Cécile Mons

Ioana Ferecatu

Sylvie Riquier

Ewen Lescop

Cécile Bouton

Marie-Pierre Golinelli-Cohen
email: marie-pierre.golinelli@cnrs.fr

Combined Biochemical, Biophysical, and Cellular Methods to Study Fe-S Cluster Transfer and Cytosolic Aconitase Repair by MitoNEET

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INTRODUCTION

For the first time in 1978, the presence of a [2Fe-2S] protein in outer mitochondrial membranes (OMMs) from beef heart was detected by electronic paramagnetic resonance spectroscopy [START_REF] Heidrich | Two iron--sulfur centers in mitochondrial outer membranes from beef heart as prepared by free--flow electrophoresis[END_REF]. It was only in 2004 that this Fe-S protein, called mitoNEET (mNT), was isolated from rat liver mitochondria and shown to be a potential target of the type II diabetes drug pioglitazone [START_REF] Colca | Identification of a novel mitochondrial protein ("mitoNEET") cross-linked specifically by a thiazolidinedione photoprobe[END_REF]. Biophysical and biochemical studies characterized mNT as a small homodimeric protein (13 kDa for each monomer), which assembles a [2Fe-2S] cluster per monomer. In mammalian cells, mNT is anchored to the OMM by its N terminus part (32 amino acids), with the vast majority of the protein located in the cytosol, including its Fe-S cluster binding domain [START_REF] Wiley | MitoNEET is an iron--containing outer mitochondrial membrane protein that regulates oxidative capacity[END_REF]. This was the first identified Fe-S protein of the OMM.

MNT belongs to the NEET protein family. Its members are characterized by the presence of at least one CDGSH iron-sulfur domain (CISD) motif ([C-X-C-X2-(S/T)-X3-P-X-C-D-G-(S/A/T)-H]) involved in the coordination of a [2Fe-2S] cluster by 3 cysteines (Cys-72, Cys-74, and Cys-83 for Homo sapiens mNT) and one histidine (His-87 for Hs mNT) [START_REF] Paddock | MitoNEET is a uniquely folded 2Fe 2S outer mitochondrial membrane protein stabilized by pioglitazone[END_REF][START_REF] Wiley | MitoNEET is an iron--containing outer mitochondrial membrane protein that regulates oxidative capacity[END_REF]. Present in all kingdoms of life, this family is composed of only three members in mammals: mNT (CISD1), Miner1 (CISD2, ERIS, Noxp70 or NAF-1), and Miner2 (CISD3) [START_REF] Inupakutika | Phylogenetic analysis of eukaryotic NEET proteins uncovers a link between a key gene duplication event and the evolution of vertebrates[END_REF].

MNT is a cluster donor protein whose cellular function is linked to iron and ROS homeostasis [START_REF] Tamir | Structure--function analysis of NEET proteins uncovers their role as key regulators of iron and ROS homeostasis in health and disease[END_REF]. Holo-mNT (i.e the form with the cluster) is capable of transferring in vitro its redox active Fe-S cluster to a bacterial apo-ferredoxin (FDX), an FDX that has lost its cluster [START_REF] Zuris | Facile transfer of [2Fe--2S] clusters from the diabetes drug target mitoNEET to an apo--acceptor protein[END_REF]. In contrast to the strict anoxia required for other Fe-S transfer proteins such as ISCU, mNT is able to perform cluster transfer under aerobic conditions, suggesting its possible involvement in Fe-S repair after oxidative stress.

Recently, we identified the cytosolic iron regulator, named cytosolic aconitase/Iron Regulatory Protein 1 (IRP1) as the first physiological protein acceptor of the mNT Fe-S cluster in an Fe-S cluster repair pathway [START_REF] Ferecatu | The drug diabetes MitoNEET governs a novel trafficking pathway to rebuild an Fe--S cluster into cytosolic aconitase/Iron Regulatory Protein 1[END_REF].

In a first part, this chapter describes methods to study Fe-S cluster transfer/repair in vitro using E. coli FDX as model recipient protein. The UV-visible absorption spectroscopy allows following the evolution of holo-protein populations (holo-mNT vs holo-FDX) as well as the redox state of the cluster. With native PAGE and Nuclear Magnetic Resonance spectroscopy, the populations of protein species (holo-and apo-mNT, holo-and apo-FDX) are precisely quantified. In a second part, we will describe in cellulo methods to study the involvement of mNT in the repair of the Fe-S cluster of cytosolic aconitase after stress challenge. Finally, we will present protocols to check the expression of the two partners in animal tissues.

In vitro Fe-S cluster transfer from mNT to a model recipient

The properties of the mNT cluster transfer reaction can be properly characterized in vitro using a model recipient apo-protein. This protein must (1) be easy to purify in large amounts;

(2) have a UV-visible absorption spectrum and a native PAGE migration different from those of holo-mNT; (3) have native PAGE migrations that differ between the apo-and holo-forms;

(4) have a holo-form that is stable over time. We used the [2Fe-2S]-containing E. coli FDX, which fulfills all these criteria. This protein is stable under aerobic conditions up to roughly 25-30°C. A hyperthermophilic FDX will be preferred for studies at higher temperatures. In section 2.1, we describe the purification of the two proteins (holo-mNT and apo-FDX) and three methods to study the cluster transfer reaction (Figure 1).

Purifications of mNT and FDX

The proteins are purified after overexpression in E. coli BL21 Star (DE3). For FDX, we have chosen to purify the holo-form, and then to remove the Fe-S cluster to obtain a homogeneous apo-FDX.

Equipment and chemical products

• Protease inhibitor SIGMA FAST EDTA free (Sigma)

• Cell disruptor: Constant Cell Disruption Systems (Constant Systems Ltd)

• Äkta Fast Protein Liquid Chromatography (FPLC) system (GE Healthcare)

General considerations

• All buffers for FPLC have to be 0.45 µm-filtered and flushed with nitrogen

• Cell lysates have to be 0.45 µm-filtered before loading on columns

• Purification of both proteins can be performed at room temperature. If a multiwavelength detector is used with FPLC, set it to 280 nm (polypeptide) and 460 nm (cluster)

• Proteins were concentrated by centrifugation using Amicon Ultra 15 mL 3K (Sigma) at 4°C

• Protein concentrations were measured using the Bradford method and BSA as standard 2.1.1 Purification of Homo sapiens holo-mNT We overexpressed a truncated form of Homo sapiens (Hs) mNT (mNT 44-108 ) without its Nterminus mitochondrial anchor, which was therefore restricted to the well-folded part of the protein [START_REF] Paddock | MitoNEET is a uniquely folded 2Fe 2S outer mitochondrial membrane protein stabilized by pioglitazone[END_REF]. Codon usage was optimized for its expression in E. coli (GenScript). The open reading frame coding for the fragment M44-T108 was cloned between NdeI and XhoI of pET22b(+) vector. Using this construct, a His-tag (LEHHHHHH) was added to its C-terminal end without effect on the biochemical properties of the protein.

Columns, buffers and reagents

• HiTrap Chelating HP 5 mL and Superdex 75 HiLoad 16/60 (GE Healthcare)

• Lysis Buffer: 50 mM NaPO 4 pH 8.0, 500 mM NaCl, 15% glycerol (1) Grow 3 L of LB culture of E. coli transformed by the plasmid with 100 mg/L ampicillin at 37°C with an agitation of 150 rpm. Three hours later, add 500 µM FeCl 3 and decrease the temperature to 20°C. When the optical density at 600 nm reaches 0.7, add 0.5 mM IPTG.

•
Twenty hours after, centrifuge the cells at 5,000 rpm for 15 min. For nuclear magnetic resonance (NMR) studies, mNT 44-108 is expressed in M9 minimal medium enriched with 0.001% thiamine-HCl and 10 µM FeCl 3 and supplemented with 1 g/L of 15 NH 4 Cl and 4 g/L of [ 13 C] D-glucose as the sole nitrogen and carbon sources, respectively.

(2) Resuspend the cells on ice in at least 100 mL Lysis Buffer containing one tablet of protease inhibitors and disrupt them using the cell disruptor. Pass the sample through the cell disruptor until it is no longer viscous (at least three times).

(3) Centrifuge the cell lysate for one hour at 4°C and 40,000 rpm. Keep the slightly red supernatant.

(4) Equilibrate the HiTrap Chelating HP column with Buffer A, which contains 10 mM imidazole and 500 mM NaCl to limit non-specific interactions. Load the supernatant on the column then wash it with Buffer A until stable low absorbance is reached. Apply a 30-mL linear gradient between Buffer A and Buffer B at 1 mL/min.

(5) Pool the red fractions containing mNT (around 40% Buffer B) and concentrate them down to 2 mL.

(6) Equilibrate the Superdex 75 HiLoad 16/60 column with Buffer C. Load the proteins and run the column with Buffer C. Apo-mNT will be eluted before the holo-form (50 and 70 mL, respectively). ( 7) Check the purity of the colored fractions on 16% SDS-PAGE gels. Then, analyze the fractions of interest on a 12% native PAGE gel to check the absence of apo-mNT. Pool pure holo-mNT fractions and concentrate them down to no more than 2 mM to avoid aggregation.

Freeze in dry ice and store at -80°C. We obtain roughly 12 mg of pure holo-mNT per liter of LB culture.

Purification of E. coli holo-FDX

E. coli FDX is overexpressed without a tag using the pET21a-FDX construct [START_REF] Nakamura | Hyperproduction of recombinant ferredoxins in escherichia coli by coexpression of the ORF1--ORF2--iscS--iscU--iscA--hscB--hs cA--fdx--ORF3 gene cluster[END_REF].

Columns and buffers

Procedures

Add 10 mM β-mercaptoethanol to all buffers just before use.

(1) Grow 6 L of LB culture of E. coli transformed with pET21a-FDX supplemented with 100 mg/L ampicillin. When the optical density at 600 nm reaches 0.7, lower the temperature to 18°C and add 500 µM FeCl 3 . Forty minutes later, induce protein overexpression by adding 0.5 mM IPTG. After 20 hours, centrifuge the culture.

(2) Cells are resuspended on ice in at least 200 mL of Buffer D with antiprotease, disrupted using the cell disruptor and then centrifuged as described for mNT.

(3) Equilibrate the HiTrap DEAE Sepharose FF column with Buffer D. Load the cell lysate on the column and wash the column with Buffer D until stable low absorbance is reached. Apply an 80-mL linear gradient between 0 and 40% Buffer E. Pool and concentrate the red fractions down to 2 mL.

(4) Equilibrate the Superdex 75 HiLoad 16/60 column with Buffer F. Load the concentrated eluate and run the column with Buffer F. The apo-FDX will be eluted before the holo-form.

Analyze the colored fractions on 16% SDS-PAGE, then pool and concentrate them.

(5) Equilibrate the MonoQ column with 93% Buffer G/7% Buffer H. Dilute the protein sample at least 4-fold with Buffer G without NaCl, load it on the column, then wash it with 10 mL of 93% Buffer G/7% Buffer H. Apply a 60-mL linear gradient from 7% to 50% Buffer H.

(6) Analyze colored fractions on 16% SDS-PAGE and on a 16% native PAGE gel. Pool the holo-FDX fractions and concentrate them. Freeze in dry ice and store at -80°C. We obtain 6-7 mg of pure holo-FDX per liter of culture.

Preparation of apo-FDX

Equipment and buffers

• NAP-5 column (GE Healthcare)

• Centricon 5 kDa (Millipore)

• Exchange Buffer: 50 mM Tris-HCl pH 7.0, 100 mM NaCl

Procedure

(1) Incubate 450 µL of holo-FDX (at roughly 1 mM) with 10 mM dithiothreitol (DTT), 10 mM EDTA at 80 °C until the protein becomes colorless, which is an evidence of cluster disassembly (at least 5 min).

(2) Equilibrate the NAP-5 column with 10 mL of Exchange Buffer. Load the protein and elute it with Exchange Buffer. Collect the eluate in 0.5 mL fractions and check for the presence of protein using the Bradford assay. Pool the fractions containing apo-FDX and concentrate them using a Centricon 5 kDa down to roughly 450 µL. Aliquot the protein (10 µL fractions), freeze in dry ice and store at -80°C.

In vitro cluster transfer reaction between holo-mNT and apo-FDX

For this reaction, holo-mNT is used as Fe-S cluster donor and DTT-reduced apo-FDX (reduction of the cysteines) as recipient. Recently, we showed that only the oxidized cluster can be transferred and that oxygen does not affect the transfer reaction [START_REF] Golinelli--Cohen | Redox Control of the Human Iron--Sulfur Repair Protein MitoNEET Activity via its Iron--Sulfur Cluster[END_REF]. To prevent cluster disassembly in holo-mNT or holo-FDX, we perform the transfer reaction at a slightly acidic pH (between 6.0 and 6.7; the mNT cluster is too labile at lower pH under aerobic conditions) and 25°C (holo-FDX is not stable enough at higher temperature) to have complete cluster transfer in a few hours. This timescale allows study of the effect of compounds on the transfer with limited change in the compound during the course of the reaction. Finally, we recommend performing the reaction under anaerobic conditions when studying the effect of oxygen-sensitive molecules on the reaction.

UV-visible absorption spectroscopy

This method uses the different spectral properties in the visible region (350-800 nm) of: (1) holo-mNT (maximum absorption at 458 nm for the oxidized form) and holo-FDX (415 and 458 nm for the oxidized form) (Figure 2A), (2) the different redox states of the proteins (Figure 2A andB) and (3) the apo-(no absorbance in the visible) and holo-proteins. Cluster transfer is monitored by recording the UV-visible absorption spectra of the reaction over time (Figure 2C). Initially, only holo-mNT contributes to the visible part of the spectrum, whereas only holo-FDX contributes when all mNT clusters have been transferred (Figure 2A). Thus, at intermediate times, the spectrum is a combination of the contribution of both holo-proteins.

While this spectroscopic method can easily follow cluster transfer, it is worth mentioning that one drawback is the difficulty of detecting species at low concentrations.

Equipment

• UV-visible absorption spectrophotometer Cary 100 (Agilent) with a temperature control apparatus. A multicell holder allows multiple reactions to be followed in parallel

• High-precision cell (Hellma Analytics, reference 105-201-QS). This cuvette is adapted to a small reaction volume (roughly 160 µL) with a reasonable background absorbance

• NAP-5 (GE Healthcare), Micro Bio-Spin 6 Columns (Bio-Rad)

Procedure

(1) If necessary, change the holo-mNT buffer using a NAP-5 column equilibrated with the desired buffer, and proceed as described for the preparation of apo-FDX. If highly concentrated proteins are used, the contribution of protein storage buffers to the reaction is negligible.

(2) Reduce apo-FDX with 5 mM DTT for 30 min at room temperature. If necessary, remove DTT using a Micro Bio-Spin 6 column.

(3) Mix holo-mNT and reduced apo-FDX together (typically 20 µM of each protein) in the desired transfer reaction buffer. Transfer the reaction mixture into the cell and place it in the thermostated spectrophotometer.

(4) Record full spectra between 240 and 900 nm (every 5 min during 2 h and then every 15 min, for example) (Figure 2C).

(5) At the end of the experiment, correct the spectra for baseline variations (for each wavelength, remove the average of the absorbances between 850 and 900 nm at the measured absorbance) (6) Calculate and plot the transfer reaction progress R over time (Figure 2C):

R= (R (t) -R initial )/(R final -R initial )
-R (t) , ratio between the absorbances at 415 and 458 nm at time t -R initial and R final , R at the beginning and at the end (when the transfer is complete) of the reaction, respectively.

Native PAGE

This method allows separation of the apo-and holo-forms of mNT and FDX and easy quantification of each species even if they are present in low amounts, but it does not give information on the redox state of the clusters (Figure 3).

Equipment

• 16% native PAGE gel, loading and electrophoresis buffers [START_REF] Arndt | Native Polyacrylamide Gels[END_REF] • Odyssey Infrared Imaging System (LI-COR)

Procedure

(1) The reaction mixture is prepared as described previously (see 2.2.1), except that the reaction takes place in a 1.5 mL centrifuge tube in a thermostated plate.

(2) Aliquots (8 µL for reactions performed with 20 µM proteins) are removed from the reaction mixture at regular times and immediately frozen in dry ice.

(3) When the reaction is complete, add 5 mM DTT to the samples (to obtain thinner bands on gels) and load them on a 16% native PAGE gel. Run it at 70 V on ice until elution of the bromophenol blue present in the loading buffer.

(4) Stain the gel using the highly sensitive Blue Silver staining [START_REF] Candiano | Blue silver: a very sensitive colloidal Coomassie G--250 staining for proteome analysis[END_REF].

(5) After destaining, scan the gel using the Odyssey Infrared Imaging System. Quantify each band (Figure 3) using LI-COR Odyssey software, calculate the ratio [holo-FDX]/[Total FDX] and plot it over time.

2.2.3. Nuclear magnetic resonance spectroscopy 1D and multi-dimensional NMR techniques allow the direct and real-time observation and quantification of the various states of mNT and FDX in a single experiment. Here, we use 15 N labeled holo-mNT (44-108) and apo-FDX at natural abundance. Each mNT state gives rise to a distinct 15 N HSQC spectrum, and their relative proportions can be assessed from cross-peak intensity. The 15 N HSQC spectra of holo-mNT in its oxidized or reduced states are very similar and well dispersed, indicating a well-folded protein. Nevertheless, the 1 H/ 15 N chemical shifts of residues V57, H58 and I102 are distinct in the reduced and oxidized states and are convenient probes of the mNT redox state (see Figure 4A). Upon cluster loss, the 15 N HSQC spectrum shows substantial reduction in 1 H spectral dispersion, due to its disordered nature [START_REF] Ferecatu | The drug diabetes MitoNEET governs a novel trafficking pathway to rebuild an Fe--S cluster into cytosolic aconitase/Iron Regulatory Protein 1[END_REF][START_REF] Golinelli--Cohen | Redox Control of the Human Iron--Sulfur Repair Protein MitoNEET Activity via its Iron--Sulfur Cluster[END_REF], as shown in figure 4B.

Equipment

• A high-field NMR spectrometer (at least 600 MHz) equipped with a triple resonance, preferentially cryogenically cooled, probe.

• For aerobic conditions, standard 3-mm or 5-mm NMR tubes can be used. For anaerobic conditions, we use a 5-mm NMR tube equipped with a controlled atmosphere valve (for example, the NE-CAV5 series from New-Era) and we prepare the sample in a glove box.

Procedure

(1) Prepare a solution (200 µL for a 3-mm tube, 500 µL for a 5-mm tube) of 50-250 µM 15 N labeled holo-mNT in the required reaction buffer with 95% / 5% H 2 O / D 2 O.

(2) Check the initial redox state of mNT by transferring the solution to the NMR tube and collect reference NMR spectra including the water-suppressed 15 N-decoupled 1 H spectrum, water-suppressed 15 N-filtered 1 H spectrum and 2D 15 N SOFAST-HMQC (Schanda & Brutscher, 2005;[START_REF] Schanda | SOFAST--HMQC experiments for recording two--dimensional heteronuclear correlation spectra of proteins within a few seconds[END_REF].

(3) Prepare a stock solution of 1-2 mM apo-FDX in 50 mM Tris-HCl pH 8.0 100 mM NaCl 95% / 5% H 2 O / D 2 O. Treat apo-FDX with 5 mM DTT for 30 min to ensure free thiols. If required, excess DTT can be removed using Micro Bio-Spin 6.

(4) Add the required volume of apo-FDX to the mNT sample to achieve the expected mNT/apo-FDX molar ratio and transfer the reaction mixture into the NMR tube. This operation represents time = 0 h of the reaction.

(5) Collect NMR spectra including the water-suppressed 15 N-decoupled 1 H spectrum and 2D

15 N SOFAST-HMQC. The reaction rate being rather slow, a series of NMR experiments can last up to 15 min and be repeated as long as the reaction is not complete.

(6) The progress of the reaction can be followed by monitoring peak intensity variations in the 2D 15 N SOFAST-HMQC for signals specific to the three mNT states. For the holo-mNT states, peak intensities can be normalized to the initial intensities, assuming that reduced and oxidized states have similar relaxation properties. The apo-mNT is highly dynamic and normalization can be done using intensities at the end of the reaction when holo-mNT has been completely converted into apo-mNT (Figure 4C). The water-suppressed 15 N decoupled 1 H spectra contain contributions from the side-chain of the single tryptophan residue (W77) in the apo-(at 10.08 ppm) and holo-FDX (at 10.02 ppm) forms that can be used to estimate the population of FDX forms.

Identification of a physiological recipient of the mNT cluster

A physiological recipient of the mNT cluster has to have the following characteristics: (1) it has to be cytosolic (localization of mNT cluster); (2) its apo-form should not be degraded in living cells; (3) its cluster has to be partially or totally disassembled by stress. Considering all these conditions, the first candidate recipient we have identified is cytosolic aconitase/IRP1 [START_REF] Ferecatu | The drug diabetes MitoNEET governs a novel trafficking pathway to rebuild an Fe--S cluster into cytosolic aconitase/Iron Regulatory Protein 1[END_REF]. In mammals, this bifunctional protein readily commutes from a [4Fe-4S]-containing form (aconitase activity) to an apo-form (RNA-binding activity) upon exposure to NO or H 2 O 2 [START_REF] Pantopoulos | Rapid responses to oxidative stress mediated by iron regulatory protein[END_REF][START_REF] Soum | Nitric oxide and peroxynitrite promote complete disruption of the [4Fe--4S] cluster of recombinant human iron regulatory protein 1[END_REF]. Interestingly, IRP1 can recycle into cytosolic aconitase after nitrosative stress involving energized mitochondria [START_REF] Bouton | Recycling of RNA binding iron regulatory protein 1 into an aconitase after nitric oxide removal depends on mitochondrial ATP[END_REF]. This part will be dedicated to the methods we used to identify and characterize IRP1 as a physiological partner of mNT after nitrosative damage of its cluster.

In vitro studies

The first step is to check if holo-mNT can transfer in vitro its cluster to IRP1, which has been obtained from cytosolic aconitase exposed to nitrosative stress leading to complete Fe-S cluster disassembly (Figure 5).

General considerations

• The disassembly of the cytosolic aconitase cluster was performed by treatment of the protein using a diazeniumdiolate NO-donor, diethylamine (DEA) NONOate. This donor has a half-life of roughly 2 min at room temperature and neutral pH [START_REF] Soum | Nitric oxide and peroxynitrite promote complete disruption of the [4Fe--4S] cluster of recombinant human iron regulatory protein 1[END_REF].

• The insertion of a functional [4Fe-4S] cluster into IRP1 is followed by measurement of the aconitase activity of the reaction. UV-visible absorption spectroscopy (poor absorption of the [4Fe-4S] cluster compared to the [2Fe-2S] cluster of mNT) and native gels (no difference in migration between the cytosolic aconitase and IRP1) cannot be used to monitor the reaction.

• All the steps until the measurement of aconitase activity require a glove box due to the high oxygen sensitivity of cytosolic aconitase.

In vitro cluster transfer reaction between holo-mNT and IRP1

Proteins and buffers

• Human cytosolic aconitase was expressed and purified as described by Brazzolotto et al. [START_REF] Brazzolotto | Human cytoplasmic aconitase (Iron regulatory protein 1) is converted into its [3Fe--4S] form by hydrogen peroxide in vitro but is not activated for iron--responsive element binding[END_REF]. Its specific activity was around 20,000 nmole.min -1 .mg -1

• Purified holo-mNT 44-108 : see 2. (2) Incubate IRP1 with 5 mM DTT for 30 min at room temperature.

(3) Incubate 100 µM holo-mNT at 25°C with 25 µM IRP1 in Transfer Buffer in a total volume of 20 µL.

(4) After 30 min, 1 h and 2.5 h, remove 2.5 µL aliquots of the reaction mixture to measure aconitase activity (see 3.1.2).

(5) The negative control reaction is performed in parallel using disassembled mNT.

(6) Check the ability of IRP1 to receive a functional cluster by chemical reconstitution using performed in Buffer B (highest reactivation expected) and one in the Transfer Buffer reaction (positive control of the experiment).

Measurement of aconitase activity

Several aconitase assays based on the conversion of cis-aconitate to isocitrate could be routinely used. One is indirect and uses excess isocitrate dehydrogenase, which catalyzes the oxidation of isocitrate into α-ketoglutarate, coupled to the reduction of NADP + , which is monitored at 340 nm [START_REF] Fansler | Aconitase from pig heart[END_REF]. Another method uses in-gel detection of aconitase activity after migration of total cell extracts on native gels (differential migration of mitochondrial and cytosolic aconitases) and incubation of the gels in a buffer containing cisaconitic acid and isocitrate dehydrogenase [START_REF] Tong | Functions of mitochondrial ISCU and cytosolic ISCU in mammalian iron--sulfur cluster biogenesis and iron homeostasis[END_REF]. In our case, we directly monitor the disappearance of cis-aconitate at 240 nm [START_REF] Drapier | Aconitases: a class of metalloproteins highly sensitive to nitric oxide synthesis[END_REF].

Buffers and chemicals

• Aconitase Buffer: 100 mM Tris-HCl pH 7.4 preheated at 37°C. Flush it with an inert gas as nitrogen before use

• cis-Aconitate solution: 20 mM cis-aconitic acid pH 7.4

Equipment

• SAFAS Monaco UV-visible spectrophotometer MC 98000 (Monaco) with a temperature controller set at 37°C.

• High-precision cell (Hellma Analytics, reference 104-QS).

Procedure.

(1) Add (685-V e ) µL of Aconitase Buffer to the cell.

(2) Add V e of the protein to be tested.

(3) Add 15 µL of the cis-aconitate solution. Use a 200 µL Pipetman or invert the cell at least three times to mix the reaction. Be careful not to form bubbles.

(4) Start the spectrophotometer and follow the absorbance at 240 nm for 20 min.

(5) Make a control cell without protein using 685 µL of Aconitase Buffer and 15 µL of cisaconitate solution. The absorbance of the control reaction should be stable over time.

(6) Calculate the specific activity of aconitase (SA) using the slope of the linear part of the kinetics (ΔA, the initial rate). 1 unit corresponds to 1 nmol of substrate consumed per minute.

𝑆𝐴 ! !" = ∆! * !"" ! * ! * !"
; ε = 3.6 mM -1 cm -1 ; V e : volume of the protein to be tested; [c]: protein concentration.

In cellulo studies

For in cellulo studies, we use HeLa cells, a human cell line from cervical cancer easily transfectable by plasmids or siRNA. Cells were exposed to NO challenge, which disrupts the Fe-S cluster of cytosolic aconitase. Then, we followed the kinetics of recovery of cytosolic aconitase activity (3.2.1). To look at the involvement of mNT in the cluster repair process, we did this experiment with cells depleted or not in mNT using RNAi technology (3.2.2) and we compared the kinetics of activity recovery in the absence and presence of mNT.

Effect of NO challenge on cytosolic aconitase activity

In these experiments, cells are treated with an NO donor (3.2.1.1) (Phase 1, Figure 6A), then the NO stress is removed and we monitor the kinetics of cytosolic aconitase activity recovery (3.2.1.2) (Phase 2, Figure 6A).

NO challenge assay

Reagents

• Diethylenetriamine NONOate (DETA-NO) (Cayman Chemical, Cat. #82120). Prepare a stock solution in basic buffer (1 M Bis-Tris Propane pH 9.0, for example) or directly in the appropriate cell medium (physiological pH around 7.4).

Procedure

Day 1. Seed HeLa cells at 3.5 x 10 5 cells/cm 2 in DMEM containing 4.5 g/L glucose and 1 mM stable L-glutamine (Sigma, Cat. #D0819), supplemented with 1% penicillin-streptomycin (Sigma, Cat. #P0781) and 10% fetal bovine serum (Lonza, Cat. #DE14-801F). Culture them overnight under 5% CO 2 and humidified atmosphere at 37°C. Day 2. Add 250 µM DETA-NO in a fresh medium and check the DETA-NO concentration by measuring the absorbance at 252 nm (ε = 7640 M -1 cm -1 ) using 100 µL of cell medium in 900 µL of distilled water. The absorbance should be around 0.4 (0.2 in the absence of DETA-NO).

Add the medium to the cells and incubate them for 16 h. Day 3. Remove the media, wash the cells twice with medium (the absorbance at 252 nm should be at 0.2), add fresh cell culture medium (t=0 of the kinetics of aconitase recovery) and then incubate them further (for 1 h, 2 h, 4 h, 6 h, 8 h, for example) at 37°C.

Preparation of mitochondria-enriched fractions and cytosolic extracts

General considerations

• Protein concentrations are measured using the BCA assay [START_REF] Smith | Measurement of protein using bicinchoninic acid[END_REF].

Equipment

• Beckman Coulter LE80K Ultracentrifuge with 70Ti rotor • 10 mL thick wall polycarbonate tubes (Beckman, Cat. # 355640)

Buffers and reagents

• Buffer A: 0.25 M sucrose, 100 mM HEPES, pH 7.4, 1/200 (v/v) protease inhibitors (Sigma, Cat. #P8340)

• Digitonin (Merck Millipore, Cat. #300410) 1% solution in DMSO stored in dark at room temperature • Trypan blue 0.4% (Sigma, Cat. #93595)

• Buffer B: 100 mM Tris-HCl, pH 7.5, 0.5% Triton X-100, 1/200 (v/v) protease inhibitors

Procedure

(1) At the desired time, remove the media and wash the cells twice with cold PBS buffer.

Keep cells on ice throughout.

(2) Harvest cells directly in cold Buffer A. Add digitonin (cell permeabilization) at a final percentage of 0.007%, mix gently several times for 15 min on ice.

(3) Check the permeabilization of cell membranes by staining an aliquot with trypan blue (v/v) and estimate the percentage of permeabilized (blue) cells under a photonic microscope.

If less than 90% of the cells are permeabilized, continue the incubation.

(4) Centrifuge the cell lysate at 10,000  g for 10 min at 4°C then separate the mitochondriaenriched fractions (pellet, see step 6) from the cytosolic fractions (supernatant, see step 5).

(5) Ultracentrifuge the resulting supernatant at 100,000  g for 1 h at 4°C in order to remove cell particles. Recover the supernatant (pure cytosol), determine the protein concentration, measure the cytosolic aconitase activity using 50 µg of protein (see 3.1.2), calculate the specific activities and plot them over time.

(6) Wash the mitochondria-enriched pellets with cold Buffer A twice and lyse the pellets with degassed Buffer B (usually with 80-100 µL, depending on the size of the pellet) and incubate for 5 min on ice. Centrifuge at 10,000  g for 5 min at 4°C, recover the supernatant and measure immediately the mitochondrial aconitase activity using 15 µL of the mitochondriaenriched fraction (see 3.1.2) and degassed buffer. The mitochondrial aconitase is particularly oxygen-sensitive, therefore mitochondria lysis and enzymatic activity measurements have to be performed under an anaerobic atmosphere using buffers flushed with nitrogen gas.

(7) Measure protein concentration and calculate the specific activities and plot them over time.

Notes

• Keep one dish untreated to measure the specific activity of cytosolic aconitase in control cytosolic extract (roughly 20 U/mg with HeLa cells). Just after NO stress relief, the activity should be very low (1-2 U/mg).

• NO challenge should not affect the cytosolic aconitase protein level (Figure 6C).

We recommend checking it by immunoblotting using standard procedures. Load 40 µg of protein extracts on an 8% reducing SDS-PAGE gel, transfer the proteins onto a membrane (Immobilon PVDF 0.45 µm, Millipore) and use specific antibodies against IRP1 (from Agro-Bio, for example) and mitochondrial aconitase.

Involvement of mNT in the Fe-S repair of cytosolic aconitase after nitrosative stress

To check the involvement of mNT in a repair pathway of the cytosolic aconitase cluster after nitrosative stress, we compare the aconitase activity recovery kinetics after NO challenge in HeLa cells with and without siRNA depletion of mNT. In the absence of mNT, slower recovery of aconitase activity indicates involvement of mNT in the repair pathway (Figure 6B).

General considerations

• In order to optimize endogenous gene silencing, a range of siRNA duplex concentrations (1 nM to 20 nM) and different volumes of the transfection reagent (30 to 50 µL for a 100 mm-diameter dish) should be tested beforehand. In the following protocol, we specify the optimized concentrations we use to deplete mNT in HeLa cells.

• At the end of the reaction, check by immunoblot the level of mNT depletion in the mitochondrial extracts (Figure 6C).

Reagents

• siRNA duplexes (Life Technologies): CISD1 (mNT-0, Cat. #s31650; mNT-1, Cat. #s31651), negative control (NC, Cat. #4390843). Reagent (the presence of serum and penicillin-streptomycin is not an impediment). Incubate the plate at 37°C. After 5 h, change the medium to ensure lower toxicity. Day 3. Proceed to an NO challenge (see 3.2.1.1).

Partner protein abundance in mammalian tissues

In the previous part, the involvement of mNT in the repair of the cytosolic aconitase was proved using cultured cell lines. To definitively demonstrate that both proteins are partners in mammals, it is necessary to check that they can be both found in at least one common tissue. Thus, we looked at the profile of expression of mNT and IRP1 in different mouse tissues by immunoblotting. Neither is ubiquitous, but they are both well expressed in several tissues, including kidney, liver, heart and brain (Figure 7).

Equipment

• Sonicator ultrasonic processor XL (Misonix Incorporated). Use it at position 7.

Reagents

• Transfer Buffer: 12.5 mM Tris-HCl, 96 mM glycine, variable percentage of ethanol

• Buffer A: 50 mM NaCl, 25 mM Tris-HCl pH 7.5, 2 mM EDTA, 0.5% Triton X-100

• Primary antibodies against IRP1 (Medimabs #MM-0074, used at a 1/1,000 dilution) and mNT (Eurogentec, antibody raised against the following peptide "CKAVYCRCWRSKKFPF", used at a 1/2,000 dilution)

• Fluorescent secondary antibodies coupled to IRDye 800CW (LI-COR Biosciences)

• Simply Blue kit (Invitrogen, Life Technologies)

Procedure

(1) Sonicate 30-100 mg of each tissue in 1 mL of Buffer A with cocktail protease inhibitor (Sigma, cat#P8340) (1/200) on ice for 15 s and then centrifuge at 14,000  g at 4°C for 15 min.

(2) Determine the protein content of supernatant using the BCA protein assay.

(3) Separate 10 µg of protein extracts on two reducing polyacrylamide SDS-PAGE gels (one 16% for detection of mNT and one 8% for IRP1).

(4) Transfer proteins onto Immobilon-FL (Millipore) membranes for 90 min at 100 mA/gel using Transblot Cell (Life Technology) in Transfer Buffer containing 10% and 20% ethanol for IRP1 and mNT detection, respectively.

(5) Perform immunoblotting using standard procedures. Incubate with primary antibodies overnight at 4°C. After washing in TBS-T, fluorescent secondary antibodies coupled to LI-COR IRDye 800CW are used at a 1/20,000 dilution (Figure 8).

(6) No protein is expressed at exactly the same level in all tissues and can be used as loading control. Thus, as loading control, perform another SDS-PAGE under the same conditions, but stain the gel using the Simply Blue kit.

CONCLUSIONS

In this chapter, we described methods to study in vitro Fe-S cluster transfer from holo-mNT to a recipient model, E. coli apo-FDX, and to the first identified physiological recipient, cytosolic aconitase/IRP1 in cellulo and in vivo. More generally, methods described in the first part can be easily adapted to study any protein involved in Fe-S cluster transfer/repair.

The second part gives a general scheme to assess physiological Fe-S cluster recipients. Purified holo-FDX (part 2.1.2)

In vitro cluster transfer reaction

Purified protein preparations

Cluster transfer analysis

  Buffer A: 50 mM NaPO 4 pH 8.0, 500 mM NaCl, 10 mM imidazole • Buffer B: 50 mM NaPO 4 pH 8.0, 500 mM NaCl, 500 mM imidazole • Buffer C: 50 mM NaPO 4 pH 8.0, 50 mM NaCl Procedures β-Mercaptoethanol cannot be used because it increases cluster degradation. The purification is performed in phosphate buffer for increased protein stability.

•

  5 mL HiTrap DEAE Sepharose FF, Superdex 75 HiLoad 16/60, and 1 mL MonoQ columns (GE Healthcare) • Buffer D: 50 mM Tris-HCl pH 7.2, 30 mM NaCl, 0.2 mM EDTA • Buffer E: 50 mM Tris-HCl pH 7.2, 1 M NaCl, 0.2 mM EDTA • Buffer F: 50 mM Tris-HCl pH 7.2, 0.1 M NaCl, 0.2 mM EDTA • Buffer G: 50 mM Tris-HCl pH 7.2, 25 mM NaCl, 0.1 mM EDTA • Buffer H: 50 mM Tris-HCl pH 7.2, 1 M NaCl, 0.1 mM EDTA

1. 1 ••

 1 Disassembled mNT: Incubate holo-mNT in Transfer Buffer at 65°C for 10 min • DEA-NONOate (DEA-NO) (Cayman Chemical, Cat. #82100). Prepare the stock solution in a basic buffer (1 M Bis-Tris Propane pH 9.0, for example) • Buffer A: 20 mM Hepes pH 7.3, 0.2 mM citrate, 30 mM ammonium acetate • Buffer B: 10 mM Hepes pH 7.6, 40 mM KCl, 3 mM MgCl 2 Transfer Buffer: 100 mM Bis-Tris pH 6.2, 100 mM NaCl, 5 mM DTT Procedure (1) Treat 290 µM human holo-IRP1 with 7 mM DEA-NO in Buffer A for 1 h at 29°C in a glove box and purify the reaction on a micro Bio-Spin 6 equilibrated with Buffer B. No residual aconitase activity should be detected.

•

  INTERFERin TM Transfection Reagent (Polyplus Transfection, Ozyme Cat. #409-10) Procedure Day 1. Seed cells in a 100 mm Petri dish to reach approximately 30% confluence at the time of transfection (3.5 x 10 5 cells/cm 2 for HeLa cells) and incubate under 5% CO 2 and humidified atmosphere at 37°C. Day 2. Transfect cells with 10 nM siRNA duplexes and 40 µL INTERFERin TM Transfection

Figure 1 .

 1 Figure 1. Scheme summarizing the different steps and methods for studying in vitro Fe-S

Figure 2 .

 2 Figure 2. UV-visible absorption spectroscopy used to monitor in vitro Fe-S cluster transfer

Figure 3 .

 3 Figure 3. Native polyacrylamide gels used to follow in vitro Fe-S cluster transfer between

Figure 4 .

 4 Figure 4. NMR fingerprints of mNT. (A) Superimposition of 1 H/ 15 N correlation spectra of

Figure 5 .

 5 Figure 5. (A) Scheme of the protocol of in vitro reactivation of the activity of cytosolic

Figure 6 .

 6 Figure 6. mNT is required to restore cytosolic aconitase activity after NO challenge in

Figure 7 .

 7 Figure 7. Level of expression of mNT and IRP1 in different mouse tissues. (A)

  Figure 1.

  Figure 7.


Loïc Clément Rambour

Loïc Denis

Florence Tupin

Jean-Marie Nicolas

Hélène Oriot

SAR TOMOGRAPHY OF URBAN AREAS: 3D REGULARIZED INVERSION IN THE SCENE GEOMETRY

Keywords: SAR tomography, structural information, spatial regularization

HAL is

INTRODUCTION

SAR tomography is the extension of the 2-D SAR imaging to three dimensions. As conventional 2-D SAR imaging uses a synthetic aperture in azimuth direction, 3-D SAR imaging is performed by a synthetic aperture in the elevation direction by collecting several images from parallel tracks. Using multibaseline interferometry techniques on the well-calibrated SAR images stack, it is possible to retrieve the localization of the scatterers in the third dimension. This approach allows separating scatterers mapped in the same resolution cell, which is likely to happen on the dense urban area due to the layover phenomenon.

To perform 3-D SAR focusing, application of the inverse discrete Fourier transform gives a resolution inversely propor-This project has been funded by ANR (the French National Research Agency) and DGA (Direction Générale de l'Armement) under ALYS project ANR-15-ASTR-0002 tional to the synthetic baseline aperture in the cross-range direction. Depending on the baseline repartition this resolution is generally much worse compared to the resolution in azimuth or range. Several spectral super-resolution techniques have been used to overcome this limitation [START_REF] Oriot | Introduction to Spectral Analysis[END_REF] but they all rely on the covariance matrix. This matrix has to be estimated which adds a strong limitation on the number of images that can be used and can be difficult when the observed scene is very heterogeneous. Compress Sensing (CS) also leads to super-resolution but by performing a direct inversion of the data under sparsity constraint. As it doesn't require the estimation of the covariance matrix, this method is far more robust for unmixing multiple close point scatterers.

CS provides a sparse reconstruction of the 3-D scene [START_REF] Zhu | Tomographic sar inversion by l 1 -norm regularization 2014;the compressive sensing approach[END_REF] but is applied pixel by pixel without exploiting any structural knowledge of the scene. Different strategies have been developed to re-process the estimated signal in order to improve its spatial coherence [START_REF] Zhu | Facade reconstruction using multiview spaceborne tomosar point clouds[END_REF], but until now the structural information has not been taken into account directly in the inversion step.

Here, we develop an algorithm that allows to inverse a tomographic stack to retrieve a sparse 3-D complex scene using structural geometric priors. To do so, we first write the transformation that allows us to express the scene in ground geometry and then we describe the inversion process. We present 3-D reconstructions obtained from a dataset of TerraSAR-X images over the city of Paris.

GROUND TO RADAR GEOMETRY

A SAR tomographic stack consists of a collection of N coregistered SAR images. The geometry of the objects and the sensors is illustrated Fig. 1. In the absence of moving objects, the value of the complex pixel in image n at position (x, r) is [START_REF] Fornaro | Threedimensional multipass sar focusing: experiments with long-term spaceborne data[END_REF]

v n (x, r) = (y,z)∈∆(x,r) u(x, y, z)exp -jξ n h(y, z) dydz + n (1)
Fig. 1. Radar and ground geometry: The geometry of the sensors (x, r, h) doesn't allow a good expression of the structural particularity of the scene. In particular, it is not natural to express the orthogonality of ground / walls or walls / roofs. We place ourselves in the ground geometry (x, y, z) to be able to take those observations into account.

Complex variables are from now on underlined. Here, ∆(x, r) is the extension of the radar resolution cell along the elevation direction h, u(x, y, z) the complex reflectivity of the scatterer located at position (x, y, z) and h(y, z) its elevation. We chose to have the azimut and x axis being the same for simplification. ξ n = 4πbn λr is the spatial frequency depending on the baseline b n , λ stand for the wavelength and θ is the incidence angle of the master antenna. n is a complex additive white noise coming from the sensors. As we can see the ground geometry that describes best the objects is not the same as the radar geometry which will depend on the incidence angle. Usually, the tomographic inversion is processed using the radar geometry, but in order to express some structural constraints we will use the ground geometry during the inversion process. We now refer to v ∈ C Nx.Nr.N as the tomographic stack and u ∈ C Nx.Ny.Nz as the 3-D unknown scene, with N r , N x , N y and N z being the number of bins in the corresponding directions. We write Φ ∈ C (Nx.Nr.N )×(Nx.Ny.Nz) the matrix that express the signal back-scattered from a scene sampled in ground geometry to the antennas. An element of Φ is then

Φ i,j+(n-1).N = δ ri,c(yj ,zj ) exp -jξ n h(y j , z j ) (2)
The function c(y, z) return the corresponding range of a point located at the position (y, z). The matrix Φ is highly sparse which can be used to speed-up the computation and its non-null elements correspond to permutations of the classical sensing matrix used in SAR tomography. We now have designed a linear operator that allows us to express the tomographic stack from the 3-D complex scene :

v = Φu + n (3)

TOMOGRAPHIC INVERSION

In this part we will derive the inversion of equation ( 3) using some basic priors on the urban topography. To achieve super resolution, we will have a number of unknowns variables far more important than the size of v. To be able to estimate u we will add two constraints. First, we will assume that the 3-D scene is sparse which is the same hypothesis as in CS.

Secondly, we will make the assumption that the objects are smooth in the x, y and z directions. Those hypothesis express the fact walls and roofs are seen as punctual objects in at least one direction and are smooth in the other two directions. We then want to solve the following minimization problem

û = argmin u ||Φu -v|| 2 2 + R(|u|) with R(|u|) = µ x • D x |u| 2 2 + µ y • D y |u| 2 2 + µ z • D z |u| 2 2 + µ L1 • |u| 1 (4)
µ x , µ y , µ z and µ 1 are the parameters allowing to tune the impact of each term in R. Here the regularization is applied to the modulus of the 3-D reflectivity. One big reason is that we don't want to risk to converge to zero as we smooth two neighboring scatterers with opposite phase. The introduction of the modulus add a lot of complexity to this problem even with a relatively simple prior. To be able to solve (4), we introduce two more variables and chose an alternating minimization scheme. We now want to solve

û = argmin u Φu -v 2 2 + R(w) s.t. u = f |f | = w (5)
The real image w will converge to the modulus of the image u and the complex image f links them. We solve (5) using a modified ADMM [START_REF] Boyd | Distributed optimization and statistical learning via the alternating direction method of multipliers[END_REF] algorithm by jointly optimizing over u and w and updating the two Lagrange multipliers :

• {û, ŵ} ← arg min u, w 1 2 ||Φu -v|| 2 2 + β 1 2 ||f (u, w) -u + d1 || 2 2 + β 2 2 w -|f (u, w)| + d2 2 2 + R(w) • d2 ← d2 + ŵ -|f (û, ŵ)| • d1 ← d1 + f (û, ŵ) -û . (6)
β 1 and β 2 are two parameters needed for the optimization and f (u, w) is given by

f (u, w) = β 1 • |u -d1 | + β 2 • (w + d2 ) β 1 + β 2 + • exp i • arg u -d1 (7)
The first step in ( 6) is easy to optimize as it is a sum of quadratic and linear functions. As the modulus is positive, the 1 norm can be seen as the sum of all the elements in the vector. The algorithm described in [START_REF] Fedorov | 3D Slicer as an image computing platform for the Quantitative Imaging Network[END_REF] gives a new tool capable to inverse a tomographic problem under structural priors. Even if the model we choose is still simple, it gives good results on urban areas as shown in the next section. As the framework is completely independent of the regularization chosen, the structure of the algorithm can be used to add even more sophisticated priors on the reconstructed data.

EXPERIMENTAL RESULT

The algorithm described in the previous section has been implemented and tested on a dataset of 40 TerraSAR-X images over the front de Seine in the south-west of Paris, France. The observed scene is presented Fig. 3 1 .The spatial (orthogonal component) and temporal baseline are shown in Fig. 2. The total spatial baseline span is more than 775m and the total temporal baseline more than 5 years with a large gap of almost two years in it. Fig. 4 we present the intensity of the 3-D inversion over the Mirabeau Tower and we also plot in Google Hearth the height of the discretized reconstruction obtained by thresholding the intensity. Fig. 5 we present the intensity of the 3-D inversion over the Ministry of Foreign Affairs and the height of the discretized reconstruction in Google Hearth.

CONCLUSION

In this paper, we have proposed a new algorithm for the inversion of a stack of tomographic images. In this approach, we add structural constraints on the modulus of the 3-D reflectivity in order to describe more accurately a dense urban scene. This algorithm has been implemented and gives good results on real images. Finally, as the priors we add can be changed, we introduce a framework capable of performing 3-D tomographic inversion under spatial constraint.

Fig. 2 .

 2 Fig. 2. Spatial Baseline (orthogonal part) vs. Temporal Baseline

Fig. 3 .

 3 Fig. 3. (Top) TerraSAR-X intensity image of the observed scene (Paris). The building back-scattering a strong signal in the center af the image is the Mirabeau Tower. In the bottom left corner is the Ministry of Foreign Affairs with a periodic appearing rooftop. (Bottom) Optical image from Geoportail of the scene.

These data have been provided by the DLR in the framework of the project LAN1746

Fig. 4. (top) Intensity of the 3-D reconstruction of the Mirabeau Tower [6]. (bottom) height of the brightest points in the 3-D reconstruction plotted in Google Hearth.


Scailteux Lucie-Marie
email: luciemarie.scailteux@chu-rennes.fr

PhD Rioux-Leclercq Nathalie

Statistician Nowak

M Doucet

; Hainry

GIRARDOT, GOLAIRE, TISSEAU & HOGENHUIS Broyer-Petit & Beyls-Noel Coeugnet

MOREAU & STAROZ M Perrot

M Tas

M Politis Saout

Crpv

Use of 5α-reductase inhibitors for benign prostate hypertrophy and risk of high-grade prostate cancer: A French population-based study

Keywords: Benign prostate hypertrophy, prostate cancer, 5 alpha reductase inhibitors, alpha blockers, population based-study ARI: 5-alpha-reductase inhibitors BPH: benign prostate hypertrophy ICD-10 codes: International Classification of Diseases, 10 th PCPT: Prostate Cancer Prevention Trial PSA

Background -To assess the association between 5α-reductase inhibitor (5-ARI) use and high grade (Gleason score 8-10) prostate cancer.

Methods -We set up a population-based nested matched case-control study using the French Health Insurance Database linked to data from all Brittany (France) path labs. Among 74,596 men with ≥ 1 drug reimbursement for symptomatic benign prostate hypertrophy

Accepted Article

. between January 1, 2010 through December 31, 2011, 767 incident prostate cancer cases between January 1, 2012 through December 31, 2013 were matched on age and delay between the first observed delivery of drug for benign prostate hypertrophy (5-ARI, alphablockers or phytotherapy) and diagnostic date of the case to five controls, using an incidence density sampling design.

Results -963 men (153 cases, 810 controls) had been exposed to 5-alpha reductase inhibitors. A statistically significant heterogeneity (p = 0.0048) was detected across cancer grades when estimating association between prostate cancer and 5-alpha reductase inhibitors long term use (≥ 2 years) versus no 5-alpha reductase inhibitor exposure: adjusted conditional odds ratio was 1. 76 [0.97-3.21] for Gleason ≥ 8, for Gleason < 8. Interpretation -Our results supported an increased risk for high-grade and a decreased risk for low-grade prostate cancer. Patients treated for longer than 2 years with 5-alpha reductase inhibitors should be informed of increased risk for the development of high-grade disease.

ClinicalTrials.

Introduction

With a high prevalence [START_REF] Ferlay | Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008[END_REF], a risk of morbidity from treatment [START_REF] Punnen | Long-term health-related quality of life after primary treatment for localized prostate cancer: results from the CaPSURE registry[END_REF] or from active surveillance with follow-up biopsies(3), primary prevention of prostate cancer is attractive. Two large randomized trials [START_REF] Thompson | The influence of finasteride on the development of prostate cancer[END_REF][START_REF] Andriole | Effect of dutasteride on the risk of prostate cancer[END_REF] have demonstrated that 5-alpha-reductase inhibitors (5-ARI) reduced overall prostate cancer risk by 20% to 25%, but they also reported a statistically significant increased risk of high-grade cancer (Gleason scores 7-10) and so far the use of 5-ARI is not recommended for primary prevention [START_REF]Drug Safety and Availability -FDA Drug Safety Communication: 5-alpha reductase inhibitors (5-ARIs) may increase the risk of a more serious form of prostate cancer[END_REF][START_REF] Oelke | EAU guidelines on the treatment and follow-up of non-neurogenic male lower urinary tract symptoms including benign prostatic obstruction[END_REF][START_REF]Synthèse avis de la commission de la transparence -Avodart[END_REF]. Reason for the observed increased risk is controversial [START_REF] Theoret | The risks and benefits of 5αreductase inhibitors for prostate-cancer prevention[END_REF] including false result through detection bias [START_REF] Cohen | Detection bias due to the effect of finasteride on prostate volume: a modeling approach for analysis of the Prostate Cancer Prevention Trial[END_REF][START_REF] Redman | Finasteride does not increase the risk of high-grade prostate cancer: a bias-adjusted modeling approach[END_REF].

Treatment with 5-ARI has clear benefits for men with lower urinary tract symptoms related to benign prostate hypertrophy [START_REF] Mcconnell | The effect of finasteride on the risk of acute urinary retention and the need for surgical treatment among men with benign prostatic hyperplasia. Finasteride Long-Term Efficacy and Safety Study Group[END_REF][START_REF] Mcconnell | The longterm effect of doxazosin, finasteride, and combination therapy on the clinical progression of benign prostatic hyperplasia[END_REF]. In those men, no difference was observed regarding the number of Gleason score 8-10 cancers in men allocated to dutasteride compared to those allocated to tamsulosin [START_REF] Roehrborn | Effect of dutasteride on prostate biopsy rates and the diagnosis of prostate cancer in men with lower urinary tract symptoms and enlarged prostates in the Combination of Avodart and Tamsulosin trial[END_REF]. Though three observational studies [START_REF] Murtola | Prostate cancer incidence among finasteride and alpha-blocker users in the Finnish Prostate Cancer Screening Trial[END_REF][START_REF] Robinson | Use of 5α-reductase inhibitors for lower urinary tract symptoms and risk of prostate cancer in Swedish men: nationwide, population based case-control study[END_REF][START_REF] Wallerstedt | Risk of Prostate Cancer in Men Treated With 5α-Reductase Inhibitors-A Large Population-Based Prospective Study[END_REF] reported somehow reassuring results, these findings did not rule out an increased risk of high-grade prostate cancer. Considering that the association between 5-ARI use and high-grade prostate cancer is still debated, we set up the CANARI study investigating the association

Accepted Article

between 5-ARI use and prostate cancer according to Gleason score (< 8 or ≥ 8), compared to 5-ARI non-users.

Patients and Methods

Study design, setting and participants. This population-based matched case-control study used data (2010 to 2013) from the comprehensive French Health Insurance Data (SNIIRAM) linked to data from all path labs located in Brittany, France.

Design description and linkage methodology were reported elsewhere [START_REF] Scailteux | Rationale and design of the CANARI study: a case-control study investigating the association between prostate cancer and 5-alpha-reductase inhibitors for symptomatic benign prostate hypertrophy by linking SNIIRAM and pathology laboratories in a specific region in France[END_REF]. Among men living in Brittany, all treated for symptomatic or complicated BPH in 2010-2011 (Supplementary Table S1), we identified incident cases of prostate cancer in 2012-2013 (Supplementary Table S2) and confirmed the diagnosis by linkage to pathology results (Gleason score). We then defined: men with high-grade prostate cancer (Gleason score ≥ 8) and men with low-grade prostate cancer (Gleason score < 8). To be eligible, a case had to have a delay of at least one year between the first observed delivery of drug for BPH and prostate cancer diagnosis. Figure 1 displays a flowchart. For each case, five controls alive and free of prostate cancer at diagnosis date of the case (index date) were randomly selected from the cohort. They were matched on age and delay between the first observed delivery of drug for BPH and index date through an incidence density sampling design.

Exposure. We defined "5-ARI users" as patients having at least two deliveries; the others were categorized as "5-ARI non-users" (Supplementary Table S1). Exposure was quantified by the cumulative duration of 5-ARI dispensed calculated from all data observed (backward from index date to January 1 st , 2010) and categorized for sake of clarity into three classes (less than 1 year, [1-2[ and ≥ 2 years).

Accepted Article

Variables and sources of data. Using SNIIRAM data [START_REF] Palmaro | Overview of drug data within French health insurance databases and implications for pharmacoepidemiological studies[END_REF], we classified cases and controls as having or not some pre-specified comorbidities (Supplementary Table S2). We also obtained dates of performed transurethral resections, prostate biopsy procedures, and PSA measurements (results unavailable). On the 2010-2013 period, French guidelines as regards prostate biopsies were the same as European guidelines which recommend 10 to 12 biopsy cores but nor more 12 (20,21). In Brittany pathology laboratories, 12 biopsy cores are usually sent for analysis.

Study size. At a 5% two-sided significance level, upon the hypothesis of 5-ARI exposure frequency of 20% among controls [START_REF] Cornu | A widespread population study of actual medical management of lower urinary tract symptoms related to benign prostatic hyperplasia across Europe and beyond official clinical guidelines[END_REF], 98 cases of high grade prostate cancer and 490 controls (1:5 case-to-control ratio) allowed to detect an odds ratio of 2.0 with 80% power, keeping in mind that matching improves power though in an unknown manner.

Statistical methods. Characteristics of patients were described according to their casecontrol status. Measure of association used odds ratio (and 95% confidence interval) through conditional logistic regression to take into account matching. Multivariate conditional logistic regression analysis was performed to further adjust on potential risk factors. As PSA measurement and number of prostate samples (biopsy or transurethral resection) were potentially in the causal pathway leading to prostate cancer, a sensitivity analysis without adjustment on those parameters was also made.

Association between prostate cancer and 5-ARI exposure was expected to be different whenever low-grade or high-grade cancer was considered. An interaction term between exposure and prostate cancer grade was introduced in the logistic model in order to allow different association strength according to the considered individual outcome (low or highgrade prostate cancer) and to test homogeneity across these individual components of the Accepted Article composite outcome [START_REF] Pogue | Designing and analyzing clinical trials with composite outcomes: consideration of possible treatment differences between the individual outcomes[END_REF]. A sensitivity analysis was made according to the recently proposed new five-tiered Gleason grade groups (GGGs) [START_REF] Epstein | A Contemporary Prostate Cancer Grading System: A Validated Alternative to the Gleason Score[END_REF].

Statistical analyses used the LOGISTIC procedure of the SAS software version 9.4 (SAS Institute Inc, Cary, NC) with a STRATA statement.

Ethical considerations.

The study got regulatory approval (CNIL: DR-2014-084); ClinicalTrials.gov identifier: NCT02873117.

Results

Participants. Among 74,596 eligible men, 859 cases of confirmed prostate cancer were identified in 2012-2013 (Figure 1); 767 cases (including 153 "5-ARI users") were available for the analysis, matched to 3835 controls (including 810 "5-ARI users").

Descriptive data. After matching, mean age was 69.3 years, and men with Gleason scores < 8 were younger than those with Gleason ≥ 8 (mean age 68.2 versus 76.0 years) (table 1).

All cases had at least one prostate sample before the index date compared to 10.6 % in controls. Cases had more frequently more than one PSA measurement than in controls (96.5 % vs. 82.4 %). Excluding the biopsy which made the diagnosis, 71.9 % of cases and 3.7 % of matched controls had at least 1 one another biopsy before diagnosis; 25.4 % of cases and 6.7 % of matched controls had TURP. Cases and controls were not markedly different as regards measured co-morbidities.

Exposure data. 153 (20.0 %) cases and 810 (21.1%) controls were "5-ARI users" before index date and 400 men had a duration of use of 5-ARI ≥ 2 years: 58 were cases (including 38 with Gleason < 8 and 20 with Gleason ≥ 8) and 342 were controls (Supplementary Table S3).

When comparing "5-ARI users" to "5-ARI non-users", no substantial differences were observed, including PSA measurement and prostate samples (table 2). Most of "5-ARI non-

Accepted Article

users" received alpha-adrenoreceptor antagonists (Tamsulosin, 35 %; Alfuzosin, 24 %), but also Serenoas repens (22 %) and Pygeum africanum (11 %).

Main results.

Matched unadjusted estimates and confounder-adjusted estimates are shown in Figure 2 and Supplementary Table S3. Conditional adjusted odds ratios for low-grade and high-grade prostate cancer were, respectively 0.80 [0.64-1.01] and 1.21 [0.74-1.98] for "5-ARI users" compared to "5-ARI non-users". As regard long term users (≥ 2 years), a statistically significant heterogeneity (p = 0.0048) was detected between prostate cancer grades: adjusted conditional odds ratio was 1.76 [0.97-3.21] for Gleason ≥ 8, and 0.65 [0.45-0.93] for Gleason < 8. A sensitivity analysis using new Gleason grading system showed similar results (Supplementary Table S4).

Discussion

In our study targeting subjects receiving drugs licensed for symptomatic or complicated benign prostate hypertrophy, a qualitative significant heterogeneity was observed across cancer grades when estimating association between prostate cancer and 5-ARI long term use (≥ 2 years) versus no 5-ARI exposure.

Our study results differ in some important aspects of clinical setting and methodology to other observational studies [START_REF] Murtola | Prostate cancer incidence among finasteride and alpha-blocker users in the Finnish Prostate Cancer Screening Trial[END_REF][START_REF] Robinson | Use of 5α-reductase inhibitors for lower urinary tract symptoms and risk of prostate cancer in Swedish men: nationwide, population based case-control study[END_REF][START_REF] Wallerstedt | Risk of Prostate Cancer in Men Treated With 5α-Reductase Inhibitors-A Large Population-Based Prospective Study[END_REF] but appear in line with PCPT and REDUCE trials(4,5). Our clinical setting was more similar to the CombAT trial [START_REF] Roehrborn | Effect of dutasteride on prostate biopsy rates and the diagnosis of prostate cancer in men with lower urinary tract symptoms and enlarged prostates in the Combination of Avodart and Tamsulosin trial[END_REF] than PCPT(4) and REDUCE trials [START_REF] Andriole | Effect of dutasteride on the risk of prostate cancer[END_REF].

In a Finnish cohort study, risk of cancer with scores 7-10 was non-significantly increased in finasteride users compared to non-users [START_REF] Murtola | Prostate cancer incidence among finasteride and alpha-blocker users in the Finnish Prostate Cancer Screening Trial[END_REF]. A Swedish population-based case-control study reported that an increasing duration of exposure to 5-ARI was associated with a decreased risk of Gleason scores 2-6 and 7; no significant association in risk of Gleason scores 8-10 was observed with increasing exposure time [START_REF] Robinson | Use of 5α-reductase inhibitors for lower urinary tract symptoms and risk of prostate cancer in Swedish men: nationwide, population based case-control study[END_REF]. Lastly, another Swedish

Accepted Article

population-based cohort study [START_REF] Wallerstedt | Risk of Prostate Cancer in Men Treated With 5α-Reductase Inhibitors-A Large Population-Based Prospective Study[END_REF] reported that 5-ARI decreased the risk for prostate cancer with Gleason scores 6 and 7, and that 5-ARI did not statistically significantly affect the long-term risk of prostate cancer with Gleason scores 8-10 over a 8-year period compared to men not taking 5-ARI; more in depth, a statistically increased risk after less than 2 years of exposure to 5-ARI (HR = 1.56), became not statistically significant (HR = 1.25) with a further adjustment on PSA before treatment and there was no increased risk thereafter: authors explained that these early detected high-grade cancers were likely potential prevalent cancers, more easily diagnosed by prostate shrinkage. Of note, a oneyear delay has been used to remove prevalent cancers detected due to the initial PSA test [START_REF] Wallerstedt | Risk of Prostate Cancer in Men Treated With 5α-Reductase Inhibitors-A Large Population-Based Prospective Study[END_REF]. Furthermore, a transient early increased risk for cancer could be related a true effect of 5-ARI selecting susceptible clones; previous studies showed no specific prostatic histologic modification in finasteride treated patients and suggested that high-grade prostate cancer could be due to the cell capacity to survive in a maybe less hormonally sensitive environment [START_REF] Bostwick | Does finasteride alter the pathology of the prostate and cancer grading?[END_REF][START_REF] Lebdai | High-grade prostate cancer and finasteride[END_REF]. These three observational studies used men free of prostate cancer as controls [START_REF] Robinson | Use of 5α-reductase inhibitors for lower urinary tract symptoms and risk of prostate cancer in Swedish men: nationwide, population based case-control study[END_REF], compared drug users to non-users [START_REF] Murtola | Prostate cancer incidence among finasteride and alpha-blocker users in the Finnish Prostate Cancer Screening Trial[END_REF], or used "non 5-ARI users" as reference [START_REF] Wallerstedt | Risk of Prostate Cancer in Men Treated With 5α-Reductase Inhibitors-A Large Population-Based Prospective Study[END_REF] (no details as regards BPH status or non 5-ARI BPH treatment were provided).

Interestingly, Murtola's and Robinson's studies also assessed the association between exposure to alpha-adrenoreceptor antagonists and prostate cancer: men using alphaadrenoreceptor antagonists had either an increased [START_REF] Robinson | Use of 5α-reductase inhibitors for lower urinary tract symptoms and risk of prostate cancer in Swedish men: nationwide, population based case-control study[END_REF] or a non-significant (15) risk of lowgrade prostate cancer. This result was thought to be related to a detection bias considering that men with lower urinary tract symptoms have a higher seeking of prostate cancer. Even in PCPT trial, a detection bias was put forward to explain the observed higher proportion of high-grade cancers in 5-ARI users; indeed 5-ARI decreased prostate volume (plus tumor shrinkage) and increased sensitivity of PSA. The last reassuring factor is that there is no

Accepted Article

increased mortality rate among long-term 5-ARI users [START_REF] Unger | Using Medicare Claims to Examine Long-term Prostate Cancer Risk of Finasteride in the Prostate Cancer Prevention Trial[END_REF]. Our study design did not allow to assess overall survival. Whatever, having been diagnosed with high grade prostate cancer is associated with stress and the need for treatment although the drug was initially given for a benign disease.

Our study has some strengths. First, we used a population-based nested matched casecontrol design which minimizes selection bias [START_REF] Sedgwick | Nested case-control studies: advantages and disadvantages[END_REF]. Selection of eligible subjects for cohort entry was made through drug claims which were collected timely and prospectively; hence recall bias does not apply and misclassification on exposure is minimized and at least nondifferential. Second, comparing treated patients, we selected subjects seeking a medical attention for symptoms justifying a drug prescription. We thought minimizing confounding by indication, compared to previous studies which used non-treated patients [START_REF] Thompson | The influence of finasteride on the development of prostate cancer[END_REF][START_REF] Andriole | Effect of dutasteride on the risk of prostate cancer[END_REF][START_REF] Murtola | Prostate cancer incidence among finasteride and alpha-blocker users in the Finnish Prostate Cancer Screening Trial[END_REF][START_REF] Robinson | Use of 5α-reductase inhibitors for lower urinary tract symptoms and risk of prostate cancer in Swedish men: nationwide, population based case-control study[END_REF][START_REF] Wallerstedt | Risk of Prostate Cancer in Men Treated With 5α-Reductase Inhibitors-A Large Population-Based Prospective Study[END_REF] as reference. Third, we thought detection bias was minimized; as PSA measurement and prostate sampling are potentially in the causal pathway leading to prostate cancer; even though no PSA results were available, the recurrence of PSA dosage and the use of prostate sampling were proxies of prostate cancer exploration; thus, we conducted a sensitivity analysis without adjustment on those parameters, showing similar results to the main analysis with full-adjustment. Fourth, we set up a two-year restriction period during which subjects had to remain free of prostate cancer diagnosis to be eligible for inclusion, reassuring us that cases were truly incident prostate cancer cases. Fifth, we had no attrition bias. Some weaknesses have to be also discussed: representativeness is more debatable than would have been a nationwide study but linkage to pathology labs imposed a restricted area. We had no information on prostate volume and body mass index but a previous study showed that such an adjustment did not materially affect the results [START_REF] Murtola | Prostate cancer incidence among finasteride and alpha-blocker users in the Finnish Prostate Cancer Screening Trial[END_REF]. Other studies suggested that prostate volume was not related to prostate cancer risk [START_REF] Ankerst | The impact of prostate volume, number of biopsy cores and American Urological Association symptom score on the sensitivity of cancer detection using the Prostate Cancer Prevention Trial risk calculator[END_REF] and could not be

Accepted Article

predictive of histological grade [START_REF] Kulkarni | Evidence for a biopsy derived grade artifact among larger prostate glands[END_REF]. We did not have information on dietary patterns but when comparing drug users between each other such healthy-user effect is thought to be less problematic. We had no valid information on family cancer status or highest level of education attained, but such an adjustment did not seem to change the results in a previous study [START_REF] Robinson | Use of 5α-reductase inhibitors for lower urinary tract symptoms and risk of prostate cancer in Swedish men: nationwide, population based case-control study[END_REF]. Lastly, there is a potential for exposure misclassification as regards short or midterm users (< 2 years) because we had no claims data before 2010; on the other hand, we are confident in long-term users (≥ 2 years) classification whenever observed.

The so-far rather re-assuring message as regards high-grade prostate cancer should be switched to more cautious information. Notwithstanding clear clinical benefits of 5-ARI, we should consider any substantial increased risk of high-grade cancer (worse prognosis) when choosing between therapies for symptomatic or complicated BPH. Patients treated for longer than 2 years with 5-alpha reductase inhibitors should be informed of increased risk for the development of high-grade disease. (c) Patients initiating treatment for symptomatic or complicated benign prostate hypertrophy within a year before index date were not eligible for the analysis. Matching on age and delay between the first observed delivery of drug for BPH and index date through an incidence density sampling design with further adjustment on diabetes, lowering-lipid drug claims, obesity, COPD, annual number of prostate sample collection(s) (i.e., biopsy or transurethral resection) before the sample which allowed the diagnosis of cancer for cases) and annual number of PSA measurement.

Accepted Article

p-value for heterogeneity across cancer grades (high-grade and low-grade) when estimating association between prostate cancer and 5-ARI long term use (≥ 2 years) versus no 5-ARI exposure (reference) = 0.0048.

Figure 1 .

 1 Figure 1. Flow chart.

Table 1

 1 Characteristics of cases (prostate cancer) and matched controls

				Overall		Gleason score of cancer
						< 8		≥ 8
				Cases	Controls	Cases	Controls	Cases	Controls
				N = 767	N = 3 835	N = 662	N = 3310	N = 105	N = 525
	Age (years), mean (SD)		69.3 (8.6)	69.3 (8.6)	68.2 (8.1)	68.2 (8.1)	76.0 (8.5)	76.0 (8.5)
	PSA measurement	a , n (%)
	None			27 (3.5)	676 (17.6)	16 (2.4)	545 (16.5)	11 (10.5)	131 (25.0)
	1-2			236 (30.8) 1764 (46.0) 191 (28.9) 1560 (47.1) 45 (42.9)	204 (39.9)
	≥ 3			552 (72.0) 1395 (36.4) 455 (86.4) 1205 (62.5) 49 (46.7)	190 (36.2)
	All prostate samples a , n (%)	767 (100)	407 (10.6)	662 (100)	357 (10.8)	105 (100)	50 (9.5)
	Previous prostate biopsy	b , n
	(%)
	None			139 (18.1) 3693 (96.3) 124 (18.8) 3183 (96.2) 15 (14.3)	510 (97.1)
	1 biopsy			562 (73.3) 128 (3.3)	478 (72.2) 114 (3.4)	84 (80.0)	14 (2.7)
	2 biopsies			55 (7.2)	14 (0.4)	51 (7.7)	13 (0.4)	4 (3.8)	1 (0.2)
	≥ 3 biopsies			11 (1.4)	0	9 (1.4)	0	2 (1.9)	0
	Transurethral resection, n (%)	195 (25.4) 256 (6.7)	163 (24.6) 219 (6.6)	32 (30.5)	37 (7.0)
	COPD, n (%)			26 (3.4)	169 (4.4)	22 (3.3)	140 (4.2)	4 (3.8)	29 (5.5)
	Diabetes mellitus, n (%)		91 (11.9)	532 (13.9)	69 (10.4)	454 (13.7)	22 (21.0)	78 (14.9)
	Lipid-lowering drug claims, n	382 (49.8) 1994 (52.0) 324 (48.9) 1704 (51.5) 58 (55.2)	290 (55.2)
	(%)
	Obesity, n (%)			38 (4.9)	132 (3.4)	31 (4.7)	118 (3.6)	7 (6.7)	14 (2.7)
	SD denotes standard deviation; data are number (%) of individuals unless stated otherwise.
	COPD denotes chronic obstructive pulmonary disease.
	(a) before index date and the date of sample which allowed the diagnosis of cancer for
	cases.
	(b) number of prostate sample collection(s), i.e. biopsy or transurethral resection, before
	index date and the date of the sample which allowed the diagnosis of cancer for cases.

Table 2

 2 Subjects' characteristics according to drug exposure

		5-ARI users	5-ARI non-users
		N = 963	N = 3639
	Age (years), mean (SD)	71.7 (8.5)	68.6 (8.5)
	PSA measurement a
	None	141 (14.6)	562 (15.4)
	1-2	396 (41.1)	1604 (44.1)
	≥ 3	426 (44.3)	1473 (40.5)
	Previous prostate samples b
	None	827 (85.9)	3211 (88.2)
	1 sample	127 (13.2)	380 (10.4)
	2 samples	9 (0.9)	45 (1.2)
	≥ 3 samples	0	3 (0.1)
	COPD	41 (4.3)	154 (4.2)
	Diabetes mellitus	125 (13.0)	498 (13.7)
	Lipid-lowering drug claims	530 (55.0)	1846 (50.7)
	Obesity	35 (3.6)	135 (3.7)
	(a) before index date and the date of the sample which allowed the diagnosis of cancer for
	cases.

(b) number of prostate sample collection(s), i.e. biopsy or transurethral resection, before index date and the date of the sample which allowed the diagnosis of cancer for cases.

This work was supported by the Fondation pour la Recherche Médicale (FRM), grant number IMD-2013-1228948, to Emmanuel OGER and Nathalie RIOUX-LECLERCQ. FRM had no role in the study design, collection, analysis, and interpretation of data; nor in the writing of the report; nor in the decision to submit the article for publication.

Data sharing:

The statistical code is available from the corresponding author. Under French law and regulatory approval, patient level data cannot be made available.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. Transparency: EO (manuscript's guarantor) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and registered) have been explained.

Details of contributors

All authors had full access to all of the data (including statistical reports and tables) and take responsibility for the integrity of the data and the accuracy of the data analysis.

Accepted Article

Legends


Hayfa Ayadi
email: ayadihayfa.ing@gmail.com

Ahmed Zouinkhi
email: ahmed.zouinkhi@enig.rnu.tn

Thierry Val

Boumedyen Boussaid
email: boumedyen.boussaid@gmail.com

M Naceur

Multi operating mode for energy adaptability in Wireless Sensor Network

Keywords: The IEEE 802.15.4 Standard, BO, SO, Energy, WSN

Wireless communication presents the most attractive sign of development in this decades. It contributes to the appearance of Wireless Sensor Network (WSN) which is invented in order to increase the supervision capacity for such phenomenon. It gets more regard from both the customers and the research society. (WSNs) is composed from numerous wireless sensor nodes which suffer from a very serious energy constraint. For this raison we are interested to the standard of IEEE 802.15.4 with beacon enabled mode. This work suggests a multi mode approach to the technology of IEEE 802.15.4 as a new technique to extend the lifetime of the network. The paper interests to network largely extended with cluster tree topology. In such a way that the network becomes divided to many subgroups. In each subgroup, a PAN coordinator is placed as a coordinator to its children. Besides, every subgroup has its specific parameters of Beacon Order (BO) and Superframe Order (SO) which depend only from the remaining energy in the battery of the nodes. When a node suffers from lack of energy, its PAN coordinator parent intervene by changing its main parameters (BO, SO) in order to postpone its death.

I. INTRODUCTION

The Wireless Sensor Network was integrated in many domains blessing to its different advantages such as the environments supervision [START_REF] Othman | Wireless sensor network applications: A study in environment monitoring system[END_REF], military domain, health care [START_REF] Lambebo | A wireless sensor network for environmental monitoring of greenhouse gases[END_REF] as well as civil engineering [START_REF] Ananya Chatterjee | Practical applications of wireless sensor network based on military, environmental, health and home applications: A survey[END_REF]. Although, WSN suffer from divers trouble such as the energy efficient and its sensibility to faults. That is why many approach was developed in order to solve that [START_REF] Mekki | Usee: A uniform data dissemination and energy efficient protocol for communicating materials[END_REF], [START_REF] Fourty | An advanced study of energy consumption in an ieee 802.15. 4 based network: Everything but the truth on 802.15. 4 node lifetime[END_REF] and [START_REF] Ayadi | A machine learning methods: Outlier detection in wsn[END_REF]. The IEEE 802.15.4 is an Low Rate Wireless Personal Area Network (LR-WPAN) standard. It is destined for network with low cast energy consumption. In addition to that the LR-WPAN have so closed characteristics such as short-range operation, easy in installation and theirs simple and flexible protocol [START_REF] Society | [END_REF].

The IEEE 802.15.4 presents one of the standard developed in order to solve the problem of the energy. This technology is a member of the WPAN family. There are two kinds of devices in this technology. The node could be a Full Function Device (FFD) device or a Reduced Function Device (RFD). A network of a LR-WPAN must be composed by at least one FFD and one RFD. The FFD node could play both roles of the PAN coordinator or a device. Although a device could be only an RFD. Many topologies could be formed by this network such as peer to peer, mesh and star topology. In star topology, the communication is established between a single PAN and a at least one node (device). Also, in peer-to-peer topology, a PAN coordinator (FFD) must be present. This topology contributes also in the mesh topology which is the most complex network. It is employed in many applications such as the ad hoc network. The architecture of the IEEE 802.15.4 is composed by four layers. Each one presents a part of the standard. The Physical layer is the responsible for the data service and the PHY management. The PHY data service enables the transmission and reception of PHY protocol data units (PPDUs) across the physical radio channel. The main role of this layer are the control of the activity of the radio transceiver, Clear Channel Assessment (CCA), Channel Selection, Link Quality Indication (LQI), Energy Detection (ED) [START_REF] Society | [END_REF].

Also the MAC sublayer has two services which are the MAC data service and the MAC management service. But the most important role are always the beacon control, GTS authority, channel access, frame validation. In addition to that the MAC block contributes in the application witch are suitable to the security mechanism. The upper layer is responsible for the configuration and the manipulation of the network as well as the message routing. It is connected to the MAC layer through the logical link control LLC and the Service-Specific Convergence Sublayer (SSCS). There are two modes allowed by this technology: beacon enabled mode and non beacon enabled mode. Each one has its advantages and main specific. For the first one, a beacon frame is send periodically to all members of network. it allows the start of the active period:Superframe Duration (SD). The Superframe Duration (SD) and the Beacon Interval (BI) are expressed with both relation [START_REF] Othman | Wireless sensor network applications: A study in environment monitoring system[END_REF] and relation [START_REF] Lambebo | A wireless sensor network for environmental monitoring of greenhouse gases[END_REF], have a direct intervention in activity period of the node that is why the idea of increasing the lifetime get through both BI and SD [START_REF] Society | [END_REF].

BI = aBaseSuper f rameDuration * 2 BO ;
(1)

1 ≤ BO ≤ 14 SD = ABaseSuper f rameDuration * 2 SO ; (2) 0 ≤ SO ≤ BO ≤ 14
The superframe of this standard is defined by the PAN coordinator which sends periodically the beacons frames in order to synchronise the network node. The period of time separated two successive beacons frames is named Beacon Interval (BI). The (BI) is divided to 16 slots which are equally sized slots. The Superframe is divided also in active and inactive period. In the first part, the node tries to communicate to other members of network by sending or receiving data. Although in the second part, all activities of this node will be stopped [START_REF] Society | [END_REF]. In the active period the node has the choice between two protocols of communication which are the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) used in the Contention Access Period (CAP) and the Time Division Multiple Access TDMA employed in the Contention Free Period (CFP), showed in figure 1. In the CAP, the slotted version of the CSMA/CA in the technology of the IEEE 802.15.4,is employed with its both version slotted and unslotted form. The slotted version presented by figure 2 is manipulated in the beacon enabled mode although the unslotted one is used in the non beacon enabled mode. The first step is to initialize the different parameters of the protocol such as the Congestion Window (CW), the Backoff Exponent (BE) and NB which is the number of times that the algorithm required to backoff. Then the node waits two Clear Channel Assessment CCA to test the channel state. If the node is idle in the first CCA so CW will lost 1 (CW-1) and continue with the second CCA. In the case that the channel is again idle so CW will be set to 0 and the data will be transmitted. In the case that the channel is busy the value of NB and BE will be decreased to 0 and CW set to 2. After that the different parameters will be updated and the node will try to access to the medium if it has another data for transmission.

II. RELATED WORK

As the Superframe Duration (SD) and the Beacon Interval (BI) are both the principal contributors in the activity duration of the node. There are considered the main manager of the energy consumption of the network across to the node. There are three possibilities to contribute in this technology via the Beacon Order (BO) and the Superframe Order (SO). The first choice is to modify just the BO and fixing the SO. The second one presents exactly the opposite by changing the SO and fixing the BO or simply to adjust both value (BO and SO). Many works were concentrated in the first approach so in [START_REF] Neugebauer | A new beacon order adaptation algorithm for ieee 802.15. 4 networks[END_REF], the authors try to improve the duty cycle of the node by just modifying the BO value with regard to the network traffic load. So in the case that the data traffic is developed then BO value decrease despite if sleeping period decrease BO will be increased. An other approach in [START_REF] Neugebauer | A new beacon order adaptation algorithm for ieee 802.15. 4 networks[END_REF] was proposed with the same standard named Beacon Order Adaptive Algorithm (BOAA). Despite it is not suitable for the large network. It is just employed in start topology of the network. Also in [START_REF] Caldeira | Intra-mobility support solutions for healthcare wireless sensor networks-handover issues[END_REF] the authors propose an other method named Duty Cycle Algorithm (DCA). This modification is based on the end-to-end delay and the transmit queue occupation. In order to get the value of the queue occupation they get the average of all the queue embedded. In [10], also, the writers choose a fixed value of SO and modify the value of BO without specifying the manner of this choice of the SO value against its importance on specifying the activity of the node. For the second choice, fixing the SO and modifying the BO, many other research groups were interested in this approach. In [START_REF] Lee | Study on a dynamic superframe adjustment algorithm for ieee 802.15. 4 lr-wpan[END_REF], the writers invent a new techniques called the Dynamic Super-frame Adjustment Algorithm (DSAA). So the PAN coordinator start by estimating the super-frame occupation and collision rate. The super-frame occupation define the delay of time in which the coordinator is busy in different communication phases. Although the collision rate is expressed with reference on the received data at the coordinator. Then the coordinator make a small comparison between theses values with other considered as a thresholds. After that, it selects the Super-frame Order.

The way to get the SO value depend from many others parameters such as the queue state of nodes, the throughput during CAP as well as the idle time in the technique of Adaptive Duty Cycle Algorithm (ADCA) [START_REF] Rasouli | Adca: adaptive duty cycle algorithm for energy efficient ieee 802.15. 4 beacon-enabled wireless sensor networks[END_REF] The decision of the change is made by the coordinator with reference to the length of the idle time. In the case that its length is more then the half of the CAP, SO will be decreased. On the other hand, the coordinator continue to examine the number of received packets and the number of pending packets during the CAP. In the case that received packets are less then received packets then SO will be decreased.

The most difficult techniques interested in the technology IEEE 802.15.4 parameters is to make the adjustment of both Beacon Order and the Superframe Order. Divers methods are proposed to adapt the duty cycle to the network condition such as the approach of the Duty cycle Self-Adaptation Algorithm (DBSAA) in [START_REF] Oliveira | A duty cycle self-adaptation algorithm for the 802.15. 4 wireless sensor networks[END_REF]. The DBSAA adjustment depend from four parameters which are super-frame occupation ratio (OR), the number of source nodes, the collision ratio (CR), as well as packets received by the coordinator.

III. ENERGY EFFICIENT

Energy stays again the main trouble in divers domains specially in wireless network. The solutions are in exploring new way to produce energy or to make very efficient solution to manage the last quantity of energy lasted in the node. This work is interested in the second solution. Its main hypothesis is that the network is really very extended network in which we propose cluster tree topology. The first step is calculating the energy consumed in different nodes of the network and in different states: emission, reception, idle, overhearing and overmitting, as mentioned in the figure 3 The approach is based on the beacon enabled mode of the T p back = (2 cst back -1) * 20symbol

The (cst back ) presents the backoff period. Also in reception phase the node consumes some quantity of energy called (Erc) which is giving by formula [START_REF] Fourty | An advanced study of energy consumption in an ieee 802.15. 4 based network: Everything but the truth on 802.15. 4 node lifetime[END_REF].

E rc = nbr SD * Eb (5)
With (nbr SD ) is the number of bit received in SD period. The energy consumed in overhearing and overmitting phase, is mentioned by equation ( 6).

E o = nbr tr * dtra * Eb * PER (6)
(Eb) is the binary energy. (dtra) shows the size of frame (bit). nbr tr is the number of bits transmitted by node. Although PER presents the error rate which is obtained as the average of packet transmitted without being well received.

Concerning energy of collision (E col ), it is get by expression [START_REF] Society | [END_REF].

E col = Tatt * U * I * Nbpk (7)
(Tatt) is among of time to access to the transmission canal. (Nbpk) is the number of try to transmit information without receiving any acknowledge from destined node.

In order to conserve more quantity of energy, the technology of the IEEE 802.15.4 gives the chance to the node to enter in sleep duration in order to manage the little portion of its power. The next expression (8) presents the energy in sleep period (E SLP ).

E SLP = Eb * (BI -SD) (8)
The energy consumed in idle state is presented by equation ( 9).

E i dle = T S IFS * U * I (9)
T S IFS presents the duration of SIFS, U is the voltage value and I defines the current value. The objective of this work is to manage the power lasted in the battery in one side and to postpone the energy fault of a specific node of the network in which the quantity of power reaches very important threshold defined by user by a way to maximise the lifetime. In other side, all the other nodes of network must not be affected by this very specific faults. So the PAN coordinator tries to intervene to its children node without affecting the activity of other members. So this approach begins by estimating the energy remaining (E R ) in the battery of node. The next step is to send this value to its PAN parent. The energy remaining (E R )is presented in the attached formula (10).

E R = Eini -Ec (10)
Ec is the sum of all types of energy consumed. It is calculated by following equation (10).

Ec = E em + E col + E o + E SLP + E rc + E idle (10)
IV. IEEE 802.15.4 PARAMETERS ADJUSTMENT

In the case of extended network every PAN collects the energy information (energy consumed EC) from all the network nodes. When it detects serious state of one node of this network that is mean that the energy remaining in the battery of this node is less then very specific threshold ET . In this case the PAN parent must intervene by changing its standard parametric (BO and SO) values which are expressed with reference to the energy values. The energy consumed in the Superframe duration E BI is showmen by the next expression [START_REF] Lee | Study on a dynamic superframe adjustment algorithm for ieee 802.15. 4 lr-wpan[END_REF].

E BI = BI T tt * E T (11)
With (BI) presents the size of Beacon Interval period,(T tt ) defines the frame length, (E T ) introduces the Energy of frame. (E R ) is the Energy Remaining. The main hypothesis in this approach is that energy consumed is again less then the energy remaining in the battery giving by equation ( 12).

E BI ≤ E R (12) (12)
The relation ( 13) is expressed used both relations ( 12) and [START_REF] Lee | Study on a dynamic superframe adjustment algorithm for ieee 802.15. 4 lr-wpan[END_REF].

BI T tt * E T ≤ E R (13)
As defined in the standard the Beacon Interval (BI) is calculated by relation [START_REF] Stanica | Simulation of vehicular ad-hoc networks: Challenges, review of tools and recommendations[END_REF].

BI = 15, 36 * 10 (-3) * 2 BO (14)
That is why equation ( 14) changes to this way (15).

(15, 36 * 10 (-3) ) * 2 BO T tt * E T ≤ E R (15)
The 2 BO is extracted by relation (16).

2 BO ≤ E R * T tt 15, 36 * 10 (-3) * E T (16)
So BO value is expressed by equation (17).

2 * log(2) ≤ E R * T tt 15, 36 * 10 (-3) * E T (17)
In the case of the majoration aspect, the BO becomes by the way mentioned in the formula (18).

BO = log( E R * T tt 15,36 * 10 (-3) * E T ) log(2) (18)
For raison of management of the last quantity of energy, just 10% of the energy remaining in the node E R1 is manipulated in order to well exploit it. It is presented by relation ( 19).

E R1 = E R * 0.1 (19)
Consequently SO becomes as mentioned in equation ( 20):

BO = log( 0.1 * E R * T tt 15,36 * 10 (-3) * E T ) log(2) (20)
For the case of the Superframe Duration SD, just 70% from the BI period is exploited. So SO is expressed by relation (21).

SO = 0.7 * log( 0.1 * E R * T tt 15,36 * 10 (-3) * E T ) log(2) (21)

V. IMPLEMENTATION AND SIMULATION RESULTS

The implemented of this approach was through the OM-NET++/INETMANET simulator for its large simulations functionalities. OMNET++/INETMANET is an open-source discrete event network simulator. So OMNET++ can be used for network simulations or just to model computer networks. OMNeT++ only arranges only the fundamental framework for developing a certain simulation module, but these models are developed separately of OMNeT++ [START_REF] Stanica | Simulation of vehicular ad-hoc networks: Challenges, review of tools and recommendations[END_REF].It has many advantages such as it supports hierarchical models, graphical analysis tools as well as graphical editor [15] Five PAN coordinators was manipulated. Each one is the main responsible over its subgroup which is composed by some children as mentioned in figure 4. The PAN coordinator is an FFD node. So it has not an energetic problem as it is known in the technology of the IEEE 802.15.4 with cluster tree topology. So PAN 1 is the responsible of node [START_REF] Caldeira | Intra-mobility support solutions for healthcare wireless sensor networks-handover issues[END_REF]10,[START_REF] Lee | Study on a dynamic superframe adjustment algorithm for ieee 802.15. 4 lr-wpan[END_REF]27,28) and formed together the subgroup 1. The PAN 2 is the main parent of the nodes (0, 1, 2, 21, 22) and compose the subgroup 2. While the subgroup 3 is designed by the PAN 3 with its three children [START_REF] Oliveira | A duty cycle self-adaptation algorithm for the 802.15. 4 wireless sensor networks[END_REF][START_REF] Stanica | Simulation of vehicular ad-hoc networks: Challenges, review of tools and recommendations[END_REF]15). Although the subgroup 4 is modeled by PAN 4 with nodes [START_REF] Fourty | An advanced study of energy consumption in an ieee 802.15. 4 based network: Everything but the truth on 802.15. 4 node lifetime[END_REF][START_REF] Ayadi | A machine learning methods: Outlier detection in wsn[END_REF][START_REF] Society | [END_REF]23,24). PAN 5 is the responsible of node (17,18,19,25,26) and formed together the subgroup 5. So the PAN sends beacons frames periodically to its children in order to synchronise its activity. Each Subgroup has its specific parameters of the Beacon Interval and the Superframe Duration. By this way, five modes in the same network were presented. The advantage of this work is to give the chance to manage every subgroup with the real energy level of its node. In this case the node number 13 in the subgroup 3 has a problem with energy. The energy consumed by this node exceeds the threshold energy which is 9360 J as mentioned in figure 5. The threshold energy presents 50% of the start value (18720J). Also this problem appears at the level of total energy consumed by this subgroup compared with other subgroups, showed by figure 6. After detecting the energy trouble the PAN coordinator intervenes in order to postpone the death of the node. Its main contribution is about changing the duty cycle of the node by modifying both the (BON) and the (SON) value based on the current level of energy which leads to decrease the duty cycle from 50 percent to 6,25 percent.

Fig. 1 .

 1 Fig. 1. Superframe composition

Fig. 2 .

 2 Fig. 2. CSMA/CA slotted algorithm

Fig. 3 .

 3 Fig. 3. Organigram of our approach

E

  em = (nbt sd * T tt * Eb) + 2 * U * I * CCA * T p back (3) (T tt) is the size of frame. (Eb) presents the binary energy. Although (nbt sd ) express the number of data frame present in SD, U is the voltage value, CCA is the Clear Channel Assessment as well as I express current value. Where (T p back ) is expressed by equation (4).

Fig. 4 .

 4 Fig. 4. multimode network

Fig. 5 .

 5 Fig. 5. CONSUMED ENERGY IN SUBGROUP3

Fig. 6 .

 6 Fig. 6. CONSUMED ENERGY IN THE SUBGROUPS

Fig. 7 .

 7 Fig. 7. Overhearing-Overmitting Energy

Fig. 8 .

 8 Fig. 8. IDLE Energy

  .It offers rich environment for simulation at different layers. INETMANET framework is an open-source model library. OMNet++ present an extensible and modular simulator. It is defined using C++ simulation library. It is an oriented object modular with discrete event network simulation. It is used for wired and wireless networks. Our simulation parameters are mentioned in table I.

Thierry VAL. Researcher is with Institut de Recherche en Informatique de Toulouse, Universite de Toulouse, 1 pl. Georges Brassens BP60073 F-31703 Blagnac Cedex France thierry.val@irit.fr

ACKNOWLEDGMENT

This work was supported by the Ministry of the Higher Education and Scientific Research in Tunisia.