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

Time-series transcriptomics reveals distinctive mRNA expression dynamics associated with gene ontology specificity and protein expression in skeletal muscle after electrical stimulation-induced resistance exercise

Resistance exercise upregulates and downregulates the expression of a wide range of genes in skeletal muscle. However, detailed analysis of mRNA dynamics such as response rates and temporal patterns of the transcriptome after resistance exercise has not been performed. We aimed to clarify the dynamics of time-series transcriptomics after resistance exercise. We used electrical stimulation-induced muscle contraction as a resistance exercise model (5 sets × 10 times of 3 s of 100-Hz electrical stimulation) on the tibialis anterior muscle of rats and measured the transcriptome in the muscle before and at 0, 1, 3, 6, and 12 h after muscle contractions by RNA sequencing. We also examined the relationship between the parameters of mRNA dynamics and the increase in protein expression at 12 h after muscle contractions. We found that the function of the upregulated genes differed after muscle contractions depending on their response rate. Genes related to muscle differentiation and response to mechanical stimulus were enriched in the sustainedly upregulated genes. Furthermore, there was a positive correlation between the magnitude of upregulated mRNA expression and the corresponding protein expression level at 12 h after muscle contractions. Although it has been theoretically suggested, this study experimentally demonstrated that the magnitude of the mRNA response after electrical stimulation-induced resistance exercise contributes to skeletal muscle adaptation via increases in protein expression. These findings suggest that mRNA expression dynamics such as response rate, a sustained upregulated expression pattern, and the magnitude of the response contribute to mechanisms underlying adaptation to resistance exercise.

Model selection reveals selective regulation of blood amino acid and lipid metabolism by insulin in humans

Insulin plays a crucial role in regulating the metabolism of blood glucose, amino acids (aa), and lipids in humans. However, the mechanisms by which insulin selectively regulates these metabolites are not fully understood. To address this question, we used mathematical modeling to identify the selective regulatory mechanisms of insulin on blood aa and lipids. Our study revealed that insulin negatively regulates the influx and positively regulates the efflux of lipids, consistent with previous findings. By contrast, we did not observe the previously reported insulin's negative regulation of branched-chain aa (BCAA) influx; instead, we found that insulin positively regulates BCAA efflux. We observed that the earlier peak time of lipids compared to BCAA is dependent on insulin's negative regulation of their influx. Overall, our findings shed new light on how insulin selectively regulates the levels of different metabolites in human blood, providing insights into the metabolic disorder pathogenesis and potential therapies.

Optogenetic decoding of Akt2-regulated metabolic signaling pathways in skeletal muscle cells using transomics analysis

Insulin regulates various cellular metabolic processes by activating specific isoforms of the Akt family of kinases. Here, we elucidated metabolic pathways that are regulated in an Akt2-dependent manner. We constructed a transomics network by quantifying phosphorylated Akt substrates, metabolites, and transcripts in C2C12 skeletal muscle cells with acute, optogenetically induced activation of Akt2. We found that Akt2-specific activation predominantly affected Akt substrate phosphorylation and metabolite regulation rather than transcript regulation. The transomics network revealed that Akt2 regulated the lower glycolysis pathway and nucleotide metabolism and cooperated with Akt2-independent signaling to promote the rate-limiting steps in these processes, such as the first step of glycolysis, glucose uptake, and the activation of the pyrimidine metabolic enzyme CAD. Together, our findings reveal the mechanism of Akt2-dependent metabolic pathway regulation, paving the way for Akt2-targeting therapeutics in diabetes and metabolic disorders.

Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome

High-throughput omics technologies have enabled the profiling of entire biological systems. For the biological interpretation of such omics data, two analyses, hypothesis- and data-driven analyses including tensor decomposition, have been used. Both analyses have their own advantages and disadvantages and are mutually complementary; however, a direct comparison of these two analyses for omics data is poorly examined.We applied tensor decomposition (TD) to a dataset representing changes in the concentrations of 562 blood molecules at 14 time points in 20 healthy human subjects after ingestion of 75 g oral glucose. We characterized each molecule by individual dependence (constant or variable) and time dependence (later peak or early peak). Three of the four features extracted by TD were characterized by our previous hypothesis-driven study, indicating that TD can extract some of the same features obtained by hypothesis-driven analysis in a non-biased manner. In contrast to the years taken for our previous hypothesis-driven analysis, the data-driven analysis in this study took days, indicating that TD can extract biological features in a non-biased manner without the time-consuming process of hypothesis generation.

Rate thresholds in cell signaling have functional and phenotypic consequences in non-linear time-dependent environments

All cells employ signal transduction pathways to respond to physiologically relevant extracellular cytokines, stressors, nutrient levels, hormones, morphogens, and other stimuli that vary in concentration and rate in healthy and diseased states. A central unsolved fundamental question in cell signaling is whether and how cells sense and integrate information conveyed by changes in the rate of extracellular stimuli concentrations, in addition to the absolute difference in concentration. We propose that different environmental changes over time influence cell behavior in addition to different signaling molecules or different genetic backgrounds. However, most current biomedical research focuses on acute environmental changes and does not consider how cells respond to environments that change slowly over time. As an example of such environmental change, we review cell sensitivity to environmental rate changes, including the novel mechanism of rate threshold. A rate threshold is defined as a threshold in the rate of change in the environment in which a rate value below the threshold does not activate signaling and a rate value above the threshold leads to signal activation. We reviewed p38/Hog1 osmotic stress signaling in yeast, chemotaxis and stress response in bacteria, cyclic adenosine monophosphate signaling in Amoebae, growth factors signaling in mammalian cells, morphogen dynamics during development, temporal dynamics of glucose and insulin signaling, and spatio-temproral stressors in the kidney. These reviewed examples from the literature indicate that rate thresholds are widespread and an underappreciated fundamental property of cell signaling. Finally, by studying cells in non-linear environments, we outline future directions to understand cell physiology better in normal and pathophysiological conditions.

Trans-omics analysis of insulin action reveals a cell growth subnetwork which co-regulates anabolic processes

Insulin signaling promotes anabolic metabolism to regulate cell growth through multi-omic interactions. To obtain a comprehensive view of the cellular responses to insulin, we constructed a trans-omic network of insulin action in Drosophila cells that involves the integration of multi-omic data sets. In this network, 14 transcription factors, including Myc, coordinately upregulate the gene expression of anabolic processes such as nucleotide synthesis, transcription, and translation, consistent with decreases in metabolites such as nucleotide triphosphates and proteinogenic amino acids required for transcription and translation. Next, as cell growth is required for cell proliferation and insulin can stimulate proliferation in a context-dependent manner, we integrated the trans-omic network with results from a CRISPR functional screen for cell proliferation. This analysis validates the role of a Myc-mediated subnetwork that coordinates the activation of genes involved in anabolic processes required for cell growth.

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A trans-omic network of insulin action in Drosophila cells was constructed

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Insulin co-regulates various anabolic processes in a time-dependent manner

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The trans-omic network and a CRISPR screen for cell proliferation were integrated

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A Myc-mediated subnetwork promoting anabolic processes is required for cell growth

FoxO-KLF15 pathway switches the flow of macronutrients under the control of insulin

KLF15 is a transcription factor that plays an important role in the activation of gluconeogenesis from amino acids as well as the suppression of lipogenesis from glucose. Here we identified the transcription start site of liver-specific KLF15 transcript and showed that FoxO1/3 transcriptionally regulates Klf15 gene expression by directly binding to the liver-specific Klf15 promoter. To achieve this, we performed a precise in vivo promoter analysis combined with the genome-wide transcription-factor-screening method "TFEL scan", using our original Transcription Factor Expression Library (TFEL), which covers nearly all the transcription factors in the mouse genome. Hepatic Klf15 expression is significantly increased via FoxOs by attenuating insulin signaling. Furthermore, FoxOs elevate the expression levels of amino acid catabolic enzymes and suppress SREBP-1c via KLF15, resulting in accelerated amino acid breakdown and suppressed lipogenesis during fasting. Thus, the FoxO-KLF15 pathway contributes to switching the macronutrient flow in the liver under the control of insulin.

Continuous variable responses and signal gating form kinetic bases for pulsatile insulin signaling and emergence of resistance

Evolutionarily conserved insulin signaling is central to nutrient sensing, storage, and utilization across tissues. Dysfunctional insulin signaling is associated with metabolic disorders, cancer, and aging. Hence, the pathway components have emerged as key targets for pharmacological interventions in addition to insulin administration itself. Despite this, activation–inactivation dynamics of individual components, which exert regulatory control in a physiological context, is poorly understood. Now, with our systems-based approach, we reveal kinetic parameters, which define the flow of information through both metabolic and growth-factor arms and thus determine signaling architecture. We also provide a kinetic basis for 1) the advantage of pulsatile-fasted insulin signaling that enables fed-insulin response and 2) the detrimental impact of repeat fed-insulin inputs that causes resistance.

Understanding kinetic control of biological processes is as important as identifying components that constitute pathways. Insulin signaling is central for almost all metazoans, and its perturbations are associated with various developmental disorders, metabolic diseases, and aging. While temporal phosphorylation changes and kinetic constants have provided some insights, constant or variable parameters that establish and maintain signal topology are poorly understood. Here, we report kinetic parameters that encode insulin concentration and nutrient-dependent flow of information using iterative experimental and mathematical simulation-based approaches. Our results illustrate how dynamics of distinct phosphorylation events collectively contribute to selective kinetic gating of signals and maximum connectivity of the signaling cascade under normo-insulinemic but not hyper-insulinemic states. In addition to identifying parameters that provide predictive value for maintaining the balance between metabolic and growth-factor arms, we posit a kinetic basis for the emergence of insulin resistance. Given that pulsatile insulin secretion during a fasted state precedes a fed response, our findings reveal rewiring of insulin signaling akin to memory and anticipation, which was hitherto unknown. Striking disparate temporal behavior of key phosphorylation events that destroy the topology under hyper-insulinemic states underscores the importance of unraveling regulatory components that act as bandwidth filters. In conclusion, besides providing fundamental insights, our study will help in identifying therapeutic strategies that conserve coupling between metabolic and growth-factor arms, which is lost in diseases and conditions of hyper-insulinemia.

An extensive and dynamic trans-omic network illustrating prominent regulatory mechanisms in response to insulin in the liver

An effective combination of multi-omic datasets can enhance our understanding of complex biological phenomena. To build a context-dependent network with multiple omic layers, i.e., a trans-omic network, we perform phosphoproteomics, transcriptomics, proteomics, and metabolomics of murine liver for 4 h after insulin administration and integrate the resulting time series. Structural characteristics and dynamic nature of the network are analyzed to elucidate the impact of insulin. Early and prominent changes in protein phosphorylation and persistent and asynchronous changes in mRNA and protein levels through non-transcriptional mechanisms indicate enhanced crosstalk between phosphorylation-mediated signaling and protein expression regulation. Metabolic response shows different temporal regulation with transient increases at early time points across categories and enhanced response in the amino acid and nucleotide categories at later time points as a result of process convergence. This extensive and dynamic view of the trans-omic network elucidates prominent regulatory mechanisms that drive insulin responses through intricate interlayer coordination.

Hepatic transcriptional responses to fasting and feeding

Mammals undergo regular cycles of fasting and feeding that engage dynamic transcriptional responses in metabolic tissues. Here we review advances in our understanding of the gene regulatory networks that contribute to hepatic responses to fasting and feeding. The advent of sequencing and -omics techniques have begun to facilitate a holistic understanding of the transcriptional landscape and its plasticity. We highlight transcription factors, their cofactors, and the pathways that they impact. We also discuss physiological factors that impinge on these responses, including circadian rhythms and sex differences. Finally, we review how dietary modifications modulate hepatic gene expression programs.

Transomics analysis reveals allosteric and gene regulation axes for altered hepatic glucose-responsive metabolism in obesity

Impaired glucose tolerance associated with obesity causes postprandial hyperglycemia and can lead to type 2 diabetes. To study the differences in liver metabolism in healthy and obese states, we constructed and analyzed transomics glucose-responsive metabolic networks with layers for metabolites, expression data for metabolic enzyme genes, transcription factors, and insulin signaling proteins from the livers of healthy and obese mice. We integrated multiomics time course data from wild-type and leptin-deficient obese (ob/ob) mice after orally administered glucose. In wild-type mice, metabolic reactions were rapidly regulated within 10 min of oral glucose administration by glucose-responsive metabolites, which functioned as allosteric regulators and substrates of metabolic enzymes, and by Akt-induced changes in the expression of glucose-responsive genes encoding metabolic enzymes. In ob/ob mice, the majority of rapid regulation by glucose-responsive metabolites was absent. Instead, glucose administration produced slow changes in the expression of carbohydrate, lipid, and amino acid metabolic enzyme-encoding genes to alter metabolic reactions on a time scale of hours. Few regulatory events occurred in both healthy and obese mice. Thus, our transomics network analysis revealed that regulation of glucose-responsive liver metabolism is mediated through different mechanisms in healthy and obese states. Rapid changes in allosteric regulators and substrates and in gene expression dominate the healthy state, whereas slow changes in gene expression dominate the obese state.

Integration of TGF-β-induced Smad signaling in the insulin-induced transcriptional response in endothelial cells

Insulin signaling governs many processes including glucose homeostasis and metabolism, and is therapeutically used to treat hyperglycemia in diabetes. We demonstrated that insulin-induced Akt activation enhances the sensitivity to TGF-β by directing an increase in cell surface TGF-β receptors from a pool of intracellular TGF-β receptors. Consequently, increased autocrine TGF-β signaling in response to insulin participates in insulin-induced angiogenic responses of endothelial cells. With TGF-β signaling controlling many cell responses, including differentiation and extracellular matrix deposition, and pathologically promoting fibrosis and cancer cell dissemination, we addressed to which extent autocrine TGF-β signaling participates in insulin-induced gene responses of human endothelial cells. Transcriptome analyses of the insulin response, in the absence or presence of a TGF-β receptor kinase inhibitor, revealed substantial positive and negative contributions of autocrine TGF-β signaling in insulin-responsive gene responses. Furthermore, insulin-induced responses of many genes depended on or resulted from autocrine TGF-β signaling. Our analyses also highlight extensive contributions of autocrine TGF-β signaling to basal gene expression in the absence of insulin, and identified many novel TGF-β-responsive genes. This data resource may aid in the appreciation of the roles of autocrine TGF-β signaling in normal physiological responses to insulin, and implications of therapeutic insulin usage.

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 level has not yet been established. We constructed a mathematical model of blood glucose control by oral glucose ingestion in three healthy human subjects, and predicted that intermittent ingestion 30 min apart was the optimal glucose ingestion patterns that minimized the peak value of blood glucose level. We confirmed with subjects that this intermittent pattern consistently decreased the peak value of blood glucose level. We also predicted insulin minimization pattern, and found that the intermittent ingestion 30 min apart was optimal, which is similar to that of glucose minimization pattern. Taken together, these results suggest that the glucose minimization is achieved by suppressing the peak value of insulin concentration, rather than by enhancing insulin concentration. This approach could be applied to design optimal dietary ingestion patterns.

Enhanced TGF-β Signaling Contributes to the Insulin-Induced Angiogenic Responses of Endothelial Cells

Angiogenesis, the development of new blood vessels, is a key process in disease. We reported that insulin promotes translocation of transforming growth factor β (TGF-β) receptors to the plasma membrane of epithelial and fibroblast cells, thus enhancing TGF-β responsiveness. Since insulin promotes angiogenesis, we addressed whether increased autocrine TGF-β signaling participates in endothelial cell responses to insulin. We show that insulin enhances TGF-β responsiveness and autocrine TGF-β signaling in primary human endothelial cells, by inducing a rapid increase in cell surface TGF-β receptor levels. Autocrine TGF-β/Smad signaling contributed substantially to insulin-induced gene expression associated with angiogenesis, including TGF-β target genes encoding angiogenic mediators; was essential for endothelial cell migration; and participated in endothelial cell invasion and network formation. Blocking TGF-β signaling impaired insulin-induced microvessel outgrowth from neonatal aortic rings and modified insulin-stimulated blood vessel formation in zebrafish. We conclude that enhanced autocrine TGF-β signaling is integral to endothelial cell and angiogenic responses to insulin.

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

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.

In Vivo Decoding Mechanisms of the Temporal Patterns of Blood Insulin by the Insulin-AKT Pathway in the Liver

Cells respond to various extracellular stimuli through a limited number of signaling pathways. One strategy to process such stimuli is to code the information into the temporal patterns of molecules. Although we showed that insulin selectively regulated molecules depending on its temporal patterns using Fao cells, the in vivo mechanism remains unknown. Here, we show how the insulin-AKT pathway processes the information encoded into the temporal patterns of blood insulin. We performed hyperinsulinemic-euglycemic clamp experiments and found that, in the liver, all temporal patterns of insulin are encoded into the insulin receptor, and downstream molecules selectively decode them through AKT. S6K selectively decodes the additional secretion information. G6Pase interprets the basal secretion information through FoxO1, while GSK3β decodes all secretion pattern information. Mathematical modeling revealed the mechanism via differences in network structures and from sensitivity and time constants. Given that almost all hormones exhibit distinct temporal patterns, temporal coding may be a general principle of system homeostasis by hormones.

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.

Type 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.

Multidimensional biochemical information processing of dynamical patterns

Cells receive signaling molecules by receptors and relay information via sensory networks so that they can respond properly depending on the type of signal. Recent studies have shown that cells can extract multidimensional information from dynamical concentration patterns of signaling molecules. We herein study how biochemical systems can process multidimensional information embedded in dynamical patterns. We model the decoding networks by linear response functions, and optimize the functions with the calculus of variations to maximize the mutual information between patterns and output. We find that, when the noise intensity is lower, decoders with different linear response functions, i.e., distinct decoders, can extract much information. However, when the noise intensity is higher, distinct decoders do not provide the maximum amount of information. This indicates that, when transmitting information by dynamical patterns, embedding information in multiple patterns is not optimal when the noise intensity is very large. Furthermore, we explore the biochemical implementations of these decoders using control theory and demonstrate that these decoders can be implemented biochemically through the modification of cascade-type networks, which are prevalent in actual signaling pathways.