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• W2149182211 abstract "Article14 May 2013Open Access The selective control of glycolysis, gluconeogenesis and glycogenesis by temporal insulin patterns Rei Noguchi Rei Noguchi Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan Search for more papers by this author Hiroyuki Kubota Hiroyuki Kubota Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan Search for more papers by this author Katsuyuki Yugi Katsuyuki Yugi Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan Search for more papers by this author Yu Toyoshima Yu Toyoshima Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan Search for more papers by this author Yasunori Komori Yasunori Komori Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan Search for more papers by this author Tomoyoshi Soga Tomoyoshi Soga Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Search for more papers by this author Shinya Kuroda Corresponding Author Shinya Kuroda Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan CREST, Japan Science and Technology Corporation, Bunkyo-ku, Tokyo, Japan Search for more papers by this author Rei Noguchi Rei Noguchi Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan Search for more papers by this author Hiroyuki Kubota Hiroyuki Kubota Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan Search for more papers by this author Katsuyuki Yugi Katsuyuki Yugi Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan Search for more papers by this author Yu Toyoshima Yu Toyoshima Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan Search for more papers by this author Yasunori Komori Yasunori Komori Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan Search for more papers by this author Tomoyoshi Soga Tomoyoshi Soga Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan Search for more papers by this author Shinya Kuroda Corresponding Author Shinya Kuroda Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan CREST, Japan Science and Technology Corporation, Bunkyo-ku, Tokyo, Japan Search for more papers by this author Author Information Rei Noguchi1, Hiroyuki Kubota2, Katsuyuki Yugi2, Yu Toyoshima2, Yasunori Komori2, Tomoyoshi Soga3 and Shinya Kuroda 1,2,4 1Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan 2Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan 3Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan 4CREST, Japan Science and Technology Corporation, Bunkyo-ku, Tokyo, Japan *Corresponding author. Department of Biophysics and Biochemistry, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113 0033, Japan. Tel.:+81 3 5841 4697; Fax:+81 3 5841 4698; E-mail: [email protected] Molecular Systems Biology (2013)9:664https://doi.org/10.1038/msb.2013.19 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions Figures & Info Insulin governs systemic glucose metabolism, including glycolysis, gluconeogenesis and glycogenesis, through temporal change and absolute concentration. However, how insulin-signalling pathway selectively regulates glycolysis, gluconeogenesis and glycogenesis remains to be elucidated. To address this issue, we experimentally measured metabolites in glucose metabolism in response to insulin. Step stimulation of insulin induced transient response of glycolysis and glycogenesis, and sustained response of gluconeogenesis and extracellular glucose concentration (GLCex). Based on the experimental results, we constructed a simple computational model that characterises response of insulin-signalling-dependent glucose metabolism. The model revealed that the network motifs of glycolysis and glycogenesis pathways constitute a feedforward (FF) with substrate depletion and incoherent feedforward loop (iFFL), respectively, enabling glycolysis and glycogenesis responsive to temporal changes of insulin rather than its absolute concentration. In contrast, the network motifs of gluconeogenesis pathway constituted a FF inhibition, enabling gluconeogenesis responsive to absolute concentration of insulin regardless of its temporal patterns. GLCex was regulated by gluconeogenesis and glycolysis. These results demonstrate the selective control mechanism of glucose metabolism by temporal patterns of insulin. Synopsis The regulation of glucose metabolism by pulse stimulations of insulin is compared with the effect of ramp stimulations. Specific network motifs mediate the differential response to these temporal patterns of stimulations that mimic in vivo patterns of insulin secretion. Temporal patterns and absolute concentration of insulin selectively control glycolysis, gluconeogenesis and glycogenesis through the different network motif in FAO hepatoma cells. Step stimulation of insulin induces the transient responses and adaptations of glycolysis (via F16P) and glycogenesis through a feedforward with substrate depletion and though an incoherent feedforward loop, respectively, and induces the sustained response of gluconeogenesis (via PEPCK) through a feedforward inhibition. Pulse stimulation of insulin, like additional secretory pattern in vivo, induces responses of glycolysis (via F16P), gluconeogenesis (via PEPCK) and glycogenesis. Ramp stimulation of insulin, like basal secretory pattern in vivo, induces only the response of gluconeogenesis (via PEPCK), but not the responses of glycolysis (via F16P) and glycogenesis. Introduction Insulin is the only hormone that lowers the concentration of blood glucose (Yki-Jarvinen, 1993; Saltiel and Kahn, 2001) by regulating hepatic glucose metabolism, including the glycolysis, gluconeogenesis and glycogenesis pathways (Pessin and Saltiel, 2000; Whiteman et al, 2002). Glycolysis is the pathway by which glucose degrades into lactate (LAC), gluconeogenesis is the pathway by which glucose is generated from pyruvate and/or LAC, and glycogenesis is the pathway by which glycogen is synthesised from glucose (Nordlie et al, 1999). Glycolysis is regulated by a key bifunctional enzyme, 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 1 (PFKFB1) (Pilkis et al, 1983; Okar et al, 2001). Insulin dephosphorylates phosphorylated-PFKFB1 (pPFKFB1) and activates its kinase activity, thereby promoting glycolysis (Probst and Unthan-Fechner, 1985). Although the mechanism by which insulin regulates PFKFB1 remains unclear, the insulin-dependent activation of an unidentified protein phosphatase presumably induces the dephosphorylation of pPFKFB1 (Pilkis et al, 1995; Okar et al, 2004). Gluconeogenesis is regulated by the protein abundance of two major rate-limiting enzymes that are involved in hepatic gluconeogenesis: phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphatase (G6Pase) (Barthel and Schmoll, 2003; Yabaluri and Bashyam, 2010). Insulin reduces the protein abundance of PEPCK and G6Pase through the AKT-signalling pathway, which leads to the suppression of gluconeogenesis (Sutherland et al, 1996; Nakae et al, 1999; Saltiel and Kahn, 2001; Whiteman et al, 2002). Glycogenesis is regulated by a balance of the enzymatic activities of glycogen synthase (GS) and glycogen phosphorylase (GP) (Bollen et al, 1998; Roach et al, 2001). Insulin promotes glycogenesis by dephosphorylation and activation of GS and/or by dephosphorylation and inactivation of GP via the AKT-signalling pathway (Srivastava and Pandey, 1998; Whiteman et al, 2002; Aiston et al, 2006). The GS activity is also allosterically activated by glucose-6-phosphate (G6P) through dephosphorylation of GS (Villar-Palasi and Guinovart, 1997), and the GP activity is allosterically inhibited by intracellular glucose (GLCin) or G6P through dephosphorylation of GP (Johnson, 1992; Aiston et al, 2003). In addition, glucokinase (GK) is a rate-limiting enzyme for glucose utilisation in the liver (Ferre et al, 1996) through glucose phosphorylation. Insulin upregulates the expression of GK via the AKT-signalling pathway (Iynedjian et al, 2000; Ribaux and Iynedjian, 2003) and the activity of GK is also regulated by interaction with its regulatory protein (Van Schaftingen et al, 1994). Many studies have also been done to elucidate hepatic glucose metabolism by use of metabolomic approach (Soga et al, 2006; Scribner et al, 2010; Massimi et al, 2012; Watanabe et al, 2012). However, how the above signalling pathways regulate glucose metabolism at system level has thus far not been examined. Blood insulin exhibits several temporal patterns, such as additional secretion (which is a transient increase of insulin in response to meals) and basal secretion (which is the sustained low secretion of insulin during fasting) (Polonsky et al, 1988b; Lindsay et al, 2003). Such temporal insulin patterns have been reported to have an important physiological role in the regulation of metabolism (Polonsky et al, 1988b, 1998). The relevance of insulin secretion abnormalities in the pathogenesis of type 2 diabetes mellitus has been recognised as important for the optimisation of the action of insulin on target tissues (Bruce et al, 1988; Polonsky et al, 1988a; Pratley and Weyer, 2001; Del Prato, 2003). These observations indicate that insulin selectively regulates metabolic processes depending on its temporal pattern. Among the target organs of insulin (liver, skeletal muscle and adipose tissue), the temporal patterns of insulin particularly affect metabolism in the liver because the temporal patterns of insulin are most evident in the portal vein, which delivers blood from the pancreas to the liver. We have previously found that additional and basal secretion-like temporal patterns of insulin selectively regulate glycogen synthase kinase-3β (GSK3β, which in turn regulates glycogenesis) and G6Pase (which regulates gluconeogenesis) in FAO rat hepatoma cells through multiplexing of the AKT-signalling pathway (Kubota et al, 2012; Purvis and Lahav, 2012). These findings demonstrate that the AKT-signalling pathway can code temporal insulin patterns for the selective regulation of downstream metabolic enzymes. However, the mechanisms by which these temporal insulin patterns selectively regulate glycolysis, gluconeogenesis and glycogenesis have not yet been examined. In this study, we measured glucose metabolism in insulin-stimulated FAO rat hepatoma cells, which show a similar signalling response to insulin as that observed in primary hepatocytes (Kubota et al, 2012). To extract the essential mechanism by which insulin selectively controls glucose metabolism, we used a simple and abstract computational model rather than a detailed biochemical model. We found that glycolysis and glycogenesis respond to temporal insulin changes whereas gluconeogenesis responds to the absolute insulin concentration. In addition, these responses occur through different networks motifs. These results demonstrate the mechanism for the selective control of glucose metabolism by temporal insulin patterns. Results Control of insulin-dependent glucose metabolism through the AKT-signalling pathway We stimulated FAO cells with an insulin step stimulation, and measured intracellular and extracellular metabolites and proteins that are involved in the glucose metabolism that is controlled through the AKT-signalling pathway in response to insulin (Figure 1). Nine intracellular metabolites and extracellular pyruvate (PYRex) were measured by capillary electrophoresis coupled to time-of-flight mass spectrometry (CE-TOF-MS). The extracellular glucose concentration (GLCex) and the intracellular glycogen content were also measured by the use of enzymatic assays (see Materials and methods). Insulin induced a gradual decrease of GLCex, which is the final output of glucose metabolism and regulated by a balance between glycolysis, gluconeogenesis and glycogenesis (Figure 1). In the glycolysis and gluconeogenesis pathways, insulin induced a transient response in fructose-1,6-bisphosphate (F16P), dihydroxyacetone phosphate (DHAP), 2,3-diphosphoglycerate (2,3-DPG), 3-phosphoglycerate (3-PG) and phosphoenolpyruvate (PEP), and induced a sustained increase in intracellular LAC. In contrast, in the glycogenesis pathway, insulin induced a transient response in glucose-1-phosphate (G1P), UDP-glucose (UDPG) and glycogen. We found that there were the two clusters of highly correlated neighbouring metabolites in the glycolysis pathway and glycogenesis pathways (Figure 1, dotted blue box). Figure 1.Insulin-dependent glucose metabolism. The changes in the indicated intracellular and extracellular metabolites, as well as pAKT, PEPCK, G6Pase, GK, pGS and pGP were measured in response to an insulin step stimulation. The black, green and red lines indicate the insulin concentrations used (0, 1 and 100 nM, respectively). The mean values and SEMs of four (pAKT, PEPCK, G6Pase, GK, pGS and pGP) and three (GLCex and glycogen) independent experiments are shown. The values at t=0 measured by CE-TOF-MS were obtained in triplicate, and the mean values and SEMs of these are indicated at t=0 using a blue dot and blue line, respectively. Highly correlated (Pearson's correlation coefficient, r>0.63) neighbouring metabolites are surrounded by a dotted blue box. The metabolites inside and outside of the black box are the intracellular and extracellular metabolites, respectively. Note that G6P at some time points were under the detection limit of CE-TOF-MS and were not shown in Figure 1, and that only the time course data of glycogen in response to 0 and 1 nM of insulin are shown because that to 100 nM insulin showed a high variation between experiments (see Materials and Methods). PYRin, intracellular pyruvate. Source data for this figure is available on the online supplementary information page. Source data for Figure 1 [msb201319-sup-0001-SourceData-S1.txt] Download figure Download PowerPoint We also measured the phosphorylation of AKT and the glycogenic metabolic enzymes, including GS and GP, as well as the protein abundance of PEPCK and G6Pase (Figure 1). Insulin induced a transient and sustained phosphorylation of AKT, which is consistent with previous observations (Kubota et al, 2012). In the gluconeogenesis pathway, insulin induced a gradual and continuous suppression of PEPCK. In the glycogenesis pathway, the phosphorylation of GS was not altered by insulin (Figure 1). However, the amount of glycogen increased in response to insulin, which means that the increase of glycogen is not mediated by the phosphorylation of GS at Ser641, which is an important phosphorylation site for the regulation of glycogenesis by insulin via pAKT (Skurat and Roach, 1995), in our system; therefore, an additional mechanism is required to explain this increase in the glycogen. The phosphorylation of GP gradually and continuously increased in response to insulin. We measured G6Pase, which is another rate-limiting enzyme involved in gluconeogenesis, but were not able to determine whether G6Pase decreased in response to insulin because of the large variation obtained between experiments (Figure 1). In addition, the protein abundance of GK was not changed by insulin (Figure 1). Thus, an insulin step stimulation induced a variety of temporal patterns in the signalling molecules, enzymes and metabolites, thereby highlighting the complexity of the insulin-induced regulation of glucose metabolism. Computational model of insulin-dependent glucose metabolism We developed a computational model of insulin-dependent glucose metabolism (Supplementary Figure 1, see Materials and methods) and examined how insulin regulates glycolysis, gluconeogenesis and glycogenesis (Figure 2A). We tried to extract the essential and simple characteristics of insulin-dependent glucose metabolism rather than describing detailed metabolic reactions by developing abstract model by aggregating the two clusters of the highly correlated neighbouring metabolites. We aggregated G1P and UDPG into a single variable, ‘G1P’, which is a major entry point for glycogenesis (Figure 1, dotted blue box). We also aggregated F16P, DHAP, DPG, 3-PG and PEP into a single variable ‘F16P’, which has been reported to be directly regulated by insulin through PFKFB1 and is thought to be a key factor in the glycolysis regulation by insulin (Figure 1, dotted blue box). In addition, we aggregated the undetected neighbouring metabolites, GLCin and G6P, into a single abstract variable, G6P. For simplification, G6Pase, GS and GK were not incorporated into the model because they were not changed by insulin in FAO cells (Figure 1, see Discussion). Although insulin regulates glucose metabolism, the details of the signalling pathways involved remain unclear. As AKT has been shown to have a major role in insulin signalling (Whiteman et al, 2002), we assumed that insulin regulates glucose metabolism via AKT. We used our previous computational model for the activation of AKT by insulin (Kubota et al, 2012) and slightly modified the parameters to fit the experimental data obtained in this study (Supplementary Figure 1H, see Materials and methods). We newly constructed the AKT-dependent glucose metabolism model, in which phosphorylated AKT (pAKT) was assumed to suppress PEPCK and facilitate the conversion of G6P to F16P, which in turn activates PFKFB1 (Supplementary Figure 1A). GLCin or G6P induces the dephosphorylation of GP via the allosteric regulation of phosphorylase phosphatase. We assumed that G6P facilitates the dephosphorylation of phosphorylated GP (pGP). The upstream G1P also exhibited a transient increase similar to that observed with glycogen, which suggests that a metabolic enzyme upstream of G1P is activated by insulin, rather than the metabolic enzyme between G1P and glycogen. We therefore assumed that some metabolite upstream of G1P is regulated by insulin via pAKT. We then connected the AKT-dependent glucose metabolism model to the insulin-dependent AKT pathway model. The connected model is hereafter denoted as the insulin-dependent glucose metabolism model (Supplementary Figure 1). We estimated the parameters of the insulin-dependent glucose metabolism model based on the experimental data shown in Figures 1 and 3A. The model appeared to reproduce the essential characteristics of the experimental results shown in Figure 2B (Figure 2A). Importantly, pAKT exhibited a transient and sustained increase (Figure 2A). In the glycolysis pathway, F16P showed a transient and adaptive response (Figure 2A). In the glycogenesis pathway, G1P and glycogen also showed both transient and adaptive responses similar to F16P (Figure 2A). In contrast, in the gluconeogenesis pathway, PEPCK exhibited a gradual and sustained decrease (Figure 2A). This decrease in PEPCK resulted in the increase of OAA (oxaloacetate) and LAC through the inhibition of the conversion of OAA to F16P and suppressed gluconeogenesis, resulting in decreases of G6P and GLCex (Figure 2A). We next examined the network structures of the pathways by specific deletion of reactions in simulations, and found that the network motif of glycolysis is a feedforward (FF) with substrate depletion, that of gluconeogenesis is a FF and that of glycogenesis is an incoherent feedforward loop (iFFL) (Supplementary Figure 2, see also Figure 6 and Table 1). We also found that GLCex is mainly regulated by gluconeogenesis and glycolysis, but not by glycogenesis (Supplementary Figure 2). Figure 2.The computational model of insulin-dependent glucose metabolism. (A) Time courses of the metabolites and enzymes in response to insulin in the insulin-dependent glucose metabolism model (Supplementary Figure 1). The black, green and red lines indicate the insulin concentrations used (0, 1 and 100 nM, respectively). (B) Time courses of the metabolites and enzymes in response to insulin. All data shown are the same as Figure 1, and only the metabolites and enzymes incorporated into the model are shown for comparison. The black, green and red lines indicate the insulin concentrations used (0, 1 and 100 nM, respectively). Download figure Download PowerPoint Figure 3.The effect of insulin pulse stimulation on glucose metabolism. (A) Time courses of pAKT, F16P, PEPCK, glycogen and GLCex in response to a 10-min pulse stimulation (blue), a step stimulation (red) and 0 nM (black) of insulin in FAO cells. The mean values and SEMs of three independent experiments are shown. The doses and duration of the insulin stimulations are shown at the bottom. (B) Time courses of pAKT, PEPCK, glycogen, GLCex and F16P in response to a 10-min pulse stimulation of the insulin-dependent glucose metabolism model. Source data for this figure is available on the online supplementary information page. Source data for Figure 3A [msb201319-sup-0002-SourceData-S2.txt] Download figure Download PowerPoint Table 1. Insulin selectively controls glycolysis, gluconeogenesis, glycogenesis and the extracellular glucose concentration via their network motifs Abbreviations: FF, feedfoward; FF with s.d., feedforward with substrate depletion; iFFL, incoherent feedforward loop. A high responsiveness to the indicated stimulations is represented by ‘+’, and a low responsiveness is represented by ‘−’. The network motif of glycogenesis forms an Incoherent Type 1 motif (Mangan and Alon, 2003) (Supplementary Figure 4). Glucose metabolism in response to a pulse stimulation of insulin We next examined how the in vivo-like temporal patterns of insulin selectively regulate glucose metabolism. Insulin secretion has two major temporal patterns in vivo: additional secretion, which is a rapid transient secretion, and basal secretion, which is a slow sustained secretion (Polonsky et al, 1988b). We first experimentally used the additional secretion-like stimulation through a pulse stimulation and examined the responses of pAKT, GLCex, PEPCK and glycogen (Figure 3A). In this study, we used F16P, PEPCK and glycogen as markers of glycolysis, gluconeogenesis and glycogenesis, respectively. As the availability of CE-TOF-MS was limited and no quantitative enzymatic assay exists for the detection of F16P, we were only able to characterise glycolysis in simulations. We found that pAKT exhibited only a transient response to the pulse stimulation, whereas it exhibited both transient and sustained responses to the step stimulation (Figure 3A), which is consistent with our previous results (Kubota et al, 2012). In contrast, GLCex, PEPCK and glycogen showed similar responses in response to both the pulse and the step stimulations of insulin (Figure 3A). As pAKT did not exhibit a sustained response to the pulse stimulation, it can be deduced that GLCex, PEPCK and glycogen do not require a sustained pAKT signal and that a transient pAKT signal is sufficient to suppress gluconeogenesis, lower GLCex, and induce glycogenesis. We computationally simulated the responses to the pulse stimulation (Figure 3B) and reproduced the transient response of pAKT, the sustained responses of GLCex and PEPCK, and the transient response of glycogen. Moreover, GLCex, PEPCK and glycogen showed similar temporal patterns in response to the step and the pulse stimulations. Thus, the model can capture the essential characteristics of the responses to the pulse stimulation, indicating that the model was able to predict the responses to the pulse stimulation. F16P exhibited a transient and adaptive response to both the step and pulse stimulations of insulin. Glucose metabolism in response to a ramp stimulation of insulin We next experimentally used the basal secretion-like stimulation of insulin through a ramp stimulation and examined the responses of pAKT, GLCex, PEPCK and glycogen (Figure 4A). We found that pAKT exhibited only a sustained response to the ramp stimulation, whereas it exhibited both transient and sustained responses to the step stimulation. In contrast, GLCex and PEPCK showed similar sustained responses in response to both the pulse and the step stimulations of insulin (Figure 4A). As pAKT did not exhibit a transient response to the ramp stimulation, it can be deduced that GLCex and PEPCK do not require a transient pAKT signal and that a sustained pAKT signal is sufficient to suppress gluconeogenesis and lower GLCex. Taken together with the result in Figure 3, either transient or sustained pAKT signal alone is sufficient to trigger the sustained suppressions of gluconeogenesis and GLCex. Moreover, considering that GLCex and PEPCK always show a similar sustained response, PEPCK is likely to mainly contribute to the regulation of GLCex by insulin. In contrast, glycogen did not respond to the ramp stimulation of insulin in spite of the transient response to the step stimulation. Given that pulse and ramp stimulation resemble the additional and basal secretion of insulin in vivo, respectively, these results demonstrate that GLCex and gluconeogenesis can respond to both additional and basal secretion of insulin, whereas glycogenesis selectively responds to additional secretion of insulin. Figure 4.The effect of insulin ramp stimulation on glucose metabolism. (A) Time courses of pAKT, F16P, PEPCK, glycogen and GLCex in response to a step stimulation (red), a ramp stimulation (blue) and 0 nM (black) of insulin in FAO cells. The mean values and SEMs of three independent experiments are shown. The step and ramp stimulations of insulin are shown at the bottom. The final concentrations of step and ramp stimulation of insulin were set at 0.1 nM, which mimics the basal secretion of insulin in vivo (Polonsky et al, 1988b; Basu et al, 1996). Note that the scale of absolute concentration of GLCex was normalised because of the variation between experiments. (B) Time courses of pAKT, PEPCK, glycogen, GLCex and F16P in response to a ramp stimulation of the insulin-dependent glucose metabolism model. Source data for this figure is available on the online supplementary information page. Source data for Figure 4A [msb201319-sup-0003-SourceData-S3.txt] Download figure Download PowerPoint We computationally predicted the responses to the ramp stimulation (Figure 4B) and reproduced the sustained response of pAKT, the sustained responses of GLCex and PEPCK, and no response of glycogen. Moreover, GLCex and PEPCK showed similar sustained patterns in response to the step and the ramp stimulations. Thus, the model can capture the essential characteristics of the responses to the ramp stimulation, indicating that the model was able to predict the responses to the ramp stimulation. F16P did not respond to the ramp stimulations of insulin in the model, suggesting that glycolysis selectively responds to additional secretion of insulin. We then computationally examined the sensitivity of glycolysis, gluconeogenesis and glycogenesis to the rate of insulin increase (Supplementary Figure 3), and confirmed that transient responses of pAKT, glycogen and F16P were sensitive to the rate of insulin increase, whereas sustained responses of pAKT, GLCex and PEPCK were insensitive to that. Taken together, these results suggest that glycolysis and glycogenesis respond to temporal changes of insulin concentration, whereas gluconeogenesis and GLCex respond to the absolute insulin concentration but not to its temporal changes. Sensitivity of glycolysis, gluconeogenesis and glycogenesis to the concentration of insulin We next examined the sensitivity of glycolysis, gluconeogenesis and glycogenesis to the concentration of insulin using step stimulations with different concentrations of insulin (Figure 5). We graphed the amplitude of the molecules at the transient peak and at the final time point in the simulation, the latter of which is a marker for the sustained phase, against the insulin concentration used (Figure 5B, solid lines), and then we experimentally validated the simulated results (Figure 5B, dots). pAKT at the transient peak and at final time point gradually increased against the insulin concentration, indicating that pAKT can code information over a wide dynamic range of insulin concentrations in both transient and sustained phases. Glycogen and F16P at the transient peak increased gradually against insulin concentration, indicating that insulin can regulate glycogenesis and glycolysi" @default.
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• W2149182211 title "The selective control of glycolysis, gluconeogenesis and glycogenesis by temporal insulin patterns" @default.
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