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p.negrel@brgm.fr, c.guerrot@brgm.fr, r.millot@brgm.fr, e.petelet@brgm.fr, tdbullen@usgs.gov

We present calciumstable isotope and strontium radiogenic isotope datafor soils and sediments developed on volcanic and igneousrocks forming small catchments in the Massif Central (France).Measurements of 44/40 Ca isotope ratios ( 44/40 Ca measured by the double spike method on TIMS and normalized to the value for seawaterCa in delta units) in rocks, sediments and soils from silicate catchments (e.g. granite and basalts) together with 87 Sr/ 86 Sr isotope ratios permit an examination ofthe relationships of these isotope systematics during weathering of silicate rocks. We have analysed the granite, weathered granite (arene) and saprolite, sediment and soil overlying the granite on one hand and the basanite, sediment and soiloverlying the basaniteon the other.

The main bedrock in the volcanic zone (e.g. Allanchecatchment) is 11 to 2.5 Mabasanite (nephelinitictoleucitic basalts) havingSiO 2 between41-45 wt. %, Na 2 O + K 2 O <5%, modal or normative nepheline or leucite and a ground mass ofclinopyroxene and plagioclase.Surrounding rocks are feldspathic basaltshavingSiO 2 between 46-49 wt. %, Na 2 O + K 2 O <5%, normative nepheline, hyperstene and olivine, with plagioclase as the main crystalline phase.The granite massif(e.g. Margeride, 332 ± 12Ma) consists of light and dark facies as a result of the fractional crystallisation of a crustal magma in a sub-horizontal laccolith, with leucogranites dated at 298±2 Ma intruding this granite. The average mineral composition is 37% quartz, 30% oligoclase, 23% K-feldspar and 10% biotite (light facies) and 31% quartz, 30% andesine, 20% K-feldspar and 19% biotite (dark facies).

Sr isotope ratios in the arene, sediment and soil diverge strongly fromthose in the granite bedrockand are positively correlated with Rb/Sr ratios. The 87 Sr/ 86 Sr and Rb/Sr ratios both increase fromthe whole rock tothe arene, reflecting the weathering of low 87 Sr/ 86 Sr, low-Rb/Sr minerals such as plagioclase and apatite. Sediments collected on ariver bank have 87 Sr/ 86 Sr ratiosgreater than that of the arenewith values increasing in the sediment from the surface down to soil.The 87 Sr/ 86 Sr vs. Rb/Srvariation observedin the volcanic area likewise confirms the weathering of low 87 Sr/ 86 Sr, low Rb/Srphases in the bedrock, and there isalinear increase in 87 Sr/ 86 Sr and Rb/Sr ratios from thosein the sediment up to the values observed in the soils.

In the volcanic area, the basanite bedrock has 44 Ca = -0.94 ± 0.05‰(n = 7), while the soils and sediments have 44/40 Ca of-0.75 to -1.13‰ and -0.79 to -1.01‰,respectively. These results suggest that Ca isotopes are not strongly fractionated during weathering of the basalt.The granite whole-rock has 44/40 Ca of -1.29‰, while thesoil and sediments have 44/40 Ca of -1.93to -2.07‰and -1.98 to -2.81‰,respectively, with values decreasing as the Ca content decreases.The 44/40 Ca ratios of arene, soil and sediment are similar to or less than that of K-feldspar, reflecting complete loss of the relatively heavy Ca from plagioclase and apatite during weathering.Comparison of the 44/40 Ca and 87 Sr/ 86 Sr ratios further revealed the roleof mineralogical assemblage in sediments and soils, particularly for the lesser 44/40 Ca -greater 87 Sr/ 86 Sr samples, when compared to the bedrock.

  44/40Ca and 87/86Sr isotopes as tracers of silicate weathering in small catchments of the Massif Central, France Philippe Négrel, Catherine Guerrot, Romain Millot, Emmanuelle Petelet-Giraud, Bullen Thomas To cite this version: Philippe Négrel, Catherine Guerrot, Romain Millot, Emmanuelle Petelet-Giraud, Bullen Thomas. 44/40Ca and 87/86Sr isotopes as tracers of silicate weathering in small catchments of the Massif Central, France. EGU General Assembly 2013, Apr 2013, Vienne, Austria. pp.EGU2013-10951-1, 2013. hal-00773535

44/40 Ca and 87/86 Sr isotopes as tracers of silicate weathering in small catchments of the Massif Central, France

	Ph. NEGREL 1 , C. GUERROT 1 , R. MILLOT 1 , E. PETELET-GIRAUD 1 , T.D. BULLEN 2
	1 BRGM, Laboratory Division, Orléans, France
	2 U.S. Geological Survey, Water Resources Division, Menlo Park, CA 94025, United States


Jad Rouhana

Andre Padilla

Sebastien Estaran

Sana Bakari

Stephane Delbecq

Yvan Boublik

Joel Chopineau

Martine Pugniere

Alain Chavanieu
email: alain.chavanieu@univ-montp1.fr

André Padilla

Sébastien Estaran

Martine Pugnière

Kinetics of Interaction between ADP-ribosylation Factor-1 (Arf1) and the Sec7 Domain of Arno Guanine Nucleotide Exchange Factor, Modulation by Allosteric Factors, and the Uncompetitive Inhibitor Brefeldin A

Keywords:

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

Summary

The GDP/GTP nucleotide exchange of Arf1 is catalyzed by Nucleotide Exchange Factors (GEF), such as Arno, which act through their catalytic Sec7 domain. This exchange is a complex mechanism that undergoes conformational changes and intermediate complex species involving several allosteric partners such as nucleotides, Mg 2+ and Sec7 domains. Using a Surface Plasmon Resonance approach, we characterized the kinetic binding parameters for various intermediate complexes. We first confirmed that both GDP and GTP counteract equivalently to the free-nucleotide binary Arf1/Arno complex stability and revealed that Mg 2+ potentiates by a factor of 2 the allosteric effect of GDP. Then, we explored the uncompetitive inhibitory mechanism of Brefeldin A (BFA) that conducts to an abortive pentameric Arf1-Mg 2+ -GDP-BFA-Sec7 complex. With BFA, the association rate of the abortive complex is drastically reduced by a factor of 42 and by contrast, the 15-fold decrease of the dissociation rate concurs to stabilize the pentameric complex.These specific kinetic signatureshave allowed distinguishing the level, nature as well as the fate in real time of formed complexes according to experimental conditions. Thus, we showed that in the presence of GDP, BFA-resistant Sec7 domain of Arno can also associate to form a pentameric complex which suggests that theuncompetitive inhibition by BFA and the nucleotide allosteric effect combine to stabilize such abortive complex.

Low molecular weight guaninenucleotide-binding (G) proteins of the Ras superfamily, also known as small GTPases for their GTP hydrolysis activity, are singlesubunit proteins activated by diverse extracellular stimuli and implicated in a number of crucial cellular processes including cell proliferation, differentiation, apoptosis, cytoskeletal organization, cell polarity and vesicular trafficking (1)(2)(3)(4). G proteins act as molecular switches and cycle between a GDPbound inactive and a GTP-bound active conformation that regulate their ability to interact with downstream effectors and regulatory proteins. The activation is a multistep process that requires the release of the bound GDP and the subsequent binding of GTP. G proteins have a slow nucleotide intrinsic dissociation rate which is accelerated upon binding of Guanine Nucleotide Exchange Factors (GEFs) (5,6). The GDP to GTP exchange process involves a transient G protein-GDP-GEF complex, promptly followed by the dissociation of GDP, forming a high-affinity free-GDP G protein-GEF complex. Then, GTP binds with subnanomolar affinity to the nucleotide binding site on the G protein, disrupting the complex between the G protein and the GEF.

Indeed, an allosteric competitive mechanism model explains the kinetics of nucleotide exchange catalyzed by GEFs (7)(8)(9) where the G proteins form stable complexes with nucleotides or with GEFs whereas they can also form transient less stable complexes with simultaneously both GEF and nucleotide. Moreover, crystallographic and biological studies revealed that nucleotide-binding on G protein presents a highly conserved coordination of Mg 2+ within the site, and disrupting the Mg 2+ coordination (for instance by a GEF) promotes a nucleotide-free state of the G proteins (10). The cation strengthens the nucleotide binding and reduces its dissociation rate, thus weakening binding of the GEF to the G protein [START_REF] Hamida-Rebai | PloS one5[END_REF](12)(13).

The Ras superfamily is divided into five major families: Ras, Rho, Arf/Arl, Ran and Rab. G-proteins of the Arf (ADP-ribosylation factor) family are major regulators of membrane traffic and organelle structure in eukaryotic cells (14,[START_REF] Casanova | Traffic[END_REF]. In mammals, there are six Arf proteins divided into three classes based on sequence homology. They have a conserved myristoylated amino-terminal amphipathic helix which ensures membrane association and they share three functionally important regions called Switch1, Switch2 and 3 Inter-switch. These Switches undergo conformational changes upon nucleotide exchange and are implicated in interactions.Arf1 is found in all eukaryotes examined to date [START_REF] Donaldson | [END_REF] and a physiological role in cell migration and in breast cancer progression has been recently reported (17). The GDP/GTP nucleotide exchange of Arf1 is under control of GEFs, such as Arno proteins (Arno, Cytohesin and Grp1). These Arno proteins share a conserved catalytic Sec7 domain of about 200 residues followed by a pleckstrin homology (PH) domain responsible for membrane targeting (18)(19)(20). Sec7 domains form an elongated superhelix of helices with an N-terminal subdomain, carrying the catalytic glutamic residue E156, and a Cterminal subdomain that closes up when associated with Arf1.Well resolved structures of Arf1 bound to GDP and GTP (21)(22)(23), isolated Sec7 domains (24) and free-nucleotide Arf1-Sec7 domain complex are available (25). Moreover, uniquely to G proteins, transient GDP-bound Arf1-GEF intermediates have been reported, using a charge reversal Arno mutant (Glu 156 to Lys) preventing the GDP release (26) or by using the natural inhibitor Brefeldin A (BFA) (26). These structural studies illustrate the conformational rearrangements and mechanisms that conduct to the formation of a binary Arf1-Sec7 complex. BFA is a fungal toxin that acts on BFA-sensitive Sec7 domain through an unusual uncompetitive mechanism and was described as an interfacial inhibitor (27). BFA binds to the protein -protein interface of the transient Arf1-GDP-Mg 2+ -Sensitive Sec7 complex and freezes it in an abortive conformation in which the GDP is maintained out of reach from the GEF catalytic site and its critical residue Glu 156 within a hydrophilic loop. Without BFA, the Glu 156 destabilizes the GDP molecule bound to Arf1 through the displacement of the Mg 2+ ion and by repulsing the GDP -phosphate (28). Several studies suggest that the free-Mg 2+ G protein-GDP intermediate is the precursor prior to GDP ejection which results in a binary G protein-GEF complex with enhanced affinity [START_REF] Hamida-Rebai | PloS one5[END_REF]29). However, Mg 2+ departure is not a prerequisite event for the binding of Arno to Arf1 as shown in the presence of BFA, where an abortive complex is made up with the two proteins, the GDP and the cation as indicated in Scheme 1.

Thus, the interaction between Arf1 and Sec7 domains, and the stabilization of a binary Arf1-Sec7 domain complex, is allosterically regulated by natural factors or reoriented to an abortive pentameric complex in presence of BFA. However, allosteric modifications of kinetic binding parameters were still unknown Here, we useda Surface Plasmon Resonance (SPR) approach for measuring the kinetic parameters that govern Arf1-Arno interaction using mono-biotinylated version of [∆17]Arf1 and Arno Sec7 domains. We theninvestigatedthe dynamics of association/dissociation of Arf1-Sec7 domain complex and theirmodulations by naturalallosteric factorsas well asby the uncompetitive toxin BFA.This strategy allowing the identification in real time of nature and fate of complexes provides a very informative analysis at qualitative and quantitative levels that should help to solve the inhibitory mechanism of new family of inhibitors of GEF Sec7 domains.

Materials and Methods.

BODIPY® TR GTP, Guanosine 5'-triphosphate, BODIPY® TR 2'-(or-3')-O-(N-(2aminoethyl)urethane)

was purchasedfrom Invitrogen (France). BFA, GDP sodium salt and GTP lithium salt were purchased from Sigma (St. Louis, MO).

Bacterial Strains and Plasmids

Escherichia coli DH5 strain was used as a host for plasmid construct encoding BirA and E. coli BL21 (DE3) strain (Novagen, Madison, WI, USA) was used as a host for protein expressions and in vivo biotinylation of proteins bearing the AviTag peptide (MSGLNDI FEAQKIEWHE) (30,31). The plasmid pCDFDuet was used for the expression of BirA. The plasmid pET42was used for the expression of N-terminal truncated human Arf1(residues 17-181, [17]Arf1) with or without an N-terminal AviTag.The plasmid pET28-a which confers to proteins an N-terminal polyhistidine tag was used for the expression of Sec7 domains bearing the AviTag peptide. All the plasmids were from Novagen, (Madison, WI, USA).

Cloning, Protein Expression and Purification.

All the proteins except BirA were obtained from expression of synthetic genes with optimized codons for expression in E. coli (Geneart, Germany). The synthetic genes coding for the Sec7 domain of human Arno (residues 50-250, Arno) or a mutant carrying four BFA-sensitizing mutations (F190Y/A191S/S198D/P208M, Arno4M) (32) were used to produce nucleotide-exchange factors used in kinetic measurement experiments. Variants of those synthetic genes encoding the additional Avitag sequence (33) in the N-Terminal region were also purchased (Geneart) to produce biotinylated-AviTag-Arno and AviTag-Arno4M proteins used in SPR experiments. The biotinylated version of G protein or GEF Sec7 domains were engineered using an AviTag construct based on the introduction of a N-terminal peptide sequence that can be enzymatically mono-biotinylated at its internal lysine residue by E. coli biotin protein ligase (BirA). The AviTag sequence does not resemble the consensus sequence of natural occurring biotinylation sites but is derived from an in vitro screen of peptide libraries (30,34). All synthetic genes are flanked by 5 'NdeI and 3' BamHI restriction sites. They were originally cloned into pMA-T and then into pET28-a vectors. The resultant plasmids pET28-a confer to proteins a polyhistidine tag and a cleavage site for Thrombin before the Avitag sequence. The cDNA encoding for [17]Arf1, digested with NdeI and BamHI, was introduced into the plasmid pET42 using T4 DNA ligase.The pCDFDuet vector (Novagen) was engineered to allow the expression of efficiently in vivo biotinylated recombinant proteins in E. coli. Briefly, the BirA gene (accession number AF044308.1) from the E. coli genomic DNA (strain JM109, Promega) was amplified by PCR using primer BirAFor 5'-ATGAAGGATAACACCGTGCCACTG and BirARev 5'-TTATTTTTCTGCACTACGCAGGGATATTTC and Phusion proofreading DNA polymerase (Finnzymes).After 15 min incubation at 72°C in the presence of ThermoPol Taq polymerase (New Englands Biolabs) to append 3'Adenosine, the PCR fragment was cloned in pCR II (Invitrogen) and then transformed in TOP10F'. Then by PCR, the gene BirA was amplified with oligonucleotides 5'-CA^TATG-BirA -3' forward and 5'-BirA -GAA-AAA -TAA-C^TCGAG-3' reverse containing the NdeI restriction site and the "E-Kstop codon-XhoI restriction site respectively. The amplified BirA gene was introduced into the pcfDUET expression vector (Novagen) by digestion with both NdeI and XhoI and the ligation was performed using T4 DNA ligase. The sequencing of the gene BirA has indicated that nucleotides coding for two C-terminal amino-acids (E and K) as well as for a stop codon were missing.

[17]Arf1 protein, in its GDP form, as well as Arno and Arno4M proteins were expressed and purified as previously described (32,35) except that for Sec7 domains used in nucleotide exchange tests, the polyhistidine tag was cleaved using thrombin. Biotinylated [17]Arf1 protein was expressed using a competent strain of E. coliBL21(DE3) transformed with a BirA pCDFDuetexpression vector and purified as previously described (35). All polyhistidine tag biotinylated Sec7 constructs were expressed and purified as followed. Briefly, E. coli BL21(DE3) strain transformed with pET28-a plasmids were grown at 37 °C in Luria-Bertani (LB) broth supplemented with 50µg/ml Kanamycin and 50µg/ml of streptomycin until OD 600nm reached approximately 0.5. Protein expression was induced with 0.4 mM isopropyl b-D-thiogalactoside (IPTG) at 18 °C for 20 h in the presence of biotine (40µM) in the media. Cells were harvested by centrifugation and resuspended in 4 ml of phosphate-buffered saline supplemented with 1% Triton X-100and a cocktail of protease inhibitors (Roche). Cells were lysed by sonication and debris was removed by centrifugation at 35,000 rpm for 30 min. The resulting Sec7 protein solutions were filtered through a 0.22-µm filter and loaded onto a 5-ml HiTrap chelating HP column (GE Healthcare, Piscataway, NJ, USA) equilibrated with lysis buffer (Tris 50 mM, NaCl 100 mM, 2mM ME, MgCl 2 1mM, pH 8). The resin was washed with 10 column volumes of lysis buffer after loading and eluted with a linear gradient of imidazole from 20 to 300 mM in the same buffer. Peak fractions were analyzed by 10% sodium dodecyl sulfatepolyacrylamide gel electrophoresis (SDS-PAGE). Positive fractions were pooled, desaltedon a HiPrep TM ∆17]Arf1 ranging from 0.5 to 5 μM with several concentrations of BFA (0, 5, 10,50 and 100 μM). Fluorescence data were fitted by using the program Origin 7.03 (Microcal, Northampton, MA). All values are means +/-one standard deviation of at least three independent experiments.Initial velocity of the nucleotide exchange reaction in function of BFA concentration was determined and the results were represented using the Michaelis-Menten plot and analyzed by the Lineweaver-Burk of saturation curves. Data were fitted with Michaelis-Menten equation using GraphPad software.

To study the effect of His-AviTags on Arno/4M to equilibrium binding constant Arf1 binding, anisotropy binding profiles were obtained from the titration of Alexa488-labeled [17]Arf1-GDP with Arno/4M bearing or not the His-AviTags (35).

Surface Plasmon Resonance Analysis of complexes formation.

Experiments were performed on a Biacore 3000 apparatus (GE Healthcare, Uppsala, Sweden) at 25 °C in 50mM Tris (pH 8.0), 150mM NaCl, various concentrations of MgCl 2 and free Mg 2+ as indicated, 2mM BME, 0.005% P20 as running buffer at a flow rate of 50µl/min. Using biotin capture kit (GE Healthcare), biotinylated

[17]Arf1 and HisTag-biotinylated-Arno or Arno4M were captured on CAP sensor chip according to the manufacturer's instructions. The immobilization levels are given in the figures and tables captions. A control surface was prepared with the same protocol without the protein. After the capture of the biotinylated protein the surfaces were saturated with an injection of biotin (40µg/ml). For the kinetic analysis of Arf1 on immobilized Arno: increasing concentrations of Arf1 solutions (12.5 nM to 1600 nM) were injected on immobilized Arno or Arno 4M during 180s followed by a dissociation of 400s. Arno coated surfaces were regenerated with a pulse (30s) of GDP (2 µM) which rapidly destabilizes the complex. For the reverse kinetics biotinylated Arf1 were captured on Cap sensor chip (200-380 RU) increasing concentrations (25 nM to 1600nM) of Arno or Arno 4M were injected. Between each run, the surface was fully regenerated with a mixture (3/1:v/v ) of 8M guanidine hydrochloride and 1M sodium hydroxide (GE Healthcare). The necessity of repetitive immobilizations of Arf1, which differ from a simple regeneration of immobilized Sec7 domain by GDP (Figure 4), results from a conformation instability of Arf1 when immobilized which could be due to a time dependent protein unfolding associated with the nucleotide departure (data not shown). The regenerability of the surface by repetitive immobilization of Arf1 avoids the problem to occur. To investigated the influence of AviTag on [17]Arf1 to the binding with Arno, the Sec7 domain was covalently bound at high level to the carboxymethyl dextran CM5 sensor chip surface, then [17]Arf1 with or without the AviTag was injected at 50 nM during 180s followed by a dissociation time of 400s. (data not shown). To obtain a 1µM concentration of Mg 2+ , 1mM of MgCl2 were mixed to 2 mM of EDTA and combined extemporaneously with Arf1 during sample injection (36) . All sensorgrams were corrected by subtracting the response from the control reference surface. The kinetic parameters were globally fitted with a 1:1 Langmuir binding (A+B = AB) or heterogeneous ligand-parallel reaction model (parallel reactions, A+B1 = AB1, A + B2 = AB2) by using BIAevaluation version 4.2 software(Biacore software handbook C16-C18). The absence of mass transferlimitation was checked for kinetics analysis. Kinetic constants are the result of at least three independent set of experiments. The validity of the fit was given by thefactor less than 10% of the Rmax value.According to the manufacturer's instructions the T value was greater than 10 for all the given fitted parameters (T value is obtained by dividing the value of the parameter by the standard error).The stoichiometric ratio (SR) is the ratio between the observed maximum binding response (Rmax) and the calculated maximum binding response (calcRmax). calcRmax is given by the molecular mass ratio of analyte to ligand, multiplied by the stoichiometry of 1 and the amount of immobilized ligand. SR= exp Rmax/ calc Rmax Equation 1calc Rmax=Ru immobilized-level x MW analyte /MW ligand

Results.

According to previous reports on nucleotide allosteric competition (9,37), scheme 1 shows a simplified model of the catalyzed GDP release from Arf1 upon the formation of a transient quaternary complex which leads to a binary Arf1-Arno complex with enhanced binding affinity. Additional steps with the release of Mg 2+ as well as the conformational changes of Arf1 are not indicated. With BFA and for sensitive Sec7 domains, a pentameric complex has been crystallized which corresponds to an abortive form where the catalyzed release of GDP by Sec7 domain is impaired [START_REF] Hamida-Rebai | PloS one5[END_REF]26,38,39). Scheme 1 indicates the rate constants delineating each association (k a1 to k a6 ) or dissociation (k d1 to k d 6) steps. The SPR approach allows us to measure some of these rate-constants giving a comprehensible flow chart that will be described in the following sections. In our study, we used a soluble truncated version of Arf1, lacking the 17 Nterminal amino-acid residues that are crucial for membrane insertion, which is fully active in catalyzed nucleotide exchangewithout the requirement of protein-phospholipid interactions and targeted by BFA in a Sec7 dependent manner (25,35,39). Sec7 domain of Arno is considered as a representative of BFAresistant Arf-GEFs and sensitivity to BFA was engineered by mutation of Phe 190 , Ala 191 , Ser 198 and Pro 208 to Tyr, Ser, Asp and Met, respectively (38).

I) The biotinylation of the proteins does not change the nucleotide exchange catalysis and the apparent equilibrium dissociation constant of Sec7 domains to [17]Arf1.

To perform SPR studies, proteins were immobilized on a new sensor chip that allows the reversible capture of biotinylated proteins and regeneration of the ligand (40). In order to circumvent deleterious effect of N-terminal additional Tags, we compared the nucleotide exchange of [17]Arf1(Scheme 2) and its mono-biotinylated-AviTag version under stimulation by BFA-resistant and BFAsensitive Sec7 domains (later referred to as Arno and Arno4M, respectively) bearing or not mono-biotinylated-His-AviTags at the Nterminus (Figure 1). The apparent inhibition constants (Kiap) of BFA for Arno and Arno4M with or without additional monobiotinylated Tags were determined. For His-AviTags-Arno4M, Arno4M, His-AviTags-Arno and Arno, Ki app are respectively 16 ± 5 µM, 11 ± 4 µM, 454 ± 226 µM and 291 ± 45 µM (Figure 1, Panel B) which indicate an equivalent sensitivity to BFA regardless of the presence of mono-biotinylated Tags and were in good agreement with previous reports (32,41).The inhibition onAviTags-Arno4M activity in function of BFA concentration using a Michaelis-Menten plot is reported onFigure 1, Panel C. The derived Lineweaver-Burk plot gave parallel lines (data not shown) which confirmed the uncompetitive inhibition mechanism of BFA on the His-AviTags-Arno4M exchange activity (35,42). At last, the non-interference of additional monobiotinylated-AviTag on [17]Arf1 was confirmed by comparison with spontaneous and catalyzed nucleotide exchanges of free-Tag [17]Arf1 version (Figure 1, Panel D).

From this, we concluded that additional His-Tag or mono-biotinylated-AviTag do not affect, neither the ability of GEFs to accelerate the nucleotide exchange on Arf1 nor the sensitivity to the uncompetitive inhibitor BFA. We then compared the equilibrium dissociation constant of [17]Arf1 to Arno/4M andmono-biotinylated-His-AviTags-Arno/4M by fluorescence anisotropy as previously described (35)and found apparent K D s were similar in the 20 nM range (data not shown). Therefore, these tagged-proteins behave similarly to their untagged versions and were used in the SPR experiments.

II) Interaction of [17]

Arf1-GDP on immobilized Arno and Arno4M conducts to a binary complex.

Our first aim was to investigate the interaction of [17]Arf1-Mg 2+ -GDP to BFAresistant and BFA-sensitive biotinylated Sec7 domains using SPR technique at 25°C, pH 8 and 150 mM NaCl. The formation of the binary Arf1-Arno complex was also studied through the action of the effectors Mg 2+ ion and nucleotides.As indicated in the schematic representation(Figure 2),using a CAP Sensor Chip, biotinylated-AviTag constructs of Sec7 domains were immobilized on streptavidinconjugated with a ss-DNA oligo. An immobilization level of 200-600 resonance units (RU) was found as a good compromise to get enough RU binding levels with Arf1-Mg 2+ -GDP and kinetic constants devoid of rebinding and non-specific effects (data not shown).As previously described ( 35), [17]Arf1 is obtained in its GDP conformation when purified in the presence of GDP and MgCl 2 . Moreover, for the present study, the GDP-bound conformational state of the purified protein has been monitored through N-1 H HSQC NMR spectra using 15N labeled [17]Arf1 (data not shown). To strengthen the nucleotide binding to Arf1 and to maintain a stable [17]Arf1-Mg 2+ -GDP association, capture, sample and running buffers were supplemented with 1mM MgCl 2 (10,43). SPR kinetic sensorgrams of [17]Arf1-Mg 2+ -GDP association with and dissociation from are shown in Figure 3, panel A where the G protein was injected at increasing concentrations from 50 nM to 1600 nM. Between injections of Arf1-Mg 2+ -GDP, regeneration of Sec7 domains was realized by a pulse of 2µM GDP. Using Biaevaluation software and a Langmuir 1:1 binding model, the association-rate constant (k a ) for the complex was determined to be 219000 M -1 s -1 , the dissociation-rate constant (k d ) to be 0.0041 s -1 and thek d /k a ratiowas 19 nM with a  2 of 1.3 (Table 1A). In accordance with the allosteric competition between nucleotides and GEFs for the binding to G proteins as well as previous reports (9,44), the complex observed in this first experience is assumed to correspond to the free nucleotide Arf1-Arno complex (named binary complex in scheme 1) (44,45). As the terms affinity and equilibrium constant (K D ) of a complex referred to association/dissociation of two equivalent partners and because, in this SPR experiment,the association occurs with Arf1-GDP whereas the dissociation is related to Arf1, in its free nucleotide form, we reported for each complex observed, the k d /k a ratio to illustrate the complex stability.

With immobilized Arno4M, similar association/dissociation profiles were obtained with a thek d /k a ratio of 18 nM and a  2 of 1.09 (Figure 3, Panel B, Table 1A). Kinetic parameters of interactions with the two Sec7 domains are equivalent which indicate the 4 mutations on Arno4M have no influence on the associationwiththe G protein. Considering a ratio of 1:1 for the binding of Arf1 to a Sec7 domain, the stoichiometric ratio (SR) represents the percentage of detected complexes over the theoretical number of possible complexes. SR was estimated from the ratio of the experimental Rmax on the calculated Rmax (Equation 1 in Materials and Methods) and revealed a SR with values of 0.3 and 0.27 for Arno and Arno4M respectively (Table 1A).We also examined the sensitivity of kinetics parameters to temperature, salt and pH have been evaluated using immobilized Arno within another set of experiments. As shown, on Table 1B, a decrease of the experimental temperature from 25 to 10 °C conducts to a progressive loss ofinthe SR value. Whereas SPR experiments performed at pH7 or in the presence of 300 mM of NaCl revealed that initial conditions conferred the highestassociation rate (k a on Table 1C).

III) Effect of Mg 2+ on the interaction between [17]Arf1 and Arno.

We performed a set of experiments to investigate the influence of free Mg 2+ on kinetic parameters of the interaction between [17]Arf1 and Sec7 domain as the removal of the cation accelerates by a factor of 20 the spontaneous nucleotide departure giving the binary G protein-GEF complex (10,36).

[17]Arf1-Mg 2+ -GDP, with or without a 10 min preincubation of 10mM EDTA, was run over on immobilized Arno (500 RU). The sensorgrams obtained from three independent trials are given in Figure 3, panel C & D and in both case, the kinetics are fitted with a Langmuir1:1 reversible binding model. In standard conditions with 1mM MgCl 2 (Figure 3, Panel D), the kinetic constants (k a : 166000 M -1 s -1 , k d 0.0038 s -1 ) were similar to data of Figure 3, panel A (Table 1A& 2) which underlines the highly reproducible level of measured constants. When free Mg 2+ was removed by EDTA, an increase of the k a by a factor of 2.37 was observed and no effect was noted on the dissociation-rate constant which confirm the influence of this cation on [17]Arf1 and Sec7 domain association. Moreover, the absence of free Mg 2+ induced an increase of the SR value at 68%(to be compared to 32% with 1mM MgCl 2 , Table 1A& 2).

It turns out that the absence of Mg 2+ destabilizes the nucleotide binding and enhances the spontaneous nucleotide release, and so we cannot rule out that changes ink a and SR values came from a nucleotide-free Arf1 population generated during the 10 min. incubation with EDTA. However, the statistical analysis with a  2 value around 10% of the Rmax rather suggests that Arf1 is still homogenous in term of population which is more compatible with a bound-GDP G protein state. To confirm the influence of Mg 2+ on kinetic constants, we performed several kinetic titration experiments by injecting [17]Arf1-GDP, from 25 to 400 nM, at 1mM and 1µM of Mg 2+ on immobilized Arno (500 RU) (Table 3). With a langmuir1:1 reversible binding model, we found the effect of the lowest Mg 2+ concentration to be similar to that of the EDTA preincubation experiments, with a 2.2fold enhancement of the association-rate constant, no appreciable change of the dissociation-rate constant and a sharp increase of the percentage of complex formation.

IV) Reverse system to study the association of Arno to [17]Arf1.

The next step was an attempt to determine the kinetic parameters of Arf1-Arno complex association/dissociation starting from a free-nucleotide Arf1 population, corresponding to the step defined by k a4 and k d4 (Scheme 1). Thus, we set a reverse SPR strategy on immobilized biotinylated [17]Arf1. As indicated in Figure 4, the biotinylated-AviTag construct of [17]Arf1 was repetitively immobilized on streptavidinconjugated with a ss-DNA oligo up to a level of ~370 RU.

Then, to generate a free-nucleotide Arf1 population, immobilized G-protein was washed 10min with a 10mM EDTA running buffer according to a previous report (44). Arno was injected at concentrations from 6.25 nM to 1600 nM with Arf1-coated to the surface fully regenerated between two Arno injections. In the presence of 1mM MgCl 2, curve analysis was compatible with a stoichiometric Langmuir 1:1 binding model with a k a of 26600 M -1 s -1 , ak d of 0.0055 s -1 andthe k d /k a ratio was around 200 nM with a  2 of 6.37. The SR value was 0.91 (Table 4). It is worthy to notethat the k a obtained with immobilized Arf1 is about 10-fold lower compared with immobilized Arno (Tables 1 &2); however the percentage of complex formation is 3-fold higher (0.91 versus 0.32).

After treatment of immobilized [17]Arf1 with EDTA, data analysis showed that a stoichiometric Langmuir 1:1 binding model was no more adequate but instead revealed the adequation with a heterogeneous ligand binding model with 2 interacting populations, each representing around 50% (Table 4). A high stablepopulation with a k d /k a ratio of 44 nM and another population with ak d /k a ratio of 338 nM. The percentage of population with high stabilitywas not increased by an extending EDTA washing time; even this would tend to destabilize the bioactive conformation of the G protein (data not shown).

Asthe association-rate constantof the binary Arf1-Arno complexwithG proteinimmobilizedis lower thanthat obtained withimmobilizedArno and also due to a drastic difference of the SR value, it was not possible to precise the influence of GDP on kinetic binding parameters, particularly k a4 and k d4 , of complex formation.In order, to understand these changes, mono_biotinylated-AviTag-[17]Arf1 and [17]Arf1 were assayed on immobilized Arno covalently bound to the surface of CM5 chip (the CM5 allows to use flowing biotinylatedproteins).In this case with Biot-AviTag-[17]Arf1, the k a and SR are decreased by a factor of ~2 when compared to [17]Arf1 (data not shown) which indicates that the additional Tag has a negative impact for the binding to Arno and could explain the lowerassociation-rate constant obtained with immobilized Arf1compared to immobilized Arno. It has to be noted that no influence of additional Tags on Arno/4M to the binding with Arf1 were detected as described above.

The instability of Arf1 and the negative impact of Avitag on kinetics parameters for immobilized Arf1 clearly indicate that the approach using immobilized Arno has to be privileged.

V) GDP and GTP counteract the Arf1-Arno complex detection with the same potency.

The nucleotide exchange activity of a GEF is not specific to the Arf1-GDP form and Arf1-GTP to GDP exchange is also stimulated by GEFs (26,44). According to the allosteric model presented in the introduction, both nucleotides counteract the stability of the binary G protein-GEF complex (9). With Arf1 and GEF Sec7 domains, this deleterious effect is illustrated on Figure 5, Panel A, which shows in the presence of 2 µM GDP the drastic disappearance of binary complex detection with immobilized Arno or Arno4M and Arf1 at 200 nM. The same effect was observed with GTP (sensorgram not shown). To investigate the relative potency of GDP and GTP to interfere on the Arf1-Arno complex stability, we measured at equilibrium the RU plateau levels of complex formation on immobilized Sec7 domains in the presence of various nucleotide concentrations. [17]Arf1 was in the flow at concentration of 800 nM. As shown for Arno on Figure 5, Panel B, the same decrease in binding levels was observed for both nucleotides with an half effect at179+/-20 nM and 140+/-20 nM for GDP and GTP, respectively. A comparable result was obtained with immobilized Arno4M (data not shown).

VI) Interaction of immobilized Arno and Arno4M with Arf1-Mg 2+ -GDP in the presence of BFA and GDP.

The next goal of our study was to investigate the modifications induced by the uncompetitive inhibitor BFA on kinetic parameters of the interaction between Arf1-Mg 2+ -GDP and GEF sec7 domains. As already mentioned, with BFA-sensitive Sec7 domain, the toxin freezes in an abortive conformation the pentameric protein-protein complexwith Mg 2+ , GDP and BFA part of the association (38,39,42). The simplified mechanism for the formation of the pentameric complex is represented in Scheme 1 (light blue) as previously described (38), however the way this complex dissociates is not completely elucidated. Previous experiments from Figures 3&5revealed that Arno and Arno4M presented the same association/dissociation profile with the G protein and that the binary complex detection with 200 nM[17]Arf1 was abolished by 2 µM GDP. However, as indicated on Figure 6, Panels A &B, despite the presence of 2µM GDP,BFA at 100 µM during the association phase restores the complex detection for both Sec7 domains, with a major effect on immobilized Arno4M compared to Arno (Table 5) as judged by the SR value. With Arno4M, the analysis of kinetic data fitted well with a langmuir1:1 reversible binding model. The k a value of 6270

Discussion.

The structure of myristoylated or non myristoylated Arf1 and [17]Arf1 bound to GDP or GTP and in complex with Sec7 domains revealed the sequential conformational changes and the role of GEFs during nucleotide exchange (21,22,24,28,36,42,46). The protein-protein interface encompasses the Switch 1 and 2 regions (residues 38-51 and 69-84, respectively) of Arf1 with the hydrophobic groove and carboxy-terminal helices of the Sec7domain(Figure 8, up). Except for Nterminal helix, Switch and Interswitch (residues 52-68) regions, the conformation of the core of the G protein is globally the same throughout the nucleotide exchange. The myristoylated N-terminal helix is directed towards the protein core in the bound-GDP state, and is extruded mediating membrane interaction in the bound-GTP (Figure 8, pdb codes 2K5U, 1U81 versus 1O3Y). If Switch 1 shows a drastic alteration with some residues inserted into the hydrophobic surface groove of the Sec7 domain, the Switch 2 is preformed on Arf1-GDP to the initial encounter with the GEF. From Arf1-GDP to Arf1-Sec7 domain complex and further in the Arf1-GTP state, strands 2 and 3 of the interswitch are translated up to 7 Åin the direction of the initial N-terminal helix pocket. With the toggle of the interswitch, a 20° rotation of the Arf core brings the -phosphate of the GDP close to the catalytic E156 which enhances the nucleotide departure and conducts to a stable binary Arf1-Arno complex with a high affinity. In the presence of nucleotides, the rapid dissociation of the complex occurs upon binding of GTP, as well as GDP, whichinduces the release of the GEF through an allosteric mechanism, that can be considered as a simplified Ping Pong Bi Bi mechanism (47,48).

In this study, taking advantages of the regenerability of the CAP sensor chip (40) coupled with a system that allows In Vivo mono-biotinylation of proteins by coexpression of BirA, we developed a SPR approach to determine kinetic parameters of the [17]Arf1 interaction to Arno Sec7 domains as well as changes induced on the type and fate of formed complexes in the presence of natural and exogenous allosteric factors.

During the association phase (Figure 8, Panel A), we observed the formation of a stable[17]Arf1-Sec7 domainbinary complex withboth immobilized Arno and Arno4M.The association-rate constant, close to 2 10 5 M -1 s -1 within independent trials, corresponds to the transient Arf1-Mg 2+ -GDP-Arno complex formation (k a2 ) followed by the departure of Mg 2+ and GDP (k d3 ) whereas the measured dissociation-rate constant, in the 5 10 -3 s -1 range, corresponds to the dissociation of the binary complex (k d4 ) (Scheme 1 and Figure 8,Panel A).The k a /k d ratio reflecting the overall complex stability was around 20 nM which could be compared to the affinity reported for some other G proteins and GEFs (eg 4.6 nM for Ras and Cdc25 Mm285 , (9,44)). In a reverse system using immobilized free-nucleotide Arf1, we attempted to measure the k a4 constant of Sec7 domain binding to free-nucleotide Arf1. Unfortunately, in contrastwith the study of Lenzen et al. based on immobilized Ras and flowing Cdc25 Mm285 , it appears that the immobilization of Arf1 could interfere with the formation of a high affinity complex and causes a 10 fold decrease of the associationrate constant (2.66 10 4 M -1 s -1 , Table 4).

The absence of free Mg 2+ decreases the GDP and GTP binding affinity by destabilizing the coordination of the nucleotide binding site and promotes the formation of the binary G protein-GEF complex (10,12).In terms of kinetic parameters of binding, our data revealed that Mg 2+ ion potentiates by a factor of 2 the counteracting allosteric effect of GDP on the binding of Arf1 to immobilized Sec7 domainas the absence of ions causes a ~2-fold reduction of the association-rate constant (Tables 2 &3).Interestingly, the absence of free Mg 2+ enhanced by a factor of ~2 the percentage of detected complexeswhich suggests a possible effect of Mg 2+ on the conformation of Arf1 suitable to interact with Arno as recently reported in a study using molecular dynamics simulations [START_REF] Hamida-Rebai | PloS one5[END_REF].

As illustrated with the nucleotide exchange assay using fluorescent labeled guanosine triphosphate (Figure 1)saturating concentrations of nucleotides did not abolish theinitial binary complex formation but its stability as we observed the release of Arf1-Mg 2+ -GTP with enhanced fluorescence.With immobilized Arno and flowing Arf1, in the presence of GDP/GTP at saturating concentrations, this allosteric nucleotide effect analyzed by SPRshowed that neither the transient complex nor the binary one were detected which indicated that the binary complex dissociation is very fast(Figure 5 and 8, panel BI).Moreover, there is no pronounced specificity toward the nature of the nucleotide todestabilize the binary complex as with 800nM of flowing Arf1, 50 % ofcomplex formation was detected at concentrations of nucleotides of 140 nM and 179 nM for GDP and GTP, respectively, in good accordance with previous reports (49).

After the characterization of kinetic parameters of thebinarycomplex formationandthe allosteric regulationsbyMg 2+ and GDP/GTP,we studied theformationof complexesinduced by BFA toxin. With BFAsensitive Sec7 domain as Arno4M, a complex, different from the binary one, is observed with a lower association-rate constant and a slower dissociation-rate constant, in the 6 10 3 M -1 s -1 and3 10 -4 s -1 range, respectively (Table 5).As this complex was observed in the presence of GDP and in accord with the uncompetitive inhibitory mechanism of BFA, we assumed it corresponds to an abortive pentameric [17]Arf1-Mg 2+ -GDP-BFA-Sec7 domaincomplex trapped by the inhibitor (Figure 8, Panel BII). The association-rate constant corresponds to the transient Arf1-Mg 2+ -GDP-Arno4M complex formation (k a2 ) followed by the association of BFA (k a 5) (Scheme 1).It is worthy to note that whereas the k a is reduced, the percentage of pentameric complex increased up to 79 % compared to 30% for the binary complex in standard conditions, highlighting the critical role of the slow dissociation-rate on the pentameric complex detection(Figure 8, Panel BII). For Sec7 domains sensitive to BFA, the inhibitor freezes the complex before the release of the nucleotide and the formation of the binary complex (26,39,42). As shown on Figure 8 (pdb code 1R8Q), the BFA interacts without leading to the interswitch toggle and no rotation of the Arf1 core occurs. The role of BFA during the slow dissociation of this pentameric complex remains unknown but the dissociation of BFA has been reported as the rate-limiting step for the liberation of GDP from Arf1 (38).

In the presence of GDP and BFA, with the BFA-resistant Sec7 domain, 2 populations of complexes were observed which behave differently from the true abortive pentameric complex. First, a 19 % population displays an association-rate constant (4.6 10 3 M -1 s -1 ) equivalent to that observed with the Arno4M pentameric complex (Table 5) but a dissociation-rate constant k d1 (1.59 10 -3 s - 1 )close to the k d found for the binary complex dissociation (eg. 4.1 10 -3 s -1 , Table 1). This is in complete agreement with the inhibitory mechanism of BFA as the toxin only freezes complexes which encompass a BFA-sensitive Sec7 domain. Without GDP during the dissociation-phase, BFA does not interfere with the interswitch toggle of Arf1 within the pentameric[17]Arf1-Mg 2+ -GDP-BFA-Arno complex. Therefore, after the conformational change of Arf1 which ends when the GDP is released, we observe the dissociation of the binary Arf1-Arno complex in the 10 -3 s -1 range (Figure 8, Panel BII). The association of BFA with complexes formed by BFA-resistant Sec7 domains has already been reported (38,39) but not the kinetic parameters and fates in real time. The second 19 % population obtained with Arno is not understood. It represents 50 % of the complex formed and has an association-rate constant k a2 (3.32 10 4 M -1 s -1 ) intermediate between the binary complex and the pentameric one. It has also a very fast dissociation-rate (3.61 10 -2 s -1 ). Due to the fast dissociation rate, one can at least suggest that allosteric effect of 13 GDP has a critical role in the dissociation. However, the exact nature of this complex remains puzzling.

The last set of experiments performed with BFA but in the absence of GDP during the association phase reveals the prevalence of the binary complex compared to the pentameric one. With Arno4M, we observed the formation of a heterogeneous population composed by a first complex (SR of 0.17) with a set of kinetic constants compatible with the binary complex and a more important (SR of 0.66) with parameters corresponding to the pentameric complex (Figure 8, Panel BIII). This result confirms that in the absence of nucleotide during the association phase, the binary complex is stable enough to be detected. Interestingly, in relation with respective kinetic constants, as shown in Figure 7 , Panels C-D-E, the ratio between binary and pentameric complex varies upon the concentration of Arf1, the binary complexbeing preponderant at low concentration and the pentameric becoming prevalent at concentrations higher than 400 nM where the Rmax of the binary complex is reached.

With Arno in the absence of flowing GDP, the binary complex is detected since the allosteric competition by the nucleotide does not occur and no additional complex was observed (Figure 8, Panel BIII). The absence of the pentameric [17]Arf1-Mg 2+ -GDP-BFA-Arno complex is in accordance with the BFA-resistance characteristic of this Sec7 domain. However, the comparison with previous experiment when BFA and GDP were present in the association phase (Figure 8, Panel BII) suggests a new role of the nucleotide allosteric effect as a prerequisite for the pentameric complex detection with Sec7 domains describe as BFA-resistant. Indeed, it seems that the uncompetitive inhibition by BFA and the nucleotide allosteric effect combine to stabilize an abortive complex as long as the nucleotide remains present. The relevance of such Arno pentameric complex in a cellular context is unknown but on purified BFA-resistant Sec7 domains an inhibition was observed with high concentrations of BFA as shown on Figure 1.

In recent years beside BFA, several inhibitors have been identified for G proteins of the Arf sub-family and their GEFs through polarization displacement fluorescence assay (SecinH3, (50)), In Silico approach (LM11, (35)), and phenotypic high-throughput screens (Golgicide A (51), Exo2, LG186, (52), AG1478, Amf-26, ( 53)). These compounds, with several present some specificity for a GEF (eg. GBF1), have clearly underlined the putative potential of these proteins as therapeutic targets. However, except the uncompetitive inhibitor BFA, their inhibitory mechanisms are not clearly understood, in part due to the fact that these compounds are not very actives in a nucleotide exchange assay on purified proteins. Moreover, structural data with the modes of binding of these compounds are not available which impaired oriented molecular optimizations. Here, the SPR approach with identification in real time of nature and fate of complexes allows a very informative analysis at qualitative and quantitative levels that should help to solve the inhibitory mechanism of new family of inhibitors.

FOOTNOTES:Jad Rouhana is a recipientof a "MENRT" grant from the French government.

Abbreviations.

The abbreviations used are: ARF, ADP-ribosylation factor; Arno, ARF nucleotide-binding site opener; Arno4M, a mutant of Arno carrying four BFA-sensitizing mutations;BFA, Brefeldin A; SPR, Surface Plasmon Resonance;RU,Resonance Unit level; GEF, Guanine Nucleotide Exchange Factors; Bodipy-GTP, guanosine 5'-triphosphate, BODIPY, 2'-(or-3')-O-(N-(2aminoethyl)urethane).

Figure Legends.

Scheme 1: Simplified model for the binding of Arno Sec7 domain and Mg 2+ -GDP to separate but interacting sites on Arf1 leading to the formation of a transient Arf1-Mg 2+ -GDP-Sec7 domain complex, which converts to the binary Arf1-Sec7 domain complex or a pentameric Arf1-Mg 2+ -GDP-BFA-Sensitive Sec7 domain complex in the presence of BFA. How the pentameric complex dissociates is not perfectly understood so far.

Scheme 2:

The GDP to GTP nucleotide exchange on Arf1 upon binding to Sec7 domain. (C) Analysis of the inhibitory mechanism of BFA on His-AviTags-Arno4M activity using a Michaelis-Menten plot. Kinetics of nucleotide exchange were performed with 50 nM of His-AviTags-Arno4M, 5 µM Bodipy-GTP, and variable concentrations of [Δ17]Arf1-Mg 2+ -GDP (0-2 µM) in the presence of BFA (0-100 µM).

FIGURE 1 :

 1 FIGURE 1:Impact of additional tags on protein activities and sensitivity to BFA.(A) Kinetics of GDP to GTP nucleotide exchange on [Δ17]Arf1 (1 µM) catalyzed by His-AviTags-Arno4M (0,2 µM) in the presence of Bodipy-GTP (1 µM) and variable concentrations of BFA (0-200 µM) as indicated. The nucleotide exchange reaction was initiated with the addition of 1 µM Bodipy-GTP and monitored by time-resolved fluorescence. AU, arbitrary units. As indicated, by measuring the nucleotide exchange of [17]Arf1 using a fluorescent GTP analog, the sensitivity of His-AviTags-Arno4M to BFA was confirmed as the kinetic exchange rate decreases with increasing concentrations of the toxin. (B) Determination of the apparent inhibition constants (Kiapp) of BFA measured with [Δ17]Arf1 and either Arno (BFA resistant) or Arno4M (BFA sensitive) with or without additional His-AviTags (HB-Arno4M and HB-Arno). Kinetics measurements were performed in the presence of 1 µM [Δ17]Arf1-Mg 2+ -GDP, 1 µM Bodipy-GTP, 0,2 µM of corresponding GEF Sec7 domain and increasing concentrations of BFA (0-200µM).

18 (

 18 D) Comparison of nucleotide exchange catalyzed by Arno on [Δ17]Arf1 (1 µM) with or without biotinylated AviTag performed in the presence of 1 µM of either AviTag-[Δ17]Arf1 (B-[Δ17]Arf1) or[Δ17]Arf1 in the absence or the presence of 0,2 µM of ARNO as indicated. AU, arbitrary units.

FIGURE 2 :

 2 FIGURE 2:Schematic representation illustrating the strategy on CAP surface with immobilized Sec7 domain. Capture of the biotinylated ligand (Sec7 domain of Arno/Arno4M, purple) on streptavidin conjugated with a ss-DNA oligo (green), subsequent injection of the [17]Arf1-Mg2+-GDP analyte sample (Arf1, red) followed by dissociation of the Arf1-Arno complex and regeneration of Arno with 2µM GDP.

FIGURE 3 :

 3 FIGURE 3: Sensorgrams of binding of [17]Arf1-Mg 2+ -GDP with Arno determined by SPR. Typical kinetics of [17]Arf1-Mg2+-GDP binding to immobilized Arno (A) and Arno4M (B) both at 220 RU. Increasing concentrations of Arf1 were injected (50, 100, 200, 400, 800 and 1600 nM, from bottom to top). Kinetics of [17]Arf1-Mg2+-GDP binding, with (C) or without (D) a 10 min preincubation of EDTA (10mM), to immobilized Arno (500 RU). Increasing concentrations of Arf1 were injected (6.25, 12.5, 25, 50, 100, 200, from bottom to top).

FIGURE 4 :

 4 FIGURE 4:Schematic representation illustrating the strategy on CAP surface with the capture of the biotinylated ligand [17]Arf1-GDP (Arf1, red) on the captured streptavidin conjugated with a ss-DNA oligo (green), the saturation with biotin of free streptavidin sites (biotin, blue circle), subsequent injection of the Arno analyte sample (Arno, purple) and complete regeneration of the CAP surface. The Binding of Arno to [17]Arf1 is assumed to be concomitant to the release of GDP.

FIGURE 5 :

 5 FIGURE 5: Complex formation detection in the presence of GDP or GTP. A) Representative sensorgrams of [17]Arf1 binding at 200 nM on immobilized Arno or Arno4M with or without 2 µM of GDP in the injection buffer.B)Effect of GDP and GTP concentrations on the binding ratio of [17]Arf1 (800 nM) on immobilized Arno (350 RU) measured on the plateau levels at equilibrium in presence of increasing concentrations of nucleotides. The mixture of [17]Arf1 with GDP and GTP was performed during injection on the chip. After the last assay with 1 µM concentration of GDP/GTP, [17]Arf1 was flowing again without nucleotide to confirm the overall GEF stability.

FIGURE 6 :

 6 FIGURE 6: [17]Arf1-Mg 2+ -GDP binding to Arno and Arno4M in the presence of BFA and GDP.Kinetics of [17]Arf1-Mg 2+ -GDP binding to immobilized (A) Arno (383 RU) and (B) Arno4M (355 RU) in the presence of 2 µM GDP and 100 µM BFA during the association time. Increasing concentrations of Arf1 were injected (25, 50, 100, 200, 400, 800 and 1600 nM, from bottom to top).

FIGURE 7 :

 7 FIGURE 7: Kinetics of [17]Arf1-Mg 2+ -GDP binding to (A) Arno (383 RU) and (B) Arno4M (665 RU) in the presence of BFA (100 µM) during the association phase. Increasing concentrations of Arf1 were injected (50, 100, 200, 400, 800 and 1600 nM, from bottom to top). Heterogeneous model components of the fitting curves for Arno4M at three concentrations of Arf1, 1600 nM (panel C), 400 nM (panel D) and

Figure 6

 6 Figure 6

Measurements of Nucleotide Exchange.

	which have shown >95% of mono-biotinylated
	proteins.
	Kinetics All kinetics experiments were performed with a
	soluble Arf1 proteintruncated of the N-terminal
	helix, which is not dependent on lipids for
	nucleotide exchange,and loaded with GDP before
	the experiments as previously described (35), on a
	multimode reader Mithras LB 940 (Berthold).
	Activation of [17]Arf1 was monitored for the
	indicated time period by fluorescence of the
	guanosine 5'-triphosphate, BODIPY, 2'-(or-3')-O-
	(N-(2-aminoethyl)urethane) (excitation, 580 nm;
	emission,	620	nm)	(Bodipy-GTP).	All
	measurements were performed at 30°C in 50 mM
	Tris (pH 8), 50 mM NaCl, 1 mM MgCl2, 2 mM 2-
	mercaptoethanol in the presence of 1 µM of
	[17]Arf1-Mg 2+ -GDP, and 1µM of Bodipy-
	GTP,with nucleotide exchange factors and BFA at
	indicated concentrations. The inhibitor Brefeldin A
	(BFA) was incubated for 5 min before initiating the
	reaction with Bodipy-GTP. The spontaneous exchange of [∆17]Arf1 was followed in the
	presence of 1µM of Bodipy-GTP and no GEF.To
	confirm the uncompetitive mechanism of BFA on
	His-AviTags-Arno4M,	enzyme	activity	of
	ARNO4M concentrations [was	determined	at	substrate
						26/10	Desalting	column,	and
						concentrated with spin concentrators (Amicon
						Ultra-15, molecular weight cutoff 10,000 Da,
						Millipore, Billerica, MA, USA). The biotin
						incorporation of the purified proteins was
						additionally confirmed by Western blot with the
						Streptavidin-HRP conjugate for detection of
						biotinylated proteins and Maldy mass spectrometry

Table 2 :

 2 Kinetic Constants of [17]Arf1-GDP binding to captured Arno, with or without a 10 min EDTA (10 mM) preincubation of the G protein, derived from data displayed in Figure 3, panel C & D.

	Langmuir1:1 reversible binding model.
		ka (1/Ms)	kd (1/s)	Rmax (RU)	kd/ka (nM)	 2	SR
	+EDTA	3.95 10 5	4.19 10	-3	223	10.6 25.5 0.68
	-EDTA	1.66 10 5	3.85 10	-3	76	23.2 5.15 0.32

Table 1B :

 1B Kinetic Constants of [17]Arf1-Mg 2+ -GDP binding to captured Arno at different temperatures at pH 8.0 and 150 mM NaCl. Langmuir1:1 reversible binding model. 2 value of 0.7 measured for all the curves simultaneously in global-local.

	SR : Stoichiometric ratio
		ka (1/Ms)	kd (1/s)	Rmax (RU)	kd/ka nM	SR
	25°C	2.56 10 5	4.76 10 -3	45	19	0.23
	20°C	1.30 10 5	3.30 10 -3	29	25	0.15
	15°C	1.20 10 5	2.27 10 -3	23	19	0.12
	10°C	9.54 10 4	2.19 10 -3	12	23	0.06

Table 1C :

 1C Effect of pH and salt concentrations on Kinetic Constants of [17]Arf1-Mg 2+ -GDP binding to captured Arno at 25°C. Langmui1:1 reversible binding model. 2 value of 0.4 measured for all the curves simultaneously in global-local.

						Page 21
	SR : Stoichiometric ratio
		ka (1/Ms)	kd (1/s)	Rmax (RU)	kd/ka nM	SR
	pH 7, NaCl 150 mM	1.67 10 5	3.81 10 -3	31	23	0.16
	pH 8, NaCl 300 mM	1.46 10 5	3.58 10 -3	64	25	0.33
						21

Table 3 :

 3 Kinetic Constants of [17]Arf1-GDP binding to captured Arno, at 1 mM and 1 µM MgCl 2 obtained by kinetic titration experiments (n=3). Langmuir1:1 reversible binding model.

		ka (1/Ms)	kd (1/s)	Rmax (RU)	kd/kanM  2 SR
	1 µM	5.05 10 5	6.14 10	-3	178	12.1	6.4	0.52
	1 mM	2.34 10 5	5.75 10	-3	106	26.5	6.4	0.31

Table 5 :

 5 Kinetic Constants of [17]Arf1-Mg 2+ -GDP binding to captured Arno4M and Arno in the presence of 100 µM BFA with 2µM GDP during association phase, derived from data displayed in Figure 6, panel A & B.

		ka (1/Ms) kd (1/s)	Rmax (RU)	kd/kanM	 2 SR
	Langmuir1:1 reversible binding model.
	Arno 4M	6.27 10 3 3.26 10	-4	192	52	0.81 0.79
	Heterogenous ligand binding model
		ka1 (1/Ms) kd1 (1/s)	Rmax1 (RU)	kd 1 /ka 1 nM  2 SR1
	Arno	4.62 10 3 1.59 10	-3	49	344	0.96 0.19
		ka2 (1/Ms) kd2 (1/s)	Rmax2 (RU)	kd 2 /ka 2 nM	SR2
		3.32 10 4 3.61 10	-2	51	1086	0.19

Table 4 :

 4 Kinetic Constants of Arno binding to captured [17]Arf1-GDP after 10 min of running buffer containing or not 10mM of EDTA.

	Langmuir1:1 reversible binding model
		ka (1/Ms) kd (1/s)	Rmax (RU)	kd/kanM  2 SR
	-EDTA	2.66 10 4	5.54 10	-3	220	220	6.37 0.91
	Heterogenous ligand binding model
	+ EDTA ka1 (1/Ms) kd1 (1/s)	Rmax1 (RU)	kd 1 /ka 1 nM  2 SR1
		1.13 10 5	5.01 10	-3	128	44	23.2 0.46
		ka2 (1/Ms) kd2 (1/s)	Rmax2 (RU)	kd 2 /ka 2 nM	SR2
		1.67 10 4	5.65 10	-3	152	338	0.54

Table 6 :

 6 Kinetic Constants of [17]Arf1-Mg 2+ -GDP binding to captured Arno4M (665 RU) and Arno (383 RU) in the presence of 100 µM BFA during association phase and without GDP, derived from data displayed in Figure 7, panel A & B.

	Heterogenous ligand binding model
		ka1 (1/Ms)	kd1 (1/s)	Rmax (RU)	kd 1 /ka 1 nM  2 SR
	Arno 4M	2.04 10 5 2.91 10 ka2 (1/Ms) kd2 (1/s) -3	77 Rmax2 (RU)	14 kd 2 /ka 2 nM	4.58 0.17 SR2
		3.20 10 3 0.70. 10	-4	302	22	0.66
	Langmuir1:1 reversible binding model
		ka (1/Ms) kd (1/s)	Rmax1 (RU)	kd/kanM  2 SR
	Arno	2.08 10 5	4.5 10	-3	55	22	2.26 0.21

Acknowledgements.We thank Sylvie Berger for helpful discussions during SPR data analysis, Corinne

Henriquet for experiments performed on CM5 chip,Kadi-Ouahabi Abderrahmane for his technical assistance.

10 M -1 s -1 and the k d value of 0.00032 s -1 (Table 5) are reduced by a factor of 42 and 15, compared to values found without GDP and BFA, and thek d /k a ratiowas 52 nM (Table 5). In another set of experiments, GDP was also present during the protein dissociation phase and comparable kinetic constants were obtained, particularly the same dissociationrate constant (0.00036 s -1 )indicating that the nucleotide has no or little influence on complex dissociation (data not shown). Analysis of the SR revealed that the complex formation reached 79% (Table 5). The binding level increases proportionally to the concentrationof BFA (data not shown) with 100 µM the higher concentration investigated which is around 10-fold the Ki app of the toxin (Figure 1). The kinetic data obtained inthe presence of BFA and GDP are compatibles with the formation and stabilization of a pentameric

[17]Arf1-Mg 2+ -GDP-BFA-Arno4M complex (see discussion). This effect was expected for Arno4M since the four mutations confer sensitivity to the toxin and allow the formation of an abortive pentameric complex (38,39,42).

With Arno, in the presence of BFA and GDP during the association phase, data analysis of complex detection revealed that a Langmuir1:1 reversible binding model is not suitable and rather curves fitted with a heterogeneous binding model showing two almost equal populations of complexes in the range of 19 % (Figure 6, Panel A, Table 5). A first population is characterized by a k a1 value of 4620 M -1 s -1 and a k d1 value of 0.0015 s -1 corresponding to an equivalent associationrate constant and a 20-fold higher dissociationrate constant when compared to Arno4M. These values suggest that Arno and Arno4M Sec7 domains both associate to form a pentameric [17]Arf1-Mg 2+ -GDP-BFA-Sec7domain complex, however with a lower complex stability for Arno (k d1 /k a1: 344 nM) due to an higher dissociation rate (see discussion). The second population of complexes obtained with Arno is more difficult to understand, it represents 50% of the amount of complexes with a higher association-rate constant k a2 (33200 M -1 s -1 ), a very rapid dissociation-rate (0.0362 s -1 ) which could be assigned to a complex with likely a destabilizing effect from the bound-nucleotide.

VII) Interaction of immobilized

Arno and Arno4M with Arf1-Mg 2+ -GDP in the presence of BFA but no GDP.

In the last part of our study, we investigated the effect of BFA on protein complex formation in the absence of the allosteric competitive action of GDP. In the presence of 100 µM BFA but no GDP in the sample solution, data analysis of the [17]Arf1-Mg 2+ -GDP binding to immobilized Arno4M were fitted with a heterogeneous ligand binding model which encompass two types of complexes. One population with kinetic constants (k a and k d in the 10 5 and 10 -3 range, respectively) which is consistent with the binary complex detection, and another set of constants compatible with the pentameric complex (k a and k d in the 10 3 and 10 -5 range, respectively)(Figure 7, Panel B, Table 6). The deconvolution of individual fitting curves obtained at different Arf1 concentrations (Figure 7, Panels C, D, and E) reveals that the ratio of pentameric (AB 2 ) versus binary (AB 1 ) complex increases with the concentration of Arf1. Thus, at 25 nM of Arf1, most of the protein-protein association detected provides from the binary complex whereas at saturating concentration, the pentameric complex is the most abundant.

By contrast, without GDP in the running buffer, data analysis of binding to immobilized Arno fitted a Langmuir1:1 reversible binding model with a complex formation of 21% (Figure 7, panel A, Table 6). In this case, values for k a and k d are in the 10 5 and 10 -3 range, respectively and therefore are characteristics of the binary complex association/dissociation. It has to be noted in the absence of GDP in the association running buffer, that no pentameric complex formation was detected with BFA-resistant Sec7 domain.


P-Y Baudin

N Azzabou

P G Carlier

N Paragios

AUTOMATIC SKELETAL MUSCLE SEGMENTATION THROUGH RANDOM WALKS AND GRAPH-BASED SEED PLACEMENT

Keywords: muscle, image segmentation, graphical models

In this paper we propose a novel skeletal muscle segmentation method driven from discrete optimization. We introduce a graphical model that is able to automatically determine appropriate seed positions with respect to the different muscle classes. This is achieved by taking into account the expected local visual and geometric properties of the seeds through a pair-wise Markov Random Field. The outcome of this optimization process is fed to a powerful graphbased diffusion segmentation method (random walker) that is able to produce very promising results through a fully automated approach. Validation on challenging data sets demonstrates the potentials of our method.

INTRODUCTION

In the field of medical image processing, skeletal muscle segmentation is a challenging problem. This is due to the fact that the basic segmentation assumption of visual discriminability between different classes is violated. Muscle regions consist of the same tissue and therefore their visual appearance is not distinctive. User-aided segmentation and delineation could address such a problem but is a time consuming and tedious process. Despite enormous progress on automatic segmentation methods, their direct application to such a clinical setting is far from being trivial. Skeletal muscles have large shape variation and no specific texture, while the contours separating them are often nonexistent or confused with noise (blood vessels, fat infiltrations).

The above issues are the predominant reason for limited prior work in the field. In [START_REF] Gilles | Fast musculoskeletal registration based on shape matching[END_REF][START_REF] Gilles | Musculoskeletal MRI segmentation using multi-resolution simplex meshes with medial representations[END_REF], the task of segmenting all the muscles in one limb was addressed. The proposed method belongs to the Deformable Model family of segmentation methods ( [START_REF] Montagnat | A review of deformable surfaces : topology , geometry and deformation[END_REF][START_REF] Mcinerney | Deformable models in medical image analysis: a survey[END_REF]), that rely on minimizing an energy balancing a data term -which pushes the model towards the target contours -and a regularization term -which imposes a smooth solution along the curve. The main drawback of such method is that only a local optimum is reached during optimization.

In [START_REF] Essafi | Hierarchical 3D diffusion wavelet shape priors[END_REF], an alternative approach was considered that aimed at introducing prior knowledge in the process. The allowed deformations of the muscle were modeled through hierarchical wavelets, resulting on a sparse local-to-global representation. Gradient descent was also used to determine the lowest potential of the designed energy. In [START_REF] Wang | 3D knowledge-based segmentation using pose-invariant higher-order graphs[END_REF], the problem was approached using a graphbased method. Higher-order pose-invariant priors were considered to model shape variability and classifications methods were used to determine model-to-image correspondences. Linear programming and dual-decomposition were adopted towards optimization of the defined objective function. However the extension of these methods in the multi-object setting is not trivial. Moreover, the drawback of all knowledge-based models is their dependency on robust statistical learning methods, which, to function properly, generally require: a) a large annotated database and b) consistent object features to be learned. Both items are difficult to observe in our application in practice. Indeed, the complexity of the skeletal muscle structure implies that the database contains dozens of segmented subjects (a long and tedious work), and, besides, muscles have few detectable consistent landmark points, while their shape varies widely among individuals.

Our approach builds upon the observation that certain regionbased methods can achieve excellent results, providing a substantial manual initialization. In [START_REF] Boykov | Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images[END_REF][START_REF] Grady | Random walks for image segmentation[END_REF], the user draws "seeds", i.e. annotates a few pixels, from which the algorithm extrapolates the full segmentation, through high performance discrete optimization processes. The method proposed in [START_REF] Grady | Random walks for image segmentation[END_REF] has the advantage of addressing the issue of incomplete contours (because of the random walks process) which is one of the main difficulties of our task. However, the performance of these methods depends heavily on the position of the initial seeds, that is also a time consuming process.

In this paper, we propose a method for generating the seeds automatically. Without loss of generality, we assume a rigid preregistration step between a segmented atlas and the target volume. Through a sampling process, a number of seeds are generated in the image domain. The clustering of these seeds with respect to the different muscle classes is achieved through a graph-based approach. This paper is organized as follows: in section 2 we detail the formulation of our method. Then, in section 3, we indicate how to solve numerically the problem, and present results obtained on 3D MR data. Section 4 concludes the paper.

AUTOMATIC SEED GENERATION

Seeds Sampling

The first step of our algorithm is to generate the seeds that will be used as initialization by the region-based algorithm -e.g. Random Walks (RW) -once labeled. For efficiency, we need to place the seeds well inside the muscles, and not too close to the contours. To achieve this, we first compute an edge map of our volume -e.g. by computing the local variance at each pixel for a given radius. Then, we generate regularly placed seeds, with a high enough density to ensure all the muscles contain several seeds. Finally, we move the seeds in the opposite direction of the gradient of the edge map for a few iterations. This way, seeds are moved away from the close-by edges by a few pixels (see figure 2).

MRF formulation

We now describe how to assign a label to the seeds we generated, according to the similarities between the test image and the reference segmentation.

Energy Form

We formulate our problem as a labeling problem, that we will solve by minimizing a first-order Markov Random Fields (MRF) energy function. Let G = (V, E) be an undirected graph, where V is the set of nodes and E is the set of edges. Given a set of labels L, we want to assign a label l ∈ L to each node p ∈ V. In this framework, the nodes in V are our unlabeled seeds, the labels in L are the indices of the muscles in the reference segmentation. We denote xp the label assigned to p, and x the collection of all assignments. A first order MRF energy is of the form:

E (x) = p∈V θp (xp) + (p,q)∈V θp,q (xp, xq) (1)
where E (•) is the energy (or cost function) we want to minimize, the unary potential θp (•) depends only on the label assigned to the node p, and the binary potential θp,q (•, •) depends on the labels assigned to each nodes of the edge (p, q).

Unary Potential

We define a unary cost θp (•) based on the knowledge that the test image is rigidly registered to the reference segmentation:

θp (m) = d (p, Mm) α /Θ α
where Mm is the set of pixels with label m in the reference segmentation, α and Θ are manually chosen parameters, and d (•, •) is a distance function between a point and a set:

d (p, R) = min { p -r , r ∈ R} if p / ∈ R 0 otherwise
The rigid registration ensures that, if we superimpose the test image and the reference segmentation, an unknown muscle in the test image is not very far from the corresponding muscle in the reference segmentation. This term will privilege the assignment to a node of a label corresponding to a geometrically close muscle, over the label of a more remote muscle. Let us now proceed with the definition of the pair-wise terms, driven from the topology and the geometry of the expected solution.

Geodesic Distance Potential

The unary term does not take into account the structure of the set of muscles. While the shape of the muscles can vary quite extensively between individuals. The topology of the set of muscles is consistent: muscle a is always "close to" muscle b, but "far from" muscle c, there is most of the time a visible contour between muscle a and b. We designed binary terms to account for this knowledge. The binary potential is the sum of two potentials:

θp,q (•, •) = w geod θ geod p,q (•, •) + worientθ orient p,q (•, •)
with weighting parameters w geod and worient.

The term θ geod p,q (•, •) is derived from the geodesic distance g (p, q) between the seeds p and q:

θ geod p,q (xp, xq) = 1/ (1 + exp (γ -g (p, q))) if xp = xq 1/ (1 + exp (g (p, q) -γ)) otherwise
where γ is a parameter. For large geodesic distances g (p, q), the case xp = xq will be more penalized than xp = xq, as it is likely that there is a contour between nodes p and q . For small g (p, q), this potential will favor xp = xq over xp = xq (cf. figure 2). We compute the geodesic distance between the seeds by running the Fast Marching algorithm [START_REF]A fast marching level set method for monotonically advancing fronts[END_REF] on the test image. We supply the algorithm with the unlabeled sampled seeds. We obtain in return a partition of the image, where each seed in included in a different region, and where the boundary between two regions is equidistant to the seeds in the regions, in the geodesic sense (i.e. a geodesic Voronoi partition).

Note that we do not get the geodesic distance between every pair of seeds, but only between geometrically proximal seeds. This is not a problem, since θ geod p,q is designed to account for the presence or the absence of a contour between two seeds, and thus is mostly a local term. We also use this newly computed edge set to determine the connectivity of the graph, obtaining a graph with mainly local edges. This is motivated by the fact that our unary cost already handle the problem at a large scale. However, we add a few random edges to the graph, to strongly reduce the diameter of the graph while not raising the complexity too much. For these non local edges, the binary cost is made only of the orientation cost.

Relative Orientation Potential

The term θ orient p,q (•, •) is intended to insure that the relative position of a pair of muscles is maintained, e.g. muscle a is located "top right of" muscle b. In a pre-processing stage, for each pair (m, n) of muscles, we computed the estimate of the probability density (the histogram) hm,n (•) of random variable:

U = (Pm -Pn) / Pm -Pn
where Pm is a randomly chosen point in muscle Mm, and Pn = arg minp∈M n Pm -p . Thus, hm,n (u) can be seen as the probability that, for any pixel in muscle m, the direction of the closest pixel in muscle n is u. We now can define the orientation cost as:

θ orient p,q (xp, xq) = -log hx p ,xq (p -q) if xp = xq 0 otherwise
This cost will be large if the orientation of the edge between two nodes is unlikely, given the labels to which the nodes are assigned.

EXPERIMENTAL VALIDATION

MRF Optimization

Minimizing the energy ( 1) is a NP-hard problem. However, it can be efficiently minimized to a good approximation with the convergent Tree-Reweighted (TRW) Message Passing algorithm ( [START_REF] Kolmogorov | Convergent tree-reweighted message passing for energy minimization[END_REF]). The TRW algorithm uses a linear programming relaxation technique and operates on the dual of the relaxed problem in order to find a lower bound to the energy of the original problem. Due to its state of the art performance among discrete optimization methods, TRW has been applied to solve many computer vision and medical imaging tasks.

Experimental Results

To evaluate our method, we used data set composed of 3D volumes of the right thigh of 15 healthy subjects, covering a wide range of morphologies (8 females, 7 males, ages range: 14 to 60), acquired with a 3T Siemens scanner and using 3pt Dixon sequence (TR=10ms, TE1=2.75 ms TE2=3.95 ms TE3=5.15 ms, rf flip angle =3°; we used the out-of-phase image), with the following resolution: 1mm×1mm×5mm. Every volume was manually segmented to obtain the ground truth against which the segmentation results are compared. We focused our evaluation on clinically relevant muscles of the thigh (13 muscles). Sampling the seeds, as described in 2.1, with grid spacing 7px×7px×5px, and 2 iterations to bring the seeds further away from the edges (filter radius = 1px), gave us between 2000 and 2500 seeds per test image. At the MRF stage (2.2), we obtained our best results with the following cross-validation estimated parameters: α = 3, Θ = 0.2 × maxp∈V,m∈L d (p, Mm), w geod = 0.1, worient = 0.01, γ = 2 × medianp,q∈V g (p, q), and U contains 26 regularly spread unitary orientation vectors. On a 2.8 GHz Intel® processor with 4 GB of RAM, total processing time is under 5 min, which adds up to the RW segmentation time (20 min).

All our results are based on a leave-one-out validation protocol: each volume is used as the reference segmentation for segmenting all the other volumes. First, we evaluated the automatic labeling process by computing the labeling error rate for each muscle (figure 3). For testing the efficiency of the binary potential, we also computed the error rate for an energy only composed of the unary potential. We observe that using the binary potential significantly reduce the error rate.

Then, we computed Dice coefficients to evaluate the segmentation results given by the RW with the labeled seeds (figure 4). These results are compared with the Dice coefficients of segmentation results with the same generated seeds with ground truth labeling. Not surprisingly, the segmentation results are consistently not as good as the ones obtained with the ground truth seeds. However, the performance difference is not that significant in most of the cases, with exceptions of large labeling errors (and thus segmentation errors), which are most probably due to the large shape variability of the corresponding muscles. In figure 5, we show cross-sections of segmentation results. We observe that small muscles tend to be more affected by segmentation errors than large ones, which points out the limitations of our model. In particular, the unary term, based on the surimposition of the registered reference atlas with the target image, is bound to be less effective for small muscles, because the distance term d (., .) is likely to be larger than in the case of large muscles. Besides, many errors are due to the absence of reliable contours between parts of muscles, which, in the case of large errors, indicates that the topology constraints are failing to insure topology or shape correctness.

CONCLUSION

Segmentation of muscles is a too often neglected problem. Standard methods are often ill-adapted to the complexity and specificity of the full segmentation of one limb, whether they be contour-based, or knowledge-based. Indeed, muscles have partial contours, no discernible texture differences, large variation inter-individuals and unremarkable shapes. However, Seed/Region-based methods show excellent contour accuracy providing the seeds are well placed. We propose a method to replace, the manual initialization of such algorithms. Since we rely on the RW to find the existent contours, our model does not need to focus on targeting the contours in the test image, but rather on identifying the muscles thanks to their relative positions and global organization. Promising results demonstrate the potentials of our method.

Future consists on alleviating the bias introduced from the rigid registration step. This can be achieved by the use of deformable registration methods. Furthermore, the proposed seedlabeling/placement process can have a statistical interpretation and therefore being associated with measures of uncertainties. The use of such measures (min-max marginals) could further enhance the performance of the random walk segmentation method when associated with seed placement confidence. Introducing soft prior knowledge to the Random Walker algorithm is also a promising direction; at least for the muscles with important volumetric mass.

Fig. 1 .

 1 Fig. 1. (left) Cross-section of an MR volume of the thigh. (right) Manual segmentation of the muscles.

Fig. 2 .

 2 Fig. 2. Sampled seeds and graph edges. Edge color represent the geodesic distance -from blue: short distances, to red: long distances.

Fig. 3 .

 3 Fig. 3. Labeling error rate per muscle (number of mis-labeled seeds over total seed number). Box-plot presentation: the boxes contain the middle 50% of the data and the median value, and the extremities of the lines indicate the min and max values, excluding the outliers (for more details, see the documentation of Matplotlib). Using the binary potentials significantly reduce the error rate.

Fig. 4 .

 4 Fig. 4. Dice coefficients on segmentation results. The expression of the Dice coefficient is: D = 2 |T ∩ R| / (|T | + |R|), where T and R are the pixel sets for the same muscle in the computed segmentation and the reference segmentation respectively.

Fig. 5 .

 5 Fig. 5. Segmentation results using automatic seeds. Segmentation errors are shown in white. The mis-labeled seeds are also represented. Please refer to the text for interpretation.