Metabolic Control Analysis Research Articles

Novel [1,2,3]triazolo[1,5-a]pyridine derivatives are trypanocidal by sterol biosynthesis pathway alteration.

Aim: To study a new series of [1,2,3]triazolo[1,5-α]pyridine derivatives as trypanocidal agents because current antichagasic pharmacologic therapy is only partially effective. Materials & methods: The effect of the series upon Trypanosoma cruzi epimastigotes and murine macrophages viability, cell cycle, cell death and on the metabolites of the sterol biosynthesis pathway was measured; also, docking in 14α-demethylase was analyzed. Results: Compound 16 inhibits 14α-demethylase producing an imbalance in the cholesterol/ergosterol synthesis pathway, as suggested by a metabolic control and theoretical docking analysis. Consequently, it prevented cell proliferation, stopping the cellular cycle at the G2/M phase, inducing cell death. Conclusion: Although the exact cell death mechanism remained elusive, this series can be used for the further rational design of novel antiparasitic molecules.

Control and Regulation of Substrate Selection in Cytoplasmic and Mitochondrial Catabolic Networks. A Systems Biology Analysis.

Appropriate substrate selection between fats and glucose is associated with the success of interventions that maintain health such as exercise or caloric restriction, or with the severity of diseases such as diabetes or other metabolic disorders. Although the interaction and mutual inhibition between glucose and fatty-acids (FAs) catabolism has been studied for decades, a quantitative and integrated understanding of the control and regulation of substrate selection through central catabolic pathways is lacking. We addressed this gap here using a computational model representing cardiomyocyte catabolism encompassing glucose (Glc) utilization, pyruvate transport into mitochondria and oxidation in the tricarboxylic acid (TCA) cycle, β-oxidation of palmitate (Palm), oxidative phosphorylation, ion transport, pH regulation, and ROS generation and scavenging in cytoplasmic and mitochondrial compartments. The model is described by 82 differential equations and 119 enzymatic, electron transport and substrate transport reactions accounting for regulatory mechanisms and key players, namely pyruvate dehydrogenase (PDH) and its modulation by multiple effectors. We applied metabolic control analysis to the network operating with various Glc to Palm ratios. The flux and metabolites’ concentration control were visualized through heat maps providing major insights into main control and regulatory nodes throughout the catabolic network. Metabolic pathways located in different compartments were found to reciprocally control each other. For example, glucose uptake and the ATP demand exert control on most processes in catabolism while TCA cycle activities and membrane-associated energy transduction reactions exerted control on mitochondrial processes namely β-oxidation. PFK and PDH, two highly regulated enzymes, exhibit opposite behavior from a control perspective. While PFK activity was a main rate-controlling step affecting the whole network, PDH played the role of a major regulator showing high sensitivity (elasticity) to substrate availability and key activators/inhibitors, a trait expected from a flexible substrate selector strategically located in the metabolic network. PDH regulated the rate of Glc and Palm consumption, consistent with its high sensitivity toward AcCoA, CoA, and NADH. Overall, these results indicate that the control of catabolism is highly distributed across the metabolic network suggesting that fuel selection between FAs and Glc goes well beyond the mechanisms traditionally postulated to explain the glucose-fatty-acid cycle.

Transomics data-driven, ensemble kinetic modeling for system-level understanding and engineering of the cyanobacteria central metabolism

In silico kinetic modeling is an essential tool for rationally designing metabolically engineered organisms based on a system-level understanding of their regulatory mechanisms. However, an estimation of enzyme parameters has been a bottleneck in the computer simulation of metabolic dynamics. In this study, the ensemble-modeling approach was integrated with the transomics data to construct kinetic models. Kinetic metabolic models of a photosynthetic bacterium, Synechocystis sp. PCC 6803, were constructed to identify engineering targets for improving ethanol production based on an understanding of metabolic regulatory systems. A kinetic model ensemble was constructed by randomly sampling parameters, and the best 100 models were selected by comparing predicted metabolic state with a measured dataset, including metabolic flux, metabolite concentrations, and protein abundance data. Metabolic control analysis using the model ensemble revealed that a large pool size of 3-phosphoglycerate could be a metabolic buffer responsible for the stability of the Calvin–Benson cycle, and also identified that phosphoglycerate kinase (PGK) is a promising engineering target to improve a pyruvate supply such as for ethanol production. Overexpression of PGK in the metabolically engineered PCC 6803 strain showed that the specific ethanol production rate and ethanol titers at 48 h were 1.23- and 1.37-fold greater than that of the control strain. PGK is useful for future metabolic engineering since pyruvate is a common precursor for the biosynthesis of various chemicals.

Mathematical study of feedback inhibition effects on the dynamics of metabolites on the central metabolism of a yeast cell: a combination of kinetic model and metabolic control analysis

The fermentation system is a metabolic system that can be considered as a complex system, since it involves metabolites linked by different reactions. Some outputs of the reaction act as input in the other reactions and they control the behaviour of the system. Glucose as the main carbon source of the yeast cells plays an important role in the cell’s reproduction cycle and product synthesis. Glucose promotes negative feedback to some reactions in the central metabolism of the yeast cell. In this paper we mathematically study the effects of the negative feedback (inhibition) by glucose and other metabolites on the dynamics of the fermentation system. We also study the sensitivity of the flux in the presence of inhibition. We found that high inhibition by glucose affects the concentration of acetyl-CoA, which will lead to the respiration pathway of the yeast cells. High inhibition by acetaldehyde affects the concentration of all metabolites, including the maximal concentration of ethanol. Based on the metabolic control analysis results, we found that glucose can be considered as the external regulating point in increasing the flux of ethanol. Although glucose acts as a negative feedback, it can also be used to promote certain processes in the metabolic pathway since it gives the highest positive control in increasing the flux of ethanol as the desired product. For the internal regulation point, the maximal concentration of ethanol can be increased significantly by regulating the maximal activity of alcohol dehydrogenase and acetaldehyde dehydrogenase simultaneously.

Thermodynamic constraints on the regulation of metabolic fluxes

Nutrition and metabolism are fundamental to cellular function. Metabolic activity (i.e. rates of flow, most commonly referred to as flux) is constrained by thermodynamics and regulated by the activity of enzymes. The general principles that relate biological and physical variables to metabolic control are incompletely understood. Using metabolic control analysis and computer simulations in several models of simplified metabolic pathways, we derive analytical expressions that define relationships between thermodynamics, enzyme activity, and flux control. The relationships are further analyzed in a mathematical model of glycolysis as an example of a complex biochemical pathway. We show that metabolic pathways that are very far from equilibrium are controlled by the activity of upstream enzymes. However, in general, regulation of metabolic fluxes by an enzyme has a more adaptable pattern, which relies more on distribution of free energy among reaction steps in the pathway than on the thermodynamic properties of the given enzyme. These findings show how the control of metabolic pathways is shaped by thermodynamic constraints of the given pathway.

Delving deeper: Relating the behaviour of a metabolic system to the properties of its components using symbolic metabolic control analysis.

High-level behaviour of metabolic systems results from the properties of, and interactions between, numerous molecular components. Reaching a complete understanding of metabolic behaviour based on the system’s components is therefore a difficult task. This problem can be tackled by constructing and subsequently analysing kinetic models of metabolic pathways since such models aim to capture all the relevant properties of the system components and their interactions. Symbolic control analysis is a framework for analysing pathway models in order to reach a mechanistic understanding of their behaviour. By providing algebraic expressions for the sensitivities of system properties, such as metabolic flux or steady-state concentrations, in terms of the properties of individual reactions it allows one to trace the high level behaviour back to these low level components. Here we apply this method to a model of pyruvate branch metabolism in Lactococcus lactis in order to explain a previously observed negative flux response towards an increase in substrate concentration. With this method we are able to show, first, that the sensitivity of flux towards changes in reaction rates (represented by flux control coefficients) is determined by the individual metabolic branches of the pathway, and second, how the sensitivities of individual reaction rates towards their substrates (represented by elasticity coefficients) contribute to this flux control. We also quantify the contributions of enzyme binding and mass-action to enzyme elasticity separately, which allows for an even finer-grained understanding of flux control. These analytical tools allow us to analyse the control properties of a metabolic model and to arrive at a mechanistic understanding of the quantitative contributions of each of the enzymes to this control. Our analysis provides an example of the descriptive power of the general principles of symbolic control analysis.

Metabolic Control Analysis of Reaction Catalysed by Chalcone Synthase to Inhibit Complex Lipid Formation in Mycobacterium tuberculosis

Tuberculosis is among the deadliest diseases occurring worldwide killing almost 2 million of people every year and the number increases every year. The drugs and medications used for treatment become ineffective due to the development of resistance in the causative pathogen, M. tuberculosis towards the drugs. This has led to the development of new approaches to study the diseases from different views. Chalcone Synthase is an enzyme involved in the flavonoid biosynthetic pathway occurring in plants and in certain bacterial species including M. tuberculosis. The enzyme is responsible for lipid biosynthesis in bacterial cell wall of M. tuberculosis. This study describes the kinetic behaviour of reaction; Chalcone Synthase catalyzes the first reaction in flavonoid biosynthesis and needs a certain threshold concentration of substrates for reaction to proceed. In this study, the steady state concentration of substrates is predicted by simulation experiment using Gepasi tool. Initial concentrations of substrates in M. tuberculosis are 4.78*10-2 mMol for p-coumaroyl-CoA and 2.23*10-7 mMol for malonyl-CoA, are taken as input which are essential for synthesis of Naringenin chalcone and release of CoA. This concentration was used to simulate the reaction. Simulation results show that after 5.00 seconds, at equilibrium constant J(R1) 0.000000e mM*ml/s, initial and final concentrations of all the five components become 1.000000 mM, at 0.000 mM reaction rate. Low and stable concentrations of products were obtained during steady state analysis which is 1.157721 mM for both Naringenin chalcone and Coenzyme. The work brings a relevant approach about the possibility of the inactivation of mycobacterial growth by reducing mycolic acid synthesis in the cell wall of mycobacterium and provides basis for new strategy to design vaccine or drug against the pathogen.

Kinetic models of metabolism that consider alternative steady-state solutions of intracellular fluxes and concentrations

Large-scale kinetic models are used for designing, predicting, and understanding the metabolic responses of living cells. Kinetic models are particularly attractive for the biosynthesis of target molecules in cells as they are typically better than other types of models at capturing the complex cellular biochemistry. Using simpler stoichiometric models as scaffolds, kinetic models are built around a steady-state flux profile and a metabolite concentration vector that are typically determined via optimization. However, as the underlying optimization problem is underdetermined, even after incorporating available experimental omics data, one cannot uniquely determine the operational configuration in terms of metabolic fluxes and metabolite concentrations. As a result, some reactions can operate in either the forward or reverse direction while still agreeing with the observed physiology. Here, we analyze how the underlying uncertainty in intracellular fluxes and concentrations affects predictions of constructed kinetic models and their design in metabolic engineering and systems biology studies. To this end, we integrated the omics data of optimally grown Escherichia coli into a stoichiometric model and constructed populations of non-linear large-scale kinetic models of alternative steady-state solutions consistent with the physiology of the E. coli aerobic metabolism. We performed metabolic control analysis (MCA) on these models, highlighting that MCA-based metabolic engineering decisions are strongly affected by the selected steady state and appear to be more sensitive to concentration values rather than flux values. To incorporate this into future studies, we propose a workflow for moving towards more reliable and robust predictions that are consistent with all alternative steady-state solutions. This workflow can be applied to all kinetic models to improve the consistency and accuracy of their predictions. Additionally, we show that, irrespective of the alternative steady-state solution, increased activity of phosphofructokinase and decreased ATP maintenance requirements would improve cellular growth of optimally grown E. coli.

Noise propagation in an integrated model of bacterial gene expression and growth.

In bacterial cells, gene expression, metabolism, and growth are highly interdependent and tightly coordinated. As a result, stochastic fluctuations in expression levels and instantaneous growth rate show intricate cross-correlations. These correlations are shaped by feedback loops, trade-offs and constraints acting at the cellular level; therefore a quantitative understanding requires an integrated approach. To that end, we here present a mathematical model describing a cell that contains multiple proteins that are each expressed stochastically and jointly limit the growth rate. Conversely, metabolism and growth affect protein synthesis and dilution. Thus, expression noise originating in one gene propagates to metabolism, growth, and the expression of all other genes. Nevertheless, under a small-noise approximation many statistical quantities can be calculated analytically. We identify several routes of noise propagation, illustrate their origins and scaling, and establish important connections between noise propagation and the field of metabolic control analysis. We then present a many-protein model containing >1000 proteins parameterized by previously measured abundance data and demonstrate that the predicted cross-correlations between gene expression and growth rate are in broad agreement with published measurements.

Identification of a co-target for enhancing efficacy of sorafenib in HCC through a quantitative modeling approach.

Sorafenib (SFB), a multi-kinase inhibitor, is the only approved drug for treating hepatocellular carcinoma (HCC). However, SFB shows low efficacy in many cases. HCC related mortality therefore remains to be high worldwide. SFB, a multi-kinase inhibitor is also known to modulate the redox homeostasis in cancer cells. To understand the effect of SFB on the redox status, a quantitative understanding of the system is necessary. Kinetic modeling of the relevant pathways is a useful approach for obtaining a quantitative understanding of the pathway dynamics and to rank the individual factors based on the extent of influence they wield on the pathway. Here, we report a comprehensive model of the glutathione reaction network (GSHnet ), consisting of four modules and includes SFB-induced redox stress. We compared GSHnet simulations for HCC of six different etiologies with healthy liver, and correctly identified the expected variations in cancer. Next, we studied alterations induced in the system upon SFB treatment and observed differential H2 O2 dynamics in all the conditions. Using metabolic control analysis, we identified glutathione S-transferase (GST) as the enzyme with the highest selective control coefficient, making it an attractive co-target for potentiating the action of SFB across all six etiologies. As a proof-of-concept, we selected ethacrynic acid (EA), a known inhibitor of GST, and verified exvivo that EA synergistically potentiates the cytotoxic effect of SFB. Being an FDA approved drug, EA is a promising candidate for repurposing as a combination therapy with SFB for HCC treatment.

A metabolic control analysis approach to introduce the study of systems in biochemistry: the glycolytic pathway in the red blood cell.

Metabolic control analysis (MCA) is a promising approach in biochemistry aimed at understanding processes in a quantitative fashion. Here the contribution of enzymes and transporters to the control of a given pathway flux and metabolite concentrations is determined and expressed quantitatively by means of numerical coefficients. Metabolic flux can be influenced by a wide variety of modulators acting on one or more metabolic steps along the pathway. We describe a laboratory exercise to study metabolic regulation of human erythrocytes (RBCs). Within the framework of MCA, students use these cells to determine the sensitivity of the glycolytic flux to two inhibitors (iodoacetic acid: IA, and iodoacetamide: IAA) known to act on the enzyme glyceraldehyde-3-phosphate-dehydrogenase. Glycolytic flux was estimated by determining the concentration of extracellular lactate, the end product of RBC glycolysis. A low-cost colorimetric assay was implemented, that takes advantage of the straightforward quantification of the absorbance signal from the photographic image of the multi-well plate taken with a standard digital camera. Students estimate flux response coefficients for each inhibitor by fitting an empirical function to the experimental data, followed by analytical derivation of this function. IA and IAA exhibit qualitatively different patterns, which are thoroughly analyzed in terms of the physicochemical properties influencing their action on the target enzyme. IA causes highest glycolytic flux inhibition at lower concentration than IAA. This work illustrates the feasibility of using the MCA approach to study key variables of a simple metabolic system, in the context of an upper level biochemistry course. © 2018 International Union of Biochemistry and Molecular Biology, 46(5):502-515, 2018.

Comparative analysis of the bioenergetics of human adenocarcinoma Caco-2 cell line and postoperative tissue samples from colorectal cancer patients.

The aim of this work was to explore the key bioenergetic properties for mitochondrial respiration in the widely-used Caco-2 cell line and in human colorectal cancer (HCC) postoperational tissue samples. Oxygraphy and metabolic control analysis (MCA) were applied to estimate the function of oxidative phosphorylation in cultured Caco-2 cells and HCC tissue samples. The mitochondria of Caco-2 cells and HCC tissues displayed larger functional activity of respiratory complex (C)II compared with CI, whereas in normal colon tissue an inverse pattern in the ratio of CI to CII activity was observed. MCA showed that the respiration in Caco-2 and HCC tissue cells is regulated by different parts of electron transport chain. In HCC tissues, this control is performed essentially at the level of respiratory chain complexes I-IV, whereas in Caco-2 cells at the level of CIV (cytochrome c oxidase) and the ATP synthasome. The differences we found in the regulation of respiratory chain activity and glycose index could represent an adaptive response to distinct growth conditions; this highlights the importance of proper validation of results obtained from in-vitro models before their extrapolation to the more complex in-vivo systems.

Establishment of a five-enzyme cell-free cascade for the synthesis of uridine diphosphate N-acetylglucosamine

In spite of huge endeavors in cell line engineering to produce glycoproteins with desired and uniform glycoforms, it is still not possible in vivo. Alternatively, in vitro glycoengineering can be used for the modification of glycans. However, in vitro glycoengineering relies on expensive nucleotide sugars, such as uridine 5′-diphospho-N-acetylglucosamine (UDP-GlcNAc) which serves as GlcNAc donor for the synthesis of various glycans. In this work, we present a systematic study for the cell-free de novo synthesis and regeneration of UDP-GlcNAc from polyphosphate, UMP and GlcNAc by a cascade of five enzymes (N–acetylhexosamine kinase (NahK), Glc–1P uridyltransferase (GalU), uridine monophosphate kinase (URA6), polyphosphate kinase (PPK3), and inorganic diphosphatase (PmPpA). All enzymes were expressed in E. coli BL21 Gold (DE3) and purified using immobilized metal affinity chromatography (IMAC). Results from one-pot experiments demonstrate the successful production of UDP-GlcNAc with a yield approaching 100%. The highest volumetric productivity of the cascade was about 0.81 g L−1 h−1 of UDP-GlcNAc. A simple model based on mass action kinetics was sufficient to capture the dynamic behavior of the multienzyme pathway. Moreover, a design equation based on metabolic control analysis was established to investigate the effect of enzyme concentration on the UDP-GlcNAc flux and to demonstrate that the flux of UDP-GlcNAc can be controlled by means of the enzyme concentrations. The effect of temperature on the UDP-GlcNAc flux followed an Arrhenius equation and the optimal co-factor concentration (Mg2+) for high UDP-GlcNAc synthesis rates depended on the working temperature. In conclusion, the study covers the entire engineering process of a multienzyme cascade, i.e. pathway design, enzyme expression, enzyme purification, reaction kinetics and investigation of the influence of basic parameters (temperature, co-factor concentration, enzyme concentration) on the synthesis rate. Thus, the study lays the foundation for future cascade optimization, preparative scale UDP-GlcNAc synthesis and for in situ coupling of the network with UDP-GlcNAc transferases to efficiently regenerate UDP-GlcNAc. Hence, this study provides a further step towards cost-effective in vitro glycoengineering of antibodies and other glycosylated proteins.

Abstract 5496: A predictive model for selective targeting of the Warburg effect through GAPDH inhibition with a natural product

Abstract Cancer cells undergo numerous adaptive processes to sustain growth and survival. One notable mechanism is by rewiring metabolism, most prominently through a phenomenon known as the Warburg effect (WE). The WE is defined by increased glucose consumption and lactate excretion in the presence or absence of oxygen. Although the WE has been extensively studied, efforts to develop successful glycolytic inhibitors have been largely unsuccessful. Targeting cancer metabolism has remained a challenge due to the lack of obvious metabolic biomarkers and difficulties achieving full enzyme inhibition without inducing toxicity in normal tissue. Although targeted cancer therapies that use genetics have been largely successful, principles for selectively targeting tumor metabolism that also depend on the environment remain unknown. In the present study, we employ metabolic control analysis to reveal that glyceraldehyde-3-phosphate dehydrogenase (GAPDH), the sixth enzyme in glycolysis, exhibits differential control properties during the WE and can be used to predict response to targeting glucose metabolism. Using high-performance liquid chromatography coupled to high-resolution mass spectrometry (HPLC-HRMS), we conducted comparative metabolomics to establish a natural product produced by Trichoderma fungi, koningic acid (KA), as a selective inhibitor of GAPDH. We expressed a fungal-derived resistant-GAPDH allele in human cells to show that KA is highly specific for GAPDH. With machine learning, integrated pharmacogenomics, and metabolomics, we demonstrate that KA efficacy is not determined by the status of individual genes, but by the quantitative extent of the WE, leading to a therapeutic window in vivo. Thus, the basis of targeting the WE can be encoded by molecular principles that extend beyond genetic status. Current work focuses on elucidating acquired resistance mechanisms of KA in cancer cells undergoing the WE. Together, these data importantly demonstrate that a complete understanding of pharmacogenomics for cancer therapy likely requires information encoded at the metabolic level. Citation Format: Maria V. Liberti, Ziwei Dai, Suzanne E. Wardell, Joshua A. Baccile, Xiaojing Liu, Xia Gao, Robert Baldi, Mahya Mehrmohamadi, Marc O. Johnson, Neel S. Madhukar, Alexander Shestov, Iok I. C. Chio, Olivier Elemento, Jeffrey C. Rathmell, Frank C. Schroeder, Donald P. McDonnell, Jason W. Locasale. A predictive model for selective targeting of the Warburg effect through GAPDH inhibition with a natural product [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5496.

Measuring autophagosome flux

ABSTRACTMacroautophagy/autophagy is a proteolytic pathway that is involved in both bulk degradation of cytoplasmic proteins as well as in selective degradation of cytoplasmic organelles. Autophagic flux is often defined as a measure of autophagic degradation activity, and many techniques exist to assess autophagic flux. Although these techniques have generated invaluable information about the autophagic system, the quest continues for developing methods that not only enhance sensitivity and provide a means of quantification, but also accurately reflect the dynamic character of the pathway. Based on the theoretical framework of metabolic control analysis, where the autophagosome flux is the quantitative description of the rate a flow along a pathway, here we treat the autophagy system as a multi-step pathway. We describe a single-cell fluorescence live-cell imaging-based approach that allows the autophagosome flux to be accurately measured. This method characterizes autophagy in terms of its complete autophagosome and autolysosome pool size, the autophagosome flux, J, and the transition time, τ, for autophagosomes and autolysosomes at steady state. This approach provides a sensitive quantitative method to measure autophagosome flux, pool sizes and transition time in cells and tissues of clinical relevance.Abbreviations: ATG5/APG5, autophagy-related 5; GFP, green fluorescent protein; LAMP1, lysosomal-associated membrane protein 1; MAP1LC3/LC3, microtubule-associated protein 1 light chain 3; J, flux; MEF, mouse embryonic fibroblast; MTOR, mechanistic target of rapamycin kinase; nA, number of autophagosomes; nAL, number of autolysosomes; nL, number of lysosomes; p-MTOR, phosphorylated mechanistic target of rapamycin kinase; RFP, red fluorescent protein; siRNA, small interfering RNA; τ, transition time; TEM, transmission electron microscopy.

Control of pancreatic β-cell bioenergetics.

The canonical model of glucose-stimulated insulin secretion (GSIS) by pancreatic β-cells predicts a glucose-induced rise in the cytosolic ATP/ADP ratio. Such bioenergetic sensitivity to metabolic fuel is unusual as it implies that ATP flux is governed, to a significant extent, by ATP supply, while it is predominantly demand-driven in other cell types. Metabolic control is generally shared between different processes, but potential control of ATP consumption over β-cell bioenergetics has been largely ignored to date. The present paper offers a brief overview of experimental evidence that demonstrates ATP flux control by glucose-fuelled oxidative phosphorylation. Based on old and new data, it is argued that ATP supply does not hold exclusive control over ATP flux, but shares it with ATP demand, and that the distribution of control is flexible. Quantification of the bioenergetic control distribution will be important from basic and clinical perspectives, but precise measurement of the cytosolic ATP/ADP ratio is complicated by adenine nucleotide compartmentalisation. Metabolic control analysis of β-cell bioenergetics will likely clarify the mechanisms by which glucose and fatty acids amplify and potentiate GSIS, respectively. Moreover, such analysis may offer hints as to how ATP flux control shifts from ATP supply to ATP demand during the development of type 2 diabetes, and why prolonged sulfonylurea treatment causes β-cell deterioration.

A rational approach for predicting the minimum composition of anti-parasite sub-unit vaccines: a multiple target vaccine hypothesis

The present article introduces a new concept, termed a multiple vaccine target hypothesis (MVTH) for determining the minimum number of vaccine targets (epitopes/antigens) necessary to construct a highly efficacious (greater than 90%) anti-parasite vaccine. Drawing inspiration from the Metabolic Control Analysis (MCA) and immunological reasoning it is proposed that for a multi-stage parasite an effective anti-parasite vaccine will necessarily act on more than one stage of the parasite in the definitive host. It is argued that the minimum number of sub-unit targets for a highly efficacious anti-parasite vaccine will vary from one parasite to the other and will be equal to twice the number of major parasite stages occurring in the human host. Quantitatively stated, C v =2n where C v the number of required targets is, and n represents the number of major parasite stages in the definitive host. Conditions when the value of C v may deviate from what is predicted by the formula given above are discussed. Extensive literature search on malaria, onchocerciasis and schistosomiasis sub-unit vaccine development suggests that subunit vaccines constructed from single components and directed to just one parasite stage are less efficacious that multivariate sub-unit vaccines, and obviously whole parasite vaccines. MVTH therefore provides a rational framework for constituting a sub-unit anti-parasite vaccine. Keywords: Vaccine, malaria, Onchocerca volvulus , schistosomiasis, parasite, equation

Genome-Scale Architecture of Small Molecule Regulatory Networks and the Fundamental Trade-Off Between Regulation and Enzymatic Activity

Metabolic flux is in part regulated by endogenous small molecules that modulate the catalytic activity of an enzyme, e.g. allosteric inhibition. In contrast to transcriptional regulation of enzymes, technical difficulties have hindered the production of a genome-scale atlas of small molecule/enzyme regulatory interactions. Here, we develop a framework leveraging the vast, but fragmented, biochemical literature to reconstruct and analyze the small molecule regulatory network (SMRN) of the model organism Escherichia coli, including the primary metabolite regulators and enzyme targets. Using metabolic control analysis, we prove a fundamental trade-off between regulation and enzymatic activity, and combine it with metabolomic measurements and the SMRN to make inferences on the sensitivity of enzymes to their regulators. Generalizing the analysis to other organisms, we identify highly conserved regulatory interactions across evolutionarily divergent species, further emphasizing a critical role for small molecule interactions in the maintenance of metabolic homeostasis.

A control analysis perspective on Katz centrality

Methods for efficiently controlling dynamics propagated on networks are usually based on identifying the most influential nodes. Knowledge of these nodes can be used for the targeted control of dynamics such as epidemics, or for modifying biochemical pathways relating to diseases. Similarly they are valuable for identifying points of failure to increase network resilience in, for example, social support networks and logistics networks. Many measures, often termed ‘centrality’, have been constructed to achieve these aims. Here we consider Katz centrality and provide a new interpretation as a steady-state solution to continuous-time dynamics. This enables us to implement a sensitivity analysis which is similar to metabolic control analysis used in the analysis of biochemical pathways. The results yield a centrality which quantifies, for each node, the net impact of its absence from the network. It also has the desirable property of requiring a node with a high centrality to play a central role in propagating the dynamics of the system by having the capacity to both receive flux from others and then to pass it on. This new perspective on Katz centrality is important for a more comprehensive analysis of directed networks.

Screening of promoters from Arthrobacter sp. CGMCC 3584 using a green fluorescent protein reporter system.

Available molecular and genetic tools for the genetic manipulation of Arthrobacter species are limited until now. In gene engineering, a continuous set of promoters with various strengths are of importance for fine-tuning gene expression in metabolic optimization and control analysis. Here, for the first time, we constructed a promoter trap system using green fluorescence protein (GFP) as a reporter, for screening and characterizing functional Arthrobacter promoters. Twenty-three Arthrobacter transformants of various GFP fluorescence strengths were isolated and characterized through the analysis of DNA sequences. Among the 23 putative promoters, 2 were selected for deletion analysis of promoter elements. As a result, the deletion of the upstream of the putative promoter P8 and P13 caused a 43.8% decrease and a 29.1% increase in the fluorescence signals, respectively. Finally, we obtained the strongest promoter P13-3 which was 4.4 times more potent than the promoter of 6-hydroxyl-D-nicotine oxidase gene which was previously reported in Arthrobacter nicotinovorans, and the obtained promoter was used to improve the production of cyclic adenosine monophosphate in Arthrobacter sp. CGMCC 3584. The screening strategy together with obtained promoters in this study would contribute to the future engineering of Arthrobacter species.