Flavien Maucourt

Aurélie Cébron

Hélène Budzinski

Karyn Le Menach

Laurent Peluhet

Sonia Czarnes

Delphine Melayah

David Chapulliot

Laurent Vallon

Gaël Plassart

Karyn Le Menach

Mylène Hugoni

Laurence Fraissinet-Tachet
email: tachet-laurence.fraissinet-tachet@univ-lyon1.fr

Laurence Fraissinet

Maucourt Flavien

Prokaryotic, microeukaryotic and fungal composition in a longterm polychlorinated biphenyl-contaminated brownfield

Keywords: polychlorinated biphenyls, metabarcoding, microbial communities, brownfield, soil, pollution

come

Introduction

Microbial diversity is crucial for soil ecosystem functioning and ecosystemic services they deliver [START_REF] Wall | Soil Ecology and Ecosystem Services[END_REF].

In the last centuries, human activities have broadly altered soil quality and functioning, including microorganisms, by using toxic compounds known as xenobiotics. Among those xenobiotics, persistent organic pollutants (POPs) are of major health and ecological concerns as many bioaccumulated and biomagnified in the trophic network [START_REF] Beaudette | Comparison of Gas Chromatography and Mineralization Experiments for Measuring Loss of Selected Polychlorinated Biphenyl Congeners in Cultures of White Rot Fungi[END_REF]. According to the Stockholm Convention from 2001, POPs included the class of polychlorinated biphenyls (PCBs) consisting in poly-chlorinated aromatic compounds (209 theoretical chlorinated forms named congeners and 150 physically synthesizable forms), used in the industry from 1929 to their prohibition at the end of 1980s as they were recognized as endocrine disruptors and carcinogenic substances [START_REF]Weltgesundheitsorganisation, International Programme on Chemical Safety[END_REF][START_REF] Pointing | Feasibility of bioremediation by white-rot fungi[END_REF]. Presently, those molecules are still recovered in organisms as well as in ecosystems, from soils to waters and sediments. PCB soil decontamination represents a challenge for management and restoration policies of impacted sites and mostly involves physico-chemical methods which are expensive, long and often generating by-products like furans or dioxins, also known as carcinogenic molecules [START_REF] Arbon | gamma.-Ray Destruction of Individual PCB Congeners in Neutral 2-propanol[END_REF]. A recent costless alternative, consisting in bioremediation (i.e. process used to treat contaminated media by living organisms) emerged from several studies highlighting the capacity of microorganisms to transform some PCB molecules [START_REF] Pieper | Bacterial Metabolism of Polychlorinated Biphenyls[END_REF][START_REF] Stella | Bioremediation of long-term PCB-contaminated soil by white-rot fungi[END_REF].

Most studies focusing on bacterial and fungal PCB transformation were conducted in vitro [START_REF] Stella | Bioremediation of long-term PCB-contaminated soil by white-rot fungi[END_REF][START_REF] Tigini | Isolation and characterisation of polychlorinated biphenyl (PCB) degrading fungi from a historically contaminated soil[END_REF][START_REF] Germain | Screening and metabolic potential of fungal strains isolated from contaminated soil and sediment in the polychlorinated biphenyl degradation[END_REF] but recent bioremediation and rhizoremediation studies of PCB contaminated sites have been reported supporting that bioremediation is a promising way to depollute PCBs [START_REF] Hashmi | PCBs attenuation and abundance of Dehalococcoides spp., bphC, CheA, and flic genes in typical polychlorinated biphenyl-polluted soil under floody and dry soil conditions[END_REF][START_REF] Sharma | Advances and perspective in bioremediation of polychlorinated biphenyls contaminated soils[END_REF][START_REF] Pino | Phytoremediation of soil contaminated with PCBs using different plants and their associated microbial communities[END_REF][START_REF] Steliga | Assessment of Biodegradation Efficiency of Polychlorinated Biphenyls (PCBs) and Petroleum Hydrocarbons (TPH) in Soil Using Three Individual Bacterial Strains and Their Mixed Culture[END_REF]. Those previous works showed the ability of microorganisms affiliated mainly with Proteobacteria, Actinobacteria, Firmicutes, Chloroflexi, Dehalococcoides and the white rot Fungi functional guild to transform PCBs [START_REF] Pieper | Bacterial Metabolism of Polychlorinated Biphenyls[END_REF][START_REF] Borja | Polychlorinated biphenyls and their biodegradation[END_REF][START_REF] Furukawa | Microbial degradation of polychlorinated biphenyls: Biochemical and molecular features[END_REF].

Other non-lignolytic Ascomycota fungi were also reported as PCB transformers such as Purpureocillium lilacinus [START_REF] Sietmann | Oxidative ring cleavage of low chlorinated biphenyl derivatives by fungi leads to the formation of chlorinated lactone derivatives[END_REF]. Because of their hyphal structure, all these filamentous fungi are suggested as good candidates to easily penetrate polluted matrices and access to poorly bio-accessible pollutants [START_REF] Kohlmeier | Taking the Fungal Highway: Mobilization of Pollutant-Degrading Bacteria by Fungi[END_REF]. Those filamentous fungi can also act as dispersion vector of bacteria [START_REF] Kohlmeier | Taking the Fungal Highway: Mobilization of Pollutant-Degrading Bacteria by Fungi[END_REF] even if bacteria already have good reported dispersal capabilities [START_REF] Wei | The population dynamics of bacteria in physically structured habitats and the adaptive virtue of random motility[END_REF]. These in vitro studies mostly analyzed the transformation of few PCBs suggesting that their depollution action with respect to all PCBs congeners is finally quite partial.

Moreover, the actual limit of in vitro PCB transformation studies is the lack of cultured microbial representatives. In contrast, in situ studies are still scarce and few characterized the cultivable fraction of Fungi retrieved in PCB-polluted-soils. Previous works showed the presence of Acremonium, Aspergillus, Paecilomyces, Penicillium, Phomopsis, Cladosporium, Doratomyces, Myceliophtora by using cultivation methods [START_REF] Tigini | Isolation and characterisation of polychlorinated biphenyl (PCB) degrading fungi from a historically contaminated soil[END_REF][START_REF] Čvančarová | Biodegradation of PCBs by ligninolytic fungi and characterization of the degradation products[END_REF][START_REF] Mouhamadou | Potential of autochthonous fungal strains isolated from contaminated soils for degradation of polychlorinated biphenyls[END_REF][START_REF] Sage | Autochthonous ascomycetes in depollution of polychlorinated biphenyls contaminated soil and sediment[END_REF]. By using 454 pyrosequencing, more recent in situ studies identified Proteobacteria, Actinobacteria, Firmicutes and Basidiomycota phyla, including species already known to transform PCBs [START_REF] Stella | Bioremediation of long-term PCB-contaminated soil by white-rot fungi[END_REF][START_REF] Zenteno-Rojas | Structure and diversity of native bacterial communities in soils contaminated with polychlorinated biphenyls[END_REF]. Other studies also reported that PCB contamination increased soil fungal richness and promoted the development of bacterial species able to metabolize these pollutants [START_REF] Ding | Responses of microbial community in rhizosphere soils when ryegrass was subjected to stress from PCBs[END_REF][START_REF] Marchal | Molecular Characterization of Fungal Biodiversity in Long-Term Polychlorinated Biphenyl-Contaminated Soils[END_REF]. But among these studies focusing on microbial diversity under PCB contaminated ecosystems, none investigated micro-eukaryotes (other than Fungi) neither archaeal diversity. Moreover, soil in situ studies are still scarce while they represent the better way to fully assess microbial diversity that could be involved in PCB transformation at the ecosystemic level.

We hypothesized that in long term PCB contaminated soils, pollution impact microorganisms composition from the three-domains-of-life and select those adapted and potentially involved in transformation of some PCBs. In the present work, we aimed at testing if microbial abundance and composition from the three-domains-of-life differ in contrasted PCB concentrations to identify PCB tolerant microorganisms including some possibly involved into PCB transformation. This work took advantage of a brownfield (a formerly factory land) that presented a spatially heterogeneous PCB exposition.

Experimental Procedures

Study site, sampling, and PCB concentrations

Soil, whose characteristics were detailed into the supplementary experimental procedures, was sampled in a peri-urban site in Pont-de-Claix, Isère, France (45,136578°N 5,697205°E). This site is a brownfield where a painting factory was established in 1956, demolished and forsaken since 2009. In 2017, engineering consultants from ENVISOL (www.envisol.net) transformed this brownfield (contaminated by PCBs for more than six decades) into an experimental site for the research and innovation CRISALID project in field of sustainable development and brownfield depollution (Centre de Réflexion ISérois en Aménagement Durable, IDfriches: http://www.crisalid-idfriches.fr). On October the 15 th 2018, twenty soil cores were randomly sampled, on a sampling grid with samples separated by at least 4 meters apart from two different geographical areas on the brownfield, called "Zone 2" and "Zone 3/4" corresponding to paint storage and production areas respectively. Briefly, for each sample, 500 g of soil was taken out, from 0 to 10 cm depth with 12-cm diameter, sieved (2-mm), homogenized manually and stored at -80°C in 50 g portions until nucleic acid extraction and PCB chemical assay of 71 congeners as described in supplementary text. Complementarily, for each sample, 375 g of sieved-soil stored few days at 4°C have been directly used for indicator PCB (iPCB) assay according to GC/MS/MS method [Hexane / Acetone exctract] -NF EN 16167 -XP X 33-012 method (Eurofins).

C-CO2 Microbial mineralization and enzymatic biological activity assay

For microbial mineralization, ten grams of dry weight soil, adjusted to 60% soil water retention capacity (WRC), were incubated at 24°C into 150 ml glass flasks hermetically sealed. A 3-mL portion of the flask atmosphere was sampled with a syringe and analyzed for CO2 content through infrared spectrophotometer (Binos 1004) [START_REF] Cébron | Impact of clay mineral, wood sawdust or root organic matter on the bacterial and fungal community structures in two aged PAHcontaminated soils[END_REF]. Microbial activity was evaluated using fluorescein diacetate (FDA) as a substrate on soil samples in triplicates at the SEMSE laboratory (https://www.semse.fr) according to a protocol derived from [START_REF] Schnürer | Fluorescein Diacetate Hydrolysis as a Measure of Total Microbial Activity in Soil and Litter[END_REF] [START_REF] Schnürer | Fluorescein Diacetate Hydrolysis as a Measure of Total Microbial Activity in Soil and Litter[END_REF]. Experimental details were developed into the supplementary text.

DNA extraction and Illumina sequencing

Three DNA extractions were conducted from 2 g of soil for each sample, pooled and quantified by Nanodrop (ND 1000 S Spectrophotometer®). A first step allowing protein removal was conducted using phenol/chloroform/isoamyl alcohol (25/24/1) using the Power Soil DNA Isolation kit according to the manufacturer recommendation (Mo Bio Laboratories, Carlsbad, CA, USA). Additionally, a DNA extraction was carried out without any biological matrix, quantified, and considered as a negative control to evaluate eventual ambient contaminations. Quality was verified by 1% agarose gel electrophoresis.

For sequencing, PCR were performed, in triplicates, on a Biorad C1000 thermal cycler (Biorad), using: 0.5 U of Taq DNA polymerase (Invitrogen, Illkirch, France), 1X PCR Buffer, 1.5 mM of MgCl2, 0.8 μM of each primer, 0.2 mM of each dNTP, 8 μg of BSA (New England Biolabs) and 20 ng of soil metagenomic DNA, in a final volume of 25 μL. The PCR programs, for the 16S and 18S rRNA amplifications, were both composed of 10 min at 94°C, 35 cycles of 1 min at 94 °C, 1 min at 58°C, and 1 min 30 sec at 72 °C, followed by 10 min at 72°C.For fungal ITS amplification, PCR conditions used were 3 min at 94 °C, 35 cycles of 45 s at 94°C, 45 s at 50°C, and 45 s at 72 °C, followed by 7 min at 72°C.The three PCR replicates from each sample were pooled, purified with Agencourt AMPure XP PCR Purification kit (Beckman Coulter, Villepinte, France), and quantified using the Quant-iT Picogreen dsDNA Assay Kit (Life Technologies, NY, USA). Prokaryotic amplification of the V3-V5 region of the 16S rRNA genes was performed using the universal primers 515F (5'-α-GTG-YCA-GCM-GCC-GCG-GTA -3' [START_REF] Caporaso | Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample[END_REF]) and 909R (5'-β-CCC-CGY-CAA-TTC-MTT-TRA-GT -3' [START_REF] Wang | Conservative Fragments in Bacterial 16S rRNA Genes and Primer Design for 16S Ribosomal DNA Amplicons in Metagenomic Studies[END_REF]). Eukaryotic amplification of the V4 region of the 18S rRNA genes was performed using the universal primers TAReuk_F (5'-α-CCA-GCA-SCY-GCG-GTA-ATT-CC -3') and TAReuk_R (5'-β-ACT-TTC-GTT-CTT-GAT-YRA -3') [START_REF] Russo | A Metaproteomic Analysis of the Response of a Freshwater Microbial Community under Nutrient Enrichment[END_REF]. Fungal amplification of the ITS-2 region was conducted using the primers fITS7 (5'-α-GTG-YCA-GCM-GCC-GCG-GGT-A-3') and ITS4 (5'-β-TCC-TCC-GCT-TAT-TGA-TAT-GC-3') [START_REF] Hugoni | Spatiotemporal variations in microbial diversity across the three domains of life in a tropical thalassohaline lake (Dziani Dzaha, Mayotte Island)[END_REF]. For all primer sets, α and β represented the two Illumina overhanging adapter sequences (TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG and GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA G, for α and β, respectively) allowing the construction of amplicon libraries by a two-step PCR. Amplicons libraries production was produced on the Illumina MiSeq platform (2 x 250-bp paired-end reads) by Biofidal, Vaulx-en-Velin, Francehttp://www.biofidal-lab.com.

Sequence processing

Prokaryotic 16S rRNA gene, eukaryotic 18S rRNA gene and ITS paired-end reads were merged with a maximum of 10% mismatches in the overlap region using Vsearch [START_REF] Rognes | VSEARCH: a versatile open source tool for metagenomics[END_REF]. Additional denoising procedures consisted in discarding reads either with no expected length (i.e. expected size between 250 to 580 bp, 250 to 450 bp, 200 to 600 bp for prokaryotic 16S rRNA genes, eukaryotic 18S rRNA genes and ITS regions, respectively) or containing ambiguous bases (N). After dereplication, the clusterisation tool ran with SWARM [START_REF] Mahé | Swarm: robust and fast clustering method for ampliconbased studies[END_REF] that used a local clustering threshold. In the present work, the aggregation distance equals 3. Chimeras were then removed using Vsearch and low abundance sequences were filtered at 0.005%, i.e. Operational Taxonomic Units (OTUs) with at least 0.005% of all sequences were kept [START_REF] Bokulich | Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing[END_REF].

Taxonomic affiliation was performed with both RDP Classifier [START_REF] Wang | Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy[END_REF] and Blastn+ [START_REF] Camacho | BLAST+: architecture and applications[END_REF] against the 132 SILVA database [START_REF] Glöckner | 25 years of serving the community with ribosomal RNA gene reference databases and tools[END_REF] for 16S and 18S rRNA genes and the ITS region. This procedure was automated in the FROGS pipeline [START_REF] Escudié | FROGS: Find, Rapidly, OTUs with Galaxy Solution[END_REF]. Then, a normalization procedure was applied to compare samples and the datasets were randomly resampled down to 5,544 sequences for prokaryotic marker, to 6,845 sequences for 18S rRNA amplicons and to 12,407 for ITS region.

Statistical analyses

A dissimilarity coefficient was measured between samples depending on total PCB contamination, using the Gower method (quantitative and symmetric [START_REF] Gower | A General Coefficient of Similarity and Some of Its Properties[END_REF]). Then, applying the distance matrix (function vegdist, in R package vegan [START_REF] Dixon | VEGAN, a package of R functions for community ecology[END_REF]), samples were grouped according to their PCB concentration by Ward method (function hclust, in R package stat [START_REF]a language and environment for statistical computing: reference index[END_REF]). A dendrogram was built using the ggdendro package in R and ggplot2 [START_REF] Wickham | A Layered Grammar of Graphics[END_REF]. Richness (Chao1) and diversity (Shannon) indices were computed with Past3 program [START_REF] Hammer | PAST: Paleontological Statistics Software Package for Education and Data Analysis[END_REF]. The comparison of richness and diversity indices for each polluted group was conducted by a Wilcoxon test (function wilcox.test in R package stat) as the mean comparison of number of DNA sequences copies obtained by qPCR method. An ordination of the samples using NMDS approach at OTU level on Bray-Curtis dissimilarity matrices was performed (function metaMDS in R package vegan) and a stress value was calculated to measure the difference between the ranks on the ordination configuration and the ranks in the original similarity matrix for each repetition [START_REF] Ramette | Multivariate analyses in microbial ecology[END_REF]. An acceptable stress value should be below 0.1. The difference between P0 group, considered as the non-polluted group, and P1,P2,P3 and P4 polluted groups was tested using PERMANOVA analysis on Bray-Curtis dissimilarity matrices using the adonis function from the package vegan. To analyze common and/or specific OTUs according to the different groups, Venn diagram was realized with the online tool Ugent (http://bioinformatics.psb.ugent.be/webtools/Venn/). Heat-maps were generated with the Phyloseq package coupled with the ggplot and vegan packages in the R software.

Quantitative PCR

qPCR analyses were performed on a CFX96 Touch Real-Time PCR System (Bio-Rad) running CFX Maestro Software 2.0 using the same primers that the ones used for metabarcoding assay without the Illumina overhanging adapter sequences. Copy numbers of 16S rRNA genes, 18S rRNA genes and ITS regions in the environmental DNA samples were determined in duplicate on the non-diluted sample and for two different sample dilutions (10-fold and 100-fold). The reaction mixture (20 µL) contained SensiFAST™ SYBR ® No-ROX Kit. For a 20 µL reaction mix, 20 µM of primers and ultra-pure sterile water were added. All reactions were performed in 96-well qPCRplates (Eurogentec) with optical tape (Eurogentec). Two microliter of diluted (10-fold and 100-fold) or non-diluted environmental DNA was added to 18 µl of mix in each well. The accumulation of newly amplified double-stranded gene products was followed online as the increase in fluorescence due to binding of SYBR Green fluorescent dye. All qPCR programs are listed in Table S4. The fluorescence signal was read in each cycle after the amplification step to ensure stringent product quantification. Specificity for the qPCR reaction was tested by melting curve analysis (65°C-97°C) and then by electrophoresis on 1% agarose gel. Standard curves for the targeted genes (16S rRNA gene,18S rRNA gene and ITS region) were generated from a mix of plasmids representative of the environment studied. Next, serial dilutions of previously titrated suspensions were performed and amplified by conventional PCR from environmental clones and then purified (NucleoSpin® Plasmid, Macherey-Nagel) and quantified for this study. All reactions were performed with standard curves spanning from 10 1 to 10 8 copies of targeted gene per mL. Mean PCR efficiencies and correlation coefficients for standard curves were as follows: for the 16S rRNA gene assay, 91.71 %, r² = 0.99, for the 18S rRNA gene assay, 81.95 %, r² = 0.99, and for the ITS region assay, 90.92 %, r² = 0.99.

Results

PCB concentration analyses and soil sample clustering

Physico-chemical characteristics of the sample site, localized in Pont de Claix (France), are listed in Table S1, and described an alkaline soil with heterogeneous texture. The twenty soil samples, randomly collected, displayed variable indicator PCB (iPCB) concentrations from 0.03 to 31.11 mg. kg -1 dry mass of soil (Table 1). Some samples presented low iPCB concentrations (between 0.03 and 0.51 mg.kg -1 dry mass of soil) while other soil samples were characterized by a higher level of iPCBs (from 1.67 to 31.11 mg.kg -1 dry mass of soil). Among the iPCBs detected in this site, the PCBs 28,[START_REF] Matturro | Microbiome changes and oxidative capability of an anaerobic PCB dechlorinating enrichment culture after oxygen exposure[END_REF]101,118,138,153 and 180 represented 10, 32, 18, 15, 10, 10 and 5% of the total iPCBs respectively, the higher chlorinated iPCB 180 being the less abundant congener. Complementarily, 71 supplemental PCBs (including the 7 iPCBs) were assayed directly in soil ranging from 0.61 to 135.84 mg.kg -1 dry mass of soil (Table S2).

Globally, the total concentration of the 71 PCBs was proportional to the total concentration of iPCBs confirming that iPCBs were reliable PCB indicators in this site. Consequently, further analyzes were conducted considering only the iPCB concentration of each sample as a proxy of soil contamination level.

A hierarchical clustering of samples was performed according to iPCB concentrations showing a segregation of the samples (Figure 1A). Three groups, named P0, P1 and P2, were represented by 5 or 6 soil samples and two other groups, named P3, contained only two samples, and P4 that regrouped the two last most PCB concentrated samples. The P0 group was characterized by the lowest iPCB concentrations from 0.03 to 0.51 mg.kg -1 dry mass of soil and considered as the non-polluted group related to the threshold of 1 mg.kg -1 established by the local decision making. P1 group contained iPCB concentrations from 1.67 to 3.83 mg.kg -1 , P2 from 4.66 to 7.69 mg.kg -1 , P3 from 10.06 to 14 mg.kg -1 and P4 from 21.60 to 31.11 mg.kg -1 . S3). Rarefaction curves indicated that some samples almost reached an asymptote in the prokaryotic dataset (Figure S1A) and that the sequencing effort was sufficient to describe microeukaryotic and fungal diversity (Figure S1B andC).

Structure of microbial communities in PCB contaminated soils

The difference in prokaryotic, microeukaryotic and fungal OTUs composition, assessed through a permutation analysis of variance (PERMANOVA), showed significant differences for P0 group compared to the other groups P1, P2, P3 and P4 for prokaryotes (p=0.002, R=0.31) while only trends were detected for microeukaryotic (p=0.072, R=0.25) and fungal communities (p=0.069, R=0.39).

Moreover, the NMDS, realized at OTU level, graphically showed a segregation between the P0 group on the first hand and the P1, P2, P3 and P4 groups on the other hand, especially for the prokaryotic dataset and supported with stress values of 0.099, 0.075 and 0.080 (Figure 1B, 1C and 1D), respectively.

The prokaryotic richness (estimated through the Chao1 index) of the P0 group was significantly lower compared to P1 group (p = 0.05) (Figure S2A). The same trend was observed for the microeukaryotic richness with a marginal significance (p = 0.06) between P0 and P1 groups, being significant between P0 and P2 groups (p = 0.02, Figure S2B). No significant differences were observed for Chao1 richness based on the fungal marker (Figure S2C). Prokaryotic diversity (estimated through the Shannon index) (Figure S2A) was significantly lower in P0 group compared to P1 group (p = 0.01) and P2 group (p = 0.03) and the same trend was observed between P0 and P3 groups, being marginally significant (p = 0.09) whereas microeukaryotic and fungal diversity was not significantly different whatever the PCB contamination level (Figure S2B andC).

Prokaryotic community composition

The present work showed a predominance of Bacteria over Archaea and the later representing less than 1% of the total prokaryotic sequences and mainly affiliated with Thaumarchaeota. The detailed analysis of bacterial composition showed that Acidobacteria, Actinobacteria and Proteobacteria were the most abundant phyla, representing 225, 318, 485 OTUs (17.76%, 25.51% and 33.71% of the total sequences), respectively (Figure 2A). Actinobacteria and Chloroflexi showed lower abundance from P0 to P4 (from 34.06% and 4.11% to 18.28% and 1.40% of the total sequences, respectively) contrary to Proteobacteria phylum with higher abundance in P4 than P0 (from 41.62% to 20.88% of the total sequences). At the class taxonomic level, the 0319-7L14, MB-A2-108 (both from Actinobacteria), P2-11E (Chloroflexi) soil groups and Oxyphotobacteria (Cyanobacteria) were enriched in P0 group. Gitt-GS-136 soil group from Chloroflexi was only enriched in P0 and P1. Thermoleophilia (Actinobacteria) and Gemmatimonadetes (Gemmatimonadetes) showed a decreasing abundance from P0 to P4 (from 8.64% and 10.87% to 2.80% and 4.37% of the total sequences, respectively). Conversely, Longimicrobia (Gemmatimonadetes) class was only detected in P4 group and the Gammaproteobacteria (Proteobacteria) showed an increasing abundance from P0 to P4 (11.23% to 30.21% of the total sequences). In the same way, Deltaproteobacteria (Proteobacteria) showed a rising abundance from P0 to P3 (11.23% to 30.21% of the total sequences) but its abundance decreased in P4 group.

At the family taxonomic level (Figure 2B), the Nocardioidaceae, three unknown families from Gaiellales and IMCC26256 order and 0319-7L14 class (all belonging to Actinobacteria) and the Rhodobacteracea family (Proteobacteria) were enriched in the P0 group. Some families showed higher abundances in the P3 group specifically such as bacteriap25 soil group, Dongiaceae and two unknown families from RCP2-54 and CCD24 classes (all belonging to Proteobacteria). Families only enriched in P4 group were Xanthomonadaceae, Acidithiobacillaceae, Rhodanobacteraceae (all belonging to Proteobacteria), Microscillaceae (Bacteroidetes), Longimicrobiaceae (Gemmatimonadetes) and an unknown belonging to Subgroup7 (Acidobacteria).

Microeukaryotic community composition

At the phylum level (Figure 3A), the microeukaryotic composition showed decreasing abundance from P0 to P4 for Cercozoa and Chytridiomycota (from 32.73% and 15.74% to 25.04% and 5.58% of the total sequences, respectively) contrary to Amoebozoa with increasing abundance from P0 to P4 (3.41% to 11.37% of the total sequences). At the class taxonomic level, Ulvophyceae (Chlorophyta) and Craspedida (Choanoflagellida) classes were enriched in P0 and Glissomonadida (Cercozoa), Chytridomycetes (Chytridiomycota) and Glomeromycetes (Glomeromycota) showed a decreasing abundance from P0 to P4 (from 13.07%, 8.55% and 2.79% to 10.63%, 4.41% and 0% of the total sequences respectively). On the contrary, an unknown class belonging to Charophyta was enriched in P4 and Arcellinida and Euamoebida (both belonging to Amoebozoa) showed an increasing abundance from P0 to P4 (2.24% and 1.16% to 3.99% and 7.37% of the total sequences respectively). In the same way, the Intramacronucleata class (Ciliophora) showed a rising abundance from P0 to P4 (6.49% to 24.51%) and specifically in P2 group (where it reached 40% of the total sequences).

At the family taxonomic level (Figure 3B), the microeukaryotic families enriched in the group P0

were Mortierellaceae (Mortierellomycota), Glomeraceae (Glomeromycota), Spizellomycetaceae

Fungal community composition

ITS dataset (Figure 4A) was dominated by Ascomycota and Basidiomycota representing 372 and 87

OTUs respectively, with 26.57 to 41.92% of Ascomycota and 23.15 to 28.99% of Basidiomycota in the different groups. The Chytridiomycota were detected with a small relative abundance whatever the groups with a decreasing abundance of sequences observed from P0 to P4 groups (from 12.43% to 4.27%, respectively). Fungal community analysis at the class level (Figure 4A) revealed that an unknown class (belonging to Mortierellomycota) only presented from the P2 to P4 groups (representing 8.07% to 9.08% of sequences). The Saccharomycetes class (belonging to Ascomycota) represented 7.93 and 4.54% of sequences only in P2 and P4 groups, respectively. An uncultured Ascomycota class was detected only in the P4 group representing 2.79% of sequences. The families Aspergillaceae, Sporomiaceae, Pleosporaceae, Pezizaceae (all belonging to Ascomycota), Powellomycetaceae (Chytridiomycota), Tricholomataceae, an unknown family from Agaricales (both belonging to Basidiomycota) presented a higher abundance in P0 group (Figure 4B). The families Halosphaeriaceae, Sympoventuriaceae (both belonging to Ascomycota), Ceratobasidiaceae, Auriculariaceae, Psathyrellaceae (all belonging to Basidiomycota), two families from Spyzellomycetales and Lobulomycetales orders (Chytridiomycota) were represented with a higher abundance in P3 group. The families Plectosphaerellaceae, Stachybotryaceae (both from Ascomycota), Pleurotaceae, Entolomataceae and families from the orders Sebacinales and Agaricales (all belonging to Basidiomycota) were represented by a drastic higher abundance in the P4 groups and one family from Saccharomycetales (Ascomycota) was characterized by a higher abundance in P2 and P4 groups.

Core versus specific microbiome among PCB pollution

Common and specific OTUs among the five PCB concentration groups (P0 to P4) were investigated using Venn diagrams. Common OTUs (or core OTUs) to the five groups consisted in 468 prokaryotic OTUs (31.68% of prokaryotic richness), 157 microeukaryotic OTUs (20.87% of eukaryotic richness) and 173 fungal OTUs (25.25% of fungal richness) (Figure S3, S4 and S5). Taxonomic information about those common and specific OTUs were then analyzed at the family level considering only known families (unknown families have been discarded from the bar diagram analysis). The most contaminated groups, consisting in all combinations of P1 to P4 (P1+P2, or P1+P2+P3 or P3+P4 etc..), contained 25.42% of the prokaryotic OTUs while less than 2.31% of the prokaryotic OTUs were specific to one group (P0, P1, P2, P3 or P4) (Figure S3). Microeukaryotic communities harbored 1.23 to 4.52% of specific OTUs to one group (P0, P1, P2, P3 or P4) (Figure S4). Concerning fungal OTUs, 1.02 to 4.49% of OTUs were specific to one group (P0, P1, P2, P3 or P4) (Figure S5). Additional results were described in the supplementary text.

Microbial abundance and activity

Prokaryotic, eukaryotic and fungal abundances were quantified using qPCR targeting the 16S rRNA gene, 18S rRNA gene and ITS region, respectively (Figure S6). An increase in microbial community abundance was observed from P0 to P1 and P2 as a trend for 16S rRNA genes and as statistically marginal or significant for 18S rRNA and fungal ITS from P0 to P1 (p = 0.08 for both), from P0 to P2 (p = 0.007 and 0.03, respectively), from P0 to P3 (p = 0.09 for both) and from P0 to P4 (p = 0.09 for both) (Figure S6). Indeed, prokaryotic 16S rRNA gene increased from 1.12x10 7 copies per gram of soil on average in P0 while being on average 3.3x10 7 , 3.2 10 7 , 2.6x10 7 and 2.7x10 7 copies per gram of soil in P1, P2, P3 and P4, respectively. Eukaryotic abundance increased from 1.4x10 5 18S rRNA gene copies per gram of soil in P0 to 6.4x10 5 , 5.4x10 6 , 9.5x10 5 and 7.9x10 5 copies per gram of soil in P1, P2, P3 and P4, respectively. For the fungal community, abundance increased from 6.8x10 4 copies per gram of soil in P0 to 3.3x10 5 , 6.0x10 6 , 4.6x10 5 and 4.0x10 5 copies per gram of soil in P1, P2, P3 and P4, respectively. These results agreed with: i) microbial mineralization assay (Figure S7A) that was significantly lower in the P0 group compared to the more polluted P4 one (p value = 0.018), and; ii) microbial non-specific enzymatic FDA hydrolase activity assay (Figure S7B) that revealed an increase of global microbial enzymatic activities along the PCB pollution gradient.

Discussion

PCB effect on microbial abundance and activity

The studied site was heterogeneously contaminated by PCB indicating that pollution concentration was not location dependent. Literature reported other PCB polluted soils that presented heterogeneous pollution with PCB concentrations varying from 1 mg.kg -1 dry mass [START_REF] Mackova | Phyto/rhizoremediation studies using long-term PCB-contaminated soil[END_REF] to over than 800 mg.kg -1 dry mass [START_REF] Marchal | Molecular Characterization of Fungal Biodiversity in Long-Term Polychlorinated Biphenyl-Contaminated Soils[END_REF]. This ranked the Pont de Claix brownfield of this study as a "moderate" polluted site despite the weakly industrial painting production activity occurring in the past decades.

One key finding is that along the statistical ranking of PCB gradient in Pont de Claix samples, an increase of microbial activity, microbial abundance and diversity related to PCB concentrations was reported.

This suggested that PCB might promote microbial growth and activity by providing carbon source or by generating a selection pressure leading resistant microbes to develop favorable coexistence.

The present work showed that prokaryotic communities were impacted by PCBs while microeukaryotic and fungal communities were impacted to a lesser extent but non-negligible. This impact on prokaryotic communities might be related to an accumulation of the well-known PCB dead-end intermediates named benzoate and chlorobenzoates [START_REF] Hu | Sphingobium fuliginis HC3: A Novel and Robust Isolated Biphenyl-and Polychlorinated Biphenyls-Degrading Bacterium without Dead-End Intermediates Accumulation[END_REF] while some fungi are able to degrade this products [START_REF] Muzikář | Biodegradation of chlorobenzoic acids by ligninolytic fungi[END_REF]. Moreover PCB molecules seem to be not lethal for most of the eukaryotic communities (including fungal one), which agreed with previous studies showing that Fungi such as Pleurotus ostreatus, Coriolopsis polysona, presented a decreased growth and/or metabolic activity instead of cell death in presence of PCBs [START_REF] Ruiz-Aguilar | Degradation of 3,3′,4,4′-tetrachlorobiphenyl by selected white rot fungi[END_REF]49]. Moreover, the low number of sequences affiliated with Archaea agreed with previous studies showing that in soils only 0.5 to 3.8% of the prokaryotes living were Archaea [START_REF] Timonen | Archaea in dry soil environments[END_REF].

Taxonomic groups with known mechanisms involved in PCB transformation

First, it is not surprising that microorganisms found in PCB-polluted sample groups with PCB degrading abilities are certainly tolerant to PCBs. In this way, among Bacteria, Gammaproteobacteria (Proteobacteria), included species known to degrade PCBs and detected in this study as one dominant class under PCB contamination, were previously reported in PCB-contaminated sites with 100 mg.kg -1 or over than 700 mg.kg -1 dry mass of total PCB [START_REF] Stella | Bioremediation of long-term PCB-contaminated soil by white-rot fungi[END_REF][START_REF] Zenteno-Rojas | Structure and diversity of native bacterial communities in soils contaminated with polychlorinated biphenyls[END_REF] suggesting their ubiquity in PCB polluted soils.

More specifically, Gammaproteobacteria (Xanthomonadaceae, Acidithiobacillaceae and Rhodanobacteraceae families) was abundant under PCB contamination in Pont de Claix and included species known to degrade PCBs thanks to their bph genes [START_REF] Khalid | Microbial and enzymatic degradation of PCBs from ewaste-contaminated sites: a review[END_REF][START_REF] Matturro | Microbiome changes and oxidative capability of an anaerobic PCB dechlorinating enrichment culture after oxygen exposure[END_REF]. Even if Chloroflexi bacteria were detected, the Dehalococcoidia (Chloroflexi) class, known to bear commonly the rdh genes [START_REF] Khalid | Microbial and enzymatic degradation of PCBs from ewaste-contaminated sites: a review[END_REF] involved in PCB transformation under anaerobic condition, was not detected in this study. Interestingly, three other classes of Chloroflexi showed lower abundance from P0 to P4 suggesting no PCB tolerance.

Concerning Fungi, (same trend in 18S rRNA and ITS datasets) species among Basidiomycota and Ascomycota were known to partially transform PCBs, especially species from the Saccharomycetes as previously reported [START_REF] Romero | Chlorinated biphenyl degradation by wild yeasts pre-cultured in biphasic systems[END_REF], retrieved as well in the present work. Even if the mechanisms involved were not demonstrated for its class, with specifically higher abundance in P2 and P4 groups, Saccharomycetes species able to transform PCB in a context of bacteria-yeast consortium are known [START_REF] Romero | Chlorinated biphenyl degradation by wild yeasts pre-cultured in biphasic systems[END_REF]. The Pleurotaceae family (Basidiomycota), including the well-known PCB degrader Pleurotus ostreatus [START_REF] Stella | Bioremediation of long-term PCB-contaminated soil by white-rot fungi[END_REF],

was specifically retrieved in this study with a higher abundance in the most contaminated groups. For other Basidiomycota such as Pleurotus ostreatus or Trametes versicolor, PCB transformation might be the result of laccases activities whereas, for Ascomycota with Acremonium sclerotigenum, it would involve peroxidase production [START_REF] Germain | Screening and metabolic potential of fungal strains isolated from contaminated soil and sediment in the polychlorinated biphenyl degradation[END_REF][START_REF] Périgon | Metabolic adaptation of fungal strains in response to contamination by polychlorinated biphenyls[END_REF]. Theses finding suggested that PCB might promote microbial growth. But considering that potential complete PCB transformation might involve diverse microorganisms harboring complex biological pathways and regarding the PCB concentrations still in these soil samples, PCB might rather generate a selection pressure as mentioned above.

Taxonomic groups with potential PCB-transforming mechanisms

The Deltaproteobacteria and Gemmatimonadetes classes could be related to PCB degradation and/or tolerate these molecules because species of these classes were retrieved in PCB polluted soil [START_REF] Khalid | Microbial and enzymatic degradation of PCBs from ewaste-contaminated sites: a review[END_REF][START_REF] Matturro | Microbiome changes and oxidative capability of an anaerobic PCB dechlorinating enrichment culture after oxygen exposure[END_REF][START_REF] Cervantes-González | Microbial diversity assessment of polychlorinated biphenyl-contaminated soils and the biostimulation and bioaugmentation processes[END_REF].

Moreover, at the family taxonomic level, Xanthomonadaceae were known to be able to degrade ethylbenzene and such bacteria might transform or tolerate PCB. Indeed, ethylbenzene are structurally close to PCBs and can present chlorine atoms [START_REF] Jayamani | Stable Isotope Probing and High-Throughput Sequencing Implicate Xanthomonadaceae and Rhodocyclaceae in Ethylbenzene Degradation[END_REF]. The present work also highlighted the presence of the Holophagae class (Acidobacteria), already reported in a work investigating active bacterial communities in a PCB polluted moorland soil [START_REF] Nogales | Identification of the metabolically active members of a bacterial community in a polychlorinated biphenyl-polluted moorland soil[END_REF]. Rhodanobacteraceae family abundant in P4 group could tolerate PCB or be involved in PCB transformation because it includes genus known to use biphenyls as sole carbon source [START_REF] Mcgenity | Taxonomy, Genomics and Ecophysiology of Hydrocarbon-Degrading Microbes[END_REF].

Concerning Fungi, the Hymenogastraceae (Basidiomycota) family presented higher abundances in groups with intermediate PCB pollution levels and was previously reported as representing 3.3% of fungal sequences from root of Salix purpurea in a polluted soil to PCB and hydrocarbons [START_REF] Gonzalez | Trees, fungi and bacteria: tripartite metatranscriptomics of a root microbiome responding to soil contamination[END_REF], suggesting their adaptability to these molecules. Other family such as Claroideoglomeraceae (Glomeromycota), abundant in P0 and P3 groups were previously retrieved in extreme petroleum hydrocarbon contaminated site [START_REF] Kong | Biodiversity of Arbuscular Mycorrhizal Fungi from Extreme Petroleum Hydrocarbon Contaminated Site[END_REF].

New taxonomic groups retrieved under PCB contamination

To our knowledge, our study reported for the first-time microbial taxa abundant in the soil samples in presence of PCBs, not previously related to these molecules or contaminated ecosystems. In this way these reported microorganisms could be subject of further PCB-transformation assays. The abundance of the Orbiliomycetes (Ascomycota) class under PCB contamination suggested their tolerance and adaptability to these molecules. It is interesting to note that non-affiliated sequences were few in the low contaminated P0 group of the brownfield compared to the other groups characterized by a higher PCB contamination level. This finding suggested that many microorganisms possibly tolerant and adapted to PCBs are still uncharacterized and knowledge about microbial diversity being present under polluted conditions, like the present work, are necessary to better understand PCB adaptation and further guide bioremediation. To our knowledge, microeucaryotes (other than fungi) were never studied in a PCB contaminated ecosystem. Concerning these under-studied microeucaryotes, Intramacronucleata (Ciliophora), Vampyrellidae (Cercozoa), Arcellinida and Euamoebida (both belonging to Amoebozoa) classes and the families such as Colpodea, Nassophorea (both belonging to Ciliophora) showed abundances increased with PCB concentrations suggesting that they might tolerate and be adapt to this molecule directly or by interacting with other adapted fungal or bacterial microorganisms.

Conclusions

This work assessed the structure of prokaryotic, micro-eukaryotic and fungal communities for the first time in a PCB gradient from a contaminated brownfield. Because complete PCB transformation might involve several microorganisms not regrouped at the same place in the environment, PCBs are still concentrated in this soil since many years. This in situ analysis highlighted that microbial community structure differed depending on PCB concentration. These molecules might act as selection pressure on bacterial and fungal species with some possibly involved in partial PCB transformation. Indeed, microbial structure of low-polluted samples was segregated from polluted samples, especially for the prokaryotic communities. Interestingly, the microbial abundance tended to be more important with increasing PCB concentrations. These results showed that PCBs seems to act as a selection pressure. These molecules impacted the three-domain of microbial life composition and could select adapted microorganisms, possibly able to transform PCBs, developing favorable coexistence. However, all these results need to be confirmed by further analyses involving greater number of samples and in vitro biotransformation demonstration. Other taxa listed here were also retrieved for the first time in polluted samples but their potential PCB tolerance and/or biotransformation remained unknown and might be promising. That is why further ecotoxicological studies will allow gaining into exhaustive microbial diversity presenting in PCB polluted ecosystems to support and guide future PCB bioremediation studies. [START_REF] Budzinski | An in vitro evaluation of human cytochrome P450 3A4 inhibition by selected commercial herbal extracts and tinctures[END_REF]and extract clean-up was carried out on a multilayer column (alumina and silica with activated copper). Extracts were purified on acid-impregnated silica and quantified by gas chromatography coupled with electron capture detection (GC-ECD) [START_REF] Barhoumi | Distribution and ecological risk of polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) in surface sediments from the Bizerte lagoon, Tunisia[END_REF] using internal standard quantification with PCBs not present in environment (PCBs 30, 103, 155, 198) and 4,4'-DDT-d8 as internal standards. Procedural blanks were performed for each sample batch and blank-correction was applied. Samples and blanks were spiked with internal recovery standards prior to extraction procedures and with a syringe standard (octachloronaphthalene) before injection to monitor IS losses; recoveries higher than 60% were accepted.

Table and Figures

Limits of detection (LODs) and limits of quantification (LOQs) were set as the concentrations yielding signal-to-noise ratios of 3 and 10, respectively. LOQs were in the range 0.03-5 ng g -1 dw of PCBs.

Table S2. Concentration of 71 different PCB by sample 696

  This work investigated the changes in prokaryotic, microeukaryotic and fungal community structure in the soil samples presenting a PCB gradient. Normalized datasets consisted in 1477 prokaryotic OTUs (representing 99,792 quality-filtered paired-end sequences and 388 to 795 OTUs per sample); 752 microeukaryotic OTUs (representing 116,365 sequences and 199 to 339 OTUs per sample); and 685 fungal OTUs (representing 248,140 sequences and 113 to 313 OTUs per sample) (Table

(

  Chytridiomycota), two unknown families belonging to the Troubouxiophycea and Chlorophyceae classes (both belonging to Chlorophyta), two other belonging to Glissomonadida and an unknown class (both belonging to Cercozoa) and a last unknown family belonging to the Craspedida class (Choanoflagellida). One unknown family belonging to the Vampyrellidae class (Cercozoa) showed higher abundance in the P3 group. Families only enriched in P4 were Colpodea, Nassophorea (both belonging to Ciliophora), an unknow family from Bdelloida class (Rotifera) and a last unknown family belonging to Charophyta.

Figure 1 .Figure 2 .Figure 3 .Figure 4 .

 1234 Figure 1. Hierarchical clustering of samples and microbial community structure by NMDS

Figure S7 .

 S7 Figure S7. Total microbial activity kinetics Total microbial activity was first (A) analyzed by mineralization kinetics expressed by µg of C-CO2 by gram of

Table 1 . Indicator PCB pollution concentration by sample

 1

	Zone 2 (Z2)

Statements & Declarations

Data availability

All data that support findings of this study have been deposited in European Nucleotide Archive (https://www.ebi.ac.uk/ena/browser/view/PRJEB46614 ) under reference PRJEB46614 for 16S rRNA dataset, PRJEB46555 and PRJEB46556 for ITS regions and eukaryotic 18S rRNA genes, respectively.

Supplementary Experimental Procedures Soil physico-chemical analyses

Physico-chemical properties of the soil were evaluated at the CESAR laboratory (http://www.labo-cesar.com/, Ceyzeriat, France) by analyzing the soil samples Z2B5, Z2F2, Z2F3, Z2F1 and Z2F4, representing the P0, P1, P2, P3 and P4 groups respectively. The cation exchange capacity (CEC), soil pH (H20), pH (KCl) and soil texture were assessed according to technical standard procedures, together with the contents of total organic carbon and total nitrogen.

C-CO2 Microbial mineralization and enzymatic biological activity assay

Three replicates per soil sample were monitored for microbial mineralization, and empty control flasks without soil were run in the same way to correct for basal atmospheric CO2 measurement. The produced CO2 of each sample was expressed as carbon mass per gram of dry weight soil. After each measurement, flasks were opened for 15 min under a fume hood for aeration to avoid anoxia through accumulation of CO2, and humidity was adjusted with sterile water. Flasks were sealed and incubated again. Measurements were performed after 3, 7, 11, 14 and 19 days of incubation. The microbial activity was evaluated on soil samples Z2B5, Z2F2, Z2F3, Z2F1 and Z2F4 (representing P0, P1, P2, P3 and P4 groups respectively). This FDA hydrolase enzymatic test is non-specific and integrates the activity of many fungal and bacterial enzymes such as esterases, lipases, proteases secreted in soils by microorganisms.

Additional PCB assay analyses

Chemical analyses were performed on the soil samples, after freeze-drying and sieving at 2 mm. Chemical analyses focused on 71 congeners of polychlorinated biphenyls (PCBs). PCB analyses were performed as described previously [START_REF] Budzinski | An in vitro evaluation of human cytochrome P450 3A4 inhibition by selected commercial herbal extracts and tinctures[END_REF]. Soil samples were extracted using

Supplementary results

Core versus specific microbiome among PCB pollution

For the prokaryotic dataset (Figure S3

Supplementary tables and figures


T Marsan

A Loiseau

Y Landon

B Watier

Comparing the weapon speed of French elite para-fencers

Introduction

Wheelchair fencing is a duelling sport practiced on a specific wheelchair and with the same three weapons as the able-bodied: foil, sabre, and sword. To participate to the main competitions, athletes must perform with two of the three weapons. While, this sport is now a Paralympic game sport, a lack of performance criteria remains for the athletes. Rules differences exist between the three weapons [START_REF] Roi | The Science of Fencing[END_REF]. With the foil and the sword, the fencer can hit his opponent only with the tip of the blade, whereas, with the sabre, the athlete can hit the opponent with the edge of the blade as well as with the tip. The sabre and the foil are conventional weapons which means that there is a priority needed to score. To obtain the priority, the athlete must attack first, parry, or avoid the attack of the opponent, usually going back. The sword however is non-conventional and the first athlete to hit the other scores. The specificity of wheelchair fencing is that it is always in close combat. Indeed, the wheelchairs are fixed at a certain distance from one to another. This distance is based on the athlete which has the lowest arm length [START_REF] Caldwell | Adaptive Sports Medicine[END_REF]. Therefore, the athletes need to move in their wheelchairs to perform an attack and touch their opponent, and, the faster they are, the more likely they are to strike first, leading to a point in the game. The athletes need to move quickly and to reach a long distance to hit their opponent so it was expected to find a relationship between their mass or arm length and the weapon speed. It has been observed, on able-bodies fencers, that there were differences in the weapon speed depending on the weapon [START_REF] Chen | Biomechanics of fencing sport: A scoping review[END_REF]) but it had not been applied to para-fencers. Hence, the objective of this study was to compare the speed of the three weapons used by para-fencers during an attack, and, to define a criterion performance, comparing the relation between the weapon speed and the anthropometric data of the athletes. The sabre was the weapon with the highest maximal speed for the able-bodied fencers [START_REF] Chen | Biomechanics of fencing sport: A scoping review[END_REF]) so it was hypothesized that it would be the same for the para-fencers.

Methods

Participants

Eleven para-fencers of the national French parafencing team volunteered for this study. Their mean ± SD age, weight, arm length, and year of practice were respectively 30.2 ± 11.3 y.o., 66.8 ± 9.8 kg, 0.65 ± 0.21 m and 18.5 ± 8.6 years. All participants had their own fencing equipment, their own wheelchair and weapons. Each athlete was using two different weapons. Therefore, there were eight athletes with a foil, eight with a sabre and six with a sword. This research was authorized by EudraCT / IDRCB n° 2020-A02919-30.

Experimentation

Two participants were facing each other. The wheelchairs of both participants were fixed onto a base plate and were connected by a metal rod as usual, so the distance between the athletes would not change after a motion of the athlete on the wheelchair. One of the participants was asked to perform five attacks at maximal speed of the opponent while the other was not moving. There was no advice on where to exactly hit their opponent on the torso. Two markers were placed on the weapon of the athlete performing the attack. One at the base of the blade on the hilt, and the other one on the blade at approximately 20 cm from the hilt, not to consider the deflection of the blade. Positions of the reflective markers were captured using a 22-cameras optoelectronic motion capture system (Vicon® System, ©Oxford Metrics Inc., UK) working at 200 Hz.

Data processing

The trajectories of the markers were smoothed using a sliding window average method. The trajectory of the marker on the blade was derivated to compute the marker velocity, then smoothed again with the same method. The direction of the weapon was computed using the marker on the hilt and on the blade and the marker velocity was projected on the instantaneous direction of the weapon. The maximal weapon speed was then computed for the three weapons. Significant differences have been assessed with a one-way ANOVA and a post-hoc analysis. The Cohen's effect size of the ANOVA was reported. Pearson correlation between maximal weapon speed and anthropometric data of the athletes was also computed.

Results and discussion

The sabre maximal speed was found the highest of all three weapons with a mean maximal speed of 5.13 m/s versus 2.98 m/s for the foil and 2.85 m/s for the sword.

The dispersion (SD) was higher for the sword (1.26 m/s) than for the sabre (0.97 m/s) and the foil (0.40 m/s). The effect size was large: which confirms the statistical differences found between the speed of the sabre and the other weapons (Figure 1). The able-bodied fencers wielding a sabre also had a higher maximal weapon speed than the ones wielding a sword or a foil although the speed was not reported. Nevertheless, Chen et al. ( 2017) reported a maximal foil speed of 2.91 m/s and 2.49 m/s for the sword for elite able-bodied fencers. With a foil maximal speed of 2.98 m/s and a sword speed of 2.85 m/s, the results of this study were close to the ones in the literature on the able-bodied fencers.

Figure 1 Weapon speed of the three fencing weapons.

Significant differences were highlighted with a *. The differences between the weapons (Figure 1) were due to the technique used to hit the opponent. Indeed, the sabre was the only of the three weapons with which it is possible to hit the opponent with the edge of the blade and not only the tip of the weapon. The motion is an edge cut with a motion of the wrist at the end of the attack [START_REF] Chen | Biomechanics of fencing sport: A scoping review[END_REF], whereas it is a thrust for the foil and the sword. The dispersion of maximal sword speed was higher than for the two other weapons. This could be due to the inter-variability of the fencing level of the participants, even if they were all on the national French para-fencing team, some of them had more experience, hence the high dispersion in term of year of practice. Differences in the pathologies between the para-fencers also exist, which could also explain the dispersion found in this study.

Foil

Sabre Sword Mass (kg) 0.01 (0.94) 0.12 (0.38) 0.48 (0.09)

Arm length (m) 0.03 (0.66) 0.14 (0.53) 0.13 (0.54)

Table 1 Correlation (R² and the p-value associated) of the weapon speed with anthropometric parameters. The higher correlation was found between the sword maximal speed and the mass of the athlete (Table 1) and it has suggested that the athletes with a lower mass had a slightly higher maximal sword speed. This seemed logical since lighter athletes could possibly move faster than the heavier ones. However, this correlation was not found with the other weapons. This might be because of the low number of athletes, and that the technique to hit was different. This correlation was not significant, and it was mostly due to the athlete with the lower sword speed. However, it could be seen as a performance criterion if the tendency would be confirmed. And such performance criterion could be of use for coaches, when trying to improve the performance of their athletes. Nevertheless, it needs validation, by increasing the number of athletes in the cohort. However, the speed of the weapons was not significantly correlated with other anthropometric parameters included the subject's height.

Conclusions

This study was the first to investigate the performance for para-fencers, and the correlation between the maximal sword speed and the mass of the athlete was presented. The maximal speed was found the highest for the sabre with 5.13 m/s in average. The maximal weapon speed was equivalent between the foil and the sword but a higher dispersion was found for the maximal sword speed in respect to the other two weapons. Inter-variability of the fencing level and of the pathology might explain the differences found within the para-fencers presented in this study. As a search for performance criterion, the correlation between the maximal sword speed and the mass of the athlete was not clear, and there is a need to increase the cohort size, to be able to conclude. The relation between the anthropometry of the athletes and the weapon maximal speed could also be researched further, to provide indications to coaches and recruiters.

Acknowledgements

This work has benefited funds managed by the National Research Agency (ANR) under the "Future Investment 3" plan within the framework of the "Priority Sport Research Program of Very High Performance" bearing the reference ANR--19-STHP-0005.