Souad Marir
email: souad.marir@univ-constantine2.dz

Faiza Belala
email: faiza.belala@univ-constantine2.dz

Nabil Hameurlain
email: nabil.hameurlain@univ-pau.fr

A Formal Model for Interaction Specification and Analysis in IoT Applications

Keywords: IoT, BRS, Formal specification, Interaction model, TQL

The Internet of Things (IoT) is a concept where connected entities can work and interact with each other in order to facilitate daily life. Although, many research efforts in the IoT realm have been to date devoted to device, networking and application service perspectives, formalization and analysis of IoT systems are still in their infancy. This paper introduces a new BRS-based approach aiming to support specification and verification of interaction and interoperability aspects in IoT systems. The proposed approach is based on a bigraphical-agent model that investigates the spatial structure of the IoT system and its logical structure defining the behaviour and interactions of its different entities. The Tree Query Logic (TQL) is used to formally express and verify some properties inherent to IoT systems

Introduction

The Internet of Things (IoT) is the connection of heterogeneous objects, geographically separated, consuming and producing data, in order to offer services. Generally considered as intelligent, IoT systems facilitate considerably every day life of human beings, with a minimum of intervention, working on interoperable networks and reacting to their environment. Developing IoT systems become more and more challenging; due to the ever growth of technologies, IoT systems are wider, distributed, heterogeneous, and involve different types of systems communication. Thus, the interaction between elements of the same system as well as the interaction between different systems is primordial. Modelling and developing IoT systems can be particularly very challenging for many reasons. For instance, the architecture of IoT systems relies on many overlapping factors that should be considered earlier in the design phase. The hardware and software components dependencies along with the complexity of the IoT systems architectures, increase significantly the difficulty of modelling these systems. The aim of this paper is twofold: in one hand, we propose a comprehensive and a generic formal model for IoT applications, reducing their complexity and design, using a judicious combination of BRS model and software agents. Our BCAM4 IoT model (Bigraphical Communicating Agent Model for the Internet of Things) gives precise semantics to possible interactions that govern the complex behaviour of these systems. On the other hand, we show how the spatial logic TQL is used to express and analyse more conveniently some relevant properties such as: communication, context awareness and interoperability, which represents a huge interest in the scientific community, we can cite the INTER-IoT project [START_REF] Project | Inter-iot -interoperability internet of things[END_REF] that aims to allow the interoperability among different IoT platforms, the VICINITY project [START_REF] Initiative | Vicinity -open virtual neighbourhood network to connect iot infrastructures and smart objects[END_REF], that proposes to define the "interoperability as a service" for IoT by guaranteeing it in the communication part of an IoT system.

Bigraphical Reactive Systems (BRS) via the bigraph model, defined by Milner in [START_REF] Milner | The space and motion of communicating agents[END_REF], emphasize on both locality and connectivity in IoT systems. They propose two types of graphs: a place graph expressing physical/logical location of IoT components, and a link graph describing their interconnections. Additionally, BRS are expressive enough to be adopted for representing IoT system dynamics; in terms of reaction rules. Besides, software agents describe the possible reasoning of each entity and how it self-adapts to context changes. The spatial logic TQL [START_REF] Conforti | Spatial tree logics to reason about semistructured data[END_REF] has emerging as an interesting tool to describe properties of several structures. Models for this logic include computational structures such as heaps, trees, graphs [START_REF] Conforti | Spatial tree logics to reason about semistructured data[END_REF], concurrent objects [START_REF] Conforti | Spatial tree logics to reason about semistructured data[END_REF] as well as process calculi and the Ambient Calculus [START_REF] Conforti | Spatial tree logics to reason about semistructured data[END_REF].

The remainder of the paper is structured as follows: in the Section 3, we present a multi-levels architecture for IoT, separating physical components (hardware of software) of IoT systems from logical ones. In this section we also explain the most important properties of IoT systems, namely interoperability and context-awareness. In Section 4, we introduce the BRS formalism together with the TQL logic. In Section 5, we propose BCAM4 IoT (Bigraphical Communicating Agent Model for the Internet of Things), a formal model extending the bigraphical agent one for the IoT in order to deal with interaction aspects in IoT systems. In Section 6, we explain how the two most challenging properties, interoperability and context-awareness could be expressed and checked in terms of TQL. Section 7 summarises the paper and discusses ongoing work.

Related work

Recently, some approaches in the literature try to apply existing formalisms to model IoT systems while favouring certain aspects to the detriment of others. In this section, we cite some distinctive approaches that deal with formal specification of IoT systems.

Authors of [START_REF] Ikram | Approaching the internet of things (iot): a modelling, analysis and abstraction framework[END_REF] used a model inspired from the nature to specify the behaviour of distributed and dynamic systems while considering the interaction unities as a basic concept. IoT applications has been shown just as a case of study.

In [START_REF] Chen | Runtime model based approach to iot application development[END_REF], authors propose a runtime model-driven approach focusing on sensor device models description, while our approach focuses on modelling the behaviour of an IoT system with its physical, logical and interaction parts support-ing the modelling of IoT systems and thus they proposed a generic architecture for IoT systems.

On the other hand, the research work in [START_REF] Bermudez-Edo | Iot-lite: a lightweight semantic model for the internet of things[END_REF] focused only on the physical part of an IoT system. Their model supports the heterogeneity of objects and may be applied to all types of IoT systems. Their contribution lies in the definition of an ontology that captures the basic considered concepts and models the dynamical process in a static way.

On another side, there is a few number of works that attempt to check formally models destined to IoT Systems, we can cite authors of [START_REF] Yamaguchi | An analysis system of iot services based on agent-oriented petri net pn2[END_REF] that use the SPIN tool (based on the Linear Temporal Logic LTL), to check whether a smart house system modelled in petri nets is working properly. In this work, they focused more on the model checking than on the IoT aspects, it is not specified how their model supports the IoT systems characteristics, in the opposite of our proposition, where we try to model IoT systems aspects.

Contrary to the above cited work, our approach consists in modelling the behaviour of an IoT system, combining the software agents and the bigraphs, which are formal tools, while emphasizing the aspect of interaction between their elements. Moreover, we use appropriate spatial logic in order to check some of their properties.

A multi-levels architecture for IoT

At a high level of abstraction, IoT applications may be considered as a set of physical and logical components connected via interaction elements. To master their complexity, we propose a generic architecture (Figure 1) dealing with the separation of the physical part from the logical one, and even the physical part can be structured hierarchically. Thus, entities of the system will be distributed between the layers, and charges of each layer are divided through the components of these layers.

Through this section, we will give, in an incremental manner, the three essential parts of the proposed architecture: Physical part, Logical part and Interaction part.

Physical part

Elements belonging to this part may be classified in four different layers :

1. Hardware: contains input hardware identifying the objects that capture data from the environment; output hardware for the objects that actuate on the environment and input/output hardware for the components used to interact in an eventual network; 2. Abstraction: constitutes an interface, containing all the protocols needed by the top layers to interact with the hardware components; 3. Communication: contains protocols of rooting information from the hardware to the software components (and vice-versa), or from one system to another;

Logical part

The logical part of this architecture is supported by a set of reactive agents which behave according to a control loop (observation, analysis, control), each layer of the physical part (except the hardware one) is managed by one agent type.

-In the abstraction layer, the agent controls each solicitation of the hardware entities by elements of the layers above and vice-versa. It also decides if the interaction can occur or not, according to the result of its observation's analysis.

-The agent that manages the communication layer is responsible of making the decisions to send or not information for internal or another system. -Finally, in the application layer, the agent makes the decisions to make a perceptible action after analysing the data received and the data produced by the specific support.

These agents are responsible of the various communications that may exist between the systems or their components.

Interaction part

In an IoT system, the interactions between objects are the most important concept requiring a particular attention. In this architecture, unidirectional arrows are used to illustrate these interactions, that may be of two types:

-Agent/Component: In the three high levelled layers of the architecture, the only interaction defined is between agents and physical components. At each time, agents obtain some information from a component, analyse it, and decide to obtain other information from the same component, or to actuate on another one. Modelling a system with a set of agents involve specifying the communications between them using interaction protocols. Specifying the way they interact is interesting and challenging because of their heterogeneity and autonomy [START_REF] Amit | Multiagent systems[END_REF]. In [START_REF] Marir | Biagent-based model for iot applications[END_REF], we modelled the architecture proposed with its different aspects, using the formalism of bi-agents, but, we didn't take care of the communication between the agents which was the weak point of this model. -Component/Component: This kind of interaction exists only between the abstraction layer entities and the hardware ones. It represents the hardware's solicitations of the system's application side and vice versa. It is shown that the abstraction agent doesn't interact directly with the hardware components, but through interfaces (this allows the heterogeneity of objects needed in real IoT systems).

The separation of IoT system components using a layered architecture facilitates its modelling. The interaction between the elements of the system becomes the most important part to define in order to reduce more this complexity. In the present paper, by extending an existing architecture [START_REF] Marir | Biagent-based model for iot applications[END_REF], we are interested in modelling and checking the two following properties:

-Interoperability: The definition of interoperability in IoT is currently being developed, so a standard definition is not yet established. In the following sections, we consider that interoperability [START_REF] Ray | A survey on internet of things architectures[END_REF] is to deal with the fact that devices must support interoperable communication protocols and can interact with other devices and also with the infrastructure; -Context awareness [START_REF] Ray | A survey on internet of things architectures[END_REF]: It is the ability of the sensor nodes to gain knowledge about the surrounding context, based on the sensed information about the physical and environmental parameters. The decisions that the sensor nodes take thereafter are context-aware.

In order to model this architecture formally, according to its hierarchical aspects, the different types of connections between its entities, and the management aspect made by agents, the formalism of bigraphical agents seems to be the most adequate. Hence, we choose the Tree Query Logic to check the considered properties using this resulted model. We are motivated by its adaptability to tree structured models as bigraphical ones. Being a spacial logic, it can be used for the analyses of a model's structure as it is given by the properties of interoperability and context awareness,that are independent of the execution scenarios. Some aspects related to the formalism of biagent and the TQL are presented in the next section.

Basic concepts

In this section, we briefly recall the formal concepts used to model and check an IoT system.

Bigraphical Reactive Systems

A Bigraphical Reactive System (BRS) [START_REF] Milner | The space and motion of communicating agents[END_REF] is the set of bigraphs representing the states of the system obtained from an initial bigraph, and the reaction rules applied successively on it. A bigraph, as shown in the Figure 2 is composed of two graphs, the place graph used to model the location of physical and logical components, and the link graph can express the connectivity between the components:

-The place graph with a set of nodes {v 0 , v 1 } and a parent function prnt:sites nodes → nodes regions, the roots are called regions (as 0 and 1 in Figure 2), and some special nodes are called sites (dashed boxes 0,1 and 2 in Figure 2).

-The link graph is defined also with a set of nodes {v 0 , v 1 }, hyperedgese 0 and the link function X ports → hyperedges Y , where X are the internal names (oriented to the bottom) and Y the external ones (oriented to the top).

In addition to that, dynamics of an specified system is defined in terms of reaction rules as explained in [START_REF] Milner | The space and motion of communicating agents[END_REF].

The Tree Query Logic

The Tree Query Logic (TQL) [START_REF] Conforti | Spatial tree logics to reason about semistructured data[END_REF] is a spatial logic used in order to reason about eventual updates of tree structures and also permits the model checking of some relevant properties. The TQL is inspired by the Ambient logic, that describes a finite unordered labelled trees called information trees, noted, i.t.'s. This logic permits the use of the quantifiers ∀ and ∃. To understand well a TQL statement, The Table 1 contains some notations: The empty information tree 0 represents the concept of nothing, having an edge leading to nothing means that there is nothing after. n[A] means that there is an edge of label n leading to something that is between [ ]. The | is the most important operator in a spatial logic, it makes the separation between the elements of the model, if the representation of the model contains at least one |, that means that the model can be separated into at least two parts. T helps avoiding repetition. ∧, ∨ and negativity as in all logics are needed for the expression of most properties.

We can express a formula F and checking it by deciding if a tree T satisfies this formula, we note F (T F ). For validating some property, its formula F is valid, if and only if, non F is not satisfiable, we note ¬(∃T.T ¬F ).

The formalism of bigraphs is used to model the behaviour and the structure of a system. In order to verify structural and dynamical properties, spatial and temporal logics are used respectively. We notice that the structure of bigraphs can be represented as trees which permits us to use the TQL as a logic of verification of some structural properties. We see in the section 6 how this can be done.

BCAM4 IoT model

In this model, we focus on the importance of defining the interaction that exists between the different elements of an IoT system. Agents in Figure 1 are responsible of the communication in an IoT system, our proposed model should deal with these communication types. This model is formally defined as: BCAM4 IoT=a

B IoT = (B, R, U, B 0 , F ) -B is the set of bigraphs -R is the set of reaction rules.
-U ⊂ V B × R is the set of controls, which is the application of a reaction rule to a specific node in the bigraph with V B the set of nodes of every bigraph

B ∈ B. -B 0 ∈ B is the first bigraph of B.
-F is the control function defining the transition between a bigraph and another according to a control u i ∈ U.

In the Definition 1, the chosen IoT application is modelled by the set of bigraphs B. Each bigraph represents a state of the system, and the transition from a state to another is defined by a control. In order to avoid the non-determinism of a particular reaction rule, there are controls U that are the application of a reaction rule to a specific node, in other words, they represent an action that changes the system's state according to a specific condition. B 0 ∈ B represents the initial state of the IoT system. The Definition 2 shows how the semantic of an agent in BCAM4 IoT is defined, each one has:

-A set of observations, -A set of controls, -A set of decisions, -An initial host.

-It observes bigraphs, -It analyses its observation, -It interacts with other agents giving them its decision The regions App, Com and Ab represent respectively the application, the communication and the abstraction layers. The nodes GS, SS, ECL and ICL represent the generic support, the specific support, the external communication layer and the internal communication layer In BCAM4 IoT, we emphasis that an agent makes actions according to its context, it analyses its observations and launches a trace of actions after interacting with agents likely to be influenced by its decision.

Example

We consider a case of a collision avoidance system as example, provided with a radar, a vibrator and a wifi card. We present a simple scenario showing the contribution of BCAM4 IoT to support the definition of the interaction semantics between agents. For instance, we present the sequence of executions materialized by the trace: 3. Each state of the system is represented by a bigraph (containing the layers Ab, Com and App), Each layer is represented as a region (dotted boxes) of the bigraph and sub systems, as ECL (External Communication Layer) and ICL (Internal Communication Layer) are represented by simple squares. (In the figure 3, the agent is not visible). The radar (input) captures a suspect sound, the abstraction agent captures the sound through the interface of the radar R1 --→. After its observation obs --→, it migrates in the information node representing the sound (RI) mgrt ---→ in order to analyse its content α -→. It interacts with the communication agent giving the resulting decision int --→: the sound will be sent to the ICL in order to be formatted, and returns to the abstraction layer mgrt ---→. After that, the ICL formats the information R2 --→ and the communication agent observes the bigraph representing this state of the system obs --→. Before migrating into this format mgrt ---→ and analysing the packet α -→, the communication agent interacts with the application agent int --→ saying that the packet will be sent to the general support and returns to its layer mgrt ---→. After this sequence of executions T , the application agent manages the highest layer and the instructions cascade down to the hardware. From T , two projection of traces can be defined:

T = B 0 R1 ≺ B 1 R2 ≺ in Figure
t Ab Ag = (B 0 , 0) R1 ≺ (B 1 , 0) obs ≺ (B 1 , 0) mgrt ≺ (B 1 , I) α ≺ (B 1 , I) int ≺ (B 1 , I) mgrt ≺ (B 1 , 0) t Com Ag =(B 1 , 1) R2 ≺ (B 1 , 1) obs ≺ (B 1 , 0) mgrt ≺ (B 1 , CF ) α ≺ (B 1 , CF ) int ≺ (B 1 , CF ) mgrt ≺ (B 1 , 1)
The trace rules defined in this section may serve to model various evolution scenarios of IoT systems. Each scenario consists of a sequence of trace rules applications. In the following section, we proceed to the checking of BCAM4 IoT, expressing the considered properties of interoperability and context-awareness, using the TQL logic, and move on to execute the checking process.

Formal analysis

The hierarchical aspect of BCAM4 IoT allows us to use the TQL in order to express structural properties of some models. These expressions are made in the purpose of checking our model in terms of a query and its verification. Here, we are interested by two properties: (i) Interoperability guaranteed by the verification of three atomic ones PICP, FRI and AI. (ii) Context-awareness.

Presence of Interoperable Communication Protocols (PICP).

This is defined in BCAM4 IoT by the existence of the node ICP (interoperable communication protocols) in the node ICL (internal communication layer) in the region 1 (communication layer) of the bigraph representing a state of the system. This property is represented in the TQL by an information tree (see figure 4) and it is noted: Formatting Raw Information into packets (FRI). This is defined in BCAM4 IoT by the ability of the node ICL (internal communication layer) to format (CF) raw information (RI) into packets. This property is represented in the TQL by an information tree (figure 5) and it is noted:

B[ region [ 1 [ subsystemN ode [ ICL [ ICP [ 0 ] ] ] | T ] ] | T ]
B[ region [ 1 [ subsystemN ode [ ICL [ ICP [ 0 ] ] | [ CF [ RI ] ] ] | T ] ] | T ]

Ability of interacting (AI).

This is defined in BCAM4 IoT by the fact that all the elements of the physical layer interact with each other, by the capacity of the abstraction agent to communicate its decision to the communication agent; from the abstraction layer (0), to the communication layer [START_REF] Project | Inter-iot -interoperability internet of things[END_REF]; and by its influence on the interfaces (input, input \ output and output) of these elements. This property is represented in the TQL by an information tree as in figure 6 and it is noted: We notice that the interoperability between the hardware components of the system is established by making the software components, and their representation in the model, interoperable.

B[ region [ 0 [ Agents [ Ag Ab [Operations [ int [ decision [content]] | [ 0 [ 1 ] ] ] | [ ctr [ interf aceN ode [ methodes ] ] ] | T] ] ] ] | T]
The context awareness property: in BCAM4 IoT, this property is defined as: every Action is triggered in the application layer according to an extracted information EI made in the general support sub system GG from the raw information RI formatted with the communication format CF received from the communication layer and captured previously. So the system must have these three states to own this property.

In the TQL, it is interpreted as follows:

B[ region [ 2 [ subsystemN ode [ GS [ CF [ RI ] ] ] ] | T ] | T ] ∧ B[ region [ 2 [ subsystemN ode [ GS [ EI [ 0 ] ] ] ] | T ] | T ] ∧ B[ region [ 2 [ subsystemN ode [ A [EI [ 0 ] ] ] ] | T ] | T]
We propose a representation of the initial state in the TQL noted as follows For example, if we proceed to the validation of the property PICP in the initial state, we find that it is true; but if we try to check for FRI in the same state, it is not valid, because in the initial state of the system, there are no raw information captured yet.

Conclusion

Development of IoT applications becomes each year more complex and challenging, this is due to, the highly dynamic cooperation among heterogeneous things, the need of ensuring interconnectivity of software and hardware (physical) entities, and the support of a huge scale of interconnected devices. This paper focused on the use of formal methods to design and specify such applications by supporting the characteristics of complex, distributed and heterogeneous systems. We have proposed an extended Biagent model (called BCAM4 IoT), that inherits the benefits of each of the implied concepts (BRS and software agents). The BRS have been adopted to specify the physical part of an IoT application dealing with the spatial distribution, the mobility and the heterogeneity of its constituents. The Agents allowed to describe the logical reasoning of IoT elements, a behavioural semantics of an agent in BCAM4 IoT considers various action types, including observation (of the context), analysis (of possible situations), control and communication (with each other).

A nice consequence of using a formal model is that trace rules have been exploited to formally specify IoT systems behaviour, dealing with context-aware feature and interoperability one. We also showed the adequacy of the TQL logic to express and validate these important properties. As a future work, we plan to investigate the formal verification of other IoT inherent properties and also, implementing a tool that permits the edition of our model BCAM4 IoT, provided with a model checker which checks the structural and dynamical properties of an IoT system using the bigraphical logic and the TQL.

Fig. 1 .

 1 Fig. 1. Multi-layered architecture for an IoT application

Fig. 2 .

 2 Fig. 2. Example of a bigraph [3] 3, {x, y} → 2, ∅

2 Definition 1 (

 21 IoT * • B IoT (BCAM for Bigraphical Communicating Agent Model), where B IoT its structural part is given by the Definition 1, and a (i) IoT * its virtual part, by the Definition The physical structure).

Definition 2 (

 2 Virtual structure of BCAM4 IoT). a (i) IoT * = (O, obs, an, ctr, mgrt, int, U, D, host 0 ) -O ⊂ B is the observation space. the function of observation obs which provides an observation o ∈ O using a bigraph and the host of the observant agent: obs(b, h) = o. the analysis function that, with an observation or a set of observations and a host, analyses this host or its sons and returns a decision: an(o, h) = α ∈ D. -The control function which gives the next succession of rules to be executed, according to a node, using the result of an analysis: ctr(α) = u ∈ U. -The migration function that provides the next host of the agent according to the current host and an observation: mgrt(o, h) = h . the interaction function int, using the decision made α ∈ D and the host of the agent with whom it interacts, the agent interacts with another one using this function: int(h, α) int :V B × D (h, α) -U, D and host 0 are respectively the space of controls, of decisions and the initial host of the agent.

Fig. 3 .

 3 Fig. 3. Example of a trace

Fig. 4 .

 4 Fig. 4. Formal notation of PICP property

Fig. 5 .

 5 Fig. 5. Formal notation of FRI property

Fig. 6 .

 6 Fig. 6. Formal notation of AI property

B

  [region[0[input]|[in\out]|[output]|[site]|[Agent[Ab[...]]]]]|[1[subsystemN ode[ICL [ICP [0]]]|[ECL[ECP |P V S]]]|[Agent[com[...]]]]|[2[GS[...]]|[SS[...]]|[Agent[app[...]]]]

Table 1 .

 1 Some TQL Logical Formula[START_REF] Conforti | Spatial tree logics to reason about semistructured data[END_REF]

	0	The empty information tree.
	n[A] Single-edge i.t.'s labelled n and leading to a subtree satisfying A.
	A|B i.t.'s splittable into two i.t.'s satisfying A and B respectively.
	T	All information trees.
	¬A All i.t.'s not satisfying A.
	A∧B i.t.'s satisfying A and B.
	A∨B i.t.'s satisfying A or B.


Dr Didier F Pisani
email: pisani@unice.fr

Olivier Dumortier

Guillaume E Beranger

Virginie Casamento

Rayane A Ghandour

Maude Giroud

Nadine Gautier

Thierry Balaguer

Jean-Claude Chambard

Kirsi A Virtanen

Jean- Claude Chambard

Pirjo Nuutila

Tarja Niemi

Markku Taittonen

Emmanuel Van Obberghen

Charlotte Hinault

Dr Ez-Zoubir Amri
email: amri@unice.fr

Visfatin expression analysis in association with recruitment and activation of human and rodent brown and brite adipocytes

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

Abstract.

Human brown adipocytes are able to burn fat and glucose and are now considered as a potential strategy to treat obesity, type 2 diabetes and metabolic disorders. Besides their thermogenic function, brown adipocytes are able to secrete adipokines. One of these is visfatin, a nicotinamide phosphoribosyltransferase involved in NAD synthesis, known to participate in insulin synthesis by pancreatic β cells. In a therapeutic context, it is of interest to establish whether a potential correlation exists between brown adipocyte activation and/or brite adipocyte recruitment, and adipokine expression. We analyzed visfatin expression, as a pre-requisite to its secretion, in rodent and human biopsies and cell models of brown/brite adipocytes. We found that visfatin was preferentially expressed in mature adipocytes and that this expression is higher in brown adipose tissue of rodents compared to other fat depots. However, using various rodent models we were unable to find any correlation between visfatin expression and brown or brite adipocyte activation or recruitment. Interestingly, the situation is different in humans, where visfatin expression was found equivalent between white and brown or brite adipocyte in vivo and in vitro. In conclusion, visfatin can be considered only as a rodent brown adipocyte biomarker, independently to tissue activation.

Keywords.

Nampt, PBEF, hMADS, UCP1, obesity, type 2 diabetes.

Introduction.

Obesity participates for a great part in the pathogenesis of type 2 diabetes (T2D), due to its association with insulin resistance. Obesity has reached epidemic proportions worldwide, with more than 1.9 billion of adults suffering from overweight and 600 million considered as clinically obese [START_REF] Kopelman | Obesity as a medical problem[END_REF][START_REF] Who | Obesity and overweight[END_REF] . Unfortunately, except for morbidly obese patients undergoing bariatric surgery, medical obesity treatments are so far not satisfactory.

Overweight and obesity are consequent to energy balance disequilibrium, in favor of inappropriate energy intake increasing white fat mass. In this context, one of the strategies which deserves consideration is the use of the thermogenic capacity of brown adipocytes to counteract the excess of energy intake to equilibrate the energy balance [START_REF] Langin | Recruitment of brown fat and conversion of white into brown adipocytes: strategies to fight the metabolic complications of obesity?[END_REF] . Brown adipocytes are characterized by a high mitochondrial content and expression of the specific uncoupling protein 1 (UCP1). These cells are endowed with a high capacity of glucose and lipid oxidation and thus making them a potential target for decreasing plasma glucose, fatty acids and lowering the risks of T2D and obesity [START_REF] Nedergaard | UCP1: the only protein able to mediate adaptive non-shivering thermogenesis and metabolic inefficiency[END_REF][START_REF] Nedergaard | New powers of brown fat: fighting the metabolic syndrome[END_REF] . Indeed, the recent discovery of brown adipocytes, thermogenically competent, in human adults open a new field in obesity associated T2D therapy (reviewed in [START_REF] Virtanen | Brown adipose tissue functions in humans[END_REF] ).

In addition to brown adipose tissue (BAT) -consisting in brown adipocytes -white adipose tissue (WAT) contains thermogenic fat cells called "brown-in-white" ("brite") or "beige" adipocytes [START_REF] Petrovic | Chronic peroxisome proliferator-activated receptor gamma (PPARgamma) activation of epididymally derived white adipocyte cultures reveals a population of thermogenically competent, UCP1-containing adipocytes molecularly distinct from classic brown adipocytes[END_REF] . These brite adipocytes originate either from the differentiation of specific progenitors resident in the tissue [START_REF] Wang | Tracking adipogenesis during white adipose tissue development, expansion and regeneration[END_REF][START_REF] Wu | Beige adipocytes are a distinct type of thermogenic fat cell in mouse and human[END_REF] , or from the conversion of differentiated white adipocytes [START_REF] Barbatelli | The emergence of coldinduced brown adipocytes in mouse white fat depots is determined predominantly by white to brown adipocyte transdifferentiation[END_REF][START_REF] Rosenwald | Bi-directional interconversion of brite and white adipocytes[END_REF] . In addition to the preventive function of BAT in the occurrence of T2D, i.e. in fat gain and glucose tolerance regulation, this tissue seems to offer beneficial effects on metabolic disorders. Indeed, transplantation of BAT in mice resulted in a reversal of high-fat diet-induced insulin resistance [START_REF] Stanford | Brown adipose tissue regulates glucose homeostasis and insulin sensitivity[END_REF] and reverses streptozotocin-induced diabetes in mice [START_REF] Kim | cprThe FGF21 analogue, LY2405319, lowers blood glucose in streptozotocin-induced insulin-deficient diabetic mice by restoring brown adipose tissue function[END_REF] . In healthy humans, cold exposure leads to a decrease in body fat by recruiting brown/brite adipocytes and increasing nonshivering thermogenesis [START_REF] Blondin | Increased brown adipose tissue oxidative capacity in cold-acclimated humans[END_REF][START_REF] Van Der Lans | Cold acclimation recruits human brown fat and increases nonshivering thermogenesis[END_REF][START_REF] Yoneshiro | Recruited brown adipose tissue as an antiobesity agent in humans[END_REF] . A recent study showed that these adipocytes may function as an anti-diabetic tissue in humans [START_REF] Chondronikola | Brown Adipose Tissue Improves Whole Body Glucose Homeostasis and Insulin Sensitivity in Humans[END_REF] . Altogether, these data explore new avenues emphasizing thermogenic adipocytes as novel important candidates to control body weight and carbohydrate metabolism through modulation of energy expenditure.

Visfatin (Nampt/PBEF) is an enzyme (nicotinamide-ribosyl-transferase) synthesizing nicotinamide mononucleotide (NMN) from nicotinamide [START_REF] Revollo | Nampt/PBEF/Visfatin regulates insulin secretion in beta cells as a systemic NAD biosynthetic enzyme[END_REF] . NMN is the substrate for nicotinamide dinucleotide (NAD) synthesis, involved in numerous cell functions [START_REF] Lin | Nicotinamide adenine dinucleotide, a metabolic regulator of transcription, longevity and disease[END_REF] . Visfatin, secreted from various cells including adipocytes, has been found in the circulation and is considered as an adipokine [START_REF] Revollo | Nampt/PBEF/Visfatin regulates insulin secretion in beta cells as a systemic NAD biosynthetic enzyme[END_REF] . Several studies demonstrate the crucial role of visfatin in β-cell function, i.e. insulin secretion, insulin receptor phosphorylation and signaling as well as transcriptional regulation of various key genes [START_REF] Revollo | Nampt/PBEF/Visfatin regulates insulin secretion in beta cells as a systemic NAD biosynthetic enzyme[END_REF][START_REF] Brown | Visfatin regulates insulin secretion, insulin receptor signalling and mRNA expression of diabetes-related genes in mouse pancreatic beta-cells[END_REF] . Thus, visfatin could be involved in metabolic disorders involving β-cell failure, such as T2D.

Since dysregulation of adipokine synthesis is associated with obesity, we hypothesize that brown adipocyte activation could participate in an increased production of visfatin and thus be involved in the regulation of insulin secretion and hence glucose metabolism. To evaluate visfatin as a potential target for future therapies of obesity and associated diseases, we analyzed here the expression of visfatin in several in vivo and in vitro models of brown adipocyte activation and brite adipocyte recruitment in rodents and humans.

Results.

Visfatin mRNA is preferentially expressed in adipocytes

Since adipose tissue is composed of adipocytes and several cell types of the stromal vascular fraction (SVF), we separated by enzymatic digestion these fractions from mouse adipose tissues isolated from interscapular (iBAT), subcutaneous (scWAT) and epididymal depots (eWAT). We measured mRNA expression of key adipocyte markers; i.e. perilipin-1 as control of adipocyte purification, and UCP1 as brown adipocyte marker. As expected, perilipin-1 and leptin were only expressed in the adipocyte fraction of the three adipose depots, while UCP1 mRNA expression was confined to the adipocyte fraction of iBAT (Fig. 1). In contrast to perilipin, leptin and UCP1, visfatin mRNA was expressed in both adipocyte and stromal vascular fractions, but with a twofold higher expression in adipocyte fraction in the three adipose tissue depots (Fig. 1).

Visfatin is highly expressed in brown adipocytes

To determine whether visfatin expression was correlated to brown adipocyte differentiation, we analyzed its expression in in vitro differentiated mouse adipocytes. For that purpose, SVF cells from the three adipose tissue depots were induced to differentiate either into white or brown/brite adipocytes (Fig. 2A). As expected, UCP1 mRNA was not detected in white adipocytes, leptin mRNA levels were low in brown/brite adipocytes. In addition, UCP1 was highly expressed in brown adipocytes derived from iBAT progenitors, lower in brown adipocytes derived from scWAT progenitors (considered as brite adipocytes) and not detected in those derived from eWAT (Fig. 2A). Conversely, visfatin mRNA was expressed in SVF differentiated either in white or brown/brite adipocytes with a significantly higher expression in iBAT-derived brown adipocytes. Then, we analyzed visfatin secretion in conditioned media and found that secreted visfatin protein levels were correlated to visfatin mRNA expression, with a higher secretion in brown adipocytes (Fig. 2B). Furthermore, visfatin secretion was not affected by β3-adrenergic receptor stimulation (Fig. 2B).

Visfatin is preferentially found in iBAT, but its expression is not correlated with BAT activation

To determine whether a correlation exists between visfatin mRNA expression and BAT activation, mice were exposed to cold (4°C, 4 days) or received a daily injection of CL316,243 for 1 week, known to activate brown adipocytes. These stimulations induced a significant increase in UCP1 mRNA levels in iBAT. However, this was not correlated to an increase in visfatin mRNA expression (Fig. 3A andB). As expected, both cold and β3-receptor agonist treatments led to an increased UCP1 expression in scWAT, but not in eWAT (Fig. 3A andB) corresponding to the recruitment and activation of inducible brite adipocytes in the scWAT.

Again, visfatin mRNA levels were not significantly modified under these stimulations (Fig. 3A andB). Interestingly, a significant increase of visfatin mRNA was observed in eWAT under both stimulations (Fig. 3A andB).

Visfatin mRNA expression was higher in mice iBAT compared to white depots, i.e. eWAT and scWAT (Fig. 3A andB). A similar profile was found in rat tissues with a higher expression in iBAT compared to eWAT (Fig. 4A andB). Concerning the correlation with BAT activation in rat adipose tissue depots, no significant increase in visfatin mRNA levels was observed upon CL316,243 stimulation (Fig. 4B). Under acute cold exposure, visfatin expression decreased (Fig. 4A).

Visfatin is expressed and secreted by human brown and brite adipocytes.

We further analyzed the expression of visfatin in scWAT and BAT biopsies from healthy volunteers (evaluated by FDG-PET/CT analysis). UCP1 mRNA expression was confined to BAT, whereas visfatin mRNA was expressed in both tissues at similar levels (Fig. 5A).

We analyzed the expression of visfatin mRNA in two human cellular models. SVF from two human subcutaneous adipose tissue samples (hADSC) were induced to differentiate into white or brown adipocytes. As expected, UCP1 mRNA expression was restricted to brown adipocytes and leptin mRNA was highly expressed in white adipocytes. By contrast, visfatin mRNA was expressed at similar levels in both white and brown adipocytes (Fig. 5B). Next, we used hMADS cells, which are able to differentiate into white adipocytes and then convert into functional brite adipocytes. Whereas UCP1 mRNA expression was confined to brite adipocytes, visfatin mRNA was expressed in both cell types, brite adipocytes showing an expression two times higher than white adipocytes (Fig. 5C). To determine a potential functional impact of this up-regulation, we analyzed the secretion of visfatin in the conditioned media. We found that secreted visfatin protein levels, evaluated to be 15% of the total visfatin (cellular + secreted), were equivalent between white and brite hMADS adipocytes, even in the presence of CL316,243 (Fig. 5D). The increased visfatin mRNA expression might augment only the intracellular visfatin level as a slight, but not significant, increase was found in cell lysates of brite adipocytes (0.053 ± 0.016 vs / 0.088 ± 0.034 ng visfatin/µg protein respectively for white and brite adipocytes).

Discussion.

Visfatin is an essential NMN/NAD biosynthetic enzyme considered as a novel adipokine, with both intra-and extracellular enzymatic functions. It is expressed in white and brown adipose tissues of rodents [START_REF] Revollo | Nampt/PBEF/Visfatin regulates insulin secretion in beta cells as a systemic NAD biosynthetic enzyme[END_REF] . Secreted visfatin acutely potentiates glucose stimulated insulin secretion, without affecting β-cell survival [START_REF] Spinnler | The adipocytokine Nampt and its product NMN have no effect on beta-cell survival but potentiate glucose stimulated insulin secretion[END_REF] . Moreover, NMN, the product of visfatin activity, has been shown to restore insulin secretion in mouse models of impaired β-cell function [START_REF] Caton | Nicotinamide mononucleotide protects against pro-inflammatory cytokine-mediated impairment of mouse islet function[END_REF] . Finally, visfatin is found in the blood circulation acting like a cytokine. Thus, targeting visfatin function or expression might represent a novel therapeutic strategy for the control of β-cell function in diabetes.

Here, we confirm that while visfatin is expressed in several adipose tissue depots, it is preferentially expressed in brown adipocytes. However, in rodents sympathetic activation in vivo does not reveal a positive correlation between visfatin expression and BAT activation. It is now well established that brown-like adipocytes appear in white adipose tissue after chronic sympathetic stimulation. These brite, or beige, adipocytes are found preferentially in subcutaneous white adipose tissue depots, where they form clusters of thermogenic competent cells able to burn lipids and glucose [START_REF] Barbatelli | The emergence of coldinduced brown adipocytes in mouse white fat depots is determined predominantly by white to brown adipocyte transdifferentiation[END_REF] . Our results demonstrate that visfatin is highly expressed and secreted in genuine brown compared to white adipocytes, but not in brite adipocytes derived from scWAT progenitors. Surprisingly, visfatin mRNA expression was found up-regulated in mouse eWAT upon cold exposure or CL316,243 treatment. This is not related to brite adipocytes recruitment, as eWAT is less sensitive to these treatments and does not display high amounts of UCP1 positive cells. However, eWAT is a highly lipolytic tissue under the control of adrenergic stimulation. Therefore, we can assume that visfatin expression could be related rather to the lipolytic function of white adipocytes.

As BAT found in adult humans correspond more to the inducible brite adipocytes described in rodents [START_REF] Lidell | Evidence for two types of brown adipose tissue in humans[END_REF] , it is not surprising to find comparable levels of visfatin mRNA in BAT and WAT biopsies from adult volunteers. Similar observations are obtained in vitro using primary cultures of cells from scWAT and differentiated in white or brite/brown adipocytes.

Brite adipocytes originate from either specific white adipose tissue resident progenitors or from the conversion of mature white adipocytes [START_REF] Rosenwald | Bi-directional interconversion of brite and white adipocytes[END_REF] . Using hMADS cell models of white to brite adipocyte conversion, we show that visfatin mRNA expression is slightly increased during the conversion of white to brite adipocytes. Furthermore, this increase in mRNA expression is not correlated to an enhanced secretion of visfatin protein. As visfatin has been described to be a PPARγ target gene in macrophages [START_REF] Mayi | Visfatin is induced by peroxisome proliferator-activated receptor gamma in human macrophages[END_REF] , and as white to brite conversion in vitro is triggered by the addition of the PPARγ agonist, rosiglitazone, it is tempting to suggest that this increased visfatin expression is due to PPARγ activation.

Conflicting data have been reported for visfatin plasma levels in various cohorts of diabetic and obese patients as well as in rodents. Unexpectedly, several studies described a positive correlation between plasma visfatin levels and obesity, T2D and metabolic syndrome, situations associated in fine to β-cell dysfunction [START_REF] El-Mesallamy | Vaspin and visfatin/Nampt are interesting interrelated adipokines playing a role in the pathogenesis of type 2 diabetes mellitus[END_REF][START_REF] Filippatos | Increased plasma levels of visfatin/pre-B cell colony-enhancing factor in obese and overweight patients with metabolic syndrome[END_REF][START_REF] Haider | Increased plasma visfatin concentrations in morbidly obese subjects are reduced after gastric banding[END_REF] . Other studies did not reveal any association between visfatin circulating levels and visceral fat mass or insulin sensitivity parameters in humans and rodents [START_REF] Berndt | Plasma visfatin concentrations and fat depot-specific mRNA expression in humans[END_REF][START_REF] Pagano | Reduced plasma visfatin/pre-B cell colony-enhancing factor in obesity is not related to insulin resistance in humans[END_REF] . Altogether, these data highlight a paradox between pathologic visfatin circulating levels and its positive involvement in β-cell function.

In conclusion, visfatin can be considered as a rodent brown adipocyte biomarker.

Nevertheless, its expression and thus secretion does not appear related to brown adipocyte activation or brite adipocyte recruitment in humans and rodents. While the currently available data do not permit to herald visfatin as a beneficial BAT-derived cytokine for the treatment of obesity and T2D, the search for other potential brown/brite adipocyte secreted molecules, acting in concert or not with visfatin, remains a burning issue.

Material and methods.

Cell culture.

Mouse primary adipocytes. Animals were euthanized by cervical dislocation for preparation of stromal vascular fractions. Interscapular, perigonadal and subcutaneous fat depots were rapidly excised, washed in PBS, and minced. Adipose tissue samples were then digested with mild agitation for 45 min at 37°C in DMEM (Lonza, BE12-707F) containing 2 mg/ml collagenase A (Roche Diagnostics, 11088793011) and 20 mg/ml BSA (Sigma-Aldrich Chemie Gmbh, A7030). Digested tissues were filtrated through 250, 100 and 27 µm nylon sheets, and centrifuged for 5 min at 500 g. Adipocyte fractions were obtained after centrifugation of the first 250 µm filtration at 500 g for 5 min, floating adipocytes were sampled for molecular analysis. The pellet containing stromal-vascular fraction (SVF) cells was submitted to red blood cell lysis procedure. SVF cells were either used directly for molecular analysis or plated and maintained in DMEM containing 10% (v/v) FCS until confluence. Differentiation was induced in the same medium supplemented with 1 µM dexamethasone (Sigma-Aldrich Chemie Gmbh, D4902), 0.5 mM isobutylmethylxanthine (Sigma-Aldrich Chemie Gmbh, I5879) and 860 nM insulin (Invitrogen, 12585014) for 2 days. Cells were then maintained for 7-10 days in the presence of 10 nM insulin for white adipogenesis or a mixture containing 10 nM insulin, 1 µM rosiglitazone (BertinPharma, 71740) and 0.2 nM triiodothyronine (Sigma-Aldrich Chemie Gmbh, T6397) for brown or brite adipogenesis.

hMADS and hADSC. The establishment and characterization of the multipotency and selfrenewal capacity of hMADS cells have been described [START_REF] Rodriguez | Adipocyte differentiation of multipotent cells established from human adipose tissue[END_REF] . White and brite adipocyte differentiation were performed as previously described [START_REF] Elabd | Human multipotent adipose-derived stem cells differentiate into functional brown adipocytes[END_REF][START_REF] Pisani | The omega6fatty acid, arachidonic acid, regulates the conversion of white to brite adipocyte through a prostaglandin/calcium mediated pathway[END_REF][START_REF] Pisani | Differentiation of Human Adipose-Derived Stem Cells into "Brite" (Brown-in-White) Adipocytes[END_REF] . For human adipose-derived stem cells (hADSC), subcutaneous adipose tissue samples were obtained as res nullus from surgeries (non-pathologic abdominoplasty). hADSC cells were isolated by enzymatic digestion as described in the mouse primary adipocytes section. Cells were then plated and maintained in culture using the protocol used for hMADS cells.

Isolation and analysis of RNA.

Total RNA was extracted using a TRI-Reagent kit according to manufacturer's instructions (Euromedex, RT-111), with an additional step for tissues consisting to homogenization in TRI-Reagent using an ULTRA TURRAX T25 (Ika). Reverse transcription and polymerase chain reaction (RT-PCR) analysis were performed as described previously [START_REF] Pisani | Differentiation of Human Adipose-Derived Stem Cells into "Brite" (Brown-in-White) Adipocytes[END_REF] . The oligonucleotide sequences are shown in Table 1. Quantitative PCR (qPCR) was performed using SYBR qPCR premix Ex TaqII from Takara (Ozyme, TAKRR820W) and assays were run on a StepOne Plus ABI real-time PCR machine (PerkinElmer Life and Analytical Sciences). The expression of selected genes was normalized to that of 36B4 housekeeping genes and then quantified using the comparative-ΔCt method.

Visfatin secretion assay.

Differentiated SVF from mouse adipose tissue or differentiated hMADS cells were incubated for 16h in fresh media (serum free, BSA 0.05%) stimulated or not with 1 µM CL316,243.

Conditioned media were sampled and centrifuged for 5 min at 400 g to remove cell debris.

Attached cells were lysed in a buffer containing 25 mM Tris-Cl (pH 7.4), 100 mM NaCl, 1 mM EDTA, 1% (v/v) Triton X-100, 0.5% (v/v) Nonidet P40, 1x protease inhibitor cocktail (Roche, 11697498001). Visfatin levels were quantified in cell lysates and conditioned media using specific ELISA kit from Enzo Life Sciences for mouse (AG-45A-0007EK-KI01) or RayBiotech for human samples (Clinisciences, EIA-VIS-1). Assays were performed following manufacturer's instruction.

Animals.

The experiments were conducted in accordance with the French and European regulations for the care and use of research animals and were approved by the local experimentation committee CIEPAL-Azur N°28 (protocol NCE-2012-57, NCE-2014-166 and MESR-00500.02). Animals were maintained under constant temperature (mice: 21 ± 2°C; rats: 23 ± 2°C) and 12:12-hour light-dark cycles, with ad libitum access to standard chow diet and water. C57Bl/6J mice (males, 10 to 14 weeks-old) and Wistar rats (males, 12 to 16 weeks-old) were from Janvier Labs (http://www.janvier-labs.com/). Brown adipocyte activation was induced by a daily intraperitoneal injection of a β3-adrenergic receptor agonist CL316,243 (1 mg/kg in a saline solution, Sigma-Aldrich Chemie Gmbh, C5976) for 7 days. Control animals were injected with vehicle only. For cold exposure, mice were exposed for 4 days and rats for one night at 4°C ± 2°C in a climate chamber.

Subjects.

The study protocol was reviewed and approved by the ethics committee of the Hospital District of Southwestern Finland, and subjects provided written informed consent following the committee's instructions. The study was conducted according to the principles of the Declaration of Helsinki. All potential subjects were screened for metabolic status, and only those with normal glucose tolerance and normal cardiovascular status (as assessed on the basis of electrocardiograms and measured blood pressure) were included. The age range of the subjects was 23-49 years. We studied a group of six healthy volunteers (2 men and 4 women). BAT was sampled in positive FDG-PET scan site, and subcutaneous WAT was derived via the same incision.

Statistical analyses.

Data are expressed as mean values ± SEM and were analyzed using InStat software (GraphPad Software). Data were analyzed by one-way ANOVA followed by a Student-Newman-Keuls posttest, or Student's t-test to assess statistical differences between experimental groups. Differences were considered statistically significant when p<0.05. Control animals were maintained near to rat thermoneutral temperature (23±2°C) or injected with the vehicle (NaCl 0.9%, w/v). Perilipin-1 was used as a control marker of adipose tissue.

Histograms represent mean±sem of 4 rats. *: p<0.05.

Figure 1 .

 1 Figure 1. Visfatin expression in mouse brown and white adipocytes and stromal vascular fractions. mRNA expression determined by RT-qPCR in Stroma Vascular Fraction (SVF) and adipocyte fraction (AF) from scWAT, eWAT and iBAT from C57BL/6 mice. Perilipin-1 was used as control of adipocyte purification, and leptin as a white adipocyte marker. Histograms represent mean±sem of 8 mice. *: p<0.05.

Figure 2 .

 2 Figure 2. Visfatin expression in mouse brown and white adipocytes. (A) Cells from SVF of the three adipose tissue depots were differentiated in white or brown adipocytes and used for mRNA level evaluation by RT-qPCR. Perilipin-1 was used as control of adipogenesis, and leptin as white adipocyte marker. (B) Intracellular and extracellular visfatin protein levels were quantified by EIA in white and brown adipocytes, derived from SVF of the three adipose tissue depots, after 16 h of incubation in the absence or presence of 1 µM CL316,243. Histograms represent mean±sem of 3 independent experiments (B). *: p<0.05.

Figure 3 .

 3 Figure 3. Visfatin is preferentially expressed in mouse BAT, but is not correlated to brown adipocyte recruitment/activation. mRNA expression was determined by RT-qPCR in iBAT, scWAT and eWAT from C57BL/6 mice exposed (4 days) at a temperature of 4±2°C (A) or received CL316,243 for 1 week (B). Control animals were respectively maintained at housing temperature (21±2°C) or injected with the vehicle (NaCl 0.9%, w/v). Perilipin-1 was used as a control marker of adipose tissue. Histograms represent mean±sem of 8 mice. *: p<0.05.

Figure 4 .

 4 Figure 4. Visfatin is preferentially expressed in rat iBAT, but is not induced with BAT activation. mRNA expression was determined by RT-qPCR in iBAT and eWAT from Wistar

Figure 5 .

 5 Figure 5. Visfatin expression and secretion in human white and brown adipocytes. (A) mRNA levels were evaluated by qRT-PCR in matched biopsies of scWAT and BAT from 6 healthy human adult patients. mRNA expression determined in human subcutaneous adipose tissue SVF-derived white and brown adipocytes (B) or hMADS adipocytes (C). Perilipin-1 was used as control of adipogenesis and leptin as white adipogenic marker. (D) Intracellular and extracellular visfatin EIA quantification in hMADS white and brown adipocytes after 16h of incubation with or without 1µM CL316,243. Histograms represent mean±sem of 6 tissue samples (A) or 3 independent experiments (B, C, D). *: p<0.05.

Table 1 . Sequence of primers used for gene expression analysis.

 1

	Genes	Species	Forward primer	Reverse Primer
	Visfatin	human	AGCCGAGTTCAACATCCTCC	AACTTTGCTTGTGTTGGGTGG
	UCP1	human	CCAGGATCCAAGTCGCAAGA	GTGTGCCCAACTGTGCAATG
	perilipin 1 human	ACCCCCCTGAAAAGATTGCTT	GATGGGAACGCTGATGCTGTT
	leptin	human	AGGGAGACCGAGCGCTTTC	TGCATCTCCACACACCAAACC
	36B4	human TGCATCAGTACCCCATTCTATCAT	AGGCAGATGGATCAGCCAAGA
	Visfatin	rat	TCTGGAAATCCGCTCGACAC	TATCCACTCCGTCCCCTTGA
	UCP1	rat	TGGCCAAGACAGAAGGATTG	GATCTTGCTTCCCAAAGAGG
	perilipin 1	rat	TCTGCTTTGCAGCGTGAAGA	GGTCTTCTGGAAGCACTCACA
	36B4	rat	ATCGTCTTTAAACCCCGCGT	CCACATTGTCTGCTCCCACA
	Visfatin	mouse	ATTCAAGGAGATGGCGTGGA	CTTCTGTAGCAAAGCGCCAC
	UCP1	mouse	CACCTTCCCGCTGGACACT	CCTGGCCTTCACCTTGGAT
	perilipin 1 mouse	AGCGTGGAGAGTAAGGATGTC	CTTCTGGAAGCACTCACAGG
	leptin	mouse	GCTGGTGAGGACCTGTTGAT	CAGGATCAATGACATTTCACACA
	36B4	mouse	TCCAGGCTTTGGGCATCA	CTTTATCAGCTGCACATCACTCAGA

Acknowledgements.

The authors greatly acknowledge IBV and IRCAN Animal core facilities. This work was supported by CNRS, INSERM, Université de Nice Sophia Antipolis, EU FP7 project DIABAT (HEALTH-F2-2011-278373), French Agence Nationale de la Recherche (ANR-10-BLAN-1105 miRBAT and ANR-RPV12004AAA-DiamiR), Aviesan/AstraZeneca, "Diabetes and the vessel wall injury" program, "Investments for the Future" LABEX SIGNALIFE #ANR-11-LABX-0028-01 and European Foundation for the Study of Diabetes (EFSD/Lilly; European Diabetes Research Program).