The liver has a central role in glucose homeostasis, as it has the distinctive ability to produce and consume glucose. On feeding, glucose influx triggers gene expression changes in hepatocytes to suppress endogenous glucose production and convert excess glucose into glycogen or fatty acids to be stored in adipose tissue. This process is controlled by insulin, although debate exists as to whether insulin acts directly or indirectly on the liver. In addition to stimulating pancreatic insulin release, glucose also regulates the activity of ChREBP, a transcription factor that modulates lipogenesis. Here we describe another mechanism whereby glucose determines its own fate: we show that glucose binds and stimulates the transcriptional activity of the liver X receptor (LXR), a nuclear receptor that coordinates hepatic lipid metabolism. d-Glucose and d-glucose-6-phosphate are direct agonists of both LXR-alpha and LXR-beta. Glucose activates LXR at physiological concentrations expected in the liver and induces expression of LXR target genes with efficacy similar to that of oxysterols, the known LXR ligands. Cholesterol homeostasis genes that require LXR for expression are upregulated in liver and intestine of fasted mice re-fed with a glucose diet, indicating that glucose is an endogenous LXR ligand. Our results identify LXR as a transcriptional switch that integrates hepatic glucose metabolism and fatty acid synthesis.
"is and cell growth , through the activation of the sterol regulatory element - binding proteins , SREBP - 1c and SREBP - 2 ( Xu et al . , 2013 ) , and the carbohydrate responsive element - binding pro - tein , ChREBP ( Xu et al . , 2013 ) . SREBP - 1c and SREBP - 2 are under the control of the nuclear receptors called the liver X receptors ( LXR ; Mitro et al . , 2007 ; Figure 2 . A schematic and simplified representation of how natural dietary factors can direct cell metabolism toward oxidative meta - bolism ( on the left ) , biosynthesis ( on the right ) , and NF - kB - induced inflammation ( at the bottom of the figure , in red ) by their binding to nuclear receptors , transcription factors , and "
") Glucose 6-phosphate (cytosol) g6p LXRa (DeBose-Boyd et al., 2001; Lehmann et al., 1997; Mitro et al., 2007) Fructose 2,6-bisphosphate (cytosol) f26p2 G6PC (Arden et al., 2012; Pedersen et al., 2007) Pyruvate (cytosol) pyruvate PDHK2 (Bowker-Kinley et al., 1998; Sugden and Holness, 2006) Xylulose 5-phosphate (cytosol) x5p PP2A (Kabashima et al., 2003) We divide the set S of signalling species into index subsets with respect to physiological meaning and state value calculation (cf. Supplementary Fig. S2 and Table S4 "
[Show abstract][Hide abstract] ABSTRACT: Systems biology has to increasingly cope with large- and multi-scale biological systems. Many successful in silico representations and simulations of various cellular modules proved mathematical modelling to be an important tool in gaining a solid understanding of biological phenomena. However, models spanning different functional layers (e.g. metabolism, signalling and gene regulation) are still scarce. Consequently, model integration methods capable of fusing different types of biological networks and various model formalisms become a key methodology to increase the scope of cellular processes covered by mathematical models. Here we propose a new integration approach to couple logical models of signalling or/and gene-regulatory networks with kinetic models of metabolic processes. The procedure ends up with an integrated dynamic model of both layers relying on differential equations. The feasibility of the approach is shown in an illustrative case study integrating a kinetic model of central metabolic pathways in hepatocytes with a Boolean logical network depicting the hormonally induced signal transduction and gene regulation events involved. In silico simulations demonstrate the integrated model to qualitatively describe the physiological switch-like behaviour of hepatocytes in response to nutritionally regulated changes in extracellular glucagon and insulin levels. A simulated failure mode scenario addressing insulin resistance furthermore illustrates the pharmacological potential of a model covering interactions between signalling, gene regulation and metabolism.
"The fact that unsaturated fatty acids can function as LXR antagonists, and thereby create a feedback mechanism, supports this suggestion further (Ou et al., 2001). Additionally, LXRs have recently been implicated in negative regulation of inflammatory gene expression (Marathe et al., 2006), and as key regulators of genes governing carbohydrate metabolism (Mitro et al., 2007). "
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