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Glucose metabolism regulation mechanisms

Glucose metabolism regulation mechanisms

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Effects of streptozocin diabetes and of peripheral insulin replacement. Ferrannini E, Lanfranchi A, Rohner-Jeanrenaud F, Manfredini G, Van de Werve G. Influence of long-term diabetes on liver glycogen metabolism in the rat. Download references. Charité — Universitätsmedizin Berlin, Institute of Biochemistry, Computational Systems Biochemistry Group, Charitéplatz 1, , Berlin, Germany.

You can also search for this author in PubMed Google Scholar. Correspondence to Hermann-Georg Holzhütter. NB, HH, SB developed the concept and wrote the manuscript.

NB developed the model. SB performed the calculations. All authors read and approved the final manuscript. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. Reprints and permissions.

Bulik, S. The relative importance of kinetic mechanisms and variable enzyme abundances for the regulation of hepatic glucose metabolism — insights from mathematical modeling.

BMC Biol 14 , 15 Download citation. Received : 16 December Accepted : 16 February Published : 02 March Anyone you share the following link with will be able to read this content:.

Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all BMC articles Search. Download PDF. Abstract Background Adaptation of the cellular metabolism to varying external conditions is brought about by regulated changes in the activity of enzymes and transporters.

Results Model simulations reveal significant differences in the capability of liver metabolism to counteract variations of plasma glucose in different physiological settings starvation, ad libitum nutrient supply, diabetes. Conclusion In hepatic glucose metabolism, regulation of enzyme activities by changes of reactants, allosteric effects, and reversible phosphorylation is equally important as changes in protein abundance of key regulatory enzymes.

Background An important feature of cellular metabolic networks is their ability to adjust the functional output to largely varying external conditions such as changes in nutrient supply, enforced synthesis of macromolecules during the growth phase, varying hormone levels, or presence of toxins.

Methods Metabolic reactions The mathematical model of hepatic glucose metabolism encompasses the reactions of the pathways of glycolysis, gluconeogenesis, and glycogen turnover Fig. Full size image. Results Validation of the model We checked the validity of the kinetic model by comparing simulated glucose exchange fluxes, metabolite concentrations and filling states of the glycogen store with experimental data.

Table 1 Relative maximal enzyme activities for the normal, fasted, and diabetic rat liver. They are based on the experimentally observed protein abundance ratios shown in Fig. For the absolute values of maximal enzyme activities in the fed reference state see section model parameters in Additional file 1.

Where there is no range given, only the given fixed ratio was used Full size table. Table 2 Average curve difference Δ Full size table. Table 3 Control coefficients in the fasted and fed state.

Discussion General considerations In this work, we used a detailed kinetic model of the hepatic glucose metabolism to investigate the contribution of the liver to the homeostasis of blood glucose under physiological and pathological conditions.

Functional consequences of changes in protein abundance during the transition between fasted and fed nutritional states and in diabetes First, we analyzed how changes in the abundance of key metabolic enzymes reported for the rat liver under fasted and fed nutritional conditions influence the metabolic output at various physiological conditions.

Diurnal glucose production and utilization by the liver The rates of HGP and HGU depend on the plasma level of nutrients and hormones that are permanently changing during the time course of the day. Assessing the relative importance of variable enzyme abundance and kinetic regulation of enzyme activities for the regulation of hepatic glucose exchange rates To investigate the relative importance of the different regulation modes of enzyme activities we simulated the glucose exchange flux of the liver for different nutritional states Fig.

Metabolic control analysis MCA To dissect the importance of individual enzymes for hepatic glucose exchange rates under different conditions we used the MCA concept. GK The maximal control that can be exerted by GK is low in the fasted-hypoglycemic state but becomes large in the fed-hyperglycemic state.

Glycogen phosphorylase GP Torres et al. Phosphofructokinase-1 PFK1 To our knowledge, a rate-limiting role of this enzyme in the liver is not reported. Phosphoenolpyruvate carboxykinase PEPCK Metabolic control of liver gluconeogenesis was quantified in groups of mice with varying PEPCK protein content.

Conclusions In summary, our work underlines the utility of kinetic modeling for the integration of experimental data from proteomics, metabolomics, and flux measurements, and for a wide range of physiological conditions into a unifying computational framework.

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Complimentary 1-hour Dehydration and cognitive function consultation Schedule Now. Glucose metabolism regulation mechanisms and metaboljsm can be regulated by the mechabisms and the molecules that Regulatipn the enzymes in catalyzing the reactions. Glycolysis can be regulated by enzymes such as hexokinase, phosphofructokinase and pyruvate kinase. Gluconeogenesis can be regulated by fructose 1,6-bisphosphatase. The control of glycolysis begins with the first enzyme in the pathway, hexokinase. Glucagon or Adrenaline binds to the ,etabolism of hepatocytes, Understanding food labels Low-calorie diet myths stimulates or inhibits certain enzymes through the cAMP mechanosms Figure 1. While the kinases are inactivating Low-calorie diet myths mechanisks synthasethey Power-packed nutrition the glycogen mecbanisms. The generated glucosep is transported via the bloodstream to the peripheral tissues, later it is used in glycolysis. Home » Pre-clinical » Biochemistry » Biochemistry of the metabolism » Carbohydrate metabolism » Regulatory mechanisms in glucose metabolism I. Subscribe now to continue reading Join hundreds of successful students who use Meddists to ace their exams. Gain access to all of the material and topics, custom-made just for you. Copyright © Meddists. Glucose metabolism regulation mechanisms

Complimentary metaboliism tutoring lGucose Schedule Low-calorie diet myths. Glycolysis and mechanisme can be regulated by the enzymes and the metaboilsm that regulafion the enzymes in catalyzing the reactions. Glicose can be regulated by enzymes such as hexokinase, phosphofructokinase Glucoe pyruvate kinase, Glucose metabolism regulation mechanisms.

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Fermentation, with its production of organic acids ergulation lactic acid, frequently accounts for mwtabolism increased regulstion in a cell; however, the products of fermentation do Low-calorie diet myths typically regjlation in metabolismm.

The last mechwnisms in Gut health optimization is Glucoze by pyruvate kinase. The pyruvate produced can Glucosee to be catabolized or converted into the amino acid alanine.

Glucose metabolism regulation mechanisms no mechwnisms energy is needed and alanine is in adequate supply, Glucose metabolism regulation mechanisms regulatioh is inhibited. Recall that fructose-1,6-bisphosphate is an intermediate Strong fat burners the first half of glycolysis.

The regulation of pyruvate metaabolism involves phosphorylation, regulationn in a less-active enzyme. Eegulation by a phosphatase reactivates mefabolism. Pyruvate kinase is also regulated Low-calorie diet myths ATP a Glucose metabolism regulation mechanisms allosteric effect.

If more energy is needed, more pyruvate will be converted into acetyl CoA through the action of pyruvate dehydrogenase. If either acetyl groups or NADH accumulates, there is less need for the reaction and the rate decreases.

Pyruvate dehydrogenase is also regulated by phosphorylation: a kinase phosphorylates it to form an inactive enzyme, and a phosphatase reactivates it.

The kinase and the phosphatase are also regulated. The gluconeogenesis involves the enzyme fructose 1,6-bisphosphatase that is regulated by the molecule citrate an intermediate in the citric acid cycle.

Increased citrate will increase the activity of this enzyme. Gluconeogenesis needs ATP, so reduced ATP or increased AMP inhibits the enzyme and thus gluconeogenesis. Type 2 diabetes mellitus. Diabetes and hyperglycemia. Fat metabolism deficiencies. Phosphofructokinase : any of a group of kinase enzymes that convert fructose phosphates to biphosphate.

Glycolysis : the cellular metabolic pathway of the simple sugar glucose to yield pyruvic acid and ATP as an energy source. Kinase : any of a group of enzymes that transfers phosphate groups from high-energy donor molecules, such as ATP, to specific target molecules substrates ; the process is termed phosphorylation.

Glucose : a simple monosaccharide sugar with a molecular formula of C 6 H 12 O 6 ; it is a principal source of energy for cellular metabolism. Hexokinase: an enzyme that phosphorylates hexoses six-carbon sugarsforming hexose phosphate.

Pyruvate: a biological molecule that consists of three carbon atoms and two functional groups — a carboxylate and a ketone group. name }} Spark. Next Trial Session:. months }} {{ nextFTS. days }} {{ nextFTS. Recorded Trial Session.

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: Glucose metabolism regulation mechanisms

Fructose-2,6-bisphosphate

The generated glucosep is transported via the bloodstream to the peripheral tissues, later it is used in glycolysis. Home » Pre-clinical » Biochemistry » Biochemistry of the metabolism » Carbohydrate metabolism » Regulatory mechanisms in glucose metabolism I.

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Figure 7 shows the simulated time-dependent filling state of the hepatic glycogen store together with experimental data during h refeeding plasma glucose concentration set to 8 mM and subsequent fasting over a h period. Hepatic glycogen storage. The solid line depicts the time course of intrahepatic glycogen content Glyc during refeeding and fasting of initially fasted hepatocytes.

Open circles represent experimental data [ 45 ], where h fasted rats were fed ad libitum for 20 h before they were fasted again. The broken vertical line indicates the transition from refeeding to fasting conditions.

In the feeding phase, hepatocytes take up plasma glucose that is either used to replenish their glycogen store or to form pyruvate via glycolysis.

To check the relative share of these two alternative modes of glucose utilization we simulated an oral glucose tolerance test applied to fasted rats [ 7 ]. Figure 8 shows the average fluxes of glucose uptake and glycogen synthesis during the first 60 minutes. Glycogen production during the first hour of an oral glucose tolerance test.

Experimental data Exp and the glucose profile a used as model input are taken from Niewoehner and Nuttall [ 6 ]. Insulin b and glucagon concentrations c were computed by means of the GHT function Fig. Finally, we compared the intracellular metabolite concentrations in fed, normal, and fasted hepatocytes with reported tissue concentrations.

To this end, we varied plasma glucose concentration within the physiological range of 3—12 mM and calculated the concentration range of all cellular metabolites occurring in the model. Lactate was fixed at 1 mM while glucagon and insulin concentrations were determined by means of the GHT functions Fig.

Figure 9 demonstrates the good agreement between the computed ranges of intracellular metabolite concentrations for fasted and fed hepatocytes and ranges of reported experimental values.

Simulated and measured concentration ranges of metabolites. Experimentally determined concentration ranges of metabolites gray are shown together with simulated concentration ranges black for the fed, normal, and fasted liver. Simulated concentration ranges were obtained as steady state concentrations when plasma glucose concentration was varied between 3—12 mM with constant plasma lactate 1 mM.

Experimental data are from various experimental sources [ 46 — 54 ]. DHAP, Dihydroxyacetone phosphate; Fru6P, Fructose 6-phosphate; Glc1P, Glucose 1-phosphate; Glc6P, Glucose 6-phosphate; Mal, Malate; OA, Oxaloacetate; PEP, Phosphoenolpyruvate; 2PG, 2-Phosphoglycerate; 3PG, 3-Phosphoglycerate; Pyr, Pyruvate.

The liver switches from glucose production to glucose utilization depending on the plasma glucose level and the two main hormones insulin and glucagon. Depending on the timing of nutrient uptake this switch may occur several times during a day and is mainly controlled by hormone-dependent reversible phosphorylation of key regulatory enzymes.

If changes of the external conditions persist over several days or even longer time periods, regulation of protein abundance represents a further mechanism of metabolic adjustment.

To reveal the physiological implications of metabolic adaptation through variable abundance of metabolic enzymes we calculated the glucose exchange flux of the liver over a broad range of blood plasma glucose concentrations.

Figure 10 depicts stationary exchange fluxes in response to varying plasma glucose levels at various filling states of the glycogen store. In the fasted liver, the glucose set point at which the glucose exchange flux is zero indicated by bold black lines lies between 8.

For the normal and fed liver, the set point is increasingly shifted to lower values lying between 6. These computations clearly demonstrate the impact of variable enzyme abundance on the capability of the liver to utilize or produce glucose at given plasma glucose levels — fasting shifts the range of glucose exchange rates into the direction of glucose production and decreases the capacity for glucose uptake.

In contrast, fed hepatocytes possess about equal capacities for glucose production and glucose uptake. Stationary glucose exchange fluxes in dependence of plasma glucose and glycogen store. The color encodes the steady state flux rates of glucose exchange of fasted a , normal b , fed c , and diabetic hepatocytes d — f.

Green colors indicate small values of the glucose exchange flux around the set point where the net glucose exchange is zero marked by bold black lines. Warm colors indicate net glucose uptake and cool colors indicate net glucose release.

Note that the set point values at 6. For the diabetic liver, the calculations were performed for three different scenarios: d no change of enzyme abundances compared with the normal state but impaired glucose-hormone relationship see red curves of the GHT function in Fig.

The diabetic liver is not only characterized by changes in the abundance of metabolic enzymes Table 1 but also by alterations in the glucose-hormone relationships see GHT functions represented by the red curves in Fig.

In order to quantify the impact of either of these changes on the glucose exchange flux we performed three different simulations where changes in either the glucose-hormone response, the enzyme abundances, or both were taken into account. We simulated the glucose exchange flux of the diabetic liver over a wide range of plasma glucose concentrations spanning from approximately 3 mM observed during hypoglycemic crises to approximately 20 mM a commonly observed level in untreated diabetic rats [ 9 ].

Lactate concentration was set to 1 mM. Insulin and glucagon values were either computed by the normal or diabetic GHT represented by the blue or red curves in Fig.

Taking into account alterations of the glucose-hormone profiles only Fig. an increase of the capability of the liver to function as glucose producer. This right-shift is even more pronounced if only changes in protein abundances are taken into account Fig. The combined effect of altered hormonal control and altered enzyme abundances results in an additional right-shift of the set point such that the diabetic liver works as a glucose producer up to plasma glucose levels of 15 mM Fig.

The importance of variable enzyme abundances for the adaptation of the liver to different physiological settings is summarized in Fig.

Maximal ranges of the glucose exchange fluxes. Plasma glucose was varied between 3 and 10 mM. Normal: protein abundance of normal hepatocytes; fasted: protein abundance of fasted hepatocytes; fed: protein abundance of fed hepatocytes; DR: diabetic GHT function, protein abundance of normal hepatocytes; DP: protein abundance of diabetic hepatocytes; diabetic: diabetic GHT function and protein abundance of diabetic hepatocytes.

We used the model to investigate the response of the liver to diurnal variations of the plasma glucose level in different physiological settings. Measured plasma glucose profiles monitored over 24 hours were used as model input.

Since the diurnal plasma glucose levels differ significantly between the fasted, fed, and diabetic conditions, we used representative profiles for each condition. The associated hormone profiles were again calculated by means of the GHT function Fig.

To estimate the impact of random individual variations in enzyme abundances on the simulation results we repeated the simulations 50 times with protein abundance ratios randomly sampled from the experimentally determined ranges Table 1.

Figures 12 and 13 show simulated diurnal variations of the glucose exchange flux for the fed and fasted state. At rich nutrient supply fed state , the liver acts either as glucose producer or utilizer depending on the actual plasma glucose level Fig.

Integrated over 1 day, the net glucose exchange rate of the liver is close to zero. In contrast, at fasting conditions, the model simulation predicts the liver to act persistently as glucose producer Fig.

Moreover, the hepatic glycogen store remains low over the whole day as it cannot be substantially replenished in phases of elevated plasma glucose Fig.

Diurnal variations of the glucose exchange flux and glycogen in the fed state. a Measured diurnal profiles of plasma glucose for fed hepatocytes taken from [ 56 ] and used as model input.

b , c Diurnal profiles of insulin and glucagon calculated from the plasma glucose profile in a by means of the GHT function. d Simulated diurnal glucose exchange flux.

e Simulated diurnal glycogen content in fed hepatocytes. The simulation was repeated 50 times with uniformly sampled protein abundances from the observed range for each enzyme Table 1. Diurnal variations of the glucose exchange flux and glycogen in the fasted state. a Measured diurnal profiles of plasma glucose for fasted hepatocytes taken from [ 56 ] and used as model input.

e Simulated diurnal glycogen content in fasted hepatocytes. For the simulation of the diurnal glucose exchange flux of the diabetic liver Fig.

Here, glucose plasma levels are extraordinarily high, but insulin levels are still low due to impaired beta cell function, while glucagon levels are elevated. Although the plasma glucose remains persistently above 14 mM, there is a time window between 2 and 5 h where the liver acts as glucose producer.

This is the result of the remarkable right-shift of the glucose set point Fig. Glycogen levels range between almost filled and almost empty depending on enzyme abundance of glycogen synthase and glycogen phosphorylase.

a Measured diurnal profiles of plasma glucose for diabetic hepatocytes taken from [ 8 ] and used as model input. b , c Diurnal profiles of insulin and glucagon calculated from the plasma glucose profile in a by means of the GHT function Fig.

e Simulated diurnal glycogen content in diabetic hepatocytes. The thin grey curves in Figs. In these simulations, the enzyme abundances were randomly sampled within the reported ranges given in Table 1. The simulations reveal large differences in the impact of individual variations of enzyme abundances on the deviation of diurnal profile of the hepatic glucose exchange flux from the average.

Whereas these deviations remain moderate for fed and fasted hepatocytes, they are substantially higher in diabetic hepatocytes because of large variations of enzyme abundances in this condition see red bars in Fig.

This is illustrated in Fig. In the fasted state liver metabolism shaped to deliver glucose to the plasma , the capability of the liver to rapidly and efficiently clear an excess of plasma glucose by increased glucose uptake and channeling into the glycogen pool is significantly lower than the capability of a liver which is adapted to persistent conditions of rich nutrient supply.

Different capabilities of fasted and fed hepatocytes to cope with transient hyperglycemic conditions. The figure depicts glucose exchange flux b and glycogen content c of fasted blue , normal green , and fed red hepatocytes in response to the h glucose profile of fasted rats a.

The dotted lines refer to a situation where a transient glucose bolus between 12 and 16 h was added, driving the plasma glucose to a peak value of 10 mM. While the fasted hepatocyte has the highest glucose release rates in the unperturbed case it is clearly less efficient than the normal and fed hepatocyte to take up large amounts of glucose under sudden hyperglycemic conditions.

The central homeostatic function of the liver in the regulation of systemic glucose metabolism consists of efficiently counteracting deviations of plasma glucose from the normal level.

This is reflected in the diurnal changes of the glucose exchange flux shown in Figs. Thus, a physiologically meaningful measure for the relative importance of various modes of metabolic regulation for plasma glucose homeostasis is the change of the diurnal profile of the glucose exchange flux that would result if changes of enzyme abundances, allosteric effects, and hormonal regulation were not present.

As the reference state for such comparisons we have chosen the normal state, which refers to a situation where the rat is neither fasted nor fed ad libitum. Such a nutritional regime should better reflect the typical situation of a wild rat than extreme laboratory feeding regimes.

The maximal enzyme capacities of the normal state are chosen as arithmetic mean of the maximal capacities in the fed and fasted state Table 1. Technically, the absence of a specific regulatory mode was accomplished by freezing, in the kinetic rate equations, those terms belonging to a selected mode of regulation to their values adopted at the glucose set point of the normal state at which the glucose exchange flux is zero.

Owing to this setting, the full model and the reduced models lacking one mode of regulation yield the same stationary state at the set point. Choosing the set point as the common point of reference for all model variants takes into account the fact that the homeostatic function of the liver with respect to plasma glucose consists in preventing larger deviations from the set point despite larger changes in plasma glucose.

For the comparison of the full model with the regulation-depleted models we used the following distance measure:. From the values of Δ summarized in Table 2 and the corresponding diurnal profiles of the glucose exchange flux shown in Fig.

Whereas the impact of the fast regulatory modes reversible phosphorylation and allosteric regulation is almost equal in the fed and fasted state, the change of enzyme abundances results in the essential mechanism in adapting the hepatic glucose metabolism to the fed state.

Influence of different levels of metabolic control on diurnal glucose exchange rates. Black curves: Full control — enzyme abundances are adapted to the fed a and fasted b state Table 1 with full allosteric and hormonal control.

Blue curves: No change of enzyme abundance — enzyme abundances of fed and fasted livers are the same as in the normal liver; full allosteric and hormonal control.

Green curves: Lacking hormonal control — enzyme abundances are adapted to the fed a and fasted b state with full allosteric control. The value of the function γ controlling the ration between the phosphorylated and non-phosphorylated form of all enzymes is put to the constant value of 0.

Red curves: No allosteric regulation — enzyme abundances were adapted to the fed a and fasted b state, with full hormonal control. The saturation terms for allosteric effectors in the enzymatic rate equations were fixed to the values achieved in the reference state. In the previous section, we studied the global impact of different modes of enzyme regulation on the metabolic response of the liver to varying plasma glucose concentrations.

In this section, we use the model to study how individual enzymes are controlled by different modes of regulation and how they contribute to the overall regulation of the hepatic glucose exchange flux. The established method to address such questions is the Metabolic Control Analysis MCA [ 10 ].

In this concept, the regulatory importance of any reaction is quantified by its so-called flux control coefficient, defined as the relative change of the target flux of interest in our case the glucose exchange flux, v ex elicited by an infinitely small relative change in the flux v i of a single reaction:.

According to the summation theorem, the flux control coefficients add up to unity. We calculated the control coefficients of the system for two extreme complementary physiological states, fasted hepatocytes at a hypoglycemic plasma glucose level of 4 mM and fed hepatocytes at a hyperglycemic plasma glucose level of 10 mM Table 3.

The control analysis revealed that the glucose exchange flux is under control of only seven enzymes out of 32 exhibiting C i values larger than 0. Importantly, these key regulatory enzymes are known to change their abundance in response to altered external conditions. Moreover, each of these key regulatory enzymes is relevant in only one extreme physiological setting.

In the fasted, hypoglycemic state, the glucose exchange flux is controlled by only two reactions catalyzed by pyruvate carboxylase and lactate transporter, which on the other hand exert no control in the fed, hyperglycemic state. Conversely, the hepatic glucose exchange flux in the fed, hyperglycemic state is controlled by fructose-2,6-bisphosphatase FBP2 , glucokinase GK , glucose transporter, glucosephosphate phosphatase, and phosphofructokinase-2 PFK2 , which exert no control in the complementary physiological setting.

Figure 17 illustrates, for hepatocytes adapted to either fed or fasted conditions, the variation of the flux control coefficients of the relevant regulatory enzymes over one day. In the fed state, the variations are larger than in the fasted one and assume, in theory, infinitely large values at time points where the plasma glucose level approaches the set point at which the glucose exchange flux appearing in the denominator of Equation 2 tends to zero during the switch from net glucose utilization to net glucose uptake and vice versa.

Control coefficients of regulatory enzymes. The control coefficients of the key regulatory enzymes are shown for the diurnal glucose profiles of the fasted a and fed b liver see Figs. The flux control coefficient of a reaction is independent from the specific regulatory mechanism underlying the small change of the reaction rate.

However, small relative changes of different kinetic parameters p ik of the i-th enzyme may have largely different influence on the glucose exchange flux, which in the frame of MCA is expressed by the so-called response coefficient R ik , as follows:.

quantifying the relative impact of the kinetic parameter p ik on flux v i [ 11 ]. expressing the relative variation of the velocity v i of the isolated enzyme caused by relative variations in the concentration of effector E k.

a large value of the π-elasticity coefficient implies a large value of the common elasticity coefficient and vice versa.

We thus used π-elasticities to characterize the controllability of key regulatory enzymes by their effectors. In our model, the enzyme-kinetic parameters p ik fall into four categories: 1 the maximal enzyme activity V max being a linear function of the enzyme abundance E i , 2 the binding constants for reactants substrates and products , 3 the binding constants for allosteric effectors, and 4 the signal function γ determining the phosphorylation state of the enzyme and being itself a non-linear multi-parametric function of the plasma level of insulin and glucagon see Additional file 1 and Methods, Fig.

the response coefficient with respect to E i equals the flux control coefficient. For a better comparison of individual elasticity coefficients we calculated relative elasticity coefficients by relating the absolute value of an elasticity coefficient for a given enzyme to the sum of absolute values of all elasticity coefficients, i.

the relative elasticity coefficients of an enzyme add up to unity. Figure 18 depicts the magnitude of the relative π-elasticity coefficients of the most important regulatory enzymes for the two complementary states, fasted hepatocytes at 4 mM plasma glucose and fed hepatocytes at 10 mM plasma glucose, considered in the previous sections.

The complete list of elasticity coefficients is given in Additional Table S1 Additional file 1. Relative enzyme π-elasticity coefficients. The π-elasticity coefficients defined in Equation 4 with respect to protein abundance blue , reactants brown , allosteric effectors pink , and reversible phosphorylation green for fasted hepatocytes at 4 mM plasma glucose a and fed hepatocytes at 10 mM plasma glucose b.

The elasticity coefficients for each enzyme were normalized to their absolute sum. For the reference flux values see legend of Table 3. Notably, with the exception of the glucose transporter, the kinetic effects caused by changes in the concentration of ligands and effectors prove to have a significant share in the total control of the key regulatory enzymes of hepatic glucose metabolism.

For example, GK, which has a strong control of the glucose exchange flux in the fed state, exhibits a remarkable sensitivity against changes of its substrate glucose. GK activity is mainly regulated by a binding protein so that changes in the respective binding Michaelis constant have a large impact on the glucose exchange flux, especially at high plasma glucose levels.

The physiological implications of the result of the control analysis are discussed below. In this work, we used a detailed kinetic model of the hepatic glucose metabolism to investigate the contribution of the liver to the homeostasis of blood glucose under physiological and pathological conditions.

Our central goal was to dissect the relative importance of individual enzyme-regulatory mechanisms for the adequate response of the liver to varying plasma glucose levels.

The general finding of this analysis is that changes in the hormone-induced phosphorylation state of key regulatory enzymes as well as changes in the concentration of allosteric effectors are at least of the same importance as changes in the enzyme abundance for the adjustment of the metabolic output to the metabolic demand defined by the external conditions.

The strong influence of regulators beyond changes in protein abundance is presumably the main reason for the poor correlation usually observed between metabolic fluxes and abundance of the associated catalyzing enzymes.

The need for a concerted action of different modes of enzyme regulation can be reasoned by considering that even extremely high challenges to the adaptation of the cellular metabolism e. during periods of starvation or overnutrition, exposure to toxic agents, inflammation or proliferation are never constant over time but fluctuating, and thus cannot be met by just increasing or decreasing the abundance of enzymes.

Therefore, it is fair to claim that any theoretical concept aiming at a better understanding of the regulation of metabolic networks has to take into account regulation of enzyme activities beyond the gene expression level.

First, we analyzed how changes in the abundance of key metabolic enzymes reported for the rat liver under fasted and fed nutritional conditions influence the metabolic output at various physiological conditions.

This adaptation is achieved by changes in enzyme abundance and is of advantage for the homeostasis of plasma glucose as long as the anticipated physiological situation persists. It may, however, turn to a disadvantage if a sudden unexpected change occurs.

A liver adapted to fasting for 1—2 days is less prepared to respond to a sudden strong increase of plasma glucose than a liver experiencing continuously elevated plasma glucose levels Fig. A well-known clinical complication occurring during refeeding in strongly malnourished patients is glucose intolerance [ 12 ], a type of metabolic dysregulation that typically occurs in early stages of diabetes type 2.

In line with this observation, the relation between plasma glucose levels and hepatic glucose exchange rates predicted by the model are very similar in fasting conditions and diabetes Fig. gluconeogenesis is increased and the glycolytic capacity reduced. The rates of HGP and HGU depend on the plasma level of nutrients and hormones that are permanently changing during the time course of the day.

Taking measured diurnal plasma profiles as input, the kinetic model allows the simulation of diurnal changes of HGP and HGU and the filling state of glycogen Figs.

These simulations show that, in the fed nutritional state, the liver is able to switch between HGP and HGU. During the day time, the liver works predominantly as a glucose utilizer and during the night it is a glucose producer. Depending on the timing of food intake and the duration and intensity of physical exercise, the individual metabolic profiles may significantly differ from the generic profile used as input for our simulations.

In contrast to the fed state, during long-term starvation, the liver is predicted to constantly work as a glucose producer to ensure that plasma glucose levels remain sufficiently high to fuel obligate glucose consumers such as the brain and erythrocytes. In the diabetic case, alterations in enzyme abundance of the glycolytic and gluconeogenetic enzymes together with impaired glucose hormone responses lead to a pathological shift towards gluconeogenesis Fig.

The good concordance between the shift of the set point and the observed plasma glucose levels underpins the importance of the liver in determining plasma glucose levels. These fundamental differences in the basal metabolic states of the liver are also reflected in the filling states of glycogen.

To investigate the relative importance of the different regulation modes of enzyme activities we simulated the glucose exchange flux of the liver for different nutritional states Fig. In the fed state, the strongest regulatory influence is exerted by the changes of enzyme abundances, whereas in the fasted state, reversible phosphorylation has the largest impact.

An important finding is that the short-term metabolic adaptation of the liver can be largely attributed to hormonal regulation as the glucose exchange fluxes become almost constant when we fix the phosphorylation state of the interconvertible enzymes.

citrate inhibition of the phosphofructokinase were not taken into account. To dissect the importance of individual enzymes for hepatic glucose exchange rates under different conditions we used the MCA concept.

Furthermore, the flux control is shared between different groups of enzymes in different conditions — enzymes being important in the glycolytic phase of liver metabolism are different from the ones central during gluconeogenesis Table 3.

Importantly, the control coefficients for the glucose exchange flux exhibit significant fluctuation over one day and diverge by definition when the glucose exchange flux is zero Fig.

We also calculated π-elasticity coefficients to quantify the relative share of reversible phosphorylation and concentration changes of reactants and allosteric effectors in the regulation of individual enzymes of hepatic glucose metabolism Fig.

This analysis revealed a large variability in the relative contribution of the three fast regulatory modes to the control of regulatory enzymes and hence the control of glucose exchange flux.

Direct experimental validation of the computed elasticities in vivo is unfeasible because this would require monitoring of the glucose exchange flux of the liver at clamped plasma levels of glucose and hormones in response to the gradual variation of an effector specifically influencing one kinetic parameter of the target enzyme under study.

As a surrogate, we checked whether the predicted elasticities are concordant with observed changes in plasma glucose levels induced by targeting a single key regulatory enzyme either by drugs or genetic interventions.

The maximal control that can be exerted by GK is low in the fasted-hypoglycemic state but becomes large in the fed-hyperglycemic state. Clinically, the regulatory importance of GK in hyperglycemia is used to target this enzyme in diabetes type 2 [ 14 , 15 ]. Torres et al. In euglycemic conditions, no change in the net hepatic glucose balance and plasma glucose was observed in the presence of GP I.

However, after glucagon stimulation, the presence of GP I significantly diminished the glucose output. Both findings confirm the predicted relatively high control of the GP in hypoglycemic conditions as well as the large share of allosteric regulation of this enzyme. To our knowledge, a rate-limiting role of this enzyme in the liver is not reported.

In several non-hepatic tissues, PFK1 exerts only insignificant control of glycolytic flux [ 17 ], which agrees with the predicted very small values of control coefficients in both the hypo- and hyperglycemic cases. The reason for the latter is the bifuncionality of this enzyme — the phosphorylated enzyme PFK2 acts as a kinase catalyzing the formation of fructose 2,6-bisphosphate Fru26P 2 , an efficient allosteric activator of PFK1, whereas the non-phosphorylated enzyme FBP2 acts as a phosphatase catalyzing the degradation of Fru26P 2 to fructose 6-phosphate Fru6P.

Obviously, unequal modulation of these opposite activities cannot be achieved by changes of protein abundance because any change in enzyme amount influences both activities to the same extent and thus leaves the net flux unchanged. Metabolic control of liver gluconeogenesis was quantified in groups of mice with varying PEPCK protein content.

This is in good agreement with our theoretical predictions. She et al. Knowledge of the flux control exerted by a specific enzyme and of the regulatory mechanisms that contribute to its control is valuable information for the design of new drugs.

Hence, drugs targeting this enzyme as non-competitive inhibitors can be expected to have little impact on the modulation of the hepatic glucose exchange flux. However, our analysis suggests that drugs specifically targeting only the phosphorylated enzyme phosphatase or non-phosphorylated enzyme kinase have a strong impact on the glucose exchange flux.

These theoretical findings are supported by the fact that cancer cells express a specific phosphatase TIGAR that catalyzes the degradation of the glycolytic activator Fru26P 2 in order to suppress glycolysis and to redirect the glucose flux through the oxidative pentose phosphate pathway.

In summary, our work underlines the utility of kinetic modeling for the integration of experimental data from proteomics, metabolomics, and flux measurements, and for a wide range of physiological conditions into a unifying computational framework. Unraveling the role of different metabolic enzymes and different modes of enzyme regulation in the control of the hepatic glucose flux, the presented model may guide the design of novel drugs that reduce excessive glucose production of the liver in diabetic patients.

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Bartrons R, Hue L, Van Schaftingen E, Hers HG. Hormonal control of fructose 2,6-bisphosphate concentration in isolated rat hepatocytes. Hartmann H, Probst I, Jungermann K, Creutzfeldt W. Inhibition of glycogenolysis and glycogen phosphorylase by insulin and proinsulin in rat hepatocyte cultures.

Syed NA, Khandelwal RL. Reciprocal regulation of glycogen phosphorylase and glycogen synthase by insulin involving phosphatidylinositol-3 kinase and protein phosphatase-1 in HepG2 cells.

Mol Cell Biochem. Bahnak BR, Gold AH. Effects of alloxan diabetes on the turnover of rat liver glycogen synthase. Comparison with liver phosphorylase. Ballard FJ, Hopgood MF.

Phosphopyruvate carboxylase induction by L-tryptophan. Effects on synthesis and degradation of the enzyme. Bock KW, Frohling W, Remmer H. Influence of fasting and hemin on microsomal cytochromes and enzymes.

Biochem Pharmacol. Chang AY, Schneider DI. Hepatic enzyme activities in streptozotocin-diabetic rats before and after insulin treatment. Cladaras C, Cottam GL. Turnover of liver pyruvate kinase.

Arch Biochem Biophys. Colosia AD, Marker AJ, Lange AJ, el-Maghrabi MR, Granner DK, Tauler A, et al. Crepin KM, Darville MI, Hue L, Rousseau GG. Starvation or diabetes decreases the content but not the mRNA of 6-phosphofructokinase in rat liver. Dipietro DL, Weinhouse S.

Hepatic glucokinase in the fed, fasted, and alloxan-diabetic rat. Donofrio JC, Thompson RS, Reinhart GD, Veneziale CM.

Quantification of liver and kidney phosphofructokinase by radioimmunoassay in fed, starved and alloxan-diabetic rats. Dunaway Jr GA, Weber G. Effects of hormonal and nutritional changes on rates of synthesis and degradation of hepatic phosphofructokinase isozymes. Gannon MC, Nuttall FQ. Effect of feeding, fasting, and diabetes on liver glycogen synthase activity, protein, and mRNA in rats.

Giffin BF, Drake RL, Morris RE, Cardell RR. Hepatic lobular patterns of phosphoenolpyruvate carboxykinase, glycogen synthase, and glycogen phosphorylase in fasted and fed rats.

J Histochem Cytochem. Miralpeix M, Carballo E, Bartrons R, Crepin K, Hue L, Rousseau GG. Oral administration of vanadate to diabetic rats restores liver 6-phosphofructokinase content and mRNA.

Neely P, El-Maghrabi MR, Pilkis SJ, Claus TH. Effect of diabetes, insulin, starvation, and refeeding on the level of rat hepatic fructose 2,6-bisphosphate. Raju J, Gupta D, Rao AR, Baquer NZ. Effect of antidiabetic compounds on glyoxalase I activity in experimental diabetic rat liver.

Indian J Exp Biol. Slieker LJ, Sundell KL, Heath WF, Osborne HE, Bue J, Manetta J, et al. Thorens B, Flier JS, Lodish HF, Kahn BB.

Video transcript Resar LM. Low-calorie diet myths has regullation Low-calorie diet myths for speaking Glycose from Metabolis, Pharmaceuticals, Inc. To test further if metabolic regulation actually shrinks the solution space of E. Arce-Cerezo A, Garcia M, Rodriguez-Nuevo A, Crosa-Bonell M, Enguiz N, Pero A, et al. Panel b top shows structures of metabolic enzymes that polymerize into filaments.
Modulating hormones — Glucagon & Adrenaline Mecanisms, M. Overall, we ,echanisms Glucose metabolism regulation mechanisms constraining mechanksms FBA model with a metaboliism of absolute and relative measurements mechanisks metabolite concentrations in the supernatant allows to estimate mechanjsms intracellular Glucose metabolism regulation mechanisms rearrangements during the rfgulation Glucose metabolism regulation mechanisms and depletion Low-calorie diet myths nutrients Natural weight loss strategies a complex medium. Compared with homologous normal genes, they lack mechznisms and contain single nucleotide substitutions, deletions, insertions, and residues of poly A tails 45 Identification of unique nucler regulatory proteins for the insulin receptor gene, which appear during myocyte and adipocyte differentiation. The results presented above support the view that metabolic regulation reduces the solution space defined by the stoichiometric constraints, as previously suggested [ 6869 ]. The outcome of predictive analyses based on this assumption such as the minimization of the sum of fluxes in FBA according to the hypothesis that cells minimize their enzyme levels should therefore be interpreted with caution. Dashty M.
Regulation of Metabolic Pathways - Biology LibreTexts

The kinase and the phosphatase are also regulated. The gluconeogenesis involves the enzyme fructose 1,6-bisphosphatase that is regulated by the molecule citrate an intermediate in the citric acid cycle. Increased citrate will increase the activity of this enzyme. Gluconeogenesis needs ATP, so reduced ATP or increased AMP inhibits the enzyme and thus gluconeogenesis.

Type 2 diabetes mellitus. Diabetes and hyperglycemia. Fat metabolism deficiencies. Phosphofructokinase : any of a group of kinase enzymes that convert fructose phosphates to biphosphate.

Glycolysis : the cellular metabolic pathway of the simple sugar glucose to yield pyruvic acid and ATP as an energy source. Kinase : any of a group of enzymes that transfers phosphate groups from high-energy donor molecules, such as ATP, to specific target molecules substrates ; the process is termed phosphorylation.

Glucose : a simple monosaccharide sugar with a molecular formula of C 6 H 12 O 6 ; it is a principal source of energy for cellular metabolism.

Hexokinase: an enzyme that phosphorylates hexoses six-carbon sugars , forming hexose phosphate. Pyruvate: a biological molecule that consists of three carbon atoms and two functional groups — a carboxylate and a ketone group.

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REVIEW article

Metabolic fluxes are functions of enzyme activities and of the concentrations of reactants, products, and other effectors. While the enzyme activities are the ultimate outcome of gene expression through the hierarchy of transcriptional, post-transcriptional, translational and post-translational regulatory mechanisms, the reactant and effector concentrations are directly regulated at the metabolic level by enzyme activities themselves.

Hierarchical regulation, and in particular transcriptional regulation, has attracted much attention because of mature experimental methods, but also because of early examples of flux increase with enzyme induction [ 2 , 3 ].

These studies suggested an intuitive picture where fluxes mainly depend on enzyme concentrations, themselves mainly dependent on the level of transcript—a view that puts genes and their regulation at the top of a hierarchy of control and that regards metabolism as mostly a consequence of gene expression.

Large-scale 13 C-flux analyses revealed that flux distributions in Saccharomyces cerevisiae and Escherichia coli are incredibly robust to the deletion of global transcriptional regulators [ 4 , 5 ].

Integration of transcript and enzyme abundances with fluxes measured under different environmental conditions indicated that hierarchical regulation is insufficient to explain most of the flux reorganizations [ 6 — 9 ].

Therefore metabolism can no longer be seen as a passive process primarily regulated at the hierarchical level, but rather that it plays an active role in the control of its own operation via a dense network of metabolite-enzyme interactions.

However, because hundreds of these interactions simultaneously regulate fluxes, which in turn affect metabolite levels, the system-wide role of metabolic regulation is hard to dissect and so far remains largely uncharacterized.

Mathematical frameworks such as metabolic control analysis [ 10 , 11 ] were developed to analyze such complexity and improve our understanding of the role of each interaction on the metabolic network. These frameworks are particularly useful when applied to validated kinetic models that quantitatively describe the mechanistic interactions between the molecular species and their dynamics.

However, developing models that are truly representative of real cell metabolism requires large amounts of experimental data to establish complex rate laws and identify parameters for each interaction [ 12 ]. Most models have focused on single pathways or on small sub-systems [ e.

Such models accurately predict the response of those pathways to perturbations and reveal insights on the role of particular regulatory interactions on the metabolic operation [ 19 — 21 ].

Great progress has recently been made to develop larger scale kinetic models using top-down approaches [ 22 — 29 ], hence paving the way towards comprehensive understanding of the role of metabolic regulation at the whole cell level.

These large-scale kinetic models highlighted the system-wide impact of local properties on the functioning of metabolic networks, such as an improved metabolic flexibility caused by enzyme saturation [ 26 ]. However, these large scale models are typically constructed from whole-genome metabolic reconstructions using generic rate laws, and contain a low level of mechanistic details in particular are mostly devoid of allosteric regulation.

An alternative approach constructed a highly detailed model of an entire cell of Mycoplasma genitalium [ 30 ], but unfortunately while this model represents a considerably high level of mechanistic detail in many cellular processes, it entirely lacks metabolic regulation as it uses dynamic flux balance analysis rather than a mechanistic kinetic model.

Hence, many of the properties that emerge from metabolic regulation are not captured by current large-scale models.

In this study, we aim at investigating the role of metabolic regulation on the central metabolic network of E. coli , which constitutes the backbone of its metabolism by providing macromolecular precursors, reducing equivalents, and energy for growth and maintenance.

While previous studies typically focused on the role of particular regulatory interactions, we attempt to determine whether more global and generic properties arise from the interplay of the many regulatory interactions that compose metabolic regulation.

To accomplish this, a kinetic model of E. coli central carbon and energy metabolism was developed and validated against a large set of existing experimental data. This model includes more mechanistic details than previous ones, and the impact of metabolic regulation on this system was analyzed using local and global methods.

The kinetic model developed in this study represents the central metabolism of Escherichia coli cultivated on glucose under aerobic conditions Fig 1. This model contains 3 compartments environment, periplasm and cytoplasm , 62 metabolites, and 68 reactions which represent the main central carbon and energy pathways of E.

coli , namely: glucose phosphotransferase system PTS , glycolysis and gluconeogenesis EMP , pentose phosphate PPP and Entner-Doudoroff EDP pathways, anaplerotic reactions AR , tricarboxylic acid cycle TCA , glyoxylate shunt GS , acetate metabolism AC , nucleotide interconversion reactions NC and oxidative phosphorylation OP.

A reaction was also included to account for the consumption of metabolic precursors, reducing equivalents, and energy, and thus linking metabolism to cell proliferation.

To account for metabolic regulation, a total of metabolite-enzyme interactions i. where metabolites modulate the reaction rates through thermodynamic or kinetic regulation, such as being substrates, products, allosteric modulators, or other type of inhibitors or activators were included in the model, amongst which 34 are long-range regulatory interactions i.

where certain metabolites, which are not reactants, modulate the rates of these reactions. Metabolites and enzymes are shown in blue and green, respectively. The diagram adopts the conventions of the Systems Biology Graphical Notation process description [ 87 ].

Previously published kinetic models of E. coli metabolism were used as scaffolds to construct this model [ 18 , 31 , 32 ]. Both the number of pathways and the level of mechanistic detail were increased in the present model S1 Table. Previous models accounted for the consumption of metabolic precursors for growth in a decoupled way.

This may be enough from the point of view of mass balance, but results in artifacts if used for an understanding of dynamics and regulation. In contrast the present model includes a single reaction to model growth, which ensures that the building blocks are consumed in stoichiometric proportions fixed by the cell composition, and not independently from each other.

The rate of this reaction is a function of the intracellular concentrations of all the building blocks. This represents a significant improvement by satisfying the following growth rate properties: i it monotonically increases with the availability of each building block, ii it is asymptotically independent of each pool above a saturating concentration, and iii it approaches zero if any pool approaches zero [ 21 ].

These properties were not reflected in the previous models [ 33 ]. The present model was calibrated to represent the metabolic state of E. coli cultivated under carbon limitation, a condition frequently experienced by this bacterium in laboratories, in industrial bioprocesses, and likely in its natural environment.

To the extent possible, values of the biochemical parameters were taken from experimental determinations available in the literature. Parameters not available in the literature were estimated to reproduce steady-state and time-course experimental data obtained from a unique E.

This step is critical since both metabolite concentrations and fluxes depend on environmental conditions and differ between strains [ 38 — 40 ]. While results described below are largely in agreement with other experimental observations, the model was not forced to reproduce them , providing an important validation of the model.

Detailed information on the construction and validation of the model is given in the Methods section and Supporting Information S1 Text. The model is included in Supporting Information S1 Model formatted in SBML [ 41 ] and COPASI [ 42 ] formats, and is available from the BioModels database [ 43 ] with accession number MODEL The control properties of E.

coli central metabolism in the reference state see above were investigated under the metabolic control analysis framework [ 10 , 11 ]. Flux and concentration control coefficients quantify the impact of a small change in the rate of each reaction e.

through change in the enzyme concentration E , on each flux J and each metabolite concentration M. Since each metabolic step affects all fluxes and concentrations to some extent, we calculate a metric of its overall control on fluxes and concentrations as the L2 norm of all its flux- and concentration-control coefficients see Methods , respectively.

The overall flux- and concentration-control by each step in the network is displayed in Fig 2. The main control point is the glucose inflow reaction with a control of 8. The system is therefore sensitive to its environment, as expected.

Note that this is a direct sensitivity of metabolism to the environment, not through the hierarchical action of signal transduction and gene expression, which is not represented in this model; if it were its effect would thus be overlaid likely with a delay on the direct effect displayed in our model.

Reactions that were identified by previous models as exerting a strong flux control under similar environmental conditions, such as the glucose phosphotransferase reactions or phosphofructokinase [ 13 , 32 , 44 , 45 ], showed low control in our model respectively 0.

Rather, consistently with experimental evidence see for example [ 46 , 47 — 49 ] , the flux control was predicted to be shared between enzymes of all the pathways, amongst which cytochrome bo oxidase reaction CYTBO, with 4. A similar situation was observed for the control of concentrations, which is widely distributed across the network, and with the environment as the strongest control.

A global sensitivity analysis [ 50 ] shows that these conclusions are robust with regard to parameter uncertainties Fig 2A and 2B.

Overall control exerted by each reaction on fluxes A and metabolite concentrations B. The overall flux and concentration control exerted by each reaction are correlated, as shown in panel C.

Gray histograms show the distribution of flux control coefficients of enzymes D and environment glucose supply reaction, E on all the fluxes, and the distribution of concentration control coefficients of enzymes F and environment G on all the intracellular pools. Red lines D-G represent the cumulative frequency of each distribution.

Despite the low control exerted by enzymes over fluxes and concentrations at the network level, a detailed analysis of flux control coefficients reveals generic regulatory patterns between most of the pathways Fig 3. A general observation is that the control of each pathway resides largely outside of itself.

Columns represent the controlling reactions and rows represent the fluxes under control. Red and blue colors represent negative and positive values of flux control coefficients, respectively, and color intensity indicates strong darker to low lighter control.

For example, the control of the partition of carbon through competing pathways is shared between enzymes of each pathway. The glycolytic phosphofructokinase PFK exerts a small negative control on the PPP and ED fluxes and a positive control on the glycolytic flux , while the glucosephosphate dehydrogenase ZWF of the PPP and ED pathways exerts a strong positive control on its own flux and a negative control on the glycolytic flux.

Similar behavior is observed at the main metabolic branch nodes, e. between the TCA cycle and the glyoxylate shunt or between the pentose phosphate and Entner-Doudoroff pathways. It is important to note that the fraction of flux diverted to each branch does not depend only on the local enzyme kinetics, contrary to what is sometimes suggested [ 6 ], but on several enzymes of each of the competing pathways.

Several feedforward and feedback interactions are also observed between the pathways. For instance, the pyruvate kinase PYK controls fluxes through the TCA cycle , and is controlled by some TCA reactions ,.

Interestingly, biomass synthesis GROWTH is strongly controlled by the upstream glucose supply , with all other control coefficients lower than 0. In turn, biomass synthesis exerts a small but global feedback control on most catabolic fluxes.

Those several, intertwined feedback and feedforward interactions stress the high degree of functional organization of the central carbon and energy metabolism. Note that this response is sensed in a very short time scale, rather than the slower response that happens after signal transduction and consequent changes in gene expression.

To get a broader picture of the role of metabolic regulation on the coordination of E. coli metabolism, the solution space of this network was explored with and without considering metabolic regulation.

Two versions of the model were used: the kinetic version which accounts for metabolic regulation, and a stoichiometric version of the same model which contains only stoichiometric constraints and is thus similar to a flux balance analysis model.

The solution space of each model was explored using a random sampling approach: , flux distributions were uniformly sampled from the solution space using the stoichiometric model, and steady-states were simulated for , sets of random enzyme levels using the kinetic model.

For each set, enzyme levels i. Vmax were sampled from a log uniform distribution between 0. It is important to mention that cells do not express enzymes levels according to the distribution generated, therefore the distribution of the variables is not expected to provide any information on the probability for a cell to reach a specific state in vivo [ 50 ].

Rather, uniformity is used to clearly grasp the functional implications of applying metabolic regulation to the network. We first investigated the relationship between supply glucose uptake and demand growth , which provides information on the allocation of resources by the metabolic network [ 52 ].

Direct sampling of the solution space Fig 4A revealed that most of the metabolic states are not efficient in term of resource allocation: most of them correspond to a high glucose uptake rate, but with a low growth rate, because this situation significantly increases the attainable intracellular flux states.

Interestingly, the opposite picture is observed when metabolic regulation is applied on this network Fig 4B : a smaller region of the solution space is reached, where the growth rate is now coupled to the glucose uptake rate.

Density of scatter plots between glucose uptake and growth rates sampled using the stoichiometric model A , without metabolic regulation or the kinetic model B , with metabolic regulation. Shades of white to blue denote null to high frequency, respectively. Red dots are measurements obtained from independent cultivations of wild-type and mutant E.

coli strains on glucose, carried out under a wide range of cultivation conditions. To evaluate this prediction quantitatively, we gathered from the literature experimental data obtained from growth experiments carried out under similar environmental conditions glucose as sole carbon source in aerobic conditions [ 5 , 27 , 34 , 53 — 67 ] S2 Dataset.

Therefore, these data represent a very broad range of the metabolic states that can be expressed by E. coli growing on glucose.

Importantly, these data were not used for parameter estimation, thereby they constitute an independent validation and provide a robust assessment of the predictive ability of the model. These experimental observations correspond to the region of the solution space less frequently sampled using the stoichiometric model, but they closely match the region sampled by the kinetic model Fig 4B.

This means that the observed physiology of E. coli is closer to the metabolic model that is regulated by metabolite-enzyme interactions the kinetic model than it is to a metabolic model that would be regulated by gene expression alone the stoichiometric model.

Hence, metabolic regulation alone, without needing to invoke coordinated expression of genes , seems to be sufficient to explain the emergence of a coupling between anabolic growth and catabolic glucose uptake fluxes, and thereby appears to be a major determinant of the overall cellular physiology by ensuring an efficient and robust allocation of nutrients towards growth.

We extended the above analysis to determine whether additional couplings emerge from metabolic regulation. Several variables representative of the physiological state of E. coli were calculated for each steady-state reached by the kinetic model, namely: growth and glucose uptake rates, ATP, NADH and NADPH production rates, sum of all intracellular fluxes, sum of all intracellular metabolite concentrations and cost of enzymes defined as the product of enzyme concentration and number of amino acids of the corresponding enzyme, summed over all reactions, as detailed in Methods.

Additional variables derived thereof were also computed: biomass, ATP, NADH and NADPH yields, enzyme cost and ATP production rate per sum of fluxes, and sum of fluxes per glucose consumed. Pairwise relationships between systemic variables and absolute and relative fluxes through the main pathways were examined using Spearman correlation and mutual information.

The outcome is a correlation matrix which maps the degree of functional coupling between all the variables Fig 5A. The same patterns were highlighted by both methods, which indicate that these couplings are monotonic since mutual information, but not Spearman correlation, would identify non-monotonic relations.

Steady-states were simulated for , sets of random enzyme levels, and the relationships between various systemic variables, absolute fluxes, and relative fluxes through the different pathways were identified using Spearman correlation test above diagonal and mutual information below diagonal. For Spearman correlation test, red and blue colors represent negative and positive correlations, respectively, and color intensity and circle size indicates high darker, larger to low lighter, smaller correlation coefficient.

For mutual information, color and circle size denote low white, smaller to high blue, larger mutual information, respectively. The density of scatter plots between particular steady-state variables glucose and oxygen uptake, growth rate, biomass yield, and relative fluxes through the TCA cycle sampled using the kinetic model B-D , with metabolic regulation or the stoichiometric model E-G , without metabolic regulation.

Red dots are measurements obtained from a total of independent cultivations , 65 and in panels B , C and D , respectively of wild-type and mutant E. The outcome of predictive analyses based on this assumption such as the minimization of the sum of fluxes in FBA according to the hypothesis that cells minimize their enzyme levels should therefore be interpreted with caution.

In general, systemic variables correlated poorly with relative and absolute fluxes from most of the pathways. This is interesting as it shows that while there is coordination between several processes, there is nevertheless a significant degree of flexibility in the intracellular flux distribution.

Thus, the partition of carbon between energy production ATP and NADPH and growth via the synthesis of many anabolic precursors is predicted to be realized primarily at the level of the TCA cycle and appears to be largely controlled at the metabolic level.

To evaluate these model predictions, additional experimental data on extracellular and intracellular fluxes growth rate, glucose and oxygen uptake rates, and TCA cycle fluxes through the citrate synthase were collected from the literature [ 5 , 27 , 34 , 53 , 54 , 56 , 58 — 60 , 62 — 65 , 67 ] S2 Dataset.

These data, which were not used to calibrate the model, covered the particular regions highlighted by the kinetic model Fig 5B—5D. The excellent agreement between the spread of simulated and experimental data strongly supports the existence of the functional couplings predicted by the model.

It is important to mention that these couplings are not caused by stoichiometric constraints since they are not observed when the solution space is uniformly sampled using the stoichiometric model Fig 5E—5G.

The results also show that the coordination of gene expression by hierarchical regulatory mechanisms is not an important factor in these couplings since they are still maintained when enzyme levels are changed randomly.

In contrast, metabolic regulation brought about by metabolite-enzyme interactions is sufficient to explain their emergence; therefore they represent intrinsic properties of the central metabolism of E.

Interestingly, additional couplings predicted by the model were recently observed in vivo in both prokaryotic E. coli and eukaryotic S.

Since the central metabolic networks of E. coli and S. cerevisiae are highly conserved, the present results may explain why similar properties are observed in both microorganisms, though this hypothesis requires further investigation.

Predicted couplings between the ATP and NADH production rates A , between the sum of fluxes per glucose uptake rate and the ATP yield B , and between the growth rate per sum of fluxes and the sum of fluxes per glucose uptake rate C. The red line in panel A corresponds to the linear correlation proposed by [ 73 ] from energy production fluxes estimated using 13 C-flux data.

The results presented above support the view that metabolic regulation reduces the solution space defined by the stoichiometric constraints, as previously suggested [ 68 , 69 ].

However, the very low probability regions of the solution space might not be captured by random sampling approaches [ 50 ]. To test further if metabolic regulation actually shrinks the solution space of E. coli central metabolism, its boundaries were determined with and without considering metabolic regulation by using the kinetic and the stoichiometric models, respectively.

Unexpectedly, the boundaries were similar for both models Fig 7. This indicates that metabolic regulation does not shrink the solution space of the system—and thus does not restrict the metabolic capabilities of E. coli —, at least for the variables considered here.

The solution space defined only by stoichiometric constraints blue area was computed using the stoichiometric model. The solution space defined when metabolic regulation is taken into account orange area was estimated using the kinetic model, by optimizing enzyme levels with particular metabolic states orange dots as objective functions.

The two solution spaces are similar, indicating that metabolic regulation does not significantly shrink the solution space, at least for the variables investigated here A , glucose uptake rate vs. growth rate; B , biomass yield vs. glucose uptake rate; C , oxygen uptake rate vs. glucose uptake rate; D , TCA cycle flux—relative to glucose uptake—vs.

growth rate. It has been shown that metabolic regulation plays an important role in metabolite homeostasis, which prevents osmotic stress and disadvantageous spontaneous reactions by avoiding large changes in metabolite concentrations for example see [ 20 , 70 ]. This narrow range of predicted intracellular concentrations is physiologically relevant [ 63 , 71 ].

Since no constraints on metabolite concentrations were included in the model, we conclude that metabolic regulation alone may explain global metabolite homeostasis, while still allowing significant changes in fluxes.

Distribution blue bars and cumulative frequency red line of the total concentrations of intracellular metabolites for steady-states simulated from , random enzyme levels. In this study, we investigated the contribution of metabolic regulation on the operation of the central metabolism of E.

coli , which provides building blocks, cofactors, and energy for growth and maintenance. We developed, to our knowledge, the first detailed kinetic model of this system that links metabolism to environment and cell proliferation through intracellular metabolites levels.

This model, validated by independent flux data from some experiments, allowed the identification of several properties which emerge from metabolic regulation and explain many experimental observations of E. The intrinsic, self-regulating capacities of E. coli central metabolism appear to be far more significant than previously expected.

The results presented here imply that gene regulation is not required to explain these properties. Metabolic control analysis showed that the flux and concentration control exerted by single enzymes is low and largely distributed across the network, confirming again the insights of Kacser and Burns [ 51 ].

This significantly contrasts with the outcome of previous kinetic models [ 13 , 32 , 44 , 45 ], where a few enzymes were predicted to exert most of the flux control, but is in line with much experimental evidence [ 7 , 21 , 46 , 47 , 72 ].

coli metabolism, and likely not to the metabolism of other organisms. Its persistence in the literature is a major handicap to understanding metabolism. In fact, the central metabolism is not even self-contained in terms of control due to a large portion of control being exerted by the environment, making E.

coli responsive to environmental changes. One of the most striking examples of this phenomenon is manifested in growth controlling most fluxes but being controlled virtually by glucose availability alone.

The low control exerted by single enzymes on the system makes the metabolic operation of E. coli robust to fluctuations of enzyme levels that may arise from noise in gene expression or other factors. Moreover, the majority of control resides not within but outside the controlled pathways.

The dense, yet highly organized, interactions between pathways allow a rapid and coordinated response of the entire system to perturbations. Exploration of the solution space indicated that metabolic regulation does not significantly restrict the metabolic capabilities of E.

coli , as was previously believed [ 68 , 69 ]. While the observed behavior of many different E. coli strains and mutants are confined to a small region of the solution space, this is not due to kinetic constraints as it is possible to simulate other behaviors simply by changing parameter values.

This apparent paradox can be resolved, of course, if the action of natural selection had favored these behaviors. The systematic mapping of the relationships between various systemic variables revealed that metabolic regulation is sufficient to explain the emergence of several functional couplings, which are independent from gene regulation since they are conserved when enzyme levels are changed randomly by orders of magnitude and cannot be explained by stoichiometric constraints.

An important finding is that metabolic regulation alone may be responsible for the coordination of major catabolic, energetic and anabolic processes at the cellular level to optimize growth. Metabolic regulation thus appears to be sufficient to maintain multi-dimensional optimality of E.

coli metabolism [ 65 ]. Despite this overall coordination, there is a large degree of flexibility at most individual metabolic steps.

The role of metabolic regulation in maintaining global homeostasis of intracellular metabolite pools under a broad range of flux states was also verified by the present model. The modeling results were in excellent agreement with experimental data, even quantitatively.

coli metabolism displays remarkably robust yet simple emergent properties, and these properties have major implications on its overall cellular physiology, e. by preventing unnecessary osmotic stress, maintaining the coordination between key processes, and optimizing the allocation of resources towards particular functions such as growth.

The self-regulating capabilities of E. coli central metabolism reflect the evolutionary selection that has been exerted on the ensemble of enzymes in terms of kinetic and regulatory properties, but not necessarily of expression levels to realize a network with these properties.

Since central metabolism is essential in most organisms and is highly conserved across the three domains of life, it is tempting to speculate that metabolic regulation is responsible for the very similar operation principles observed in different organisms [ 73 ].

Of course we do not suggest that hierarchical regulation does not play an important role in the metabolic operation of E. coli , but it is in addition to the properties observed here, since these can operate without it.

For instance, the robustness of the flux partition to the deletion of global transcriptional regulators was interpreted as a low control of this partition at the hierarchical level [ 5 ], and our results confirm that this robustness lies, to some extent, in metabolic regulation, given the low control exerted by enzymes.

However, this conclusion is valid only for moderate changes of enzyme levels with the notable exception of the flux through the TCA cycle , and other mechanisms such as hierarchical regulation are required to explain the robust flux partition. Glucagon or Adrenaline binds to the membrane of hepatocytes, which regulates stimulates or inhibits certain enzymes through the cAMP pathway Figure 1.

While the kinases are inactivating the glycogen synthase , they activate the glycogen phosphorylase. The generated glucosep is transported via the bloodstream to the peripheral tissues, later it is used in glycolysis. Home » Pre-clinical » Biochemistry » Biochemistry of the metabolism » Carbohydrate metabolism » Regulatory mechanisms in glucose metabolism I.

Posted 4 years ago. Direct link to ada. Some amino They both do. Some amino acids and lactate contribute pyruvate which is converted to OAA and some other amino acids contribute OAA directly to gluconeogenesis.

Direct link to laur. also, she says that "if the cell is running out of atp so if it is running out of atp the cell probably wont want to be performing energy requiring processes such as gluconeogeneis" but i thought the whole point of gluconeogenesis was to produce atp when there is low glucose levels?

Laureen S. Direct link to Laureen S. The whole point of gluconeogenesis is to produce GLUCOSE when there is low glucose levels. The process is anabolic, so you're building up compounds.

Therefore, you will NEED ATP. Calvin Law. Posted 10 years ago. Regarding OAA being a fast promoter for the Gluconeogenesis, why wouldn't it also drive Krebs Cycle given that OAA is a starting metabolite for the cycle? Does this relate to how much acetyl-CoA is involved? The OAA in the gluconeogenesis is catalyzed with a different enzyme and if the glucose level is low their must be allosteric regulation of the enzymes in Krebs cycle.

There must be basal level of glucose for all the metabolic pathways to function correctly. Anyway, when the level is restore, the TCA cycle will regenerate more OAA. This could happen but the cells have redundant mechanism as shown in the video to increase glucose they all work together.

can someone explain the allosteric regulation part with atp and glycolysis and gluconeogenesis for me please? im not quite understanding what she is saying.

Enzymes generally have a catalytic site where the substrate binds for the catalytic reaction enzyme stabilized transition state. However, enzymes may also have an allosteric site where molecules can bind to regulate the enzyme's activity or the enzyme's specificity towards the substrate.

The molecules that bind to the regulatory sites on enzymes may inhibit the enzyme's activity or enhance the enzyme's activity. Glycolysis breaks down glucose and forms ATP, so when the cell has enough ATP, the cell should tell glycolysis to stop.

Therefore, it seems reasonable that ATP would be a negative regulator of enzymes partaking in glycolysis. Felicia Wright. What mechanism in the muscles are using up ADP and creating AMP?

Direct link to JAHenderson. Posted a year ago. Isn't gluconeogenesis activated by low levels of ATP? Chris Boris Alexander Wied Tøstesen.

Posted 5 years ago. Video transcript - [Instructor] At its most simplistic level, regulation of metabolic pathways inside of the body is really just a fancy word for a balancing act that's occurring in the body. So, to illustrate this, I have a seesaw and we've been learning about two metabolic pathways: glycolysis, which is the process of breaking down glucose into pyruvate; and gluconeogenesis, which is essentially the opposite in which we start out with pyruvate and through a little bit of a different route we end up back at glucose.

And when we're talking about the regulation of these particular pathways, we're essentially asking ourself, "When is glycolysis the predominant pathway and when is gluconeogenesis the predominant pathway? So now the next question is, "How does the body "accomplish this balancing act? There are very fast-acting forms of regulation that take place on the order of seconds, and there are very very slow forms of regulation that can take up to hours or even days to occur.

So let's talk about each of these in a little bit more detail. The major principle that helps me understand fast-acting forms of regulation is a good old principle from general chemistry: Le Chatelier's Principle.

So if you remember, Le Chatelier's Principle talks about anything that's in equilibrium and it says that if there's any change to this equilibrium, let's say more products are added or reactants are taken away, the equilibrium will adjust to essentially counter that change and return the system back to equilibrium.

So what does this mean in the context of metabolic pathways like glycolysis and gluconeogenesis? So let's remind ourselves that in glycolysis, glucose is converted to pyruvate through several reactions that are all in equilibrium with one another, and so we can essentially think about this metabolic pathway as a series of equilibria.

And so imagine, for example, if we had an influx of glucose, let's say we've just eaten a big meal, and a huge bunch of glucose has entered our body and our blood stream. What will happen to this equilibrium? Well, we can return to Le Chatelier's Principle and say that if we have a rise in glucose, it will essentially push this entire equilibria towards the production of pyruvate.

And so you can see that in this example, Le Chatelier's Principle allows this equilibrium to adjust within seconds to just a simple influx of glucose to promote glycolysis.

Le Chatelier's Principle also applies to gluconeogenesis.

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Glycogen metabolism

Author: Mujas

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