Masashi Fujii

Glucose Homeostatic Law: Insulin Clearance Predicts the Progression of Glucose Intolerance in Humans

Homeostatic control of blood glucose is regulated by a complex feedback loop between glucose and insulin, of which failure leads to diabetes mellitus. However, physiological and pathological nature of the feedback loop is not fully understood. We made a mathematical model of the feedback loop between glucose and insulin using time course of blood glucose and insulin during consecutive hyperglycemic and hyperinsulinemic-euglycemic clamps in 113 subjects with variety of glucose tolerance including normal glucose tolerance (NGT), impaired glucose tolerance (IGT) and type 2 diabetes mellitus (T2DM). We analyzed the correlation of the parameters in the model with the progression of glucose intolerance and the conserved relationship between parameters. The model parameters of insulin sensitivity and insulin secretion significantly declined from NGT to IGT, and from IGT to T2DM, respectively, consistent with previous clinical observations. Importantly, insulin clearance, an insulin degradation rate, significantly declined from NGT, IGT to T2DM along the progression of glucose intolerance in the mathematical model. Insulin clearance was positively correlated with a product of insulin sensitivity and secretion assessed by the clamp analysis or determined with the mathematical model. Insulin clearance was correlated negatively with postprandial glucose at 2h after oral glucose tolerance test. We also inferred a square-law between the rate constant of insulin clearance and a product of rate constants of insulin sensitivity and secretion in the model, which is also conserved among NGT, IGT and T2DM subjects. Insulin clearance shows a conserved relationship with the capacity of glucose disposal among the NGT, IGT and T2DM subjects. The decrease of insulin clearance predicts the progression of glucose intolerance.

Selective control of up-regulated and down-regulated genes by temporal patterns and doses of insulin

Transcriptomic analysis and mathematical modeling reveal selective gene regulation by insulin. Insulin highs and lows Insulin is released by the pancreas and enables tissues such as skeletal muscle, fat, and liver to take up glucose. The pancreas secretes a basal amount of insulin, and eating triggers a rapid, transient increase in insulin release. Sano et al. identified a set of genes that increased in expression and a set that decreased in expression in response to insulin in hepatoma cells. Stimulating the cells with different doses and temporal patterns of insulin revealed that the up-regulated genes responded more rapidly than did the down-regulated genes to transient high concentrations of insulin stimulation; the down-regulated genes responded to lower concentrations of insulin than did the up-regulated genes. Simple mathematical modeling of the insulin-stimulated transcriptional pathway in two parts [(i) insulin to nucleus and (ii) transcription and mRNA degradation] suggested that the suppression of down-regulated genes occurred at steps before transcription and that transcription and transcript degradation rates were higher for the up-regulated genes. Livers from rats receiving a single dose of insulin exhibited increased or decreased expression of a subset of the regulated genes identified in the hepatoma cells. In particular, a gene involved in cholesterol biosynthesis was stimulated by insulin in culture and in vivo, and genes involved in gluconeogenesis were suppressed. Secretion of insulin transiently increases after eating, resulting in a high circulating concentration. Fasting limits insulin secretion, resulting in a low concentration of insulin in the circulation. We analyzed transcriptional responses to different temporal patterns and doses of insulin in the hepatoma FAO cells and identified 13 up-regulated and 16 down-regulated insulin-responsive genes (IRGs). The up-regulated IRGs responded more rapidly than did the down-regulated IRGs to transient stepwise or pulsatile increases in insulin concentration, whereas the down-regulated IRGs were repressed at lower concentrations of insulin than those required to stimulate the up-regulated IRGs. Mathematical modeling of the insulin response as two stages—(i) insulin signaling to transcription and (ii)transcription and mRNA stability—indicated that the first stage was the more rapid stage for the down-regulated IRGs, whereas the second stage of transcription was the more rapid stage for the up-regulated IRGs. A subset of the IRGs that were up-regulated or down-regulated in the FAO cells was similarly regulated in the livers of rats injected with a single dose of insulin. Thus, not only can cells respond to insulin but they can also interpret the intensity and pattern of signal to produce distinct transcriptional responses. These results provide insight that may be useful in treating obesity and type 2 diabetes associated with aberrant insulin production or tissue responsiveness.

Small-Volume Effect Enables Robust, Sensitive, and Efficient Information Transfer in the Spine

Why is the spine of a neuron so small that it can contain only small numbers of molecules and reactions inevitably become stochastic? We previously showed that, despite such noisy conditions, the spine exhibits robust, sensitive, and efficient features of information transfer using the probability of Ca2+ increase; however, the mechanisms are unknown. In this study, we show that the small volume effect enables robust, sensitive, and efficient information transfer in the spine volume, but not in the cell volume. In the spine volume, the intrinsic noise in reactions becomes larger than the extrinsic noise of input, resulting in robust information transfer despite input fluctuation. In the spine volume, stochasticity makes the Ca2+ increase occur with a lower intensity of input, causing higher sensitivity to lower intensity of input. The volume-dependency of information transfer increases its efficiency in the spine volume. Thus, we propose that the small-volume effect is the functional reason why the spine has to be so small.

Stochasticity in Ca2+ Increase in Spines Enables Robust and Sensitive Information Coding

A dendritic spine is a very small structure (∼0.1 µm3) of a neuron that processes input timing information. Why are spines so small? Here, we provide functional reasons; the size of spines is optimal for information coding. Spines code input timing information by the probability of Ca2+ increases, which makes robust and sensitive information coding possible. We created a stochastic simulation model of input timing-dependent Ca2+ increases in a cerebellar Purkinje cells spine. Spines used probability coding of Ca2+ increases rather than amplitude coding for input timing detection via stochastic facilitation by utilizing the small number of molecules in a spine volume, where information per volume appeared optimal. Probability coding of Ca2+ increases in a spine volume was more robust against input fluctuation and more sensitive to input numbers than amplitude coding of Ca2+ increases in a cell volume. Thus, stochasticity is a strategy by which neurons robustly and sensitively code information.

Laguerre Filter Analysis with Partial Least Square Regression Reveals a Priming Effect of ERK and CREB on c-FOS Induction

Signaling networks are made up of limited numbers of molecules and yet can code information that controls different cellular states through temporal patterns and a combination of signaling molecules. In this study, we used a data-driven modeling approach, the Laguerre filter with partial least square regression, to describe how temporal and combinatorial patterns of signaling molecules are decoded by their downstream targets. The Laguerre filter is a time series model used to represent a nonlinear system based on Volterra series expansion. Furthermore, with this approach, each component of the Volterra series expansion is expanded by Laguerre basis functions. We combined two approaches, application of a Laguerre filter and partial least squares (PLS) regression, and applied the combined approach to analysis of a signal transduction network. We applied the Laguerre filter with PLS regression to identify input and output (IO) relationships between MAP kinases and the products of immediate early genes (IEGs). We found that Laguerre filter with PLS regression performs better than Laguerre filter with ordinary regression for the reproduction of a time series of IEGs. Analysis of the nonlinear characteristics extracted using the Laguerre filter revealed a priming effect of ERK and CREB on c-FOS induction. Specifically, we found that the effects of a first pulse of ERK enhance the subsequent effects on c-FOS induction of treatment with a second pulse of ERK, a finding consistent with prior molecular biological knowledge. The variable importance of projections and output loadings in PLS regression predicted the upstream dependency of each IEG. Thus, a Laguerre filter with partial least square regression approach appears to be a powerful method to find the processing mechanism of temporal patterns and combination of signaling molecules by their downstream gene expression.

Increase in hepatic and decrease in peripheral insulin clearance characterize abnormal temporal patterns of serum insulin in diabetic subjects

Insulin plays a central role in glucose homeostasis, and impairment of insulin action causes glucose intolerance and leads to type 2 diabetes mellitus (T2DM). A decrease in the transient peak and sustained increase of circulating insulin following an infusion of glucose accompany T2DM pathogenesis. However, the mechanism underlying this abnormal temporal pattern of circulating insulin concentration remains unknown. Here we show that changes in opposite direction of hepatic and peripheral insulin clearance characterize this abnormal temporal pattern of circulating insulin concentration observed in T2DM. We developed a mathematical model using a hyperglycemic and hyperinsulinemic-euglycemic clamp in 111 subjects, including healthy normoglycemic and diabetic subjects. The hepatic and peripheral insulin clearance significantly increase and decrease, respectively, from healthy to borderline type and T2DM. The increased hepatic insulin clearance reduces the amplitude of circulating insulin concentration, whereas the decreased peripheral insulin clearance changes the temporal patterns of circulating insulin concentration from transient to sustained. These results provide further insight into the pathogenesis of T2DM, and thus may contribute to develop better treatment of this condition.Glucose homeostasis: roles of insulin clearanceType 2 diabetes mellitus (T2DM) is one of the fastest growing public health problems, characterized by chronic hyperglycemia with the failure of glucose homeostasis. Evaluating alteration in biological functions regulating circulating glucose concentration is complicated due to the mutual relation between circulating glucose and insulin. A team led by Wataru Ogawa at Kobe University designed clinical experiments for breaking such feedback relations, and a team led by Shinya Kuroda at University of Tokyo developed mathematical models for specifically quantifying the functions from the clinical data. The estimated model parameters revealed the significant increase in hepatic and decrease in peripheral insulin clearance, which occur before and after insulin delivery into systemic circulation, respectively, from healthy to T2DM subjects. Model analysis suggested these insulin clearances centrally regulate the dynamics of circulating insulin concentration in the glucose-insulin regulatory system.

Trans-omic Analysis Reveals Selective Responses to Induced and Basal Insulin across Signaling, Transcriptional, and Metabolic Networks

Summary The concentrations of insulin selectively regulate multiple cellular functions. To understand how insulin concentrations are interpreted by cells, we constructed a trans-omic network of insulin action in FAO hepatoma cells using transcriptomic data, western blotting analysis of signaling proteins, and metabolomic data. By integrating sensitivity into the trans-omic network, we identified the selective trans-omic networks stimulated by high and low doses of insulin, denoted as induced and basal insulin signals, respectively. The induced insulin signal was selectively transmitted through the pathway involving Erk to an increase in the expression of immediate-early and upregulated genes, whereas the basal insulin signal was selectively transmitted through a pathway involving Akt and an increase of Foxo phosphorylation and a reduction of downregulated gene expression. We validated the selective trans-omic network in vivo by analysis of the insulin-clamped rat liver. This integrated analysis enabled molecular insight into how liver cells interpret physiological insulin signals to regulate cellular functions.

Logical design of oral glucose ingestion pattern minimizing blood glucose in humans

Excessive increase in blood glucose level after eating increases the risk of macroangiopathy, and a method for not increasing the postprandial blood glucose level is desired. However, a logical design method of the dietary ingestion pattern controlling the postprandial blood glucose 2 level has not yet been established. We constructed a mathematical model of blood glucose control by oral glucose ingestion in 3 healthy human subjects, used the model to predict an optimal glucose ingestion pattern, and showed that the optimal ingestion pattern minimized the peak value of blood glucose level. Subjects orally ingested 3 doses of glucose by bolus or over 2 hours, and blood glucose, insulin, C-peptide and incretins were measured for 4 hours. We constructed an ordinary differential equation model that reproduced the time course data of the blood glucose and blood hormone levels. Using the model, we predicted that intermittent ingestion 30 minutes apart was the optimal glucose ingestion patterns that minimized the peak value of blood glucose level. We confirmed with subjects that this intermittent pattern decreased the peak value of blood glucose level. This approach could be applied to design optimal dietary ingestion patterns. In Brief As a forward problem, we measured blood glucose and hormones in three human subjects after oral glucose ingestion and constructed a mathematical model of blood glucose control. As an inverse problem, we used the model to predict the optimal oral glucose ingestion pattern that minimized the peak value of blood glucose level, and validated the pattern with the subjects. Highlights Modeling blood glucose concentrations predicts an intermittent ingestion pattern is optimal Human validation shows ingestion at 30-minute intervals limits peak blood glucose We provide a strategy to design optimal dietary ingestion patterns

Small and many is the strategy for robust and efficient information transfer in dendritic spines

A dendritic spine is a small structure on the dendrites of a neuron that processes input timing information from other neurons. Tens of thousands of spines are present on a neuron. Why are spines so small and many? Because of the small number of molecules in the spine volume, biochemical reactions become stochastic. Therefore, we used the stochastic simulation model of N-methyl-D-aspartate receptor (NMDAR)-mediated Ca2+ increase to address this issue. NMDAR-mediated Ca2+ increase codes the input timing information between prespiking and postspiking. We examined how much the input timing information is encoded by Ca2+ increase against prespiking fluctuation. We found that the input timing information encoded in the spine volume (10-1 μm3) is more robust against prespiking fluctuation than that in the cell volume (103 μm3). We further examined the mechanism of the robust information transfer in the spine volume. We demonstrated that the necessary and sufficient condition for robustness is that the stochastic NMDAR-mediated Ca2+ increase (intrinsic noise) becomes much larger than the prespiking fluctuation (extrinsic noise). The condition is satisfied in the spine volume, but not in the cell volume. Moreover, we compared the information transfer in many small “spine-volume” spines with that in a single large “cell-volume” spine. We found that many small “spine-volume” spines is much more efficient for information transfer than a single large “cell-volume” spine when prespiking fluctuation is large. Thus, robustness and efficiency are two functional reasons why dendritic spines are so small and many. Significance A dendritic spine is a small platform for information processing in a neuron, and tens of thousands of spines are present on a neuron. Why are spines so small and many? Here we addressed this issue using stochastic simulation of NMDAR-mediated Ca2+ increase in a spine. We demonstrated that smallness of a spine enables the robust information transfer against input fluctuation, and that many small spines are much efficient for information transfer than a single large cell. This is the first demonstration that shows the advantage of the “small and many” of spines in information processing. The “small and many” strategy may be used not only in spines of a neuron, but also in other small and many intracellular organelles.

Trans-omic analysis reveals fed and fasting insulin signal across phosphoproteome, transcriptome, and metabolome

The concentration and temporal pattern of insulin selectively regulate multiple cellular functions. To understand how insulin dynamics are interpreted by cells, we constructed a trans-omic network of insulin action in FAO hepatoma cells from three networks—a phosphorylation-dependent cellular functions regulatory network using phosphoproteomic data, a transcriptional regulatory network using phosphoproteomic and transcriptomic data, and a metabolism regulatory network using phosphoproteomic and metabolomic data. With the trans-omic regulatory network, we identified selective regulatory networks that mediate differential responses to insulin. Akt and Erk, hub molecules of insulin signaling, encode information of a wide dynamic range of dose and time of insulin. Down-regulated genes and metabolites in glycolysis had high sensitivity to insulin (fasting insulin signal); up-regulated genes and dicarboxylic acids in the TCA cycle had low sensitivity (fed insulin signal). This integrated analysis enables molecular insight into how cells interpret physiologically fed and fasting insulin signals. Highlights We constructed a trans-omic network of insulin action using multi-omic data. The trans-omic network integrates phosphorylation, transcription, and metabolism. We classified signaling, transcriptome, and metabolome by sensitivity to insulin. We identified fed and fasting insulin signal flow across the trans-omic network.