Crowet Jm

Kruglik Sg D , Belloy N

Prof Laurent Debelle

Structural analysis of nonapeptides derived from elastin

Elastin-derived peptides are released from the extracellular matrix remodeling by numerous proteases and seem to regulate many biological processes, notably cancer progression. The canonical elastin peptide is VGVAPG which harbors the XGXXPG consensus pattern allowing interaction with the elastin receptor complex located at the surface of cells. Besides these elastokines, another class of peptides has been identified. This group of bioactive elastin peptides presents the XGXPGXGXG consensus sequence but the reason for their bioactivity remains unexplained. In order to better understand their nature and structure-function relationships, herein we searched the current databases for this nonapeptide motif and observed that the XGXPGXGXG elastin peptides define a specific group of tandemly repeated patterns. Further, we focused on four tandemly repeated human elastin nonapeptides, i.e. AGIPGLGVG, VGVPGLGVG, AGVPGLGVG and AGVPGFGAG. These peptides were analysed by means of optical spectroscopies and molecular dynamics. UV-circular dichroism and Raman spectra are consistent with a mixture of β-turn, β-strand and random chain secondary elements in aqueous media. Quantitative analysis of their conformations suggested that turns corresponded to half of the total population of structural elements while the remaining half was equally distributed between β-strand and unordered chains. These distributions were confirmed by molecular dynamics simulations. Altogether, our data suggest that these highly dynamic peptides harbor a type II β-turn located in their central part. We hypothesize that this structural element could explain their specific bioactivity.

Statement of Significance

Elastin fragmentation products, the so-called elastin peptides, may exhibit a bioactivity towards normal and tumor cells. This phenomenon depends on the sequence motif they harbor. While XGXXPG sequences bioactivity is explained by the presence of a type VIII β-turn allowing interaction with the elastin receptor complex, the structural reasons for XGXPGXGXG specific activity remain unexplained. Using data mining, we show that elastin nonapeptides define a specific class of tandemly repeated features. Further, spectroscopic and numerical simulations methods suggest the presence of a type II β-turn in their conformation. This structural element could explain their bioactivity.

Introduction

Elastin is the extracellular matrix protein responsible for the structural integrity and function of tissues undergoing reversible extensibility or deformability [START_REF] Mecham | The Extracellular Matrix: an Overview[END_REF]. This protein is extremely stable and resistant and undergoes virtually no turnover. Nevertheless, during aging, mechanical stress and elastase activities contribute to the fragmentation of this macropolymer into elastin-derived peptides (EDP) [START_REF] Duca | Matrix ageing and vascular impacts: focus on elastin fragmentation[END_REF].

Elastin is characterized by its elasticity and seems devoid of any other biological activity [START_REF] Mecham | The Extracellular Matrix: an Overview[END_REF]. In contrast, EDP have been shown to regulate numerous biological processes and are thought to be involved in several aging-related pathologies such as atherosclerosis [START_REF] Maurice | Elastin fragmentation and atherosclerosis progression: The elastokine concept[END_REF][START_REF] Gayral | Elastin-derived peptides potentiate atherosclerosis through the immune Neu1-PI3Kγ pathway[END_REF] and cancer [START_REF] Pocza | Locally generated VGVAPG and VAPG elastin-derived peptides amplify melanoma invasion via the galectin-3 receptor[END_REF][START_REF] Coquerel | Elastin-derived peptides: Matrikines critical for glioblastoma cell aggressiveness in a 3-D system[END_REF][START_REF] Devy | Elastin-derived peptides enhance melanoma growth in vivo by upregulating the activation of Mcol-A (MMP-1) collagenase[END_REF][START_REF] Toupance | Elastin-derived peptides increase invasive capacities of lung cancer cells by post-transcriptional regulation of MMP-2 and uPA[END_REF]. Most biologically active EDP, elastokines, possess a GXXPG consensus sequence adopting a type VIII β-turn structure [START_REF] Brassart | Conformational dependence of collagenase (matrix metalloproteinase-1) up-regulation by elastin peptides in cultured fibroblasts[END_REF] involved in their binding to the elastin receptor complex [START_REF] Blanchevoye | Interaction between the Elastin Peptide VGVAPG and Human Elastin Binding Protein[END_REF]. The interaction of GXXPG-containing elastokines with the elastin receptor complex and the consequences of their interaction has been considerably documented during the last decades. For a comprehensive review on this topic, the reader is referred to [START_REF] Scandolera | The Elastin Receptor Complex: A Unique Matricellular Receptor with High Anti-tumoral Potential[END_REF].

Besides the classical GXXPG-containing sequences, another class of elastokines has also been reported. In 1988, Long and colleagues reported that nonapeptide sequences from elastin were chemoattractant for fibroblasts [START_REF] Long | Chemotaxis of fibroblasts toward nonapeptide of elastin[END_REF]. In 1989, they further demonstrated that this biological activity could be extended to endothelial cells [START_REF] Long | Elastin repeat peptides as chemoattractants for bovine aortic endothelial cells[END_REF]. Strikingly, since then, this peculiar class of EDP has been mostly ignored until 2007, when Maeda and colleagues demonstrated that these peptides could promote macrophages migration via a specific, but unknown, receptor [START_REF] Maeda | Induction of macrophage migration through lactose-insensitive receptor by elastin-derived nonapeptides and their analog[END_REF]. More recently, their biological activity has been further linked to lung carcinoma progression [START_REF] Toupance | Elastin-derived peptides increase invasive capacities of lung cancer cells by post-transcriptional regulation of MMP-2 and uPA[END_REF] and to promotion of invasion in triple-negative cancer cells via MMP-14 and MMP-2 [START_REF] Salesse | Elastin molecular aging promotes MDA-MB-231 breast cancer cell invasiveness[END_REF].

The relatively low interest encountered by these nonapeptide EDP can be explained by the fact that considerable advances have been made on both GXXPG-containing EDP and their receptor biology. As a consequence, few groups considered investigating on these peculiar peptides.

The nonapeptide AGVPGLGVG and other EDPs share similar structural features: random coil and β-turns conformations. However, AGVPGLGVG induces angiogenesis, tumor progression, secretion of proteases higher than other EDPs in carcinomas. Moreover, in vitro, it behaves like amyloid-like peptides harboring the XGGXG motif by forming cross β-sheets at the supramolecular level [START_REF] Da Silva | Structural characterization and in vivo pro-tumor properties of a highly conserved matrikine[END_REF]. Very recently, Brassart and colleagues have shown that the receptor mediating AGVPGLGVG biological effects, and identified as lactose-insensitive, is the ribosomal protein SA (RPSA) [START_REF] Brassart | Tumour cell blebbing and extracellular vesicle shedding: key role of matrikines and ribosomal protein SA[END_REF].

The aim of this work is to analyze the structure and conformational behavior of these peptides. Our structural analysis was conducted using sequence analysis, spectroscopic methods and molecular dynamics simulations.

Our results show that elastin nonapeptides define a peculiar class among peptides harboring the consensus X-G-X-P-G-X-G-X-G sequence. In elastin, these peptides are mostly tandemly repeated. Further our data show that they are engaged in a conformational equilibrium between random coil and turn conformations at the considered concentrations and temperatures. Molecular dynamics simulations suggest that the dominant conformation of these peptides is a type II β-turn. The functional significance of this conformation is discussed.

Material and Methods

Sequence analysis

Sequence retrieval. Sequences harboring the X-G-X-P-G-X-G-X-G consensus motif were searched using Python scripts on UniProtKB (Swiss-Prot and TrEMBL) database Fasta files where spliced variants were ignored. Only exact matches were retrieved and further cleaned informatically and manually so as to ensure that a sequence belonging to a given species (identified by its unique accession number) was present only once in our results. For each sequence hit, the collected data were its accession number, the originating organism, the length of the protein and the position(s) of the nonapeptide sequence(s).

Pattern analysis. Nonapeptide exact matches were classified and distributed as a function of the taxonomic hierarchical classification lineage of the source organism of the corresponding parent protein. Hits were either individual hits, i.e. only one occurrence was found in the sequence, or multiple ones. In the latter case, we distinguished three possibilities.

Multiple hits could be found in different regions of the protein and each occurrence was isolated from the others. Another possibility was that the repetitions could overlap, i.e. the end of the first occurrence (-X-G) was the beginning of the following one (X-G-). Finally, we observed that nonapeptide sequences could also be tandemly repeated, one after the other. The tree figure has been made with the Python module ETE3 [START_REF] Huerta-Cepas | ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data[END_REF] and the Logo sequences with WebLogo (https://github.com/weblogo/weblogo) [START_REF] Crooks | WebLogo: A Sequence Logo Generator[END_REF].

Optical spectroscopy

Molecular compounds. Powder samples of amino acids were purchased from Sigma-Aldrich (Saint Quentin Fallavier, France). Lyophilized samples of nonapeptides were obtained from Genecust (Luxemburg, Luxemburg) as zwitterionic peptides in acetate salts.

Their purity was at least 95% as determined by mass spectrometry.

Solution samples. Lyophilized powder of amino acids and peptides was dissolved in pure water from a Millipore filtration system (Guyancourt, France). The ionic strength of all peptide samples was increased by adding 150 mM NaCl to stock solutions. Upon dissolution, the pH was between 4 and 4.5. Raman spectra were recorded between 10°C and 80°C.

Circular dichroism (CD) spectra were obtained at room temperature between 100 and 200 µM.

CD spectroscopy. Room temperature ultraviolet-circular dichroism (UV-CD) spectra were analyzed on a JASCO J-810 spectrophotometer (Lisses, France) within the 190-300 nm spectral region. A path length of 1 mm and a spectral resolution of 0.2 nm were selected. Each spectrum corresponding to an average of five scans was recorded with a speed of 100 nm per min. The measured ellipticity for each sample, referred to as , was further normalized to obtain the so-called mean residue ellipticity, , by using the expression

= ⁄
, where n, c, and l are the length of the peptide, its molar concentration, and the optical path length, respectively. The normalized ellipticity was expressed in deg cm 2 dmol -1 .

Raman spectroscopy. Room temperature Stokes Raman spectra were analyzed in bulk samples at right angle on a Jobin-Yvon T64000 spectrometer (Longjumeau, France) at single spectrograph configuration, 1200 grooves/mm holographic grating and a holographic notch filter. Raman data corresponding to 1200 s acquisition time for each spectrum were collected on a liquid nitrogen cooled CCD detection system (Spectrum One, Jobin-Yvon). The effective slit width was set to 5 cm -1 . Solution samples were excited by the 488 nm line of an Ar + laser (Spectra Physics, Evry, France), with 200 mW power at the sample.

Post-record spectroscopic data treatment. Buffer subtraction and smoothing of the observed spectra was performed using the GRAMS/AI Z.00 package (Thermo Galactic, Waltham, MA, USA). Final presentation of Raman spectra was done by means of SigmaPlot package 6.10 (SPSS Inc., Chicago, IL, USA).

Secondary structure analysis. Quantification of protein secondary structures using amide I profile decomposition is a method used commonly and its intrinsic limits are readily known [START_REF] Lefèvre | Protein Secondary Structure and Orientation in Silk as Revealed by Raman Spectromicroscopy[END_REF][START_REF] Dolui | Order, Disorder, and Reorder State of Lysozyme: Aggregation Mechanism by Raman Spectroscopy[END_REF]. For elastin material, its overall accuracy is ± 5% as far as the final structural estimates are concerned [START_REF] Debelle | Bovine Elastin and k-Elastin Secondary Structure Determination by Optical Spectroscopies[END_REF]. The initial step of the reconstruction is to guess how many underlying bands are present in the complex amide I profile to be analyzed. This is achieved using a second derivative analysis of the smoothed spectrum. For our data, 4 components were suggested. This information was provided to the curve fitting software to reconstruct the experimental spectrum using Gaussian/Lorentzian mixture profiles. The curve fitting procedure was successful when the fit was good (low chi square value), the number of components was unchanged and their width at half height were consistent. The secondary structure estimates were obtained by adding the fractional areas of amide I structural components belonging to the same secondary structure group.

Atomistic molecular dynamics simulations

Ten independent simulations of 500 ns were performed for each of the 4 human elastin nonapeptides using amber99SB-ILDN force field [START_REF] Lindorff-Larsen | Improved side-chain torsion potentials for the Amber ff99SB protein force field[END_REF] and Gromacs 2016.5 software [START_REF] Abraham | GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers[END_REF].

Peptides were built in an extended conformation with PyMOL (25) and solvated with TIP3P

water in a 5 nm cubic box. All studied systems were first minimized by steepest descent for 5000 steps. Then, a 1-ns simulation with the peptides under position restraints was run before the production simulations were performed. Different seeds were used for each simulation during the velocities assignments. Periodic boundary conditions were used with a 2 fs time step. The dynamics were carried out under NPT conditions (310 K and 1 bar). The temperature was maintained using the v-rescale method [START_REF] Bussi | Canonical sampling through velocity rescaling[END_REF] with τT = 0.1 ps, and an isotropic pressure was maintained using the Parrinello-Rahman barostat [START_REF] Parrinello | Polymorphic transitions in single crystals: A new molecular dynamics method[END_REF] with a compressibility of 4.5 × 10 -5 bar -1 and τP = 2 ps. Short-range non-bonded interactions were treated with a cutoff of 1.0 nm and long-range interactions were calculated with the particle mesh Ewald method [START_REF] Essmann | A smooth particle mesh Ewald method[END_REF] with a grid spacing of 0.16 nm. Bond lengths were maintained with the LINCS algorithm [START_REF] Hess | LINCS: A linear constraint solver for molecular simulations[END_REF] and long range dispersion corrections for energy and pressure were applied. The 3D structures were analyzed with both PyMOL and VMD (30) softwares.

The secondary structures were computed with DSSP [START_REF] Kabsch | Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features[END_REF] and the different types of β-turns have been assigned based on the ΦΨ angles of two residues following each other as defined by Lewis et al. in 1973 (32) and Hutchinson et al. in 1994 (33). Distances between each i and i+3 Cα have been computed and, if this distance was less than 0.7 nm and the amino acids were not part of an helix conformation, the ΦΨ angles of residues i+1 and i+2 were considered. To assign a peculiar type of β-turn, Hutchinson and colleagues (33) considered a range of ±30° or ±40° around the standard angles with a deviation of 50° allowed for one of the angle.

The distribution of ΦΨ angles of residues i+1 and i+2 we observed during the simulation permitted to easily identify type I, I', II, II' and VIII β-turn populations as broad plot regions (Fig. S1 in the Supporting Material). As these regions were larger than those proposed by

Hutchinson and colleagues [START_REF] Hutchinson | A revised set of potentials for β-turn formation in proteins[END_REF], we adapted the considered ΦΨ angles ranges to encompass the ranges we observed. The main effect of this change was the reduction of unassigned turn contribution, i.e. type IV β-turns. The corresponding figure and ΦΨ angle ranges for type I, I', II, II' and VIII β-turns are provided as supplemental data (Fig. S1, Table S1 in the Supporting Material). As in Hutchinson, other β-turn types are considered with a range of ± 40° around the standard angles and type IV β-turns correspond to otherwise unassigned β-turns.

Clustering was performed using the gromos method (34) with a cut-off of 0.1 nm on Cα atoms of residues 2 to 6.

In order to assess the quality of our simulations, CD spectra have been computed from the peptide trajectories by using the SESCA software [START_REF] Nagy | SESCA: Predicting Circular Dichroism Spectra from Protein Molecular Structures[END_REF] with the basis set HBSS-3SC1.

Results

Sequence analysis.

The retrieval of sequences possessing the X-G-X-P-G-X-G-X-G yielded 96 293 unique sequences. These sequences (Table S2 in the Supporting Material) were classed as a function of their taxonomy (Figure 1).

CD spectroscopy

In order to describe the structural behavior of the nonapeptides in solution, their CD spectra were recorded in different (polar vs low dielectric constant) environments. Routinely, methanol is considered as a primary step in studying the peptides conformational behavior in hydrophobic environments, such as membrane interior or hydrophobic pockets of proteins [START_REF] Hernández | Secondary conformation of short lysine-and leucine-rich peptides assessed by optical spectroscopies: Effect of chain length, concentration, solvent, and time[END_REF].

The analysis of the CD spectra in water (150 mM NaCl), in a water/methanol mixture (50/50, v/v), as well as in methanol (Fig. 2) reveal a progressive conformational change of the nonapeptides in going from water to methanol solutions.

The CD spectra recorded in water all present a double negative band shape, i.e. composed of a deep minimum at ~198 nm followed by a weaker and broader negative band centered at ~225 nm. It is a matter of fact that random chains are generally identified by a deep negative band at ~198 nm [START_REF] Perczel | Quantitative analysis of cyclic β-turn models[END_REF][START_REF] Lopes | Distinct circular dichroism spectroscopic signatures of polyproline II and unordered secondary structures: Applications in secondary structure analyses[END_REF]. However, a negative double band, as that observed in the case of the nonapeptides (Fig. 2), is an indicator of turns. For instance, certain β-turns are known to give rise to a negative double band. It is to be mentioned that β-turns are recognized as the most frequent ones in proteins (39)(40) [START_REF] Kaur | A neural network method for prediction of β-turn types in proteins using evolutionary information[END_REF]. Particularly, the CD fingerprints of four categories of β-turns, i.e. type-I (37), -II and -II' [START_REF] Hernández | Octreotide Used for Probing the Type-II′ β-Turn CD and Raman Markers[END_REF][START_REF] Hernández | Low concentration structural dynamics of lanreotide and somatostatin-14[END_REF][START_REF] Hernández | From bulk to plasmonic nanoparticle surfaces: the behavior of two potent therapeutic peptides, octreotide and pasireotide[END_REF] and -VIII (45) β-turns are identified by a double negative band, with unequal ellipticities. It is interesting to note that amongst all the mentioned β-turns, that corresponding to type-VIII is formed by a deep minimum at ~198 nm followed by a broad shoulder at ~220 nm (45), i.e. strikingly similar to that observed in the case of the presently analyzed nonapeptides. However, this overwhelming resemblance between CD markers does not permit rejection of possible unordered chains, of which the CD marker can be overlapped with the negative band at ~198 nm. In the same manner, a probable overlap may occur between the CD fingerprint of β-strands, generally characterized by a unique negative band at ~215 nm, and the ~225 nm negative band from β-turns. Nevertheless, any uncertainty about the presence of PPII (polyproline II) type chains can be discarded because of the recent CD studies highlighting the fact that the PPII fingerprint is composed of a deep negative band at ~198 nm followed by a positive one centered at ~220 nm. The presence of the latter band cannot be suspected in the CD spectra displayed in Figure 2.

In methanol (Fig. 2), the CD signals of the nonapeptides are composed of a broad positive band peaking at ~205 nm followed by a weak and broad negative band at ~225 nm. It is worth noting that the CD spectra observed in water/methanol mixture (1:1, v/v) are somehow an overlap of those observed in water and methanol. To provide a reliable assignment for the CD signal observed in methanol, we can recall that while a type-II β-turn gives rise to a positive band located between 190 and 210 nm (37), the observed negative band at ~225 nm in methanol can be ascribed to β-strands and/or to type-I' β-turn [START_REF] Perczel | Quantitative analysis of cyclic β-turn models[END_REF]. Based on the observed CD spectra, no unordered chain can be expected in the presently studied nonapeptides.

Raman spectroscopy

Raman spectra of the nonapeptides were recorded in the middle wavenumber region (1800-550 cm -1 ). They presented a low water contribution enabling an accurate analysis of the observed bands (Fig. 3A-B). Whatever the nonapeptide, the spectra were identical for the two tested concentrations, i.e. 2.5 mM and 20 mM (data not shown). Indeed, no substantial change, assignable to either a possible conformational transition or a detectable molecular aggregation, could been observed. Because the spectral features were more pronounced at 20 mM (higher Raman intensities due to higher concentration), we will limit our discussion to the Raman spectra recorded at that concentration.

The tentative assignments of the Raman spectra of the four studied nonapeptides are reported in Table 2. These assignments have been performed in keeping with the features observed in the Raman spectra of their constitutive amino-acids (Fig. S2 in the Supporting Material). The bands observed in the 1500-1320 cm -1 range, mainly arising from the bending modes of CH2 and CH3 side chain moieties, as well as those located below 1200 cm -1 in amino acids spectra, were taken as references for assigning the observed peaks in the spectra of peptides.

The spectrum of hN6 is characterized by the occurrence of the six Phe characteristic Raman bands, referred to as F1 to F6 [START_REF] Hernández | Vibrational Analysis of Amino Acids and Short Peptides in Hydrated Media. VIII. Amino Acids with Aromatic Side Chains: l-Phenylalanine, l-Tyrosine, and l-Tryptophan[END_REF][START_REF] Hernández | Characteristic Raman lines of phenylalanine analyzed by a multiconformational approach[END_REF]. Being all of in-plane type and localized in the Phe phenyl ring, the wavenumbers of these six characteristic markers remained very close to those collected from the free amino acid F (Fig. 3B). The Raman spectra of hN3, hN4 and hN5 (Fig. 3A) do not present such narrow and resolved aromatic markers, but their striking spectral shape similarity is to be emphasized (Fig. 3). Two regions corresponding to amide I (1700-1640 cm -1 ) and amide III (1300-1230 cm -1 ) vibrations provide valuable structural information through their decomposition into band components [START_REF] Hernández | Octreotide Used for Probing the Type-II′ β-Turn CD and Raman Markers[END_REF][START_REF] Hernández | Low concentration structural dynamics of lanreotide and somatostatin-14[END_REF][START_REF] Hernández | From bulk to plasmonic nanoparticle surfaces: the behavior of two potent therapeutic peptides, octreotide and pasireotide[END_REF]. It is noteworthy that while amide I vibrations results from the backbone C=O bond stretch motion, more or less coupled to its adjacent N-H angular bending, amide III vibrations mainly arise from the backbone N-H angular bending. The Raman spectra obtained for the free amino acids (Fig. S2) are devoid of amide (I and III) vibrations. However, the presence of low intensity bands arising from the side chains of Val, Leu, Ile and Pro, falling within the amide III range should be stressed.

These bands can be naturally superimposed to those relative to amide III vibrations in peptides. Nevertheless, because of their weakness, one cannot expect a considerable distortion in the structural analysis on the basis of amide III vibrations. In all nonapeptides, amide I and amide III vibrations are characterized by two strong, broad and incompletely resolved bands peaking at ~1685 and ~1265 cm -1 , respectively. To go farther in extracting the structural information from amide vibrations, the decomposition of the amide I region for four nonapeptides is displayed in Figure 4. Through a systematic investigation on the structural analysis of the peptide chains (42-44, 48, 49), in aqueous environment, we have been led to select four band components located at ~1690±5 (assignable to random chain), ~1660±5

(assignable to β-strand), ~1675±5 and ~1650±5 (both assignable to turns) to decompose the amide I region in nonapeptides. The populations corresponding to different secondary structural elements, as estimated by the normalized areas of the band components (expressed in percent) are reported in Table 3. As it can be seen between 50% to 60% of the total population is formed by turn structures, and the rest is equally distributed between β-strands and random chains.

Solution samples containing nonapeptides at 20 mM were heated up to 80°C. Little changes appeared in their Raman spectra, but slight changes could be observed in the amide III region. Figure 5 exemplifies this behavior for hN5 and hN6. The main effect is a wavenumber downshift of the spectral profile consistent with a progressive shift from turn to β-strand upon increasing temperature. Albeit the turns remain the major population of secondary structures in the 10-80° C temperature range (data not shown), they show a little tendency to be transformed into β-strands, presumably because of the weakening/breakdown of the inter-turn stabilizing hydrogen bonds occurring upon thermal annealing.

Atomistic molecular dynamics simulations

To get a more detailed representation of the structures that can be adopted by the four nonapeptides, 10 molecular dynamic simulations of 500 ns have been performed for each peptide. The adopted conformations, analyzed with DSSP [START_REF] Kabsch | Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features[END_REF], are presented on Figure 6A and Figure S3 in the Supporting Material. The figures show that the peptides mainly adopt random coil conformations with some residues favoring turns but no stable structure can be observed. The formation of β-turns is evidenced by distances between Cα atoms of residues i and i+3 being less than 0.7 nm with residues i+1 and i+2 being non helical, which is mainly observed in the middle of the peptides between residues 3 and 6 and notably for the hN3 peptide (Fig. S4 in the Supporting Material, panel A). The type of β-turns can be identified by analyzing the ΦΨ angles of two amino acid residues following each other as determined by

Lewis et al. [START_REF] Lewis | Chain reversals in proteins[END_REF] and Hutchinson et al. [START_REF] Hutchinson | A revised set of potentials for β-turn formation in proteins[END_REF]. Figure 6B shows that type II β-turns are formed during a third to a half of the simulation time (52.9 ± 4.1% for hN3, 37.8 ± 4.7% for hN4, 33.9 ± 2.8% for hN5 and 33.5 ± 3.8% for hN6) for the PG residues being at positions 4 and 5.

PG residues are strongly favored at i+1 and i+2 positions in experimental structure of type II β-turns and they are known to promote this type of β-turn. The population of type II β-turns is greater for the hN3 peptide than for the hN4, hN5 and hN6 peptides. Figures S4B-E show that type I, I', II, II' and VIII β-turns can also be formed in smaller amounts at other positions of the peptides and that type VIII β-turns are mainly observed at the start of the peptides (up to 3% of the simulation time). A proline at position 4 has been shown to promote the formation of this type of turn [START_REF] Hutchinson | A revised set of potentials for β-turn formation in proteins[END_REF]. Analysis of the simulation underlines that the structures are rapidly changing on the nanosecond time scale from extended to more packed conformations which mainly corresponds to type II β-turns (Fig. S4F). The population of β-turns has also been analyzed through clustering methods and the central structures of the dominant cluster population of each peptide is presented as an insert in Figure 6B. Altogether, our analysis of MD data strongly suggests that the PG motif of the nonapeptides could mainly adopt a type II β-turn conformation.

To test the agreement of these results with the measured CD spectra, CD spectra were computed from our simulated trajectories with the SESCA software [START_REF] Nagy | SESCA: Predicting Circular Dichroism Spectra from Protein Molecular Structures[END_REF]. The results (Fig. S5 in the Supporting Material) show a very good agreement between the measured and computed spectra despite noticeable differences around ~220 nm. This suggests that the computed profiles might have failed to detect several experimental signals. Nevertheless, they reinforce our proposal that β-turns and notably type II β-turns are the prevailing structures adopted by the nonapeptides.

Discussion

Among elastin peptides, elastin nonapeptides have been identified initially as chemoattractants for fibroblasts [START_REF] Long | Chemotaxis of fibroblasts toward nonapeptide of elastin[END_REF]. Now, there are growing evidences that these elastin peptides could define a peculiar class of matrikines.

Brassart and colleagues have recently shown that hN5 (i.e. AGVPGLGVG) plays a key role in tumor progression by promoting tumor cell blebbing and extracellular vesicle shedding following its interaction with the ribosomal protein SA [START_REF] Brassart | Tumour cell blebbing and extracellular vesicle shedding: key role of matrikines and ribosomal protein SA[END_REF]. The structure of hN5 has been analyzed previously by CD, FT-IR and NMR spectroscopies [START_REF] Da Silva | Structural characterization and in vivo pro-tumor properties of a highly conserved matrikine[END_REF]. This report concluded that the structure of the peptide was a mixture of random coils and β-turns. Our data fully agree with these findings but they also expand our knowledge of the nature and structural behavior of these elastin peptides.

The occurrence of the X-G-X-P-G-X-G-X-G consensus sequence was analyzed in a large sequence data bank. Our results show that among the observed hits, the group of elastin sequences defines a specific group (Fig. 1). Further, our analysis underlines the fact that these peptides mostly occur in tandem repeats in human exon 26 (and corresponding sequences in other species). Finally, as far as human sequences are concerned, we observed that similar sequences were also present in numerous species (Table 1). The most striking observation is that hN6 is present in numerous species and is tandemly repeated five times in mouse sequence. In contrast, hN3 is only found in 3 species. These observations suggest that, besides their matrikines activity, these sequences could play a key, and yet undescribed, role in elastin biology, possibly elastin assembly. Indeed, among elastin sequences, these sequences appear much more conserved that the canonical elastin hexapeptide sequence (say VGVAPG), which occurrence is mostly limited to human exon 24 sequence. Further works are needed to test this hypothesis.

Our data (Fig. 1) show that the X-G-X-P-G-X-G-X-G consensus sequence can be found in most filum and/or species. To the best of our knowledge, sequences harboring this consensus have caught the attention only in elastin because these elastin peptides are bioactive [START_REF] Toupance | Elastin-derived peptides increase invasive capacities of lung cancer cells by post-transcriptional regulation of MMP-2 and uPA[END_REF][START_REF] Long | Chemotaxis of fibroblasts toward nonapeptide of elastin[END_REF][START_REF] Long | Elastin repeat peptides as chemoattractants for bovine aortic endothelial cells[END_REF][START_REF] Maeda | Induction of macrophage migration through lactose-insensitive receptor by elastin-derived nonapeptides and their analog[END_REF][START_REF] Salesse | Elastin molecular aging promotes MDA-MB-231 breast cancer cell invasiveness[END_REF][START_REF] Brassart | Tumour cell blebbing and extracellular vesicle shedding: key role of matrikines and ribosomal protein SA[END_REF]. Up to now, neither a biological activity nor a dedicated receptor have been described

for the non-elastin sequences.

As evidenced by the consensus profile reported for each of them (Fig. 1), the non-elastin sequences (96 259 sequences) present a sequence pattern that is mostly dominated by the occurrence of G, A or K at X positions. These residues are not favored in elastin sequences where AGVPGFGVG is the prevailing motif. This is remarkably pronounced when nonelastin animal sequences are considered. Their consensus is GGGPGGGGG. These important sequence differences suggest that nonapeptides found in non-elastin sequences would most probably be structured and behave differently than elastin nonapeptides. Nevertheless, a comprehensive structural analysis is required to address this point.

In the present study, we focused our analysis on the nonapeptide sequences found in human exon 26 aiming at understanding their structure and structural behavior. First, this investigation was performed using optical spectroscopies. The results gathered by both CD and Raman spectra are consistent with previous reports underlining a conformational equilibrium between random, strand and turns conformations within elastin sequences [START_REF] Debelle | Elastin: molecular description and function[END_REF].

The Raman data underline that the conformation of these peptides is dominated by β-turns as deduced from the quantitative analysis of their amide I profiles. These conformations appear very favored as increasing the temperature up to 80°C did not drastically change the spectra and hence their structure, albeit a small increase of the β-strands content could be observed upon heating. Altogether our experimental data suggest that the structures of the nonapeptides are dominated by β-turns and that these conformations could be engaged in a conformational equilibrium with extended and random structures.

In order to have a better understanding of the structural behavior of these peptides, molecular dynamics simulations were undertaken in explicit water. Ten independent 500-ns simulations were performed for each of the 4 nonapeptides. The results obtained are extremely consistent with our experimental data. Indeed, they show that the most common structures observed during the simulation is a type II β-turn occurring at the X-P-G-X motif.

Quantitatively, this structure seems more abundant in hN3 (about 50%), than hN4, hN5 or hN6 (25-35%). This finding is in very good agreement with the conclusions of Lessing and colleagues [START_REF] Lessing | Identifying Residual Structure in Intrinsically Disordered Systems: A 2D IR Spectroscopic Study of the GVGXPGVG Peptide[END_REF] who observed that the preference for the X-Pro-Gly sequence to form a β-turn increased with the complexity and hydrophobicity of the side chain at position X (Gly < Ala < Val). In contrast to hN4, hN5 and hN6 which harbor a valyl residue before PG, hN3 possesses an isoleucyl residue at that position. Thus, the higher propensity of hN3 to adopt a β-turn when compared to the other three nonapeptides is consistent with the work of Lessing and colleagues.

Since the first report of their bioactivity on bovine fibroblasts ( 12), nonapeptides sequences have been tested on various cellular systems: bovine endothelial cells [START_REF] Long | Elastin repeat peptides as chemoattractants for bovine aortic endothelial cells[END_REF], rat macrophages ( 14) and human tumor cell lines [START_REF] Toupance | Elastin-derived peptides increase invasive capacities of lung cancer cells by post-transcriptional regulation of MMP-2 and uPA[END_REF][START_REF] Brassart | Conformational dependence of collagenase (matrix metalloproteinase-1) up-regulation by elastin peptides in cultured fibroblasts[END_REF]. Nevertheless, among those publications few used more than one peptide sequence, thereby forbidding the comparison of their respective effects. In the work of Maeda and colleagues, hN5 and hN6 were identified as chemoattractants for rat macrophages [START_REF] Maeda | Induction of macrophage migration through lactose-insensitive receptor by elastin-derived nonapeptides and their analog[END_REF]. In this work, the authors also analyzed the effect of the canonical VGVAPG sequence. Their conclusion was that the two nonapeptides could compete for receptor binding on EBP. Indeed, while VGVAPG appeared as an exclusive ligand for EBP, they underlined that hN5 and hN6 could also bind EBP and a lactoseinsensitive receptor, later identified as RPSA. Their work therefore suggest that the EBPbinding motif can be harbored by both hexa-and nonapeptides while RPSA binding motif can be only adopted by nonapeptides. It has been established that VGVAPG bioactivity relies on its ability to adopt mainly a type VIII β-turn conformation [START_REF] Blanchevoye | Interaction between the Elastin Peptide VGVAPG and Human Elastin Binding Protein[END_REF]. Our molecular dynamics simulations show that the major conformation of nonapeptides could be the type II β-turn, but also that type VIII turns can be present. Our experimental data also suggest that both type VIII and type II β-turn can be present in the conformation of the nonapeptides. Thus, at the difference of VGVAPG, nonapeptides could be favorably structured to bind EBP and RPSA.

However, it has to be emphasized that the sequences of the hexa-and nonapeptides are different. That way, binding affinity could also be supported by favorable residue side chain interactions in addition to the locally adopted conformation. Sequence differences could also contribute to the differential bioactivity exhibited by these peptides.

Altogether our data suggest that the structural signature of elastin nonapeptide is a type II β-turn. Consequently, it is reasonable to propose that this conformation could be relevant of the observed bioactivity of these peptides. This proposal is supported by the observations made by Toupance and co-workers where they measured a lesser biological effect of hN6 towards human tumor cells than hN5 [START_REF] Toupance | Elastin-derived peptides increase invasive capacities of lung cancer cells by post-transcriptional regulation of MMP-2 and uPA[END_REF]. In our work, hN6 is predicted to present less β-turn conformations than hN5. Therefore, it seems coherent that the bioactivity of hN5 is greater than that of hN6 supporting the proposal that the turn motif is the conformation sustaining bioactivity of these peptides. If our prediction is correct, then one can anticipate that hN3 would be a peptide more bioactive that others.

Despite decades of efforts, the structure/function relationship of RPSA is still an open question [START_REF] Digiacomo | Looking into laminin receptor: critical discussion regarding the non-integrin 37/67-kDa laminin receptor/RPSA protein: Looking into laminin receptor[END_REF]. Notably, the interaction site of elastin nonapeptides on RSPA is unknown. In contrast, RPSA laminin binding site was described as a secondary function acquired during evolution [START_REF] Ardini | The 67-kDa laminin receptor originated from a ribosomal protein that acquired a dual function during evolution[END_REF]. This site corresponds to the 161-180 region of the human sequence. However, the crystal structure of the protein (54) locates this binding site mostly inside the fold.

Therefore, it is thought that RPSA could possess multiple sites and/or associate with other compounds to exert its functions. Consequently, our current knowledge of RPSA structure cannot explain where and how it binds elastin nonapeptides. Further structural explorations are required to address this question.

Conclusion

Our data throw new light on the nature, structure and conformational behavior of elastin nonapeptides. These peptide sequences exhibit a peculiar composition as demonstrated by our bioinformatic analysis of current databases. Further, experimental (CD and Raman spectroscopies) and theoretical data (molecular dynamics) strongly suggest that the dynamic structure of these peptides is dominated by the type II β-turn conformation. We propose that the observed bioactivity of these peptides towards stromal and tumor cells could be driven by the recognition of this structural feature by their cognate receptor, RPSA. Molecular modeling of their interaction is underway. Component positions are ± 5 cm -1 . During the computation, their widths were kept between 15 and 25 cm -1 . The figures correspond to the normalized areas of the components expressed in per cent of the whole Amide I profile. Fractional areas estimate the global secondary structures with an accuracy of ±5% [START_REF] Debelle | Bovine Elastin and k-Elastin Secondary Structure Determination by Optical Spectroscopies[END_REF]. hN3, hN4, hN5 and (B) hN6, compared to F. Spectra were recorded at 20 mM. See Table 2 for tentative assignments. 3.

Figure captions

Figure 1 .

 1 Figure 1. Nonapeptide occurrences and distribution. The X-G-X-P-G-X-G-X-G consensus

Figure 2 .

 2 Figure 2. Ultraviolet-circular dichroism (UV-CD) spectra of the four nonapeptides observed

Figure 3 .

 3 Figure 3. Room temperature Stokes Raman spectra in the middle wavenumber region for (A)

Figure 4 .

 4 Figure 4. Band decomposition in the amide I region of the nonapeptides observed at 20 mM.

Figure 5 .

 5 Figure 5. Effect of temperature on the amide III vibrations of nonapeptides. The spectra were

Figure 6 .

 6 Figure 6. Conformational analysis of the molecular dynamics simulations. A, secondary

  Most sequences were from Bacteria (71 504 sequences for 75 718 occurrences with 2 129 overlaps and 297 tandem repeats) then Eukariota(22 919 sequences for 24 828 occurrences with 811 overlaps and 297 tandem repeats), Archae (1 630 sequences for 1 694 occurrences with 16 overlaps and 6 tandem repeats) and Virus (240 sequences for 245 occurrences with no overlaps and no tandem repeats). Strikingly, when these results are observed, it comes that elastins define a specific group. They exhibit numerous occurrences per sequences (171 for 38) with no overlap and a very high ratio of tandem repeats. This specificity of elastin nonapeptides is even more evident when the most

frequent residues occurring at positions X are considered. While other group mostly favor the presence of G, A, K, P or L at these positions, elastins allow almost none of those with the AGVPGFGVG being the dominating motif. In fact, out of the 140 million sequences searched, this motif was always found in elastin sequences but one bacterial sequence.

The nonapeptide sequences found in elastin entries are reported in Table

1

where they are compared. The sequences are reported as a function of their alphabetical order. In human elastin, nonapeptides can be observed in exon domains 18, 20 or 26. Single occurrences are observed in the two first exons while they appear almost exclusively as tandem repeats in exon 26. Strikingly, the human repeats can also be observed in other species. For instance, the human elastin motif AGVPGFGVG is also present in mouse exon 26. Overall, human exon 26 (or its equivalent in other species) is a sequence domain characterized by tandem repeats of these nonapeptides. As a consequence, those are supposedly important for the biological function of the polymer and we decided to further analyze the structural behavior of the 4 human exon 26 nonapeptides, i.e. AGIPGLGVG (hN3), VGVPGLGVG (hN4), AGVPGLGVG (hN5) and AGVPGFGAG (hN6).

Table 1 .

 1 Nonapeptide stack of elastin Uniprot entries Species, name of the species. Acc#, accession number of the origin sequence. #, number of the hit within the sequence. Position, position of the hit. Exon, exon number (when available). Human sequences appear in bold faces.

	Species	Acc#	#	Sequence	Position	Exon
	Oreochromis niloticus	I3JQ46	8	AGGPGSGIG	657 -665	18
	Odobenus rosmarus divergens	A0A2U3ZFC3	5	AGIPGFGAG	541 -549
	Canis lupus familiaris	J9NW15	5	AGIPGFGVG	571 -579	23
	Homo sapiens	P15502	3	AGIPGLGVG	576 -584	26
	Rhinopithecus roxellana	A0A2K6NWE2	3	AGIPGLGVG	562 -570	26
	Pan troglodytes	A0A2I3SVF0	3	AGIPGLGVG	580 -588	27
	Macaca mulatta	A0A1D5R663	2	AGIPGVGPG	312 -320	15
	Papio anubis	A0A2I3MW49	3	AGVPGFGAG	472 -480	16
	Cercocebus atys	A0A2K5LP54	3	AGVPGFGAG	512 -520	18
	Felis catus	A0A337S1Z3	4	AGVPGFGAG	404 -412	18
	Ailuropoda melanoleuca	G1MA94	4	AGVPGFGAG	499 -507	23
	Canis lupus familiaris	J9NW15	6	AGVPGFGAG	580 -588	23
	Myotis lucifugus	G1QCG1	2	AGVPGFGAG	483 -491	24
	Myotis lucifugus	G1QCG1	3	AGVPGFGAG	492 -500	24
	Myotis lucifugus	G1QCG1	4	AGVPGFGAG	501 -509	24
	Sus scrofa	A0A287AXU0	5	AGVPGFGAG	599 -607	25
	Homo sapiens	P15502	6	AGVPGFGAG	603 -611	26
	Rattus norvegicus	Q99372	3	AGVPGFGAG	642 -650	26
	Rattus norvegicus	Q99372	4	AGVPGFGAG	658 -666	26
	Mus musculus	P54320	2	AGVPGFGAG	613 -621	27
	Mus musculus	P54320	3	AGVPGFGAG	622 -630	27
	Mus musculus	P54320	4	AGVPGFGAG	631 -639	27
	Mus musculus	P54320	5	AGVPGFGAG	640 -648	27
	Mus musculus	P54320	6	AGVPGFGAG	649 -657	27
	Mustela putorius furo	M3YXB8	4	AGVPGFGAG	608 -616	27
	Pan troglodytes	A0A2I3SVF0	6	AGVPGFGAG	607 -615	27
	Erinaceus europaeus	A0A1S3W547	3	AGVPGFGAG	584 -592
	Erinaceus europaeus	A0A1S3W547	4	AGVPGFGAG	593 -601
	Enhydra lutris kenyoni	A0A2Y9J5B3	4	AGVPGFGAG	615 -623
	Tursiops truncatus	A0A2U4A8K1	1	AGVPGFGAG	87 -95
	Ursus maritimus	A0A384CQV9	6	AGVPGFGAG	583 -591
	Mesocricetus auratus	A0A1U8CCM2	2	AGVPGFGAG	643 -651
	Mesocricetus auratus	A0A1U8CCM2	3	AGVPGFGAG	652 -660
	Mesocricetus auratus	A0A1U8CCM2	4	AGVPGFGAG	661 -669
	Mesocricetus auratus	A0A1U8CCM2	5	AGVPGFGAG	670 -678
	Dipodomys ordii	A0A1S3GKU4	6	AGVPGFGAG	587 -595
	Erinaceus europaeus	A0A1S3W547	5	AGVPGFGGG	602 -610
	Balaenoptera acutorostrata scammoni	A0A384AI97	3	AGVPGFGPG	584 -592
	Felis catus	A0A337S1Z3	2	AGVPGFGVG	386 -394	18
	Felis catus	A0A337S1Z3	3	AGVPGFGVG	395 -403	18
	Canis lupus familiaris	J9NW15	3	AGVPGFGVG	553 -561	23
	Canis lupus familiaris	J9NW15	4	AGVPGFGVG	562 -570	23
	Saimiri boliviensis boliviensis	A0A2K6UXV4	1	AGVPGFGVG	491 -499	25
	Sus scrofa	A0A287AXU0	3	AGVPGFGVG	581 -589	25
	Sus scrofa	A0A287AXU0	4	AGVPGFGVG	590 -598	25
	Callithrix jacchus	A0A2R8P8B8	3	AGVPGFGVG	523 -531	26
	Mustela putorius furo	M3YXB8	2	AGVPGFGVG	590 -598	27
	Mustela putorius furo	M3YXB8	3	AGVPGFGVG	599 -607	27
	Odobenus rosmarus divergens	A0A2U3ZFC3	4	AGVPGFGVG	532 -540
	Enhydra lutris kenyoni	A0A2Y9J5B3	2	AGVPGFGVG	597 -605
	Enhydra lutris kenyoni	A0A2Y9J5B3	3	AGVPGFGVG	606 -614
	Ursus maritimus	A0A384CQV9	4	AGVPGFGVG	565 -573
	Ursus maritimus	A0A384CQV9	5	AGVPGFGVG	574 -582
	Dipodomys ordii	A0A1S3GKU4	4	AGVPGLGAG	569 -577
	Dipodomys ordii	A0A1S3GKU4	5	AGVPGLGAG	578 -586
	Danio rerio	F8W4J7	4	AGVPGLGGG	568 -576	18
	Ailuropoda melanoleuca	G1MA94	3	AGVPGLGVG	490 -498	23
	Saimiri boliviensis boliviensis	A0A2K6UXV4	2	AGVPGLGVG	500 -508	25
	Rhinopithecus bieti	A0A2K6MP70	3	AGVPGLGVG	522 -530	25
	Bos taurus	P04985	1	AGVPGLGVG	562 -570	26
	Bos taurus	P04985	2	AGVPGLGVG	571 -579	26
	Bos taurus	P04985	3	AGVPGLGVG	580 -588	26
	Homo sapiens	P15502	5	AGVPGLGVG	594 -602	26
	Rattus norvegicus	Q99372	2	AGVPGLGVG	633 -641	26

Table 2 .

 2 Tentative assignment of the Raman bands observed in the middle wavenumber region. refer to the six characteristic Raman bands of phenylalanine. Amide I and amide III vibrations give rise to a broad and strong Raman band peaking at ~1685 and 1255 cm -1 , respectively.

	AGIPGLGVG	VGVPGLGVG	AGVPGLGVG	AGVPGFGAG	Tentative
	hN3	hN4	hN5	hN6	assignment
	1700-1620 (br) 1700-1620 (br) 1700-1620 (br)	1700-1620 (br)	Amide I
				1605 (s)	F1
				1586 (m)	F2
	1465 (s)	1468 (s)	1466 (s)		P, I, L, V, A
	1451 (s)	1452 (s)	1452 (s)	1454 (s)	V, L, I
	1421 (s)	1421 (s)	1420 (s)	1421 (s)	G, A, L, I, V, P
	1391 (m)	1394 (m)	1395 (m)	1392 (m)	G, A, L, I, V, P
	1343 (s)	1345 (s)	1343 (s)	1341 (s)	A, L, I, V, P
	1315 (s)	1313 (s)	1317 (s)	1320 (s)	A, L, I
	1300-1240 (br) 1300-1240 (br) 1300-1240 (br)	1300-1240 (br)	Amide III
				1207 (s)	F3
				1183 (m)
	1161 (m)	1175 (m)	1161 (m)	1159 (m)	V, L, I, P
	1128 (s)	1127 (s)	1126 (s)	1126 (m)	V, L, I
				1101 (m)	P
				1031 (s)	F4
	1031 (m)	1034 (m)	1031 (m)		G, I, P
	1011 (m)	1014 (m)	1014 (m)		A, V
				1004 (vs)	F5
	960 (s)	962 (s)	960 (s)		V, L, I
	937 (s)	938 (s)	937 (s)	937 (br)	A, V, L, I
	891 (s)	891 (s)	891 (s)		V
	857 (m)	860 (m)	856 (sh)	851 (br)	L, P
	845 (m)	840 (m)	843 (m)		A, V, L, I, P
				758 (br)	P
	736 (br)	735 (br)	737 (br)		V, L, I, P
				621 (m)	F6
	(vs) very strong; (s) strong; (m) medium; (br) broad Raman bands.
	F1 to F6

Table 3 .

 3 Underlying secondary structure elements in the Amide I profile of the nonapeptides and fractional areas.

	Position (assignment)	hN3	hN4	hN5	hN6
	~1690 (random)	20	20	24	25
	~1680 (turn)	41	40	37	38
	~1660 (β-strand)	26	23	21	25
	~1640 (turn)	13	17	18	12
	Fractional areas
	Random	20	20	24	25
	Turn	54	57	55	50
	β-strand	26	23	21	25

Acknowledgments

The authors would like to thank the MAgICS chair (Université de Reims Champagne Ardenne) and the Centre National de la Recherche Scientifique (CNRS) for financial support.

Molecular dynamics calculations were performed on the ROMEO high performance computing center (https://romeo.univ-reims.fr).

Acknowledgments

The authors would like to thank the MAgICS chair (Université de Reims Champagne Ardenne) and the Centre National de la Recherche Scientifique (CNRS) for financial support.

Molecular dynamics calculations were performed on the ROMEO high performance computing center.

Authors contributions

HB and KSG performed the Raman and CD measurements and analyzed the spectra. BS conceived and ran the simulations. CJM, TJ and BN performed the sequence analyses. BS and DM funded the project and edited the manuscript. DL supervised the work and wrote the manuscript.


MD Bilal Abdi

PharmD Noemie Basset

MD Emmanuel Perrot

MD Marc-Antoine Benderra

MD Ahmed Khalil

MD Stephane Oudard

MD Pascal Blanchet

MD Laurent Brureau

MD Florence Coulet

MD Olivier Cussenot

PhD Geraldine Cancel-Tassin

DNA damage repair gene germline profiling for metastatic prostate cancer patients of different ancestries

Keywords: DNA damage repair gene, germline, metastatic, mutation, prostate cancer

Background: Germline and somatic mutations in DNA damage repair genes (DDRg) are now recognized as new biomarkers for the management of metastatic prostate cancers (mPC). We evaluate the frequency of germline DDRg mutations among French mPC patients of European and African ancestries.

Methods: Targeted next-generation sequencing of 21 DDRg was performed on germline DNA from 557 mPC patients, including 15.1% of cases with an African origin.

Results: Forty-seven germline mutations in 11 DDR genes were identified in 46 patients of the total cohort (8.3%). BRCA2 (4.1%) and ATM (2.0%) were the most frequently mutated genes. There was no difference in DDRg mutation frequency between mPC patients of European ancestry and those of African origin. Germline mutations of BRCA2 were associated with a positive family history of breast cancer (p=0.02). The mean age at metastatic stage (59.7 vs 67.0; p = 0.0003) and the mean age at death (65.2 vs 73.9; p = 0.0003) were significantly earlier for carriers of BRCA2 mutation than for non-carriers. Moreover, Cox model showed that BRCA2 positive status was statistically associated with poorer survival (Hazard ratio: 0.29; 95%Confidence interval 0.18-0.48; p<0.0001).

Conclusion:

We showed that, in France, BRCA2 and ATM are the main predisposing DDR genes in mPC patients, with a particular aggressiveness for BRCA2 leading to early metastatic stage and death.

INTRODUCTION

Prostate cancer (PC) is the most frequent malignancy in men in Northern America and Europe. [START_REF] Ferlay | Global Cancer Observatory: Cancer Today[END_REF] Recognized risk factors for PC are age, familial history of PC or other cancers, and ethnicity with a higher risk of PC for men of African ancestry compared to European one. [START_REF] Saunders | Identification of Germline Genetic Variants that Increase Prostate Cancer Risk and Influence Development of Aggressive Disease[END_REF] Numerous genetic variants have also been associated with PC susceptibility, but only few rare predisposition genes have been identified to date. [START_REF] Saunders | Identification of Germline Genetic Variants that Increase Prostate Cancer Risk and Influence Development of Aggressive Disease[END_REF] Among them, inherited mutations of DNA damage repair genes (DDRg) seemed to be the most frequent ones, and have been detected in 4.6 % of localized PC patients. [START_REF] Pritchard | Inherited DNA-Repair Gene Mutations in Men with Metastatic Prostate Cancer[END_REF] The frequency of these germline mutations was even higher among the patients with an aggressive form of the disease [START_REF] Wu | Rare Germline Pathogenic Mutations of DNA Repair Genes Are Most Strongly Associated with Grade Group 5 Prostate Cancer[END_REF] , notably in metastatic prostate cancer (mPC) ones. Indeed, Pritchard et al. [START_REF] Pritchard | Inherited DNA-Repair Gene Mutations in Men with Metastatic Prostate Cancer[END_REF] reported that this frequency reached 11.8% among 692 mPC patients. Since then, numerous studies were performed on mPC and metastatic castration-resistant PC (mCRPC) patients, and showed that germline mutation frequency of these genes ranged from 7.3 to 20.1%. [START_REF] Hart | Determining the frequency of pathogenic germline variants from exome sequencing in patients with castrate-resistant prostate cancer[END_REF][START_REF] Annala | Treatment Outcomes and Tumor Loss of Heterozygosity in Germline DNA Repair-deficient Prostate Cancer[END_REF][START_REF] Antonarakis | Germline DNA-repair Gene Mutations and Outcomes in Men with Metastatic Castration-resistant Prostate Cancer Receiving First-line Abiraterone and Enzalutamide[END_REF][START_REF] Castro | PROREPAIR-B: A Prospective Cohort Study of the Impact of Germline DNA Repair Mutations on the Outcomes of Patients With Metastatic Castration-Resistant Prostate Cancer[END_REF][START_REF] Yadav | Contribution of Inherited DNA-Repair Gene Mutations to Hormone-Sensitive and Castrate-Resistant Metastatic Prostate Cancer and Implications for Clinical Outcome[END_REF][START_REF] Wei | Prognostic Value of Germline DNA Repair Gene Mutations in De Novo Metastatic and Castration-Sensitive Prostate Cancer[END_REF][START_REF] Boyle | Pathogenic Germline DNA Repair Gene and HOXB13 Mutations in Men With Metastatic Prostate Cancer[END_REF][START_REF] Ledet | Comparison of germline mutations in African American and Caucasian men with metastatic prostate cancer[END_REF] In addition to these germline mutations, somatic DDRg mutations have been identified in tumoral tissues from PC patients, and approximately 20-25% of mPC tumors harbored mutations in DDRg. [START_REF] Teply | Treatment strategies for DNA repair-deficient prostate cancer[END_REF] In mPC patients, the response to treatment has been shown to vary depending on whether or not a DDRg mutation is present. [START_REF] Chung | Clinical implications of genomic evaluations for prostate cancer risk stratification, screening, and treatment: a narrative review[END_REF] Mutation carriers better responded to treatment with Poly(ADP) ribose polymerase (PARP) inhibitors [START_REF] Castro | Targeting DNA Repair: The Role of PARP Inhibition in the Treatment of Castration-Resistant Prostate Cancer[END_REF] , carboplatin-based chemotherapy [START_REF] Pomerantz | The association between germline BRCA2 variants and sensitivity to platinum-based chemotherapy among men with metastatic prostate cancer[END_REF] , first line of next-generation hormonal therapy [START_REF] Antonarakis | Germline DNA-repair Gene Mutations and Outcomes in Men with Metastatic Castration-resistant Prostate Cancer Receiving First-line Abiraterone and Enzalutamide[END_REF] and radium-223 therapy [START_REF] Velho | Efficacy of Radium-223 in Bone-metastatic Castrationresistant Prostate Cancer with and Without Homologous Repair Gene Defects[END_REF] , whereas they showed a lower response rate to taxane [START_REF] Jäger | Mutations in BRCA2 and taxane resistance in prostate cancer[END_REF] , compared to non-carriers. Germline and tumoral mutations in DDRg are then recognized as new biomarkers for the management of mPC [START_REF] Marshall | Therapeutic targeting of the DNA damage response in prostate cancer[END_REF] , and germline genetic testing for a panel of DDRg that includes at least BRCA1, BRCA2, ATM, PALB2, CHEK2, MLH1, MSH6, MSH2 and PMS2 is now recommended for all men with high-risk or metastatic PC. [START_REF]National Comprehensive Cancer Network Clinical Practice Guidelines in Oncology (NCCN Guidelines®)[END_REF] In order to better define and personalize this genetic testing, it is needed to define the frequency of these DDRg mutations among mPC patients of different geographic and ethnic origins. For example, in their recent study, Darst et al. [START_REF] Darst | Germline Sequencing DNA Repair Genes in 5545 Men With Aggressive and Nonaggressive Prostate Cancer[END_REF] reported that BRCA2 and PALB2 had the most statistically significant gene-based associations among 155 DDRg sequenced in germline DNA from 5 545 European-ancestry men (2 775 nonaggressive and 2 770 aggressive PC cases, including 467 mPC patients). However, until now, most of the large studies on DDRg germline mutation were performed on mPC patients from North America [START_REF] Pritchard | Inherited DNA-Repair Gene Mutations in Men with Metastatic Prostate Cancer[END_REF][START_REF] Annala | Treatment Outcomes and Tumor Loss of Heterozygosity in Germline DNA Repair-deficient Prostate Cancer[END_REF][START_REF] Yadav | Contribution of Inherited DNA-Repair Gene Mutations to Hormone-Sensitive and Castrate-Resistant Metastatic Prostate Cancer and Implications for Clinical Outcome[END_REF][START_REF] Boyle | Pathogenic Germline DNA Repair Gene and HOXB13 Mutations in Men With Metastatic Prostate Cancer[END_REF][START_REF] Ledet | Comparison of germline mutations in African American and Caucasian men with metastatic prostate cancer[END_REF][START_REF] Darst | Germline Sequencing DNA Repair Genes in 5545 Men With Aggressive and Nonaggressive Prostate Cancer[END_REF] , and few included mPC cases from North and South of Europe [START_REF] Pritchard | Inherited DNA-Repair Gene Mutations in Men with Metastatic Prostate Cancer[END_REF][START_REF] Castro | PROREPAIR-B: A Prospective Cohort Study of the Impact of Germline DNA Repair Mutations on the Outcomes of Patients With Metastatic Castration-Resistant Prostate Cancer[END_REF][START_REF] Darst | Germline Sequencing DNA Repair Genes in 5545 Men With Aggressive and Nonaggressive Prostate Cancer[END_REF] . Moreover, only one study including 188 African American patients compared the frequency of DDR gene germline mutations among mPC cases of African and European origins. [START_REF] Ledet | Comparison of germline mutations in African American and Caucasian men with metastatic prostate cancer[END_REF] In order to contribute to the inventory of the DDRg germline mutational profiles, according to the geographic origin and the ancestry, we report results of the germline sequencing of 21 DDRg on a new population of 557 mPC French patients, with a European or an African ancestry.

MATERIALS AND METHODS

The patients selected for this study were recruited in the PROGENE study (FWA00006032), based on mPC disease status. They all provided written informed consent in order to participate in this study that complied with the Declaration of Helsinki and was approved by the CCP Ile de France IV (IRB: 00003835). Family history of prostate or breast cancer was defined as positive if at least one firstdegree relative of the patient had this malignancy. Ancestry was based on self-report and on skin phenotype selected by the clinician in the medical questionnaire. Patients of Asian ancestry (N = 7) or from North African origin (N=18), 2 patients born in India and 1 originating from Southern America were classified as "other", and were excluded from comparison between patients from African and European ancestries.

Germline DNA was extracted from saliva or blood using standard protocol. A custom targeted DNA panel was designed for full exon and splice site coverage of 21 DNA repair genes (List in Table 2).

Sequencing libraries were generated from germline DNA using SeqCap EZ Prime kit (Roche Diagnostics) and read using an Illumina NextSeq 500 platform (Illumina, San Diego, CA). The mean coverage was X150 for each sample. Bioinformatical analysis was done using the module SeqNext (Seqpilot). Sequencing files were annotated using ClinVar and VarSome version 11.2 records. Variants reported as pathogenic or likely pathogenic (Classification of variants according to ACMG/AMP recommendations and/or according to national and international databases) were considered positive.

T-test was used for comparing mean ages at metastatic stage or at death between carriers or noncarriers of germline mutation. We used logistic regression to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between carrier status of a germline mutation and family history of cancer (either breast or prostate), adjusting for age at diagnosis and ancestry in the total population, or adjusting for age at diagnosis only when considering patients from a specific ancestry. Logistic regression was also used to analyze association between germline mutation carrier status and hormone sensitive/castration resistance status, with adjustment for ancestry. Adjustment for ancestry was performed by considering 5 classes: European, African, Asian, North African and Others. Comparisons between carriers and non-carriers of a BRCA2 mutation were performed by first considering patients carrying non-BRCA2 DDRg mutation as non-carriers, and a secondly, by excluding these patients with another DDRg mutation. Cox proportional hazards analysis was carried out for survival analyses, using Schoenfeld's global test to verify assumption of proportionality. All tests were two-sided, and p < 05 was considered to indicate statistical significance.

RESULTS

The characteristics of the patients are presented in Table 1. The mean age (SD) at the metastatic stage was 66.5 years old (9.3). Ancestry distribution was 79.9% European, 15.1% African, and 5.0% other. Typology of metastases was classified as only in distant lymph nodes (33.9%), bone metastasis (52.8%) and visceral metastasis (13.3%). 270 (48.5%) cancers were castration resistant at the inclusion. 146 (26.2%) patients had a positive family history of PC, and 43 (7.7%) had a personal history of cancer other than prostate.

Forty-seven germline mutations in 11 DDR genes were identified in 46 patients of the total cohort (8.3%). BRCA2 (4.1%) and ATM (2.0%) were the most frequently mutated genes in our series of mPC cases (Table 2, Supplementary table 1). The frequency of identified mutations was less than 1% for the other 9 genes (BARD1, BRCA1, BRIP1, CHEK2, MRE11A, MSH2, MSH6, NBN and PMS2). All mutations were unique except three ones: one ATM mutation (c.2921+1G>A) that was found in two patients, one of European ancestry and one of African ancestry, and two BRCA2 mutations (c.5946delT and del ex12-ex13) that were observed in two metastatic cases of European ancestry (Supplementary Table 1). The patient who carried a MSH2 germline mutation was diagnosed with thyroid and colon cancers at the same age as prostate cancer.

There was no difference in DDRg mutation frequency between mPC patients of European ancestry and those of African origin (8.3% vs 7.1%; p =0.93). The frequencies of BRCA2 and ATM mutations were thus 3.6% and 1.2%, for patients of African ancestry, and 4.0% and 2.2% for European men (Supplementary Table 2). Similarly, the frequency of mutations was not statistically different between hormone-sensitive and castrate-resistant mPC patients (6.0% vs 10.4%; p = 0.06).

Germline mutations of BRCA2 were associated with a positive family history of breast cancer (10.0% of the BRCA2 mutation carriers had at least one first degree relative with breast cancer vs 3.3% for the non-carriers; p=0.02; Supplementary table 3). This association was restricted to carriers of BRCA2 germline mutations, and wasn't observed when we compared mPC patients with or without DDRg mutations (12.9% of the mutation carriers had at least one first degree relative with breast cancer vs 7.6% for the non-carriers; p=0.17). Similarly, a higher proportion of BRCA2 germline mutation carriers had a positive family history of PC (7.5% vs 2.9% for the non-carriers), but the difference wasn't statistically significant.

The mean age at metastatic stage (62.1 vs 67.0 years old; p = 0.0009) and the mean age at death (68.3 vs 73.9 years old; p = 0.0022) were significantly earlier for carriers of DDRg mutation than for non-carriers (Supplementary table 4). These results were mainly driven by BRCA2 mutation carriers. Indeed, the mean age at metastatic stage (59.7 vs 66.8; p = 0.0004) and the mean age at death (65.2 vs 73.8; p = 0.0003) were even significantly earlier for carriers of BRCA2 mutation than for noncarriers. Similar results were found when we excluded the nonBRCA2 DDRg mutation carriers from the analyses, with mean age at metastatic stage (59.7 vs 67.0; p = 0.0003) and mean age at death (65.2 vs 73.9; p = 0.0003) for carriers of BRCA2 mutation versus non-carriers. In agreement with those results, when we excluded the BRCA2 mutation carriers, the mean age at metastatic stage (64.5 vs 67.0; p = 0.22) and the mean age at death (72.3 vs 73.9; p = 0.57) didn't remain significantly different between carriers of DDRg mutation and non-carriers. When we stratified the patients by ancestry, the same results were found among patients of European ancestry, but no significant difference in mean age at metastatic stage or death was observed among carriers or not of DDRg mutation from African ancestry (Supplementary table 4). Moreover, Cox model showed that BRCA2 positive status was statistically associated with poorer survival in the total cohort of mPC patients (Hazard ratio: 0.29; 95% Confidence interval: 0.18-0.48; p<0.0001), but that positive status for overall DDRg mutations, with or without BRCA2, was not (p = 0.13 and p = 0.92, respectively).

DISCUSSION

In our study, BRCA2 was the most frequently mutated gene in mPC patients of either European or African origin (4.0% and 3.6%, respectively). Similarly, the frequency of germline BRCA2 mutation was the highest among analysed DDR genes in almost all reported studies on mPC, mCRPC and lethal PC, [START_REF] Pritchard | Inherited DNA-Repair Gene Mutations in Men with Metastatic Prostate Cancer[END_REF][START_REF] Annala | Treatment Outcomes and Tumor Loss of Heterozygosity in Germline DNA Repair-deficient Prostate Cancer[END_REF][START_REF] Castro | PROREPAIR-B: A Prospective Cohort Study of the Impact of Germline DNA Repair Mutations on the Outcomes of Patients With Metastatic Castration-Resistant Prostate Cancer[END_REF][START_REF] Yadav | Contribution of Inherited DNA-Repair Gene Mutations to Hormone-Sensitive and Castrate-Resistant Metastatic Prostate Cancer and Implications for Clinical Outcome[END_REF][START_REF] Boyle | Pathogenic Germline DNA Repair Gene and HOXB13 Mutations in Men With Metastatic Prostate Cancer[END_REF][START_REF] Ledet | Comparison of germline mutations in African American and Caucasian men with metastatic prostate cancer[END_REF][START_REF] Matejcic | Pathogenic Variants in Cancer Predisposition Genes and Prostate Cancer Risk in Men of African Ancestry[END_REF][START_REF] Plym | DNA Repair Pathways and Their Association With Lethal Prostate Cancer in African American and European American Men[END_REF] with frequency ranging from 1.9% to 5.4% (Table 2). Accordingly, reviews of the literature concluded that the DDR gene with the highest prevalence of germline mutation was BRCA2 in those patients. [START_REF] Lang | A systematic review of the prevalence of DNA damage response gene mutations in prostate cancer[END_REF][START_REF] Shore | Systematic Literature Review of the Epidemiology of Advanced Prostate Cancer and Associated Homologous Recombination Repair Gene Alterations[END_REF] Only, Darst et al. [START_REF] Darst | Germline Sequencing DNA Repair Genes in 5545 Men With Aggressive and Nonaggressive Prostate Cancer[END_REF] reported a frequency of BRCA2 mutation that wasn't the highest one, but this study included Finnish patients who carried no BRCA2 mutation. This is in agreement with previous results from Ikonen et al. [START_REF] Ikonen | BRCA1 and BRCA2 mutations have no major role in predisposition to prostate cancer in Finland[END_REF] who screened 444 unselected PC patients for six BRCA2 recurrent germline mutations in Finland, and found no mutation. They concluded that BRCA2 mutations have no major role in predisposition to prostate cancer in Finland.

ATM was the second most frequently mutated DDR gene (2.0%) in our total cohort of mPC patients. Most of the other large studies on mPC and mCRPC had reported a similar frequency of ATM germline mutation, ranging from 1.6 to 2.1% (Table 2). However, two studies, performed on mPC [START_REF] Boyle | Pathogenic Germline DNA Repair Gene and HOXB13 Mutations in Men With Metastatic Prostate Cancer[END_REF] and mCRPC [START_REF] Annala | Treatment Outcomes and Tumor Loss of Heterozygosity in Germline DNA Repair-deficient Prostate Cancer[END_REF] found a less frequent rate of ATM mutations (0.3%). This could be due to the smaller size of their cohort, the origin of the patients, or to a difference in interpretation of ATM variants as it has been shown that the classification of its variants might be discordant from one laboratory to another. [START_REF] Feliubadaló | A Collaborative Effort to Define Classification Criteria for ATM Variants in Hereditary Cancer Patients[END_REF] Two other genes, CHEK2 and PALB2, had different mutation frequency between studies (Table 2).

Concerning CHEK2, it could be explained by the higher or lesser frequency of the c.1100delC Eastern European founder mutation observed according studies. Indeed, in our study and the one by Castro et al. [START_REF] Castro | PROREPAIR-B: A Prospective Cohort Study of the Impact of Germline DNA Repair Mutations on the Outcomes of Patients With Metastatic Castration-Resistant Prostate Cancer[END_REF] , this mutation was rare, whereas it accounted for 50% of CHEK2 mutations in the study by Pritchard et al. [START_REF] Pritchard | Inherited DNA-Repair Gene Mutations in Men with Metastatic Prostate Cancer[END_REF] . Darst et al. [START_REF] Darst | Germline Sequencing DNA Repair Genes in 5545 Men With Aggressive and Nonaggressive Prostate Cancer[END_REF] found a higher rate of PALB2 mutations (1.1%), compared to the other large studies (Table 2), but even in this study, its frequency varied according the geographical origin of the populations, ranging from 2.6% in US patients to 0.0% in Australian ones.

We observed no difference in DDRg mutation frequency between patients of African and European origins. The same observation was made by Ledet et al. [START_REF] Ledet | Comparison of germline mutations in African American and Caucasian men with metastatic prostate cancer[END_REF] who didn't find difference in mutation frequency between African and European American patients when they considered all the DDRg they had analysed. Particularly, the frequency of BRCA2 wasn't different between patients of African and European origins in the two studies. To counteract one of the limitations of our study which is the small number of patients of African ancestry, we combined our data with those of Ledet et al. [START_REF] Ledet | Comparison of germline mutations in African American and Caucasian men with metastatic prostate cancer[END_REF] and, again, found comparable BRCA2 mutation frequency between patients of both origins (4.7% and 4.1%, among patients of European and African ancestry, respectively; p=0.75). However, both studies, and even their combination, are underpowered to draw conclusion on population differences, and studies including, in particular, a larger number of metastatic PC patients of African ancestry are needed to address this point. Another limitation of these two studies is that ancestry was self-reported.

Conversely, we found an association between the presence of BRCA2 germline mutation and a family history of breast cancer in this population of metastatic PC patients, which is in agreement with our previous observation of a higher frequency of this mutation among PC patients, both metastatic or not, who have a family history of breast cancer. [START_REF] Leon | Bayesian predictive model to assess BRCA2 mutational status according to clinical history: Early onset, metastatic phenotype or family history of breast/ovary cancer[END_REF] This association was restricted to BRCA2, and wasn't observed for all DDR genes. When we stratified the patients according ancestry, the frequency of patients with a positive family history of breast cancer was higher among the BRCA2 mutation carriers (9.2%) than the non-carriers (3.2%) from European ancestry, even if the difference was not statistically significant (p=0.06). This tendency wasn't observed among the patients of African ancestry (Supplementary table 3). In their study, Ledet et al. [START_REF] Ledet | Comparison of germline mutations in African American and Caucasian men with metastatic prostate cancer[END_REF] reported that family history of breast cancer predicted the existence of germline mutations in DDR genes in European but not African-American patients. The absence of association observed among mPC patients of African ancestry could be due to the smaller number of these patients, compared to the European ones, in the two studies or may reflect the specific importance of familial history of cancers among the European patients. However, these associations would need to be validated in larger populations as our results related to familial information self-reported by the patients, that weren't clinically confirmed.

Finally, germline mutations of DDR genes were associated with both an early age at metastatic disease and an early age at death in our study. These associations were mostly driven by BRCA2 and indeed, patients with a germline mutation of BRCA2 had a poorer overall survival. This is in agreement with previous studies performed on BRCA2 which showed that mutation of this DDR gene is associated with a poor outcome [START_REF] Castro | Germline BRCA mutations are associated with higher risk of nodal involvement, distant metastasis, and poor survival outcomes in prostate cancer[END_REF][START_REF] Castro | Effect of BRCA Mutations on Metastatic Relapse and Causespecific Survival After Radical Treatment for Localised Prostate Cancer[END_REF] and worse prognosis. [START_REF] Wei | Prognostic Value of Germline DNA Repair Gene Mutations in De Novo Metastatic and Castration-Sensitive Prostate Cancer[END_REF] However, this seemed not to be true for all DNA damage repair genes, and studies, including not only BRCA2 but also other DRR genes, didn't find difference in response to treatment or overall survival in patients who carried or not mutations of those genes. [START_REF] Yadav | Contribution of Inherited DNA-Repair Gene Mutations to Hormone-Sensitive and Castrate-Resistant Metastatic Prostate Cancer and Implications for Clinical Outcome[END_REF][START_REF] Mateo | Clinical Outcome of Prostate Cancer Patients with Germline DNA Repair Mutations: Retrospective Analysis from an International Study[END_REF][START_REF] Carlson | Impact of mutations in homologous recombination repair genes on treatment outcomes for metastatic castration resistant prostate cancer[END_REF] Large studies, with sufficient statistical power, are therefore needed to evaluate the impact of mutations of each DDR gene on the clinical outcomes of mPC patients.

CONCLUSIONS

Our study showed that in Western Europe (France), PALB2 and CHEK2 germline mutations remain rare, while those of BRCA2 and ATM are the main predisposing ones in mPC patients, with a particular aggressiveness for BRCA2 leading to early metastatic stage and death. The small disparities in the prevalence of BRCA2 mutations between patients of European and African ancestry suggest that this gene remains the most targetable event based on germline analysis in mPC patients from the French population regardless of their ancestry.

Table 1 :

 1 Characteristics of the patients

	Ancestry	All	European	African	Other
	Number (%)	557 (100%)	445 (79.9%)	84 (15.1%)	28 (5.0%)
	Mean age at diagnosis (years) ± SD	64.6 ± 9.0	64.0 ± 8.8	67.1 ± 10.9	67.1 ± 11.0
	Mean age at metastatic status (years) ± SD	66.5 ± 9.3	66.2 ± 9.0	68.2 ± 10.7	67.6 ± 10.4
	Castration-resistant
	Number (%)	270 (48.9%)	217 (49.2%)	43 (51.8%)	10 (35.7%)
	Mean age at CRPC (years) ± SD	68.2 ± 9.8	67.7 ± 9.5	70.7 ± 10.3	67.4 ± 13.8
	Deceased
	Number (%)	351 (63.0%)	296 (66.5%)	46 (54.8%)	9 (32.1%)
	Mean age at death (years) ± SD	73.3 ± 10.3	73.1 ± 10.0	75.1 ± 11.7	71.8 ± 13.4
	Deceased due to prostate cancer
	Number (%)	135 (24.3%)	100 (22.5%)	27 (32.1%)	8 (28.6%)
	Mean Age at death (years) ± SD	71.3 ± 10.4	71.3 ± 10.2	71.5 ± 10.6	71.0 ± 14.1
	Typology of metastasis
	Distant lymph nodes	189 (33.9%)	153 (34.4%)	25 (29.8%)	11 (39.3%)
	Bone metastasis	294 (52.8%)	236 (53.0%)	48 (57.1%)	10 (35.7%)
	Visceral metastasis	74 (13.3%)	56 (12.6%)	11 (13.1%)	7 (25.0%)
	Positive family history of prostate cancer	146 (26.2%)	138 (31.0%)	5 (6.0%)	3 (10.7%)
	Positive family history of breast cancer	70 (12.6%)	65 (14.6%)	2 (2.4%)	3 (10.7%)
	Personal history of cancer (other than	43 (7.7%)	34 (7.6%)	6 (7.1%)	3 (10.7%)
	prostate)

Table 2 :

 2 Number and frequency of germline mutation carriers in DNA repair genes found among patients with metastatic prostate cancer.

			This	Darst et	Pritchard	Yadav et	Boyle et	Ledet et	Castro et	Annala et
			study	al.	et al.	al.	al.	al.	al.	al.
	Chr	Gene	mPC	mPC	mPC	mPC	mPC	mPC	mCRPC	mCRPC
			(N=557) (N=467) (N=692) (N = 704) (N = 317) (N = 867) (N = 419) (N = 319)
	11	ATM	11 (2.0%) 10 (2.1%) 11 (1.6%) 13 (1.8%) 1 (0.3%) 9 (1.1%) 8 (1.9%) 1 (0.3%)
	2	BARD1	1 (0.2%)	-	0	1 (0.1%) 1 (0.3%)	-	0	-
	15	BLM	0	1 (0.2%)	-	-	2 (0.6%) 2 (0.4%)	-	-
	17	BRCA1	1 (0.2%) 2 (0.4%) 6 (0.9%) 3 (0.4%) 3 (0.9%) 7 (0.8%) 4 (1.0%) 1 (0.3%)
	13	BRCA2	23 (4.1%) 9 (1.9%) 37 (5.4%) 14 (2.0%) 6 (1.9%) 42 (4.9%) 14 (3.3%) 16 (5.0%)
	17	BRIP1	1 (0.2%) 1 (0.2%) 1 (0.2%) 3 (0.4%)	0	-	0	-
	16	CDH1	0	-	-	-	0	-	-	-
	22	CHEK2	4 (0.7%) 10 (2.1%) 10 (1.9%) 14 (2.0%) 5 (1.6%) 16 (1.9%) 2 (0.5%)	-
	4	FAM175A	0	-	1 (0.2%)	-	0	-	0	-
	3	MLH1	0	6 (1.3%)	0	0	0	-	0	0
	11	MRE11A	1 (0.2%)	0	1 (0.1%)	0	0	-	2 (0.5%)	-
	2	MSH2	1 (0.2%)	-	1 (0.1%)	0	0	1 (0.1%) 1 (0.2%)	0
	2	MSH6	1 (0.2%) 2 (0.4%) 1 (0.1%)	0	0	4 (0.5%)	0	0
	8	NBN	2 (0.4%) 3 (0.6%) 2 (0.3%) 2 (0.3%)	0	4 (0.5%)	0	-
	16	PALB2	0	5 (1.1%) 3 (0.4%) 2 (0.3%)	0	4 (0.5%)	0	2 (0.6%)
	7	PMS2	1 (0.2%)	-	2 (0.3%)	-	1 (0.3%) 3 (0.4%)	0	-
	5	RAD50	0	11 (2.4%)	-	3 (0.4%)	-	-	0	-
	17	RAD51C	0	-	1 (0.1%)	0	0	-	0	0
	17	RAD51D	0	-	3 (0.4%)	0	0	2 (0.2%)	0	0
	17	TP53	0	1 (0.2%)	-	3 (0.4%)	0	4 (0.5%)	-	1 (0.3%)
	7	XRCC2	0	-	0	0	0	-	0	-
		mPC: metastatic prostate cancer; mCRPC: metastatic castration-resistant prostate cancer

ACKNOWLEDGEMENTS

We thank the patients for their participation in this study, and Cecile Gaffory and Valerie Ondet for their technical assistance.

Funding statement This work was supported by grant from the Fonds de Dotation pour l'Innovation dans la prise en charge du Cancer de Prostate (FDCP) and from the French National Cancer Institute (INCa) [grant number: 2017-016].

Conflict of interest disclosure

The authors declare no competing interests.

Ethics approval statement The PROGENE study was approved by the CCP Ile de France IV (IRB: 00003835). The study was performed in accordance with the Declaration of Helsinki.

Data availability statement

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.