a simplified manner and to build predictive models by parameterizing kinetic rate equations for the EM. In this procedure, the selection of a set of EM is a key step which is followed by an estimation of their reaction rates and of the associated confidence bounds. In this paper, we present a method for the computation of reaction rates of cellular reactions and EM as well as an algorithm for the selection of EM for process modeling. The method is based on the dynamic metabolic flux analysis (DMFA) proposed by Leighty and Antoniewicz (2011, Metab Eng, 13(6), 745–755) with additional constraints, regularization and analysis of uncertainty. Instead of using estimated uptake or secretion rates, concentration measurements are used directly to avoid an amplification of measurement errors by numerical differentiation. It is shown that the regularized DMFA for EM method is significantly more robust against measurement noise than methods using estimated rates. The confidence intervals for the estimated reaction rates are obtained by bootstrapping. For the selection of a set of EM for a given st oichiometric model, the DMFA for EM method is combined with a multiobjective genetic algorithm. The method is applied to real data from a CHO fed-batch process. From measurements of six fed-batch experiments, 10 EM were identified as the smallest subset of EM based upon which the data can be described sufficiently accurately by a dynamic model. The estimated EM reaction rates and their confidence intervals at different process conditions provide useful information for the kinetic modeling and subsequent process optimization.     

Regulation and control of compartmentalized glycolysis in bloodstream formTrypanosoma brucei

Unlike other eukaryotic cells, trypanosomes possess a compartmentalized glycolytic pathway. The conversion of glucose into 3-phosphoglycerate takes place in specialized peroxisomes, called glycosomes. Further conversion of this intermediate into pyruvate occurs in the cytosol. Due to this compartmentation, many regulatory mechanisms operating in other cell types cannot work in trypanosomes. This is reflected by the insensitivity of the glycosomal enzymes to compounds that act as activity regulators in other cell types. Several speculations have been raised about the function of compartmentation of glycolysis in trypanosomes. We calculate that even in a noncompartmentalized trypanosome the flux through glycolysis should not be limited by diffusion. Therefore, the sequestration of glycolytic enzymes in an organelle may not serve to overcome a diffusion limitation. We also search the available data for a possible relation between compartmentation and the distribution of control of the glycolytic flux among the glycolytic enzymes. Under physiological conditions, the rate of glycolytic ATP production in the bloodstream form of the parasite is possibly controlled by the oxygen tension, but not by the glucose concentration. Within the framework of Metabolic Control Analysis, we discuss evidence that glucose transport, although it does not qualify as the sole rate-limiting step, does have a high flux control coefficient. This, however, does not distinguish trypanosomes from other eukaryotic cell types without glycosomes.     

氨基酸生产的代谢工程研究进展与发展趋势

氨基酸发酵是我国发酵工业的支柱产业,近年来,随着代谢工程的快速发展,氨基酸的代谢工程育种蓬勃发展。传统的正向代谢工程、基于组学分析与计算机模拟的反向代谢工程以及借鉴自然进化的进化代谢工程,都有越来越多的应用。在氨基酸的工业生产中涌现出了一系列具有高效生产、抗逆性强等优良性状的菌株。日益剧烈的市场竞争对菌株的选育提出了新的要求,如开发高附加值氨基酸品种、菌株代谢的动态调控、适应新工艺的要求等。文中介绍了氨基酸生产相关的代谢工程研究进展以及未来的发展趋势。     

Quorum-sensing linked RNA interference for dynamic metabolic pathway control in Saccharomyces cerevisiae

Some of the most productive metabolic engineering strategies involve genetic modifications that cause severe metabolic burden on the host cell. Growth-limiting genetic modifications can be more effective if they are ‘switched on’ after a population growth phase has been completed. To address this problem we have engineered dynamic regulation using a previously developed synthetic quorum sensing circuit in Saccharomyces cerevisiae. The circuit autonomously triggers gene expression at a high population density, and was linked with an RNA interference module to enable target gene silencing. As a demonstration the circuit was used to control flux through the shikimate pathway for the production of para-hydroxybenzoic acid (PHBA). Dynamic RNA repression allowed gene knock-downs which were identified by elementary flux mode analysis as highly productive but with low biomass formation to be implemented after a population growth phase, resulting in the highest published PHBA titer in yeast (1.1 mM).     

Integrating cybernetic modeling with pathway analysis provides a dynamic, systems-level description of metabolic control

Cybernetic modeling strives to uncover the inbuilt regulatory programs of biological systems and leverage them toward computational prediction of metabolic dynamics. Because of its focus on incorporating the global aims of metabolism, cybernetic modeling provides a systems-oriented approach for describing regulatory inputs and inferring the impact of regulation within biochemical networks. Combining cybernetic control laws with concepts from metabolic pathway analysis has culminated in a systematic strategy for constructing cybernetic models, which was previously lacking. The newly devised framework relies upon the simultaneous application of local controls that maximize the net flux through each elementary flux mode and global controls that modulate the activities of these modes to optimize the overall nutritional state of the cell. The modeling concepts are illustrated using a simple linear pathway and a larger network representing anaerobic E. coli central metabolism. The E. coli model successfully describes the metabolic shift that occurs upon deleting the pta-ackA operon that is responsible for fermentative acetate production. The model also furnishes predictions that are consistent with experimental results obtained from additional knockout strains as well as strains expressing heterologous genes. Because of the stabilizing influence of the included control variables, the resulting cybernetic models are more robust and reliable than their predecessors in simulating the network response to imposed genetic and environmental perturbations.     

Identification of optimal measurement sets for complete flux elucidation in metabolic flux analysis experiments

Metabolic flux analysis (MFA) methods use external flux and isotopic measurements to quantify the magnitude of metabolic flows in metabolic networks. A key question in this analysis is choosing a set of measurements that is capable of yielding a unique flux distribution (identifiability). In this article, we introduce an optimization-based framework that uses incidence structure analysis to determine the smallest (or most cost-effective) set of measurements leading to complete flux elucidation. This approach relies on an integer linear programming formulation OptMeas that allows for the measurement of external fluxes and the complete (or partial) enumeration of the isotope forms of metabolites without requiring any of these to be chosen in advance. We subsequently query and refine the measurement sets suggested by OptMeas for identifiability and optimality. OptMeas is first tested on small to medium-size demonstration examples. It is subsequently applied to a large-scale E. coli isotopomer mapping model with more than 17,000 isotopomers. A number of additional measurements are identified leading to maximum flux elucidation in an amorphadiene producing E. coli strain.     

A feedback control system for the precise and continuous regulation of photosynthetic photon flux density

A simple and inexpensive feedback control system that provides continuous and precise control of photosynthetic photon flux density (PPFD) in a whole plant cuvette is described. A ‘Plexiglass’ tank is interposed between a light source and cuvette and PPFD changed by varying the level of dyed liquid in the tank. The amount of liquid pumped into or drained from the tank is a function of the difference (error) between a defined set point value of PPFD and that measured in the cuvette. The set point can be varied as a function of time, can follow the output of a quantum sensor measuring ambient PPFD or can be driven by values of PPFD read from a data file. Within the 0.4 to 0.64 μm waveband, the dye acts as a neutral density filter so that there is no change in spectral distribution with PPFD. Photosynthetic photon flux density in the cuvette was controlled to better than 20 μmol m⁻²s⁻¹ when the set point was varied from 200 to 1100 μmol m⁻²s⁻¹ over 3 min. When the set point was held constant or changed less rapidly, errors did not exceed 5 μmol m⁻²s⁻¹. Net photosynthesis of Western redcedar (Thuja plicata Donn.) seedlings held at 18 °C closely followed rapid changes in PPFD.     

Flux response of glycolysis and storage metabolism during rapid feast/famine conditions in Penicillium chrysogenum using dynamic 13C labeling

The scale‐up of fermentation processes frequently leads to a reduced productivity compared to small‐scale screening experiments. Large‐scale mixing limitations that lead to gradients in substrate and oxygen availability could influence the microorganism performance. Here, the impact of substrate gradients on a penicillin G producing Penicillium chrysogenum cultivation was analyzed using an intermittent glucose feeding regime. The intermittent feeding led to fluctuations in the extracellular glucose concentration between 400 μM down to 6.5 μM at the end of the cycle. The intracellular metabolite concentrations responded strongly and showed up to 100‐fold changes. The intracellular flux changes were estimated on the basis of dynamic ¹³C mass isotopomer measurements during three cycles of feast and famine using a novel hybrid modeling approach. The flux estimations indicated a high turnover of internal and external storage metabolites in P. chrysogenum under feast/famine conditions. The synthesis and degradation of storage requires cellular energy (ATP and UTP) in competition with other cellular functions including product formation. Especially, 38% of the incoming glucose was recycled once in storage metabolism. This result indicated that storage turnover is increased under dynamic cultivation conditions and contributes to the observed decrease in productivity compared to reference steady‐state conditions.     

Beyond topology: coevolution of structure and flux in metabolic networks

Interactions between the structure of a metabolic network and its functional properties underlie its evolutionary diversification, but the mechanism by which such interactions arise remains elusive. Particularly unclear is whether metabolic fluxes that determine the concentrations of compounds produced by a metabolic network, are causally linked to a network's structure or emerge independently of it. A direct empirical study of populations where both structural and functional properties vary among individuals’ metabolic networks is required to establish whether changes in structure affect the distribution of metabolic flux. In a population of house finches (Haemorhous mexicanus), we reconstructed full carotenoid metabolic networks for 442 individuals and uncovered 11 structural variants of this network with different compounds and reactions. We examined the consequences of this structural diversity for the concentrations of plumage‐bound carotenoids produced by flux in these networks. We found that concentrations of metabolically derived, but not dietary carotenoids, depended on network structure. Flux was partitioned similarly among compounds in individuals of the same network structure: within each network, compound concentrations were closely correlated. The highest among‐individual variation in flux occurred in networks with the strongest among‐compound correlations, suggesting that changes in the magnitude, but not the distribution of flux, underlie individual differences in compound concentrations on a static network structure. These findings indicate that the distribution of flux in carotenoid metabolism closely follows network structure. Thus, evolutionary diversification and local adaptations in carotenoid metabolism may depend more on the gain or loss of enzymatic reactions than on changes in flux within a network structure.     

INCA 2.0: A tool for integrated,dynamic modeling of NMR- and MS-based isotopomer measurements and rigorous metabolic flux analysis

Metabolic flux analysis (MFA) combines experimental measurements and computational modeling to determine biochemical reaction rates in live biological systems. Advancements in analytical instrumentation, such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), have facilitated chemical separation and quantification of isotopically enriched metabolites. However, no software packages have been previously described that can integrate isotopomer measurements from both MS and NMR analytical platforms and have the flexibility to estimate metabolic fluxes from either isotopic steady-state or dynamic labeling experiments. By applying physiologically relevant cardiac and hepatic metabolic models to assess NMR isotopomer measurements, we herein test and validate new modeling capabilities of our enhanced flux analysis software tool, INCA 2.0. We demonstrate that INCA 2.0 can simulate and regress steady-state ¹³C NMR datasets from perfused hearts with an accuracy comparable to other established flux assessment tools. Furthermore, by simulating the infusion of three different ¹³C acetate tracers, we show that MFA based on dynamic ¹³C NMR measurements can more precisely resolve cardiac fluxes compared to isotopically steady-state flux analysis. Finally, we show that estimation of hepatic fluxes using combined ¹³C NMR and MS datasets improves the precision of estimated fluxes by up to 50%. Overall, our results illustrate how the recently added NMR data modeling capabilities of INCA 2.0 can enable entirely new experimental designs that lead to improved flux resolution and can be applied to a wide range of biological systems and measurement time courses.     

Mass flux balance-based model and metabolic pathway engineering analysis for serine alkaline protease synthesis by Bacillus licheniformis

A mass flux balance-based stoichiometric model of Bacillus licheniformis for the serine alkaline protease (SAP) fermentation process has been established. The model considers 147 reaction fluxes, and there are 105 metabolites that are assumed to be in pseudo-steady state. Metabolic flux distributions were obtained from the solution of the model based on the minimum SAP accumulation rate assumption in B. licheniformis in combination with the off-line extracellular analyses of the metabolites that were the sole carbon source citrate, dry cell, organic acids, amino acids, and SAP; variations in the intracellular fluxes were demonstrated for the three periods of the batch bioprocess. The flux distribution maps showed that the cells completed the TCA cycle and utilized the gluconeogenesis pathway, pentose phosphate pathway, and anaplerotic reactions throughout the fermentation; however, the glycolysis pathway was inactive in all the periods of the fermentation. The flux values toward SAP increased throughout the bioprocess and slightly decreased in the last period; however, SAP selectivity values were almost the same in Periods II and III and higher than Period I. The diversions in the pathways and certain metabolic reactions depending on the bioprocess periods are also presented and the results indicated that the intracellular amino acid fluxes played an important role in the SAP fermentation process.