Metabolic Control Analysis Research Articles

Oxidative phosphorylation K0.5ADP in vitro depends on substrate oxidative capacity: Insights from a luciferase-based assay to evaluate ADP kinetic parameters

The K0.5ADP of oxidative phosphorylation (OxPhos) identifies the cytosolic ADP concentration which elicits one-half the maximum OxPhos rate. This kinetic parameter is commonly measured to assess mitochondrial metabolic control sensitivity. Here we describe a luciferase-based assay to evaluate the ADP kinetic parameters of mitochondrial ATP production from OxPhos, adenylate kinase (AK), and creatine kinase (CK). The high sensitivity, reproducibility, and throughput of the microplate-based assay enabled a comprehensive kinetic assessment of all three pathways in mitochondria isolated from mouse liver, kidney, heart, and skeletal muscle. Carboxyatractyloside titrations were also performed with the assay to estimate the flux control strength of the adenine nucleotide translocase (ANT) over OxPhos in human skeletal muscle mitochondria. ANT flux control coefficients were 0.91 ± 0.07, 0.83 ± 0.06, and 0.51 ± 0.07 at ADP concentrations of 6.25, 12.5, and 25 μM, respectively, an [ADP] range which spanned the K0.5ADP. The oxidative capacity of substrate combinations added to drive OxPhos was found to dramatically influence ADP kinetics in mitochondria from several tissues. In mouse skeletal muscle ten different substrate combinations elicited a 7-fold range of OxPhos Vmax, which correlated positively (R2 = 0.963) with K0.5ADP values ranging from 2.3 ± 0.2 μM to 11.9 ± 0.6 μM. We propose that substrate-enhanced capacity to generate the protonmotive force increases the OxPhos K0.5ADP because flux control at ANT increases, thus K0.5ADP rises toward the dissociation constant, KdADP, of ADP-ANT binding. The findings are discussed in the context of top-down metabolic control analysis.

Gene expression regulates metabolite homeostasis during the Crabtree effect: Implications for the adaptation and evolution of Metabolism

A key issue in both molecular and evolutionary biology has been to define the roles of genes and phenotypes in the adaptation of organisms to environmental changes. The dominant view has been that an organism's metabolic adaptations are driven by gene expression and that gene mutations, independent of the starting phenotype, are responsible for the evolution of new metabolic phenotypes. We propose an alternate hypothesis, in which the phenotype and genotype together determine metabolic adaptation both in the lifetime of the organism and in the evolutionary selection of adaptive metabolic traits. We tested this hypothesis by flux-balance and metabolic-control analysis of the relative roles of the starting phenotype and gene expression in regulating the metabolic adaptations during the Crabtree effect in yeast, when they are switched from a low- to high-glucose environment. Critical for successful short-term adaptation was the ability of the glycogen/trehalose shunt to balance the glycolytic pathway. The role of later gene expression of new isoforms of glycolytic enzymes, rather than flux control, was to provide additional homeostatic mechanisms allowing an increase in the amount and efficiency of adenosine triphosphate and product formation while maintaining glycolytic balance. We further showed that homeostatic mechanisms, by allowing increased phenotypic plasticity, could have played an important role in guiding the evolution of the Crabtree effect. Although our findings are specific to Crabtree yeast, they are likely to be broadly found because of the well-recognized similarities in glucose metabolism across kingdoms and phyla from yeast to humans.

Divergences in the Control of Mitochondrial Respiration Are Associated With Life-Span Variation in Marine Bivalves.

The role played by mitochondrial function in the aging process has been a subject of intense debate in the past few decades, as part of the efforts to understand the mechanistic basis of longevity. The mitochondrial oxidative stress theory of aging suggests that a progressive decay of this organelle's function leads to an exacerbation of oxidative stress, with a deleterious impact on mitochondrial structure and DNA, ultimately promoting aging. Among the traits suspected to be associated with longevity is the variation in the regulation of oxidative phosphorylation, potentially affecting the management of oxidative stress. Longitudinal studies using the framework of metabolic control analysis have shown age-related differences in the flux control of respiration, but this approach has seldom been taken on a comparative scale. Using 4 species of marine bivalves exhibiting a large range of maximum life span (from 28 years to 507 years), we report life-span-related differences in flux control at different steps of the electron transfer system. Increased longevity was characterized by a lower control by NADH (complex I-linked) and Succinate (complex II-linked) pathways, while respiration was strongly controlled by complex IV when compared to shorter-lived species. Complex III exerted strong control over respiration in all species. Furthermore, high longevity was associated with higher citrate synthase activity and lower ATP synthase activity. Relieving the control exerted by the electron entry pathways could be advantageous for reaching higher longevity, leading to increased control by complex IV, the final electron acceptor in the electron transfer system.

Identification of a rate-limiting step in a metabolic pathway using the kinetic model and in vitro experiment

Identification of the rate-limiting step in a metabolic pathway is an important challenge in metabolic engineering for enhancing pathway flow. Although specific enzyme activities (Vmax) provide valuable clues for the identification, it is time-consuming and difficult to measure multiple enzymes in the pathway because different assay protocols are required for each enzyme. In the present study, we propose a method to simultaneously determine the Vmax values of multiple enzymes using a kinetic model with a time course of the intermediate concentrations through an invitro experiment. To demonstrate this method, nine glycolysis reactions for converting glucose-6-phosphate (G6P) to pyruvate in Escherichia coli were considered. In a reaction mixture containing G6P and cofactors, glycolysis was initiated by adding a crude cell extract obtained from stationary phase cells. The Vmax values were optimized to minimize the difference between the measured and simulated time-courses using a kinetic model. Metabolic control analysis using the kinetic model with the estimated Vmax values revealed that fructose bisphosphate aldolase (FBA) was the rate-limiting step in the upper part of glycolysis. The addition of FBA in the reaction mixture successfully increased the glycolytic flux invitro. Furthermore, invivo, the specific glucose consumption rate of an FBA overexpression strain was 1.4 times higher than that of the control strain during the stationary phase. These results confirmed that FBA was the rate-limiting step in glycolysis under the stationary phase. This approach provides Vmax values of multiple enzymes in a pathway for metabolic control analysis with a kinetic model.

Dynamic Inventory of Intermediate Metabolites of Cyanobacteria in a Diurnal Cycle.

SummaryCyanobacteria are gaining importance both as hosts for photoautotrophic production of chemicals and as model systems for studies of diurnal lifestyle. The proteome and transcriptome of cyanobacteria have been closely examined under diurnal growth, whereas the downstream effects on the intermediary metabolism have not received sufficient attention. The present study focuses on identifying the cellular metabolites whose inventories undergo dramatic changes in a fast-growing cyanobacterium, Synechococcus elongatus PCC 11801. We identified and quantified 67 polar metabolites, whose inventory changes significantly during diurnal growth, with some metabolites changing by 100-fold. The Calvin-Benson-Bassham cycle intermediates peak at midday to support fast growth. The hitherto unexplored γ-glutamyl peptides act as reservoirs of amino acids. Interestingly, several storage molecules or their precursors accumulate during the dark phase, dispelling the notion that all biosynthetic activity takes place in the light phase. Our results will guide metabolic modeling and strain engineering of cyanobacteria.

Enzyme activities predicted by metabolite concentrations and solvent capacity in the cell.

Experimental measurements or computational model predictions of the post-translational regulation of enzymes needed in a metabolic pathway is a difficult problem. Consequently, regulation is mostly known only for well-studied reactions of central metabolism in various model organisms. In this study, we use two approaches to predict enzyme regulation policies and investigate the hypothesis that regulation is driven by the need to maintain the solvent capacity in the cell. The first predictive method uses a statistical thermodynamics and metabolic control theory framework while the second method is performed using a hybrid optimization–reinforcement learning approach. Efficient regulation schemes were learned from experimental data that either agree with theoretical calculations or result in a higher cell fitness using maximum useful work as a metric. As previously hypothesized, regulation is herein shown to control the concentrations of both immediate and downstream product concentrations at physiological levels. Model predictions provide the following two novel general principles: (1) the regulation itself causes the reactions to be much further from equilibrium instead of the common assumption that highly non-equilibrium reactions are the targets for regulation; and (2) the minimal regulation needed to maintain metabolite levels at physiological concentrations maximizes the free energy dissipation rate instead of preserving a specific energy charge. The resulting energy dissipation rate is an emergent property of regulation which may be represented by a high value of the adenylate energy charge. In addition, the predictions demonstrate that the amount of regulation needed can be minimized if it is applied at the beginning or branch point of a pathway, in agreement with common notions. The approach is demonstrated for three pathways in the central metabolism of E. coli (gluconeogenesis, glycolysis-tricarboxylic acid (TCA) and pentose phosphate-TCA) that each require different regulation schemes. It is shown quantitatively that hexokinase, glucose 6-phosphate dehydrogenase and glyceraldehyde phosphate dehydrogenase, all branch points of pathways, play the largest roles in regulating central metabolism.

Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches

Metabolic pathway modeling plays an increasing role in drug design by allowing better understanding of the underlying regulation and controlling networks in the metabolism of living organisms. However, despite rapid progress in this area, pathway modeling can become a real nightmare for researchers, notably when few experimental data are available or when the pathway is highly complex. Here, three different approaches were developed to model the second part of glycolysis of E. histolytica as an application example, and have succeeded in predicting the final pathway flux: one including detailed kinetic information (white-box), another with an added adjustment term (grey-box) and the last one using an artificial neural network method (black-box). Afterwards, each model was used for metabolic control analysis and flux control coefficient determination. The first two enzymes of this pathway are identified as the key enzymes playing a role in flux control. This study revealed the significance of the three methods for building suitable models adjusted to the available data in the field of metabolic pathway modeling, and could be useful to biologists and modelers.

Metabolic control analysis of hepatic glycogen synthesis in vivo

Multiple insulin-regulated enzymes participate in hepatic glycogen synthesis, and the rate-controlling step responsible for insulin stimulation of glycogen synthesis is unknown. We demonstrate that glucokinase (GCK)-mediated glucose phosphorylation is the rate-controlling step in insulin-stimulated hepatic glycogen synthesis in vivo, by use of the somatostatin pancreatic clamp technique using [13C6]glucose with metabolic control analysis (MCA) in three rat models: 1) regular chow (RC)-fed male rats (control), 2) high fat diet (HFD)-fed rats, and 3) RC-fed rats with portal vein glucose delivery at a glucose infusion rate matched to the control. During hyperinsulinemia, hyperglycemia dose-dependently increased hepatic glycogen synthesis. At similar levels of hyperinsulinemia and hyperglycemia, HFD-fed rats exhibited a decrease and portal delivery rats exhibited an increase in hepatic glycogen synthesis via the direct pathway compared with controls. However, the strong correlation between liver glucose-6-phosphate concentration and net hepatic glycogen synthetic rate was nearly identical in these three groups, suggesting that the main difference between models is the activation of GCK. MCA yielded a high control coefficient for GCK in all three groups. We confirmed these findings in studies of hepatic GCK knockdown using an antisense oligonucleotide. Reduced liver glycogen synthesis in lipid-induced hepatic insulin resistance and increased glycogen synthesis during portal glucose infusion were explained by concordant changes in translocation of GCK. Taken together, these data indicate that the rate of insulin-stimulated hepatic glycogen synthesis is controlled chiefly through GCK translocation.

Metabolic Control Analysis for Drug Target Prioritization in Trypanosomatids.

To validate therapeutic targets in metabolic pathways of trypanosomatids, the criterion of enzyme essentiality determined by gene knockout or knockdown is usually being applied. Since, it is often found that most of the enzymes/proteins analyzed are essential, additional criteria have to be implemented for drug target prioritization. Metabolic control analysis (MCA), often in conjunction with kinetic pathway modeling, offers such possibility for prioritization. MCA is a theoretical and experimental approach to analyze how metabolic pathways are controlled. It involves strategies to perform quantitative analyses to determine the degree in which an enzyme controls a pathway flux, a value called flux control coefficient ([Formula: see text]). By determining the [Formula: see text] of individual steps in a metabolic pathway, the distribution of control of the pathway is established, that is, the identification of the main flux-controlling steps. Therefore, MCA can help in ranking pathway enzymes as drug targets from a metabolic perspective. In this chapter, three approaches to determine [Formula: see text] are reviewed: (1) In vitro pathway reconstitution, (2) manipulation of enzyme activities within parasites, and (3) in silico kinetic modeling of the metabolic pathway. To perform these methods, accurate experimental data of enzyme activities, metabolite concentrations and pathway fluxes are necessary. The methodology is illustrated with the example of trypanothione metabolism of Trypanosoma cruzi and protocols to determine such experimental data for this metabolic process are also described. However, the MCA strategy can be applied to any metabolic pathway in the parasite and general directions to perform it are provided in this chapter.

Drug Target Selection for Trypanosoma cruzi Metabolism by Metabolic Control Analysis and Kinetic Modeling.

In the search for therapeutic targets in the intermediary metabolism of trypanosomatids the gene essentiality criterion as determined by using knock-out and knock-down genetic strategies is commonly applied. As most of the evaluated enzymes/transporters have turned out to be essential for parasite survival, additional criteria and approaches are clearly required for suitable drug target prioritization. The fundamentals of Metabolic Control Analysis (MCA; an approach in the study of control and regulation of metabolism) and kinetic modeling of metabolic pathways (a bottom-up systems biology approach) allow quantification of the degree of control that each enzyme exerts on the pathway flux (flux control coefficient) and metabolic intermediate concentrations (concentration control coefficient). MCA studies have demonstrated that metabolic pathways usually have two or three enzymes with the highest control of flux; their inhibition has more negative effects on the pathway function than inhibition of enzymes exerting low flux control. Therefore, the enzymes with the highest pathway control are the most convenient targets for therapeutic intervention. In this review, the fundamentals of MCA as well as experimental strategies to determine the flux control coefficients and metabolic modeling are analyzed. MCA and kinetic modeling have been applied to trypanothione metabolism in Trypanosoma cruzi and the model predictions subsequently validated in vivo. The results showed that three out of ten enzyme reactions analyzed in the T. cruzi anti-oxidant metabolism were the most controlling enzymes. Hence, MCA and metabolic modeling allow a further step in target prioritization for drug development against trypanosomatids and other parasites.

Statistical inference in ensemble modeling of cellular metabolism.

Kinetic models of metabolism can be constructed to predict cellular regulation and devise metabolic engineering strategies, and various promising computational workflows have been developed in recent years for this. Due to the uncertainty in the kinetic parameter values required to build kinetic models, these workflows rely on ensemble modeling (EM) principles for sampling and building populations of models describing observed physiologies. Sensitivity coefficients from metabolic control analysis (MCA) of kinetic models can provide important insight about cellular control around a given physiological steady state. However, despite considering populations of kinetic models and their model outputs, current approaches do not provide adequate tools for statistical inference. To derive conclusions from model outputs, such as MCA sensitivity coefficients, it is necessary to rank/compare populations of variables with each other. Currently existing workflows consider confidence intervals (CIs) that are derived independently for each comparable variable. Hence, it is important to derive simultaneous CIs for the variables that we wish to rank/compare. Herein, we used an existing large-scale kinetic model of Escherichia Coli metabolism to present how univariate CIs can lead to incorrect conclusions, and we present a new workflow that applies three different multivariate statistical approaches. We use the Bonferroni and the exact normal methods to build symmetric CIs using the normality assumptions. We then suggest how bootstrapping can compute asymmetric CIs whilst relaxing this normality assumption. We conclude that the Bonferroni and the exact normal methods can provide simple and efficient ways for constructing reliable CIs, with the exact normal method favored over the Bonferroni when the compared variables present dependencies. Bootstrapping, despite its significantly higher computational cost, is recommended when comparing non-normal distributions of variables. Additionally, we show how the Bonferroni method can readily be used to estimate required sample numbers to attain a certain CI size.

Glucose 6-Phosphate Accumulates via Phosphoglucose Isomerase Inhibition in Heart Muscle.

Metabolic and structural remodeling is a hallmark of heart failure. This remodeling involves activation of the mTOR (mammalian target of rapamycin) signaling pathway, but little is known on how intermediary metabolites are integrated as metabolic signals. We investigated the metabolic control of cardiac glycolysis and explored the potential of glucose 6-phosphate (G6P) to regulate glycolytic flux and mTOR activation. We developed a kinetic model of cardiomyocyte carbohydrate metabolism, CardioGlyco, to study the metabolic control of myocardial glycolysis and G6P levels. Metabolic control analysis revealed that G6P concentration is dependent on phosphoglucose isomerase (PGI) activity. Next, we integrated ex vivo tracer studies with mathematical simulations to test how changes in glucose supply and glycolytic flux affect mTOR activation. Nutrient deprivation promoted a tight coupling between glucose uptake and oxidation, G6P reduction, and increased protein-protein interaction between hexokinase II and mTOR. We validated the in silico modeling in cultured adult mouse ventricular cardiomyocytes by modulating PGI activity using erythrose 4-phosphate. Inhibition of glycolytic flux at the level of PGI caused G6P accumulation, which correlated with increased mTOR activation. Using click chemistry, we labeled newly synthesized proteins and confirmed that inhibition of PGI increases protein synthesis. The reduction of PGI activity directly affects myocyte growth by regulating mTOR activation.

Intracellular metabolism analysis of Clostridium cellulovorans via modeling integrating proteomics, metabolomics and fermentation

Abstract A consolidated bioprocess for cellulosic n-butanol production has been developed by engineering Clostridium cellulovorans to overexpress a bifunctional aldehyde/alcohol dehydrogenase. Rational metabolic engineering is important to further improve butanol production. This study aimed to investigate intracellular metabolism and identify the key regulators of cellulosic butanol formation in C. cellulovorans via integrated Omics and fermentation kinetics data analysis. First, comparative proteomics and metabolomics analyses of wild type and n-butanol producing mutant strain were conducted, which quantified 624 host cell proteins and 474 primary and secondary metabolites. Compared to wild type, most cellulases in cellulolysis were up-regulated, but three glycolysis enzymes and three enzymes in central pathway were down-regulated in the n-butanol producing strain. Second, a dynamic model integrating Omics and fermentation data was developed to identify key regulators in butanol biosynthesis, which were ranked by further metabolic control analysis. Finally, rational metabolic engineering was performed in C. cellulovorans by overexpressing two genes (thl and hbd) identified as important factors limiting butanol biosynthesis, which improved butanol yield and C4/C2 ratio. This study demonstrated a research approach to integrate multi-Omics and fermentation data of C. cellulovorans and guide its rational metabolic engineering, which can also be applied to other microorganisms.

Investigation of the methylerythritol 4-phosphate pathway for microbial terpenoid production through metabolic control analysis

BackgroundTerpenoids are of high interest as chemical building blocks and pharmaceuticals. In microbes, terpenoids can be synthesized via the methylerythritol phosphate (MEP) or mevalonate (MVA) pathways. Although the MEP pathway has a higher theoretical yield, metabolic engineering has met with little success because the regulation of the pathway is poorly understood.ResultsWe applied metabolic control analysis to the MEP pathway in Escherichia coli expressing a heterologous isoprene synthase gene (ispS). The expression of ispS led to the accumulation of isopentenyl pyrophosphate (IPP)/dimethylallyl pyrophosphate (DMAPP) and severely impaired bacterial growth, but the coexpression of ispS and isopentenyl diphosphate isomerase (idi) restored normal growth and wild-type IPP/DMAPP levels. Targeted proteomics and metabolomics analysis provided a quantitative description of the pathway, which was perturbed by randomizing the ribosome binding site in the gene encoding 1-deoxyxylulose 5-phosphate synthase (Dxs). Dxs has a flux control coefficient of 0.35 (i.e., a 1% increase in Dxs activity resulted in a 0.35% increase in pathway flux) in the isoprene-producing strain and therefore exerted significant control over the flux though the MEP pathway. At higher dxs expression levels, the intracellular concentration of 2-C-methyl-d-erythritol-2,4-cyclopyrophosphate (MEcPP) increased substantially in contrast to the other MEP pathway intermediates, which were linearly dependent on the abundance of Dxs. This indicates that 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase (IspG), which consumes MEcPP, became saturated and therefore limited the flux towards isoprene. The higher intracellular concentrations of MEcPP led to the efflux of this intermediate into the growth medium.DiscussionThese findings show the importance of Dxs, Idi and IspG and metabolite export for metabolic engineering of the MEP pathway and will facilitate further approaches for the microbial production of valuable isoprenoids.

Metabolic control analysis of L-tryptophan production with Escherichia coli based on data from short-term perturbation experiments

E. coli strain NT1259 /pF112aroFBLkan was able to produce 14.3 g L−1 L-tryptophan within 68 h in a fed-batch process from glycerol on a 15 L scale. To gain detailed insight into metabolism of this E. coli strain in the fed-batch process, a sample of L-tryptophan producing cells was withdrawn after 47 h, was separated rapidly and then resuspended in four parallel stirred-tank bioreactors with fresh media. Four different carbon sources (glucose, glycerol, succinate, pyruvate) were supplied individually with varying feeding rates within 19 min and the metabolic reactions of the cells in the four parallel reactors were analyzed by quantification of extracellular and intracellular substrate, product and metabolite concentrations. Data analysis allowed the estimation of intracellular carbon fluxes and of thermodynamic limitations concerning intracellular concentrations and reaction energies. Carbon fluxes and intracellular metabolite concentrations enabled the estimation of elasticities and flux control coefficients by applying metabolic control analysis making use of a metabolic model considering 48 enzymatic reactions and 56 metabolites. As the flux control coefficients describe connections between enzyme activities and metabolic fluxes, they reveal genetic targets for strain improvement. Metabolic control analysis of the recombinant E. coli cells withdrawn from the fed-batch production process clearly indicated that (i) the supply of two precursors for L-tryptophan biosynthesis, L-serine and phosphoribosyl-pyrophosphate, as well as (ii) the formation of aromatic byproducts and (iii) the enzymatic steps of igps and trps2 within the L-tryptophan biosynthesis pathway have major impact on fed-batch production of L-tryptophan from glycerol and should be the targets for further strain improvements.

Increase in lysophosphatidate acyltransferase activity in oilseed rape (Brassica napus) increases seed triacylglycerol content despite its low intrinsic flux control coefficient.

Summary Lysophosphatidate acyltransferase (LPAAT) catalyses the second step of the Kennedy pathway for triacylglycerol (TAG) synthesis. In this study we expressed Trapaeolum majus LPAAT in Brassica napus (B. napus) cv 12075 to evaluate the effects on lipid synthesis and estimate the flux control coefficient for LPAAT.We estimated the flux control coefficient of LPAAT in a whole plant context by deriving a relationship between it and overall lipid accumulation, given that this process is a exponential.Increasing LPAAT activity resulted in greater TAG accumulation in seeds of between 25% and 29%; altered fatty acid distributions in seed lipids (particularly those of the Kennedy pathway); and a redistribution of label from 14C‐glycerol between phosphoglycerides.Greater LPAAT activity in seeds led to an increase in TAG content despite its low intrinsic flux control coefficient on account of the exponential nature of lipid accumulation that amplifies the effect of the small flux increment achieved by increasing its activity. We have also developed a novel application of metabolic control analysis likely to have broad application as it determines the in planta flux control that a single component has upon accumulation of storage products.

Cell free biosynthesis of isoprenoids from isopentenol.

Cell-free systems are growing in importance for the biosynthesis of complex molecules. These systems combine the precision of traditional chemistry with the versatility of biology in creating superior overall processes. Recently, a new synthetic pathway for the biosynthesis of isoprenoids using the substrate isopentenol, dubbed the isopentenol utilization pathway (IUP), was demonstrated to be a promising alternative to the native 2C-methyl-d-erythritol-4-phosphate (MEP) and mevalonate (MVA) pathways. This simplified pathway, which contains a minimum of four enzymes to produce basic monoterpenes and only depends on ATP and isopentenol as substrates, allows for a highly flexible approach to the commercial synthesis of isoprenoid products. In this work, we use metabolic reconstitution to characterize this new pathway in vitro and demonstrate its use for the cell-free synthesis of mono-, sesquit-, and diterpenoids. Kinetic modeling and sensitivity analysis were also used to identify the most significant parameters for taxadiene productivity, and metabolic control analysis was employed to elucidate protein-level interactions within this pathway, which demonstrated that the IUP enzymatic system is primarily controlled by the concentration and kinetics of choline kinase (CK) and not regulated by any pathway intermediates. This is a significant advantage over the natural MEP or MVA pathways as it greatly simplifies future metabolic engineering efforts, both in vitro and in vivo, aiming at improving the kinetics of CK. Finally, we used the insights gathered to demonstrate an in vitro IUP system that can produce 220 mg/L of the diterpene taxadiene, in 9 hr, almost 3-fold faster than any system reported thus far.

Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties.

A persistent obstacle for constructing kinetic models of metabolism is uncertainty in the kinetic properties of enzymes. Currently, available methods for building kinetic models can cope indirectly with uncertainties by integrating data from different biological levels and origins into models. In this study, we use the recently proposed computational approach iSCHRUNK (in Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models), which combines Monte Carlo parameter sampling methods and machine learning techniques, in the context of Bayesian inference. Monte Carlo parameter sampling methods allow us to exploit synergies between different data sources and generate a population of kinetic models that are consistent with the available data and physicochemical laws. The machine learning allows us to data-mine the a priori generated kinetic parameters together with the integrated datasets and derive posterior distributions of kinetic parameters consistent with the observed physiology. In this work, we used iSCHRUNK to address a design question: can we identify which are the kinetic parameters and what are their values that give rise to a desired metabolic behavior? Such information is important for a wide variety of studies ranging from biotechnology to medicine. To illustrate the proposed methodology, we performed Metabolic Control Analysis, computed the flux control coefficients of the xylose uptake (XTR), and identified parameters that ensure a rate improvement of XTR in a glucose-xylose co-utilizing S. cerevisiae strain. Our results indicate that only three kinetic parameters need to be accurately characterized to describe the studied physiology, and ultimately to design and control the desired responses of the metabolism. This framework paves the way for a new generation of methods that will systematically integrate the wealth of available omics data and efficiently extract the information necessary for metabolic engineering and synthetic biology decisions.

Mechanistic inferences on metabolic dysfunction in posttraumatic stress disorder from an integrated model and multiomic analysis: role of glucocorticoid receptor sensitivity.

Posttraumatic stress disorder (PTSD) is associated with neuroendocrine alterations and metabolic abnormalities; however, how metabolism is affected by neuroendocrine disturbances is unclear. The data from combat-exposed veterans with PTSD show increased glycolysis to lactate flux, reduced TCA cycle flux, impaired amino acid and lipid metabolism, insulin resistance, inflammation, and hypersensitive hypothalamic-pituitary-adrenal (HPA) axis. To analyze whether the co-occurrence of multiple metabolic abnormalities is independent or arises from an underlying regulatory defect, we employed a systems biological approach using an integrated mathematical model and multiomic analysis. The models for hepatic metabolism, HPA axis, inflammation, and regulatory signaling were integrated to perform metabolic control analysis (MCA) with respect to the observations from our clinical data. We combined the metabolomics, neuroendocrine, clinical laboratory, and cytokine data from combat-exposed veterans with and without PTSD to characterize the differences in regulatory effects. MCA revealed mechanistic association of the HPA axis and inflammation with metabolic dysfunction consistent with PTSD. This was supported by the data using correlational and causal analysis that revealed significant associations between cortisol suppression, high-sensitivity C-reactive protein, homeostatic model assessment of insulin resistance, γ-glutamyltransferase, hypoxanthine, and several metabolites. Causal mediation analysis indicates that the effects of enhanced glucocorticoid receptor sensitivity (GRS) on glycolytic pathway, gluconeogenic and branched-chain amino acids, triglycerides, and hepatic function are jointly mediated by inflammation, insulin resistance, oxidative stress, and energy deficit. Our analysis suggests that the interventions to normalize GRS and inflammation may help to manage features of metabolic dysfunction in PTSD.

Gamma-glutamylcysteine synthetase and tryparedoxin 1 exert high control on the antioxidant system in Trypanosoma cruzi contributing to drug resistance and infectivity

Trypanothione (T(SH)2) is the main antioxidant metabolite for peroxide reduction in Trypanosoma cruzi; therefore, its metabolism has attracted attention for therapeutic intervention against Chagas disease. To validate drug targets within the T(SH)2 metabolism, the strategies and methods of Metabolic Control Analysis and kinetic modeling of the metabolic pathway were used here, to identify the steps that mainly control the pathway fluxes and which could be appropriate sites for therapeutic intervention. For that purpose, gamma-glutamylcysteine synthetase (γECS), trypanothione synthetase (TryS), trypanothione reductase (TryR) and the tryparedoxin cytosolic isoform 1 (TXN1) were separately overexpressed to different levels in T. cruzi epimastigotes and their degrees of control on the pathway flux as well as their effect on drug resistance and infectivity determined. Both experimental in vivo as well as in silico analyses indicated that γECS and TryS control T(SH)2 synthesis by 60–74% and 15–31%, respectively. γECS overexpression prompted up to a 3.5-fold increase in T(SH)2 concentration, whereas TryS overexpression did not render an increase in T(SH)2 levels as a consequence of high T(SH)2 degradation. The peroxide reduction flux was controlled for 64–73% by TXN1, 17–20% by TXNPx and 11–16% by TryR. TXN1 and TryR overexpression increased H2O2 resistance, whereas TXN1 overexpression increased resistance to the benznidazole plus buthionine sulfoximine combination. γECS overexpression led to an increase in infectivity capacity whereas that of TXN increased trypomastigote bursting. The present data suggested that inhibition of high controlling enzymes such as γECS and TXN1 in the T(SH)2 antioxidant pathway may compromise the parasite's viability and infectivity.