A system biology approach revealed the nature and the cause of the different metabolic features of weak and strong antibiotic producers of the Streptomyces genus

A Millan-Oropeza, C Henry, C Lejeune, M David, M.

Introduction Bibliography

.

Results

A system biology approach revealed the nature and the cause of the different metabolic features of weak and strong antibiotic producers of the Streptomyces genus.

A. Millan-Oropeza 1,2 ¶ , C. Henry 2 ¶ , C. Lejeune 1 , M. David 1 and M.-J. Virolle 1 ¶ 1 Institute for Integrative Biology of the Cell (I2BC), Group "Energetic Metabolism of Streptomyces", CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, F-91198, Gif-sur-Yvette cedex, France.

2 Institut National de la Recherche Agronomique, Plateforme d'Analyse Protéomique de Paris Sud-Ouest (PAPPSO), MICALIS (UMR 1319), Université Paris-Saclay, Jouy-en-Josas, France.

Streptomycetes are efficient producers of chemically diverse bio-active molecules useful to human health including life-saving antibiotics. The biosynthesis of these so called "secondary metabolites" usually occurs in the period of slow or no growth. It is thought to be triggered by nutritional limitations, especially in phosphate, that are correlating with energy shortage. Despites numerous important scientific contributions over the past 40 years, a systemic understanding of the regulation of the biosynthesis and of the function of these bio-active metabolites for the producing bacteria remain incomplete. The model strains, Streptomyces lividans (SL) and Streptomyces coelicolor (SC), are extensively studied by the scientific community to address these questions. These closely related species possess identical biosynthetic pathways directing the synthesis of three well characterized secondary metabolites (CDA, RED and ACT) but only S. coelicolor expresses them at a high level. A comparative proteomic analysis of these strains, grown on phosphate limited R2YE medium, with glucose as main carbon source, was carried out in order to establish the specific metabolic features linked to weak (SL) and strong (SC) antibiotic production and a possible cause of such differences. Furthermore, a novel view of the function of the antibiotics for the producing bacteria in the specific context of their production is proposed.

B

Conclusions

We demonstrated that the ability of SL and SC to produce antibiotics was anti-correlated with their ability to accumulate TAG (2, 3). The low expression of the two component system in SC lead to a severe Pi limitation and thus energy shortage that the strain tries to compensates by activating its oxidative metabolism. The activation of the oxydative metabolism requires the feeding of the Krebs cycle by acetylCoA resulting into a very low TAG content of this strain. TAG and antibiotics biosynthesis compete for common precursors, acetylCoA or its derivatives. This is true for the biosynthesis of polyketide antibiotics (ACT) that are directly using acetylCoA or malonylCoA as precursors but that is also true for that of peptidic antibiotics (CDAand RED). Indeed these are synthesised from amino acids, often derived from intermediates of the Krebs cycle, and acetylCoA is feeding the Krebs cycle for the synthesis of the latters. Furthermore the produced antibiotics are proposed to play important roles in the regulation of the energetic metabolism of the bacteria in condition of phosphate scarcity. For instance, CDA and RED were proposed to promote cell death and lysis of a fraction of the population, via membrane damages, to feed and especially provide phosphate to the surviving one (7). The provided Phosphate would allow the temporary activation of the oxidative metabolism of the strain to re-establish its energy balance. However when phosphate is exhausted, oxydative phosphorylation has to be stopped and ACT was proposed to fulfill such function.

The two component system PhoR/PhoP is know to regulate positively phosphate (Pi) scavenging and uptake and negatively nitrogen (N) assimilation (1). Consequently in SC, the toning down of PhoR/PhoP regulatory role was correlated with low abundance of proteins involved in Pi scavenging and uptake leading to severe Pi limitation as well as with high N availability. High N availability is likely to be responsible for the inhibition of TAG biosynthesis and thus for the low TAG content of this strain (2, 3). Inhibition of TAG biosynthesis is predicted to lead to high acetylCoA availability, that together with high N availability, would fuel the Krebs cycle to support the oxidative metabolism characteristic of this strain (2). The up-regulation of enzymes generating reduced co-factors belonging to the Krebs cycle or to amino acid degradative pathways in SC is consistent with the previously reported high ATP content of this strain (2).

In SC, the activation of the oxidative metabolism constitutes an attempt for the strain to restore its energy balance. To do so most of the acetylCoA, generated by glycolysis or amino acids degradation, will be used to feed the Krebs cycle rather than being stored as TAG.

However, an active oxidative metabolism requires sufficient Pi to generate ATP and even if SC was shown to actively consume its polyphosphate stores (2), at some point, it will face a situation of Pi starvation linked to the weak expression of proteins of the Pho regulon involved in Pi supply.

Oxidative phosphorylation cannot proceed in this context and should be stopped.

We propose that the benzochromane quinone ACT might fulfill such a function.

1 Martín JF, Rodríguez-García A, Liras P ( 2017

Figure 1 :

 1 Figure 1: Heatmap representation of proteins of the Pho regulon belonging to phosphate metabolism with significant abundance change (ANOVA, adjusted p value < 0.01) between S. coelicolor and S. lividans grown on R2YE medium limited in phosphate (1mM) with either glucose or glycerol as main carbon source, for 36, 48 and 72 h at 28°C. The quantification methods are displayed in the vertical bar indicating those proteins quantified by SC (orange) or XIC (black).

Figure 2 :

 2 Figure 2: Cultures of S. lividans, its ppk mutant (3, 5, 6) and S. coelicolor grown on R2YE glucose limited in phosphate (1mM). Estimation of growth (continuous lines, mg of dry biomass per plate) of S. lividans (black circles), its ppk mutant (grey circles) and S. coelicolor (white circles), and evolution of the levels of ACT produced extracellularly and intracellularly by the ppk mutant of S. lividans (grey and grey hatched histograms, respectively) and S. coelicolor (plain white and hatched histograms, respectively) (A). Quantification of intracellular ATP levels in S. lividans (black circles), its ppk mutant (grey circles) and S. coelicolor (open circles). The error bars represent the standard deviation of at least three independent experiments. (2)

  ) The master regulator PhoP coordinates phosphate and nitrogen metabolism, respiration, cell differentiation and antibiotic biosynthesis: comparison in Streptomyces coelicolor and Streptomyces avermitilis. J Antibiot (Tokyo). 70(5):534-541. 2 Esnault C, Dulermo T, Smirnov A, Askora A, David M, Deniset-Besseau A, Holland IB. and Virolle MJ (2017) Strong antibiotic production is correlated with highly active oxidative metabolism in Streptomyces coelicolor M145" Scientific Reports 7(1):200. 3 Le Maréchal P, Decottignies P, Marchand CH, Degrouard J, Jaillard D, Dulermo T, Froissard M, Smirnov A, Chapuis V, Virolle MJ. (2013) Comparative proteomic analysis of the wild-type and the ppk mutant of Streptomyces lividans revealed the importance of storage lipids for antibiotic biosynthesis. Appl Environ Microbiol. vol. 79 no. 19 5907-5917. 4 Smirnov A, Esnault C, Prigent M, Holland IB, Virolle MJ. (2015) Phosphate Homeostasis in Conditions of Phosphate Proficiency and Limitation in the Wild Type and the phoP Mutant of Streptomyces lividans. PLoS One. 15;10(5):e0126221. doi: 10.1371/journal.pone.0126221. 5 Ghorbel S, Kormanec J, Artus A and Virolle MJ (2006) Transcriptional studies and regulatory interactions between the phoR-phoP operon and the phoU, mtpA, and ppk genes of Streptomyces lividans TK24. J Bacteriol. 188(2):677-86. 6 Chouayekh H and Virolle MJ (2002) The polyphosphate kinase is involved in the phosphate control of actinorhodin production in Streptomyces lividans. Mol. Microbiol. 43(4):919-30.) 7 Tenconi E, Traxler MF, Hoebreck C, van Wezel GP, Rigali S. (2018) Production of Prodiginines Is Part of a Programmed Cell Death Process in Streptomyces coelicolor. Front Microbiol. 6;9:1742. The ability to produce antibiotics and that to accumulate storage lipids of the TriAcylGlycerol family are anti-correlated in SL and SC


Dr Fabienne Rajas
email: fabienne.rajas@univ.lyon1.fr

Dr Gilles Mithieux
email: gilles.mithieux@univ.lyon1.fr

Master Role of Glucose-6 Phosphate in Cell Signalling and Consequences of its Deregulation in the Liver and Kidneys

Keywords: Carbohydrate response element binding protein, ChREBP, Chronic kidney disease, Diabetes, Fasting, Glycogen storage disease type I, Hepatocellular adenoma and carcinoma, Lipids, Non alcoholic fatty liver disease, Steatosis Converting enzyme, ChREBP: carbohydrate response element binding protein, CKD: chronic kidney disease, G6P: glucose-6 phosphate, G6Pase: glucose-6 phosphatase (G6Pase), hepatocellular adenoma, HCC: hepatocellular carcinoma, NAFLD: non-alcoholic fatty liver diseases, PKC: protein kinase C, PPP: pentose phosphate pathway, SITR2: Sirtuin 2, TG

come

Maintaining normoglycemia is a finely tuned, crucial physiological function. Indeed, most mammals, including humans, are incapable of tolerating hypoglycemia for more than a few minutes. Glucose is the major source of energy for most cells and provides precursors for biomolecule synthesis, such as nucleotides. Once a glucose molecule has passed from the bloodstream into a cell, it is rapidly phosphorylated into glucose-6 phosphate (G6P) and enters the glycolysis pathway (Figure 1). In addition, G6P enters the pentose phosphate pathway (PPP), where it is oxidized by glucose-6 phosphate dehydrogenase to produce ribose 5-phosphate, an intermediate metabolite in nucleotide synthesis required for DNA replication, as well as the production of the biological reductant NADPH. Glucose can also be stored as glycogen in the liver and skeletal muscle. When the storage capacity of glucose under the form of glycogen is exceeded, G6P is transformed into fat via de novo lipogenesis pathway. While hepatic lipid synthesis is a normal physiological process, excessive storage of lipids in the liver leads to the development of steatosis that represents a pre-pathological state possibly evolving in nonalcoholic fatty liver diseases (NAFLD). Thus, G6P is situated at the crossroad of several metabolic pathways and thus plays a key role in cell metabolism. In this review, we will focus on G6P metabolism in the liver and kidneys, the two major glucose-producing organs. We will also analyze the consequences of its deregulation in two metabolic diseases: type 2 diabetes and glycogen storage disease type I (GSDI). Indeed, abnormal plasma concentrations of glucose result in deleterious effects that can alter the normal functioning of the whole organism.

Decreased glucose levels are of particular danger for several tissues, especially in neuronal cells or erythrocytes that require glucose ceaselessly under normal physiological conditions. Thus, hypoglycemia can lead to impaired brain function and in more extreme situations it can lead to death. Conversely, hyperglycemia results in non-enzymatic glycosylation (glycation) and thus loss-of-function of proteins, glucose-induced oxidative damage and other adverse effects such as macrovascular and microvascular complications. While type 2 diabetes is characterized by an overproduction of glucose and hyperglycemia, GSDI is due to an absence of glucose production leading to severe hypoglycemia between two meals. Thus, these two pathologies could be considered as "mirror" diseases. However, they share similar hepatic and renal metabolic perturbations. Therefore, type 2 diabetic patients and GSDI patients are both prone to hepatic steatosis and tumor development and to chronic kidney disease (CKD) that appear with time (Figure 2).

Maintenance of blood glucose

Summary points § This chapter focuses on metabolic adaptions during the fed-to-fasted period to maintain blood glucose. § In the fed state, glucose is stored under the form of glycogen in the liver and muscles. § During the post-prandial period, glucose is mainly produced from glycogen degradation (glycogenolysis) in the liver, to maintain glycemia. § Hepatic glycogen metabolism is controlled by the coordinated action of two enzymes, glycogen synthase and glycogen phosphorylase, both of which are regulated by phosphorylation (regulated by pancreatic insulin or glucagon) and allosteric modulators such as glucose and glucose-6 phosphate. § During long-term food deprivation, glucose is produced by gluconeogenesis in the liver, kidney and intestine. § Gluconeogenesis is the synthesis of glucose from non-sugar carbon compounds. The main precursors are lactate and alanine in the case of liver gluconeogenesis, whereas glutamine is an essential substrate for gluconeogenesis in the kidneys and intestine. § Glucose homeostasis is balanced by nutrient sensing and hormones that control tissuespecific rates of glucose utilization and production. Insulin induces glucose utilization by tissues and inhibits glucose production, while glucagon activates glucose production.

The maintenance of blood glucose between 0.7 and 1.2 mg/dl (4-7 mmol/l) is a critical requirement for the body to maintain metabolism and function of several organs including the brain, kidneys or intestine. While glucose is generally considered as a major source of energy, ketone bodies represent the major energy source of several organs, including the brain, during fasting periods. Thus, during fasting, body reduces glucose oxidation to supply PPP for the synthesis of nucleotides, which is essential for cell proliferation [START_REF] Soty | Gut-Brain Glucose Signaling in Energy Homeostasis[END_REF]. Thereby, global glucose turnover decreases and glucose is preserved to supply 3-carbon skeletons to the body during fasting.

During the fed-to-fasted transition, glucose metabolism is finely tuned by a multi-organ crosstalk involving hormonal regulation and both central and splanchnic nervous systems [START_REF] Donovan | Peripheral and central glucose sensing in hypoglycemic detection[END_REF]. Insulin and glucagon that are produced by β and α cells, respectively, in the endocrine pancreas, are the main regulators of blood glucose levels. In addition to insulin and glucagon, counter-regulatory hormones such as epinephrine are also involved in this tight regulation. The liver and kidneys play a central role in glucose homeostasis by coordinating the storage/utilization and the synthesis of glucose between two meals and fasting in response to hormonal signaling. Moreover, reabsorption of filtered glucose in the renal proximal tubules is also an essential process to prevent excretion of glucose in the urine.

In the fed state, the presence of high insulin level favors tissue glucose utilization through the glycolytic pathway and storage of glucose in glycogen in the liver and muscles (around 30-40% of alimentary glucose). Glycogen is a polymer composed of glucose molecules forming long linear chains linked by α(1-4) bonds and fixed on main ramification chains by α(1-6) branching bonds [START_REF] Roach | Glycogen and its metabolism: some new developments and old themes[END_REF]. At a molecular level, glucose inhibits glycogen phosphorylase phosphatase, preventing glycogen degradation, whereas G6P allosterically activates glycogen synthase, thus favoring glycogen synthesis [START_REF] Agius | Role of glycogen phosphorylase in liver glycogen metabolism[END_REF]. Hepatic glycogen represents around 5 % of the weight of the liver. A deregulation of its storage and/or metabolism in the liver and/or muscle is responsible for glycogen storage diseases (GSD), which are a family of rare genetic diseases [START_REF] Hicks | Glycogen storage diseases: a brief review and update on clinical features, genetic abnormalities, pathologic features, and treatment[END_REF][START_REF] Weinstein | Inborn Errors of Metabolism with Hypoglycemia: Glycogen Storage Diseases and Inherited Disorders of Gluconeogenesis[END_REF]. Hypoglycemia is the primary manifestation of the hepatic GSDs (types 0,I, III, VI IX and XI). Contrarily to the liver and muscle, glycogen is not accumulated in the normal kidneys. However, when G6P is present in excessive concentrations, this metabolite can be stored as glycogen in the kidneys, as observed in GSDI and Fanconi-Bickel syndrome (due to GLUT 2 mutations), leading to nephromegaly [START_REF] Berry | Diabetes-like renal glomerular disease in Fanconi-Bickel syndrome[END_REF][START_REF] Martens | Renal Function in Glycogen Storage Disease Type I, Natural Course, and Renopreservative Effects of ACE Inhibition[END_REF]. In diabetes, renal glycogen accumulation was reported in hyperglycemic conditions, but further studies are necessary to better understand the tubular formation of glycogen deposits and their relevance in the diabetic kidney [START_REF] Vallon | Pathophysiology of the diabetic kidney[END_REF].

In the fasted state, the decrease in glycemia reduces intracellular glucose and G6P levels, activating glycogen degradation. Glycogen stores are considered to contribute to endogenous glucose production for about 10-12h in rodents and 20h in human subjects [START_REF] Mutel | Control of Blood Glucose in the Absence of Hepatic Glucose Production During Prolonged Fasting in Mice[END_REF][START_REF] Soty | Gut-Brain Glucose Signaling in Energy Homeostasis[END_REF]. During this post-absorptive period, the liver accounts for at least 80% of glucose production, mainly ensured by glycogenolysis. The stimulation of hepatic glycogenolysis during fasting or adrenergic stimulation is dependent on cAMP-PKA signaling. Indeed, both glycogen synthase and glycogen phosphorylase are phosphorylated by PKA, which results in a coordinated inhibition of glycogen synthase and activation of glycogen phosphorylase.

Furthermore, the increase in glucagon activates the gluconeogenic pathway by inducing the expression of phosphoenolpyruvate carboxykinase-cytosolic form (encoded by Pck1) and glucose-6 phosphatase (G6Pase). Gluconeogenesis (GNG) is the de novo synthesis of glucose from non-carbohydrate substrates that takes place only in the liver, kidney and intestine (Soty et al., 2017). G6Pase catalyzes the hydrolysis of G6P into glucose and inorganic phosphate, leading to the production of glucose in the bloodstream. This enzyme is expressed exclusively in gluconeogenic organs, but not in the muscles. Therefore, G6P is not converted into glucose in the muscle and thus not released in the bloodstream for glucose maintenance, but it is used solely in glycolysis to produce energy for muscle contraction. While the contribution of hepatic glucose production decreases during fasting, the contribution of renal GNG is increased to represent more than 50% of glucose production in fasting rats or humans [START_REF] Gerich | Renal gluconeogenesis: its importance in human glucose homeostasis[END_REF][START_REF] Owen | Liver and kidney metabolism during prolonged starvation[END_REF][START_REF] Pillot | Protein feeding promotes redistribution of endogenous glucose production to the kidney and potentiates its suppression by insulin[END_REF]. Moreover, we showed that intestinal GNG takes place to maintain glycemia during prolonged fasts [START_REF] Croset | Rat small intestine is an insulin-sensitive gluconeogenic organ[END_REF][START_REF] Penhoat | Intestinal gluconeogenesis is crucial to maintain a physiological fasting glycemia in the absence of hepatic glucose production in mice[END_REF]. It is noteworthy that glucose produced by the gut exerts in the post-absorptive state beneficial metabolic effects. By delivering glucose directly in the portal vein, where glucose is sensed by the nervous system, it induces a gut-brain glucose signal [START_REF] Soty | Gut-Brain Glucose Signaling in Energy Homeostasis[END_REF]. This portal glucose signal positively controls different metabolic functions, such as food intake and insulin sensitivity. Thus, the regulation of endogenous glucose production is finely regulated in the three gluconeogenic organs, allowing them to coordinately maintain blood glucose. While glucagon is mainly known to modulate hepatic glucose production [START_REF] Jiang | Glucagon and regulation of glucose metabolism[END_REF], it has also been shown to modulate the transcription of G6pc in the kidneys and intestine, thus modulating renal and intestinal glucose production [START_REF] Mutel | Control of Blood Glucose in the Absence of Hepatic Glucose Production During Prolonged Fasting in Mice[END_REF]. Recently, we have demonstrated a possible involvement of a crosstalk between the kidneys and intestine, via the vitamin D system, and the intestine and liver, via the aforementioned neural gut-brain axis, which might take place in the situation of impaired renal glucose production, such as during CKD [START_REF] Kaneko | The role of kidney in the inter-organ coordination of endogenous glucose production during fasting[END_REF].

Glucose phosphorylation and dephosphorylation.

Summary points § This chapter focuses on glucose phosphorylation/dephosphorylation during the fed-tofasting state cycle in the liver and kidneys. § Glucose-6 phosphate is the first intermediate of glucose metabolism. § The postprandial elevation in G6P has a major role in stimulating glycogen storage. § Glucokinase is the gatekeeper of glucose metabolism in hepatocytes and strongly regulates hepatic glycogen synthesis. § Glucokinase activity is regulated by an inhibitory protein, GKRP, which sequesters glucokinase in the nucleus during fasting. § In the kidneys, glucose phosphorylation is mediated by hexokinase. § Glucose-6 phosphatase is a key enzyme for endogenous glucose production. This enzyme ensures the last common step of both glycogenolysis and gluconeogenesis pathways i.e. the hydrolysis of glucose-6 phosphate into glucose. § Glucose-6 phosphatase is composed of two subunits (a G6P translocase, G6PT, and a catalytic subunit, G6PC), both localized in the membrane of the endoplasmic reticulum. § The transcription factor ChREBP (carbohydrate-response element binding protein) is a central metabolic coordinator orchestrating the fate of glucose. § Insulin-mediated lipogenesis is activated via the regulation of sterol regulatory elementbinding protein-1c (SREBP-1c), while glucose-mediated lipogenesis is activated via ChREBP.

In the liver and kidneys, extracellular and intracellular glucose concentrations are in equilibrium due in part to glucose transporter GLUT 2, characterized by a low affinity for glucose (15-20 mM) [START_REF] Thorens | Glucose transporters in the regulation of intestinal, renal, and liver glucose fluxes[END_REF] (Figure 1). Once in the cell, glucose is phosphorylated by glucokinase, specifically in the hepatocytes, or by hexokinases (Figure 1A). Glucokinase (GCK, also named hexokinase IV; encoded by Gck), exhibits kinetic properties quite distinct from those of other mammalian hexokinases [START_REF] Agius | Hormonal and Metabolite Regulation of Hepatic Glucokinase[END_REF]. In vivo, the expression and/or activity of GCK is induced in response to an oral glucose and fructose load [START_REF] Oosterveer | LRH-1-dependent glucose sensing determines intermediary metabolism in liver[END_REF][START_REF] Van Schaftingen | Short-term control of glucokinase activity: role of a regulatory protein[END_REF]. GCK activity is regulated by an inhibitory protein, GKRP that sequesters GCK in the nucleus during fasting. The interaction of GCK and GKRP is also induced by fructose-6 phosphate [START_REF] Van Schaftingen | Short-term control of glucokinase activity: role of a regulatory protein[END_REF]. On the contrary, elevated blood glucose (above 5 mM) and fructose-1 phosphate enables dissociation of GCK and translocation in the cytoplasm, where GCK is active [START_REF] Agius | Hormonal and Metabolite Regulation of Hepatic Glucokinase[END_REF]. It was recently shown that Sirtuin 2 (SIRT2) allows glucosedependent dissociation of GCK from GKRP by de-acetylating GKRP in the presence of elevated levels of NAD+ [START_REF] Watanabe | Sirt2 facilitates hepatic glucose uptake by deacetylating glucokinase regulatory protein[END_REF]. Consistent with the major role of GCK in control of blood glucose homeostasis, transcription of Gck in the liver is induced by insulin through the transcription factor SREBP-1c and repressed by glucagon. Unlike other hexokinase, GCK is not inhibited by G6P and has relatively low affinity for glucose [START_REF] Van Schaftingen | Short-term control of glucokinase activity: role of a regulatory protein[END_REF]. In addition, glucagon is predicted to favor sequestration of GCK in the nucleus and thereby increased hepatic glucose production and impaired hepatic glucose uptake [START_REF] Agius | Hormonal and Metabolite Regulation of Hepatic Glucokinase[END_REF]. Thus, GCK enables hepatocytes to efficiently trap glucose when blood glucose is increased, playing a central role in glucose homeostasis. In the last decade, GCK activators and allosteric inhibitors of GCK-GKRP interaction have been proposed as a therapy for diabetes, with inconclusive results [START_REF] Raimondo | Glucokinase regulatory protein: complexity at the crossroads of triglyceride and glucose metabolism[END_REF].

As mentioned above, G6P can enter three metabolic pathways including glycogen synthesis, glycolysis, and PPP as illustrated on Figure 1 [START_REF] Adeva-Andany | Liver glucose metabolism in humans[END_REF]. Thus G6P may undergo isomerization into glucose-1 phosphate for glycogen synthesis, isomerization into fructose 6-phosphate and oxidation into gluconolactone, which initiates PPP. Fructose-6 phosphate may further lead to UDP-N-acetylglucosamine production (hexosamine pathway) or it can enter glycolysis to generate pyruvate and acetyl-CoA, via tricarboxylic acid cycle into the mitochondria. PPP is widely known to play a key role in reductive biosynthesis, given that this pathway is the major source of NADPH. While it was proposed that NADPH could be essential for glutathione reduction, it has been recently suggested that PPP is not an efficient pathway to protect the liver from oxidative stress [START_REF] Jin | Pentose phosphate pathway activity parallels lipogenesis but not antioxidant processes in rat liver[END_REF]. In addition, G6P and/or its metabolites (xylulose-5 phosphate or fructose-2,6-biphosphate) may act as signaling molecules to regulate transcription of specific glucose-responsive genes, by recruiting carbohydrate response element binding protein (ChREBP) and its partner Mlx [START_REF] Abdul-Wahed | Sweet Sixteenth for ChREBP: Established Roles and Future Goals[END_REF][START_REF] Adeva-Andany | Liver glucose metabolism in humans[END_REF]. ChREBP, which recognizes conserved carbohydrate response elements (ChoREs) in gene promoters, regulates genes involved in glycolysis (Lpk) and fatty acid synthesis (Fasn, Acaca, Scd1) [START_REF] Benhamed | The lipogenic transcription factor ChREBP dissociates hepatic steatosis from insulin resistance in mice and humans[END_REF]Dentin et al., 2012b). Recent data confirmed that the majority of ChREBP target genes are involved in metabolic processes in the liver and white adipose tissue, as well as in insulin signaling and tumorigenesis [START_REF] Poungvarin | Genome-Wide Analysis of ChREBP Binding Sites on Male Mouse Liver and White Adipose Chromatin[END_REF]. The various hormonal and nutritional regulations of ChREBP expression and activity during fasting and upon carbohydrate feeding have been detailed in a recent review [START_REF] Abdul-Wahed | Sweet Sixteenth for ChREBP: Established Roles and Future Goals[END_REF]. Thus, ChREBP is considered as a glucose sensor and a general integrator of metabolic signals according to nutritional states.

As soon as the intestinal absorption of glucose is completed (post-prandial period), hepatic G6P is mainly derived from glycogen breakdown, while GNG becomes the major source of G6P after prolonged fasting. In its phosphorylated form, glucose cannot be exported in the circulation and thus it is retained in the cell. In glucose producing organs, G6P is then dephosphorylated into glucose by G6Pase (Figure 1B). G6P is first translocated from the cytosol into the endoplasmic reticulum (ER) by the G6Pase transporter (G6PT encoded by SLC37A4), and subsequently dephosphorylated by the catalytic subunit of G6Pase (G6PC encoded by G6PC1).

Both subunits are deeply embedded in the ER membrane and the catalytic site of G6PC faces the ER lumen. Glucose is finally released from the cytosol into the bloodstream through GLUT2.

Indeed, GLUT2 deficiency leads to Fanconi-Bickel disease (GSD type XI) characterized by ketotic hypoglycemia during periods of fasting and hepatomegaly secondary to glycogen accumulation [START_REF] Thorens | GLUT2, glucose sensing and glucose homeostasis[END_REF]. However, liver-GLUT2 deficiency in mice does not result in major perturbations in hepatic glucose production, which has suggested that glucose produced in the ER lumen could be released from a vesicle trafficking to the plasmatic membrane [START_REF] Burcelin | Liver hyperplasia and paradoxical regulation of glycogen metabolism and glucose-sensitive gene expression in GLUT2null hepatocytes. Further evidence for the existence of a membrane-based glucose release pathway[END_REF][START_REF] Guillam | Normal hepatic glucose production in the absence of GLUT2 reveals an alternative pathway for glucose release from hepatocytes[END_REF]. It is noteworthy that glucose release from the ER lumen by a pathway that would compartmentalize glucose away from the cytosol, where it could be rephosphorylated by GCK, could represent an interesting adaptation during fasting.

Deregulation of glucose homeostasis in diabetes and glycogen storage disease type

I.

Summary points § This chapter described two metabolic diseases, i.e. type 2 diabetes and glycogen storage disease type I, which are characterized by glucose metabolism impairments. § Type 2 diabetes is characterized by an overproduction of glucose and hyperglycemia, while GSDI is characterized by a lack of endogenous glucose production and hypoglycemia. § Type 2 diabetes and non-alcoholic fatty liver diseases (NAFLD) are chronic metabolic diseases that are associated with over-nutrition. § GSDI is a rare genetic metabolic disease due to deficiency in G6Pase activity. GSD type Ia patients have mutations in G6PC, whereas GSD type Ib patients have mutations in G6P transporter (SLC37A4). § When the storage capacity of glucose in the form of glycogen is exceeded, G6P is transformed into fat via the de novo lipogenesis pathway, leading to steatosis. § Type 2 diabetes and GSDI patients both develop hepatic steatosis and long-term pathologies, including liver cancer and chronic kidney disease. In these diseases, hepatic and renal metabolism is characterized by an increased metabolism downstream of G6P. § Lipid accumulation in the liver and kidneys play a key role in the occurrence of hepatic and renal damage. § Hepatic steatosis is the major hallmark of NAFLD. § The transcription factor ChREBP is a major mediator for promoting the conversion of dietary carbohydrates to fat for energy storage. § In diabetes, NAFLD can progress to liver fibrosis and non-alcoholic steatohepatitis, and finally to the development of hepatocellular carcinomas (HCC). However, HCC can also occur from the transformation of hepatocellular adenomas (HCA) developed in fatty liver. In GSDI, most adult patients develop multiple HCA that can transform into HCC with time. § In diabetes and GSDI, nephropathy can progress to interstitial fibrosis and glomerulosclerosis leading to renal failure. § A common metabolic feature of CKD and NAFLD is ectopic lipid accumulation, which is suspected to induce organ damage and dysfunction.

Hepatic and renal metabolic disruption in type 2 diabetes and GSDI

Type 2 diabetes is currently considered as an epidemic metabolic strongly linked to the development of obesity, which can be generated from a decrease in physical activity and an increase in caloric consumption in combination with genetic predisposition to insulin resistance [START_REF] Chen | The worldwide epidemiology of type 2 diabetes mellitus--present and future perspectives[END_REF]. Indeed, type 2 diabetes is characterized by insulin resistance of the tissues capable of capturing glucose under the action of insulin and / or the lack of insulin production due to the decline in β-cell function. The lack of appropriate insulin signaling, associated with endogenous glucose overproduction leads to hyperglycemia [START_REF] Lin | Hormonal regulation of hepatic glucose production in health and disease[END_REF]. Thus the liver and kidney are chronically exposed to hyperglycemia. In the liver, it was shown that glucose uptake is impaired in obese diabetic mice, due to a decreased activity of SIRT2, blocking the dissociation of GCK and GKRP [START_REF] Watanabe | Sirt2 facilitates hepatic glucose uptake by deacetylating glucokinase regulatory protein[END_REF]. This has also been associated with low hepatic GCK mRNA expression in diabetic patients [START_REF] Haeusler | Decreased expression of hepatic glucokinase in type 2 diabetes[END_REF]. Indeed, high glucose represses the expression of the Gck gene and concomitantly induces G6pc gene expression [START_REF] Arden | Elevated Glucose Represses Liver Glucokinase and Induces Its Regulatory Protein to Safeguard Hepatic Phosphate Homeostasis[END_REF]. Thus glucose production is activated in diabetes, consequence an overall increase in GNG and of G6P production [START_REF] Haeusler | Decreased expression of hepatic glucokinase in type 2 diabetes[END_REF]. This leads to aberrant glucose sensing that results in the accumulation of triglycerides (TG) in the liver and the development of steatosis via the concomitant activation of de novo lipogenesis and suppression of lipid oxidation. Thus diabetes, characterized by glucose metabolism dysfunction, is tightly linked to liver disease, more precisely to non-alcoholic fatty liver disease (NAFLD) [START_REF] Bhatt | Fatty liver disease in diabetes mellitus[END_REF][START_REF] Hazlehurst | Non-alcoholic fatty liver disease and diabetes[END_REF]. Indeed, up to 70% of diabetic patients may present NAFLD [START_REF] Hazlehurst | Non-alcoholic fatty liver disease and diabetes[END_REF][START_REF] Williamson | Prevalence of and risk factors for hepatic steatosis and nonalcoholic Fatty liver disease in people with type 2 diabetes: the Edinburgh Type 2 Diabetes Study[END_REF]. Moreover, hypertriglyceridemia is a common lipid abnormality in persons with type 2 diabetes. In the kidneys, similar metabolic perturbations (increased de novo lipogenesis, decreased lipid oxidation, activation of hexosamine pathway…) are observed, leading to the development of chronic kidney disease (CKD) [START_REF] Vallon | Pathophysiology of the diabetic kidney[END_REF]), which will be described below. GSDI (also known as the von Gierke's disease) is a rare genetic disease, with an incidence of 1/100,000 live births, due to a deficiency in G6Pase. Mutations in the gene encoding G6PC are responsible for GSD Type Ia and mutations in G6PT are responsible for GSD Type Ib [START_REF] Bruni | Enzymatic characterization of four new mutations in the glucose-6 phosphatase (G6PC) gene which cause glycogen storage disease type 1a[END_REF][START_REF] Chevalier-Porst | Mutation analysis in 24 French patients with glycogen storage disease type 1a[END_REF][START_REF] Kishnani | Diagnosis and management of glycogen storage disease type I: a practice guideline of the American College of Medical Genetics and Genomics[END_REF]. Consequently, patients with GSDI cannot produce glucose during short fasts and may suffer from severe hypoglycemia episodes [START_REF] Kishnani | Diagnosis and management of glycogen storage disease type I: a practice guideline of the American College of Medical Genetics and Genomics[END_REF]. The deficiency in G6Pase leads to the accumulation of G6P in the liver and kidneys, leading to a complete metabolic reprogramming, characterized in part by increased glycolysis and lipogenesis (Gjorgjieva et al., 2016a). Furthermore, we have recently demonstrated a strong reduction in hepatic GCK flux in GSDIa mice and an activation of glycogen turnover [START_REF] Hijmans | Hepatocytes contribute to residual glucose production in a mouse model for glycogen storage disease type Ia[END_REF]. GSDI patients show hepatomegaly and severe steatosis caused by strong glycogen and lipid accumulation, respectively. This is associated with lactic acidosis, hyperuricemia, hypercholesterolemia and hypertriglyceridemia [START_REF] Bandsma | Disturbed lipid metabolism in glycogen storage disease type 1[END_REF][START_REF] Kishnani | Diagnosis and management of glycogen storage disease type I: a practice guideline of the American College of Medical Genetics and Genomics[END_REF]. Furthermore, in these patients de novo lipogenesis and cholesterol synthesis were found to be increased 40-fold and 7-fold, respectively [START_REF] Bandsma | Increased de novo Lipogenesis and Delayed Conversion of Large VLDL into Intermediate Density Lipoprotein Particles Contribute to Hyperlipidemia in Glycogen Storage Disease Type 1a[END_REF]. Production of VLDL was unchanged compared to control values, but conversion of VLDL into intermediate density lipoproteins was relatively delayed in GSDI patients. Lipid vesicles are present in abundance in GSDI livers, mainly in the periportal zone, which corresponds to the location of the highest expression of G6Pase in the liver [START_REF] Rajas | Immunocytochemical localization of glucose 6-phosphatase and cytosolic phosphoenolpyruvate carboxykinase in gluconeogenic tissues reveals unsuspected metabolic zonation[END_REF]. Associated with glycogen accumulation, lipids contribute further to the development of hepatomegaly. Recently, our results highlighted the crucial role that lipids also play in the development of CKD in GSDIa, independently of the occurrence of NAFLD [START_REF] Monteillet | Intracellular lipids are an independent cause of liver injury and chronic kidney disease in non alcoholic fatty liver disease-like context[END_REF]. While hepatic manifestations of GSDI have been long known, CKD was acknowledged as a complication of GSDI later on, in the 1980's, when renal histology of GSDI patients revealed glomerulosclerosis, interstitial fibrosis and tubular atrophy [START_REF] Kishnani | Diagnosis and management of glycogen storage disease type I: a practice guideline of the American College of Medical Genetics and Genomics[END_REF]. Indeed, G6Pase deficiency results in glycogen and lipid accumulation in renal proximal tubules of the kidney cortex. It has been reported that most adult GSDI patients over 25 years of age develop first signs of CKD, which can progress to renal failure with aging [START_REF] Kishnani | Diagnosis and management of glycogen storage disease type I: a practice guideline of the American College of Medical Genetics and Genomics[END_REF]. Many patients also present nephrocalcinosis (due to hypercalciuria and hypocitraturia) that enhance the likelihood of urine calcium precipitation. Because of hyperuricemia, uric acid nephrolithiasis and gout nephropathy can develop. Unfortunately, the loss in renal function, glomerulosclerosis and interstitial fibrosis can further take place and these defects can lead to renal failure at later stage. Thus, type 2 diabetes and GSDI share similar hepatic and renal metabolic dysfunctions leading to hepatic steatosis and CKD [START_REF] Rajas | Glycogen storage disease type 1 and diabetes: learning by comparing and contrasting the two disorders[END_REF].

Deregulation of glucose homeostasis and hepatic steatosis

When the storage capacity of glucose in the form of glycogen in the liver is exceeded, glucose is stored under the form of lipids. As previously mentioned, high G6P concentrations activate glycolysis, leading to the production of pyruvate. Pyruvate may then be transported in the mitochondria, where it is converted into acetyl-CoA, a precursor for the synthesis of fatty acids and cholesterol. Fatty acids can then be elongated, desaturated, esterified and used to synthetize TG. Triglycerides are stored in lipid vesicles in the cytosol, transported to the adipose tissue via the export of very low density lipoproteins (VLDL), or metabolized through the β-oxidation pathway [START_REF] Postic | Contribution of de novo fatty acid synthesis to hepatic steatosis and insulin resistance: lessons from genetically engineered mice[END_REF]. On a molecular level, lipid accumulation in the liver can be due to excessive lipid synthesis or import of fatty acids, a decrease in lipid degradation or problems in the export of VLDL [START_REF] Postic | Contribution of de novo fatty acid synthesis to hepatic steatosis and insulin resistance: lessons from genetically engineered mice[END_REF]. In insulin-resistant states, hyperglycemia and hyperinsulinemia are responsible for enhanced lipogenesis partly through the activation of ChREBP and SREBP-1c. Indeed, inhibition of ChREBP expression in obese diabetic ob/ob mice leads to reversal of hepatic steatosis [START_REF] Dentin | Liver-specific inhibition of ChREBP improves hepatic steatosis and insulin resistance in ob/ob mice[END_REF][START_REF] Iizuka | Deficiency of carbohydrate-activated transcription factor ChREBP prevents obesity and improves plasma glucose control in leptin-deficient (ob/ob) mice[END_REF].

In GSDI, lipid synthesis is activated by ChREBP, but independently of liver X receptor (LXR) and SREBP1c [START_REF] Grefhorst | Carbohydrate-response-element-binding protein (ChREBP) and not the liver X receptor α (LXRα) mediates elevated hepatic lipogenic gene expression in a mouse model of glycogen storage disease type 1[END_REF]. The lack of SREBP1c activation could be due to low insulin signaling in GSDI [START_REF] Tong | The glucose-responsive transcription factor ChREBP contributes to glucose-dependent anabolic synthesis and cell proliferation[END_REF]. Besides lipogenesis, ChREBP is known to potentiate glycolysis and nucleotide biosynthesis, and it can have an inhibitory role on oxidative phosphorylation (OXPHOS) [START_REF] Tong | The glucose-responsive transcription factor ChREBP contributes to glucose-dependent anabolic synthesis and cell proliferation[END_REF]. Thus ChREBP could be a metabolic switch orchestrating the metabolic reprogramming in GSDI and diabetic cells.

Contrarily to what is generally believed, there is no clear link between hepatic insulinresistance and fat storage in the liver. For example, despite severe hepatic steatosis, GSDIa mice are protected against diabetes induced by high-fat/high-sucrose diet [START_REF] Abdul-Wahed | A link between hepatic glucose production and peripheral energy metabolism via hepatokines[END_REF].

More likely, the consequences of hepatic steatosis are dependent on the specific nature of generated lipid species and/or their subcellular localization. The fractional contribution of hepatic de novo lipogenesis is also dependent on dietary carbohydrate intake. For example, fructose increases lipogenesis more strongly than glucose, through a significant activation of ChREBP [START_REF] Kim | ChREBP regulates fructoseinduced glucose production independently of insulin signaling[END_REF]. Moreover, this induces the expression of the hepatokine FGF21, which appears to exert a protective effect on the liver against steatosis [START_REF] Fisher | A critical role for ChREBP-mediated FGF21 secretion in hepatic fructose metabolism[END_REF].

NAFLD encompasses a large spectrum of disease seriousness ranging from simple steatosis to inflammation, fibrosis, and potentially cirrhosis. Simple hepatic steatosis is thought to be a benign reversible condition characterized by the accumulation of TG, in the absence of indicators of liver disease. However, NAFLD is a fertile ground for the development of hepatic tumors. In a subset of obese/diabetic individuals, NAFLD may transition to non-alcoholic steatohepatitis (NASH) characterized by the development of inflammation and fibrosis. However, hepatocarcinogenesis is also observed in non-fibrotic, non cirrhotic NAFLD livers [START_REF] Baffy | Hepatocellular carcinoma in non-alcoholic fatty liver disease: An emerging menace[END_REF][START_REF] Calzadilla Bertot | The Natural Course of Non-Alcoholic Fatty Liver Disease[END_REF]). An uncertain fraction of tumors in nonfibrotic livers likely arise through transformation of HCA [START_REF] Baffy | Hepatocellular carcinoma in non-alcoholic fatty liver disease: An emerging menace[END_REF][START_REF] Stoot | Malignant transformation of hepatocellular adenomas into hepatocellular carcinomas: a systematic review including more than 1600 adenoma cases[END_REF] (Figure 3). Interestingly, the same HCA-to-HCC transformation has been recently described in GSDI livers [START_REF] Gjorgjieva | Dietary exacerbation of metabolic stress leads to accelerated hepatic carcinogenesis in glycogen storage disease type Ia[END_REF]. Despite the important levels of accumulated glycogen and lipids in the liver, as well as the important metabolic imbalance, GSDI patients present low-grade hepatic inflammation [START_REF] Kishnani | Diagnosis and management of glycogen storage disease type I: a practice guideline of the American College of Medical Genetics and Genomics[END_REF]. Hepatic transaminase (AST / ALT) levels are also usually normal, especially in patients with optimal metabolic control and in patients not bearing hepatic tumors [START_REF] Kim | Necrotic foci, elevated chemokines and infiltrating neutrophils in the liver of glycogen storage disease type Ia[END_REF]. The absence of oxidative stress and inflammatory responses in the case of GSDI might be the reason as to why hepatic fibrosis is not associated with GSDI [START_REF] Kishnani | Diagnosis and management of glycogen storage disease type I: a practice guideline of the American College of Medical Genetics and Genomics[END_REF]. Indeed, GSDI patients and related animal models do not present fibrosis in the liver, contrarily to other types of glycogen storage diseases. Consequently, these patients do not develop cirrhosis. The comparison of HCC occurrence in NAFLD and GSDI livers argue for a dominant role of metabolic reprogramming in the molecular induction of tumor development.

Recently, we showed that GSDIa hepatocytes exhibited the main characteristics of cancer cell metabolism, with a Warburg-like metabolic reprogramming that predisposes GSDIa livers to tumor development [START_REF] Gjorgjieva | Dietary exacerbation of metabolic stress leads to accelerated hepatic carcinogenesis in glycogen storage disease type Ia[END_REF]. Indeed, we observed a hyperactivation of the glycolysis pathway characterized by an overexpression of the M2 isoform of pyruvate kinase in the tumors and an increase in lactate production. Moreover, OXPHOS analyses revealed a decrease in mitochondrial respiration with a reduction of pyruvate oxidation. This offers advantages to proliferating cells, providing substrates for cellular membrane biogenesis and amino acids and nucleotide synthesis for cell division. Altogether, these metabolic perturbations were associated with a decrease in cellular defenses, including decreased autophagy, chronic ER-stress and loss of tumor suppressors such as p53, HNF1α, PTEN (Gjorgjieva et al., 2016a[START_REF] Gjorgjieva | Dietary exacerbation of metabolic stress leads to accelerated hepatic carcinogenesis in glycogen storage disease type Ia[END_REF]. Interestingly, as observed in GSDIa, a decrease in autophagy levels has also been reported in other NAFLD [START_REF] Koga | Altered lipid content inhibits autophagic vesicular fusion[END_REF][START_REF] Mao | Autophagy: a new target for nonalcoholic fatty liver disease therapy[END_REF][START_REF] Yang | Defective Hepatic Autophagy in Obesity Promotes ER Stress and Causes Insulin Resistance[END_REF]. Furthermore, lipid accumulation and defective autophagy induce ER stress in NAFLD conditions [START_REF] Yang | Defective Hepatic Autophagy in Obesity Promotes ER Stress and Causes Insulin Resistance[END_REF].

Interestingly, there is growing evidence indicating that ChREBP plays a central role in hepatocarcinogenesis. Indeed, the suppression of ChREBP in hepatoma and colorectal cancer cells led to a metabolic switch from aerobic glycolysis to mitochondrial respiration coupled with reduced lipogenesis and nucleotide synthesis and decreased proliferative and tumorigenic potential in vivo [START_REF] Tong | The glucose-responsive transcription factor ChREBP contributes to glucose-dependent anabolic synthesis and cell proliferation[END_REF]. It was also shown that advanced glycation end products (AGEs) that are elevated in diabetic patients stimulate both the expression and activity of ChREBP and promote cancer cell proliferation [START_REF] Chen | Advanced glycation end products increase carbohydrate responsive element binding protein expression and promote cancer cell proliferation[END_REF]. In GSDIa, the overexpression of ChREBP has been linked to glucose and lipid metabolism reprogramming [START_REF] Abdul-Wahed | A link between hepatic glucose production and peripheral energy metabolism via hepatokines[END_REF][START_REF] Gjorgjieva | Dietary exacerbation of metabolic stress leads to accelerated hepatic carcinogenesis in glycogen storage disease type Ia[END_REF]. In this context, enhanced ChREBP could partly account for increased proliferation of hepatocytes by favoring cancer cell metabolism. Further investigation is required to unravel the exact role of ChREBP in hepatocarcinogenesis in the context of NAFLD.

In conclusion, both diabetic and GSDI patients develop severe hepatic steatosis and are prone to the development of hepatic tumors. It is noteworthy that tumor occurrence is higher in GSDI patients than in diabetes. Interestingly, molecular mechanisms involved in hepatocarcinogenesis in this steatotic context seem different from HCC induced by some viral infections and alcoholic liver diseases. By comparing hepatocarcinogenesis in diabetes and GSDI, we can propose a dominant role of hepatic metabolic reprogramming in this process. Thus, a rational therapeutic approach for the treatment of NAFLD, before the development of hepatic tumors, could be to increase hepatic energy expenditure and thereby increase hepatic fat oxidation. Recently, it was shown that T3-glucagon conjugate or glucacon like peptide 1 agonistgastric inhibitory peptide-glucagon tri-conjugate improved glycemia and NAFLD in diabetes, without the harmful effects of each hormone [START_REF] Finan | A rationally designed monomeric peptide triagonist corrects obesity and diabetes in rodents[END_REF][START_REF] Finan | Chemical Hybridization of Glucagon and Thyroid Hormone Optimizes Therapeutic Impact for Metabolic Disease[END_REF]. Liver-targeted mitochondrial uncouplers, which can dissipate the inner mitochondrial membrane proton gradient in the liver are also a promising class of agent for the treatment of NAFLD in the context of diabetes [START_REF] Perry | Controlled-release mitochondrial protonophore reverses diabetes and steatohepatitis in rats[END_REF]. Interestingly, lipid-lowering treatments that increased fatty acid oxidation by using pharmacological activators of PPARα have recently shown to prevent hepatic damages in both diabetes and GSDI [START_REF] Monteillet | Intracellular lipids are an independent cause of liver injury and chronic kidney disease in non alcoholic fatty liver disease-like context[END_REF][START_REF] Musso | Non-alcoholic steatohepatitis: emerging molecular targets and therapeutic strategies[END_REF][START_REF] Waskowicz | Bezafibrate induces autophagy and improves hepatic lipid metabolism in glycogen storage disease type Ia[END_REF] .

Deregulation of glucose homeostasis and chronic kidney disease

The molecular mechanism leading to CKD in GSDI patients is thought to be very similar to the one observed in diabetic patients [START_REF] Mundy | Glycogenosis type I and diabetes mellitus: a common mechanism for renal dysfunction?[END_REF][START_REF] Rajas | Glycogen storage disease type 1 and diabetes: learning by comparing and contrasting the two disorders[END_REF][START_REF] Yiu | The angiotensin system mediates renal fibrosis in glycogen storage disease type Ia nephropathy[END_REF]. Indeed, the increased flux downstream of G6P leads to the accumulation of ectopic lipids in the kidneys (Figure 4). This results in the activation of the Renin-Angiotensin system (RAS) observed in both diabetic and GSDI CKD [START_REF] Mundy | Glycogenosis type I and diabetes mellitus: a common mechanism for renal dysfunction?[END_REF][START_REF] Yiu | The angiotensin system mediates renal fibrosis in glycogen storage disease type Ia nephropathy[END_REF]. Lipids are precursors for the de novo synthesis of glycerolipids, including diacylglycerol (DAG). DAG is a potent activator of protein kinase C (PKC) and acts through binding to a specific domain of the regulatory subunit of the enzyme. The activation of PKC leads to the activation of RAS. In RAS, angiotensinogen (Agt) is the precursor of Angiotensin II (Ang II) that is produced from two sequential enzymatic reactions: the conversion of Agt in Angiotensin I by renin and the conversion of Ang I in Ang II by Ang converting enzyme (ACE). Ang II stimulates the proliferation of mesangial cells, glomerular endothelial cells, and fibroblasts, and acts as a profibrogenic factor. Many profibrotic effects of RAS are mediated by transforming growth factor-β1 (TGF-β1). Indeed, renal fibrosis development is induced by TGF-β1, which activates the expression of the extracellular matrix genes [START_REF] Lan | Diverse Roles of TGF-β/Smads in Renal Fibrosis and Inflammation[END_REF][START_REF] Meng | TGF-β/Smad signaling in renal fibrosis[END_REF]. In addition, TGF-β1 is a potent inducer of epithelial-to-mesenchymal transition that characterizes the phenotype of renal interstitial fibrosis. Thus several treatments inhibiting the RAS / TGF-β1 axis, such as ACE inhibitors, are used to retard progression of CKD in diabetic and GSDI patients (Gjorgjieva et al., 2016b;[START_REF] Martens | Renal Function in Glycogen Storage Disease Type I, Natural Course, and Renopreservative Effects of ACE Inhibition[END_REF][START_REF] Patney | Treatment of Diabetic Kidney Disease With Hypertension Control and Renin Angiotensin System Inhibition[END_REF]. Interestingly, as lipids are a major contributor to the development of CKD, lipid-lowering drugs, such as fenofibrate via the activation of PPARα, could be suitable therapeutic strategy to prevent nephropathy. Some studies already evoked renoprotective effects of fenofibrate in diabetes and GSDI [START_REF] Lewis | Diabetes: Should we use fibrates in patients with diabetes and mild CKD?[END_REF][START_REF] Monteillet | Intracellular lipids are an independent cause of liver injury and chronic kidney disease in non alcoholic fatty liver disease-like context[END_REF][START_REF] Ting | Benefits and safety of long-term fenofibrate therapy in people with type 2 diabetes and renal impairment: the FIELD Study[END_REF]. Unfortunately, while lipid-lowering effects of fibrate were corrective of both pathologies featuring the liver and kidneys of GSDIa mice, this strategy did not insure normal glycemia [START_REF] Monteillet | Intracellular lipids are an independent cause of liver injury and chronic kidney disease in non alcoholic fatty liver disease-like context[END_REF].

To conclude, by comparing hepatic and renal diseases in diabetes and GSDI, we emphasize the striking similarities of molecular metabolism dysfunction in these two diseases. A stimulated metabolism down-stream of G6P is a common causal feature leading to the accumulation of ectopic lipids in the liver and kidneys. In the liver, hepatic steatosis results in HCA/HCC development with time. In the kidneys, renal lipids are responsible for kidney injuries, finally responsible for the loss of renal filtration function. It is a noteworthy observation that, while diabetes and GSDI are characterized by abnormal glucose metabolism, lipid-lowering drugs might represent a potential therapeutic approach to prevent both NAFLD and CKD development in both diabetes and GSDI. phosphate, PPP: pentose phosphate pathway. Illustrations were done with the help of Servier Medical Art.

Figure 2 :

 2 Figure 2: Development of chronic kidney disease and hepatic tumors in type 2 diabetes

Figure 3 :

 3 Figure 3: Schematic steps of the development of hepatocellular adenomas and

Figure 4 :

 4 Figure 4: CKD progression until the end-stage kidney disease. After the first alterations of

Acknowledgements

The authors thank all past and current colleagues in the laboratory who contributed to the work reviewed.

Legends of figures

Figure 1: Schematic glucose metabolism in hepatocytes in the fed state and postabsorptive state. Under fed conditions (A), glucose promotes the dissociation of GCK from GKRP, which leads to the cytoplasmic translocation of GCK. Thus, glucose is phosphorylated into G6P, which can enter three metabolic pathways including glycogen synthesis, glycolysis, and PPP. Glucose is then stored under the form of glycogen. G6P is also degraded during glycolysis, providing energy and pyruvate in further decarboxylated to acetyl-CoA, which enters the intramitochondrial tricarboxyl acid cycle (TCA). Alternatively, G6P is degraded in PPP, which provides NADPH and nucleotides. When glucose is in excess, de novo lipogenesis is activated and G6P is transformed into fat leading to steatosis. (B) Under fasted conditions, glycogenolysis and gluconeogenesis provide G6P as a substrate for glucose production. G6P is then dephosphorylated by G6Pase, which is localized in endoplasmic reticulum. In the liver, glycerol, lactate and alanine are used as gluconeogenic substrates. Moreover, GCK is sequestered in the nucleus, bound to GKRP, limiting the phosphorylation of glucose. GLUT2: glucose transporter 2; GCK: glucokinase; G6Pase, glucose-6 phosphatase; G6P: glucose-6 phosphate; F6P, fructose-6


Takai- Eddine Kennouche
email: takai-eddine.kennouche@univ-grenoble-alpes.fr

Florent Cadoux
email: florent.cadoux@grenoble-inp.fr

Nicolas Gast
email: nicolas.gast@univ-grenoble-alpes.fr

Benoît Vinot

ASGriDS: Asynchronous Smart-Grids Distributed Simulator

Keywords: real-time simulation, asynchronous, cosimulation, distributed simulation, HIL, Smart Grid Communication

We present ASGriDS, an asynchronous Smart Grid simulation framework. ASGriDS is multi-domain, it simultaneously models the power network along with its physical loads/generators, controllers, and communication infrastructure. ASGriDS provides a unified workflow in a pythonic environment, to describe, run and control complex SmartGrid deployment scenarios. ASGriDS is an event-driven simulator that can run in either real-time or accelerated real-time. As it is modular and its components interact asynchronously, it can run either locally on a distributed infrastructure, also in hardware-in-theloop setups, and on top of emulated/physical communication links. In this paper, we present the design of our simulator and we demonstrate its use with a generation control problem on a low voltage network. We use ASGriDS to deploy a realtime controller based on optimal power flow, on top of TCP and UDP based communication network, under various packet loss conditions.

I. INTRODUCTION

The Smart Grid is a cyber -physical energy system, where the physical processes of a power system are strongly integrated with a communication network infrastructure and a set of possibly complex controllers. This vision of the modern power grid is motivated by the new challenges that are currently raised by the need to massively integrate distributed renewable energy generators. In addition to that, the Smart Grid is expected to satisfy growing reliability and efficiency demands, due to the fast transition to electrical power in public transportation systems and the growing usage of electrical vehicles.

The Smart Grid needs to solve a variety of reliability and efficiency challenges [START_REF] Moslehi | A Reliability Perspective of the Smart Grid[END_REF]. In this context, Information and Communication Technologies (ICT) come into play as an enabler for more intelligence to manage a complex set of operational, monitoring and optimization constraints, and allow for innovative solutions and novel applications. On the other hand, understanding the ICT infrastructure's performance limits, such as managing packet transmission delays, is crucial for safety and stability of the power grid and efficiency of control, monitoring and recovery of the network.

New techniques are needed to properly prototype, test and validate a modern Smart Grid. Traditional testing and domain-specific simulation techniques and software tools only allow for partial representation of the system; for instance, there exists many domain-specific tools for the simulation of either the physics of a power grid, or for the simulation of telecommunication networks, but none of these tools are capable of embracing the complexity of a Smart Grid. Such tools are simply not sufficient to trace the complex behavior of intertwined power and telecommunication networks: for this purpose, multi-domain frameworks must be developed.

ASGriDS is developed to provide a modular and generic simulation description environment, that relies on an eventdriven architecture to model power network components, and an asynchronous communication module that is scalable and reliable. Our framework is capable of running both local and distributed simulations. The nodes are asynchronous in the sense that they don't rely on heavy explicit synchronization, but they rely instead on the fact that their execution is driven by local clocks that are globally synchronous enough. This choice of implementation allows for realistic modeling of a real asynchronous Smart Grid behavior. It is also technically required to be able to account for real-time execution and Hardware-In-the-Loop (HIL) integration. In addition, our framework is capable of modeling complex communication network behavior, through the use of Linux network emulation that permits rich tuning capabilities of delay, jitter, loss, duplication, reordering, corruption and rate. This allows the modeling of wired and wireless networks [START_REF] Hemminger | Network emulation with NetEm[END_REF].

The rest of the paper is organized as follows. In Section II, we review the state of the art of Smart Grid simulators, and put that in contrast with our own proposal. We describe the architecture and design choices of ASGriDS in Section III and evaluate its performance in Section IV. Finally, we present in Section V a case study concerning photovoltaic production control, in a low voltage network and lossy communication.

II. RELATED WORK

The Electric Power and Communication Synchronizing Simulator (EPOCH) [START_REF] Hopkinson | EPOCHS: A platform for agent-based electric power and communication simulation built from commercial off-the-shelf components[END_REF], is a simulation framework that aims to study complex scenarios involving combined power and communication networks. EPOCH runs in a federated simulation environment, using PSCAD and PSLF for power simulations and UC Berkeley's Network Simulator 2 (NS-2) [START_REF]The Network Simulator -ns-2[END_REF] for communication network simulations, with fixed time-stepping clock synchronization.

The Global Event-Driven Co-Simulation Framework (GECO) [START_REF] Lin | GECO: Global Event-Driven Co-Simulation Framework for Interconnected Power System and Communication Network[END_REF] is another framework that targets power system monitoring and control using a communication network. It also combines PSLF power system simulator and NS-2, but differs than EPOCH in that it uses a global event-driven cosimulation environment. GECO ensures full synchronization of events, by encapsulating dynamic power system simulation runs in discrete events queued in a global events queue and ordered with the network simulator events.

Another co-simulation framework is the Integrated co-Simulation of Power and ICT systems for Real-time Evaluation (INSPIRE) [START_REF] Georg | INSPIRE: Integrated co-simulation of power and ICT systems for realtime evaluation[END_REF]. INSPIRE co-simulation environment consists of running DIgSILENT PowerFactory for power simulation, in conjunction with OPNET Modeler with a model of IEC 61850 communication protocol. Both are orchestrated with dynamic time-stepped clock.

A different simulation approach is followed in MECSYCO [START_REF] Vaubourg | Multi-agent Multi-Model Simulation of Smart Grids in the MS4SG Project[END_REF], where the authors adhere to strict formalism using a Discrete Event System Specification (DEVS) to describe their multi-agent system. The interaction of their systems, though, is always strictly synchronized and does not provide any real time capabilities or the possibility of interaction with components outside the simulator.

T-RECS [START_REF] Achara | A Virtual Commissioning Tool for Software-Based Control of Electric Grids -Design, Validation, and Operation[END_REF] is another interesting simulator, its stated objectives are close to what we intend to develop in ASGriDS, in that it incorporates network emulation for more realistic control over the communication medium, and provides an API for the interaction with pre-existing components. However, as pointed by their authors in [START_REF] Achara | A Virtual Commissioning Tool for Software-Based Control of Electric Grids -Design, Validation, and Operation[END_REF], it cannot scale to more than ten or a few tens of nodes, whereas ASGriDS can scale to hundreds.

It is noticeable that a limiting factor for realism and scalability of simulation, is the constraints of strong event/time synchronization among simulation components, and/or the implementation of a global event scheduler. Time synchronization can be a very fit choice, if the goal is to model synchronous systems, and/or to guarantee reproducible simulations and easy testing/debugging of certain system's aspects, but it falls short to capture the nature of an actually asynchronous system: such a system may only be accurately modelled by means of an asynchronous simulation framework, without a compromise in terms of accuracy [START_REF] Ghosh | The Role of Modeling and Asynchronous Distributed Simulation in Analyzing Complex Systems of the Future[END_REF]. On the other hand, a global event scheduler, does not allow for any real-time execution, and limit the possibility to interact with other real-time components of the system.

Our work is intended to provide scalability of simulation, realism of modeling and flexibility of scenario description, through an asynchronous design and event-driven node model.

• Asynchronism: ASGriDS does not enforce explicit time synchronization of the different nodes. Each node is driven by its local system's clock. Asynchronous communication is also used to decouple network I/O operations from nodes internal operations. • Real-time: The simulation nodes all run in real-time, in the sense that they are driven by system's real-time clock. This allows the nodes to interact with the outside world -for example, with a real communication infrastructure. • Scalability: Scalability comes as a consequence of the modularity, event-driven and asynchronism of our architecture, as we show in section IV. The socket-based communication module, also permits scalability both locally and on a distributed deployment, without sacrificing of its key features of asynchronism and real-time execution. Our framework provides the necessary architecture for complex modeling of power/communication interactions, using either emulation of the ICT network, simulation models or interaction with real setup.

III. DESIGN OF ASGRIDS

The general overview of ASGriDS's architecture is outlined in Figure 1. The components shown in the figure represent the different aspects of a Smart Grid, whether it is the power networks simulation, the optimization and control or the network communication. These components, as seen in the figure, interact with each other at two separate but complimentary planes, a Deployment Plane and a Communication Plane. In this section, we describe these two operational planes of the simulator, after which we discuss the various building blocks of ASGriDS and how it integrates both network emulation for ICT modeling, and electrical network simulation.

A. Deployment Plane

At the Deployment Plane level, ASGriDS deploys and orchestrates the simulation, the traffic flowing between the components at this level is exclusively the traffic necessary to control the various distributed elements of the simulator.

The distinction between this and the actual simulation traffic is important, for instance in a distributed scenario one might want to change the behavior of a controller reployed remotely, or provide a remote node with a new production profile, or schedule some events, this will incur network traffic generated by the object-proxying mechanism that we rely on, this traffic isn't what we would like to observe or interact with, but it is necessary for simulation deployment. On a local scenario, this traffic will simply be the communication between system processes/threads. ASGriDS basically triggers a "remote object" mode when run distributively that allows the user to handle simulation components, and network nodes as if they were running locally. This was possible using the concept of object-proxying and remote method invocation, and their implementation provided by the free and open-source library RPyC [START_REF]RPyC -Transparent, Symmetric Distributed Computing -RPyC[END_REF].

B. Smart Grid Communication Plane

At the Smart Grid Communication Plane level, ASGriDS simulates the actual communication network of interest for the end-users, where the traffic that is flowing is supposed to represent a real Smart Grid deployment traffic. As we mentioned before, ASGriDS can be deployed on a variety of ICT models, whether emulated, simulated or real. By default, it allows very easy and flexible manipulation of the communication link between simulated nodes, through binding to a linux local network interface (local loop or virtual interface). Network impairments (delay, packet loss, duplication. . . ) can then be controlled by the user through an interface to linux traffic shaping (tc) [START_REF] Hubert | Linux advanced routing & traffic control[END_REF] utility and the netem module [START_REF] Hemminger | Network emulation with NetEm[END_REF].

C. Event-Driven Real-Time Node

The basic component of the proposed framework is a realtime Event-Driven "Node", that is shown in Figure 2. The "Node" component of ASGriDS, is an event-driven. It can be deployed as a process, a thread or a remote object in a distributed simulation. It interacts with its environment is callback-based, for instance when handling network events as in Figure 2. Most importantly, a node can be easily deployed to interact with any real-time source of events, such as hardware component (e.g. real communication network) or software implementation of a certain behavior, that can already be available to the user through external libraries, either to test/validate software/hardware components or to implement new ones in a configuration as close to reality as possible. This renders the framework fit for a variety of HIL and Software-In-the-Loop (SIL) setups.

D. Asynchronous Communication

In order for ASGriDS's "Node" to truly communicate in a network, it is supported by a communication layer module, and the interaction between the two happens through a callback mechanism, as shown in Figure 2. In designing the module, our objective is a complete decoupling from the node through asynchronism and non-blocking network Input/Output operations, and pluggability in the sense that the communication We chose to implement this asynchronous communication ("AsyncComm") module around asyncio [START_REF]Asyncio -Asynchronous I/O -Python 3.7.3 documentation[END_REF]. asyncio is a standard python library, it features flexible and industryproven asynchronous API, and provides the necessary blocks to implement a communication layer that is completely asynchronous and modular, through its concurrent execution model and customizable event-loops.

E. Electrical Simulation Integration

In ASGriDS, electrical simulation is handled as a background service, controlled at the Deployment Plane, and interfacing with various other simulation components at the Smart Grid Communication Plane. For instance, this service keeps track of the electrical network state by solving load flow equations in real time. It provides an interface to other nodes so that they can access their local electrical state (i.e. voltage).

In our experiments (that we present in Section IV and V), we implement this component around pandapower, a free and open-source python library that provides access to various power system simulations such as load flow and optimal power flow solvers.

IV. PERFORMANCE OF ASGRIDS

In this section, we study the scalability of ASGriDS in terms of CPU and memory consumption. Our assumption is that for low-voltage distribution networks and micro-grids, a scalable simulator should be able to handle in the order of hundreds of network nodes. For an entire distribution network, one to two additional orders of magnitude may be necessary.

To measure the scalability of ASGriDS, we design a set of experiments according to the architecture in Figure 1. Every component of the simulation is run as an independent computation node, that is either part of the simulated grid (Smart Grid Communication Plane), i.e. a load or a generator (PV or otherwise) or part of the simulation management (Deployment Plane). At the simulation plane, each node simulates either a network load or a PV generator. These nodes communicate with a special node acting as a central allocator as follows: each node reports its voltage measurements and current production/consumption levels, and receives production setpoints in the case if PV generators. The allocator is running a control algorithm, that observes the network state through the voltage measures and acts accordingly to keep the network stable within a defined voltage limit.

A. System Resources Consumption

We define a set of benchmarks by using pandapower's implementation of Washington Case300 network as a basecase. By default this network includes 193 loads (which corresponds to 193 nodes in our simulator). To vary the number of nodes of the network, we add or remove randomly nodes of this network. Every simulated node will be generating fake producation/consumption values in the electrical network, these values will be reported to an allocator (controller) along with voltage measurements gathered from pandapower's power flow analysis, the allocator will then pandapower's optimal power flow solver to generate new setpoints for the nodes. In Figure 3, we report the maximal memory usage of our simulator during the first 3 minutes of simulation as a function of of the number of nodes deployed. We observe that the memory consumption remains limited and roughly equals 140 MB plus 50 kB per simulated node. Note that we also run the tests with other scenarios than the Washington Case300 and obtain similar values.

In all of our experiments, the CPU usage for our simulator is mainly dominated by the load flow. To verify that pandapower was fit for the need of our simulator, we measured the time taken by this library to solve load flow and optimal power flow equations. Our measures show that solving one load flow takes less than 100 ms while computing an optimal power flow takes a delay between 0.5 s for small networks, and 2.5 s for ∼200 nodes network.

V. CASE STUDY: OPF-BASED CONTROLLER ON A LOSSY

NETWORK To demonstrate the capabilities of ASGriDS, we present a case study where we deploy CIGRE's low voltage network [START_REF]Benchmark Systems for Network Integration of Renewable and Distributed Energy Resources, version 7[END_REF] in ASGriDS. CIGRE's loads become "Nodes" capable of PV power generation, by incorporating load and production profiles from [START_REF] Espinosa | Dissemination document low voltage networks models and low carbon technology profiles[END_REF]. The low voltage network consists of 37 nodes distributed over residential, industrial and commercial sub-networks, and includes 6 residential loads, 8 commercial and one industrial. This benchmark encapsulates the most relevant technical aspects of a real electrical grid, and it is developed for the purpose of modeling and simulating modern micro-grids efficiently [START_REF] Papathanassiou | A BENCHMARK LOW VOLTAGE MICROGRID NETWORK[END_REF].

In the deployed simulations, every load from the CIGRE network corresponds to a "Node" in ASGriDS, running 24h recorded consumption and PV production profiles from [START_REF] Espinosa | Dissemination document low voltage networks models and low carbon technology profiles[END_REF], read from a database. In the simulations, for practical reasons, we simulate the systems in accelerated real time (x300) so that 24h simulated time corresponds to 5min realtime. An allocator (controller) "node" runs the control loop as described in the petri net [START_REF] Peterson | Petri Nets[END_REF] diagram in Figure 4 and submits new PV production setpoints to all concerned nodes every cycle (15min simulation, 3s accelerated realtime). A power flow solver runs in the background (using pandapower) to provide timely voltage measurements to nodes (Deployment Plane). Each node reports this voltage value along with its consumption/production, to the allocator (Communication Plane). When a node receives a new setpoint, it implements the maximum between the new setpoint and the current maximum production capacity. We use the experiments to observe the performance of an Optimal Power Flow based controller described in section V-A. The controllers will be operating with a communication packet loss ranging between no loss (ideal communication), and 60 % of packet loss (extreme conditions). The network is deployed on an emulated link, and losses are controlled through linux's netem module. Recall that the network losses affect the transmission between the allocator node and the loads. The communication between the nodes and the power flow analyzer is not affected by the losses as such communications are done through the deployment plane.

A. Optimal Power Flow Controller

We implement the Optimal Power Flow (OPF) control loop around the OPF formulation in Equation ( 1). Where P is the production capacity every PV producer.

max i∈PV generators P i subject to V min V g,i V max , j ∈ bus L k L max,k , k ∈ transformer L l L max,l , l ∈ line P i P i,max (1)
In our experiments, we use pandapower to solve the OPF (with default parameters). The constraints are 100 % loading for branch elements, and ±5% p.u. around nominal voltage for bus voltages. The value of P i,max is a forecast of what we think could be the maximal production of the PV panel i.

B. Numerical Results

In Figure 5 we plot the performance of the OPF controller, in terms of the rate of voltage violations (voltage outside 5% of nominal value) detected from all measurements in the network, as a function of various losses configure in the communication channel, while using either TCP or UDP as a transport protocol. The baseline (in red), is when there is no control. We observe that the OPF in TCP mode consistently outperforms the OPF in UDP mode, although the difference is small for packet loss of 0%, 10%, 20% and 30% the performance is very good in both cases. With 60% packet loss, we notice that the controller manages to perform better with UDP, we explain that by the fact that TCP might exhibit high retransmission rate with such high packet loss, thus delivers outdated measurements and control information, the effect that is absent in UDP. We have presented in this work, ASGriDS, a framework for the design, prototyping and testing of Smart Grid deployment scenarios through distributed and scalable simulation. In AS-GrIDS, real-time control strategies can be implemented, and deployed on a communication network. ASGriDS allows the integration of emulated communication links, and/or the deployment over a physical ICT infrastructure. It provides a consistent workflow for describing complex multi-domain Smart Grid deployment scenarios. It is scalable and flexible, through a modular and asynchronous design. ASGriDS permits the various simulated components to exhibit behavior at various level of accuracy and complexity, and allows the integration of simulated power-network components, and the deployment of control strategies on physical communication hardware, or in combination with other hardware and software network components. A typical use-case is demonstrated, analyzing control performance over a lossy link, with two modes of communication, TCP and UDP. ASGriDS is publicly available on a github repository [START_REF]Takienn/asgrids[END_REF] together with the results described in this paper. We plan to continue its development, especially in distributed setups, with real ICT infrastructure and in HIL configurations. In this paper, we illustrated the capabilities of ASGriDS with a simple OPF controller described in [START_REF] Moslehi | A Reliability Perspective of the Smart Grid[END_REF]. For future work, we plan to use ASGriDS to develop controllers that are fault tolerant and that can deal with measurement uncertainty and non-ideal communication.

Fig. 1 .

 1 Fig. 1. Simulation Deployment Architecture

Fig. 2 .

 2 Fig. 2. Node -Network interaction

Fig. 3 .

 3 Fig. 3. ASGriDS's CPU and Memory consumption for various network sizes

Fig. 4 .

 4 Fig. 4. Power flow analysis and production control, running in parallel

Fig. 5 .

 5 Fig. 5. Voltage violations with TCP/UDP communication on a lossy network