Tawfik Addi

Stéphane Poitevin

Nathalie Mckay

El Mecherfi

Omar Kheroua

Noemie Jourde-Chiche

Alix De Macedo

Bertrand Gondouin

Claire Cerini

Philippe Brunet

El Mecherfi Kamel Eddine

Kheroua Omar

Noémie Jourde-Chiche

Françoise Dignat-George

Stéphane Burtey

Dr Laetitia Dou
email: laetitia.dou@univ-amu.fr

Mechanisms of tissue factor induction by the uremic toxin indole-3 acetic acid through aryl hydrocarbon receptor/nuclear factor-kappa B signaling pathway in human endothelial cells

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Cardiovascular disease (CVD) is the leading cause of death in patients with chronic kidney disease (CKD) [START_REF] Go | Chronic Kidney Disease and the Risks of Death, Cardiovascular Events, and Hospitalization[END_REF][START_REF] Tonelli | Chronic Kidney Disease and Mortality Risk: A Systematic Review[END_REF]. Thrombosis is a key event in CVD; and CKD is associated with an increased thrombotic risk [START_REF] Daneschvar | Deep vein thrombosis in patients with chronic kidney disease[END_REF][START_REF] Wattanakit | Chronic kidney disease and venous thromboembolism: epidemiology and mechanisms[END_REF][START_REF] Carney | Thrombosis: New mechanism of thrombus formation in CKD[END_REF]. In patients with CKD, the balance of the coagulation system is dysregulated, with higher levels of factor VII, factor VIII, thrombin and tissue factor (TF) [START_REF] Jalal | Disorders of hemostasis associated with chronic kidney disease[END_REF][START_REF] Huang | Blood coagulation system in patients with chronic kidney disease: a prospective observational study[END_REF]. Accumulation of some uremic toxins normally eliminated by the kidney is correlated with CVD and mortality in patients with CKD (Barreto et al. 2009;[START_REF] Moradi | Cardiovascular burden associated with uremic toxins in patients with chronic kidney disease[END_REF][START_REF] Han | p-Cresyl sulfate aggravates cardiac dysfunction associated with chronic kidney disease by enhancing apoptosis of cardiomyocytes[END_REF][START_REF] Storino | Cardiovascular mortality in chronic kidney patients: the role of uremic toxins[END_REF]. The uremic toxin indole-3 acetic acid (IAA) is an independent predictor of cardiovascular events and mortality in patients with CKD [START_REF] Dou | The cardiovascular effect of the uremic solute indole-3 acetic acid[END_REF]. IAA is a uremic indolic toxin derived from the metabolization of dietary tryptophan by the gut microbiota [START_REF] Fernandez-Prado | Nutrients Turned into Toxins: Microbiota Modulation of Nutrient Properties in Chronic Kidney Disease[END_REF]. Because of its proteinbinding [START_REF] Jourde-Chiche | Proteinbound toxins--update 2009[END_REF], IAA is poorly removed by dialysis [START_REF] Neirynck | Review of protein-bound toxins, possibility for blood purification therapy[END_REF].

In cultured endothelial cells, IAA promotes the expression of a proinflammatory phenotype [START_REF] Gondouin | Indolic uremic solutes increase tissue factor production in endothelial cells by the aryl hydrocarbon receptor pathway[END_REF][START_REF] Dou | The cardiovascular effect of the uremic solute indole-3 acetic acid[END_REF]. IAA also increases the endothelial expression and procoagulant activity of TF [START_REF] Gondouin | Indolic uremic solutes increase tissue factor production in endothelial cells by the aryl hydrocarbon receptor pathway[END_REF], the principal initiator of blood coagulation [START_REF] Camerer | Cell biology of tissue factor, the principal initiator of blood coagulation[END_REF][START_REF] Ruf | The Aryl Hydrocarbon Receptor-Activating Effect of Uremic Toxins from Tryptophan Metabolism: A New Concept to Understand Cardiovascular Complications of Chronic Kidney Disease[END_REF].

Tissue factor expression is induced by various stimuli, like pro-inflammatory cytokines, lipopolysaccharides (LPS), growth factors, or thromboxane A2, via different transcription factors: NF-B, AP-1, NFAT or Egr-1 [START_REF] Bode | Regulation of tissue factor gene expression in monocytes and endothelial cells: Thromboxane A2 as a new player[END_REF]. Interestingly, we have described that indolic toxins induce TF expression by a new pathway: the aryl hydrocarbon receptor (AhR) pathway [START_REF] Gondouin | Indolic uremic solutes increase tissue factor production in endothelial cells by the aryl hydrocarbon receptor pathway[END_REF]. AhR was initially described as a receptor of some environmental contaminants, especially 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) [START_REF] Hankinson | The Aryl Hydrocarbon Receptor Complex[END_REF][START_REF] Mandal | Dioxin: a review of its environmental effects and its aryl hydrocarbon receptor biology[END_REF]; and IAA is an endogenous agonist of AhR [START_REF] Heath-Pagliuso | Activation of the Ah receptor by tryptophan and tryptophan metabolites[END_REF]. In resting cells, AhR forms a complex with HSP90, XAP2 and p23 in the cytoplasm [START_REF] Heid | Role of heat shock protein 90 dissociation in mediating agonist-induced activation of the aryl hydrocarbon receptor[END_REF][START_REF] Petrulis | The role of chaperone proteins in the aryl hydrocarbon receptor core complex[END_REF]. After ligand binding to AhR, the complex dissociates, resulting in AhR translocation into the nucleus and dimerization with the aryl hydrocarbon nuclear translocator (ARNT). The AHR/ARNT heterodimer binds to the XRE (Xenobiotic Response Element) sequences on the promoters of AhR target genes like CYP1A1, CYP1A2, or CYP1B1 [START_REF] Dolwick | In vitro analysis of Ah receptor domains involved in ligand-activated DNA recognition[END_REF][START_REF] Fujii-Kuriyama | Molecular mechanisms of AhR functions in the regulation of cytochrome P450 genes[END_REF]. In addition to this direct genomic pathway, a non-genomic inflammatory AhR pathway, independent of ARNT, has been described (Zhao et al. 2002;[START_REF] Sciullo | Initial and extended inflammatory messages of the nongenomic signaling pathway of the TCDD-activated Ah receptor in U937 macrophages[END_REF][START_REF] Matsumura | The significance of the nongenomic pathway in mediating inflammatory signaling of the dioxin-activated Ah receptor to cause toxic effects[END_REF][START_REF] Kim | Inflammatory pathway genes belong to major targets of persistent organic pollutants in adipose cells[END_REF]. AhR ligands, such as TCDD, induce the expression of proteins involved in inflammation like IL-6, RANTES, TNF, and COX-2 (Zhao et al. 2002;[START_REF] Sciullo | Initial and extended inflammatory messages of the nongenomic signaling pathway of the TCDD-activated Ah receptor in U937 macrophages[END_REF][START_REF] Matsumura | The significance of the nongenomic pathway in mediating inflammatory signaling of the dioxin-activated Ah receptor to cause toxic effects[END_REF][START_REF] Kim | Inflammatory pathway genes belong to major targets of persistent organic pollutants in adipose cells[END_REF]). In the non-genomic pathway, AhR acts as a signaling molecules that interacts with multiple signaling pathways [START_REF] Borlak | Cross-talk between aryl hydrocarbon receptor and mitogenactivated protein kinase signaling pathway in liver cancer through c-raf transcriptional regulation[END_REF][START_REF] Henklová | Role of mitogen-activated protein kinases in aryl hydrocarbon receptor signaling[END_REF][START_REF] Ma | The aryl hydrocarbon receptor at the crossroads of multiple signaling pathways[END_REF]Puga et al. 2009a). We recently reported that a non-genomic pathway of AhR involving p38MAPK/NF-B signaling is crucial for the induction of endothelial COX-2 by IAA [START_REF] Dou | The cardiovascular effect of the uremic solute indole-3 acetic acid[END_REF].

Until now, the mechanisms through which AhR controls TF expression in endothelial cells were poorly understood. The objective of the present work was therefore to determine the signaling pathways and the transcriptions factors involved in AhR-mediated TF induction by IAA.

METHODS

Endothelial cell culture

HUVEC were obtained from umbilical cord vein by collagenase digestion as described [START_REF] Jaffe | Culture of human endothelial cells derived from umbilical veins. Identification by morphologic and immunologic criteria[END_REF]) and grown to the fourth passage in EGM-2 medium (Lonza, France) (containing 2% fetal bovine serum), under standard cell culture conditions (humidified atmosphere at 37°C, 5% CO2). Human aortic endothelial cells (HAoEC) and cardiac-derived microvascular endothelial cells (HMVEC-C) were obtained from Lonza. HMVEC-C were grown to the fifth passages in EGM-2 MV medium (Lonza, France) (containing 5% fetal bovine serum) under standard cell culture conditions. HAoEC were grown to the fifth passage in EGM-2 medium under standard cell culture conditions.

Effect of the uremic toxin IAA

Cells were incubated in the presence of IAA (Sigma-Aldrich, France) at 50µM, the highest concentration described in uremic patients (Vanholder et al. 2003). They were treated during indicated times, with or without the AhR Inhibitor CH-229131 at 10µM (Sigma-Aldrich), the NF-B inhibitor BAY 11-7082 at 10µM (Merck Chemicals), the PKC inhibitor Bisindolymaleimide I at 5µM (Merck Chemicals), the p38 inhibitor SB203580 at 10µM (Sigma-Aldrich), the ERK1/2 inhibitor PD98059 at 10µM (Sigma-Aldrich), and the transcription inhibitor Actinomycin D at 1µg/ml (Sigma-Aldrich). Because IAA was diluted in ethanol, ethanol 1/1000 was used as control. In some experiments, human serum albumin (LFB, France) was added in the culture medium, at the concentration found in human serum (4g/dL).

SiRNA knockdown of AhR

HUVEC were transfected with siRNA control (Negative Universal Control, Stealth™ RNAi, Life Technologies, France) or a pool containing three Silencer® Select siRNA directed against AhR (1200[START_REF] Armstead | Tissue factor expression in vital organs during murine traumatic shock: role of transcription factors AP-1 and NF-kappaB[END_REF]and 1998, Life Technologies, France) by magnetofection using SilenceMag beads (OZ Biosciences, France), according to the manufacturer's instructions. The knockdown of AhR was verified by western blot on cell extracts 48 hours after transfection (Supplemental Fig. 1). TF induction and p50 nuclear translocation were determined 48 hours after transfection.

RNA extraction and quantitative RT-PCR analysis of mRNA expression

Total RNA was extracted by an RNeasy mini-kit (Qiagen, France). RT was performed on 500ng of total RNA using the Takara PrimeScript™ RT reagent Kit (Ozyme, Saint Quentin en Yvelines, France) followed by qPCR on 25ng of cDNA using the Takara SYBR qPCR Premix Ex Taq (Ozyme). We quantified the following target genes: TF, CYP1A1, and CYP1B1. The housekeeping gene HPRT was used for normalization of the target gene values. The sequences of primers were as follows: TF forward: 5'TGCAGTAGCTCCAACAGTGC3', TF reverse: 5'GAGTGTATGGGCCAGGAGAA3'; CYP1A1 forward: 5'GACAGATCCCATCTGCCCTA 3',

CYP1A1

reverse: 5'ATAGCACCATCAGGGGTGAG 3; CYP1B1 forward: 5' TGATGGACGCCTTTATCCTC 3', CYP1B1 reverse: 5' CCACGACCTGATCCAATTCT 3'; HPRT forward: 5'GGATTATACTGCCTGACCAAGGAAAGC 3', HPRT reverse: 5' GAGCTATTGTAATGACCAGTCAACAGG3'.

All PCR reaction efficiencies were determined with MxPro software (Agilent) and were always between 90% and 110%. The fusion curves were analyzed to assess the specificity of detected fluorescence. Fold change of mRNA expression versus control condition (ethanol)

was calculated using the 2 -ΔΔCt method. The transcript for the housekeeping gene HPRT was used for data normalization. Green QPCR Master Mix (Takara bio, France). Specific primer sequences for promoters were as follows: TF forward, 5'-GCCCTCCCTTTCCTGCCATAGA-3', TF reverse: 5'-CCTCCCGGTAGGAAACTCCG-3'; CYP1A1 forward: 5'-ACGCAGACCTAGACCCTTTGC-3', CYP1A1

Western blotting and densitometry analysis of Western blots

reverse: 5'-CGGGTGCGCGATTGAA-3'; CYP1B1 forward: 5'-ATATGACTGGAGCCGACTTTCC-3', CYP1B1 reverse: 5'-GGCGAACTTTATCGGGTTGA-3'. Fold enrichment was calculated using the 2 -ΔΔCt method, where ΔΔCt represents the difference between threshold cycles of experimental rabbit polyclonal antibodies against AhR over rabbit control IgG.

Transient transfection of HUVEC and Luciferase Promoter Assay

The for 20 min at room temperature, incubated with HUVEC for 1 h at 37 °C on a magnetic plate in the 5% CO2 incubator and then replaced with growth medium. After 48h, HUVEC were treated with IAA or ethanol (vehicle control) for 6h. Cell were lysed in a reporter lysis buffer and lysates were assayed for luciferase activities using the luciferase assay system and the GloMax®-Multi detection system (Promega, France). The pSV-β-Galactosidase control vector (Promega) was cotransfected so that the transfection efficiencies could be normalized with the β-galactosidase activities determined using the b-Glo® assay system (Promega).

Patients

We performed a single center prospective study in 92 hemodialyzed patients, selected according to the following criteria: age >18 years; no cardiovascular event (myocardial infarction, stroke, peripheral vascular disease with amputation or need for angioplasty), infection, or surgical intervention (except for vascular access angioplasty) in the last 3 months; no pregnancy; no recent history of malignancy; no intake of corticosteroids or immunosuppressive agents. Patients had been dialyzed at least 3 times a week for a minimum of 6 months. Patients' blood samples were drawn before the mid-week hemodialysis session. Blood samples were drawn in BD Vacutainer tubes containing lithium heparin for biochemical analyses, EDTA for hemoglobin measurement, citrate for TF measurement, and in BD Vacutainer SST tubes for IS, IAA, p-cresylsulfate, and 2microglobulin measurement. Standard laboratory procedures were used for blood chemistry evaluations. Total IS, IAA, and p-cresylsulfate were measured by HPLC, according to [START_REF] Calaf | Determination of uremic solutes in biological fluids of chronic kidney disease patients by HPLC assay[END_REF].

Informed consent was obtained from all individual participants included in the study. The study was approved by the local ethics committee and conforms to the principles outlined in the Declaration of Helsinki.

Measurement of TF by enzyme-linked immunosorbent assay

TF was quantified in citrated human plasma and in cell lysates of HUVEC with the enzymelinked immunosorbent assay (ELISA) kit Quantikine Human Coagulation Factor III/Tissue Factor (R&D Systems, Lille, France) according to the instructions of the manufacturer.

Statistical analyses

For in vitro studies, statistical analyses were performed with the Prism (GraphPad Software Inc, CA).

RESULTS

Study of IAA-induced TF expression in endothelial cells

We previously demonstrated that IAA up-regulates TF expression in HUVEC [START_REF] Gondouin | Indolic uremic solutes increase tissue factor production in endothelial cells by the aryl hydrocarbon receptor pathway[END_REF]). Here we studied the effect of IAA on TF mRNA and protein expression in other types of human endothelial cells that are relevant to the cardiovascular events observed in patients: arterial (HAoEC, aortic endothelial cells), and microvascular (HMVEC-C, cardiacderived microvascular endothelial cells). In all human endothelial cells studied, IAA significantly increased the mRNA (Fig. 1A) and protein (Fig. 2B) expression of TF after 4 hours of incubation.

We then added human serum albumin at 4g/dL in the culture medium to increase the protein-bound fraction of IAA. After 4 hours of incubation, the mRNA (Fig. 1C) and protein (Fig. 1D) induction of TF by IAA was similar with or without addition of albumin.

To test whether transcription was the main way controlling TF expression after IAA stimulation in endothelial cells, we added the transcription inhibitor Actinomycin D (Fig. 1E).

Actinomycin D abolished the induction of TF mRNA expression by IAA, as well as the basal expression of TF mRNA. These findings suggest that the expression of TF induced by IAA in endothelial cells is controlled at the transcriptional level.

IAA induces TF expression via AhR activation but no AhR binding on TF promoter

We confirmed the involvement of AhR in IAA-mediated TF expression using an AhR pharmacological inhibitor CH223191, and AhR siRNA. The up-regulation by IAA of TF mRNA (Fig. 2A) and protein (Fig. 2B) expression was abolished by CH223191 and by AhR siRNA.

CH223191 and AhR siRNA alone had no significant effect on basal TF mRNA and protein expression (Fig. 2A and2B).

AhR can regulate transcription directly by binding on gene promoter in a so called genomic pathway, or indirectly by activating other transcription factors in a non-genomic pathway.

We first studied by ChIP experiments whether AhR binds directly to the TF promoter to induce TF expression in HUVEC. ChIP experiments demonstrated no enrichment of AhR on the promoter of TF, showing that AhR was not directly recruited to the TF promoter in endothelial cells following IAA stimulation (Fig. 2C). As expected, AhR was recruited to the CYP1A1 and CYP1B1 promoters, known to contain XRE sequences, after HUVEC stimulation by IAA (Fig. 2C). This confirms that IAA induces AhR recruitment to the promoters of target genes known to be up-regulated by the genomic pathway. We next analyzed the nuclear translocation of AhR by studying AhR expression in nuclear and cytoplasmic extracts after stimulation of HUVEC by IAA in presence of the AhR inhibitor CH223191. IAA increased the nuclear level of AhR, which was strongly decreased when cells were incubated with the AhR inhibitor CH223191 (Fig. 2D). In parallel, IAA caused a decrease in cytoplasmic level of AhR, which was prevented by CH223191. We next examined the effect of IAA on the upregulation of CYP1A1 and CYP1B1 in HUVEC in presence of CH223191. IAA induced a marked increase in mRNA expression of CYP1A1 and CYP1B1, which was abolished by the presence of CH223191 (Fig. 2E and2F).

The NF-B site in TF promoter is necessary for TF induction by IAA Because AhR did not directly bind to TF promoter after HUVEC stimulation by IAA, we studied the regulation of TF transcription using a TF promoter-luciferase reporter system, containing upstream binding sites for transcription factors known to up-regulate TF: AP-1, NF-B, Egr-1, and Sp1 [START_REF] Oeth | Regulation of the tissue factor gene in human monocytic cells. Role of AP-1, NF-kappa B/Rel, and Sp1 proteins in uninduced and lipopolysaccharide-induced expression[END_REF][START_REF] Li | NF-kappaB transcription factor p50 critically regulates tissue factor in deep vein thrombosis[END_REF]). We focused here on transcription factors involved in TF up-regulation by inflammatory stimuli: AP-1 and NF-B [START_REF] Bode | Regulation of tissue factor gene expression in monocytes and endothelial cells: Thromboxane A2 as a new player[END_REF]. Luciferase plasmids containing either a wild-type TF promoter (-227 WT), or one containing a NF-B non-binding mutant (-227 mNF-B), or one containing AP-1 nonbinding sites (-227 mAP1), were transfected into HUVEC. In cells transfected with the wildtype TF construct, the luciferase activity was 70% higher in IAA-stimulated cells that in control cells (Fig. 3A). The mutation of AP-1 sites in the TF promoter construct (-227 mAP1)

significantly decreased the luciferase activity in both control and IAA-stimulated cells (Fig. 3A). However, the -227-mAP-1 promoter activity was 53% higher in IAA-treated cells than in ethanol-treated control cells. This led us to study the nuclear level of the AP-1 subunits c-Fos and c-Jun in IAA-stimulated HUVEC. The nuclear level of both c-fos and c-jun was not higher in IAA-treated cells than in control cells (Supplemental Fig. 2).

We next studied the role of NF-B in the effect of IAA on TF transcription in using the NF-B non-binding mutant (-227 mNF-B) construct. The induction of the promoter activity by IAA was significantly lower in the mutant NF-B construct than in the wild-type. Interestingly, the activity of the TF promoter mutated for the NF-B site was similar in IAA-treated cells and in ethanol-treated control cells (Fig. 3A). In addition, in control cells, no difference in promoter activity was observed between cells transfected with the -227WT and the -227 mNF-B. These data indicate that the NF-B site is crucial for IAA-mediated TF gene transcription.

Cooperative role between AhR and NF-B in TF induction by IAA

Because the mutation of the NF-B site resulted in decrease in TF promoter activity, we studied the involvement of NF-B pathway in TF induction by IAA. We incubated HUVEC with IAA for 4 hours in the presence of the pharmacological inhibitor of IB kinase (IK) BAY 11-7082. BAY 11-7082 drastically decreased the induction of TF mRNA (Fig. 3B) and protein (Fig. 3C) by IAA. This suggests that NF-B activation is involved in TF induction by IAA.

Subsequently, we studied the level of the NF-B p50 subunits in nuclear extracts of HUVEC stimulated during 30 min by IAA. IAA increased the nuclear level of NF-B p50 (Fig. 3D) and p65 (Supplemental Fig. 3). We then analyzed the effect of pharmacological inhibitors of IK (BAY 11-7082) and AhR (CH223191). Nuclear translocation of p50 was inhibited by the IK inhibitor BAY 11-7082 (Fig. 3D). Interestingly, the increase in p50 nuclear level was also inhibited by the AhR inhibitor CH223191 (Fig. 3D) and by AhR siRNA (Supplemental Fig. 4),

showing that IAA activated NF-B via AhR.

Involvement of p38 MAPK in IAA-mediated TF induction

We studied the involvement of Protein Kinase C (PKC) and of p38 and ERK1/2 MAP Kinases in TF induction by IAA. HUVEC were stimulated by IAA for 4 hours in presence of the PKC inhibitor Bisindolymaleimide I, the p38 inhibitor SB203580, and the ERK1/2 inhibitor PD98059. TF mRNA induction by IAA was significantly decreased by the p38 and PKC inhibitors, but not significantly modified by the ERK1/2 inhibitor (Fig. 4A). The p38 and PKC inhibitors also decreased TF protein induction by IAA (Fig. 4B). This suggests that the p38 and the PKC pathways are involved in IAA-mediated TF up-regulation.

We finally evaluated the effect of p38 and PKC inhibitors on IAA-induced NF-B activation.

The increase in nuclear p50 level caused by IAA (Fig. 4C) was inhibited by SB203580 but not by Bisindolymaleimide I, suggesting that p38 but not PKC is involved in IAA-induced NF-B nuclear translocation.

Taken together, our data showed that IAA up-regulates TF expression by an inflammatory non genomic AhR/p38 MAPK/NF-B pathway (Fig. 5).

Circulating TF levels are independently related to IAA levels in hemodialyzed patients

We studied a cohort of 92 hemodialyzed (HD) patients (Table 1) and measured the plasma levels of circulating TF (cTF). Mean cTF values were 133 ± 66 pg/mL (median 131 pg/mL) and ranged from 36 to 410 pg/mL. At baseline, cTF levels negatively correlated with residual

DISCUSSION

In this study, we demonstrated that TF induction by IAA is mediated by an AhR non genomic pathway involving p38MAPK and NF-B activation.

IAA is a protein-bound uremic toxin, mostly produced from tryptophan metabolism by intestinal bacteria [START_REF] Fernandez-Prado | Nutrients Turned into Toxins: Microbiota Modulation of Nutrient Properties in Chronic Kidney Disease[END_REF]. IAA is an endogenous AhR agonist, which has similar endothelial effects to those of exogenous AhR-activating pollutants like TCDD, benzo [a] pyrene, PCB126, and PCB77 (Sallée et al. 2014). It induces the production of reactive oxygen species, up-regulates the expression of inflammatory molecules COX-2, IL-8, ICAM-1, and MCP-1 [START_REF] Dou | The cardiovascular effect of the uremic solute indole-3 acetic acid[END_REF], and increases the expression and the procoagulant activity of TF in HUVEC [START_REF] Gondouin | Indolic uremic solutes increase tissue factor production in endothelial cells by the aryl hydrocarbon receptor pathway[END_REF]). Here, we show that IAA increases TF expression in endothelial cells from other vessel types: arterial (aortic) and microvascular (cardiac-derived). We also observed that human serum albumin did not modify IAA effect on tissue factor induction, supporting that IAA, whether free or protein-bound, induces TF upregulation.

TF is usually not expressed by endothelial cells under normal conditions. However, it could be expressed in some pathologic conditions in vivo [START_REF] Bode | Regulation of tissue factor gene expression in monocytes and endothelial cells: Thromboxane A2 as a new player[END_REF]. In vitro induction of TF expression in endothelial cells is mediated by various intracellular signaling pathways and by the transcription factors NF-B, AP-1, NFAT, and Egr-1, depending on the stimulus (LPS, IL-1β, TNF-α, thrombin and VEGF) [START_REF] Bode | Regulation of tissue factor gene expression in monocytes and endothelial cells: Thromboxane A2 as a new player[END_REF]. Using AhR siRNA, we previously described a new pathway of TF induction by IAA that is mediated by AhR activation [START_REF] Gondouin | Indolic uremic solutes increase tissue factor production in endothelial cells by the aryl hydrocarbon receptor pathway[END_REF]. We confirm here that an AhR-specific antagonist, CH223191, is able to reduce IAA-mediated TF expression.

The induction of TF in response to stimuli is mainly regulated at the transcriptional level; but some stimuli may also increase the stability of TF mRNA and/or protein [START_REF] Crossman | The regulation of tissue factor mRNA in human endothelial cells in response to endotoxin or phorbol ester[END_REF][START_REF] Brand | Tissue factor mRNA in THP-1 monocytic cells is regulated at both transcriptional and posttranscriptional levels in response to lipopolysaccharide[END_REF][START_REF] Ahern | Regulation of human tissue factor expression by mRNA turnover[END_REF][START_REF] Reddy | Dexamethasone enhances LPS induction of tissue factor expression in human monocytic cells by increasing tissue factor mRNA stability[END_REF]) . The regulation of TF by IAA was well studied in vascular smooth muscle cells by the group of Chitalia. They showed that IAA prolongs the half-life of TF protein by decreasing its ubiquitination [START_REF] Chitalia | Uremic serum and solutes increase post-vascular interventional thrombotic risk through altered stability of smooth muscle cell tissue factor[END_REF].

They further demonstrated in vascular smooth muscle cells that AhR directly interacts with TF and reduces the level of TF ubiquitination [START_REF] Shivanna | The Aryl Hydrocarbon Receptor is a Critical Regulator of Tissue Factor Stability and an Antithrombotic Target in Uremia[END_REF], and that the ubiquitin ligase STUB1 dynamically interacts with TF [START_REF] Shashar | Targeting STUB1-tissue factor axis normalizes hyperthrombotic uremic phenotype without increasing bleeding risk[END_REF]). In our study in endothelial cells, TF mRNA up-regulation by IAA was abolished in presence of the transcription inhibitor actinomycin D, supporting that transcription is the major mechanism of TF mRNA induction by IAA in endothelial cells. However, one cannot exclude that a posttranslational regulation of TF exists, like in vascular smooth muscle cells.

The classical genomic pathway of AhR target genes up-regulation is based on dimers of ligand-activated AhR and AhR nuclear translocator (ARNT), which directly activate gene expression. Upon ligand binding to AhR, it is translocated into the nucleus where it associates with its heterodimerization partner ARNT. The AHR/ARNT heterodimer binds to DNA sequence motif, referred to as XRE, within the promoters of target genes. In the XRE (5′-TNGCGTG-3′), a minimum core sequence of 5′-GCGTG-3′ is required for AHR/ARNT binding [START_REF] Swanson | DNA binding specificities and pairing rules of the Ah receptor, ARNT, and SIM proteins[END_REF] and induction of target genes like xenobiotic-metabolizing enzymes CYP1A1 and CYP1B1 [START_REF] Dolwick | In vitro analysis of Ah receptor domains involved in ligand-activated DNA recognition[END_REF][START_REF] Fujii-Kuriyama | Molecular mechanisms of AhR functions in the regulation of cytochrome P450 genes[END_REF].

Recently, a genomic, ARNT-independent pathway for AhR-mediated gene expression has been reported [START_REF] Jackson | Ah Receptor Pathway Intricacies; Signaling Through Diverse Protein Partners and DNA-Motifs[END_REF], with a novel non-consensus XRE (NC-XRE) described in the promoter of PAI-1 [START_REF] Huang | A novel nonconsensus xenobiotic response element capable of mediating aryl hydrocarbon receptor-dependent gene expression[END_REF] and in p21Cip1 promoter [START_REF] Jackson | Ah receptor-mediated suppression of liver regeneration through NC-XRE-driven p21Cip1 expression[END_REF]. The AhR-dependent gene expression mediated through the NC-XRE does not involve the ARNT as a component of the NC-XRE bound AhR complex. We report here that IAA activates the classical genomic pathway of AhR in HUVEC. IAA induces AhR nuclear translocation and AhR binding to the promoters of its target genes CYP1A1 and CYP1B1, leading to upregulation of CYP1A1 and CYP1B1 mRNA. The AhR inhibitor CH223191 reduces AhR nuclear translocation mediated by IAA and abolishes IAA-induced expression of CYP1A1 and CYP1B1. CH223191 was initially described as an inhibitor of TCDD binding to AhR, which inhibits AhR nuclear translocation induced by TCDD [START_REF] Kim | Novel compound 2-methyl-2H-pyrazole-3-carboxylic acid (2methyl-4-o-tolylazo-phenyl)-amide (CH-223191) prevents 2,3,7,8-TCDD-induced toxicity by antagonizing the aryl hydrocarbon receptor[END_REF]. One can suppose that CH223191 also inhibits IAA binding to AhR. Importantly, we showed that AhR does not directly bind to the TF promoter, supporting that the classical genomic pathway of AhR activation is not involved in TF up-regulation by IAA. This result agrees with the absence of description in the literature of an XRE sequence in the TF promoter, excludes the presence of a NC-XRE in the TF promoter, and suggests the activation of the non-genomic pathway of AhR by IAA.

Once activated by ligand, AhR can also elicit inflammation through the non-genomic pathway, which does not require the participation of ARNT [START_REF] Matsumura | The significance of the nongenomic pathway in mediating inflammatory signaling of the dioxin-activated Ah receptor to cause toxic effects[END_REF]. We supposed that AhR activation could initiate alternative signaling pathways without direct DNA binding of AhR on TF gene promoter. It has been shown that AhR could activate others transcription factors such as AP-1 (Di [START_REF] Meglio | Activation of the aryl hydrocarbon receptor dampens the severity of inflammatory skin conditions[END_REF][START_REF] Ito | Crucial Role of the Aryl Hydrocarbon Receptor (AhR) in Indoxyl Sulfate-Induced Vascular Inflammation[END_REF]) and NF-B [START_REF] Borlak | Cross-talk between aryl hydrocarbon receptor and mitogenactivated protein kinase signaling pathway in liver cancer through c-raf transcriptional regulation[END_REF][START_REF] Vogel | A new cross-talk between the Aryl hydrocarbon receptor and RelB, a member of the NF-κB family[END_REF][START_REF] Vogel | Interaction of Aryl hydrocarbon receptor and NF-κB subunit RelB in breast cancer is associated with Interleukin-8 overexpression[END_REF]Vogel et al. ,2013;;[START_REF] Øvrevik | AhR and Arnt differentially regulate NF-κB signaling and chemokine responses in human bronchial epithelial cells[END_REF] to drive expression of genes, by a protein kinase-dependent non-genomic route. We previously reported that IAA induces inflammatory endothelial COX-2 expression via non-genomic pathway of AhR involving NF-B [START_REF] Dou | The cardiovascular effect of the uremic solute indole-3 acetic acid[END_REF]. To determine the transcription factors involved in TF up-regulation by IAA, we studied IAA-induced TF transcription using a classic proximate TF promoter model, with binding sites for transcription factors AP-1, NF-B, Egr-1, and Sp1. We clearly demonstrated the role of NF-B in IAA-mediated TF expression. We showed that the NF-B site is essential in TF promoter activity induced by IAA. In addition, an inhibitor of IB Kinase, which avoids the phosphorylation of IB and the subsequent NF-B activation, strongly inhibited TF mRNA and protein induction by IAA. Then, we showed that IAA increased the nuclear level of the NF-B p50 and p65 subunits, which are critical regulators of TF [START_REF] Li | NF-kappaB transcription factor p50 critically regulates tissue factor in deep vein thrombosis[END_REF]). This does not exclude that other NF-KB subunits could be activated by IAA. Interestingly, IAA-induced p50 nuclear translocation was decreased when AhR was inhibited; supporting that nuclear translocation of p50 was mediated by AhR activation. All these results demonstrated the crucial role of NF-B in AhR-mediated TF induction by IAA. We also observed that the mutation of the AP-1 site significantly decreased TF promoter activity in both control and IAA-stimulated cells, but IAA still increased TF promoter activity when the AP-1 site was mutated. Furthermore, IAA did not increase nuclear level of c-Jun and c-fos subunits of AP-1. These results suggest than the AP-1 site is required for basal TF expression and is only partially involved in TF induction by IAA. One cannot exclude that other AP-1 subunits may be involved, or that AP-1 cooperates with NF-B to induce TF expression as described in the literature for other agonists [START_REF] Mackman | Lipopolysaccharide-mediated transcriptional activation of the human tissue factor gene in THP-1 monocytic cells requires both activator protein 1 and nuclear factor kappa B binding sites[END_REF][START_REF] Parry | Transcriptional regulation of tissue factor expression in human endothelial cells[END_REF][START_REF] Armstead | Tissue factor expression in vital organs during murine traumatic shock: role of transcription factors AP-1 and NF-kappaB[END_REF][START_REF] Xia | 66 %) Statins 30 (32%) Antiplatelet drugs 49 (53%) Anticoagulant drugs 23 (25%) Erythropoietin therapy 71 (77%) Hemoglobin (/.1 Plasma TF (pg/mL) 131 (36; 410Serum calcium level (mmol/L) 2.34 ± 0.14 Serum phosphate level (mmol/L) 1.53 (0.54; 3.17) Serum urea level (mmol/L) 19.9 ± 5.4 Serum creatinine level (µmol/L) 759 (213; 1533) Serum cholesterol level (mmol/L) 4.3 ± 1.0 Serum LDL-cholesterol level (mmol/L) 2.45 ± 0.87 Serum HDL-cholesterol level (mmol/L) 1.05 (0.28; 2.57) Serum triglyceride level (mmol/L)) 1.3 (0.6; 3.7) Serum 2microglobulin level (mg/L) 32.2 (17.4; 66.9) Serum p-cresylsulfate level (µM) 104 (4.3; 443.6) Serum Indole-3 acetic acid level (µM) 4.3 (1.0; 19.1) Serum indoxyl sulfate level (µM[END_REF][START_REF] Xia | The Effects of NF-κB and c-Jun/AP-1 on the Expression of Prothrombotic and Proinflammatory Molecules Induced by Anti-β2GPI in Mouse[END_REF].

Studies show the cross-talk of AhR and multiple signal transduction pathways, notably MAP Kinases [START_REF] Henklová | Role of mitogen-activated protein kinases in aryl hydrocarbon receptor signaling[END_REF]Puga et al. 2009a); and conventional AhR activators such as TCDD and benzo [a] pyrene activate MAP kinases [START_REF] Henklová | Role of mitogen-activated protein kinases in aryl hydrocarbon receptor signaling[END_REF]Puga et al. 2009a). In our previous study, we demonstrated that IAA induces p38 MAPK and ERK phosphorylation [START_REF] Dou | The cardiovascular effect of the uremic solute indole-3 acetic acid[END_REF]. We found here that p38 MAPK, but not ERK, is involved in TF up-regulation by IAA. We previously showed that AhR inhibition decreases IAA-induced p38 phosphorylation [START_REF] Dou | The cardiovascular effect of the uremic solute indole-3 acetic acid[END_REF], supporting that AhR is the upstream regulator of p38.

Here, we observed that p38 inhibition decreased TF up-regulation by IAA. Furthermore, IAAinduced p50 nuclear translocation was decreased by inhibitors of both AhR and p38 MAPK.

Therefore, in addition to the activation of the classical genomic pathway, the binding of IAA to AhR initiates a cytoplasmic signaling pathway that activates p38 MAPK, which then activates NF-B to induce TF expression.

We also observed the involvement of PKC in TF up-regulation by IAA, but the absence of inhibition of p50 nuclear translocation by the PKC inhibitor suggests that PKC is not involved in the AhR/p38 cytoplasmic signaling pathway that leads to p50 nuclear translocation. PKCs are usually known for their key role in cytoplasmic signal transduction; they can also directly regulate gene expression through signal transduction within the nucleus, a process distinct from the cytoplasmic signaling pathways [START_REF] Lim | Protein kinase C in the immune system: from signalling to chromatin regulation[END_REF]. Within the nucleus, PKCs can directly regulate gene transcription through either phosphorylating specific histone residues or phosphorylating transcription factors [START_REF] Lim | Protein kinase C in the immune system: from signalling to chromatin regulation[END_REF]. For example, PKCα can phosphorylate NF-κB p65 at S276, which is important for transcriptional activation of NF-κB [START_REF] Lim | Protein kinase C in the immune system: from signalling to chromatin regulation[END_REF]. Our results allow to hypothesize that PKC could play a role in IAA-induced TF transcription as an epigenetic regulator that could be involved in histone or NF-KB phosphorylation, rather than as a cytoplasmic signal transducer involved in AhR/p38 signaling pathway.

TF upregulation induced by IAA has functional consequences on thrombogenicity. Indeed, the increase TF protein abundance induced by IAA, like uremic serum and the indolic uremic toxin indoxyl sulfate, is associated with increased procoagulant activity in endothelial cells and vascular smooth muscle cells [START_REF] Chitalia | Uremic serum and solutes increase post-vascular interventional thrombotic risk through altered stability of smooth muscle cell tissue factor[END_REF][START_REF] Gondouin | Indolic uremic solutes increase tissue factor production in endothelial cells by the aryl hydrocarbon receptor pathway[END_REF], and increases thrombogenicity in an ex-vivo model [START_REF] Chitalia | Uremic serum and solutes increase post-vascular interventional thrombotic risk through altered stability of smooth muscle cell tissue factor[END_REF]). An antibody against endothelial TF prevents platelet deposition on cultured endothelial cells induced by uremic serum [START_REF] Serradell | Uremic medium disturbs the hemostatic balance of cultured human endothelial cells[END_REF], suggesting that the presence of endothelial TF is responsible for the enhanced platelet deposition. Patients with CKD display high levels of cTF and increased TFdependent procoagulant activity [START_REF] Gondouin | Indolic uremic solutes increase tissue factor production in endothelial cells by the aryl hydrocarbon receptor pathway[END_REF][START_REF] Shivanna | The Aryl Hydrocarbon Receptor is a Critical Regulator of Tissue Factor Stability and an Antithrombotic Target in Uremia[END_REF].

cTF levels in CKD patients are related to some uremic toxins that are AhR agonists.

Kynurenine levels are associated with cTF levels and hypercoagulability in HD patients [START_REF] Pawlak | Hypercoagulability is independently associated with kynurenine pathway activation in dialysed uraemic patients[END_REF]; cTF levels are correlated with indoxyl sulfate in CKD patients not on dialysis [START_REF] Gondouin | Indolic uremic solutes increase tissue factor production in endothelial cells by the aryl hydrocarbon receptor pathway[END_REF]. We found here that cTF level positively correlated with serum cholesterol, HDL-cholesterol, and with the uremic toxins 2-microglobulin and IAA in simple correlation analysis. However, we did not found a positive correlation between cTF and indoxyl sulfate in HD patients, as we had previously observed in not dialyzed CKD patients (Goundouin et al. 2013). In multivariate linear regression analysis, only serum IAA was significantly and independently associated with cTF level. We previously identified IAA as an independent predictor of cardiovascular events and mortality in CKD patients [START_REF] Dou | The cardiovascular effect of the uremic solute indole-3 acetic acid[END_REF]. TF up-regulation is part of the mechanisms by which IAA may induce atherothrombosis in patients with CKD. Furthermore, cTF negatively correlated with residual renal function (in simple analysis) in HD patients in the present study, and with estimated GFR in not dialyzed CKD patients in our previous study [START_REF] Gondouin | Indolic uremic solutes increase tissue factor production in endothelial cells by the aryl hydrocarbon receptor pathway[END_REF]. This suggests that the preservation of renal function in HD patients could be advantageous to prevent the increase in cTF levels.

In CKD patients, we recently showed high level of AhR agonists and an activation of the AhR pathway in cells from CKD patients [START_REF] Dou | Aryl hydrocarbon receptor is activated in patients and mice with chronic kidney disease[END_REF]. AhR has been proposed as a therapeutic target to reduce TF levels, for a better management of increased thrombosis in CKD [START_REF] Shivanna | The Aryl Hydrocarbon Receptor is a Critical Regulator of Tissue Factor Stability and an Antithrombotic Target in Uremia[END_REF]. However, targeting AhR would also result in inhibition of the AhR genomic pathway that is crucial in detoxification processes. Therefore, it would be interesting to specifically target the thrombotic/inflammatory non genomic pathway of AhR activation, while preserving the AhR genomic pathway. Thus, understanding of signal transduction pathways related to AhR thrombotic/inflammatory non genomic pathway is of interest to find therapeutic targets.

In conclusion, we demonstrated that IAA induces TF expression via an AhR/ p38

  human TF wild-type promoter (-227 hTF WT), the TF mutant AP-1 (-227 hTF mAP1) and the TF-B mutant (-227 hTF mTF-B) reporter plasmids in pGL2 basic were a gift from Nigel Mackman (Addgene plasmid # 15442, 15443, 15444 respectively). HUVEC (1.5 x 10 5 cells) were plated into 6-well plates in EGM-2 medium 16 h before transfection. Cells were then incubated 1h before transfection with Opti-MEM TM reducedserum medium (Gibco, Life Technologies, France). Transfection was performed using Lipofectamine 2000 (Life Technologies, France) associated with magnetofection using Combimag TM beads (OZ Bioscience, Marseille, France) according of the manufacturer's instructions. Briefly, reporter plasmids (1 µg) were mixed with 2 µl of Lipofectamine in 200 µl of Opti-MEM reduced serum medium for 20 min then 1 µl of Combimag TM beads was added to DNA-Lipofectamine complexes. The DNA-Lipofectamine-beads complexes were incubated

  FIGURE CAPTIONS

Fig. 2

 2 Fig. 2 IAA induces AHR activation but no binding of AhR on TF promoter

Fig. 3 Fig. 4

 34 Fig. 3 NF-B is activated by IAA and is involved in Tissue Factor up-regulation

Fig. 5 A

 5 Fig. 5 A diagram shows the AhR-related mechanisms underlying IAA-induced TF

Study of NF-B and AP-1 nuclear levels

  HUVEC were incubated with 50µM IAA, or with ethanol diluted 1/1000 (vehicle control) during 30 min, with or without the AhR Inhibitor CH-229131 at 10µM (Sigma-Aldrich). Some experiments were also performed on HUVECs transfected with AhR siRNA or control siRNA,

	10µM (Sigma-Aldrich), nuclear extracts were prepared using Cayman Chemical's Nuclear
	Extraction Kit (Interchim, France). NF-B p50 was detected in nuclear extracts by Cayman
	Chemical's NF-B (human p50) combo transcription factor assay kit (Interchim). This kit is a
	and cells were stimulated with IAA 50µM forty-eight hours after transfection. Nuclear 96-well ELISA with a specific double stranded DNA sequence containing the NF-KB response
	extracts were prepared using Cayman Chemical's Nuclear Extraction Kit (Interchim, France). element. NF-KB p50 contained in nuclear extracts is detected with a specific primary
	Cytosolic extracts were prepared using 1ml of lysis buffer containing 20mM MOPS, 50mM β-antibody and a secondary antibody HRP-conjugated that provides a colorimetric readout à
	glycerolphosphate, 50mM sodium fluoride, 1mM sodium orthovanadate, 5mM EGTA, 2mM 450 nm.
	EDTA, 1% NP40, 1mM Endothelial p65 expression in nuclear extracts was measured after 30 min incubation of dithiothreitol (DTT), 1mM benzamidine, 1mM
	phenylmethanesulphonylfluoride (PMSF) and 10 μg/mL leupeptin and aprotinin. The cells HUVEC with IAA 50µM, and detected in nuclear extracts by the Cayman Chemical's NF-B
	were incubated 10 min on ice and then collected with a scraper. The cytosolic extracts were (human p50/p65) combo transcription factor assay kit (Interchim).
	obtained after centrifugation of cell lysates at 18,000 g for 15 min. Protein concentrations AP-1 subunits c-Fos and c-Jun were detected in nuclear extracts by the AP1 (c-
	were measured with the Bicinchoninic Acid Kit (BCA1, Sigma-Aldrich). Fos/FosB/Fra1/c-Jun/JunB/JunD) transcription factor assay kit (Abcam, Paris, France).
	Samples were mixed with LDS sample buffer and proteins were separated using 4-12% Chromatin immunoprecipitation (ChIP) assay
	NuPAGE TM protein gel (Life technologies). Proteins were transferred to nitrocellulose HUVEC monolayers (6x10 6 cells) were treated with IAA (50 µM) or ethanol (vehicle) for 60
	membrane and blocked with 5% skim milk for 1 h at room temperature. The membrane was min. Cells were fixed by adding formaldehyde directly to the medium to a final concentration
	incubated overnight at 4°C with antibodies directed against AhR (Santa Cruz, France), against of 1% and incubated for 10 min at room temperature then 40 min at 4°C. ChIP assay was
	NF-kB p50 (Cell Signaling, Ozyme, France), or actin (Cell Signaling, France), and then with the performed using the EZ-Magna ChIP™ A/G Chromatin Immunoprecipitation Kit (Merck
	secondary peroxidase-conjugated antibody (Beckman-Coulter, France). Revelations were Millipore, France), according to the kit instructions. Immunoprecipitations were performed
	done by chemiluminescence (ECL Western blotting substrate, Pierce). Gel images were overnight at 4°C with 5 µg of rabbit IgG (control IgG) or rabbit polyclonal antibody against
	captured using the Syngene GBox (Ozyme) and analyzed with the software geneSys (Ozyme). AhR (Santa Cruz Biotechnology, Tebu-Bio, France). Real-time PCR quantification of ChIP
	enrichments were run on a MX3000P instrument (Stratagene) using the Brilliant II SYBR
	After HUVEC incubation with 50µM IAA during 30 min, with or without the AhR Inhibitor CH-
	229131 at 10µM (Sigma-Aldrich), the NF-B inhibitor BAY 11-7082 at 10µM (Merck
	Chemicals), the PKC inhibitor Bisindolymaleimide I at 5µM, the p38 inhibitor SB203580 at

  Significant differences were revealed by the Wilcoxon signed rank test or by the Mann Whitney test. Data are expressed as mean ± SEM of independent experiments performed on different cell preparations.

	In CKD patients, data are expressed as mean ± standard deviation (SD) for values with
	normal distribution or median (min; max) for non-normally distributed values. Numerical
	variables were tested for normality by the Shapiro-Wilk test. Correlations between plasma
	TF and continuous variables were obtained using Spearman correlation coefficients;
	statistical analyses were performed with the Prism (GraphPad Software Inc, CA) software. To
	identify factors independently associated with plasma TF, multiple linear regression analyses
	were performed with the R software. A p value lower than 0.05 was considered significant.

Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Table 1 .

 1 Baseline characteristics of the HD population (n=92)

	MAPK/ NF-

Table 2 . Two-tailed Spearman correlations of baseline characteristics with TF plasma levels in the HD cohort (n=92)

 2

	TF
	r	p

Table 3 .

 3 Multivariate linear regression analysis for evaluating the relation between independent variables and TF plasma levels in HD patients (n=92)

	TF

ACKNOWLEDGEMENTS

This work was supported by funding from the Aix-Marseille University, the Institut National de la santé et de la Recherche médicale (INSERM), the European Uremic Toxins (EUTox) Work Group, the University of Oran 1 Ahmed Benbella, and the Algerian Ministry of Higher Education and Scientific Research.

We thank K. Fallague and C. Scagliarini for technical assistance.

Conflict of interest:

The authors declare that they have no conflict of interest.

Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.


Chafic Jaber

Andreas Kanstein

Ludovic Apvrille

Amer Baghdadi

Renaud Pacalet

Shared resources high-level modeling in embedded systems using virtual nodes

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

System-on-chip design, that is, integrating multiple functionalities on a single die, has reached a very high level of complexity due to the available integration density. Taking an example from the domain of mobile devices, one can integrate multiple telephony modems and an operating system (OS) like Linux or Symbian, and have them share a single external memory [START_REF]Integrating Operating Systems with Freescale's Cellular Software Platform[END_REF]. In a true multitasking OS, the device must be able to handle simultaneous voice and data calls, while also handling complex imaging tasks like image or video capture, in part sharing processing engines, and all serviced by a single external memory. Both HW and SW architectures must be designed carefully with worst-case concurrent use cases in mind, to avoid any negative user experience.

Modeling the use case behavior and the candidate system architectures at an early stage is therefore essential for a successful system design. Fast architecture evaluation requires system modeling at a very high abstraction level. Furthermore, due to typical development cycles, the HW design needs to be closed earlier, sometimes much earlier than the SW design, providing another reason for high-level modeling. As illustrated in the example, shared resources, like the integrated applications processor and the external memory, have a great influence on the performance of the system-on-chip. Our approach extends the DIPLODOCUS modeling methodology [START_REF] Apvrille | A UML-based environment for system design space exploration[END_REF] with modeling and simulation techniques for shared resources [START_REF] Jaber | A High-Level System Modeling for Rapid HW/SW Architecture Exploration[END_REF]. This paper defines the concept of the virtual node to model shared resources and estimate their impact on system performance. A virtual node ensures -given a parameterized policy -the scheduling of accesses to shared resources (e.g. CPUs, busses, memories). The virtual node concept helps to build a well-structured simulation model, and also facilitates the creation of simple and reusable architecture component models.

The rest of the paper is organized as follows: Section 2 presents the virtual node concept and Section 3 presents our system modeling methodology and environment where we integrated the virtual node concept. Section 4 discusses related work on system modeling with a special focus on shared resources modeling, and finally Section 5 concludes this paper and gives future guidelines.

Modeling Accesses to Shared Resources

A Demonstrative Example

Let us consider a simple application example composed of three tasks: T1, T2 and T3; where T1 and T3 are two dependent tasks and exchange data between each other, while T2 is independent. This application will be executed by a hardware architecture composed of two CPUs (CPU1 and CPU2), one bus (BUS1) and one memory (MEM1). The two CPUs share the bus BUS1 to access the shared memory MEM1. We consider, as well, that T1 and T2 will execute on CPU1, while T3 will execute on CPU2. In this scenario, when T1 and T3 exchange data, the data will be transmitted using the BUS1 and the MEM1. This communication involves two shared architecture nodes, the bus and the memory, whose access is determined with access policies: the arbitration policy. The communication latency is the sum of time needed to access shared resources and the contention on them when CPU1 and CPU2 want to access the bus at the same time for example).

Another level of contention is on the CPU scheduling level; a scheduling policy should indeed select a task to execute among the non-blocked ones. For instance, if the scheduling policy of CPU1 is priority based, and T1 is of higher priority than T2, then T1 will be chosen to execute. In this case, T2 execution is delayed until T1 finishes its execution, including the communication cost with T3 that may vary depending on the contention on the bus and the memory.

Finally, we can identify several types of shared resources. In fact each architecture node is shared between multiple elements: the CPU is shared between different tasks, the bus is shared between multiple CPUs and the memory is also shared between multiple CPUs using the bus.

This simple example shows clearly how the overall performance of the system depends significantly on shared resources access control and contention. It justifies the three types of resource requests that we have identified:

1-Computation requests generated by application tasks to computation nodes (e.g. CPUs). 2-Communication requests generated by computation nodes to the communication nodes (e.g. Bus) in order to transfer data generated/requested by tasks.

3-Storage requests generated by computation

and communication nodes to storage nodes (e.g. memories) As each resource can be shared between different requesters, resources should have an access policy that select a request among pending ones. We generalized this by introducing the virtual node concept, explained in the following section.

The Virtual Node

We define the "Virtual Node" (VN) as a generic modeling component that controls the access to a resource by implementing an access policy. It allocates the controlled resource to a requester, for example the VN of a CPU allocates the CPU to a task that is ready to execute, or the VN of a bus allocates the bus bandwidth to a CPU that is trying to reach the memory or other architecture nodes that are connected to the bus.

A request is generated by a requester to access to a resource. It specifies the resource amount that the requester needs. For example, a storage request shall specify the size of data to transfer. In addition, a request has a priority in the case when the VN's access policy is priority based.

Figure 1 shows the shared resources view of the example introduced in section 2.1. Each architecture node is controlled by a virtual node; for example "VN4CPU1" is the virtual node that controls the access to the CPU1. Requests are stored in queues (one queue for each virtual node). We have three types of queues: computation, communication and storage queues. The virtual node, using its access policy, chooses among the requests in the queue which one will acquire the access to the resource.

Virtual nodes hierarchy

Embedded systems can concurrently execute different real-time heterogeneous applications; for instance in a modern mobile device multimedia application such as video or audio could execute concurrently with control applications (telecommunication protocols). These applications may have specific scheduling requirements (soft real time, intensive data transfer or execution loop, etc); furthermore applying one access policy to all applications is not the optimal solution [START_REF] Shin | Hierarchical Scheduling Framework for Virtual Clustering of Multiprocessors[END_REF]. Hierarchical stacking of virtual nodes is our solution to optimize resources sharing when heterogeneous groups of requesters request the resource. A main virtual node controls a hardware resource and a secondary virtual node controls each group of requesters. This approach allows us to optimize the access policies to satisfy requirements of all groups. We use, as well, the hierarchical composition of virtual nodes for computation, communication and storage resource sharing.

Figure 2 shows an example of two classes of applications; "App1" is controlled using a round robin policy while "App2" is controlled by a priority based policy. The CPU is shared between the two applications by a time sharing policy implemented by the main VN. The main VN (VN4CPU) allocates a time slot of the CPU to an application; the secondary VN (VN4App1 or VN4App2) controlling this application allocates the available execution time to one or more tasks depending on its access policy.

Modeling Methodology and Tool

We integrated the shared resources modeling, presented in the previous section, in the DIPLODOCUS UML modeling methodology [START_REF] Muhammad | Abstract application modeling for system design exploration[END_REF][START_REF] Jaber | A High-Level System Modeling for Rapid HW/SW Architecture Exploration[END_REF]. We furthermore defined a translation of DIPLODOCUS models into a simulator based on SystemC in order to execute the system model and estimate its performance. The following subsections give a brief overview of the DIPLODOCUS methodology and the simulation and analysis of its models.

DIPLODOCUS Methodology

DIPLODOCUS is a UML profile [START_REF] Apvrille | A UML-based environment for system design space exploration[END_REF] targeting design space exploration at a high level of abstraction. It adopts the Y modeling paradigm [START_REF] Keutzer | System-Level Design: Orthogonalization of Concerns and Platform-Based Design[END_REF][START_REF] Kienhuis | An Approach for Quantitative Analysis of Application-Specific Dataflow Architectures[END_REF] which consists of modeling separately the application and the architecture, and then integrating both in a mapping model. Application and architecture models are totally independent from each other, and so a designer can easily evaluate candidate architectures using the same application model. It also permits to explore the mapping of two different applications on a given architecture during first stages of projects. DIPLODOCUS is supported by the TTool [START_REF] Labsoc | The TURTLE Toolkit[END_REF] toolkit that can automatically generate LOTOS or UPPAAL code for formal verification.

We extended the DIPLODOCUS modeling with the notion of virtual nodes to enable the analysis of performance issues related to shared resources, such as contention [START_REF] Jaber | A High-Level System Modeling for Rapid HW/SW Architecture Exploration[END_REF].

Mapping Model Simulation and Analysis

We developed a SystemC-based simulation environment where simulation code can be directly generated from UML DIPLODOCUS models.

Hardware architecture resources are instantiated from a library of pre-defined abstract models for architecture nodes that can be customized by setting the appropriate performance parameters (e.g. pipeline on a CPU, etc.), thus reducing the modeling effort. The designer can also use pre-defined access policies (with or without preemption): round-robin, fixed priority based, time slice scheduling, first-come-first-served. In addition, new access policies can be easily defined.

Simulations produce VCD waveforms containing temporal characteristics of the analyzed system, i.e. of the application, the architecture and of the VNs. In order to get a global view of the system, our simulator provides, for each resource, the utilization factor and the average contention delay on each resource thanks to add-on observers. Buffer overflow situations on storage nodes are also indicated. At last, application temporal behaviors are summarized in terms of end-toend latency of the application, tasks' execution time, and the ratio of a task being ready or waiting to be scheduled by the VN of a computation node.

The designer can make design decisions based on simulation results: for example, he/she can evaluate the access policies of shared resources, tasks' memory mapping and the optimal capacity of resources (CPU frequency, memory size and hierarchy, bus speed …).

Related Work

The back annotation techniques like MESH [START_REF] Bobrek | Modeling shared resource contention using a hybrid simulation/analytical approach[END_REF] and the one proposed in Schnerr & al. [START_REF] Schnerr | High performance timing simulation of embedded software[END_REF] focus on the modeling of task scheduling and extract contention attributes related to communication and memories from low level simulations. In our approach, we extract this information from the high-level simulation of our models. They utilize analytical and simulation techniques to estimate shared resources contention. Final code is used to estimate the performance. On the contrary, our methodology is applied early in the design flow, and so before the code is released.

On the other hand, early architecture exploration methodologies like Sesame [START_REF] Pimentel | A systematic approach to exploring embedded system architectures at multiple abstraction levels[END_REF] offer a clear distinction between application and architecture concerns, and facilitate flexible system-level performance evaluation. So far Sesame only provides schedulers to allocate computation resources to the application processes: it does not model communication architecture arbitration nor memory mapping.

Kempf et al. [START_REF] Kempf | A modular simulation framework for spatial and temporal task mapping onto multi-processor SoC plateforms[END_REF] present a simulation framework for MP-SoC platforms. They use a virtual processing unit (VPU) to schedule the execution of tasks mapped to a processor. The important difference to our approach is that we generalize the notion of a virtual node to model accesses policies to any type of architecture resources, and that we are able to extract performance result of any shared resource.

Hierarchical scheduling methodologies for processors [START_REF] Lipari | A methodology for designing hierarchical scheduling systems[END_REF] or bus [START_REF] Meyerowitz | A Tool for Describing and Evaluating Hierarchical RealTime Bus Scheduling Policies[END_REF] try to optimize resources sharing between multiple groups with different scheduling requirements. Our approach applies the hierarchical control to all shared resources, and more importantly at a high level of abstraction.

Conclusion and Future Work

This paper describes an important part of our methodology for the rapid investigation of the performance impact of contention on shared resources. We have extended DIPLODOCUS with a modular and extendible generic virtual node to model shared resources. Our virtual node model is hierarchical and can model the sharing of computation, communication and storage resources.

In addition to the aspects presented in this paper, we have also extended DIPLODOCUS with test bench modeling and simulation observers to support model analysis. To evaluate our methodology, industrial case studies are conducted over 4G telecommunication systems [START_REF] Jaber | A High-Level System Modeling for Rapid HW/SW Architecture Exploration[END_REF].

Figure 1 :

 1 Figure 1: Shared resources modeling with virtual nodes

Figure 2 :

 2 Figure 2: Virtual nodes hierarchy example