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

Distinct dynamic regulation of pectoralis muscle metabolomics by insulin and the promotion of glucose-lipid metabolism with extended duration

Birds' glycolipid metabolism has garnered considerable attention due to their fasting blood glucose levels being nearly twice those of mammals. While skeletal muscle is the primary insulin-sensitive tissue in mammals, the effects of insulin on chicken skeletal muscle remain unclear. In this study, the insulin-responsive metabolites were identified in broiler's pectoralis muscle (after 16 h of fasting) using widely targeted metabolomics. Glycolipid concentrations were measured using kits, and the expression of key genes involved in glucose metabolism was assessed via quantitative real-time PCR (qRT-PCR). The insulin tolerance test, performed by injecting 5 IU/kg body weight of insulin, demonstrated a rapid drop in blood glucose levels from 0 to 15 min, with a consistent reduction observed at 120 min (P < 0.01). Insulin did not alter glucose and glycogen content in chicken pectoralis; however, low-density lipoprotein (LDL, P < 0.05) levels were upregulated in the early phase (15 min). With an extended insulin duration (120 min), pectoralis glucose content increased (P < 0.05), accompanied by a reduction in TG levels (P < 0.05). Metabolomic analysis revealed that insulin promotes the downregulation of 63 out of 71 metabolites at 15 min and the upregulation of 101 out of 134 metabolites at 120 min, mainly associated with lysine degradation and thyroid hormone signaling pathways, respectively. 7 metabolites were dynamically modulated in the same manner over time (2 up-up and 5 down-down). Early insulin inhibited glycolysis, evidenced by the reduction in phosphoenolpyruvate levels and hexokinase 2 (HK2) expression; however, insulin promoted glucose uptake through the activation of glucose transporter 4 (GLUT4) and enhanced glycolysis, accompanied by elevated fatty acid metabolism at the later phase. In conclusion, insulin dynamically regulates the metabolomics of the pectoralis muscle over time. Initially, chicken muscle tissues downregulate metabolic activities to accommodate the new signaling state, followed by significant upregulation to meet heightened metabolic demands. Extended insulin monitoring promotes glucose uptake and glycolysis, alongside enhanced fatty acid metabolism. This research provides insights into the potential mechanisms of insulin action in chicken muscles.

Monitoring autophagic flux in vivo revealed its physiological response and significance of heterogeneity in pancreatic beta cells

Autophagy plays an essential role in preserving cellular homeostasis in pancreatic beta cells. However, the extent of autophagic flux in pancreatic islets induced in various physiological settings remains unclear. In this study, we generate transgenic mice expressing pHluorin-LC3-mCherry reporter for monitoring systemic autophagic flux by measuring the pHluorin/mCherry ratio, validating them in the starvation and insulin-deficient model. Our findings reveal that autophagic flux in pancreatic islets enhances after starvation, and suppression of the flux after short-term refeeding needs more prolonged re-starvation in islets than in the other insulin-targeted organs. Furthermore, heterogeneity of autophagic flux in pancreatic beta cells manifests under insulin resistance, and intracellular calcium influx by glucose stimulation increases more in high- than low-autophagic flux beta cells, with differential gene expression, including lipoprotein lipase. Our pHluorin-LC3-mCherry mice enable us to reveal biological insight into heterogeneity in autophagic flux in pancreatic beta cells.

Comparison of hepatic responses to glucose perturbation between healthy and obese mice based on the edge type of network structures

Interactions between various molecular species in biological phenomena give rise to numerous networks. The investigation of these networks, including their statistical and biochemical interactions, supports a deeper understanding of biological phenomena. The clustering of nodes associated with molecular species and enrichment analysis is frequently applied to examine the biological significance of such network structures. However, these methods focus on delineating the function of a node. As such, in-depth investigations of the edges, which are the connections between the nodes, are rarely explored. In the current study, we aimed to investigate the functions of the edges rather than the nodes. To accomplish this, for each network, we categorized the edges and defined the edge type based on their biological annotations. Subsequently, we used the edge type to compare the network structures of the metabolome and transcriptome in the livers of healthy (wild-type) and obese (ob/ob) mice following oral glucose administration (OGTT). The findings demonstrate that the edge type can facilitate the characterization of the state of a network structure, thereby reducing the information available through datasets containing the OGTT response in the metabolome and transcriptome.

Signaling and Gene Expression in Skeletal Muscles in Type 2 Diabetes: Current Results and OMICS Perspectives

Skeletal muscles mainly contribute to the emergence of insulin resistance, impaired glucose tolerance and the development of type 2 diabetes. Molecular mechanisms that regulate glucose uptake are diverse, including the insulin-dependent as most important, and others as also significant. They involve a wide range of proteins that control intracellular traffic and exposure of glucose transporters on the cell surface to create an extensive regulatory network. Here, we highlight advantages of the omics approaches to explore the insulin-regulated proteins and genes in human skeletal muscle with varying degrees of metabolic disorders. We discuss methodological aspects of the assessment of metabolic dysregulation and molecular responses of human skeletal muscle to insulin. The known molecular mechanisms of glucose uptake regulation and the first results of phosphoproteomic and transcriptomic studies are reviewed, which unveiled a large-scale array of insulin targets in muscle cells. They demonstrate that a clear depiction of changes that occur during metabolic dysfunction requires systemic and combined analysis at different levels of regulation, including signaling pathways, transcription factors, and gene expression. Such analysis seems promising to explore yet undescribed regulatory mechanisms of glucose uptake by skeletal muscle and identify the key regulators as potential therapeutic targets.

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

Quantitative metabolic fluxes regulated by trans-omic networks

Cells change their metabolism in response to internal and external conditions by regulating the trans-omic network, which is a global biochemical network with multiple omic layers. Metabolic flux is a direct measure of the activity of a metabolic reaction that provides valuable information for understanding complex trans-omic networks. Over the past decades, techniques to determine metabolic fluxes, including 13C-metabolic flux analysis (13C-MFA), flux balance analysis (FBA), and kinetic modeling, have been developed. Recent studies that acquire quantitative metabolic flux and multi-omic data have greatly advanced the quantitative understanding and prediction of metabolism-centric trans-omic networks. In this review, we present an overview of 13C-MFA, FBA, and kinetic modeling as the main techniques to determine quantitative metabolic fluxes, and discuss their advantages and disadvantages. We also introduce case studies with the aim of understanding complex metabolism-centric trans-omic networks based on the determination of metabolic fluxes.