The metabolic core and catalytic switches are fundamental elements in the self-regulation of the systemic metabolic structure of cells

Background

Experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a metabolic core formed by a set of enzymatic reactions which are always active under all environmental conditions, while the rest of catalytic processes are only intermittently active. The reactions of the metabolic core are essential for biomass formation and to assure optimal metabolic performance. The on-off catalytic reactions and the metabolic core are essential elements of a Systemic Metabolic Structure which seems to be a key feature common to all cellular organisms.

Methodology/Principal Findings

In order to investigate the functional importance of the metabolic core we have studied different catalytic patterns of a dissipative metabolic network under different external conditions. The emerging biochemical data have been analysed using information-based dynamic tools, such as Pearson''s correlation and Transfer Entropy (which measures effective functionality). Our results show that a functional structure of effective connectivity emerges which is dynamical and characterized by significant variations of bio-molecular information flows.

Conclusions/Significance

We have quantified essential aspects of the metabolic core functionality. The always active enzymatic reactions form a hub –with a high degree of effective connectivity- exhibiting a wide range of functional information values being able to act either as a source or as a sink of bio-molecular causal interactions. Likewise, we have found that the metabolic core is an essential part of an emergent functional structure characterized by catalytic modules and metabolic switches which allow critical transitions in enzymatic activity. Both, the metabolic core and the catalytic switches in which also intermittently-active enzymes are involved seem to be fundamental elements in the self-regulation of the Systemic Metabolic Structure.     

Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals

A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically infeasible, even in simpler systems like dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct structural connectivity from network activity monitored through calcium imaging. We focus in this study on the inference of excitatory synaptic links. Based on information theory, our method requires no prior assumptions on the statistics of neuronal firing and neuronal connections. The performance of our algorithm is benchmarked on surrogate time series of calcium fluorescence generated by the simulated dynamics of a network with known ground-truth topology. We find that the functional network topology revealed by Transfer Entropy depends qualitatively on the time-dependent dynamic state of the network (bursting or non-bursting). Thus by conditioning with respect to the global mean activity, we improve the performance of our method. This allows us to focus the analysis to specific dynamical regimes of the network in which the inferred functional connectivity is shaped by monosynaptic excitatory connections, rather than by collective synchrony. Our method can discriminate between actual causal influences between neurons and spurious non-causal correlations due to light scattering artifacts, which inherently affect the quality of fluorescence imaging. Compared to other reconstruction strategies such as cross-correlation or Granger Causality methods, our method based on improved Transfer Entropy is remarkably more accurate. In particular, it provides a good estimation of the excitatory network clustering coefficient, allowing for discrimination between weakly and strongly clustered topologies. Finally, we demonstrate the applicability of our method to analyses of real recordings of in vitro disinhibited cortical cultures where we suggest that excitatory connections are characterized by an elevated level of clustering compared to a random graph (although not extreme) and can be markedly non-local.     

Glycosomes: A comprehensive view of their metabolic roles in T. brucei

Peroxisomes are single-membrane cellular organelles, present in most eukaryotic cells and organisms from human to yeast, fulfilling essential metabolic functions in lipid metabolism, free radical detoxification, differentiation, development, morphogenesis, etc. Interestingly, the protozoan parasite species Trypanosoma contains peroxisome-like organelles named glycosomes, which lack hallmark peroxisomal pathways and enzymes, such as catalase. Glycosomes are the only peroxisome-like organelles containing most enzymatic steps of the glycolytic pathway as well as enzymes of pyrimidine biosynthesis, purine salvage and biosynthesis of nucleotide sugars. We present here an overview of the glycosomal metabolic peculiarities together with the current view of the raison d'être of this unique metabolic peroxisomal sequestration.     

Evaluation of the Performance of Information Theory-Based Methods and Cross-Correlation to Estimate the Functional Connectivity in Cortical Networks

Functional connectivity of in vitro neuronal networks was estimated by applying different statistical algorithms on data collected by Micro-Electrode Arrays (MEAs). First we tested these “connectivity methods” on neuronal network models at an increasing level of complexity and evaluated the performance in terms of ROC (Receiver Operating Characteristic) and PPC (Positive Precision Curve), a new defined complementary method specifically developed for functional links identification. Then, the algorithms better estimated the actual connectivity of the network models, were used to extract functional connectivity from cultured cortical networks coupled to MEAs. Among the proposed approaches, Transfer Entropy and Joint-Entropy showed the best results suggesting those methods as good candidates to extract functional links in actual neuronal networks from multi-site recordings.     

Estimating causal interaction between prefrontal cortex and striatum by transfer entropy

Transfer entropy (TE) is an information-theoretic measure for the investigation of causal interaction between two systems without a requirement of pre-specific interaction model (such as: linear or nonlinear). We introduced an efficient algorithm to calculate TE values between two systems based on observed time signals. By this method, we demonstrated that the TE correctly estimated the coupling strength and the direction of information transmission of two nonlinearly coupled systems. We also calculated TE values of real local field potentials (LFPs) recorded simultaneously in the lateral prefrontal cortex (LPFC) and the striatum of the behavioral monkey, and observed that the TE value from the LPFC to the striatum was stronger than that from the striatum to the LPFC, consistent with anatomical structure between the two areas. Moreover, the TE value dynamically varied dependent on behavior stages of the monkey. These results from simulated and real LFPs data suggested that the TE was able to effectively estimate functional connectivity between different brain regions and characterized their dynamical properties.     

Yeast protein glycation in vivo by methylglyoxal. Molecular modification of glycolytic enzymes and heat shock proteins

Protein glycation by methylglyoxal is a nonenzymatic post-translational modification whereby arginine and lysine side chains form a chemically heterogeneous group of advanced glycation end-products. Methylglyoxal-derived advanced glycation end-products are involved in pathologies such as diabetes and neurodegenerative diseases of the amyloid type. As methylglyoxal is produced nonenzymatically from dihydroxyacetone phosphate and d-glyceraldehyde 3-phosphate during glycolysis, its formation occurs in all living cells. Understanding methylglyoxal glycation in model systems will provide important clues regarding glycation prevention in higher organisms in the context of widespread human diseases. Using Saccharomyces cerevisiae cells with different glycation phenotypes and MALDI-TOF peptide mass fingerprints, we identified enolase 2 as the primary methylglyoxal glycation target in yeast. Two other glycolytic enzymes are also glycated, aldolase and phosphoglycerate mutase. Despite enolase's activity loss, in a glycation-dependent way, glycolytic flux and glycerol production remained unchanged. None of these enzymes has any effect on glycolytic flux, as evaluated by sensitivity analysis, showing that yeast glycolysis is a very robust metabolic pathway. Three heat shock proteins are also glycated, Hsp71/72 and Hsp26. For all glycated proteins, the nature and molecular location of some advanced glycation end-products were determined by MALDI-TOF. Yeast cells experienced selective pressure towards efficient use of d-glucose, with high methylglyoxal formation as a side effect. Glycation is a fact of life for these cells, and some glycolytic enzymes could be deployed to contain methylglyoxal that evades its enzymatic catabolism. Heat shock proteins may be involved in proteolytic processing (Hsp71/72) or protein salvaging (Hsp26).     

Global self-organization of the cellular metabolic structure

Background

Over many years, it has been assumed that enzymes work either in an isolated way, or organized in small catalytic groups. Several studies performed using “metabolic networks models” are helping to understand the degree of functional complexity that characterizes enzymatic dynamic systems. In a previous work, we used “dissipative metabolic networks” (DMNs) to show that enzymes can present a self-organized global functional structure, in which several sets of enzymes are always in an active state, whereas the rest of molecular catalytic sets exhibit dynamics of on-off changing states. We suggested that this kind of global metabolic dynamics might be a genuine and universal functional configuration of the cellular metabolic structure, common to all living cells. Later, a different group has shown experimentally that this kind of functional structure does, indeed, exist in several microorganisms.

Methodology/Principal Findings

Here we have analyzed around 2.500.000 different DMNs in order to investigate the underlying mechanism of this dynamic global configuration. The numerical analyses that we have performed show that this global configuration is an emergent property inherent to the cellular metabolic dynamics. Concretely, we have found that the existence of a high number of enzymatic subsystems belonging to the DMNs is the fundamental element for the spontaneous emergence of a functional reactive structure characterized by a metabolic core formed by several sets of enzymes always in an active state.

Conclusions/Significance

This self-organized dynamic structure seems to be an intrinsic characteristic of metabolism, common to all living cellular organisms. To better understand cellular functionality, it will be crucial to structurally characterize these enzymatic self-organized global structures.     

Global Self-Regulation of the Cellular Metabolic Structure

Background

Different studies have shown that cellular enzymatic activities are able to self-organize spontaneously, forming a metabolic core of reactive processes that remain active under different growth conditions while the rest of the molecular catalytic reactions exhibit structural plasticity. This global cellular metabolic structure appears to be an intrinsic characteristic common to all cellular organisms. Recent work performed with dissipative metabolic networks has shown that the fundamental element for the spontaneous emergence of this global self-organized enzymatic structure could be the number of catalytic elements in the metabolic networks.

Methodology/Principal Findings

In order to investigate the factors that may affect the catalytic dynamics under a global metabolic structure characterized by the presence of metabolic cores we have studied different transitions in catalytic patterns belonging to a dissipative metabolic network. The data were analyzed using non-linear dynamics tools: power spectra, reconstructed attractors, long-term correlations, maximum Lyapunov exponent and Approximate Entropy; and we have found the emergence of self-regulation phenomena during the transitions in the metabolic activities.

Conclusions/Significance

The analysis has also shown that the chaotic numerical series analyzed correspond to the fractional Brownian motion and they exhibit long-term correlations and low Approximate Entropy indicating a high level of predictability and information during the self-regulation of the metabolic transitions. The results illustrate some aspects of the mechanisms behind the emergence of the metabolic self-regulation processes, which may constitute an important property of the global structure of the cellular metabolism.     

Full-scale model of glycolysis in Saccharomyces cerevisiae.

We present a powerful, general method of fitting a model of a biochemical pathway to experimental substrate concentrations and dynamical properties measured at a stationary state, when the mechanism is largely known but kinetic parameters are lacking. Rate constants and maximum velocities are calculated from the experimental data by simple algebra without integration of kinetic equations. Using this direct approach, we fit a comprehensive model of glycolysis and glycolytic oscillations in intact yeast cells to data measured on a suspension of living cells of Saccharomyces cerevisiae near a Hopf bifurcation, and to a large set of stationary concentrations and other data estimated from comparable batch experiments. The resulting model agrees with almost all experimentally known stationary concentrations and metabolic fluxes, with the frequency of oscillation and with the majority of other experimentally known kinetic and dynamical variables. The functional forms of the rate equations have not been optimized.     

Induction of the activity of glycolytic enzymes correlates with enhanced hydrolysis of sucrose in the cytosol of transgenic potato tubers

The aim of this work was to define the metabolic factors which regulate the respiratory pathways in trangenic potato tubers. We previously found that respiration is enhanced in transgenic tubers which express a yeast invertase and a glucokinase from Zymomonas mobilis . In this study we investigated glycolysis in three further transgenic potato lines with profound changes in the mobilization of sucrose. We studied antisense ADPglucose pyrophosphorylase lines which are characterized by a reduction in starch accumulation and a significant build up of sucrose and related metabolic intermediates. We also report the generation of two novel double transgenic lines where the yeast invertase is expressed specifically in tubers of the ADPglucose pyrophosphorylase antisense line, targeted to either the cytosol or apopolast. We evaluated whether the localization of sucrose cleavage had an impact on the glycolytic induction, and assessed if invertase expression in the high-sucrose background had any further effects on glycolysis. We found that induction of the glycolytic enzymes only occurs when the invertase is targeted to the cytosol, and that the extent of this induction was comparable in the wild type and antisenseADPglucose pyrophosphorylase backgrounds. We conclude that the signal regulating glycolysis is directly linked to cytosolic sucrose hydrolysis.     

Modeling of uncertainties in biochemical reactions

Mathematical modeling is an indispensable tool for research and development in biotechnology and bioengineering. The formulation of kinetic models of biochemical networks depends on knowledge of the kinetic properties of the enzymes of the individual reactions. However, kinetic data acquired from experimental observations bring along uncertainties due to various experimental conditions and measurement methods. In this contribution, we propose a novel way to model the uncertainty in the enzyme kinetics and to predict quantitatively the responses of metabolic reactions to the changes in enzyme activities under uncertainty. The proposed methodology accounts explicitly for mechanistic properties of enzymes and physico‐chemical and thermodynamic constraints, and is based on formalism from systems theory and metabolic control analysis. We achieve this by observing that kinetic responses of metabolic reactions depend: (i) on the distribution of the enzymes among their free form and all reactive states; (ii) on the equilibrium displacements of the overall reaction and that of the individual enzymatic steps; and (iii) on the net fluxes through the enzyme. Relying on this observation, we develop a novel, efficient Monte Carlo sampling procedure to generate all states within a metabolic reaction that satisfy imposed constrains. Thus, we derive the statistics of the expected responses of the metabolic reactions to changes in enzyme levels and activities, in the levels of metabolites, and in the values of the kinetic parameters. We present aspects of the proposed framework through an example of the fundamental three‐step reversible enzymatic reaction mechanism. We demonstrate that the equilibrium displacements of the individual enzymatic steps have an important influence on kinetic responses of the enzyme. Furthermore, we derive the conditions that must be satisfied by a reversible three‐step enzymatic reaction operating far away from the equilibrium in order to respond to changes in metabolite levels according to the irreversible Michelis–Menten kinetics. The efficient sampling procedure allows easy, scalable, implementation of this methodology to modeling of large‐scale biochemical networks. Biotechnol. Bioeng. 2011;108: 413–423. © 2010 Wiley Periodicals, Inc.     

Enolase takes part in a macromolecular complex associated to mitochondria in yeast

In eucaryotes, glycolytic enzymes are classically regarded as being localised in the cytosol. Recently, we have shown that part of the cellular pool of the glycolytic enzyme, enolase, is tightly associated with the mitochondrial surface in the yeast Saccharomyces cerevisiae (N. Entelis, I. Brandina, P. Kamenski, I.A. Krasheninnikov, R.P. Martin and I. Tarassov, A glycolytic enzyme, enolase, is recruited as a cofactor of tRNA targeting toward mitochondria in Saccharomyces cerevisiae, Genes Dev. 20 (2006) 1609-1620). Here, using enzymatic assays, we show that all glycolytic enzymes are associated with mitochondria in yeast, to extents similar to those previously reported for Arabidopsis cells. Using separation of mitochondrial complexes by blue-native/SDS-PAGE and coimmunoprecipitation of mitochondrial proteins with anti-enolase antibodies, we found that enolase takes part in a large macromolecular complex associated to mitochondria. The identified components included additional glycolytic enzymes, mitochondrial membrane carriers, and enzymes of the TCA cycle. We suggest a possible role of the enolase complex in the channeling of pyruvate, the terminal product of glycolysis, towards the TCA cycle within mitochondria. Moreover, we show that the mitochondrial enolase-containing complex also contains the cytosolic tRNA(CUU)Lys, which is mitochondrially-imported, and its presumed import carrier, the precursor of the mitochondrial lysyl-tRNA synthetase. This suggests an unsuspected novel function for this complex in tRNA mitochondrial import.     

Experimental determination of control of glycolysis in Lactococcus lactis

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

The Number of Catalytic Elements Is Crucial for the Emergence of Metabolic Cores

Background

Different studies show evidence that several unicellular organisms display a cellular metabolic structure characterized by a set of enzymes which are always in an active state (metabolic core), while the rest of the molecular catalytic reactions exhibit on-off changing states. This self-organized enzymatic configuration seems to be an intrinsic characteristic of metabolism, common to all living cellular organisms. In a recent analysis performed with dissipative metabolic networks (DMNs) we have shown that this global functional structure emerges in metabolic networks with a relatively high number of catalytic elements, under particular conditions of enzymatic covalent regulatory activity.

Methodology/Principal Findings

Here, to investigate the mechanism behind the emergence of this supramolecular organization of enzymes, we have performed extensive DMNs simulations (around 15,210,000 networks) taking into account the proportion of the allosterically regulated enzymes and covalent enzymes present in the networks, the variation in the number of substrate fluxes and regulatory signals per catalytic element, as well as the random selection of the catalytic elements that receive substrate fluxes from the exterior. The numerical approximations obtained show that the percentages of DMNs with metabolic cores grow with the number of catalytic elements, converging to 100% for all cases.

Conclusions/Significance

The results show evidence that the fundamental factor for the spontaneous emergence of this global self-organized enzymatic structure is the number of catalytic elements in the metabolic networks. Our analysis corroborates and expands on our previous studies illustrating a crucial property of the global structure of the cellular metabolism. These results also offer important insights into the mechanisms which ensure the robustness and stability of living cells.     

Consideration of the gain, enzymatic capacity utilization, and response time properties of metabolic networks as a function of operating point and structure

Computer simulation is used to examine the gain, utilization of enzymatic capacity and response time properties of a simple metabolic network consisting of a sequence of irreversible enzyme reactions controlled by non-allosteric feedback inhibition. The latter properties are chosen because interpretation of a metabolic network's structure can be aided by envisioning the network to maximize a single objective such as flux control. However, by examining the properties as a function of the network's operating point (extent of saturation of the first enzymatic reaction) and structural characteristics, it is found that apparent trade-offs occur between definable objectives. It is suggested that greater consideration of the trade-offs that may occur could help generate metabolic network heuristics and facilitate the interpretation of metabolic networks in terms of the functional role they play in the intact organism.     

Design of glycolysis

The design of the glycolytic pathway resulting from the continuous refinement of evolution is discussed with regard to three aspects. 1. Functional and structural properties of individual enzymes. The catalytic constants of the glycolytic enzymes are remarkably optimized; the turnover numbers are within one order of magnitude. The same is true for the molarities of catalytic centres in the cytosol, as is noted for yeast. Functional properties of the enzymes are reflected in their tertiary and quaternary structures. 2. Regulatory mechanisms of single enzymes. A classification of the various types of enzymic control mechanisms operating in the glycolytic pathway is given. In addition to the usual Michaelis-Menten saturation kinetics and the various types of inhibition there is control by positive and negative effectors based on oligomeric structures (fast acting, fine control) as well as regulation by chemical interconversion structures (fast acting, fine control) as well as regulation by chemical based on enzymes cascades (slow acting, very effective). 3. Functional and regulatory mechanisms of the whole glycolytic reaction pathway. A prominent feature is the high enzyme:substrate ratio, which guarantees fast response times. However, a quantitative treatment of the overall kinetics is limited by an incomplete knowledge of the enzymes' dynamic and chemical compartmentation as well as some of their control properties. From an analysis of the oscillatory state, certain control points in the glycolytic chain can be located that coincide with major branching points to other metabolic pathways. These points are controlled by fast-acting cooperative enzymes that operate in a flip-flop mechanism together with the respective antagonistic enzymes, preventing futile cycles. The gating enzymes leading to the glycogen store and the citric acid cycle are of the slow-acting but very effective interconvertible type. The combination of all the complex and intricate features of design yields a glycolytic network that enables the cell to respond to its various metabolic needs quickly, effectively and economically.