Nair Olguín

Marie Champomier-Vergès

Patricia Anglade

Fabienne Baraige

Ricardo Cordero-Otero

Albert Bordons

Monique Zagorec

Cristina Reguant
email: cristina.reguant@urv.cat

Transcriptomic and proteomic analysis of Oenococcus oeni PSU-1 response to ethanol shock

Keywords:

The correct development of malolactic fermentation depends on the capacity of Oenococcus oeni to survive under harsh wine conditions. The presence of ethanol is one of the most stressful factors affecting O. oeni performance. In this study, the effect of ethanol addition (12% vol/vol) on O. oeni PSU-1 has been evaluated using a transcriptomic and proteomic approach. Transcriptomic analysis revealed that the main functional categories of the genes affected by ethanol were metabolite transport and cell wall and membrane biogenesis. It was also observed that some genes were overexpressed in response to ethanol stress (for example, the heat shock protein Hsp20 and a dipeptidase). Proteomic analysis showed that several proteins are affected by the presence of ethanol. Functions related to protein synthesis and stability are the main target of ethanol damage. In some cases the decrease in protein concentration could be due to the relocation of cytosolic proteins in the membrane, as a protective mechanism.

The omic approach used to study the response of O. oeni to ethanol highlights the importance of the cell membrane in the global stress response and opens the door to future studies on this issue.

Introduction

Oenococcus oeni is the most important of the lactic acid bacteria involved in malolactic fermentation (MLF) in wine. However, bacterial growth and MLF are not always successful due to the harsh environmental conditions of wine [START_REF] Davis | Practical implications of malolactic fermentation: a review[END_REF];

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3 [START_REF] Malherbe | Understanding problem fermentations -A review[END_REF]. Several studies have been made of how O. oeni responds under stress conditions such as pH, temperature, sulfite concentration and ethanol content [START_REF] Versari | Leuconostoc oenos and malolactic fermentation in wine: a review[END_REF]. However, ethanol seems to be one of the parameters that most limits O. oeni survival in wine. Therefore, if control over MLF in the wine industry is to be improved, it is essential to understand the mechanisms involved in ethanol stress and tolerance in O. oeni.

The toxicity of ethanol is generally attributed to its interaction with membranes at the aqueous interface, resulting in perturbed membrane structure and function [START_REF] Weber | Adaptation mechanisms of microorganisms to the toxic effects of organic solvents on membranes[END_REF]Beney and Gervais, 2001). Studies on O. oeni have shown that exposing cells to ethanol increases the permeability of the cytoplasmic membrane and enhances passive proton influx and the concomitant loss of intracellular material (Da [START_REF] Da Silveira | Membrane fluidity adjustments in ethanol-stressed Oenococcus oeni cells[END_REF]. The permeability of the membrane to protons dissipates the proton motive force and affects ATP synthesis, which is no longer available for growth (Capucho and San Romão, 1994;[START_REF] Salema | The proton motive force generated in Leuconostoc oenos by L-malate fermentation[END_REF][START_REF] Weber | Adaptation mechanisms of microorganisms to the toxic effects of organic solvents on membranes[END_REF]. This may explain the high mortality when O. oeni cells were directly inoculated into a wine-like medium supplemented with 12-16% ethanol [START_REF] Da Silveira | Membrane fluidity adjustments in ethanol-stressed Oenococcus oeni cells[END_REF][START_REF] Chu-Ky | Combined cold, acid and ethanol shocks in Oenococcus oeni: effects on membrane fluidity and cell viability[END_REF].

Nonetheless, in concentrations up to 12%, ethanol has no significant effect on malolactic activity, but, according to Capucho and San Romão (1994), it does strongly inhibit cell growth. These authors suggest that the mechanisms regulating cell growth are more sensitive to ethanol than the malolactic enzyme itself. On the other hand, we have also found that a number of O. oeni citrate pathway genes are over-expressed in the presence of ethanol, suggesting that the citrate metabolism takes part in the response to this stress [START_REF] Olguín | Influence of ethanol and pH on the gene expression of the citrate pathway in Oenococcus oeni[END_REF].

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4 Two-dimensional gel electrophoresis (2-DE) has provided invaluable information on the adaptive response of microorganisms to changes in external conditions [START_REF] Champomier-Vergès | Lactic acid bacteria and proteomics: current knowledge and perspectives[END_REF]. For instance, Silveira and co-workers (2004) found that ethanol triggers alterations in the protein patterns of O. oeni cells that are directly stressed with 12% ethanol for 1 hour and cells pre-adapted in 8% ethanol. It has also been shown that cell cultures acclimated with 10% ethanol survived better in wine, probably due to the differential expression of certain proteins (Cecconi et al., 2009). Functional analysis of gene expression using comparative transcriptomics is also providing insight into stress responses and regulation mechanisms in lactic acid bacteria (LAB). Preliminary microarray analysis of the Lactobacillus plantarum response to several stress conditions revealed unanticipated stress response profiles that correlate specifically with lactate-and pH-induced stress [START_REF] Siezen | Genome data mining of lactic acid bacteria: the impact of bioinformatics[END_REF][START_REF] Pieterse | Unravelling the multiple effects of lactic acid stress on Lactobacillus plantarum by transcription profiling[END_REF]. However, no current studies use microarray analysis of O. oeni.

The aim of the present study was to evaluate the cell response of O. oeni PSU-1 after 12% ethanol shock using transcriptomic and proteomic approaches. In order to study the effect of ethanol alone on O. oeni cells, the assays were performed in rich medium (MRS) at pH 5.0 [START_REF] De Man | A medium for the cultivation of lactobacilli[END_REF] with the addition of ethanol.

Materials and Methods

Growth conditions

O. oeni PSU-1 was cultured at 30ºC in a two-liter flask containing MRS broth medium supplemented with L-malic acid (4 g l -1 ) and fructose (5 g l -1 ) at pH 5.0. When cultures reached the late exponential phase (OD 600nm ≈ 1) they were divided and put into two sterile flasks. Immediately, 12% (v/v) of ethanol was added to one flask and 12% (v/v) of water was added to the other (control). The latter was used as a control assay to

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evaluate the possible effect of culture dilution on the proteome. At this moment, the quantities of L-malic acid and fructose remaining in the medium were 0.03 g l -1 and 0.28 g l -1 respectively. The pH of the medium was 4.35. Both flasks were incubated at 28ºC. All assays were performed in triplicate using independent cultures and the growth was monitored by counting colonies on plates of MRS medium [START_REF] De Man | A medium for the cultivation of lactobacilli[END_REF], supplemented as described above. Samples were taken at time zero just before water/ethanol was added, and then at one, three and five hours after addition.

L-malic acid and fructose contents were measured using Boehringer enzymatic kits (Mannheim) on culture supernatants stored at -20ºC until use. pH measurements were taken using a GLP31 pH-meter (Crison Instruments, Barcelona, Spain).

Transcriptome analysis

O. oeni cells were harvested by centrifugation, frozen in liquid nitrogen and kept at -80ºC until RNA extraction. Total RNA extractions were performed using the Roche RNeasy kit following the manufacturer's instructions (Mannheim, Germany). RNA concentrations were calculated by measuring absorbance at 260 nm using a NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific SL, Alcobendas, Spain). cDNA was subsequently prepared. Samples were hybridized to the arrays for 18 hours at 42ºC. After washing, the arrays were scanned in a Roche Nimblegen MS 200 scanner. Raw data files (Pair and XYS files) were obtained from images using Nimblescan v2.6 software (Roche Nimblegen). Normalized gene expression values were obtained with Nimblescan software using the robust multichip average (RMA) algorithm as described by Irizarry et al. (2003a;2003b). Data univariate (ANOVA) analyses of transcriptomic data were conducted using SPSS version 17.0 (SPSS Inc., Chicago, IL, USA). Variable means showing statistical significance were compared using Bonferroni post-test comparisons at a significance level of 0.05, after testing the homogeneity of variance assumption between the various groups. The results were submitted to GEO (Gene Expression Omnibus Database, NCBI) under accession number GSE62036.

Arrays (090324_Oenococcus

Array validation by real-time qPCR

Several genes were selected by real-time qPCR for validation of the microarray data. The primers used for these analyses are shown in Table 1. Genes OEOE_0289, OEOE_0422 and OEOE_0665 were selected because they have been studied by qPCR before (Beltramo et al. 2006[START_REF] Olguín | Influence of ethanol and pH on the gene expression of the citrate pathway in Oenococcus oeni[END_REF][START_REF] Olguín | Multigenic expression analysis as an approach to understanding the behaviour of Oenococcus oeni in wine-like conditions[END_REF]. Genes OEOE_1325 and OEOE_1565 were selected due to their involvement in malolactic fermentation. The other genes (OEOE_0258, OEOE_0394, OEOE_0411, OEOE_1325, OEOE_0008, OEOE_0238 and OEOE_1290) were randomly selected with the sole objective of validating the methodology. Real-time qPCR was performed on the same RNA samples used for the microarray analysis. Reverse transcription and real-time qPCR were performed as previously described [START_REF] Olguín | Multigenic expression analysis as an approach to understanding the behaviour of Oenococcus oeni in wine-like conditions[END_REF]. Primers were designed to be about 18-23 bases long, to contain over 50% G/C and to have a melting bp. Clone Manager Professional Suite software was used to select primer sequences and analyze secondary structures and dimer formation. In this work four genes were assayed as internal controls for qPCR -ldhD, dpoIII, gyrA and gyrB -using the primers described by [START_REF] Desroche | Determination of an internal control to apply reverse transcription quantitative PCR to study stress response in the lactic acid bacterium Oenococcus oeni[END_REF]Constantini et al. (2011). Of these, the ldhD gene, coding for lactate dehydrogenase, was the one that showed the least variation under the experimental conditions used (data not shown). For this reason ldhD was used as the internal control. Real-time PCR was performed in 25 µl final volume containing 5 µl of diluted cDNA, 1.5 µl of each primer at 5 µM, 4.5 µl of RNAse free water and 12.5 µl of SYBR Green Master Mix (Applied Biosystems, Foster City, CA, USA). Amplifications were carried out using a Real Time PCR System 7300 (Applied Biosystems). The threshold value used in this study was automatically determined by the instrument. Results were analyzed using the comparative critical threshold (∆∆CT) method, in which the amount of target RNA was adjusted to a reference (internal target RNA) as previously described [START_REF] Livak | Analysis of relative gene expression data using real-time quantitative PCR and the 2-∆∆CT method[END_REF].

Proteome analysis

Protein extract preparation. Cells cultured in the presence or absence of ethanol were harvested by centrifugation, washed with 10 mM of Tris-HCl buffer (pH 8.0) and frozen at -80ºC until analysis. Pellets were then resuspended to a final OD 600nm ≈ 60 in 0.1 M of Tris-HCl buffer (pH 7.5) and cellular extracts obtained using a cell disrupter (BASIC Z; Constant Systems Ltd., Daventry, United Kingdom) at a pressure of 2.5 kbar. The suspension was first centrifuged at 4,500 x g for 15 min at 4ºC to remove unbroken cells and cellular debris. The supernatant was ultra-centrifuged at 50,000 x g for 30 min at 4ºC to remove cell envelope components. Protein concentration was estimated using the

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Bradford method following the manufacturer's instructions (Coomassie Protein Assay Reagent; Pierce Biotechnology, Rockford, IL, USA).

Sample preparation and protein electrophoresis. Protein samples and 2-DE were performed as described by [START_REF] Sánchez | Proteomic analysis of global changes in protein expression during bile salt exposure of Bifidobacterium longum NCIMB 8809[END_REF], with some modifications. The extract was treated with 1 µl of Benzonase (Merck KGaA, Darmstadt, Germany) and 1 µl of 1M MgCl 2 to remove nucleic acids. The mixture was vortexed and centrifuged at 3,500

x g for 2 min at 4ºC. After the addition of four volumes of deionized water and vigorous vortexing, samples were centrifuged at 3,500 x g for 10 min at 4ºC. The upper phase was removed and proteins precipitated by adding 3 volumes of methanol and centrifuging at 3,500 x g for 4 min. The pellets were then resuspended in solubilization buffer. The second dimension electrophoresis was run at 11 mA/gel for 15 h at 4ºC. Image analysis. Spots were detected and their volume quantified with Prodigy SameSpots software (Nonlinear Dynamics), analyzing images of at least three gels for each time and condition. A sample taken at time zero (just before water/ethanol addition) was chosen as the . Protein expression was deemed to have changed if the mean normalized spot volume varied at least twofold and was confirmed by analysis of variance at a significant level of P < 0.05. Reproducibility was assessed by performing three independent experiments, and sets of five gels were analyzed.

Identification of proteins by peptide mass fingerprinting. Individual spots were excised from the gels and submitted to tryptic digestion, and mass spectrometry analyses were performed as previously described [START_REF] Guillot | Proteomic analysis of Lactococcus lactis, a lactic acid bacterium[END_REF]. The mass of the peptides was determined by MALDI-TOF MS on a Voyager DE STR instrument (Applied Biosystems) at the PAPSSO platform of the INRA Center in Jouy-en-Josas. Proteins were identified against the O. oeni NC_008528 database.

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Results and Discussion

Functional analysis using comparative transcriptomics and proteomics could provide insight into stress responses and regulation mechanisms in O. oeni. Our main aim was to evaluate which genes and proteins are most affected by ethanol shock.

The growth of O. oeni PSU-1 in MRS medium and the effect of ethanol on population development were monitored by counting plates (data not shown). In all conditions, the population remained constant during the 3-hour assay, and cell populations of more than 10 7 CFU ml -1 were detected.

Transcriptional profiling after ethanol shock

Transcriptional analysis was carried out using mRNA from the control (water addition) at t=0h or at t=1h, and mRNA from ethanol treated samples at t=1h and t=3h.

The rough data were first analyzed to get an indication of reproducibility, and spot intensities were compared between pairs of filters. Scatter plots of normalized spot intensities (arbitrary units) from 1611 individual spots were generated and showed good reproducibility between filters (Figure 1A is a representative example). Averaged spot intensities from a sample taken at t =0h versus t=1h after water addition (control) show a better correlation (Figure 1B) than the ethanol-treated samples (Figure 1, C andD).

In order to validate the results obtained from the microarray analysis, real-time qPCR was performed with the same RNA from the original microarray experiment.

Eleven genes, some related to stress response, were selected : nadE, canH, pyrB, hsp18, trhD, amt, citE, atpB, qnnR, mleA-2 and mleR. There was a general accordance between microarray and real-time qPCR data for all the genes tested (Table 2). Of the eleven genes, eight were clearly correlated using both techniques. The three remaining genes (canH, citE, mleR) Transcriptomic data were grouped by functional categories in order to identify biological processes influenced by ethanol shock. Time zero, just before water/ethanol addition, was used as the reference condition to normalize data. In the control condition (water addition) some genes decreased their expression, probably due to changes in nutrient concentration (data not shown). However, the greatest changes in gene expression were observed for ethanol addition. Table 3 shows the number of genes from each functional category with altered expression in samples obtained one hour after the addition of 12% ethanol (t=1h) . The presence of ethanol appeared to influence gene expression in a wide range of functional classes. A total of 1611 genes were detected by the microarray. Of these, 170 genes decreased their expression after ethanol shock and 30 genes increased their expression in the presence of ethanol. Some groups seemed to be less affected by ethanol (cell mobility and secretion, coenzyme metabolism, secondary metabolites and signal transduction mechanisms), while others were more affected (amino acid transport and metabolism, cell envelope biogenesis in the outer membrane and transcription). Transcriptomic data analysis was also performed in samples obtained 3 hours after ethanol addition (t=3h), but no significant difference was observed in comparison with t=1h samples (data not shown).

Table 4 shows the transcriptomic analysis of the relative expression of the genes between time zero and 1 hour after the addition of 12% ethanol. The table shows all upregulated genes with known functions. A selection of the most inhibited genes has also been included for each functional category. Microarray data revealed that transport systems were widely inhibited in response to ethanol shock. In particular, permeases

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11 involved in metabolite transport, such as amino acids and carbohydrates, and inorganic ions were down-regulated.

As far as amino acid transport is concerned, it should be pointed out that five of the seven genes encoding for amino acid permeases that were down-regulated are related to glutamate and/or gamma-aminobutyrate transport (GABA). The other two genes are generic amino acid transporters. Gene OEOE_1747 encoding a possible GABA permease showed one of the strongest inhibitions (sevenfold). The glutamate/GABA antiporter (OEOE_0883) was also down-regulated in response to ethanol. Other inhibited amino acid transporter genes (OEOE_1806, OEOE_1427, OEOE_0388) showed high homology with orthologue glutamate/GABA transport genes in other LAB species (data not shown). It has been reported that the conversion of glutamate into GABA may confer resistance to bacterial cells, including some LAB species, under acidic conditions because of the consumption of an intracellular proton in the reaction (Cotter and Hill, 2003). However, the gene of glutamate decarboxylase, the enzyme responsible for GABA production from glutamate, has not been found in O.

oeni. However, an aminotransferase gene that transforms GABA into succinate semialdehyde and L-glutamate is present in the O. oeni PSU-1 genome (OEOE_0387).

GABA can be assimilated as a nitrogen and/or carbon source in bacteria such as Escherichia coli [START_REF] Bartsch | Molecular analysis of two genes of the Escherichia coli gab cluster: nucleotide sequence of the glutamate:succinic semialdehyde transaminase gene (gabT) and characterization of transport system in Oenococcus oeni during rehydration[END_REF] and Corynebacterium glutamicum [START_REF] Zhao | Identification and characterization of γ-aminobutyric acid uptake system GabP Cg (NCgl0464) in Corynebacterium glutamicum[END_REF], but no information is available about LAB in this respect. The inhibition of glutamate and GABA transport after ethanol shock observed in this study may account for the cell growth arrest due to stress. [START_REF] Vasserot | Transport of glutamate in Oenococcus oeni 8403[END_REF] described the inability of O. oeni to uptake L-glutamate in non-energy generating cells (membrane potential).

Similar findings were described for Lactobacillus casei [START_REF] Strobel | Transport of amino acids in Lactobacillus casei by proton-motive-force-dependent mechanisms[END_REF] and Lactococcus lactis [START_REF] Smid | Mechanism and energetics of dipeptide transport in membrane vesicles of Lactococcus lactis[END_REF]. Two genes involved in the transport of

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spermidine/putrescine were down-regulated. Like glutamate transport, the uptake of these two polyamines has been associated with an energy-producing state/membrane potential of the cell in E. coli [START_REF] Kashiwagi | Excretion and uptake of putrescine by the PotE protein in Escherichia coli[END_REF]. Both putrescine and spermidine protect against oxidative stress [START_REF] Tkachenko | Putrescine as a factor protecting Escherichia coli against oxidative stress[END_REF]. This protective mechanism may also be a target of ethanol damage, which inhibits the uptake of these polyamines.

In contrast to the previously mentioned down-regulated functions, a dipeptidase A gene (OEOE_1783) was over-expressed in response to ethanol. This is in line with the increase in protease or peptidase activity in response to stress reported by other authors (Manca de [START_REF] Manca De Nadra | A proteolytic effect of Oenococcus oeni on the nitrogenous macromolecular fraction of red wine[END_REF][START_REF] Ritt | Oligopeptide assimilation and transport by Oenococcus oeni[END_REF].

Multiple genes involved in carbohydrate transport were negatively affected, which may partly explain the decrease in energy production that led to the arrest of the nitrogenated-compound transport mentioned above. Only an ATPase (OEOE_1456) related to sugar transport was induced after ethanol shock. However, other ATPase components related to defense mechanisms were inhibited (OEOE_0722 and OEOE_0735). Some genes related to cell wall and membrane biogenesis were also significantly affected. The most inhibited were two genes with acetyl transferase function. The gene encoding for a rod shape-determining protein (MreB) was inhibited threefold. This protein has been reported to have a cytoskeletal, actin-like role in bacterial cell morphogenesis and seems to be essential for cell survival since its deletion causes inflated morphology and, finally, cell lysis [START_REF] Jones | Control of cell shape in bacteria: helical, actin-like filaments in Bacillus subtilis[END_REF]. These transcriptional changes are indicative of cell envelope damage due to ethanol action. However, the down-regulation of several N-acetylmuramidase genes (OEOE_0735, OEOE_0588, OEOE_1734) is the cell's protective response against ethanol, which prevents cell wall

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weakening since these genes encode for proteins with autolysin activity [START_REF] Delcour | The biosynthesis and functionality of the cell-wall of lactic acid bacteria[END_REF][START_REF] Govindasamy-Lucey | Varying influence of the autolysin, N-acetyl muramidase, and the cell envelope proteinase on the rate of autolysis of six commercial Lactococcus lactis cheese starter bacteria grown in milk[END_REF].

Gene expression related to defense mechanisms, DNA replication, recombination, repair and transcription was widely affected after ethanol shock (Table 4). Among the genes inhibited under these functional categories were multidrug and antimicrobial ATPase and transport systems. Several transcriptional regulators were also significantly down-regulated -for example, various members of the xenobiotic responsive element (xre) family (OEOE_0047) -but no information is available regarding their activator/repressor role.

Altogether, it seems that ethanol shock triggers the transcriptional inhibition of several cell defense mechanisms in response to the immediate effect of an external threat to the cell, such as the presence of ethanol. Presumably the ability to recover and reactivate these cell protection mechanisms is part of the adaptation response that allows O. oeni strains to survive under wine conditions (Beltramo et al., 2006;[START_REF] Olguín | Influence of ethanol and pH on the gene expression of the citrate pathway in Oenococcus oeni[END_REF]. Meanwhile, the activation of the widely studied stress protein Hsp20 in O. oeni [START_REF] Guzzo | A small heat shock protein from Leuconostoc oenos induced by multiple stresses and during stationary growth phase[END_REF]2000) confirms the importance of this gene as a marker of stress response in O. oeni.

Changes in proteins of O. oeni in response to ethanol shock

Changes in O. oeni soluble proteome were followed for the first five hours after 12% ethanol shock. The protein profile of O. oeni PSU-1 at t=0h was characterized to generate a standard grid, which was used for subsequent comparative studies of samples obtained after water/ethanol addition. The high-resolution map that was obtained revealed approximately 215 spots (Fig. 2A), indicating a better resolution than in a

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previous study [START_REF] Silveira | Effect of adaptation on cytoplasmic and membrane protein profiles of Oenococcus oeni[END_REF]. A larger quantity of spots was detected by Cecconi et al. (2009), even though they used a larger gradient (pH 3-10).

A comparison of proteome profiles from 12% ethanol-treated or control cultures for each sampling time revealed quantitative and qualitative modifications of the spot patterns that could be recognized by simple visual comparison of the two conditions.

Further analysis of the gels confirmed that ethanol-treated and control populations (12% water addition) had different responses. Two spots decreased their intensity in the control condition. One of these spots could not be identified; the other was spot 7, which also diminished in the ethanol-treated samples (Fig. 2A). After ethanol treatment, intensity increased in one spot (spot 36) and decreased in 44. This is in agreement with a previous report in which most proteins decreased in concentration after ethanol shock [START_REF] Silveira | Effect of adaptation on cytoplasmic and membrane protein profiles of Oenococcus oeni[END_REF].

Of the 45 spots found in different quantities when compared to the proteome reference gel (t=0h), 35 were identified, corresponding to 31 different proteins (Table 5). Unfortunately spot 36 (Fig. 2B), the only one detected that increased in response to ethanol shock, could not be identified.

As shown in Table 5, the proteins identified are involved mainly in nucleotide transport and metabolism (22.86%), translation, ribosomal structure and biogenesis (17.14%), cell envelope biogenesis (14.26%) and posttranslational modification, protein turnover and chaperone functions (11.43%). Seven spots were classified in five other functional categories and for the last five spots (14.29%) no function could be predicted.

Three proteins (PyrG, PyrE and Zwf) matched two different spots (spots 1 and 2, 7 and 8, and 26 and 27 respectively). This may be due to the presence of co-and posttranslational modifications that affect their pI and/or mass.

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As mentioned above, image analysis showed only subtle changes in the protein kinetics in the control assay with added water. Therefore all further analysis focused on the gels obtained from the ethanol-treated cultures. The fold-change value was derived from the mean normalized volumes of four groups of three gels, each group corresponding to t=0h (reference gel), 1, 3 and 5 h. Fold-changes between control at t=0h and ethanol-treated samples at t=5 h are indicated in Table 5. On the basis of their predicted function, proteins were classified into ten different functional categories.

The kinetic changes were also analyzed and seven different patterns observed. Some representative proteins are depicted in Fig. 2B. Within a given functional category, several kinetics were observed; for instance, spots 2 (PyrG) and 7 (PyrE) both belong to the pyrimidine biosynthetic pathway. These patterns of expression suggest a complex response to ethanol during the first 5 hours of exposure (Fig. 2B). Indeed, one protein (spot 36) increased after one hour and then remained stable, whereas other spots decreased after one hour and remained stable (spots 7 and 23), or decreased only after 3 hours (spots 2, 11, 27), or showed a gradual decrease over time (spots 11 and 21).

However, we were unable to find a clear-cut link between functional categories and the type of kinetics.

Our results suggest that protein synthesis and stability decrease when cells are directly submitted to 12% ethanol since more than half the proteins (53.13%) that decrease in concentration are related to these protective functions. The concentration of the molecular chaperone DnaK, a stress-induced protein in several lactic acid bacteria [START_REF] Kilstrup | Induction of heat shock proteins DnaA, GroEL, and GroES by salt stress in Lactococcus lactis[END_REF][START_REF] Lim | Identification of stress-inducible proteins in Lactobacillus delbrueckii subsp. bulgaricus[END_REF], decreases after ethanol shock (Table 5). This decrease in DnaK concentration in the cytosolic fraction may be related to the recruitment of this chaperone to the membrane, as described for Bacillus subtilis DnaK after short-term ethanol stress (Seydlova et al., 2012). A similar phenomenon of

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16 membrane association has been described for heat shock protein Lo18 in O. oeni (Weidmann et al., 2010). It has been widely reported that O. oeni cells respond to the presence of ethanol by decreasing the fluidity of their membranes (Da [START_REF] Da Silveira | Membrane fluidity adjustments in ethanol-stressed Oenococcus oeni cells[END_REF][START_REF] Chu-Ky | Combined cold, acid and ethanol shocks in Oenococcus oeni: effects on membrane fluidity and cell viability[END_REF]. It has also been suggested that this decrease in fluidity stems from the changed lipid-to-protein ratio, which plays an important role in regulating fluidity. The recruitment of several stress proteins to the membrane could play a protective role of protein stabilization and membrane fluidity regulation, as described for Lo18 (Weidmann et al., 2010).

Other stress proteins such as ClpC and ClpE also diminish in the presence of ethanol. These two proteins have ATPase activity and can function either as molecular chaperones or as regulating components of a proteolytic complex by associating to ClpP protease (Beltramo et al., 2004). Therefore this association of each of the ATPases with ClpP to form a two-subunit complex in response to ethanol stress may account for the decrease in the cytosolic fraction of these proteins in the single form. About 14% of the proteins that decrease in concentration are related to cell envelope biogenesis. One of them, identified as dTDP-D-glucose 4,6-dehydratase (RmlB), is involved in cell wall lipopolysaccharide biosynthesis. In the proteomic study by [START_REF] Silveira | Effect of adaptation on cytoplasmic and membrane protein profiles of Oenococcus oeni[END_REF], RmlB was detected in the membrane-associated protein extract of cells pre-adapted in 8% ethanol but not in the control condition or after 1 hour of 12% ethanol shock. In our study RmlB was present in all the samples, although it decreased over time in the presence of ethanol. This suggests that rmlB is initially down-regulated after ethanol shock, which decreases the RmlB protein level, but is subsequently over-expressed when cells are adapted and so may be an indicator of cell acclimation. We have also observed that in some O. oeni strains there is a correlation between high levels of rmlB transcripts and a better malolactic performance (Olguín et al., 2010). The present study also shows that proteins such as MurC, MurD and GlmS, which are involved in murein biosynthesis, are down-regulated after a 12% ethanol shock (Table 5). Altogether, these results suggest that ethanol stress causes important changes in cell wall composition. It is clear that resistance to stress depends on the genes involved in peptidoglycan and teichoic acid biosynthesis [START_REF] Delcour | The biosynthesis and functionality of the cell-wall of lactic acid bacteria[END_REF].

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Nonetheless, there is little biochemical or genetic data available on the biosynthesis pathways of the cell wall constituents in lactic acid bacteria. Further study is required in this area.

Global evaluation of transcriptomic and proteomic changes

Little correlation has been found among the 31 identified proteins changing in abundance and with their gene expression analyzed by microarray. Only two genes, lactoylglutathione lyase (OEOE_0531) and glucosamine 6-phosphate aminotransferase (OEOE_0635), showed the same behavior at protein and gene level; in both cases their expression decreased after ethanol addition. These two genes could therefore be useful molecular reporters of the metabolic state of cells in response to ethanol stress. Cecconi et al. ( 2009) reported a similar result for one of these proteins, glucosamine 6-phosphate aminotransferase (OEOE_0635), which was less abundant in cells not acclimated to ethanol (as in the present study) than in acclimated cells. Thus we might suggest that the non-activation of these genes indicate cellular metabolic robustness against the induced stress.

The other proteins that decreased in concentration showed no changes in gene expression. These differences may be accounted for by posttranscriptional regulation, changes in protein localization and, most probably, protein degradation due to ethanol damage. However, a considerable number of the genes that are differentially expressed,

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according to transcriptomic analysis, encode for membrane-associated proteins (e.g. permeases), whereas in this work only the soluble proteome was analyzed. Nonetheless, the combination of both transcriptomic and proteomic approaches confirmed the functions that are mainly affected by short-term ethanol stress in O. oeni.

In conclusion, the transport of metabolites and cell wall and membrane biogenesis are the main functional categories affected by ethanol shock. These results highlight the importance of the membrane as a barrier to stress and as a key element for cell protection. One of the mechanisms of response to cell damage is the recruitment of several stress proteins to the membrane. This is the first study to present a transcriptomic analysis of O. oeni and to combine this data with a proteomic analysis. This dual approach opens the door to future studies on the behavior of O. oeni under wine-related conditions.

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the succinic semialdehyde dehydrogenase gene (gabD). Journal of Bacteriology 172, 7035-7042. Beltramo, C., Grandvalet, C., Pierre, F., Guzzo, J., 2004 *Fold-change between ethanol-treated and control gels for each spot at T=5h.

  oeni expression 4-plex array) were developed by Roche NimbleGen (Madison, WI, USA) and samples were analyzed at the Functional Genomics Core of the Institute for Research in Biomedicine (IRB, Barcelona, Spain). cDNA library preparation and amplification were performed from 25 ng total RNA using WTA2 (Sigma-Aldrich, Madrid, Spain) with 17 cycles of amplification. Labeling, hybridization and washing were performed according to the Roche Nimblegen expression guide v5.1. For each sample, 1 µg cDNA was labeled by Cy3 nonamer primers and Klenow polymerization. A hybridization mixture with 2 µg Cy3-labeled

  ) above 60ºC. The length of the PCR products ranged from 92 to 130

  displayed lower numerical values by microarray, indicating no this technique. Overall, the correlation between real-time qPCR and microarray was good, suggesting that the microarray gene expression measurements were valid.

Figure 1 .

 1 Figure legends

Figure 2 .Figure 1 .

 21 Figure 2. A) Reference map of proteins extracted from O. oeni PSU-1 cells in the late-

  Several spot numbers for the same protein entry indicate that the protein was identified in several spots.

Table 2 .

 2 Validation of microarray data by real-time qPCR Microarray and b RT-qPCR fold changes between: t=0h and t=1h after ethanol addition.

	Gene name (code)	Microarray a	qPCR b
	nadE (OEOE_0008)	+1.92	+1.71
	canH (OEOE_0238)	+0.08	+2.99
	pyrB (OEOE_0258)	+3.12	+6.92
	hsp18 (OEOE_0289)	+2.04	+1.64
	trhD (OEOE_0394)	+3.18	+4.50
	amt (OEOE_0411)	+2.50	+6.11
	citE (OEOE_0422)	+0.99	+2.11
	atpB (OEOE_0665)	+0.10	+0.52
	qnnR (OEOE_1290)	+3.72	+2.91
	mleA-2 (OEOE_1325)	+2.54	+3.84
	mleR (OEOE_1565)	-0.40	+4.44

a

Table 3 .

 3 Number of genes of Oenococcus oeni PSU-1 with altered expression 1h after ethanol addition according to functional group.

	Functional Group

Table 4 .

 4 Relative expression of genes differentially expressed between 0h and 1h after ethanol addition. All up-regulated genes with known functions are included (on grey background). Only a selection of the most inhibited genes are included for each functional category. The gene names are taken from the NCBI database for Oenococcus oeni PSU-1 complete genome b The information in the description column is taken from the Computational Biology and Bioinformatics The relative expression was described by the fold change value of genes after ethanol addition with respect to 0h

	a Gene	b Description	c Relative Expression
	Amino Acid Transport and Metabolism
	OEOE	Dipeptidase A. Cysteine peptidase. MEROPS family C69	2.09
	OEOE	Threonine dehydrogenase or related Zn-dependent dehydrogenase	3.18
	OEOE	Gamma-aminobutyratepermease or related permease	-7.25
	OEOE	Permease of the drug/metabolite transporter (DMT) superfamily	-5.26
	OEOE	ABC-type amino acid transport system. permease and periplasmic	-5.25
		component
	OEOE	Spermidine/putrescine ABC transporter permeaseprotein	-2.65
	OEOE	Glutamate gamma-aminobutyrate antiporter	-2.60
	OEOE	Spermidine/putrescine-binding periplasmic protein	-2.26
	OEOE	Spermidine/putrescine ABC transporter permease protein	-2.05
	Carbohydrate Transport and Metabolism
	OEOE	ABC-type sugar transport system. ATPase component	2.39
	OEOE	ABC-type sugar transport system. periplasmic component	-4.47
	OEOE	Permease of the major facilitator superfamily	-4.18
	OEOE	ABC-type maltose transport system. permease component	-3.91
	OEOE	Permease of the major facilitator superfamily	-3.53
	OEOE	ABC-type sugar transport system. permease component	-3.01
	Cell Envelope Biogenesis. Outer Membrane
	OEOE	Predicted glycosyltransferase	-4.07
	OEOE	O-acetyltransferase family protein	-3.89
	OEOE	D-alanine-activating enzyme	-3.37
	OEOE	Rod shape-determining protein MreD	-3.37
	OEOE	Lyzozyme M1 (1.4-beta-N-acetylmuramidase)	-3.26
	Defense mechanisms
	OEOE	ABC-type multidrug transport system. ATPase component	-8.43
	OEOE	ABC-type antimicrobial peptide transport system. permease	-3.34
		component
	OEOE	ABC-type antimicrobial peptide transport system. ATPase	-3.21
		component
	DNA Replication. Recombination and Repair
	OEOE	Rossmann fold nucleotide-binding protein for DNA uptake	-6.15
	OEOE	RNase HII	-3.82
	OEOE	DNA replication and repair protein RecF	-3.58

a c

Table 5 .

 5 Identification of the differentially expressed proteins of O. oeni PSU-1 growing in the presence of ethanol

	Spot	Gene	Gene	Functional category	Fold	Theoretical Mr	Theoretical
	no § .	symbol	name	Protein name (EC number)	change*	(kDa)	pI
				Nucleotide transport and metabolism
	1-2	OEOE_1786	pyrG	CTP synthase (UTP-ammonia lyase) (EC 6.3.4.2)	-5.6	60.11	5.52
	3	OEOE_0138	nrdL	Ribonucleotide reduction protein	-5.4	18.05	5.52
	4	OEOE_1069	apt	Adenine/guanine phosphoribosyltransferase or related PRPP-binding protein	-3.6	19.25	5.7
				(EC 2.4.2.7)
	5	OEOE_1124	hpt	Hypoxanthine/guanine phosphoribosyltransferase (EC 2.4.2.8)	-2.7	20.93	4.99
	6	OEOE_0437	--	Deoxynucleoside kinase (EC 2.7.1.113)	-2.1	24.74	5.07
	7-8	OEOE_0263	pyrE	Orotate phosphoribosyltransferase (EC 2.4.2.10)	-3	23.13	5.67
				Coenzyme metabolism
	9 OEOE_1036 pdxS Pyridoxine biosynthesis enzyme, SOR/SNZ family	-3.1	31.44	5.58
				Translation, ribosomal structure and biogenesis
		OEOE_0982	proS	Prolyl-tRNA synthetase (EC 6.1.1.15)	-5.2	64.33	5.41
		OEOE_0321	glnS	Glutamyl-and glutaminyl-tRNA synthetase (EC 6.1.1.17 -6.1.1.24)	-2.7	57.05	5.94
		OEOE_0440	serS	Seryl-tRNA synthetase (EC 6.1.1.11)	-2.4	49.86	5.68
		OEOE_1694	gatA	Asp-tRNAAsn/Glu-tRNAGln aminotransferase A subunit (EC 6.3.5.6 -	-2.4	52.5	5.31
				6.3.5.7)
		OEOE_0806	def	N-formylmethionyl-tRNA deformylase (EC 3.5.1.88)	-2.3	21.08	5.27
		OEOE_1699	map	Methionine aminopeptidase (EC 3.4.11.18)	-2.2	29.95	5.08
				Energy production and conversion
		OEOE_1248 eutG Iron-binding alcohol dehydrogenase / aldehyde dehydrogenase family domain	-3.8	99.1	6.04
				(EC1.1.1.1)
				Cell envelope biogenesis
		OEOE_0635	glmS	Glucosamine 6-phosphate synthetase, amidotransferase and phosphosugar	-3.7	66.17	5.28
				isomerase domains (EC 2.6.1.16)
		OEOE_0565	galU	UDP-glucose pyrophosphorylase (EC 2.7.7.9)	-2.9	32.56	5.37
		OEOE_1269	murC	UDP-N-acetylmuramate-alanine ligase (EC 6.3.2.8)	-2.7	48.13	6.03
		OEOE_1147	murD	UDP-N-acetylmuramoylalanine-D-glutamate ligase (EC 6.3.2.9)	-2.5	48.65	6.16
		OEOE_1447	rmlB	dTDP-D-glucose 4,6-dehydratase (EC 4.2.1.46)	-2.1	37.46	5.8
			Posttranslational modification, protein turnover, chaperones
		OEOE_1114	sufC	Fe-S-cluster assembly ABC-type transport system, ATPase component	-2	28.22	5.71
		OEOE_0514	clpC	ATP-binding subunit of Clp protease and DnaK/ DnaJ chaperones (subunit of	-3.2	91.48	5.86
				DnaK/J)
		OEOE_0640	clpE	ATP-binding subunit of Clp protease and DnaK/ DnaJ chaperones (subunit	-2.5	81.29	5.37
				clpE)
		OEOE_1309	dnaK	Molecular chaperone	-2.9	66.2	4.89
				Carbohydrate metabolism
	26-27	OEOE_0135	zwf	Glucose-6-phosphate 1-dehydrogenase (EC 1.1.1.49)	-2.9	55.67	6.44
		OEOE_1523	gnd	6-phosphogluconate dehydrogenase (EC 1.1.1.44)	-2.1	32.86	4.83
				Amino acid transport and metabolism
		OEOE_0845 appF ABC-type oligopeptide transport system, ATPase component	-2.7	34.49	5.74
				Signal transduction mechanisms
		OEOE_0807 typA Stress response membrane GTPase	-3.1	68.22	5.24
				General function prediction only
		OEOE_1270	arc1	EMAP domain	-2.3	22.47	6.1
		OEOE_0070	ara1	Aldo/keto reductase related enzyme	-2.1	31.68	5.6
		OEOE_1072	obg	Predicted GTPase	-3	48.2	5.47

Acknowledgments

This work was supported by grants AGL2006-03700ALI and AGL2009-7368ALI (MECD) from the Spanish Ministry of Education and Science. Nair Olguín is grateful to the Catalan government (Generalitat de Catalunya) for a pre-doctoral fellowship and to the Spanish Ministry of Education for a mobility grant (TME2008-01339). Nair Olguín would also like to thank Dr Christian Beauvallet (INRA, Jouy-en-Josas, France) for his support and collaboration in the protein electrophoresis analyses.

• Proteomic results show a decrease in protein biosynthesis and stability.

• Global analysis confirms that the cell membrane is the main target of ethanol damage.


Alice Rodriguez

Emmanuelle Pouydebat

M Gema

Chacón Cda

Marie-Hélène Moncel

Raphaël Cornette

Ameline Bardo

Laurence Chèze

Radu Iovita hi Antony Borel
email: antony.borel@mnhn.fr

Right or left? Determining the hand holding the tool from use traces

Currently, approximately 90% of the human population is right-handed. This handedness is due to the lateralization of the cerebral hemispheres and is controlled by brain areas involved in complex motor tasks such as making stone tools or in language. In addition to describing the evolution of laterality in humans, identifying hand preference in fossil hominids can improve our understanding of the emergence and development of complex cognitive faculties during evolution. Several fields of prehistory like palaeoanthropology or lithic analysis have already investigated handedness in fossils hominins but they face limitations due to either the incomplete or the composite state of the skeleton remains or to results replication or method application failure. Wear analysis could provide new complementary data about hand preference evolution and the development of certain complex cognitive functions using indirect evidence (use traces, micro-scars in particular) of the hand holding the stone tool during use. Controlled experiment has been carried out in order to establish a reference collection of tools used with the left and tools used with the right hand. Wear analysis was performed on this corpus using "classical" microscopic approach and geometric morphometric analysis. A machine learning algorithm, the k-NN method, was applied to verify if use traces (micro-scars) could help determine the hand holding the tool during use. The best model, based on parameters referring to invasiveness of micro-scars, was able to correctly determine the hand holding the tool with 75% accuracy.

Introduction:

Hand preference is defined by the hand with which an individual performs a specific task (e.g. [START_REF] Annett | Left, right, hand and brain: the right shift theory[END_REF][START_REF] Uomini | La latéralité manuelle préhistorique, les outils et le langage[END_REF]. Several studies have demonstrated that hand preference is linked to the lateralization of the cerebral hemispheres and is related to areas of the brain similar to those involved in the control of other essential functions such as language (e.g. [START_REF] Corballis | From mouth to hand: Gesture, speech, and the evolution of right-handedness[END_REF][START_REF] Meguerditchian | On the origins of human handedness and language: A comparative review of hand preferences for bimanual coordinated actions and gestural communication in nonhuman primates[END_REF][START_REF] Stout | Stone Tool-Making and Brain Activation: Position Emission Tomography (PET) Studies[END_REF][START_REF] Uomini | Shared Brain Lateralization Patterns in Language and Acheulean Stone Tool Production: A Functional Transcranial Doppler Ultrasound Study[END_REF][START_REF] Willems | Neural evidence for the interplay between language, gesture, and action: A review[END_REF]. Thus, in addition to describing the evolution of laterality in humans, identifying hand preference in fossil hominids can improve our understanding of the emergence and development of complex cognitive faculties during evolution (e.g. [START_REF] Steele | Can the Archaeology of Manual Specialization Tell Us Anything About Language Evolution? A Survey of the State of Play[END_REF][START_REF] Stout | Neural correlates of Early Stone Age toolmaking: technology, language and cognition in human evolution[END_REF].

Currently, between 74% and 96% of the human population preferentially uses the right hand [START_REF] Annett | Left, right, hand and brain: the right shift theory[END_REF][START_REF] Llaurens | Why are some people left-handed? An evolutionary perspective[END_REF][START_REF] Porac | Lateral Preferences and Human Behavior[END_REF]. Lefthanders are a minority in all human populations. The highest rate of left-handers recorded in a population is found among the Eipo in Papua New Guinea and is estimated at 26.9% [START_REF] Faurie | Variation in the Frequency of Left-handedness in Traditional Societies[END_REF].

The control of fine manual actions (called "complex" motor actions) is attributed to the left hemisphere which would explain the preferential use of the right hand for this type of action (e.g. [START_REF] Greenfield | Language, tools and brain: The ontogeny and phylogeny of hierarchically organized sequential behavior[END_REF][START_REF] Johnson-Frey | A Distributed Left Hemisphere Network Active During Planning of Everyday Tool Use Skills[END_REF][START_REF] Lewis | Cortical Networks Related to Human Use of Tools[END_REF][START_REF] Schluter | Cerebral dominance for action in the human brain: the selection of actions[END_REF]. Some authors have thus hypothesized the development of a strong manual preference in relation to the manufacture and complexity of tools [START_REF] Corballis | Straw monkeys[END_REF][START_REF] Stock | Skeletal evidence for variable patterns of handedness in chimpanzees, human hunter-gatherers, and recent British populations[END_REF][START_REF] Uomini | La latéralité manuelle préhistorique, les outils et le langage[END_REF][START_REF] Uomini | The prehistory of handedness: Archaeological data and comparative ethology[END_REF]. Other studies show a greater effect of the nature and demands of the task as well as the type of object involved and the speed of the action [START_REF] Forrester | Human handedness: An inherited evolutionary trait[END_REF][START_REF] Forrester | Target animacy influences chimpanzee handedness[END_REF][START_REF] Forrester | Target animacy influences gorilla handedness[END_REF][START_REF] Pouydebat | Hand preference in fast-moving versus slowmoving actions in capuchin, Sapajus spp., and squirrel monkeys, Saimiri sciureus[END_REF][START_REF] Quaresmini | Social environment elicits lateralized behaviors in gorillas (Gorilla gorilla gorilla) and chimpanzees (Pan troglodytes)[END_REF][START_REF] Rogers | Hand and paw preferences in relation to the lateralized brain[END_REF]. The complexity of the task is therefore probably not the only criterion explaining the emergence of a strong manual preference.

Establishing a direct link between strong manual preference and the complexity of (stone) tools is difficult and, so far, concrete archaeological data do not support it. Moreover, few studies highlight the fact that the right hemisphere is also involved and essential in activities such as tool use [START_REF] Frey | Modulation of Neural Activity during Observational Learning of Actions and Their Sequential Orders[END_REF][START_REF] Hartmann | It takes the whole brain to make a cup of coffee: the neuropsychology of naturalistic actions involving technical devices[END_REF][START_REF] Stout | Stone tools, language and the brain in human evolution[END_REF] In non-human primates, especially great apes, similarly strong lateralization as in humans is missing [START_REF] Vallortigara | survival with an asymmetrical brain: advantages and disadvantages of cerebral lateralization[END_REF]. This differs according to the species but also within the same species at an individual level or a population level [START_REF] Cashmore | The evolution of handedness in humans and great apes: a review and current issues[END_REF][START_REF] Chapelain | Hand Preferences for Bimanual Coordination in 77 Bonobos (Pan paniscus): Replication and Extension[END_REF][START_REF] Hopkins | Comparative and familial analysis of handedness in great apes[END_REF][START_REF] Marzke | Tool making, hand morphology and fossil hominins[END_REF][START_REF] Mcmanus | The history and geography of human handedness[END_REF]. Differences in hand preference among nonhuman primates appear to be due to complex relationships between various factors, such as the environment (life in the wild or captivity), or the complexity and nature of the task (type, speed) (e.g. [START_REF] Fagot | Manual Laterality in Nonhuman Primates: A Distinction Between Handedness and Manual Specialization[END_REF][START_REF] Hopkins | Hand Preferences for Coordinated Bimanual Actions in 777 Great Apes: Implications for the Evolution of Handedness in Hominins[END_REF][START_REF] Mcgrew | On the other hand: Current issues in and meta-analysis of the behavioral laterality of hand function in nonhuman primates[END_REF][START_REF] Mosquera | Complex Tasks Force Hand Laterality and Technological Behaviour in Naturalistically Housed Chimpanzees: Inferences in Hominin Evolution[END_REF][START_REF] Pouydebat | Hand preference in fast-moving versus slowmoving actions in capuchin, Sapajus spp., and squirrel monkeys, Saimiri sciureus[END_REF].

The origin of this peculiarity during human evolution is for the moment unknown. So far, data about fossil hominin hand preference are mainly coming from the skeleton, either directly inferred from forelimb asymmetries or indirectly from the skull. The examination of bone asymmetries on the upper limbs of fossil hominids could deliver direct indications of hand preference. However, as suggested or stated in several studies, observed differences in hand entheses (the surfaces where ligaments are attached on bones) among individuals with the same occupation could be due to numerous variables including inter-individual genetic variability, nutrition or hormone levels, and not just manual activities [START_REF] Foster | Using Enthesis Robusticity to Infer Activity in the Past: A Review[END_REF][START_REF] Karakostis | Evidence for precision grasping in Neandertal daily activities[END_REF][START_REF] Karakostis | Occupational manual activity is reflected on the patterns among hand entheses[END_REF][START_REF] Karakostis | Morphometric patterns among the 3D surface areas of human hand entheses[END_REF].

On the other hand, other measurements of upper limb asymmetries have been used to identify human hand preference [START_REF] Shaw | Is 'hand preference' coded in the hominin skeleton? An in-vivo study of bilateral morphological variation[END_REF][START_REF] Trinkaus | Postcranial robusticity in Homo. II: Humeral bilateral asymmetry and bone plasticity[END_REF]. Based on such observation, [START_REF] Sparacello | Patterns of humeral asymmetry among Late Pleistocene humans. Comptes Rendus Palevol, Hominin biomechanics, virtual anatomy and inner structural morphology: From head to toe[END_REF] studied a sample of humeri coming from 107 Late Pleistocene individuals and obtained a distribution of hand preference similar to the distribution obtained with recent human samples (i.e. right: 74.8%, left: 15.0%, ambiguous: 10.3%). Others suggested for example that the articular constraints model could help in the identification of hand preference [START_REF] Lazenby | Articular constraint, handedness, and directional asymmetry in the human second metacarpal[END_REF] or that the analysis of trabecular bone could contribute to the identification of right/left directional asymmetry [START_REF] Stephens | Trabecular architecture in the thumb of Pan and Homo: implications for investigating hand use, loading, and hand preference in the fossil record[END_REF].

Moving on to the skull, analyses of cranial endocast asymmetries analyses (e.g. [START_REF] Balzeau | Shared Pattern of Endocranial Shape Asymmetries among Great Apes, Anatomically Modern Humans, and Fossil Hominins[END_REF][START_REF] Holloway | Volumetric and asymmetry determinations on recent hominid endocasts: Spy I and II, Djebel Ihroud I, and the salèHomo erectus specimens, with some notes on neandertal brain size[END_REF][START_REF] Holloway | Brain endocast asymmetry in pongids and hominids: Some preliminary findings on the paleontology of cerebral dominance[END_REF][START_REF] Poza-Rey | Brain asymmetries and handedness in the specimens from the Sima de los Huesos site (Atapuerca, Spain)[END_REF] suggested possible relationships between hand preference, endocranial shape, and functional capacities in hominins. Other studies, investigating striations on teeth surfaces, show that even for the Middle Palaeolithic and species such as Homo erectus in Asia, or Homo heidelbergensis and Homo neanderthalensis in Europe, the right hand was already the chosen hand for the majority of individuals, both adults or young individuals (e.g. Bermúdez de [START_REF] Bermúdez De Castro | Rates of anterior tooth wear in Middle Pleistocene hominins from Sima de los Huesos (Sierra de Atapuerca, Spain)[END_REF][START_REF] Bermúdez De Castro | Buccal striations on fossil human anterior teeth: evidence of handedness in the middle and early Upper Pleistocene[END_REF][START_REF] Condemi | Vergisson 4: a left-handed Neandertal: CONDEMI et al[END_REF][START_REF] Estalrrich | Handedness in Neandertals from the El Sidrón (Asturias, Spain): Evidence from Instrumental Striations with Ontogenetic Inferences[END_REF][START_REF] Fiore | Handedness in the Krapina Neandertals: A Re-Evaluation[END_REF][START_REF] Frayer | More than 500,000 years of right-handedness in Europe. Laterality: Asymmetries of Body[END_REF][START_REF] Frayer | Right handed Neandertals: Vindija and beyond[END_REF][START_REF] Lozano | Right handedness of Homo heidelbergensis from Sima de los Huesos (Atapuerca, Spain) 500,000 years ago[END_REF][START_REF] Poza-Rey | Brain asymmetries and handedness in the specimens from the Sima de los Huesos site (Atapuerca, Spain)[END_REF][START_REF] Xing | Dental Scratches and Handedness in East Asian Early Pleistocene Hominins: Handedness of East Asian Hominin[END_REF]. This could be the case as early as 1.8 Ma as suggested by the study of labial striations on the OH-65 fossil's anterior teeth [START_REF] Lozano | Right-handed fossil humans[END_REF].

For younger periods, analysis of wall-paintings and striations on bone tools may give clues about the hand preference of their authors or users respectively (see [START_REF] Spenneman | Handedness data on the European neolithic[END_REF] for an example concerning Neolithic).

These studies do not provide data supporting a ratio of left-and right-handers clearly different from now in Prehistory (see [START_REF] Llaurens | Why are some people left-handed? An evolutionary perspective[END_REF]. However, the more we go back in time the more the application of these methods to extinct hominin species have serious limitations.

Relatively bad conservation of skeletal remains often prevents any analysis of traces that are relevant for determining hand preference. Also, apart from the limited number of individuals which could be studied (see table 2.1 of [START_REF] Faurie | Origins, Development, and Persistence of Laterality in Humans[END_REF] for a summary of fossil hominin data for laterality and corresponding references) palaeoanthropology faces the problem of the partial skeleton remains which implies sometimes to study composite anatomical elements composed of bones coming from different individuals (see [START_REF] Cashmore | Can hominin 'handedness' be accurately assessed?[END_REF].

On the contrary, stone tools are often abundant in Palaeolithic sites. The data they could provide regarding hand preference are therefore likely to be statistically more representative if whole sets of assemblages are analysed. Different studies have intended to set up a methodology to determine hand preference based on the observation of manufacture features appearing on stone tools (size and shape of flake scatter patterns: [START_REF] Newcomer | Experimental Flake Scatter-Patterns: A New Interpretative Technique[END_REF]dorsal cortex position: Toth, 1985; occurrence of cortex and relic margins: [START_REF] Bradley | Analysis of replication of two Acheulian artefact assemblages[END_REF] orientation of the cone of percussion: [START_REF] Rugg | Inferring handedness from lithic evidence[END_REF]resharpening techniques: Cornford, 1986; combination of technical features: [START_REF] Bargalló | Identifying handedness at knapping; an analysis of the scatter pattern of lithic remains[END_REF][START_REF] Bargalló | In pursuit of our ancestors' hand laterality[END_REF][START_REF] Bargalló | Can hand laterality be identified through lithic technology? Laterality: Asymmetries of Body[END_REF]. These methods give variable results and a recent work from [START_REF] Ruck | Determining hominid handedness in lithic debitage: a review of current methodologies[END_REF], applying Toth's, Rugg and Mullane's and Bargalló and Mosquera's methods, showed that these methods were not able to determine the hand holding the hammer for the studied flakes production better than a random guess (see also [START_REF] Patterson | Comments on Toth's Right-Handedness Study[END_REF][START_REF] Pobiner | The Use of Stone Tools to Determine Handedness in Hominids[END_REF][START_REF] Uomini | Handedness in Neanderthals[END_REF][START_REF] Uomini | Lithic indicators of handedness: assessment of methodologies and the evolution of laterality in hominids[END_REF].

The identification of the knapper hand preference based on the spatial organization of debitage remains have also been investigated giving successful results in experimental conditions but the authors highlight the difficulties in applying such method in an archaeological context [START_REF] Bargalló | Identifying handedness at knapping; an analysis of the scatter pattern of lithic remains[END_REF]. Some of these methods should be investigated further in order to improve their rate of correct determination and their applicability to archaeological samples. However, producing a stone flake is a quick and punctual activity. Therefore, any variation during this short production moment might modify the types of features appearing on the flake. The poor rates of success in identifying hand preference from patterns in flake production may be due to the high variability of factors influencing the production of any particular flake. On the other hand, a repeated activity or movement is likely to leave behind a stronger "signature" of the hand preference. Therefore, focusing on stone tool use rather than stone tool production could provide better results in identifying hominids hand preference. A handful of studies have attempted to extract hand preference from stone tool use (based on asymmetries of the tool: Brinton, 1896; based on direction of rotation of boring artefacts: [START_REF] Cahen | Stone Tools, Toolkits, and Human Behavior in Prehistory [and Comments and Reply[END_REF][START_REF] Cahen | Not less than two, not more than three[END_REF][START_REF] Keeley | The Functions of Paleolithic Flint Tools[END_REF] see also [START_REF] Uomini | Prehistoric Technology. 40 Years Later: Functional Studies and the Russian Legacy[END_REF]. Others simply discuss why it is difficult to do so [START_REF] D'errico | Technology, Motion, and the Meaning of Epipaleolithic Art[END_REF][START_REF] Semenov | Prehistoric technology: an experimental study of the oldest tools and artefacts from traces of manufacture and wear[END_REF][START_REF] Weber | -Gröbern: Beiträge Zur Jagd Des Mittelpaläolithischen Menschen, Veröffentlichungen Des Landesmuseums Für Vorgeschichte[END_REF]. However, to our knowledge, most of them only briefly mention this question and none is based on an experimental reference collection that was statistically tested to allow the identification of specific criteria corresponding to the use of the tool in the right or left hand.

Here, we propose to examine if hand preference can be identified from use-wear on stone tools through qualitative (traditional wear observation) and quantitative (measurements and geometric morphometric) wear descriptors. A machine learning algorithm is used for the first time on these data in order to test which of the descriptors is/are the most relevant to recognize the hand holding the tool based on the traces of use.

Materials and methods

Experimental setup

Tools:

Numerous variables (morphology and raw material type of the tool, worked material, working angle, direction of movement, etc.) can have an effect on the formation of traces on the surface of a stone tool. In order to characterise traces of use related to hand preference but also quantify their variability, it is first essential to limit the effect of these variables and particularly the effect of the form of the tool. Therefore, the form of the tool was standardized using soda-lime glass replicas of a Levallois point [START_REF] Iovita | Projectile impact fractures and launching mechanisms: results of a controlled ballistic experiment using replica Levallois points[END_REF]; figure 1). The use of this glass has the advantage to simulate the type of conchoidal fracture of many raw materials used in prehistory.

These artefacts measured 64.5 mm long, 36.5 mm at the maximal width and 6 mm at the maximal thickness, with a weight of 15.8 g on average.

Figure 1 (1 column)

Users:

A total of 20 volunteers participated in the experiment, 10 right-handed subjects (5 women and 5 men) and 10 left-handed subjects (5 women and 5 men). They were beginners in using stone tools; none of them had used prehistoric tools before. The subjects were not aware of the fact that the aim of the experiment was related to hand preference in order to limit modification of their behaviour. Their hand preference was documented along with a number of other variables, including name, age, sex, size, weight, hand size, circumference of the last phalanx of each finger of the hand holding the tool, palm length, palm width, expertise, previous injuries in order to prevent them from identifying the aim of the study.

Worked material:

Wooden (Pinus) sticks with no bark were provided to each subject. These sticks were of standardized diameter (18 mm) and length (50 cm).

Task:

Each individual had to sit on a chair and to saw wooden sticks with three different tools.

Each tool was used for a total duration of 20 minutes. The number of strokes was not constrained but number of strokes (forward + backward = 1) per minute was measured during the 5 th , 10 th and 15 th minutes in order to approximate the speed of the sawing movement (in mvt/min.

Analysis

Description of micro-scars

Each stone tool was observed with a stereomicroscope (Zeiss Discovery V8 with Plan S 1.0x objective; FWD = 81 mm; it is equipped with a ToupTek camera with a resolution of 5 MPixels) before use at 10x (objective FOV = 23 mm) and 80x (objective FOV = 2.9 mm) magnifications. Then, the tools were cleaned (in a solution of DERQUIM LM02 phosphate free neutral soap at 5% in ultrasonic-bath during 15 min), and moulded (using Heraeus PROVIL®novo Light Regular silicone) and casted (using Axson F180 polyurethane resin) to keep a record of their original morphology before use. The tools were then used for a total duration of 20 minutes each.

After use, they were cleaned again (same procedure as before use) and observed at 10x and 80x magnifications (with the Zeiss Discovery V8).

Micro-scars were documented before and after use on both sides of the tool. Their amount, position (ventral or dorsal face), type, orientation, and (position of) maximum extent on the surface perpendicularly to the edge were recorded (Figure 2). The type of micro-fractures was described following the typology of the [START_REF] Ho | The Ho Ho Classification and Nomenclature Committee Report[END_REF]: fracture, feathered and step. We added the type "abrupt" to refer to scars which were not straight as a fracture but too steep to enter in the category "feathered". Three categories were used for the orientation of the scars: perpendicular to the edge, oriented distally (towards the point) and proximally (towards the butt).

Statistical tests were performed in R (R Core Team, 2019). As the assumptions of normality and equality of variance for t-test were not met, we used non-parametric Mann-Whitney U tests to verify if the number of micro-scars and if their invasiveness were different depending on the hand holding the tool during use. The Cochran's rule being respected, we used chi-square test to examine if the type of micro-scars, their orientation and the face where they appeared were independent of the user, of the user's sex and of the hand holding the tool during use.

Figure 2 (1 column)

Edge modification visualization and quantification

Photograph of each tool was taken before and after use. The same camera was not available during the whole study. Pictures were taken with a Nikon D300 equipped with a Tamron 16-300mm F/3.5-6.3 Di II VC PZD objective before use (pictures were taken at a focal length of 200mm) and with a NIKON 7100 with an AF-D Micro NIKKOR 60mm 1:2.8G ED objective after use.

As they were taken with different conditions, the two batches of photos (i.e. from before and after use) were first scaled. They were then binarized with Adobe Photoshop. The Momocs [START_REF] Bonhomme | Momocs: Outline Analysis Using R[END_REF] and geomorph [START_REF] Adams | Geomorph: Software for geometric morphometric analyses[END_REF] libraries for R were used for the analysis of the shape of the tool edges. The outlines of the tools were automatically extracted from the binarized images. The outlines were then aligned and cut to keep only the area of interest displaying the modifications due to use: the used edge of each tool (Figure 3). The remaining curves corresponding to the used edges of each tool were defined by two landmarks, one at each extremity, and by 1000 equidistant "sliding-semilandmarks" distributed between these two points. A high number of points was chosen because the sought shape differences are due to the microscaring of the edges and can be very fine. The space between each "semilandmark" was then optimized by allowing them to slip between the previous and the next point in order to minimize the Procruste distance with the pseudo-homologous point of the Procrustes mean shape (see [START_REF] Gunz | Semilandmarks: a method for quantifying curves and surfaces[END_REF]). This optimization aims to limit the effect of the former arbitrary spacing of the points [START_REF] Gunz | Semilandmarks: a method for quantifying curves and surfaces[END_REF][START_REF] Perez | Differences between sliding semi-landmark methods in geometric morphometrics, with an application to human craniofacial and dental variation[END_REF], here equidistant. This optimization and the superposition of the curves were carried out by Generalized Procrustean Analysis (GPA).

The new coordinates obtained by GPA were used to calculate a Principal Component Analysis (PCA) and to visualize, with transformation grids [START_REF] Klingenberg | Visualizations in geometric morphometrics: how to read and how to make graphs showing shape changes[END_REF], the axes on which the shape variations were the most important according to the manual preference. A Procrustes multivariate analysis of variance (Procrustes ANOVA using the function "procD.lm" of package geomorph in R)

was used to test if the shape differences visualized according to the recorded factors (e.g. hand holding the tool during use) were statistically significant. In other words, it allowed us to check if specific scar distributions and specific used edge morphologies can be linked to the hand holding the tool during use (i.e. left or right hand). The relationship between individuals' sex and the resulting used edge shape was also tested.

Figure 3 (2 columns)

k-Nearest Neighbours model

In order to verify if the qualitative and quantitative descriptors of scars could be used to discriminate a tool used with the right hand from a tool used with the left hand, we used the k Nearest Neighbours (k-NN) machine learning algorithm. It was computed using the "caret" library [START_REF] Kuhn | caret: Classification and Regression Training[END_REF] used leave-on-out cross-validation method to evaluate the models. In this method, the whole sample set of analysed artefacts but one is used as a training set and the removed artefact is used as a testing set. This procedure is repeated as many times as the number of artefacts in the sample set. In addition, we used kappa statistics (value between -1 and 1 which measures the agreement between observed and expected accuracy; negative values indicate disagreement, and the degree of agreement increase from >0 to 1), sensitivity (a measure of the proportion of attributed classes -i.e. positives -which are correctly attributedi.e. true positives) and specificity (a measure of the proportion of rejected classesi.e. negatives -which are correctly rejectedi.e.

true negatives) to evaluate the performance of the selected model.

Results

Scars description

The scars qualitative description showed that independently of the hand holding the tool during use: 1) tools have more extended scars on their dorsal face than on the ventral face, 2)

tools have mostly continuous scars on the dorsal face and discontinuous scars on the ventral face,

3) scars can be distributed all along the used edge and 4) scars are mostly perpendicular to the edge. Therefore, no significant difference of position, type and orientation of scars between tools used by left-handers and right-handers was noticed (Figure 4 and Supplementary figures 1 to 7). The hypothesis of independence between the hand holding the tool and the type of microscars appearing on the surface of the tool could be rejected (X² = 43.371, df = 4, p-value < 0.0001). However, independence could not be rejected concerning the hand holding the tool and the orientation of micro-scars (X² = 0.66197, df = 2, p-value = 0.7182) and the face where microscars were located (X² = 2.1323, df = 1, p-value = 0.1442).

Among other chi-squared tests performed on this dataset we can notice that the type of micro-scars is not independent from the sex of the user (X² = 89.084, df = 4, p-value < 0.0001) and that the type of micro-scars, the orientation and the face where appeared micro-scars are not

Figure 6 (2 columns)

3.3.

k Nearest Neighbours model Among the 21 tested models, only five gave an accuracy of at least 75%, all are based on descriptors concerning the maximum scar extent on the surface (perpendicularly to the edge). The best model includes the maximum scar extent and the position of the scar with the maximum extent on the surface (perpendicularly to the edge) on both faces. With a k value set up at 5, this model provided an accuracy of 75% (95% confidence interval: 62%, 85%) with an acceptable kappa of 0.51, a sensitivity of 85% and a specificity of 67% (Table 1).

Table 1 (2 columns)

4. Discussion 4.1. Can hand preference be identified from micro-scars?

We could not document any significant difference in position, type, and orientation of scars between tools used by left-handers and right-handers. The microscopic observation showed that tools have more extended scars on their dorsal face than on the ventral face regardless of which hand the user preferred. Knowing that tools were always held with the same orientation during the sawing action (with the dorsal face on the right) this result is counter intuitive. Indeed, during the sawing action we usually observe a tilt of the tool toward the right for right-handers and towards the left for left-handers usually resulting in more scars on the face placed on the left for right-handers and on the right for left-handers. This result shows that hand preference has no influence on the distribution of scars on either of the tool's faces. Combined with the fact that these tools had a centred cutting edge, this result may show that the edge morphology and surface structure may have an effect on the distribution of scars (when the glass casts were made, imperfections on the edges were removed by polishing the ventral side of the edge only; this might have affected the resilience of this face).

On the other hand, the type of micro-scars seems to be related to the hand holding the tool during use. This is also a counter-intuitive result. However, as the tool had to be held in the same orientation by left-handers and right-handers, the forces applied by left-handers, theoretically symmetrical to those applied by right-handers, are applied on the opposite surface of the tool. As mentioned above, the surface might have different resilience to contact during use and, as a consequence, different types of micro-scars might have been produced. This should be tested in future experiments. The suggestion that one of the surfaces (i.e. the dorsal face) of the tools might be weaker than the other is somewhat supported by the fact that irrespective of which hand was holding the tool, the dorsal face exhibited more scars.

Comparison of the edges before and after use did not show significant difference between the amount of removed material and the location of modifications on tools used by left-handers and right-handers. Right-handed subjects produced significantly more invasive and more numerous micro-scars than left-handers but this is very likely to be due to an individual factor as, apart from hand preference, the sex and the user seem to have a negligible effect on the characteristics of the micro-scars. This was confirmed by geometric morphometric analyses which show that the shape of the used edges depends more on the user and user's sex than on the hand preference.

Based on the data gathered through microscopic observation of scars and quantification of edge modification with geometric morphometric, the k Nearest Neighbours method highlighted the importance of the maximum scar extent (on the ventral face) and of the position of the more invasive scar in discriminating hand preference during stone tool use. The model integrating these variables gave 75% of good identification of the hand preference of the user.

Importance of individual characteristics

We decided not to choose experts at this step of the project because expertize in using stone flakes (here more exactly soda lime-glass) is a complicated notion. How can we define an expert user of stone flakes? Beginners are sometimes performing very well in such tasks. Like for nowadays tools, a beginner might be more likely to damage the tool quicker than an expert might but can we really consider someone as an expert stone flake user in the current world? Few have more experience than others do and finding enough experienced people to produce statistically acceptable data is not straightforward. In addition, experts may not have been the users of all the archaeological pieces; expertise is not an information that we have archaeologically and we intended to find parameters, which do not depend on the expertise to identify hand preference.

Thus, all of the individuals involved in this experiment were beginners in prehistoric tool use. This lack of skill may be one of the reason why several scar features seem to be more related to individual characteristics than to hand preference. The variability of grip and movements, even if somehow constrained, likely influence scars production which may follow a pattern linked to the way each individual used the tool. It can be supposed that in prehistory, frequent use of stones as tools implies a certain degree of know-how and the transmission of this knowledge among members of a particular community. A certain regularity in the way stone tools were used might be expected. If so, individual characteristics might not exert such influence on scars features in archaeological assemblages. However, in any discipline, experts often develop idiosyncratic techniques which can be used to identify them. If stone tool use follows such a model, individual characteristics may be very important in the process of scars formation and, for a specific previously identified task, (even if unlikely because of the numerous variables possibly affecting tool surfaces) it might be possible to evaluate the minimum number of different users within an assemblage. This has to be tested in the future, involving experimenters skilled in stone tool use.

Hand holding the tool versus hand preference

The hand preference of an individual may vary depending on the action carried out [START_REF] Provins | The Specificity of Motor Skill and Manual Asymmetry: A Review of the Evidence and Its Implications[END_REF]. For instance, in the frame of this experiment, one individual declared himself as left-handed but spontaneously used the right hand to saw the wood during the whole experiment.

Also, some of the individuals asked to change the hand holding the tool (which was not allowed here) because of pain due to this unusual task. In an unconstrained context (as in Prehistory), it is likely that individuals would change hand if they feel pain or discomfort. Such modification of the hand used during the tasks would highly complicate the identification of left-handed and righthanded people based on archaeological stone tools. However, we can think that, during Prehistory, stone tool use was a more frequent activity and pain and discomfort may have been less common.

In any case, such behaviours (as well as the case of ambidextrous people) demonstrate that what we might intent to determine in an archaeological context is the hand holding the tool rather than the hand preference of the user. Only the study of large samples through multi-proxy examination might allow approaching hand preference.

Archaeological application

The results of this experiment, the k-NN results in particular, are promising and show that it might be possible to identify the hand holding the tool during use based on micro-scars extent and location. However, even if 75% of correct identification is acceptable, it remains valid for a constrained experimental context in wood sawing activity only. We can expect a drastic drop of accuracy in unconstrained situations. Indeed, it is for instance essential to consider that one tool could have been used several times, for various activities, and by different users. Furthermore, in an archaeological assemblage, it is very unlikely to deal only with artefacts used in sawing wood activities. It is now essential to extend the experimental reference corpus with unstandardized pieces (used for different activities), to improve the model with other traces (residues, micropolishes, striations), and to validate the results with blind-tests before any application on archaeological assemblages. Such an application could be considered when a strong enough (i.e. more than 90% accuracy) experimental model will be ready. It requires a large experimental collection on which each of the traces will be examined and documented. Our model involves parameters that are not dependent on the population ratio of right and lefthanders and is only based on repeatable measurements and description. Once improved, we will be able to propose a new proxy which could provide different result concerning hand preference ratio in Prehistory or reinforce the obtained results from the other disciplines.

Conclusion

Wear analysis, geometric morphometric and machine learning provided complementary results in order to examine if the hand holding the tool during use could be identified based on micro-scars. However, the k-NN method showed that the data provided by geometric morphometric and numerous data coming from wear analysis do not help in improving the This first experiment, with standardized tools dedicated to hand preference identification, yielded promising results and already highlighted variables that could be used as discriminants.

Nevertheless, the experimental corpus needs to be extended and the model tested further to provide reliable results from archaeological assemblages.

Acknowledgments

We thank Simon Puaud and Florent Détroit for their help and advices during the A. Bardo research was funded by the Fyssen foundation.

We are grateful to the two anonymous reviewers and editors who provided constructive comments, which contributed to improve this manuscript.

Break

Feathered

Step Abrupt

After Vaughan (1985) Towards the point Perpendicular

  ) (Whole experiment: min = 98.33 mvt/min., max = 191.33 mvt/min., mean = 135.96 mvt/min., SD = 23.59 mvt/min.; for left hand users: min = 98.33 mvt/min., max = 191.33 mvt/min., mean = 141.74 mvt/min., SD = 25.37 mvt/min.; for right hand users: min = 100.3 mvt/min., max = 166 mvt/min., mean = 129.9 mvt/min., SD = 20.41 mvt/min.; see the dataset in supplementary material). Before the experiment proper, a five-minute test was carried out without any instructions to observe how individuals hold and use the tool spontaneously, and to evaluate the constrains of our controlled experiment on the tool grasping mode. The first tool of the proper experiment was used with the only instruction to use the predefined edge. For the second and third tools, the participants had to use the predefined edge and keep the wooden stick horizontal on their knees. Each experiment was recorded individually and documented by one observer using a Samsung NV24HD video camera.

  in R. This algorithm uses Euclidean distances to find the k-Nearest Neighbours of the artefact to be classified. The class which is the most frequent among the k Nearest Neighbours is given to the unknown artefact (see Domínguez-Rodrigo and Baquedano, 2018 for an example of application on cut-marks on bones). Twenty-one combinations of different descriptors were tested: 1) Number of scars on the ventral and dorsal faces of the tool 2) types of scars on both faces 3) types of scars on dorsal face 4) types of scars on ventral face 5) directions of scars on both faces 6) directions of scars on dorsal face 7) directions of scars on ventral face 8) types and directions of scars on both faces 9) number, types and directions of scars on both faces 10) the 59 first principal components of the PCA on geometric morphometric data 11) the 2 first principal components of the PCA on geometric morphometric data 12) the 59 first principal components of the PCA on geometric morphometric data and the number of scars on both faces 13) position of the scar with the maximum extent on the surface perpendicularly to the edge on ventral face 14) position of the scar with the maximum extent on the surface perpendicularly to the edge on dorsal face 15) position of the scar with the maximum extent on the surface perpendicularly to the edge on both faces 16) maximum scar extent on the surface perpendicularly to the edge on ventral face 17) maximum scar extent on the surface perpendicularly to the edge on dorsal face 18) maximum scar extent on the surface perpendicularly to the edge on both faces 19) maximum scar extent and position of the scar with the maximum extent on the surface perpendicularly to the edge on both faces 20) all descriptors from microscopic observation 21) all descriptors from microscopic observation and geometric morphometricsWe normalized the descriptors so their values were comprised between 0 and 1 to avoid emphasizing descriptors with larger ranges of values. Each configuration was tested with each odd number between 3 and 59 as k value (3 being the minimum odd number for such model and 59 the maximum in our case as we had 60 artefacts). The k value of the final model was chosen based on the obtained accuracy and kappa for each model. As our sample size (N = 60) is quite small, we

  However, right-handed subjects produced significantly more micro-scars than left-handed subjects (X ̅ = 81.42, sd = 39.84 for the tools used by right-handers versus X ̅ = 53.04, sd = 18.60 for the left-handers; Mann-Whitney U one-tailed test: W = 240, p-value = 0.0011). Micro-scars were also significantly less invasive on tools used by left-handed subjects (X ̅ = 2.45 mm, sd = 1.13 mm for the tools used by right-handers versus X ̅ = 1.63 mm, sd = 0.73 mm for the left-handers; Mann-Whitney U one-tailed test: W = 771, p-value < 0.0001).

  independent from the user (respectively X² = 587.75, df = 76, p-value < 0.0001; X² = 240.43, df = 38, p-value < 0.0001 and X² = 114.05, df = 19, p-value < 0.0001).

Figure

  Figure4(2 columns) and Supplementary figures 1 to 7

Figure

  Figure 5 (1 column) and Supplementary figures 8 to 18

  accuracy of the model of classification. Nevertheless, counter-intuitive results may indicate a bias related to the structure of the tool used in this experiment and these non-significant variables from geometric morphometric and wear analysis should not be rejected definitively before further experiments. Moreover, the heterogeneity of raw materials used in prehistoric times may lead to similar results. The best model (75% accuracy) is obtained when based on the maximum scar extent and the position of the scar with the maximum extent on the surface of the tool perpendicularly to the edge on both faces. Other variables show overlap for tools used with the left hand and tools used with the right hand, and are likely to depend rather on individual characteristics.

  preparation of the experiments and Myriam Méziou, Anne-Cécile Haussonne and Anthony Herrel for logistic and funding administration. We are grateful to all the volunteers who participated. Data acquisition and analysis were partly done thanks to the 2D/3D imaging and the Material characterisation platforms of the MNHN. The surface scanning laboratory of the IPHES also provided assistance for data acquisition. Part of A. Rodriguez research was possible thanks to a grant to the Museum National d'Histoire Naturelle of Paris financed by the project "Robes de pierre", part of the POLYRE ('L'Aventure Polychrome') Chair of Excellence, supported by Sorbonne Universités. The PreMHo project is supported by funding from the French National Research Agency through LabEx ANR-10-LABX-0003-BCDiv, in the program "Investissements d'avenir" ANR-11-IDEX-0004-02. M.G. Chacón. research is funded by CERCA Programme/Generalitat de Catalunya, by Spanish Ministry of Science, Innovation and Universities (MICINN) of the Spanish Government, project nº HAR2016-76760-C3-1-P and the Catalonian Government, research group's nº 2017 SGR 836.

Fig. 1 :Fig. 2 :Fig. 3 :

 123 Fig.1: Soda-lime glass replica used in the experiment.

Fig. 4 :Fig. 5 :Fig. 6 :

 456 Fig.4: Examples of position and orientation of micro-scars for left and right-handers.

Table 1 :

 1 Results of the k Nearest Neighbours models. Models providing the best accuracy are in bold.

19 Maximum scar extent and posi�on of the scar with the maximum extent on the surface perpendicularly to the edge on both

	Model n°Included variables	K-value giving best results	Accuracy	95%CI	Kappa Sensi�vity Specificity
	1	Number of scars on the ventral and dorsal faces of the tool	23	0.65	0.5160, 0.7687	0.3092	0.7407	0.5758
	2	Types of scars on both faces	9 & 11	0.6833	0.5504, 0.7974	0.3537	0.5926	0.7576
	3	Types of scars on dorsal face	5	0.7	0.5679, 0.8115	0.398	0.7037	0.6970
	4	Types of scars on ventral face	31	0.6	0.4654, 0.7244	0.1864	0.5185	0.6667
	5	Direc�ons of scars on both faces	17 & 27	0.6	0.4654, 0.7244	0.2131	0.7037	0.5152
	6	Direc�ons of scars on dorsal face	41	0.6333	0.499, 0.7541	0.2834	0.7778	0.5152
	7	Direc�ons of scars on ventral face	21	0.6333	0.499, 0.7541	0.2691	0.6667	0.6061
	8	Types and direc�ons of scars on both faces	25	0.65	0.516, 0.7687	0.3	0.6667	0.6364
	9	Number, types and direc�ons of scars on both faces	27	0.6167	0.4821, 0.7393	0.2333	0.6296	0.6061
	10	The 59 first principal components of the PCA on geometric morphometrics data	55 & 57 & 59	0.55	0.4161, 0.6788	0	0	1
	11	The 2 first principal components of the PCA on geometric morphometrics data	31	0.5833	0.4488, 0.7093	0.1776	0.6667	0.5152
	12	The 59 first principal components of the PCA on geometric morphometrics data and the number of scars on both faces	13 & 15	0.65	0.516, 0.7687	0.3	0.6667	0.6364
	13	Posi�on of the scar with the maximum extent on the surface perpendicularly to the edge on ventral face	17 & 47	0.7333	0.6034, 0.8393	0.4539	0.6296	0.8182
	14	Posi�on of the scar with the maximum extent on the surface perpendicularly to the edge on dorsal face	21 & 23	0.5833	0.4488, 0.7093	0.1611	0.5556	0.6061
	15	Posi�on of the scar with the maximum extent on the surface perpendicularly to the edge on both faces	11	0.7333	0.6034, 0.8393	0.4613	0.7037	0.7576
	16	Maximum scar extent on the surface perpendicularly to the edge on ventral face	25 & 37	0.75	0.6214, 0.8528	0.4966	0.7407	0.7576
	17	Maximum scar extent on the surface perpendicularly to the edge on dorsal face	21 & 23	0.7333	0.6034, 0.8393	0.4613	0.7037	0.7576
	18	Maximum scar extent on the surface perpendicularly to the edge on both faces	23	0.75	0.6214, 0.8528	0.5	0.7778	0.7273
			5	0.75	0.6214, 0.8528	0.5066	0.8519	0.6667
		faces
	20	All descriptors from microscopic observa�on	5	0.75	0.6214, 0.8528	0.5	0.7778	0.7273
	21	All descriptors from microscopic observa�on and geometric morphometrics	9	0.75	0.6214, 0.8528	0.5066	0.8519	0.6667

Left-handers Right-handers

MIN PC1


Julie Meneghel

Eric Dugat-Bony

Françoise Irlinger

Valentin Loux

Marie Vidal

Stéphanie Passot

Catherine Béal

Séverine Layec

Fernanda Fonseca
email: fonseca@grignon.inra.fr.

Draft Genome Sequence of Lactobacillus delbrueckii subsp. bulgaricus CFL1, a Lactic Acid Bacterium Isolated from French Handcrafted Fermented Milk

Lactobacillus delbrueckii subsp. bulgaricus (L. bulgaricus) is a lactic acid bacterium widely used for the production of yogurt and cheeses. Here, we report the genome sequence of L. bulgaricus CFL1 to improve our knowledge on its stress-induced damages following production and end-use processes.

L actobacillus delbrueckii subsp. bulgaricus (L. bulgaricus) is a lac- tic acid bacterium that is traditionally used as a dairy starter. L. bulgaricus is mainly involved in milk acidification and more generally plays a role in the preservation and development of flavor, texture, and vitamins of dairy products [START_REF] Van De Guchte | The complete genome sequence of Lactobacillus bulgaricus reveals extensive and ongoing reductive evolution[END_REF][START_REF] Ott | Origin of acetaldehyde during milk fermentation using 13 C-labeled precursors[END_REF]. In addition, this bacterium is exploited to produce lactic acid for use in the food, cosmetics, and chemical sectors [START_REF] Datta | Lactic acid: recent advances in products, processes and technologies-a review[END_REF].

Aimed at enhancing its industrial utilization, considerable research efforts have been undertaken to characterize the diversity of L. bulgaricus strains. Since 2006, the genome sequences of six different strains were published: ATCC 11842 (1), ATCC BAA-365 (4), 2038 (5), CNCM I-1519 and CNCM I-1632 [START_REF] Mcnulty | The impact of a consortium of fermented milk strains on the gut microbiome of gnotobiotic mice and monozygotic twins[END_REF], and CRL871 [START_REF] Laiño | Draft genome sequence of Lactobacillus delbrueckii subsp. bulgaricus CRL871, a folateproducing strain isolated from a northwestern Argentinian yogurt[END_REF]. Actually, the production of lactic acid bacteria by traditional fermentation, their stabilization (by freezing and/or drying methods), and final use (in yogurt, cheese-making, or biomolecule production) are responsible for the undesirable degradation of their viability and functionality. Therefore, scientific work has also focused on optimizing the formulation and operating conditions of starter production and the study of L. bulgaricus behavior under related stress conditions, especially considering the sensitive strain CFL1 (8-16). However, the majority of these studies are descriptive, and there is a clear need for comprehension of the underlying molecular cellular damage mechanisms.

Consequently, we sequenced the genome of L. bulgaricus CFL1 (available at CIRM-BIA, Rennes, France), isolated Ͼ20 years ago from handcrafted fermented milk, by using Illumina MiSeq technology. Paired-end sequences (2 ϫ 250-bp long) were merged using FLASH [START_REF] Mago | FLASH: fast length adjustment of short reads to improve genome assemblies[END_REF] and assembled using SPAdes (version 3.1.1, with default parameters) [START_REF] Bankevich | SPAdes: a new genome assembly algorithm and its applications to singlecell sequencing[END_REF], which generated 44 large contigs (Ͼ1,000 bp), with an average sequencing coverage of 115-fold, N 50 contig of 116 Kbp, and a largest contig of 266 Kbp. The unclosed draft genome is 1,757,917 bp in length and has a GϩC content of 49.8%. Gene prediction and annotation were performed using the Inte-grated Microbial Genomes (IMG) system [START_REF] Markowitz | IMG: the integrated microbial genomes database and comparative analysis system[END_REF], according to the standard operating procedure of the Department of Energy-Joint Genome Institute (DOE-JGI) microbial genome annotation pipeline (MGAP version 4) [START_REF] Mavromatis | The DOE-JGI standard operating procedure for the annotations of microbial genomes[END_REF]. The genome is composed of 1,882 predicted genes comprising 1,794 coding DNA sequences, 20 rRNAs, and 68 tRNAs.

The analysis of the genome of L. bulgaricus CFL1 revealed general consistency with previously sequenced strains, including GϩC content, carbohydrate metabolism, and genes involved in exopolysaccharide and lipopolysaccharide production. However, a disparity regarding the detected clustered regularly interspaced short palindromic repeat (CRISPR)-associated (Cas) systems should be emphasized. They consist of defense systems against plasmid and phage invasions that are classified into three major groups (types I, II, and III) [START_REF] Bhaya | CRISPR-Cas systems in bacteria and archaea: versatile small RNAs for adaptive defense and regulation[END_REF]. Whereas in L. bulgaricus, only type II or III seems to be present, if any [START_REF] Urshev | Distribution of clustered regularly interspaced palindrome repeats CRISPR2 and CRISPR3 in Lactobacillus delbrueckii ssp. bulgaricus strains[END_REF], interestingly, CFL1 appeared to possess both of these CRISPR-Cas system types simultaneously.

The availability of the genome sequence and annotation of L. bulgaricus CFL1 will allow a deeper insight into its stress response mechanisms, thus making it possible to identify potential preservation issues and to better understand cell damages following production and end-use processes, especially through transcriptomic and proteomic approaches.

Nucleotide sequence accession numbers. The draft genome sequences of L. bulgaricus CFL1 have been deposited at the EMBL database under accession numbers CZPS01000001 to CZPS01000044.

Genome Announcements 2 genomea.asm.org March/April 2016 Volume 4 Issue 2 e00052-16 on May 31, 2016 by guest http://genomea.asm.org/ Downloaded from

ACKNOWLEDGMENTS

We thank the INRA GeT-PlaGE platform (http://get.genotoul.fr) for sequencing the genome, the INRA MIGALE bioinformatics platform (http: //migale.jouy.inra.fr) for providing computational resources and support, and the IMG E/R pipeline for generating annotations. crossmark Genome Announcements March/April 2016 Volume 4 Issue 2 e00052-16 genomea.asm.org 1 on May 31, 2016 by guest http://genomea.asm.org/ Downloaded from