B Bessagnet

R Guillaume

Rosset

F Cousin

C Liousse

H Cachier

B Guillaume

R Rosset

Aerosol modelling and validation during ESCOMPTE 2001

Keywords: Aerosol model, Aerosol inventory, Inorganic and organics formation, Size distribution

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

Introduction

A previous paper [START_REF] Cousin | Interaction between local and regional pollution during ESCOMPTE 2001: impact on surface ozone concentrations (IOP2a and 2b)[END_REF] has dealt with dynamics and gas-phase chemistry for 24 June, during the ESCOMPTE 2001 campaign. Here, this work is extended to account for aerosol pollution. Particulate pollution has recently received much interest, due to its 1 environmental and health impacts. Up to now, general efforts have been made to model gaseous pollution but there is presently an urgent need for incorporating aerosol processes in air quality simulations in view of forthcoming European legislations on particles. Aerosols have been introduced as air quality indicators through PM-10 (particles of diameters below 10 mm) and in the near future, through PM-2.5 (diameters below 2.5 mm). Aerosols are known to have an impact on gaseous chemistry [START_REF] Ravishankara | Heterogeneous and multiphase chemistry in the troposphere[END_REF]. They also are essential for the closure of radiative budgets [START_REF] Charlson | Climate forcing by anthropogenic aerosols[END_REF][START_REF] Penner | The contribution of carbonaceous aerosols to climate change[END_REF][START_REF] Adams | General circulation model assessment of direct radiative forcing by the sulphate-nitrate-ammonium-water inorganic aerosol system[END_REF]. Threat to human health is also an important issue [START_REF] Donaldson | Ultrafine (nanometre) particle mediated lung injury[END_REF].

Aerosol impacts are closely dependent on their chemical composition and size, a very complex issue due to their chemical diversity and evolution in the atmosphere. Carbon is an essential component of the atmospheric particulate matter, mainly issued from combustion emissions [START_REF] Cachier | Biomass burning sources[END_REF]. This component comprises of two fractions: the organic carbon (OC), reactive in the atmosphere and the non-reactive black carbon (BC) [START_REF] Cachier | Carbonaceous combustion aerosols[END_REF]. Inorganic components such as nitrates, sulphates, ammonium also form a significant part of the aerosols. In most aerosol modelling works, aerosols species are generally assessed as independent passive tracers and any interaction with their gaseous environment is not taken into account. This limiting constraint is overcome with the use of an aerosol module which may simulate in some way gas-particle interactions and the chemical and size evolution (''ageing'') of the resulting heterogeneous aerosols. For this purpose, the ORganic and Inorganic Spectral Aerosol Module (ORISAM) has been developed [START_REF] Bessagnet | Fractal modelling of carbonaceous aerosols-application to car exhaust plumes[END_REF] and implemented in the 3D mesoscale model Meso-NH-C previously upgraded for dynamics, radiation and gas-phase chemistry [START_REF] Tulet | Description of the mesoscale non-hydrostatic chemistry model and application to a transboundary pollution episode between northern France and southern England[END_REF].

In this work, ORISAM has been tested and improved using an episode of the ESCOMPTE experiment [START_REF] Cros | The ESCOMPTE program: an overview[END_REF]. Although primarily devoted to gas-phase chemistry and ozone photochemical production, ES-COMPTE has produced an important set of experimental data documenting the aerosol phase and its evolution [START_REF] Cachier | Aerosol studies during the ESCOMPTE experiment: an overview[END_REF]. Indeed, seven representative sites were equipped at ground level (Fig. 1) for the study of particles and some flight tracks devoted to aerosol and gas-phase studies also allowed to gain some information on the particle vertical distribution. As furthermore expected, it has been shown that the domain was subjected to intense primary emissions and secondary anthropogenic and biogenic aerosols formations. All these conditions have resulted in a unique opportunity to test ORISAM. The period of our study, 24 June 2001, during IOP2b, was identified as one characteristic period of sea-breeze regimes and asso-ciated high pollution levels [START_REF] Cousin | Interaction between local and regional pollution during ESCOMPTE 2001: impact on surface ozone concentrations (IOP2a and 2b)[END_REF], probably one of very favourable conditions for gas-particle interactions.

A new BC and OCp emission inventory was specifically designed for ESCOMPTE, and simulations were then performed and compared with aerosol measurements at surface stations and along aircraft flight tracks.

Model descriptions

The organic and inorganic spectral aerosol module

In order to simulate aerosol size distributions, two methods are commonly used: the modal and the sectional ones. In the modal representation, each aerosol subpopulation (mode) is described by an analytical distribution function (e.g. lognormal in [START_REF] Binkowski | The Regional Particulate Matter Model. 1. Model description and preliminary results[END_REF]. This approach has the advantage of limiting the number of prognostic variables but forces the distribution function for each mode. In the sectional approach, the size distribution which is approximated by a set of contiguous, non-overlapping discrete size bins (e.g. [START_REF] Jacobson | Development and application of a new air pollution modelling system. Part II. Aerosol module structure and design[END_REF][START_REF] Gong | Canadian Aerosol Module: a size-segregated simulation of atmospheric aerosol processes for climate and air quality models. 1. Module development[END_REF], appears more suitable for a detailed description of the microphysical processes. This has been adopted here for its flexibility in solving aerosol multicomponent systems involving the processes of coagulation, condensation, absorption and chemical reactions.

In the ORISAM module, first developed by [START_REF] Bessagnet | Fractal modelling of carbonaceous aerosols-application to car exhaust plumes[END_REF], there are four bins from 0.03 to 10 mm. These four sectional bins are chosen as follows: bin 1 between 0.03 and 0.156 mm, bin 2 between 0.156 and 0.625 mm, bin 3 between 0.625 and 2.5 mm and bin 4 between 2.5 and 10 mm.

A detailed aerosol speciation has been adopted (Table 1) to better represent aerosol composition. Initially, this aerosol module comprised six chemical species: anthropogenic carbon, secondary OC, sulphates, nitrates, ammonium and water. Here, carbonaceous anthropogenic primary species have been partitioned into two primary carbon components, BC and primary organic carbon (OCp). We also have partitioned secondary organic aerosols to differenciate between their anthropogenic or biogenic origins. Nitric acid is produced in the gas phase by NO x oxidation. Ammonium is considered a primary species converted into the aerosol phase by ammonia neutralization with nitric and sulphuric acids. Secondary Organic Aerosol (SOA) are formed from condensable Volatile Organic Compounds (VOC) using aerosol yields [START_REF] Moucheron | Development and testing of a process model for secondary organic aerosols[END_REF] and partition coefficients [START_REF] Pankow | An absorption model of gas/particle partitioning of organic compounds in the atmosphere[END_REF]. Aerosol growth follows condensation/ absorption processes on preexisting particles followed with subsequent coagulation. We also have added the nucleation process for sulphates according to [START_REF] Kulmala | Parameterizations of sulphuric acid/water nucleation rates[END_REF]. A detailed description can be found in [START_REF] Bessagnet | Aerosol modelling with CHIMERE-Preliminary evaluation at the continental scale[END_REF].

The mesoscale non-hydrostatic chemistry model (Meso-NH-C)

The ORISAM module has been implemented on-line within the mesoscale non-hydrostatic Chemistry model (Meso-NH-C), and a full description of that is given in [START_REF] Cousin | Interaction between local and regional pollution during ESCOMPTE 2001: impact on surface ozone concentrations (IOP2a and 2b)[END_REF]. The ORISAM module and the gasphase chemical module ReLACS [START_REF] Crassier | Development of a reduced chemical scheme for use in mesoscale meteorological models[END_REF] derived from RACM [START_REF] Stockwell | A new mechanism for regional atmospheric chemistry modelling[END_REF] are also coupled on-line with Meso-NH.

Particle dry deposition has also been implemented in Meso-NH-C together with chemistry schemes modified for nitrates following [START_REF] Jacob | Heterogeneous chemistry and tropospheric ozone[END_REF] so as to account for particle impact on gaseous species.

Emission inventories

For the gas phase, the GENEMIS anthropogenic emission inventory [START_REF] Wickert | Generation of an Emission Data Base for Europe[END_REF] was first adopted at 9 km horizontal and 1 h time resolutions. For primary aerosols, since no inventory was available at comparable resolutions, two different approaches have been adopted.

In the large domain (Fig. 3a) first, we use global BC and primary OCp inventories at 11 Â 11 resolutions. These inventories were derived from [START_REF] Cooke | Construction of a 11 Â 11 fossil fuel emission data set for carbonaceous aerosol and implementation and radiative impact in the ECHAM4 model[END_REF] with an updated algorithm for the year 1997. In order to increase the resolution, CO emission inventories developed for mesoscale purposes were used to scale BC and OCp emissions. Since CO emissions are mainly due to combustion sources, they are strongly related to BC and primary OCp. Consequently, inventories were spatialised at 9 km using the GENEMIS inventory for CO. An average CO emission value for each 11 Â 11 cell in the large domain was computed from this 9 km inventory so as to obtain BC/CO and OC/CO ratios. Then, CO emissions at 9 km resolution were used to scale out primary BC and OCp aerosol emission inventories at this same 9 km resolution. With such values at 9 km resolution, computing average BC and primary OCp in each 11 Â 11 cell gave BC and primary OCp values in the updated inventory. Time evolution of BC and OCp emissions were thus assigned to follow CO emissions.

The second approach for the small domain (Fig. 3b) was based on the new fuel consumption data set established on a daily basis for the ESCOMPTE campaign (Franc -ois et al., 2004). Bottom up primary BC and OCp emission inventories were then determined for the 2001 campaign in the ESCOMPTE domain. These inventories were obtained with a 1 km resolution (Fig. 2a), including combustion of fossil fuels and wood subject to an imposed typical daily evolution. Fig. 2b displays BC emissions scaled with the CO Genemis emissions. When comparing these two figures, it appears in the new inventory that traffic emissions are better described and primary sources more accurately located. Finally, as previously explained, hourly CO variations (specific to the ESCOMPTE campaign) were used to obtain BC and primary OCp hourly inventories. Carbonaceous particle emission size distributions were defined using BC and OC measurements obtained with a 13stage impactor (Dekati) at Marseilles on 23 June [START_REF] Aulagnier | Aerosol experimental studies in ES-COMPTE[END_REF]. These distributions display a common mode in the submicrometer size range for BC and OC and an additional coarse mode for OC. These results at Marseilles are in agreement with other measurements performed at Vallon Dol, a station located 10 km north of Marseilles [START_REF] Mallet | Microphysical and optical properties of aerosol particles in urban zone during ESCOMPTE[END_REF]. In order to initialise ORISAM, we have used two lognormal functions for the two emission modes, with their characteristics listed in Table 2.

In the ESCOMPTE domain, with strong industrial and urban sources and intense traffic, sulphate formation rapidly occurs very near emission sources, in relation with high water contents within the combustion plumes. Another possibility could be heterogeneous sulphate formation at the particle surfaces in connection with high-aerosol concentrations in these plumes. These processes are not explicitly simulated. Nevertheless, since they can significantly affect initial sulphate concentrations, following [START_REF] Kasibhatla | A threedimensional global model investigation of seasonal variations in the atmospheric burden of anthropogenic sulphate aerosols[END_REF] and [START_REF] Tan | Budget and export of anthropogenic SO x from East Asia during continental outflow conditions[END_REF], we have considered that a fraction (2% here) of total SO x emissions is rapidly transformed into SO 4 2À .

Aerosol modelling and comparison with experimental data

Model configuration and meteorological situation on 24 June 2001

Two-way nested grid simulations were performed using the large domain at 9 km resolution (Fig. 3a) and the small domain at 3 km resolution (Fig. 3b)s oa st o figure out the importance of trans-boundary pollution inputs. It was the same configuration as in [START_REF] Cousin | Interaction between local and regional pollution during ESCOMPTE 2001: impact on surface ozone concentrations (IOP2a and 2b)[END_REF] with dynamic and gas-phase chemistry only, but here it was further supplemented with ORISAM, all models being run in a nested way in the large and small domains.

One crucial problem is chemical initialisation. For gaseous species, initial fields was provided by the global chemistry transport model MOCAGE [START_REF] Dufour | Observed and modelled chemical weather during ES-COMPTE[END_REF] which also gave the boundary conditions of the large domain every 3 h. The MOCAGE resolution was 0.251. For carbonaceous aerosols, the MOCAGE CO fields were also used to derive BC and OCp initial fields. For this purpose, using these CO modelled fields and domain averaged measured BC and OC values, uniform surface BC/CO and OC/CO ratios were derived all over the large domain. CO boundary conditions also gave boundary conditions for BC and primary OCp for the large domain. Then, BC and primary OCp initial fields for the small domain were deduced through direct nesting in the large domain. Other aerosol species were initialised to zero. As in [START_REF] Cousin | Interaction between local and regional pollution during ESCOMPTE 2001: impact on surface ozone concentrations (IOP2a and 2b)[END_REF], the dynamical and gasphase chemistry simulations for 24 June began on 21 June 00 UTC. For aerosols run in an on-line mode, spinup began on 23 June, 12.00 UTC, due to much higher computational costs. As shown in [START_REF] Cousin | Interaction between local and regional pollution during ESCOMPTE 2001: impact on surface ozone concentrations (IOP2a and 2b)[END_REF], 24 June is characteristic in the Marseilles region of high-pollution episodes following strong northerly mistral winds which have previously cleansed the lower atmosphere. Southerly sea-breeze regimes then appear early in the morning at coastal stations. After progressively penetrating inland, winds turn to the southwest, driving the pollutant plume from the Berre pond within the Durance valley. More generally, high-pollution (gaseous and particulate) levels are reestablished. During 24 June, temperatures were about 15-18 1C in the morning, increasing inland during the day to 28-29 1C, sometimes over 30 1C. Relative humidities were high in the morning and the evening (about 90%). To illustrate this situation, Fig. 3 shows PM-10 fields obtained from the summation of all modelled aerosols species (listed in Table 1), with superimposed winds at 12.00 UTC. The model clearly reproduces chanelling of particle pollution plumes within the Durance valley, as already observed for gases [START_REF] Cousin | Interaction between local and regional pollution during ESCOMPTE 2001: impact on surface ozone concentrations (IOP2a and 2b)[END_REF].

BC concentrations

Comparisons between simulated and measured BC concentrations were carried out in order to test the improvements that could be expected with the use of the ad hoc new ESCOMPTE combustion particle emission inventory. Fig. 4 illustrates this comparison for the BC diurnal evolution on 24 June at the stations of Marseilles, Martigues, Dupail, Plan d'Aups and Vallon Dol. For the two sites within the source regions (Marseilles and Martigues), this comparison shows fair agreement both at concentration levels and trends. At Martigues (Fig. 4b), the morning peak is not observed with the correct intensity, perhaps due to close vicinity of intense point sources. However, later this day, BC levels are well simulated. At Marseilles (Fig. 4a), a double peak is observed early in the morning. The second peak is the classical urban traffic peak, whereas the origin of the first peak is not so common. Interestingly, this double peak is reproduced only with the ad hoc ESCOMPTE-derived BC inventory, not in the GENEMIS inventory. In France, the first summer Saturday is the ''music day'' celebrated by street festivals. The day under study, 24 June, was a Sunday, the day just after this celebration. Thus, the first peak can be attributed to unusually dense traffic at night (2.00-3.00 UTC), between 23 Saturday and 24 Sunday. The model can reproduce this, since ad hoc BC emission evolutions have been specifically designed for the ESCOMPTE campaign, which is not the case for GENEMIS. The comparison between simulated and measured BC concentrations is not so satisfactory at rural sites. For example at Dupail (Fig. 4c), though the mid-day peak is well captured by the model, probably due to sea-breeze transport of the pollutant plumes from Berre and Marseilles, there is a constant significant Fig. 3. PM-10 surface concentrations (scales in mgm À3 at the right) with superimposed wind fields (vector at bottom left is for 10 m s À1 ) on 24 June 2001, at 12.00 UTC in: (a) the large domain (the square delineates the small domain) and (b) the small domain (segments represent ARAT 39 flight trajectory described in section 4.6.2). Isocontour is plot for 15 mgm À3 underestimation of the BC concentrations. This feature points out to the limitations of BC initialization by CO fields. At remote sites such as Dupail, background concentrations issued from the CO-based initialization procedure result in BC underestimation. At Plan d'Aups, a 800 m elevation site (Fig. 4d), the situation is still worse, probably due to steep very local orographic effects non-fully resolved by the model. There, the mid-day peak probably denotes rapid shifting from the free troposphere at night to the boundary layer with pollution from Marseilles and Aix. Eventually, at Vallon Dol, north of Marseilles, the second peak in the morning is well captured by the model. The first peak is measured but not simulated, while in the afternoon, the model overestimates BC levels, perhaps due to very fine orographic effects around Vallon Dol.

Particulate organic matter concentrations

Due to logistical and technical reasons, measurements at the various sites were made at different time resolutions, the number of samples obtained during 24 June 2001 vary from one to four. Model POM values were obtained by averaging all simulated values over the same time period as the duration of individual measurements. To account for non-carbon atoms in organic matter, primary POM concentrations were deduced from primary OCp concentrations by applying a scaling factor of 1.3 (a minimum value for scaling total primary and secondary OC). Total POM includes both primary POM, anthropogenic ASOA and biogenic BSOA secondary organic aerosols.In sources regions and suburban sites (Marseilles (3.42 mgm À3 measured, 6 mgm À3 modelled), Martigues and Realtor (Fig. 5a,b), concentration levels and trends are well reproduced with a tendency for overestimation at night. Conversely, at remote sites (Dupail and Plan d'Aups, Fig. 5c,d), there is a clear trend to underestimate actual concentrations, though analytical artefacts (linked to complex OC analysis) are not excluded, especially for aged aerosols. A reason could be found in the underestimation of primary BC aerosols (as previously seen in Fig. 4c andd), which consequently limits the aerosol surface available for semi-volatile OC condensation. Both BC and OC underestimations are particularly significant at Plan d'Aups. Another cause of discrepancy between experimental and model results could be related to temperature. Organic gas condensation processes onto particle surfaces are known to be limited at high temperatures [START_REF] Sheehan | Estimated effects of temperature on secondary organic aerosol concentrations[END_REF]. This result was discussed by [START_REF] Liousse | 0D-Modelling of Carbonaceous Aerosols over Greater Paris focusing on the organic particle formation[END_REF] who have shown that ORISAM displays a clear temperature dependence in SOA formation. In our case study, temperatures can reach 30-35 1C. It is possible that the temperature condensation threshold above which SOA formation declines, is somewhat too low in the model, thus possibly explaining model underestimation in the afternoon.

Sulphate concentrations

Comparisons between model results and measurements highlight a general sulphate underestimation, with overestimation only found at Martigues (Fig. 6b), tightly surrounded by strong industrial sulphate sources. At this site, the model overestimates primary sulphate concentrations, particularly in the morning before onset of the dispersing sea-breeze circulations. Several reasons can explain this general underestimation of sulphates. First, even with high SO 2 levels, sulphate formation might be too slow in the model. Homogeneous sulphate production is very slow and less efficient when compared to heterogeneous aqueous production. In our case study, with prevailing dry anticyclonic conditions, no cloud could account for an effective pathway for sulphate production. Moreover, the meteorological analyses forcing the model gave too low relative humidity (RH) at several stations, due to underestimation of the marine boundary layer RH. One consequence would be quite inefficient SO 4 production. To test this modelling artefact, using a 1D version of the Meso-NH-C model, two simulations at Realtor have been made, one using the modelled RH (obtained from 3D simulations) and the other using measured RH during ESCOMPTE. In Fig. 7, it can be observed that sulphate (and other inorganic species) mean concentrations increase with measured RH. Another cause for biases can be found in the ratio value selected for primary sulphate emissions. In the absence of any measurements, a value of 2% has been applied to SO x emission for generating primary sulphates [START_REF] Kasibhatla | A threedimensional global model investigation of seasonal variations in the atmospheric burden of anthropogenic sulphate aerosols[END_REF][START_REF] Tan | Budget and export of anthropogenic SO x from East Asia during continental outflow conditions[END_REF]. Other studies refer to different possible values for primary sulphate emissions, ranging from 1.4% [START_REF] Benkovitz | Sulphate over the North Atlantic and adjacent continental regions: evaluation for October and November 1986 using a three-dimensional model driven by observation-derived meteorology[END_REF]u pt o5 % [START_REF] Eliassen | Modelling of long-range transport of sulphur over Europe: a two-year model run and some model experiments[END_REF].

Nitrates and ammonium

As mentioned in Section 2.2, heterogeneous chemistry of nitrate precursors (NO 2 ,N O 3 and N 2 O 5 hydrolysis) has been introduced. The overall effect is an increased HNO 3 production in the gas phase, mainly in the morning and the evening (not presented here). Condensation also results in an increase of particle mass concentrations.

Nevertheless, at all stations, nitrates and ammonium are underestimated in the model. First, this can be explained by modelled low RH on 24 June (Fig. 7), even in the morning and evening when RH are the highest.

In Fig. 8, nitrates and ammonium aerosol concentrations at Realtor are displayed. The model simulates significant nitrate aerosol concentrations only in the morning. During this period, nitrate concentrations are highest in the gas phase, with low temperatures and high humidities favouring the condensation process. However, at other time periods, nitrates are almost zero. Other physical processes could presumably occur, allowing for nitrates to be generated in the aerosol phase. Some studies have hypothesised that terrigeneous mineral aerosols can react with and neutralise HNO 3 (g) [START_REF] Dentener | Role of mineral aerosol as a reactive surface in the global troposphere[END_REF][START_REF] Tabazadeh | Nitric acid scavenging by mineral and biomass burning aerosols[END_REF]. Furthermore, [START_REF] Cachier | Aerosol studies during the ESCOMPTE experiment: an overview[END_REF] have shown from detailed impactor results that nitrates were mainly in the coarse aerosol fraction, probably attached to dust and sea-salt particles. In our study, measurements show that sea-salt concentrations are very low this day but carbonate concentrations are significant. At present, in ORISAM, ISORROPIA does not account for carbonates which is probably one major cause of model nitrate underestimation. It was shown also that ammonium could decrease in the accumulation mode, sometimes in the coarse mode too. This artefact has already been noticed by [START_REF] Jacobson | Studying the effects of calcium and magnesium on size-distributed nitrate and ammonium with EQUISOLV II[END_REF].

4.6. Total aerosol investigations 4.6.1. PM-10 concentrations and size distributions at the surface At all stations, simulated PM-10 values (Fig. 9) are too low when compared to measurements. During daytime, a ratio of 2-4 can be found between model and experimental values. This underestimation was to be expected since the model does not consider resuspended particles and dust which may substantially contribute to the total PM loading [START_REF] Miguel | Allergens in paved road dust and airborne particles[END_REF]. Lack of prexisting sea-salt particles could also contribute to this discrepancy, though to a lesser extent. It must be underlined that during the day wind speeds were stronger (2.25 m s À1 ) than at nighttime (1 m s À1 ), which is consistent with this hypothesis.

Focusing on submicrometer aerosols, we have compared particle numbers measured at Marseilles with those in the first and second bins of the model. Experimental data were obtained with a TSI 3010 model CN counter, whereas in the model, the first bin refer to particles with a diameter between 0.03 and 0.156 mm and between 0.156 and 0.625 mm in the second one. This comparison proves to be satisfactory for the first bin (Fig. 10a) though the traffic peaks appear to be somewhat overestimated. The situation is quite different in bin 2, since modelled particle numbers in this bin let appear an important underestimation. This comparison confirms that aerosol underestimation is mainly due to underestimated growth processes (e.g. condensation) and lack of dust resuspension. Furthermore, for all secondary organic and inorganic species, boundary conditions values for the large domain are set to zero. Therefore, there is no transboundary transport of aged aerosols which could generate an important loading by secondary aerosols. This absence could contribute to underestimation of the daytime values.

Aerosol vertical distribution

On 24 June, the morning ARAT aircraft flight (flight 39) was devoted to gas and aerosol measurements. During this flight (Fig. 3b), total particle numbers were measured with a TSI 3022 CN counter. Size distributions (in numbers) were measured in the size range 0.01-3 mm. Fig. 11 displays the comparison between measurements and model results for total particle numbers. Almost all particles are found in model bin 1. This is particularly true when remembering that particles of radii between 0.01 and 0.03 mm are not simulated, which could potentially contribute to underestimation of particle numbers in the submicrometer size range. During the flight, aerosol numbers concentrations are fairly well simulated at all levels in the sampled atmosphere. At 6.45 UTC, the aircraft flew close to the surface, south of Marseilles and the Berre pond. Measurements denote a small increase in particle numbers. At the end of flight 39, the ARAT heading to Avignon airport flew through the Fos-Berre industrial plume. The aircraft then was at 960 hPa (about 400 m) within the boundary layer. The model has well captured the plume which confirms accurate timing of sea-breeze advance at the surface in the simulations. Due to reduced aerosol simulations and our treatment of boundary conditions, the model cannot detect transboundary aerosol transport. [START_REF] Cousin | Interaction between local and regional pollution during ESCOMPTE 2001: impact on surface ozone concentrations (IOP2a and 2b)[END_REF] have shown that regional transport of an ozone layer issued from the Barcelona area has an impact in the ESCOMPTE domain on 24 June. A similar effect could be expected for particles, though if in the ESCOMPTE domain, the particle pollution sources could be more restricted to local emissions. Due to the shorter aerosol spin-up, regional particle layers probably have not enough time to be generated in the model.

Conclusions

The ORISAM aerosol model initially incorporating coagulation and condensation processes has been improved and coupled on-line with the Meso-NH-C model to better account for interactions between atmospheric dynamics, gaseous chemistry and aerosol processes. Simulations at fine grid resolutions were performed with a new ad hoc emission combustion particle inventory based on the new ESCOMPTE detailed inventory for gases and combustion fuels. One observes overall good agreement between measured and modelled BC concentrations, particularly in urban and industrial source areas. Aerosol modelling fairly reproduces the concentration levels of particulate organic matter, despite underestimation of organics in the afternoon. Ongoing developments aim at to better representing secondary aerosol formation in the model. Aerosol yields can also be replaced by new explicit mechanistic chemical scheme for SOA formation (e.g. [START_REF] Griffin | Secondary organic aerosol. 1. Atmospheric chemical mechanism for production of molecular constituents[END_REF]. Inorganic species are underestimated in the model. For sulphates, even with high sulphur dioxide levels, sulphate oxidation is underestimated. One reason lies in the underestimation of relative humidities affecting the condensation processes. Another important factor requiring further consideration is more precise assessment of primary sulphate emissions especially in areas with many industrial plumes such as the ESCOMPTE domain. Nitrates and ammonium are also affected by RH underestimation. After implementing heterogeneous chemistry for nitrates, aerosol concentrations of nitrates and ammonium increase, particularly in the morning and the evening. Nevertheless, our modelling study underlines the major biaises due to neglecting the terrigeneous species, thus leading to major nitrate and ammonium underestimations. This points out to necessary further developments with incorporation of pre-existing coarse carbonates or dust in order to capture the attachment of acidic gases. Consequently, modelled PM-10 concentrations are underestimated versus measurements. The situation is much more satisfactory for submicron particles. The model could reproduce particle numbers in this submicrometer size range, at the surface and in the atmospheric column. Future improvements for this sizerange could be identified related to transboundary conditions providing higher background conditions thus enhancing submicron particle concentrations directly or indirectly by offering additional surface areas to further gas-to-particle conversion.

As an extension to a previous paper [START_REF] Cousin | Interaction between local and regional pollution during ESCOMPTE 2001: impact on surface ozone concentrations (IOP2a and 2b)[END_REF] devoted to dynamics and gas-phase chemistry for 24 June 2001, this study further considers aerosols coupling in an on-line mode, within Meso-NH-Chemistry using the ORISAM model [START_REF] Bessagnet | Fractal modelling of carbonaceous aerosols-application to car exhaust plumes[END_REF]. During this exploratory phase of aerosol modelling, some agreement but also limitations have been pointed out, useful for further developments of OR-ISAM, particularly as regards terrigeneous and secondary organic aerosol components.

Fig. 1 .

 1 Fig. 1. Orographic map (height isocontours) of the central ESCOMPTE domain with the main stations and valleys quoted in the text.

Fig. 2 .

 2 Fig. 2. BC emission inventory (a) ad hoc ESCOMPTE 2001 inventory and (b) derived from the CO Genemis inventory (scale in mgm À2 s À1 at the right), for 24 June at 7.00 UTC.

Fig. 4 .

 4 Fig. 4. BC time series (scale in mgm À3 at the left) for 24 June at: (a) Marseilles, (b) Martigues, (c) Dupail, (d) Plan d'Aups and (e) Vallon Dol (grey curve (a) and squares for model, dark curve for measurements)

Fig. 5 .

 5 Fig. 5. Particulate Organic Matter (POM) concentrations (scales in mgm À3 at the left) at different time periods (in UTC) on 24 June at: (a) Martigues, (b) Realtor, (c) Dupail and (d) Plan d'Aups (measurements in dark, average model values in grey).

Fig. 6 .

 6 Fig. 6. Sulphate aerosol concentrations (scale in mgm À3 at the left) for different time periods on 24 June at: (a) Marseilles, (b) Martigues, (c) Realtor, (d) Dupail (measurements in dark, model in grey)

Fig. 8 .

 8 Fig. 8. (a) Nitrates and (b) ammonium aerosol concentrations (scales in mgm À3 at the left) for different time periods on 24 June at Realtor (measurements in dark, model in grey).

Fig. 9 .

 9 Fig. 9. PM-10 time series (scales in mgm À3 on the left) for 24 June at: (a) Marseilles, (b) Martigues, (c) Dupail, (d) Realtor and (e) Plan d'Aups. (squares for the model, dark curve for measurements)

Fig. 10 .

 10 Fig. 10. Particle number time series (scale in part cm À3 on the left) for 24 June at Marseilles in: (a) the first bin and (b) the second bin.

Fig. 11 .

 11 Fig. 11. Time series during the ARAT morning flight 39 for particle numbers (scale in part cm À3 on the left). Bin 1 particles between 0.03 and 0.156 mm, total model particle between 0.03 and 3 mm, CNC total particle number between 0.01 and 3 mm.

Table 2

 2 Characteristics of the lognormal function for BC and primary OCp emissions (sigma is the dispersion parameter)

	Median diameter (mm) Sigma BC (%) OC (%)
	Mode 1 0.197	1.89	100	60
	Mode 2 2.905	2.02	0	40

Acknowledgments

The authors warmly acknowledge J. Escobar, D. Gazen, J. Duron, S. Prieur and L. Cabanas for their assistance. We would like to thank Fabien Aulagnier for his help on measurements. Computational resources were provided by the Institute for Development and Resources in Intensive Scientific computer. This work is supported by ADEME and CNRS.


V Izotov

A V Sidorov

V A Skalyga

V G Zorin

T Lamy

L Latrasse

T Thuillier

Experimental and Theoretical Investigation of the Preglow in ECRIS

The experimental study of pulsed mode operation of the PHOENIX-V2 Electron Cyclotron Resonance Ion Source at 28 GHz has clearly demonstrated, when increasing the repetition rate of the HF power injection at frequencies higher than 1 Hz, the reality of a transient current peak occurring at the very beginning of the plasma discharge. This regime was named the Preglow, as an explicit reference to the classical Afterglow occurring at the microwave pulse end. After the transient Preglow peak, the plasma regime relaxes to the classical steady state one. Experimental argon pulses for charge states from 2+ to 8+ are presented. The current observed during the preglow peak can reach intensities of the order of 1 mA for low charge states (Ar4+). A 0-dimension theoretical model of ECR gas breakdown in a magnetic trap is presented in detail. Results of the simulation are compared with the experimental Preglow peaks and discussed.

I. INTRODUCTION ELECTRON Cyclotron

Resonance Ion Sources (ECRIS) can be used in continuous or pulsed operation. The pulsed operation, permitting to generate the well known Afterglow mode (AFG) [START_REF] Briand | A newly designed ECR source for the lead injector of CERN[END_REF][START_REF] Sortais | Pulsed ECR ion source using the afterglow mode[END_REF], has been used daily at CERN for pulsed heavy ion beams production [START_REF] Hill | Pulsed ECR source in afterglow operation at CERN[END_REF]. When varying the parameters (confinement magnetic field, microwave frequency and power, gas pressure, gas nature …), different plasma regimes can be observed. If one can find stable and reproducible regimes, new applications for ECRIS may be found. For example, within the EURISOL design study, the Beta Beam project [4] may take profit of direct, fast and efficient multi ionization of 6He and 18Ne gases. The generation of 2 × 1013 ions of 6He2+ or 8 × 1011 ions of 18Ne6+, grouped in a 50 to 100 μs pulse, would permit a direct injection into a post accelerator LINAC. When injecting pulsed HF power with a repetition rate of 1 Hz into the PHOENIX-V2 ECRIS, it can be noticed a fast pressure decrease leading to instabilities during the pulse. This fast pressure decrease is associated with a fast transient intensity peak occurring at the beginning of the plasma discharge. Due to the high drain current of the high voltage supply during this peak, it is supposed that the pressure drop is the consequence of the ionic pumping and that the instabilities are due to the inadequate HF coupling in such low plasma density. This peak has been named Preglow mode (PGW) [START_REF] Sortais | ECRIS development for stable and radioactive pulsed beams[END_REF] in analogy with the AFG which occurs at the microwave pulse end. To generate such a peak in experiments, it appears to be necessary to operate with high repetition rates (typically 1 Hz). In such experimental conditions, a low density plasma with hot electrons can be confined from pulse to pulse if its lifetime is higher than the delay between HF pulses. The presence of this low density plasma at the very beginning of the RF pulse seems to be an important condition to generate PGW. This paper is organized as follows: in the first section the experimental setup is described and an example of experimental argon PGW pulses are presented and quantitatively described. After a second section, where a theoretical 0-dimension simulation program intending to explain experimental data is presented, the simulation results are presented and compared with the experimental ones leading to a global discussion and to the conclusion.

II.

EXPERIMENTAL DESCRIPTION The experiments have been performed on the PHOENIX-V2 ECRIS test bench described in details in [START_REF] Thuillier | High Intensity Ion Beams Prospects for Accelerators with PHOENIX 28 GHz[END_REF]. The PHOENIX-V2 source is a modified version of the original PHOENIX one. The radial confinement magnetic field has been improved and the length of the hexapole reduced from 320 mm to 240 mm. The plasma chamber volume is ~ 0.7 liter. The minimum-|B| structure of the ECRIS is obtained by the superposition of a radial hexapolar field (Br = 1.3 Tesla at the plasma chamber walls) with an axial magnetic field forming a magnetic bottle tunable within the following ranges: at the injection side the maximum magnetic induction is Bzinj ≤ 1.66 T, in the middle plane 0.45 T ≤ Bzmid ≤ 0.95 T, and at the extraction side Bzextr ≤ 1.31 T. The plasma is heated with a 28 GHz frequency microwave delivered by a Gycom gyrotron [START_REF] Bykov | 28 GHz 10 kW gyrotron system for electron cyclotron resonance ion source[END_REF] with a 10 Hz repetition rate and a microwave pulse duration in the range of 5 to 15 ms. The total beam extracted from the ion source is analyzed through a 90° magnetic spectrometer and collected in a Faraday cup. The plasma electrode hole has a diameter of 10 mm and the extraction voltage is ranging from 30 to 40 kV. These values were chosen to get good beam transmission efficiency (> 80 %) throughout the 90° dipole to the analysis Faraday cup. The high transmission efficiency permits to consider that the ionic spectra obtained give a confident picture of the ECRIS plasma boundary conditions where the ions are extracted from. The technique used to analyze the experimental ionic pulses is described in [START_REF] Thuillier | Study of pulsed electron cyclotron resonance ion source plasma near breakdown: The Preglow[END_REF]. A typical time evolution of the multi-charged argon intensities for different charge states is presented on Fig. 1, where the PGW peaks for charge states from 2+ to 8+ can be seen. The HF pulse begins at t = 0 and ends at t = 10 ms, the spectrum is optimized on the Ar4+ PGW peak due to a high gas flow used for the experiment. On the vertical scale the intensity between two sticks is 500 μA, the signals have been shifted up by this value for better view. The PGW peaks, whatever is the charge state, appear in the time range from ~ 1 to ~ 1.5 ms, this implies a fast ionization process with a hot and dense electron population. After 2 ms, the steady state is reached for every charge state, and at the end of the HF pulses (10 ms) the AFG peak increases for increasing charges. If we count the number of ions in a time window of 100 μs centered on the PGW peak maximum for each charge state, we see that the maximum number is available on the Ar 2+ , as shown in Fig. 2.

III. THEORETICAL PLASMA MODEL

In order to have a good understanding of the plasma characteristics evolution, a detailed analysis of the gas discharge ignition is necessary. The following description of PGW phenomenon is based on calculations which have been made with the zero-dimension plasma confinement model in a magnetic trap, which was firstly proposed in [START_REF] Semenov | Scaling for ECR Sources of Multicharged Ions with Pumping at Frequencies from 10 to 100 GHz[END_REF]. Significant additions to this model and calculations for different argon charge states have been performed and are presented here.

A microwave induced plasma ignition of a rarefied gas in a magnetic trap under the ECR conditions may be separated into two stages for which the growth rate of the plasma density is determined by different processes governed by the evolution of the electron energy distribution function (EEDF). At the first stage, the density is low and the electrons undergo a fast energy increase, the dominating process is the neutral gas ionization due to collisions with hot electrons. The plasma density at this stage grows fast, the gas ionization degree is low and the power absorbed by the plasma is less than the one absorbed at the steady state of the discharge. At the second stage, the increase of the density slows down significantly, the step by step ionization process goes further modifying the EEDF, the average charge state increases, and the power absorbed by the plasma is approximately equal to the value at the steady state.

The initial stage of the plasma ignition, when the density is sufficiently low, can be described with the theory for a microwave breakdown of a rarefied gas in axi-symmetric mirror magnetic traps under the electron cyclotron resonance conditions [START_REF] Suvorov | K teorii SVCh proboja razrezhennogo gaza v adiabaticheskoj magnitnoj lovushke pri elektronno-tsiklotronnom rezonanse[END_REF]. In this work, an important factor which significantly affects the EEDF shape is the superadiabaticity effect. When the plasma density is so low that the electron collision time is much longer than the interaction time of an electron with the HF wave in the resonance region, the energy transfer can be described like for a single electron. The ECR energy increase of the lone electron in a mirror trap is governed by stochasticity, and this remains true when the phase change of the microwave between two electron passes through the ECR zone is much more than 2π [START_REF] Suvorov | K teorii SVCh proboja razrezhennogo gaza v adiabaticheskoj magnitnoj lovushke pri elektronno-tsiklotronnom rezonanse[END_REF]. With the electron energy increase, the electron travel time from mirror to mirror decreases and consequently the phase variation of the heating wave decreases too, this means that at a certain electron energy value the stochastic mechanism of electron heating is destructed. At this stage the interaction of hot electrons with a microwave is called superadiabatic interaction. The consequence of this interaction is an electron energy limitation which is called superadiabaticity effect. Hence, while the plasma density is low, the plasma is unable to absorb more energy than a certain value, because the energy of every electron is limited. In [START_REF] Suvorov | K teorii SVCh proboja razrezhennogo gaza v adiabaticheskoj magnitnoj lovushke pri elektronno-tsiklotronnom rezonanse[END_REF] it has been shown that the following EEDF is formed under such conditions:

( ) ( ) ( t N F eff st st e ν ε ε εε exp 1 2 1 0 - = - )(1)
where 1(x) is the unit Heaviside function, N e0 the initial number of electrons, the energy boundary value determined by the superadiabaticity effect (ω : HF frequency, E : electric field amplitude of the wave), where τ * is the electron density increase characteristic time. In the case of axi-symmetric magnetic traps, as it has been analytically shown in [START_REF] Suvorov | K teorii SVCh proboja razrezhennogo gaza v adiabaticheskoj magnitnoj lovushke pri elektronno-tsiklotronnom rezonanse[END_REF], the formation time of the EEDF (1) is much shorter than τ *, the EEDF obtained was later confirmed by numerical calculations performed with the General ECRIS Model code developed by FARTECH, Inc. and based on a 1D bounce-averaged Fokker-Planck modeling for the electrons [START_REF] Edgell | Modeling of electron cyclotron resonance ion source plasmas[END_REF]. An example of such a superadiabatic EEDF at the very beginning of an ECR discharge is plotted in Fig. 3. When the plasma density increases further, the absorbed heating power becomes insufficient to maintain the EEDF (1). This happens when the total energy, absorbed since the beginning of the discharge, equals to the sum of the energy lost in the plasma leaks from the trap and in the ionization processes. In order to describe this process, it is necessary to use the electronic energy balance (equation 2) which takes into account the energy introduced by the microwave power, the energy losses caused by the plasma leaks from the trap and by the ionization processes.

4 / 3 2 / 1 E st ω ε ∝ 1 * ) ( - = τ ν eff
n i i i e i i I N N k (2)
Here N e is the electron density, T e the electron temperature, P the microwave power flow density, L the trap length, k i, i+1 the ionization rate from the charge state i to the charge state i +1, N i the ion density for the charge i, I i the ionization potential of an ion with charge i, τ e the electron life time, Δψ the difference between the plasma potential at the centre of the trap and behind the magnetic plug, and n the electron number for the considered atom. In the framework of the considered model, since this moment the EEDF is assumed to be Maxwellian.

For modeling the plasma discharge dynamics, the energy balance equation and a non-stationary differential equations system for the ionization balance are used. The ionization balance is described by the following set of equations for all ionization states i:

e i i i i i i i N N k N k t N ⋅ - = ∂ ∂ + - - ) ( 1 , 1 , 1 o i ex ch i o i ex ch i i i N N k N N k N 1 . . 1 . . + + + - -τ (3)
Here τ i is the ion lifetime for different charge states i, the charge exchange rate of ion with charge i on neutrals, k i,i+1 are the electron impact ionization rate coefficients calculated for the Maxwellian EEDF:

∫ ∫ >= =< ε ε ε ε ε σ ε σ d F d v F v k ) ( ) ( ) ( ) ( (4)
Here σ is the ionization cross section, v the electron velocity, F(ε) the EEDF. For σ, data from Povyshev (Joint Institute for Nuclear Research, Dubna) [START_REF] Povyshev | Electron impact ionization cross-sections of H[END_REF] are used. The equation for the electrons is written as:

The neutral gas density equation is:

e , extr N N k Q S t I dt dN 0 1 0 0 ) 1 ( ) ( - - + = ∑ = - n i o i ex ch i N N k 2 . . ( 6
)
where I(t) is the number of gas atoms injected per cubic centimeter per second into the trap, S the flux of neutrals which comes from the chamber walls due to the ion neutralization on them, Q extr an extraction coefficient taking in account the proportion of the ion flux extracted through the plasma electrode hole (not reaching the plasma chamber walls), including such coefficient into equations allows to model the ion pumping effect. The system is supplemented by the plasma quasi-neutrality condition:

∑ = × = n i i i e e N i N 1 τ τ (7)
Equations ( 3), ( 5)-( 7) do not directly depend on the shape of EEDF. The dependence on the EEDF is present in calculations of T e and k i,i+1 (equations 2 and 4).

The previous system of equations should also be supplemented by equations for plasma lifetime, and then it is necessary to determine a confinement regime of the plasma.

A mirror-confinement regime for a plasma composed of hot electrons and cold ions crucially depends on the ordering of the characteristic timescales such as the Coulomb electron scattering into the loss-cone time τ c = ln R/ν ei , the thermal ion escape time from the trap τ T = LR/V T , and the gas dynamic confinement time τ g = LR/V s .

Here, R is the mirror ratio, ν ei the electron-ion Coulomb collision frequency,

M T V i T = the ion thermal velocity, M T z V e s > < =
the ion sound velocity for the average charge <z> of the ion population, T i and T e are the ion and electron temperatures and M is the ion mass. At low plasma densities, we have:

g T c τ τ τ >> >> T c
. Therefore, the loss-cone turns out to be essentially empty and the electron confinement time e τ τ τ >> ≈ [START_REF] Pastukhov | Collisional losses of electron from an adiabatic trap in a plasma with a positive potential[END_REF].

Accordingly, to keep the plasma quasi-neutrality, an ambipolar field in the trap should delay ion escape. This is achieved by means of ambipolar potential "humps" formed in the vicinities of the magnetic plugs. In this case the ion confinement time can be roughly estimated as [START_REF] West | Calculations of Ion Charge-State Distribution in ECR ion Sources[END_REF], [START_REF] Rognlien | Transition from Pastukhov to collisional confinement in a magnetic and electrostatic well[END_REF]:

⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ × Δ × × × ≈ i i T i T k e z ϕ τ τ exp ( 8
)
where τ i is the confinement time of an ion with charge state z i , e the electron charge, k the Boltzmann constant, and Δϕ the height of the potential "hump". When the plasma density increases, the electron confinement time decreases, and at some point, drops below the ion thermal escape time from the trap. Thereafter, the potential "humps" have vanished, and ambipolar field starts contributing to ion ejection from the trap (Δϕ becomes negative). Due to frequent ion-ion collisions, the confinement times for different kinds of ions appear to be almost the same, and thus, equal to the electron confinement time, which is determined by Coulomb scattering if

g c τ τ > : c e i τ τ τ ≈ ≈ (9)
or by gas-dynamic processes if the plasma density is so high that g c τ τ < [START_REF] Mirnov | Gazodinamicheskaja linejnaya lovushka dlya uderzhanija plazmy[END_REF]:

g e i τ τ τ ≈ ≈ (10)
In ( 9) the EVDF (Electron Velocity Distribution Function) loss-cone is still empty, while in [START_REF] Suvorov | K teorii SVCh proboja razrezhennogo gaza v adiabaticheskoj magnitnoj lovushke pri elektronno-tsiklotronnom rezonanse[END_REF] it is filled with electrons. Such regime of non-equilibrium plasma confinement was called quasi-gasdynamic confinement regime [START_REF] Golubev | Formation of multicharged ions and plasma stability at quasigasdynamic plasma confinement in a mirror magnetic trap[END_REF].

Plasma losses determined by transversal diffusion in the magnetic field were not calculated due to the complexity of a minimum-|B| structure. However, because it can significantly change quantitative variables of PGW, transversal lifetime was included into calculations as a free parameter, as well as the microwave power absorption rate. These free parameters were adjusted to provide the best agreement between experimental and calculated ion currents for all charge states at the steady state of the discharge. The initial density of neutrals is fixed by the initial pressure in the plasma chamber and is set to 2 × 10 11 cm -3 .

IV. COMPARISON OF NUMERICAL AND EXPERIMENTAL RESULTS

A. Direct Comparison of Experimental and Simulated PGW Peaks

The previous described model has been used in order to simulate experimental argon PGW peaks shown on Fig. 1. The time evolution of the ionic currents has been computed simultaneously for all argon charge states with a predefined set of free parameters. For each argon charge state, the experimental current intensity and their corresponding numerical fit are shown in Fig. 4. The best fit has been obtained with the free parameters set as follows: absorption rate = 25 % and upper limit for transversal lifetime = 600 µs. There is a good agreement between numerical simulations and experimental data for charges lower than 4+ where the PGW peak intensity is well reproduced. However these peak intensity values are overestimated for higher charges, it can be explained by the approximate calculation of the transversal ion lifetime which strongly depends on the ion charge states. In Fig. 4c) we have plotted the calculated electron energy averaged over the EEDF, after about 1ms due to the transition between the superadiabatic EEDF and the Maxwellian one, the average electron energy begins to collapse.

B. Gaussian Fit of the Experimental PGW and Parameters Simulation

In order to study the PGW peak characteristics, several parameters have been introduced for convenience based on the results of a Gaussian fit of the PGW peaks. The PGW parameters, presented in Fig. 5, are as follows: Imax is the peak maximum current, FWHM is the peak full width at half-maximum, TImax is the time to reach the maximum intensity (t=0 being microwave trigger) and T10 is the time to reach 10 % of Imax. The PGW peak is not symmetric, so the fits were made using mainly the left part of the curve, ie for time t ≤ TImax. To check the validity of the simulation of the peak intensities, the microwave power has been varied (three different levels of microwave power have been injected: 3000W, 4500W, 5000W) keeping all the other ECRIS parameters constant. The charge state dependence of I max has been plotted Fig. 7 with the results of the numerical calculations (the P values in the simulation were respectively 15 W/cm 2 , 22.5 W/cm 2 , and 25 W/cm 2 . 6 and Fig. 7 that the proposed numerical model is able to predict the main PGW parameters dependences for charge states q = 2, 3 and 4. A huge deviation appears for higher charge states, not due to charge exchange processes which were included in the simulation. The evolution of the I max values obtained by the simulations for the consecutive charge states presents one or two maxima, they could be due to competitive effects that have not been yet identified by the authors. The general agreement between calculations and experimental results allows us to propose an explanation of the PGW phenomenon based on the results of the computer modeling. In Fig. 4-c) the Ar 4+ PGW peak is presented together with the calculation of the average electron energy in the discharge. After t ≈1 ms, the fall away of <E> is connected with the plasma density overrunning the level where the EEDF transits from superadiabatic to Maxwellian. Further plasma density increase leads to a decrease of <E>. The longitudinal lifetime is decreasing as a result of the <E> dropping which provides a fast plasma outlet from the ECRIS and the formation of a PGW peak.

The only experimental parameter we could not change during experiments was the microwave frequency, the PHOENIX-V2 ion source having only one microwave injection port. The relatively good agreement between experimental results and simulations, in the case of Ar 4+ , allows us to make a qualitative numerical estimation of the evolution of the PGW parameters with the microwave frequency. In this simulation, other parameters of modeling were chosen to maintain the same charge state distribution along the microwave frequency scale. It is visible on Fig. 8 that the PGW intensity is increasing and the time width of the signal is decreasing for higher microwave frequencies. The use of frequencies higher than 28 GHz associated with higher microwave power injection will permit to increase the plasma density and then should permit to increase PGW efficiency for high charge states. CONCLUSION To produce neutrino beams, the use of intense and pulsed accelerated radioactive ion beams is under evaluation for the Beta Beam project connected to the EURISOL facility. When using radioactive ion beams, efficiency is an important parameter, the work presented here does not evaluate nor measure the efficiency, however this work shows the possibility to produce intense pulsed beams about one order of magnitude below the expected intensities for the beta beam project and shows a potential increase of this intensity when using higher microwave frequencies. The authors expect that future study of the Preglow phenomenon under conditions of 60 GHz microwave ECR heating should effectively show the Preglow peak FWHM reduction, and an intensity increase leading to the expected intensity level.

Fig. 1 .

 1 Fig. 1. Example of experimental PGW peaks for argon. The microwave power is 3100 W, the extraction voltage is 30 kV, the pulse duration is 10 ms and the plasma electrode diameter is 10 mm.

Fig. 2 .

 2 Fig. 2. Number of ions in a 100 μs time window centered on the PGW peak maximum for each charge state.

Fig. 3 .

 3 Fig. 3. Example of a superadiabatic EEDF.

Fig. 4 .

 4 Fig. 4. Experimental argon pulses and their numerical fits for a) Ar 2+ , b) Ar 3+ , c) Ar 4+ , d) Ar 6+ , e) Ar 7+ , f) Ar 8+ . Calculated <E> is also plotted on c).

Fig. 5 .Fig. 6 ,Fig. 6 .

 566 Fig. 5. Experimental PGW along with a Gaussian fit.The variations of such PGW parameters as T 10 , T Imax , FWHM and Imax with the ion charge states are shown

Fig. 7 .

 7 Fig. 7. Imax for different Ar charge states with increasing microwave power: a) 3000 W b) 4500 W c) 5000 WIt is visible on Fig.6and Fig.7that the proposed numerical model is able to predict the main PGW parameters dependences for charge states q = 2, 3 and 4. A huge deviation appears for higher charge states, not due to charge exchange processes which were included in the simulation. The evolution of the I max values obtained by the simulations for the consecutive charge states presents one or two maxima, they could be due to competitive effects that have not been yet identified by the authors. The general agreement between calculations and experimental results allows us to propose an explanation of the PGW phenomenon based on the results of the computer modeling. In Fig.4-c) the Ar 4+ PGW peak is presented together with the calculation of the average electron energy in the discharge. After t ≈1 ms, the fall away of <E> is connected with the plasma density overrunning the level where the EEDF transits from superadiabatic to Maxwellian. Further plasma density increase leads to a decrease of <E>. The longitudinal lifetime is decreasing as a result of the <E> dropping which provides a fast plasma outlet from the ECRIS and the formation of a PGW peak.The only experimental parameter we could not change during experiments was the microwave frequency, the PHOENIX-V2 ion source having only one microwave injection port. The relatively good agreement between experimental results and simulations, in the case of Ar 4+ , allows us to make a qualitative numerical estimation of the evolution of the PGW parameters with the microwave frequency. In this simulation, other parameters of modeling were chosen to maintain the same charge state distribution along the microwave frequency scale. It is visible on Fig.8that the PGW intensity is increasing and the time width of the signal is decreasing for higher microwave frequencies. The use of frequencies higher than 28 GHz associated with higher microwave power injection will permit to increase the plasma density and then should permit to increase PGW efficiency for high charge states.

Fig. 8 .

 8 Fig. 8. Microwave frequency influence on PGW parameters

ACKNOWLEDGMENT

The authors wish to pay tribute to Pr. Richard Geller who pushed us with all his strength to go forward in the PGW study.


Nadine Assrir

Anna Pavelkova

Régine Dazzoni

Rémi Ducasse

Nelly Morellet

Eric Guittet

Sylvie Rebuffat

Séverine Zirah

Yanyan Li

Ewen Lescop
email: ewen.lescop@cnrs.fr

Initial molecular recognition steps of McjA precursor during microcin J25 lasso peptide maturation

Keywords: lasso peptide, NMR, intrinsically disordered protein, SDS micelles, biosynthesis

Microcin J25 (MccJ25) has emerged as an excellent model to understand the maturation of ribosomal precursor peptides into the entangled lasso fold. MccJ25 biosynthesis relies on the posttranslational modification of the precursor McjA by the ATPdependent protease McjB and the lactam synthetase McjC. Here, using NMR spectroscopy we show that McjA is an intrinsically disordered protein without detectable conformational preference, which emphasizes on the active role of the maturation machinery on the three-dimensional folding of MccJ25. We further show that the Nterminal region of the leader peptide is involved in interaction with both maturation enzymes and identify a predominant interaction of V43-S55 in the core McjA sequence with McjC. Moreover we demonstrate that residues K23-Q34 in the N-terminal McjA leader peptide tend to adopt a helical conformation in presence of membrane mimics, implying a role in directing McjA to the membrane in the vicinity of the lasso synthetase/export machinery. These data provide valuable insights into the initial molecular recognition steps in MccJ25 maturation process.

Introduction

Lasso peptides are 15 to 24-residue peptides produced by bacteria and characterized by an extraordinary rotaxane topology [1] . In this highly constrained structure, a macrolactam ring is established by an isopeptide linkage between the Nterminal residue of the peptide and the side chain of a glutamate or aspartate at positions 7, 8 or 9. The C-terminal tail of the peptide further threads through the N-terminal ring and is firmly trapped by steric interactions or/and disulfide bonds [1b, 2] . This unique compact structure confers lasso peptides with high stability against proteolysis or thermal and chemical denaturation. These peptides naturally act as receptor antagonists or enzyme inhibitors, the latter action being sometimes associated with antibacterial and/or antiviral properties. Due to both extraordinary stability and attractive biological activities, lasso peptides constitute a valuable scaffold for drug design. The proof of concept of their usage in drug design has been provided by Marahiel and coll. [3] , with the development of integrin antagonists based on a lasso peptide scaffold. However, synthesis of the lasso topology by conventional chemical methods is still an unsolved challenge. It is therefore crucial to understand in details their biosynthesis by producing bacteria to permit engineering novel lasso peptides endowed with desired biological activities. Microcin J25 ( MccJ25) is a lasso peptide produced by Escherichia coli with potent antibacterial activity against Enterobacteriaceae such as certain Escherichia, Salmonella and Shigella species [START_REF] Rebuffat | [END_REF]4] . This activity relies on the inhibition of bacterial RNA polymerase (RNAP) by obstructing its secondary channel [5] and on the production of reactive oxygen species in the respiratory chain [5b, 6] . MccJ25 consists of a 21-residue lasso peptide including a macrolactam ring closed by the G1 Nterminal amino group and the E8 side chain (Scheme 1). The rotaxane topology of the peptide is provided by two bulky amino acids of the C-terminal tail, F19 and Y20, located on each side of the ring, which lock the tail into the ring [7] . MccJ25 has emerged as an excellent model to understand the maturation of lasso peptides [8] . Its biosynthesis gene cluster encodes the linear precursor McjA, two maturation enzymes McjB and McjC and an ATP-binding cassette (ABC) transporter McjD [9] . McjA [8a] . The role of each enzyme has also been delineated [8b] . McjB acts as an ATPdependent cysteine protease responsible for the cleavage of the leader peptide in McjA (between residues K37 and G38). McjC was demonstrated to be a lactam synthetase and as such catalyzes the formation of the macrolactam bond. McjB and McjC were proposed to form a complex that was termed lasso synthetase [8b] . The processing of McjA was observed to occur at the bacterial inner membrane [10] The disorder propensity was calculated using IUPRED [11] (empty squares), DISPRO [12] (filled circles), META-disorder [13] (dashed line) and PONDR-FIT [14] (continuous line). Residues identified as disordered by the algorithm DISOPRED2 [15] are indicated by a star on the protein sequence.

Results

Amino acid sequence analysis of the precursor McjA.

Several bioinformatics tools have been made available in the last years for the study of disordered regions in proteins (see ref [16] for examples). To predict the disordered regions in McjA, we used three predictors IUPRED [11] , DISPRO [12] , DISOPRED2 [15] and the meta-predictors META-disorder (MD) [13] and PONDR-FIT [14] (Figure 1). The different algorithms gave different disorder predictions in the case of McjA: although all of them predicted McjA to be mostly disordered, several identified regions with reduced disorder. For example, according to DISPRO calculations, the regions K23-K30 and H42-I54 have a very low propensity to be disordered. PONDR-FIT predicted a very low disorder propensity in the region A40-I50, but high disorder propensity in the region K23-K30. IUPRED predicted low disorder propensity at the extremities of the protein but relatively high disorder propensity ( > 0.4) between residues F5 to L35. In contrast, the meta-predictor META-disorder did not identify regions with low disorder propensity and mostly predicted McjA to be completely disordered. We also used PSIPRED [17] to predict the secondary structure elements of McjA (Figure 1). Several regions (F5-F7, G24-K29, F47-I51 and P53-F56) were shown to harbor β-strand propensity and a short αhelix was predicted in the region A32-L35. Therefore, despite some divergence in the predictions obtained from the different tools, the sequence analysis confirmed that McjA is likely essentially disordered in absence of partners. However bioinformatics tools could not provide a consistent picture of possible transiently formed secondary structures in McjA. All residues were assigned at the exception of the three N-terminal residues that were not visible on the spectra. The backbone chemical shifts were analyzed using Talos+ [18] and δ2d [19] approaches (Figure 3A) to assess the flexibility and the propensity to form secondary structure elements (polyproline helix PPII, β-strand, α-helix, or random coil). The analysis by Talos+ revealed that RCI S 2 values along the McjA sequence were in the 0.3-0.6 range, which indicated the absence of any stable secondary structure. However, a slightly increased rigidity was apparent for some regions such as H4-F5, V18-P21, I26-K29 and A40-I54 that were characterized by larger than averaged S 2 values. We then performed δ2d analysis from the same set of chemical shifts (Figure 3A). The regions with elevated S 2 values corresponded to regions where δ2d identified propensity to form PPII (H42-E45) or β-strand (G49-T52) or both (V18-P21 and P53-S55). However the measured populations were globally very low (below 13% at the exception of residues I50-G51 with propensities up to 20% shift analysis based on Talos+ RCI algorithm [18] to probe the S 2 squared order parameter (dashed line) and on the δ2d algorithm [19] (continuous line) that estimates the propensity to form β-strand (blue) or polyproline II (red) elements. The populations of α-helix derived from δ2d analysis were very low (below 3%) and are not represented. 15 To get more insight into McjA dynamics, we collected 15 N R1 and R2 relaxation rates and { 1 H}-15 N heteronuclear NOE (hetNOE) values that report on H-N bond reorientation properties (Figure 3BCD). At the exception of the C-terminal extremity that exhibited shorter relaxation rates and smaller hetNOE values, as expected for highly mobile regions, the 15 N R1 parameter and the hetNOE values were relatively flat over the protein sequence at the two magnetic fields. In particular the low hetNOE averaged values (0.44 at 950 MHz, -0.05 at 600 MHz) were typical of highly flexible molecules. The 15 N R2 profile showed more pronounced variations. The regions encompassing the residues I26-A32 and V43-I50 had slightly larger R2 values than the rest of the protein sequence, which might reflect slightly increasing rigidity in these regions, as already noticed from RCI S 2 analysis.

In contrast, the glycine-rich region G38-G41 showed smaller R2 values that might reveal increased flexibility. The λex and λNOE parameters obtained from the HET-SOFAST experiment [20] report on the H2O/HN exchange properties and on the structural compactness and heterogeneity of polypeptide chains in solution, respectively. Low and high λex values indicate rapidly and slowly exchanging amide protons, respectively. Two types of behaviors were observed (Figure 3E): the F5-N17 and K30-H42 regions globally showed rapidly exchanging amide protons (λex < 0.6), whereas the V18-K29 and E45-G58 fragments globally had slowly exchanging amide protons. To check whether this behavior could be explained by the residue-type dependent solvent-exchange properties, we predicted the solvent exchange rates for the McjA sequence using the Sphere server [21] (data not shown). The Sphere-predicted exchange rates had a globally similar profile as compared to the observed λex values, indicating that the measured λex can be well explained by the amino acidtype dependent solvent exchange properties and more importantly, that amide protons are poorly protected from solvent-exchange all along the sequence, in agreement with a largely unfolded protein and limited local ordering. The λNOE parameter reports on 1 H-

Characterization of SDS micelles-bound McjA

Since the processing of McjA might occur near the bacterial membrane, we next envisaged that McjA might fold partially or completely upon membrane interaction. To support this, a previous circular dichroism (CD) study has shown that the helical contents of His6-tagged McjA increased up to 10-15% upon interaction with the negatively-charged SDS micelles [8a] that are considered as good mimics of bacterial membranes [22] . However the localization of the newly formed helical region(s) could not be identified by Very similar results were obtained from Talos+ analysis (see Figure 5A). Taken together, the δ2d algorithm identified an average value of 13% for helical propensity over the protein sequence, which nicely correlated with the previous CD measurements carried out at the same SDS concentration [8a] . Errorbars were calculated as described in Experimental Section.

We then measured the 15 N R1 and R2 relaxation and { 1 H}- 15 N heteronuclear NOE parameters to probe McjA dynamics (Figure 5BCD). The relaxation parameters were homogeneous over the protein sequence at the exception of the two regions showing partial folding as α-helices and of the C-terminal extremity. The fragments identified to probe helical conformations were characterized by lower R1 values and elevated R2 and { 1 H}- 15 N heteronuclear NOE values, in agreement with more restricted motions. In contrast, the C-terminal extremity showed elevated R1 values and lower R2 and { 1 H}- 15 N heteronuclear NOE that are collectively indicative of significant mobility at the ps-ns timescale.

We next questioned the relative positioning of McjA with respect to the SDS micelles using paramagnetic probes. The water soluble neutral Gd(DPTA-BMA) [23] and the hydrophobic stearic acid probes were used to identify the solvent-accessibility and membrane-binding regions, respectively. In the latter case, we used modified stearic acids in which the paramagnetic doxyl groups were inserted near the headgroup at position 5 (5-DSA) or at the extremity at position 16 (16-DSA), respectively [24] . When adding 5 mM Gd(DPTA-BMA) to the McjA solution, all amino acid residues showed reduced intensity in the 15 N HSQC spectrum (Figure 5E, black squares). The less affected residues were located in the region K23-Q34 that corresponded to the main helix in McjA, thus suggesting that this helix is less accessible to the solvent compared to other McjA regions. In contrast, the residues mostly affected by Gd(DPTA-BMA) were located at the N-terminal (residues K3, H4, H6, N8, S11-G13) and at the C-terminal (G51, T52, S55-F56 and G58) extremities, which suggests the high solvent accessibility of these residues. The 5-DSA and 16-DSA paramagnetic agents are expected to induce line-broadening, and therefore reduction in NMR signal intensity, for residues near the surface or near the center of the micelle, respectively [25] . The addition of 600 µM 5-DSA (Figure 5E, blue squares) also led to significant intensity changes. The less affected amino acids were K9-K14, N17-S20, K23-I26, I28-L35, K37, G39-A40 and H42-V48, suggesting that they are not in direct contact with the SDS micelles, which correlates well with Gd(DPTA-BMA) data. In contrast, the residues K3, F5, N8, K15, A22, Q27, G38, G51-I54 were affected upon addition of 5-DSA, revealing their close proximity to the micelle. Surprisingly the addition of 16-DSA, which is supposed to probe a different depth within the micelle compared to 5-DSA, gave very similar intensity changes as compared to 5-DSA (Figure 5E). Such an observation is not unprecedented [25b, 26] and may be explained by an increase of the flexibility of the spin-label in the micelles [26] or by specific interaction between the peptide and the spinlabel [25b] . 5-DSA and 16-DSA therefore only revealed the tendency of residues to interact with the fatty interior of the micelles. 6). The N-terminal extremity (from K3 to L10) and the C-terminal G51-I54 stretch were significantly affected by both types of paramagnetic agents, which suggests that they are exposed to both bulk solvent and micellar environments (arrows in Figure 6). This can be explained by their mixed charged (K3, H4, H6, K9) and hydrophobic (F5, F7, P53, I54) property. The regions S12-K14, N16-S20 and G39-V48 are highly solvent-exposed, as expected from their amino acid compositions. K15 interacts more with the micellar environment, possibly with the negatively charged SDS headgroups. Residue A22 strongly interacts with the membrane. The K23-Q34 region that shows helical propensity is protected from both the solvent and the core of the micelle. The helixmicelles interactions are therefore stabilized by the hydrophobic surface formed by A22-V25-I26-I28-A32 that is more deeply embedded in the micelle, whereas the positively charged lysines K23-K29-K30 may create salt bridges with the surface exposed SDS negative charges.

Discussions

MccJ25 maturation is a complex process and involves multiple peptide-protein and protein-protein interactions. Some intriguing questions remain about the role of the leader peptide, the order of the reactions as well as how the lasso topology is acquired. Different roles have been assigned to the leader peptide in various ribosomal peptide systems. For example, it has been proposed [27] to act as a secretion signal, as a chaperone to assist in the folding of the precursor peptide, as a recognition motif for the enzymes during post-translational modifications, to protect the peptide from degradation, or to keep it inactive during processing, but also to promote efficiency and ensure order and fidelity of the post-translational modification reactions [28] . In the case of the MccJ25 lasso peptide maturation, we show here that the leader sequence of the precursor McjA does not induce the prefolding of the precursor peptide. This emphasizes on the crucial role of the maturation machinery on the acquisition of the lassoed shape from the precursor. The study of the interactions established between the precursor and the enzymes carried out here indicates that in the leader peptide, only the N-terminal extremity (F5-S12) is prone to interact with the enzymes McjC and McjB. This is consistent with a recent report that McjB-like proteins harbor leader recognition elements [29] . It is therefore tempting to predict that the region F5-S12 may anchor the precursor on the maturation enzymes. To this regard, an in vivo study [30] has shown that removal of the first 15 N-terminal amino acids of McjA only resulted in a 5-fold decrease in MccJ25 production suggesting that the McjA/enzyme interaction mediated by the leader peptide is dispensable for maturation/export of the lasso peptide. Therefore, the leader peptide / enzyme interaction is not crucial for the reaction but may contribute to accelerate MccJ25 maturation. The most striking structural feature of the leader peptide observed in our study is the ability of the central region K23-Q34, which is located in close vicinity to the K37-G38 McjB cleavage site, to fold into an α-helix in contact with SDS micelles used here as a surrogate of bacterial membranes. This sequence has a limited propensity to fold in a helix as a result of the bioinformatics and solution-state analysis, thus highlighting the role of the membrane mimics to drive peptide helical folding. McjA maturation has been proposed to occur near the bacterial membranes [10] in agreement with the idea that the mature lasso peptide must be rapidly exported after production via the ABC transporter McjD to protect the host bacteria. Therefore, the leader peptide of McjA may function as directing the precursor to the bacterial inner membrane to increase its local concentration near the maturation/export machinery. Our hypothesis is further supported by an in vivo study [30] that demonstrated that the region Q27-S31 of McjA, which forms the center of the helix observed in presence of SDS micelles, is absolutely required for MccJ25 production in the culture medium. One cannot exclude, however, that the helical region formed in the leader peptide in contact to an hydrophobic environment might be also involved in additional protein-protein interactions during maturation, as already suggested for microcin B17 [31] . To this regard, we did not observe any significant interaction between residues Q27-S31 and the enzymes under our conditions, although such contacts might exist in later steps. Lasso peptide maturation involves the McjB-mediated proteolytic cleavage of McjA between residues K37 and G38 as well as the McjC-mediated macrolactam ring formation between residues G38 and E45 (G1 and E8 in MccJ25 sequence numbering). The peptide however must adopt the correct conformation (prefolding) as a β-hairpin structure prior to the closure reaction [7] to avoid the formation of branched-cycling peptide.

Experimental Section

Cloning, expression and purification of

Cloning, expression and purification of MBP-McjB / MBP-McjC

The genes mcjB and mcjC were cloned into a modified plasmid pMAL-c5e to be fused at the C-terminus of an N-terminal His6 tagged maltose binding protein (MBP), generating plasmids pMAL-mcjB and pMAL-mcjC. E. coli BL21(DE3) cells transformed with these plasmids were grown in LB medium at 37°C until OD600 reached 0.7-1. Protein expression was induced by addition of 0.1 mM IPTG and cells continued to grow at 20°C for 20 h. Cells were resuspended in buffer A (50 mM Tris-HCl, 300 mM NaCl, 2 mM tris(2-carboxyethyl)phosphine (TCEP), pH 8) supplemented with protease inhibitors cocktail (Roche). Lysis was performed with a cell disruptor. The cleared supernatant was loaded on a Histrap FF crude column (GE Healthcare life sciences -15 N correlation experiments were collected using the BEST-HSQC or SOFAST-HMQC [32] pulse sequences. The backbone resonance assignment of McjA was obtained using the BEST-version of 3D HNCO, HN(CO)CA, HNCA, HN(CO)CACB, HNCACB and HN(CA)N triple resonance experiments [33] . 15 N R1 and R2 relaxation rates were collected in an interleaved manner as a pseudo-3D experiment with an interscan delay of 6 s. For optimal peak dispersion in the 15 N dimension, the spectral resolution was increased using optimized 15 N spectral width obtained by the ASCOM method [34] . 16 and 10 relaxation delays were typically used for the 15 N R1 and R2 measurements respectively. The intensities extracted for each residue using NMRPipe [36] tools were then fitted to a two-parameter exponential function and errorbars were estimated from Monte-Carlo analysis. { 1 H}- 15 N heteronuclear NOE values were obtained from the ratio in intensities extracted from 1 H-15 N correlation maps with and without a 5 s 1 HN presaturation period achieved by a train of 120° pulses. Errorbars were calculated from peak intensities and the estimated noise level from the two spectra using NMRPipe [36] tools.

For the HET-SOFAST experiment, three 15 N SOFAST-HMQC spectra were collected without 1 H RF irradiation as a reference and under water or aliphatic proton selective saturation. The aliphatic-proton selective saturation was achieved by a 1.5 ms 180° REBURP shaped pulse [35] centered at 1.5 ppm. The water selective saturation was achieved using a 10 ms REBURP shaped pulse centered at 4.7 ppm. Saturation was obtained by the rapid scan repetition (d1 = 0.1 s). The λex and λNOE parameters were extracted respectively from the ratios of intensities Iex/Iref and INOE/Iref, where Iex, INOE and Iref are the cross-peak intensities observed in the spectra under water-selective saturation (Iex) or aliphatic proton-selective saturation (INOE) conditions and in absence of irradiation (Iref). To limit radiation damping effects, the HET-SOFAST experiment were collected with a room-temperature probe. Data processing and analysis were performed using NMRPipe [36] and CCPNMR [37] softwares.

Scheme 1 .

 1 Scheme 1. (A) Scheme illustrating the maturation process of McjA into the lasso peptide MccJ25 accomplished by the enzymes McjB/McjC in presence of ATP. The primary structures of the precursor McjA and of MccJ25 are shown with their corresponding amino acid numbering. The ring, loop and Cterminal regions of MccJ25 are colored magenta, green and yellow, respectively. (B) NMR structure of MccJ25 (PDB Code 1Q71). The amino acids are labeled according to their position in MccJ25 sequence. MccJ25 structure is colored using the same convention as in panel A.

  is composed of a 37-residue leader peptide at the N-terminus and the 21-residue structural core sequence of MccJ25 at the Cterminus. The enzymes McjB and McjC are responsible for the post-translational modification of McjA that results in the mature bioactive MccJ25 peptide. McjD is involved in the export of mature MccJ25 in the environment and simultaneously confers immunity to the producing bacteria. We have reconstituted in vitro the maturation of MccJ25 from its precursor McjA by the enzymes McjB and McjC in the presence of ATP

  , which suggests the requirement for McjA and the enzymes McjB/McjC to colocalize near McjD at the cell membrane for efficient maturation and export. To date, the molecular mechanisms responsible for the acquisition of the three-dimensional fold of lasso peptides still remain poorly understood, in part due to the absence of structural information for the protein partners and the complexes thereof. In this study, we established by NMR spectroscopy that free McjA is largely disordered without detectable transient structural preference. To further explore the molecular interactions of McjA in the vicinity of the lasso synthetase machinery at the bacterial inner membrane, we studied in details the structural and dynamic properties of McjA in presence of SDS micelles or in presence of the maturation enzymes.

Figure 1 :

 1 Figure 1: Bioinformatics analysis of the McjA amino acid sequence. The amino acid sequence of McjA is shown at the top of the figure together with the secondary structure elements as predicted by PSIPRED.The disorder propensity was calculated using IUPRED[11] (empty squares), DISPRO

Figure 2 :

 2 Figure 2: 15 N BEST-HSQC spectrum of 200 µM 15 N-labeled McjA at 600 MHz in 40 mM MES pH 6 and 282 K. The crosspeaks are labeled according to their amino acid type (one letter code) and numbering in McjA sequence.

Figure 3 :

 3 Figure 3: Probing McjA internal dynamics by NMR observables. (A) Chemical shift analysis based on Talos+ RCI algorithm[18] to probe the S 2 squared order

  NOE parameters (D) obtained at 600 MHz (triangles) and 950 MHz (circles) 1 H frequencies. (E) λex (empty circles) and λNOE (filled circles) obtained from the HET-SOFAST experiment. All NMR experiments were carried out for 50 µM 15 N McjA in 40 mM MES at pH 6 and at 282 K. The green and grey backgrounds represent the McjA amino acid regions corresponding to the residues forming the ring (G1-E8) and the loop and C-terminus (Y9-G21) in MccJ25 sequence, respectively. Errorbars were calculated as described in Experimental Section.

  this technique. Due to their small size, SDS micelles are compatible with solution-state NMR techniques. Therefore the SDS micelles-bound McjA was characterized by NMR spectroscopy using a 15 N McjA sample without N-terminal tag, to avoid potential artifacts. Successive additions of 2 mM SDS to a 50 µM 15 N-labeled McjA were performed to optimize SDS concentration. The complete disappearance of McjA signals in the 15 N SOFAST-HMQC experiment was observed in presence of 2 mM SDS (data not shown), while SDS at 4 mM induced the reappearance of McjA signals (Figure , top right panel). At 4mM SDS, about half resonances were strong and narrow, but the remaining signals were weak and broad, suggesting residual line-broadening. We therefore increased SDS concentration up to 8 mM which led to high quality spectra (Figure , lower panel). Under these conditions, SDS formed micelles and under the assumption of 80 SDS molecules per micelle, about 0.5 McjA molecule was interacting with one micelle. The NMR spectrum of McjA dramatically changed upon SDS interaction suggesting a major conformational and dynamic change in the SDS-bound state. The NMR resonances were assigned using a set of triple resonance experiments (Figure , lower panel) and the resulting chemical shifts were analyzed using the δ2d algorithm (Figure 5A). A significant propensity (40-60%) to form an α-helical conformation was observed in the K23-Q34 region and to a lower propensity (~15%) in the small segment around E45-Y46.

Figure 4 :

 4 Figure 4: 15 N SOFAST-HMQC spectra collected on a sample containing 100 µM 15 N-13 C labeled McjA in 40 mM MES pH 6 at 298 K and 800 MHz without SDS and in presence of 4 and 8 mM SDS concentrations. The assignment of the SDS-bound form of McjA is shown at 8 mM SDS concentration.

Figure 5 :

 5 Figure 5: Interaction between McjA and SDS. The McjA amino acid sequence is shown on top. Residues found by Talos+ analysis to adopt a helical conformation are coloured on a red background. The secondary structure propensity as obtained from δ2d-based analysis of chemical shifts is shown in panel A. The R1 and R2 15 N relaxation and { 1 H}-15 N heteronuclear NOE parameters are shown in panel B, C and D respectively. Panel E represents the paramagnetic effects as judged from the intensity change (Ipara/Iref) upon addition of 5 mM Gd(DPTA-BMA) (black circles) used as a paramagnetic

Figure 6 :

 6 Figure 6: Model of the interaction between McjA and SDS micelles. The interior of the SDS micelle is shown in black and the SDS surface-exposed negative charges are indicated. The helical fragments identified from 13 C chemical shift analysis are shown as tubes. The helical central region K23-Q34 would be able to interact with the negatively charged surface of the SDS micelle via its three positively charged residues K23, K29 and K30 that are located on one face of the helix and the hydrophobic residues A22-V25-I26-I28-A32 would be oriented towards the interior of the micelle. The arrows illustrate that the regions K3-L10 and G51-I54 would transiently interact either with the bulk water or the micelles.

Figure 5 :

 5 Figure 5: Interactions of McjA with McjB and McjC enzymes. 15 N SOFAST-HMQC spectra were collected for a 25 µM 15 N-labeled McjA solution in absence of in presence of MBP, McjB and McjC in 40 mM MES pH 6, 0.02% DOC, 200 mM NaCl. The figure shows the intensity drop (I/I0) for each McjA

  Interestingly our NMR study demonstrates that the region V43-S55 in McjA interacts with McjC and to a much lesser extent to McjB. This suggests that McjC binding to McjA acts as the initial peptideprotein recognition step. To this regard, the region V43-S55 that largely defines the β-hairpin conformation in the final lassoed conformation might already undergo a conformational change upon McjC binding to facilitate the progress along the reaction coordinates. Relevant to MccJ25 biosynthesis, ATP-dependent activation of the carboxyl side-chain of E45 by McjC likely constitutes the first event of the maturation process. This initial interaction and E45 adenylation may subsequently drive the complete pre-folding of McjA. Unexpectedly, minimal interaction was observed between the maturation enzymes and the K37-A40 region of McjA that encompasses the proteolytic cleavage site. We then speculate that these residues must remain solvent-exposed in the McjA/McjC complex for optimal accessibility to McjB for the cleavage reaction.ConclusionsOur work addresses the molecular recognition events that occur during the initial steps of McjA maturation into MccJ25. We propose that the central part of the leader peptide adopts an αhelical structure which acts as a membrane anchoring region to localize McjA at the membrane and that McjC initially recognizes McjA through its C-terminus. However, additional work is needed for a deeper characterization of the maturation pathway and in particular, through the availability of the long-awaited high-resolution structures of the enzymes and complexes thereof.

  1 H dipolar interactions that depend on 1 H local spin density and dynamics. High λNOE values (> 0.75)

	were observed for most of the protein sequence, indicating
	highly mobile regions. Nevertheless, the I26-K29 and V43-F56
	regions showed lower λNOE values (< 0.65) that are usually
	indicative of slightly more restricted mobility. Taken together this
	NMR study revealed that McjA is a largely intrinsically
	disordered protein with no significant formation of residual
	secondary structure elements, although two regions (I26-K29
	and V43-F56) show consistent slightly increased rigidity
	compared to the rest of the protein.

15

  ). MBP-McjB and MBP-McjC were eluted with 250 mM imidazole and further purified on a Superdex 200 10/300 GL (GE Healthcare life sciences) equilibrated with NMR buffer (40 mM MES, pH 6). NMR samples of 50 µM 15 N and 200 µM 15 N-13 C labeled McjA were prepared in 40 mM MES buffer (pH 6) in 90% H2O / 10% D2O. NMR experiments were collected on a Bruker AVANCE I 600 MHz or a Bruker AVANCE III 800 MHz or 950 MHz equipped with TCI cryoprobes, unless specified. 1 H

	NMR Spectroscopy

Acknowledgements

This work was financed by the ANR grant (no. BLAN_NT09_692063). We thank Philippe Durand (ICSN) for his help on the CNBr-based KSI cleavage reaction. Financial support from the TGIR-RMN-THC Fr3050 CNRS for conducting the research is gratefully acknowledged.