text:
M Courel
email: courel@cirad.fr

; G M Rios
email: rios@crit.univ_monpt2.fr

; M Dornier

; M Reynes

Membrane Distillation, Osmotic Distillation and Membrane Contactors, 2-4 July, Cetraro (CS)

Il manque les titres de figures Introduction

Osmotic evaporation (OE) is a recent membrane technology that was patented by Deblay in 1991 [START_REF] Deblay | Process for at least partial dehydration of an aqueous composition and devices for implementing the process[END_REF] allowing concentrating fragile aqueous solutions such as fruit juices [START_REF] Courel | La concentration de jus de fruits par évaporation osmotique[END_REF]. OE consists of extracting water from an aqueous stream by means of a concentrated salt solution. The liquids are circulated along the 2 faces of a macro porous hydrophobic membrane. Because of its hydrophobicity the polymer cannot be wetted by the liquids; vapour-liquid interfaces are thus formed at each extremity of the pores that contain air at atmospheric pressure (Fig. 1). The presence of salt depresses the vapour pressure of the brine and results in creating a vapour pressure gradient between dilute and salt solution.

Mass transfer and hydrodynamics

The mass transfer mechanism can be decomposed in 3 steps: the water is transported from the bulk feed through a concentration boundary layer to the membrane surface where it evaporates. The vapour is transferred across the membrane under the influence of a vapour pressure gradient and condenses at the surface of the strip solution. The water then passes through a diluted brine boundary layer to the bulk stream. The evaporation flux can be summarized by the following equations:

The molar flux N is proportional to the bulk vapour pressure gradient ∆P w ; K is the overall permeability of the system that can be expressed in terms of mass transfer resistances respectively in the feed (k f ), in the strip solution (k s ) and in the membrane (k m ).

The vapour transfer across the membrane has been widely studied for membrane distillation process and the same theory can be applied for OE. The porous polymer filled with air can be considered as a stagnant air film opposing resistance to the diffusion of water vapour. According to the type of membrane used, other resistances coming from the porous structure ie Knudsen diffusion or Poiseuille viscous flow might be taken into account. [START_REF] Hogan | Thermal and isothermal membrane distillation[END_REF]. The phenomenon of concentration polarisation is intimately linked to the hydrodynamics of the process. The data summarized in table 1 show that according to the module geometry and the liquid hydrodynamics used by the various authors, the concentration polarisation on brine side varies from 0 effects to 50% flux reduction.

macroporous hydrophobic membrane P wf >> P ws

Dilute aqueous solution

Concentrated salt solution

C feed C f interface C strip C s interface vapour air ] ) k 1/ + k 1/ + k 1/ ( [1/ = 1/K -1 s m f P . K = N w ∆
Taking into account these considerations, a new module was designed to try and increase the flux performances and to study the hydrodynamics of OE.

Materials and methods

The design of a new laboratory scale module is shown in Fig. 2. The fluids are circulated into square channels evenly distributed so that the velocity of the fluids is identical on the whole membrane surface. The membrane is sandwiched between the 2 symmetrical half cells with a surface area of 40 cm 2 . A commercial TF200 flat-sheet membrane from Gelman company was used for this work (pore diameter = 0.2 µm; porosity = 60%; thickness = 165 µm) made of a thin PTFE layer supported by a PP net. Distilled water was extracted by calcium chloride aqueous solutions of 5.5 +/-0.05 mol.l -1 at a temperature of 25 +/-0.2 °C. The concentration of the brine was maintained constant by using a large volume of solution and controlled by density measurements. The recirculation flow rates of the 2 solutions were controlled with rotameters with an accuracy of 2 %.

Results

Flux measurement procedure

The kinetics of each OE experiment was followed by recording the weight loss of the water reservoir from an electronic balance. The slope of the kinetic curve was calculated by least square regression to give the corresponding OE flux with an accuracy of less than 1 %. The noise introduced by the circulating fluids was measured and attained a maximum of 4 % of the OE signal (Fig. 3). The maximum stability was achieved between 180 and 360 minutes and this time interval was used to calculate the mean characteristic flux of an experiment. Reproducibility tests indicated that the OE flux could be measured under standard conditions with an accuracy of 10% within a 95% prediction interval.

Effect of circulation flow rate and concentration of the brine

One of the objectives of this work was to estimate the maximum flux attainable under specific temperature and brine concentration conditions. Using water and CaCl 2 5,3 M, the flow rate of the brine was varied from 0,3 to 2,7 l.min-1 covering a range of Reynolds numbers from 170 to 1400 corresponding to a laminar flow regime. The results presented Fig. 4. indicate evaporation fluxes from 5,7 to 12,0 l.h -1 .m -2 which is much higher than the performances reported in the literature. The OE flux increases with brine flow rate because of increased Reynolds number and decreased boundary layer resistance. Concentration polarisation in the brine affects significantly the flux as a 9 fold flow rate increase results in a 100% flux increase.

Another set of experiments was conducted at constant brine flow rate and temperature and the salt concentration was varied from 3.45 to 5.30 mol.l -1 . The results shown in Fig. 5 indicate the flux dependence on the driving force which is related to the water activity of the salt solution.

Conclusions

Considering the low osmotic evaporation fluxes reported in the literature as well as the lack of information about the hydrodynamic aspect of this technique, a new laboratory scale OE module was developed. A series of experiments was carried out with pure water and CaCl 2 solutions varying the flow rate and the concentration of the brine. The OE fluxes obtained in this work showed a significant improvement towards the results published by various authors. In spite of important concentration polarisation in the brine, the relative high fluxes can be attributed to high mass transfer coefficients achievable in the new lab-scale OE module.

Table 1 -

 1 OE fluxes obtained by various authors; T = 35-40°C; water or dilute fruit juice; NaCl or CaCl2 aqueous solutions; flat sheet PTFE or hollow fibre PP membranes.Concentration polarisation can play a significant role in the performance of OE systems. The resistances offered by the liquid films bounding either side of the membrane may cause severe flux decay. Mass transfer across boundary layers is generally supported by the film theory[3][4][5][START_REF] Sheng | Mass and heat transfer mechanisms in the osmotic distillation process[END_REF]

	REFERENCE	MENGUAL [3]	GODINO [4]	GOSTOLI [5]	SHENG [6]	HOGAN [7]
	Geometry	stirred cell	stirred cell	stirred cell plate & frame hollow fibre
	Hydro-dyna	200	50 to 350	1200	5.8 to 12.0	0.085 to
	mics	rpm	rpm	rpm	l.min -1	0.255 m.s -1
	Flux	0.39; 3.90?	2.95 to 4.20	max 2.7	1.25 to 1.80	1.30
	(l.h.m -2 )
	Shear	∞ agitation	+ 7 fold	no	+ 2 fold	+ 3 fold
	effect on	+ 40 to 50 %	+ 30 %	polarisation	+ 36 %	no effect
	flux


Benjamin Marie
email: benjamin.marie@mnhn.fr

Alison Gallet

Fish metabolome from sub-urban lakes of the Paris area (France) and potential influence of noxious metabolites produced by cyanobacteria

Keywords: Untargeted metabolomics, environmental assessment, cyanobacteria, biomarkers, sentinel species

The recent democratization of high-throughput molecular phenotyping allows the rapid expansion of promising untargeted multi-dimensional approaches (e.g. epigenomics, transcriptomics, proteomics, and/or metabolomics). Indeed, these emerging omics tools, processed for ecologically relevant species, may present innovative perspectives for environmental assessments, that could provide early warning of eco(toxico)logical impairments.

In a previous pilot study (Sotton et al., Chemosphere 2019), we explore by 1 H NMR the bioindicative potential of metabolomics analyses on the liver of 2 sentinel fish species (Perca fluviatilis and Lepomis gibbosus) collected in 8 water bodies of the peri-urban Paris' area (France). In the present study, we further investigate on the same samples the high potential of high-throughput UHPLC-HRMS/MS analyses. We show that the LC-MS metabolome investigation allows a clear separation of individuals according to the species, but also according to their respective sampling lakes. Interestingly, similar variations of Perca and Lepomis metabolomes occur locally indicating that site-specific environmental constraints drive the metabolome variations which seem to be influenced by the production of noxious molecules by cyanobacterial blooms in certain lakes. Thus, the development of such reliable environmental metabolomics approaches appears to constitute an innovative bio-indicative tool for the assessment of ecological stress, such as toxigenic cyanobacterial blooms, and aim at being further follow up.

INTRODUCTION

In the past decades, the use of aquatic organisms as bio-indicator has been widely adopted to monitor and to manage the quality of water bodies worldwide [START_REF] Lorenz | Bioindicators for ecosystem management, with special reference to freshwater systems[END_REF]. In parallel, the difficulties and the rational limits of a direct chemical monitoring for providing sufficient information to adequately assess the risks from anthropogenic chemicals in the natural environment was progressively documented (Reyjol et al., 2014). More recently, the recent democratisation of high-throughput molecular phenotyping has allowed the rapid expansion of promising untargeted multi-dimensional approaches (e.g. epigenomics, transcriptomics, proteomics, metabolomics), that now represent promising perspectives for environmental assessments [START_REF] Bahamonde | Defining the role of omics in assessing ecosystem health: Perspectives from the Canadian environmental monitoring program[END_REF]Cordier et al., 2020). It appears now very likely that emerging omics-based analyses, such as metabolomics, developed for ecologically relevant aquatic species, may support the development of new ecological evaluation tools, that could provide early warning of ecotoxicological impairments [START_REF] Marie | Disentangling of the ecotoxicological signal using "omics" analyses, a lesson from the survey of the impact of cyanobacterial proliferations on fishes[END_REF]). The metabolome is defined as the set of primary and secondary metabolites synthesized at a given time and thus represents its global metabolic chemical picture. This set of metabolites is assumed to represent a final endpoint of the phenotypic response of an organism that can potentially be modified when the ecological and environmental stress conditions change (Bundy et al., 2008). In this way, metabolomic studies have become a relevant approach to investigate physiological processes involved in the homeostatic responses of the organisms encountering environmental stresses from potentially multiple origins (Capello et al., 2016;Good et al., 2020). However, despite its high potential to support the understanding the molecular mechanisms implicated in the ecotoxicological responses of aquatic organisms [START_REF] Sardans | Ecological metabolomics: Overview of current developments and future challenges[END_REF]Viant, 2008), such environmental metabolomics investigations remains still limited. Indeed, numerous studies have compared the metabolite profiles of individuals collecting from a limited set of environments, often including only one contaminated site and a unique reference location (e.g. Capello et al., 2016;[START_REF] Melvin | NMRbased lipidomics of fish from a metal (loid) contaminated wetland show differences consistent with effects on cellular membranes and energy storage[END_REF][START_REF] De Marco | Organ-Specific Metabolome Deciphering Cell Pathways to Cope with Mercury in Wild Fish (Golden Grey Mullet Chelon auratus)[END_REF]. As previously suggested, the selection of a unique "good" reference site may crucially orientate the results of the analyses, and larger efforts should be provided at understanding similarities and differences among various references sites [START_REF] Simmons | Omics for aquatic ecotoxicology: Control of extraneous variability to enhance the analysis of environmental effects[END_REF][START_REF] Marjan | How does reference site selection influence interpretation of omics data?: Evaluating liver transcriptome responses in male rainbow darter (Etheostoma caeruleum) across an urban environment[END_REF]. Such approaches would gain at investigating larger sets of environments regarding their respective positions within environmental gradients of global ecotoxicological pressures [START_REF] Jeffries | Presence of natural and anthropogenic organic contaminants and potential fish health impacts along two river gradients in Alberta, Canada[END_REF][START_REF] Davis | Linking field-based metabolomics and chemical analyses to prioritize contaminants of emerging concern in the Great Lakes basin[END_REF][START_REF] Meador | Metabolomic profiling for juvenile Chinook salmon exposed to contaminants of emerging concern[END_REF]. Among several aquatic organisms, such as molluscs or arthropods, fishes present remarkable features that make them especially relevant for bio-indication purposes. In fact, they occupy almost any aquatic habitat, being capable of experiencing different and variable environmental conditions [START_REF] Harris | The use of fish in ecological assessments[END_REF]. Fishes are also responsive to numerous abiotic (temperatures, water velocities, sediment loads, hypoxia, …) or biotic (famine, predation, parasitism, …) pressures, together with anthropogenic stressors, such as contaminants, that represent additional constraints that fish may experience in disturbed ecosystems [START_REF] Mcclanahan | Global baselines and benchmarks for fish biomass: comparing remote reefs and fisheries closures[END_REF]. Environmental stressors are known to affect the specific physiological, developmental and reproductive traits, that allow the different fish species to colonize and occupy various ecosystems [START_REF] Hamilton | Population-level consequences for wild fish exposed to sublethal concentrations of chemicals-a critical review[END_REF]. Nowadays, cyanobacterial blooms are one of the common sources of stress encountered by organisms living in modern freshwater lentic systems around the world [START_REF] Harke | A review of the global ecology, genomics, and biogeography of the toxic cyanobacterium, Microcystis spp[END_REF][START_REF] Burford | Perspective: Advancing the research agenda for improving understanding of cyanobacteria in a future of global change[END_REF]. Then, various efforts have been attempted to characterize the potential effect of cyanobacteria proliferation on various fish species according to experimentation performed in the lab, in mesocosms, or from field sampling [START_REF] Amado | Oxidative stress generation by microcystins in aquatic animals: Why and how[END_REF]Ferrao-Filho et al., 2011;Sotton et al., 2017aSotton et al., , 2017b;;[START_REF] Manach | Physiological effects caused by microcystin-producing and non-microcystin producing Microcystis aeruginosa on medaka fish: A proteomic and metabolomic study on liver[END_REF]. To this end, we initially investigated by 1 H NMR technics the liver metabolome of fish collected from different lakes of the peri-urban Paris area (France), and encountering low or high cyanobacterial proliferation [START_REF] Sotton | Specificity of the metabolic signatures of fish from cyanobacteria rich lakes[END_REF]. This pilot study leads to the description of putative metabolite concentrations, corresponding to specific NMR signals, that were significantly correlated with higher cyanobacterial occurrence. The present study investigates the bio-indicative potential of high-throughput molecular phenotyping supported by LC-HRMS approaches on 2 sentinel fish specimens (Perca fluviatilis and Lepomis gibbosus) that were previously investigated by 1 H NMR [START_REF] Sotton | Specificity of the metabolic signatures of fish from cyanobacteria rich lakes[END_REF]. We hypothesis that LC-MS-based metabolomics approach may present a higher potential for environmental diagnosis. Indeed, such investigations provide valuable multimetric characters indicative of biological or ecological conditions, together with a prospective mechanistic under-standing of the cause-effect relationships. With regard to the cyanobacterial occurrence and the associated production of bioactive metabolites in the different lakes, we compare here the metabolome of two different sentinel fish species. Indeed, these organisms could present rather different or comparable sensitivity and responsiveness to local eco(toxico)logical constraints (i.e. potentially the production of noxious molecules by cyanobacteria), and may support the general interest for omics-based environmental approach for the characterization of cyanobacterial bloom threat.

MATERIALS AND METHODS

Fish sampling

Field sampling campaigns were performed during late summer 2015 (7-10 th September), as this "end-of-the-bloom" period appears more appropriate to observe cyanobacterial effects on fishes. Eight peri-urban pounds around Paris' area (Île-de-France region, France) were selected for their respective eutrophication levels and the presence or the absence of recurrent cyanobacterial blooms: Cergy-Pontoise (Cer), Champs-sur-Marne (Cha), Maurepas (Mau), Rueil (Rue), Verneuil (Ver), Varennes-sur-Seine (Var), Fontenay-sur-Loing (Fon) and Triel (Tri) pounds (Supplementary figure S1) (Maloufi et al., 2016;[START_REF] Sotton | Specificity of the metabolic signatures of fish from cyanobacteria rich lakes[END_REF]. These sites were sampled by the Hydrosphère company (www.hydrosphere.fr) with electric fishing device (FEG 8000, EFKO, Leutkirch, Germany) for capturing live caught fish, that were then promptly sacrificed for liver collection. The investigation of the fish guild indicates that only the perch (Perca fluviatilis) and pumpkinseed sunfish (Lepomis gibbosus) were presents in all or almost all these pounds (supplementary table S1) and were further selected as sentinel species for further molecular phenotyping analyses by metabolomics. Briefly, live caught fishes (n=5-10 young-of-the-year per pounds and per species) were briefly euthanized by neck dislocation, directly measured (12.04.8 cm), weighed (9.32.6 g), and then liver of each individual was shortly sampled, flash-frozen in liquid nitrogen and kept at -80°C until analyses, in accordance with European animal ethical concerns and regulations. In every lake, sub-surface chlorophyll-a equivalent concentrations attributed to the fourmain phytoplankton groups (Chlorophyta, Diatoms, Cyanobacteria and Cryptophyta) were measured with an in-situ fluorometer (Fluoroprobe II, Bbe-Moldenke, Germany) (Supplementary figure S2). Sub-surface water samples filtered on 20-µm mesh size were also collected for phytoplanktonic community analysis and further metabolomics characterisation, and then kept at -80°C until analysis as previously described [START_REF] Sotton | Specificity of the metabolic signatures of fish from cyanobacteria rich lakes[END_REF]. Physico-chemical parameters of the water column (temperature, conductivity, pH, dissolved O 2 and nitrate concentrations) were monitored with a YSI DSS probe, as previously described [START_REF] Sotton | Specificity of the metabolic signatures of fish from cyanobacteria rich lakes[END_REF].

Liver metabolite extraction and metabolomics analyses

The liver extraction was performed on 132 individuals (comprising 78 Perca and 54 Lepomis) with methanol/chloroform/water (ratio 2/2/1.8 -22 mL.g -1 at 4°C) and the polar fraction was analyzed on a 600-MHz NMR spectrometer equipped with a 5-mm cryoprobe (Advance III HD Bruker, Germany) with a noesygppr1d pulse sequence as previously described [START_REF] Sotton | Specificity of the metabolic signatures of fish from cyanobacteria rich lakes[END_REF]. 1 H-NMR spectra were treated with Batman R-package for deconvolution, peak assignment and quantification of 222 putative metabolites (Hoa et al., 2014). The liver extracted polar phase was additionally injected (2 L) on C 18 column (Polar Advances II 2.5 pore -Thermo), then eluted at a 300 μL.min -1 flow rate with a linear gradient of acetonitrile in 0.1% formic acid (5 to 90 % in 21 min) with an ultra-high-performance liquid chromatography (UHPLC) system (ELUTE, Bruker). Consecutively, the individual metabolite contents were analysed using an electrospray ionization hybrid quadrupole timeof-flight (ESI-Qq-TOF) high-resolution mass spectrometer (Compact, Bruker) at 2 Hz speed on positive MS mode on the 50-1500 m/z range. The feature peak list was generated from recalibrated MS spectra (< 0.5 ppm for each sample, as an internal calibrant of Na formate was injected at the beginning of each sample analysis) within a 1-15 min window of the LC gradient, with a filtering of 5,000 count of minimal intensity, a minimal occurrence in at least 50% of all samples, and combining all charge states and related isotopic forms using MetaboScape 4.0 software (Bruker). Additionally, five pools of six different individuals randomly selected for Perca and Lepomis (quality check samples) and the phytoplanktonic biomass extracts from the 8 lakes were similarly eluted then analysed on positive autoMS/MS mode at 2-4 Hz on the 50-1500 m/z range for further metabolite annotation. Molecular networks were performed with Global Natural Product Social network (http://gnps.ucsd.edu) and/or MetGem (http://metgem.github.io) softwares, as previously described [START_REF] Kim Tiam | Insights into the Diversity of Secondary Metabolites of Planktothrix Using a Biphasic Approach Combining Global Genomics and Metabolomics[END_REF][START_REF] Manach | Global metabolomic characterizations of Microcystis spp. highlights clonal diversity in natural bloom-forming populations and expands metabolite structural diversity[END_REF] for cyanobacterial and fish metabolite annotation was assayed using inhouse (Le [START_REF] Manach | Global metabolomic characterizations of Microcystis spp. highlights clonal diversity in natural bloom-forming populations and expands metabolite structural diversity[END_REF] or CyanoMetDB [START_REF] Jones | CyanoMetDB, a comprehensive public database of secondary metabolites from cyanobacteria[END_REF], and GNPS, Mona, human metabolome database (HMDB) and Massbank MS/MS spectral libraries. Water samples of each lake, concentrated on a 20-µm mesh size, were also extracted with 75% methanol (2 min sonication, 5 min centrifugation at 15,000 g -4°C) and then similarly analysed in triplicates on LC-HRMS on autoMS/MS positive mode, as earlier described, and metabolite list annotated with same pipelines.

Data matrix treatment

The resulting the intensity data tables of the 222 metabolites ( 1 H NMR) and 1252 analytes (LC-MS) were further treated for normalization with Pareto's scaling, inspected data representation by PCA and heatmap with hierarchical clustering (Euclidean distance) and then analysed to investigate the influences of "Species" and "Lakes" parameters on the datasets by PERMANOVA, PLS-DA or multi-block PLS-DA using MixOmics and Mixomics DIABLO R Package [START_REF] Rohart | mixOmics: An R package for 'omics feature selection and multiple data integration[END_REF][START_REF] Singh | DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays[END_REF], MetaboAnalyst 5 [START_REF] Chong | Using metaboanalyst 4.0 for comprehensive and integrative metabolomics data analysis[END_REF] and MicrobiomeAnalyst tools [START_REF] Chong | Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data[END_REF].

RESULTS AND DISCUSSION

Distinction of Perca and Lepomis liver LC-MS metabolome

Figure 1 illustrates the global metabolite profiles analyzed by LC-MS for each fish (comprising Perca and Lepomis) on a heatmap with hierarchical classifications and individual plot representation of principal component analyses (PCA). A very strong and significant discrimination between the "species" was achieved (PERMANOVA F-value = 94.92; R 2 = 0.43; P-value <0.001), when a slighter but still significant discrimination can be also retrieved for "lakes" (PERMANOVA F-value = 4.96; R 2 = 0.22; P-value <0.001). In parallel, a molecular network performed with fragmentation data was obtained by LC-MS/MS analyses of Perca and Lepomis metabolomes. This approach supports the global annotation process of the fish liver metabolites considering both structural identities and similarities [START_REF] Nothias | Feature-based molecular networking in the GNPS analysis environment[END_REF]. The GNPS and the t-SNE representation of the molecular network highlights that most, if not all, of the known metabolites (presenting structural identity or analogy hits) are largely shared between the two species (e.g. nucleic acids, carnitines, glutathiones, lipids, saccharides, …). Moreover, numerous species-specific cluster metabolites remain uncharacterized, with relative molecules and clusters presenting no match within the public chemical databases (supplementary figure S2) (da [START_REF] Da Silva | Illuminating the dark matter in metabolomics[END_REF]. Taken together, these analyses illustrate for these two species the various molecular specificities of their respective metabolomes. It also shows the substantial portion of the specific liver molecular metabolism that still remains uncharacterized and that could be related to species-specific nutritional, physiological or toxicological functions [START_REF] Brusle | The structure and function of fish liver[END_REF].

Comparison of Perca and Lepomis LC-MS metabolome between lakes

In addition, the LC-MS datasets of the two fish species considered separately show even clearer discriminations of the metabolomes of fish originating from the different lakes. The un-supervised PCA and the heatmap with hierarchical classification (Figure 2) present distinct environmental signatures that support reliable discrimination of the lake of sampling for both Perca and Leptomis (Fig. 2A-B and 2C-D, respectively). These observations strongly suggest that the locality seems to globally influence the local LC-MS metabolome composition for both species. However, the liver metabolomes of individuals from Fontenaysur-Loing lake (Fon) exhibit quite species-specific relative positions on PCA (Figure 2A and2C). Such differences of the relative position of metabolomes from Fon between the two species suggests that: i/ for most localities, the specific "lake" signature of the metabolome appears in good agreement between the two species, ii/ for some specific environments (e.g. Fon), the metabolome signatures are variable from one species to the other one. Thus, the species-specific signature of the Perca and Lepomis metabolomes in Fon lake may rather traduce the specific responsiveness and/or sensitivity to a local factor occurring at this particular location. Interestingly, the two hierarchical classifications performed on LC-MS metabolome datasets of Perca and Lepomis, respectively, overall show a global similarity of the metabolome of fish originating from the same lake, together with an overall distinction of 3 principal lake clusters. Indeed, these analyses show very similar lake relationships for both species, grouping together all fish from the lakes of 'Cer and Mau', from 'Cha, Fon and Rue', and from Tri, Var and Ver' for Perca, or 'Tri and Ver' for Lepomis, as this latter was not retrieved in Var (Figure 2B and2D). This observation suggests that these localities could somehow present comparable, if not analogous, environmental conditions. This hypothesis was firstly considered regarding NMR metabolomics and potential explicative environmental factors (physico-chemical and biological factors -supplementary table S2) and had suggested a potential influence of cyanobacterial proliferation (BBEcya and pH) [START_REF] Sotton | Specificity of the metabolic signatures of fish from cyanobacteria rich lakes[END_REF]. Indeed, as initially shown, the Fon, Tri, Var and Ver lakes encounter remarkable high amounts of cyanobacteria belonging to the Planktothix, Pseudo-Anabaena, Aphanizomenon, Anabaena or Microcystis genera. Differently, the other lakes (Cer, Mau, Cha and Rue) exhibit phytoplanktonic biomasses principally dominated by green algae, diatoms or cryptophytes (Fig. 3A-B). A molecular network performed with LC-MS/MS data obtained from the phytoplanktonic biomass of these 8 lakes shows that noticeable amounts of cyanopeptides are observed in the water column of the cyanobacterial-rich lakes (Fon, Tri, Var and Ver), with the specific presence of most noxious cyanopeptides (microcystins, cyanopeptolins and anabaenopeptins) being detected in Tri, Var and Ver lakes. In a different manner, Fon water exhibits rather less toxic cyanopeptides [START_REF] Janssen | Cyanobacterial peptides beyond microcystins-A review on cooccurrence, toxicity, and challenges for risk assessment[END_REF], such as microginins and aeruginosins (Fig. 3C). Taken together, these observations suggest that the proliferation and the subsequent production of such potentially noxious cyanobacterial metabolites could be also considered as an explicative factor of the local metabolome singularity for the liver of Perca and Lepomis from Tri, Var and Ver lakes, as previously postulated [START_REF] Sotton | Specificity of the metabolic signatures of fish from cyanobacteria rich lakes[END_REF].

Discriminating metabolites between the different lake groups

Thus, we further investigate the fish metabolome distinctions considering together fishes from lakes presenting noxious cyanobacterial metabolites ("Tri/Var/Ver" group for Perca and "Tri/Ver" group for Lepomis), or not ("Cer/Mau/Cha/Fon/Rue" group). For this purpose, we used PLS-DA to identify metabolites that discriminate the individuals belonging to these two lake groups (Figure 4). Indeed, while unsupervised PCA were first used to evaluate the global dispersion between species and sampling groups, a supervised model such as partial least square differential analysis (PLS-DA) allows to maximize the separation between the different sample classes (e.g. "lake groups") and to further provide information on discriminating features (variable importance on projection -VIP). For both fish species, the metabolomes present a consistent discriminant analysis scoring (Perca: accuracy = 1, performance R 2 = 0.78, predictability Q 2 = 0.72; Lepomis: accuracy = 0.93, R 2 = 0.59, Q 2 = 0.29) and large sets of discriminating variables (Perca VIP >1.5 =109 and Lepomis VIP >1.5 =93) (Figure 4A-B). Among these metabolites, 33 components have been successfully annotated considering their respective molecular mass, the isotopic pattern, together with the fragmentation pattern of all components with GNPS and MetGem algorithms and a manual curation. Overall, the metabolite semi-quantification of the annotated best VIP reveals largely similar variations between Perca and Lepomis for these two groups of lakes (Figure 4C; Supp. fig. S3). Indeed, except for the reduced-glutathione, which present higher concentrations in the livers of Perca from "Tri/Var/Ver" lakes, when livers of Lepomis exhibits lower concentrations than in the those of fish of the "Cer/Mau/Cha/Fon/Rue" group, all other metabolites present similar quantification patterns between the two species. This discrepancy suggests that glutathione-dependent redox mechanisms could be more extensive in Perca than in Lepomis livers. Globally, the main differences in the different metabolite concentration between these two lake groups show that fishes collected from the "Cer/Mau/Cha/Fon/Rue" present higher energetic, anti-oxidant/detoxification and lipids reserves (e.g. various nucleic acids, glutathiones, flavonoids and lipo-phosphocholines) than in "Tri/Var/Ver" lakes. In addition, higher amounts of amino acids, di-peptides and acylcarnitines in the liver of fish originating from the "Tri/Var/Ver" lakes could be related to higher protein catabolism and betaoxidation, that could denote of higher energetic requirements for those specific organisms. Interestingly, the "Tri/Var/Ver" lakes present much more cyanobacteria and related noxious metabolites, that may represent a patent stress source for fishes colonizing those environments. Taken together, these observations are in agreement to the hypothesis of an occurrence of higher stress conditions locally occurring in the Tri, Var and Ver lakes, that could be associated with the local production of potentially noxious metabolites by cyanobacterial proliferation (Sotton et al., 2017a(Sotton et al., , 2017b;;[START_REF] Manach | Physiological effects caused by microcystin-producing and non-microcystin producing Microcystis aeruginosa on medaka fish: A proteomic and metabolomic study on liver[END_REF]. In general, various genetic or phenotypic factors (such as development stages, contamination levels, predator/parasite pressures, food availability, …) could influence and explain the local discrepancy of the metabolome of fishes collected from different environments (reviewed in Marie 2020). However, in the present case, the fact that Perca and Lepomis metabolomes analyzed in parallel present similar metabolite variations clearly indicates that local environmental constraints may drive such phenotypic co-variations. Then, these present results claim for the high informativeness of LC-MS-based metabolomic imprints for molecular phenotyping on sentinel species and the exploration of environmental stresses [START_REF] Lohr | Metabolomic profiles differ among unique genotypes of a threatened Caribbean coral[END_REF].

Comparison of 1 H NMR and LC-MS metabolomics for fish livers

Considering the previous 1 H NMR metabolomics datasets retrieved from the same samples on these two fish species [START_REF] Sotton | Specificity of the metabolic signatures of fish from cyanobacteria rich lakes[END_REF], the liver metabolome analyzed by NMR shows more limited specificity according to "species" or "lakes" factor than those analyzed by LC-MS. Although the 222 potential metabolites quantified by NMR on 132 fish (78 Perca and 54 Lepomis) present no obvious structuration of the individual dataset according to "species" or "lakes" variables, when observed on PCA or hierarchical classification, slight but significant relations are however supported by PERMANOVA (F-values = 4.15 and 3.63; R 2 = 0.03 and 0.17; P-values < 0.01 and 0.01, for "species" and "lakes" respectively, Supp. fig. S4). Indeed, according to the relatively lower performances of the hierarchical classifications and PERMANOVA and the PLS-DA (Supp. fig. S4 andS5), the NMR liver metabolomes exhibit a limited discrimination on "lakes" for both species, when compared to LC-MS metabolomics performances. Overall, regarding both hierarchical classification, individual plots of PCA or PERMANOVA performances, the dataset obtained from the LC-MS metabolome analysis presents a clearer discriminant pattern between the two species or the 8 lakes, than the NMR dataset generated on the same samples (with the same extraction procedure) (Fig. 1 and 2; Supp. fig. S4 andS5). This observation is further confirmed by direct comparison of the NMR and LC-MS metabolome dataset using MixOmics Diablo tool for multi-block PLS-DA (Supp. fig. S6 andS7). Indeed, although the two datasets appear mostly congruent, the individual plot distribution carries much more variance on the PLS-DA projection for the LC-MS analysis than for the NMR, considering both fish species. On one side, the 1 H NMR metabolomics allows the reliable quantification of main liver metabolites (belonging principally to amino acids, sugars, TCA metabolites, …), by detecting characteristic chemical groups, with regards to their respective position within the molecules [START_REF] Martin | Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a largescale, multi-instrument inter-laboratory study[END_REF]. These primary metabolites are key contributors to the cellular metabolism and are supposed to be responsive to variable physiological conditions of fish encountering peculiar biological outcomes and/or environmental constraints [START_REF] Roques | Proton-NMR Metabolomics of Rainbow Trout Fed a Plant-Based Diet Supplemented with Graded Levels of a Protein-Rich Yeast Fraction Reveal Several Metabolic Processes Involved in Growth[END_REF]. However, this approach remains focused on few principal metabolites involved in main cellular pathways which measured cellular contents (expressed in ppm) can be globally involved in multiple regulation processes, and not being specific of subtle molecular regulation processes. On the other hand, 1 H NMR presents only low sensitivity and an overall limited discrimination capability for molecules presenting similar chemical structures, such as lipids, that exhibits an aggregated and unspecific signal on NMR spectra [START_REF] Emwas | NMR spectroscopy for metabolomics research[END_REF]. For this reason, it is supposed to present lower discriminating power than more selective and less qualitative, but more sensitive, high-resolution mass spectrometry-based metabolomics approaches. On the other side, although LC-MS-based metabolomic remains a largely selective analytical approach (according to both LC separation and ionisation respective properties of each molecule), it presents un-precedent analytical capabilities in terms of sensitivity, dynamic range and number of characterized components and offer now unprecedented perspectives for the investigation for molecular phenotyping applied to environmental sciences (Beyoglu and Idle, 2020). We assume, that a single LC-MS fingerprint analyse does not embed the whole metabolite picture of the biological compartment (the fish liver, in the present case), especially because of its selectivity performances [START_REF] Gika | Untargeted LC/MSbased metabolic phenotyping (metabonomics/metabolomics): The state of the art[END_REF]. Indeed, this noticeable selectivity can have multifactorial origin along the analytical procedures and be related to both the extraction solvent, the selected chromatographic gradient and column, together with the ionisation principle and mode. However, it provided specific and precise measurements of a large number of components that can endorse high-throughput and indepth phenotyping. For comparison, 1 H NMR metabolomics allow the global quantification of the CH 2 -CH 2 or CH 2 -CH=CH fatty acid bounds [START_REF] Marchand | multidimensional 1 H NMR lipidomics workflow to address chemical food safety issues[END_REF], when LC-MS metabolomics can discriminate and characterize hundreds of different lipids belonging to various sub-classes [START_REF] Dreier | Ecotoxico-lipidomics: An emerging concept to understand chemical-metabolic relationships in comparative fish models[END_REF], providing undoubtedly more informative and discriminant features. For these reasons, recent efforts are still been performed in order to compared the inputs of both NMR-and LC-MS-based metabolomics in order to explore the respective advantages and caveats of both approaches for environmental investigations [START_REF] Ameline | Identification and analytical characterization of seven NPS, by combination of 1H NMR spectroscopy, GC-MS and UPLC-MS/MS®, to resolve a complex toxicological fatal case[END_REF][START_REF] Hayden | Metabolomics approaches for the discrimination of disease suppressive soils for Rhizoctonia solani AG8 in cereal crops using 1H NMR and LC-MS[END_REF][START_REF] Labine | The use of nuclear magnetic resonance (NMR) and mass spectrometry (MS)-based metabolomics in environmental exposure assessment[END_REF]. In addition, multi-omics integration tools, such as MixOmics DIABLO [START_REF] Singh | DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays[END_REF], also provides innovative solutions for the comparison of heterogeneous dataset joint matrices obtained from a single set of samples or individuals (supplementary figures S6-S7). However, the robustness and the repeatability of the operational analytical pipeline constitute key requirements of first order for environmental metabolomics, as recently experienced by clinic development of environmental toxicology [START_REF] Silva | 1H NMR-based metabolite profiling for biomarker identification[END_REF]. Nowadays, it also appears even more crucial to carefully take into account the socioeconomic and the environmental costs when considering the development of different pipelines for operational and sustainable metabolomic applications [START_REF] Jones | Metabolomics and its use in ecology[END_REF].

Use of LC-MS metabolomics fingerprint for environmental assessment?

Although the value of LC-MS metabolomics for investigating the impacts of environmental stressors or contaminants, and their respective mode of action has been well-explored in medical sciences [START_REF] Beyoğlu | Metabolomic and lipidomic biomarkers for premalignant liver disease diagnosis and therapy[END_REF][START_REF] Zhang | Mass spectrometry-based metabolomics in health and medical science: a systematic review[END_REF] or in ecotoxicology laboratorybased studies on aquatic models (Le [START_REF] Manach | Physiological effects caused by microcystin-producing and non-microcystin producing Microcystis aeruginosa on medaka fish: A proteomic and metabolomic study on liver[END_REF][START_REF] Huang | A multi-omic approach to elucidate low-dose effects of xenobiotics in zebrafish (Danio rerio) larvae[END_REF][START_REF] Ekman | Metabolite profiling of fish skin mucus: a novel approach for minimally-invasive environmental exposure monitoring and surveillance[END_REF][START_REF] Gil-Solsona | Untargeted metabolomics approach for unraveling robust biomarkers of nutritional status in fasted gilthead sea bream (Sparus aurata)[END_REF], such methods have been used only faintly in field research so far [START_REF] Bundy | Environmental metabolomics: a critical review and future perspectives[END_REF][START_REF] Lankadurai | Environmental metabolomics: an emerging approach to study organism responses to environmental stressors[END_REF]. Apart from a limited number of evidence of the utility of NMR-based metabolomics in environmental fish studies (Capello et al., 2016;[START_REF] Sotton | Specificity of the metabolic signatures of fish from cyanobacteria rich lakes[END_REF][START_REF] Wei | Systemic homeostasis in metabolome, ionome, and microbiome of wild yellowfin goby in estuarine ecosystem[END_REF], few other examples indicate that field-based LC-MS metabolomics constitutes an emerging and powerful, but still underused, approach for increasing our understanding of in situ biological, physiological, ecological or ecotoxicological processes [START_REF] Meador | Metabolomic profiling for juvenile Chinook salmon exposed to contaminants of emerging concern[END_REF][START_REF] Reverter | Fish mucus metabolome reveals fish life-history traits[END_REF][START_REF] Goode | Environmental metabolomics as an ecological indicator: Metabolite profiles in juvenile fish discriminate sites with different nursery habitat qualities[END_REF]. So far, aquatic ecotoxicology has been widely using fishes as reference models to investigate both physical or chemical contaminants [START_REF] Li | Ecotoxicological effects of pyraclostrobin on tilapia (Oreochromis niloticus) via various exposure routes[END_REF][START_REF] Mallik | Ecotoxicological and physiological risks of microplastics on fish and their possible mitigation measures[END_REF], and their deleterious effects on diverse biological mechanisms including their behaviour [START_REF] Almroth | Assessing the effects of textile leachates in fish using multiple testing methods: From gene expression to behavior[END_REF] or specific enzymatic activities [START_REF] Villeneuve | Case Study in 21st Century Ecotoxicology: Using In Vitro Aromatase Inhibition Data to Predict Short-Term In Vivo Responses in Adult Female Fish[END_REF], on short or longer terms [START_REF] Thoré | Towards improved fish tests in ecotoxicology-efficient chronic and multi-generational testing with the killifish Nothobranchius furzeri[END_REF]. Among the large list of the characters of interest on which ecotoxicological studies aim at being dedicated, the metabolome appears as one of the main promising functional traits (Martynuik et al., 2016). Indeed, metabolomics seem to present many key advantages for environmental studies, such as its immediate connexion with the global physiology of the organisms [START_REF] Rosato | From correlation to causation: analysis of metabolomics data using systems biology approaches[END_REF], its pertinence for all model or nonmodel organisms [START_REF] Hillyer | Metabolomics as a tool for in situ study of chronic metal exposure in estuarine invertebrates[END_REF][START_REF] Reverter | Fish mucus metabolome reveals fish life-history traits[END_REF], the multi-variable character of the dataset and its statistical strength [START_REF] Chu | Integration of metabolomic and other omics data in population-based study designs: An epidemiological perspective[END_REF][START_REF] Mendez | A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification[END_REF], and its appliance for non-lethal and minimal-stress approaches (Colette et al., 2016). However, applying metabolomics to ecotoxicological questions is still currently limited by a lack of conceptual, technical and data frameworks, which require to be tailored to reach its full potential for applied environmental purposes [START_REF] Walker | Functional Traits 2.0: The power of the metabolome for ecology[END_REF]. Our analysis constitutes one of the first attempts to push forward the potential of highthroughput molecular phenotyping, and especially through LC-MS-based metabolomics, for environmental assessment. Indeed, this organism molecular phenotyping supported by multi-variable chemometric investigation offers rich biological information that serves at describing specific phenotypic plasticity. Moreover, this molecular variability/responsiveness can further be confronted to local environmental factors (such as noxious cyanobacterial metabolites in the present case) in order to demonstrate correlation or causality relationships. As described by Pompfret and co-workers (2019), environmental metabolomics exhibit very promising perspectives for operational bio-monitoring applications, according to its reliability, its reproducibility, and its high predictive potential. However, these authors also point out that the responsiveness and robustness of the bio-indicative object, which is characterized through the analytical prism of the metabolomics, remains crucial and have to be carefully evaluated and tested with an appropriate experimental design (Pompfret et al., 2019). However, for ethical concerns, fish bio-monitoring would also gain at been less invasive and deleterious for the organisms. To this end, non-lethal mucus sampling has been investigated by LC-MS and have demonstrated the remarkable informativeness of this approach for environmental studies [START_REF] Reverter | Fish mucus metabolome reveals fish life-history traits[END_REF], but these efforts still remain explorative and require additional investigations.

Conclusions

The main challenge facing us remains to be able to develop more integrative approaches to connect chemical, biological and ecological assessment, in the context of anthropized natural environments confronting multi-stressor pressures. A major caveat of the use of fish environmental bio-indication by metabolomics remains maybe the lack of standardized methodology and, overall, of dedicated databases [START_REF] Viant | Use cases, best practice and reporting standards for metabolomics in regulatory toxicology[END_REF] fulfilled by studies considering together different species, populations, development stages, seasons and environments. These data could be used to provide baseline reference values that would further support machine learning or artificial neural network tools for the training of decision-making models [START_REF] Mendez | A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification[END_REF].

Legends of figures

Figure 1. Visualization of the dataset structuration for LC-MS metabolomics (comprising 132 fish and 1252 analytes) on PCA (A) and heatmap with hierarchical clustering (B) for Perca (n=78) and Lepomis (n=54) fish collected during the 7-10 th of September 2015 within 8 pounds of the peri-urban Paris' area. between brackets indicate when the metabolite concentration is more important in the Cha/Cer/Fon/Mau/Rue (green) or Tri/Var/Ver (red) lake group. * indicates that this metabolite was also observed as being negatively correlated with cyanobacteria occurrence within the different lake according to NMR analysis [START_REF] Sotton | Specificity of the metabolic signatures of fish from cyanobacteria rich lakes[END_REF].

Figure 2 .

 2 Figure1. Visualization of the dataset structuration for LC-MS metabolomics (comprising 132 fish and 1252 analytes) on PCA (A) and heatmap with hierarchical clustering (B) for Perca (n=78) and Lepomis (n=54) fish collected during the 7-10 th of September 2015 within 8 pounds of the peri-urban Paris' area. Figure 2. Individual plot of PCA (A and C) and heatmap with hierarchical classification (B and D) of LC-MS liver metabolomes of Perca (n=78; A-B) and Lepomis (n=54; C-D) collected during the 7-10 th of September 2015 within 8 pounds of the peri-urban Paris' area. Figure 3. Phytoplankton composition estimated by BBE measurment of the 8 lake subsurface water (A), corresponding cyanobacteria relative composition for Fon, Tri, Var and Ver (B), and molecular networking of metabolites extracted from the filtered biomass of the respective water of the 8 lakes generated with t-SNE algorithm, with cyanobacteria peptide clusters indicated in bold and potentially more noxious family clusters (microcystin, cyanopeptolmin and anabaenopeptin) indicated in red (C).Figure 4. Individual plots of PLS-DA, testing the difference between the different lake groups

Figure 3 .

 3 Figure1. Visualization of the dataset structuration for LC-MS metabolomics (comprising 132 fish and 1252 analytes) on PCA (A) and heatmap with hierarchical clustering (B) for Perca (n=78) and Lepomis (n=54) fish collected during the 7-10 th of September 2015 within 8 pounds of the peri-urban Paris' area. Figure 2. Individual plot of PCA (A and C) and heatmap with hierarchical classification (B and D) of LC-MS liver metabolomes of Perca (n=78; A-B) and Lepomis (n=54; C-D) collected during the 7-10 th of September 2015 within 8 pounds of the peri-urban Paris' area. Figure 3. Phytoplankton composition estimated by BBE measurment of the 8 lake subsurface water (A), corresponding cyanobacteria relative composition for Fon, Tri, Var and Ver (B), and molecular networking of metabolites extracted from the filtered biomass of the respective water of the 8 lakes generated with t-SNE algorithm, with cyanobacteria peptide clusters indicated in bold and potentially more noxious family clusters (microcystin, cyanopeptolmin and anabaenopeptin) indicated in red (C).Figure 4. Individual plots of PLS-DA, testing the difference between the different lake groups

Figure 4 .

 4 Figure1. Visualization of the dataset structuration for LC-MS metabolomics (comprising 132 fish and 1252 analytes) on PCA (A) and heatmap with hierarchical clustering (B) for Perca (n=78) and Lepomis (n=54) fish collected during the 7-10 th of September 2015 within 8 pounds of the peri-urban Paris' area. Figure 2. Individual plot of PCA (A and C) and heatmap with hierarchical classification (B and D) of LC-MS liver metabolomes of Perca (n=78; A-B) and Lepomis (n=54; C-D) collected during the 7-10 th of September 2015 within 8 pounds of the peri-urban Paris' area.Figure 3. Phytoplankton composition estimated by BBE measurment of the 8 lake subsurface water (A), corresponding cyanobacteria relative composition for Fon, Tri, Var and Ver (B), and molecular networking of metabolites extracted from the filtered biomass of the respective water of the 8 lakes generated with t-SNE algorithm, with cyanobacteria peptide clusters indicated in bold and potentially more noxious family clusters (microcystin, cyanopeptolmin and anabaenopeptin) indicated in red (C).Figure 4. Individual plots of PLS-DA, testing the difference between the different lake groups presenting, or not, high content of potentially noxious cyanobactérial metabolites (Cha/Cer/Fon/Mau/Rue and Tri/Var/Ver, respectively) according to the LC-MS metabolomes of Perca (A) and Lepomis (B), and corresponding list of annotated best VIP list (metabolite score > 1.5) of Perca and Lepomis metabolomes (C). Accuracy, predictability and quality performances and number of VIP metabolite score > 1.5) for sampling lake discrimination are indicated for each species. Dots

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Notes

The authors declare not conflict of interest ACKNOWLEDGMENTS We would like to thanks B. Sotton for his collaboration on NMR data acquisition and treatment. We are also greatful to A. Paris for its support and prolific discussions. This work was supported by grants from CNRS (Défi ENVIROMICS "Toxcyfish" project). The NMR and the MS spectra were respectively acquired at the Plateau technique de Résonance Magnétique Nucleaire and the Plateau technique de spectrométrie de masse bio-organique, Muséum National d'Histoire Naturelle, Paris, France. This work benefitted from the French GDR "Aquatic Ecotoxicology" framework which aims at fostering stimulating scientific discussions and collaborations for more integrative approaches.