Marilisa Neri

Marc Baaden

Vincenzo Carnevale

Claudio Anselmi

Amos Maritan

Paolo Carloni
email: carloni@sissa.it.

Microseconds Dynamics Simulations of the Outer-Membrane Protease T

Keywords: molecular dynamics, conformational fluctuations, Coarse-grained, microseconds, hybrid model, Omptin

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

Introduction

Recently, molecular dynamics (MD) studies have lead to the suggestion that enzymatic function results from a subtle interplay between chemical kinetics and molecular motion. Examples include the dihydrofolate reductase enzyme [START_REF] Rod | Correlated motion and the effect of distal mutations in dihydrofolate reductase[END_REF], whose completion of the catalytic cycle has been suggested to require coupled motions, and the proteases superfamily [START_REF] Piana | Role of conformational fluctuations in the enzymatic reaction of HIV-1 Protease[END_REF][START_REF] Cascella | Evolutionarily conserved functional mechanics across pepsin-like and retroviral aspartic proteases[END_REF][START_REF] Carnevale | Convergent dynamics in the protease enzymatic superfamily[END_REF], whose biological function has been proposed to be affected by conformational fluctuations.

These conclusions are being corroborated by a variety of experimental biophysical studies [START_REF] Fersht | Structure and mechanism in protein science: a guide to enzyme catalysis and protein folding[END_REF][START_REF] Eisenmesser | Enzyme Dynamics during Catalysis[END_REF][START_REF] Benkovic | A perspective on enzyme catalysis[END_REF][START_REF] Daniel | The Role of Dynamics in Enzyme Activity[END_REF][START_REF] Luo | Anticorrelated motions as a driving force in enzyme catalysis: the dehydrogenase reaction[END_REF][START_REF] Wolf-Watz | Linkage between dynamics and catalysis in a thermophilic-mesophilic enzyme pair[END_REF].

A major limitation of MD in addressing this issue is obviously the accessible time scale (currently up to the subµs timescale). In principle, one could achieve much longer time scales by coarse-grained (CG) potential based MD approaches [START_REF] Tirion | Large amplitude elastic motions in proteins from a single-parameter, atomic analysis[END_REF][START_REF] Noguti | Collective variable description of smallamplitude conformational fluctuations in a globular protein[END_REF]. However, while CG potentials are useful to understand large-scale phenomena such as the folding process (13) they can not describe the exquisite molecular recognition events among enzymes and their substrates, which are key for the enzymatic function.

Recently, we have presented a hybrid approach, the MM/CG model [START_REF] Neri | Coarse-grained model of proteins incorporating atomistic detail of the active site[END_REF], with the goal to preserve the advantages of both MM and CG approaches, i.e. the necessary details associated with the biological activity and the "large" accessible time scale. Specifically, the amino acid residues involved in the ligand binding are treated with atomic detail (MM region) by means of a molecular mechanics force field, whereas the rest of the protein is treated at the CG level. This approach allows a fast and efficient description of the mechanical coupling between the dynamics of the active site with the enzymatic substrate (the MM region) and that of the protein environment (the CG region).

So far, MM/CG simulations have been applied to selected members of the aspartic protease family. These calculations were meant as a validation of the approach versus all-atom MD simulations [START_REF] Neri | Coarse-grained model of proteins incorporating atomistic detail of the active site[END_REF]. The MM/CG approach turned out to correctly reproduce both the local and the global features of two cytoplasmatic proteins (the aspartic proteases from HIV-1 PR [START_REF] Piana | Role of conformational fluctuations in the enzymatic reaction of HIV-1 Protease[END_REF] and β-secretase (BACE) (3)). Furthermore, the calculations reproduced the structural fluctuations of the substrate in the binding cavity, which play an important role for the enzymatic activity [START_REF] Piana | Role of conformational fluctuations in the enzymatic reaction of HIV-1 Protease[END_REF][START_REF] Cascella | Evolutionarily conserved functional mechanics across pepsin-like and retroviral aspartic proteases[END_REF]. These test calculations covered the same timescale as reference MD simulations used for comparison (10 ns for HIV-1 PR and 8 ns for BACE). However, the computational cost of this scheme turned out to be about two orders of magnitude smaller than that of the corresponding all-atom MD [START_REF] Neri | Coarse-grained model of proteins incorporating atomistic detail of the active site[END_REF].

In this paper, we use the MM/CG approach to study the relevant case of the Michaelis complex of a membrane protease, the outer-membrane protease T (OmpT) [START_REF] Vandeputte-Rutten | Crystal Structure of the Outer Membrane Protease Ompt from Escherichia Coli Suggests a Novel Catalytic Site[END_REF] on the µs time scale.

OmpT is a defense protein expressed by Gram-negative bacteria belonging to the omptin protein family [START_REF] Mangel | Omptin: an Escherichia coli outer membrane proteinase that activates plasminogen[END_REF], which has been shown to be important for the virulence of Y. pestis and clinical E. coli isolates [START_REF] Sodeinde | A surface protease and the invasive character of plague[END_REF]. The protein cleaves peptides preferentially between two consecutive basic amino acid residues [START_REF] Kramer | Identification of active site serine and histidine residues in Escherichia coli outer membrane protease OmpT[END_REF][START_REF] Dekker | Substrate specificity of the integral membrane protease OmpT determined by spatially addressed peptide libraries[END_REF].

The isoenzyme from E. coli, for which the X-ray structure is available [START_REF] Vandeputte-Rutten | Crystal Structure of the Outer Membrane Protease Ompt from Escherichia Coli Suggests a Novel Catalytic Site[END_REF], features a deep groove formed by loops L4 and L5 on one side and L1, L2, L3 on the other one (Figure 1a). This groove constitutes the active site of the enzyme. Initially, OmpT was classified as a novel-type serine protease; however, the relatively large distance between the putative catalytic S99 and H212 (∼9 Å) observed in the X-ray structure [START_REF] Vandeputte-Rutten | Crystal Structure of the Outer Membrane Protease Ompt from Escherichia Coli Suggests a Novel Catalytic Site[END_REF] has suggested that the protease may work by a novel mechanism involving the D210-H212 and D83-D85 pairs. H212 and D83 groups have been suggested to activate a water molecule for the nucleophilic attack, while D85 and D210 contribute to polarizing the substrate scissile peptide bond [START_REF] Kramer | Identification of active site serine and histidine residues in Escherichia coli outer membrane protease OmpT[END_REF][START_REF] Dekker | Substrate specificity of the integral membrane protease OmpT determined by spatially addressed peptide libraries[END_REF]. A peculiar H-bond network formed by these residues orients both the substrate and the nucleophilic water, promoting the cleavage of the peptide bond. MD simulations of the enzyme on a 10-ns time scale have further supported this scenario [START_REF] Baaden | OmpT: molecular dynamics simulations of an outer membrane enzyme[END_REF][START_REF] Tai | Three hydrolases and a transferase: comparative analysis of active-site dynamics via the BioSimGrid database[END_REF].

A comparison of MM/CG and all-atom MD simulations of the enzyme in the free state shows that the MM/CG approach is equally well suited for membrane proteins [START_REF] Neri | Molecular dynamics simulations of outer-membrane protease T from E. coli based on a hybrid coarse-grained/atomistic potential[END_REF] and suggests that fluctuations of the active site cleft may play a role for substrate recognition. Here, we extend our investigations to the Michaelis complex of the protein with a model substrate (Ala-Arg-Arg-Ala). We carried out simulations on four OmpT/ARRA Michaelis complexes (A-D), which differ for: (i) the protonation state of the putative catalytic residue D83; (ii) for the N-and C-terminal tails of the substrate, which are considered in the Zwitterionic form (charged state) or capped with acetyl and N-methyl groups (neutral state), respectively, following all-atom MD calculations on the same system.

The aim of our MM/CG simulations is twofold. First, we investigated the role of conformational fluctuations of the substrate in the active site on the µs time-scale. Results show that both large-scale motions of the protein and the electrostatic field, evaluated on such a time-scale, impact on the function of the enzyme. Second, we used the MM/CG approach to investigate the effects of mutations for the enzymatic function. We focused on protein mutants S99A and H212A, which are experimentally known to be much less efficient than the wild-type (wt) (residual activity within 0% and 4% relative to the wt ( 18)). Our calculations, on the ∼ 0.1 µs time scale, provide a structural basis for the dramatic decrease in the catalytic activity. Because of its relatively cheap computational cost (two order of magnitude faster than standard all-atom MD), the methodology may be helpful to investigate structure/function relationships of high-throughput site-directed mutagenesis data.

Methods

The MM/CG model A detailed description of the MM/CG approach is reported elsewhere [START_REF] Neri | Coarse-grained model of proteins incorporating atomistic detail of the active site[END_REF], therefore we only summarize its principles here.

A small part of the protein (e.g. the enzymatic active site) is investigated in atomic detail, while the rest is treated with a CG approach using a modified by only considering C α centroids. An interface region (I) is located between the two MM and CG regions, bridging the large discontinuity between full-atom and CG descriptions. The total potential energy of the system reads:

V = E M M + E CG + E I + E M M/I + E CG/I + E SD ,
where the first three terms represent the interactions within the MM, CG and I regions, respectively, whereas the fourth and fifth represent the crossterms potentials. The last term, E SD , mimics stochastic and frictional forces acting on the system due to the solvent [START_REF] Doi | Introduction To Polymer Physics[END_REF][START_REF] Van Der Spoel | Gromacs User Manual version 3[END_REF].

In regions MM and I, all atoms are explicitly considered and, consequently, E M M , E I and E M M/I energy terms have all the same formulation (i.e. the GROMOS96 43a1 force field [START_REF] Van Gunsteren | Biomolecular Simulation: The GROMOS96 manual and user guide[END_REF]).

E CG takes the following form:

E CG = 1 4 i K b (|R i -R i+1 | 2 -b 2 i i+1 ) 2 + i>j V 0 [1 -exp (-B ij (|R i -R j | -b ij ))] 2 . ( 1
)
The first term in Eq. ( 1) takes into account bonded interactions between consecutive CG (C α ) centroids, identified by the position vectors R i and R i+1 , and K b is the relative bond force constant. b ij is the equilibrium distance, corresponding to the native distance between CG atoms. The second term in Eq. ( 1) describes the non-bonded interactions between CG atoms. V 0 is the interaction well depth and B ij is the modulating exponent of the Morse potential. The latter, along with V 0 , have been obtained so as to reproduce features of all-atom MD calculations on a test system [START_REF] Neri | Coarse-grained model of proteins incorporating atomistic detail of the active site[END_REF].

At the interface between the I and CG regions, bonds between consecutive C α belonging to the I and CG regions ensure backbone connectivity.

In addition, a term describing the non-bonded interactions is added: the interface atom, that is either a C α or a C β , interacts with the C α in the CG region with the same potential as in Eq. [START_REF] Rod | Correlated motion and the effect of distal mutations in dihydrofolate reductase[END_REF].

A simple solvation model using explicit water molecules is also implemented: if the active site of the enzyme is solvent exposed a water drop is constructed around the MM and I regions. If a water molecule exits from the drop, its velocity is reflected toward the inside [START_REF] Neri | Molecular dynamics simulations of outer-membrane protease T from E. coli based on a hybrid coarse-grained/atomistic potential[END_REF] to avoid evaporation.

Investigated Systems

We focused on two OmpT protomers in complex with their substrate ARRA.

The simulations differ in the protonation state of D83 at the active site and in the N-and C-termini of the peptide (see Table 1). The protonated aspartate might orient the putative attacking water molecule. The neutral termini help to reduce artefacts relative to the short length of the peptide substrate. The catalytic histidine residue H212 was assumed to be δ-protonated as in (20) (Figure 1bc). Prior to the MM/CG simulations we equilibrated the systems A, B, C and D via 24 ns of standard all-atom MD starting from the complex model described in [START_REF] Baaden | OmpT: molecular dynamics simulations of an outer membrane enzyme[END_REF]. S99A and H212A mutants were built by simply replacing these residues with alanine. We also performed MD simulations of a reference system (diglycine) to compare the electrostatic properties of key residues in the active site of OmpT. The systems underwent MM/CG simulations for the time scales summarized in Table 1.

Computational Details

Preliminary MM simulations on four protomers of the OmpT-ARRA complex described in [START_REF] Baaden | OmpT: molecular dynamics simulations of an outer membrane enzyme[END_REF] were performed for 24 ns using the Yasara software and the associated Yamber2 force field [START_REF] Krieger | Making optimal use of empirical energy functions: Force-field parameterization in crystal space[END_REF]. Details of these equilibration simulations are provided as supplementary material.

Hybrid MM/CG simulations were performed using a modified version of the Gromacs 3.2.1 program [START_REF] Neri | Coarse-grained model of proteins incorporating atomistic detail of the active site[END_REF]. The enzymatic active site was treated at atomic detail with the GROMOS96 43a1 force field [START_REF] Van Gunsteren | Biomolecular Simulation: The GROMOS96 manual and user guide[END_REF], as aforementioned (MM and I regions in Figure 1a). ∼ 400 SPC water molecules [START_REF] Berendsen | Interaction models for water in relation to protein hydration[END_REF] were added. This constitutes a water layer of ∼ 15 Å around the MM region (Figure 1a). This approach has been shown to accurately describe structural and dynamic features of the active site of two aspartic proteases in complex with their substrate ( 14) and of OmpT in the free state [START_REF] Neri | Molecular dynamics simulations of outer-membrane protease T from E. coli based on a hybrid coarse-grained/atomistic potential[END_REF].

The entire systems were composed of ∼ 4, 400 particles.

The analyses of the trajectories of A and B were performed over the last 0.95 µs, that is after the equilibration phase (Figure 1SIab). The analyses of the trajectories of C and D were not carried out, as we will discuss later in the results section.

The leap-frog stochastic dynamics algorithm was used to integrate the equations of motion with a time step ∆t = 2 fs and a friction coefficient

γ i = m i /τ
, where τ = 0.5 ps is the time constant for the coupling and m i is the mass of the ith particle. A cut-off distance of 14 Å was used for the electrostatics1 . A cut-off of 14 Å was also used for the van der Waals interactions. The pair list was updated every 10 steps. The SHAKE algorithm ( 29) was used to keep bonds containing hydrogens at a fixed length.

Following the 24 ns of equilibration at an all-atom MM level, the systems were relaxed by a 1-ns MM/CG simulation with positional restraints on the OmpT/ARRA complexes to minimize the energy of the solvent. Then further 1 ns with positional restraints on OmpT were performed to allow the ligand to accommodate itself inside the binding pocket under the MM/CG potentials.

Finally, we performed an atomic-force field based MD simulation of a reference system. This is diglycine (AceGGNMe) in a periodic box of 779 water molecules. The GROMOS96 43a1 [START_REF] Van Gunsteren | Biomolecular Simulation: The GROMOS96 manual and user guide[END_REF] and SPC (28) force fields were used for the dipeptide and water, respectively. Room conditions (T=300 K, P=1 bar) were achieved by coupling the system with a Berendsen thermo-stat [START_REF] Berendsen | Molecular dynamics with coupling to an external bath[END_REF] with τ =1.0 ps and a Berendsen barostat (30) with compressibility of 4.5 10 -10 bar -1 in all three dimensions. The time step of the integration was 2 fs. Electrostatic and van der Waals interactions were calculated using a cut-off of 18 Å and 14 Å, respectively. 0.015 µs of trajectory were collected.

The following properties were calculated: (i) large scale motions as eigenvectors of the covariance matrix of the C α 's ( 31); (ii) the analysis of the cosine content [START_REF] Hess | Similarities between principal components of protein dynamics and random diffusion[END_REF][START_REF] Hess | Convergence of sampling in protein simulations[END_REF]; (iii) the electric field in the MM region, using the electrostatic term in the GROMOS96 43a1 force field [START_REF] Van Gunsteren | Biomolecular Simulation: The GROMOS96 manual and user guide[END_REF]. This analysis has been used for qualitative comparisons between different systems.

Results

MM/CG of systems A and B

The structure of A and B (Figure 1bc) is well maintained within the time C i computed for the first 8 eigenvectors of the covariance matrix is lower than 0.5 (see Figure 1SIcd), meaning that the largest fluctuations are related to the potential.

A and B appear to be productive Michaelis complexes: a water molecule bridges H212 and D83, thus pointing towards the substrate carbonyl carbon. The average distance during the simulations between the catalytic water and the carbon of the scissile CO-NH bond in A and B is 3.91(±0.35) Å and 3.50(±0.30) Å, respectively. This water molecule is located in a position prone for a nucleophilic attack on the carbonyl group of the substrate (see Figure 2ab). At times a second water molecule bridges H212 and D83 and the H212-water(s)-D83 interaction is replaced by a direct H-bond between D83 and S99. Most of the key contacts at the active sites are maintained during the dynamics of both systems (Table 2, Figure 1bc): (i) Hδ@H212 H-bonding to Oδ@D210 or to O@D210; (ii) the salt bridge between substrate R2 and E27 and D208; (iii) the salt bridge between substrate R3 and D97 and D85; (iv) H S99 backbone H-bonding to D83 backbone; (v) D85 backbone H-bonding to D97 backbone. In addition (Table 2, Figure 1bc), (i) at times, S99 H-bonds to D85 and D97 and N @H212 H-bonds with H@R3 of the substrate; (ii) H @H101H-bonds to either S99 or D83.

Structural fluctuations of the substrate in the binding cavity have been shown to play a functional role for cytoplasmatic proteases (4) such as HIV-1 protease (2) and BACE [START_REF] Cascella | Evolutionarily conserved functional mechanics across pepsin-like and retroviral aspartic proteases[END_REF]. For these enzymes it was found that the distance between the catalytic dyad and the substrate fluctuates around characteristic values corresponding to different mutual positions of the catalytic water relative to the substrate carbonyl carbon: only conformations in which the distance between the enzyme and the substrate is at a minimum turned out to be catalytically efficient [START_REF] Piana | Role of conformational fluctuations in the enzymatic reaction of HIV-1 Protease[END_REF][START_REF] Cascella | Evolutionarily conserved functional mechanics across pepsin-like and retroviral aspartic proteases[END_REF]. Because those motions are correlated to the the large-scale motions of the proteins, the enzyme might play a role for the reaction by steering the substrate into its appropriate reactive conformation.

In order to ascertain whether this is also the case for OmpT, we have chosen to monitor the substrate motion within the β-barrel: (i) the distance δ of the center of mass of the ARRA peptide from the center of mass of the D83-D85 pair of residues; δ is affected by the width of the cleft and, therefore, its fluctuations also modulate the position of the water inside the catalytic cleft. (ii) the distance ξ of the center of mass of ARRA from the center of mass of the β-barrel (see Figure 3, left panel). ξ is affected by the distance between the substrate and the catalytic water. Thus, δ and ξ can be used as suitable descriptors of the enzyme "active" conformations [START_REF] Carnevale | Convergent dynamics in the protease enzymatic superfamily[END_REF] to evaluate the presence of a functional mechanical coupling between the substrate and large-scale conformational fluctuations of the entire enzyme.

In A, δ ∼ 7.8(0.5) Å and ξ ∼ 24.1(0.4) Å, showing a sharp Gaussian-like distribution (see Figure 5SIab). In B, both quantities feature bimodal distributions: δ fluctuates from δ 1 = 7.3 Å to δ 2 = 9.8 Å (Figure 3a) and ξ from ξ 1 = 25.9 Å to ξ 2 27.5 Å (Figure 3b). Interestingly, the transitions from ξ 1 to ξ 2 and from δ 1 to δ 2 occur at the same time, suggesting that the oscillation of the cleft is correlated with the oscillation of the substrate along the axis of the β-barrel (the linear correlation between the two data sets δ and ξ is 0.60 over ∼10 3 data points). No such transitions were observed in the all-atom simulations used for equilibration, where the δ / ξ space is explored to a much lower extent (see supplementary material for details).

The presence of relevant correlations in OmpT is investigated by computing the projection of the top 10 eigenvectors of the covariance matrix on the trajectory (X • V i ) [START_REF] Amadei | Essential dynamics of proteins[END_REF]. The correlation can be assessed in a quantitative way by analyzing the scatter plot in which the projection at each time step is plotted versus the δ and ξ distances. We find a significant correlation only with the second largest eigenvector (V 2 , Figure 3a'b'). In fact, two mostly populated regions corresponding to δ 1 and δ 2 and to ξ 1 and ξ 2 , are visible.

We conclude that V 2 induces relevant variations in the relative distances of the active site, showing high correlations with both distances. In contrast, no correlations are found with the first eigenvector V 1 , which represents the first most dominant large-scale motion. Because the cosine content associated to V 1 is relatively larger (C 1 ∼ 0.5, while C 2 ∼ 0.1; see Figure 1SId), V 1 might not represent a real "coherent" motion, but, rather, a random diffusion of less structured parts of the protein.

The large-scale motion described by V 2 mostly affects the solventexposed loops embracing the active site, as found in previous work [START_REF] Neri | Molecular dynamics simulations of outer-membrane protease T from E. coli based on a hybrid coarse-grained/atomistic potential[END_REF].

In addition, the loops L2, L3, L4 and L5 as well as the substrate are more mobile in system B with respect to A (Figure 4SIa). This is consistent with the fact that the substrate is significantly anticorrelated to the motion of the loops in system B (Figure 4SIb).

Thus, large-scale fluctuations in B allow a well defined motion of the substrate within the catalytic cleft concerted with that of loops, similarly to what is found for several proteases (4), as HIV-1 PR (2) and BACE (3).

Our approach also allows to investigate the polarization of the reactants relative to a refence system (here the Gly-Gly dipeptide) in water. To this aim, we monitor the electric field along the substrate carbonyl group C@R2 of the substrate and the catalytic water C 2 axis (Figure 1de), which has been proposed to be the nucleophile agent for the protein.

As for the substrate, the field of A does not show any preferential direction with respect to the C=O bond: the angle α between the field and the vector identified by the C=O bond (Figure 1d) is spread within 0 • and 180 • : the standard deviation value is large around its average value ( α = 125 • (44 • )), and not much smaller than that of the reference system here investigated, for which the field is completely isotropic ( α = 100 • (59 • )). In contrast, in B the field is partially aligned with the C=O bond (Figure 1d) with far lower spread ( α = 140 • (10 • )). We conclude that the carbonyl carbon is expected to be more electrophilic and thus reactive than in A and in aqueous solution.

Similar conclusions can be drawn for the catalytic water: the field in A is orthogonal to the water C 2 axis (Figure 1e, the average angle β between the field and the C 2 axis of the catalytic water is ∼ 104 • (14 • )); hence it is not expected to significantly affect the nucleophilic power of the water molecule. Notice that in this case the spread is much smaller than that of the reference system ( β ∼ 93 • (60 • )). In B, in contrast, the angle β shows a bimodal distribution, either orthogonal ( β ∼ 90 • (12 • )), or aligned to the C 2 axis ( β ∼ 20 • (8 • )). Thus, in the latter case the field in B might render the catalytic water more nucleophilic than in A and in aqueous solution.

The fluctuation of the β angle between β 1 and β 2 values in B is driven by V 2 (see Figure 6SI). This means that the functional oscillations of the substrate allow the catalytic water molecule to take a well defined geometric configuration, able to stabilize a more negative charge distribution on the oxygen and resulting in an enhanced nucleophilicity of the oxygen atom itself.

We conclude that the polarization effect may be used by the enzyme in order to enhance the electrophilicity of the carbonyl carbon and the nucleophilicity of the water oxygen. The same calculation in the time scale typical of all-atom MD simulations (∼ 50 ns) shows no preferential directions of the field in both systems (the α and β distributions are spread between 0 • and 180 • ). This is somewhat confirmed in the all-atom equilibration simulations where no significant spread in the α and β distributions is reached (see supplementary material Figure 11SI) due to insufficient sampling.

MM/CG of systems C and D

The Michaelis complex in C and D disrupts already after 10 ns (Fig-

ure 2SIab): The NH + 3 terminal groups, which formed salt bridges with D210 and E27 at the beginning of the dynamics, rotate and form a salt bridge with D83 and an H-bond with N @H212 (Figure 2SIab). Consequently, the carbonyl carbon, which undergoes the nucleophilic attack, moves away from the putative catalytic dyad composed by H212 and D83 (see Figure 2SIab), whilst the oxygen of such a group flips, pointing toward the D210 and H212

dyad. Such a non-productive Michaelis complex, in which there is no putative nucleophilic agent in the close proximity of the carbonyl carbon, is maintained for further ∼0.04µs, after which we decided to stop the simulation. We conclude that the presence of the charged termini in addition to the two positive arginines affects the structure of the Michaelis complexes in both C and D.

MM/CG of H212A and S99A mutants

Here we compare MM/CG simulations of H212A and S99A OmpT, which show a residual activity ranging within 0% and 4% [START_REF] Kramer | Identification of active site serine and histidine residues in Escherichia coli outer membrane protease OmpT[END_REF], with respect to wt. We ran the simulations on the mutants for protomers A and B for a shorter timescale than that of the wt (0.15 µs), as we are solely interested in constructing structural models.

In H212A, the mutation disrupts the H-bonds with N @H212 and the amide group of the substrate, which contributes to maintain the position of the substrate inside the enzymatic cleft. In the wt, R2 and R3 of the substrate form salt bridges with E27 and D208 and with D85 and D97, respectively. These two residues rotate in both A and B and their side chains face the solvent (Table 1 and Figure 7SIab). At the end of the simulation, the carbonyl carbon, which is cleaved in the wt enzyme, moves away from the putative catalytic dyad H212-D83 and the cleft is filled by water.

In S99A, the replacement of S99 with alanine disrupts the S99-H @His101 H-bond (Figure 8SI). The H atom of the same histidine H-bonds to D83 in A and B.

In A, the breaking of the S99-H101 H-bond causes a rearrangement of H101 and consequently D83, which is protonated, rearranges and H-bonds to D97 (Table 2 and Figure 8SIa). Consequently, the water-mediated interaction between the proton of D83 and the substrate is lost, and the latter fluctuates allowing a rotation of the side chain of R2 of the substrate. As a result, R2 H-bonds with D210 and the catalytic residue H212 is permitted to move further apart from the active site (Table 2). This causes a drastic change of the substrate configuration and its partial detachment (Figure 8SIa).

In B, D83, which is ionized, forms a stable H-bond with N @H101.

However, because of the lack of S99-D97 H-bonding, D97 moves away, in turn causing the loss of H-bond interactions between the backbone of D85 and D97 (Figure 8SIb). As a result, the salt bridge between D85, D97 and R3 is lost, allowing the side chain of the latter residue to rotate and become solvated. In this case, a partial detachment of the substrate occurs (Table 2, Figure 8SIb).

Discussion

MM/CG simulations have been used to investigate the fluctuations of OmpT in complex with its substrate ARRA on the µs time scale as well as the effect of key point mutations at the active site. We have focused on four systems (A-D), which differ for the protonation state of D83 and the charge of the substrate ( AceARRANMe or NH 3 + ARRA COO -), which have first been simulated by all-atom MD simulations on a shorter time scale.

The complexes with substrates with charged tails (C and D) evolved to non-productive Michaelis complexes, because the charged tail groups interact with the residues among the catalytic cleft causing a distortion of the ARRA peptide and its detachment from the binding pocket. Interestingly the preliminary 24 ns all-atom simulations did not show a disruption of the Michaelis complex, which points to a less efficient exploration of conformational space compared to the MM/CG approach, where the systems are described by smoother potential energy surfaces. Obviously, our investigation does not rule out the presence of longer substrates with charged tail groups, as those substrates may have their termini located outside the catalytic cleft.

In contrast, the complexes with the substrates with neutral tails provided productive Michaelis complexes and both remained stable over 1-µs dynamics simulations. The two complexes (A and B in Figure 1bc) exhibit different protonation states of D83: in A, the aspartic acid is protonated while in B it is ionized. The systems are characterized by significant differences in the electrostatic polarization of the reactants. In B, the active site polarizes both the catalytic water and the carbonyl carbon, rendering the first more nucleophilic and the second more electrophilic relative to a reference system in water (diglycine). In contrast, in A no significant polarization is observed and the electric field acting on the carbonyl carbon bond and on the catalytic water is very similar to that calculated for the reference system diglycine in water solution.

System B is also characterized by large conformational fluctuations of the substrate triggered by global large-scale motions (Figure 3a'b'), which populate significantly different conformations (see Figure 3ab and Figure 3SI). This contrasts to A in which the substrate fluctuates around a well defined conformation (see Figure 3SIa and Figure 5SIab). The large-scale fluctuations of complex B might have functional implications as found in the Aspartyl Proteases superfamily (2-4). However, quantum chemical calculations on the reaction mechanism are required to discriminate between the two protomers and further address this issue.

We have next used the MM/CG approach on H212A and S99A mutants (for both A and B protomers) to provide the structural basis for the much lower activity of these mutants relative to wt. This result, which is easy to rationalize for the H212A mutant, as it is part of the putative catalytic dyad, is rather intriguing for S99A, which is not involved directly in the catalytic cleft. Our simulations suggest that in H212A the ARRA peptide detached spontaneously in both systems due to the loss of the H-bond interaction between N @H212 and the amide group of the substrate, present in the wt. S99A, indeed, allowed a similar detachment of the peptide due to the disruption of the geometry of the active site in both systems, in spite of the fact that this residue is not located at the active site. In A the loss of the H-bond interaction between S99 and D83 allows the rotation of the latter residue causing the breaking of interactions between D83 and the substrate. In contrast, in B the detachment of the peptide is due to the loss of the salt bridge between R3 and D85-D97 after the breaking of the S99-D97 interaction. We conclude that not only first-shell H-bond interactions (such as those formed by H212), but also second shell H-bonding (such as that of S99) play an important role for the stability of the geometry of the active site. Removing any of those stabilizing interactions may cause a high instability of the active site and therefore a reduced activity.

In conclusion, our MM/CG approach emerges as a useful tool to investigate µs simulations of enzymes, which is presently difficult with all-atom MD. Our conclusions about the electric field acting on the reactants and about the motion of the substrate inside the catalytic cleft cannot be drawn if one focuses on the typical time scale of all-atom MD (0.01-0.1 µs). This suggests that this approach, within its limitations deriving from the use of a coarse-grained model for modeling the most of the protein in solution, may provide useful information -complementary to all atom MD-on phenomena occuring on relatively long time scales. In addition, it may be useful for computational molecular biology, allowing to test the effect of point mutations via a computationnally affordable method. Thus, MM/CG, by allowing to run more numerous and longer simulations than all-atom MD, is expected, on the one hand to improve our confidence in the results, and on the other one it may strengthen the interaction between molecular biology experiments and simulations.

Additional information on MM/CG simulations of systems A-B Wild-type OmpT

The root mean square deviations (RMSD) of the C α of A and B is stable after 0.05µs and fluctuates around an average value of ∼3 Å, as shown in figure 1SIab.

We evaluate the convergence of the simulations after 50ns by the analysis of the cosine content C i of the principal component p i , defined as:

C i = 2 T T T 0 cos(4πt)p i (t)dt 2 T T 0 p 2 i (t)dt -1 , ( 1SI
)
where T is the time of simulation and p i is the principal component associated to the ith eigenvector. C i can take values between 0, no cosine, and 1, a perfect cosine. It was demonstrated that insufficient sampling can lead to behaviors that resemble a functional motion, but describing a random motion [Hess(2000), Hess(2002)]. In Figure 1SIcd are reported the C i values associated to the the first 8 eigenvectors of A and B computed after 50ns. The picture shows that the cosine content is lower than 0.5 for both the systems. The RMSD values for the C α atoms of the substrate relative to the MM/CG snapshot at 0.05 µs of the wt as a reference structure (Figure 3SIab), are either characterized by small fluctuations (∼ 0.4 Å) around an average value of ∼ 2.3 Å (system A , see Figure 3SIa) or they show many peaks featuring fluctuations of larger amplitude of ∼ 1.7 Å (system B , see Figure 3SIb). In addition, based on a calculation of the root mean square fluctuations (RMSF) of each residue, we conclude that loops L2, L3 and L4 as well as the substrate in B are more mobile than in A (Figure 4SIa). This is consistent with the analysis of the large scale motions of the systems, which suggest that the motions of the substrate in the catalytic cleft are concerted with that of the loops, the correlation being larger for B (Figure 4SIb).

The δ and ξ distances, defined in the Main Text, are used in this work to characterize the motion of the substrate inside the catalytic cleft. In A, as shown in Figure 5SIab, δ and ξ oscillate around an average value ( δ ∼ 7.8(0.5) Å and ξ ∼ 24.1(0.4) Å) with a sharp Gaussian-like distribution. In contrast, in B the distances feature bimodal distributions (see Main Text).

H212A and S99A OmpT

For both systems, in H212A, the side chains of R2 and R3 of the substrate, which form a salt bridge with E27-D208 and D85-D97, respectively, rotate and face the solvent at the end of the simulation (0.05 µs, Figure 7SIab). 2 In S99A-system A, R2 side chains interacts at 0.05 µs with D210 breaking the D210-H212 H-bond. This causes a motion of H212 away from the catalytic site (see Figure 8SIa). In S99A-system B, the salt bridge between R3 and D85-D97 is disrupted after 0.02 µs. This causes, as for H212A and S99A of A mutants, a motion of the substrate carbonyl carbon away from the catalytic dyad D83-H212 (see Figure 8SIb).

Additional information on MM equilibration simulations of systems A, B, C and D

The OmpT-ARRA complex described in [Baaden and Sansom(2004)] was optimized using a molecular mechanics based approach with the Yasara software and the Yamber2 forcefield [Krieger et al.(2004)]. A limited region (see Figure 9SI) around the active site was free to move in order to speed up the calculations and be able to compare the four systems A, B, C and D. First a protocol was established where the complex is gradually solvated. The box is filled with water at a density of 0.997 g/l, then a steepest descent minimization is run followed by simulated annealing and short molecular dynamics at 298 K. After solvating the box, the density is adapted using an NPT algorithm within Yasara. All equilibrations are a combination of steepest descents followed by 500 steps of simulated annealing and 0.6 to 6 ns of molecular dynamics. During these steps the protein, peptide and catalytic water were first kept fixed, then parts were released, then instead of keeping them fixed some springs were used and finally all restraints were removed. Each of the systems Aeq, Beq, Ceq and Deq underwent 24 ns of unrestrained equilibration.

Characteristic structural parameters were analyzed for all four simulations (Figure 10SI). The simulations with neutral termini are clearly separated from the ones with ionized residues with respect to the δ and ξ parameters as shown in Figure 10SIabd. The evolution of the catalytic water-scissile carbon distance (Figure 10SIc) indicates that simulation Beq shows the shortest distances and might thus visit reactive transition states more often than the other protomers. Only a small fraction of the conformational δ-ξ space is explored compared to the MM/CG simulations.

The polarization of the active site is characterised by the α and β angles defined in the main text. Figure 11SIab

scale investigated ( 1

 1 µs): the C α root mean square deviations (RMSD) of A and B rise during the first ∼0.05µs and then fluctuate around an average value for the remainder of the simulations (Figures 1SIab), suggesting that this specific protein requires large sampling to equilibrate. The analysis of the cosine content (C i ) (32, 33) ascertains the convergence of the simulations:

Figure 1 .

 1 Figure 1.

Figure 2 .

 2 Figure 2.

Figure 3 .

 3 Figure 3.

Figure 1 :

 1 Figure 1:

Figure 3 :

 3 Figure 3:

Figure 1SI :

 1SI Figure 1SI: RMSD of C α atoms plotted as a function of simulated time for A: (a) and B: (b). Pictures (c) and (d) depict the cosine content, defined in Eq. 1SI, of the first 8 eigenvectors of the covariance matrix after 50ns, for A and B, respectively.

FigureFigure 3SI :FigureFigure 5SI :

 3SI5SI Figure 2SI: MM/CG snapshots of the active site after 10 ns for C: (a) and D: (b).The substrate is depicted with a transparent effect.

2 Figure 6SI :

 26SI Figure 6SI: Projection of the trajectory X of B on the eigenvector V 2 of the covariance matrix plotted versus the time evolution of the angle β defined in the main text. The calculation was carried out every 100 ps.

Figure

  Figure 7SI: H212A OmpT MM/CG simulation: Active site structure after 150 ns for A: (a) and B: (b). The substrate is depicted with a transparent effect.

Figure

  Figure 8SI: S99A OmpT MM/CG simulation: Active site structure after 150 ns of the S99A mutant for A: (a) and B: (b).

Figure 9SI :

 9SI Figure 9SI: OmpT MM simulation: simulation system highlighting the box around the active site that is free to move. It is hydrated using a cell with the size 40 x 30 x 40 Å.

Figure 10SI :

 10SI Figure 10SI: OmpT MM simulations Aeq, Beq, Ceq and Deq: (a-c) Structural parameters plotted as function of simulated time. Time evolution of the distance between the center of mass of the C α atoms of the substrate and the center of mass of the dyad D83-D85: (a) and the center of mass of the β-Barrel: (b). Time evolution of the distance between the catalytic water molecule and the scissile C: (c). ξ versus δ plot illustrating the exploration of conformational space: (d).

Figure 11SI :

 11SI Figure 11SI: Time evolution of the electric field angles with catalytically important groups for OmpT MM simulations Aeq, Beq, Ceq and Deq. (a) α angle between the electric field and the carbonyl bond. (b) β angle between the electric field and the water C2 axis.

Table 1 :

 1 Systems and mutants of OmpT/ARRA complexes investigated in this work.

	Complex id	Simulated Time (µs)
	Production runs at the MM/CG level
	A: protonated D83, neutral N-and C-termini (AceARRANMe)	1.00
	B: deprotonated D83, neutral N-and C-termini (AceARRANMe)	1.00
	C: protonated D83, charged N-and C-termini (NH 3 + ARRACOO -)	0.05
	D: deprotonated D83, charged N-and C-termini (NH 3 + ARRACOO -)	0.05
	S99A-A	0.16
	S99A-B	0.16
	H212A-A	0.15
	H212A-B	0.15
	All-atom reference simulations
	Aeq: equilibration	0.024
	Beq: equilibration	0.024
	Ceq: equilibration	0.024
	Deq: equilibration	0.024
	Diglycine (AceGGNme)	0.015

Table 2 :

 2 MM/CG simulations of A and B, H212A-A, H212A-B, S99A-A and S99A-B: selected MD-averaged distances ( Å ) within the active site of the protein. Standard deviations (STD) are reported in parenthesis.

		A	B	H212A-A H212A-B S99A-A S99A-B
	Hδ@H212-Oδ@D210	2.6(±0.8)	2.5(±0.7)	//	//	7.0(±3.5)	2.8(±0.7)
	Hδ@H212-O@D210	5.2(±0.8)	4.3(±1.2)	//	//	7.0(±2.1)	5.0(±0.8)
	N @H212-H@R2	4.7(±0.7)	5.0(±1.6)	//	//	6.4(±2.2)	5.0(±0.7)
	N @H212-C@R2	4.4(±0.5)	6.2(±1.4)	//	//	6.5(±1.9)	4.4(±0.7)
	N @H212-Oδ@D83	5.8(±1.0)	8.0(±1.5)	//	//	11.3(±1.8)	7.9(±1.4)
	Cζ@R2-Cδ@E27	4.8(±0.5)	5.0(±0.8)	6.7(±2.4)	5.0(±0.7)	6.2(±1.7)	6.3(±1.4)
	Cζ@R2-Cγ@D208	5.0(±0.7)	5.2(±0.9)	9.0(±2.3)	8.4(±0.3)	7.6(±2.1)	6.6(±0.7)
	Cζ@R2-Cγ@D210	8.5(±0.7)	8.0(±0.9)	10.1(±3.0)	4.0(±0.4)	9.5(±1.6)	6.8(±1.2)
	Cζ@R3-Cγ@D85	6.0(±1.0)	6.5(±1.3)	9.7(±1.6)	7.0(±1.7)	9.4(±1.4)	6.7(±1.2)
	Cζ@R3-Cγ@D97	7.0(±1.5)	8.0(±1.8)	11.6(±2.1)	9.0(±1.6)	11.8(±3.4)	15.0(±3.1)
	H @H101-Oγ@S99	3.1(±0.9)	4.2(±1.2)	4.8(±1.0)	3.0(±0.7)	//	//
	Oδ@D97-Hγ@S99	5.5(±1.3)	6.6(±2.1)	3.5(±2.3)	8.2(±2.6)	//	//
	Hδ@D83-O@R2	4.5(±1.1)	//	5.5(±1.8)	//	7.5(±1.4)	//
	Hδ@D83-Oδ@D97	8.8(±1.2)	//	7.9(±1.3)	//	4.0(±1.2)	//
	H @H101-Oδ@D83	3.7(±1.0)	3.5(±1.1)	4.6(±2.0)	3.1(±0.9)	5.0(±1.1)	3.5(±0.9)
	H@D83-O@S(A)99	2.0(±0.2)	2.0(±0.3)	2.0(±0.2)	2.0(±0.2)	2.2(±0.4)	4.0(±0.6)
	O@D83-H@S(A)99	2.0 (±0.3)	2.4(±0.5)	2.5(±0.6)	2.0(±0.2)	2.6(±0.7)	2.5(±0.9)
	H@D85-O@D97	2.1(±0.4)	2.3(±0.3)	2.3(±0.7)	2.4(±0.7)	3.0(±1.2)	5.0(±1.8)
	O@D85-H@D97	2.0(±0.4)	2.6(±1.0)	2.1(±0.4)	2.7(±0.7)	3.5(±1.6)	5.3(±1.7)

This very crude assumption in the treatment of the electrostatics appears to be justified by the simplicity of the model used. Careful checks were made on energy conservation. In addition, test calculations with a longer cut-off for electrostatics (36 Å) provided very similar results to those with the shorter cut-off (data not shown).

Acknowledgments

The authors thank Alessandra Magistrato and Michele Cascella for discussions. We would like to acknowledge financial support by MURST-FIRB and IIT.

Supplementary Information (SI) Microseconds Dynamics Simulation of the Outer-Membrane Proteases T

Marilisa Neri, Marc Baaden, Vincenzo Carnevale, Claudio Anselmi, Amos Maritan and Paolo Carloni


Pascal Cotte
email: pascal@cotte.com

Lionel Simonot

MONA LISA'S SPOLVERO REVEALED

Keywords: Mona Lisa, multispectral technique, spolvero, under-drawing, signal noise ratio, spatial resolution, L.A.M. technique, Leonardo da Vinci

On the request of the Louvre Museum, Mona Lisa's portrait was digitised with Lumiere Technology's high-resolution multispectral camera. The data from this digitisation revealed for the first time the spolvero (a drawing transfer technique) of the portrait. These discoveries change the history of this iconic symbol of Western painting because they are proof that a preparatory drawing was employed. These discoveries were possible because the multispectral camera produces high-resolution images of 13 filters, including 3 IR filters with a high signal-to-noise ratio and optimal spatial resolution. The images are processed with the layer amplification method (L.A.M.), which amplifies weak signals and thus reveals new details. A test board was made to compare the L.A.M. with traditional IR reflectography.

-Research aim

This article outlines and explains our methodology employed to highlight the Mona Lisa's spolvero. We first present the performance in terms of signal-to-noise ratio (SNR) and the spatial resolution of the camera, as well as the multispectral imaging treatments we applied, including the layer amplification method (L.A.M.). To validate the approach, a test board is conducted and the method is compared with IR reflectography. Finally, the highlighting of the spolvero on the Mona Lisa is presented.

-The multispectral camera

Inside the Louvre Museum, a multispectral camera [START_REF] Cotte | Crisatel High-Resolution Multispectral System in Proceedings of PICS'03 -Digital Photography Conference[END_REF] digitised the Mona Lisa. [START_REF] Cotte | Spectral imaging of Leonardo Da Vinci's Mona Lisa: An authentic smile at 1523 dpi with additional infrared data[END_REF][START_REF] Elias | La Joconde analyseé à l'aide d'une camera multispectrale Techniques de l'ingeńieur[END_REF][START_REF] Ribés | Analyse multispectrale et reconstruction de la réflectance spectrale des tableaux de maître[END_REF] (Fig. 1) The multispectral camera uses a TH-7834 CCD sensor originally designed for the SPOT [START_REF] Bardoux | Linear CCD array TH-7834B performances near 10 MHz[END_REF] image weather satellite. It is a 12,000 pixel linear array of a 6.5 µm square. It offers a dynamic range (DR) of 10,000:1 [16] and an effective SNR of 9, 230:1, or 79. [START_REF] Walmleys | Leonardo's portrait of Ginevra di Benci: a reading of the X-radiographs and infrared reflectoraphs, in Leonardo da Vinci's technical practice[END_REF]

dB. (see appendix A)

The camera uses a 210 mm focal length optic and a symmetrical lens formula. Geometric distortion is low and not measurable.

One of the main optical characteristics of this camera is its ability to memorise a specific focus for each filter with 2 advantages: always having a maximum contrast transfer function (CTF) for each wavelength (in appendix B are the measurements of the ISO 16097-1 test pattern [17]) and the cancelling of sagittal and longitudinal chromatic aberration, defects that are inevitable otherwise. This is a second considerable technical advantage compared with hyperspectral cameras because the focus and therefore the resolution are optimised for one wavelength and are significantly worse at the edge of the spectrum.

We increase the DR of the image by using the binning method [START_REF][END_REF]. We use a binning of max 4x4 pixels, and therefore there is a gain in SNR of 10 dB. (appendix C)

3-2 -Multispectral image processing

The processing techniques specific to multi-and hyperspectral imaging are derived from methods developed for satellite imagery, among which are spectral angle mapping [START_REF] O'sullivan | An Extended Spectral Angle Map for Hyperspectral and Multispectral Imaging[END_REF] and spectral correlation mapping [START_REF] De Carvalho Júnior | Spectral Correlation Mapper (SCM): An Improvement on the Spectral Angle Mapper (SAM)[END_REF]. There are classification methods for assessing the similarity of spectra to a reference used, for example, to identify spectra of areas, for example, forest or agricultural, or in the case of works of art to identify pigments.

For the detection of underlying drawings, the problem is different because the carbon black pigment used for spolvero has no spectral signature. The aim is to detect very small spectral differences related to carbon black, obviously without a priori knowledge of the area where the underlying drawings are located. The usual contrast-enhancement techniques for each spectral band, especially in IR, are a first approach, but in most cases, they are insufficient.

The pictorial layers of old, classic paintings are generally approximately 1 mm thick, and their optical properties (absorption and scattering) allow the light to penetrate them according to the wavelength. However, the extreme complexity of the interaction of light and matter involves all the components of the pictorial layer: the surface varnish, the varieties of pigments, the sizes of the pigment grains and their shape, the binders (e.g. oil, mastic, wax, egg), the driers (e.g. lead), additives (e.g. glues, varnishes, mastics, glass), their preparation method, superposed pigment layers, the thickness of each layer, preparatory drawings and surface preparations (e.g. calcium carbonate, calcite), and ageing factors of materials. Without precise knowledge of the structure of the pictorial layer, it is impossible to establish a prediction model to obtain an image in the exact depth desired. To overcome this apparent impossibility, some methods combine images from 2 or more filters [START_REF] Daffara | [END_REF]22]. The L.A.M. technique is a protocol that generalises this approach, and various calculations are performed with variable coefficients to increase the contrast. Stretching and equalising the histogram based on Shannon entropy extends the dynamics of the resulting images [23].

All these calculations produce many images. Similar to medical echography, a single echo image is difficult to understand without observing the other images. Therefore, to make the technique efficient, the images are watched at a video rate of several per second, and the remarkable ones are isolated. All the L.A.M. images are recalculated for each interesting detail and the contrast optimised. The images of the detail are displayed as thumbnails on large photo plates for a quicker selection. For the same detail, it is often interesting to superimpose the information given by several L.A.M. images to complete it and form an opinion when there is a doubt about an observation. As with any detection technique, our method can present false positives. Thus, this type of analysis must be entrusted to a technical expert who verifies that the information is found in several wavelength combinations. The expert must have the knowledge required to select the best L.A.M. image and be a specialist in painting techniques to correctly interpret the detected signals.

This method has been spread by and taught at Bologna University, Alma Mater Studiorum. It is widely used for analysing paintings [START_REF] Cotte | Lumière on the Lady with an Ermine[END_REF], drawings, and palimpsests [START_REF]Centre Léon Robin de recherches sur la pensée antique[END_REF]. This method completes the list of scientific examinations conducted on works of art to analyse the lower layers of the pictorial matter to find, for example, preparatory drawings, repaints, and restoration. In most cases, this method provides better results than IR reflectography.

-Validation of the method and comparison with IR reflectography

To illustrate the difficulty of detecting spolvero, namely, by IR reflectance, and test the effectiveness of the proposed method, we perform a test board (Fig. 3) with known materials, thicknesses, and pattern. This test is founded on the method of spolvero used by Leonardo da Vinci to prepare a painting.

a)

A transparent sheet is pierced with needle holes following the contours to make a spolvero; b)

A wooden board covered with white gesso is divided into 8 zones; c)

A very fine carbon black powder is applied on the transparent sheet to make the spolvero on the gesso;

d)
This process leaves the spolvero, a series of black dots on the gesso; e)

Using a micrometric paint applicator (10 µm step), increasing thicknesses of lead white are applied on each area. The paint is made with lead white mixed with 17% vol. of walnuts oil with litharge. The fluidity is 450 poises. The thickness was measured after 1 year. The fluidity of the paint and the oxidation of the binder over time considerably reduce the final thickness. Due to the irregularities in the surfaces of the wooden support, there are important variations in the thicknesses of the lead white layer when dry, and they are presented as an indication in Table 1.

To the naked eye (Fig. 4a) observing the final product, the spolvero is a little visible in zones 10 and 20 (10 and 20 µm thick), nearly disappears in zone 30 (70 µm), and is invisible in the remaining zones with thicker layers. A new camera in 2016 featuring an internal mechanical scanning system able to create high-quality images of 16 M. pixels was used to perform infrared reflectography at 1700 nm on this test board; the same was used by the Louvre for the Mona Lisa. [START_REF] Frank | Catalogue de l'exposition Léonard de Vinci[END_REF] Two halogen lamps illuminate the test board. This type of IR InGaAs image sensor has traditionally been used in many museum laboratories [START_REF] Bonanno | La riflettografia infrarossa tra fisica, arte e tecnologia[END_REF]. To ensure that the results are comparable with L.A.M. images, the histogram of the image (Fig. 4b) has been stretched and equalised. The image shows the spolvero 30 (70 µm thick of lead white) but struggles with the spolvero 40, where the contour is barely perceptible. The other spolveri remain invisible.

4-1-2 -Use of the multispectral camera with a1000 nm IR filter

The image obtained by the 1000 nm filter of the multispectral camera is presented in Fig. 4c, whose histogram has been stretched and equalised as it was on the 1700 nm IR reflectography (Fig. 4b). The 2 images obtained are comparable: The spolvero 40 remains difficult to observe. For this example, scanning at 1700 nm offers no advantage over scanning at 1000 nm.

4-2 -Results analysis

This difference in results obtained by using the L.A.M. technique and NIR reflectography is partially explained by the 2 main technical characteristics of the camera previously exposed in part 1: its good CTF and SNR, with the use of multiple NIR narrow filters and the camera's high resolution of 240,000,000 pixels. The image quality allows high amplification (approximately 1:4000) of weak signals and high discrimination of information.

However, other characteristics accentuate this difference: The CTF for IR wavelengths greater than 1000 nm is lower than the CTF in the visible range because as the wavelength increases, the diffraction limit induces a decrease of the spatial resolution. The minimum size with a numerical optical aperture of N=8 (used by the multispectral camera), according to the Rayleigh criterion (1.22𝜆N), is 5.4 µm for 550 nm (median value visible spectrum), and it is 16.6 µm for 1700 nm and 24.4 µm for 2500 nm. The resolution at 1700 nm, resp. 2500 nm, is therefore ~10 times, resp. ~25 times lower than in the visible range. Of course, the wavelengths at 1700 nm or 2500 nm go more deeply inside the pictorial layer [START_REF] Daffara | [END_REF]22]. However, the stronger scattering of these IR wavelengths by the pictorial layer can make it impossible to detect important details, namely, the IR can go through without seeing them, located just below the surface of the painting.

On the test board scanned with the multispectral camera at 1000 dpi (39 pixels/mm), we make spectral measurements (Fig. 5) of an area corresponding to a black dot on the spolvero, (approx. ø 0.3 to 0.6 mm) and a white area immediately next to it. After calibration, we reconstruct the diffuse reflectance spectra with an average of a 9 pixel matrix (3x3). We calculate the CIELab colorimetric coordinates (illuminant D65) of the corresponding areas. The color difference between the black point and the white zone is of course important (∆E00 = 51) when not covered with lead white. Zones 10 to 60, covered with lead white, respectively give ∆E00 = 13.64 -11.11 -2.09 -1.52 -0.82 -0.65. For values below 1, we consider that visual discrimination between the 2 areas is no longer possible, which is confirmed by observation. (Fig. 4a)

By analysing the reflectance spectra over the 400-1000 nm range, we observe significant differences in area 10 but irregularities in the wavelength. The greater the thickness of lead white, the smaller the difference between the curves and less the black points are visible. In zones 40, 50, and 60, the curves at the beginning of the visible range (400-500 nm) are almost superimposed, the difference is approximately 5% in the remaining curves for zone 40, 3% in zone 50, and 1% in zone 60. Additionally, the greater the thickness of lead white, the more the curves converge in the infrared, to finally overlap perfectly in zone 60, and there is no difference after 900 nm: the spolvero cannot be detected by using only infrared. We can be assumed that the curves continue to meet after 1000 nm and beyond. This could explain why IR reflectography at 1700 nm is blind to spolvero if the SNR and CTF are too low and that it is better to use the visible spectrum.

Zone 60

Black point White area

For zones 30 to 70, filters in the central band from 500 nm to 800 nm are those that record the highest contrast, but a simple difference calculation between those filters has very little chance of providing an interesting result because the choice of filters based on this measurement is not representative of the entire image. It is necessary to find solutions that offer a more specific calculation for each pixel of the entire image and are applicable to the whole zone.

-Results: Mona Lisa's spolvero revealed

To search for preparatory drawings and repaints under Mona Lisa's pictorial layers, the scientific team in the Louvre laboratory used 4 infrared techniques:

1-NIR photography (full portrait) high resolution (CCD sensor 900-1000 nm) [START_REF] Martin | The Painter's palette in Mona Lisa Inside the Painting[END_REF] 2-IR reflectrography (full with a camera Hamamastu C 2400-03 IR and Vidicon tube N 2606-06 sensitive up to 2000 nm with D.R. 46dB.

Several hundreds of shots (600 x 600 pixels) spaced a few centimetres apart were assembled to obtain a very high-resolution image. [START_REF] Mottin | Reading the image in Mona Lisa Inside the Painting[END_REF] The face of Mona Lisa alone is printed in high-resolution page and enlarged in a 300 x 450 mm format. [START_REF] Lambert | Photographic imaging in Mona Lisa Inside the Painting[END_REF] 3-NIR photography (full portrait) with the multispectral camera (800, 900, 1000 nm) [START_REF] Lahanier | High definition imagerie : a first synthesis in Mona Lisa Inside the Painting[END_REF] 4-IR reflectography (full portrait) made in multiple shoots with the same 16 M pixel OSIRIS camera that we used for the test board. (IR 1700 nm) [START_REF] Frank | Catalogue de l'exposition Léonard de Vinci[END_REF] Perhaps for the aforementioned reasons (see § 4.2), these 4 methods did not allow us to see the spolveri present in many places (even on the enlarged one), as we uncovered with the L.A.M. technique. There is more difficulty in dark areas, where the black dots are confused with their environment.

We present the discoveries made in the 3 areas in Fig. 6. On the Mona Lisa's forehead (Fig. 6 point 1), as it appears today (Fig. 7a), the edge of the veil that covers the head is represented by a simple line that regularly follows the hair limit, a few millimetres below from left to right; the enlarged Louvre's IR reflectography at 2000 nm [START_REF] Lambert | Photographic imaging in Mona Lisa Inside the Painting[END_REF] (Fig. 7b) shows us much the same image as the NIR filter R900 of the multispectral camera. (Fig. 7c). No spolvero are visible in these images even when applying image processing to increase the contrast. This L.A.M. image (Fig. 7d) was obtained by calculating the numerical derivative of the reflectance R600, R640, and R900: 𝐿. 𝐴. 𝑀. = '-)

5-1 -Mona Lisa's forehead

! !"" "! #"" # + 𝑅 $%% , + 𝑝𝑅 $&% . Eq. ( 1
)
Where Rλ is the reflectance at wavelength lambda, and p is a coefficient that increases the weight of reference reflectance R640 (p= 1.064 in the presented image).

On the L.A.M. image, we observe a spolvero on the forehead (drawing lines with black dots), almost at the same location, but the lines are more inclined to Mona Lisa's right. This is a preparatory drawing of a face turned to the right. It outlines a different veil belonging to a previous project of Leonardo's. He draws 2 lines between which Leonardo probably designed the embroidery, as often found in contemporaneous Florentine paintings, such as the Portrait of wealthy Maddalena Strozzi Doni executed by Raphael in 1506. (Oil on panel 63 × 45cm, Palazzo Pitti, Florence.) This finding is proof that this work evolved in its composition and that Leonardo did not remain fixed on 1 version.

Notably, the L.A.M. image simultaneously shows the black dots of the transfer (highlighted in orange) and the lines drawn by the artist to connect these dots (highlighted in yellow). (Fig. 7e)

There is another horizontal line below, but it does not cover the entire width of the forehead. Some black dots appear along this line, but the contrast is too low to hypothesise that it is a spolvero. On Mona Lisa's hands (Fig. 6 point 2), the L.A.M. image in Fig. 8d shows a series of black dots connected by a line: This is the spolvero of an original preparatory drawing.

5-2 -Mona Lisa's hand

The best L.A.M. images to allow visibility of the black dots of the spolvero use filters only in the visible range at 720 nm and 480 nm. In this specific case, the operation is not performed directly on the reflectance but on the K𝜆/S𝜆 ratios between the absorption K and backscattering S coefficients obtained from the reflectance by the Kubelka-Munk function [START_REF] Kubelka | [END_REF][START_REF] Kubelka | New Contributions to the Optics of Intensely Light-Scattering Materials I[END_REF]. This operation highlights the differences in absorption and is much more sensitive than the differences in reflectance.

𝐿. 𝐴. 𝑀. = ')

(("! $%" ) % #! $%"
, -𝑝 )

(("! &'" ) % #! &'" ,. Eq. ( 2
)
Where Rλ is the reflectance at wavelength lambda, and p is a coefficient to optimise the contrast (p=1.082 in the presented image).

Other L.A.M. images, not presented in this article, show that the spolvero goes all the way around the right hand, but the contrasts of the black dots are weak and more difficult to observe.

The IR reflectography at 2000 nm (Fig. 8b), [START_REF] Mottin | Reading the image in Mona Lisa Inside the Painting[END_REF] does not allow us to distinguish the spolvero, as the visible reflectance R720 (Fig. 8c). A similar-but not a spolvero-analysis revealed this preparatory drawing (Fig. 9d) that was found in the sky to the right of Mona Lisa's head (Fig. 6 point 3). It is strange because it does not touch the edge of the head. It seems to belong to a previous project that Leonardo abandoned, perhaps the same project we discovered in §5.1. Considering the scale of the portrait, the position near the head, the orientation, the dimensions of the object (6.5 mm x 8.2 mm), and its height of 16 mm, a possibility is that it represents a hair needle with a pearl.

5-3 -Preparatory drawing in the sky

For which project? For which portrait? These questions may be best answered by art historians.

This image was obtained by this calculation:

𝐿. 𝐴. 𝑀. = [𝑅 *%% -𝑝𝑅 +$% ] Eq. ( 3
)
Where Rλ is the reflectance at wavelength lambda, and p = 0.852. Stretching and equalising the histogram amplifies the dynamics of the image.

The IR reflectography [START_REF] Lambert | Photographic imaging in Mona Lisa Inside the Painting[END_REF] of Fig. 9b is a good example of the result obtained at a wavelength of 2000 nm. The IRs probably pass through the pictorial layer and the underlying drawing. The IRs, as is the NIR 800 nm (Fig. 9c), are blind to this detail in the sky.

-Discussion and conclusion

We have demonstrated that a drawing underlying a thick layer of lead white can be highlighted by using a multispectral camera and the L.A.M. technique. IR alone is insufficient; it is necessary to combine spectral bands, especially in the visible range. The Louvre's 4 IR and NIR techniques from 800 nm to 2000 nm do not allow these drawings to be observed, probably for the reasons explained in this article.

Perhaps IR cameras would have the performance required to capture them in the near future, but there would have to be improvements in SNR, spatial resolution, and spectral band.

As with IR reflectometry, a limitation of this method is when the underlying drawing is located under a very dark pictorial layer. Notably, the more absorbent (dark) the pictorial layer, the more difficult it is to detect spolvero. This is confirmed with the Mona Lisa, where the results presented correspond only to light areas containing lead white. This reason is why we assume the test board is made only with lead white.

We have also detected spolvero in darker areas but they were less obvious. We came to the same conclusions with a test board covered with a layer of 90% lead white and mixed with 10% ultramarine blue: The L.A.M. technique remains more efficient than IR to observe the spolvero or the preparatory drawings. It is conceivable that multiple scans combined with HDR software could achieve the necessary dynamics to overcome the limits of this technique.

The discovery of the spolvero is not surprising [START_REF] Ravaud | Catalogue de l'exposition Léonard de Vinci, L'art de la matière, l'art et la manière[END_REF]. This method was taught in the workshop of Verrocchio, Leonardo's master, and has been highlighted in other paintings by Leonardo. We observe it in the L.A.M. image in Fig. 10 on the portrait of The Lady with an Ermine [START_REF] Cotte | Lumière on the Lady with an Ermine[END_REF]. This discovery of the spolvero is important to understand how this work has evolved but also because 1 -This is the first time that its use has been demonstrated on The Mona Lisa.

2 -It places the work in a period in the history of painting during which this technique was widely used.

3 -It proves the existence of a cartone (drawing on paper). A cartone can be copied indefinitely; perhaps other versions are made from this drawing. We are thinking of the Prado Museum's version.

4 -It makes it possible to estimate the degrees of freedom that Leonardo allows himself during the execution of the painting.

5 -It enriches the database with information on Leonardo's technique (e.g. measurements of hole spacing, black dots, width of the pencil line, nature of the pencil line).

◊ ◊ ◊

We would like to thank the Louvre Museum for authorising multispectral digitisation and thus making it possible to conduct this research and publish this article. The visible or invisible information the image can only be extracted by algorithms under the condition that it is not drowned out by the electronic noise of the image. To amplify weak signals, it is therefore important to have a source of information with the highest possible DR and SNR. For example, a weak signal when digitised with a usual CMOS sensor would only contain 2 bits (4 grey levels)almost unusable for paint analysis-but with the multispectral camera's CCD, the same signal would have 5.7 bits (52 grey levels) and would thus become relevant information. The camera digitises each filter individually, and this allows optimisation of the exposure time of the pixels to light to reduce the amplification gain and thus obtain a maximum SNR for each filter. This is a considerable technical advantage compared with hyperspectral cameras, which have an SNR optimised for a wavelength but are often much worse at the ends of the spectral domain.

APPENDICES

Appendix B -Spatial resolution -Contrast Transfer Function

The camera uses a 210 mm focal length optic and a symmetrical lens formula. Geometric distortion is low and not measurable. The CTF of a camera is the measure of its ability to reproduce spatial frequencies (details of an image). The measurements were made on the ISO 16097-1 test pattern [17], which includes pairs of black and white lines at different frequencies. The setting of the camera is 1200 dpi, 1 CCD pixel of 6.5 µm sees 21 µm on the test pattern. In practice, the CTF is calculated by using the line pattern contrast determined at the different spatial frequencies of the target. The minimum spatial frequency f0 is obtained over large bright areas in the edges of the target. One of the main optical characteristics of this camera is its ability to memorise a specific focus for each filter with 2 advantages: to always have a maximum CTF (Table A1) for each wavelength and to cancel sagittal and longitudinal chromatic aberration, and defects are inevitable otherwise. This is a second considerable technical advantage compared with hyperspectral cameras because the focus and therefore the resolution are optimised for 1 wavelength and are significantly worse at the edge of the spectrum. For comparison purposes, measurements made for the 400, 760, and 1000 nm filters while maintaining focus on the central band at 560 nm show poor CTFs.

Table A1: CTF values for all filters of the Lumiere Technology multispectral camera with a specific focus for each filter individually. The CTFs of the 400, 760, and 1000 nm filters are also given with a focus identical to the 560 nm filter at 277.90 mm.

It is possible to slightly improve the CTF by software processing algorithms such as deconvolution matrices. However, this improvement is at the cost of a significant decrease in image dynamics (DR and SNR) and is not used for L.A.M. analysis of multispectral imaging.

C(f ) on raw files

Cycles/mm size 400 440 480 520 560 600 640 680 720 760 800 900 1000 Panchro 400 760 1000 f 0 77% 83% 84% 83% 79% 83% 83% 80% 81% 81% 84% 72% 53% 92% 43% 67% 24% Variable focus for 13 Filters from 400 nm to 1000 nm and Panchro without filter

Fig. 1 :

 1 Fig. 1: Mona Lisa's multispectral scanning Fig. 2: Quantum Efficiency x Filters transmittance of the camera. 3-1 -Performances of the camera Lumiere Technology developed of this camera in the framework of the project CRISATEL, European Research Programme IST2. The fundamental technical characteristics have been designed to offer maximal optical and electronic performance. A CCD sensor is used with 13 filters that divide the visible and near-infrared (NIR) light spectrum. The whole has good efficiency in the spectral range [380-1050 nm]. (Fig. 2)

Fig. 3

 3 Fig. 3 Making a test board with an increasing thickness of lead white covering spolveri.

Fig. 4 :

 4 Fig. 4: Final result of the test board covered with lead white. 4a: Visible colour photography. ; 4b: Osiris camera, IR Reflectography at 1700nm. 2540 pixels x 450 pixels. ; 4c: IR image 1000 nm of the multispectral camera. 3229 pixels x 566 pixels. (after binning) ; 4d: L.A.M. Technique. 3229 pixels x 566 pixels (after binning) 4-1 -Comparison of imaging results 4.1.1 -OSIRIS camera -IR Reflectography 1700 nm

4- 1 - 3 -

 13 L.A.M. technique with the multispectral camera. The L.A.M. technique (Fig. 4d) shows the spolveri of the 30 (70 µm), 40 (180 µm), and 50 (270 µm) and the contours of the 60 (350 µm) and 70 (360 µm). Zones 10, 20, and 30 are the result of an L.A.M. from the same calculation, and zones 40, 50, 60, and 70 are from 4 different calculations.

Fig. 5 :

 5 Fig.5: Spectra of a black point (blue line) and the white area (orange line) next to it. Measurements performed on an area not covered with lead white, and on the zones10, 30, 40, 50, 60.

Fig. 6 :

 6 Fig. 6: Locations of the 3 areas studiedThe L.A.M. technique found the spolveri mainly in the light layers, which scatter light in the lower strata. There is more difficulty in dark areas, where the black dots are confused with their environment.

Fig. 7 :

 7 Fig. 7: Detail Mona Lisa's forehead: 7a -Colour image D65 ; 7b -IR reflectography 2000 nm. © Louvre [28]; 7c-NIR filter R900 of the multispectral camera. © P.Cotte; 7d -L.A.M. Technique. © P.Cotte ; 7e -Colour image D65 + outline spolvero.

Fig. 8 :

 8 Fig. 8: Detail of Mona Lisa's hands: 8a -Colour image D65 ; 8b -IR reflectography 2000 nm. © Louvre [27]; 8c -Visible reflectance R720. © P.Cotte ; 8d -L.A.M. Technique. © P.Cotte.

Fig. 9 :

 9 Fig. 9: Detail on Mona Lisa's head: 9a -Colour image D65 ; 9b -IR reflectography 2000 nm. © Louvre [28; 9c -NIR R800. © P.Cotte ; ]; 9d -L.A.M. Technique. © P.Cotte

Fig. 10 :

 10 Fig. 10: The Lady with an Ermine, Leonardo Da Vinci. L.A.M. image. © P.Cotte The spolvero (line of black dots) placed behind the ermine's head draws a different project belonging to the garment. Its presence shows that perhaps the ermine was not in Leonardo da Vinci's initial project.

Table 1 :

 1 Thicknesses applied with the applicator (wet coat) and estimated with a micrometre comparator (when the coats are dry).

Appendix A -Dynamic Range and Signal-to-Noise Ratio

  Dynamic range (DR) is the ratio between the largest and the smallest values that the image can assume. It can be expressed in dB. The higher the DR, the greater the nuances and information the image offers. Signal-to-noise ratio (SNR) is a measure that compares the level of a desired signal to the level of the background noise in dB. The multispectral camera uses a TH-7834 CCD sensor originally designed for the SPOT[START_REF] Bardoux | Linear CCD array TH-7834B performances near 10 MHz[END_REF] image weather satellite and other uses. It is a 12,000 pixel linear array of a 6.5 µm square. It offers a DR of 10,000:1[16]. Considering the manufacturer's data (Quantum Efficiency, Photo response non uniformity and Dark Signal nonuniformity) and camera operating parameters, we obtain an effective SNR of 9,230:1 or 79.3 dB. By comparison, a camera with a CMOS sensor typically has an effective SNR of approximately 446:1 or 53 dB.

28/09/2020 Page

Appendix C -Binning to increase the DR and SNR One advantage of having a hyper high-resolution image of 240,000,000 pixels with the multispectral camera is that it allows us to increase the dynamic range of the image by using the binning method [START_REF] O'sullivan | An Extended Spectral Angle Map for Hyperspectral and Multispectral Imaging[END_REF]. Binning is usually performed on the sensor by combining the outputs of neighbouring photosites, or by mathematical algorithms. It reduces the resolution but increases the DR and the SNR by lowering the background noise.

However, the excessive reduction of the resolution with binning is also a loss of information; there is therefore a compromise between a gain in dynamics and loss of spatial resolution. We use a binning of max 4x4 pixels and therefore have a gain in SNR of 10 dB.


J Quénon

G Arsenos

G Bailo

R Baptista

I De Barbieri

G Bruni

F Freire

A Theodoridis

S Vouraki

V Thénard

Assaf Chios

Frizarta Lacaune Boutsko

Assaf Corriedale Merino

Increasing resilience of small ruminants farming systems: three management strategies

93) 'The non-geneticists aiming at robustness and multifunctionality' Group 2 (n = 34) 'The farmers aiming at increasing production efficiency' Group 3 (n = 145) 'The geneticists aiming at increasing production efficiency'

No overlap between groups and livestock systems: breeding management strategies and views on sustainability do not seem to depend on livestock species/farming system

Introduction

 Small ruminant livestock are of socio-economic and environmental importance to many rural communities around the world (FAO, 2009) their sustainability is a crucial issue (Joy et al., 2020;Leite et al., 2021)  Choosing breeding goals adapted to such issue (Phocas et al., 2016), by selecting traits that enhance:

 Resilience = buffer, adaptive and transformative capacity in a changing/uncertain context (Dumont et al., 2020)  Resistance to heat stress (Sejian et al., 2019;Sánchez-Molano et al., 2020)  Resistance to parasitism and diseases (Hine et al., 2022;Doeschl-Wilson et al., 2022)  Efficiency = production related to the use of the necessary resources  Feed intake (Amarilho-Silveira et al., 2022)  Land use (Hennessy et al., 2021) Introduction  Small ruminant livestock are of socio-economic and environmental importance to many rural communities around the world (FAO, 2009) their sustainability is a crucial issue (Joy et al., 2020;Leite et al., 2021)  Choosing breeding goals adapted to such issue (Phocas et al., 2016), by selecting traits that enhance:

 Resilience = buffer, adaptive and transformative capacity in a changing/uncertain context (Dumont et al., 2020)  Resistance to heat stress (Sejian et al., 2019;Sánchez-Molano et al., 2020)  Resistance to parasitism and diseases (Hine et al., 2022;Doeschl-Wilson et al., 2022)  Efficiency = production related to the use of the necessary resources

Introduction

 Small ruminant livestock are of socio-economic and environmental importance to many rural communities around the world (FAO, 2009) their sustainability is a crucial issue (Joy et al., 2020;Leite et al., 2021)  Choosing breeding goals adapted to such issue (Phocas et al., 2016), by selecting traits that enhance:

 Resilience/robustness = buffer, adaptive and transformative capacity in a changing/uncertain context (Dumont et al., 2020)  Resistance to heat stress (Sejian et al., 2019;Sánchez-Molano et al., 2020)  Resistance to parasitism and diseases (Hine et al., 2022;Doeschl-Wilson et al., 2022)  Efficiency = production related to the use of the necessary resources

What criteria (genetic or not) do farmers/breeders use?

Which traits do they think are relevant to increase the sustainability of their farm?

«

  Qualitative data:Building categorical variables = data abstraction(Girard et al., 2008) :"From a abundant diversity of responses to an acceptable one" Quantitative data:Calculation of relative indicators e.g. % of artificial insemination used Factorial analysis of mixed data (FAMD): analysing pattern of relationships described by both quantitative and categorical data  Hierarchical clustering: discriminating and characterising groups of small ruminants' farmers with contrasted breeding practices Dim 1 (15.7%) Axis 1 determined by the level of integration of small ruminants' farmers in the sociotechnical system of breed selection and performance recording This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787 Results 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR) 18 Dim 1 (15.7%) Axis 1 determined by the level of integration of small ruminants' farmers in the sociotechnical system of breed selection and performance recording  Farmers  Don't know EBVs  Not enrolled in performance controlling organisations  Using natural mating only  Don't use genetic criteria to buy breeding animals Low level of integration in the sociotechnical system of breed selection and performance recording organisationsThis project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787 Results 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR) 19 Dim 1 (15.7%) Axis 1 determined by the level of integration of small ruminants' farmers in the sociotechnical system of breed selection and performance recording  Farmers  Don't know EBVs  Not enrolled in performance controlling organisations  Using natural mating only  Don't use genetic criteria to buy No need new traits for sustainability  Sustainability is no relevant objective  Satisfied with the current indexes  Production-driven management for culling No need new traits for sustainability  Sustainability is no relevant objective  Satisfied with the current indexes  Production-driven management for culling Little interest in adding new traits in the selection indexes nor in increasing the sustainability of their farming system High interest in adding robustness-related traits in the current selection indexes to increase sustainability of their farming system  Sustainability is a crucial objective  Unsatisfied with the current indexes  Ask for robustness-and health-related traits in the indexes Low level of integration in the sociotechnical system High level of integration in the sociotechnical system High interest in adding robustness traits in the indexes to increase sustainability Group 1 (n = 93) 'The non-geneticists aiming at robustness and multifunctionality' Level of structuring of the breeding selection system  Level of dissemination of technologies and knowledge on genetics (e.g. AI, indexes, genomics)  Shared knowledge among farmers on specific topics (e.g. sustainability/robustness/resilience) Interest in the issue of sustainability varied among farmers  Confidence in genetic tools and their relevance to increasing resilience varied among farmers to what extent SMARTER objectives fit farmers' expectations (vs. breeders')? Qualitative data: Building categorical variables = data abstraction (Girard et al., 2008) : "From a abundant diversity of responses to an acceptable one" Qualitative data: Building categorical variables = data abstraction (Girard et al., 2008) : "From a abundant diversity of responses to an acceptable one" Qualitative data: Building categorical variables = data abstraction (Girard et al., 2008) : "From a abundant diversity of responses to an acceptable one" farmers aiming at increasing production efficiency' -Production-driven flock management -Mostly Greek dairy sheep farmers -Low % of meadows/grassland in the UAA -Low use of pesticides -Selecting on production traits to increase sustainability of their farming system. 'The geneticists aiming at increasing production efficiency' -Mostly Spain and Italian breeders + Uruguayan farmers -Large flocks -Low % of meadows/grassland -High use of pesticides -Demanding flock configuration practices:-higher use of artificial insemination -higher replacement rate -Strong knowledge in genetics -Enrolled in performance control recording organisations -Satisfied with the current indexes to ensure the sustainability of their system This project has received funding from the European UnionToo much emphasis is placed on the ram's paper, on his genetic potential, and the breeders themselves place too much emphasis on this. » « What 'geneticists' dont understand is that genetic progress will be difficult to maintain as it is because. climate change will force farmers to adapt rather than rely on animal adaptation or selection alone»

to redefine genetic selection criteria to increase the sustainability of the small ruminants sectors

  Choosing breeding goals adapted to such issue(Phocas et al., 2016), by selecting traits that enhance:

	 Resilience/robustness = buffer, adaptive and transformative capacity in a changing/uncertain context (Dumont et al., 2020)  Resistance to heat stress (Sejian et al., 2019; Sánchez-Molano et al., 2020)  Resistance to parasitism and diseases (Hine et al., 2022; Doeschl-Wilson et al., 2022)  Efficiency = production related to the use of the necessary resources  Feed intake (Amarilho-Silveira et al., 2022)  Land use (Hennessy et al., 2021) Introduction  SMARTER (SMAll RuminanTs breeding for Efficiency and Resilience) H2020 project aims  Adjusting breeding objectives to small ruminants farmers' expectations, actual breeding practices and views on sustainability (Perucho et al., 2019; Kosgey et al., 2006) 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR) Introduction  SMARTER (SMAll RuminanTs breeding for Efficiency and Resilience) H2020 project aims

 Feed intake

(Amarilho-Silveira et al., 2022)

 Land use

(Hennessy et al., 2021)

Introduction  Small ruminant livestock are of socio-economic and environmental importance to many rural communities around the world (FAO, 2009) their sustainability is a crucial issue (Joy et al., 2020; Leite et al., 2021) 

to redefine genetic selection criteria to increase the sustainability of the small ruminants sectors  Adjusting breeding objectives to small ruminants farmers

' expectations, actual breeding practices and views on sustainability

(Perucho et al., 2019; Kosgey et al., 2006)

08/09/2022

Julien Quénon -73rd EAAP Annual Meeting -Porto (POR) 7

 Final dataset: 272 individuals described by 12 active (+ 29 supplementary)

Acknowledgments 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR)

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787 Smarter Credits: V.Thénard This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR)

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR)

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR) This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR) This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787 This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787 Material and methods 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR) 8 1This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787 Material and methods 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR) 9 This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787 Material and methods 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR) 15 This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787 Material and methods 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR) 16 This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787 Results 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR) 17 This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787 Livestock farming system Discussion 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR) 24 Group 1 (n = Group 1 Group 2 Group 3 Dairy goat Dairy sheep Meat sheep Wool-meat sheep No. Of farms This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787 Discussion 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR) 25 * * * * * This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787 Discussion 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR) 26 This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787 Material and methods 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR) 30 This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787 Material and methods 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR) 32 This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787  Final dataset: 272 individuals described by 12 active (+ 29 supplementary) variables I. Crops management: ∅ II. Flock management:  V1 -Replacement rate (%)  V2 -Percentage of artificial insemination used in the flock (%)  V3 -Use of AI: Only natural mating / Only artificial insemination / Both AI and NM Material and methods 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR) 35 1. DATA COLLECTION 2. DATA EDITING This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 772787 Results 08/09/2022 Julien Quénon -73rd EAAP Annual Meeting -Porto (POR) 42