Zhi-Qiang Wang
email: zwang3@kent.edu

Jesús Tejero

Chin-Chuan Wei

Mohammad Mahfuzul Haque

Jerome Santolini

Mohammed Fadlalla

Ashis Biswas

Dennis J Stuehr
email: stuehrd@ccf.org

Arg375 tunes tetrahydrobiopterin functions and modulates catalysis by inducible nitric oxide synthase

NO Single turn over Arg375

Nitric oxide synthase Tetrahydrobiopterin Midpoint potential NO synthase enzymes (NOS) support unique single-electron transitions of a bound H 4 B cofactor during catalysis. Previous studies showed that both the pterin structure and surrounding protein residues impact H 4 B redox function during catalysis. A conserved Arg residue (Arg375 in iNOS) forms hydrogen bonds with the H 4 B ring. In order to understand the role of this residue in modulating the function of H 4 B and overall NO synthesis of the enzyme, we generated and characterized three mutants R375D, R375K and R375N of the oxygenase domain of inducible NOS (iNOSoxy). The mutations affected the dimer stability of iNOSoxy and its binding affinity toward substrates and H 4 B to varying degrees. Optical spectra of the ferric, ferrous, ferrous dioxy, ferrous-NO, ferric-NO, and ferrous-CO forms of each mutant were similar to the wild-type. However, mutants displayed somewhat lower heme midpoint potentials and faster ferrous heme-NO complex reactivity with O 2 . Unlike the wild-type protein, mutants could not oxidize NOHA to nitrite in a H 2 O 2 -driven reaction. Mutation could potentially change the ferrous dioxy decay rate, H 4 B radical formation rate, and the amount of the Arg hydroxylation during single turnover Arg hydroxylation reaction. All mutants were able to form heterodimers with the iNOS G450A full-length protein and displayed lower NO synthesis activities and uncoupled NADPH consumption. We conclude that the conserved residue Arg375 (1) regulates the tempo and extent of the electron transfer between H 4 B and ferrous dioxy species and (2) controls the reactivity of the heme-based oxidant formed after electron transfer from H 4 B during steady state NO synthesis and H 2 O 2 -driven NOHA oxidation. Thus, Arg375 modulates the redox function of H 4 B and is important in controlling the catalytic function of NOS enzymes.

Introduction

Nitric oxide (NO), an inorganic radical, plays an important role in physiology and pathology [1][2][3]. In mammals NO is largely produced by nitric oxide synthase (NOS) enzymes from L-arginine (Arg). NOSs are flavo-heme enzymes that catalyze a stepwise oxidation of L-Arg to form nitric oxide (NO) and L-citrulline [4][5][6]. The overall biosynthetic reaction consumes 1.5 NADPH and 2 O 2 and involves two steps: the first being Arg hydroxylation to form N-hydroxy-L-Arg (NOHA) as a bound intermediate, and the second being NOHA oxidation to form citrulline and NO (Scheme 1). Three mammalian NOS isozymes have been characterized: neuronal NOS (nNOS, type I), macrophage inducible NOS (iNOS, type II) and endothelial NOS (eNOS, type III) [7][8][9]. Each NOS is only active as a homodimer [10,11], with each monomer being comprised of a N-terminal oxygenase domain that binds Fe-protoporphyrin IX (heme), the substrate Arg, and the cofactor 6R-tetrahydrobiopterin (H 4 B) [12,13], and a C-terminal flavoprotein domain that binds FAD, FMN, and NADPH [14][15][16]. A central calmodulin (CaM) binding motif links the two domains [10,[15][16][17][18]. The heme is ligated to a cysteine thiolate and acts in conjunction with H 4 B to catalyze a reductive activation of molecular oxygen in both steps of NO synthesis [19][20][21]. During catalysis, NADPH-derived electrons are transferred to FAD and FMN in the NOS flavoprotein domain. CaM binding activates NO synthesis by triggering inter-domain electron transfer between the reduced FMN and ferric heme groups in the NOS homodimer (Fig. 1) [22][23][24][25][26]. Once ferrous heme forms, it can bind O 2 , obtain an electron from H 4 B, generate reactive heme dioxy species, and catalyze substrate oxidation in each NOSoxy domain [22][23][24]27].

H 4 B was found to play novel structural and electronic roles in NOS [12,20,28,29]. It promotes dimer assembly of iNOS heme-containing monomers, increases Arg binding affinity, modifies heme midpoint potential and induces a high spin shift of the heme iron [30][31][32][33]. Moreover, H 4 B performs a critical redox function in NOS: it reduces the heme ferrous dioxy intermediate (Fe II O 2 ) that is formed during oxygen activation and becomes an enzyme-bound H 4 B radical [20,21,[34][35][36][37][38][39][40]. NOS flavoprotein domain then catalyzes reduction of the biopterin radical to H 4 B which remains bound in the enzyme to be used again in the second step of NO synthesis. The one-electron redox cycling of bound H 4 B in NOS enzymes is unique and differs from other enzymes like tryptophan/phenylalanine/tyrosine hydroxylases which also use H 4 B as a cofactor but only catalyze two-electron oxidation of H 4 B to generate H 2 B, and need a steady supply of fresh H 4 B during catalysis [27,41].

Crystal structures of nNOS, iNOS, and eNOS oxygenase domain dimers (i.e., NOSoxy) [12,13,15,42] reveal that H 4 B binds near the heme with its 2-amino-4-hydroxy-pyrimidine ring hydrogen bonded with a heme propionate group [43,44]. The H 4 B ring also makes π-stacking and/or hydrogen-bonding interactions with several conserved residues that are provided by both subunits of the NOSoxy dimer (Fig. 2). Point mutagenesis has been utilized to probe the roles of these residues in modulating the functions of H 4 B and catalytic properties of NO synthesis [37,38,[45][46][47][48]. For instance, the conserved residue Trp457 in iNOS and its corresponding amino residue Trp678 in nNOS were found to regulate delivery of both electrons required for O 2 activation (i.e., kinetics of ferric heme reduction by the NOS flavoprotein domain and reduction of the heme-dioxy intermediate by H 4 B) [37,38,45]. Here we are particularly interested in a conserved Arg residue (Arg375 in iNOS), which is situated on the substrate binding helix and forms hydrogen bonds with O4 and N5 of the H 4 B ring (Fig. 2). To investigate the role of Arg375 in regulating the redox function of H 4 B and overall NO synthesis of the enzyme, we used point mutagenesis to generate the R375D, R375K and R375N in iNOSoxy. We then characterized each mutant with regard to their dimer content, H 4 B and substrate Arg binding, heme midpoint potential, extent of H 4 B radical formation, and a range of catalytic properties. Our results indicate that conserved residue Arg375 modulates the redox function of H 4 B and is important in controlling the catalytic function of NOS enzymes. org/) with the crystal structure of the mouse iNOSoxy dimer (PDB entry 1NOD [12,79]). B; Hydrogen bond interactions (dashed lines) between the H 4 B cofactor and iNOS. Ser112, Ile456 and Trp457 make H-bonds through the main chain carbonyl groups; Arg375 interacts through its side chain.

Materials and methods

Materials

All reagents and materials were obtained from Sigma, Aldrich, Alexis, or sources described previously [37,38].

Mutagenesis

Site-directed mutagenesis of Δ65iNOSoxy domain (amino acid 66-498) was performed using the QuikChange XL Mutagenesis Kit (Agilent Technologies-Stratagene, La Jolla, CA). Mutations were confirmed by DNA sequencing at the Cleveland Clinic Genomics Core Facility. Mutation bases (bold and underlined) and a silent restriction site (Italic) were introduced to the primers: DNA isolation, restriction enzyme digestion, transformation, etc. were carried out using standard protocols.

Protein expression and purification

Wild type and mutants with a six-histidine tag at C terminus were over expressed in BL21 using pCWori vector and purified as reported previously by Ni 2 + -nitrilotriacetate (NTA) chromatography [46]. Concentrations of NOS proteins were determined from the 444 nm absorbance of the ferrous-CO complex, using an extinction coefficient 76 mM -1 cm -1 [49].

Dimerization assessment

Protein stock solutions were incubated overnight at 4 °C in the presence of 1 mM H 4 B and 10 mM Arg. The dimer contents of the proteins were estimated by chromatography on an Amersham Pharmacia Biotech Superdex-200 HR size-exclusion column equilibrated with 40 mM EPPS buffer (pH 7.4), containing 10% glycerol, 250 mM NaCl and 3 mM DTT. The dimer:monomer ratios were derived from measuring the integrated area of the corresponding elution peaks determined at 280 nm. Molecular weights of the protein peaks were estimated relative to protein molecular weight standards. [46,50,51]. In short, enzymes were incubated at room temperature with NOHA, DTT and different concentrations of H 4 B in 96-well plates. Reactions were initiated by adding 30 mM H 2 O 2 and stopped after 10 min by adding catalase. Griess reagent solution was then added to enable detection of nitrite production as determined by the absorbance change at 550 nm. Nitrite was quantitated based on NaNO 2 standard solutions.

Midpoint potential titration of the mutants

Midpoint potential measurements were performed as previously described with some modifications [52]. Spectroelectrochemical titrations were carried out at room temperature in the presence of mixed dye (40 μM neutral red, 20 μM benzyl viologen, and 100 μM methyl viologen). Proteins (~15 μM), which were incubated with 10 mM Arg and 1 mM H 4 B at 4 °C overnight, were added to 40 mM phosphate buffer (pH 7.0) including 200 μM H 4 B, 10 mM Arg, 0.1% Tween and the mixture of dyes described above. Protein samples and working electrode (Au) in the cell were made anaerobic by alternating cycles of vacuation and flushing with nitrogen. The anaerobic reference electrode (AgCl(s)/Ag(s)/Cl(aq)) and auxiliary electrode (Ag(s)/KCl(aq)) were promptly placed into the cell under N 2 pressure. Potential titrations were performed by gradually adding small amount of current to the system using a Radiometer PGP201 potentiostat/galvanostat. The spectra were then recorded when the potential in the system were stabilized after each addition. The midpoint potential was determined using the absorbance changes at 400 and 645 nm, which indicate the ferric to ferrous heme transition. The midpoint potential was calculated according to the Nernst equation and is reported relative to the standard hydrogen electrode.

Single-turnover Arg hydroxylations

Arg hydroxylation single-turnover reactions were carried out in a Hi-Tech SF-61 stopped flow apparatus equipped for anaerobic work and coupled to a Hi-Tech MG-6000 diode array detector, as reported previously [20,37]. The R375 mutants were pre-incubated with Arg plus H 4 B or H 2 B on ice overnight before performing experiments because of their lower affinity toward substrate and cofactor. An anaerobic solution that contained dithionite-reduced enzyme were transferred into the stopped-flow instrument and rapidly mixed with air-saturated EPPS buffer at 10 °C. Ninety-six spectral scans were obtained after each mixing. Sequential spectral data were fit to different reaction models using the Specfit/32 global analysis software, Version 3.0 (Spectrum Software Associates), which could calculate the number of different enzyme species, their spectra, and their concentrations versus time during the single turnover reactions. Data from eight to ten sequential reactions were averaged to obtain the final traces.

EPR spectra measurement

EPR spectra were recorded on a Bruker ESP 300 electron paramagnetic resonance spectrometer equipped with an ER 035 NMR gauss meter and a Hewlett-Packard 5352B microwave frequency counter. All spectra were obtained at 150 K using a microwave power of 2 mW, microwave frequency of 9.45 GHz, modulation amplitude of 5.0 G, and modulation frequency of 100 kHz. 10 scans per sample were accumulated to improve the signal to noise ratio. Spin quantitations were calculated by double integration. We observed some variations in the protein concentration (around 25%). Thus, in order to improve the measurement, protein samples were thawed after EPR data collection and protein concentration were calculated. Double integration values were divided by the final protein concentration. Radical concentrations versus time were fit to an A → B→ C kinetic model, where B is the radical, using the Origin 7.5 software (OriginLab, Northampton, MA).

Product stoichiometry analysis for Arg hydroxylation single-turnover reaction

NOHA production from Arg was measured using the following method. Reaction samples collected from single turnover reactions run in anaerobic vessels were derivatized with o-phthalaldehyde, and then analyzed by reverse-phase HPLC with fluorescence detection as described previously [20,37]. In summary, after reactions were completed, sample solutions were filtered through an Amicon Centricon device (10,000 MW cut-off) prior to derivatization for 2 min. An aliquot was then immediately injected into a Hewlett-Packard ODS-Hypersil column and eluted with a gradient of buffer A (5 mM ammonium acetate, pH 6.0, 20% methanol) and buffer B (100% methanol). Retention times and concentrations of amino acids were calculated based on Arg and NOHA standard solutions.

Ferrous NO oxidation

Reduced protein was generated by the method described previously [53,54]. Small amount of anaerobic NO-saturated buffer were added to the reduced protein to form ferrous NO complex. Such Fig. 5. Spectra and kinetics of iNOSoxy Fe II NO oxidation reactions. Anaerobic ferrous iNOSoxy proteins in the presence of Arg (10 mM) and H 4 B (400 μM) were titrated by small amounts of anaerobic NO-saturated buffer to form the ferrous NO complex, which was then rapid-mixed in a stopped flow spectrophotometer with air-saturated buffer at 10 °C. Left panels contain spectra of the beginning ferrous-NO and ending ferric heme species as calculated by global analysis of the spectral data, while right panels show the concentration of both species versus time after mixing. Data are representative of three experiments.

solution was transferred to the stopped-flow instrument by a gastight syringe and rapidly mixed with air saturated buffer. Sequential spectral data of 96 scans were fit to A → B reaction models using the Specfit global analysis software.

2.11.

Heterodimer studies of Arg375 mutant monomer and G450A iNOS full length monomer 60 μM iNOS wild type or mutant was incubated with urea (5 M) for 2.5 h at 15 °C to form protein monomer. For the heterodimer formation, the resulting urea-treated monomer was incubated with G450A iNOS full length protein (ratio 7:1) at 30 °C for 1 h in 40 mM EPPS, 10% glycerol, 250 mM NaCl buffer containing 1 mg/ml BSA, 1 mM H 4 B, 3 mM DTT, 10 mM Arg and then further incubated at 4 °C overnight. Nitrite productions of heterodimer were assayed as described previously in 96 well plates [46]. In short, about 50-80 μl heterodimers were incubated with cofactor FAD, FMN, and other reagents including SOD and catalase in each well, and NADPH was added to initiate the reaction at 37 °C. After 1 h, excess NADPH was consumed by adding lactate dehydrogenase (LDH) and sodium pyruvate. Absorbance difference at 550 and 650 nm was measured after adding Griess reagent to each well. Nitrite productions were then calculated according to a standard curve made with nitrite solutions of known concentration.

NO synthesis of heterodimers were determined by spectrophotometric oxyhemoglobin assay [38,55] using extinction coefficient of 38 mM -1 cm -1 for methemoglobin minus oxyhemoglobin. All sample solutions contained 100 μM H 4 B, 5 mM Arg, 0.3 mM DTT, 0.1 mg/ml BSA, 10 U/ml SOD, 346 U/ml catalase, 1 mM oxyhemoglobin and 4 μM FMN/FAD. Reactions were initiated by adding NADPH (100 μM final concentration), and the absorbance changes at 401 nm were recorded at room temperature. NADPH oxidation was monitored at 340 nm using an extinction coefficient of 6.22 mM -1 cm -1 .

Antagonist experiments were done in a similar way except that 300 nM wild type iNOSoxy monomer was included during the heterodimer formation of the mutants.

Results and discussion

3.1. Spectral property, stability, binding affinity for H 4 B and Arg, and dimer content of mutants Mutations at Arg375 in iNOSoxy changed enzyme's binding affinity toward substrate Arg and cofactor H 4 B significantly. When the proteins were purified in the absence of Arg and H 4 B, adding high concentration of Arg and H 4 B at room temperature afterwards or incubating the enzyme overnight at 4 °C could not cause a complete high-spin shift in the heme iron spin state, which is a characteristic of substrate and cofactor binding (data not shown). Mutants were also found to be less stable compared to the wild type: two mutants R375N and R375D displayed a P420 peak instead of a P450 peak after the addition of CO to their dithionite reduced forms [56]. To maximize mutants' stability and their binding affinity for substrate and cofactor, we henceforth utilized mutant proteins that were purified in the presence of 1 mM Arg and 2 μM H 4 B for our studies, unless noted otherwise.

All three mutants displayed the Soret absorbance peaks at 418 nm when purified in the presence of H 4 B and Arg, indicating that they contained low-spin heme iron (Fig. 3) which is also observed in the wild type NOSoxy and other mutant like W457A iNOSoxy when purified in the absence of substrate and cofactor [46,[57][58][59]. Overnight incubation of proteins at 4 °C with additional H 4 B and Arg induced Soret peak maxima shift to 395 nm for R375K and R375N, and 388 nm for R375D, which indicates that Arg and H 4 B could bind and stabilize the heme iron in a five-coordinate high-spin state as occurs in the wild type NOS (Table 1, Fig. 3). After addition of the reducing agent dithionite and CO gas to the solution, mutants generated stable ferrous-CO complexes with maximal Soret absorbance at 444 nm (Table 1, Fig. 3). Arg375 mutants were also able to form other heme species including ferric/ferrous NO and ferrous dioxy which displayed similar Soret band maxima as that of the corresponding wild type heme species. These spectral properties demonstrate that the mutation does not perturb the electronic properties of the heme significantly. This finding agrees well with the crystal structures of R375 iNOSoxy mutants which showed that there are very limited structural changes around the heme pocket compared to the wild-type iNO-Soxy. 1 We also estimated the possible conformation changes after the mutations and observed that Lys residue in R375K can still maintain the interaction with the heme, as well as forming the hydrogen bonding with O4 of H 4 B without significant rearrangements of the protein structure. However, the side chains of Asn and Asp are too short to make direct interactions with H 4 B in the absence of noticeable structural changes (Supplementary Fig. S1).

The dimer-monomer ratio of each Arg375 mutant was estimated by gel filtration chromatography [38,46,58]. After incubating the enzyme with sufficient amounts of substrate and cofactor, R375K mutant was approximately 100% dimeric. However, only approximately 38% and 30% dimer were obtained for R375N and R375D, respectively. Such decreases in dimer content were also observed in R375A and Trp678 mutants where Trp 678 is another conserved amino residue involved in the hydrogen bonding and π stacking with H 4 B ring in nNOSoxy [38].

Peroxide assay

We next examined whether the mutants could oxidize NOHA to nitrite in a H 2 O 2 -driven reaction. Measurements of the reaction end product-nitrite were carried out photometrically using the Griess Assay in a 96-well microplate at 37 °C as described previously [46,50,51]. For this experiment we used mutants purified in the absence of Arg and with the presence of H 4 B. Protein solutions were first incubated with 1 mM H 4 B overnight at 4 °C, then were diluted to the desired concentration and further incubated with various concentration of NOHA (1 mM, 2 mM, 10 mM) or H 4 B (100 μM, 500 μM, 1 mM) at 30 °C for 30 min. H 2 O 2 solution was added to initiate the reaction, then the reaction mixtures were incubated for 10 min at 30 °C.

Wild type iNOSoxy protein being as a positive control displayed normal activity comparable to our previous results [46]. However, none of the mutants showed activities under these experimental conditions regardless of increasing the enzyme concentration from 110 nM to 1.1 μM. To test whether the lower activity was attributed to the lower binding ability of substrate NOHA and H 4 B, we measured the spectra of these mutants in the presence of 1 mM NOHA and 0.5 mM H 4 B. About 80-90% of heme iron of R375K and R375N were in the high spin state suggesting normal binding of NOHA and H 4 B toward the enzyme under the assay conditions. However, heme iron of R375D mainly existed in the low spin state. In peroxide assay system, H 2 O 2 binds directly to the NOS ferric heme forming reactive heme-oxy species and further react with Arg or NOHA to produce nitrite in the presence or absence of H 4 B [46]. Since the NOS heme does not need to acquire electrons from the reductase domain or H 4 B [50,51,58]. This method can be used to test the reactivity of the heme pocket of the oxygenase domain for NOS enzymes. For R375K and R375N mutants, their lower activities compared to wild type iNOSoxy may be either due to the lower affinity of the enzyme for H 2 O 2 or the reaction generating a heme-oxy species that is less reactive toward NOHA than wild type iNOSoxy. In the case of R375D, low binding affinity toward NOHA plus the two factors mentioned above contribute to its lower activity.

Heme midpoint potential

The NOS ferric heme midpoint potential is important in determining the thermodynamic driving force for electron transfer between the reductase domain flavins and the ferric heme. We next measured the heme midpoint potential for R375K and R375D. A spectroelectrochemical reductive titration was performed for the ferric enzyme in the presence of Arg, H 4 B, and mediator dyes [52].

Representative data were shown in Fig. 4. The heme midpoint potential were determined to be -294 ± 3 mV for R375K and -283 ± 3 mV for R375D, which are about 30 to 40 mV more negative than the wild type iNOSoxy when measured under similar conditions (-263 mV [52]; -253 mV2 ).

In NOSs, the heme iron has a proximal cysteine thiolate ligand [12,13,42] (Fig. 2). Studies have shown that the strength of the heme-thiolate bond determines the heme midpoint potential of the enzyme. One conserved residue Trp409 in nNOS (Trp188 in iNOS) engages in π-stacking with the heme and forms hydrogen bond to the proximal cysteine ligand. Mutation on this residue turns out to alter the heme midpoint potential greatly by either weakening or strengthening the heme-thiolate bond [60]. Interestingly, our Arg375 mutation lowers the heme midpoint potential, indicating that such mutation could fine tune the heme environment even though spatially Arg375 is far from the cysteine thiolate ligand. These effects are probably mediated through the direct interaction of the Arg375 side chain with the heme propionate. The decrease in the redox potential creates unfavorable thermodynamic change for the electron transfer from the flavins in the reductase domain and the ferric heme. In addition, the lower heme midpoint potential also implied the conceivable changes of the Fig. 6. Global kinetic model for NO synthesis by NOS. During steady state catalysis, the enzyme molecules engage in a productive cycle that releases free NO and in a futile cycle that releases a higher oxide of nitrogen (nitrate). Reduction of ferric enzyme to ferrous (k r ) enables the heme to bind O 2 and initiates catalytic reactions. After NO is made, an immediate product of catalysis is the ferric heme-NO complex (Fe III -NO), which can either release NO (k d ) or become reduced (k r ′) to generate a ferrous heme-NO complex (Fe II -NO). The ferrous heme-NO complex dissociates extremely slowly and instead regenerates the active ferric enzyme by reacting with O 2 (k ox ). The Fe II O 2 intermediate can also undergo an uncoupled reaction that generates ferric enzyme and superoxide. heme reactivity which is supported by NOHA oxidation reaction described above.

Oxidation rate of the ferrous heme-NO (Fe II NO) complex (kox)

The differences in kox values between the wild type and mutants often indicate subtle changes of the heme environment. We next determined kox for the wild-type and Arg375 mutants by mixing their ferrous heme-NO complexes with an air-saturated buffer in the stopped-flow spectrophotometer, and following their subsequent conversion to ferric enzyme [61][62][63][64]. The starting Fe II NO complex and ending ferric enzyme were observed for all proteins, and their conversion fit well to a monophasic transition (Fig. 5). The calculated kox values of WT, R375K, N and D are 3.11 ± 0.15, 6.35 ± 0.35, 7.72 ± 0.33 and 8.73 ± 0.37 s -1 , respectively. All mutants showed higher kox rates, which were about 2.1 to 2.8 times faster than that of the wild-type iNOSoxy. In combination with the results of the heme midpoint potential measurements, mutation at Arg375 lowered the heme midpoint potential and increased the oxidation rate of the ferrous heme-NO complex. Such relationship was observed in other mutants as well. For example, W409F, W409Y nNOSoxy mutants and mesoheme-nNOSoxy also displayed lower heme midpoint potentials and high kox values relative to their wild-type enzyme [54,60,61,[65][66][67]. For iNOSoxy, replacing Trp188 to His increases the heme midpoint potential by 80 mV due to the strengthened heme-thiolate bond, since His is able to form stronger hydrogen bonding with proximal Cys ligand, and its kox rate of W188H was found to decrease by 40% relative to wild type iNOSoxy correspondingly. 3 The correlation between the kox rate and heme midpoint potential provides a good indication about the heme environment and the characteristics of the heme-thiolate bond. NOS heme Fe II -NO complexes oxidize at much higher rates compared to any other heme protein including Hb or Mb [28,54,65,68,69]. Such difference is attributed in part by the heme-thiolate bond, causing the NOS heme Fe II NO complex to have a lower midpoint potential. Our results on Arg375 further indicate that besides the difference between the heme proximal ligands (cysteine in NOSs versus histidine in Hb, Mb and other heme proteins), modifications of nearby amino residue in the heme pocket could also modulate the heme midpoint potentials and the kox rate of NOS enzymes.

The rate of ferrous NO oxidation (kox) is one of the key parameters controlling the steady state NO synthesis in full length enzymes [63,70] (Fig. 6). During NO synthesis, practically all generated NO binds to the NOS ferric heme forming Fe III NO before exiting the enzyme. Part of the Fe III NO is reduced by the attached reductase domain and the resulting Fe II NO complex must react with O 2 (kox in Fig. 6) afterwards to return to the catalytic cycle [63,70]. Unlike other heme-thiolate proteins [54,71], NOS enzyme posses relatively high oxidization rate of Fe II NO complex which is crucial to avoid the formation of a dead-end product during catalysis. The observed faster kox rates of Arg375 mutants possibly increase the steady-state activity of iNOS by speeding the return of enzyme molecules to the productive cycle (Fig. 6) [62,70,72].

Kinetics of heme transitions, H 4 B radical formation and decay during Arg hydroxylation

Previous studies show that H 4 B performs a novel redox function in NOS: it reduces the ferrous dioxy intermediate (Fe II O 2 ) that forms during oxygen activation. This step is critical because it enables NOS to form the heme-oxy species that react with Arg or NOHA (Scheme 2). Since Arg375 forms hydrogen bond with the H 4 B ring, we investigated whether mutation affects the redox function of H 4 B during single catalytic turnover Arg hydroxylation.

First, we monitored the heme transitions that occur during a single turnover Arg hydroxylation reaction run at 10 °C in the stoppedflow spectrophotometer. Air-saturated buffer was mixed with anaerobic ferrous iNOSoxy proteins that contained H 4 B and Arg and the subsequent heme transitions were followed by UV-visible scanning [20,37,73]. Figs. 7 and 8 contain representative data collected during reactions catalyzed by wild type, R375K, R375N and R375D iNOSoxy. In Fig. 7, the left panels show the light absorbance spectra of the three enzyme species that were detected during each reaction, while the right panels show the concentration of each species changed with time during the reaction. In all mutants we could identify a beginning ferrous species, a heme-dioxy (Fe II O 2 ) intermediate, and an ending ferric species. Their Soret absorbance maxima for each heme species matched very well among wild-type and mutants. The ending ferric species for all mutants displayed a 390-393 nm peak, which indicated that their heme groups were predominantly in high spin state at the end of the single turnover reactions. In Fig. 8, Panel A shows the scanning traces collected at different time after mixing anaerobic ferrous R375D proteins with air-saturated buffer at 10 °C. Panel B contains cross section analysis of the kinetic data at 426 nm for the decay of the heme ferrous dioxy and 393 nm for the formation of the ferric enzyme. These results showed that Arg375 mutations still permitted buildup of a Fe II O 2 intermediate, and did not significantly alter its spectral characteristics or those of the ferrous or ferric species. Formation and disappearance of the Fe II O 2 intermediate were best described by monophasic transitions, consistent with previous reports [20,37,73]. The mutants showed similar Fe II O 2 formation rate as of the wild type iNOSoxy (Table 2). However, the transition of the Fe II O 2 intermediate to ferric enzyme was found to be different among mutants: R375N and R375K mutants demonstrated similar or faster rate, respectively, compared to wild type, but much slower Fe II O 2 decay rate was observed in the R375D mutant (Table 2). Our previous results show that the electron transfer from H 4 B to ferrous dioxy species in wild type iNOSoxy increases the ferrous dioxy decay rate by 40 fold compared to the H 2 B-bound protein (H 2 B cannot reduce the ferrous dioxy species). To elucidate the redox role of H 4 B in Arg375 mutants, we performed the single turnover Arg hydroxylation reactions using H 2 B-bound R375D mutant as well. Three heme species were observed, whose spectral features were identical 3 Z.-Q.Wang, and D. J. Stuehr, unpublished data. We next studied H 4 B radical formation and decay during Arg hydroxylation. Rapid-freeze experiments showed that a radical with g value of 2.0 built up in Arg375 mutants the single turnover reactions (Fig. 9). The spectra and saturation properties of the mutant radicals were either identical or similar to those of the H 4 B radical formed in wild-type iNOSoxy [13,20,37,42,74]. Signal intensities were used to calculate the amount of H 4 B radical per heme that was present at each time point. The kinetic data were fit to a two step process including the radical formation and decay (Fig. 10). The relative rates of H 4 B radical formation are consistent with the different ferrous dioxy disappearance rates we observed in the mutants, and imply that there is faster ferrous dioxy reduction by H 4 B in the R375K and R375N mutants compared to the wild type. Apparently R375 mutations alter the tempo of electron transfer: in R375K and R375N mutants, H 4 B transfers an electron faster than in the wild type protein, while H 4 B transfers an electron slower in the R375D mutant. Our kinetic data also suggest the mutations do not greatly affect the stability of the H 4 B radical since all mutants displayed similar H 4 B decay rates compared to the wild type within experimental error.

Our data analysis also shows that a normal level of H 4 B radical accumulated in the R375K mutant reaction (about 0.78 per heme) compared to wild type iNOSoxy [37]. However, there was less H 4 B radical accumulation (0.4 and 0.2 radical per heme, respectively) during the single turnover reactions of R375N and R375D iNOSoxy. Increasing the H 4 B concentration in the reactions from 1 to 5 mM did not alter the results in any way (data not shown), suggesting that the observed differences were not due to incomplete H 4 B binding. The poor radical buildup observed in these mutants might be due to their incomplete dimer formation, or possibly some unreacted H 4 B remaining in the enzyme.

We then studied the stoichiometry of Arg hydroxylation in the Arg375 mutants. NOHA was the only amino acid product in all cases and its formation depended completely on H 4 B (data not shown). The R375K, R375N and R375D mutants consistently catalyzed about 0.30, 0.25 and 0.11 of Arg hydroxylation on a per heme basis during the single turnover Arg hydroxylation reactions, respectively, which are all less extensive than in the wild type (0.55, Table 2).

Previous studies suggest that a complete and well coupled electron transfer between H 4 B and Fe II O 2 occurred for wild type NOS proteins in Arg hydroxylation single turnover reactions. Thus, one would expect that Arg hydroxylation should generate 1 NOHA/heme. However, we and others [20,[73][74][75] typically observe substoichiometric NOHA formation in single turnover reactions ranging from 0.2 to 0.8 NOHA formed per heme, which implies that subsequent steps (i.e. conversion of the iron-peroxo intermediate to iron-oxo or its reaction with Arg) become uncoupled in the wild type iNOSoxy within this setting. We recently identified a heme-oxy reaction intermediate in the W188H iNOSoxy [60], further studies should be able to shed light on this issue. Here, the mutation at Arg375 even further decreased the product stoichiometry compared to the wild type protein. One possibility is that Arg375 mutations might diminish the reactivity of the heme-based oxidant toward Arg, since this effect will only occur after H 4 B reduces the Fe II O 2 intermediate. Our H 2 O 2 -driven NOHA oxidation results supported this assumption since the heme-based oxidant formed when ferric NOS reacts with H 2 O 2 is thought to be similar or identical to the species that hydroxylates NOHA during normal NOS catalysis [33,61]. For the R375N and D mutants, their incomplete enzyme dimerization would also diminish the product stoichiometry.

Activities of mutant heterodimers

To test whether mutation at Arg375 affect the NO synthesis activity, we made heterodimers by incubating the monomer of wild type iNOSoxy (or mutants) and full-length G450A iNOS. The point mutation Gly450 to Ala prevents itself forming homodimer, thus the reductase domain from G450A is only capable of transferring electron to the iNOSoxy which enables NO production from the oxygenase domain [46,76,77]. We first converted oxygenase domains of wild type and mutants to monomer by adding urea, then incubated such monomer with full-length G450A protein in the presence of H 4 B and Arg to form heterodimer. We next measured the rates of NO synthesis and NADPH oxidation of these heterodimers in the presence of Arg and H 4 B at 25 °C using the spectrophotometric oxyhemoglobin assay. Results are summarized in Table 3.

The NO synthesis activity of the heterodimer containing iNOSoxy wild type was very close to the native wild type full length iNOS enzyme, which validates the experimental approach. All mutants showed lower NO synthesis activities with only 35%, 17% and 7% of the wild type for R375K, N and D, respectively. However, their concurrent rates of NADPH oxidation were also reduced to a lesser degree. In general, NO synthesis from Arg displays a stoichiometry of 1.5 NADPH oxidized per NO formed for wild type full length NOSs [18,78]. This minimum value was observed for our wild-type heterodimer iNOS at 25 °C, while a stoichiometry of 3.5, 7.5, and 29 NADPH oxidized per NO formed were found for R375K, R375N and R375D, respectively (Table 3). This indicates that a majority of the NADPH oxidation is uncoupled from their NO synthesis in the Arg375 mutants.

Nitrite production of heterodimer measured by Griess assay also showed the lower activity of mutants compared to the wild type, which is consistent with the results from our NO measurements (Table 3). One could argue that the lower activity might be due to the low affinity of mutant monomer toward the G450A subunit. To test this, we measured the capability of each mutant monomer to "compete" or "antagonize" wild type iNOSoxy monomer in forming a heterodimer with full length G450A iNOS. First, 300 nM wild type iNOSoxy monomer formed heterodimer with G450A iNOS. If mutant monomers could compete with wild type effectively, then addition of excess of mutant monomer (~3 μM) to the protein solution should reduce the nitrite production to a level similar to that of the heterodimer we formed by mixing mutant monomer and G450 iNOS in the absence of wild type iNOSoxy monomer. Our results in Table 3 clearly indicate that Arg375 mutants compete successfully and completely with wild-type iNOSoxy monomer in forming the heterodimer, eliminating the possibility that lower activity is associated with a lower binding affinity of the mutant monomer to full length G450A iNOS. These results are also consistent with our previous heterodimer studies of R375A mutant [46].

The catalytic profiles we observed for heterodimer are consistent with our single turnover Arg hydroxylation results. Since all the heterodimers have the same reductase domain subunit, lower NO synthesis observed in the mutants solely comes from the mutant oxygenase domain itself and/or the inter-domain electron transfer. Negative shift of the heme midpoint potential of Arg375 mutants disfavored the electron transfer from reductase domain to the oxygenase domain, which can decrease the NO synthesis activity. Also the less efficient electron transfer from H 4 B and/or reduced reactivity of heme-based oxidant further lower the NO synthesis rate in Arg375 mutants.

Conclusions

Mutation of the conserved residue Arg375, whose side chain H-bonds with in the ring of H 4 B, greatly changes iNOS properties and catalytic behavior. The decreased binding affinity toward substrate and cofactor can be overcome by purifying the enzyme in the presence of substrates and cofactor and later incubating the enzyme with additional amount of substrate and cofactor. However, the maximum dimer formation obtained in the R375N and D mutant are only 38% and 30%, respectively. Such a low percentage in these two mutants partially contributes to their lower Arg hydroxylation observed in the single turnover reactions and decreased NO synthesis rate. Mutation at Arg375 also perturbs the heme environment which leads to the negative shift of the heme midpoint potential, faster ferrous NO oxidation rate and vanished peroxide activity in H 2 O 2 -drived NOHA oxidation compared to the wild type protein. Although replacement of Arg375 by Lys (K) increases the tempo of electron transfer between H 4 B and the ferrous dioxy species formed during O 2 activation, R375K still shared similar properties like other mutants, for instance, lower NOHA production in single turnover reactions and lower NO synthesis activities and uncoupled NADPH oxidation. We attribute these properties mainly to the reduced reactivity of heme-based oxidant formed after the electron transfer from H 4 B. Our results suggest that the conserved residue Arg375 modulates the redox and structural functions of H 4 B, and is thereby important in controlling the catalytic behavior of NOS enzymes.

Supplementary materials related to this article can be found online at doi:10.1016/j.jinorgbio.2011.11.015.

Scheme 1 .Fig. 1 .

 11 Scheme 1. Biosynthesis of NO from Arginine by NOS enzymes.

Fig. 2 .

 2 Fig. 2. Protein environment of the H 4 B cofactor. A, Interactions between H 4 B and iNOS. Several iNOS residues and the heme cofactor form a hydrogen bonding network with the H 4 B cofactor. Relevant iNOS residues are shown in green; H 4 B (yellow), Heme (pink) and the substrate L-Arg (blue) are shown as sticks. Two water molecules that mediate H-bonding interactions are shown as red spheres. The hydrogen bonding interactions are shown as yellow dashes. Note the π-stacking interaction between the Trp457 side chain and H 4 B. The figure was made using PyMOL (http://www.pymol.org/) with the crystal structure of the mouse iNOSoxy dimer (PDB entry 1NOD[12,79]). B; Hydrogen bond interactions (dashed lines) between the H 4 B cofactor and iNOS. Ser112, Ile456 and Trp457 make H-bonds through the main chain carbonyl groups; Arg375 interacts through its side chain.

  R375K_Fw: 5′ ACT GGG TGG CGA TTG TGC CTC CCA TGT CGG GAT CCA TCA CCC CTG TCT TCC 3′ R375K_Rev: 5′ GGA AGA CAG GGG TGA TGG ATC CCG ACA TGG GAG GCA CAA TCG CCA CCC AGT 3′ R375N_Fw: 5′ ACT GGG TGT TCA TTG TGC CTC CCA TGT CGG GAT CCA TCA CCC CTG TCT TCC 3′ R375N_Rev: 5′ GGA AGA CAG GGG TGA TGG ATC CCG ACA TGG GAG GCA CAA TCG ACA CCC AGT 3′ R375D_Fw: 5′ GGA AGA CAG GGG TGA TGG ATC CCG ACA TGG GAG GCA CAA GA ACA CCC AGT 3′ R375D_Rev: 5′ GGA AGA CAG GGG TGA TGG ATC CCG ACA TGG GAG GCA CAA TCT TCA CCC AGT 3′

Fig. 3 .

 3 Fig. 3. Spectral properties of the R375 mutants. The spectra of proteins which were purified in the presence of 0.5 mM Arg and 3 μM H 4 B are shown in solid lines. Dashed lines show the spectra of mutants in the presence of 10 mM Arg and 200 μM H 4 B after incubation proteins with high concentration of Arg and H 4 B overnight at 4 °C. A difference spectrum created by subtracting the spectrum of the ferric enzyme from the spectrum of the enzyme ferrous-CO complex is shown in dotted line in each panel.

Fig. 4 .

 4 Fig. 4. Potentiometric reductive titration of R375K iNOSoxy. Left Panel shows spectra recorded during titration of the ferric enzyme in the presence of H 4 B, Arg, and mediator dyes. Right panel plots the measured potentials vs. the logarithmic ratio of ferric/ferrous heme.

  1 Z.-Q.Wang, M. Aoyagi, E. D. Getzoff and D. J. Stuehr, unpublished data.

Scheme 2 .

 2 Scheme 2. Arginine hydroxylation single turnover reaction by NOS enzyme.

Fig. 7 .

 7 Fig. 7. Stopped-flow analysis of heme transitions and kinetics during single-turnover Arg hydroxylation catalyzed by R375K, R375N and wild type iNOSoxy. Anaerobic ferrous iNO-Soxy proteins (4 μM) in the presence of Arg (10 mM) and H 4 B (400 μM) were mixed in a stopped flow instrument with air-saturated buffer at 10 °C and diode array spectra were collected. Left panels contain three heme species that were detected during each reaction as calculated by global analysis of the spectral data, while right panels show their concentration profiles versus time after mixing. Data are representative of three similar experiments.

Fig. 8 .

 8 Fig. 8. Heme transitions and kinetics of R375D iNOSoxy during Arg hydroxylation single turnover reactions. Panel A contains spectral scans collected at 0.0089 s (…), 0.00135 s (-) and 13.35 s (--) after mixing anaerobic ferrous proteins with airsaturated buffer at 10 °C. Panel B contains cross section analysis of the kinetic data at 426 nm and 393 nm. Data are representative of three similar experiments.

Fig. 9 .

 9 Fig. 9. H 4 B radical formation and decay during Arg hydroxylation in Arg375 mutants and wild-type iNOSoxy. Anaerobic ferrous iNOSoxy proteins were rapidly mixed with an airsaturated solution at 10 °C to start the reaction. Reactions were aged at the various times (16 ms to 5 s), and then quenched by rapid freezing. EPR traces are shown for Arg375 mutants and wild-type iNOSoxy.

Fig. 10 .

 10 Fig. 10. Calculated concentrations of the H 4 B radical versus time during Arg hydroxylation in Arg375 mutants and wild-type iNOSoxy. The relative concentration of H 4 B radical was calculated by double integration of the EPR signals shown in Fig. 9 and divided by the protein concentration.

Table 1

 1 Soret band peak absorbance values and dimeric contents of the wild-type and mutant enzymes. UV-visible spectra were recorded at room temperature. The dimerization contents were estimated by gel filtration chromatography at 4 °C.

		WT	R375K	R375D	R375N
	Fe III (-Arg, -H 4 B) 418 nm	418 nm	418 nm	418 nm
	Fe III (+Arg, + H 4 B) 395 nm	395 nm	388 nm	394 nm
	Fe II	411 nm	412 nm	413 nm	412 nm
	Ferrous-CO	444 nm	444 nm	445 nm	444 nm
	Ferrous-NO	437, 567 nm 437, 567 nm 439, 568 nm 437, 567 nm
	Dimer content	100%	100%	30%	38%

Table 2

 2 Rates of heme and H 4 B redox transitions and product yield during Arg single turnover reactions. Ferrous wild type and mutant iNOSoxy proteins containing substrates Arg and H 4 B were rapid-mixed at 10 °C with air-saturated buffer to start the reactions. Subsequent heme transitions were followed by stopped-flow rapid-scanning spectroscopy. Rates were calculated by Specfit global analysis of diode array spectral data. H 4 B radical formation and decay were monitored by EPR. NOHA productions were calculated based on HPLC results described in the Experimental section. Values are the means ± S.D. of three determinations.

	NOS	Fe II O 2	Fe II O 2	H 4 B radical	H 4 B radical	NOHA
		formation	decay	formation	decay (s -1 )	produced per
		(s -1 )	(s -1 )	(s -1 )		heme
	WT	67.8 ± 3.0	20.8 ± 0.5	11.1 a	0.7 ± 0.5	0.55 ± 0.06
	R375K 55.9 ± 3.3	29.7 ± 1.1	30.2 ± 9.8	1.6 ± 0.5	0.30 ± 0.10
	R375N 72.4 ± 3.2	17.9 ± 0.5	34.8 ± 4.6	1.2 ± 0.6	0.25 ± 0.08
	R375D NA	0.13 ± 0.01 8.8 ± 1.6	0.3 ± 0.1	0.11 ± 0.02

a Values from ref

[39]

.

Table 3

 3 Catalytic activities of heterodimers formed by the Arg375 mutants or wild type iNO-Soxy and G450A iNOS. NO synthesis and NADPH oxidation were measured at room temperature and NO 2 -production measurement was performed at 30 °C as described under "Materials and methods." The values are the mean ± S.D. of three measurements. WT, wild type.

		Heterodimer			Native
		R375K	R375N	R375D	WT	WT iNOS
	NO synthesis (min -1 )	17.6	8.6 ± 0.3 1.7 ± 0.1 51.8	50.9
		± 0.6			± 0.4	± 0.2
	NADPH oxidation	62.5	64.2	50.1	80.3	78.2
	(min -1 )	± 4.0	± 3.8	± 4.4	± 6.2	± 3.1
	NO 2 -production (μM)	7.8 ± 0.2 6.3 ± 0.1 1.8 ± 0.1 30.3	NA
	(NO 2 -/μM G450A/min)				± 0.7
	NO/NADPH	3.6	7.5	29.4	1.6	1.5
	Antagonist Study	29%	30%	13%	100%	NA

Z.-Q. Wang et al. / Journal of Inorganic Biochemistry 108 (2012) 203-215

J. Santolini and D. J. Stuehr, unpublished data.

Acknowledgments

We thank Manisha Sharma and John MacDonald for excellent technical support. This work was supported by the National Institutes of Health Grants GM51491 and CA53914 (D. J. S.), American Heart Association Beginning Grant-in-aid 0565297B (Z-Q. W.), KSU Farris Innovation Award (Z-Q. W.) and KSU Tuscarawas Faculty Professional Development Release Time Award (Z-Q. W.).

Abbreviations


A-G Allais

M Bouhier

T Edmond

N Maciol

S Nicolas

M Boffety

F Galland

M Chami

K Ebert

SOFI: A 3D simulator for the generation of underwater optical images

Keywords: GPU, GPGPU, underwater, radiative transfer, image modeling

We present an original simulator -called SOFI -for the synthetic generation of underwater optical images. The simulator architecture is flexible and relies on flow diagrams in order to allow the integration of various models for image generation which are based on the underwater optical phenomena. The objective is also to ensure real time or quasi real time performance so it takes advantage of the latest technologies, such as GPGPU, and relies on GPU programming under CUDA. Two kinds of models for image generation are presented and should be integrated in SOFI: (1) the OSOA model based on the radiative transfer theory and (2) global image modeling which describes globally how an image is deteriorated under the effects of sea water.

I. INTRODUCTION

Nowadays, optical data collected on the seabed form an essential tool in the underwater field of research for two main reasons. On one hand, this kind of information allows one to complete visual information at a medium or large scale provided for instance by acoustic echosounder images. Thus, scientists use them to analyze and to improve their knowledge of smaller underwater areas [START_REF] Jerosch | Spatial distribution of mud flows, chemoautotrophic communities, and biogeochemical habitats at Hakon Mosby Mud Volcano[END_REF]. On the other hand, optical underwater images can be used for the accurate positioning of underwater vehicles in their environment [START_REF] Barufaldi | An integrated visionbased positioning s ystem for video stabilization and accurate local navigation and terrain mapping[END_REF][START_REF] Fleischer | Global position determination and vehicle path estimation from a vision sensor for real-time video mosaicking and navigation[END_REF][START_REF] Santos-Victor | Using vision for underwater robotics: video mosaics and station keeping[END_REF]. In this latter field of research, visual information could be coupled to navigation sensor measurements in order to refine the position. Recent developments on lighting source such as LED projectors and on camera systems having a high sensitivity and definition, contributed to the expansion of the use of underwater optical images for scientific and technical purposes. Therefore, underwater image processing became a more and more important issue the last few years.

However, despite the improvement of advanced technology in sensor and lighting source devices, observations collected during field experiment at sea revealed that the quality of underwater images is significantly dependent on the angular distribution of the incoming light field and on the water t urbidity which induces absorption a nd scattering properties of l ight by marine particulates. Thus, standard image processing techniques are highly dependent on environmental conditions. Most of the time, those techniques do not take into account the physical phenomena which are at the origin of the image formation. Therefore, it is of great interest to develop image processing algorithms which need to be validated for a large set of experimental conditions. Nevertheless, the acquisition of a high number of real images at sea in various experimental conditions is very expensive and time-consuming. One interesting way to overcome these problems and to make easier the validation step of any algorithms for processing underwater images consists in using a realistic simulator of underwater images. Such a simulator should take into account optical and lighting properties as close as possible to real-world conditions. A simulator is helpful as well to perform pre-cruise analysis to optimize the set up and operational p hase of a submarine vehicle at sea. For these purposes, a 3D simulator -so-called SOFI -is being developed to generate synthetic optical underwater images. This paper describes and presents the main characteristics and originality of SOFI.

II. THE SIMULATOR

A. Short description of the simulator SOFI ("Simulateur Optique pour la Formation d'Image" French acronym for the Image Synthesis Simulator) is an operational underwater exploration mission simulator for submarine vehicles such as AUVs (Autonomous Underwater Vehicles) or ROVs (Remote Operated Vehicles) operating in a realistic three dimensional environment. The challenge is to take into account vehicle's embedded sensors, light sources, seabed and ocean's properties to reach a realistic image generation (Fig. 1). The complete architecture of the SOFI simulator is represented in Fig. 2. SOFI includes features such as:

• A GUI (Graphical User Interface),
• Network capacities,

• An environment server to take into account environment data (maps, …),

• A simulation engine including kinematic and hydrodynamic vehicle behavior models,

• A 3D rendering engine to get images without any degradation,

• A post-processing flow diagram for image synthesis with medium degradation.

B. Specific features

The main innovative feature developed in the simulator relies on flow diagrams entirely processed and accelerated through graphic hardware for the image and video synthesis. This technique r efers to GPGPU which stands for General-Purpose computing on Graphics Processing U nits [START_REF] Balz | Real-time sar simulation of complex scenes using programmable graphics processing units[END_REF]. The whole image synthesis rendering pipeline is performed through GPU (Graphic Processing Units) using the DirectX technology (Microsoft Application P rogramming I nterfaces), the HLSL language (High Level Shading Language) a nd the NVIDIA CUDA architecture (Compute Unified Device Architecture) [START_REF] Garland | Parallel computing experiences with CUDA[END_REF]. Manipulation and visualization of the inputs (raw data such as color images without lighting a nd depth map, matrices, vectors and scalars), outputs (synthesized images) and processes (illumination models, convolution, space transforms, …) are described by a set of boxes and links (Fig. 3). Generic boxes and several predefined processes have been implemented using HLSL or CUDA for basic algebra subroutines, the Fast Fourier Transform [START_REF] Fialka | FFT and convolution performance in image filtering on GPU[END_REF] or the Beer-Lambert illumination model. Then, the GUI allows one to create the flow diagram for image synthesis by combining, ordering and editing any set of algorithms. The integration of new technologies in the simulator associated with the flexibility offered to the user to create its own series of algorithms reveals the huge potential of this simulator. It shows that it can be used to a much larger extent than the field of underwater imagery.

C. Hardware accelerated flow diagram

Three dimensional e nvironment data are rendered using Direct3D pipeline (3D part of Microsoft's D irectX) which means that all inputs are processed a nd maintained within a GPU device context. The rendering process produces two dimensional r aw images. The idea is to perform the whole image synthesis using 2D post-processes, in order to take benefit from parallel computing, without losing the depth information. Us ing a raw image and a depth map makes possible to perform the 3 dimensional image synthesis like a 2 dimensional i mage processing which is a highly parallelizable process. The raw data are then processed with algorithms which are coded in HLSL and/or CUDA, using various libraries such as CUFFT (Fast Fourier Transform CUDA library) and CUBLAS (Basic Linear Algebra Subroutines CUDA library). As the main performance obstacle is memory bottleneck [START_REF] Ryoo | Optimization principles and application performance evaluation of a multithreaded GPU using CUDA[END_REF], which appears when data are transferred

D. GPU hardware choice

The simulation tasks have been dispatched between CPU (for sequential base tasks such as vehicle simulation) and GPU (for highly parallelizable tasks such as 3D rendering and image synthesis). The choice of GPU programming for image synthesis tasks was motivated by the gain in performance linked to a high memory bandwidth a nd a large amount of Floating Point Operations per Second (FLOPs) (Tab. I) [START_REF] Che | A performance study of general-purpose applications on graphics processors using CUDA[END_REF]. In addition, hardware performance evolves faster for GPU than for CPU. Since GPU enables floating point operations on latest hardware, processes such as algebra subroutines a nd Fourier transforms a re possible. O ur first analyses showed that this technology seems appropriate to integrate in the simulator the models used for the generation o f underwater images: the radiative transfer model of propagation of light in the ocean, such as the OSOA model [START_REF] Chami | Radiative transfer model for the computation of radiance and polarization in an ocean-atmosphere system: polarization properties of suspended m atter for remote s ensing[END_REF], or the global underwater image generation model such as the McGlamery's model [START_REF] Mcglamery | A computer model for underwater camera systems[END_REF].

E. RESULT

The architecture of the simulator is now available with the possibility for the user to create his own set of algorithms for image generation, programmed in both languages HLSL and CUDA. The GUI for the simulator is presented in Fig. 4. First tests have been performed and a first flow diagram for image generation has been integrated. It includes a series of processes whose complexity could be comparable to some models of underwater image generation; it consists of:

• rendering raw scene and depth data as textures,

• applying the Beer Lambert illumination model using the previously computed images,

• adding a model for distortion for the lens radial bias,

• adding marine snow based on Perlin noise,

• applying a spotlight,

• processing a depth dependent Gaussian blur.

This flow diagram reaches 60 frames per second, thus ensuring real time performance.

III. IMAGE MODELS

In order to generate synthetic underwater optical images, the simulator requires the integration of realistic image models which rely on the physical phenomena as observed in the ocean. Consequently, the development of the simulator cannot be separated from the study of optical underwater mechanisms which explain light propagation i n the ocean. Underwater images typically suffer from low contrast, limited range, diminished colors with a dominating blue-green hue as well as strong blur. The prominent reasons for these artifacts are the attenuation of light in the water, the directional scattering by particulates, and the strong water absorption in the red spectral range. Thus, the study of the marine visibility within a water body using artificial lighting has been of interest for several decades.

We can distinguish two ways to gain understanding on the processes i nvolved in underwater visibility. One way could consist of using radiative transfer theory based on optical properties of marine particulates to study light propagation i n the ocean. Another way is to use image modeling to describe globally how an image is deteriorated under the effects of sea water. Both models, i .e. radiative transfer and global image models, have first been studied and are now under integration in our simulator in order to obtain realistic underwater images.

A. Radiative transfer and the OSOA model 1) The OSOA model

Radiative transfer theory is used to model the propagation of light in the ocean close to the seabed and its interaction with suspended matter [START_REF] Mobley | Light and water: radiative transfer in natural waters[END_REF]. Integrating a radiative transfer model in our simulator is a challenging task because this type of model is quite complex a nd the final product ( simulator SOFI + radiative transfer mode) is very time consuming. So, extensive radiative transfer computations a re first performed using the OSOA radiative transfer model [START_REF] Chami | Radiative transfer model for the computation of radiance and polarization in an ocean-atmosphere system: polarization properties of suspended m atter for remote s ensing[END_REF]. The OSOA model solves the vector radiative transfer equation for the coupled atmosphere-ocean system using the successive orders of scattering method. Given a set of inherent optical properties (IOPs) in the water column, the OSOA model outputs the angular distribution of the radiance field. The originality of the OSOA model, when comparing to other widely used models such as Hydrolight [START_REF] Mobley | A numerical model for the computation of radiance distributions in natural waters with wind-roughened surfaces[END_REF], is to account for the polarization state of light in the water mass. The IOPs required for the computations are the absorption coefficient a, the scattering coefficient b and the phase functions of all compounds in the water. The phase function of the particulates i s of special interest as it describes the angular distribution of the light scattering that results from an incident beam interacting with a volume of water. The bio-optical model of the IOPs close to the seabed considers the pure seawater itself, the suspended particulates, called total suspended matter (TSM), and the yellow substance absorption ( aYS) of dissolved matter. The IOP model includes measurements of the spectral absorption of pure seawater from Smith and Baker [START_REF] Smith | Optical properties of the clearest natural waters (200-800 nm)[END_REF] and Morel et al. [START_REF] Morel | Optical properties of the "clearest" natural waters[END_REF], as well as the parameterization of the scattering of the water from Morel [START_REF] Morel | Optical properties of pure water and pure sea water[END_REF]. The IOPs of the particulates are modeled using Mie Theory [START_REF] Bohren | Absorption and scattering of light by small particles[END_REF]. The absorption of dissolved matter is modeled following Bricaud [START_REF] Bricaud | Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains[END_REF]. Based on these considerations, we focused on 3 types of water for the radiative transfer computations as described in Tab. II. As the IOPs are highly variable with wavelengths, the computations have been performed in the whole spectral range between 400 and 850 nm.

2) OSOA-based look-up tables

The OSOA simulations provide a set of look-up-tables describing the underwater radiance distributions of the light source, the target, and the radiances measured by the camera depending on a defined geometry and on the IOPs. Then, the look-up tables will be used as inputs by the SOFI simulator to compose synthetic optical underwater images.

Further, the generation of look-up tables is also and above all devoted to study the influence of the optical properties of marine particles on the diffusion of the underwater light field.

Thus, the simulations performed using the OSOA model confirm some well-known results about the light propagation in the ocean. For example, Fig. 5 represents t he normalized downwelling irradiance E d (λ,z) as a function of the wavelength λ and the distance z. It leads to the conclusion t hat light penetration in clear sea water (case A) is highest for the blue spectral range with a penetration at 15m distance of about 83%. This maximum shifts towards the green spectral range for turbid waters ( case C) and the penetration si gnificantly decreases because the downwelling irradiance drops to only 33% of the initial irradiance at 15m distance. Such a behavior of the irradiance profile was expected but with the extensive simulations, it is now quantified for a quasi-continuous range of wavelengths and distances.

In the same way, Fig. 6 presents t he ratio R of upwelling radiance to downwelling irradiance E d for case A and case C relative to distances and wavelengths. I n that case, we can notice that the reflectance ration R rapidly decreases from the blue to the red spectral range in clear waters. On the contrary, in turbid waters, the highest radiance i s located in the green radiations, which is induced by the particulate scattering.

3) Comparison between OSOA model and Beer-Lambert law

As we said, the aim of these simulations is not only to provide numerical data used as inputs by the SOFI simulator but also to have a detailed insight in the mechanisms of light propagation underwater under artificial illumination. The final goal will be to obtain a simple parametric model which can be used to model the image degradation while taking into account all the optical phenomena encountered in the ocean. The results of the radiative transfer simulations are then compared t o the simplest and widely used description for light propagation along the vertical profile of the ocean which is the Beer-Lambert law. The Beer-Lambert law describes the attenuation of light as an exponential decrease, depending on the attenuation coefficient. It is thus interesting to analyze in which case such a law is a good approximation of the complete radiative transfer.

The radiative transfer equation not only accounts for the losses of radiation due to attenuation but also for the gains of radiation due to multiple scattering. Fig. 7 represents the normalized relative difference of the reflectance ratio R derived with the OSOA model and the Beer-Lambert approximation. This figure re veals t hat in clear water (case A), the single scattering scheme is prominent in the blue radiations whereas the multiple scattering process must be considered in the green to red radiations. I n turbid water however, the multiple scattering must absolutely be taken into account as it is the major phenomenon.

B. Global image modeling

Contrary t o the OSOA model which is based on radiative transfer and whose use is quite complex and not immediate to generate images, the global image formation models are now [START_REF] Mcglamery | A computer model for underwater camera systems[END_REF][START_REF] Jaffe | Monte-Carlo m odeling of underwater image formationvalidity of the linear and small-angle approximations[END_REF][START_REF] Negahdaripour | Investigation of photometric stereo method for 3-d shape recovery from underwater imagery[END_REF][START_REF] Trucco | Self-tuning underwater image restoration[END_REF][START_REF] Schettini | Underwater image processing: state of the art of restoration and image enhancement methods[END_REF] and no more on light propagation. Assuming a "perfect" image which would have been captured on a sensor with no diffusing media, the influence of the diffusing media on the image is then modeled as a degradation of this "perfect" image [START_REF] Mcglamery | A computer model for underwater camera systems[END_REF]. This approach has the advantage to operate only few parameters (attenuation coefficient, camera-to-scene distance, …) and allows one to identify the main physical phenomena and the key parameters involved in the degradation of the quality of underwater images [START_REF] Negahdaripour | Investigation of photometric stereo method for 3-d shape recovery from underwater imagery[END_REF][START_REF] Trucco | Self-tuning underwater image restoration[END_REF][START_REF] Schettini | Underwater image processing: state of the art of restoration and image enhancement methods[END_REF].

Based on the state of the art [START_REF] Mcglamery | A computer model for underwater camera systems[END_REF][START_REF] Jaffe | Monte-Carlo m odeling of underwater image formationvalidity of the linear and small-angle approximations[END_REF][START_REF] Negahdaripour | Investigation of photometric stereo method for 3-d shape recovery from underwater imagery[END_REF], t he global image formation model that will be implemented in the SOFI simulator assumes that the underwater image is the sum of three components. Two components hold information about the scene: the direct component r epresents t he light which propagates to the camera without being scattered and the scattered component represents the light reflected by the scene and affected by scattering processes. The third component refers to the light which is backscattered to the camera without reaching the scene ( Fig. 8). This model assumes that the degradation due to light propagation in the water (attenuation and scattering) can be described by a convolution on the sensor plane.

ΔR = R ss -R ms /R ms Using such an approach (see [START_REF] Mcglamery | A computer model for underwater camera systems[END_REF][START_REF] Jaffe | Monte-Carlo m odeling of underwater image formationvalidity of the linear and small-angle approximations[END_REF][START_REF] Negahdaripour | Investigation of photometric stereo method for 3-d shape recovery from underwater imagery[END_REF] for details), it is then possible to determine the three components and finally to determine the image observed on the camera. To illustrate the interest of such a global image formation model, we have placed few pens on the floor and captured a 256x256 pixel image with a standard CCD camera (Fig. 9). The whole set-up is in the air (scene and camera). Then, Fig. 10 (a) and Fig. 10 (b) represent the image simulation corresponding to the scene which would have been observed in underwater environment, assuming a 6m scene-camera di stance and assuming that the light source is white and punctual, at a 4m distance on the left from the camera. Fig. 10 As we said, this image is composed of three additive components, each of them describing a macroscopic physical phenomenon involved in the degradation of the image quality. Thus, the use of a global image formation model allows one to study separately the different sources of degradation on underwater i mages. One objective of high interest could be to study and predict general tendencies on the degradation of the image quality due to the variations of some image formation parameters (camera-to-scene distance, attenuation coefficient, etc). For example, in Fig. 10 (a), the main source of degradation is due to the absorption of red radiations, whereas in Fig. 10 (b), a veiling light, due to the backscattering phenomenon, is observed over the whole image and degrades the contrast of the objects. In this paper, we have presented the SOFI simulator which is based on the latest technologies used for computer graphics. Its flexible architecture relies on flow diagrams and allows one to take advantage of the full potential of the technologies to integrate any set of algorithms a s soon as they are highly parallelizable processes. Although the simulator still needs to be validated with models based on optical phenomena encountered underwater, the first results are promising and show t hat it should not be limited to the only field on underwater imagery. The next step in the simulator development concerns the integration of the two physics-based models which have been described in this paper: the OSOA model via the integration of look-up tables and the global image formation model. While the radiative transfer theory is initially used to study light propagation underwater, i ntegrating the OSOA model to generate images is an innovative a nd interesting way of research as it will allow us to compare complex but accurate models to more simple models such as the McGlamery's model. A first insight into differences of radiances which could be observed has been shown through the comparison between the OSOA model and the Beer-Lambert law. Furthermore, to complete the validation, the images generated by the simulator should be compared to ground-truth images. But the key issue is not to point up the differences of the models but rather to specify the validity domain of global models. Using simple and global image models has already shown their interest as they can be used to study and develop image algorithms appropriate to the intrinsic nature of the images [START_REF] Liu | Underwater image transmission and blurred image restoration[END_REF][START_REF] Schechner | Clear underwater vision[END_REF]. One point of interest will be to use the global models to design, develop and assess quantitatively image processing algorithms appropriate to underwater images or novel optical instrumentation for underwater vehicles.

Figure 1 .

 1 Figure 1. Environment for the synthetic generation of underwater optical images SOFI aims at enabling users to integrate their own image formation physical models and process them in real time (depending on the model). Real time is a useful feature because the simulator is able to be operated in a complete operational platform c onnected with mission planning software and hardware interfaces like localization and navigation systems.

Figure 2 .Figure 3 .

 23 Figure 2. Architecture of the SOFI simulator

Figure 4 .

 4 Figure 4. GUI of the SOFI simulator

Figure 5 .Figure 6 .

 56 Figure 5. Downwelling radiance ratio Ed(z)/Ed(z=0) for case A and case C

  the formation of the image on the sensor

Figure 7 .Figure 8 .

 78 Figure 7. Relative difference of the reflectance ratio R between model OSOA and Beer-Lambert law for case A and case C

  (a) corresponds to case A water characteristics, whereas Fig. 10 (b) corresponds to case C water characteristics.

Figure 9 .Figure 10 .

 910 Figure 9. Image (256x256 pixels) acquired with a standard CCD camera into the air.

TABLE I .

 I PERFORMANCE COMPARISON

Type of processor CPU (core i7 980 XE) GPU (Nvidia Tesla C2050) Tera 100 (140000 Intel Xeon 7500)

	FLOPs	107.55 GFlops	933 TFlops	1.25 PFlops
	Price (€)	950€	1090€	50M€

TABLE II .

 II OPTICAL CHARACTERIZATION OF THE DEFINED SIMULATION CASES A, B, C

E d (z) / E d (z=0)

This work has been partially supported by French National Research Agency (ANR) through COSINUS program (project OSIFIOST n° ANR-08-COSI-008), with an additional support through the Pôle Mer PACA.