Marion Urvoy
email: marion.urvoy@outlook.fr

Raphaël Lami

Catherine Dreanno

David Daudé

S Rodrigues

Michèle Gourmelon

Stéphane L'helguen

Claire Labry

Alice M S Rodrigues

Quorum sensing disruption regulates hydrolytic enzyme and biofilm production in estuarine bacteria

Keywords: Estuary, quorum sensing, biofilm, hydrolytic enzymes, leucine-aminopeptidases, β-glucosidases

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

Significance statement

In this study, we isolated estuarine bacteria and evaluated their N-acylhomoserine lactone (AHL)based quorum sensing and quorum quenching abilities using various techniques, which has never been done in an estuary ecosystem before. Thus, our work contributes to the depiction of communication pathways among estuarine bacteria, from genes to metabolites. We then studied the impact of quorum sensing-mediated microbial interactions on biofilm production, dissolved and cellbound leucine aminopeptidases and β-glucosidases. To this extent, the AHL communication pathways were disrupted using a lactonase assay among 28 phylogenetically diverse estuarine bacterial isolates. To the best of our knowledge, this approach was only used on two environmental strains before. Furthermore, quorum sensing regulation of both dissolved and cell-bound enzymes has never been investigated simultaneously in environmental strains. This experiment evidenced the broad involvement of quorum sensing in the regulation of biofilm as well as cell-bound and dissolved leucine-aminopeptidases and β-glucosidases. As such, this study provides evidence that bacterial cellcell communication can potentially influence global biogeochemical cycles through the modulation of prokaryotic functions and lifestyle.

Introduction

Heterotrophic microbial communities play a central role in biogeochemical carbon (C) and nutrients cycling in the oceans [START_REF] Azam | Microbial structuring of marine ecosystems[END_REF]. As organic matter mostly consists of unavailable polymeric substrates, heterotrophic prokaryotes need to cleave them into smaller molecules that can be transported across their cell membranes [START_REF] Arnosti | Microbial Extracellular Enzymes and the Marine Carbon Cycle[END_REF]. These activities rely on the expression of a diverse array of dissolved (cell-free) and cell-bound extracellular hydrolytic enzymes. Those enzymes initiate organic matter mineralization through the 'microbial loop' [START_REF] Pomeroy | The Ocean's Food Web, A Changing Paradigm[END_REF][START_REF] Azam | The Ecological Role of Water-Column Microbes in the Sea[END_REF], ultimately affecting C export, nutrient cycling and energy flow through the food web [START_REF] Chróst | Microbial Ectoenzymes in Aquatic Environments[END_REF][START_REF] Azam | Microbial structuring of marine ecosystems[END_REF].

Estuaries are important transition zones where organic matter is heavily transformed before reaching coastal areas. Those mineralization processes are particularly intense in the maximum turbidity zone, where aggregate concentrations may reach several grams per liter. Marine bacteria living on such aggregates frequently reside in complex multi-layered environments called biofilms, where they are embedded in a matrix of extracellular polymers [START_REF] Dang | Microbial Surface Colonization and Biofilm Development in Marine Environments[END_REF][START_REF] Flemming | Biofilms: An emergent form of bacterial life[END_REF]. Those particle-attached microorganisms are often more active than their free-living counterparts in terms of bacterial production and enzymatic activity [START_REF] Crump | Phylogenetic analysis of particle-attached and free-living bacterial communities in the Columbia River, its estuary, and the adjacent coastal ocean[END_REF][START_REF] Millar | Particle-associated and cell-free extracellular enzyme activity in relation to nutrient status of large tributaries of the Lower Mississippi River[END_REF]. These enzymes actively solubilize particulate organic matter into dissolved compounds, impacting both local organic matter composition and C export to coastal waters [START_REF] Azam | Microbial structuring of marine ecosystems[END_REF][START_REF] Dang | Microbial Surface Colonization and Biofilm Development in Marine Environments[END_REF]. Dissolved enzymes, which may be actively secreted or result from viral lysis and grazing [START_REF] Baltar | Watch out for the "living dead": Cell-free enzymes and their fate[END_REF], are also involved in the mineralization of organic matter. They often account for a large proportion of total enzymatic activities [START_REF] Baltar | Watch out for the "living dead": Cell-free enzymes and their fate[END_REF][START_REF] Thomson | Resolving the paradox: Continuous cell-free alkaline phosphatase activity despite high phosphate concentrations[END_REF].

Despite their important ecological implications, the mechanisms that regulate bacterial colonization, cell-bound and dissolved hydrolytic enzyme expression among these microbial consortia have been poorly characterized, especially in estuarine environments. In situ enzymatic activity of bacterial communities likely results from the communities' genetic potential (determined by the bacterial community composition), as well as the differential expression of those genes, driven by environmental factors [START_REF] Arnosti | Microbial Extracellular Enzymes and the Marine Carbon Cycle[END_REF]. Indeed, distinct microbial communities with specific taxonomic and functional compositions may colonize organic matter particles and express various hydrolytic enzymes [START_REF] Delong | Phylogenetic Diversity of Aggregate-Attached Vs Free-Living Marine Bacterial Assemblages[END_REF][START_REF] Middelboe | Attached and Free-Living Bacteria: Production and Polymer Hydrolysis During a Diatom Bloom[END_REF][START_REF] Rieck | Particle-associated differ from free-living bacteria in surface waters of the baltic sea[END_REF]. Hydrolytic enzyme production is also tightly regulated at the transcriptional level [START_REF] Chróst | Microbial Ectoenzymes in Aquatic Environments[END_REF][START_REF] Arnosti | Microbial Extracellular Enzymes and the Marine Carbon Cycle[END_REF].

In particular, the concentration and composition of organic matter are key factors driving hydrolytic enzyme production through substrate induction, end-product repression or catabolic repression [START_REF] Chróst | Ecology of Microbial Enzymes in Lake Ecosystems[END_REF][START_REF] Arnosti | Microbial Extracellular Enzymes and the Marine Carbon Cycle[END_REF].

Quorum sensing (QS) is a widespread communication system allowing bacterial assemblages to synchronize their gene expression at the population level [START_REF] Miller | Quorum Sensing in Bacteria[END_REF][START_REF] Waters | Quorum sensing: Cell-to-cell communication in bacteria[END_REF]. This mechanism relies on the production, diffusion and sensing of small diffusible molecules called autoinducers (AIs). When the concentration of AIs increases under favorable conditions (i.e. high cellular density, low mass-transfer properties), they bind their cognate receptors and regulate the transcription of their target genes [START_REF] Waters | Quorum sensing: Cell-to-cell communication in bacteria[END_REF][START_REF] Papenfort | Quorum sensing signal-response systems in Gram-negative bacteria[END_REF]. These target genes are involved in diverse functions such as bioluminescence, motility, biofilm formation, virulence and hydrolytic enzyme production [START_REF] Miller | Quorum Sensing in Bacteria[END_REF][START_REF] Hmelo | Quorum Sensing in Marine Microbial Environments[END_REF]. In Gram-negative bacteria, the most well-characterized QS system is based on the synthesis of Nacylhomoserine lactones (AHLs) [START_REF] Papenfort | Quorum sensing signal-response systems in Gram-negative bacteria[END_REF][START_REF] Hmelo | Quorum Sensing in Marine Microbial Environments[END_REF]. These cell-cell QS-based interactions can be disrupted by quorum quenching (QQ) mechanisms [START_REF] Kalia | Quorum sensing inhibitors: An overview[END_REF][START_REF] Grandclément | Quorum quenching: Role in nature and applied developments[END_REF]. They rely on the enzymatic degradation of signal molecules or on the production of QS inhibitors that block the signal production or reception, among other mechanisms [START_REF] Kalia | Quorum sensing inhibitors: An overview[END_REF][START_REF] Grandclément | Quorum quenching: Role in nature and applied developments[END_REF].

QS and QQ are widespread mechanisms among aquatic microbial communities. A large diversity of QS and QQ genes have been identified in metagenomics datasets [START_REF] Doberva | Diversity of quorum sensing autoinducer synthases in the Global Ocean Sampling metagenomic database[END_REF][START_REF] Muras | High prevalence of quorum-sensing and quorum-quenching activity among cultivable bacteria and metagenomic sequences in the mediterranean sea[END_REF]. AHLs have been detected in various types of aquatic environments such as marine aggregates [START_REF] Hmelo | Possible influence of bacterial quorum sensing on the hydrolysis of sinking particulate organic carbon in marine environments[END_REF]Jatt, Tang, Liu, Zhang, and X. H. Zhang, 2015), corals and sponges [START_REF] Tait | Quorum sensing signal production and inhibition by coral-associated vibrios[END_REF] and bacterial mats [START_REF] Decho | Autoinducers extracted from microbial mats reveal a surprising diversity of N-acylhomoserine lactones (AHLs) and abundance changes that may relate to diel pH[END_REF]. Similarly, numerous bacteria isolated from aquatic environments exhibit AI production or quenching capacities in culture [START_REF] Gram | Possible quorum sensing in marine snow bacteria: production of acylated homoserine lactones by Roseobacter strains isolated from marine snow[END_REF][START_REF] Romero | Quorum quenching in cultivable bacteria from dense marine coastal microbial communities[END_REF][START_REF] Tourneroche | Bacterialfungal interactions in the kelp endomicrobiota drive autoinducer-2 quorum sensing[END_REF]. To our knowledge, no QS study has been conducted in estuarine ecosystems.

In aquatic environments, biofilm production, emission of QS compounds and hydrolytic activities are inherently linked. Densely packed biofilm favors both high enzymatic activities and the accumulation of QS compounds [START_REF] Dang | Microbial Surface Colonization and Biofilm Development in Marine Environments[END_REF][START_REF] Flemming | Biofilms: An emergent form of bacterial life[END_REF][START_REF] Hmelo | Quorum Sensing in Marine Microbial Environments[END_REF]. QS mechanisms are also known to regulate the biofilm formation, maturation and dispersion [START_REF] Lami | Quorum Sensing in Marine Biofilms and Environments[END_REF]. In this vein, it was shown that the addition of QS compounds to both natural bacterial communities and bacterial cultures modified the activity of extracellular hydrolytic enzymes [START_REF] Hmelo | Possible influence of bacterial quorum sensing on the hydrolysis of sinking particulate organic carbon in marine environments[END_REF]Jatt, Tang, Liu, Zhang, and X. Zhang, 2015;[START_REF] Krupke | Quorum Sensing plays a complex role in regulating the enzyme hydrolysis activity of microbes associated with sinking particles in the ocean[END_REF][START_REF] Su | Quorum sensing system of Ruegeria mobilis Rm01 controls lipase and biofilm formation[END_REF], as well as their biofilm production [START_REF] Liu | Detection of Diverse N-Acyl-Homoserine Lactones in Vibrio alginolyticus and Regulation of Biofilm Formation by Lactone In vitro[END_REF][START_REF] Su | Quorum sensing system of Ruegeria mobilis Rm01 controls lipase and biofilm formation[END_REF]. Nevertheless, the role of QS as a regulatory mechanism of those bacterial functions involved in crucial biogeochemical processes remains underexplored, especially in estuaries.

In this study, we investigated the prevalence of AHL-based QS and QQ circuits among bacterial strains isolated in the Aulne estuary (Bay of Brest, France), combining bioassays, liquid chromatography coupled to mass spectrometry (UHPLC-HRMS/MS) and genomics approaches. We then explored whether AHL-based QS regulates biofilm production as well as dissolved and cell-bound leucine aminopeptidase (LAM) and β-glucosidase (β-glc) synthesis among the identified AHL-producing bacteria. An original approach of our work is the use of enzyme-driven QQ to disrupt the expression of QS circuits in several strains. Taken together, our results revealed that the studied estuarine bacteria synthesize a diverse array of AHLs. The inhibition of AHL signals led to contrasted effects on biofilm production and hydrolytic enzyme activities in several of the targeted strains, highlighting the importance of QS in the regulation of estuarine bacteria physiology and ultimately in biogeochemical cycles.

Results

Production of QS metabolites among estuarine bacteria

A total of 299 bacterial strains were isolated from the Aulne estuary (Table S1) and screened for their ability to produce AHLs, using the Pseudomonas putida F117 and Escherichia coli MT102 biosensors. Among them, only a few strains (19 strains, 6%) clearly induced at least one biosensor compared with the negative control (ratio > 1.5, p < 0.05, Table S2). In addition, 56 strains (19%) induced either a questionable induction (1.2 < ratio < 1.5, p < 0.05) or an inhibition (ratio < 0.8, p < 0.05) of the green fluorescent protein (GFP) production. To increase the probability of finding AHLproducing bacteria, we selected all 75 strains (25% of total isolated strains) for the analytical assessment of AHL production. Among those 75 bacteria, AHLs were detected in the supernatant of 28 strains using a UHPLC-HRMS/MS approach. Among those 28 strains, 26 (9% of total isolated strains) showed an AHL production that was quantifiable using commercially available standards (Table 1, see the Experimental procedures for the full AHL names). The two additional strains (Pseudomonas sp. AF02-5 and Sphingobacterium sp. AF02-2) seemed to produce an unsaturated C16-HSL (C16:1-HSL) given its mass, molecular formula and the presence of its characteristic fragments.

Unfortunately, this AHL could not be confirmed nor quantified because the corresponding standard is not commercially available. Such AHLs are qualified as "putative" in this study (Table 1).

Overall, the AHLs produced by the isolated estuarine strains were diverse. A total of 14 different AHLs (Table 1) were identified, with a side-chain length ranging from 4 to 14 C atoms. Both hydroxyl and carbonyl substitutions were found at the C3 position among the identified AHLs. We also found several other putative AHLs (C6:1-HSL, C12:1-HSL, C16:1-and 3-OH-C7-HSL), which also could not be confirmed nor quantified. It is worth noting that the χ 2 test and Fischer's exact test showed no significant impact of the salinity or the sampling fraction (i.e. attached or free-living) on the number of isolated AHL-producing strains (data not shown).

Taxonomic identification of AHL-producing isolates

The 28 AHL-producing bacteria were either Gammaproteobacteria (27 strains) or Bacteroidetes (1 strain). More precisely, they belonged to the genera Vibrio (10 strains), Pseudomonas (10 strains), Acinetobacter (5 strains), Obesumbacterium (1 strain), Morganella (1 strain) and Sphingobacterium (1 strain, Fig. 1, Table 1, Table S3). For samples at salinity 0, only Pseudomonas-affiliated strains (6 strains) were detected as AHL producers, except one strain affiliated with Sphingobacterium faecium.

There was a greater diversity of AHL-producing isolates retrieved at salinity 5, corresponding to the maximum turbidity zone. Those strains were affiliated with the Pseudomonas, Acinetobacter, Morganella and Obesumbacterium genera. At salinity 25, all AHL-producing strains were affiliated with Vibrio species (2 strains affiliated with V. echinoideorum in April and 8 strains affiliated with V. alginolyticus in July). Interestingly, all the Vibrio strains were found to produce 3-OH-C6-HSL, while none of the other strains produced this type of AHL. The AHL production among the other Pseudomonas and Acinetobacter species was much more diverse as they produce both long and short-chain AHLs.

Identification of strains capable of QQ

AHL-producing strains were further assessed for their capacities to produce anti-AHL compounds (Fig. 1, Fig. S1), a common feature in environmental bacteria [START_REF] Romero | Determination of whether quorum quenching is a common activity in marine bacteria by analysis of cultivable bacteria and metagenomic sequences[END_REF]. None of the 28 tested strains showed significant quenching of C6-HSL. Indeed, only Vibrio sp. CFL-9 and Vibrio sp.

CFL-25 significantly inhibited the biosensor-specific GFP production (by 13 and 12%, respectively, p < 0.05). However, they also presented a cytotoxic effect as the biosensor viability was reduced by 18% and 12%, respectively, compared with the control (p < 0.01, data not shown). As viability reduction accounted for the GFP reduction, those strains were not counted as AHL quenchers.

A total of 10 strains (36% of the tested strains) significantly quenched C14-HSL (p < 0.05). They were affiliated with the Pseudomonas genus (7 strains), with the addition of Acinetobacter sp. DF3-8, Vibrio sp. EF02-7 and Morganella sp. DF3-16. All these strains exhibited low quenching abilities, with a reduction of GFP production between 6% and 20%, except for Pseudomonas sp. AF3-2 and AF3-9, which decreased GFP production by 46% and 29%, respectively. In addition, Vibrio sp. CFL-9 and Vibrio sp. CFL-25 reduced the biosensor-specific GFP production, but with a similar reduction in the biosensor viability (data not shown), so those strains were not counted as AHL quenchers.

Effects of lactonase on AHL production

We then tested whether QS circuits regulated biofilm production and hydrolytic enzyme synthesis. To this end, QS mechanisms were inhibited using a lactonase preparation active over a broad range of AHLs, including among others C4-HSL, C6-HSL, C8-HSL, C9-HSL, C10-HSL, C11-HSL, 3oxo-C6-HSL, 3-oxo-C10-HSL, 3-oxo-C12-HSL, 3-OH-C10-HSL and 3-OH-C11-HSL. [START_REF] Hiblot | Differential Active Site Loop Conformations Mediate Promiscuous Activities in the Lactonase SsoPox[END_REF][START_REF] Rémy | Lactonase Specificity Is Key to Quorum Quenching in Pseudomonas aeruginosa[END_REF][START_REF] Mion | Disrupting quorum sensing alters social interactions in Chromobacterium violaceum[END_REF]. We first evaluated whether the AHL production of the 28 AHLproducing strains was affected by the lactonase treatment (Table 2). Among those 28 strains, 14 were found to significantly induce at least one of the biosensors (specific fold induction > 1.5, p < 0.05) at the end of the lactonase assay, allowing the assessment of the lactonase efficiency.

Interestingly, the AHL production of 11 of them was significantly reduced by the lactonase treatment (ratio lac/ctr ranged from 0.0 to 0.6, p < 0.05), confirming the broad spectrum of the lactonase treatment. Three strains (Pseudomonas sp. AF02-5 and Vibrio sp. CFL-9 and E14) activated one biosensor in the control condition but were not significantly impacted by the lactonase treatment.

The 14 other strains did not sufficiently induce the biosensors in the control condition at the end of the assay, preventing the assessment of the lactonase efficiency. Indeed, 5 Vibrio strains (CFL-25, EF3-1, EFL-3, EFL-16 and EF02-15) induced a questionable GFP fold induction between 1.21-1.33, which is too low to demonstrate a significant effect of the lactonases. The remaining 9 strains did not induce the biosensors (ratio < 1.2). Among them, 5 strains were in the "questionable induction" during the initial screening. The difference in the growth stage at which the screening was performed could explain the absence of induction since the AHL production and degradation is growth phasedependent.

Effects of lactonase treatment on biofilm production

Among the 28 tested strains, 25 were found to produce biofilm in the tested set-up. Two of those strains had their biofilm production significantly impacted by the lactonase treatment (Fig. 1, Table 3): a decrease in biofilm production was seen in Pseudomonas sp. BF02-28 (ratio lac/ctr = 0.48, p < 0.05) while an increase was observed in Vibrio sp. E14 (ratio lac/ctr = 2.29, p < 0.01). Interestingly, among the 8 other Vibrio strains tested, biofilm production seemed reduced in 5 strains (Vibrio sp. CFL-9, CFL-25, EFL-3, EFL-16 and EF3-1) but increased in 3 strains (Vibrio sp. E15, EF02-7 and EF02-14), although not significantly (Table 3). In addition, biofilm production seemed to decrease in Acinetobacter sp. DF3-15 and DF02-2 (ratio lac/ctr = 0.6 and 0.8 respectively, p < 0.1).

Effect of lactonase treatment on LAM activity

Dissolved LAM production was found in 61% of the strains, especially in Vibrio and Acinetobacter species. However, they were mostly negligible compared with cell-bound LAM, except for Vibrio sp.

CFL-9 and CFL-25, for which dissolved LAM represented most of the total activity (Table 3). Upon lactonase addition, dissolved LAM production was affected in 19 out of 28 strains (p < 0.05, Fig. 1, Table 3, Fig. S2). Dissolved LAM production increased (ratio lac/ctr between 1.25 and 7.47, p < 0.05) in 18 strains (7 Pseudomonas-, 1 Sphingobacterium-, 1 Acinetobacter-, 1 Obesumbacterium-, 1 Morganellaand 7 Vibrio-affiliated strains). By contrast, LAM production decreased in Pseudomonas sp. AF3-9 (ratio lac/ctr = 0.19, p < 0.01). Interestingly, among the 12 tested strains that did not produce dissolved LAM in the absence of AHLs, 8 strains showed a detectable production with the lactonase treatment.

The lactonase treatment affected cell-bound LAM production in 11 out of 28 strains (p < 0.05, Fig. 1, Table 3, Fig. S2). Among them, 5 Pseudomonas-affiliated strains (A14, A20, AF3-2, AF3-9, AF02-5) had their cell-bound LAM production increased (ratio lac/ctr between 1.30 and 2.44, p < 0.05). By contrast, 6 strains (Vibrio sp. CFL-9, E15, EFL-16, EF02-7; Obesumbacterium sp. DF3-3; and Acinetobacter sp. DF3-8) exhibited a decreased cell-bound LAM (ratio lac/ctr between 0.59 and 0.89, p < 0.05).

Effect of lactonase treatment on β-glc activity

Production of dissolved β-glc was not frequent, with only 3 strains exhibiting a detectable hydrolysis of MUF-glc in the absence of lactonase (Sphingobacterium sp. AF02-2, Pseudomonas sp. B20 and Pseudomonas sp. BF02-28, Table 3). Among them, dissolved β-glc represented an important fraction of total β-glc in Pseudomonas sp. B20 and BF02-28 (respectively 57 and 72%, Table 3).

Pseudomonas sp. B20 was significantly impacted by the lactonase treatment (ratio lac/ctr = 1.33, p < 0.01, Fig. 1, Table 3). The strains that did not exhibit a detectable amount of dissolved β-glc in the absence of lactonase also did not produce a detectable amount of dissolved β-glc upon lactonase treatment.

Overall, 22 strains out of 28 produced cell-bound β-glc and most of them exhibited low activity, with the exception of Sphingobacterium sp. AF02-2, Obesumbacterium sp. DF3-3 and Vibrio sp. CFL-9 and CFL-25. Among the 28 strains, 7 strains had their cell-bound β-glc significantly impacted by the lactonase treatment (p < 0.05, Fig. 1, Table 3, Fig. S2). Acinetobacter sp. DF3-8 (ratio lac/ctr = 1.32, p < 0.05) and Pseudomonas sp. A20 (production not detected in control treatment, p < 0.01) exhibited an increase in cell-bound β-glc with the lactonase treatment while 5 Vibrio sp. (CFL-9, E14, EFL-3, EFL-16 and EF3-1) exhibited decreased activity (ratio lac/ctr between 0.83 and 0.63, p < 0.05).

Identification of QS, QQ, hydrolase and biofilm-related genes in estuarine bacteria genomes

The genomes of four strains of Pseudomonas sp. (AF3-9 and B20), Vibrio sp. E14 and Acinetobacter sp. DF3-8 were sequenced to find AHL-related QS and QQ genes, carbohydrate-active enzymes (CAZymes), proteases and biofilm-related genes (pili, flagella, fimbriae and chemotaxis) (Table 4). Those strains were chosen because they covered the taxonomic diversity of the AHLproducing strains and exhibited marked responses to lactonase addition. The genome assembly metrics are given in Table S5. Pseudomonas sp. AF3-9 and B20 possessed complex AHL-based QS circuits, with each having two putative AHL synthases (luxI-type and hdtS-type), several AHL receptors and three AHL acylases. Interestingly, the AHL receptors included several luxR solos, that is to say, without an accompanying synthase [START_REF] Fuqua | The QscR quorum-sensing regulon of Pseudomonas aeruginosa: An orphan claims its identity[END_REF]. The hdtS synthases were not located near an AHL receptor. Acinetobacter sp. DF3-8 and Vibrio sp. E14 encoded, respectively, one luxI/luxR and one luxM/luxN tandem and no QQ enzymes. The phylogenetic trees of the QS and QQ genes are presented in Fig. S3.

All the strains contained numerous genes coding for hydrolases, including metallopeptidases (which include LAM) and glycoside hydrolases (which include β-glc). Interestingly, one of the Pseudomonas sp. AF3-9 solo luxR genes was located directly next to two serine peptidases (Fig. S4).

They also all possessed numerous genes involved in motility and adhesion (pili, flagella, fimbriae and chemotaxis), which are linked to biofilm development [START_REF] Wolska | Genetic control of bacterial biofilms[END_REF][START_REF] Berne | Bacterial adhesion at the single-cell level[END_REF][START_REF] Rossi | Biofilm and motility in response to environmental and host-related signals in Gram negative opportunistic pathogens[END_REF].

Discussion

Although QS-dependent phenotypes have been extensively studied in strains of medical interest, much less work has been conducted on environmental strains. In particular, the question of a possible implication of QS mechanisms in the regulation of bacterial phenotypes involved in key biogeochemical processes remains open [START_REF] Hmelo | Quorum Sensing in Marine Microbial Environments[END_REF]. In this study, several strains were isolated from the Aulne estuary (Bay of Brest, France), which is known to host high hydrolytic activities [START_REF] Labry | High alkaline phosphatase activity in phosphate replete waters: The case of two macrotidal estuaries[END_REF]. We then inhibited their AHL-based QS communication to test whether this mechanism regulated biofilm and extracellular hydrolytic enzyme production (β-glc and LAM), which are known to play a major role in organic matter biodegradation [START_REF] Chróst | Ecology of Microbial Enzymes in Lake Ecosystems[END_REF][START_REF] Arnosti | Microbial Extracellular Enzymes and the Marine Carbon Cycle[END_REF]. One limitation to this approach is that it is restricted to the cultivable bacterial fraction.

Estuarine bacteria produce diverse QS signals

Several estuarine bacterial strains were found to produce AHLs quantifiable by UHPLC-HRMS/MS. They were affiliated with the genera Vibrio, Pseudomonas, Acinetobacter, Obesumbacterium, Morganella and Sphingobacterium based on 16S rRNA gene sequencing. Pseudomonas, Vibrio and Acinetobacter species are frequently isolated in coastal and marine environments [START_REF] Elliot | Indicator organisms for estuarine and marine waters[END_REF][START_REF] Baffone | Detection of free-living and plankton-bound vibrios in coastal waters of the Adriatic Sea (Italy) and study of their pathogenicity-associated properties[END_REF][START_REF] Abd-Elnaby | Characterization and Bioremediation potential of marine psychrotolerant Pseudomonas spp. isolated from the Mediterranean Sea, Egypt. Egypt[END_REF] and have been well studied for their QS properties [START_REF] Eberl | N-Acyl Homoserinelactone-mediated Gene Regulation in Gram-negative Bacteria[END_REF][START_REF] Miller | Quorum Sensing in Bacteria[END_REF]. The production of AHLs has also previously been reported in Obesumbacterium [START_REF] Priha | Quorum Sensing Signalling and Biofilm Formation of Brewery-Derived Bacteria , and Inhibition of Signalling by Natural Compounds[END_REF] and Morganella morganii [START_REF] Guzman | Green tea (Camellia sinensis) extract inhibits biofilm formation in acyl homoserine lactone-producing, antibioticresistant Morganella morganii isolated from Pasig River, Philippines[END_REF]. To our knowledge, AHL production has not previously been reported in Sphingobacterium isolates, although AHL synthase sequences matching the Sphingomonadaceae family have been reported in environmental metagenomics databases [START_REF] Doberva | Diversity of quorum sensing autoinducer synthases in the Global Ocean Sampling metagenomic database[END_REF]. The AHL-producing isolates were phylogenetically different depending on the salinity at which they were isolated, suggesting that the bacterial community members engaged in QS communication may vary along the estuary.

The identified bacterial estuarine strains involved in QS communication produced a diverse array of QS molecules, with 14 AHLs detected and quantified in total. This finding is consistent with previous studies showing that a single marine strain can produce a large, diverse array of QS signals [START_REF] Doberva | Large diversity and original structures of acyl-homoserine lactones in strain MOLA 401, a marine Rhodobacteraceae bacterium[END_REF]. This broad spectrum of emitted AHLs suggests that estuarine bacterial communities produce and respond to multiple AHL signals emitted in the environment. Most strains produced short-and medium-length AHLs (C6-HSL to C12-HSL), and a majority of them were substituted at the C3 position, two structural properties that could increase AHL solubility in brackish water and seawater [START_REF] Hmelo | Possible influence of bacterial quorum sensing on the hydrolysis of sinking particulate organic carbon in marine environments[END_REF]. Interestingly, all the Vibrio strains were found to produce 3-OH-C6-HSL, while none of the other type of strains produced this type of AHL, suggesting a particular signal specificity within those strains. This is in contrast with previous studies that reported no clear pattern of QS activity in Vibrionaceae strains, including Vibrio sp. [START_REF] Tait | Quorum sensing signal production and inhibition by coral-associated vibrios[END_REF][START_REF] Freckelton | Quorum sensing interference and structural variation of Quorum Sensing mimics in Australian Soft Coral[END_REF]. The AHL production among Pseudomonas and Acinetobacter was much more diverse, with no clear production pattern. Interestingly, the sequencing data showed that the two Pseudomonasaffiliated strains had complex QS circuits, with several AHL synthases and receptors, including luxR solos. As QS compounds have been less studied in Obesumbacterium, Morganella and Sphingobacterium, our study contributes to describe their QS circuits, revealing the production of 3oxo-C6-HSL and 3-oxo-C8-HSL in Obesumbacterium, C4-HSL in Morganella and the possible production of C16:1-HSL in Sphingobacterium.

QS and QQ circuits co-occur in estuarine bacteria

Interestingly, based on our bioassays, 10 out of 28 AHL-producing strains (36%) also showed the QQ ability to interfere with long-chain AHLs, while none of them interfered with short-chain AHLs.

The sequencing data suggested that Pseudomonas sp. AF3-9 and B20 quenching abilities result from the degradation of AHLs based on the emission of acylases, a QQ enzyme that cleaves the acyl side chain of AHLs. No QQ enzymes (i.e. acylase or lactonase) were found in Acinetobacter sp. DF3-8 and Vibrio sp. E14 genomes, although Acinetobacter sp. DF3-8 presented a QQ activity during the bioassay. Such an observation concerning Acinetobacter suggests that estuarine bacteria could be a source of new QQ enzymes for biotechnological applications. The observation of bacteria that simultaneously emit pro-and anti-QS compounds has been evidenced in a few strains, for example, in Agrobacterium tumefaciens [START_REF] Zhang | The quormone degradation system of Agrobacterium tumefaciens is regulated by starvation signal and stress alarmone (p) ppGpp[END_REF], a marine Shewanella sp. [START_REF] Tait | Turnover of quorum sensing signal molecules modulates cross-kingdom signalling[END_REF], in two isolates related to Endozoicomonas [START_REF] Freckelton | Quorum sensing interference and structural variation of Quorum Sensing mimics in Australian Soft Coral[END_REF] or Acinetobacter and Burkholderia strains isolated from the rhizosphere (Chan et al. 2011). Such co-occurrence of QS and QQ features in our collection of estuarine strains raises the question of the co-expression of these two phenotypes, especially within the natural environment. In any case, our data highlight the complexity of QS regulation within the natural estuarine communities. They also point out the importance of QS-based cell-cell cooperation and QQ-based competition within these communities.

QS regulates biofilm formation and hydrolytic enzyme production in estuarine bacteria

We then investigated the role of QS in biofilm formation and hydrolytic enzyme synthesis in the 28 AHL-producing estuarine strains, two important physiological features when considering the role of bacteria in the marine C cycle [START_REF] Arnosti | Microbial Extracellular Enzymes and the Marine Carbon Cycle[END_REF][START_REF] Dang | Microbial Surface Colonization and Biofilm Development in Marine Environments[END_REF]. To this extent, we used a lactonase-based QS disruption approach. Lactonases have been shown to catalyze the opening of the lactone ring of AHLs, thus preventing their use as a signal molecule. SsoPox lactonase has been shown to inhibit a range of behaviors under QS control, including biofilm and protease production, in P. aeruginosa [START_REF] Hraiech | Inhaled lactonase reduces pseudomonas aeruginosa quorum sensing and mortality in rat pneumonia[END_REF][START_REF] Guendouze | Effect of quorum quenching lactonase in clinical isolates of Pseudomonas aeruginosa and comparison with Quorum sensing inhibitors[END_REF][START_REF] Mion | Quorum Quenching Lactonase Strengthens Bacteriophage and Antibiotic Arsenal Against Pseudomonas aeruginosa Clinical Isolates[END_REF][START_REF] Rémy | Lactonase Specificity Is Key to Quorum Quenching in Pseudomonas aeruginosa[END_REF] and Chromobacterium violaceum [START_REF] Mion | Disrupting quorum sensing alters social interactions in Chromobacterium violaceum[END_REF]. They also modulated in vitro [START_REF] Schwab | Signal disruption leads to changes in bacterial community population[END_REF] and in situ [START_REF] Huang | Evaluation of biological and enzymatic quorum quencher coating additives to reduce biocorrosion of steel[END_REF] bacterial community composition. Contrary to some other wellknown QS inhibitors, such as 5-fluoro-uracil or the brominated furanone C-30, lactonases do not induce cytotoxic effects that may bias the experiments [START_REF] Mahan | Effects of Signal Disruption Depends on the Substrate Preference of the Lactonase[END_REF].

Most of the AHL-producing strains (89%) were also biofilm producers. The lactonase treatment significantly impacted the biofilm production in two of them, with contrasting effects: Pseudomonas sp. BF02-28 exhibited a significant biofilm decrease (-52%), while Vibrio sp. E14 biofilm production was increased (+128%). By contrast, the lactonase treatment did not significantly modify biofilm production in other Vibrio strains. Previous studies have mostly reported the inhibition of biofilm production in the presence of lactonases [START_REF] Muras | Acyl homoserine lactone-mediated quorum sensing in the oral cavity: a paradigm revisited[END_REF][START_REF] Paluch | Prevention of biofilm formation by quorum quenching[END_REF][START_REF] Rémy | Lactonase Specificity Is Key to Quorum Quenching in Pseudomonas aeruginosa[END_REF], although biofilm upregulation has also been described in such conditions [START_REF] Su | Quorum sensing system of Ruegeria mobilis Rm01 controls lipase and biofilm formation[END_REF][START_REF] Mahan | Effects of Signal Disruption Depends on the Substrate Preference of the Lactonase[END_REF]. Those contrasting effects probably result from the fact that biofilm production is under complex and interconnected regulatory mechanisms, including AHL-based QS, furanosyl diester borate (AI-2)-based QS and c-diGMP production, among others [START_REF] Wolska | Genetic control of bacterial biofilms[END_REF][START_REF] Hmelo | Quorum Sensing in Marine Microbial Environments[END_REF][START_REF] Lami | Quorum Sensing in Marine Biofilms and Environments[END_REF]. In another report, [START_REF] Liu | Detection of Diverse N-Acyl-Homoserine Lactones in Vibrio alginolyticus and Regulation of Biofilm Formation by Lactone In vitro[END_REF] noticed that the addition of 3-oxo-C10-HSL differentially affected the biofilm formation of V. alginolyticus isolates with apparent strain specificity and a dose-dependent response. Clearly, our results revealed that biofilm production is under the control of QS in some estuarine microbial communities, but they also suggest the complexity of QSdependent regulations of biofilm production in such ecosystems. Biofilm formation among estuarine bacteria may drive their capacities to colonize particulate organic matter [START_REF] Simon | Microbial ecology of organic aggregates in aquatic ecosystems[END_REF][START_REF] Dang | Microbial Surface Colonization and Biofilm Development in Marine Environments[END_REF][START_REF] Sivadon | Biofilm formation as a microbial strategy to assimilate particulate substrates[END_REF]. Thus, QS involvement in biofilm formation possibly shapes the diversity of particle-attached communities. However, we evaluated QS involvement in monospecific cultures while biofilm formation is a complex multi-species phenomenon in natural environments. As such, it will be necessary to evaluate QS involvement on complex community assemblages in the future to obtain a better picture of the QS-based interactions at play, as has been done in a few studies [START_REF] Huang | Evaluation of biological and enzymatic quorum quencher coating additives to reduce biocorrosion of steel[END_REF][START_REF] Schwab | Signal disruption leads to changes in bacterial community population[END_REF] We then assessed whether the expression of AHL-based QS regulates the activity of hydrolytic enzymes. Most studies to date that have examined the possible regulation of hydrolytic enzyme activity by QS in isolated strains have focused on dissolved extracellular enzymes [START_REF] Gram | Possible quorum sensing in marine snow bacteria: production of acylated homoserine lactones by Roseobacter strains isolated from marine snow[END_REF]Jatt, Tang, Liu, Zhang, and X. H. Zhang, 2015;[START_REF] Guendouze | Effect of quorum quenching lactonase in clinical isolates of Pseudomonas aeruginosa and comparison with Quorum sensing inhibitors[END_REF][START_REF] Li | Involvement of Exogenous N-Acyl-Homoserine Lactones in Spoilage Potential of Pseudomonas fluorescens Isolated From Refrigerated Turbot[END_REF][START_REF] Su | Quorum sensing system of Ruegeria mobilis Rm01 controls lipase and biofilm formation[END_REF][START_REF] Mahan | Effects of Signal Disruption Depends on the Substrate Preference of the Lactonase[END_REF]. By contrast, most studies have been carried out on natural communities focused on cell-bound [START_REF] Hmelo | Possible influence of bacterial quorum sensing on the hydrolysis of sinking particulate organic carbon in marine environments[END_REF] or total activities [START_REF] Krupke | Quorum Sensing plays a complex role in regulating the enzyme hydrolysis activity of microbes associated with sinking particles in the ocean[END_REF]. Thus, in this study we decided to bridge this gap by assaying both dissolved and cell-bound activities of two hydrolytic enzymes: LAM, which are involved in N cycling and considered to be a broader proxy for proteases activity [START_REF] Steen | Substrate specificity of aquatic extracellular peptidases assessed by competitive inhibition assays using synthetic substrates[END_REF], and β-glc, which are broad-specificity enzymes involved in C cycling and carbohydrate dynamics [START_REF] Chróst | Characterization and significance of β-glucosidase activity in lake water[END_REF]. Interestingly, the lactonase treatment significantly impacted the activity of dissolved and cell-bound LAM in, respectively, 71% and 39% of the tested strains, suggesting that QS might regulate LAM production in numerous estuarine bacteria. The influence of QS on β-glc production appeared less pronounced, with dissolved and cell-bound activities being significantly modified by the lactonase treatment in 4% and 26% of the tested strain, respectively. However, β-glc production was not as widespread as LAM production: only 3 and 17 strains produced dissolved and cell-bound β-glc, respectively, versus 18 and 28 for LAM, respectively. Clearly, our study evidenced that hydrolytic enzyme activities are under QS regulation in many strains. To our knowledge, only [START_REF] Jatt | Quorum sensing in marine snow and its possible influence on production of extracellular hydrolytic enzymes in marine snow bacterium Pantoea ananatis B9[END_REF] and [START_REF] Su | Quorum sensing system of Ruegeria mobilis Rm01 controls lipase and biofilm formation[END_REF] have described such an effect after QS inhibition in Pantoea and Ruegeria strains isolated from marine snow, respectively. Our study demonstrated a broader effect because more phylogenetically diversified strains were tested. This clearly points out the need to investigate at a much larger scale this question in aquatic environments, as QS communication may represent an underestimated mechanism of N and C recycling by marine bacteria.

The effect of AHL-based QS inhibition on the activity of hydrolytic enzymes was contrasted as both increase and decrease in dissolved and cell-bound activities were observed. Indeed, the cellbound LAM activity of several Pseudomonas strains increased, while those of other genera (Acinetobacter, Obesumbacterium, Sphingobacterium and Vibrio) decreased. Similar results were observed for β-glc activity, which decreased in some Vibrio strains but increased in one Pseudomonas strain and one Acinetobacter strain. Such complex effects have been reported after AHL addition to marine snow: [START_REF] Krupke | Quorum Sensing plays a complex role in regulating the enzyme hydrolysis activity of microbes associated with sinking particles in the ocean[END_REF] found that 3-oxo-C8-HSL induced phosphatase activity but decreased aminopeptidase and lipase activities after 6 h of incubation with communities from the Atlantic Ocean. [START_REF] Su | Quorum sensing system of Ruegeria mobilis Rm01 controls lipase and biofilm formation[END_REF] also highlighted that 3-oxo-C8-HSL increased galactosaminidase and β-xylosidase activities but decreased β-glucosidase and mannosidase activities in marine particles collected in the Yellow Sea of China. A few strains exhibited interesting behavior, where dissolved and cell-bound activities were differentially affected by the lactonase treatment. For example, the inhibition of QS circuits induced a decrease in Pseudomonas sp. AF3-9 dissolved LAM, but an increase in cell-bound LAM. The opposite phenomenon was observed for Acinetobacter sp. DF3-8 and Vibrio sp. CFL-9. This overall variability is probably linked to the complexity of hydrolase regulation, which are under tight control given their metabolic cost [START_REF] Chróst | Ecology of Microbial Enzymes in Lake Ecosystems[END_REF].

Collectively, our findings revealed that estuarine bacteria can synthesize a large array of AHLs and present a wide range of quenching activities. Our results also clearly demonstrated the importance of AHL-based QS regulation on a range of phylogenetically different estuarine strains, suggesting an underestimated role of QS in microbial N and C cycling in aquatic environments. In the particular case of the Aulne estuary, for which high enzymatic activities have been reported [START_REF] Labry | High alkaline phosphatase activity in phosphate replete waters: The case of two macrotidal estuaries[END_REF], QS involvement in hydrolase synthesis may have important consequences on biogeochemical fluxes of nutrients to coastal waters. The effects of QS inhibition were contrasted, illustrating the complexity of those cell-cell regulations. Thus, our study paves the way for future studies, highlighting the need to introduce concepts and tools of chemical ecology in marine microbial ecology to address broader questions relative to ecosystem functioning.

Experimental procedures Standard N-acylhomoserine lactones

All AHLs used in this study were purchased from Sigma-Aldrich (Darmstadt, Germany). The

Isolation of estuarine bacterial strains

Bacteria were isolated from the surface water of the Aulne estuary (Bay of Brest) in April and July 2019. Three type of waters were sampled along the salinity gradient in order to cover a large range of suspended matter concentrations: riverine waters (salinity 0), brackish waters in the maximum turbidity zone (salinity 5) and brackish waters under greater marine influence (salinity 25, Table S1).The salinity was measured in situ with a thermosalinometer (WTW Cond 330i) using the practical salinity scale and as such, is reported with no unit [START_REF] Unesco | The international system of units (SI) in oceanography[END_REF]. All culturing steps were performed at the in situ water temperature (i.e. 12 and 22°C for April and July, respectively) using modified Luria Broth medium (mLB) containing 1% (w/v) peptone and 0.5% (w/v) yeast extract and adjusted to the salinity at which the strains were isolated (0, 5 or 25) with artificial seawater (54 mM MgCl 2 , 10 mM CaCl 2 , 0.16 mM SrCl 2 , 9 mM KCl, 2.4 mM NaHCO 3 , 840 mM KBr, 440 mM H 3 BO 3 , 71 mM NaF, 0.40 M NaCl and 30 mM Na 2 SO 4 , pH 8.2). For the 0 salinity mLB, 1% (v/v) of artificial seawater was added to ensure the presence of essential ions.

For each sample, four different isolation strategies were conducted to isolate bacteria from the total, particle-attached (> 3 µm) and free-living (0.2-3 µm) fractions of sampled water. Thus, mLB agar plates (mLB amended with 1.5% w/v agar) were inoculated by (i) spreading 100 µL of total water, (ii) spreading 100 µL of < 3 µm-filtered water (Whatman Nuclepore™ PC membrane, GE Healthcare Life Sciences, Little Chalfont, UK), (iii) depositing > 3 µm filters or (iv) depositing 0.2 µm filters (Whatman Nuclepore™ PC membrane) used with 3 µm-filtered water. Single colonies were isolated, purified, grown in mLB and stored at -80°C using 10% (v/v) DMSO.

Identification of AHL-producing strains

We then determined whether the isolated bacterial strains could produce AHLs, following previously published protocols. Briefly, bacterial supernatants were screened with bacterial biosensors, P. putida F117 (pRK-C12) [START_REF] Andersen | gfp-based N-acyl homoserine-lactone sensor systems for detection of bacterial communication[END_REF]) and E. coli MT102 (pJBA132) [START_REF] Riedel | Nacylhomoserine-lactone-mediated communication between Pseudomonas aeruginosa and Burkholderia cepacia in mixed biofilms[END_REF]. Those biosensors produce GFP in the presence of AHL. P. putida F117 allows a good detection of long-chain AHLs while E. coli MT102 preferentially detects short-chain AHLs [START_REF] Andersen | gfp-based N-acyl homoserine-lactone sensor systems for detection of bacterial communication[END_REF][START_REF] Riedel | Nacylhomoserine-lactone-mediated communication between Pseudomonas aeruginosa and Burkholderia cepacia in mixed biofilms[END_REF].

Both biosensors were grown at 30°C (150 rpm) in LB (0.5% w/v yeast extract, 1% w/v peptone, 1% w/v sodium chloride) using, respectively, 20 µg mL -1 of gentamicin or 25 µg mL -1 of tetracycline.

An overnight culture of each biosensor was inoculated at a 600 nm optical density (OD 600 , measured using a NanoDrop 2000c with a 1 cm cuvette, Thermo Scientific) of 0.02 into fresh LB medium and dispensed into a 96-well black microplate (150 µL per well). Bacterial strains to be tested for AHL production were grown in mLB (in the salinity at which they were isolated) until an OD 600 of 0.6-1.2 was reached (typically 12 to 48 h). Cultures were centrifuged at 12,000 g for 10 min and filtered through a 0.2 µm filter (Corning® PES syringe filter). The resulting supernatants were stored at -20°C until screening (1 week maximum) and then were added to the biosensors microplate in triplicate (50 µL/well) after thawing at ambient temperature.

Blanks consisted of sterile LB medium amended with mLB at the corresponding salinity. Negative controls for the test samples consisted of biosensor strains with sterile mLB amendments, as the mLB was found to modify growth and fluorescence production of the biosensors depending on the salt concentration. Positive controls consisted of biosensor strains amended with 10 µM commercial AHL (C6-HSL for E. coli MT102 and 3-oxo-C10-HSL for P. putida F117) with a final DMSO concentration of 1% (v/v). In consequence, a second negative control was prepared with DMSO (1% v/v). GFP fluorescence (excitation: 485 nm, emission: 535 nm) and OD 600 were read on a Spark Tecan Infinite M200PRO microplate reader (Tecan, Switzerland) after an overnight incubation (14-16 h) at 30°C under low agitation (100 rpm). Specific fold induction of fluorescence was calculated by dividing the specific GFP fluorescence (FLUO GFP /OD 600 ) of the test sample by the specific GFP fluorescence of the negative control (each containing mLB with the same salinity). Specific fold inductions were arbitrarily classified into inhibition (< 0.8), no modification (0.8-1.2), questionable induction (1.2-1.5), induction (1.5-3) and strong induction (> 3, see Table S1). All fold inductions that were not significantly different from the control were also counted in the 0.8-1.2 category.

Quantification of AHLs produced by isolated strains

Bacterial strains that induced a response with AHL biosensors were further investigated to confirm AHL production using non-targeted UHPLC-HRMS/MS (MS). For each strain, 50 mL of supernatant was obtained as described above. Supernatants were extracted with 50 mL of ethyl acetate (EtOAc, overnight incubation at room temperature). Aqueous and organic phases were separated, and the aqueous phase was extracted again with 50 mL of EtOAc. The 2 × 50 mL EtOAc extracts were pooled and evaporated using a rotary evaporator. Crude extracts were dissolved in 500 µL of EtOAc, evaporated to dryness using a GeneVac HT-4X and stored at -20°C until analysis. Extracts were then dissolved in 500 µL of methanol for UHPLC-HRMS/MS analysis, which was performed using a Q Exactive Focus Orbitrap System coupled to an Ultimate 3000™ UHPLC system (Thermo Fisher Scientific, Waltham, Massachusetts, USA). The column was a Phenomenex Luna Omega Polar C18 (150 × 2.1 mm, 1.6 µm particle size). The mobile phase was composed of 0.1% (v/v) formic acid in

Identification of strains capable of QQ

AHL-producing strains were tested for both long-and short-chain AHL quenching. QQ identification relied on the use of the biosensors E. coli MT102 and P. putida F117 and followed previously published protocols [START_REF] Blanchet | Quorum sensing and quorum quenching in the Mediterranean Seagrass Posidonia Oceanica microbiota[END_REF]. Briefly, biosensors were grown as described above, except that 1 µM of a commercial AHL was added to the LB medium before dispatching into a 96-well plate (C6-HSL for E. coli MT102 and C14-HSL for P. putida F117). Supernatants exhibiting QQ capacities reduced GFP production compared with the control. Supernatants were produced as described above but were stored at 4°C until screening (4 h maximum) because QQ enzymes may be denatured upon freezing. Controls were realized as described previously for the screening of AHLproducing strains, except that positive controls contained 1 µM of AHLs and negative controls contained the equivalent quantity of DMSO (1% v/v). The fold reduction of fluorescence was calculated as described previously. To check that loss of fluorescence was not caused by cytotoxicity, 100 µL of each well of the biosensor assay was reacted with 30 µL of 0.01% (w/v) resazurin and incubated for 4 h at 30°C under low agitation [START_REF] Tourneroche | Bacterialfungal interactions in the kelp endomicrobiota drive autoinducer-2 quorum sensing[END_REF]. The resulting fluorescence (excitation: 530 nm, emission: 590 nm) corresponds to the reduction of resazurin to resorufin by viable cells with active metabolism.

Finally, the pH was checked at the end of separate cultures as it can affect AHLs degradation. The two biosensors and three representative strains (Pseudomonas sp. AF3-9, Acinetobacter sp. DF3-8 and Vibrio sp. E14) were grown in the same conditions as during the QQ assays, in 4 mL. The mean pH at the end of the incubation was 7.1 (range 6.5-7.9), which is near neutral pH.

Taxonomic identification of AHL-producing strains

AHL-producing bacteria were identified using partial 16S rRNA gene sequencing. For each bacterial strain, a single colony grown on an mLB agar plate was resuspended in 50 µL of MilliQ water and incubated at 95°C for 15 min. The polymerase chain reaction (PCR) mix contained 1 µL of this lysate, 21 µL of GoTaq Green Master Mix (Promega) and 1.5 µL of each universal primer 27F (10 µM, 5′-AGAGTTTGATCMTGGCTCAG-3′) and 1492R (10 µM, 5′-ACGGYTACCTTGTTACGACTT-3′). One cycle of denaturation (95°C for 5 min) was followed by 35 cycles at 95°C for 30 s, 56°C for 30 s and 72°C for 30 s. The final extension was realized at 72°C for 5 min. The PCR products were purified using the NucleoSpin Gel and PCR Clean-up Kit (Macherey-Nagel). The final products were sequenced using the Sanger technique by Eurofins Genomics (Köln, Germany) using the same primers. Each sequence was manually checked using Bioedit and blasted against the EzTaxon-e database (http://eztaxone.ezbiocloud.net, [START_REF] Kim | Introducing EzTaxon-e: A prokaryotic 16s rRNA gene sequence database with phylotypes that represent uncultured species[END_REF], from which reference sequences were retrieved. The pairwise sequence similarity between the isolates and their closest relatives was also calculated using the EzTaxon-e server (Table S3). All sequences were aligned in Muscle (MEGA 7.0), and a phylogenetic tree was constructed by maximum likelihood using the Tamura-Nei model. Geobacter sulfurreducens (NR 132673.1) and Geobacter soli (NR 134039.1) sequences were used as an outgroup. The reliability of each node in the tree was assessed by bootstrapping over 500 replicates and nodes with boostrap values inferior to 50 were collapsed. The resulting tree was displayed using the ggtree package (version 2.2.4) in R.

Biofilm production in isolated strains

Biofilm production was quantified using a crystal violet (CV) assay as described previously [START_REF] Blanchet | Quorum sensing and quorum quenching in the Mediterranean Seagrass Posidonia Oceanica microbiota[END_REF] with slight modifications. Briefly, 200 µL of culture was grown in mLB medium (at the isolation salinity) in a 96-well microplate (Greiner Bio-One, Cat.-No 655 160) for 24 h (at 22°C) or 48 h (at 12°C) under low agitation (100 rpm). Each subsequent step was performed gently to preserve the biofilm. The supernatant of each culture was removed and non-adherent cells were washed with 200 µL of phosphate-buffered saline (PBS, pH 7.4, VWR). Microplates were dried at 60°C for 1 h. Biofilms were stained with 0.2% (w/v) CV for 15 min. Unbound CV was then removed and the wells were washed 3 times with sterile water. CV in biofilm was dissolved with 200 µL of a destaining solution (50% ethanol, 10% acetic acid in MilliQ water, v/v) and the resulting OD was measured at 540 nm (OD 540 ). Strains with OD 540 < 0.1 were considered non-adherent to the plate surface.

Enzymatic activity assay on isolated strains

The potential LAM and β-glc activities were quantified using model fluorogenic substrates: Lleucine-7-amido-4-methylcoumarin (LLMCA) and 4-methylumbelliferyl-β-D-glucopyranoside (MUFglc), respectively. Those substrates release a fluorescent moiety upon hydrolysis, 7-amido-4methylcoumarin (MCA) and 4-methylumbelliferone (MUF), respectively [START_REF] Chróst | Microbial Ectoenzymes in Aquatic Environments[END_REF]. LAM and βglc were assayed in the supernatant (which corresponds to dissolved cell-free enzymes) and in total culture (which includes dissolved and cell-bound extracellular enzymes). To perform the assay, 100 µL of either supernatant or total culture was transferred into a 96-well microplate and amended with 1000 µM of LLMCA or 150 µM of MUF-glc, which were saturating concentrations (not shown).

Fluorescence was monitored for 3-4 h using a Spark Tecan Infinite M200PRO with excitation/emission wavelengths of 364/460 nm for MUF and 380/440 nm for MCA. Activities were determined as the slope of the linear part of the curve (AU min -1 ). Cell-bound activity was determined by subtracting the mean dissolved fraction activity from total fraction activity. All reported activities are specific (normalized using OD 600 ).

Table and Figure legends

Fig 1 . A) The phylogenetic tree of the strains for which AHLs were detected using MS. The tree was constructed by maximum likelihood using the Tamura-Nei model as implemented in MEGA 7.0. Nodes with bootstrap value inferior to 50 were collapsed. B) The ability of each strain to: quench C6-HSL and C14-HSL, produce hydrolytic enzymes (dissolved glucosidases, cell-bound glucosidases, dissolved proteases and cell-bound proteases) and produce biofilm. Red: producer, white: non-producer, grey: not tested. C) The impact of lactonase treatment on the production of hydrolytic enzymes and biofilm for each strain. White: non-significant effect of lactonase treatment, red: significant downregulation, green: significant upregulation, grey: not tested, black cross: no production in control treatment without lactonase. Significance threshold = 0.05.

Table 1. AHLs quantified in the supernatants of the bacterial strains isolated from the Aulne estuary. AHL concentrations are in nM. Putative AHLs are molecules that possess the characteristic fragmentation spectrum of AHLs but for which we do not have commercial standards.

Table 2. Mean value of specific GFP fold induction of the biosensors in the control condition (no lactonase) and the specific GFP ratio of lactonase treatment to control at the end of the lactonase assay, averaged over triplicate measurements. Only strains that were considered to activate the biosensors in the control condition at the end of the lactonase assay are reported for readability, that is to say strains with a specific fold induction > 1.5 that is significantly different from negative control A) The phylogenetic tree of the strains for which AHLs were detected using MS. The tree was constructed by maximum likelihood using the Tamura-Nei model as implemented in MEGA 7.0. Nodes with bootstrap value inferior to 50 were collapsed. B) The ability of each strain to: quench C6-HSL and C14-HSL, produce hydrolytic enzymes (dissolved glucosidases, cell-bound glucosidases, dissolved proteases and cell-bound proteases) and produce biofilm. Red: producer, white: non-producer, grey: not tested. C) The impact of lactonase-treatment on the production of hydrolytic enzymes and biofilm for each strain. White: non-significant effect of lactonase-treatment, red: significant down-regulation, green: significant up-regulation, grey: not tested, black cross: no production in control treatment without lactonase. Significance threshold = 0.05.

  following abbreviations are used: N-butyryl-DL-homoserine lactone (C4-HSL), N-hexanoyl-DLhomoserine lactone (C6-HSL), N-(3-hydroxyhexanoyl)-DL-homoserine lactone (3-OH-C6-HSL), N-(3oxohexanoyl)-DL-homoserine lactone (3-oxo-C6-HSL), N-octanoyl-DL-homoserine lactone (C8-HSL), N-(3-hydroxyoctanoyl)-DL-homoserine lactone (3-OH-C8-HSL), N-(3-oxooctanoyl)-DL-homoserine lactone (3-oxo-C8-HSL), N-decanoyl-DL-homoserine lactone (C10-HSL), N-(3-hydroxydecanoyl)-DLhomoserine lactone (3-OH-C10-HSL), N-(3-oxodecanoyl)-DL-homoserine lactone (3-oxo-C10-HSL), Ndodecanoyl-DL-homoserine lactone (C12-HSL), N-(3-hydroxydodecanoyl)-DL-homoserine lactone (3-OH-C12-HSL), N-(3-oxododecanoyl)-DL-homoserine lactone (3-oxo-C12-HSL) and N-tetradecanoyl-DLhomoserine lactone (C14-HSL). All AHLs were dissolved in dimethyl sulfoxide (DMSO).

  water (A) and 0.1% (v/v) formic acid in acetonitrile (B). The elution gradient started with 99% (v/v) of A, keeping this composition constant for 4 min. The proportion of B was linearly increased to 100% (v/v) over 10 min and left at 100% (v/v) for 5 min. The analytical method includes full-scan MS (50 to 750 m/z) with successive data-dependent MS/MS (dd-MS²) scan, allowing the acquisition of high-resolution data of the parent ion and daughter ion masses in a single chromatographic run. The selected parent ions were fragmented using 15, 30 and 40 eV. AHL fragmentation spectra are characterized by the systematic presence of one abundant daughter ion (102.055 m/z), which corresponds to the lactone ring. Three other fragments are usually observed (56.050, 74.061 and 84.045 m/z). AHLs were confirmed and quantified using commercially available standards.

  (p < 0.05). * p < 0.05, ** p < 0.01, *** p < 0.001. Lac: lactonase treatment. ctr: control (buffer amendment). Table 3. Mean values of biofilm production, dissolved LAM, cell-bound LAM, dissolved β-glc and cellbound β-glc measured at the end of the lactonase assays for control (ctr) and lactonase treatment (lac) conditions with lactonases (lac) averaged over triplicated measurements. The ratio corresponds to the control over test condition. Significance was assessed using the t-test (° p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001). Significant results are highlighted in red and bolded when p < 0.05. nd.: not detected. nt.: not tested. -: ratio not calculable.Table 4. Number of functionally annotated genes related to QS, QQ, hydrolases and biofilm production in each draft genome.

Fig

  Fig. 1. A)The phylogenetic tree of the strains for which AHLs were detected using MS. The tree was constructed by maximum likelihood using the Tamura-Nei model as implemented in MEGA

Table 1 .Table 3 .

 13 AHLs quantified in the supernatants of the bacterial strains isolated from the Aulne estuary. AHL concentrations are in nM. Putative AHLs are molecules that possess the characteristic fragmentation spectrum of AHLs but for which we do not have commercial standards Mean value of specific GFP fold induction of the biosensors in the control condition (no lactonase) and the specific GFP ratio of lactonase-treatment to control at the end of the lactonase assay, averaged over triplicate measurements. Only strains that were considered to activate the biosensors in the control condition at the end of the lactonase assay are reported for readability, that is to say strains with a specific fold induction > 1.5 that is significantly different from negative control (p < 0.05). * p < 0.05, ** p < 0.01, *** p < 0.001. Lac: lactonase-treatment. ctr: control (Mean values of biofilm production, dissolved LAM, cell-bound LAM, dissolved β-glc and cell-bound β-glc measured at the end of the lactonase assays for control (ctr) and lactonasetreatment (lac) conditions averaged over triplicated measurements. The ratio corresponds to the control over test condition. Significance was assessed using the t-test (° p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001). Significant results are highlighted in red and bolded when p < 0.05. nd.: not detected. nt.: not tested. -: ratio not calculable.

Acknowledgments

This work was supported by the French National programme EC2CO (Ecosphère Continentale et Côtière) and by an ISblue (Interdisciplinary graduate school for the blue planet) project (ANR-17-EURE-0015), co-funded by a grant from the French government under the programme 'Investissements d'Avenir'. This work was also carried out in conjunction with the European Marine Biological Resource Centre (EMBRC-ERIC), EMBRC-France and BIO2MAR platform (http://bio2mar.obsbanyuls.fr). French state funds are managed by the ANR within the Investments of the Future programme under the grant number ANR-10-INSB-02.

We would like to thank the crew of the Hésione -Isabelle Bihannic, Thierry Le Bec and Erwan Amice -for their help during sampling. We would also like to thank the BIO2MAR platform (http://bio2mar.obsbanyuls.fr) for providing technical support and access to instrumentation. In addition, we are deeply grateful to Amine Boukerb (University of Rouen) for performing the wholegenome sequencing, Alexandre Cormier (SeBiMer, IFREMER) for assembling the genomes and Cyril Noël (SeBiMer, IFREMER) for his constructive advice on bioinformatics. Finally, we would like to thank Daniel Delmas for his perspicacious comments and support throughout this research project.

Data availability

The genome assemblies of Pseudomonas sp. AF3-9, Pseudomonas sp. B20, Acinetobacter sp. DF3-8 and Vibrio sp. E14 are available under the DDBJ/ENA/GenBank accession numbers JAHPJF000000000, JAHPJG000000000, JAHPJH000000000 and JAHPJI000000000, respectively. The AHL-related genes databases are available upon request.

Effect of lactonases on enzymatic activities and biofilm production

QS involvement in biofilm formation and expression of β-glc and LAM was assessed by comparing their production with and without lactonase, which degrade AHLs and thus inhibit QS circuits [START_REF] Grandclément | Quorum quenching: Role in nature and applied developments[END_REF]. A broad-spectrum lactonase preparation based on SsoPox lactonase from the archaeon Saccharolobus solfataricus was obtained from Gene&GreenTK (Marseille, France), which is specialized in the development of enzymes, including quorum quenching lactonases. The commercial preparation obtained from Gene&GreenTK was produced heterogeneously in E. coli, purified to homogeneity by size-exclusion chromatography [START_REF] Hiblot | Differential Active Site Loop Conformations Mediate Promiscuous Activities in the Lactonase SsoPox[END_REF][START_REF] Guendouze | Effect of quorum quenching lactonase in clinical isolates of Pseudomonas aeruginosa and comparison with Quorum sensing inhibitors[END_REF][START_REF] Rémy | Lactonase Specificity Is Key to Quorum Quenching in Pseudomonas aeruginosa[END_REF] and eluted in HEPES buffer (50 mM HEPES, 150 mM NaCl and pH 8.0). Bacterial isolates that were shown to produce AHLs were pre-cultured overnight in mLB (in the salinity at which they were isolated) at the appropriate temperature (12 or 22°C). The pre-cultures OD 600 was measured using a NanoDrop 2000c with 1 cm cuvette. They were diluted to a theoretical initial OD 600 of 0.001 and amended with the lactonase preparation (at a final concentration of 0.5 mg L -1 ) or the equivalent amount of HEPES buffer. All cultures were done in triplicate, starting from the same pre-culture. Two hundred microliters of each culture were immediately transferred into a microplate for biofilm production assay (see above procedure). Cultures were then incubated for 24 h (at 22°C) or 48 h (at 12°C) under low agitation (100 rpm). At the end of the incubation, OD 600 and enzymatic activities (see above procedure) were quantified (one assay per replicate). The resulting supernatants were also tested with P. putida F117 and E. coli MT102 as described above to ensure the proper AHL reduction upon lactonase treatment. For each parameter (biofilm production, specific enzymatic activity and specific GFP fold induction), the effect of the lactonase treatment was assessed by calculating the ratio of control to test sample value (ratio lac/ctr ).

Whole-genome sequencing of representative strains

The genome of four strains (Pseudomonas sp. AF3-9, Pseudomonas sp. B20, Acinetobacter sp. DF3-8 and Vibrio sp. E14) was sequenced to identify the genes involved in QS, QQ, hydrolytic enzymes and biofilm production. Those strains were chosen because they covered the taxonomic diversity of the AHL-producing strains and exhibited marked responses to lactonases addition. The four selected strains were grown to stationary phase in mLB (at the salinity at which they were isolated) at 12°C (Pseudomonas sp. AF3-9 and Pseudomonas sp. B20) or 22°C (Acinetobacter sp. DF3-8 and Vibrio sp. E14). Cells were harvested by centrifugation and genomic DNA was extracted using the GenElute Bacterial Genomic DNA Kit (Sigma-Aldrich) according to the manufacturer's instructions. DNA was sequenced using an Illumina MiSeq. Data quality was assessed using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc) and reads that met the following criteria were kept: (1) read length ≥ 50 base pairs (bp) and (2) phred score per base ≥ 30x. Adapters were filtered away based on an internal Illumina adapter database. De novo assembly of genomes from raw reads was performed using Unicycler (v0.4.8) with a minimal contig length of 500 bp [START_REF] Wick | Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads[END_REF]. Contigs were then searched for similarities against the UniVec database (03-20-2017) using BLASTN [START_REF] Altschul | Basic local alignment search tool[END_REF] to remove potential contaminants from the genome assemblies.

Sequencing coverage was estimated by first mapping paired reads to the genome with Bowtie2 (v2.3.5) [START_REF] Langmead | Fast gapped-read alignment with Bowtie 2[END_REF] in 'sensitive' mode and then computing the coverage with Mosdepth (v0.2.7) [START_REF] Pedersen | Mosdepth: Quick coverage calculation for genomes and exomes[END_REF]. The completeness of the genome assembly was assessed using BUSCO (v4.0.0) [START_REF] Simão | BUSCO: Assessing genome assembly and annotation completeness with single-copy orthologs[END_REF] in 'genome' mode specifying the profile as appropriate (i.e., Pseudomonadales or Vibrionales, release April 2019, Table S5). The obtained drafts were checked for the number of contigs, %GC content, total assembly size, N50 values and percentage of coverage. Automatic gene prediction was done with the Prokka pipeline (v1.14.5) [START_REF] Seemann | Prokka: Rapid prokaryotic genome annotation[END_REF] using default parameters, specifying the corresponding genus.

The following analyses were done using the predicted protein sequence output from Prokka. To search for AHL synthases, AHL receptors, AHL acylases and AHL lactonases, reference databases were built using the reviewed UniProt/Swiss-Prot database (with the respective keywords: "acylhomoserine-lactone synthase", "IPR036693", "AHL acylase" and "AHL lactonases") and manually inspected. In addition, three sequences from UniProt/ TrEMBL were added to represent the HdtS and AinS AHL synthase families and the AinR AHL receptors. Predicted proteins were blasted against the custom reference databases using a cut-off E-value of 10 -15 . Shortlisted proteins were then blasted against the complete UniProt/Swiss-Prot database and proteins with a better match in the UniProt/Swiss-Prot database were removed from the hit list. Finally, the protein domains of those hits were annotated using InterProScan (v5.36.75) [START_REF] Jones | InterProScan 5: Genome-scale protein function classification[END_REF]. Hits containing the appropriate domains were considered putative QS genes (see Table S4 for domain list). They were aligned to their custom reference databases using MUSCLE as implemented in Geneious Prime (v2021.0.3) and consensus neighbor-joining trees were built using 500 bootstraps.

Genes related to motility and adherence likely to be involved in biofilm production (flagella, pili, fimbriae, chemotaxis) were predicted using VFanalyzer [START_REF] Liu | VFDB 2019: A comparative pathogenomic platform with an interactive web interface[END_REF], specifying the appropriate genus. Proteases and protease inhibitors were predicted using BLASTN against the MEROPS protease database (v12.1) [START_REF] Rawlings | The MEROPS database of proteolytic enzymes, their substrates and inhibitors in 2017 and a comparison with peptidases in the PANTHER database[END_REF] with a cut-off E-value of 1e-50 and 70% of identity. CAZymes were predicted against the CAZy database (Lombard et al., 2014) using dbCAN2 (HMMdb release v9.0) [START_REF] Zhang | DbCAN2: A meta server for automated carbohydrate-active enzyme annotation[END_REF]. Only CAZymes that were predicted by HMMER, Diamond and Hotpep tools were selected.

Statistical analysis


Nadine Martin
email: nadine.martin@gipsa-lab.grenoble-inp.fr

Corinne Mailhes

A non-stationary index resulting from time and frequency domains

Detecting the presence of non-stationarity events in a signal is a challenge that is still not taken up. The aim of this paper is to make a contribution to this key issue. We already proposed a non-stationarity detection defined in time-frequency domain in order to control the invariance of the time-frequency statistics. In this paper, in order to be not limited by the time and frequency resolution of a time-frequency approach, we propose another test in frequency domain. In frequency domain, the problem can be cast by taking advantage of the normalized-variance properties of a spectral estimator when analyzing non-stationary signals. This second test will confirm, invalidate or detect new frequency localizations of non-stationarities. Finally, the main contribution of the paper is to propose a stationary index defined so as to merge the information given by these two tests and to allow an alarm to be raised for a high level of non-stationarities. Applications on real-world signals show the pertinence of this new index.

Introduction

When processing signals, knowing that the underlying physical phenomena are nonstationary is of the first importance. Moreover, lots of processing methods rely on a stationarity assumption, which could be useful to test before any analysis. The aim of this paper is to propose a full methodology, for testing this stationarity assumption without any a priori hypothesis about the nature of the possibly non-stationarity events. In order to be of practical use, the methodology proposed will conclude with an index ranged between 0 and 100 so as to evaluate a level of non-stationarity.

In many applications, and above all in condition monitoring, Fourier analysis and spectrograms are standard and powerfull tools for studying time-varying signals. Great progress has been made in this area in order to improve resolution but this last point is not of importance when the issue is not to estimate and to detect. In addition, the main advantages of these classical transforms are their robustness and the fact that statistical properties are theoretically well known, which is of the first concern for this study. Finally, devising tests from these signal representations is motivated by the TetrAS project, in which signal-processing tools are developed around Fourier-based transforms (http://www.gipsa-lab.inpg.fr/index.php?id=304) and in which takes place this work.

According to the definition of the stationarity of a process, a stationarity test should result in the control of its statistical properties versus time. Such an approach first requires a time-observation scale. The time-observation scale will be set by the time resolution of the time-frequency representation, which will infer the definition of the minimal size of the non-stationary event the test would be able to detect. This size becomes then an a priori assumption. Second, in order to control the process statistics, this approach needs a way of measuring and tracking either the probability law parameters, or the moment invariance. The tests proposed in this paper completes the one presented in a previous publication [START_REF] Martin | A criterion for detecting nonstationary event[END_REF] and will be limited to the second order. Lastly, the methodology aims at estimating also both time and frequency locations of each nonstationary occurrence detected. Casting a test in a time-frequency domain plan is obviously well-fitted for solving this localization problem.

Early papers on the subject (see [START_REF] Martin | A criterion for detecting nonstationary event[END_REF] for the references) deal with optimal criterions, which rules out the time-frequency interest, deal with methods adapted to some particular event detection as certain transients, or need a preliminary training data set. More recently, a method based on surrogates, stationarized data get from the data itself in order to get rid of a well-defined structuration in time [START_REF] Borgnat | Stationarization via surrogates[END_REF] , do not seem to be welladapted to mixed signals. Its current implementation needs a number of choices, that the method proposed in this paper tries to avoid. Although the test gives also a final index, no time-frequency localizations are provided. This paper is organized as follows. Section 2 describes the framework of this study. In section 3, the test proposed in [START_REF] Martin | A criterion for detecting nonstationary event[END_REF] is recalled whereas a new test is elaborated upon. In section 4 a classification based upon a signal non-stationarity level is proposed from an original index computed with the proposed tests. In all of the sections, the performance of the methodology is dicussed around a number of real-world signals. Finally, Section 5 looks at the conclusions that can be drawn from the work.

Framework

Let [ ]

x n be a discrete time signal of length N and frequency sampling F s . The

observation set [ ] k x n L is a subset of 2 R so that [ ] ( ) [ ] { } 2 , /
, and for a given

k x x n n k S n k k = ∈ ∃ L R , (1)
[ ] [ ] [ ] 2 1 2 jk / 0 1 , e N m K x m s S n k x m w m nD N F π - - = = - ∑ , (2)
with [ ] w m a time window, D the window time-shift and K being the Fourier bin number, or a gliding correlogram evaluated from a biaised autocorrelation estimate, Figure 1 shows time-frequency representations of four signals with segment length equal to the close power of 2 of 2 % signal length, 50% overlapping, no zero padding and Hanning window. The first one is a magnetic Barkhausen Noise (F s =200 KHz over 0.2 s) measured with a magnetoresistive magnetic field sensor in order to evaluate the microstructure and the stress behaviour of ferromagnetic steels [START_REF] Padovese | Time-frequency and Time-Scale analysis of Barkhausen noise signals[END_REF] , the second one is a bioacoustic signal (F s =15 364 Hz over 17.64 s ) measured with an hydrophone in order to characterize dolphin whistles [START_REF] Millioz | Short Time Fourier Transform Probability Distribution for Time-Frequency Segmentation[END_REF] , the third one is an acoustic signal (F s =5 KHz over 20 s after a decimation by a factor 4) measured with an hydrophone during a boat passing, and the fourth one is a vibratory signal (F s =6 365 KHz over 10.3 s) measured with an accelerometer on a test bench in order to identify vibrations induced by a gearbox.

[ ] [ ] [ ] ( ) 1 1 * 2 jk / 0 1 1 , e N N m K x m u m s S n k x u x u m w m F N π - - - = =   = -     ∑ ∑ . ( 3
)
[ ] k

x n L is then an horizontal line at frequency k in each spectrogram of Figure 1for n varying from 1 to P, the number of signal-windows. The union of sets

[ ] k x n L , with k
varying from 1 to K, contains all the time-frequency points of a spectrogram. The aim of the tests proposed is to extract from these sets a sub-set, which will contain all the points corresponding to non-stationary events. Proceeding with detection theory is then possible thanks to the use of Fourier-based transforms.

Two non-stationary detection tests

The definition of a stationary process excuses for the nonexistence of an unique efficient test. An ergodic random process is completely stationary if its probability density function remains unaltered under a time shift. This definition is a severe requirement and should be relaxed. Many tests were derived according to the way you relax. In a previous paper of the authors [START_REF] Martin | A criterion for detecting nonstationary event[END_REF] , a time-frequency test was proposed. Being used in the final section, this test will be summed up in this section. Then, using the result of this time-frequency test, a second test based on the signal spectrum only will confirm, refute or eventually detect other types of non-stationarities. It will be the subject of the second sub-section.

Time-frequency test

As we did in [START_REF] Martin | A criterion for detecting nonstationary event[END_REF] , let us define two hypotheses:

-H 0 where the signal [ ] x S n k under this hypothesis. See [START_REF] Huillery | On the Description of Spectrogram Probabilities with a Chiquared Law[END_REF] for further details about the chi-square approximation.

0 k H n L is defined as [ ] ( ) [ ] [ ] ( ) { } 0 0 0 , / , min for a given x H x x S H k n k k p p et E S n k k = ∈ = L L , (4) with [
-H 1 where [ ]

x n is nonstationary and where

[ ] 1 k H n L is by definition the complement of [ ] 0 k H n L .
The two parameters of the law 0 p , parameter r, the equivalent degree of freedom, and parameterα are defined as, , which allow α to be estimated as,

[ ] ( ) [ ] ( ) ( ) [ ] ( ) ( ) [ ] ( ) ( ) 2 2 2 E , E , 2 Varn , = r , x x x x S n k S n k S n k E S n k - = and [ ] ( ) , b α E S k r ν = , (5) with [ ] ( ) Varn
[ ] [ ] ( ) [ ] [ ] [ ] 0 0 / , 1 ˆ, b H b n S n k k H α k S n k r card k ∈ = ∑ L L , (6)
[ ] α k being an estimation of α at frequency k .

As a matter of fact, if a deterministic signal [ ]

d n is eventually present under H 0 , [ ] , x S n k is proportional to a noncentral chi-square variable, ( ) 2 '
' r χ δ , with the same proportionality parameter α , a degree of freedom ' r , and with noncentrality parameter

[ ] [ ] ' , , d b r S n k S n k δ =
. In this case, the normalized variance,

[ ] ( ) ( ) ( ) 2 2 ' 4 Varn , ' x r S n k r δ δ + = + . ( 7
)
depends on the method parameters and mostly on the deterministic signal, whether the estimator is either a spectrogram or a correlogram. Nevertheless, the normalized variance in ( 7) is always lower than the normalized variance in (5). So, if we use the gamma distribution of the only-noise case in a detection test instead of the non-central chi-square distribution, the detector will always work but the real Pfa will be lower.

Afterwards, considering 0 p as equal to a gamma law as in (4), we can write at each frequency k the decision rule,

[ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] 1 1 0 1 , , , k x H k r H H is true S n k n x Pfa Pfa H is true S n k n S n k k k k η λ α ∈ ∈ > = × < L L , (8)
which allows making a decision between the two hypothesis H 0 and H 1 . Knowing that a gamma variable is proportional to a chi-square one 2 r χ , a given probability of false alarm Pfa has permitted the calculation of the detection threshold x n L being unknown, an iterative algorithm is proposed to be able to apply this decision rule. See [START_REF] Martin | A criterion for detecting nonstationary event[END_REF] for more details. Each class is defined as This time-frequency test was applied to signals of Figure 1with a false alarm probability equal to 10 -4 . Figure 2 shows the map defined by (10) for each signal. Figure 3 shows time projections, referred to as

[ ] Pfa k η after inverting the following integral, [ ] ( ) [ ] [ ] 2 2 1 r r Pfa Pfa Pfa k k k s Pfa g ds g u du α χ χ η λ η α α +∞ +∞ =   = =     ∫ ∫ , (9) where
( ) [ ] [ ] [ ] [ ] [ ] [ ] 1 1 1 ˆĈlass = , / , c c c c k I I x H x Pfa Pfa k Pfa Pfa Pfa S n k n k k S n k k λ α λ α + + ≤ < ∈ ≤ <       ∪ L (
[ ] 1 H
P n , and frequency projections, referred to as

[ ] 1 H P k , defined as [ ] [ ] [ ] [ ] ( ) 1 1 1 1 , . k H H k k H H P n card n P k card n   =     = ∪ L L (11)
The transient structures of the Barkhausen noise are well detected whereas the stationary narrow-band frequencies present all over the time duration of the signal (close to 30 kHz and 50 kHz) are fortunately undetected. These frequencies may come from some external interferences of the measurement equipment. In this application, this test has performed a source separation in the time-frequency domain. In the second signal, the non-stationary parts of the signal, the dolphin whistles are very well localised in the submarine noise. In the third signal, the signal is globally stationary apart a very localized frequency event corresponding to amplitude modulations in a band around 1 450 Hz. This band is well marked in the frequency projection, see Figure 3. The last signal is totally stationary, except for one non-significant point.

Normalized variance test

To round out the time-frequency test, a second test limited to second order also is designed in the frequency domain only.

In the previous sub-section it was noticed that, first, the normalized variance of a spectrum for a noise-only signal is constant (see ( 5)), and second, this variance is lower when a deterministic signal being present (see (7)). Furthemore, if a signal contains a non-stationarity at a frequency, this variance increases and if the non-stationarity is high, the variance will exceed its variations due to its own variance. These properties are tolerant vis-à-vis the Gaussian noise assumption. A test based on a normalised variance could then be able to detect non-stationary events.

The test proposed in this section aims at detecting the high variations of the normalized variance according to a stationary case. Based upon previous time-frequency test and the definition of the normalised variance recalled in (5), the frequency test of this section is defined as 12) is equal to 1 when using a spectrogram (from (2) and r=2 from ( 5)) or 0.49 when using a correlogram (from (3) and r=4.08 from ( 5)). Figure 5 shows the results for the dolphin whistles and, in particular, the stationary spectrum estimated by the first time-frequency test. The normalized variance shows that the variance is around 1 for most of the frequencies excepted for the frequency band containing the dolphin whistles, so the non-stationary part of the signal. The last plot shows the frequency alarms referred to as V[k] and given by

[ ] [ ] [ ] [ ]
ˆx k µ , [ ] [ ] ( ) [ ] [ ] 0 0 1 kH x p k p n H k k card n µ γ ∈ = ∑ L L , ( 13
[ ] [ ] [ ] [ ] 0 1 0 if ( ) . 1 if ( ) P P V k varn k H V k varn k H ζ ζ = ≤ = > (14)
Figure 6 shows the results for the three other signals. The magnetic Barkhausen noise is non-stationary for almost all the frequencies, which confirms the result of the time-frequency test (see Figure 3). For the boat passing signal, alarms are not all confirmed and some new frequencies are detected. More insights are necessary in this case, which will be the purpose of the next session. The gearbox vibration signal is totally stationary, no point of the variance exceeds the threshold, even the non-significant point of the first test, which is then not confirmed. This last example is interesting for the normalised variance shows several points well below 1, which characterizes deterministic components in the signal, which is indeed the case.

Classification from a stationary index

After having proposed tests, a final indicator is necessary in order to merge the information given by the two tests and also to provide one numerical index, which will allow a quick decision just after the signal acquisition. This index is targeted to assess the occurrence of non-stationary events without any considerations about their nature, then their time-frequency shape.

In that context, several indexes are defined. The first two merge the results of the timefrequency detection map defined in (10) by calculating the detection number referred to as Nb det_time and Nb det_freq and a percentage referred to as %det_time and %det_freq in the time and frequency projections The following three convey the contribution of the normalized test according to the time-frequency test,

[ ] ( ) [ ] [ ] [ ] [ ] ( ) [ ] ( ) 1 1 1 _ _ 1, _ _ 1, _ _ 1, = 1
Nb V k conf freq Nb K Nb V k new freq Nb K Nb V k fa freq Nb = = =   - × ×       × ×     = - × - ×     ∑ ∑ ∑ 100 / , . K (16)
where

[ ] ( ) 1 1 0 H P k - is a binary-value vector deduced from [ ] 1 H P k and [ ] 1 0 H P k the orthogonal one. The product [ ] ( ) [ ] 1 1 0 H P k V k   - ×    
between the two binary vectors gives the detections confirmed referred to as conf_freq. When the first is replaced by its orthogonal, the product gives the new detections referred to as new_freq and the product between the two orthogonal vectors referred to as fa_freq gives the false alarms, that is to say the detections non-confirmed by the second test.

The stationary index proposed is then defined by

_ _ % _ % _ Stat index time freq det time Rev det freq - = × , (17) with
= < - ≤ < - ≤ - > . ( 20
)
The last class corresponds, for example, to 40% time detection and 40% frequency detection in the time-frequency map, which is without any doubt a case of alarm relatively to the stationarity of the signal. Including this classification, Table 3 compares the results obtained with an expert interpretation. For the four studied results the stationary index proposed performs well.

Conclusions

This paper has proposed a stationary index defined in percentage in order to characterize the presence of stationary events in a signal without taking the nature of theses stationarities into account. This index does not need high computations and is easily and fast computed from a spectrogram or a gliding correlogram of a signal. This test is of interest to control all signal acquisitions as a pre-analysis before applying more specific processing. It can also be used in order to localise the non-stationarities, which can be the input of some algorithms. Future works will include another time test in order to reinforce the time index as the normalized-variance test does for the frequency index.

Figure 1 .

 1 Figure 1. Time signals and their spectrograms (with segment length equal to the close power of 2 of 2 % signal length, 50%overlapping, no zero padding and Hanning window), (from left to right) a magnetic Barkhausen noise, dolphin whistles, a boat passing and gearbox vibrations.

  x n is equal to [ ] b n , a stationary random process with zero mean, constant variance and time-frequency estimation [ ]

  , x S n k the normalized variance of the observation. The first moment about zero of [ ] ν , can be estimated from the average of the points belonging to

  minus the Pfa th -quantile of a 2 r χ and depends on the given Pfa only. The partition of [ ] k

  The result of this test is represented as a time-frequency map. Each element of the value of Pfa that detects this element at the last iteration.

Figure 2 .

 2 Figure 2. Results of the time-frequency test applied on spectrograms computed with (from left to right) the magnetic Barkhausen noise, the dolphin whistles, the boat passing and the gearbox vibrations. Horizontal axis is time in s whereas vertical axis is frequency in Hz.

Figure 3 .

 3 Figure 3. Time projection in s (up) and frequency projection in Hz (bottom) of the detection map get with the time-frequency test applied on the four studied signals, (from left to right) the magnetic Barkhausen noise, the dolphin whistles, the boat passing and the gearbox vibrations.

  over the p th signal window (see (2)), P the number of signal-windows in equation (2), and where [ ]

  ) results of the previous test and is the mean value of varn k is then equal to a constant depending on the window shape and of the average number.

  a Blackman window and without averaging due to the time-frequency estimator, then [ ] ˆP varn k in (

  The threshold ζ should be defined from the standard deviation of [ ] ˆP varn k considered as a random process. As the derivation of the confident interval is a tricky task, a simulation procedure with 20 000 runs of a white Gaussian noise has determined the value of the threshold ζ according to the false alarm probability Pfa varnorm and the signal-window number P.

Figure 4 .Figure 4

 44 Figure 4. Simulation results of the normalised variance of a spectrum estimated by spectrograms (on the left) or gliding correlograms (on the right) versus the probability of false alarm Pfa varnorm (vertical axis) and the threshold ζ (horizontal axis) and the signal-window number P (number over the figure)

Figure 5 .

 5 Figure 5. Detailed results of the normalized variance test applied to the spectrogram of the dolphin whistle signal (the second signal), (left, up to bottom) spectrum [ ] ˆp k γ

Figure 6 .

 6 Figure 6. Results of the normalized variance test with Pfa varnorm =10 -4 applied to the spectrogram of the three other signals, (left to right) the magnetic Barkhausen noise with P=77 then ζ =2.3, the boat passing with P=96 then ζ =2.3, the gearbox vibrations with P=63 then ζ =3, (up to bottom) normalised variance [ ] ˆP varn k with threshold in green dotted line, frequency alarm plot V[k].

1 H

 1 P k respectively, providing the number of zero values in vectors[ ]

Table 1

 1 makes use of the simulation results and suggests a final choice of the threshold ζ for different ranges of the signal-window number P and for 4 values of the false alarm probability Pfa

varnorm . It is important to notice that the value of P will lie in the range

[30,150]

due to the fact that, in the time-frequency test proposed, the length of the signal-window is fixed to the close power of 2 of 2 % signal length with 50% overlapping.

Table 1 . Choice of the detection threshold in the normalized variance test according to the simulation process and for both the spectrogram and gliding correlogram.

 1

				Spectrogram				Gliding correlogram
	P Pfa varnorm	from20 to 30	from 30 to 70	from 70 to 150	from 150 to 400	> 400	from 20 to 30	from 30 to 70	from 70 to 150	from 150 to 400	> 400
	10 -5	5	3,7	2,5	2	1,6	2,4	1,7	1,3	1	0,8
	10 -4	4	3	2,3	1,8	1,5	1,9	1,4	1,1	0,9	0,75
	10 -3	3	2,4	2	1,6	1,4	1,4	1,2	1	0,8	0,7
	10 -2	2	1,8	1,6	1,4	1,2	1	0,9	0,8	0,7	0,6

Table 2 . Values of the indexes proposed for the four studied signals

 2

		t-f test			Normvar test % t-f test
	Signal	%det_time %det_freq %conf_freq %new_freq %fa_freq Rev%det_freq	Stat_index_ti me-freq	Redondancy_ freq
	Barkhausen	72,7%	81,3%	76,6%	1,4%	4,7%	78,0%	56,7%	92,7%
	Dolphin	76,9%	24,9%	18,7%	0,0%	6,2%	18,7%	14,4%	75,1%
	Boat	70,8%	9,9%	2,0%	0,0%	7,8%	2,1%	1,5%	20,7%
	Gearbox	1,6%	0,1%	0,0%	0,0%	0,1%	0,0%	0,0%	0,0%

Table 2

 2 shows the values of these indexes for the four signal studied throughout this paper. Finally, relying upon a wider data base analysis, a symbolic classification is proposed and defined as

					Symbolic Classification
	_ Stat index time freq _ -	0%		Stationary
	0%	_ Stat index time freq _	4%	Warning	!
	4%	_ Stat index time freq _	16%	Serious warning	!!
	_ Stat index time freq _	16%		Alarm	!!!

Table 3 . For the four studied signals, comparison of the stationary index proposed with an expert interpretation

 3

	Signal	Stat_index_t ime-freq	Classification given by the tests	Expert view
	Magnetic Barkhausen Noise	56,7%	Alarm!!!	Strongly Non-Stationary
	Dolphin Whistles	14,4%	Serious Warning!!	Non-Stationary, Presence of non-linear modulations locally
	Boat Passing	1,5%	Warning!	Non-Stationary, small variations of central frequencies of narrow band patterns & small amplitude variations
	Gearbox Vibrations	0,0%	No Alarm	Stationary