D Sadoun

C Dubois

Y Ghamri-Doudane

B Grau

Formal Rule Representation and Verification from Natural Language Requirements Using an Ontology

Keywords: Knowledge representation, OWL ontology, NL requirements, formal verification; 10

The development of a system is usually based on shared and accepted requirements. Hence, to be largely understood by the stakeholders, requirements are often written in natural language (NL). However, checking requirements completeness and consistency requires having them in a formal form. In this article, we focus on user requirements describing a system behaviour, i.e. its behavioural rules. We show how to transform behavioural rules identified from NL requirements and represented within an OWL ontology into the formal specification language Maude. The OWL ontology represents the generic behaviour of a system and allow us to bridge the gap between informal and formal languages and to automate the transformation of NL rules into a Maude specification.

Introduction

Requirements correspond to a specification of what should be implemented. Among other, they describe how a system should behave. Stakeholders of a system development often use natural language (NL) for a broader understanding, which may lead to various interpretations, as NL texts can contain semantic ambiguities or implicit information and be incoherent. Thus, requirements have to be checked and this requires them to be represented in a formal language. A transformation of NL requirements into formal specifications is usually costly in human and material resources and would benefit of an automatic method. A direct transformation is difficult, if not impossible [START_REF] Guissé | From regulatory texts to brms: how to guide the acquisition of business rules?[END_REF], which leads to the need of an intermediate representation to reduce the gap between the two formalisms. Both works of [START_REF] Guissé | From regulatory texts to brms: how to guide the acquisition of business rules?[END_REF] and [START_REF] Selway | Formalising natural language specifications using a cognitive linguistics/configuration based approach[END_REF] propose a first step in the formalization process by transforming NL specifications into SBVR. Similarly, in [START_REF] Njonko | From natural language business requirements to executable models via sbvr[END_REF], the authors use SBVR as an intermediate representation to transform NL business rules into semi-formal models such as UML. The tool NL2Alloy [START_REF] Bajwa | Nl2alloy: A tool to generate alloy from nl constraints[END_REF] also uses SBVR as a pivot representation to generate Alloy6 code from NL constraints. To our knowledge, only NL2Alloy proposes a complete chain of transformation from NL to formal specifications, but it does not perform formal verifications on the intermediate representations to validate it. Indeed, verifying extracted information needs formal knowledge representation and inference mechanisms. However, controlled natural languages as SBVR or semi-formal representation models as UML often lack validation mechanisms and inference engines. These shortcomings have led many researchers to explore the transformation of SBVR or UML into languages such as OWL and SWRL [START_REF] Karpovic | Requirements for semantic business vocabularies and rules for transforming them into consistent owl2 ontologies[END_REF][START_REF] Sukys | Transformation framework for sbvr based semantic queries in business information systems[END_REF] or as Maude [START_REF] Durán | Tracing properties of uml and ocl models with maude[END_REF].

We propose an OWL-DL ontology based on description logics as an intermediate representation. We use this ontology to guide the automatic identification of behavioural rules from NL requirements analysis and to represent them formally [START_REF] Sadoun | From natural language requirements to formal specification using an ontology[END_REF]. Behavioural rules are represented in the ontology in order to be transformed into a formal specification language. Indeed, OWL allows us to check the consistency and the completeness of the modelled rules. However, it cannot represent state evolution or sequential rules application. Hence, to simulate and validate the whole system behaviour, we propose to transform the ontology model into a formal specification Maude. In this article, we focus on the ontology conception choices and the transformation process that enable us to automate the production of formal specifications and to maintain the link between NL requirements and their formal representation.

This work has been done in the framework of the project ENVIE VERTE7 which aims to allow a user to configure her own smart space by describing her requirements in natural language. A smart space is a set of communicating objects (sensors, actuators and control processes) that may influence, under well defined conditions, the behaviour of the smart space devices (physical processes). The behavioural rules determine desired component interactions.

2 Ontology of a system behaviour

Conceptualisation choices

An ontology defines concepts (C), properties (P) and individuals (I) of a domain. Concepts and properties of an ontology are defined by terminological axioms (A). We represent an ontology O as a tuple < C, P, A, I, I C , I P > where: -C is a set of concepts; -P is a set of binary properties; -A is a set of terminological axioms; -I is a set of individuals; -I C is a function that associates to each concept a set of individuals; -I P is a function that associates to each property a set of couples of individuals or of couples individual/value.

The ontology of a system behaviour has to define the components of the system, their characteristics and the way they behave. In this framework, it is important to highlight a distinction between two kinds of individuals within ontologies: 1) individuals representing entities; 2) individuals representing a type characterizing entities, which lead us to distinguish two sorts of concepts: individual concepts and generic concepts. This distinction is pertinent for both NL requirement analysis and the automatic ontology translation into the formal language Maude. Based on that, we define two high level concepts to represent a system behaviour: Component (C C ⊆ C) and Type (C T ⊆ C) (cf. figure 1). Representing the system behaviour requires taking into account the dynamic aspects of its operation. Thus, we modelled two super-properties in the ontology: 1) Relation for describing an interaction between two components of the system; 2) Attribute for describing a characteristic of a component, defined as follows:

(P ) ⊆ (I C [C C ] ∪ I C [C T ]) × (I C [C C ] ∪ I C [C T ] ∪ V).
We also distinguish two types of attributes:

dynamic attribute whose value may evolve over the time, as the balance of a bank account; static attribute whose value is not set to change, such as a bank account ID. This last kind of attribute corresponds to definitional properties of a concept that can be used to identify and distinguish its individuals.

The result of our conceptualisation choices is the ontology illustrated in Figure 1. The ontology is divided in two parts: the upper level ontology models a 8 We note D P R to define for each property P its domain D and its range R.

Behavioural rules

As concepts and properties, Behavioural rules participate to the domain definition, by modelling its dynamic aspects. They are formed as antecedent → consequent. The antecedent defines conditions under which the rule applies.

The consequent defines the result of its application. Each of them corresponds to a conjunction of predicates denoting instances of a property P (i x , i y ) with (i x , i y ) ∈ I P (P ), since, in our approach, rule identification is guided by property instance identification [START_REF] Sadoun | From natural language requirements to formal specification using an ontology[END_REF]. Within the ontology, we model a behavioural rule as two sets of predicates P k (i x , i y ) with P k a binary predicate referring to a property instance and i x an individual, a literal (value of a basic data type) or a variable. We defined a concept Predicate as a sub-concept of the concept Type (cf. figure 1), associated to the two properties Antecedent & Consquent (cf. figure 1) on which the behavioural rules are constituted. We distinguish two types of behavioural rules: 1) rules describing the general behaviour of the system that is independent of the user needs; 2) rules specific to the user requirements. We propose to model within the ontology the two concepts Requirement-Pattern and User-Requirement. Requirement-Pattern is a set of different generic patterns of rules. Its individuals are defined by an expert of the domain to guide the NL requirement analysis. User-Requirement is a set of behavioural rules specified by a user. Its individuals are created automatically from NL requirements analysis and linked to their model pattern by the property Rule-pattern (cf. figure 1). Within the ontology five requirement patterns have been defined for guiding the identification of behavioural rules of a smart space.

Population of the ontology

In [START_REF] Sadoun | From natural language requirements to formal specification using an ontology[END_REF], we proposed an approach for ontology population based on the identification of property instances in sentences which leads to recognize triples of individuals. Instance property recognition enables to resolve some ambiguities and to infer implicit individuals. The creation of User-Requirement individuals exploits these property instances and depends on two verifications based on the use of OWL reasoning and SQWRL queries. First, for each requirement pattern represented in the ontology, we check that all the predicates (i.e. property instances) specializing it have been recognized and do not introduce any inconsistency in the ontology, then, that the resulting rule, i.e. the individual of User-Requirement is correctly formed. If this two verifications hold, an instance of the concept User-Requirement is created. During the ontology population process, several instances of User-Requirement can be associated to an instance of Requirement-Pattern via the property Rule-Pattern (cf. Figure 1). Each of them is associated with the sentence number it is extracted from. It enables to keep the link between textual requirements and formal rules.

We collected user requirements of a smart space behaviour configuration via a platform available on the web 10 . We collected about hundred sentences 11 (2171 words). Figure 2 presents an example of an individual of User-Requirement that specializes an instance of Requirement-Pattern. It was created automatically from the NL requirement analysis and was identified from the sentence number 1 "When I enter a room the door opens automatically." of the analysed user requirements. Right elements in bold are instances identified from user requirements analysis. Elements preceded by a question mark ' ?' correspond to variables. The left property in bold is a super-property 12 that determines the type of property to identify from user requirements analysis.

Within the hundred sentences, 62 were manually annotated as containing a behavioural rule. From user requirements analysis, a total of 28 rules were completely identified and created in the ontology and 34 rules were partially recognized. During the ontology reasoning, two rules among the 28 were rejected, being inconsistent with two existing rules and 3 were identified as containing an additional (incorrect) predicate. As within the ontology, identified individuals are linked to the sentence they were extracted, a precise feedback is returned to the Fig. 2. A user requirement created from NL requirement analysis user, highlighting missing and incorrect information in order to let her correct or complete the concerned requirement. Once all the necessary checks have been performed successfully, the validated rules are transformed into Maude.

3 From the ontology to the Maude formal specifications

The formal specification language Maude

Maude13 enables to describe the dynamic of a system, i.e. its state changes, and provides different tools for checking it. The state space of a system is represented by a signature Σ that defines sorts (i.e. types) of constants and variables manipulated by Maude and operators that will act upon the manipulated data and by a set of equations E built between terms using the signature. Within Maude, the evolution of the system state is described by rewriting rules of the form R : t → t , where t and t are terms formed on the signature. Rewriting rules rewrite each term of the left hand side of the rule into a term of the right hand side. The rewriting mechanism allows for specification animation and verification of certain properties as the reachability or the non-reachability of particular states.

Maude defines an object-oriented module that offers an object-oriented syntax which is well adapted for concurrent systems, using sets of objects, and a communication mechanism based on message transmission between objects. We use it as a target module for the transformation of the ontology model.

In an object-oriented module, objects are of the form <O : C|a 1 : v 1 , ..., a n : v n > with O the object identifier, C the object class, a i (i ∈ 1..n) its attribute names and v i (i ∈ 1..n) the corresponding attribute values. Messages represent the dynamic interaction between objects. They have the form msg M es : Oid, T 1 , ..., T k → M sg . with msg a keyword, M es the message name, Oid the type of the recipient object and T i (i ∈ 1..k) the types of the message arguments. The state of a system, called configuration, corresponds to a multiset of objects and messages. It is defined using a Maude equation of the form: eq Conf = Ob 1 ... Ob m M es 1 ... M es n . with eq a keyword, Conf the configuration name, Ob i and M es i the objects and messages of the state system.

We represent a Maude object oriented model as a tuple <C,M,Σ,E,R> with:

-C is the set of class names with, for each class, its set of pairs (attribute, type); -M denotes the set of message names; -Σ corresponds to the typing environment. Each element (constant or variable) is associated to its type; -E corresponds to the set of equations representing the state of the system (its configuration) with E = E O ∪ E M such that:

• E O : the set of configurations-objects pairs;

• E M : the set of configurations-messages pairs.

• R contains the rewriting rules.

Transformation approach

In this section, we propose a mapping between the ontological elements and the object-oriented Maude elements for an automatic translation. Ontological elements to translate are those contributing to the representation of the system state evolution. They correspond to User-Requirement instances and the elements necessary for their definition: concepts Component and Type, properties (attributes and relations), individuals and their property values. Figure 3 illustrates this mapping. The set of relations P R is represented in Maude by a set of messages M between two objects as they represent evolving relations.

The set of attributes P A is translated as object attributes. Finally, instances of User-requirement are translated as rewriting rules with an antecedent and a consequent built on objects, messages, attributes, literals (i.e. values of basic types) and variables.

The dynamic evolution of a rewriting rule depends on messages and dynamic attributes (cf. section 2.1). When a rule applies, messages of the antecedent are not rewritten and some new messages may appear in the consequent, also dynamic attributes values may change and new attributes may appear in the consequent as in Figure 4, which illustrates a rewriting rule created from the user requirement R-1 (cf. Figure 2) and extracted from the sentence number 1 "When I enter a room the door opens automatically." the dynamic attribute Turn-on of the object Actuator is created in the consequent part.

OWL Ontology object oriented model Maude Individual of the concept Component

(∈ IC ) Object (∈ E) Individual of the concept Type (∈ IT ) Attribute value (∈ E) Sub-concept of the concept Component (∈ CC ) Class (∈ C) Sub-concept of the concept Type (∈ CT ) Sort Oid (∈ Σ) Relation (∈ PR) Message (∈ M) Attribute (static & dynamic) (∈ PA) Attribute (∈ Σ) Instance of User-Requirement (∈ IRU )
Rewriting rule (∈ R) The algorithm 1 details the function T rad R (cf. Algorithm T rad O ) that translates the user requirements (I RU ) modelled in the ontology into rewriting rules describing the system behaviour within Maude. These rules are formed by binary predicates representing ontology properties. Each predicate may have as argument individuals, literals or variables. Existing objects have been declared in Σ 0 and created in E within the function T rad E (cf. Algorithm T rad O ). Variables and literals still need to be declared. For each predicate of the properties Antecedent and Consequent, getter-functions are called to get its name (the property to which it refers) and its domain and range values. These values are inputs of the function updateObjects that creates objects or updates their values if they already exist. For example, during the creation of the rewriting rule R-1 (cf. Figure 4) the object Actuator has been created from the predicate Managed-type, then updated by the predicate Managed-zone and finally updated in the consequent of the rule by the predicate Turn-on that represents a dynamic attribute.

User requirements verification in Maude

Maude incorporates a variety of validation and verification tools [START_REF] Clavel | The maude formal tool environment[END_REF] including a model checker [START_REF] Eker | The maude {LTL} model checker[END_REF]. A model-checker enables the model exploration. From an initial configuration, it explores the possible states of the represented system based on rewriting rules application. The model-checking allows us to check undesirable state reachability as states resulting from the simultaneous application of rules in contradiction i.e. that can be triggered at the same time and contains in their consequents predicates in opposition (as Turn-on and Turn-off ) on the same object. Then we say that the rules are inconsistent. Hence, the rule created from the sentence 88 "When a sensor detects a hot temperature in any Algorithm 1: Type declaration and rewriting rules creation room combined with smoke in this room, close all the doors and windows." was identified as inconsistent with the rule number 1. Model checking also allows us to check the completeness of the specified system by checking the reachability of desirable states. For example, in the framework of a smart space, it is necessary to check if all physical processes can reach the states on and off at least once. Thus, a message can be returned to the user. As it was the case for the lack of a rule that turns off the physical process light-bathroom.

; if isV ariableOrLitteral(v D ) then Σ ← Σ ∪ {(v D ,t D )}; if isV ariableOrLitteral(v R ) then Σ ← Σ ∪ {(v R ,t R )}; if p ∈ P R then //p is a relation Msg-Antecedent ← Msg-Antecedent ∪ {(p, v D , v R )}; Objs-Antecedent ←

Conclusion

We proposed an approach for behavioural rules representation and formalization from user requirements written in natural language. The core of this approach is an OWL-DL ontology that encompasses the general behaviour of a system. The ontology is used as a pivot representation as it defines a framework for guiding the identification of behavioural rules and allows us to implement an automated transformation of them into a formal specification in Maude. We described an application of our approach on the domain of smart spaces and showed how representing the behaviour of smart space by a Maude specification enabled us to check its consistency and completeness.

9 IFig. 1 .

 91 Fig. 1. The ontology of smart space behaviour

Fig. 3 .

 3 Fig. 3. Correspondence between our ontology model and Maude model

Fig. 4 . 1 3. 3

 413 Fig. 4. A Maude rewriting rule translated from the behavioural rule R-1

  updateObjects(Objs-Antecedent,{(v D ,t D ,∅,∅)});Objs-Antecedent ← updateObjects(Objs-Antecedent,{(v R ,t R ,∅,∅)}); else //p is an attribute Objs-Antecedent ← updateObjects(Objs-Antecedent,{(v D ,t D ,p,v R )}); Objs-Consequent ← Objs-Antecedent; for each c-predicate in C-predicates do //predicates of the consequent p ← get-PredicateName(c-predicate) ; v D ← get-PredicateDomainValue(c-predicate) ; v R ← get-PredicateRangeValue(c-predicate) ; t D ← get-Domain(p,A); t R ← get-Range(p,A) ; if isDynamic(p,A) then //p is an dynamic attribute Objs-Consequent ← updateObjects(Objs-Consequent,{(v D ,t D ,p,v R )}) else if p ∈ P R then //p is a relation Msg-Consequent ← Msg-Consequent ∪ {(p, v D , v R )}; R ← R ∪ {(Objs-Antecedent,Objs-Consequent,Msg-Antecedent,Msg-Consequent)};

  1. sub-properties of Relation are defined exclusively between two sub-concepts of Component. Within OWL, each property is defined as an ObjectProperty. Formally P R is the set of properties P of type Relation such that D P R 8 with D ⊆ C C and R ⊆ C C et I P (P ) ⊆ I C [C C ] ×I C [C C ] 9 . 2. sub-properties of Attribute are defined between a sub-concept of Component or Type and an OWL type. Within OWL, each property is defined as ObjectProperty between sub-concepts of Component and sub-concepts of Component or Type, or as a DataProperty between Component or Type and an OWL type. Formally P A is the set of properties P of type Attribute such that D P R 8 with D ⊆ C C ∪ C T and R ⊆ C C ∪ C T ∪ T and I P

  Input: I RU ,I P , P R , A, Σ0

	Output: R,Σ
	R ← ∅; Σ ← Σ0; Objs-Antecedent ← ∅; Objs-Consequent ← ∅;
	Msg-Antecedent ← ∅; Msg-Consequent ← ∅; Class-Attributes-Values ← ∅;
	for each i

RU in I RU do A-predicates ← get-RangeValue(i RU ,Antecedent,I P ) ; C-predicates ← get-RangeValue(i RU , Consequent,I P ) ; for each a-predicate in A-predicates do //predicates of the antecedent p ← get-PredicateName(a-predicate) ; v D ← get-PredicateDomainValue(a-predicate) ; v R ← get-PredicateRangeValue(a-predicate) ; t D ← get-Domain(p,A); t R ← get-Range(p,A)

A language and tool for relational model verification. http://alloy.mit.edu/alloy/

Funded by DIGITEO, projet DIM LSC 2010.

http://maude.cs.uiuc.edu/

http://perso.limsi.fr/sadoun/Application


Benoît Sottona

Alain Parisa

Séverine Le Manacha

Alain Blonda

Charlotte Duvala

Qin Qiaoa

Arnaud Catherinea

Audrey Combesb

Valérie Pichonb

Cécile Bernarda

Benjamin Mariea
email: bmarie@mnhn.fr

Specificity of the metabolic signatures of fish from cyanobacteria rich lakes

Keywords: fish, cyanobacteria, metabolomics, ecotoxicology, NMR

With the increasing impact of the global warming, occurrences of cyanobacterial blooms in aquatic ecosystems are becoming a main worldwide ecological concern. Due to their capacity to produce potential toxic metabolites, interactions between the cyanobacteria, their cyanotoxins and the surrounding freshwater organisms have been investigated during the last past years. Non-targeted metabolomic analyses have the powerful capacity to study simultaneously a high number of metabolites and thus to investigate in depth the molecular signatures between various organisms encountering different environmental scenario, and potentially facing cyanobacterial blooms.

In this way, the liver metabolomes of two fish species (Perca fluviatilis and Lepomis gibbosus) colonizing various peri-urban lakes of the Île-de-France region displaying high biomass of cyanobacteria, or not, were investigated. The fish metabolome hydrophilic fraction was analyzed by 1H-NMR analysis coupled with Batman peak treatment for the quantification and the annotation attempt of the metabolites. The results suggest that similar metabolome profiles occur in both fish species, for individuals collected from cyanobacterial blooming lakes compared to organism from non-cyanobacterial dominant environments. Overall, such environmental metabolomic pilot study provides new research perspectives in ecology and ecotoxicology fields, and may notably provide new information concerning the cyanobacteria/fish ecotoxicological interactions.

Introduction

Direct and indirect anthropogenic disturbances affect the global functioning and stability of freshwater ecosystems. Global warming, physical alteration, habitat loss, water withdrawal, pollutions, overexploitation and the introduction of non-native species constitute the main threats affecting freshwater ecosystems and their biocenoses [START_REF] Dudgeon | Freshwater biodiversity: importance, threats, status and conservation challenges[END_REF][START_REF] Bunn | Grand challenge for the future of freshwater ecosystems[END_REF][START_REF] Revenga | Prospects for monitoring freshwater ecosystems towards the 2010 targets[END_REF]. Over the past century, transformation of natural landscapes for industrial and urban needs have led to a general increase of chemical nutrients concentrations in freshwater ecosystems, and notably of phosphorus and nitrogen species that have caused the massive eutrophication of freshwater bodies [START_REF] Schwarzenbach | Global Water Pollution and Human Health[END_REF]. One of the main consequences of this non-natural eutrophication process is a shift in the composition of the primary producers due to the appearance of noxious autotrophic bacteria, such as cyanobacteria [START_REF] Chorus | Toxic cyanobacteria in water: a guide to their public health consequences, monitoring and management[END_REF][START_REF] O'neil | The rise of harmful cyanobacteria blooms: The potential roles of eutrophication and climate change[END_REF]. In worldwide freshwater ecosystems, cyanobacteria are mainly present during summers, forming large surface scums generally accompanied by the presence of toxic compounds that have been pointed out to affect all trophic food web compartments [START_REF] Codd | Cyanobacterial toxins: risk management for health protection[END_REF]Ferrao-Filho et al., 2011;[START_REF] Sotton | Nodularin and cylindrospermopsin: a review of their effects on fish[END_REF].

Microcystis, Aphanizomenon, Anabaena, Cylindrospermopsis and Planktothrix are the main bloom-forming freshwater cyanobacterial genera found during summers in lentic waterbodies, and all have been often reported to produce potent toxic compounds, so-called cyanotoxins [START_REF] Codd | Introduction: Cyanobacteria, Cyanotoxins, Their Human Impact, and Risk Management[END_REF]. Among them, the microcystins (MCs), a family of hepatotoxins consisting of more than 230 variants, are the most studied due to their high biological activity and their wide occurrence during freshwater cyanobacterial blooms [START_REF] Catherine | Microcystins and Noduarins[END_REF]. For exposed macro-organisms, MCs may induce inhibition of the protein phosphatases 1 (PP-1) and 2A (PP-2A) as well as occurrence of a cellular oxidative stress via the formation of reactive oxygen species (ROS), with different physiological consequences depending on the organism and the species studied [START_REF] Amado | Oxidative stress generation by microcystins in aquatic animals: Why and how[END_REF][START_REF] Malbrouck | Effects of microcystins on fish[END_REF]. The effects of freshwater cyanobacteria and their respective cyanotoxins, notably the MCs, have been widely studied on fish maintained in microcosm or mesocosm conditions, and fish have been proposed as valuable indicators of environmental disturbances associated to cyanobacteria proliferations [START_REF] Malbrouck | Effects of microcystins on fish[END_REF][START_REF] Bols | Ecotoxicology and innate immunity in fish[END_REF][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]. However, the actual knowledge on the genuine cyanobacteria bloom impairs for natural populations of fishes is mainly deduced from short-time experimentation generally performed in micro-or mesocosms with high concentrations of purified toxins. Furthermore, most of these studies were focused on the mechanisms involved in the dynamic of the MC accumulation-detoxification. It appears that there is still a lack in our understanding of the real ecotoxicological effects of cyanobacterial biomass, particularly on the natural ichtyofauna population. Indeed, cyanobacterial blooms are producing at the same time a "cocktail" of potentially bioactive compounds, and are also potentially modifying other important ecological parameters of the water bodies.

The metabolome of an organism is constituted by the set of primary metabolites synthesized at a given time. It represents its metabolic chemical picture, which can potentially be altered when the ecological and environmental stress conditions change [START_REF] Fiehn | Metabolomics -the link between genotyopes and phenotypes[END_REF][START_REF] Bundy | Environmental metabolomics: a critical review and future perspectives[END_REF].

The observed metabolome regulations represent the chemical phenotypic response of an organism that could potentially response to counter-act the negative effects of stressors present in the ecosystems, and thus to adapt to the variable conditions [START_REF] Hamilton | Population-level consequences for wild fish exposed to sublethal concentrations of chemicals ??? a critical review[END_REF]. In this way, metabolomic studies have become a relevant approach to describe and analyze the integrated response of the organisms under specific environmental context and scenario [START_REF] Franzosa | Sequencing and beyond: integrating molecular "omics" for microbial community profiling[END_REF][START_REF] Mclean | Eco-omics": A Review of the Application of Genomics, Transcriptomics, and Proteomics for the Study of the Ecology of Harmful Algae[END_REF][START_REF] Hultman | Multiomics of permafrost, active layer and thermokarst bog soil microbiomes[END_REF]. Then, the changes in the primary metabolite concentrations help to provide valuable and useful information concerning the physiological processes involved in the homeostatic or dys-regulated responses of the organisms encountering environmental stresses from potentially multiple origins. Nuclear magnetic resonance (NMR)-based metabolomics has been proved to be a powerful approach to address hypotheses relating to fish physiology or pollutant induced toxicity or diseases [START_REF] Samuelsson | Contributions from metabolomics to fish research[END_REF]Viant 2008). However, despite its high potential for the understanding of the molecular mechanisms implicated in the ecotoxicological responses of organisms, the investigation of new qualitative and quantitative biomarkers characterizing the interactions between populations and their biotopes in various ecological context remains still rare and difficult to achieve (Sardans et al., 2013;[START_REF] Cappello | Insights into the mechanisms underlying mercuryinduced oxidative stress in gills of wild fish (Liza aurata) combining 1H NMR metabolomics and conventional biochemical assays[END_REF]2017;[START_REF] Qiao | An integrated omic analysis of hepatic alteration in medaka fish chronically exposed to cyanotoxins with possible mechanisms of reproductive toxicity[END_REF]. Thus, as toxic cyanobacterial blooms may represent important ecotoxicological and ecological constraints in freshwater environments, it can be supposed that organisms exposed to cyanobacterial blooms exhibit characteristic metabolome signatures, compared to others nonexposed to cyanobacterial dominant conditions. Their metabolism may respond and/or counteract the potential negative effects of the cyanobacteria and thus adapt to the local environmental pressures. To date, no studies have been carried out using a global metabolomic approach in order to assess the specific metabolic changes that could be observed in fish exposed to cyanobacterial blooms in contrasted aquatic natural ecosystems. In this way, during the summer 2015, young fish of two representative and common species of freshwater lakes from the European temperate regions, the perch (Perca fluviatilis) and the pumpkinseed sunfish (Lepomis gibbosus), have been sampled in eight peri-urban lakes of the Île-de-France region contrasted by their phytoplanktonic community composition ("presence" or "absence" of cyanobacterial blooms). 1H-NMR metabolomic analyses were performed on the fish liver in order to investigate the global metabolome local specificities of the two fish species collected from a gradient of distinct ecological contexts (comprising the cyanobacteria dominance) and to further identify the metabolic signatures related to these potential specific phenotypic responses.

Experimental section

Lakes and physico-chemical parameters measurements

In-situ sampling campaigns were performed during summer 2015 in eight lakes of the Île-de-France region (France), chosen for their different dominant phytoplankton communities already described in previous studies and notably the presence or the absence of recurrent cyanobacterial blooms [START_REF] Catherine | Design and application of a stratified sampling strategy to study the regional distribution of cyanobacteria (Ile-de-France, France)[END_REF][START_REF] Maloufi | Environmental heterogeneity among lakes promotes hyper ß-diversity across phytoplankton communities[END_REF]. Cergy "les Galets" lake (C), Champs-sur-Marne "la Sablière" lake (CM), Maurepas "la Courance" lake (M), Rueil "Saint Cucufa" lake (R), Verneuil "la grosse Pierre" lake (V), Varenne-sur-Seine "Le grand Marais" lake (VS), Fontenay-sur-loing "le grand Fontenay" lake (F) and Triel lake (T) (Supplementary figure S1) were thus studied and sampled with electric fishing device for capturing fish alive. Previous investigation of the fish guild populating every one of these lakes (using both electric device and fish net sampling) had indicated that only the perch (Perca fluviatilis) and pumpkinseed sunfish (Lepomis gibbosus) were presents in all or almost all the lakes and were further selected as bioindicative species for further metabolomic analyses.

In all lakes, dissolved oxygen (O2) concentrations, pH, temperatures and conductivity were measured in the water column using a multiparameter probe (YSI EXO2) (Supplementary table S1).

Phytoplankton sampling

In every lake, sub-surface chlorophyll-a equivalent concentrations attributed to the four-main phytoplankton groups (Chlorophyta, Diatoms, Cyanobacteria, Cryptophyta) were measured with an in-situ fluorometer (Fluoroprobe II, Bbe-Moldenke, Germany) and samples of water were filtered through 1.2 μm GF/C filters (Nucleopore, Whatman) and stored at -80°C until total chlorophyll-a concentrations analyses using the ethanolic extraction as described by Yepremian and co-workers (2017). Samples of water were fixed in Lugol iodine solution and kept at 4°C until the identification. The estimation (%) of the abundance of the different cyanobacterial genera was performed using an Utermohl's counting chamber and an inverted microscope as described by [START_REF] Catherine | Patterns and multi-scale drivers of phytoplankton species richness in temperate peri-urban lakes[END_REF]. In parallel, phytoplankton biomass was collected using an Apstein's type phytoplankton net (20-µm mesh size) and kept at -80°C until metabolite characterization by mass spectrometry (MS) and MCs content analyses.

Metabolite characterization of phytoplanktonic biomass by mass spectrometry

The collected biomass from the sampled lakes were freeze-dried and then sonicated in 75% methanol, centrifuged at 4°C (15,000 g, 10 min). This step was performed twice, and the pooled supernatants were collected with a Pasteur pipet and kept at -80°C prior to analysis. The supernatant was transferred and acidified with formic acid and 5 µL were analyzed in triplicats on an HPLC (Ultimate 3000, ThermoFisher Scientific) coupled to an electrospray ionization and quadrupole time-of-flight hybrid mass spectrometer (ESI-QqTOF, QStar® Pulsar i, Applied Biosystems®, France).

High-performance liquid chromatography (HPLC) of 5 µL of each of the metabolite extracts was performed on a C18 column (Discovery Bio wide pore, 15cm x 1mm, 5 µm, Sigma) at a 50 µL.min-1 flow rate with a gradient of acetonitrile in 0.1% formic acid (10 to 80% for 60 min).

The metabolite contents were then analyzed on positive mode using information dependent acquisition (IDA), which allows switching between mass spectrometry (MS) and tandem mass spectrometry (MS/MS) experiments, as previously described [START_REF] Marie | Effects of a toxic cyanobacterial bloom (Planktothrix agardhii) on fish: Insights from histopathological and quantitative proteomic assessments following the oral exposure of medaka fish (Oryzias latipes)[END_REF]. The data was acquired and analyzed with the Analyst QS software (Version 1.1). Peak lists were generated from MS spectra acquired between 10 and 55 min, filtering noise threshold at 2% maximal intensity and combining various charge states and related isotopic pattern. Metabolite annotation was attempted according to the accurate mass of the molecules, then to their respective MS/MS fragmentation pattern with regard to an in-house database of more than 700 cyanobacterial metabolites.

A molecular network was created using the online workflow at GNPS [START_REF] Wang | Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking[END_REF]. The data was filtered by removing all MS/MS peaks within +/-17 Da of the precursor m/z. The data was then clustered with MS-Cluster with a parent mass tolerance of 1.0 Da and a MS/MS fragment ion tolerance of 0.5 Da to create consensus spectra. Further, consensus spectra that contained less than 2 spectra were discarded. A network was then created where edges were filtered to have a cosine score above 0.6 and more than 5 matched peaks. Further edges between two nodes were kept in the network if and only if each of the nodes appeared in each other's respective top 10 most similar nodes. The spectra in the network were then searched against GNPS' spectral libraries. The library spectra were filtered in the same manner as the input data.

All matches kept between network spectra and library spectra were required to have a score above 0.6 and at least 5 matched peaks. All GNPS results are available at http://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=a158701fa7894ab08c0df4c60994ccf1

Cyanotoxin quantification

The 75% methanol extracts of the phytoplankton biomass were further analyzed by ELISA (enzyme-linked immunosorbent assay), with the microcystins (Adda-specific) Kit (AD4G2 antibody, Abraxis), in order to globally measure the total MC content, and integrating the signal obtained for all MC variants in presence. Prior to analysis, samples were dissolved with the ELISA sample diluent to reach a methanol concentration below 5% to avoid any interactive effect and to stay in the detection range of the kit (0.1-5 µg.L-1) for all samples. The results were expressed in microcystin-LR equivalents (µg eq. MC-LR .mg-1 dry weight).

The free and bound β-Methylamino-L-alanine (BMAA) were also measured in the phytoplankton biomass according to the hydrophobic interaction liquid chromatography (HILIC)/MS-MS based methods described previously [START_REF] Combes | Development of an analytical procedure for quantifying the underivatized neurotoxin β-N-methylamino-lalanine in brain tissues[END_REF][START_REF] Faassen | A collaborative evaluation of LC-MS/MS based methods for BMAA analysis: Soluble bound BMAA found to be an important fraction[END_REF]. This method used solid-phase extraction based on mixed mode sorbent to concentrate and clean up the phytoplankton extract that contained free BMAA. After the acidic lysis of the phytoplankton biomass, the bound fraction of BMAA was analyzed by LC/MS-MS. This quantitative method has proved to be specific and reliable in a range of concentration level from 0.25 to 1.6 ng mg-1 dry weight.

Fish sampling and tissue extraction procedure for metabolomic analysis

Immature individuals of perch (Perca fluviatilis) and pumpkinseed sunfish (Lepomis gibbosus), two representative fish species of European freshwater lakes (that were selected according to their presence in almost, if not, all investigated lakes), were targeted by electric fishing (FEG 8000, EFKO, Leutkirch, Germany) performed in the riparian area of every lake. Alive caught fish (n<6-10 per lake and per species) were directly measured, weighed (Table S2), briefly euthanized by neck dislocation and then liver of each individual was shortly sampled, deepfrozen in liquid nitrogen and kept at -80°C until metabolomics analyses, in accordance with animal ethical concerns and regulations.

Individual liver metabolome extraction was carried out using the methanol/chloroform/water (ratio 2/2/1.8) method, on the basis of existing literature [START_REF] Lin | Evaluation of metabolite extraction strategies from tissue samples using NMR metabolomics[END_REF][START_REF] Wu | High-throughput tissue extraction protocol for NMR-and MS-based metabolomics[END_REF].

Briefly, fresh frozen livers were weighed, homogenized in ice-cold methanol (8 mL per gram of tissue, AnalaR Normapur, min. 99.8 %, VWR, Pennsylvania, USA) and ice cold milliQ water (2.5 mL.g-1), and then vortexed for 1 min. Subsequently, ice cold chloroform (4 mL.g-1, Normapur, 99.3 %, VWR, Pennsylvania, USA) and milliQ water (4 mL.g-1) were added to extract the hydrophobic metabolites. Then, the mixture was vortexed for 1 min and incubated on ice for 10 min to obtain a complete solvent partition. The resulting supernatant was then centrifuged at 4°C for 10 min at 2,000 g, resulting in a biphasic solution. The upper polar and lower non-polar layers were carefully removed. The upper polar fraction was then transferred to 2-mL Eppendorf tubes, dried under Speed-vac device (Speed-vac Plus SC110A, Savant) and then kept at -80°C until NMR analysis. Prior to 1H-NMR measurement, the polar tissue extracts were dissolved in 550 µL of 0.1 M sodium phosphate buffer (pH 7.0) prepared in D2O (10% v/v) containing 0.25 mM sodium-3-tri-methylsilylpropionate (TMSP) as an internal standard.

Finally, the resulting samples were transferred to 5-mm NMR tubes (Norell, France) and immediately analyzed by 1H-NMR.

1H-NMR spectroscopy and metabolite quantification.

All NMR data were recorded at 298 K on a 600 MHz Bruker AVANCE III HD spectrometer equipped with a 5-mm TCI CryoProbe (1H-13C-15N) with Z-gradient. One-dimensional 1H-NMR spectra were acquired using a standard Bruker noesygppr1d pulse sequence to suppress water resonance. Each spectrum consisted of 256 scans of 32,768 data points with a spectral width of 7.2 kHz, a relaxation delay of 2.5 s and an acquisition time of 2.3 s.

The relative metabolite quantification was performed using the BATMAN (an acronym for Bayesian AuTomated Metabolite Analyser for NMR spectra) R-package (Hoa et al., 2014), which deconvolutes peaks from 1-dimensional 1H-NMR spectra to automatically assign chemical shifts to specific metabolites from a target list and then estimate their respective concentrations. This can be achieved thanks to an implementation of a Bayesian-based modelling procedure. BATMAN uses, in a two-component joint model, resonances of every assigned proton from a list of catalogued metabolites (this library is provided with the BATMAN R-package, and includes the chemical shift information of various targeted metabolites measured at pH 7.0) and noisy information to finally reconstruct the empirical NMR spectrum. But, in absence of confirmatory analytical methods, they were further mentioned as candidate metabolic biomarkers sharing the same 1H-NMR parameters with the catalogued metabolites. Therefore, 222 potential metabolites were quantified from Bruker spectra files using the following parameters: i) the chemical shift regions belonging to the two following regions: 0.5 to 4.60 ppm and 5.40 to 10.0 ppm, ii) 400 burn-in iterations, iii) 200 post-burn-in iterations and iv) 5000 iterations for batman rerun. Calculations were performed on a HP Z820 workstation using two 3.30 GHz Intel Xeon© CPU E5 processors and 64 Go RAM by activating 12-parallel threads processing.

Statistical exploration of data

The mixOmics library [START_REF] Rohart | mixOmics: An R package for 'omics feature selection and multiple data integration[END_REF] was used to carry out the multivariate analyses [START_REF] Tenenhaus | Variable selection for generalized canonical correlation analysis[END_REF]. Regularized canonical correlation analysis (rCCA) is a multivariate statistical method used to assess correlations between two multivariate datasets acquired on the same individuals. Here, it was used as a factorial discriminant analysis that modeled the relationships between the species (Perca and Lepomis), the quantitative chemical data measured in the different lakes (total Chl-a concentrations, Chl-a estimated concentrations related to cyanobacterial biomass (BBEcya), MCs concentrations, O2 concentrations, pH and conductivity), together with the lake of sampling, constituting the Y matrix, and the semiquantitative levels of metabolites determined by the BATMAN algorithm with no normalization of the data (X matrix) (supplementary data 1). The rcc() function was used to define the canonical correlations and the canonical variates, the network() function was used to produce the network of interactions, using default parameters. Cross-validation of MANOVA results was obtained thanks to a bootstrap-based procedure with corrected p-value (P<0.05) by applying a random assignment of any statistical individual to a given group of treatments [START_REF] Krishnamoorthy | A parametric bootstrap solution to the MANOVA under heteroscedasticity[END_REF]. In order to evaluate the effects of the experimental factors (species, chemical data and their interaction) on the relative concentrations of metabolites highlighted by multivariate analyses and the subsequent relevant networks, simple two-way ANOVAs followed by a Student-Newman-Keuls post-hoc test were performed.

Results

Phytoplankton, microcystins and chemical conditions of the studied lakes

During 2015 summer sampling campaign (Supplementary fig. S1), distinct phytoplanktonic compositions and concentrations were observed in the eight lakes targeted (Fig. 1). Among them, F, T, V and VS, exhibited remarkable high and/or dominance of cyanobacterial-specific chlorophyll-a concentrations reaching 106, 14.8, 135 and 48.3 µg eq. Chl a L-1, respectively (Fig. 1A). In the other lakes, chlorophyll-a concentrations of the total phytoplanktonic community ranged between 7.2 and 241.5 µg. Chl a L-1 (supplementary table S1) but were mainly dominated by chlorophytes, diatoms and/or cryptophytes (Fig. 1A), major phytoplanktonic phyla, that are so far not known to produce any toxins nor other ecological concerns as cyanobacteria does. In these lakes, only low concentrations of cyanobacterialspecific chlorophyll-a, ranging from 2.9 to 10.9 µg eq. Chl a L-1, were measured (Fig. 1A).

Furthermore, in the cyanobacteria-dominated lakes, different cyanobacterial genera were observed during samplings (Fig. 1B). F and T were dominated by Planktothrix (94% of the cyanobacteria present in the sample) and Pseudanabaena (71%), respectively, whereas V and VS, were both dominated by Microcystis (Fig 1B). Furthermore, we notice that the modeste blue green signal observed in C corresponds to 100% Spirulina sp., a cyanobacteria genus that is so far known not to produce any known toxic compounds, as it was further confirmed.

Microcystin concentration was investigated in order to estimate the amount of the cyanobacterial bioactive compounds present in the various lake water (Fig. 2; supplementary table S2). MC-LR equivalent concentrations were measured at concentrations ranging from above the quantification limit (> 1 µg.g-1 DW) to 3367 µg.g-1 DW eq. MC-LR. The highest MCs concentrations were found in lakes dominated by cyanobacteria and particularly in F, V and VS where 2067 µg.g-1, 166 µg.g-1 and 3367 µg.g-1 MC-LR eq. were measured, respectively. Interestingly, V that exhibited the highest cyanobacterial concentrations (dominated by Microcystis) measured in this study, did not exhibit the highest MCs concentrations (3367 µg.g-1 MC-LR eq. in VS) illustrating the fact that MCs concentrations are not linearly linked to the cyanobacterial biomass measured in the lakes, as it is also dependent on the production of metabolite by the dominant cyanobacterial clones, which may or not be producing MCs. On one side, none of these samples presented detectable amount of the potential neurotoxin BMAA (neither in free or bound form), saxitoxins nor anatoxins. On the other side, various classes of cyanobacterial secondary metabolites comprising different variants of hepatotoxic microcystins, together with various other potentially bioactive metabolite families, such as anabaenopeptins, aeruginosins, microginins and cyanopeptolins, were detected in the phytoplanktonic biomass of collected from the lakes where cyanotoxin-producing cyanobacteria reached higher concentrations (Fig. 2; supplementary figure S2-S3).

Metabolomic analysis of sampled fish

1H-NMR raw files (supplementary fig. S4) were preprocessed thanks to the R BATMAN library [START_REF] Hao | Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN[END_REF] to get a relative quantification of a preselected set of metabolites replacing an analytical assignment based on different and complementary analyses. This approach allows for each individual spectrum to determine a metabolite quantification, even for very low concentrated metabolites when compared to the main ones, as the differences in their relative concentrations could have been validated by both uni-and multivariate statistical tests [START_REF] Hao | Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN[END_REF]. A multivariate analysis was performed by using a rCCA analysis between the matrix of the relative concentrations of the 222 metabolites (X) and the dummy matrix (Y) corresponding to the following variables (supplementary data S1): i.e. fish species, cyanobacterial-specific Chl-a and MCs concentrations, O2, pH, conductivity and the total Chlorophyll-a concentrations and their interaction using the mixOmics package in R and a MANOVA bootstrap procedure applied to the dataset in order to highlight the significant effects of the environmental variables on the observed metabotypes (i.e. the specific metabolic profiles according to the environmental factors).

MANOVA bootstraps reveal significant effects of species (p = 0), cyanobacterial-specific chla (BBEcya) concentrations (p < 10-43), MC concentrations (p < 10-11), pH (p < 10-28), O2 (p < 10-28), conductivity (p < 10-47), total chlorophyll-a concentrations (p < 0.011) from the rCCA model. However, a clear species effect is observable through the dimension 1 (p < 10-16) with Perca and Lepomis clearly separated by the first dimension whatever the lake considered (fig. 2A). On the dimension 2, a clear effect correlated with cyanobacterial biomass is observable (F = 1931, p < 10-16), as fish coming from cyanobacterial dominated lakes (in green) are clearly separated by this dimension whatever the fish species considered (Fig. 3A). In addition, pH, O2 concentrations and conductivity variables (F = 599 and p < 10-16, F = 867 and p < 10-16, and F = 419 and p < 10-16, respectively) are also significantly correlated to the different metabotypes observed on this dimension but to a much lesser extent compared to cyanobacterial biomass contents (Fig. 3A). In addition, no significant interactive effects between species and the environmental factors could have been observed in this analysis suggesting that the metabotypes of the two species seem to be similarly driven by the different environmental factors.

The relevance network based on the second rCCA dimension specifically highlights metabolites discriminating the fish from dominated and non-dominated cyanobacteria lakes and linking to the correlated environmental factors (mainly BBEcya concentrations but also pH) (Fig. 3B).

Overall, it appears that all these metabolites shown by this network exhibit negative correlation with both cyanobacterial concentrations and pH values. In addition, two-way ANOVA (Table 1) were performed on all putative metabolites highlighted in the figure 3B. A significant effect of the BBEcya and pH is shown for putative metabolites annotated as 16a-hydroxyestrone, acetylcholine, androsterone, glycerophosphocholine, isovalerylcarnitine, L-glutamine and Lpalmitoylcarnitine whereas for glycerol, homo-L-arginine and L-arginine, a significant effect of the BBEcya concentrations, only, have been confirmed by two-way ANOVA analyses (Table 1). Furthermore, no interaction effect between the species and the BBEcya concentrations or pH has been observed for any metabolites suggesting the occurrence, for both fish species and for these two discriminating environmental factors, the occurrence of a similar metabolic responses. Negative correlations between relative concentrations of certain metabolites and the BBEcya concentrations and the pH conditions are corresponding to lowest relative concentrations of these metabolites in the liver of fish captured in cyanobacterial dominated lakes (Table 1; supplementary figure S5-S6). Globally, it appears that liver of fish sampled in T, F and V lakes, and in VS lake in a lesser extent, exhibits the most significant differences in the relative concentrations of the highlighted metabolites compared to fish coming from other investigated lakes, where cyanobacteria are notably in low or not dominant proportions.

Discussion

Diversity of ecosystems: environmental, chemical and biological conditions

Cyanobacterial blooms are frequent phenoma in peri-urban lakes from the Île-de-France region due to the eutrophic and hyper-eutrophic states of these environments. Interestingly, these freshwater aquatic ecosystems are characterized by a wide range of environmental conditions and anthropogenic pressures that influence the phytoplankton biomass and compositions present in these ecosystems [START_REF] Maloufi | Environmental heterogeneity among lakes promotes hyper ß-diversity across phytoplankton communities[END_REF][START_REF] Catherine | Cost effective prediction of the eutrophication status of lakes and reservoirs[END_REF]2016). During summer 2015 sampling, in the lakes from Rueil (R), Cergy (C), Maurepas (M) and Champs-sur-Marne (CM), diatoms or chlorophyta were dominant and cyanobacteria were present with chl-a concentrations never exceeding 5 µg eq. chl-a L-1 (supplementary table S1). In the other lakes, the Microcystis genera appeared largely dominant, such as in Varenne-sur-Seine (VS) and in Verneuil (V) lakes with cyanobacterial-specific chl-a (BBEcya) concentrations reaching 135 and 48 µg eq. chl-a L-1, respectively. In the Fontenay (F) lake, Planktothrix genera was found to be dominant with BBEcya concentration reaching 106 µg eq. chl-a L-1. Interestingly, in Triel (T) lake, the cyanobacteria belonging to the Pseudanabaena and Anabaena genera were present at relatively lower but even substantial BBEcya concentrations (around 15 µg eq. chl-a L-1).

However, even though cyanobacterial amount in T appears maybe not as high as in other cyanobacterial-dominated lakes of this study (F, V and VS), the presence in this T lake of noticeable and recurrent amounts of other contaminants, such as heavy metals, polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs) was measured in both water and fish samples [START_REF] Teil | Polychlorinated Biphenyls, Polybrominated Diphenyl Ethers, and Phthalates in Roach from the Seine River Basin (France): Impact of Densely Urbanized Areas[END_REF][START_REF] Gaspéri | Contamination de la Seine par les micropolluants organiques : Effet des conditions hydriques et de l'urbanisation[END_REF][START_REF] Azimi | Influence of the water quality improvement on fish population in the Seine River (Paris , France) over the 1990 -2013 period[END_REF], and would indicate why the fish present in the T lake exhibit an even more divergent metabolic profile, when compared to those of the non-cyanobacteria-dominated (R, C, M and CM) and the other cyanobacteria-dominated (V, VS and C) lakes.

Which cyanobacterial-related parameters may influence the fish metabolome?

Cyanobacteria are well known to produce a wide variety of potentially toxic secondary metabolites including MCs, that have been detected in remarkable amount especially in the VS and F lakes, together with various other cyanobacterial bioactive compounds, such as those that have been highlighted by the global network analysis, including cyanopeptolins, aeruginosins, microginins and anabaenopeptins. Previous experimental studies have been able to show that cyanobacterial exposure could induce significant physiological impairs in fish exhibiting different metabolomic dysregulation, in relation with the various secondary metabolite content of the investigated strains (Sotton et al., 2017a;2017b). Nevertheless, the genuine ecological consequences of the production of this chemical bioactive (including potent toxic) metabolites by cyanobacteria remain largely uncovered.

Indeed, in addition to the production of such potentially toxic secondary metabolites, changes in physico-chemical parameters of the water-bodies have also been reported during cyanobacterial bloom episodes. Massive decrease of dissolved O2 concentrations may occur during important bloom senescence events, due to the bacterial degradation of cyanobacterial cell biomass, inducing sometimes spectacular massive death of various fish species, also described as fish-kill phenomenon (Pearl and Paul 2011). During the present study, no noticeable depletion of the dissolved O2 concentrations of neither surface nor bottom water or any fish-kill have been observed, indicating that the high cyanobacterial biomass (observed in V, VS, T and F lakes) were not in senescence. Furthermore, O2 concentrations does not appear to significantly drive the liver metabolic differences observed in the fish species studied, suggesting that no metabolic disturbances linked to O2 concentration variation were observed here. Among the other environmental factors measured on the studied lakes that are noticeably correlated with metabolic differences in fish livers based on rCCA analyses, the pH exhibits large variations between sampled aquatic ecosystems. Indeed, it appears that lakes dominated by cyanobacteria show higher pH values (above 9), excepted in Fontenay (F) lake where the value is more similar (between 7.1 and 8.3) to those found in lakes where cyanobacteria are in less abundance (supplementary table S1). Such high pH value phenomenon has been already reported to be directly related with cyanobacteria photosynthesis process that removes carbon dioxide from the water and increases hydroxide ion concentration [START_REF] Lopez-Archilla | Phytoplankton diversity and cyanobacterial dominance in a hypereutrophic shallow lake with biologically produced alkaline pH[END_REF].

However, in the present study, it is not possible to directly conclude whether the elevated pH observed in cyanobacterial dominated lakes is a consequence of the photosynthetic activity of cyanobacteria or is due to local geochemical conditions of the respective lake. To better characterize the causality of these elevated pH in further investigation, it should be suitable to monitor pH value before, during and at the end of the bloom. Further studies need now to be performed in order to disentangle the specific and/or synergic effects of the various bioactive metabolite production together with those of other physicochemical parameters associated with cyanobacterial blooms.

Metabolic changes correlated to perturbed lakes: cyanobacterial concentrations and pH as environmental drivers of fish liver metabolome?

Thanks to NMR and multivariate analyses, the present results show that comparable metabolic changes are observable in both fish species exposed to higher cyanobacterial biomass (F, VS, T and V lakes). in these analyses the most constraining factors correlated to the metabolic changes in fish were the cyanobacterial concentrations and the pH values that exhibit the strongest correlations with the changes of relative concentrations of various metabolites in the two fish species sampled. In this study, it is also very likely that the combination of both high pH values and high cyanobacterial biomass could lead to the more contrasted metabolome changes in fish. This would be in agreement with the fact that fish from F lake, presenting high cyanobacterial and MCs concentrations but moderate pH value around 7.5, exhibit intermediate metabolomes compared to fish captured in T, V and VS (high cyanobacterial and MCs concentrations and high pH) and fish captured in other lakes (C, CM, M, R), not dominated by cyanobacteria.

The present results indicate that MCs may not be in this case the most constraining factor for fish from those natural ecosystems. However, in addition to MC cyanobacteria produce a large set of bioactive compounds that could also influence the fish health and the complexity of such secondary metabolite mixtures aims at being more characterized in an ecotoxicological point of view. One should take also into account that fish may not necessarily be exposed by direct ingestion of cyanobacterial cells, and that trophic transfer may be another source of cyanobacterial bioactive (including toxic) metabolites [START_REF] Sotton | Trophic transfer of microcystins through the lake pelagic food web: Evidence for the role of zooplankton as a vector in fish contamination[END_REF]. It would be now interesting to consider other fish species feeding on phytoplankton in order to test whether they are indeed exposed to higher cyanotoxin contents, by direct ingestion of cyanobacterial cells, and whether they exhibit similar or even more drastic metabolome alterations than such carnivorous ones.

Interestingly, the fish collected from the T lake exhibits one of the most divergent global metabotypes, suggesting that it could potentially be influenced by the occurrence of multi-stress conditions, comprising cyanobacteria proliferations together with the other heavy metal, PCB and PAH contaminants, that have been observed in this specific environments [START_REF] Catherine | Patterns and multi-scale drivers of phytoplankton species richness in temperate peri-urban lakes[END_REF]. Indeed, T lake was not characterized by the highest concentration of cyanobacteria observed during this study. However, due to the presence of other pollutants already monitored in past studies in this pound and not in the other one [START_REF] Teil | Polychlorinated Biphenyls, Polybrominated Diphenyl Ethers, and Phthalates in Roach from the Seine River Basin (France): Impact of Densely Urbanized Areas[END_REF][START_REF] Gaspéri | Contamination de la Seine par les micropolluants organiques : Effet des conditions hydriques et de l'urbanisation[END_REF][START_REF] Azimi | Influence of the water quality improvement on fish population in the Seine River (Paris , France) over the 1990 -2013 period[END_REF], this observation suggests that additive and/or synergistic effects of multi-pollutants together with cyanobacterial bloom seem to be involved in similar metabolic variations than those of fish from pounds which are the most stressed by cyanobacterial blooms.

But such assumption now need further investigations before been fully validated. On order to fully validate potential metabolite biomarkers of cyanobacterial exposure pressure on fish liver metabolome, further investigation would now aim at investigating a larger set of lake from a wider geographic area, and also to firmly confirm the metabolite identification by orthogonal approaches such as 2-D NMR, spiking of standard, and or to develop in parallel mass spectrometry based metabolomic investigations.

Conclusion

The present study demonstrates that cyanobacterial blooms occurrence can locally be correlated with metabolic divergences in relation to the fish response to environmental stress. Such metabolomic analyses support experimentally observed processes but also give us innovative perspectives for the characterization of the environmental and genuine interactions between fish and cyanobacteria proliferations.

In this way, further studies should be performed in order to investigate longitudinal variations along seasonal variations, fish life history, populational genetic distance and the specific changes of fish metabolomes during bloom episodes, which could help us to better understand and characterize the main environmental factors potentially disturbing fish physiology. Also, the understanding of the ecosystem functioning under ecotoxicological pressure induced by cyanobacterial blooms would gain at investigating other fish species feeding directly on phytoplankton organisms. It should highlight whether higher metabolome and toxicological changes would be driven by directly feeding on cyanobacterial cells and higher contact with with the various potentially toxic secondary metabolites, or by other ecological parameters impacted by cyanobacterial proliferations. Furthermore, complementary omic analyses, such as proteomics and transcriptomics, would provide additional support to generate system biology understanding of the global organism response to environmental stress, and help at characterizing potential biomarkers useable at the fish population levels in various aquatic environments.

Legends of figures

Figure 1 .

 1 Figure 1. Phytoplanktonic chlorophyll-a concentrations (µg.L-1 eq. Chl-a) in the sampled lakes (A) and the cyanobacterial genus composition observed in the cyanobacteriadominated lakes (B). ND = non-detected. Cergy (C), Champs-sur-Marne (CM), Maurepas lake (M), Rueil lake (R), Verneuil lake (V), Varenne-sur-Seine lake (VS), Fontenay lake (F) and Triel lake (T).

Figure 2 .

 2 Figure 2. Diversity of metabolites observed in the lakes, dominated or not by cyanobacterial proliferations revealed by molecular networking based on MS/MS fragmentation data. When various phospholipids are observed in all lakes, potentially bioactive metabolites known to be produced by certain cyanobacteria, such as microcystins (hepatotoxin), aeruginosins, cyanopeptolins, anabaenopeptins, microginins, are only observed in cyanobacterial dominated lakes.

Figure 3 .

 3 Figure 3. 1H-NMR liver metabolomes and relevance network of fish sampled in the different lakes. The individual plots of regularized canonical correlations analysis (rCCA) for dimensions 1-2 (A). Perch individuals are on the left side and pumpkinseed individuals on the right side of the graphic. Lakes are represented by their respective letters that in blue correspond to control lakes and in green to perturbated lakes. Cergy (C), Champs-sur-Marne (CM),Maurepas lake (M), Rueil lake (R), Verneuil lake (V), Varenne-sur-Seine lake (VS), Fontenay lake (F) and Triel lake (T). Relevance network providing from rCCA analysis on the dimension 2 (B). Putative metabolites above a correlation threshold of |0.3| were kept, revealing that the 2 most correlated variables are pH and BBEcya (cyanobacterial specific content of chlorophyll a measured by BBE flurorescence probe). Green edges correspond to negative correlations with the discriminant ecological factors.

Fig

  Fig. 1. 686

Table Putative

 Putative

	metabolites	BBECya	pH
		F-value	p-value	F-value	p-value
	16a-Hydroxyestrone	26.604	1.02e -06	8.295	0.00472
	Acetylcholine	43.903	1.08e -09	5.892	0.01672
	Androsterone	19.932	1.85e -05	10.776	0.00135
	Glycerophosphocholine	38.040	1e -08	8.892	0.00348
	Glycerol	29.455	3.1e -07	3.908	0.0504
	Homo-L-arginine Isovalerylcarnitine L-Arginine L-Glutamine L-Palmitoylcarnitine	32.501 32.028 30.269 30.004 24.965	8.95e -08 1.08e -07 2.22e -07 2.47e -07 2.05e -06	1.795 9.392 2.228 14.025 12.888	0.18287 0.0027 0.13820 0.000280 0.000483

Table 1 :

 1 Effects of significantly-related environmental factors on the putative metabolite expressions revealed by two-ways ANOVA analyses performed on the environmental parameter and metabolite matrix for all individuals (supplementary data 1). Bold p-values correspond to p-values < 0.05.

Acknowledgments

The NMR and the MS spectra were respectively acquired at the Plateau technique de Résonance Magnétique Nucléaire and the Plateau technique de spectrométrie de masse bio-organique, UMR 7245 CNRS/MNHN Molécules de Communication et d'Adaptation des Microorganismes, Muséum National d'Histoire Naturelle, Paris, France. This work was supported by grants from the Sorbonne Universités "DANCE", "Procytox" projects and from CNRS (Défi ENVIROMICS "Toxcyfish" project) and from the "region Île-de-France" (R2DS N°2015-11) awarded to Dr. Benjamin Marie. The authors would like to thank the French minister for the research for the financial supports to Séverine Le Manach. Qin Qiao PhD was founded by the China Scolarship Council.

Conflicts of interest

None

Declaration of interest