The importance and challenges of in vivo-like enzyme kinetics

Combining systems and synthetic biology for in vivo enzymology

Enzymatic parameters are classically determined in vitro, under conditions that are far from those encountered in cells, casting doubt on their physiological relevance. We developed a generic approach combining tools from synthetic and systems biology to measure enzymatic parameters in vivo. In the context of a synthetic carotenoid pathway in Saccharomyces cerevisiae, we focused on a phytoene synthase and three phytoene desaturases, which are difficult to study in vitro. We designed, built, and analyzed a collection of yeast strains mimicking substantial variations in substrate concentration by strategically manipulating the expression of geranyl-geranyl pyrophosphate (GGPP) synthase. We successfully determined in vivo Michaelis-Menten parameters (KM, Vmax, and kcat) for GGPP-converting phytoene synthase from absolute metabolomics, fluxomics and proteomics data, highlighting differences between in vivo and in vitro parameters. Leveraging the versatility of the same set of strains, we then extracted enzymatic parameters for two of the three phytoene desaturases. Our approach demonstrates the feasibility of assessing enzymatic parameters directly in vivo, providing a novel perspective on the kinetic characteristics of enzymes in real cellular conditions.

Non-consecutive enzyme interactions within TCA cycle supramolecular assembly regulate carbon-nitrogen metabolism

Enzymes of the central metabolism tend to assemble into transient supramolecular complexes. However, the functional significance of the interactions, particularly between enzymes catalyzing non-consecutive reactions, remains unclear. Here, by co-localizing two non-consecutive enzymes of the TCA cycle from Bacillus subtilis, malate dehydrogenase (MDH) and isocitrate dehydrogenase (ICD), in phase separated droplets we show that MDH-ICD interaction leads to enzyme agglomeration with a concomitant enhancement of ICD catalytic rate and an apparent sequestration of its reaction product, 2-oxoglutarate. Theory demonstrates that MDH-mediated clustering of ICD molecules explains the observed phenomena. In vivo analyses reveal that MDH overexpression leads to accumulation of 2-oxoglutarate and reduction of fluxes flowing through both the catabolic and anabolic branches of the carbon-nitrogen intersection occupied by 2-oxoglutarate, resulting in impeded ammonium assimilation and reduced biomass production. Our findings suggest that the MDH-ICD interaction is an important coordinator of carbon-nitrogen metabolism.

In Situ UV-Vis-NIR Absorption Spectroscopy and Catalysis

This review highlights in situ UV-vis-NIR range absorption spectroscopy in catalysis. A variety of experimental techniques identifying reaction mechanisms, kinetics, and structural properties are discussed. Stopped flow techniques, use of laser pulses, and use of experimental perturbations are demonstrated for in situ studies of enzymatic, homogeneous, heterogeneous, and photocatalysis. They access different time scales and are applicable to different reaction systems and catalyst types. In photocatalysis, femto- and nanosecond resolved measurements through transient absorption are discussed for tracking excited states. UV-vis-NIR absorption spectroscopies for structural characterization are demonstrated especially for Cu and Fe exchanged zeolites and metalloenzymes. This requires combining different spectroscopies. Combining magnetic circular dichroism and resonance Raman spectroscopy is especially powerful. A multitude of phenomena can be tracked on transition metal catalysts on various supports, including changes in oxidation state, adsorptions, reactions, support interactions, surface plasmon resonances, and band gaps. Measurements of oxidation states, oxygen vacancies, and band gaps are shown on heterogeneous catalysts, especially for electrocatalysis. UV-vis-NIR absorption is burdened by broad absorption bands. Advanced analysis techniques enable the tracking of coking reactions on acid zeolites despite convoluted spectra. The value of UV-vis-NIR absorption spectroscopy to catalyst characterization and mechanistic investigation is clear but could be expanded.

Sensitivity analysis and adaptive mutation strategy differential evolution algorithm for optimizing enzymes' turnover numbers in metabolic models

Genome-scale metabolic network model (GSMM) based on enzyme constraints greatly improves general metabolic models. The turnover number ( k cat ${k}_{\mathrm{cat}}$ ) of enzymes is used as a parameter to limit the reaction when extending GSMM. Therefore, turnover number plays a crucial role in the prediction accuracy of cell metabolism. In this work, we proposed an enzyme-constrained GSMM parameter optimization method. First, sensitivity analysis of the parameters was carried out to select the parameters with the greatest influence on predicting the specific growth rate. Then, differential evolution (DE) algorithm with adaptive mutation strategy was adopted to optimize the parameters. This algorithm can dynamically select five different mutation strategies. Finally, the specific growth rate prediction, flux variability, and phase plane of the optimized model were analyzed to further evaluate the model. The enzyme-constrained GSMM of Saccharomyces cerevisiae, ecYeast8.3.4, was optimized. Results of the sensitivity analysis showed that the optimization variables can be divided into three groups based on sensitivity: most sensitive (149 k cat ${k}_{\mathrm{cat}}$ c), highly sensitive (1759 k cat ${k}_{\mathrm{cat}}$ ), and nonsensitive (2502 k cat ${k}_{\mathrm{cat}}$ ) groups. Six optimization strategies were developed based on the results of the sensitivity analysis. The results showed that the DE with adaptive mutation strategy can indeed improve the model by optimizing highly sensitive parameters. Retaining all parameters and optimizing the highly sensitive parameters are the recommended optimization strategy.

Data integration across conditions improves turnover number estimates and metabolic predictions

Turnover numbers characterize a key property of enzymes, and their usage in constraint-based metabolic modeling is expected to increase the prediction accuracy of diverse cellular phenotypes. In vivo turnover numbers can be obtained by integrating reaction rate and enzyme abundance measurements from individual experiments. Yet, their contribution to improving predictions of condition-specific cellular phenotypes remains elusive. Here, we show that available in vitro and in vivo turnover numbers lead to poor prediction of condition-specific growth rates with protein-constrained models of Escherichia coli and Saccharomyces cerevisiae, particularly when protein abundances are considered. We demonstrate that correction of turnover numbers by simultaneous consideration of proteomics and physiological data leads to improved predictions of condition-specific growth rates. Moreover, the obtained estimates are more precise than corresponding in vitro turnover numbers. Therefore, our approach provides the means to correct turnover numbers and paves the way towards cataloguing kcatomes of other organisms.

Single-molecule analysis of actin filament debranching by cofilin and GMF

Eukaryotic cells contain branched actin networks that are essential for endocytosis, motility, and other key cellular processes. These networks, which are formed by filamentous actin and the Arp2/3 complex, must subsequently be debranched to allow network remodeling and to recycle the Arp2/3 complex. Debranching appears to be catalyzed by two different members of the actin depolymerizing factor homology protein family: cofilin and glial maturation factor (GMF). However, their mechanisms of debranching are only partially understood. Here, we used single-molecule fluorescence imaging of Arp2/3 complex and actin filaments under physiological ionic conditions to observe debranching by GMF and cofilin. We demonstrate that cofilin, like GMF, is an authentic debrancher independent of its filament-severing activity and that the debranching activities of the two proteins are additive. While GMF binds directly to the Arp2/3 complex, cofilin selectively accumulates on branch-junction daughter filaments in tropomyosin-decorated networks just prior to debranching events. Quantitative comparison of debranching rates with the known kinetics of cofilin-actin binding suggests that cofilin occupancy of a particular single actin site at the branch junction is sufficient to trigger debranching. In rare cases in which the order of departure could be resolved during GMF- or cofilin-induced debranching, the Arp2/3 complex left the branch junction bound to the pointed end of the daughter filament, suggesting that both GMF and cofilin can work by destabilizing the mother filament-Arp2/3 complex interface. Taken together, these observations suggest that GMF and cofilin promote debranching by distinct yet complementary mechanisms.

Inverting glucuronidation of hymecromone in situ by catalytic nanocompartments

Glucuronidation is a metabolic pathway that inactivates many drugs including hymecromone. Adverse effects of glucuronide metabolites include a reduction of half-life circulation times and rapid elimination from the body. Herein, we developed synthetic catalytic nanocompartments able to cleave the glucuronide moiety from the metabolized form of hymecromone in order to convert it to the active drug. By shielding enzymes from their surroundings, catalytic nanocompartments favor prolonged activity and lower immunogenicity as key aspects to improve the therapeutic solution. The catalytic nanocompartments (CNCs) consist of self-assembled poly(dimethylsiloxane)-block-poly(2-methyl-2-oxazoline) diblock copolymer polymersomes encapsulating β-glucuronidase. Insertion of melittin in the synthetic membrane of these polymersomes provided pores for the diffusion of the hydrophilic hymecromone-glucuronide conjugate to the compartment inside where the encapsulated β-glucuronidase catalyzed its conversion to hymecromone. Our system successfully produced hymecromone from its glucuronide conjugate in both phosphate buffered solution and cell culture medium. CNCs were non-cytotoxic when incubated with HepG2 cells. After being taken up by cells, CNCs produced the drug in situ over 24 hours. Such catalytic platforms, which locally revert a drug metabolite into its active form, open new avenues in the design of therapeutics that aim at prolonging the residence time of a drug.

Modeling and Simulation of Batch Sugarcane Alcoholic Fermentation Using the Metabolic Model

The present work sought to implement a model different from the more traditional ones for the fermentation process of ethanol production by the action of the fungus Saccharomyces cerevisiae, using a relevant metabolic network based on the glycolytic Embden–Meyerhof–Parnas route, also called “EMP”. We developed two models to represent this phenomenon. In the first model, we used the simple and unbranched EMP route, with a constant concentration of microorganisms throughout the process and glucose as the whole substrate. We called this first model “SR”, regarding the Portuguese name “sem ramificações”, which means “no branches”. We developed the second model by simply adding some branches to the SR model. We called this model “CR”, regarding the Portuguese name “com ramificações”, which means “with branches”. Both models were implemented in MATLABTM software considering a constant temperature equal to 32 °C, similar to that practiced in sugar and ethanol plants, and a wide range of substrate concentrations, ranging from 30 to 100 g/L, and all the enzymes necessary for fermentation were already expressed in the cells so all the enzymes showed a constant concentration throughout the fermentation. The addition of common branches to the EMP route resulted in a considerable improvement in the results, especially predicting ethanol production closer to what we saw experimentally. Therefore, the results obtained are promising, making adjustments consistent with experimental data, meaning that all the models proposed here are a good basis for the development of future metabolic models of discontinuous fermentative processes.

Parameter Reliability and Understanding Enzyme Function

Knowledge of the Michaelis-Menten parameters and their meaning in different circumstances is an essential prerequisite to understanding enzyme function and behaviour. The published literature contains an abundance of values reported for many enzymes. The problem concerns assessing the appropriateness and validity of such material for the purpose to which it is to be applied. This review considers the evaluation of such data with particular emphasis on the assessment of its fitness for purpose.

Maximization of non-idle enzymes improves the coverage of the estimated maximal in vivo enzyme catalytic rates in Escherichia coli

MOTIVATION

Constraint-based modeling approaches allow the estimation of maximal in vivo enzyme catalytic rates that can serve as proxies for enzyme turnover numbers. Yet, genome-scale flux profiling remains a challenge in deploying these approaches to catalogue proxies for enzyme catalytic rates across organisms.

RESULTS

Here we formulate a constraint-based approach, termed NIDLE-flux, to estimate fluxes at a genome-scale level by using the principle of efficient usage of expressed enzymes. Using proteomics data from Escherichia coli, we show that the fluxes estimated by NIDLE-flux and the existing approaches are in excellent qualitative agreement (Pearson correlation > 0.9). We also find that the maximal in vivo catalytic rates estimated by NIDLE-flux exhibits a Pearson correlation of 0.74 with in vitro enzyme turnover numbers. However, NIDLE-flux results in a 1.4-fold increase in the size of the estimated maximal in vivo catalytic rates in comparison to the contenders. Integration of the maximum in vivo catalytic rates with publically available proteomics and metabolomics data provide a better match to fluxes estimated by NIDLE-flux. Therefore, NIDLE-flux facilitates more effective usage of proteomics data to estimate proxies for kcatomes.

AVAILABILITY

https://github.com/Rudan-X/NIDLE-flux-code.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metabolic response to point mutations reveals principles of modulation of in vivo enzyme activity and phenotype

The relationship between sequence variation and phenotype is poorly understood. Here, we use metabolomic analysis to elucidate the molecular mechanism underlying the filamentous phenotype of E. coli strains that carry destabilizing mutations in dihydrofolate reductase (DHFR). We find that partial loss of DHFR activity causes reversible filamentation despite SOS response indicative of DNA damage, in contrast to thymineless death (TLD) achieved by complete inhibition of DHFR activity by high concentrations of antibiotic trimethoprim. This phenotype is triggered by a disproportionate drop in intracellular dTTP, which could not be explained by drop in dTMP based on the Michaelis-Menten-like in vitro activity curve of thymidylate kinase (Tmk), a downstream enzyme that phosphorylates dTMP to dTDP. Instead, we show that a highly cooperative (Hill coefficient 2.5) in vivo activity of Tmk is the cause of suboptimal dTTP levels. dTMP supplementation rescues filamentation and restores in vivo Tmk kinetics to Michaelis-Menten. Overall, this study highlights the important role of cellular environment in sculpting enzymatic kinetics with system-level implications for bacterial phenotype.

Characterization of different biocatalyst formats for BVMO-catalyzed cyclohexanone oxidation

Cyclohexanone monooxygenase (CHMO), a member of the Baeyer-Villiger monooxygenase family, is a versatile biocatalyst that efficiently catalyzes the conversion of cyclic ketones to lactones. In this study, an Acidovorax-derived CHMO gene was expressed in Pseudomonas taiwanensis VLB120. Upon purification, the enzyme was characterized in vitro and shown to feature a broad substrate spectrum and up to 100% conversion in 6 h. Furthermore, we determined and compared the cyclohexanone conversion kinetics for different CHMO-biocatalyst formats, that is, isolated enzyme, suspended whole cells, and biofilms, the latter two based on recombinant CHMO-containing P. taiwanensis VLB120. Biofilms showed less favorable values for K_S (9.3-fold higher) and k_cat (4.8-fold lower) compared with corresponding K_M and k_cat values of isolated CHMO, but a favorable K_I for cyclohexanone (5.3-fold higher). The unfavorable K_S and k_cat values are related to mass transfer- and possibly heterogeneity issues and deserve further investigation and engineering, to exploit the high potential of biofilms regarding process stability. Suspended cells showed only 1.8-fold higher K_S , but 1.3- and 4.2-fold higher k_cat and K_I values than isolated CHMO. This together with the efficient NADPH regeneration via glucose metabolism makes this format highly promising from a kinetics perspective.

Modeling Microtubule Counterion Distributions and Conductivity Using the Poisson-Boltzmann Equation

Microtubules are highly negatively charged proteins which have been shown to behave as bio-nanowires capable of conducting ionic currents. The electrical characteristics of microtubules are highly complicated and have been the subject of previous work; however, the impact of the ionic concentration of the buffer solution on microtubule electrical properties has often been overlooked. In this work we use the non-linear Poisson Boltzmann equation, modified to account for a variable permittivity and a Stern Layer, to calculate counterion concentration profiles as a function of the ionic concentration of the buffer. We find that for low-concentration buffers ([KCl] from 10 μM to 10 mM) the counterion concentration is largely independent of the buffer's ionic concentration, but for physiological-concentration buffers ([KCl] from 100 to 500 mM) the counterion concentration varies dramatically with changes in the buffer's ionic concentration. We then calculate the conductivity of microtubule-counterion complexes, which are found to be more conductive than the buffer when the buffer's ionic concentrations is less than ≈100 mM and less conductive otherwise. These results demonstrate the importance of accounting for the ionic concentration of the buffer when analyzing microtubule electrical properties both under laboratory and physiological conditions. We conclude by calculating the basic electrical parameters of microtubules over a range of ionic buffer concentrations applicable to nanodevice and medical applications.

Thermodynamics and Kinetics of Glycolytic Reactions. Part I: Kinetic Modeling Based on Irreversible Thermodynamics and Validation by Calorimetry

In systems biology, material balances, kinetic models, and thermodynamic boundary conditions are increasingly used for metabolic network analysis. It is remarkable that the reversibility of enzyme-catalyzed reactions and the influence of cytosolic conditions are often neglected in kinetic models. In fact, enzyme-catalyzed reactions in numerous metabolic pathways such as in glycolysis are often reversible, i.e., they only proceed until an equilibrium state is reached and not until the substrate is completely consumed. Here, we propose the use of irreversible thermodynamics to describe the kinetic approximation to the equilibrium state in a consistent way with very few adjustable parameters. Using a flux-force approach allowed describing the influence of cytosolic conditions on the kinetics by only one single parameter. The approach was applied to reaction steps 2 and 9 of glycolysis (i.e., the phosphoglucose isomerase reaction from glucose 6-phosphate to fructose 6-phosphate and the enolase-catalyzed reaction from 2-phosphoglycerate to phosphoenolpyruvate and water). The temperature dependence of the kinetic parameter fulfills the Arrhenius relation and the derived activation energies are plausible. All the data obtained in this work were measured efficiently and accurately by means of isothermal titration calorimetry (ITC). The combination of calorimetric monitoring with simple flux-force relations has the potential for adequate consideration of cytosolic conditions in a simple manner.

Characterization of maximal enzyme catalytic rates in central metabolism of Arabidopsis thaliana

Availability of plant-specific enzyme kinetic data is scarce, limiting the predictive power of metabolic models and precluding identification of genetic factors of enzyme properties. Enzyme kinetic data are measured in vitro, often under non-physiological conditions, and conclusions elicited from modeling warrant caution. Here we estimate maximal in vivo catalytic rates for 168 plant enzymes, including photosystems I and II, cytochrome-b6f complex, ATP-citrate synthase, sucrose-phosphate synthase as well as enzymes from amino acid synthesis with previously undocumented enzyme kinetic data in BRENDA. The estimations are obtained by integrating condition-specific quantitative proteomics data, maximal rates of selected enzymes, growth measurements from Arabidopsis thaliana rosette with and fluxes through canonical pathways in a constraint-based model of leaf metabolism. In comparison to findings in Escherichia coli, we demonstrate weaker concordance between the plant-specific in vitro and in vivo enzyme catalytic rates due to a low degree of enzyme saturation. This is supported by the finding that concentrations of nicotinamide adenine dinucleotide (phosphate), adenosine triphosphate and uridine triphosphate, calculated based on our maximal in vivo catalytic rates, and available quantitative metabolomics data are below reported KM values and, therefore, indicate undersaturation of respective enzymes. Our findings show that genome-wide profiling of enzyme kinetic properties is feasible in plants, paving the way for understanding resource allocation.

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Enzyme turnover numbers (k _cats) are essential for a quantitative understanding of cells. Because k _cats are traditionally measured in low-throughput assays, they can be inconsistent, labor-intensive to obtain, and can miss in vivo effects. We use a data-driven approach to estimate in vivo k _cats using metabolic specialist Escherichia coli strains that resulted from gene knockouts in central metabolism followed by metabolic optimization via laboratory evolution. By combining absolute proteomics with fluxomics data, we find that in vivo k _cats are robust against genetic perturbations, suggesting that metabolic adaptation to gene loss is mostly achieved through other mechanisms, like gene-regulatory changes. Combining machine learning and genome-scale metabolic models, we show that the obtained in vivo k _cats predict unseen proteomics data with much higher precision than in vitro k _cats. The results demonstrate that in vivo k _cats can solve the problem of inconsistent and low-coverage parameterizations of genome-scale cellular models.

An "energy-auxotroph" Escherichia coli provides an in vivo platform for assessing NADH regeneration systems

An efficient in vivo regeneration of the primary cellular resources NADH and ATP is vital for optimizing the production of value-added chemicals and enabling the activity of synthetic pathways. Currently, such regeneration routes are tested and characterized mainly in vitro before being introduced into the cell. However, in vitro measurements could be misleading as they do not reflect enzyme activity under physiological conditions. Here, we construct an in vivo platform to test and compare NADH regeneration systems. By deleting dihydrolipoyl dehydrogenase in Escherichia coli, we abolish the activity of pyruvate dehydrogenase and 2-ketoglutarate dehydrogenase. When cultivated on acetate, the resulting strain is auxotrophic to NADH and ATP: acetate can be assimilated via the glyoxylate shunt but cannot be oxidized to provide the cell with reducing power and energy. This strain can, therefore, serve to select for and test different NADH regeneration routes. We exemplify this by comparing several NAD-dependent formate dehydrogenases and methanol dehydrogenases. We identify the most efficient enzyme variants under in vivo conditions and pinpoint optimal feedstock concentrations that maximize NADH biosynthesis while avoiding cellular toxicity. Our strain thus provides a useful platform for comparing and optimizing enzymatic systems for cofactor regeneration under physiological conditions.

Biochemical characterization of Ty1 retrotransposon protease

Ty1 is one of the many transposons in the budding yeast Saccharomyces cerevisiae. The life-cycle of Ty1 shows numerous similarities with that of retroviruses, e.g. the initially synthesized polyprotein precursor undergoes proteolytic processing by the protease. The retroviral proteases have become important targets of current antiretroviral therapies due to the critical role of the limited proteolysis of Gag-Pol polyprotein in the replication cycle and they therefore belong to the most well-studied enzymes. Comparative analyses of retroviral and retroviral-like proteases can help to explore the key similarities and differences which may help understanding how resistance is developed against protease inhibitors, but the available information about the structural and biochemical characteristics of retroviral-like, and especially retrotransposon, proteases is limited. To investigate the main characteristics of Ty1 retrotransposon protease of Saccharomyces cerevisiae, untagged and His₆-tagged forms of Ty1 protease were expressed in E. coli. After purification of the recombinant proteins, activity measurements were performed using synthetic oligopeptide and fluorescent recombinant protein substrates, which represented the wild-type and the modified forms of naturally occurring cleavage sites of the protease. We investigated the dependence of enzyme activity on different reaction conditions (pH, temperature, ionic strength, and urea concentration), and determined enzyme kinetic parameters for the studied substrates. Inhibitory potentials of 10 different protease inhibitors were also tested. Ty1 protease was not inhibited by the inhibitors which have been designed against human immunodeficiency virus type 1 protease and are approved as antiretroviral therapeutics. A quaternary structure of homodimeric Ty1 protease was proposed based on homology modeling, and this structure was used to support interpretation of experimental results and to correlate some structural and biochemical characteristics with that of other retroviral proteases.

Laccase: addressing the ambivalence associated with the calculation of enzyme activity

Laccase (benzenediol: oxygen oxidoreductase) a unique multi-copper oxidase enzyme has been studied rigorously since its identification. However, there is ambivalence associated with various aspects of laccase, e.g., assay conditions and calculations. Our aim was to minimize its ambivalence, thus, total of five formulas (F1-F5) were used to determine laccase activity of white and blue laccase. In case of enzymatic profiling of blue laccase, its activity ranged from 0.04 to 464.3 U L^-1 whereas in case of white laccase it ranged from 0.05 to 1404.7 U L^-1. The affinity of laccase at various enzyme concentration (0.3-0.9 mg mL^-1), time (5 and 10 min) along with various substrates, i.e., 2, 2-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), guaiacol (GCL), 2,6-dimethoxyphenol (DMP) and syringaldazine (SYZ), and its concentration (ABTS 0.5-1.5 mM, GCL 20-30 mM, DMP 1-5 mM, SYZ 10-30 mM) were inferred. The optimal substrate concentrations were 1.5 and 0.5 mM ABTS for blue and white laccase, respectively, with 30 mM GCL and 2 mM DMP being the common parameter. The optimal substrate concentrations were 0.5 mM ABTS, 20 mM GCL, 1 mM DMP and 30 mM SYZ for commercial laccase. It was observed that the optimal protein load and reaction time was 0.3 mg mL^-1 and 5 min in all the cases, however, in case of white laccase it was 0.6 mg mL^-1 at 10 min for DMP and in case of commercial laccase it was 0.9 mg mL^-1 and 5 min for SYZ. In the present study, F1 was most appropriate among the five formula used as it incorporates all the significant factors and use of single formula will help reduce future ambiguity.

Stepwise engineering of Saccharomyces cerevisiae to produce (+)-valencene and its related sesquiterpenes

(+)-Valencene and (+)-nootkatone are high value-added sesquiterpenoids found in grapefruit. The synthesis of (+)-nootkatone by chemical oxidation from (+)-valencene cannot meet the increasing demand in natural aromatics markets. Development of a viable bioprocess using microorganisms is attractive. According to the yields of β-nootkatol and (+)-nootkatone by strains harboring different expression cassettes in the resting cell assay, premnaspirodiene oxygenase from Hyoscyamus muticus (HPO), cytochrome P450 reductase from Arabidopsis thaliana (AtCPR) and alcohol dehydrogenase (ADH1) from Saccharomyces cerevisiae were finally selected and overexpressed in CEN·PK2-1Ca, yielding β-nootkatol and (+)-nootkatone with 170.5 and 45.6 mg L-1 ethyl acetate, respectively. A combinational engineering strategy including promoter change, regulator ROX1 knockout, squalene pathway inhibition, and tHMGR overexpression was performed to achieve de novo (+)-valencene production. Subsequent culture investigations found that galactose as the induced carbon source and a lower temperature (25 °C) were beneficial to target accumulation. Also, replacing the inducible promoters (GAL1) of HPO and AtCPR with constitutive promoters (HXT7 and CYC1) dramatically increased the β-nootkatol accumulation from 108.2 to 327.8 mg L-1 ethyl acetate in resting-cell experiments using (+)-valencene as a substrate. Finally, the total terpenoid titer of the engineered strain of PK2-25 using glucose as a carbon source was improved to 157.8 mg L-1 cell culture, which was 56 times the initial value. We present a new candidate for production of (+)-valencene and its related sesquiterpenoids with attraction for industry.

Improving formaldehyde consumption drives methanol assimilation in engineered E. coli

Due to volatile sugar prices, the food vs fuel debate, and recent increases in the supply of natural gas, methanol has emerged as a promising feedstock for the bio-based economy. However, attempts to engineer Escherichia coli to metabolize methanol have achieved limited success. Here, we provide a rigorous systematic analysis of several potential pathway bottlenecks. We show that regeneration of ribulose 5-phosphate in E. coli is insufficient to sustain methanol assimilation, and overcome this by activating the sedoheptulose bisphosphatase variant of the ribulose monophosphate pathway. By leveraging the kinetic isotope effect associated with deuterated methanol as a chemical probe, we further demonstrate that under these conditions overall pathway flux is kinetically limited by methanol dehydrogenase. Finally, we identify NADH as a potent kinetic inhibitor of this enzyme. These results provide direction for future engineering strategies to improve methanol utilization, and underscore the value of chemical biology methodologies in metabolic engineering.

Designing artificial metabolic pathways, construction of target enzymes, and analysis of their function

Artificial design of metabolic pathways is essential for the production of useful compounds using microbes. Based on this design, heterogeneous genes are introduced into the host, and then various analysis and evaluation methods are conducted to ensure that the target enzyme reactions are functionalized within the cell. In this chapter, we list successful examples of useful compounds produced by designing artificial metabolic pathways, and describe the methods involved in analyzing, evaluating, and optimizing the target enzyme reaction.

Mathematical models of lignin biosynthesis

BACKGROUND

Lignin is a natural polymer that is interwoven with cellulose and hemicellulose within plant cell walls. Due to this molecular arrangement, lignin is a major contributor to the recalcitrance of plant materials with respect to the extraction of sugars and their fermentation into ethanol, butanol, and other potential bioenergy crops. The lignin biosynthetic pathway is similar, but not identical in different plant species. It is in each case comprised of a moderate number of enzymatic steps, but its responses to manipulations, such as gene knock-downs, are complicated by the fact that several of the key enzymes are involved in several reaction steps. This feature poses a challenge to bioenergy production, as it renders it difficult to select the most promising combinations of genetic manipulations for the optimization of lignin composition and amount.

RESULTS

Here, we present several computational models than can aid in the analysis of data characterizing lignin biosynthesis. While minimizing technical details, we focus on the questions of what types of data are particularly useful for modeling and what genuine benefits the biofuel researcher may gain from the resulting models. We demonstrate our analysis with mathematical models for black cottonwood (Populus trichocarpa), alfalfa (Medicago truncatula), switchgrass (Panicum virgatum) and the grass Brachypodium distachyon.

CONCLUSIONS

Despite commonality in pathway structure, different plant species show different regulatory features and distinct spatial and topological characteristics. The putative lignin biosynthes pathway is not able to explain the plant specific laboratory data, and the necessity of plant specific modeling should be heeded.

Administration of exercise-conditioned plasma alters muscle catalase kinetics in rat: An argument for in vivo-like Km instead of in vitro-like Vmax

Maximal velocity (V_max) is a well established biomarker for the assessment of tissue redox status. There is scarce evidence, though, that it does not probably reflect sufficiently in vivo tissue redox profile. Instead, the Michaelis constant (K_m) could more adequately image tissue oxidative stress and, thus, be a more physiologically relevant redox biomarker. Therefore, the aim of the present study was to side-by-side compare V_max and K_m of an antioxidant enzyme after implementing an in vivo set up that induces alterations in tissue redox status. Forty rats were divided into two groups including rats injected with blood plasma originating from rats that had previously swam until exhaustion and rats injected with blood plasma originating from sedentary rats. Tail-vein injections were performed daily for 21 days. Catalase V_max and K_m measured in gastrocnemius muscle were increased after administration of the exercise-conditioned plasma, denoting enhancement of the enzyme activity but impairment of its affinity for the substrate, respectively. These alterations are potential adaptations stimulated by the administered plasma pointing out that blood is an active fluid capable of regulating tissue homeostasis. Our findings suggest that K_m adequately reflects in vivo modifications of skeletal muscle catalase and seems to surpass V_max regarding its physiological relevance and biological interpretation. In conclusion, K_m can be regarded as an in vivo-like biomarker that satisfactorily images the intracellular environment, as compared to V_max that could be aptly parallelized with a biomarker that describes tissue oxidative stress in an in vitro manner.

Beyond the Michaelis-Menten equation: Accurate and efficient estimation of enzyme kinetic parameters

Examining enzyme kinetics is critical for understanding cellular systems and for using enzymes in industry. The Michaelis-Menten equation has been widely used for over a century to estimate the enzyme kinetic parameters from reaction progress curves of substrates, which is known as the progress curve assay. However, this canonical approach works in limited conditions, such as when there is a large excess of substrate over enzyme. Even when this condition is satisfied, the identifiability of parameters is not always guaranteed, and often not verifiable in practice. To overcome such limitations of the canonical approach for the progress curve assay, here we propose a Bayesian approach based on an equation derived with the total quasi-steady-state approximation. In contrast to the canonical approach, estimates obtained with this proposed approach exhibit little bias for any combination of enzyme and substrate concentrations. Importantly, unlike the canonical approach, an optimal experiment to identify parameters with certainty can be easily designed without any prior information. Indeed, with this proposed design, the kinetic parameters of diverse enzymes with disparate catalytic efficiencies, such as chymotrypsin, fumarase, and urease, can be accurately and precisely estimated from a minimal amount of timecourse data. A publicly accessible computational package performing such accurate and efficient Bayesian inference for enzyme kinetics is provided.

Effect of pH and Temperature on <I>Bacillus subtilis</I> FNCC 0059 Oxalate Decarboxylase Activity

BACKGROUND AND OBJECTIVE

Bacillus subtilis is a bacterium that can produce the oxalate decarboxylase (ODC) enzyme. This enzyme decomposes oxalate. ODC enzyme activity is influenced by temperature and pH. This study was conducted to determine the optimum pH and temperature for ODC enzyme activity in B. subtilis FNCC 0059.

MATERIALS AND METHODS

B. subtilis was cultivated in oxalate medium at different incubation temperatures for 24 h. The tested temperatures were 25, 29, 33, 37, 41 and 45°C. In separate experiments, cultures were grown in oxalate medium at varying pH from 4-8.5. The cell growth assay and enzyme activity were performed and all the experiments were done with 3 replicates. Data were analyzed using one-way analysis of variance.

RESULTS

The optimum temperature of 37°C could produce maximum ODC enzyme activity (0.041 U mL-1) enzyme activity increased to the point of the optimum pH and then decreased 5.6% when the pH was increased 0.5 from the optimum. Maximum ODC enzyme activity (0.0413 U mL-1) was achieved at pH 5.5.

CONCLUSION

Temperature and the pH of the medium affected ODC enzyme activity. Optimum medium condition of B. subtilis was reached at 37°C and pH 5.5.

Predictive sulfur metabolism - a field in flux

The key role of sulfur metabolites in response to biotic and abiotic stress in plants, as well as their importance in diet and health has led to a significant interest and effort in trying to understand and manipulate the production of relevant compounds. Metabolic engineering utilizes a set of theoretical tools to help rationally design modifications that enhance the production of a desired metabolite. Such approaches have proven their value in bacterial systems, however, the paucity of success stories to date in plants, suggests that challenges remain. Here, we review the most commonly used methods for understanding metabolic flux, focusing on the sulfur assimilatory pathway. We highlight known issues with both experimental and theoretical approaches, as well as presenting recent methods for integrating different modeling strategies, and progress toward an understanding of flux at the whole plant level.