A whole-body model for glycogen regulation reveals a critical role for substrate cycling in maintaining blood glucose homeostasis

Identification and Characterization of a Novel Insulin-like Receptor (LvRTK2) Involved in Regulating Growth and Glucose Metabolism of the Pacific White Shrimp Litopenaeus vannamei

The insulin receptor (IR) plays a crucial role in the growth and metabolism of animals. However, there are still many questions regarding the IR in crustaceans, particularly their role in shrimp growth and glucose metabolism. In this study, we identified a novel insulin-like receptor gene in Litopenaeus vannamei and cloned its full length of 6439 bp. This gene exhibited a highly conserved sequence and structural characteristics. Phylogenetic analysis confirmed it as an unreported RTK2-type IR, namely, LvRTK2. Expression pattern analysis showed that LvRTK2 is primarily expressed in female reproductive and digestive organs. Through a series of in vivo and in vitro experiments, including glucose treatment, exogenous insulin treatment, and starvation treatment, LvRTK2 was confirmed to be involved in the endogenous glucose metabolic pathway of shrimp under different glucose variations. Moreover, long-term and short-term interference experiments with LvRTK2 revealed that the interference significantly reduced the shrimp growth rate and serum glucose clearance rate. Further studies indicated that LvRTK2 may regulate shrimp growth by modulating the downstream PI3K/AKT signaling pathway and a series of glucose metabolism events, such as glycolysis, gluconeogenesis, glycogen synthesis, and glycogenolysis. This report on the characteristics and functions of LvRTK2 confirms the important role of RTK2-type IRs in regulating shrimp growth and glucose metabolism.

Experimental hemodialysis in diet-induced ketosis and the potential use of dialysis as an adjuvant cancer treatment

Numerous in vivo studies on the ketogenic diet, a diet that can induce metabolic conditions resembling those following extended starvation, demonstrate strong outcomes on cancer survival, particularly when combined with chemo-, radio- or immunological treatments. However, the therapeutic application of ketogenic diets requires strict dietary adherence from well-informed and motivated patients, and it has recently been proposed that hemodialysis might be utilized to boost ketosis and further destabilize the environment for cancer cells. Yet, plasma ketones may be lost in the dialysate-lowering blood ketone levels. Here we performed a single 180-min experimental hemodialysis (HD) session in six anesthetized Sprague-Dawley rats given ketogenic diet for five days. Median blood ketone levels pre-dialysis were 3.5 mmol/L (IQR 2.2 to 5.6) and 3.8 mmol/L (IQR 2.2 to 5.1) after 180 min HD, p = 0.54 (95% CI - 0.6 to 1.2). Plasma glucose levels were reduced by 36% (- 4.5 mmol/L), p < 0.05 (95% CI - 6.7 to - 2.5). Standard base excess was increased from - 3.5 mmol/L (IQR - 4 to - 2) to 0.5 mmol/L (IQR - 1 to 3), p < 0.01 (95% CI 2.0 to 5.0). A theoretical model was applied confirming that intra-dialytic glucose levels decrease, and ketone levels slightly increase since hepatic ketone production far exceeds dialytic removal. Our experimental data and in-silico modeling indicate that elevated blood ketone levels during ketosis are maintained during hemodialysis despite dialytic removal.

Digital twin predicting diet response before and after long-term fasting

Today, there is great interest in diets proposing new combinations of macronutrient compositions and fasting schedules. Unfortunately, there is little consensus regarding the impact of these different diets, since available studies measure different sets of variables in different populations, thus only providing partial, non-connected insights. We lack an approach for integrating all such partial insights into a useful and interconnected big picture. Herein, we present such an integrating tool. The tool uses a novel mathematical model that describes mechanisms regulating diet response and fasting metabolic fluxes, both for organ-organ crosstalk, and inside the liver. The tool can mechanistically explain and integrate data from several clinical studies, and correctly predict new independent data, including data from a new study. Using this model, we can predict non-measured variables, e.g. hepatic glycogen and gluconeogenesis, in response to fasting and different diets. Furthermore, we exemplify how such metabolic responses can be successfully adapted to a specific individual’s sex, weight, height, as well as to the individual’s historical data on metabolite dynamics. This tool enables an offline digital twin technology.

Fasting and diet are central components of prevention against cardiovascular disease. Unfortunately, there is little consensus regarding which diet schemes are optimal. This is partially because different clinical studies contribute with different non-connected pieces of knowledge, which have not been fully integrated into a useful and interconnected big picture. In principle, mathematical models describing meal responses could be used for such an integration. However, today’s models still lack critical mechanisms, such as protein metabolism and a dynamic glycogen regulation. Herein, we present a) a new expanded model structure including these mechanisms; b) a set of parameters which can simultaneously describe a wide array of complementary estimation data, in both healthy and diabetic populations; c) a personalisation-script, which allows these generic parameters to be tuned to an individual/sub-population, using demographics (age, weight, height, diabetes status) and historic metabolic data. We exemplify how this personalisation can be used to predict new independent data, including a new clinical study, where a qualitatively new prediction is validated: that an oral protein tolerance test gives a clear response in plasma glucose, after, but not before, a 48h fasting period. Our combined model, parameters, and fitting script lay the foundation for an offline digital twin.

Representing Stimulus Information in an Energy Metabolism Pathway

We explored a computational model of astrocytic energy metabolism and demonstrated the theoretical plausibility that this type of pathway might be capable of coding information about stimuli in addition to its known functions in cellular energy and carbon budgets. Simulation results indicate that glycogenolytic glycolysis triggered by activation of adrenergic receptors can capture the intensity and duration features of a neuromodulator waveform and can respond in a dose-dependent manner, including non-linear state changes that are analogous to action potentials. We show how this metabolic pathway can translate information about external stimuli to production profiles of energy-carrying molecules such as lactate with a precision beyond simple signal transduction or non-linear amplification. The results suggest the operation of a metabolic state-machine from the spatially discontiguous yet interdependent metabolite elements. Such metabolic pathways might be well-positioned to code an additional level of salient information about a cell's environmental demands to impact its function. Our hypothesis has implications for the computational power and energy efficiency of the brain.

Virtual metabolic human dynamic model for pathological analysis and therapy design for diabetes

A virtual metabolic human model is a valuable complement to experimental biology and clinical studies, because in vivo research involves serious ethical and technical problems. I have proposed a multi-organ and multi-scale kinetic model that formulates the reactions of enzymes and transporters with the regulation of hormonal actions at postprandial and postabsorptive states. The computational model consists of 202 ordinary differential equations for metabolites with 217 reaction rates and 1,140 kinetic parameter constants. It is the most comprehensive, largest, and highly predictive model of the whole-body metabolism. Use of the model revealed the mechanisms by which individual disorders, such as steatosis, β cell dysfunction, and insulin resistance, were combined to cause diabetes. The model predicted a glycerol kinase inhibitor to be an effective medicine for type 2 diabetes, which not only decreased hepatic triglyceride but also reduced plasma glucose. The model also enabled us to rationally design combination therapy.

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A standard of virtual metabolic human dynamic models is proposed

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It integrates the three scales of molecules, organs, and whole body

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It gets insight into pathological mechanisms of type 1 and type 2 diabetes

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It enables the computer-aided design of medication treatment for diabetes

Mechanisms of muscle insulin resistance and the cross-talk with liver and adipose tissue

Insulin resistance is a metabolic disorder affecting multiple tissues and is a precursor event to type 2 diabetes (T2D). As T2D affects over 425 million people globally, there is an imperative need for research into insulin resistance to better understand the underlying mechanisms. The proposed mechanisms involved in insulin resistance include both whole body aspects, such as inflammation and metabolic inflexibility; as well as cellular phenomena, such as lipotoxicity, ER stress, and mitochondrial dysfunction. Despite numerous studies emphasizing the role of lipotoxicity in the pathogenesis of insulin resistance, an understanding of the interplay between tissues and these proposed mechanisms is still emerging. Furthermore, the tissue-specific and unique responses each of the three major insulin target tissues and how each interconnect to regulate the whole body insulin response has become a new priority in metabolic research. With an emphasis on skeletal muscle, this mini-review highlights key similarities and differences in insulin signaling and resistance between different target-tissues, and presents the latest findings related to how these tissues communicate to control whole body metabolism.

Deletion of the Glutaredoxin-2 Gene Protects Mice from Diet-Induced Weight Gain, Which Correlates with Increased Mitochondrial Respiration and Proton Leaks in Skeletal Muscle

Aims: The aim of this study was to determine whether deleting the gene encoding glutaredoxin-2 (GRX2) could protect mice from diet-induced weight gain. Results: Subjecting wild-type littermates to a high fat diet (HFD) induced a significant increase in overall body mass, white adipose tissue hypertrophy, lipid droplet accumulation in hepatocytes, and higher circulating insulin and triglyceride levels. In contrast, GRX2 heterozygotes (GRX2^+/-) fed an HFD had a body mass, white adipose tissue weight, and hepatic and circulating lipid and insulin levels similar to littermates fed a control diet. Examination of the bioenergetics of muscle mitochondria revealed that this protective effect was associated with an increase in respiration and proton leaks. Muscle mitochondria from GRX2^+/- mice had a ∼2- to 3-fold increase in state 3 (phosphorylating) respiration when pyruvate/malate or succinate served as substrates and a ∼4-fold increase when palmitoyl-carnitine was being oxidized. Proton leaks were ∼2- to 3-fold higher in GRX2^+/- muscle mitochondria. Treatment of mitochondria with either guanosine diphosphate, genipin, or octanoyl-carnitine revealed that the higher rate of O₂ consumption under state 4 conditions was associated with increased leaks through uncoupling protein-3 and adenine nucleotide translocase. GRX2^+/- mitochondria also had better protection from oxidative distress. Innovation: For the first time, we demonstrate that deleting the Grx2 gene can protect from diet-induced weight gain and the development of obesity-related disorders. Conclusions: Deleting the Grx2 gene protects mice from diet-induced weight gain. This effect was related to an increase in muscle fuel combustion, mitochondrial respiration, proton leaks, and reactive oxygen species handling. Antioxid. Redox Signal. 31, 1272-1288.

Glucose-6 Phosphate, A Central Hub for Liver Carbohydrate Metabolism

Cells efficiently adjust their metabolism according to the abundance of nutrients and energy. The ability to switch cellular metabolism between anabolic and catabolic processes is critical for cell growth. Glucose-6 phosphate is the first intermediate of glucose metabolism and plays a central role in the energy metabolism of the liver. It acts as a hub to metabolically connect glycolysis, the pentose phosphate pathway, glycogen synthesis, de novo lipogenesis, and the hexosamine pathway. In this review, we describe the metabolic fate of glucose-6 phosphate in a healthy liver and the metabolic reprogramming occurring in two pathologies characterized by a deregulation of glucose homeostasis, namely type 2 diabetes, which is characterized by fasting hyperglycemia; and glycogen storage disease type I, where patients develop severe hypoglycemia during short fasting periods. In these two conditions, dysfunction of glucose metabolism results in non-alcoholic fatty liver disease, which may possibly lead to the development of hepatic tumors. Moreover, we also emphasize the role of the transcription factor carbohydrate response element-binding protein (ChREBP), known to link glucose and lipid metabolisms. In this regard, comparing these two metabolic diseases is a fruitful approach to better understand the key role of glucose-6 phosphate in liver metabolism in health and disease.

Spatial and temporal studies of metabolic activity: contrasting biochemical kinetics in tissues and pathways during fasted and fed states

The regulation of nutrient homeostasis, i.e., the ability to transition between fasted and fed states, is fundamental in maintaining health. Since food is typically consumed over limited (anabolic) periods, dietary components must be processed and stored to counterbalance the catabolic stress that occurs between meals. Herein, we contrast tissue- and pathway-specific metabolic activity in fasted and fed states. We demonstrate that knowledge of biochemical kinetics that is obtained from opposite ends of the energetic spectrum can allow mechanism-based differentiation of healthy and disease phenotypes. Rat models of type 1 and type 2 diabetes serve as case studies for probing spatial and temporal patterns of metabolic activity via [²H]water labeling. Experimental designs that capture integrative whole body metabolism, including meal-induced substrate partitioning, can support an array of research surrounding metabolic disease; the relative simplicity of the approach that is discussed here should enable routine applications in preclinical models.

Glucagon-Induced Acetylation of Energy-Sensing Factors in Control of Hepatic Metabolism

The liver is the central organ of glycolipid metabolism, which regulates the metabolism of lipids and glucose to maintain energy homeostasis upon alterations of physiological conditions. Researchers formerly focused on the phosphorylation of glucagon in controlling liver metabolism. Noteworthily, emerging evidence has shown glucagon could additionally induce acetylation to control hepatic metabolism in response to different physiological states. Through inducing acetylation of complex metabolic networks, glucagon interacts extensively with various energy-sensing factors in shifting from glucose metabolism to lipid metabolism during prolonged fasting. In addition, glucagon-induced acetylation of different energy-sensing factors is involved in the advancement of nonalcoholic fatty liver disease (NAFLD) to liver cancer. Here, we summarize the latest findings on glucagon to control hepatic metabolism by inducing acetylation of energy-sensing factors. Finally, we summarize and discuss the potential impact of glucagon on the treatment of liver diseases.

Insulin: a review of analytical methods

Insulin is an important polypeptide hormone that regulates carbohydrate metabolism.

Norepinephrine stimulates glycogenolysis in astrocytes to fuel neurons with lactate

The mechanism of rapid energy supply to the brain, especially to accommodate the heightened metabolic activity of excited states, is not well-understood. We explored the role of glycogen as a fuel source for neuromodulation using the noradrenergic stimulation of glia in a computational model of the neural-glial-vasculature ensemble (NGV). The detection of norepinephrine (NE) by the astrocyte and the coupled cAMP signal are rapid and largely insensitive to the distance of the locus coeruleus projection release sites from the glia, implying a diminished impact for volume transmission in high affinity receptor transduction systems. Glucosyl-conjugated units liberated from glial glycogen by NE-elicited cAMP second messenger transduction winds sequentially through the glycolytic cascade, generating robust increases in NADH and ATP before pyruvate is finally transformed into lactate. This astrocytic lactate is rapidly exported by monocarboxylate transporters to the associated neuron, demonstrating that the astrocyte-to-neuron lactate shuttle activated by glycogenolysis is a likely fuel source for neuromodulation and enhanced neural activity. Altogether, the energy supply for both astrocytes and neurons can be supplied rapidly by glycogenolysis upon neuromodulatory stimulus.

Although efficient compared to computers, the human brain utilizes energy at 10-fold the rate of other organs by mass. How the brain is supplied with sufficient on-demand energy to support its activity in the absence of neuronal storage capacity remains unknown. Neurons are not capable of meeting their own energy requirements, instead energy supply in the brain is managed by an oligocellular cartel composed of neurons, glia and the local vasculature (NGV), wherein glia can provide the ergogenic metabolite lactate to the neuron in a process called the astrocyte-to-neuron shuttle (ANLS). The only means of energy storage in the brain is glycogen, a polymerized form of glucose that is localized largely to astrocytes, but its exact role and conditions of use are not clear. In this computational model we show that neuromodulatory stimulation by norepinephrine induces astrocytes to recover glucosyl subunits from glycogen for use in a glycolytic process that favors the production of lactate. The ATP and NADH produced support metabolism in the astrocyte while the lactate is exported to feed the neuron. Thus, rapid energy demands by both neurons and glia in a stimulated brain can be met by glycogen mobilization.

In vivo and in silico dynamics of the development of Metabolic Syndrome

The Metabolic Syndrome (MetS) is a complex, multifactorial disorder that develops slowly over time presenting itself with large differences among MetS patients. We applied a systems biology approach to describe and predict the onset and progressive development of MetS, in a study that combined in vivo and in silico models. A new data-driven, physiological model (MINGLeD: Model INtegrating Glucose and Lipid Dynamics) was developed, describing glucose, lipid and cholesterol metabolism. Since classic kinetic models cannot describe slowly progressing disorders, a simulation method (ADAPT) was used to describe longitudinal dynamics and to predict metabolic concentrations and fluxes. This approach yielded a novel model that can describe long-term MetS development and progression. This model was integrated with longitudinal in vivo data that was obtained from male APOE*3-Leiden.CETP mice fed a high-fat, high-cholesterol diet for three months and that developed MetS as reflected by classical symptoms including obesity and glucose intolerance. Two distinct subgroups were identified: those who developed dyslipidemia, and those who did not. The combination of MINGLeD with ADAPT could correctly predict both phenotypes, without making any prior assumptions about changes in kinetic rates or metabolic regulation. Modeling and flux trajectory analysis revealed that differences in liver fluxes and dietary cholesterol absorption could explain this occurrence of the two different phenotypes. In individual mice with dyslipidemia dietary cholesterol absorption and hepatic turnover of metabolites, including lipid fluxes, were higher compared to those without dyslipidemia. Predicted differences were also observed in gene expression data, and consistent with the emergence of insulin resistance and hepatic steatosis, two well-known MetS co-morbidities. Whereas MINGLeD specifically models the metabolic derangements underlying MetS, the simulation method ADAPT is generic and can be applied to other diseases where dynamic modeling and longitudinal data are available.

A mechanism based approach to management of children with end-stage liver disease

Due to parallel advances in surgical and acute care disciplines, liver transplantation (LT) has revolutionized the outlook for children with end-stage liver disease (ESLD). Contrary to advances in technical aspects of LT and the peri-operative care, pre-transplant management of ESLD remains quite a formidable challenge. Areas covered: This review provides mechanisms based management strategies to address common complications of ESLD including malnutrition, amended metabolic pathways, gastrointestinal dysfunction, and development of ascites. Clinically relevant discussion of each paradigm is followed by an account of high impact therapeutic interventions which can be used as guides for formulating management plans. A tabulated summary of the suggested interventions is also provided. Indeed, execution of a dynamic plan tailored to the evolution of pathophysiologic derangements can further enhance outcomes of pediatric LT. Expert commentary: LT has evolved as a dependable therapeutic option for a variety of fatal pediatric liver diseases. However, relative organ shortage remains a formidable challenge. Similarly, consumer expectations continue to grow for sustained improvement of graft and patient survival after LT. In this environment, the level of sophistication applied to the management ESLD before LT stands out as a major opportunity with lasting impact on the future of pediatric LT.

A Computational Model of Hepatic Energy Metabolism: Understanding Zonated Damage and Steatosis in NAFLD

In non-alcoholic fatty liver disease (NAFLD), lipid build-up and the resulting damage is known to occur more severely in pericentral cells. Due to the complexity of studying individual regions of the sinusoid, the causes of this zone specificity and its implications on treatment are largely ignored. In this study, a computational model of liver glucose and lipid metabolism is presented which treats the sinusoid as the repeating unit of the liver rather than the single hepatocyte. This allows for inclusion of zonated enzyme expression by splitting the sinusoid into periportal to pericentral compartments. By simulating insulin resistance (IR) and high intake diets leading to the development of steatosis in the model, we identify key differences between periportal and pericentral cells accounting for higher susceptibility to pericentral steatosis. Secondly, variation between individuals is seen in both susceptibility to steatosis and in its development across the sinusoid. Around 25% of obese individuals do not show excess liver fat, whilst 16% of lean individuals develop NAFLD. Furthermore, whilst pericentral cells tend to show higher lipid levels, variation is seen in the predominant location of steatosis from pericentral to pan-sinusoidal or azonal. Sensitivity analysis was used to identify the processes which have the largest effect on both total hepatic triglyceride levels and on the sinusoidal location of steatosis. As is seen in vivo, steatosis occurs when simulating IR in the model, predominantly due to increased uptake, along with an increase in de novo lipogenesis. Additionally, concentrations of glucose intermediates including glycerol-3-phosphate increased when simulating IR due to inhibited glycogen synthesis. Several differences between zones contributed to a higher susceptibility to steatosis in pericentral cells in the model simulations. Firstly, the periportal zonation of both glycogen synthase and the oxidative phosphorylation enzymes meant that the build-up of glucose intermediates was less severe in the periportal hepatocyte compartments. Secondly, the periportal zonation of the enzymes mediating β-oxidation and oxidative phosphorylation resulted in excess fats being metabolised more rapidly in the periportal hepatocyte compartments. Finally, the pericentral expression of de novo lipogenesis contributed to pericentral steatosis when additionally simulating the increase in sterol-regulatory element binding protein 1c (SREBP-1c) seen in NAFLD patients in vivo. The hepatic triglyceride concentration was predicted to be most sensitive to inter-individual variation in the activity of enzymes which, either directly or indirectly, determine the rate of free fatty acid (FFA) oxidation. The concentration was most strongly dependent on the rate constants for β-oxidation and oxidative phosphorylation. It also showed moderate sensitivity to the rate constants for processes which alter the allosteric inhibition of β-oxidation by acetyl-CoA. The predominant sinusoidal location of steatosis meanwhile was most sensitive variations in the zonation of proteins mediating FFA uptake or triglyceride release as very low density lipoproteins (VLDL). Neither the total hepatic concentration nor the location of steatosis showed strong sensitivity to variations in the lipogenic rate constants.

Influence of plasma macronutrient levels on hepatic metabolism: role of regulatory networks in homeostasis and disease states

Multilevel regulations by metabolic, signaling and transcription pathways form a complex network that works to provide robust metabolic regulation in the liver. This analysis indicates that dietary perturbations in these networks can lead to insulin resistance.

Quantitative analysis of drug effects at the whole-body level: a case study for glucose metabolism in malaria patients

We propose a hierarchical modelling approach to construct models for disease states at the whole-body level. Such models can simulate effects of drug-induced inhibition of reaction steps on the whole-body physiology. We illustrate the approach for glucose metabolism in malaria patients, by merging two detailed kinetic models for glucose metabolism in the parasite Plasmodium falciparum and the human red blood cell with a coarse-grained model for whole-body glucose metabolism. In addition we use a genome-scale metabolic model for the parasite to predict amino acid production profiles by the malaria parasite that can be used as a complex biomarker.

Elucidation of the molecular mechanisms underlying adverse reactions associated with a kinase inhibitor using systems toxicology

BACKGROUND/OBJECTIVES

Targeted kinase inhibitors are an important class of agents in anticancer therapeutics, but their limited tolerability hampers their clinical performance. Identification of the molecular mechanisms underlying the development of adverse reactions will be helpful in establishing a rational method for the management of clinically adverse reactions. Here, we selected sunitinib as a model and demonstrated that the molecular mechanisms underlying the adverse reactions associated with kinase inhibitors can efficiently be identified using a systems toxicological approach.

METHODS

First, toxicological target candidates were short-listed by comparing the human kinase occupancy profiles of sunitinib and sorafenib, and the molecular mechanisms underlying adverse reactions were predicted by sequential simulations using publicly available mathematical models. Next, to evaluate the probability of these predictions, a clinical observation study was conducted in six patients treated with sunitinib. Finally, mouse experiments were performed for detailed confirmation of the hypothesized molecular mechanisms and to evaluate the efficacy of a proposed countermeasure against adverse reactions to sunitinib.

RESULTS

In silico simulations indicated the possibility that sunitinib-mediated off-target inhibition of phosphorylase kinase leads to the generation of oxidative stress in various tissues. Clinical observations of patients and mouse experiments confirmed the validity of this prediction. The simulation further suggested that concomitant use of an antioxidant may prevent sunitinib-mediated adverse reactions, which was confirmed in mouse experiments.

CONCLUSIONS

A systems toxicological approach successfully predicted the molecular mechanisms underlying clinically adverse reactions associated with sunitinib and was used to plan a rational method for the management of these adverse reactions.

Physiology of proglucagon peptides: role of glucagon and GLP-1 in health and disease

The preproglucagon gene (Gcg) is expressed by specific enteroendocrine cells (L-cells) of the intestinal mucosa, pancreatic islet α-cells, and a discrete set of neurons within the nucleus of the solitary tract. Gcg encodes multiple peptides including glucagon, glucagon-like peptide-1, glucagon-like peptide-2, oxyntomodulin, and glicentin. Of these, glucagon and GLP-1 have received the most attention because of important roles in glucose metabolism, involvement in diabetes and other disorders, and application to therapeutics. The generally accepted model is that GLP-1 improves glucose homeostasis indirectly via stimulation of nutrient-induced insulin release and by reducing glucagon secretion. Yet the body of literature surrounding GLP-1 physiology reveals an incompletely understood and complex system that includes peripheral and central GLP-1 actions to regulate energy and glucose homeostasis. On the other hand, glucagon is established principally as a counterregulatory hormone, increasing in response to physiological challenges that threaten adequate blood glucose levels and driving glucose production to restore euglycemia. However, there also exists a potential role for glucagon in regulating energy expenditure that has recently been suggested in pharmacological studies. It is also becoming apparent that there is cross-talk between the proglucagon derived-peptides, e.g., GLP-1 inhibits glucagon secretion, and some additive or synergistic pharmacological interaction between GLP-1 and glucagon, e.g., dual glucagon/GLP-1 agonists cause more weight loss than single agonists. In this review, we discuss the physiological functions of both glucagon and GLP-1 by comparing and contrasting how these peptides function, variably in concert and opposition, to regulate glucose and energy homeostasis.

The Molecular Size Distribution of Glycogen and its Relevance to Diabetes

Glycogen is a highly branched polymer of glucose, functioning as a blood-glucose buffer. It comprises relatively small β-particles, which may be joined as larger aggregate α-particles. The size distributions from size-exclusion chromatography (SEC, also known as GPC) of liver glycogen from non-diabetic and diabetic mice show that diabetic mice have impaired α-particle formation, shedding new light on diabetes. SEC data also suggest the type of bonding holding β-particles together in α-particles. SEC characterisation of liver glycogen at various time points in a day/night cycle indicates that liver glycogen is initially synthesised as β-particles, and then joined by an unknown process to form α-particles. These α-particles are more resistant to degradation, presumably because of their lower surface area-to-volume ratio. These findings have important implications for new drug targets for diabetes management.

Lipoic acid prevents fructose-induced changes in liver carbohydrate metabolism: role of oxidative stress

BACKGROUND

Fructose administration rapidly induces oxidative stress that triggers compensatory hepatic metabolic changes. We evaluated the effect of an antioxidant, R/S-α-lipoic acid on fructose-induced oxidative stress and carbohydrate metabolism changes.

METHODS

Wistar rats were fed a standard commercial diet, the same diet plus 10% fructose in drinking water, or injected with R/S-α-lipoic acid (35mg/kg, i.p.) (control+L and fructose+L). Three weeks thereafter, blood samples were drawn to measure glucose, triglycerides, insulin, and the homeostasis model assessment-insulin resistance (HOMA-IR) and Matsuda indices. In the liver, we measured gene expression, protein content and activity of several enzymes, and metabolite concentration.

RESULTS

Comparable body weight changes and calorie intake were recorded in all groups after the treatments. Fructose fed rats had hyperinsulinemia, hypertriglyceridemia, higher HOMA-IR and lower Matsuda indices compared to control animals. Fructose fed rats showed increased fructokinase gene expression, protein content and activity, glucokinase and glucose-6-phosphatase gene expression and activity, glycogen storage, glucose-6-phosphate dehydrogenase mRNA and enzyme activity, NAD(P)H oxidase subunits (gp91(phox) and p22(phox)) gene expression and protein concentration and phosphofructokinase-2 protein content than control rats. All these changes were prevented by R/S-α-lipoic acid co-administration.

CONCLUSIONS

Fructose induces hepatic metabolic changes that presumably begin with increased fructose phosphorylation by fructokinase, followed by adaptive changes that attempt to switch the substrate flow from mitochondrial metabolism to energy storage. These changes can be effectively prevented by R/S-α-lipoic acid co-administration.

GENERAL SIGNIFICANCE

Control of oxidative stress could be a useful strategy to prevent the transition from impaired glucose tolerance to type 2 diabetes.

Quantifying the contribution of the liver to glucose homeostasis: a detailed kinetic model of human hepatic glucose metabolism

Despite the crucial role of the liver in glucose homeostasis, a detailed mathematical model of human hepatic glucose metabolism is lacking so far. Here we present a detailed kinetic model of glycolysis, gluconeogenesis and glycogen metabolism in human hepatocytes integrated with the hormonal control of these pathways by insulin, glucagon and epinephrine. Model simulations are in good agreement with experimental data on (i) the quantitative contributions of glycolysis, gluconeogenesis, and glycogen metabolism to hepatic glucose production and hepatic glucose utilization under varying physiological states. (ii) the time courses of postprandial glycogen storage as well as glycogen depletion in overnight fasting and short term fasting (iii) the switch from net hepatic glucose production under hypoglycemia to net hepatic glucose utilization under hyperglycemia essential for glucose homeostasis (iv) hormone perturbations of hepatic glucose metabolism. Response analysis reveals an extra high capacity of the liver to counteract changes of plasma glucose level below 5 mM (hypoglycemia) and above 7.5 mM (hyperglycemia). Our model may serve as an important module of a whole-body model of human glucose metabolism and as a valuable tool for understanding the role of the liver in glucose homeostasis under normal conditions and in diseases like diabetes or glycogen storage diseases.