Laurie Boithias
email: l.boithias@gmail.com

Sabine Sauvage

Lobat Taghavi

Georges Merlina

Jean-Luc Probst

José Miguel Sánchez Pérez

Occurrence of metolachlor and trifluralin losses in the Save river agricultural catchment during floods

Keywords:

Rising pesticide levels in streams draining intensively managed agricultural land have a detrimental effect on aquatic ecosystems and render water unfit for human consumption. The Soil and Water Assessment Tool (SWAT) was applied to simulate daily pesticide transfer at the outlet from an agriculturally intensive catchment of 1110 km 2 (Save river, south-western France). SWAT reliably simulated both dissolved and sorbed metolachlor and trifluralin loads and concentrations at the catchment outlet from 1998 to 2009. On average, 17 kg of metolachlor and 1 kg of trifluralin were exported at outlet each year, with annual rainfall variations considered. Surface runoff was identified as the preferred pathway for pesticide transfer, related to the good correlation between suspended sediment exportation and pesticide, in both soluble and sorbed phases. Pesticide exportation rates at catchment outlet were less than 0.1% of the applied amount. At outlet, SWAT hindcasted that (i) 61% of metolachlor and 52% of trifluralin were exported during high flows and (ii) metolachlor and trifluralin concentrations exceeded European drinking water standards of 0.1 g L -1 for individual pesticides during 149 (3.6%) and 17 (0.4%) days of the 1998-2009 period respectively. SWAT was shown to be a promising tool for assessing large catchment river network pesticide contamination in the event of floods but further useful developments of pesticide transfers and partition coefficient processes would need to be investigated.

Introduction

Rising pesticide levels in stream waters draining intensively managed agricultural land have become a widespread problem throughout Europe in recent decades. Intensive agriculture is known to have a detrimental effect on soils, surface water and groundwater quality, leading to acute problems such as soil erosion and water contamination [e.g. [START_REF] Soulsby | Fine sediment influence on salmonid spawning habitat in a lowland agricultural stream: a preliminary assessment[END_REF][START_REF] Zalidis | Impacts of agricultural practices on soil and water quality in the Mediterranean region and proposed assessment methodology[END_REF][START_REF] Probst | Linking land use variables and invertebrate taxon richness in small and medium-sized agricultural streams on a landscape level[END_REF][START_REF] Zeiger | Impact of organic farming systems on runoff formation processes-a long-term sequential rainfall experiment[END_REF]. Excessive loading of pesticides, transferred into the environment through various pathways (e.g. surface runoff, subsurface and groundwater flows) either in solution or sorbed onto particles, may be harmful to terrestrial and aquatic ecosystems [START_REF] Cuppen | Impact of the fungicide carbendazim in freshwater microcosms. I. Water quality, breakdown of particulate organic matter and responses of macroinvertebrates[END_REF][START_REF] Van Den Brink | Impact of the fungicide carbendazim in freshwater microcosms. II. Zooplankton, primary producers and final conclusions[END_REF][START_REF] Niemi | Microbial toxicity and impacts on soil enzyme activities of pesticides used in potato cultivation[END_REF][START_REF] Becker | Pesticide contamination of water alters the metabolism of juvenile silvercatfish, Rhamdia quelen[END_REF], rendering stream water and groundwater unfit for human consumption.

In Europe, pesticides are considered hazardous substances in accordance with current directives regarding water [9,10]. Drinking water quality standard should not exceed 0.1 g L -1 for an individual pesticide concentration and 0.5 g L -1 for all pesticide concentration [11]. River basins were adopted as territorial management units and the scientific community was asked to provide reliable modelling tools to evaluate pesticide source contribution to water pollution and to quantify pesticide river loads. To model pesticide fate at catchment scale, spatially variable land management and landscape characteristics, temporally variable climatology and hydrology as well as dissipation processes in the river need to be taken into account. Therefore, the combination of watershed models and river water quality models is needed to calculate pesticide fluxes to the river and transformation processes in the river channel [START_REF] Holvoet | Monitoring and modeling pesticide fate in surface waters at the catchment scale[END_REF].

Various models describe the pesticide fate, allowing a better understanding of the processes involved. Among the very first, the Chemical Migration and Risk Assessment (CMRA) methodology [START_REF] Onishi | Computer-based environmental exposure and risk assessment methodology for hazardous materials[END_REF] included the Agricultural Runoff Management (ARM) [START_REF] Donigian | Modeling Pesticides and Nutrients on Agricultural Lands[END_REF] and Chemicals, Runoff and Erosion from Agricultural Management Systems (CREAMS) [START_REF] Knisel | CREAMS: A Field-scale Model for Chemicals, Runoff and Erosion from Agricultural Management Systems[END_REF] models. It simulates the transport and fate of both dissolved and sediment-sorbed contaminants and by predicting acute and chronic impacts, provides risk assessment on aquatic biota. Many one-dimension, river and catchment scale models simulating pesticide fate have been then developed [e.g. [START_REF] Leonard | GLEAMS: Groundwater Loading Effects of Agricultural Management Systems[END_REF][START_REF] Brown | The Enhanced Water Quality Models QUAL2E and QUAL2E-UNCAS: Documentation and User Manual[END_REF][START_REF] Brown | SWAT-a semi-empirical model to predict concentrations of pesticides entering surface waters from agricultural land[END_REF][START_REF] Haverkamp | POWER a new approach of watershed modelling[END_REF]. The Soil and Water Assessment Tool (SWAT, [START_REF] Arnold | Large area hydrologic modeling and assessment. I. Model development[END_REF]) is a semi-distributed model that provides long-term continuous predictions, including hydrology, plant growth, nutrients and suspended sediments from the field to the catchment outlet at daily time-step. Two main processes describe the pesticide transfer in both the soluble and the sorbed phases: the pesticide load generated in the hydrological unit and the fate of the load in the river.

Few works have been published so far on pesticide fate modelling using the SWAT model. Molecules of a wide range of solubility were simulated (e.g. atrazine, metolachlor, trifluralin, diazinon and chlorpyrifos) in catchments ranging from 30 to 15,000 km 2 [START_REF] Neitsch | Pesticides Fate and Transport Predicted by the Soil and Water Assessment Tool (SWAT)-Atrazine, Metolachlor and Trifluralin in the Sugar Creek Watershed[END_REF][START_REF] Holvoet | Sensitivity analysis for hydrology and pesticide supply towards the river in SWAT[END_REF][START_REF] Larose | Hydrologic and atrazine simulation of the Cedar Creek watershed using the SWAT model[END_REF][START_REF] Luo | Dynamic modeling of organophosphate pesticide load in surface water in the northern San Joaquin Valley watershed of California[END_REF]. To our knowledge, no work has been published on pesticide modelling in both the dissolved and sorbed phases at flood-event scale, i.e. during a few days of high flow.

This study had four objectives: (i) to assess the performance of the SWAT model in the Save catchment (1110 km 2 ) in the Gascogne region, an agriculturally intensive area of south-western France, in predicting daily pesticide river loads and concentrations at the catchment outlet; (ii) to test the sensitivity of SWAT long-term response, in terms of pesticide exportations, to interannual hydrological constraint by using an 11-year constant pesticide supply; (iii) to hindcast earlier pesticide data in order to make the model reliable for predicting river network contamination (e.g. exceeding drinking water standards) depending on the climatic context and possible flood events; (iv) to identify factors controlling exportations and preferred pathways.

Material and methods

Study area

The River Save drains an area of 1110 km 2 which is mostly farmed with intensive agriculture. It is located in the Coteaux de Gascogne region (south-western France) near Toulouse (Fig. 1). The River Save has its source in the Pyrenees piedmont. It joins the River Garonne after a 140 km course at a 0.4% average slope. Altitudes range from 663 m in the piedmont to 92 m at the Garonne confluence. The Larra gauging station elevation is 114 m (Fig. 1).

The climate is oceanic. The Save river hydrological regime is mainly pluvial with a maximum discharge in May and low flows during the summer (July-September). Annual precipitation is 600-900 mm and annual evaporation is 500-600 mm (1998-2008). The hydrology is complex and subject to large climatic variations: annual average rainfall is 721 mm with a 99 mm standard deviation. The catchment lies on detrital sediments. Calcic soils represent over 90% of the whole catchment with a clay content ranging from 40% to 50%. Non-calcic silty soils represent less than 10% of the soil in this area (50-60% silt) [START_REF] Revel | Erosion due to cultivation of calcareous clay soils on the hillsides of Southwest France. I. Effect of former farming practices[END_REF].

Because of its high clay content, the catchment substratum is relatively impermeable. River discharge is consequently supplied mainly by surface and subsurface runoff and groundwater is limited to alluvial and colluvial phreatic aquifers. The mean interannual discharge for the River Save is 6.1 m 3 s -1 . The annual discharge of 'dry' years is approximately 4.1 m 3 s -1 whereas for 'wet' years it is about 8.1 m 3 s -1 . Low water discharge is about 1.3 m 3 s -1 (data from the Compagnie d'Aménagement des Coteaux de Gascogne (CACG) at the Larra gauging station). During low flows, river flow is sustained upstream by the Neste canal (about 1 m 3 s -1 ).

90% of the catchment area is used for agriculture. The upstream part of the catchment is a hilly agricultural area mainly covered with pasture -a 5-year rotation including one year of corn and 4 years of grazed fescue -and sometimes forest. The lower part is devoted to intensive agriculture with mainly a 2-year crop rotation Various pesticides are applied in the catchment throughout the year depending on the crops. Our study focuses on the most applied pesticides: each year, 23 tons of metolachlor, a highly soluble chemical (S w = 488 mg L -1 , log K ow = 2.9), and 18 tons of trifluralin, a poorly soluble chemical (S w = 0.221 mg L -1 , log K ow = 4.83), are applied on the catchment. Both pesticides are herbicides. They are applied each year on sunflower in early April (Table 1). On average, sunflower fields cover 18.4% of the catchment (20,600 ha).

Observed discharge data

The River Save has been monitored for discharge since 1965. At the Larra hydrometric station, hourly discharges (Q) were obtained from CACG. The hourly discharge was plotted by the rating curve H(Q) in which the water level (H) was measured continuously and then averaged for each day.

Observed water quality data

Nitrate and suspended sediment monitoring

Nitrate loads and suspended sediment concentrations were monitored continuously from January 2007 to March 2009 at the Larra gauging station, both manually and automatically, as described previously in Oeurng et al. [START_REF] Oeurng | Dynamics of suspended sediment transport and yield in a large agricultural catchment, south-west France[END_REF][START_REF] Oeurng | Temporal variability of nitrate transport through hydrological response during flood events within a large agricultural catchment in south-west France[END_REF][START_REF] Oeurng | Fluvial transport of suspended sediment and organic carbon during flood events in a large agricultural catchment in southwest France[END_REF]: an automatic water sampler, connected to a probe, was programmed to activate pumping water on the basis of water level variations ranging from 10 cm (during low flows) to 30 cm (during high flows) for the rising and falling stages. Grab sampling was also undertaken near the probe position at weekly intervals.

Pesticide monitoring

Pesticides were monitored from March 2008 to March 2009 at the Larra station with weekly grab sampling during low flow and daily grab sampling during flood events. Laboratory analyses were performed as described in Taghavi et al. [START_REF] Taghavi | The role of storm flows in concentration of pesticides associated with particulate and dissolved fractions as a threat to aquatic ecosystems. Case study: the agricultural watershed of Save river (Southwest of France)[END_REF][START_REF] Taghavi | Flood event impact on pesticide transfer in a small agricultural catchment (Montousse at Aurade, south west France)[END_REF]. Additional data from Agence de l'Eau Adour-Garonne (AEAG) were used for longterm total pesticide concentration comparison (Source: Système d'Information sur l'Eau du Bassin Adour-Garonne, data exported in 2009).

Load calculation

Based on the high frequency of data collection, a linear interpolation method was applied between two neighbouring sampling points to construct the continuous nitrate, suspended sediment and pesticide concentration series and thus calculate continuous daily loads through the product of concentration and water volume. Yearly loads were calculated by totalling daily loads. SWAT is a physically based agro-hydrological model [START_REF] Arnold | Large area hydrologic modeling and assessment. I. Model development[END_REF]. It operates at a daily time-step and was designed to predict the impact of management practices on water quality in ungauged catchments. It allows the addition of flows by including measured data from point sources. SWAT discretises catchments into sub-basins. Sub-basins are then subdivided into Hydrological Response Units (HRUs). HRUs are areas of homogenous land use, soil type and slope. HRUs outputs are inputs for the connected stream network. One sub-basin is drained by one reach. Authors refer to Neitsch et al. [START_REF] Neitsch | Soil and Water Assessment Tool Theoretical Documentation-Version[END_REF] for detailed description of the model.

The pesticide component in SWAT

Pesticide processes in SWAT are divided into three components: (i) pesticide processes in land areas, (ii) transport of pesticides from land areas to the stream network, and (iii) instream pesticide processes.

SWAT uses algorithms from GLEAMS (Groundwater Loading Effects on Agricultural Management Systems) [START_REF] Leonard | GLEAMS: Groundwater Loading Effects of Agricultural Management Systems[END_REF] to model pesticide movement and fate in land areas. The partitioning of a pesticide between the dissolved and sorbed phases is defined by a soil adsorption coefficient. Algorithms governing movement of soluble and sorbed forms of pesticide from land areas to the stream network were taken from the EPIC (Erosion-Productivity Impact Calculator) model [START_REF] Williams | Simulator for water resources in rural basins[END_REF]. The SWAT model incorporates a simple mass-balance method [START_REF] Chapra | Surface Water-Quality Modeling[END_REF] to model the transformation and transport of pesticides in streams. Only one pesticide can be routed through the stream network in a given simulation. The fraction of pesticide in each phase is a function of the pesticide's partition coefficient and the reach segment's suspended solid concentration. Degradation is based on half-life. Authors refer to Neitsch et al. [START_REF] Neitsch | Soil and Water Assessment Tool Theoretical Documentation-Version[END_REF] for further details.

SWAT data inputs

Spatialised data used in this study were:

-Digital Elevation Model with a resolution of 25 m × 25 m from Institut Géographique National (IGN) France (BD TOPO R).

-Soil data on the scale of 1:80,000 from CACG and digitised by Cemagref de Bordeaux [START_REF] Macary | Méthode de changement d'échelle pourl'estimation du potentiel de contamination des eaux de surface par l'azote[END_REF] and soil properties for the SWAT soil database [START_REF] Lescot | A decision support to choose between changes of agricultural practices: a spatially distributed cost-effectiveness assessment framework[END_REF]. -Land use data [START_REF] Macary | Méthode de changement d'échelle pourl'estimation du potentiel de contamination des eaux de surface par l'azote[END_REF] from Landsat 2005 with associated management practices: spatial and temporal average of planting/seedling dates, amounts, type and date of fertilisation, pesticide application and irrigation, grazing, tillage and harvest operations dates from a 3 year survey (2003-2005) with catchment farmers, applied for each year of simulation. -Meteorological data from 5 stations (Fig. 1) with daily precipitation from Météo-France. Missing data were generated by linear regression equation from data from the nearest stations with complete measurements. Two stations in the upstream section had a complete set of measurements of daily minimum and maximum air temperature, wind speed, solar radiation and relative humidity that were used to simulate the reference evapotranspiration in the model by Penman-Monteith [START_REF] Penman | Natural evaporation from open water, bare soil, and grass[END_REF][START_REF] Monteith | Evaporation and environment[END_REF] method. -Point source data: the Save river network is connected upstream to the Neste canal. Daily discharge was given by CACG. Since it is water from a mountainous agricultural extensive area, concentrations of nitrate and pesticides were set constant and equal to 2 and 0 mg L -1 respectively. Since water is derived by a dam where sediments are trapped, suspended sediment concentration was set constant and equal to 10 mg L -1 .

In this study, version 2009.93.3 of ArcSWAT was used. The catchment was discretised into 73 sub-basins with a minimal area of 500 ha. 1642 HRUs were generated integrating 8 land uses classes, 23 soil classes and 5 slope classes (%: 0-1, 1-3, 3-5, 5-8 and 8 and over).

Whole simulation was carried out daily from January 1998 to March 2009 (excluding 4 years' warm-up from 1994 to 1997). The sensitivity of 15 parameters governing discharge, nitrate and suspended sediment dynamic was tested using the ArcSWAT2009 sensitivity analysis tool [START_REF] Van Griensven | A global sensitivity analysis tool for the parameters of multi-variable catchment models[END_REF]. Calibration of discharge, nitrate, suspended sediment and pesticide at daily time-step was performed manually. Pesticide input values are given in Table 2.

Model evaluation

The performance of the model was evaluated using the Nash-Sutcliffe efficiency (E NS ) index [START_REF] Nash | River flow forecasting through conceptual models. Part I. A discussion of principles[END_REF] and the coefficient of .swq 0.025 0.025 determination (R 2 ). E NS ranges from negative infinity to 1 whereas R 2 ranges between 0 and 1. We refer to Krause et al. [START_REF] Krause | Comparison of different efficiency criteria for hydrological model assessment[END_REF] for further discussion on these evaluation criteria. Daily E NS and R 2 were applied on daily discharge for low flows (below the 6.1 m 3 s -1 mean annual discharge), high flow (over 6.1 m 3 s -1 ) and total (1998-2006 for calibration and 2007-2009 for validation). They were also calculated for nitrate loads and suspended sediment concentrations for the 2007-2009 period. Daily and monthly R 2 were calculated for dissolved, sorbed and total pesticides concentrations for calibration period (2008)(2009). Due to data limitation, E NS was not calculated on pesticide concentration. In this study we deemed E NS satisfactory when higher than 0.36 [START_REF] Van Liew | Hydrologic simulation of the little Washita river experimental watershed using SWAT[END_REF] and R 2 satisfactory when higher than 0.5 [START_REF] Green | Hydrologic evaluation of the soil and water assessment tool for a large tile-drained watershed in Iowa[END_REF]. Daily SWAT interpolated rainfall and simulated water yield (i.e. the amount of water flowing down the outlet) were totalled for each year. Daily simulated nitrate and suspended sediment loads at the Larra outlet were totalled for each year and for low flow and high flow (using the 6.1 m 3 s -1 threshold). Incoming suspended sediment in a given reach is the total of the amount of suspended sediments coming from the HRUs related to the reach, added to the amount entering from the upstream reach.

Pesticide simulation

Daily pesticide loads were totalled for each year, for April flood (11/04/08-30/04/08) and June flood (14/05/08-18/06/08), and for low flow and high flow (using the 6.1 m 3 s -1 threshold). Total pesticide concentration is the concentration of pesticide as measured in unfiltered water. Simulated total concentration is calculated as the total of dissolved and sorbed pesticide concentration. The particulate fraction is the fraction of pesticide in the sorbed phase and is calculated as the sorbed pesticide load divided by the total pesticide load. The exportation rate is calculated as the ratio from pesticide load exported at outlet and the amount of pesticide applied on the catchment. As pesticide toxicity is more relevant as concentration than as load, long-term simulation concentrations (1998-2009) were compared to European drinking water quality standards of 0.1 g L -1 for a single pesticide and of 0.5 g L -1 for both pesticides being modelled.

Correlation analyses were performed on 1998-2008 interannual-averaged metolachlor and trifluralin (both dissolved and sorbed) loads from reach and HRUs to relate them to reach and HRUs variables such as slope, soil classes, rainfall, water, suspended sediment and nitrate yields.

Results

Discharge, nitrate and suspended sediment simulation

According to sensitivity analysis, parameters governing discharge and nitrate were mostly parameters governing runoff and groundwater transfer (CN2, RCHRG DP, GWQMN). Parameters governing suspended sediment were mostly parameters governing runoff (CN2) and in-stream processes (CH N2, SPCON, SPEXP). Fig. 2 focuses on discharge simulated daily from January 2007 to March 2009. The goodness-of-fit indices for daily discharge were satisfactory during both calibration and validation period (Table 3). They were also satisfactory for high flow but unsatisfactory for low flow (Table 3).

Nitrate daily load predictions (Fig. 3(a)) were correlated to observations for the 2007-2009 period (Table 3). Observed and simulated cumulated nitrate loads in 2007 were 2514 and 2388 tons respectively, they were 3047 and 3018 tons respectively in 2008 (Fig. 3(b) and(c)). Considering daily and annual loads prediction on 2007-2009, the model was considered to hindcast past daily and annual nitrate loads back to 1998 with little error. Annual nitrate loads were correlated to annual water yield (R 2 = 0.84, p < 0.05).

Daily simulated suspended sediment concentrations fitted observations (Fig. 4(a)) although E NS and R 2 were unsatisfactory (Table 3). Observed and simulated annual suspended sediment loads were 9000 and 15,000 tons respectively in 2007 and 58,000 and 64,000 tons respectively in 2008 (Fig. 4(b) and (c)). Considering concentration and load prediction on 2007-2009, the model was considered to reconstruct past annual loads back to 1998 with little error. Annual suspended sediment loads were correlated to annual water yield (R 2 = 0.59, p < 0.05). On average across the 73 reaches, the annual ratio of deposited/incoming suspended sediment in the reach is of 54%.

Pesticide simulation

Simulation results were shown to be poorly sensitive to application date change for both molecules (results not shown) although pesticide losses are known to be determined mainly by the period of time between application and the first rainfall event and by the application dose [START_REF] Martins | Atrazine, deethylatrazine, and deisopropylatrazine in surface runoff form conservation tilled watersheds[END_REF][START_REF] Müller | Hydrological characterisation of pesticide loads using hydrograph separation at different scales in a German catchment[END_REF]. SWAT pesticide component parameters, including the soil adsorption coefficient (K oc ), were also poorly sensitive (results not shown) except the channel partition coefficient between water and suspended sediment (CHPST KOC).

SWAT performances

The range of daily simulated concentrations of metolachlor and trifluralin followed the range of respective measurements during both the calibration 2008-2009 period (Fig. 5) and the long-term validation 1998-2009 period (Fig. 6). During the calibration period, simulated dissolved and sorbed molecule concentrations during low flow matched respective observations. Flood concentration peaks of dissolved metolachlor were predicted although overestimated during April and June flood events. Sorbed pesticide concentration peaks during the same period were underestimated. Trifluralin concentration peaks in May were skipped. The model did not simulate the trifluralin concentration peak in early 2009. Simulated partition between soluble and sorbed phases of both molecules roughly followed the observed partition.

In terms of loads, average simulated annual loads at outlet were in the range of observed annual loads (Table 4). The simulated metolachlor and trifluralin particulate fractions at catchment outlet fitted the observed particulate fractions and were consistent with the fractions simulated in surface runoff out of the HRUs (Table 4). At flood scale, simulation followed the measured range of values (Table 5). However, metolachlor loads were underestimated whereas trifluralin loads were overestimated during the April flood and underestimated during the June flood. R 2 for monthly concentrations of metolachlor and trifluralin were over 0.38 except

Table 4

Observed (2008-2009) and simulated (1998-2008) average annual loads of metolachlor and trifluralin in each catchment compartment (mg ha -1 yr -1 ) and particulate fraction out of HRUs and at catchment outlet.

Metolachlor (mg ha -1 yr -1 )

Trifluralin (mg ha for sorbed and total trifluralin. R 2 for daily concentrations did not exceed 0.26 (Table 6).

Long-term pesticide exportation balances at outlet

During the 1998-2008 period, high flows represented 17% of the time considering the 6.1 m 3 s -1 threshold. 50% of nitrate load and 57% of suspended sediment load were exported during high flow. Annual pesticide loads are shown in Fig. 7. The total metolachlor load varied between 0.1 kg yr -1 (2003) and 80 kg yr -1 (2000), whereas trifluralin varied between 0.01 kg yr -1 (2003) and 2.3 kg yr -1 (2000). Average total metolachlor and trifluralin annual loads were 16.7 kg (SD = 23 kg) and 0.8 kg (SD = 1 kg) respectively. The total metolachlor and trifluralin exported were around 0.072% and 0.005% of the applied amount respectively (exportation rate was 1% out of the HRUs for both molecules). At outlet 61% of the total metolachlor and 52% of the total trifluralin were exported during high flows (Table 7). Out of the 4108 simulated days (1998-2009), metolachlor at Larra outlet exceeded the European standard threshold of 0.1 g L -1 for 149 days and trifluralin exceeded this same threshold for 17 days (3.6 and 0.4% of the time period respectively). Maximum metolachlor concentration was 5.4 mg L -1 , predicted in July 2001 whereas maximum trifluralin concentration was 0.2 mg L -1 , predicted in April 1998 (Fig. 6). Considering the sum of total metolachlor and total trifluralin concentrations, the threshold of 0.5 g L -1 was exceeded during 24 days at catchment outlet.

Pesticide transfer controlling factors

At catchment scale, the simulated preferred pathway of pesticide transfer was surface runoff (Table 4) with associated nitrate and suspended sediment exportations. At sub-basin scale, trifluralin and metolachlor in the sorbed phase were correlated to suspended sediment loads (R 2 = 0.69 and 0.64, respectively). Trifluralin and metolachlor in the dissolved phase were poorly correlated to nitrate loads (R 2 = 0.21 and 0.16, respectively). Eventually, trifluralin and metolachlor in the dissolved phase were better correlated to suspended sediment loads (R 2 = 0.33 and 0.67 respectively). At HRU scale, the correlation analysis did not show any correlation between suspended sediment loads, metolachlor and trifluralin loads in the dissolved phase and catchment variables. Metolachlor and trifluralin loads in the sorbed phase correlated weakly to suspended sediment yields (R 2 = 0.24 and 0.43 respectively).

Discussion

Discharge, nitrate and suspended sediment simulation

Overall E NS and R 2 for daily discharge were over the satisfactory threshold. However, gaps between observed and simulated values are explained by errors in observed and simulated values. Errors in observed values can stem from the precision of the sensor and from the use of a rating curve. Errors in simulated values can be attributed to (i) actual local rainfall storms that were not well represented by the SWAT rainfall data interpolation and (ii) the flow uncertainty in the dense network of canals diverted from the river network to bring part of river flow to many watermills. E NS for low flow was below the satisfactory threshold. Low E NS during low flow has to be related to its generally poor performance in periods of low flow: with only minor simulation errors the denominator of the equation tends towards zero and E NS approaches negative infinity. Low E NS is however of minor concern since pesticides were shown to be mostly exported during floods. Save river discharge simulation quality is however comparable to Oeurng et al. [START_REF] Oeurng | Assessment of hydrology, sediment and particulate organic carbon yield in a large agricultural catchment using the SWAT model[END_REF].

Daily nitrate prediction is related to daily discharge prediction, as it is a very soluble nutrient. Bias is therefore linked to the inaccuracy of discharge predictions mentioned above. Uncertainty in point-source nutrient input also explains bias. In addition, averaged land use and associated management practice inputs may not reflect well enough the actual and local land use and management practices. They also can evolve over the modelled period depending on agricultural policy trends.

E NS and R 2 for daily suspended sediments concentration were below the satisfactory threshold. Calibration of sediment is difficult in the Save catchment considering the dense network of canals diverted from the river network. The succession of dams and gates traps sediments till their random emptying back to river network. Uncertainty in point-source suspended sediments input may also explain bias. Suspended sediment results were however consistent with Oeurng et al. [START_REF] Oeurng | Assessment of hydrology, sediment and particulate organic carbon yield in a large agricultural catchment using the SWAT model[END_REF].

4.2. Pesticide simulation 4.2.1. SWAT performances R 2 of daily pesticide concentration was below the satisfactory threshold. As highlighted by Luo et al. [START_REF] Luo | Dynamic modeling of organophosphate pesticide load in surface water in the northern San Joaquin Valley watershed of California[END_REF] possible time shifts in the precipitation, agricultural activity, and measurements for flow and water quality data render daily pesticide statistics poor. Errors in pesticide concentration and load predictions may be related to (i) an inadequate calibration of the parameters governing flow and soluble (e.g. nitrate) and particulate (e.g. suspended sediment) phases transport and to (ii) an inadequate calibration of pesticide component parameters.

Sorbed pesticide underestimation has to be related to the high deposition rate of suspended sediment in the channel. Also, as K oc was shown to be poorly sensitive and as CHPST KOC is set as a constant value, both partition coefficients modelled in HRUs and in reaches vary depending on suspended sediment concentration. They may not reflect actual variations: metolachlor and trifluralin measurements at outlet show various inversions (i.e.

[soluble] < [sorbed] for metolachlor and [soluble] > [sorbed] for tri- fluralin) of the partition coefficient Kd, as defined by Taghavi et al. [START_REF] Taghavi | Flood event impact on pesticide transfer in a small agricultural catchment (Montousse at Aurade, south west France)[END_REF] at catchment outlet, that were not modelled. Regarding agricultural activity, i.e. land use and associated management practices, the model did not simulate any trifluralin concentration rise in early 2009. The land use input map was based on a 2005 land cover satellite image that may not reflect long-term actual operations: e.g. canola, representing 1% of the catchment land use in 2005, has been grown increasingly over the past five years in the northern part of the catchment. Up to 2009, canola was managed with an average spread of 1 kg ha -1 of trifluralin in August. This was not taken into account in the model because SWAT land use approximation was set to skip land use representing less than 10% of the sub-basin area. Reliable pesticide supply input data are therefore a necessary condition to achieve satisfactory simulation outputs.

A last source of error is the modelled transfer pathways: SWAT simulates pesticide transfers through surface runoff and subsurface lateral flow but not through groundwater flow, drainflow nor atmospheric deposition [START_REF] Neitsch | Soil and Water Assessment Tool Theoretical Documentation-Version[END_REF]. This may lead to additional errors in soluble pesticide simulation although leaching of pesticides into deep groundwater and a possible input of pesticides into surface waters by outflowing groundwater is known to be negligible [START_REF] Röpke | DRIPS-a DSS for estimating the input quantity of pesticides for German river basins[END_REF][START_REF] Richards | Pesticide concentration patterns in agricultural drainage networks in the Lake Erie Basin[END_REF]. In addition, no point-source, such as the cleaning of the equipment, was modelled in this study [START_REF] Holvoet | Monitoring and modeling pesticide fate in surface waters at the catchment scale[END_REF][START_REF] Reichenberger | Mitigation strategies to reduce pesticide inputs into ground-and surface water and their effectiveness: a review[END_REF].

Finally, daily pesticide total concentrations R 2 were in the lower range of the values mentioned by Neitsch et al. [START_REF] Neitsch | Pesticides Fate and Transport Predicted by the Soil and Water Assessment Tool (SWAT)-Atrazine, Metolachlor and Trifluralin in the Sugar Creek Watershed[END_REF]. They reported R 2 ranging from 0.41 to 0.28 for daily metolachlor total concentration and R 2 ranging from 0.51 to 0.02 for daily trifluralin total concentration.

Long-term pesticide exportation balance at outlet

Simulation showed that pesticides were exported mainly during flood events. Role of floods in pesticide exportation was previously shown at the Save catchment outlet [START_REF] Taghavi | The role of storm flows in concentration of pesticides associated with particulate and dissolved fractions as a threat to aquatic ecosystems. Case study: the agricultural watershed of Save river (Southwest of France)[END_REF]. As interannual average, exportation rates of both pesticides have to be related to their application time (April) and the month of maximum discharge (May). However, simulated values of exportation during a similar high flow period were less than measured values in a small catchment or at field scale [START_REF] Louchart | Herbicide transport to surface waters at field and watershed scales in a Mediterranean vineyard area[END_REF][START_REF] Rabiet | Assessing pesticide concentrations and fluxes in the stream of a small vineyard catchment-effect of sampling frequency[END_REF]. The size of the drained area, but also the soil and the land use may modulate the exportation.

Pesticide exportations from land to outlet were less than 1% of applied amount. Such a value was reported by various studies on metolachlor in France, Switzerland and Québec [START_REF] Novak | Effects of soil type upon metolachlor losses in subsurface drainage[END_REF][START_REF] Leu | Variability of herbicide losses from 13 fields to surface water within a small catchment after a controlled herbicide application[END_REF][START_REF] Poissant | Pesticides in fluvial wetlands catchments under intensive agricultural activities[END_REF] and on other pesticides [START_REF] Rabiet | Assessing pesticide concentrations and fluxes in the stream of a small vineyard catchment-effect of sampling frequency[END_REF][START_REF] Poissant | Pesticides in fluvial wetlands catchments under intensive agricultural activities[END_REF][START_REF] Berenzen | A comparison of predicted and measured levels of runoff-related pesticide concentrations in small lowland streams on a landscape level[END_REF]. About 93% and 99% of metolachlor and trifluralin respectively entering the stream network does not reach the outlet, suggesting high deposition (discussed above) and degradation in stream water. The latter would be consistent with the carbon consumption by river biota shown by Sánchez-Pérez et al. [START_REF] Sánchez-Pérez | Effects of wastewater treatment plant pollution on in-stream ecosystems functions in an agricultural watershed[END_REF] on a similar catchment of Gascogne.

Trifluralin concentration exceeded the 0.1 g L -1 maximum permissible level less than metolachlor concentration. In the model, trifluralin was less applied than metolachlor among the catchment, its soil half-life was 60 days (instead of 90 days for metolachlor) and its transport was more likely depending on land and river bed erosion. It is worth noting that the European standard of 0.5 mg L -1 was exceeded by a pool of only two molecules during 0.6% of the simulation time.

Pesticide transfer controlling factors

Runoff was shown to be the preferred simulated pathway for pesticide exportation. Luo et al. [START_REF] Luo | Dynamic modeling of organophosphate pesticide load in surface water in the northern San Joaquin Valley watershed of California[END_REF][START_REF] Luo | Management-oriented sensitivity analysis for pesticide transport in watershed-scale water quality modeling using SWAT[END_REF] already reported the control of runoff on pesticide transfer simulation. In the environment, pesticides may be sorbed onto mineral suspended matter, Particulate Organic Carbon (POC) and complexed by Dissolved Organic Carbon (DOC) [START_REF] Taghavi | The role of storm flows in concentration of pesticides associated with particulate and dissolved fractions as a threat to aquatic ecosystems. Case study: the agricultural watershed of Save river (Southwest of France)[END_REF]. Better correlation was found between dissolved phases and suspended sediment than between dissolved phases and nitrate. Although pesticide transfer through groundwater is not yet modelled in SWAT, contrarily to nitrate, the relationships highlighted a control of both surface and sub-surface runoffs during floods on dissolved phase exportation. This is consistent with the pesticides' actual ability to sorb onto DOC, i.e. smaller than the 0.45 m mesh filter, and its transfer through sub-surface flow [START_REF] Taghavi | The role of storm flows in concentration of pesticides associated with particulate and dissolved fractions as a threat to aquatic ecosystems. Case study: the agricultural watershed of Save river (Southwest of France)[END_REF].

Conclusions

The SWAT model was applied to simulate pesticide transfer at the outlet of a large intensive agricultural catchment. Simulation results were deemed to be satisfactory, taking into account that the modelled transfer processes were simplified and do not represent all actual transfers. Further improvements of the SWAT model may be investigated. The transfer of pesticide in the dissolved phase from land to river through groundwater could be tested to assess possible water-table effect. Also, investigating the variations of the partition coefficient between observed dissolved and sorbed phases during floods in various points in the river (areas of rapid, deep, etc.) would help to assess the accuracy of the SWAT modelled partition at flood-event scale.

However, extrapolation to other chemicals is conceivable and SWAT was shown to be promising for providing a robust decision tool for water quality managers. Floods are quick events. Such a tool would help (i) to target 'what and when' to monitor, (ii) to highlight pesticide concentration peaks without cost-intensive field measurements and predict future peaks and (iii) to evaluate exported loads as contamination indicator. Suggestion of localised mitigation practices to reach water policy objectives such as the European Water Framework Directive is made possible.

Fig. 1 .

 1 Fig. 1. Localisation of Save catchment, Larra gauging station and meteorological stations.

2. 4 .

 4 Modelling approach 2.4.1. The SWAT model

2. 7 .

 7 Water quality simulation 2.7.1. Discharge, nitrate and suspended sediment simulation

Fig. 2 .

 2 Fig. 2. Observed and simulated daily discharge (m 3 s -1 ) at the Larra gauging station (January 2007-March 2009).

Fig. 3 .

 3 Fig. 3. Observed and simulated daily (a) nitrate load (tons) at the Larra gauging station (January 2007-March 2009), (b) 2007 accumulation (tons) and (c) 2008 accumulation (tons).

Fig. 4 .

 4 Fig. 4. Observed and simulated daily (a) suspended sediment concentration (mg L -1 ) at the Larra gauging station (January 2007-March 2009), (b) 2007 accumulation (tons) and (c) 2008 accumulation (tons).

Fig. 5 .

 5 Fig. 5. Observed and simulated daily pesticide concentrations (g L -1 ) at the Larra gauging station (2008-2009): (a) metolachlor and (b) trifluralin.

Fig. 6 .

 6 Fig. 6. Observed and simulated daily pesticide total concentrations (g L -1 ) at the Larra gauging station (1998-2009): (a) metolachlor and (b) trifluralin.

Fig. 7 .

 7 Fig. 7. Simulated annual dissolved and sorbed pesticide loads (kg yr -1 ) at Larra outlet (1998-2008): (a) metolachlor and (b) trifluralin.

Table 1

 1 Spatial and temporal 3-year survey averaged management practices for sunflower grown on the Save catchment including metolachlor and trifluralin spreading.

	Type of operation	Date of	Quantity
		operation	(kg ha -1 )
	Pesticides spreading: trifluralin	05 April	0.874
	Fertilizer: 15-15-15	05 April	193.3
	Sowing	10 April	-
	Pesticides spreading: metolachlor	15 April	1.12
	Fertilizer: 15-15-15	16 May	193.3
	Harvest	01 October	-
	of sunflower and winter wheat. Fertilizers are generally applied
	from late winter to spring. Average nitrogenous supply through-
	out the catchment is approximately 72 kg N ha -1 , i.e. 320 kg ha -1
	nitrate-equivalents. About 150 mm of water is supplied by irriga-
	tion of corn.

Table 2

 2 Manually calibrated values of pesticides parameters: half-lifes, Koc and CHPST KOC and degradation rates in the channel water and in the sediment bed (respectively CHPST REA and SEDPST REA).

	Parameters	Input file	Metolachlor	Aclonifen
	Soil half-life Met/Tri (days)	pest.dat	90	60
	Koc Met/Tri (mg kg -1 /mg L -1 )	pest.dat	667	13,196
	CHPST KOC Met/Tri (m 3 g -1 )	.swq	0.1	2.6
	CHPST/SEDPST REA (days -1 )

Table 3

 3 Goodness-of-fit indices for daily discharge, nitrate load and suspended sediment simulation (p < 0.05).

	Periods	R 2	ENS

Table 5

 5 Total and dissolved metolachlor and trifluralin loads (g) during April and June 2008 floods at Larra outlet.

		Observed	Simulated	Observed	Simulated
	HRUs
	Runoff		Dissolved: 1653		Dissolved: 475
			Sorbed: 317		Sorbed: 1237
			Partition: 0.16		Partition: 0.72
	Lateral flow		Dissolved: 200		Dissolved: 10
	Outlet
	Flow water	Dissolved: 204	Dissolved: 135	Dissolved: 60	Dissolved: 2
		Sorbed: 27	Sorbed: 14	Sorbed: 195	Sorbed: 5
		Partition: 0.12	Partition: 0.09	Partition: 0.77	Partition: 0.72
		Metolachlor		Trifluralin
	2008 floods	April (11/04-30/04)	June (14/05-18/06)	April (11/04-30/04)	June (14/05-18/06)
	Measured total load (g)	5015	16,692	66	3285
	Simulated total load (g)	3588	11,283	92	422
	Measured dissolved load (g)	4591	16,178	13	186
	Simulated dissolved load (g)	3262	10,257	26	117

-1 yr -1 )

Table 6

 6 Daily and monthly R 2 for metolachlor and trifluralin concentration (dissolved, sorbed and total) at Larra outlet(2008)(2009).

		Metolachlor		Trifluralin
		Daily	Monthly	Daily	Monthly
	Dissolved	0.25	0.43	0.21	0.60
	Sorbed	0.01	0.38	0.01	0.15
	Total	0.26	0.45	0.02	0.16

Table 7

 7 Average metolachlor and trifluralin daily loads (g d -1 ) during high flow and low flow in dissolved and sorbed phases and percentage of pesticide load exported during high flow at Larra outlet (1998-2008).

	Dissolved

Acknowledgements

This work was performed as part of the EU Interreg SUDOE IVB program (SOE1/P2/F146 AguaFlash project, http://www.aguaflashsudoe.eu) and funded by ERDF and the Midi-Pyrénées Region.

We sincerely thank the CACG for discharge data, Météo-France for meteorological data, AEAG for long-term pesticide concentration measurements, Arnaud Mansat and Erwan Motte for their data-processing expertise, and Hugues Alexandre for IT support.


Jiantao Zhao

Christopher Sauvage

Frédérique Bitton

Mathilde Causse

Meta-analysis of GWAS provides new insights into genetic control of tomato flavor

Tomato flavor has changed over the course of long-term domestication and intensive breeding. To understand the genetic control of flavor related traits, we performed a metaanalysis of genome-wide association studies (GWAS) for 18 traits, using 775 tomato accessions and 2,316,117 SNPs from three GWAS panels (Sauvage et al., 2014;Bauchet et al., 2017 andTieman et al., 2017). In a first step, we imputed SNP data to increase genome coverage for two out of three panels and ran EMMAX software for each panel separately. From each run, the genomic inflation factor and standard errors of the beta coefficients were implemented in two meta-GWAS models: the inverse variance-weighted fixed effect and the Han Eskin random-effect model that takes heterogeneity across studies into account. Then, from the meta-GWAS results, we conducted a statistical fine-mapping of the candidate loci following a heuristic linkage disequilibrium (LD) approach. We discovered 305 significant associations for the contents of sugars, acids, amino acids and flavor-related volatiles. We showed that fruit citrate and malate contents have been impacted by selection during domestication and improvement, while sugar content has undergone less stringent selection. Results suggest that it may be possible to significantly increase volatiles that positively contribute to consumer preferences while reducing unpleasant volatiles, by selection of the relevant allele combinations. Our results provide genetic insights into the influence of human selection on tomato flavor and demonstrate the benefits obtained from meta-analysis.


Thierry Prangé
email: thierry.prange@parisdescartes.fr

Philippe Carpentier

Anne-Claire Dhaussy

Peter Van Der Linden

Eric Girard

Nathalie Colloc'
email: colloch@cyceron.fr

Nathalie Colloc'h

Comparative study of the effects of high hydrostatic pressure per se and high argon pressure on urate oxidase ligand stabilization

Keywords: homotetramer, dissociation, enzymatic mechanism, argon, high pressure, HPMX

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

Introduction

Pressure is a reversible thermodynamic variable, which increases the energy of biochemical systems and hence allows access to their low-lying excited states (Akasaka, 2006;Akasaka et al., 2013;Espada et al., 2016;Frauenfelder et al., 1990Frauenfelder et al., , 2007;;Winter, 2019;Frauenfelder et al., 2009). Studies of proteins under pressure also give access to their dynamic behavior in excited states, and to properties of their transient conformational states. Many studies using NMR or crystallography under high hydrostatic pressure have demonstrated that these high energy conformers are functionally relevant, designed either as functional or kinetic intermediates (Louis & Roche, 2016;Nielsen et al., 2013;Williamson & Kitahara, 2019;Kalbitzer et al., 2013;Fourme, Girard et al., 2012;[START_REF] Collins | [END_REF]Dhaussy & Girard, 2015;Fourme et al., 2009). These conformational states correspond to local minima in the energy landscape of a given protein along its reaction pathways.

Internal protein cavities present in the native state play an important role in pressure-induced structural modifications. Upon increasing pressure, above a critical value, cavities either collapse or become hydrated. Both processes drive pressure-induced denaturation, and trigger the unfolding process [START_REF] Collins | Proc. Natl. Acad. Sci. U.S.A[END_REF]Nagae et al., 2012;[START_REF] Xue | Proc Natl Acad Sci U S A[END_REF]. However, proteins partial unfolding behavior is already observed at lower pressure (Fuglestad et al., 2017;Nagae et al., 2012;Silva & Weber, 1993;[START_REF] Xue | Proc Natl Acad Sci U S A[END_REF]. More specifically, when high pressure is applied to multimeric structures, denaturation usually begins by the dissociation of subunits, leading to lower order oligomeric states (Winter, 2019). This process is likely due to the fact that the association of subunits is stabilized by polar and ionic bonds that are the first to be disrupted by pressure.

Noble gases such as xenon, krypton and more recently argon are known to map hydrophobic tunnels and cavities within proteins (Prangé et al., 1998), deciphering pathways for gaseous ligands like molecular oxygen, carbon monoxide or hydrogen (Colloc'h et al., 2017;Knapp et al., 2009;Kalms et al., 2016;Engilberge et al., 2020;Luna et al., 2012Luna et al., , 2008;;Tilton et al., 1984;Whittington et al., 2001;Hahn et al., 2021;Winter et al., 2011;Montet et al., 1997).

In the present study, we investigate the pressure-induced structural effects on the tetrameric enzyme urate oxidase (uricase; EC 1.7.3.3; UOX), with a competitive inhibitor (8-azaxanthine) bound at the interface between subunits. The UOX/8-azaxanthine complex appears to be a suitable model system to study the effect of pressure on the stability of an enzyme-ligand complex.

UOX is involved in the purine degradation pathway and is active as a homotetramer. Without any cofactor, it catalyzes the oxidation of uric acid to 5-hydroxy-isourate in the presence of molecular oxygen, and generates hydrogen peroxide as by-product (Scheme 1). UOX from Aspergillus flavus in complex with the competitive inhibitor 8-azaxanthine (8-aza) crystallizes in the orthorhombic space group I222 with one monomer per asymmetric unit (Colloc'h et al., 1997). The tetramer is composed of two face-to-face 16-stranded -barrel dimers, delimiting a central tunnel of 50 Å long and 12 Å in diameter. The dimer composed of two face-to-face monomers is termed A-B, while the dimer that forms the 16-stranded -barrel is termed A-C (Fig. 1, upper part). The active site is located at the interface between monomers A and C, where the ligand (here the 8-aza molecule) is hydrogen-bonded to the side chains of the two tweezer amino acids Arg 176 and Gln 228, and to the main chain nitrogen of the Thr 57* (* strands for the symmetric monomer), and forms a - stacking interaction with Phe 159 (Colloc'h et al., 1997). In the peroxo hole, on the opposite side of the phenylalanine, the water molecule responsible for the catalytic hydration step is hydrogen-bonded between side chains of Asn 254 and Thr 57*, acting as peroxo hole tweezers. A well conserved H-bond network connects the ligand to the catalytic water molecule, through two water molecules, the His 256 side chain, and the catalytic diad Lys 10* and Thr 57* side chains. This network was proposed to act as a proton shuttle during the different steps of the reaction mechanism (Gabison et al., 2008(Gabison et al., , 2010(Gabison et al., , 2011;;Wei et al., 2017;Bui et al., 2014;McGregor et al., 2021).

The functional relevance of a large buried hydrophobic cavity close to the active site in each monomer was probed using both crystallography under moderate pressure of noble gas (less than 5 MPa) and high pressure (Colloc'h & Prangé, 2014;Girard et al., 2010). This cavity was proposed to have an allosteric role acting as a connecting vessel to provide the flexibility necessary to the neighboring active site for function.

Above a critical hydrostatic pressure, which depends on the ligand presence and concentration, the tetramer in complex with 8-aza slowly begins to unfold (Girard et al., 2010). In order to determine the different factors involved in UOX pressure-induced destabilization, we compare the direct effects of hydrostatic pressure per se with the effects of argon-mediated pressure on the structure of a complex UOX/8-aza produced in excess of ligand. In the following, the two types of pressure, either hydrostatic pressure per se and high argon pressure, will be referred to as HHP and HArP respectively, and each (P,T) state of UOX crystals will be designated by the acronym "UOX-type-pressure in MPa-temperature in K or room temperature (RT)". This comparative study aims at investigating the first glimpse of the pressure-induced UOX tetramer dissociation, to identify a potential intermediate sub-state, and to confirm the allosteric role of the cavity in the enlargement of the active site necessary for efficient enzymatic activity.

Materials and methods

Crystallization

Purified urate oxidase from Aspergillus flavus expressed in S. cerevisiae was a gift from the Sanofi Company (Montpellier, France). 8-azaxanthine was purchased from Sigma-Aldrich (Lyon, France).

Crystals were routinely prepared at room temperature (291K) by mixing 20 L of enzyme (20 mg/mL at pH 7.5-8.0) with 20 L of a 8% PEG 4000 solution at the same pH (buffer = Tris/acetate 50 mM), in the presence of a large excess of 8-azaxanthine (10 mg). Large diamond-shape colorless crystals usually grow in about two days. They were stabilized by increasing the PEG concentration to 10 % in the final solution. They usually grow with approximate size 0.4 x 0.6 mm, and were stable at 291 K over several weeks.

Pressurization cells

High hydrostatic pressure macromolecular crystallography (HPMX) were performed using a Diamond Anvil Cell (DAC) described previously (Fourme et al., 2001;Fourme, Girard et al., 2012;Girard et al., 2007). We used two large aperture DACs designed by Roger Fourme and colleagues, and manufactured by the BETSA Company (Nangis, France). The DACs have an Inconel gasket elastic enough to allow a reversible decrease of pressure, a drilled hole of 400 m in diameter and 200 m depth and an aperture of 90° allowing a complete data set to be recorded on crystals belonging to highly symmetric space groups in a single rotation. The pressure within the DAC compression chamber was monitored through the pressure-dependent fluorescence of a ruby chip used as an internal probe. We used the DACs to compress UOX crystals at 210 and 310 MPa, for which the pressure transmitter is solely the mother liquor.

At low argon pressure, 3.5 and 6.5 MPa, we used a quartz capillary pressure cell, in which the pressure is transmitted by the static pressurized argon gas (Schiltz et al., 1994). At moderate argon pressure, 12 and 22 MPa, we used the "soak-and-freeze" method as described in (Lafumat et al., 2016). At high argon pressure (60 MPa, 100 MPa, 150 MPa and 200 MPa), we used the same approach but with a twostage compressor apparatus to reach higher pressure (van der Linden et al., 2014). For these two latter techniques, UOX crystals are compressed by static gaseous argon prior to being frozen in the liquid phase of argon at 77 K while they are still under pressure. They were thereafter depressurized at cryogenic temperature.

Data recording and processing

Three high hydrostatic pressure (HHP) data collections with a DAC were recorded at room temperature on UOX in the presence of an excess of 8-aza (UOX-HHP-0.1-RT as a reference, UOX-HHP-210-RT and UOX-HHP-310-RT) at the CRISTAL beamline (SOLEIL synchrotron, Gif sur Yvette, France), with a Rayonix SX-165 detector at an incident X-ray wavelength  = 0.58183 Å (21 keV), a beam size of 50 m x 50 m and a crystal-to-detector distance of 124.5 mm. Data collection at short wavelengths reduces the absorption of the diamond windows and increases the accessible reciprocal space allowing higher resolution to be reached (Fourme, Honkimäki et al., 2012).

Particular attention was paid to minimize radiation damage for data collections at room temperature, in order to avoid misinterpretations due to potential unwanted X-ray induced structural changes. Figure S1 illustrates the typical strategy used to collect data on a large crystal which fitted the DAC cavity well. The DAC is placed on a support and is vertically rotated with an available rotation range of -45° to 45° thanks to the large geometrical opening of our pneumatic DAC (Fourme et al., 2001;Fourme, Girard et al., 2012). Prior to data collection, the cavity is centered with X-rays using a highly attenuated beam (dashed square in Fig S1 ). A typical data collection is thus performed with 50 m x 50 m beam size on several zones (blue squares) using to a 10° rotation at each position.

Hence, large crystals of UOX (300 x 300 x 180 m 3 ) that fit optimally into the DAC cavity were selected to have maximum of diffracting material and power. With such large crystals compared to the small beam, full data collection on a single crystal at a given pressure were performed by merging 3 to 5 partial datasets of 20 to 30° rotation ranges collected on well separated zones with at least 50 m of translation between each of them. In the present study, a single crystal was exploited for data measurements at ambient pressure by collecting 20° data from each of 5 zones, and a second crystal was exploited for data measurements at high pressure by collecting 30° data on each of 3 zones situated on one side of the crystal, at 210 MPa and 30° each from 3 zones on the other side of the sample at 310 MPa. This corresponds to a translation of more than 100 m between the two sample parts used for data collection. Moreover, owing to the diffracting power of the bulky crystals and to the orthorhombic I222 space group, the complete data set was collected with an exposure time of 4 s, an oscillation range of 1° per frame over a full rotation of 90°, and an X-ray beam flux of 1.9  10 10 photon/s (26 fold attenuated) at 21keV. The two data collections under argon pressure in quartz capillaries were performed at room temperature on UOX in the presence of an excess of 8-aza (UOX-HArP-3.5-RT and UOX-HArP-6.5-RT) with a MAR CCD Mosaic detector at the MAX II synchrotron, beamline I711 (Lund, Sweden), with 1° rotation per frame and a full rotation of 120°. The six other data collections under argon pressure (12,22,60,100,150 and 200 MPa) were performed at 100 K on UOX in the presence of an excess of 8-aza (UOX-HArP-12-T100, UOX-HArP-22-T100, UOX-HArP-60-T100, UOX-HArP-100-T100, UOX-HArP-150-T100, and UOX-HArP-200-T100) using the flash-cooled crystals recovered from the pressurization devices, with a Pilatus 6M detector at the ESRF beamline ID30B (Grenoble, France) (McCarthy et al., 2018), with 0.1° rotation per frame and the same range of 120°. To discriminate argon atoms from water molecules or other ions, the six data sets were collected at a wavelength of 1.7 Å, to locate argon atoms using their anomalous signal at low energy (Fig. S2).

All data sets were processed using the XDS software (Kabsch, 2010). The resolution limits were determined using various criteria in the highest bin, including a I/(I) ratio around 2, and a Rmeas lower than 80 %. The integrated intensities were scaled and merged with SCALA and TRUNCATE, and the structures were solved by molecular replacement with MOLREP, all from the CCP4 suite of programs (Winn et al., 2011) . Molecular replacement was performed using the pdb 2iba structure as the starting model for the data in the I222 space group and a reconstructed dimer of 2iba for the data in the P21212 space group. Argon sites and their occupancy were initially estimated from their peaks in anomalous f"± maps, using the neighboring sulfur atoms (from Met and Cys residues) as calibration (Fig. S2).

All data were refined with REFMAC5 (Murshudov et al., 1997) and the resulting models analyzed and rebuilt with the graphic tool Coot (Emsley et al., 2010) All water molecules in the final models were kept only if they were located at correct H-bond distances and if their unconstrained B-factors remained lower than 60 Å 2 .

The data processing and refinement statistics are given in the Tables 1 and2, respectively. The final models and associated data sets were deposited in the Protein Data Bank (the pdb files IDs are reported at the end of Table 2).

Calculations of cavity volumes were performed using VOIDOO (Kleywegt & Jones, 1994) with a grid spacing of 0.6 Å and a probe radius of 1.1 Å, using 20 randomly generated orientations of the protein model. Calculations of thermodynamic parameters were performed using the PISA program (Krissinel, 2015) from the CCP4 package. Figures were prepared using PYMOL (Version 2.4 Schrodinger, LLC).

Results

UOX structures under high hydrostatic pressure per se (HHP)

Although recorded at different synchrotrons, the consistency and reproducibility of all the experiments under pressure are illustrated by the similar linear dependencies of the unit cell volume of UOX with pressure (Fig. S3).

In the present study, three structures of UOX in the presence of an excess of 8-aza were refined from data sets collected at the CRISTAL beamline: a reference data set at ambient pressure (UOX-HHP-0.1-RT), and two high pressure data sets at 210 and 310 MPa (UOX-HHP-210-RT and UOX-HHP-310-RT respectively). The pressure degrades the quality of the crystal, with a resolution decrease from 1.9 Å to 2.15 Å and 2.4 Å for the UOX-HHP-0.1-RT, UOX-HHP-210-RT and UOX-HHP-310-RT structures respectively, while the average thermal B-factors increase from 21 Å 2 to 29 Å 2 and 49 Å 2 . Consistently, the number of modelled water molecules decreases from 261 to 179 and 115. We observe a clear destabilization of UOX at 310 MPa, which is close to the pressure of denaturation since at 340 MPa UOX crystals weakly diffract and dissolve at 400 MPa.

The estimated dose per irradiation zone was calculated using the program RADDOSE-3D (Bury et al., 2018), taking the experimental parameters described above into account. The average dose on each exposed region was evaluated to be about 11 kGy (Average Dose Exposed region) and the maximum dose to be 95 kGy. Hence, the present data collections were recorded with absorbed doses one order of magnitude below the established values that should not be exceeded to solve structures at room temperature (100 -500 kGy) (Leal et al., 2013;de la Mora et al., 2020). A recent study experimentally demonstrated that, when structures are solved at room temperature with low dose, minimizing global radiation damage, the specific radiation damage that would modify the structures are of much less concern (Gotthard et al., 2019). This ensures that the differences between the three UOX-HHP-RT structures, which are discussed below, are unlikely to be related to radiation damage issues. This strategy was reinforced by a recent study experimentally showing the benefits of using high X-ray energies for macromolecular crystallography (Storm et al., 2021).

The structural differences between ambient and high-pressure structures remain small. The UOX-HHP-0.1-RT structure displays an r.m.s deviation on the C of 0.18 Å with UOX-HHP-210-RT and 0.19 Å with UOX-HHP-310-RT (Fig. S4). The main difference between these structures is, as expected, the diminution of the volume of the internal hydrophobic cavity, of 38 % between UOX-HHP-210-RT and UOX-HHP-0.1-RT. This decrease is consistent with that of 16 % at 150 MPa compared to ambient pressure described in previous work (Girard et al., 2010). At 310 MPa, there is no further contraction, but a slight expansion of 8 % compared to UOX-HHP-210-RT. However, the calculation of the cavity volume might be imprecise for a structure at such a moderate resolution. No electron density for any water molecules that could have penetrated the cavity was evidenced whatever the applied pressure.

The main chain overall thermal B-factors increased on average by 9 Å 2 between the two UOX-HHP-0.1-RT and UOX-HHP-210-RT structures. Interestingly, amongst the 47 least pressure-sensitive residues (i.e. with B-factors increasing by less than 5 Å 2 ), 13 are located along the central tunnel and 19 belong to the A-C interface forming the -barrel, among which 10 are specifically located in the active site. In contrast, 23 amongst the 44 most pressure-sensitive residues (i.e. with B-factors increasing by more than 13 Å 2 ) belong to the face-to-face A-B interface (Fig. 1, lower part). There is a large increase of average B-factor (19 Å 2 ) between the two UOX-HHP-210-RT and UOX-HHP-310-RT structures, revealing that the tetramer becomes destabilized by pressure. Here also, half of the most sensitive residues (i.e. with B-factors increasing by more than 24 Å 2 ) belong to the A-B interface (Fig. S5). This behavior strongly suggests that the pressure-induced destabilization appears precisely at this face-to-face interface, which therefore represents the weak point of the quaternary structure leading to the disruption of the tetramer.

The active site, located at the -barrel A-C interface, is clearly enlarged by pressure. This expansion can be quantified by the distance between the residues Asn 254 and Thr 57* (the clamp holding the catalytic water termed W1) that increases from 5.35 Å (UOX-HHP-0.1-RT) to 5.63 Å (UOX-HHP-210-RT) and to 5.88 Å (UOX-HHP-310-RT). Notably, there is a backward displacement of Asn 254 side chain that disrupts the H-bond with W1 (distance of 2.8, 3.2 Å and 3.4 Å for UOX-HHP-0.1-RT, UOX-HHP-210-RT and UOX-HHP-310-RT respectively), as shown in Fig. 2.

On the other hand, the positions of 8-aza and the catalytic water W1 above the inhibitor are observed to be very stable. Similarly, the conserved network of H-bonds linking 8-aza to the catalytic water molecule W1, through two conserved water molecules (8-aza→W2→W3→ His256→Lys 10*→ Thr 57*→W1), remains almost unmodified, except the W2-W3 distance that expands from 3 Å (UOX-HHP-0.1-RT) to 3.3 Å (UOX-HHP-210-RT and UOX-HHP-310-RT).

Interestingly, at high pressure, a second 8-aza molecule is observed at the entrance of the channel leading to the active site, while this latter is still occupied by the first ligand. The occupancies of this second ligand are estimated to be 50 % and 40 % in the refined structures UOX-HHP-210-RT and UOX-HHP-310-RT respectively. This second inhibitor is undetectable at 0.1 MPa. It is located between the two subunits of the -barrel and stabilized through a -stacking interaction with Phe 258, and also loosely stabilized by the formation of H-bonds with Asp 58* and Lys 61* side chains. A water molecule located above the ligand is H-bonded to the Asp 58* side chain, an environment that closely mimics the active site. In addition, a network of hydrogen-bonds links the two inhibitors, from the N9 atom of the first 8-aza molecule to the O6 atom of the second one through two water molecules, W2 and a new one termed here W4 (Fig. 3).

UOX structures under argon pressure (HArP)

Previous studies of UOX structures under pressurized noble gas have shown that the main gas binding site is the large internal hydrophobic cavity close to the active site. At higher pressure, Xe and Kr also bind at the bottom of a surface pocket within each subunit with a low occupancy [START_REF] Colloc'h | Current Trends in X-Ray Crystallography[END_REF]Marassio et al., 2011). In the present study, 8 structures of UOX in presence of an excess of 8-aza under Ar pressure were refined (UOX-HArP-3.5-RT, UOX-HArP-6.5-RT, UOX-HArP-12-T100, UOX-HArP-22-T100, UOX-HArP-60-T100, UOX-HArP-100-T100, UOX-HArP-150-T100, and UOX-HArP-200-T100) showing that argon binds to the internal cavity with occupancies increasing with the applied pressure from 20 % at 3.5 MPa to nearly saturation at 150 MPa (Fig. S6). Because of its high critical pressure, argon becomes visible in the minor binding site only at 12 MPa with an occupancy of 25 % that increases to 50 % at 100 MPa and 80 % at 150 MPa. No other binding sites are evidenced in the anomalous maps, demonstrating that noble gases have mainly two binding sites in the monomer of UOX (Fig. S2 andS7).

Interestingly, a high pressure of argon of 60 MPa and above induces a loss of two of the 222-fold symmetries in the 4 homotetramer, leading to a less symmetric ()2 homotetramer. Consequently, the crystal space group changes from I222 to P21212 (Fig. 4, upper part). This HArP-induced disruption of the symmetry at the A-B interface is interpreted as the very first step of pressure-induced dissociation of the tetrameric organization into a transient dimeric state constituted by the -barrel A-C. The structural differences related to the 4 -> ()2 transition within the tetramer are quantified by the least-squares deviations between main chain atoms of the A subunit with those of the B subunit within the AB dimer (Fig. 4, lower part). Surprisingly at a local level, the r.m.s. displacements appear rather low, while noticeable shifts are observed in five short regions, with a prominence for residues 123-125 in the A-B interface. In the I222 habit of the 4 tetramer, the 123-125 residues are in close contact with their symmetric mates. Hence, this segment needs to be refined as a two-orientation disordered loop to avoid unwanted steric clashes. This disorder is likely due to the averaging of the 222 local symmetry of the 4 tetramer over the 222 crystallographic symmetry. In the ()2 tetramer, the two-fold symmetry related to the segment 123-125 is no longer affected by a steric clash. This disorder is thus fully resolved and the segment 123-125 can be perfectly traced in the two independent chains.

Regarding the other striking regions, the two loops 50-52 and 264-267 are involved in the crystalline packing interface and the loop 196-202 is involved in the A-B interface. These three loops can be considered as the mobile parts of the monomer that further make the tetramer inner 222 symmetries to fit the crystallographic symmetries as well as possible. The residue Glu 22 is embedded in the loop 21-26 which is always very disordered.

In the eight HArP structures, the active site remains unmodified whatever the argon pressure. The distance between the peroxo hole tweezers also remains unchanged (distance of 5.4 Å between Asn 254 and Thr 57* side chains), the H-bond between the catalytic water W1 and Asn 254 is unaffected, and the H-bond between W2 and W3 is conserved, contrary to what is observed in the HHP structures.

However, and similarly to the HHP structures, a second 8-aza molecule becomes visible in the electron density map at 150 MPa and 200 MPa, bound at the entrance of the channel leading to the active site.

The occupancy of this second ligand is estimated to be about 30 % in UOX-HArP-150-T100 and about 60 % in UOX-HArP-200-T100. The same H-bond network links the two 8-aza molecules through W2 and W4 (Fig. 3) similarly to what is observed in the HHP structures.

Discussion

Argon labelling

Usually, argon labelling, like other noble gases, induces only marginal structural changes since it maintains a good isomorphism between labelled and non-labelled structures (Schiltz et al., 2003;Vitali et al., 1991). This results in very low r.m.s. deviations when comparing their C traces. The noble gas occupancies in binding sites of a given protein depend on both the atom physicochemical properties and the applied pressure. It can usually be modelled by the Hill-Langmuir isotherm saturation curve (Marassio et al., 2011) that can be written as:

𝒇 = 𝒇 𝒎𝒂𝒙( 𝑷 𝑷 𝒄 ) 𝟏+( 𝑷 𝑷 𝒄 ) 𝒂
where Pc is the critical pressure at which the occupancy f reaches half of its saturation (fmax/2) and α is the curve steepness coefficient (or sigmoidality that reflects a cooperative binding). For a given site, the value of Pc depends essentially on both the Henry's coefficient of the dissolved gas in the protein solution and on the gas polarizability (i.e. the protein/noble-gas interaction strength). Pc is thus ranked as: Pc Xe << Pc Kr << Pc Ar . Experimentally, the critical pressure to populate a given protein binding site is observed to be rather low for xenon (~ 0.5 -3 MPa), intermediate for krypton (~ 1 -20 MPa), and high for argon . In a given binding site, the noble gas occupancies in UOX are, as expected, in the order of their polarizabilities (Fig. S6) (Quillin et al., 2000;[START_REF] Colloc'h | Current Trends in X-Ray Crystallography[END_REF].

Contrary to what happens in many cases where the number of binding sites increases with pressure (Colloc'h et al., 2017;Kalms et al., 2016;Engilberge et al., 2020;Prangé et al., 1998), we only observed two noble gas binding sites in UOX whatever the pressure. The main noble gas binding site highlights a putative dioxygen storage site, since the hydrophobic cavity where it binds was proposed to serve as a transient reservoir for dioxygen on its way to the active site (Colloc'h & Prangé, 2014). The minor binding site is a surface pocket largely deepened in the presence of the gas and located in the vicinity of residues lining the central tunnel. Interestingly, this pocket is also deepened in UOX-HHP-210-RT structure (but not in UOX-HHP-310-RT), with however no exogenous molecule bound into it, revealing the intrinsic dynamics of this pocket.

HArP reveals the first step of tetramer dissociation

The most important feature observed in UOX HArP structures is a transition from the body-centered space group of the crystals to a less-ordered primitive space group. The transition I222 → P21212 is triggered by argon at a pressure between 22 MPa and 60 MPa. This corresponds to a loss of a symmetry between the four equivalent monomers, according to the ()4 → ()2 scheme. This transition is not observed in the HHP structures when the pressure transmitter is solely the mother liquor. However, we can speculate that the cluster of pressure-sensitive residues along the A-B interface could be indicative of the future loss of the two-fold symmetry between these two monomers.

Subdomains that are the most pressure sensitive usually indicate the presence of a mispacked state. In our case, this would correspond to the region encompassing the Asp 123 residue.

A thermodynamic analysis of the UOX monomer by PISA shows that the tetramer is, as expected, the most stable assembly (G = -164 kcal/mol), and that both dimers are equally stable with a G = -50 kcal/mol. Interestingly, their free energy of assembly dissociation is lower for the face-to-face A-B dimer (Gdiss = 30 kcal/mol) than for the -barrel A-C dimer (Gdiss = 42 kcal/mol), showing that the face-to-face dimer is thermodynamically less stable than the -barrel dimer. In line with these calculations, the structures under very high argon pressure (60 MPa and above) confirm that the faceto-face dimer A-B seems to be less stable than the -barrel dimer A-C, since pressure induces the disruption of the tetramer symmetry precisely at the A-B interface. We can then suggest, based on the loss of symmetry induced by a high pressure of argon, that pressure would begin to dissociate the tetrameric UOX via the formation of an intermediate A-C sub-state constituted by the most stable 16stranded -barrel.

In contrast, it has been shown recently that controlled dehydration of UOX crystals from bacillus sp.

TB-90, using the humid air and glue-coating method, induces a phase transition from a P21212 space group to a I222 space group (Hibi et al., 2014(Hibi et al., , 2016;;Hibi & Itoh, 2021). Dehydration has an effect opposite to pressure, inducing a modification from a homotetramer with the four chains in the asymmetric unit to a more ordered tetramer with the 16-stranded -barrel dimer in the asymmetric unit.

Comparison between HHP and HArP effects at 200 MPa

The difference between hydrostatic pressure per se and high pressure induced by noble gas has been mainly studied in the context of high pressure nervous syndrome studies, to discriminate between the pure pressure effect and the pressure effect of breathing helium by divers. Theoretical studies on the NMDA receptor have shown that a pressure of 2.5 MPa helium induces more deformation than a 2.5

MPa pressure per se, mainly through protein-lipid interactions (Bliznyuk et al., 2019). Although our globular model cannot be compared to a membrane-embedded protein, it is interesting to note that argon pressure induces a disruption of the symmetry not observed at a similar high pressure per se.

The UOX-HArP-200-T100 structure is closer to the UOX-HHP-210 RT structure than to the UOX-HHP-310 RT structure (Fig. S4), suggesting that a part of the Ar pressure-induced structural modifications at 200 MPa may arise from the direct effect of pressure (per se), even if it is difficult to take into account the effect due to the difference in data collection temperatures (100 K and RT).

Tetramer stabilization by the inhibitor bound at interface

A protein interacts with a ligand through a spatial reorganization, implying that the complexation process is associated with a volume change. Thus, applying pressure, which is thermodynamically conjugated to volume, modifies de facto the protein/ligand interaction by adjusting voids at the interface between the protein and its ligand. Pressure is a way to induce a denser packing, with a smaller volume at the interface, leading to a more stable complex. Moreover, pressure by exploring the conformational landscape could favor a sub-state corresponding to a local minima which in turn favors ligand binding (Levin et al., 2019;Luong et al., 2015;Winter, 2019).

We have previously shown by fluorescence spectroscopy experiments that UOX displays an enhanced pressure stability upon inhibitor binding, in a concentration-dependent manner (Girard et al., 2010).

Without inhibitor, the pressure-induced dissociation of the tetramer (denaturation) was observed around 175-200 MPa. This limit is shifted toward higher pressure depending on the concentration of the inhibitor, around 220-250 MPa with an equimolar concentration and around 350 MPa with an excess of 8-aza. The previously determined crystallographic structure of UOX under a hydrostatic pressure of 150 MPa (pdb 3f2m) was obtained in the presence of an equimolar concentration of 8-aza. With such a ratio, the diffraction was lost above 200 MPa. The stabilizing effect of 8-aza in excess is here confirmed, since diffraction was now lost at about 400 MPa. This result highlights that the ligand bound at the interface strengthens the dimer and further protects the tetrameric organization against pressure effects.

Moreover, both HHP and HArP show that a second inhibitor molecule is stabilized in the channel connecting the solvent to the active site. The Lys 61* residue, which is H-bonded to this second 8-aza, is amongst the least pressure-sensitive residues, confirming that pressure consolidates this area.

In the previous study of UOX at 150 MPa in the presence of an equimolar concentration of 8-aza, we observed that three loops surrounding the channel leading to the active site were particularly pressuresensitive with the largest B-factor increases (Girard et al., 2010). In the presence of an excess of 8-aza, the B-factor increase of two of these loops (51 -55 and 161 -173) remains average, confirming that 8aza in excess stabilizes the active site against the effect of pressure. On the other hand, the loop 260 -275, which is part of the face-to-face A-B interface, still displays a high pressure sensitivity, in line with a destabilization of this interface. It is interesting to note that the two loops 51 -55 and 260-275 are also those which differ between the two A and B monomers when the symmetry is disrupted.

Differences of temperature in data collections

HHP data collections within a DAC and HArP data collections in capillaries below 7 MPa were performed at room temperature, but at 100 K for HArP data collections at 12 MPa and above. The mosaicity of the crystals is indeed affected upon freezing (Table 1). The structural modifications and the intrinsic dynamics of flexible loops induced by pressure lead to a degradation of the order and resolution together with an associated increase of thermal B-factors. This is especially visible at 310

MPa, a pressure quite close to that which leads to a total loss of diffraction. On the other hand, the freezing of crystals under high pressure of Ar leads experimentally to immobilise transient sub-states in crystals, which thus display diffraction to higher resolution. Hence, the second 8-aza becomes clearly more visible in UOX-HArP-200-T100 structure than in both UOX-HHP-210-RT and UOX-HHP-310-RT MPa structures. However, the treatment under argon pressurization followed by a depressurization step then a freezing step is quite difficult to conduct, leading sometimes to a degradation of the quality of diffraction.

HHP reveals an excited sub-state of UOX catalytic cycle

The enzyme conformation in the UOX-HHP-210-RT and UOX-HHP-310-RT structures, that display a broader active site characterized by an elongated peroxo hole tweezers (Asn 254 -Thr 57*), can correspond to a sub-state where the active site needs to accommodate a larger ligand. This occurs during the catalytic mechanism, when the non-planar 5-hydroxy-isourate product is bound before release. The larger distance between Asn 254 and Thr 57* also corresponds to a sub-state in which molecular oxygen is bound in the active site in place of the catalytic water (Colloc'h & Prangé, 2014).

In previous work, it was proposed through the dissection of the complete UOX catalytic reaction that a base-oxidative-system (BOS) plays a central role in the mechanism (Gabison et al., 2011). It consists of the peroxo hole tweezers Thr 57*/Asn 254 acting together with the triad Lys 10*/His 256/Lys 61* which would deprotonate and then extract an electron from uric acid. The clear stabilization of Lys 61* upon pressurization suggests again that pressure stabilizes an active sub-state of UOX that occurs along the catalytic cycle.

Interestingly, the proton shuttle mechanism (Gabison et al., 2010(Gabison et al., , 2011)), which runs from 8-aza to W1 through W2, W3, His 256, Lys 10* and Thr 57* is weakened at 210 and 310 MPa. Indeed, at these pressures, the H-bond between W2 and W3 becomes elongated, since W2 is engaged in an H-bond with W4 that interlinks the two inhibitors. The HHP structures thus suggest the existence of a mechanism with two sub-states, a sub-state that processes the first ligand through the proton pathway exchange (the proton shuttle) and a sub-state where an H-bond system stabilizes the entrance of a second ligand.

Similarly, Asn 254, which is displaced backward by pressure, has been shown to explore different conformations. It has been proposed that Asn 254 dynamics could modulate W1 stability in the peroxo hole and favor its displacement out of the peroxo hole (McGregor et al., 2021). It is worth noting that this Asn residue is located between the active site and the hydrophobic cavity proposed to be a transient reservoir for di-oxygen close to the peroxo hole (Colloc'h & Prangé, 2014). The HHP structures of UOX could then represent excited substates where Asn 254 moves backward to allow the di-oxygen to migrate from the hydrophobic cavity to the peroxo-hole.

We have suggested that the swelling of the active site can occur through the contraction of the neighboring hydrophobic cavity that has been proposed to act as a connecting vessel to give flexibility to the active site (Colloc'h & Prangé, 2014;Girard et al., 2010). Its role is confirmed here in both the HHP and HArP structures. In the UOX-HHP-210-RT structure, the active site is enlarged, while the hydrophobic cavity volume is reduced. Conversely, in the UOX-HArP-200-T100 structure, the binding of argon in the cavity prevents its contraction and thus induces no enlargement of the active site. This confirms that argon restrains the intrinsic flexibility of the cavity. Consequently, the proton shuttle is not weakened when Ar is bound in the internal cavity, as was the case in HHP structures.

Accordingly, the argon atom bound in the hydrophobic cavity prevents the enlargement of the active site, thus stabilizing the UOX tetramer against pressure effects.

Pressure inhibits substrate affinity of the enzyme

Pressure has been shown to both enhance or inhibit enzymatic activities (Eisenmenger & Reyes-De-Corcuera, 2009;Akasaka et al., 2008;Winter, 2019). For example, it can promote or hamper a catalytically active state of an enzyme with a better/lower ligand binding affinity or improve/reduce reaction rates by increasing/decreasing conformational flexibility. In the present case, enzymatic assays under pressure have previously shown that pressure decreases the enzymatic activity of UOX with a loss of substrate affinity, while the catalytic efficiency was unmodified and the protein was still tetrameric (Girard et al., 2010). Moreover, it was shown by electron spin resonance spectroscopy and quantum mechanical studies that residues from the face-to-face A-B interface participate in the stabilization of the active site (Gabison et al., 2011). The loss of substrate affinity induced by pressure might thus be explained by both the contraction of the allosteric cavity leading to the swelling in the active site and the destabilization of the face-to-face A-B interface.

Conclusion

Both ways of applying pressure, either per se (HHP) or noble-gas mediated (HArP), provide This study also highlights that high pressure applied to a protein-ligand complex, whatever the transmission medium, shifts the thermodynamic equilibrium towards the completion of the complex formation, according to Le Chatelier's principle. Pressure leads to the saturation of this complex and populates new specific sites within the protein. The comparison between the structures under high hydrostatic pressure per se, in which the cavity is empty, and the structures under high argon pressure, in which argon is bound, demonstrates the role of the allosteric cavity that provides the functional flexibility to the active site, necessary for the catalytic process.

High pressure NMR experiments have widely been used to characterize oligomeric states upon unfolding and to analyze structural perturbations and unfolding transitions. Here we show that crystallography under pressure (HPMX) is a valuable complementary tool which gives additional understanding in analyzing the structural perturbations at atomic level, and is even able to catch the first steps of the oligomeric dissociation preceding denaturation in the case of multimeric proteins like urate oxidase.

Scheme 1 Left: The reaction catalysed by urate oxidase (UOX) from uric acid to 5-hydroxy-isourate.

Right: The analogue 8-azaxanthine in which the 8-carbonyl group is replaced by a nitrogen atom.

  complementary information about the UOX mechanism. The UOX-HArP structures show that upon pressure, UOX does not transit directly from a tetrameric association to a monomeric form, but via a transient dimeric intermediate state constituted of the 16-stranded -barrel built by the A and C chains. Very high argon pressures (above 22 MPa) thus allow the characterization of this intermediate excited sub-state with a partial unfolding behavior.

Figure 1

 1 Figure 1 Upper left: The tetramer of urate oxidase in cartoon representation with the A chain in green, the B chain in cyan, the C chain in slate, and the D chain in salmon. The 8-azaxanthine molecules are shown in sphere representation. Upper right: Same representation, viewed along the central tunnel. Lower left: Molecular surface of UOX tetramer, colored in red where the B-factors increase more than 13 Å 2 between the UOX-HHP-0.1-RT and UOX-HHP-210-RT structures, in cyan where the B-factors increase less than 5 Å 2 and in grey otherwise. The 8-azaxanthine molecules are shown in ball-and-stick representation. Lower right: Same representation, viewed along the central tunnel.

Figure 2

 2 Figure 2 From left to right, the active site in the UOX-HPP-0.1-RT, UOX-HPP-210-RT and UOX-HPP-310-RT structures, showing the progressive elongation of the catalytic water W1 tweezers and of the hydrogen bond between W2 and W3.

Figure 3

 3 Figure 3 Left: In the UOX-HHP-210-RT, UOX-HHP-310-RT and UOX-HArP-200-T100 structures, the second ligand molecule (8-aza 2) is located within the channel ending at the active site occupied by 8-aza 1. It is connected to the first ligand through the two water molecules W2 and W4 and is stacked over Phe 258. Right: The electron density of the second ligand observed in the HArP-200-T100 structure (2Fo-Fc map, level at 1.5 sigmas above background).

Figure 4

 4 Figure 4 Upper: View of the HArP transition from an 4 tetramer with three 2-fold axes (left, either the UOX-HPP-RT structures or the UOX-HArP structures up to 22 MPa) to a less-ordered ()2 tetramer with one two-fold axis (right, the UOX-HArP structures at 60 MPa and above). The asymmetric units are coloured in green and the symmetry-generated subunits in the tetramer are coloured in red. Lower: The r.m.s. deviations of the two  and  subunits of the UOX-HArP-200-T100 structure, relevant to the loss of the 2-fold symmetry between them.

Table 1

 1 Data collection and processing statisticsValues for the outer shell are given in parentheses.

	Acronym	UOX-	UOX-	UOX-	UOX-	UOX-	UOX-	UOX-	UOX-	UOX-	UOX-	UOX-
		HHP-	HHP-	HHP-	HArP-	HArP-	HArP-	HArP-	HArP-	HArP-	HArP-	HArP-
		0.1-	210-	310-	3.5-	6.5-	12-	22-	60-	100-	150-	200-
		RT	RT	RT	RT	RT	T100	T100	T100	T100	T100	T100
	Diffraction source	SOLEIL, CRISTAL beamline	MAXII, I711			ESRF, ID30 beamline
					beamline
	Wavelength (Å)	0.5818	0.5818	0.5818	0.9775	0.9775	1.77	1.77	1.77	1.77	1.77	1.77
	Pressure (MPa)	0.1	210	310	3.5	6.5	12	22	60	100	150	200
	Temperature (K)	291	291	291	291	291	100	100	100	100	100	100
	Space group	I222	I222	I222	I222	I222	I222	I222	P21212	P21212	P21212	P21212
	a (Å)	80.34	79.53	79.13	80.23	80.32	80.89	79.79	78.23	78.43	77.40	78.55
	b (Å)	96.16	95.63	95.40	96.11	96.12	95.50	95.15	95.27	95.18	95.34	96.68
	c (Å)	105.51	104.75	104.67	105.39	105.41	105.23	104.28	104.5	104.4	104.66	105.58
	Mosaicity (°)	0.07	0.15	0.09	0.09	0.11	0.36	0.28	0.20	0.19	0.22	0.34
	Resolution range (Å)	29.77 -	29.59 -	29.51 -	4.78 -	20.00 -	40.50 -	70.00 -	47.60 -	47.60 -	47.70 -	71.32 -
		1.90	2.15	2.40	1.60	1.60	2.36	1.69	1.86	1.64	2.19	1.72
	Total No of	107 997	69 635	51 523	220 558	187 218	175 236	490 648	607 475	908 151	405 680	531 793
	reflections	(150493)	(10 163)	(7 586)	(21 323)	(18 022)	(15 472)	(7 193)	(1 545)	(19 326)	(11 521)	(11 199)
	No of unique	30 145	21 075	15 109	53 681	52 954	47 439	39 125	53 842	84 438	39 208	73 215
	reflections	(4 400)	(3 071)	(2 216)	(5 277)	(5 301)	(4 120)	(2 310)	(499)	(4955)	(11 521)	(2 671)

† Estimated Rr.i.m. = Rmerge[N/(N -1)] 1/2 , where N = data multiplicity.

Table 2

 2 Structure solution and refinement statisticsValues for the outer shell are given in parentheses.

	Acronym	UOX-	UOX-	UOX-	UOX-	UOX-	UOX-	UOX-	UOX-	UOX-	UOX-	UOX-
		HHP-	HHP-	HHP-	HArP-	HArP-	HArP-	HArP-	HArP-	HArP-	HArP-	HArP-
		0.1-RT	210-RT	310-RT	3.5-RT	6.5-RT	12-T100	22-T100	60-T100	100-T100	150-T100	200-T100
	Resolution range	20.0 -	20.0 -	20.0 -	14.78 -	14.42 -	40.48 -	70.0 -	47.70 -	47.65 -	47.70 -	46.38 -
	(Å)	1.90	2.15	2.40	1.60	1.60	2.36	1.69	1.86	1.64	2.19	1.80
	No of reflections,	25 754	17 795	13 313	50 892	50 192	14 823	37 292	51 064	80 214	37 198	68 695
	working set	(1 746)	(1 122)	(869)	(3 677)	(3 714)	(72)	(668)	(659)	(1 305)	(2 053)	(3 402)
	No of reflections,	1390	944	693	2 729	2 707	739	1 831	2 726	4 143	1 959	3 583
	test set	(83)	(57)	(50)	(187)	(173)	(8)	(34)	(34)	(56)	(124)	(170)
	Final Rcryst	0.174	0.189	0.188	0.151	0.156	0.198	0.154	0.165	0.177	0.174	0.212
		(0.269)	(0.309)	(0.349)	(0.206)	(0.300)	(0.863)	(0.646)	(0.227)	(0.297)	(0.317)	(0.386)
	Final Rfree	0.222	0.248	0.270	0.178	0.183	0.264	0.196	0.208	0.205	0.226	0.243
		(0.316)	(0.340)	(0.366)	(0.245)	(0.305)	(1.154)	(0.673)	(0.225)	(0.361)	(0.333)	(0.391)
	No of non-H atoms
	Proteins	2362	2362	2362	2362	2362	2362	2362	4724	4824	4829	4724
	Ion	1	1	1	1	1	1	6	2	2	2	2
	Ligand	11	22	22	11	11	20	16	17	22	44	44
	Water	261	179	115	229	221	203	435	531	652	580	473
	Argon	-	-	-	1	1	2	2	4	4	4	4
	Total	2 635	2 564	2 500	2 604	2 596	2 588	2 821	5 277	5 504	5 459	5 247
	r.m.s. deviations

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Acknowledgements

The authors acknowledge the SOLEIL (Gif-sur-Yvette, France), the European Synchrotron Radiation Facility (ESRF, (Grenoble, France) and the MAX II synchrotrons (Lund, Sweden) for provision of synchrotron radiation facilities. They thank Pierre Fertey of the CRISTAL beamline (SOLEIL) for his technical help and assistance during data collection under high pressure per se, Yngve Cerenius and the staff members of I117 beamline (MAX II) for data collection in quartz capillaries under moderate argon pressure, and the staff members of ID30-B beamline (ESRF) for data collection under high argon pressure. The authors acknowledge access to the ESRF MX high-pressure laboratory for the preparation of the frozen UOX crystals under argon pressure. The use of the different large aperture DACs built by BETSA has been made possible through the IBS High Pressure platform. IBS acknowledges integration into the interdisciplinary Research Institute of Grenoble (IRIG, CEA).

The authors also have special thoughts for Roger Fourme, deceased in 2012, who developed HPMX with such great enthusiasm.